THE POTENTIAL OF BIOLOGICAL SLUDGE AMENDED

THE POTENTIAL OF BIOLOGICAL SLUDGE AMENDED
THE POTENTIAL OF BIOLOGICAL SLUDGE AMENDED
COMBUSTION COAL ASH RESIDUES AS ARTIFICIAL PLANT
GROWTH MEDIA: A LABORATORY COLUMN STUDY TO ASSESS
THE INFLUENCE OF WEATHERING ON ELEMENTAL RELEASE
by
BONOKWAKHE HEZEKIEL SUKATI
Submitted in partial fulfilment of the requirements for the degree
MSc (Agric) Soil Science
In the Faculty of Natural and Agricultural Sciences
University of Pretoria
Supervisor: Mr C. de Jager
Co-supervisor: Prof. J.G. Annandale
June 2012
© University of Pretoria
DECLARATION
I hereby certify that this thesis is my own original work, except where duly acknowledged. I also
certify that no plagiarism was committed in writing this work and has not been previously
submitted partly or fully for a degree to any other University.
Signed:
Date:
Bonokwakhe Hezekiel Sukati
i
DEDICATION
This work is dedicated to my family especially my parents Mrs Hleziphi N. Sukati and the late
Mr Richard M. Sukati who tirelessly prayed and extended support to my success. The work is
also dedicated to my children; Sakhiwo, Lindelwa, Mthobisi and Uya Sukati, to my siblings
Mbongeni W., Nombulelo N., Thobile F. and Hlengiwe P. Sukati, and to my fiancée Gcinaphi
Manana.
ii
ACKNOWLEDGEMENT
First and formost I wish to extend my sincere gratitudes to Sasol Synfuels in Secunda who
funded this project and to Professor J.G. Annandale and Mr P.C. De Jager who gave me the
opportunity to be part of this work and provided guidance that lead to my prestigious MSc
(Agric) Soil Science Degree. My appreciation is also due to the laboratory manager Mr Charl D.
Hertzog who allowed me to carry out all the experiments in the laboratories and tirelessly helped
in the development of the columns and in the analysis of my samples.Thanks to the Departmental
Administration and to my colleaques in Soil Science and other desciplines that contributed much
to my study.
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TABLE OF CONTENTS
DEDICATION ........................................................................................................................... ii
ACKNOWLEDGEMENT ......................................................................................................... iii
TABLE OF CONTENTS .......................................................................................................... iv
LIST OF TABLES .................................................................................................................. viii
LIST OF FIGURES ....................................................................................................................x
ABSTRACT ............................................................................................................................. xv
1. Introduction .........................................................................................................................1
1.1 Specific objectives .............................................................................................................6
1.2 Thesis lay out.....................................................................................................................6
CHAPTER 2: LITERATURE REVIEW .....................................................................................8
2.1 Fine and gasification ashes characteristics ..........................................................................8
2.2 Sources and mobility of macronutrients (N, P, K, Ca and Mg) in coal ash ........................ 14
2.3 Sources and mobility of micronutrients (Zn, Cu, Mn, Mo, Fe, and B) in coal ash ............. 20
2.4 Sludge characteristics....................................................................................................... 22
2.5 Possible benefits of amending coal ash with biological sludge ......................................... 24
2.6 The potential of biological sludge – coal ash mixtures as artificial growth media ............. 26
2.7 Unsaturated packed low tension column system ............................................................... 27
2.8 Conclusion....................................................................................................................... 34
CHAPTER 3: MATERIALS AND METHODS ........................................................................ 36
3.1 Mixture formulations ....................................................................................................... 36
3.2 Calculations involving the packing of the columns ........................................................... 38
3.3 Packing of the columns .................................................................................................... 39
3.4 The development of the unsaturated column system, specifications and set up ................. 39
3.5 Leaching procedure and collection of leachate ................................................................. 40
CHAPTER 4: PARTICLE SIZE DISTRIBUTION AND WATER RETENTION OF
BIOLOGICAL SLUDGE – COAL ASH MIXTURES .............................................................. 43
4.1 Introduction ..................................................................................................................... 43
4.2 Materials and Methods ..................................................................................................... 46
4.2.1 Particle size analysis .................................................................................................. 46
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4.2.2 Assessing water retention characteristics ................................................................... 46
4.3 Results and Discussions ................................................................................................... 48
4.3.1 Particle size analysis of fine and gasification ash ....................................................... 48
4.3.2 Changes in water holding capacity of fine ash and gasification ash over time ............ 50
4.3.3 The contribution of water locked-up in hydrated minerals to water retention ............. 52
4.3.4Change in water holding capacity of the mixtures over time ....................................... 54
4.4 Conclusion....................................................................................................................... 56
CHAPTER 5: NITROGEN DYNAMICS IN SLUDGE-COAL ASH MIXTURES AS
INFLUENCED BY WEATHERING ........................................................................................ 58
5.1 Introduction ..................................................................................................................... 58
5.2 Materials and methods ..................................................................................................... 60
5.2.1 Selection of mixtures ................................................................................................. 60
5.2.2 Determination of inorganic nitrogen (NH4+, NO3-and NO2 -) ..................................... 62
5.2.3 Determination of ammonium (NH4+) ......................................................................... 63
5.2.4 Indirect determination of nitrate (NO3-) ..................................................................... 63
5.2.5 Indirect determination of nitrite (NO2-) ...................................................................... 64
5.3 Results and discussion ..................................................................................................... 64
5.4 Conclusion....................................................................................................................... 71
CHAPTER 6: ELEMENTAL DETERMINATION IN SLUDGE, FINE AND GASIFICATION
ASHES, ELEMENTAL RELEASE, SALINITY AND pH OF MIXTURES ............................. 72
6.1 Introduction ..................................................................................................................... 72
6.2 Materials and methods ..................................................................................................... 74
6.2.1 Mixture analysis with X-ray Fluorescence Spectroscopy (XRF) ................................ 74
6.2.2 Mixture analysis with Inductively coupled plasma mass spectroscopy (ICP-MS)....... 75
6.2.3 Phosphorus determination of mixtures ....................................................................... 75
6.2.4 Analysis of leachate with Inductively Coupled Plasma Optical Emission Spectrometry
(ICP-OES) ......................................................................................................................... 75
6.2.5 Salinity and pH determination ................................................................................... 77
6.3 Results and discussion ..................................................................................................... 77
6.3.1 pH changes of mixtures as influenced by leaching ..................................................... 77
6.3.2 Electrical Conductivity (EC) changes of mixtures as influenced by leaching ............. 82
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6.3.3 Calcium content of Sasol sludge, fine and gasification ashes measured in 2006, 2007,
2008 and 2011.................................................................................................................... 85
6.3.4 Calcium content of mixtures ...................................................................................... 87
6.3.5 Calcium leaching from mixtures ................................................................................ 89
6.3.6 Magnesium content of Sasol sludge, fine and gasification ashes measured in 2006,
2007, 2008 and 2011 .......................................................................................................... 94
6.3.7 Magnesium content of mixtures ................................................................................. 96
6.3.8 Magnesium leaching from mixtures ........................................................................... 97
6.3.9 Potassium content of Sasol sludge, fine and gasification ashes measured in 2006, 2007,
2008 and 2011.................................................................................................................. 101
6.3.10 Potassium content in mixtures ............................................................................... 102
6.3.11 Potassium leaching from mixtures ......................................................................... 104
6.3.12 Sodium content of Sasol sludge, fine and gasification ashes measured in 2006, 2007,
2008 and 2011.................................................................................................................. 106
6.3.13 Sodium content of mixtures ................................................................................... 107
6.3.14 Soluble sodium released ........................................................................................ 109
6.3.15 Phosphorus content of Sasol sludge, fine and gasification ashes measured in 2006,
2007, 2008 and 2011 ........................................................................................................ 111
6.3.16 Phosphorus content in mixtures ............................................................................. 112
6.3.17 Soluble phosphorus released .................................................................................. 114
6.3.18 Micronutrients ....................................................................................................... 116
6.3.19 Iron and manganese in mixtures ............................................................................ 116
6.3.20 Soluble Fe and Mn ................................................................................................ 117
6.3.21 Zinc and copper ..................................................................................................... 120
6.3.22 Solubility of zinc and copper in mixtures ............................................................... 120
6.3.23 Boron and molybdenum in mixtures ...................................................................... 123
6.4 Conclusions ................................................................................................................... 125
CHAPTER 7: ASSESSING THE CATION EXCHANGE CAPACITY PROPERTIES OF THE
VARIOUS MIXTURES .......................................................................................................... 128
7.1 Introduction ................................................................................................................... 128
7.2 Materials and methods ................................................................................................... 130
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7.2.1 Ammonium acetate procedure for the measurement of CEC for unleached and leached
mixtures ........................................................................................................................... 130
7.2.2 Lithium chloride procedure for the measurement of CEC for leached mixtures ........ 132
7.2.3 Potassium chloride (KCl) method for the measurement of CEC for leached mixtures
........................................................................................................................................ 133
7.3 Results and discussion ................................................................................................... 134
7.3.1 Cation exchange capacity (CEC) determination of selected unleached and leached
mixtures using ammonium acetate (NH4OAc) procedure .................................................. 134
7.3.2 Statistical comparison of CEC (NH4OAc) of unleached and leached mixtures .......... 137
7.3.3 Cation exchange capacity (KCl) of leached mixtures ............................................... 138
7.3.4 Cation exchange capacity (LiCl) of leached mixtures .............................................. 138
7.3.5 Statistical comparison CEC (NH4OAc), CEC (LiCl) and CEC (KCl) procedures...... 141
7.3.6 The contribution of sludge, fine and gasification ashes to cation exchange capacity of
the mixtures ..................................................................................................................... 143
7.4 Conclusions ................................................................................................................... 146
CHAPTER 8: GENERAL DISCUSSION ............................................................................... 147
CHAPTER 9: CONCLUSIONS AND RECOMMENDATIONS ............................................. 152
9.1 Conclusions ................................................................................................................... 152
9.2 Recommendations.......................................................................................................... 153
10. References ..................................................................................................................... 154
11 Appendices ..................................................................................................................... 167
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LIST OF TABLES
Table 2. 1: Characterization of Sasol’s biological sludge (using digestion method), fresh
gasification and fine ashes (using X-ray Fluorescence Spectroscopy (XRF)) (Sasol Synfuels,
2008) ........................................................................................................................................ 11
Table 2. 2: Characterization of Sasol sludge (Sasol Synfuel, 2008) ........................................... 24
Table 4. 1: Mixtures with highest and lowest water holding capacities (WHC). ........................ 54
Table 5.1:Mixtures selected for inorganic nitrogen analysis ...................................................... 61
Table 5.2: pH values in selected mixtures for the 1st and 10th eluviation cycles. ........................ 66
Table 6.1: ICP – OES theoretical and actual analytical ranges for each element and wavelengths
used in the analysis (Essington, 2004) ....................................................................................... 76
Table 6.2: Standards used to calibrate the ICP - OES................................................................ 77
Table 6.3: The Ca contentfor the Sasol fine and gasification ashes as determined by X-ray
Fluorescence Spectroscopy (XRF) measured in 2008 and 2011 by two laboratories. ................. 86
Table 6.4: Comparison of total Ca content determined using acid digestion and XRF in fine and
gasification ashes based on 95 % confidence intervals ............................................................... 87
Table 6.5: Measured and calculated means for Ca, in selected mixtures using microwave
digestion method ....................................................................................................................... 89
Table 6.6: Magnesium content in Sasol fine and gasification ashes as determined by X-ray
Fluorescence Spectroscopy (XRF) measured in 2008 and 2011. ................................................ 95
Table 6.7: Comparison of total Mg content determined using acid digestion and XRF in fine and
gasification ashes based on 95% confidence intervals ................................................................ 95
Table 6.8: Measured and calculated means for Mg, in selected mixtures using microwave
digestion method ....................................................................................................................... 97
Table 6.9: Potassium content in Sasol fine and gasification ashes as determined by X-ray
Fluorescence Spectroscopy (XRF) measured in 2008 and 2011. .............................................. 102
Table 6.10: Comparison of total K content determined using acid digestion and XRF in fine and
gasification ashes based on 95% confidence intervals .............................................................. 102
Table 6.11: Measured and calculated means for K, in selected mixtures using microwave
digestion method ..................................................................................................................... 103
Table 6.12: Sodium content in Sasol fine and gasification ashes as determined by X-ray
Fluorescence Spectroscopy (XRF) measured in 2008 and 2011. .............................................. 107
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Table 6.13: Comparison of total Na content determined using acid digestion and XRF in fine
and gasification ashes based on 95 % confidence intervals ...................................................... 107
Table 6.14: Measured and calculated means for Na, in selected mixtures using microwave
digestion method ..................................................................................................................... 108
Table 6.15: Phosphoruscontent in Sasol fine and gasification ashes as determined by X-ray
Fluorescence Spectroscopy (XRF) measured in 2008 and 2011. .............................................. 112
Table 6.16: Comparison of total P content determined using acid digestion and XRF in fine and
gasification ashes based on 95 % confidence intervals ............................................................. 112
Table 6.17: Measured and calculated means for P, in selected mixtures using microwave
digestion method ..................................................................................................................... 113
Table 6.18: Measured and calculated Cu, Mn, Zn and Fe in selected mixtures ........................ 117
Table 7.1: Cations replaced by NH4 in step 1 and NH4 replaced by K in the minus step......... 138
Table 7.2: Comparing mean CEC of the reference material (kaolinite and illite) included
determined by the NH4OAc, LiCl and KCl procedures to that reported in literature. ................ 143
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LIST OF FIGURES
Fig. 3. 1: a) Sludge content in mixtures (wet mass of sludge expressed as a percentage of the
total wet mass), b) gasification ash content in mixtures and c) fine ash content in mixtures ....... 37
Fig. 3. 2: a) Side view of column base showing opening to which vacuum was connected, b) Top
view of column base showing grooved ‘floor’, drainage outlet and imbedded O-ring, c) Side
view of column base showing the imbedded O-ring to ensure a vacuum tight seal between
column and base, d) Bottom view of column base showing drainage outlet and threaded opening
to which a Schott Duran glass bottle were screwed/fitted........................................................... 41
Fig. 3. 3: a) The transparent polyethylene column (length: 0.3 m, internal diameter: 0.105 m), b)
The five layered mesh placed at the outflow boundary of the column or at the base of the
column, c) The securing of the mesh by the column in the column base, d) The column assembly
consisting of the transparent column, the column base and the Schott bottle. ............................. 41
Fig. 3. 4: The low tension column battery on each bench, connected to the vacuum system (blue
pipe from column connected to the red main vacuum line) and collection plastic bottles. .......... 42
Fig.4. 1: Particle size distribution of fine and gasification ash. The error bars are standard
deviations. The values above the bars are the coefficient of variance (n = 30)............................ 50
Fig.4. 2: a) Change in gravimetric water content (kg kg-1) of fine ash from the 1st to 10th
eluviation cycle, b) Volumetric water content (%) of fine ash from the 1st to 10th eluviation cycle,
c) Gravimetric water content (kg kg-1) of gasification ash from 1st to 10th eluviation cycle, d)
Volumetric water content (%) of gasification ash from the 1st to 10th eluviation cycle. .............. 52
Fig.4. 3: a) Assessment of water retention in fine ash (mixture 1), b) Assessment of water
retention in gasification ash (mixture 11), c) Assessment of water retention in mixture 46 with
50% fine ash and 50% sludge and d) Assessment of water retention in mixture 51 with 50%
gasification ash and 50% sludge ................................................................................................ 53
Fig.4. 4: a) Gravimetric water content (kg kg-1) of the various mixtures after the 1st eluviation
cycle, b) Volumetric water content (%) of the various mixtures after the 1st eluviation cycle, c)
Gravimetric water content (kg kg-1) of the various mixtures after the 10th eluviation cycle d)
Volumetric water content (%) of the various mixtures after the 10th eluviation cycle. The arrows
indicate an increase in fine ash content. ..................................................................................... 55
Fig.5. 1: Total inorganic N (NH4+, NO3- and NO2-) released by selected mixtures calculated for
ten eluviation cycles. The arrows indicate the increasing gradient in fine ash. ........................... 65
x
Fig.5. 2: Inorganic N (NH4+, NO3- and NO2-) released by selected mixtures calculated for
eluviation cycles 1 and 10. The arrows indicate the increasing gradient in fine ash. ................... 67
Fig.5. 3: a) Log millimolar NH4+:NO3- ratio based on the ammonium and nitrate released after
ten eluviation cycles, b) Log millimolar NH4+:NO2- ratio based on the ammonium and nitrite
released after ten eluviation cycles, c) Log millimolar NO3-:NO2 - ratio based on the nitrate and
nitrite released after ten eluviation cycles. ................................................................................. 68
Fig.5. 4: NH4+, NO3- and NO2- release trends for selected eluviation cycles; 1, 5, 8 and 10 in a, b,
c and d respectively (expressed as N). ....................................................................................... 70
Fig.6. 1: a) The change in pH of the pore solution upon sludge addition for the first eluviation
cycle, b) frequency distribution of the pore solution pH for the first eluviation cycle, c) The
change in pH of the pore solution upon sludge addition for the tenth eluviation cycle, and d)
frequency distribution of the pore solution pH for the tenth eluviation cycle. The SL in a and c
means sludge. The blue rectangles in a and c demarcate proposed optimum pH range suitable for
plant growth. ............................................................................................................................. 81
Fig.6. 2: Salinity comparison for the first and tenth eluviation cycles. The blue rectangle in
demarcates proposed optimum salinity range for plant growth................................................... 83
Fig.6. 3: a) The change in electrical conductivity of the pore solution upon sludge addition to the
various treatment groups (with sludge increasing from 0 to 50%, fine ash decreasing and
gasification ash increasing – illustrated in chapter 3) for the first eluviation cycle, b) frequency
distribution of the pore solution electrical conductivity for the first eluviation cycle, c) The
change in electrical conductivity of the pore solution upon sludge addition for the tenth
eluviation cycle and d) frequency distribution of the pore solution electrical conductivity for the
tenth eluviation cycle. The SL in a and c means sludge. The blue rectangles in a and c demarcate
proposed optimum EC range for plant growth. .......................................................................... 84
Fig.6. 4: The variation in calcium content of Sasol sludge, fine and gasification ashes based on
analyses done in 2006, 2007, 2008 and 2011 (at least 1 sample per year). The method used was
acid digestion using hydrofluoric acid (HF) acid and a mixture of perchloric acid (HClO4) and
nitric acid (HNO3) (as indicated in section 6.2.2)....................................................................... 85
Fig.6. 5: Calculated Ca content of the mixtures based on the mean content depicted in Fig 6.3.
of sludge, fine and gasification ash to total Ca content of mixtures ............................................ 88
xi
Fig.6. 6: a) Cumulative amount of soluble Ca (mmol kg-1) released after 10 eluviation cycles and
b) Cumulative soluble Ca (%) in mixtures released after 10 eluviation cycles. The arrows in a
and b indicate the direction of increasing gasification ash content of each sludge treatment group.
................................................................................................................................................. 90
Fig.6. 7: a) Log molar Ca:Mg ratio based on the cumulative calcium and magnesium released
after ten eluviation cycles, b) Log molar Ca:K ratio based on the cumulative calcium and
potassium released after ten eluviation cycles, c) Log molar Ca:Na ratio based on the cumulative
calcium and sodium released after ten eluviation cycles, and d) Log millimolar Ca:P ratio based
on the cumulative calcium and phosphorus released after ten eluviation cycles. The SL in a, b, c
and d means sludge. .................................................................................................................. 93
Fig.6. 8: The variation in magnesium content of the Sasol sludge, fine and gasification ashes
based on analysis performed in 2006, 2007, 2008 and 2011 as determined by digestion method.
................................................................................................................................................. 94
Fig.6. 9: Contribution of sludge, fine and gasification ash to Mg content of mixtures ................ 96
Fig.6. 10: a) Cumulative amount of Mg (mmol kg-1) released after 10 eluviation cycles and b)
Cumulative soluble Mg fraction (%) in mixtures released after 10 eluviation cycles. The arrows
in a and b indicate the direction of increasing gasification ash content of each sludge treatment
group......................................................................................................................................... 99
Fig.6. 11: a) Log millimolar Mg:K ratio based on the cumulative magnesium and potassium
released after ten eluviation cycles, b) Log millimolar Mg:Na ratio based on the cumulative
magnesium and sodium released after ten eluviation cycles and c) Log millimolar Mg:P ratio
based on the cumulative magnesium and phosphorus released after ten eluviation cycles. ....... 100
Fig.6. 12: Potassium content in Sasol sludge, fine and gasification ashes measured in 2006,
2007, 2008 and 2011 as determined by digestion method ........................................................ 101
Fig.6. 13: Contribution of sludge, fine and gasification ash to total K content of mixtures ....... 103
Fig.6. 14: a) Cumulative amount of K (mmol kg-1)released after 10 eluviation cycles and b)
Cumulative soluble K fraction (%) in mixtures released after 10 eluviation cycles. The arrows in
a and b indicate the direction of increasing gasification ash content of each sludge treatment
group....................................................................................................................................... 104
xii
Fig.6. 15: a) Log millimolar K:Na ratio based on the cumulative potassium and sodium released
after ten eluviation cycles and b Log millimolar K:P ratio based on the cumulative potassium and
phosphorus released after ten eluviation cycles ........................................................................ 105
Fig.6. 16: Sodium content in Sasol sludge, fine and gasification ashes measured in 2006, 2007,
2008 and 2011 as determined by digestion method .................................................................. 106
Fig.6. 17: Contribution of sludge, fine and gasification ash to total Na content of mixtures ..... 108
Fig.6. 18: a) Cumulative amount of Na (mmol kg-1 ) released after 10 eluviation cycles and b)
Cumulative soluble Na fraction (%) in mixtures released after 10 eluviation cycles. The arrows
in a and b indicate the direction of increasing gasification ash content of each sludge treatment
group....................................................................................................................................... 110
Fig.6. 19: Phosphorus content in Sasol sludge, fine and gasification ashes measured in 2006,
2007, 2008 and 2011 as determined by digestion method ........................................................ 111
Fig.6. 20: Contribution of sludge, fine and gasification ash to total P content of mixtures ....... 113
Fig.6. 21: a) Cumulative amount of P (mmol kg-1) released after 10 eluviation cycles and b)
Cumulative soluble P fraction (%) in mixtures released after 10 eluviation cycles. The arrows in
a and b indicate the direction of increasing gasification ash content of each sludge treatment
group....................................................................................................................................... 115
Fig.6. 22: a) Contribution of sludge, fine and gasification ash to total Fe content of mixtures, b)
Cumulative amount of Fe (mmol kg-1) released after 10 eluviation cycles, c) Contribution of
sludge, fine and gasification ash to total Mn content of mixtures and d) Cumulative amount of
Mn (mmol kg-1) released after 10 eluviation cycles. The arrows in b and d indicate the direction
of increasing gasification ash content of each sludge treatment group. ..................................... 119
Fig.6. 23: a) Contribution of sludge, fine and gasification ash to total Zn content of mixtures, b)
Cumulative amount of Zn (mmol kg-1)released after 10 eluviation cycles, c) Contribution of
sludge, fine and gasification ash to total Cu content of mixtures and d) Cumulative amount of Cu
released after 10 eluviation cycles. The arrows in b and d indicate the direction of increasing
gasification ash content of each sludge treatment group........................................................... 122
Fig.6. 24: a) Cumulative amount of B (mmol kg-1) released after 10 eluviation cycles and b)
Cumulative amount of Mo (mmol kg-1) released after 10 eluviation cycles. The arrows in a and b
indicate the direction of increasing gasification ash content of each sludge treatment group. ... 124
xiii
Fig.7. 1: a) Cation exchange capacity means for selected unleached mixtures determined by
ammonium acetate (NH4OAc) procedure, b) Cation exchange capacity means for selected
leached mixtures determined by NH4OAc procedure, c) Cation exchange capacity of leached
mixtures determined by lithium chloride (LiCl) method and d) cation exchange capacity of
leached mixtures determined by potassium chloride (KCl) method. Means with the same letter
are not significantly different from each other and means with different letters are significantly
different from each other. ........................................................................................................ 140
Fig.7. 2: Comparison of the CEC of selected leached mixtures as measured by NH4OAc, LiCl
and KCl methods..................................................................................................................... 141
Fig.7. 3: a) Sludge and fine ash contribution to CEC for selected mixtures (1, 21, 22, 31, 39 and
46) with sludge content increasing (0 to 50%), fine ash decreasing (100 to 50%), gasification ash
content 0%, b) Sludge and gasification ash contribution to CEC for selected mixtures (11, 12, 30,
38, 45 and 51) with sludge content increasing and c) fine and gasification ash contribution to
CEC for selected mixtures (1, 6 and 11) without sludge. ......................................................... 145
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ABSTRACT
Sasol biological sludge, coal fine and gasification ash were the three waste streams involved in
this study. The main concern is that on their own they are not suitable as growth mediums, the
ash is alkaline (pH>12) with high salinity (total dissolved solids of 8000 mg ℓ-1). Fine ash is
microporous (particle size diameter <250 µm) and forms cemented layers that can restrict root
growth while, gasification ash in macroporous (most particle size diameter ranged between 1 and
75 mm) and has a low water holding capacity. Sludge is unstable and can inhibit gaseous
exchange. However, these wastes potentially, have physical, biological and chemical attributes
that make them suitable as hospitable growth medium. Sludge can promote micro-fauna activity
and, provide plant available nitrogen (N) as well as phosphorus (P) the ash is poor in. On a short
term bases and in the long term it can also contribute to cation exchange capacity (CEC). Fine
ash can increase water holding capacity and gasification ash can improve gaseous exchange. It
was hypothesized that if the ash was treated with sludge, pH will be reduced to between 5.5 and
8, and weathering will reduce salinity to less than 400 mSm-1, increase CEC and increase plant
available N and P. Therefore, the main purpose of this laboratory column study was to establish
combinations of these waste streams that hold promise as plant growth media, based on various
chemical and physical criteria link to hospitable plant growth media, as well as the influence of
weathering on the release of essential plant nutrients. A total of 51 mixtures (each weighing 2.6
kg) were formulated based on wet mass basis and divided into 6 groups based on sludge content
(0, 10, 20, 30, 40 and 50%) and packed into columns, subjected to wetting and drying for 1 year
(10 wetting and drying cycles) by passing through deionized water equivalent to the pore volume
and allowing the mixtures to dry in between. The leachates were analysed using Inductively
Coupled Plasma Optical Emission Spectrometry (ICP-OES) and Kjeldahl procedures (for N
release). Total elemental analysis was done using X-ray Fluorescence Spectroscopy (XRF) and
acid digestion method. Particle size distribution was done using the sieve method. Cation
exchange properties were assessed using ammonium acetate (NH4OAc), lithium chloride (LiCl)
and potassium chloride (KCl) methods. Results indicated that sludge was critical for these
mixtures,at a minimal content of 10% it increased the water holding capacity of the mixtures. In
the mineralization of inorganic N at a lower limit of 20% sludgeenabled the production of plant
available NH4+ and NO3 - and less NO2 -. Increasing sludge to 50% further reduced the production
of NO2 - in the mixtures. In terms of elemental release, mixtures without sludge were dominated
xv
by Na and the order of abundance was as follows; Na>K>Ca>Mg>P on mmol kg-1 but the
introduction of sludge at a lower content limit of 10% changed the abundance of the elements as
follows; P>Mg>Ca>Na>K on mmol kg-1. Sludge content as low as 10% reduced the pH of the
mixtures to between 7.6 and 8 and EC to less than 400 mSm-1. However, increasing sludge to
50% increased the leachate EC dramatically and kept the EC high (415 mSm-1) till the end.
Introduction of sludge at a low limit of 10 % content increased the CEC above 8 cmolc kg-1. The
effects of fine ash on the water holding capacity of the mixtures were seen at the 10 % level, for
example, mixture 13 with 10% fine ash had 0.3 mg kg-1, while mixture 12 with 0% fine ash had
0.27 mg kg-1. Increasing fine ash content above 40% increased pozzalanic properties, pH (>8),
EC (>400 mSm-1), Na release and reduced CEC.Gasification ash is the biggest waste stream and
utilizing these wastes as growth media will mean that it realistically will always dominate these
mixtures. This study showed that on its own it will be a challenging environment. However, the
amendent with sludge and fine ash resulted in some chemically and physically favourable
changes in these media. It can be concluded that the main objective has been achieved and bio
assay evalution of theses mixtures is recommended.
Keywords:water holding capacity;nitrogen;elemental release; cation exchange capacity.
xvi
CHAPTER 1
1. Introduction
Fine and gasification ashesare byproducts of coal combustion which is a process intended to
generate energy and the quantity of ash produced worldwide is estimated to exceed 6 x 108
million tons annually (Jayasinghe et al., 2009). South Africa produces approximately 28 million
tons of ash annually (Reynolds et al., 2000) and Sasol Synfuels in Secundaconsumes over 45
million tons of low grade, high ash coal (lignite and sub-bituminous coals) annually and
produces 4 and 7 million tons of fine and gasification ashes respectively (Ginster & Matjie, 2005
&Mahlaba et al., 2011). According to Jala and Goyal (2006) and Haynes (2009), lignites and
sub-bituminous coals tend to have low suphur (S) contents and are high in calcium (Ca) resulting
in alkaline ash.Such coals may also have high moisture content and low ash fusibility (potential
to slagging) (Katalambula & Gupta, 2009). Consequently, ash properties depend on the physical
and chemical characteristics of the coal gasified, coal particle size and more significantly on the
gasification process (Jankowski, et al., 2005).
Sasol fine ash is a combination of about 83% fly ash and the remaining 17% is made up of both
gasification ash and bottom ash fines with particles of less than 250 µm (Mahlaba et al.,
2011).The mineralogy of Sasol fine ash characterized using X-ray diffraction (XRD) technique
consists of major phases such as anamorphous phase (a phase with non-fixed elemental
proportions and has no ordered crystalline structure), mullite (Al6Si2 O13) and quartz (SiO2) and
someminor and trace mineral phases. Physically, fine ash has 60% of particle sizes falling
between 5µm and 75 µm in diameter.Gasification ash is a combination of red and white fused
sintered clinkers with heterogenous texture varying from fine material to large irregularly shaped
aggregates ranging from 4 to 75 mm (Matjie et al., 2008). The mineralogy of gasification ash
consists of major oxides like quartz (SiO2), mullite (Al6Si2O13) and anorthite (CaAl2Si2O8) and
some minor oxides (Ginster & Matjie, 2005 & Matjie et al., 2008). Fly ash isgenerally grey in
colour, abrasive, mostly alkaline, andrefractory in nature and is generated from the combustion
of powdered coal.This type of ashconsists of fine particles resulting from fused clay minerals
mainly comprising aluminium-silicate ((AlO)2SiO3) that gives it pozzolanicproperties
(cementitious characteristics) and Sasol fly ash is dominated by 59% of silt-sized particles
(Ahmaruzzaman, 2010 &Mahlaba et al., 2011).Generally, finer particles of ashes are spherical in
1
shape and the spheres may be solid, hollow (cenospheres) or encapsulating (plerospheres)
(Kopsick & Angino, 1981&Matjie et al., 2008).
Sasol ashes are alkaline with a pH greater than 12 and have a high salinity of approximately
8000 mg l-1 (Mahlaba et al., 2011). However, coal ash may be rich in non available plant
nutrients such as boron (B), but occasionally can supply plant available potassium (K), Ca and
magnesium (Mg). Alkaline ash is generally a poor source of plant available nitrogen (N) and
phosphorus (P) (Jankowski et al., 2006). These plant nutrients are generally released from the
ash minerals by processes of weathering such as hydrolysis, carbonation, oxidation, hydration
and dissolution (McConnell, 1998 & Brady & Weil, 2008). Chemical weathering over time
induces transformation of the ash components converting alumino-silicate glass (mineral phases)
to non-crystalline clay minerals and the formation of these secondary minerals is shown by an
increase in cation exchange capacity (CEC) (Zevenbergen et al., 1999). It was envisaged that the
increase in CEC during weathering results from aluminium-Silicon rich phases that form during
mineral transformation with increased adsorption capability (Gitari et al., 2009).
Deposition and management of ash is a great challenge to energy producing plants since there is
pressure on governments from the international community to reduce greenhouse gases and
environmental pollution. Ash generally is used in cement and concrete manufacturing,
agriculture as soil amendment and in waste stabilization, but if not used, it is generally “land
filled” as part of daily management practice or is washed out with water into artificial lagoons
(Jala & Goyal 2004, Haynes, 2009 & Gitari et al., 2009).Sasol adopted landfilling as their major
daily management practice (Mahlaba et al., 2011). Landfilling degrades soil and can endanger
human health and the environment through the release of toxic elements to subsurface aquifers
that serve as drinking water supplies close to ash disposal sites (Wang & Wang, 1992, Asokan et
al., 2005 & Jankowski et al., 2005).
Sludge is a redundant byproduct of industrial wastewater treatment processes (Wang, 1996) and
can be derived from various processes that influence its properties (Snyman & Van der Waals,
2004).Sasol biological sludge is a byproduct of the aerobic activated biosolid treatment process
and has a pH of 6.8. Generally, sludge contains mainly organic N, P and high organic matter
2
(OM) content that makes it a potential source of nutrients for plant growth (Snyman & Van der
Waals, 2004, Snyman & Herselman, 2006).
Nitrogen and P are contained in appreciable
amounts while K content is generally low because most K compounds are water soluble and
remain in the sewage effluent or the liquid portion during sludge dewatering (Rechcigl, 1995).
Nitrogen is present in sludge mainly in the organic form and needs to be mineralized into
inorganic forms (nitrate - NO3-, nitrite - NO2- and ammonium - NH4+). Sludge contains variable
quantities of organic N and inorganic N (NO3-, NO2-and NH4+) (Snyman & Van der Waals,
2004).
According to Herselmen et al. (2005) sludge disposal on land either for beneficial or non
beneficial use is the most adopted strategy for sludge management. However, this management
strategy increases the risk of environmental pollution. Another least adopted sludge management
method is incineration which not only generates carbon dioxide (CO2), a greenhouse gas, but
also numerous flue gases and toxic residues. The ash produced during incineration is even more
hazardous, as it contains high concentrations of heavy metals (Moldes et al., 2007).
To minimize the transfer of toxic materials to the environment, Jala and Goyal (2004) and
Haynes (2009), suggest that establishment of vegetation and the raising of forests on fly ash
basins and landfill sites can stabilize the ash against wind and water erosion and reduce the
leaching of metals and metalloids through water loss as evapotranspiration. However, ash and
sludge on their own are not suitable as growth mediums. Ash is alkaline and can induce
deficiencies of essential plant nutrients such as P and trace elements such as, iron (Fe),
manganese (Mn), zinc (Zn) and copper (Cu) (Haynes, 2009). Unweathered alkaline ash can also
contain high levels of soluble salts that may induce osmotic stress on plants and microorganisms,
creating conditions difficult to assimilate water. This is an important parameter that can limit the
suitability of any growth medium to support life if not leached (Brady & Weil, 2008, & Haynes,
2009). Furthermore, in highly alkaline conditions, the dominant inorganic N form is often
ammonia (NH3) which volatilizes, and high NH3 concentrations have a negative effect on
microbial activity (Keen & Prosser, 1987). An important nitrifying bacterium, for example,
Nitrobacter which converts NO2- to NO3-, is inhibited by high NH3 concentrations.
Nitrosomonas, which convert NH4+ to NO2- are less sensitive to high pH and therefore alkaline
3
conditions can also lead to a build-up of NO2-, another biotoxic compound (Keen & Prosser,
1987).
Physically, gasification ash has a low water holding capacity and thus will not be able to provide
enough water for shallow rooted plants. This is another factor that makes plant establishment
difficult, limiting options for vegetative capping. Inversely, too much fine ash can restrict root
growth due to the natural compaction of its particles and or formation of solid cemented layers
(Haynes, 2009). Unstable sludge, on the other hand, is anaerobic, a condition that inhibits
gaseous exchange.
To establish a vegetative cover under such extreme conditions, engineering experts have
suggested capping the Sasol ash heaps with topsoil. The philosophy behind capping the ash
dump with topsoil was to try and establish a vegetation cover consisting of shallow and deep
rooted plants which will help minimize pollutant transfer to the environment and increase water
transfer to the atmosphere. Furthermore, established vegetation will also stabilize the ash dump
against wind and water erosion (Jala & Goyal, 2004). However, capping the ash dump will
require importing a significant amount of valuable and irreplaceable topsoil as growth medium
from elsewhere, there by seeking additional enriched inputs. It seems worthwhile, therefore, to
investigate the feasibility of combining these waste streams as an alternative or as part of a more
integrated waste disposal strategy to transform the outer layers of the ash dump into a suitable
growth medium for plants. This follows the fact that these wastes potentially have chemical,
physical and biological attributes that make them suitable to engineer a hospitable growth
medium for plants.
Sludge can promote the establishment of micro-fauna in the ash as it contains microorganisms
such as bacteria, viruses, fungi and yeasts, parasitic worms and protozoa (Snyman & Van der
Waals, 2004) that the ash is poor in. There can also be an introduction of bacteria species
responsible for N mineralization such as autotrophs, Nitrosomonas that convert NH4+ to NO2and autotrophs, Nitrobacter species that convert NO2- to NO3- (DinÇer & Kargi, 2000). Sludge
will also increase the content of plant available N and P in which the ash is poor in. Moreover,
sludge is a source of organic material and humified or stabilized sludge can promote the
4
establishment of soil micro-flora and fauna, increasing water holding capacity and contribute to
CEC (a function of organic matter functional groups) (Brady and Weil, 2008 & Essington,
2004). Therefore, stabilized sludge will generally increase the fertility of the artificial growth
medium.
Fine ash consists of fine particles (Jala & Goyal, 2004) that can contribute to the microporosity
of the medium and increase the ability to retain water. Conversely, gasification ash is
macroporous (Jala & Goyal, 2004), a characteristic that can provide the necessary aeration
needed for numerous microbial mediated processes, essential for a functioning soil medium, for
example, nitrogen mineralization and nitrification. This characteristic can further ensure rapid
infiltration and minimize run-off from the ash dump. The high hydraulic conductivity of the
gasification ash will also increase capillary rise resulting in more sustainable transfer of water to
the atmosphere and greater cumulative evaporation.
It has been shown that Sasol sludge amended gasification ash can support vegetation. Annandale
et al. (2004) conducted a preliminary rehabilitation trial on the ash dump in which sludge was
surface incorporated as an alternative to importing topsoil as a growth medium to establish a
vegetative cap on the ash. In this trial, several perennial grasses and shrubs were screened with
the primary objective of establishing which species could adapt to the substrate conditions. Of all
the perennial grasses tested, Chloris gayana and Cynadon dactylon indicated the best
establishment, good overall cover, dense stand and best survival. Some of the grasses and shrubs
could not survive. It was therefore evident that a better understanding was needed of the
chemistry and essential plant nutrient release behaviour of these waste combinations.
In this study it was hypothesised that the incorporated sludge will increase water holding
capacity of the ashes, add P and N into the mixtures, increase elemental release, reduce the high
pH and salinity in ash to optimum levels suitable for plant growth through dilution effects and
also increase the CEC of the mixtures.It was also hypothesized that subjecting the mixtures to
wetting and drying cycles (eluviation cycles) will induce weathering processes that can reduce
pH, salinity, increase elemental release and increase CEC through the transformation of primary
minerals to secondary minerals. The addition of fine ash was expected to increase water holding
5
capacity of the mixtures. The main purpose of this work was therefore to establishvarious
biological sludge – coal ash combinations that have the potential of supporting plant growth and
to assess the release of essential plant nutrients as influenced by weathering under laboratory low
tension column system.
1.1 Specific objectives
i)
To establishbiological sludge – coal ash combinations suitable to serve as
growthmedia with sufficient available essential plant nutrients, also physically and
biologically suitable.
ii)
To assess elemental release as influenced by weathering, physical (particle size
distribution and water holding capacity) and biological (nitrogen mineralization)
characteristics of the mixtures.
iii)
To assess pH and salinity dynamics in the mixtures as influenced by sludge
incorporation and weathering.
iv)
To determine the cation exchange capacity of the mixtures usingammonium acetate
(NH4OAc), potassium chloride (KCl) and lithium chloride (LiCl) as influenced by
sludge incorporation and weathering.
1.2 Thesis lay out
The outlay of the thesis included several chapters attempting to achieve the above objectives.
Chapter 2
This section focused on the literature study covering the physical, biological and chemical
characteristics of Sasol sludge, fine and gasification ashes; the potential of the individual wastes
and the biological sludge-coal ash mixtures in releasing essential plant nutrients and subsequent
support of plant growth. It also covered literature on the development and advantages of
unsaturated low tension columns.
Chapter 3
Generic materials and methods were covered under this section and these included the
development and set up of unsaturated low tension columns, mixture formulations and the
6
approach followed in packing the columns, carrying out wetting and drying cycles and the
collection and preparation of leachates for analysis.
Chapter 4
This section dealt with the physical characteristics of the mixtures and this includedparticle size
distribution and water holding and release capacity of the mixtures.
Chapter 5
The focus in this section was on the biological characteristics of the mixtures. To achieve this
microbial mineralized inorganic nitrogen species (NH4+, NO2- and NO3-) were determined in the
leachates.
Chapter 6
This section covered the chemical characteristics of the mixtures that included pH and salinity
changes and the release of Ca, Mg, P, K, Na, Mn, Cu, B, Mo, Zn and Feas influenced by
weathering induced by eluviation cycles. It also incorporated work done in 2006, 2007, 2008 and
2011 that covered the characterization of Sasol sludge, fine and gasification ashes.
Chapter 7
This chapter covered the determination of cation exchange capacity (CEC) for fresh
mixturesusing ammonium acetate (NH4OAc) method, and the determination of CEC for leached
mixtures using NH4OAc, lithium chloride (LiCl) and potassium chloride (KCl) methods.
Basically this section compares the efficiency of these methods in determining CEC in biological
sludge-coal ash mixtures and covers the effect of sludge and weathering in the development of
negative charges in the mixtures.
Chapter 8
General discussion
Chapter 9
Conclusions and Recommendations of the study.
7
CHAPTER 2: LITERATURE REVIEW
2.1 Fine and gasification ashes characteristics
Fine and gasification ashes are inevitable co-products of coal gasification process employed by
Sasol Synfuels in Secunda to produce synthesis gases (Ginster & Matjie, 2005). Seven million
tons of gasification ash is produced annually from the combustion of low grade coal (lignite and
sub-bituminous coals) that has low sulphur (S) content and high in calcium (Ca) resulting in
basic ash (Ginster & Matjie, 2005, Jala & Goyal, 2006 & Haynes, 2009). The fine ash is a
combination of about 83% fly ash and the remaining 17% is made up of both gasification ash
and bottom ash fines with particles of less than 250 µm (Mahlaba et al., 2011). Gasification ash
is a combination of red and white fused sintered clinkers with heterogenous texture varying from
fine material to large irregularly shaped aggregates (Matjie et al., 2008). Fly ash (generally grey
in color, abrasive, mostly alkaline, andrefractory in nature) is generated from the combustion of
powdered coal and has fine particles resulting from fused clay minerals mainly comprising
aluminium silicate (AlO)2SiO3) giving it the pozzolanic (cementitious characteristics) properties
(Ahmaruzzaman, 2010). Sasol fly ash is dominated by 59% of silt-sized particles (Mahlaba et al.,
2011).
Coal ash properties generally depend on the physical and chemical characteristics of the coal, the
coal particle size distribution and more significantly on the combustion process (Jankowski, et
al., 2005). For example, the Ca to S molar ratio of over 2.5 in the feedstock results in the ashes
containing not only calcium sulphate (CaSO4) but also calcium oxide (CaO) (Anthony et al,
2003). The presence of significant amounts of CaO and oxides of iron (Fe), and magnesium
(Mg) make the ash basic (Tsai, 1982). The mineralogy of weathered Sasol fine ash was
characterized by Mahlaba et al. (2011) using X-ray diffraction (XRD) technique and found that it
contained major phases; amorphous phase ( a phase with non fixed elemental proportions and
has no ordered crystalline structure), mullite (Al6Si2O13) and quartz (SiO2), minor phases; calcite
(CaO3), magnetite (FeFe2O4), ettringite (Ca6Al2(SO4)3(OH)12.26H2O) and sillimanite (Al2SiO5),
trace mineral phases; pyrrhotite (Fe9S10), and analcime (NaAlSi2O6.H2O), periclase (MgO) and
hematite (Fe2O3). Sasol fresh gasification ash contains of major oxides; quartz (SiO2),mullite
(Al6Si2O13) and anorthite (CaAl2Si2O8); minor oxides; diopside (CaMgSi2O6), hematite (Fe2O3),
crystobalite (SiO2) and anhydrite (CaSO4) (Ginster & Matjie, 2005 & Matjie et al., 2008).
8
Sasol fine ash contains 60% of the amorphous phase (Mahlaba et al., 2011) that represents the
pozzolanic nature of the ash. A pozzolan can be described as a siliceous - aluminous material
that is formed, when calcium hydroxide (Ca(OH)2) chemically reacts with silicic acid (H4SiO4,
or
Si(OH)4).
The
resultant
products
formed
include
calcium
silicate
hydrate
(Ca9Si6O18(OH)6·8(H2O)) and Strätlingite (Ca2 Al2SiO2(OH)10·3H2O) (Matschei et al., 2007)
depending on the presence of Ca, Al and Si in the ash. These pozzolans have a cementitious
characteristic that they acquire after addition of lime during the combustion process. Basically
the Ca, aluminium (Al) and silicon (Si) in ash react with the free lime in the presence of water to
form these cementitious materials (Haynes, 2009). The pozzolans in the ash are important as
adsorption sites for pollutants such as chloride and can possibly increase water holding capacity
(Mahlaba et al., 2011).
Physically, gasification ash is the coarse grained material that consists of agglomerated dark grey
granular particles with a very porous surface texture (Kopsick & Angino, 1981, Jala & Goyal,
2004 & Cheng, 2005). Sasol gasification ash has fine to large irregularly shaped aggregates of
sizes ranging from 4 to 75 mm (Matjie et al., 2008). In characterizing Sasol fine ash Mahlaba et
al. (2011)found that particles falling between 5µm and 75 µm constituted 60%.This percentage
was greater than particles that fell between1 – 5 µm (16%) and between 75 – 425µm
(30%).Generally, finer particles of ashes are spherical in shape showing a complete melting of
silicates which occurs during combustion at temperatures above 1350 oC and pressures greater
than 2000kPa (Matjie et al., 2008).
The spheres of both ashes may be solid, hollow
(cenospheres) or encapsulating (plerospheres) (Kopsick & Angino, 1981). Micrographic
evidence indicated that most of the particles in fine ash occur as solid spheres of amorphous
glass that forms during cooling of the melt phase (Tishmack & Burns, 2004). In addition, only a
few hollow spheres and some spheres packed with other numerous small spheres or crystals of
minerals, may be present (Trivedi & Sud, 2002). The crystals of minerals formed are a result of
cooling of the minerals and non-mineral inorganic elements in the coal mineral matter that melt
and form liquid phases during the gasification process (Matjie et al., 2008). Fine ash has a low
particle density, a high surface area and light grey particles (Asokan et al., 2005 & Jala & Goyal,
2006).
9
Coal ash is generally rich in elements such as Ca, Mg, sodium (Na), potassium (K), phosphorus
(P),boron (B) and minor elements (Jankowski et al., 2005). However, sulphate (SO4), (K), and
Ca total content generally increase with decrease in particle sizes (Bendz et al., 2007). Sasol
freshfly and fine ashes together with weathered fine ash contain elements which are classified
as major (> 1%) in coal ash and include; Si, Al, Ca, Fe, Mg and Na, also contains minor (0.1 –
1%) elements that include; K, S and P, characterized by (Mahlaba et al., 2011) using X-ray
Fluorescence (XRF). Calcium is generally a dominant cation in fine ash followed by Mg, Na and
K (Maiti et al., 1990). However, these claims differed slightly when compared to cation
dominance in Sasol fine and gasification ashes; the Ca was the most dominant cation followed
by Mg, K and Na (Table 2.1). A major portion of K is localized in the interior glassy matrix
(principally an alumino-silicate glass containing elements such as; Ca, Fe, Mg and P and is
derived from crystallized minerals) the external glass is enriched with Mg (Matjie, et al., 2005
&Jala & Goyal, 2006). A number of metals and metalloids may also be present as carbonates,
oxides, hydroxides, and sulphates, including; cadmium (Cd), arsenic (As), selenium (Se), lead
(Pb), nickel (Ni), copper (Cu), chromium (Cr), cobalt (Co), molybdenum (Mo), beryllium (Be) in
lower but still significant concentrations (Jankowski et al., 2005).
As discussed earlier, coal ash can be acidic or alkaline depending on the S content of the feed
stock used for combustion (Carlson & Adriano, 1993). Sulphur in coal exists as pyritic sulphur
(FeS2), suphate sulphur (SO4), organic sulphur, and as elemental sulphur (S).
During
combustion S is oxidized to SO2, SO3 then eventually to suphuric acid (H2SO4) when it reacts
with water molecules that make the ash acidic (Chatterjee, 1940 & Ryan, 1997). According to
Jala & Goyal (2006), anthracite coals (highest ranked coal that has the highest heating value of
13600 British thermal unit per pound of coal2 and contains 94% carbon) are generally high in S
and produce acidic ash while lignite coals (lowest ranked coal thathas a heating value of 7000
British thermal unit per pound of coal2 and contains 72% carbon) tend to be lower in S but higher
in Ca content and produce alkaline ash. Some of the alkaline ashes can have pH values
exceeding 12 (Edmunds, 2002 &Haynes, 2009). As indicated earlier on that the feed stock for
Sasol is low grade coal (lignite and sub-bituminous coals) that has low S content and high in Ca
increase the pH for both fine and gasification ashes to above 12(table 2.1). The pH for fine ash is
10
mostly contributed by fly ash that forms 83% of fine ash and has a pH value of 12.5 (Mahlaba et
al., 2011). The presence and high concentrations of soluble salts (Na+, K+, Mg2+ Ca2+ and Cl-) in
alkaline and unweathered ash give the ash a saline status and a high electrical conductivity (EC)
of 1300 mSm-1(Haynes, 2009).
Table 2.1: Characterization of Sasol’s biological sludge (using digestion method), fresh
gasification and fine ashes (using X-ray Fluorescence Spectroscopy (XRF)) (Sasol Synfuels,
2008)
Parameter
Gasification Ash
Fine ash
Units
pH
10.80
> 12
Unit less
Volatile Matter
4.6
16.9
%
Total N
< 0.04
0.04
%
Total C
5.7
2.67
%
C/N Ratio
N was below detection
66.75
Unit less
Ca
5.75
5.07
%
Mg
0.973
0.954
%
P
0.251
0.229
%
K
0.904
0.230
%
Na
0.383
0.143
%
Fe
1.93
0.56
%
Cu
246
17.4
mg kg-1
Mn
200
316
mg kg-1
Zn
220
20
mg kg-1
Al
Below detection
2.029
%
S
Below detection
0.123
%
The leaching of thesechemical constituents from the ash depends on several factors such asthe
nature of the mineral phases present, patterns and speciation of the chemical constituents existing
in the ash andelements combined with the glass phases are more resistant to leachate solutions.
The presence of a non porous continuous outer surface and a dense particle interior (in fly and
fine ash) restricts metal leachability from residues.Other physicochemical factors that also
influence leaching include; the type of the leaching medium (considering the particle size
distribution), pH, the complexing agents (functional groups)present in the solid sample and
various reaction kinetics (Van der Sloot et al., 1981,Anthony et al, 2003 &Saikia, 2006).
Kopsik & Angino (1981), conducted a laboratory based column study on the leaching of Ca, Mg,
Na, K, Fe, Mn, Zn and Cu from six fly ash samples (collected from six different places) and
11
three bottom ash samples (collected from three different places) separately using distilled water
and to assess pH change. Column specifications used; 1.0 m in length and 0.05 m internal
diameter. Fly ashes had finer sized particles ranging from 0.5 to 100 µm, while bottom ashes
were coarse grained with particle sizes above 100 µm. The samples were packed to a height of
0.46 m. Distilled water was passed from the bottom to prevent preferential flow and the columns
were kept saturated between leachings. Three leaching patterns were observed in their work. The
most prevalent trend was characterized by a large initial release of all elements except for Ca in
both fly and bottom ash samples and leveling off of concentrations later in the leaching process.
The next trend observed was that there was a constant release pattern for Ca in all ashes and
subsequent leaching did not lower its concentration. This was explained by the presence of
soluble Ca-sulfates and oxide in the ash that represented a constant source of Ca for release.
The final trend involved a delayed release curve (in fly ash) in which a short period of time
elapsed before the maximum concentration in the leachate was observed. Their explanation was
that the leaching behavior was related to the morphology of the fly ash. The delay in elemental
release was because of the large total surface (due to small particle size diameter) available for
the reaction with the leaching solution. Because of the aluminosilicate material in fly ash the
leaching water initiated pozzolanic reactions that developed an impermeable layer delaying
leaching of the elements more especially iron (Fe), manganese (Mn), zinc (Zn) and copper (Cu).
Iron concentration was the lowest in the leachates of all samples and the reason given was that it
occurred in the matrix or as extraneous sulphide material that is not susceptible to leaching. In
the beginning of the experiment one bottom ash and two fly ashes were acidic (pH 3-5); four fly
ashes and two bottom ashes were alkaline (pH 9.1-11.8). The pH remained approximately the
same after the study for most of the samples. This period (two weeks) was not enough to realize
significant changes in pH.
Clearly coal ash can serve as a source of plant available Ca and Mg but is a poor source of N or
plant available P and can only occasionally supply plant available K (Jankowski et al., 2006).
Some studies show that only 1-3% fine ash material is soluble in water and if the water extracts
are analysed they indicate Ca and Na as the principal cations extracted. However, the
concentration of ions in leachates is controlled by the solubility of particular minerals present in
12
the ash (Iyer, 2002). The controlling minerals include CaCO3, dolomite (CaMg(CO3)2) and
MgOamongst others (Jankowski et al., 2006). The susceptibility of these minerals to hydrolysis
depends on the surface area of the ash, reaction time and water/solid ratio. However, pH still
remains the major factor controlling the extractability of a number of elements including
essential plant nutrients (Iyer, 2002).
Weathering processes are the only factors that influence elemental leaching and the formation of
secondary minerals and subsequent release of elements in ash.
Processes involved in the
weathering include; hydrolysis, carbonation, oxidation, hydration and dissolution (McConnell,
1998& Brady & Weil, 2008). Basically hydrolysis in this context refers to the ionization of
carbonic acid into hydrogen (H+) and bicarbonate (HCO3). The free H+ replaces other ions in a
mineral’s atomic structure altering its chemical composition into a weaker secondary mineral.
The oxidation process involves the reaction of oxygen (O2) with iron (Fe) in a mineral to form an
iron oxide mineral, for example, hematite may be formed and this mineral is insoluble
(McConnell, 1998). The hydration process involves the chemical union of a mineral with one or
more water molecules (Brady and Weil, 2008).
McConnell (1998) and Brady and Weil (2008) describe dissolution as a process by which a gas,
solid or another liquid dissolve in a solvent. In the case of carbonation process, CO2 diffuses into
moist ash and reacts with water (H2O) producing a carbonic acid (H2CO3) which eventually
dissolves minerals during weathering. Dissolution rates differ from mineral to mineral, hence the
rates decrease in this order; Ca-plagioclase>Na-Plagioclase>K- feldspar > quartz(Kump et al.,
2000).In ash, chemical weathering over time effects an alteration of the ash components and the
aluminosilicate glass property to non-crystalline clay minerals. The formation of the clay mineral
is generally indicated by an increase in CEC (Zevenbergen et al., 1999). Gitari et al., (2009)
suggest that the increase in CEC is attributable to the Al-Si rich phases that form during mineral
transformation with increased adsorption capability.
The rate of weathering processesdepend on mineralogy of the ash components exposed, the
reactive surface area of these minerals, the supply of water, its residence time in the ash and
initial pH, the abundance of organic acids, and the temperature of ash solutions (Kump et al.,
13
2000). Most of the elements are released after long equilibration times when the alkalinity of the
ash is significantly depleted and pH of the leachate approaches circum-neutral or acidic levels
(Gitari et al., 2009). The reduction of pH is the result of carbonation.
Management of ash is a great challenge to the energy producing plants since there is pressure on
governments to reduce green house gases and environmental pollution. Ash generally used in
cement and concrete manufacturing, agriculture as soil amendment and in waste stabilization, but
if not used, it is generally “land filled” as part of daily management practice or is washed out
with water into artificial lagoons (Jala & Goyal 2004, Haynes, 2009 &Gitari et al., 2009).Sasol
adopted landfilling as their daily management practice.
The contamination of the environment could be through the release of toxic constituents to
subsurface aquifers that serve as drinking water supplies nearby the disposal area (Wang &
Wang, 1992, Asokan et al., 2005 &Jankowski et al., 2005). As remediation strategy to reduce
environmental contamination Jala and Goyal (2004) and Haynes (2009), suggested that
establishment of vegetation and the raising of forests on ash basins and landfill site can serve a
variety of functions like stabilizing the ash against wind and water erosion and reduction of the
leaching of the metals and metalloids through water loss as evapotranspiration.
2.2 Sources and mobility of macronutrients (N, P, K, Ca and Mg) in coal ash
Potassium is localized in the interior glassy matrix in ash (Jala & Goyal, 2006). Generally,K can
exist as; water – soluble, exchangeable, fixed and structural forms. Hence the order of K forms
availability to plants is as follows: water – soluble>exchangeable>non-exchangeable>mineral K.
The availability of any of these K forms to plants is related to the structural and surface
chemistry of the minerals (Huang et al., 2005 & Huo-Yan et al., 2010). The water-soluble K is
the K+ in solution immediately available to plants and potentially subject to leaching. The
balance between water-soluble K and exchangeable K depends on factors such as pH, CEC and
clay mineralogy and could be influenced by the alteration of ions in soil solution and the total
concentration of soluble anions (Huo-Yan et al., 2010). Exchangeable K is the portion of the K
that is electrostatically bound / adsorbed as an outer-sphere complex to the surfaces of minerals
and organic matter. Potassium in this form is readily exchanged with other cations. The non14
exchangeable K is slowly available and does not only become adsorbed but fits in between layers
of swelling minerals and become an integral part of the crystal, while mineral K is relatively
unavailable and is in the crystal structure of the minerals (Huang et al., 2005&Brady & Weil,
2008).
Mineral dissolution, precipitation, sorption and desorption of chemical species are responsible
for the release and/or sequestration of essential elements such as K (Brown et al., 1999). Slow
dissolution of minerals in ash is a dominating mechanism for the non-exchangeable and mineral
K release. The release may also proceed via selective exchange of interlayer K. Other factors that
influence K release include particle size and chemical composition. For example, minerals with
finest particles may undergo ‘layer weathering’ implying a rapid initial release which results in
mixed-layer clays that strongly retain the remaining K (Simonsson et al., 2009). Larger particles
release K through ‘edge weathering’ (Murashkina et al., 2007). Redox processes and formation
of hydroxyaluminium interlayers also affect K release. The reduction of structural Fe3+ to Fe2+
promotes fixation of interlayer cations like K (Simonsson et al., 2009). An increase in pH as well
may enhance the fixation of K probably as an indirect result of a reduction in hydroxyaluminium
interlayering of minerals. However, K release or leachability from the interior glassy matrix in
ash may be observed at pH 6 to above 10 (Jala & Goyal, 2006 & Simonsson et al., 2009).
Alternate wetting and drying of these minerals may aid in slow release of fixed potassium
(Sharma et al., 2010). This is made possible by the chemical weathering processes that are
initiated by the introduction of water. Water also leaches out the soluble K in solution. The
dissolution of calcite and gypsum in ash increases the concentration of Ca2+ and Mg2+ which
further increase K desorption by replacing it from the exchange site (Kolahchi & Jalali, 2007).
The K in the inter layers can be exchanged by Ca2+ and H+ enhancing its release (Rahmatullah &
Mengel et al., 2000).
Phosphorus fractions include non-occluded inorganic phosphate (phosphate in solution,
phosphate adsorbed to mineral surfaces and some phosphate in minerals) and occluded
phosphate (Phosphate fractions held by Fe, Al and Ca) (Mengel et al., 2001). Phosphorus release
is dependent on pH which is a function of Ca compounds (carbonates and phosphates) in alkaline
15
conditions like in ash. The weathering degree of occluded P in Ca compounds is high (Peltovuori
et al., 2002). The solubility of P may also be controlled by chermisorption (chemical bonding to
surface) of P on calcite (CaCO3). A surface complex of calcium carbonate P with a well defined
chemical composition may form on the calcite (Pizzeghello et al., 2011). An increase in pH
desorbs the P from the Ca-P compounds by increasing competition between hydroxyl ions and
the adsorbed P (Jin et al., 2006). The depletion of non-occluded P through leaching creates a
gradient that accelerates P release from the exchange site and from the Ca-P compounds. In this
study it is also expected that as weathering progresses P will increasingly be associated with Ca.
The initial P release in ash is generally rapid until equilibrium is reached. Based on modelling,
the fast, intermediate and slow P release is attributed to; the dissolution of poorly crystalline
metastable calcium phosphates converting to hydroxyapatite, desorption of adsorbed P from
carbonate surfaces and dissolution of calcium hydroxyapatite respectively. A combination of
desorption and diffusion-dissolution reactions control the initial fast and final slow release of P
(Shuriatmadari et al., 2006). The P forms released include species such as HPO42- and H2PO4 with HPO42- dominating under alkaline conditions (Brady and Weil, 2008).
Calcium occurs in primary minerals such as Ca phosphates and Ca carbonates (present as calcite
or dolomite) in alkaline conditions (Mengel et al., 2001).In Sasol ashes Ca is contained in
CaCO3, Ca6Al2(SO4)3(OH)12.26H2O and in lime (CaO) (Mahlaba et al., 2011). Calcite in ash also
remains as the main source of Ca (Kolahchi & Jalali, 2007). This cation (Ca2+) generally
competes strongly with metals for adsorption sites on the exchange complex. The adsorption of
Ni, Cd, Pb and Zn that are also present in ash is reduced by the presence of Ca (Wang, 1997).
The retention of calcium on the exchange site is determined by factors such as; valence,
hydration size and/or the relative energies of hydration of various cations and clay mineralogy
(Agbenin, 2006).
The release of Ca cations from the minerals is initiated by hydrogen ions (H+) and also chelating
agents causing dissolution of the minerals. The rate of Ca leaching increases with an increase in
the addition of water and with the content of Ca bearing minerals. The diffusion of CO2 into
moist ash causes the carbonation and the transformation of Ca toCaCO3which inturn dissolves to
16
form aqeous forms of Ca(HCO3)2 which is more water soluble and this is an important means by
which Ca leaching takes place.
Magnesium (Mg) in ash is localized in the external glassy matrix (Jala & Goyal, 2006) and is
present in the divalent, Mg2+, form in nature. Sasol ash containes MgO as the main source of Mg
(Mahlaba et al., 2011). However, Kolahchi and Jalali (2007) claimed that gypsum (CaSO4.2(H2O))
in ash remains the main source of Mg in ash. There are generally two pools of Mg; non
exchangeable (Mg located in minerals), exchangeable (Mg associated with cation exchange sites
on clay surfaces) and soluble Mg (Brady & Weil, 2008 & Mikkelsen, 2010).
The release of non exchangeable Mg is gradual from the minerals and is through the processes of
weathering. But Mg naturally adsorbed on the exchange sites is generally replaced by cations
present in the soil solution such as K. In this case 2 K+ ions are required to replace 1Mg2+ ion
which then becomes soluble. The behavior of Mg is thus similar to that of Ca ions in the
exchange site, but the adsorption of Ca is much stronger than the adsorption of Mg. This is due
to the fact that Mg has a much larger hydrated radius than Ca cations (Mikkelsen, 2010).
Sodium silicate and halite (NaCl) are sources of Na in ash. Other Na sources may include Trona
(Na2CO3.NaHCO3.2H2O), Nahcolite (NaHCO3) and Soda (Na2CO3.10H2O) that generally occur
in very dry and alkaline soil and soda pans(Dijkistra et al., 2006). In Sasol ashes Na is contained
in NaAlSi2O6.H2O (Mahlaba et al., 2011). Generally Na+ together with other cations (Ca2+, Mg2+,
and K+) are important in defining the status of salt affected soil media as saline, sodic and sodicsaline. Brady and weil (2008) defined saline soils as containing sufficient salinity to give
effective electrical conductivity (ECe) values greater than 4.0 mS/m but have an exchangeable
sodium percentage (ESP) less than 15 or sodium adsorption ratio (SAR) of less than 13. The pH
of saline soils is below 8.5. While sodic soils have low soluble salts (ECe less than 4.0 mS/m) but
with ESP and SAR values above 15 and 13 respectively. The pH of such soil media exceeds 8.5.
Saline-sodic soils on the other hand have ECe greater than 4.0 mS/m and ESP greater than 15 or
SAR greater than 13. Alkaline coal ash is not an exception to saline-sodic conditions since it has
a pH of greater than 12 and an ECe of more than 4.0 mS/m (Haynes, 2009). Based on sodium
17
effect on the status of alkaline soil media it is important to understand its sorption and desorption
processes that influence the status of ash.
Sodium competes with other exchangeable cations (Ca2+, Mg2+, and K+) for the adsorption site
on clay minerals. A closer competitor seems to be Ca2+. The adsorption selectivity of either Na+
or Ca2+ depends on both ionic strength and clay mineralogy. Both the latter and the former
adsorption parameters effect changes to the electrical potential of the diffuse double layer (DDL)
which further influences the selectivity. A decrease in electric potential increases preference for
Na+ while an increase in electric potential increases preference for Ca+. The affinity of the
external surfaces of the clay minerals for Ca+ increases with an increase in solution ionic
strength. As ESP decreases (exchangeable Ca2+ increases) preference for Na+ increases due to a
decrease in the electric potential (Kopittke et al., 2006). Calcium ions have a larger interaction
with mineral surfaces than Na+ due to the fact that Ca2+ forms monodentate inner sphere
complex. Sodium ions have a weak interaction with the mineral surfaces forming outer sphere
complexes (Rahnemaie et al., 2006). Therefore, the removal of excess Na+is effected by the
introduction of Ca2+. A significant supply of soluble sources of Ca2+ in the solution helps in the
dissolution rate of the mineral and in replacing the Na+ from the exchange site. This replaced Na+
becomes available for plant uptake or leached out (Qadir et al., 2005). The dissolution of soluble
salts as well such as halite release Na.However, the release is relatively slow as a result of
kinetically controlled dissolution of less soluble mineral phases (Dijkistra et al., 2006).
Nitrogen amongst macronutrients is the most limiting nutrient to plant growth (Yuan et al., 2008)
and is contained by amino acids, for example, lysine and alanine which are the building blocks of
proteins contained in organic matter (OM). Amino acids are the major sources of organic N and
precursor of ammonium (NH4+) production (Pietri & Brookes, 2008). Organic matter largely of
biological origin is present in Sasol ash in relatively low levels (4.6 – 16.9%) as a source of
organic N (Sasol Synfuels, 2008).
Generally, during N mineralization process, microorganisms attack humic compounds and in the
process amino compounds (R-NH2) are formed that are further hydrolyzed producing N as NH4+
(Brady and Weil, 2008). Microbial mineralization of NH4+ from soil organic matter forms the
18
principal source of plant available N. The processes of ammonification (microbial transformation
of organic N to NH4+) and nitrification (the oxidation of NH4+ to nitrate (NO3-)) make the
inorganic N species available to plants and microbes and further make N susceptible to leaching,
volatilization and denitrification losses (Vernimmen et al., 2007). Understanding the individual
processes helps in conceptualizing the N speciation. Nitrification is an aerobic and autotrophic
process that converts NH4+ to nitrite (NO2) by autotrophs, Nitrosomonas bacteria species as the
first step and then the NO2 is converted to NO3- by autotrophs, Nitrobacter species as the second
step (DinÇer & Kargi, 2000). Nitrification can also be reversed by anaerobic bacteria in a
dissimilatory nitrate reduction to ammonium (DNRA), reducing NO3 - to NO2 and then to NH4+.
Mineralization as well can be reversed by the immobilization (at high carbon/nitrogen (C/N)
ratio) process converting NO3 - and NH4+ into organic forms (Brady & Weil, 2008).
Denitrification process on the other hand is an anoxic, heterotrophic process that converts NO3to nitrogen gas (N2O, NO and N2) by nitrifying organisms (DinÇer & Kargi, 2000). Another
contributing process to N loss is volatilization which occurs mainly at high pH (alkaline
conditions) where NH4+ is converted to ammonia (NH3) N gas (Brady & Weil, 2008).
The mineralization process of N depends on several factors such as; pH, temperature, aeration,
soil type, moisture, type of organic matter, and the supply of essential nutrients like P (Serna &
Pomares, 1992 & Vernimmen et al., 2007). However, the main focus is on pH which is the
master variable affecting most N speciation in soil (Mørkved et al., 2007), temperature and
aeration.
Generally, the optimum pH for nitrification ranges between 8 and 9 and some researchers have
reported that at pH between 5 and 5.5 nitrification ceases. It has also been reported that the
optimum pH for Nitrosomonas is 8.5 to 8.8 and that for Nitrobacter is 8.3 to 9.3. In contrast
some studies have indicated that the optimum pH for Nitrobacter is 7.7. The activities of
Nitrosomonas cease at pH 9.6 (Shammas, 1986). Pietri and Brookes (2008) reported that the
optimum pH for ammonification ranges from 6.0 to 8.0 and for nitrification ranges from 7.5 to
8.0. Mørkved et al, (2007) reported that nitrification is generally detected in soils with pH greater
or equal to 4. In terms of pH it is clear that ammonification dominates at higher pH values than
nitrification. However, denitrification (N2O emission) occurs mainly under anaerobic and
19
slightly acidic conditions. DinÇer & Kargi, (2000) reported that denitrifying organisms can
tolerate pH values between 6 and 9 while an optimum pH ranges between 7 and 8.
From this discussion it can be deduced that a reduction in pH and anaerobic conditions lead to N
losses through denitrification as N2O, NO and N2. An increase in aeration combined with
reduced pH produces NO3- ions than NH4+ ions. The former N ions are susceptible to leaching.
Higher pH values at the same time increase N losses through volatilization as NH3. In contrast to
N losses, NH4+ ions are further adsorbed to negatively charged clay minerals as exchangeable
forms or sometimes entrapped in the interlayers of clay minerals as nonexchangeable (Brady &
Weil, 2008).
According to Shammas (1986) the nitrification rate is a function of temperature within the range
of 5 to 35o C while the maximum rate occurring at 30 oC. Other researchers have reported 26oC
and some reported 27oC as the optimum temperature. At temperatures below 15 o C nitrification
drops sharply and is reduced by 50% at 12 oC. The formation of both NO2- and NO3 - is strongly
inhibited at temperatures of 10oC or less. At low temperatures biostatic effect affects the
activities of nitrifiers (Sierra, 2002). It is clear that temperatures between 26 to 30 oC can
maximize nitrification increasing N availability to plants.
2.3 Sources and mobility of micronutrients (Zn, Cu, Mn, Mo, Fe, and B) in coal ash
Gupta et al. (2008) described micronutrients as trace elements or trace minerals that are required
by plants in extremely small quantities and they include; Zn, Cu, Mn, Mo, Fe, Cl, and B (Fageria
et al. 2002 & Brady & Weil, 2008). Some minerals contain micronutrients which define their
amounts and distribution. Boron in ash occurs in borax, Mg hydroxides, Ca carbonates. Organic
matter also adsorbs significant amounts of B(Rahnemaie et al., 2006). Copper occurs in
carbonates under alkaline conditions. Iron is contained in ferromagnesium silicates and
precipitates as Fe oxides or hydroxides during weathering. In Sasol ash Fe occurs in FeFe2O4,
Fe2O3 and in Fe9S10 (Mahlaba et al., 2011). Molybdenum is a constituent of oxides, molybdates
and sulfides and has a similar chemistry as P, thus it can associate with Ca. Manganese occurs in
carbonates (rhodochrosite - MnCO3), silicates (rhodanate - NaSCN), simple oxides (manganite MnO(OH)) and complex oxides (braunite - Mn2+Mn3+6[O8|SiO4]). Zinc as well is a constituent of
20
carbonates (smithsonite - ZnCO3), sulfides (sphalerite–(Zn,Fe)S) and silicates (hemimorphite Zn4Si2O7(OH)2•(H2O)) (Fageria et al. 2002).
The release of micronutrients is controlled by sorption-desorption processes which further
depend on several factors such as; pH, redox potential, nature of the mineral, organic matter,
CaCO3, ionic strength, simultaneous presence of competing metals, soil temperature and
moisture content (White & Zasoski, 1999, Fageria et al. 2002, Wei et al. 2006, Singh et al. 2006,
Jalali & Moharrami, 2007). Boron availability is highest at pH 5.5 – 7.5 and adsorption increases
above and below this range on clay and Al and Fe hydroxyl surfaces. Calcium carbonate also
adsorbs B at higher pH levels. Copper adsorption increases as pH increases from 4 – 7,however,
pH levels above 6 induce hydrolysis of hydrated Cu which then increases its adsorption to clay
minerals and organic matter (Fageria et al. 2002). Copper precipitates as carbonate of
hydroxides(Malachite – Cu2CO3(OH)2 and Azurite - Cu3(CO3)2(OH)2) at higher pH and forms
strong bond with soil organic matter (Wei et al., 2006).
Iron solubility decreases as the pH
increases from 4 – 9. As pH increases to above 5, Feo (metallic) oxidizes to Fe2+ (ferrous) and
Fe2+ oxidizes to Fe3+ (ferric). The ferric iron is reduced to ferrous which becomes readily
available in acidic conditions and precipitates in alkaline conditions. Under aerated conditions Fe
solubility is controlled by dissolution and precipitation of Fe3+ forming secondary minerals such
as Goethite - FeO(OH) and Haematite - Fe2O3 . Manganese solubility increases as soil pH
decreases and the reduction of Mn4+ toMn3+and Mn2+ at pH lower than 5. At higher pH levels
Mn adsorption increases on organic colloids (Fageria et al. 2002). Addition of organic matter
increases Mn availability through complexation and can supply electrons for the reduction of Mn
oxides hence increasing its availability (Wei et al., 2006). Molybdenum solubility increases with
increase in pH with less adsorption on pH >5. The Mo form, MoO42-, polymerizes in solution
under acidic conditions (pH <5) and sorption on Fe oxides increases, decreasing Mo availability.
The adsorption of Zn on hydrous oxides of Al, Fe and Mn increases as pH increases above 5.5.
But at pH above 7, Zn solubility increases due to solubilization of organic matter and also forms
Zn(OH)+ and increased complexation of Zn with lower positive charge (Fageria et al. 2002).
Possible secondary zinc minerals that can form under alkaline conditions include: smithsonite
(ZnCO3),
hydrozincite
[Zn5(OH)6(CO3)2],
willemite
[Zn3(PO4)2•4H2O] (Essington. 2004).
21
(Zn2SiO4)
Franklinite,
(hopeite
Organic matter during humification produces water insoluble (humic acids or humin) and water
soluble (fulvic acids) compounds that react and form complexes with cations (Fageria et al.
2002). Organic matter therefore, has a strong ability to dissolve and complex with non-available
elements such as Fe, Zn and Mn and increases their solubility and availability (Wei, 2006).
Organic matter is the main source of B an increase in organic matter and pH, increases B
availability. Copper as well is highly bound by organic matter more especially at soil pH 6.5.
Iron forms complexes with organic acids and such complexes enhance Fe solubility. Organic
matter also reduces Mo availability through the formation of complexes (Fageria et al. 2002).
2.4 Sludge characteristics
Sludge is an unwanted by-product of wastewater treatment process (Wang, 1997) and is derived
from various processes that influence its characteristics (Snyman & Van der Waals, 2004).Sasol
biological sludge is a byproduct of the aerobic activated biosolid treatment process. According to
Gray (1990) this process consists of two phases; aeration and sludge settlement. In the aeration
phase waste water is added to the aeration compartment that contains mixed microbial
populations (heterotrophic bacteria and autotrophic microorganisms) and air is added to oxygen
for the respiration of the organisms. There is continuous agitation to ensure adequate food and
maximize oxygen concentration gradient to enhance mass transfer and to help disperse metabolic
end products from within the floc. As the settled waste water enters the aeration tank it displaces
the mixed liquor into a sedimentation reservoir. In phase two the flocculated biomass settles
rapidly out of suspension to form sludge with the clarified effluent, which is almost free from
solids, subsequently discharged as the final effluent. The added microorganisms use organic
matter as food source to produce more microorganisms which are eventually settled out; CO2 is
dispersed to the atmosphere; water (H2O) leaves as part of final effluent; energy is used by the
microorganisms to maintain their life systems. The process requires an adjusted pH for the well
being and operation of the microorganisms. The pH of a well digested sludge is on the acidic
side usually pH 6 or less. Sasol sludge has a pH of 6.8 (Sasol Synfuel, 2008).During digestion
CO2 forms carbonic acid (H2CO3) (if it is oxic and suboxic but under redoxic conditions, for
example, anaerobic digestors it will be methane (CH4)) when mixed with water and tends to
drive the pH of the wastewater down if the wastewater does not have sufficient alkalinity to
buffer the acid formation (Junkins et al., 1983).
22
Similarly, Sasol sludge contains significant levels of plant nutrients and organic matter (82.4%)
that make it possible to use in agriculture (Table 2.2). Total N (7.1%) and P (0.451%) are
contained in appreciable amounts while K content is generally low (0.262%) (Table 2.2). The K
content is generally low in Sasol sludge, because most K compounds are water soluble and
remain in the sewage effluent or the aqueous fraction during sludge dewatering (Rechcigl, 1995).
Nitrogen is present mainly in the organic form that must be mineralized before made available to
plants (Snyman & Van der Waals, 2004). However, Rechcigl (1995) claimed that a proportion of
inorganic N by far is the largest fraction (50 to 90%) of the total N in any sludge and only 10 to
30% of the total P in anaerobic sludges is organic P. This may depend on the source of the
wastewater and the processing that determines the type of the sludge. In South Africa both
municipal wastewater and sludge contain variable amounts of organic N, nitrate (NO3-), nitrite
(NO2-) and ammonium (NH4+) (Snyman & Van der Waals, 2004) that makes it difficult to rule
out which of them dominates.
There are several disposal methods that are used in the management of sludge. Sludge disposal
on landfor beneficial or non beneficial use is the most used strategy for sludge management
(Herselmen et al., 2009). However this management strategy increases the risk of environmental
pollution.Another method of sludge disposal is incineration.This management strategy does not
only generate carbon dioxide (CO2), a greenhouse gas, but also possibly a myriad of flue gases
and toxic residues (Moldes et al., 2007).The ash produced during incineration is more hazardous
as well since it contains high concentrations of heavy metals. Therefore, in South Africa the
annual produced dry sludge that amounts to 310000 tons, 30% is used in agriculture, 67% land
filled, 3% other and non is incinerated (Herselmen et al., 2009).
There are some risks associated with the use of sewage sludge in agriculture. There is a
possibility of ground water and surface water nitrate and phosphate contamination. Heavy metals
such as Cu and Zn that mainly comes from domestic sources, Cd and Pd mainly from industrial
sources may as well contaminate both ground water and surface water (Snyman & Van der
Waals, 2004). A decrease in pH of the medium treated with sludge increases the mobility of
these heavy metals. Zinc and Cu are important plant micronutrients that sludge can supply but
concentrations of 150 to 200 mg kg -1 Zinc and 21 mg kg-1 Cu in dry matter of tissues are
23
considered toxic to plants. (Mengel et al., 2001, Snyman & Van der Waals, 2004). Another risk
of sludge usage in agriculture is that it contains pathogenic organisms such as bacteria, viruses,
fungi and yeasts, parasitic worms and protozoa. If humans or animals are exposed to some of
these pathogenic organisms in the environment or in agriculture can contract diseases (Snyman
& Van der Waals, 2004).
Table 2.2: Characterization of Sasol sludge (Sasol Synfuel, 2008)
Parameter
Sludge
Units
pH
6.8
Unit less
Moisture
(A.D.)
13.1
%
Moisture Loss
77.7
%
Tot. Moisture
80.7
%
Solids(A.D.)
86.9
%
Ash (A.D.)
4.5
%
Volatile solids
82.4
%
Ash: Dry
5.2
%
Total N
7.9
%
Total C
56.3
%
C/N Ratio
7.1
Unit less
Ca
0.4
%
Mg
0.1
%
P
0.5
%
K
0.3
%
Na
0.2
%
Fe
0.5
%
Cu
100
mg kg-1
Mn
97
mg kg-1
Zn
113
mg kg-1
2.5 Possible benefits of amending coal ash with biological sludge
Sludge incorporated in ash ismore likely to increase the contents of plant available N and P in
which the ash is poor in as characterized by Sasol Synfuel (2008). But the ash may be rich in non
available essential plant nutrients such as Na, B, and SO4 but occasionally can supply plant
available K, Ca and Mg (Jankowski et al., 2006). As discussed above these plant nutrients are
generally released from the minerals by the processes of weathering (McConnell, 1998 & Brady
& Weil, 2008) andthe controlling minerals in ash include CaCO3, CaMg(CO3)2 and MgO
(Jankowski et al., 2006). The susceptibility to hydrolysis depends on the surface area of the ash,
24
reaction time and water/solid ratio. The high levels of calcium oxide (CaO) and magnesium
oxide (MgO) in ash also play a major role of precipitating heavy metals in sludge and thus
reducing their toxicity to plants which may be growing in an ash-sludge medium (Fang et al.,
1999).
The clay minerals in the ash contribute to CEC through inorganic functional groups (Essington,
2004). Chemical weathering over time effects an alteration of the ash components and
aluminosilicate glass to non-crystalline clay minerals. The formation of these clay minerals is
generally indicated by an increase in CEC (Zevenbergen et al., 1999). It is envisaged that the
increase in CEC is attributable to the aluminium-silicon rich phases that form during mineral
transformation with increased adsorption capability (Gitari et al., (2009).
The release of trace elements may also be accelerated by the presence of soluble organic matter
from sludge which forms soluble metal organic complexes. If the presence of insoluble organic
matter is significantly high it can reduce thebioavailability of trace elements (Singh & Agrawal,
2008). But Li and Shuman (1996) maintain that the addition of sludge retains heavy metals due
to creation of new sorbing surfaces (increased CEC) by the sorption of organic ligands. Organic
matter increases CEC through its deprotonated functional groups such as carboxylic and phenolic
groups (Essington, 2004). Soluble organic matter can also reduce soluble salt concentration
which is high in unweathered ash deposits with electrical conductivity (EC) of >13 dSm-1
(Haynes, 2009).
Sludge can also promote the establishment of micro-fauna in the ash as it contains
microorganisms such as bacteria, viruses, fungi and yeasts, parasitic worms and protozoa
(Snyman & Van der Waals, 2004) that the ash in poor in. Furthermore, there is introduction of
bacteria species responsible for N mineralization such as autotrophs, Nitrosomonas that converts
NH4+-N to NO2-N and autotrophs, Nitrobacter species that converts NO2-N to NO3— -N (DinÇer
& Kargi, 2000).
Physically, Sasol fine ash consists of fine particles with an average diameter of less than 250 µm
(Mahlaba et al., 2011). This characteristic of fine ash can contribute to the microporosity of the
25
medium and increase the ability to retain water. Inversely gasification ash is macroporous with
average particle sizes rangingfrom 4 to 75 mm (Matjie et al., 2008)that can provide the necessary
aeration needed for numerous microbial mediated processes, essential for a functioning growth
medium, e.g. mineralization and nitrification. This characteristic also ensures rapid infiltration
and minimize run-off from the ash dump. The increased unsaturated hydraulic conductivity will
also increase capillary rise resulting in more sustainable transfer of water to the atmosphere and
greater cumulative evaporation. The addition of sludge in fine ash increases its organic matter
content that further increases water infiltration (Snyman & Van der Waals, 2004), while, in
gasification ash it may increase the retention of water.
2.6 The potential of biological sludge – coal ash mixtures as artificial growth media
Saikia et al. (2006) conducted a laboratory study in columns (0.018 m internal diameter) in a
mixture of fly ash, municipal solid waste (all waste generated by a community excluding
industria and agricultural process waste) and sewage sludge incinerator ash. They observed that
there was a rapid initial pH increase which became approximately constant or decreased. The
explanation given was that the initial sharp rising of the pH values was due to the solubility of
alkaline materials like carbonates, which neutralize leachants acidities coming from the ash.
Saikia et al. (2006), observed high concentrations of mobile metal species like; Pb, chromium
(Cr), selenium (Se), arsenic (As), molybdenum (Mo), cadmium (Cd) and B) in the leachants as
the pH was decreasing. The decrease in pH of the leachant in the subsequent leaching favored
the formation of mobile metal species and decreased the ability of metal ions to form surface
complexes with hydrous oxides and silicates present in the residues.
Recently, Annandale et al. (2004) conducted an onsite rehabilitation trial on the Sasol ash dump
by amending combustion coal ash with industrial sludge, as an alternative to importing top soil
as a growth medium to establish vegetative cap on the ash dump. Substrates of ash alone and ash
amended with sludge were developed. In this trial several perennial grasses and shrubs were
screened with the primary objective of establishing which species could adapt to the climate and
substrate conditions. Of all the perennial grasses germinated on the sludge treated ash, Chloris
gayana and Cynadon dactylon indicated the best establishment, good overall cover, dense stand
and best survival. Some of the grasses and shrubs could not survive. This trial was provided the
26
basic needed information on the functionality of the waste combinations as artificial growth
media. It was therefore evident that the physical and essential plant nutrient release behavior of
these waste combinations needed to be elucidated and quantified, using appropriate column
system in order to gain predictive capability on the most suitable growth medium combinations.
Based on the research done by Annandale et al. (2004) it is clear that both ash and sludge have
physical, biological and chemical attributes that provide the potential to support plant growth.
Most of the work in the literature has been done in the field and plants planted are used as
indicators of elemental release by the mixtures. The treatment of ash with sludge is usually done
with the objective of increasing the fertility of the media. The soils and plant biomass are
analyzed to quantify available/released essential plant nutrients. Such analysis poorly represents
the chemical dynamics of the mixtures, that is, it does not give information on the retention and
release mechanisms responsible.
2.7 Unsaturated packed low tension column system
Soil columns have been used for more than three centuries with early investigations appearing in
1703 and can be described as discrete blocks of soil located either out doors or in a laboratory
(Goss et al., 2010 & Lewis & Sjoström, 2010). This is generally achieved by encasing the soil
column in a rigid and impermeable shell material, both for structural reasons and to prevent fluid
loss, but, the technical approach adopted in constructing columns is not standard, as a result, the
smallest column ever reported measured 0.01 m in diameter and 0.014 m in length and was used
to investigate the release of heavy metals from contaminated soils (Voegelin et al., 2003) while
the largest measured up to 2 m x 2 m x 5 m (Mali et al., 2007). Due to the differences in design,
soil columns can be operated under saturated and unsaturated regimes. Columns can further be
classified into two broad categories; packed columns that use disturbed material and monolithic
columns that use undisturbed material (Lewis & Sjoström, 2010). However, for this discussion
focus is on packed unsaturated columns.
Packed soil columns, are built using disturbed material packed into a rigid container and
compacted. The objective of packing is to produce a homogenous soil column having a bulk
density similar to that observed naturally and avoiding the formation of stratifying layers or
preferential flow pathways (Lewis & Sjoström, 2010). Such columns tend to lack macropores,
27
channeling, and native soil structure due to the packing and compaction (Singh et al., 2002).
Macropores and channeling are difficult to eliminate but can easily be minimized by ensuring
that the column diameter is thirty times as large as the maximum particle size of the material
used to pack the column (Bi et al., 2010). During packing and compaction it is a challenge to
pack the whole column at the same field bulk density. Hence various effective packing methods
have been successfully used to achieve the desired density but the focus is on dry or damp
method.
The dry and damp packing technique involves loading small discrete amounts of dry or damp
soil into the column (Lewis & Sjoström, 2010) ensuring close contact of the particles of the
packed material allowing elimination of macropores. However, Bi et al. (2010) argues that this
method is tedious and usually results in layering of the soil, results in stratification and defies
reproducibility of the soil column. But they further suggested that the sample can be loaded into
the column with the help of a stainless steel spatula depositing the material in layers thinner than
0.01 m and in some cases at 0.15 m and then mechanically packing it either by hand or with
some type of ram (Lewis & Sjoström, 2010). The material packed also needs to be homogenized
if large particles exist by grinding manually using a pestle and mortar to smaller particles to
ensure reproducible column packing (Bi et al., 2010). To minimize layering and ensure hydraulic
connectivity the surface of the soil is slightly scarified after compaction before the addition of
another layer (Lewis & Sjoström, 2010).
One other successful technique used for packing in smaller soil columns is vibration which
depends on power (Lewis & Sjoström, 2010). Small soil columns can also be compacted by
slightly taping the column side wall with an object if the bulk density is not very high (Bi et al.,
2010 & Schwab et al., 2008) and variation in density can also be controlled by dropping the soil
column from a height of 0.03 m consecutively for three times on a hard surface (Hansen, 2010),
but this method is very arbitrary because the 0.03 m height and frequency of dropping may
achieve a density lower or higher than field bulk density for some columns.
This packing method has the advantage of reproducibility, that is, the lack of heterogeneities and
macropores should lead to reproducible bulk densities and dispersivities (Lewis &
28
Sjoström,2010). However, sidewall flow is a preferential flow which is a concern with packed
unsaturated column. To minimize this some methods have been proposed and tried. Suggested
successfully used methods include; roughening the sidewall by gluing sand to it (Sentenac et al.,
2001) confining the soil column with a flexible latex membrane to overcome side wall flow
(Charbeneau, 2000) and wetting the inside of the column then packing it with a swelling clay
such as montmorillonite (Lewis & Sjoström, 2010). In the latter approach, the excess (dry) clay
is allowed to fall out of the column while the hydrated clay forms a liner on the column wall.
However, there is a possibility to have a flow at the soil - membrane boundary and the clay
mineral is highly reactive and will participate in chemical reactions.
The sidewall can also be coated with paraffin and extra paraffin should be added along the soil
tube interface to prevent preferential flow (Shan et al., 2005). Goss et al. (2010) suggested that
injecting petroleum between the casing and the soil can reduce preferential flow. This was
supported by Steiner et al. (2010) that petroleum jelly can seal the gap between the soil column
and the casing around it in order to prevent edge-flow effects. However, the application of
petroleum can negatively affect the solution chemistry.
Other undesirable forms of preferential flow include macropore flow or fingering in packed soil
columns (Lewis & Sjoström, 2010). Macropore flow refers to any flow which takes place outside
of the normal pore structure of the soil, such as in wormholes or decayed roots. While these may
play a more significant role in monolith-type soil columns, macropores still exist in apparently
homogeneous packed soil columns on account of the heterogeneity of the soil grains themselves
(Cortis & Berkowitz, 2004 & Oswald et al., 1997). Fingering also occurs when instability
develops in the wetting front as it moves through coarse unsaturated soils such as sands and is a
function of the soil grain size, with silts having fingers on the order of 1 m in diameter and
coarse sands having fingers on the order of 1 cm. Fingering can persist until the soil has either
been fully dried or fully saturated and is most likely to occur when the soil being infiltrated is
initially extremely dry (Lewis & Sjoström, 2010). This is not necessarily bad if the purpose is to
simulate field conditions.
29
Suction is needed in unsaturated soil columns in order to extract the pore water and maintain the
column under unsaturated condition. However, Lewis and Sjoström (2010) argues that
attempting to sample pore water by applying suction to an open ended pipe attached to the base
of a soil column normally fails because only air is drawn in. But this is normally not the case; it
depends on how close the columns are to saturation. For this reason, rigid porous materials are
used as an interface between the sampling device and the soil to ensure that pore liquids in the
soil are in hydraulic contact with liquid within the sampling device (Plummer et al., 2004, Chu et
al., 2003, Hutchison et al., 2003, Magesan et al., 2003, Powelson & Mills, 2001 & Vogeler,
2001). Rigid porous materials that are used in soil columns installed with a suction device
include; ceramic, porous polytetrafluoroethylene (PTFE), fritted glass, porous stainless steel,
porous plastic and fibreglass wicks and to help in choosing a rigid porous material for an
experimental apparatus, the bubbling pressure (air entry pressure - the pressure where the largest
pores that can retain water against gravity evacuates) of the material must be considered.
Mechanical dispersion and molecular diffusion occurs in unsaturated columns and may affect the
results. This is caused by deviations in the microscopic fluid velocity caused by differences in
pore sizes and geometries, creating localized dilutions (Lewis & Sjoström, 2010). Mechanical
dispersion is a linear function of the dispersivity – which is a property of the soil – and the fluid
velocity. However, this is a function of pore size distribution. Molecular diffusion in contrast is
driven by concentration gradients (Fick’s law) and will occur regardless of whether the fluid is
moving. Unless the fluid is nearly immobile, mechanical dispersion dominates and molecular
diffusion effects can often be neglected (Leij & van Genuchten, 2002). The dispersivity of
unsaturated soils is inversely related to the soil moisture content (Lewis & Sjoström, 2010) and
may be nearly an order of magnitude higher in unsaturated soil than that of an identical saturated
soil. The flux density also appears to have a lesser effect on the dispersivity in unsaturated soil
(Toride et al., 2003).
There is a significant relationship between the diameter of a column and the measured
dispersivity. Larger column diameters (≥ 0.076 m) tend to produce greater experimental
dispersivities than columns with diameters < 0.076 m, which may be on account of the greater
difficulty in uniformly packing larger columns (Bromly et al., 2007). However, there is less
relationship between the column length and dispersivity. Dispersivities in larger columns having
30
diameters ≥ 0.076 m can be grouped according to their lengths. Columns longer than 0.107 m
produced greater dispersivities than columns < 0.107 m (Bromly et al., 2007). By extension,
once the fluid flow in a saturated soil column is forced to a value that is approximately an order
of magnitude higher than that of the unsaturated regime, the saturated hydrodynamic dispersion
can be expected to overtake the unsaturated dispersion. Pressure differentials in a saturated soil
column between the upper and lower boundaries may be much lower than those of an
unsaturated soil column, leading to potentially higher fluid flow velocities since macropores are
less resistant against flow and consequently higher hydrodynamic dispersions (Lewis &
Sjoström, 2010).
The draining system may be designed to prevent saturated conditions in the bottom of the
column or to create a water table within the column depending on the purpose of the experiment
(Hansen et al., 2000). At the bottom of the column a peristaltic pump can be connected to
continuously collect leachate by applying constant suction (Zhao, 2009). In circular soil columns
the draining system is often formed as a funnel and typically consists of different layers of sand
with varying particle size, or it simply consists of a filter placed on a perforated platform,
possibly combined with a thin layer of sand (Hansen et al., 2000). A similar structure was
constructed by Shan et al. (2005) using small polyvinyl chloride (PVC) tubes with an open strip
facing up filled with quartz sand to collect leachate from the soil column. In large-scale
lysimeters, which are often used for waste products, the bottom of the lysimeter is often lined
with an impervious material with a draining layer placed above such as geotextile with a fixed
synthetic draining layer to prevent particles from entering the leachate collecting system (Hansen
et al., 2000).
According to Hansen et al. (2000) a low suction can be applied constantly or periodically to the
draining system and a zero-tension lysimeter allows the soil solution to drain freely through the
test material while with low-tension lysimeter and the equilibrium-tension lysimeter, suction is
applied. In a low tension lysimeter and in an equilibrium-tension lysimeter, soil solution can be
extracted from finer pores also by establishing good contact between the sampling point and the
test material and applying suction. Draining from the test materials differs in quantity when
31
vacuum is applied or not applied, and when vacuum is applied at fixed or variable levels (Hansen
et al., 2000).
Static tension refers to the fact that the columns are connected to a vacuum system and a static
tension of approximately -10 kPa can be applied at the base of the columns. This enables the
removal of saturated environments within the packed material. Therefore, static tension systems
are arguably a better approach compared to free draining systems to simulating release and
sequestering dynamics of unsaturated porous systems. Firstly, in free draining columns water
perching or ponding occurs at the outflow boundary resulting in artificially wetter conditions at
best and potentially anaerobic conditions at worst, compared to unconfined porous medium.
Secondly, applying tension at the base of the columns helps to minimize these boundary
conditions and ensure that the columns drain better ensuring conditions closer to that of
unconfined porous media. Thirdly, the ability to apply vacuum allows better control of the water
content and aeration of the columns. Different tensions can be applied to simulate different
aeration scenarios if needed and wetting and drying cycles under laboratory condition can be
accelerated through vacuum drying or force aeration of the columns. Lastly, pore solution
chemistry can be investigated directly and residence time can be controlled.
In free draining systems the only means to gain any insight into pore solution chemistry is
through the analysis of the free drainage collected. Little control over the residence time of
solution in free draining steady state flow systems exists. In order to interpret data in context the
assumption must be made that the chemistry of the free drainage more or less mirrors pore
solution chemistry. A better approach is to approximate the nearness of the residence time to
“equilibrated” or more correctly steady state. This is done by using, for example, the Damköhler
number:
(Da) = R.K.L.V-1
(Eq. 2.1)
Where R is the retardation coefficient of the element of interest, L is the column length, V the
pore velocity (ms-1) and K kinetic constant based on the rate of elution (Andrés & Fransisco,
2008). Preferential flow paths can develop in any artificially packed system. This will decrease
tortuosity resulting in shorter residence times and less contact of the percolating solution with the
32
total surface area of the medium, this will also influence pore velocity calculations. All
conclusions made and recommendation forwarded about nearness to equilibrium, solution
composition, ion concentration and ion activities are, therefore, transport and pathway
dependent. The advantage of a constant tension system is that entrained pore solutions, in pores
that can retain water against gravity, can be sampled directly after it was allowed to equilibrate
for a specific time. The intricacies of relating the chemistry of the free drainage to pore solution
chemistry and the nearness to equilibrium are circumvented. This allows the comparison of pore
solution chemistry of different residence times to that of free drainage.
Soil column studies therefore allow for the assessment of elemental transport, the evaluation of
transport models, monitoring the fate and mobility of contaminants in the soil (Lewis &
Sjoström, 2010). To achieve this, column techniques incorporate the use of models, equations
and break through curves (Mahmood-Ul-Hassan et al., 2008). Such experiments involve packing
the relevant soil material into columns where the transport, mobility, bioavailability and
chemical behavior of contaminants in the soil matrix is to be quantified (Wang et al., 2009).
Secondly, columns enable hydraulic conductivity studies. Early investigations (1703) that
introduced soil column technology concentrated on identification of the components of water
balance and this was aimed at determining the proportion of natural precipitation leaving the soil
as a result of deep drainage and surface runoff (Goss et al., 2010). Preferential flow in
macropores is an important component since it can lead to rapid transport of surface applied
contaminants to the subsurface and make difficult agricultural water management. In addition
wetting fronts propagate in macropores to significant depths and bypass the soil matrix pore
space (Akay & Fox, 2007). Field studies using hydrometric methods and/or natural and artificial
tracers enable assessing macropore flow under field conditions, but face climatic variability
complexities and difficulties associated with field assessment of hydraulic properties. Thus
hydraulic conductivity is better assessed using small undisturbed soil columns under laboratory
conditions (Lamy et al., 2009).
Lastly columns enable the study of the influence of organic acids on mobility of heavy
metals.Dissolved organic matter could contribute effective organic ligands to form complexes
33
with heavy metals in the soil. The soluble complexes with heavy metals can be transported
downward and possibly deteriorate ground water quality. The addition of soluble organic ligands
has been found to decrease the sorption of trace metals by soils because they form soluble
complexes with heavy metals (Li & Shuman, 1997).
2.8 Conclusion
This review shows the potential ofcombining ash and sludge can be a better utilization
management tool which can create a more conducive environment to establish a vegetative cap.
Sludge has high levels of available inorganic N and P that ash is poor in and also contains both
soluble and insoluble organic matter, with the former complexing trace elements increasing their
mobility and the latter contributing to the retention of elements. Thus sludge can contribute to
CEC of the medium and can provide C, a source of energy to microorganisms responsible for
essential processes such as N mineralization and nitrification. Introduction of sludge can also
introduce important bacterial microorganisms that decompose organic material. Ash as well
contains plant nutrients such as Ca, Mg, K and Na that sludge is poor in. Physically gasification
ash is macroporous and this characteristic can provide the necessary aeration needed for
numerous microbially mediated processes essential for a functioning growth medium, e.g.
mineralization and nitrification. The increased hydraulic conductivity can also increase capillary
rise resulting in more sustainable transfer of water to the atmosphere and greater cumulative
evaporation. This characteristic can ensure rapid infiltration and minimize run-off from the ash
dump. Fine ash, on the other hand is micro porous and this characteristic can contribute to the
micro porosity of the medium and increase the ability to retain water. The production of C,
humic and fulvic acids by sludge during decomposition can contribute to the reduction of the
high pH of the ash.
Weathering processes such as dissolution, hydrolysis, oxidation and hydration can help in the
release of the elements from the clay minerals that contain them. Other factors such as pH, ionic
strength, electric potential, concentrations of ions in the solution, temperature, affinity of
adsorption by the exchange surfaces, type of clay mineral, point of zero charge, cation exchange
capacity and carbon-nitrogen ratio govern the absorption and release of the plant nutrients.
34
However, pH remains the master variable in control of adsorption and desorption of micro- and
macro-nutrients in soils and artificial soil materials like coal ash-industrial sludge mixtures.
A more realistic combination of ash and sludge was evident with on-site rehabilitation trial
which was conducted by Annandale et al. (2004), where substrates of ash alone and ash amended
with sludge were developed. Several perennial grasses and shrubs were screened with the
primary objective of establishing which species could adapt to the climate and substrate
conditions. Chloris gayana and Cynadon dactylon established the best, with a dense stand and
best survival. However, some of the grasses and shrubs could not survive on certain substrates.
This trial was used as a precursor to the functionality of the waste combinations as growth
media. It was therefore evident that the physical and essential plant nutrient release behaviour of
these waste combinations needed to be elucidated and quantified. Sometimes, plant biomass is
analyzed to estimate available essential plant available nutrients. Generally, such an analysis
poorly represents the chemical dynamics of growth mwdia, that is, it does not give information
on the retention and release mechanisms responsible. This can only be done under laboratory
conditions using appropriate methods using carefully designed column systems.
Packed
unsaturated low tension columns set-up in a laboratory can improve aeration and oxidation; they
can also enable the study of the medium’s hydraulic conductivity and nutrient retention and
release. Such columns can also give one predictive capability into the most suitable growth
media combinations. However, various options may be expected since no one combination will
always be better than others.
35
CHAPTER 3: MATERIALS AND METHODS
The materials and methods covered in this section included: mixture formulation technique,
components of each mixture, calculations involving parking of columns, the development of the
low tension column system and leaching procedure. Materials and methods regarding analysis of
elements (macro and micronutrients), pH and salinity, particle size distribution and cation
exchange capacity (CEC) are dealt with under their respective chapters.
3.1 Mixture formulations
Biological sludge, fine and gasification ashes were collected from Sasol Synfuels in Secunda.
Each waste stream was randomly collected from its source to ensure correct representation of the
site. Both gasification and fine ash were collected from freshly dumped heap and fresh fine ash
dam respectively. Freshly processed sludge was collected directly from the final point (at stage
of disposal) of the plant. For each of the wastes 300 kg was collected and transferred into a
tightly closing plastic container. The plastic containers with samples were later, during the same
day, enclosed in a cold room at 4 oC to minimize chemical reactions and biological activities that
may occur.
The general approach followed in developing the mixtures are illustrated in Fig. 3.1 a, b and c).
The mixtures were expressed on a wet mass basis. The sludge was viewed as a suspension and
mixtures containing 10, 20, 30, 40, 50% (Fig 3.1 a) represented the amount of the sludge
suspension added relative to the two solid phases. A previous approach revealed that sludge as a
solid and base sludge additions on a dry mass bases proved to be unpractical. In a preliminary
trial it was evident that the high liquid content(10 – 13% solids) of the sludge resulted in the final
volumes of mixtures containing greater than 60% sludge to be more than the volume of the
columns.
The fine and gasification ash are similar to soil, the liquid phase is a minor component (12 – 13%
moisture) of the natural mass in their ‘natural state’. The gasification and fine ash content in the
mixtures are represented in Fig. 3.1 b and c respectively.
36
SLUDGE PERCENTAGE IN MIXTURES
60
Group 6
50 % sludge
50
Group 5
40 % sludge
40
Group 4
30 % sludge
30
Group 3
20 % sludge
20
10
Group 2
10 % sludge
Group 1
0 % sludge
0
a
NUMBER OF MIXTURES
GASIFICATION ASH PERCENTAGE IN
MIXTURES
110
100
Group 1
0 % sludge
90
Group 2
10 % sludge
70
FINE ASH PERCENTAGE IN MIXTURES
Group 4
30 % sludge
60
Group 5
40 % sludge
50
Group 6
50 % sludge
40
30
20
10
0
b
c
Group 3
20 % sludge
80
NUMBER OF MIXTURES
110
100
90
Group 1
0 % sludge
Group 2
10 % sludge
Group 3
20 % sludge
80
70
60
50
Group 4
30 % sludge
Group 5
40 % sludge
Group 6
50 % sludge
40
30
20
10
0
NUMBER OF MIXTURES
Fig. 3.1: a) Sludge content in mixtures (wet mass of sludge expressed as a percentage of the total
wet mass), b) gasification ash content in mixtures and c) fine ash content in mixtures
37
A total of 51 mixtures (hand mixed) were formulated containing varying amounts of sludge, fine
and gasification ashes. There wereno replications included because of the gradient in sludge,
gasification ash and fine ash content represented by the various mixtures. The mixtures were
conveniently divided into 6 groups based on sludge content (Fig 3.1 a, b and c). Group 1
(mixtures 1 – 11) represented treatments that received no sludge while fine ash content was
reduced (100 – 0%) and gasification ash content increased (0 – 100%). Group 2 (mixtures 12 –
21) represented treatments that received 10% sludge while fine ash content was increased and
gasification ash content was reduced. Group 3 (mixtures 22 – 30), group 4 (31 – 38), group 5 (39
– 45) and group 6 (46 – 51) received 20, 30, 40 and 50% sludge respectively while the fine ash
decreased and gasification ash increased.
3.2 Calculations involving the packing of the columns
The columns were packed at a wet bulk density (ρ b = Mtotal / Vtotal) of approximately 1200 kg m3
. In order to leave adequate space for the addition of deionized water to the mixture only 0.25 m
of the column height was considered for total volume calculation. Based on this, the mass of
mixture that was prepared to be packed in each column was calculated:
Volume = Πr2h
(3.1)
Where r is the radius and h is the height
Column volume (cm3)
= Π x (5.25 cm)2 x 25 cm
= 2164.75 cm3
Total mass of mixture
= bulk density x volume
3
(3.2)
3
= 1.2 g/cm x 2164.75 cm
= 2597.7 g = 2.6 kg
The amount of deionized water that must be added to each mixture was approximated by
calculating the pore volume of the mixture (Tan, 2005, Lal & Shukla, 2004).
Total porosity = 1 – ρb / ρs
(3.3)
Where:
Dry bulk density (ρb) = Msolids / Vtotal
(3.4)
Particle size density (ρs) = Msolids / Vsolids
(3.5)
Pore volume = (1 – ρb / ρs)x Vtotal
(3.6)
38
To estimate the porosity and pore volume, the equivalent dry bulk density or more specifically
the dry mass of the mixtures were needed. The particle density was taken as 2650 kg m-3. Sub
samples (100 g) of the mixtures were prepared, weighed off in duplicate into pre-weighed
beakers and the oven dried at 105oC for 24 hours. The beakers with their content were then
cooled in a desiccator, and afterwards the dry mass percentage of each treatment was determined
directly (Eq. 3. 7) (Tan, 2005). The pore volume (Eq. 3.6) was estimated for all samples/mixtures
and was found to be 1.1 L (1101.86 cm3).
Dry mass of sample (%) = 100 x [wet mass (g) – oven dry mass (g)] / [dry mass (g)]
(3.7)
3.3 Packing of the columns
Small discrete amounts of the wet mixture were loaded into the column to ensure close contact of
the particles of the packed material and to eliminate macropores. The loading of the material into
the column was carried out on increments of0.1kg. The material was deposited in layers of about
0.09 m highequivalent to 0.9 kg of the sample in mass. After each layer the material was then
mechanically packed using a vibrating table for about 15 seconds. To minimize layering and
ensure hydraulic connectivity the surface of the material was slightly scarified after compaction
before the addition of the next layer.
3.4 The development of the unsaturated column system, specifications and set up
A static low tension column set up was developed in order to investigate the interaction of
sludge, fine and gasification ashes mixtures and the result elemental release in an aerobic
environment under alternating wetting and drying conditions. Static tension under these
conditions referred to columns connected to a vacuum system and a static tension of
approximately -10 kPa that could be applied at the base of the columns. This was done to prevent
the accumulation of moisture at the base of the column and encourage aeration.
The column ensemble consisted of the following:
1) A column base cut from polypropylene with an internal diameter of 0.11 m (out rim with a 0.01
m thickness) the depth of the base was 0.045 m (Fig 3.2 a – d),
39
2) An O-ring about 0.004 m diameter (Fig 3.2 b and c) was fitted prior in the column bases to
ensure vacuum tight seal with column,
3) A 0.3 m long transparent polyethylene column, with an internal diameter of 0.105 m (Fig 3.3 a)
fitted on the base,
4) A ‘depth filter’ placed at the base of the column consisting of five layers of filtering material
(Fig 3.3 c). The filtering material were placed in an order of decreasing pore size: The first layer
directly in contact with the mixture was a polypropylene mesh with a pore size of 0.002
m,followed by three nylon meshes with a decreasing pore size of 25, 10 and 5 µm (Fig 3.3 b).
The last mesh at the outflow boundary was again a 0.002 m polypropylene mesh and
5) A 0.0005 m3 Schott Duran glass bottle that can screw in at the bottom of the column bases (Fig
3.3 d). The column bases were connected to the vacuum system as illustrated in Fig 3.4. A main
vacuum line, connected to a vacuum regulator on the bench, serviced eight columns. In total, two
rows of 24 columns were mounted back-to-back on one bench (Fig 3.4).
3.5 Leaching procedure and collection of leachate
The approach followed was to simulate wetting and drying cycles. A total of ten wetting and
drying cycles were simulated in a period of 12 months starting in January 2010 and ending in
December 2010 and in each eluviation cycle the amount of deionized water (1.1 L) estimated
based on the calculated porosity and pore volume of the media was added to the columns. This
amount of deionized water that passed through the columns was equivalent to approximately one
pore volume. The free drained leachates that collected in Schott bottles, shown in Fig 3.4, were
removed after free drainage stopped. Clean Schott bottles were then connected to the column
bases and a vacuum of -10 kPa was applied in order to collect the pore solutionthat was allowed
to equilibrate with the mixtures for 24 hours. The volume and the mass of the collected solutions
were determined for mass balance purposes. Solution pH and electrical conductivity (EC) of both
the free drained and pore solutions were immediately determined. Inorganic nitrogen analysis as
well was carried out within 24 hours after eachleaching to avoid volatilization and
denitrification. After inorganic nitrogen analysis, pH and EC determination the leachates were
40
a
b
c
d
Fig. 3.2: a) Side view of column base showing opening to which vacuum was connected, b) Top
view of column base showing grooved ‘floor’, drainage outlet and imbedded O-ring, c) Side
view of column base showing the imbedded O-ring to ensure a vacuum tight seal between
column and base, d) Bottom view of column base showing drainage outlet and threaded opening
to which a Schott Duran glass bottle were screwed/fitted.
a
b
c
d
Fig. 3.3: a) The transparent polyethylene column (length: 0.3 m, internal diameter: 0.105 m), b)
The five layered mesh placed at the outflow boundary of the column or at the base of the
column, c) The securing of the mesh by the column in the column base, d) The column assembly
consisting of the transparent column, the column base and the Schott bottle.
41
Fig. 3.4: The low tension column battery on each bench, connected to the vacuum system (blue
pipe from column connected to the red main vacuum line) and collection plastic bottles.
refrigerated at 4 oC to minimize chemical and biological activities that could occur before further
analysis.
42
CHAPTER 4: PARTICLE SIZE DISTRIBUTION AND WATER RETENTION OF
BIOLOGICAL SLUDGE – COAL ASH MIXTURES
4.1 Introduction
Sasol gasification ash had large irregularly shaped aggregates of sizes ranging from 4 to 75 mm
as characterized by Matjie et al.,(2008) while fine ash consisted of particles that fell between
5µm and 75 µm and constitute about 60%.This percentage was greater than particles that fell
between1 – 5 µm (16%) and between 75 – 425µm (30%)as characterized by Mahlaba et
al.(2011).
Generally, finer particles of ashes are spherical in shape showing a complete melting of silicates
which occurs during combustion at temperatures above 1350 oC and pressures greater than
2000kPa (Matjie et al., 2008). The spheres of both ashes may be solid, hollow or encapsulating
(Kopsick & Angino, 1981). Micrographic evidence indicated that most of the particles in fine ash
occur as solid spheres of amorphous glass that form during cooling of the melt phase (Tishmack
& Burns, 2004). In addition, only a few hollow spheres and some spheres packed with other
numerous small spheres or crystals of minerals, may be present (Trivedi & Sud, 2002). The
crystals of minerals formed are a result of cooling of the minerals and non-mineral inorganic
elements in the coal mineral matter that melt and form liquid phases during the gasification
process (Matjie et al., 2008). Fine ash has a low particle density, a high surface area and light
grey particles (Asokan et al., 2005 & Jala & Goyal, 2006).
Sasol gasification ash was characterized by Matjie et al. (2008) and found thatit contained
minerals (contribute to water retention through hydration during weathering)by weight such as;
quartz (10.7%), anorthite (13.1 %), mullite (17.7%), crystobalite (1.8%) and diopside (0.7%).
While for weathered Sasol fine ash Mahlaba et al. (2011) found that it contained mullite (18 %)
and quartz (10 %) as major mineral phases and magnetite (2%), attringite (3%), calcite (3%) and
sillimanite
(Al2SiO5)(1.5%)
as
minor
phases.
Periclase
(0.75%),
analcime
(NaAlSi2O6 ·H2O)(0.75%), pyrrhotite (0.3%) and hematite (0.5%) were found as trace mineral
phases. Mahlaba et al. (2011) characterized weathered Sasol fine ash as having a high water
holding capacity (moisture content ranging between 27 and 37%) that can sustain hydration
43
reactions. These minerals together with the physical and pozzolanic properties can contribute to
CEC through surface charge development and to water holding capacity.
A pozzolan is a siliceous and aluminousmaterial that is formed, for example, when calcium
hydroxide (Ca(OH)2) chemically reacts with silicic acid (H4SiO4, or Si(OH)4). The resultant
products formed include calcium silicate hydrate (Ca9Si6O18(OH)6·8(H2O)) and Strätlingite
(Ca2Al2SiO2(OH)10·3H2O) (Matschei et al., 2007)depending on the presence of Ca, Al and Si in
the ash. These pozzolans have a cementitious characteristic that they acquire after addition of
lime during the combustion process. Basically the Ca, Al and Si in ash react with the free lime in
the presence of water to form these cementitious materials (Haynes, 2009). The pozzolans in the
ash are important as adsorption sites for pollutants such as chloride and can possibly increase
water holding capacity (Mahlaba et al., 2011).
Sasol sludge contains a significant amount of organic matter (82.4%) as characterized by Sasol
Synfuel (2008). The organic matter in sludge can contribute to water holding capacity if
combined with coarse grained medium like gasification ash and may increase water infiltration
rate, hence reduces runoff and erosion when combined with fine ash (Snyman & Van der Waals,
2004). Sasol sludge also contains 80.7% moisture ( Sasol Synfuel, 2008) a characteristic that
inhibits gaseous exchange making it difficult to use as artificial soil medium. However, this is a
transient property and can dry out under conditions of high atmospheric demand.
Particle size distribution does not only predict water holding capacity but also total pore space,
pore size distribution, bulk density and air filled porosity (Benito et al., 2005). Gasification ash is
macroporous, therefore, can provide the necessary aeration needed for numerous microbial
mediated processes, essential for functioning of a growth medium. It can also ensure rapid
infiltration and minimize run-off from the ash dump. However, a rapid water percolation can
increase the rate of loss of nutrients and increase the transportation heavy metals that end up
contaminating both the environment and ground water (Brady & Weil 2008). It is clear that
gasification ash has a low capacity to hold water and nutrients. The microporosity of fine ash can
contribute to the microporosity of the medium, increase the ability to retain water and increase
44
the release of nutrients. Some of the minerals present in the ash through hydration and humified
or stabilized sludge can also increase water holding capacity of an artificial soil medium.
However, the pozzolanic nature of the fine ash can increase hydration of minerals and cause
hardness of the growth medium and physical compaction.
According toHandreck and Black (1984) and Jayasinghe et al., (2009) an ideal artificial soil
medium must generally have a medium to coarse texture, equivalent to a particle-size
distribution between 200 and 3000 µm. However, an ideal artificial growth medium must have
20% of its particle size in the range between 100 and 250µm to be able to have a good balance
between airfilled porosity and ability to supply readily available water (Handreck & Black,
1984).Clearly, ranges lower than 0.1 mm can clog pores, increase non plant available water and
decrease airfilled porosity (Benito et al., 2005). None of these wastes has a particle size
distribution that nicely fits in this range, but it is highly possible to achieve it when the wastes
are combined. A pore space of about 50% in a soil medium can be shared equally by air and
water (Brady & Weil, 2008). Neither gasification nor fine ash has a pore space of 50%.
Gasification ash has a pore space of greater than 50% and fine ash has a pore space of less than
50%. An artificial medium must also have a bulk density of less than 400 kg m-3 (Jayasinghe et
al., 2009), but this density is much lower than the bulk density of an unconfined medium that
ranges between 1000 to 1800 kg m-3 (Brady and Weil, 2008). The bulk density range of fine ash
is similar to the density of an unconfined medium, it ranges between 1000 and 1800 kg m-3,
which is far above the optimum bulk density (400 kg m-3) of an artificial soil medium suggested
by Jayasinghe et al., 2009.
A measurement of particle size distribution can help in better understanding the interaction
between chemical, physical and biological parameters of the mixtures. It was hypothesized that
an increase in fine ash content will increase water holding capacities of the mixtures by ensuring
a particle size distribution of between 0.1 and 0.25 mm in the mixtures. Conversely, it was
hypotheised that the incorporation and increase of gasification ash content will reduce water
holding capacities of the mixtures. Futher, it was also hypothesized that the incorporation and
increase in sludge content will increase the water holding capacities of the mixtures. Therefore
the main aim of this chapter was to assess variation in particle size distribution of the mixtures
45
and the influence each waste has on water holding capacity when combined as artificial soil
medium. This is a laboratory based study where packed unsaturated low tension columns will be
used, a system that can allow the study of the ability of the various media to retain water against
the application of vacuum. This can be a useful predictor and predictive capability on the most
suitable combinationswith respect to pontential water holding capacity.
4.2 Materials and Methods
4.2.1 Particle size analysis
The sieve method described by Soil Science Society of South Africa (1990) and Smith and
Mullins, (1991) was used. From the bulk samples collected at Secunda, 200 g samples of each of
the materials (gasification and fine ash) werecollected. In total thirty replicateswere collected
from both gasification and fine ash for particle size analysis. Nine sieves (woven wire) were
arranged in descending order of their apertures as follows; 8, 4, 2, 1, 0.5, 0.25, 0.1, 0.05 mm-pan.
The 200 g sample of each of the wastes was transferred to the top sieve (8 mm) then washed with
distilled water through the sieves while shaking and slightly tapping the column of sieves. Wetsieving was necessary to help force finer particles (finer particles aggregate and block the sieve
apertures) through the sieve apertures. To determine the dry mass of each sample the contents of
each sieve was transferred into a pre-weighed beaker and oven dried at 105oC for 24 hours. The
oven dried samples in each sieve was then expressed as a percentage of the total oven dried
sample. The percent particles collected from each sieve was used to estimate the particle size
distribution of the different mixtures. This approach helped to establish the range upon which the
optimum particle size distribution falls.
4.2.2 Assessing water retention characteristics
In total 51 different mixtures were formulated containing varying amounts of gasification ash,
fine ash and sludge. The general approach followed in developing the mixtures were illustrated
in Fig. 3.1 a, b and c in chapter 3. The approach followed was to simulate natural wetting and
drying cycles. A total of ten wetting and drying cycles were simulated in a period of 12 months.
In each eluviation cycle the amount of distilled water (1.1 L) estimated based on the calculated
porosity and pore volume (Eq. 3.3 and 3.6) of the media was added to the columns. This amount
of distilled water that passed through the columns was equivalent to approximately one pore
46
volume and the system was allowed to equilibrate for 24 hours. The free drained leachates that
collected in Schott bottles were removed after 24 hours. Clean Schott bottles were then
connected to the column bases and a vacuum of -10 kPa was applied in order to collect the pore
solution. This matric potential was selected to ensure attainment of field capacity in the mixtures
and to ensure minimal removal of finer particles during vacuum application. The volume, as well
as the mass of the collected solution, was determined for mass balance purposes. The water
holding capacities of the mixtures were taken as the amount of water retained by the mixtures
after the free drainage took place and the pore solution, extractable with vacuum of -10 kPa, was
removed.
The water content of the mixtures was then expressed on both a gravimetric and volumetric
basis. Gravimetrically, the water content was calculated as the ratio between the mass of water
(Mw) that remained in the mixtures afterthe application of a static tension of – 10 kPa and the
oven dry mass (Ms) of the mixtures(Eq. 4.1). Volumetrically, the water content was calculated as
the ratio between the volume of water (Mw) that remained in the mixtures afterthe application of
a static tension of – 10 kPa and the volume (VT) occupied by mixtures(Eq. 4.3).
Some of the water retained after the application of vacuum was not necessarily water that resided
in micro pores or adsorbed on surfaces, that can easily be removed by normal oven drying at 105
0
C.It was expected that some of the water retained was trapped by hydrated minerals and
therefore chemically bound (crystal water). This type of water is at a very low energy state and
have low propensity to change phase. It was not expected that the energy applied when drying at
105 0C would be enough to liberate crystal water. With the oven drying method it will not be
possible to distinguish between water retained and potential plant available and chemically
bound water. Equation 4.3 calculates the volumetric water content (in percentage) of the
mixtures by expressing the volume of water retained as a fraction of the total volume of the
initial mixture.
=
(Eq.4.1)
47
=
× 100
(Eq. 4.2)
=
× 100
(Eq. 4.3)
Where:
MT = Mw + Ms + Mg
(Eq. 4.4)
VT = Vw + Vs + Vg
(Eq. 4.5)
Mw: mass of water retained (kg)
Ms: mass of solids (kg)
Mg: mass of gas (kg)
MT: Total mass of mixture (kg)
Vw: volume of water retained (m3)
Vs: volume of solids (m3)
Vg: Volume of gas (m3)
(Radcliffe & Šimůnek, 2010)
A separate experiment was also set-up to investigate the pozzolanic nature of the mixtures
caused by hydration. A sample of 200 g (replicated 3 times) of each mixture was oven dried at
105 0C for 24 hours and then reweighed to determine water content. The same mixture was
further saturated with distilled water, oven dried at 105 0C for 24 hours and then reweighed to
determine water content. The saturation with distilled water, oven drying at 105 0C for 24 hours
and reweighing to determine water content was carried out five times (this was done assess the
locking up of water by hydration). After these wetting and drying cycles the mixture was then
dried at 120 0C for 24 hours and then reweighed to determine water content. Further drying was
carried out by increasing the temperature by 10 units, that is to 130, 140, 150 and 160 0C.
Increasing the temperature was an attempt to forcefully remove all the water added in the
mixtures including water molecules involved in hydration.
4.3 Results and Discussions
4.3.1 Particle size analysis of fine and gasification ash
Fine ash generally did not have a diverse particle size distribution (Fig. 4.1). Particles ranging
between 100 - 250 µm were consistently the dominant fraction of fine ash (CV = 9.2%). Particles
greater than 250 µm were less than 10% of the total oven dry mass and the contribution of these
48
particles was more variable. Particles ranging between 50 – 100 µm constituted 34.6% (CV =
15.1) of the total oven dry mass. These results slightly differed from Mahlaba et al.
(2011)findings,who established that particles falling between 5µm and 75 µm were the dominant
fraction (60%) of fine ash. These authors further found that particles that fell between1 – 5 µm
and 75 – 425µm constituted 16 and 30% respectively.
Particles greater than 1 mm dominated the particle size distribution of the gasification ash (Fig.
4.1). On average particles greater than 1 mm constituted more than 75 % of the total oven dry
mass. These results were in agreement with Matjie et al. (2008) who found that gasification ash
particles are heterogenous varying from 4 to 75 mm. The contribution of particles greater than 1
mm to the total mass was also consistent and the variation between the 30 replicates in the end
exhibited a variation of only 6.6%. The contribution of particles less than 1 mm was variable and
repeated analyses of these fractions yielded CV values greater than 34% for the size fraction
smaller than 1 mm. Particles less than 2 mm was on average 36.2% of the total mass of the
gasification ash and the replicates exhibited a 12% variation.
It was clear from the results (Fig. 4.1) that none of the ashes alone are ideal growth media with
respect to having readily available water and maintaining adequate aeration. Fine ash constitutes
finer particles that are capable of increasing water holding capacity through smaller pore spaces
and a larger surface, while gasification ash seemed to be macroporous a property that is weak in
holding water against gravity.
49
60
CV=9.2
50
CV=15.
Particle size (%)
40
CV=26.
30
CV=19.
CV=27.
20
CV=22.
CV=38.
CV=17.
CV=20.
10
CV=52.
CV=61.
CV=44.
CV=34.4
CV=32.
0
<50
50-100
100-250
250-500 500-1000 1000-20002000-40004000-8000 >8000
Screen sizes (micrometers)
Fine ash particle size distribution (%)
Gasification particle size distribution (%)
Fig.4. 1: Particle size distribution of fine and gasification ash. The error bars are standard
deviations. The values above the bars are the coefficient of variance (n = 30).
4.3.2 Changes in water holding capacity of fine ash and gasification ash over time
The water holding capacity, as reflected by the gravimetric and volumetric water content of the
fine ash after free drainage and vacuum extraction, increased from eluviation cycles 1 to 5 by
8.09% (calculated by dividing the difference between the water contents of the 1 st and 5th
eluviation cycles by the water content of the 1st eluviation cycle and multiply by 100%),
gradually decreased by 10.9% (calculated by dividing the difference between the water contents
of the 6th and 10th eluviation cycles by the water content of the 6th eluviation cycle and multiply
by 100%) from eluviation cycles 6 to 10 (Fig. 4.2 a and b). The overall decrease in water
retention over 10 eluviation cycles was 3.78% (calculated by dividing the difference between the
water contents of the 1st and 10th eluviation cycles by the water content of the 1 st eluviation cycle
and multiply by 100%). This difference was quit small because the material maybe just settled,
50
initially the hydration was more significant and masked the settling of particles and decrease in
overall pore volume.
The overall water holding capacity of fine ash was significantly higher than in gasification ash
(Table 4.3).In this study it was attributed to the dominant 50 - 250 µm particle range which was
lacking in the gasification ash. Gasification is dominated by particles larger than 1 mm (Fig. 4.1)
Jala & Goyal, (2004). Attributable water retention by fine ash to the fine particles of the material
with an average diameter of less than 200 µm.Furthermore fine ash contains pozzolanic materials
likecalcium
silicate
hydrate
(Ca9Si6O18(OH)6·8(H2O))
and
Strätlingite
(Ca2Al2SiO2(OH)10·3H2O). From the chemical formula it is clear that water will be locked up by
these minerals that formed resulting in the retention of water molecules (Haynes, 2009).The
contribution of hydration on water retention is further discussed in section 4.3.3.
Variable but gradual increased by 10.61% (calculated by dividing the difference between the
water contents of the 1st and 7th eluviation cycles by the water content of the 1st eluviation cycle
and multiply by 100%) in gravimetric water content was observed with gasification ash from
eluviation cycles 1 to 7 and a rapid drop by 15.91% (calculated by dividing the difference
between the water contents of the 8th and 10th eluviation cycles by the water content of the 8th
eluviation cycle and multiply by 100%) during cycles 8 to 10 (Fig. 4.2c and d) without a definite
decrease in the volume of the material. The overall decrease in water retention over 10 eluviation
cycles was 7.0%.The gasification ash therefore settled more over time and resulted in a greater
decrease in water retention over time. The difference between the overall drop in water content
between the fine and gasification was 3.22%. Gasification ash particles contain a large number of
completely empty spheres and spheres packed with other numerous small spheres or crystals
(Trivedi & Sud, 2002) that may retain water to a certain extent.
51
b b a
b b
0.5
b b b
Gravimetric water content (kg kg1)
Gravimetric water content (kg kg1)
0.6
b c
0.4
0.3
0.2
0.1
0
1
2
3
4
5
6
7
8
9
10
0.6
0.5
0.4
0.3
b b b b b b a b b b
0.2
0.1
0
1
2
3
4
Wetting cycle
6
7
8
9
10
Wetting cycle
a
c
45
40
35
30
25
20
15
10
5
0
b b b
1
2
3
b
4
a b
5
6
b b
7
8
b
9
Volumetric water content (%)
Volumetric water content (%)
5
c
10
Wetting cycle
45
40
35
30
25
20
15
10
5
0
b b b b
b b
a b b
c
1
5
7
2
3
4
6
8
9
10
Wetting cycle
b
d
Fig.4. 2: a) Change in gravimetric water content (kg kg-1) of fine ash from the 1st to 10th
eluviation cycle, b) Volumetric water content (%) of fine ash from the 1st to 10th eluviation cycle,
c) Gravimetric water content (kg kg-1) of gasification ash from 1st to 10th eluviation cycle, d)
Volumetric water content (%) of gasification ash from the 1st to 10th eluviation cycle.
4.3.3 The contribution of water locked-up in hydrated minerals to water retention
The mass of fine ash gradually increased from 82.5 to 84.9 g (2.9% increase) with repeated
saturation with distilled water followed by oven drying at 105 0C (Fig. 4.3 a).The increase in
water holding capacity was attributed to the hydration of minerals present in the ash. Possible
hydrated solid phases are calcium silicate hydrate (Ca9Si6O18(OH)6·8(H2O)), Strätlingite
(Ca2Al2SiO2(OH)10·3H2O) and ettringite (Ca6Al2(SO4)3(OH)12•26(H2O))(Matschei et al., 2007).
Attempting to remove this water through oven drying by increasing the temperature from 120 to
160 0C was slightly successful. The mass reduced from 84.9 to 82.6 g (2.7% reduction). The
mass at 105 0C increased from 82.5 to 84.9 g and this was 2.9% increase and only 0.2%
remained in the mixture (100% fine ash) due to pozzalinity. The addition of sludge gradually
enhanced the loss of water in fine ash despite the continuous saturation. For example, the
addition of sludge in mixture 46 (50% fine ash, 0% gasification ash and 50% sludge) increased
water loss, the mass of the mixture reduced from 83.7 to 77.5 g ( 7.4% reduction) (Fig. 4.4 c) and
52
this was caused by loss of water held up by the sludge when the temperature was increased (this
was result of combustion).
With gasification ash the mass varied but did not change significantly despite continuous
saturation and temperature increase (Fig. 4.3 b) and this was because of the combustion. The
bottom line here was that gasification ash had no pozzalinic effect. The addition of water
increased the mass of the mixture from 108.0 to 108.3 g (0.3 % increase) and oven drying
reduced the mass from 108.3 to 107.8 g (0.5% reduction) (Fig. 4.3 b) The addition of sludge
significantly increased water loss, for example, mixture 51 had its mass reducing from 19.0 to
14.2 g (25% decrease) despite continuous addition of water (Fig 4.3 d). The drastic reduction in
the mass of the mixture could be caused by loss of water held up by the sludge when the
120
120
100
100
b b
80
b
a
a
b b
b b
Sample mass (g)
Sample mass (g)
temperature was increased (The material volatilzed as CO2 at these high temperatures).
b
60
40
a a
a
a a
a
a
40
20
0
105 105 105 105 105 120 130 140 150 160
a
b
Temperature increase (0C)
a
a
a
a
ab ab b
90
80
70
60
50
40
30
20
10
0
bc bc c
Sample mass (g)
Sample mass (g)
a
60
0
105 105 105 105 105 120 130 140 150 160
c
a
80
20
90
80
70
60
50
40
30
20
10
0
a
105 105 105 105 105 120 130 140 150 160
Temperature increase (0C)
a
ab ab ab ab ab ab bc bc bc
105 105 105 105 105 120 130 140 150 160
d
Temparater increase (0C)
Temperature increase (0C)
Fig.4. 3: a) Assessment of water retention in fine ash (mixture 1), b) Assessment of water
retention in gasification ash (mixture 11), c) Assessment of water retention in mixture 46 with
50% fine ash and 50% sludge and d) Assessment of water retention in mixture 51 with 50%
gasification ash and 50% sludge
53
4.3.4Change in water holding capacity of the mixtures over time
Seemingly, a combined increase of fine ash and sludge from 10 to 50% content increased water
holding capacity of the mixtures (Fig.4.4 aand b). An addition of 50% sludge to 50% fine ash
(mixture 46) appreciably increased the water content of the mixtureafter the first eluviation
cycle. The addition of the same amount of sludge (50%) to 50% gasification ash (mixture 51)
increased water retention but the increase was less than in mixture 46. Mixture 46 (50% fine ash
and 50% sludge) retained the highest water over all combinations and mixture number 11 (100%
gasification ash) had the least water water holding capacity after the first eluviation cycle. Other
mixtures without sludge (mixtures 1 to 10) showed a constant decrease in water retention with an
increase in gasification ash and a decrease in fine ash content.
After the tenth eluviation cycle water holding capacity of mixture 51 (50% gasification ash and
50 % sludge) decreased significantly from the first eluviation cycle and remained the mixture to
hold the least water, followed by mixtures 11 (100% gasification ash), 12 (90% gasification ash
and 10% sludge), 30 (80% gasification ash and 20 % sludge), 38 (70 % gasification ash and 30
% sludge) and 45 (60% gasification ash and 40% sludge) after the tenth eluviation cycle (Table
4.1, Fig. 4.4 c and d).This indicated that Sludge addition does not increase the water holding
capacity of the gasification ash much. Mixture 46 (50% fine ash and 50% sludge) lost
Table 4.1: Mixtures with highest and lowest water holding capacities (WHC).
Mixtures
with
highest
WHC
Description of
mixtures
1
22
31
39
40
46
100% FA, 0%
GA & 0% SL
80% FA, 0%
GA & 20% SL
70% FA, 0%
GA & 30% SL
60% FA, 0%
GA & 40% SL
50% FA, 10%
GA & 40% SL
50% FA, 0%
GA & 50% SL
WHC after
eluviation
cycle 1 (kg
kg-1)
WHC after
eluviation
cycle 10 (kg
kg-1)
Mixtures
with lowest
WHC
0.46
0.44
11
0.71
0.55
12
0.88
0.65
30
0.98
0.65
38
0.96
0.62
45
0.99
0.63
51
Note: FA = fine ash, GA = gasification ash and SL = sludge
54
Description of
mixtures
0% FA, 100%
GA & 0% SL
0% FA, 90%
GA & 10% SL
0% FA, 80%
GA & 20% SL
0% FA, 70%
GA & 30% SL
0% FA, 60%
GA & 0% SL
0% FA, 40%
GA & 0% SL
WHC after
eluviation
cycle 1 (kg
kg-1)
WHC after
eluviation
cycle 10 (kg
kg-1)
0.20
0.19
0.27
0.20
0.37
0.22
0.51
0.24
0.61
0.26
0.80
0.31
a
20% SL
10% SL
No SL
1 3 5 7 9 111315171921232527293133353739414345474951
Number of mixture
Volumetric water content (%)
60
50
40
50% SL
Gravimetric water content (kg kg-1)
40% SL
30% SL
10 % SL
20 % SL
30 % SL
1.1
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
c
No SL
30 % SL 40 % SL
50 % SL
1 3 5 7 9 111315171921232527293133353739414345474951
Number of mixtures
60
No SL
30
20
10
0
50
40
No SL
10 % SL
20 % SL
30 % SL
40 % SL
50 % SL
30
20
10
0
1 3 5 7 9 111315171921232527293133353739414345474951
b
20 % SL
10 % SL
40 % SL 50 % SL
Volumetric water content (%)
Gravimetric water content (kg kg-1)
1.1
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1 3 5 7 9 111315171921232527293133353739414345474951
Number of mixture
d
Number of mixture
Fig.4. 4: a) Gravimetric water content (kg kg-1) of the various mixtures after the 1st eluviation
cycle, b) Volumetric water content (%) of the various mixtures after the 1 st eluviation cycle, c)
Gravimetric water content (kg kg-1) of the various mixtures after the 10th eluviation cycle d)
Volumetric water content (%) of the various mixtures after the 10th eluviation cycle. The arrows
indicate an increase in fine ash content.
its moisture content significantly after the tenth eluviation cycle (Fig. 4. d, e and f). Mixtures 31,
39, 40 and 46 maintained their gravimetric water content above 0.6 kg kg -1 even after the tenth
eluviation cycle; while all other mixtures had their moisture content falling lower than 0.6 kg kg 1
(Table 4.1, Fig. 4.4 c and d). Mixture 31 (70% fine ash and 30% sludge) remained with the
highest percent gravimetric and volumetric water holding capacity after the tenth eluviation cycle
followed by mixtures 24 (60% fine ash, 20% gasification ash and 20 % sludge) and 32 (60% fine
ash, 10% gasification ash and 30% sludge). Mixtures without sludge and dominated by by
gasification ash, 5 to 11 generally maintained almost the same gravimetric and volumetric water
content even after the tenth eluviation cycle.
55
It was evident that adding sludge to both fine and gasification ash increased both gravimetric and
volumetric water content of the ashes, but a mixture of 50% gasification ash and 50% sludge
retains less water compared to a similar mixture of 50% fine ash and 50 % sludge after the first
eluviation cycle. The former (gasification – sludge mixture) lost more water than the latter (fine
ash – sludge mixture) after the tenth eluviation cycle.
The increase in water retention in the mixtures of fine ash was an attribute of fine ash particles
contributing to microporosity of the medium, the pozzolanic nature and hydration of minerals
present such as phyllosilicates and other hydrated minerals (layered double hydroxides)
(Zevenbergen et al., 1999, Dermatas & Meng, 2003). Furthermore, weathering of ash to
secondary minerals by the wetting and drying cycles exposed new mineral phases and increased
the reactive surface area that played an important role in the development of CEC and AEC. The
reactive surfaces contributed not only to cation and anion exchange but also to water holding
capacity (Zevenbergen et al., 1999). Sludge amendment greater than 20%, did not drastically
improve the water holding capacity of the mixtures. This is because humified or stabilized sludge
generally contributes more to CEC than acting as a surface for water retention. The negative
effect of gasification ash on water holding capacity was due to the fact that gasification ash is
macroporous in nature with an average particle size of greater than 1 mm dominant.
4.4 Conclusion
Gasification ash was found to be macroporous with average particle sizes of greater than 1 mm
dominating and fine ash was dominated by particle sizes between 100 and 250 µm. The addition
of sludge (10 to 50%) significantly increased water content of the mixtures (mixtures 12 to 51
described in chapter 3). Increasing fine ash (10 to 100%) as well increased the water holding
capacities of mixtures 22(80% fine ash, 0% gasification ash and 20% sludge), 31(70% fine ash,
0% gasification ash and 30% sludge), 39(60% fine ash, 0% gasification ash and 40% sludge) and
46(50% fine ash, 0% gasification ash and 50% sludge) (dominated by particles between 100 and
250 µm) for the first eluviation cycle. Alternatively, increasing gasification ash drastically
reduced the water holding capacity of mixtures 11(0% fine ash, 100% gasification ash and 0%
sludge), 12(0% fine ash, 90% gasification ash and 10% sludge), 30(0% fine ash, 80%
gasification ash and 20% sludge), 38(0% fine ash, 70% gasification ash and 30% sludge), 45(0%
fine ash, 60% gasification ash and 40% sludge) and 51(0% fine ash, 50% gasification ash and
56
50% sludge) dominated by particles greater than 8mmafter the first eluviation cycle. After the
tenth eluviation cycle mixture 31 retained the highest volumetric water content (37.2 %)
followed by mixtures 24(60% fine ash, 20% gasification ash and 20% sludge) with 35.9 % and
32 (60% fine ash, 10% gasification ash and 30% sludge)with 35.6 % volumetric water content.
With gravimetric water content mixtures 31 and 39 retained the highest water followed by
mixtures 32, 40 (50% fine ash, 10% gasification ash and 40% sludge) and 46. Mixtures 11, 12,
30, 38, 45 and 51 exhibited the lowest water holding capacities even after the tenth eluviation
cycle. Mixtures 1 to 5 (described in chapter 3) had increased gravimetric and volumetric water
content after the tenth eluviation cycle due to the dominant particles between 100 and 250 µm
and pozzolanic nature through the hydration of the siliceous and aliminous material like calcium
silicate hydrate (Ca9Si6O18(OH)6·8(H2O)) and Strätlingite (Ca2Al2SiO2(OH)10·3H2O), while
mixtures 6 (50% fine ash, 50% gasification ash and 0% sludge) to 11 (dominated by particles
greater than 8 mm) had reduced water holding water capacity.It was evident that mixtures; 17,
18, 19, 20, 21, 22, 23, 24, 25, 31, 32, 33, 39, 40 and 46 (descrbed in chapter 3) with 50 to 80%
fine ash, 0 to 40% gasification ash and 10 to 50 % sludge were dominated by particle diameters
between 0.1 to 0.250 mm can provide plant available water.
57
CHAPTER 5: NITROGEN DYNAMICS IN SLUDGE-COAL ASH MIXTURES AS
INFLUENCED BY WEATHERING
5.1 Introduction
Sasol fine and gasification ash contain lower total nitrogen (less than 0.04%) content relative to
the N content for sludge (7.9%) as characterized by Sasol (2008). South African municipal
wastewater and sludge contain variable amounts of organic N, nitrate (NO3-), nitrite (NO2-) and
ammonium (NH4+) but the organic form of N in sludge is dominant and needs to be mineralized
before it becomes available to plants (Snyman & Van der Waals, 2004). Mineralization can be
described as the conversion of organic nitrogen into plant available inorganic forms (NH4+ and
NO3-) with the help of microbial activities (Deenik, 2006). Essential processes involved in
mineralization such as ammonification (the transformation of organic N to NH4+) and
nitrification (the oxidation of NH4+ to NO3-) liberate N from organic matter and make it available
to plants and microbes. However, mineralized N is also subjected to leaching and denitrification
losses (Vernimmen et al., 2007). Sasol biological sludge contains 82.4 % organic matter as
characterized by Sasol (2008) that can be subjected to mineralization.
Ammonification process
Amino acids are the major sources of organic N and precursor of ammonium (NH4+) production
(Pietri & Brookes, 2008). During N mineralization process, microorganisms(Bacillus,
Clostridium, Proteus, Pseudomonas, and Streptomyces) attack humic compounds and in the
process amino compounds (R-NH2) are formed and further hydrolyzed producing N as NH4+.
This microbial transformation of organic N to NH4+ is termed as ammonification (Brady and
Weil, 2008). This process is affected by pH. Pietri and Brookes (2008) reported that the optimum
pH for ammonification ranges from 6.0 to 8.0.
Nitrification process
This process occurs under aerobic conditions and oxidizes NH4+ to NO3- (summarized in Eq. 5.1)
Nitrate results from the oxidation of nitrite (NO2-) which inturn is a product of NH4+ oxidation
(Kieber et al., 2005). The conversion of NH4+ to NO2- is facilitated strictly by aerobic autotrophic
bacteria (Nitrosomonas, Nitrosolobus and Nitrosospira) and the formation of NO3 from NO2 is
made possible by another group of autotrophs, Nitrobacter (Mengel and Kirkby, 2001 & Brady
58
& Weil, 2008). The process is catalyzed by enzymes contained in the microorganisms. The
bacteria contain the enzyme ammonium monooxygenase that oxidizes NH4+ to hydroxyalamine
which then is oxidized to NO2- by hydroxyalamine oxydoreductase and eventually the NO2 - is
oxidized to NO3- by nitrite oxydoreductase (Canfield, et al., 2010). Nitrate can also be formed
by the oxidation of ammonia (NH3) by autotrophic bacteria. In this case NH3 is initially oxidized
to NO2- then to hydroxylamine (NH2OH) and eventually to NO3-. Heterotrophic microorganisms
can also produce NO3- by using NH3 as an electron donor. This enables them to oxidize NH3 to
NO3- (Mengel and Kirkby, 2001).
NH4+ (aq) + 2O2 (g) →NO3- (aq) + 2H+(aq) + H2O(l)
(Eq. 5.1, Essington, 2004)
During nitrification, pH remains as one of the factors that control this process (Vernimmen et al.,
2007). pH conditions that are slightly acidic to neutral are preferred by the nitrifying bacteria
(Mengel and Kirkby, 2001). Shammas, (1986) reported that the optimum pH for Nitrosomonas is
8.5 to 8.8 and that for Nitrobacter is 8.3 to 9.3. The activities of Nitrosomonas cease at pH 9.6.
Under highly alkaline conditions, NH3 becomes a dominant inorganic N species and at high
concentrations it has a negative effect on microbial activity and basically poisons these systems.
For example, important nitrifying bacteria, Nitrobacter, are inhibited by high NH3
concentrations. Temperature also affects nitrification; Shammas (1986) stated that nitrification
rate is a function of temperature within the range of 5 to 35o C while the maximum rate occurring
at 30 oC. However, Sierra, (2002) claimed that the formation of both NO2- and NO3 - is strongly
inhibited at temperatures of 10 oC or less because at low temperatures biostatic effect affects the
activities of nitrifiers. Redox reactions also affect the formation of NH4+, NO3- and NO2 -. Carbon
(C) contained in organic matter is an excellent donor of electrons while NO3 - is an electron
acceptor. An abundance of electrons reduces NO3 - to NH4+ (Reddy et al., 2000). In this case a
low C/N ratio would favour mineralization (Mengel & Kirkby, 2001).
Immobilization process
This process refers to the assimilation of inorganic N. It involves the transformation of NO3 - to
NH4+ and then to organic forms. Causes of this process include high C/N ratio that encourages
reduction of NO3- to NH4+ and when there is short supply of N by organic matter forcing the
59
microbes to search for NO3 - to NH4+ from soil solution. Immobilization and mineralization occur
simultaneously (Mengel & Kirkby, 2001 & Brady & Weil, 2008).
Denitrification process
Denitrification is an anaerobic process by which heterotrophic bacteria reduce NO3- to gases
such as NO, NO2 and N2. Another contributing process to N loss is volatilization which occurs
mainly at high pH (alkaline conditions) where NH4+ is transformed to NH3 (Eq. 5.2) (DinÇer &
Kargi, 2000 & Brady & Weil, 2008).
NH4+ (aq) + OH-(aq) →NH3 (g) + H2O (l)
(Eq. 5.2, Reddy et al., 2000)
In this chapter it was hypothesized that the ashes will not provide any species of nitrogen and
sludge will provide all the plant inorganic nitrogen (NH4+, NO3- and NO2-) in the mixtures. The
main purpose therfore was to assess the contribution of Sasol sludge, fine and gasification ashes
to the production of NH4+, NO3- and NO2 - in sludge ash mixtures as influenced by weathering. It
was envisaged that all the processes involved in N mineralization would occur due to the
conducive conditions brought about by sludge and created aerated conditions. Further, the
production of these N species would provide insight to microbial activities.
5.2 Materials and methods
5.2.1 Selection of mixtures
The fifty-one sludge-coal ash mixtures were divided into six groups based on sludge content
(described in chapter 3, Fig. 3.1-3.3). From each group three mixtures were selected based on
fine and gasification ash content. In this case all three mixtures had the same sludge content but
varying quantities of both fine and gasification ashes (Table 5.1). The purpose was to select
samples that represented the various treatments the best. The analysis in these samples was done
to assess the measured inorganic N (ammonium + nitrate + nitrite) and individual N species;
ammonium (NH4+), nitrate (NO3-) and nitrite (NO2-) to calculated total inorganic N and NH4+,
NO3- and NO2-species in non selected mixtures. The calculation of total inorganic N and
individual N species content in a mixture was based on the N contribution of the individual waste
components; sludge, fine and gasification ashes. To calculate total inorganic N content in a
60
particular mixture, for example, the measured total inorganic N content of fine ash alone was
multiplied by the percent content of fine ash in that mixture. Similarly the measured total
inorganic N content of gasification ash alone was multiplied by the percent content of
gasification ash in that mixture. The same procedure was followed to calculate the sludge
contribution to inorganic N species content in the mixture. The calculated total inorganic N
species content of that particular mixture was then obtained by summing the three products.
Table 5.1:Mixtures selected for inorganic nitrogen analysis
Group number
1 (0% sludge)
2 (10% sludge)
3 (20% sludge)
4 (30% sludge)
5 (40% sludge)
6 (50% sludge)
Selected
mixtures
1
6
11
12
17
21
22
26
30
31
35
38
39
42
45
46
48
51
Fine ash content (%)
Gasification ash content (%)
Sludge content (%)
100
50
0
0
50
90
80
40
0
70
30
0
60
30
0
50
30
0
0
50
100
90
40
0
0
40
80
0
40
70
0
30
60
0
20
50
0
0
0
10
10
10
20
20
20
30
30
30
40
40
40
50
50
50
The total inorganic N and NH4+, NO3- and NO2- were determined on selected pore volume
solutions. These solutions were collected from the mixtures by applying vacuum of -10 kPa at
the base of each column to remove some of intertesial water that remained in the mixtures after
free drainage took place. The pore solutions were sampled 24 hours after free drainage stopped
(Chapter 3, section 3.5). The pore volume solution was preferred to cleam information about the
N dynamics of the various mixtures bcause this solution has been in contact with the mixtures for
longer time cover free drained solution because it was exposed to several chemical reactions for
24 hours before removal.
61
5.2.2 Determination of inorganic nitrogen (NH4+, NO3-and NO2-)
The inorganic nitrogen was determined by means of Kjeldahl distillation described by Bremner
(1965) and Tan (2005). A 5 ml aliquot of the solution was pipetted into a distillation flask and
diluted with 20 ml of de-ionized water to a total volume of 25 ml. Twenty five millimeters of a
strong alkali, 50 % sodium hydroxide (NaOH) freshly prepared on a mass bases was pipetted
into the same distillation flask containing the diluted aliquot making a total volume of 50 ml. The
NaOH was added to convert ammonium (NH4+) to ammonia (NH3). The sample was swirled for
a few seconds before 2 g of Devardas Alloy was added to reduce nitrate (NO3-) and nitrite (NO2-)
to NH3. After the addition of the Devardas Alloy the flask was immediately connected to a Bϋchi
321 Kjeldahl distiller (Manufacturer – LABEQUIP Ontario, Canada) and distilled for six min
into a 500 ml beaker containing 25 ml of 0.6 M boric acid (H3BO3) to collect NH3. The 0.6 M
H3BO3 was prepared by dissolving 200 g using 3000 ml of deionised water and added a
combination of 57 ml methylene blue (C16H18ClN3S) and 117 ml methyl red (C15H15N3O2) as
indicator then made up to volume (5000 ml). The NH3 plus H3BO3 during distillation formed
ammonium borate (NH4+ + H2BO3-).
2NH3 + 2H3BO3 → 2NH4H2BO3
(Eq. 5.3)
The indicator changed to a green colour indicating the completion of NH3 distillation. The 6 min
elapsed when the beaker was almost half full (approximately 230 ml). A measuring cylinder was
used to determine the volume of the distilled solution. To estimate total N the ammonium borate
was back titrated using 0.01 M hydrochloric acid (HCl) to H3BO3.
2NH4H2BO3 + 2HCl → 2NH4Cl + 2H3BO3
(Eq. 5.4)
The colour changed at the end point of the titration from green to permanent faint pink (the
colour of the H3BO3). To calculate total inorganic N in solution the volume (L) of HCl used to
titrate the ammonium borate was multiplied by the H+ (mol) added divided by the volume of
aliquot (L), the outcome was multiplied by the volume of aliquot (L) and the product (mol L -1)
was divided by the dry mass (kg) of the mixture. The outcome (mol L -1/kg mix) was multiplied
62
by N molar mass (14.007 g mol-1) then divided the product by 1000 g to convert the N to mg kg 1
.
5.2.3 Determination of ammonium (NH4+)
To determine NH4+, a 5 ml aliquot of the same solution was pipetted into the distillation flask
and diluted with 20 ml of de-ionized water pushing up to a total volume of 25 ml. the difference
with the previous step was that no electron donor (reducing agent) was added ( Devarda’s alloy)
to convert oxidized N to NH3. Twenty five millimeters of a 50 % NaOH solution (prepared on a
mass bases) was pipetted into the same flask with the diluted aliquot to obtain a final volume of
50 ml. The flask was swirled for a few seconds to enhance chemical reaction. The NaOH was
added to convert NH4+ to NH3. The flask was connected to the Bϋchi 321 Kjeldahl distiller and
distilled for 6 min into a 500 ml beaker containing 25 ml of 0.6 M H3BO3 prepared as described
above including the methylene blue and methyl red indicators. From this point, to determine
NH4 in the sample the same procedure described in section 5.2.2 was followed.
5.2.4 Indirect determination of nitrate (NO3-)
After the determination of NH4+ the flask with the aliquot was cooled to prepare for the
determination of NO3 - + NO2- left in the aliquot. Two grams of the Devarda’s Alloy was added
into the same flask to convert the NO3- and NO2 - to NH4 and immediately connected to the Bϋchi
321 Kjeldahl distiller. The Bϋchi was turned on for the solution to start boiling and then switched
off to recede, after which the distillation continued for 6 min into a 500 ml beaker containing 25
ml of 0.6 M H3BO3 prepared as described above including the methylene blue and methyl red
indicators. A measuring cylinder was used to determine the volume of the distilled solution. The
NH4+ accumulating plus H3BO3 during distillation formed ammonium borate which changed to a
green colour indicating the completion of NH4+ distillation. A measuring cylinder was used to
determine the volume of the distilled solution. To determine NH4 the ammonium borate was
back titrated using 0.01 M HCl. The colour changed at the end point of the titration from green to
permanent faint pink which was the colour of the 0.6 M H3BO3.To estimate NO3- + NO2 represented by NH4+ the volume (L) of HCl used to titrate the ammonium borate was multiplied
by the H+ (mol) added divided by the volume of aliquot (L), the outcome was multiplied by the
volume of aliquot (L) and the product (mol L-1) was divided by the dry mass (kg) of the mixture.
63
The outcome (mol L-1/kg mix) was multiplied by N molar mass (14.007 g mol-1) then divided the
product by 1000 g to convert the NH4+ to mg kg-1.
5.2.5 Indirect determination of nitrite (NO2-)
In this procedure NH4+ + NO3 - were determined (to subtracted from total inorganic N and remain
with NO2 -) by pipetting a separate 5 ml of aliquot into a distillation flask and diluted with 20 ml
of de-ionized water increasing the volume to a total of 25 ml. Sulfamic acid (1 ml) was also
pipetted into the same flask with diluted aliquot, swirled the flask for a few seconds to reduce
NO2- to N2. After swirling only NH4+ + NO3 - were left. The Devarda’s Alloy (2 g) was added to
reduce the NO3- to NH4+and 2 g of MgO was added to convert the NH4+ to NH3. The flask was
then immediately connected to the Bϋchi and distilled to a volume of 50 ml into a 50 ml conical
flask containing 10 ml of 0.6 M H3BO3 prepared as described above including the methylene
blue and methyl red indicators. A measuring cylinder was used to determine the volume of the
distilled solution. The NH3 accumulating plus H3BO3 during distillation formed ammonium
borate which changed to a green colour indicating the completion of NH4+ distillation. A
measuring cylinder was used to determine the volume of the distilled solution. To determine NH3
the ammonium borate was back titrated using 0.0025 mol L-1 sulphuric acid (H2SO4). The colour
changed at the end point of the titration from green to permanent faint pink which was the colour
of the 0.6 M H3BO3.To estimate NH4+ + NO3 - represented by NH4+ the volume (L) of HCl used
to titrate the ammonium borate was multiplied by the H+ (mol) added divided by the volume of
aliquot (L), the outcome was multiplied by the volume of aliquot (L) and the product (mol L -1)
was divided by the dry mass (kg) of the mixture. The outcome (mol L -1/kg mix) was multiplied
by N molar mass (14.007 g mol-1) then divided the product by 1000 g to convert the NH4 to mg
kg-1. Finally, NO2 - concentration was calculated as the difference between total inorganic N
(NH4+ + NO3- + NO2-) and the combination of ammonium and nitrate (NH4+ + NO3-).
5.3 Results and discussion
Generally, the inorganic N species, NH4+ and NO2- were detected in all selected mixtures (1 to
51) while NO3 - was detected in mixtures 22 to 51 (Fig 5.1). The detection of only NH4+ and NO2 in all mixtures with no or low sludge content (0 to 10%) indicated that ammonification and the
first part of oxidation (conversion of NH4+ to NO2-) occurred (Fig. 5.1)
64
Concentration N-NH4 (mg kg-1)
30
50 % sludge
25
40 % sludge
20
0 % sludge
30 % sludge
10 % sludge
20 % sludge
15
10
5
0
1
6
11
12
17
Nitrite - N
21
22
26 30 31 35
Selected mixtures
Nitrate - N
38
39
42
45
46
48
51
Ammonium - N
Fig.5. 1: Total inorganic N (NH4+, NO3- and NO2-) released by selected mixtures calculated for
ten eluviation cycles. The arrows indicate the increasing gradient in fine ash.
However, it seems that the second step of nitrification was inhibited in these mixtures. The initial
high pH range of 9.4 to 11.7 of the mixtures, were above the optimum pH for microbially
mediated ammonification (between 6.0 and 8.0) and for nitrification (between 7.5 to 8.0)
generaly reported (Pietri & Brookes 2008). From this it was deduced that the ammonifying
bacteria(Bacillus,Clostridium, Proteus, Pseudomonas, and Streptomyces) and the Nitrosomonas
bacteria potentially present were quite resilient and not severly inhibited / less affected by the
extreme conditions (high pH and salinity). Under these conditions NO3 - produced was close to
zero (Fig. 5.1, mixtures) which indicates that Nitrobacterbacteria (converting NO2 - to NO3-) were
more sensitive andtheir activity inhibited. It could also be possible that denitrification of NO3 could have occurred resulting to low levels of the NO3- as a result of high pH and volatilization
could also occur since the pH of the ash was close to the pKa (9.2) of NH4+/NH3. Another reason
why especial NO2 - was detected in the mixtures with low sludge content was that Sasol inject
significant amount of NH3 into the electrostatic precipitators (in the steam plant) to assist in with
the removal of fly ash (personal communication with Sasol). The ash is alkaline and it is
expected that much would have volatilize, however, it is reasonable to expect that some nitrogen
remained in the fly ash and transferred to fine ash since fine ash is made of 83% fly ash and 17%
gasification ash and fine particles less than 250 µm (Mahlaba et al., 2011). The NH3was
converted by autotrophic bacteria into NO3- by initially oxidizing the NH3 to NH2OH then NO2 and finally to NO3-, however, conditions were not conducive (high pH 9.4 - 11.7) for the
Nitrobacter bacteria to swiftly convert the NO2- to NO3-.
65
Nitrate was detected in mixtures containing 20 to 50% sludge (Fig. 5.1) because the sludge
played a role in buffering the pH and the enrichment of ash with organic N increased
mineralization and propensity for NO3 - to be generated. The pH was reduced from a high and
narrow range11.3 - 11.7 toa lower and wider range 9.0 - 10.3 for the mixtures without sludge and
from pH range; 9.4 – 10.0to7.7 – 8.2 for mixtures with sludge after the tenth eluviations cycle
(Table 5.2). Such conditions were expected to be more conducive for higherNitrobacter bacteria
activity responsible for the formation of nitrate. However, a reduction in NO2 -species (Fig. 5.1)
was observed in mixtures containing 20 to 50% sludge and a gradually increase in the NH4+ and
NO3- species. The increase in NH4+ and NO3 - was a result of more N which was available for
mineralization and suitable conditions for both ammonifying and the nitrification bacteria
created by the reduction in pH and salinity over time. Hence the activities of microorganisms
resulted to the quantities of NH4+ species in mixtures 35, 42, 46, 48 and 51 becoming equivalent
on average to the quantities of NO3- species (Fig. 5.1). The reduction of NO2-, evident in
mixtures 35 and 51 (Fig. 5.1), was due to high oxidation rate of NO2 -to NO3-by Nitrobacter.
Mixture 48 had the highest mineralized total N (24.4 mg kg-1) than any other mixture and
mixture number 12 had the least (10.5 mg kg-1)for all the ten eluviation cycles. Mixtures with
high percent gasification ash content, 11, 12, 30, 38, 45 and 51, had the least mineralized total N
within their groups (0 to 50% sludge)due to the fact that it maintained a higher pH (11.7) which
negatively impacted mineralization (Table 5.2).
Table 5.2: pH values in selected mixtures for the 1st and 10th eluviation cycles.
Eluviation
cycle no.
1
11.
3
9.6
11
12
1 (pH)
11.7 9.4
10 (pH)
9.0
8.2
1 (ECmSm-1)
580 527
319
10 (ECmSm-1)
118 88
140
Note: EC – electrical conductivity
Selected mixtures
35
38
39
17
22
30
42
45
46
48
51
9.6
8.3
9.8
8.1
9.9
8.0
9.9
8.0
9.5
8.1
10.0
7.7
9.8
7.7
9.6
7.8
9.8
7.8
9.6
7.7
9.4
7.8
520
455
540
505
463
511
551
533
536
549
622
122
222
148
227
191
319
302
243
284
329
220
Total inorganic N for the first eluviation cycle was significantly lower than the total inorganic N
for the tenth eluviation cycle and this was evident in mixtures (12 to 51) with sludge (10 to 50%)
(Fig. 5.2). The high inorganic N for the tenth eluviation cycle was due to the reduction in pH (9.4
66
– 10.0 to 7.7 – 8.3)of the mixtures over time and an increase in N mineralization. This pH range
(7.7 – 8.3) was suitable for both ammonification and nitrification processes (Pietri & Brookes
2008). However, at this stage NO3- was close to zero (due to leaching) in all mixtures and the
total inorganic N was made up of only NH4+ and NO2-. The lower total inorganic N for the first
eluvation cycle was due to high pH (11.3 – 11.7) which created an overall non conducive
environment for all the microbes. However, NH4+ and NO3- were mineralized more than NO2 - at
Concentration of inorganic N (mg kg-1)
this stage due to high oxidation rate of NO2- to NO3-.
3
50 % sludge
2.5
40 % sludge
30 % sludge
2
1.5
20 % sludge
0 % sludge
10 % sludge
1
0.5
0
1
6
11
12
17
21
22
26
30
31
35
38
39
42
45
46
48
51
Selected mixtures
Eluviation cycle 1
+
-
Eluviation cycle 10
-
Fig.5.2: Inorganic N (NH4 , NO3 and NO2 ) released by selected mixtures calculated for
eluviation cycles 1 and 10. The arrows indicate the increasing gradient in fine ash.
The total inorganic N for the first and the tenth eluviation cycles did not differ much in mixtures
without sludge (Fig. 5.2). This was because of the small change in pH range (from 11.3 - 11.7 to
9.6 – 10.3) that inhibited mineralization. As discussed earlier on mixture 46 (50% fine ash and
50% sludge) appeared to have the highest mineralized total N compared to all mixtures for both
the first and the tenth eluviation cycles, mixtures 11 (100% gasification ash) and 12 (0% fine ash,
90% gasification ash and 10% sludge) had the least total N for the tenth eluviation cycle and the
first eluviations cycle respectively (Fig 5.2). For the first eluviation cycle mixtures 46 and 12 had
1.7 and 0.97 mg kg -1 respectively while for the tenth eluviation cycle mixture 46 had 2.5 mg kg -1
and mixture 11 had 1.1 mg kg-1.
67
Log molar NH4:NO3
20 % sludge
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
30 % sludge
No sludge
1
6
11
Log molar NH4:NO2
50 % sludge
10 % sludge
12
17
21
22
a
26 30 31 35 38
Selected mixtures
39
42
45
46
48
51
50 % sludge
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
30 % sludge
40 % sludge
20 % sludge
No sludge
1
b
40 % sludge
6
11
10 % sludge
12
17
21
22
26 30 31 35 38
Selected mixtures
39
42
45
46
48
51
1.5
50 % sludge
30 % sludge
Log molar NO3:NO2
1
No sludge
0.5
10 % sludge
20 % sludge
40 % sludge
0
-0.5
1
6
11
12
17
21
22
26
30
31
35
38
39
42
45
46
48
51
-1
-1.5
c
Selected mixtures
Fig.5. 3: a) Log molar NH4+:NO3- ratio based on the ammonium and nitrate released after ten
eluviation cycles, b) Log millimolar NH4+:NO2- ratio based on the ammonium and nitrite
released after ten eluviation cycles, c) Log millimolar NO3-:NO2 - ratio based on the nitrate and
nitrite released after ten eluviation cycles.
68
The release of each N species was also viewed in relation to the release of the other species. The
release of NH4+was more favoured over the release of NO2- in all mixture over all eluviation
cycles (Fig.5.3 b). This indicates that the conversion rate of NH4+to NO2- was low. The release of
NH4+was more favoured over the release of NO3- in mixtures with 20% sludge but reduced in
mixtures with 30 to 50% sludge slightly favouring NO3- due to the introduction of sludge that
helped to reduce pH and salinity. At this stage the environment was conducive for bacteria
converting NO2 - to NO3- (Fig.5.3 a). The release of NO3- was favoured only in mixtures with
20% sludge and then fluctuated in mixtures with 30 to 50% sludge (Fig.5.3 c).
To assess the release trends of NH4+, NO3- and NO2- eluviation cycles 1, 5, 8 and 10 marked as a,
b, c and d respectively were selected based on changes they showed on the trends (Fig. 5.4). The
mineralization of NH4+seemed not to change much for all the eluviation cycles but increased
with increase in sludge content. Nitrate in eluviation cycles 1 and 5 increased with increase in
sludge due to a decrease in pH and an increase in the oxidation of NO2 - to NO3-. The saw tooth
characterstics for leaching 5 was mixtures dominated with gasification ash. It also seemed that
there was flush or pulse of nitrate production during leaching 4 – 5. This lead to a decrease in
NO2- for the first and the fifth eluviation cycles. An increase in NO2 - was shown by the 8th and
the 10th eluviation cycles. At this stage NO3- was reducing to lower levels. The rate of oxidizing
NH4+ to NO2- was higher than the rate at which NO2- was converted to NO3- or most of the NO3 was leached at this point (this seemed that NO3- hand flushed out).
The reduction in pH could be the result of carbonation where carbon dioxide (CO2) liberated
from the breakdown of the sludge formed carbonic acid (H2CO3) and The oxidation of NH4
results in an increase in the concentration of NO3 - and 2 moles of H+ in the solution (Essington,
2004 &Brady & Weil, 2008). The biodegradation of sludge released dissolved organic carbon
(DOC) thatcontained humified compounds such as fulvic acid (FA) and humic acid (HA)
(Mulder & Cresser, 1994 & Singh & Agrawal, 2010) which also contributed to a reduction in the
pH.The reduction in salinity for all the mixtures could be attributable to the leaching of the salts
(Na+, K+, Ca2+, Mg2+, Cl–, SO42- ,HCO3 - , CO32-, and NO3-) from the first to the tenth eluviation
cycles (Mulder & Cresser, 1994, Li & Shuman 1997 & Sparks 2004).Electrical conductivity
(EC) and elemental release will be discussed in the following chapter (chapter 6).
69
3.5
3
3
2.5
2
1.5
1
0.5
0
1
c
Ammonium
Nitrate
Nitrite
2.5
2
1.5
1
0.5
0
1
b
3.5
3
3
2.5
2
1.5
1
0.5
0
1
6 11 12 17 21 22 26 30 31 35 38 39 42 45 46 48 51
Selected mixtures
Ammonium
Nitrate
Nitrite
d
6
11 12 17 21 22 26 30 31 35 38 39 42 45 46 48 51
Selected mixtures
Ammonium
3.5
Concentration of inorganic N (mgkg-1)
Concentration of inorganic N (mg kg-1)
a
6 11 12 17 21 22 26 30 31 35 38 39 42 45 46 48 51
Selected mixtures
Concentration of inorganic N (mg kg-1)
Concentration of inorganic N (mgkg-1)
3.5
Nitrate
Nitrite
2.5
2
1.5
1
0.5
0
1
6 11 12 17 21 22 26 30 31 35 38 39 42 45 46 48 51
Selected mixtures
Ammonium
Nitrate
Nitrite
Fig.5. 4: NH4+, NO3- and NO2- release trends for selected eluviation cycles; 1, 5, 8 and 10 in a, b, c and d respectively (expressed as
N).
70
5.4 Conclusion
The extreme conditions (caused by high pH and salinity) of mixtures with zero and 10% sludge
(mixtures 1 to 21 described in chapter 3) negatively affected the nitrifying bacteria (Nitrobacter)
and as a result only NH4+ and NO2-nitrogen species were detected and no nitrate. The addition of
sludge moderated these ash extreme conditions by reducing the pHfrom a higher range (11.7-8.3)
to a lower range (10.3-7.6) and salinity from a higher range (622-95 mSm-1) to a lower range
(415-88 mSm-1) of all mixtures (chapter 6) with 20 to 50% sludge. The lower pH range increased
the oxidation of NO2 -to NO3-. The NO3 - and NH4- species were mostly contributed by sludge
while fine ash contributed more to the NO2- species. This was because the purposely added NH3
by Sasol to enhance the removal of fly ash in the processing plant was converted to NO2- and the
oxidation rate of this species to NO3- was minimal due to the extreme conditions caused by high
pH and salinity. Mixtures 35 (30% fine ash, 40% gasification ash and 30% sludge), 42 (30%
fine ash, 30% gasification ash and 40% sludge), 46 (50% fine ash, 0% gasification ash and 50%
sludge), 48 (30% fine ash, 20% gasification ash and 50% sludge) and 51 (0% fine ash, 50%
gasification ash and 50% sludge) had high amounts of NH4+ and NO3- and relatively little NO2-.
These mixtures provided plant available NH4+ and NO3-desired in a functional growth media.
Mixture 48 had the highest mineralized total inorganic N (24.4 mg kg -1) compared to all mixtures
and for all eluviation cycles, while mixture 12 (0% fine ash, 90% gasification ash and 10%
sludge) exhibited the least (10.5 mg kg-1). Mixtures 11(0% fine ash, 100% gasification ash and
0% sludge), 12 (0% fine ash, 90% gasification ash and 10% sludge), 30 (0% fine ash, 80%
gasification ash and 20% sludge), 38 (0% fine ash, 70% gasification ash and 30% sludge), 45
(0% fine ash, 60% gasification ash and 40% sludge), 51 (0% fine ash, 50% gasification ash and
50% sludge) had the lowest inorganic N within their groups due to high gasification content.
71
CHAPTER 6: ELEMENTAL DETERMINATION IN SLUDGE, FINE AND
GASIFICATION ASHES, ELEMENTAL RELEASE, SALINITY AND pH OF
MIXTURES
6.1 Introduction
Industrial sludge contains a significant amount of organic nitrogen (N), phosphorus (P), calcium
(Ca), magnesium (Mg), potassium (K), sulphur (S) and trace elements which makes it a good
source of nutrients for plants (Rechcigl, 1995 & Wong & Su, 1997). Ash as well is rich in
essential plant nutrients listed for sludge, sodium (Na), boron (B), aluminium (Al), silicon (Si)
and numerous trace elements as residues from the coal source (Zevenbergen et al., 1999,
Dermatas & Meng, 2003, Junkowski et al., 2006, Bendz et al., 2007,). The Al and Si are the
basic building blocks necessary for the formation of clay minerals (phyllo silicates) and other
hydrated minerals, for example, layered double hydroxides (Zevenbergen et al., 1999, Dermatas
& Meng, 2003) that contribute to chemical reactivity and fertility of the ash. While sludge has a
high content of organic carbon that ash is poor in and also provides plant available N and P
(Snyman & Van der Waals, 2004, Snyman & Herselman, 2006). Therefore, amending ash with
sludge does not only alter the adverse properties it has but also build a soil-like matrix that is
capable of providing plant available nutrients in correct quantities (Rendell & McGinty, 2010).
The release of the elements from the sludge-ash matrix is important for plant uptake but there are
several factors that control their release. Amongst them pH remains the master variable that
governs elemental mobility processes (Bendz et al., 2007). In soils the optimum pH for the
maximum release of nutrients is between 5.5 and 6.5 (Brady and Weil, 2008). However,
Handreck and Black (1984)ealier on claimed that at pH 6 to 7.5 most nutrients are reasonably
available to plants but maximum availability accurs between pH 6 and 7 in artificial soil media.
Other than pH, elemental release also depends on the mineralogy of the ash components
exposed, the reactive surface area of these minerals, the supply of water and its residence time in
the ash and initial pH, the abundance of organic acids, and the temperature of ash solutions
(Kump et al., 2000). Most of the elements are released after long equilibration times when the
alkalinity of the ash is significantly depleted and pH of the leachate approaches circum-neutral or
acidic levels (Gitari et al., 2009). Elemental release also depends on the extent of carbonation,
72
dissolution of the minerals or flushing of the soluble salts as a function of the degree of
saturation.
Particle size distribution is another important factor that contributes to elemental release.
Chemical reactivity is generally confined to the clay-sized particles and results from the
combined reactive surface functional groups and specific surface area (Essington 2004 & Brady
and Weil, 2008). More functional groups are expected and charge development will occur as
weathering progress and secondary minerals starts to form (charge development will be
discussed later). Therefore the incorporation of fine particles definitely enhances chemical
reactivities that result in the release of elements and the addition of sludge contributes to the
reduction of not only pH but also to the salinity of the medium.
Subjecting the ash – sludge mixtures to wetting and drying cycles induces change in pH and
salinity of the mixtures and the addition of water enhance chemical reactions and increases the
mobility/leaching of the elements. It is imperative therefore to carry out elemental analysis in
fresh samples/mixtures and in both pore and free drained leachates. The analysis of fresh
samples could be done using X-ray Fluorescence Spectroscopy (XRF) analytical technique that
is capable of analyzing elements in a press powder from fluorine (with atomic number 9) to
uranium (with atomic number 92) in the periodic table with detection limits varying from 0.5
ppm for heavier elements to 100 ppm for the lightest elements (Loubser & Verryn, 2008). For
lighter elements the XRF technique may not be as reliable so the acid digestion method could be
used coupled with Inductively coupled plasma mass spectroscopy (ICP-MS). The digestion
technique analyses both macro and trace elements accurately. To analyze the pore and free
drained leachates for both macro and trace elements the Inductively Coupled Plasma Optical
Emission Spectrometry (ICP-OES) could be used. All samples were subjected to membrane
filtration (0.45 µm) to reduce colloidal interference.
In this study it was hypothesiesd that the addition of sludge and weathering processes brought
about by eluviation cycles will reduce the high pH to between 5.5 and 8 and salinity to less than
400 mSm-1of the mixtures to optimum levels suitable for plant growth and futher increase the
73
solubility of major and trace elements. Therefore, the main purpose of this chapter was to
assess;change in pH, salinity and elemental release as influenced by eluviation cycles.
6.2 Materials and methods
6.2.1 Mixture analysis with X-ray Fluorescence Spectroscopy (XRF)
X-ray Fluorescence Spectroscopy (XRF) analytical technique was used to measure the total
chemical composition of the solid samples. The technique relies on the software (UniQuant) that
enables it to analyses raw spectral data qualitatively and quantitatively. It is capable of analyzing
elements from fluorine (with atomic number 9) to uranium (with atomic number 92) in the
periodic table with detection limits varying from 0.5 ppm for heavier elements to 100 ppm for
the lightest elements. However, specific detection limits for MnO, MgO, CaO, Na2O, K2O, P2O5,
Cu, Zn and Fe2O3 were 13, 118, 100, 265, 50, 100, 2, 4 and 97 ppm respectively (Loubser &
Verryn, 2008). Seemingly, these detection limits were above the lower limit (0.5 ppm) and some
like 118 and 265 were even higher than the miximum limit (100 ppm).
In preparation for the press powder analysis each sample was milled in a tungsten carbide
milling pot (with insignificant sample contamination) such that at least 80 % of the particles fell
below 75µm. A small amount (20 g) of each sample was transferred into a plastic zip-lock bag,
added 5 drops of polyvinyl alcohol (used as a binder) then thoroughly mixed between thumbs.
The mixed sample was then pressed at a pressure of 20 ton/cm2 for two min in collapsible
aluminium holders for mechanical support, using a polished piston. The sample was then dried at
110 oC before analysis. During the analysis radiation with sufficient energy was emitted by the xray tube and illuminated the sample hence exciting the atom of the element by ejecting one or
more strongly held electrons in the inner orbital. Electrons held at an outer or higher orbital
replaced the ejected electron and in the process fluorescence secondary photons were emitted to
the detector that converted the photons to pulses. The pulses were further processed by a Multichannel Analyser interpreting its characteristics that relate to the atoms present in the sample
(Loubser & Verryn, 2008). The fused beads technique (for the analysis of major elements)
requiring high temperatures (1000 oC) in the muffle furnace could not be executed because the
samples contained significant amounts of organic matter (sludge) that ignited in the furnace. To
validate the results coming from this technique the digestion method was then carried out.
74
6.2.2 Mixture analysis with Inductively coupled plasma mass spectroscopy (ICP-MS)
This is generally a three acid digestion method standard for soils. A sample of 0.2 g was digested
with hydrofluoric acid (HF) acid. It was further digested with a mixture of perchloric acid (HClO4)
and nitric acid (HNO3) in a ratio of 1:3 to dryness. The sample was then dissolved in a 20%
hydrochloric acid and finally analysed by ICP-MS. For the mass spectroscopy 0.5 g of sample
was digested by the HF acid and then digested with a mixture of HClO4 and nitric acid in a ratio
of 1:3 to dryness as well. The sample was then dissolved in a 20% HNO3 acid and analysed by
ICP-MS.
6.2.3 Phosphorus determination of mixtures
A portion of sample was fused with a sodium peroxide (Na2O2)/ sodium carbonate (Na2CO3)
mixture, then leached with nitric acid (HNO3) and deionised water. Ferric solution was added,
and an excess of ammonia (NH3) was added. The solution was filtered, and the residue was
further digested with HNO3 and HClO4 acids. If necessary, the sample was treated to remove any
As or Si, and then diluted to a known volume. An aliquot of an ammonium molybdate
(NH4)2MoO4) / vanadate (NH4VO3) solution was added to a portion of the solution and
transferred to a separating funnel. This reagent complexed with the P and turned to a yellow
colour - the intensity of the colour was proportional to the P concentration. An aliquot of Methyl
isobutyl ketone (MIBK-organic compound (CH3)2CHCH2COCH3) was added to extract the P
from the reagent, and then the funnel was shaken well. The phosphorus, complexed with the
(NH4)2MoO4) / NH4VO3 reagent was extracted into the MIBK, which was separated from the
aqueous phase. Calibration standards were prepared similarly. The standards and samples were
read by UV/visible spectrometry, and the P concentration calculated.
6.2.4 Analysis of leachate with Inductively Coupled Plasma Optical Emission Spectrometry
(ICP-OES)
An axially viewed Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) was
used to determine soluble elements (Mg, Ca, Na, K, P, Fe, Zn, Cu, B, Mo and Mn) in the
leachates at different wave lengths (Table 6.1). Prior to analysis 20 ml aliquots were vacuum
filtered through 0.45 µm membrane filters using a vacuum pump (vaccum was equal to
approximately -60 kPa) to remove colloidal particles. The membrane filtered leachates were
75
allowed to collect in 20 ml polyethylene tubes fitted underneath. The polyethylene tubes were
then transferred into plastic trays accommodating 55 tubes at a time and covered with
parafilm(Parafilm® M Barrier Film)and enclosed in an air tight plastic container to prevent
possible evaporation. Method blanks (deionized water treated the same way as the samples) and
instrument blanks (deionized water) were also prepared in order to isolate and correct for
contamination emanating from the membrane equipment, membranes, glass ware or from the
deionized water. Before analysis the ICP-OES was calibrated using standards (MERCK
CertiPUR® grade standards – Table 6.2) with ranges specific to the elements (Table 6.1).
Elemental analysis was performed both in the samples and in blanks with the ICP-OES. The
elemental analysis of the blanks was subtracted from the elemental analysis of the mixtures to
obtain the quantity of elements contributed by the mixtures alone. Cumulative elemental
concentrations in each mixture were caluculated by summing up concentrations leached per
eluviation cycle.
Table 6.1: ICP – OES theoretical and actual analytical ranges for each element and wavelengths
used in the analysis (Essington, 2004)
Element
Theoretical
Theoretical
Actual
Actual Analytical
Wavelength
Analytical ranges
Wavelengths
ranges (mg L-1)
-1
(nm)
(mg L )
(nm)
P
214.9
0.1 – 1000
178.3
0.0 – 120
K
766.5
0.4 – 1000
766.5
0.0 – 120
Ca
317.9
0.0 – 1000
315.9
0.0 – 600
Mg
279.1
0.0 – 1000
279.1
0.0 – 120
Na
589.0
0.0 – 500
330.3
0.0 – 120
Mn
257.6
0.0 – 100
260.6
0.0 – 120
Cu
324.8
0.0 – 200
324.8
0.0 – 300
Fe
259.9
0.0 – 500
259.9
0.0 – 60
Zn
213.9
0.0 – 200
213.9
0.0 – 3
Mo
202.1
0.0 – 500
202.1
0.0 – 6
B
249.7
0.0 – 200
249.7
0.0 – 3
76
Table 6.2: Standards used to calibrate the ICP - OES
Standard
Grade
Concentration mg L-1
Na
MERCK CertiPUR®
10000
®
Ca
MERCK CertiPUR
10000
K
MERCK CertiPUR®
10000
®
Mg
MERCK CertiPUR
10000
®
P
MERCK CertiPUR
10000
Multi element
MERCK CertiPUR®
100
standard solution for
Cu, Zn, Mn, Mo, Fe
&B
Catalog number
170881
170308
170342
170331
170340
109487
6.2.5 Salinity and pH determination
Solution pH and electrical conductivity (EC) for both the free drained and pore solutions were
immediately (within 24 hours) determined to avoid chemical changes. A multi-parameter analyser
(Consort C830) with a 0.01 pH resolution coupled with epoxy electrode was used. The pH meter was
initially calibrated using buffer solutions pH 7.0 (potassium dihydrogen phosphate/di-sodium
hydrogen phosphate, CertiPUR® from MERCK, catalogue number 199002) and pH 4.01 (potassium
hydrogen phthalate, CertiPUR® from MERCK, catalogue number 199001) to ensure accurate
readings. The reading of samples commenced immediately after calibration. Calibration was
repeated after every 10 pH readings to reduce erroneous results. Similarly, an electrical conductivity
meter (Consort C861) with a 0.001 µS cm-1 resolution coupled with conductivity electrode was used.
The EC meter was calibrated with EC 1.41 mS cm-1 calibration solution (potassium chloride
solution, CertiPUR® from MERCK, catalogue number1012030500) and the reading of samples
commenced immediately. The calibration of the EC meter was also repeated every after 10 readings.
All the EC readings were expressed in mS m-1.
6.3 Results and discussion
6.3.1 pH changes of mixtures as influenced by leaching
pH is often called the master variable that vastly affects numerous essential chemical reactions and
processes. It affects the rates of anion and cation exchange and attenuation, redox reactions,
microbial activity, solution speciation of elements, surface charge characteristics as well as mineral
precipitation and dissolution. Low pH increases the solubility of elements such as Mn, Al and Fe and
induces the deficiency of Ca, Mg and P, but under alkaline conditions Cu, Fe, Zn, and Mn
77
precipitate (Sparks, 2004 & Brady & Weil 2008). The effect of pH on surface charge characteristics
indirectly affects anion and cation exchange and attenuation. Clay minerals carry a permanent
negative charge that results from isomorphous substitution, for example, Si+ is replaced by
Al3+(Mulder & Chresser, 1994). At this point cation retention becomes more pronounced at pH
values greater than the point of zero charge (pzc). When the solution pH is lower than the pzc, the
surface will exhibit a net positive charge and the surface affinity for anions increases (Prasad and
Power, 1997, Essington, 2004, Brady and Weil, 2008).
Fine ash indicated a high pH value of 11.3 (indicative of P alkalinity) and this value was 1.2 units
lower than pH 12.5 obtained by Mahlaba et al. (2011) in his characterization of Sasol fine ash.
Previous analysis in 2008 of Sasol fine ash, gasification ash and sludge were characterized and had
pH values of >12.0, 10.8 and 6.8 respectively. Gasification ash (mixture 11 - 100% gasification ash)
exhibited a higher pH value (11.7) than in 2008. The high pH in both ashes was expected because
they result from the combustion of lignite coals (low grade coals) that contain low sulphur and high
Ca that subsequently maintains the pH at high values (> 12) (Haynes, 2009). The Ca in the ashes is a
result of limestone (CaCO3) that is added and undergoes calcination during coal gasification to retain
S and CO2 (Merrick, 1984). Mixing the gasification ash and fine ash did not influence the leachate
pH of mixtures without sludge (mixtures 1 to 11) had pH values greater than 10. But the
incorporation of sludge reduced the pore solution pH of the ashes, for example, mixture 14 (20%
fine ash, 70% gasification ash and 10% sludge) had the lowest pH (8.3) after the first leaching cycle
(Fig. 6.1, a).
Generally, the incorporation of sludge suddenly reduced the pH to a mean of 10 and a median of 9.8
(Fig.6, b). The frequency indicated that 45.1% of the mixtures had the pH falling in the range 9.80 ±
0.38 with 8.3% as coefficient of variation. Even after the tenth eluviation cycle mixtures without
sludge retained pH values above 8.4 and the abrupt transition of pH from the mixtures without
sludge (1 to 11) to treatments that received varying amounts of sludge (12 to 51) resulting from the
addition of sludge was still distinct (Fig.6, c). Mixture 7 (40% fine ash, 60% gasification ash and 0%
sludge) maintained the highest pH value (10.3) while mixture 49 (20% fine ash, 30% gasification
ash and 50% sludge) showed the lowest pH (7.6). However, the overall pH mean was reduced to 8.2
78
and the median brought down to 8.0. At this stage the frequency indicated that 37.2% of the
mixtures had the pH falling in the range 7.9 ± 0.31 with 8.0% as coefficient of variation (Fig.6, d).
Subjecting the columns to more eluviation / leaching gradually decreased the pH of all mixtures
(with and without sludge) with time. This was attributed to the removal of soluble alkalinity and
various reactions possibly occurred resulting in the release of protons (H+) into the solution.
Generally, a decrease or increase in pH is the net effect of various reactions. Firstly, the
biodegradation of sludge in the ash environment released dissolved organic carbon (DOC) that
parted a tanned colour to the solutions. This was an indication that some of the decomposition
products were polar and water soluble to some extent. It is reasonably to expect that, the DOC
released also contained humified compounds such as fulvic acid (FA) and humic acid (HA) (Mulder
& Cresser, 1994 & Singh & Agrawal, 2010). From literature FA is often isolated from sludge
amended soil (Sposito et al., 1978). Fulvic acid contains numerous of functional groups including
carboxylic groups (-COOH, pKa = 4-6). The greater solubility under alkaline conditions is caused by
the deprotonation and ionisation of the various functional groups at pH conditions greater than their
respective pKa values. This makes the organic molecule more polar and thus water soluble.
Carboxylic groups, for example, will be completely ionised (Eq. 6.1) at the pH measured for the
various treatments (Essington, 2004 &Kleber & Johnson, 2010). The ionisation / deprotonation of
these functional groups will obviously also contribute to the increase in proton activity in solution.
R-COOH0 → R-COO- + H+
(Eq. 6.1)
Secondly, the carbonation process produced protons. During this process CO2 was liberated from the
breakdown of the sludge and subsequently formed carbonic acid (H2CO3) with the solution through
carbonation reaction. The dissociation /deprotonation of H2CO3 formed HCO3- and CO32- increasing
the aqueous proton concentration and activity (Eq. 6.2 & 6.3) (Essington, 2004 &Brady & Weil,
2008).
H2CO30 →HCO3- + H+
(Eq. 6.2)
HCO3- →CO32- + H+
(Eq. 6.3)
79
Thirdly, sludge generally contains bacteria that feature in nitrogen mineralization, the availability of
organic matter and a reduced pH in mixtures with sludge favoured the nitrification process. The
oxidation of NH4 results in an increase in the concentration of NO3 - and produced 2 moles of H+ in
the solution (Eq. 6.4) (Essington, 2004&Brady & Weil, 2008).
NH4+(aq) + 2O2 (g) →NO3-(aq) + H2O(l)+ 2H+(aq)
(Eq. 6.4)
Finally, Sasol coal ashes contain appreciable amounts of aluminium (Al) (aluminium is the second
most abundant after silicon, 6.75%) iron and to a lesser extent manganese. In general when exposed
to weathering these hydroxo cations first undergohydrolysis and then precipitated. In the pH ranges
of soil the net reaction involving the precipitation of these minerals are usually associated with the
release of protons (H+) into the solution (Essington, 2004, Brady & Weil, 2008). However, in
alkaline environments trivalent hydoxo cations in solution often occur in anionic forms, for example,
M(OH)4- andthe mol fraction of Al(OH)4 -at pH 9 is, for example, 0.82 and at pH 10 = 0.98. A
balanced generic precipitation reaction of a metal hydroxide involving M3+ in the anionic hydrolysis
form M(OH)4- actually shows a consumption of a proton (Eq.6.5),
M(OH)4-(aq) + H+(aq) M(OH)3 (s) + H2O( l)
(Eq. 6.5)
However, the preceding reaction involving the hydration and hydrolysis of a M 3+, liberated from the
ash matrix to formM(OH)4-, resulted in the generation of protons(Lindsay, 1979).It should be noted
that these are elements in solution. It was also evident that increasing sludge from 10 to 50% after all
the eluviation cycles (1 to 10) could not further reduce the pH because of the onset of some strong
buffering reaction or reactions with high buffering capacity that occurred. The pH range was close to
the pKa of NH4+ / NH3 (9.2) therefore the transformation of ammonium to ammonia resulted in the
release of protons into the solution buffering the pH in this range for the sludge amended treatments.
The hydrolysis (Eq. 6.6) of silicon has a pKa of 9.71 and produces protons that add to the buffering
capacity of the system.
H4SiO40 H3SiO4- + H+
pKa = 9.71 (Eq. 6.6)
80
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
No % SL
20 % SL
30 % SL
40 % SL
50 % SL
pH
pH
10 % SL
1
3
5
7
10 % SL
1
Number of mixture
25
3
5
7
10
30 % SL
40 % SL
50 % SL
Number of mixture
20
Mean = 8.2
Median = 8.0
Standard deviation = 0.7
Coefficient of variance = 8.0 %
18
16
14
FREQUENCY
15
20 % SL
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
c
Mean = 10.0
Median = 9.8
Standard deviation = 0.8
Coefficient of variance = 8.3 %
20
12
10
8
6
4
5
2
0
0
8.65
b
No % SL
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
a
FREQUENCY
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
9.03
9.42
9.80
10.19
10.57
10.96
11.34
11.73
7.86
12.12
d
pH
8.17
8.47
8.78
9.09
9.40
9.70
10.01
10.32
10.63
pH
Fig.6. 1: a) pH of the pore solution upon sludge addition for the first eluviation cycle, b) frequency distribution of the pore solution pH
for the first eluviation cycle, c) pH of the pore solution upon sludge addition for the tenth eluviation cycle, and d) frequency
distribution of the pore solution pH for the tenth eluviation cycle. The SL in a and c means sludge. The gray shaded rectangles in a and
c demarcate proposed optimum pH range suitable for plant growth.
81
6.3.2 Electrical Conductivity (EC) changes of mixtures as influenced by leaching
Salinity is defined as the concentration of dissolved mineral salts (Na+, K+, Ca2+, Mg2+, Cl–,
SO42- ,HCO3- , CO32-, and NO3 -) in a growth medium. Literature reveals that EC values greater
than 400 mSm-1 denote saline conditions that can induce salt toxicity to most plants. An optimum
EC suitable for most plants fall between 70 and 400 mSm-1. However, some tolerant plants can
survive at EC values greater than 1000 mSm-1 (Handrek, 1984, Essington, 2005 & Sparks, 2004).
High salinity (>400 mSm-1) can limit the chemical suitability of the medium to support plants
(Sparks, 2004). Calcium to S molar ratios of over 2.5 in the feedstock (coal) results in the ash
containing not only CaSO4 but also CaO (Anthony, 2003). Such basic coals also contain
appreciable amounts of salts like Na+, K+, Ca2+ and Mg2+ that control salinity (Tsai, (1982).
Most of the salinity was contributed immensely by fine ash (580 mSm-1) and followed closely by
gasification ash (527 mSm-1) (Fig.6.2, a). These EC values are greater than 400 mSm-1 therefore
denote salt toxic conditions (saline) not suitable for the growth of most plants. This was expected
because alkaline and unweathered ash deposits are generally saline with high levels of soluble
salts and a high electrical conductivity (EC) of 1300 mSm-1 (Haynes, 2009). The high salinity in
the Sasol ash could be attributed to the results from the interaction of the ash with saline brines
(which has reported total dissolved solids content in the order of 8000 mg l-1) in the form of
slurry during hydraulic or fluid disposal (Mahlaba et al., 2011). It was therefore expected that the
salinity would gradually decrease as the content gradient for both ashes decreased from mixture
1 (100% fine ash) to 51 (50% sludge and 50% gasification ash) and salinity dilution effect
caused by sludge in mixtures with sludge (mixtures 12 to 51 described in chapter 3). The
addition of sludge generally increased the release of salts from all the mixtures with sludge after
the first eluviation cycle for the pore solution (Fig.6.3, a). But had the least salts released due to
the small amount of sludge (10%) added.Generally, the incorporation of sludge increased the
salinity to a mean of 500 mSm-1 and a median of 526 mSm-1 for all mixtures with sludge. The
frequency indicated that the EC for 41.2% of the mixtures ranged 563.4 ± 58.6 (with 19.0% as
coefficient of variation) after the first eluviation cycle (Fig.6.3, b). The breakdown of sludge
produced dissolved organic carbon (DOC). It was reasonable to expect that the tanned colour
(observed in chapter 3 on the column set up) of the leachates indicated the presence of FA and
HA and their conjugated bases humates and fulvates form complexes with metals through
82
chelation, this in turn, promoted the dissolution of metals from minerals (Mulder & Cresser,
1994). Dissolved organic carbon also contributed to EC due to its ionization caused by the
oxidative breakdown. The increase in EC and concentrations of K+ Ca2+ Mg2+ and Na+ for the
sludge treated mixtures were a contribution of these processes
The effect of abovementioned
processes was evident throughout the study as a result mixtures with sludge maintained a higher
salinity. After the 10th leaching the EC values of the treatments that did not receive sludge were
below 200 mS m-1, while some mixtures that received 10, 30, 40 and 50% sludge had EC values
between 200 and 300 mS m-1 (Fig.6.3 c). Even mixture 13 had an increased salinity. Leaching
did decrease salinity as indicated by the decrease in EC over time and on average the EC was
less than half than initial (Fig.6.2). The frequency distribution showed that the EC of 21.6% of
the mixtures (some mixtures with 0 to 30% sludge) fell in the range 124.3 ± 36.3 with 40.7% as
coefficient of variation after the tenth eluviation cycle (Fig.6.3, c and d). This EC range was
within the optimum and acceptable EC range (70 to 400 mSm-1) for good growth media. Only
mixtures 40 and 43 that proved to be saline after the tenth eluviation cycle with EC values above
400 mSm-1 (Fig.6.3 C). The complexation of the salts by soluble ligands maintained a higher
salinity even after the tenth eluviation cycle (Mulder & Cresser, 1994 & Li & Shuman 1997)
(Fig.6.3 c). According to Handreck and Black (1984) and Brady and Weil, (2008) the most
suitable EC range for plant growth falls between 70 and 400 mSm-1. It was only mixtures 40 and
Electrical conductivity (mSm-1)
43 that had their EC beyond 400 mSm-1 after ten eluviation cycles.
700
650
600
550
500
450
400
350
300
250
200
150
100
50
0
No sludge
10 sludge
20 sludge
1
3
5
7
30 sludge
50 sludge
40 sludge
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Number of mixture
Eluviation cycle 1
Eluviation cycle 10
Fig.6. 2: Salinity comparison for the first and tenth eluviation cycles. The gray shaded rectangle
demarcates proposed optimum salinity range for plant growth.
83
No
SL
50 % SL
10 % SL
20 % SL
40 % SL
30 % SL
Electrical conductivity (mSm-1)
Electrical conductivity (msm-1 )
700
650
600
550
500
450
400
350
300
250
200
150
100
50
0
700
650
600
550
500
450
400
350
300
250
200
150
100
50
0
25
20
3 5 7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
c
Number of mixtures
Number of mixture
Mean = 211.1
Median = 203
Standard deviation = 86.0
Coefficient of variance = 40.7 %
12
Mean = 500.0
Median = 526
Standard deviation = 92.9
Coefficient of variance = 18.6 %
50 % SL
30 % SL
10 % SL
No SL
1
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
a
40 % SL
20 % SL
10
Frequency
Frequency
8
15
10
6
4
5
2
0
0
124.33
153.56 212.11 270.67 329.22 387.78 446.33 504.89 563.44 622.00 680.56
b
d
ELECTRICAL CONDUCTIVITY (mSm-1)
160.67
197.00
233.33
269.67
306.00
342.33
378.67
415.00
451.33
ELECTRICAL CONDUCTIVITY (mSm-1)
Fig.6. 3: a) Electrical conductivity of the pore solution upon sludge addition to the various treatment groups (with sludge increasing from 0 to 50%, fine ash
decreasing and gasification ash increasing – illustrated in chapter 3) for the first eluviation cycle, b) frequency distribution of the pore solution electrical
conductivity for the first eluviation cycle, c) Electrical conductivity of the pore solution upon sludge addition for the tenth eluviation cycle and d) frequency
distribution of the pore solution electrical conductivity for the tenth eluviation cycle. The SL in a and c means sludge. The gray shaded rectangles in a and c
demarcate proposed optimum EC range for plant growth.
84
6.3.3 Calcium content of Sasol sludge, fine and gasification ashes measured in 2006, 2007,
2008 and 2011
The calcium content had been measured over time for the Sasol sludge (in 2007, 2008 and 2011),
fine ash (2006 and 2011) and gasification ash (in 2008 and 2011) by various laboratories (Fig
6.4). Statistically the Ca content of sludge (mean 0.6%) was found to be significantly lower at (a
significance level of α = 5%) level than the Ca content of gasification ash (mean 6.8%) and fine
ash (mean 5.1%). The Ca content of the fine ash was also significantly lower than that of the
gasification ash at 5% level. The computed least significant difference (LSD) was 0.7 and the
coefficient of variation (CV) was 15.0 %. The variability in Ca content as indicated by longer
error bars was significantly higher in gasification ash (with 95% confidence interval of between
4.6 and 9.0%) than in fine ash (with 95% confidence interval of between 4.8 and 5. %) and had a
minimum, maximum and median of 5.7%, 8.2% and 6.8% respectively. Fine ash had a narrower
variability (with shorter error bars) and a minimum, maximum and median of 5.0%, 5.4% and
5.1% respectively. The high variability in particle size distribution (illustrated in chapter 4
Fig.4.1) of gasification ash (80% > 2 mm) could be the source of the variability in Ca content.
Sludge had the lowest Ca content and variability (with shortest error bars and a 95% confidence
interval between 0.5 and 0.7%) and a minimum, maximum and median of 0.5%, 0.7% and 0.63%
respectively and a CV of 9.3%.
8
A
7
Mean
6
Ca (%)
5
Maximum
Upper quartile
Median
Lower quartile
Minimum
B
4
3
2
1
C
0
Fine ash
Gasification ash
Industrial wastes
Sludge
Fig.6. 4: The variation in calcium content of Sasol sludge, fine and gasification ashes based on analyses done in
2006, 2007, 2008 and 2011 (at least 1 sample per year). The method used was acid digestion using hydrofluoric acid
(HF) acid and a mixture of perchloric acid (HClO4) and nitric acid (HNO3) (as indicated in section 6.2.2).
85
Table 6.3: The Ca contentfor the Sasol fine and gasification ashes as determined by X-ray
Fluorescence Spectroscopy (XRF) measured in 2008 and 2011 by two laboratories.
Year
2011
2011
2011
2008
Mean
Standard deviation
Coefficient of Variation (%)
Ca (%) in gasification ash
7.1
7.3
5.2
6.6
1.2
18.1
Ca (%) in fine ash
5.7
5.9
5.8
0.1
2.3
X-ray Fluorescence Spectroscopy (XRF) was used in 2008 and 2011 to measure calcium oxide
content in Sasol fine and gasification ashes (Table 6.3). TheCa content varied significantly over
the two years in gasification ash with a standard deviation of 1.2 and a large CV of 18.1%
confirming the variability. This variability was further confirmed by the wider 95% confidence
interval (3.6 to 9.5%) provided by XRF method in gasification ash. The acid digestion method
gave a narrower 95 % confidence interval (4.6 to 9.0%) (Table 6.4). The variability of Ca for
fine ash was narrow as indicated by the standard deviation of 0.1 and a low coefficient of
variation of 2.3%. Again this was confirmed by both methods; XRF gave a narrower 95%
confidence interval of 5.4 to 6.2% and acid digestion gave a 95% confidence interval of 4.8 to
5.5% (Table 6.4).
Gasification ash showed a significantly higher Cacontent with a mean of
9.2% compared to Cacontent for fine ash with a mean of 8.1%.
In comparing the two methods (acid digestion and XRF) 95% confidence intervals were
computed and the confidence intervals for both methods in fine ash overlapped (indicating a
significant difference between the means obtained by the two methods) and in gasification ash
the confidence intervals for acid digestion fitted into the XRF confidence intervals (indicating
that there was no significant difference between the means obtained by the two methods (Table
6.4)
86
Table 6.4: Comparison of total Ca content determined using acid digestion and XRF in fine and
gasification ashes based on 95 % confidence intervals
Ash material
Acid digestion
Mean
XRF
Lowe
Upper
r limit
limit
Stdev
CV
Mean
(%)
Lower
Upper
limit
limit
Stdev
CV
(%)
Fine
5.1
4.8
5.5
0.1
2.3
5.8
5.4
6.2
0.2
3.1
Gasification
6.8
4.6
9.0
1.2
18.1
6.6
3.6
9.5
1.0
15.0
Gasification ash at 95% had the confidence interval of between 4.6 and 9.0% and fine ash with
confidence interval of between 4.8 and 5.5% with the acid digestion method while with XRF
method gasification ash at 95% had the confidence intervalof between 3.6 and 9.5% and fine ash
had confidence interval of between 5.4 and 6.2%.
6.3.4 Calcium content of mixtures
Ca was more abundant than other alkaline and alkaline earth metals followed by Mg, Na and K
in all mixtures. However, in mineral soils this arrangement differs slightly, Ca still is more
abundant but followed by K, Mg and Na (Essington, 2004). The Ca content of the sludge was
significantly lower than that of gasification ash and fine ash (Table 6.2). It was therefore
expected that with increasing sludge content the Ca content of the mixtures will decrease (Fig
6.5). Total elemental analysis was also performed on selected mixtures: that contained 100%
fine ash (mixture 1), a combination of 50% fine and 50% gasification ash (mixture 6), 100%
gasification ash (mixture 11), a combination of sludge, fine and gasification ash (mixture 26), a
combination of 50% sludge and 50% fine ash (mixture 46) and a combination of 50% sludge and
50 % gasification ash (mixture 51). This was done to assess the variability and potential error
that resides in calculating the elemental content by summing the factional contribution of
gasification ash, fine ash and sludge. The Ca content of a mixture, for example, was the
measured Ca content of fine ash alone multiplied with the percent fractional contribution content
of fine ash in the mixture. Similarly the measured Ca content of gasification ash alone was
multiplied by the percent content of gasification ash in the mixture. The same procedure was
followed to calculate the contribution of sludge to the Ca content in the mixture. The calculated
Ca content of a mixture was then obtained by summing the three products.
87
Ca (mmol kg-1)
2000
1800
1600
1400
1200
1000
800
600
400
200
0
1 3 5
7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Number of mixtures
Sludge
Gasification ash
Fine ash
Fig.6. 5: Calculated Ca content of the mixtures based on the mean content depicted in Fig 6.3.
of sludge, fine and gasification ash to total Ca content of mixtures
The approach followed was to establish a 95% confidence interval based on the measured data
(Ca means in selected mixtures) and compared to the predicted Ca content of the mixtures in an
attempt to establish some significant differences between the means. It was found that the
calculated means for mixtures 1, 6, 11, 46 and 51 fell within the 95% confidence interval.
However, the calculated mean (1263.4 mmol kg-1) for mixture 26 fell outside the confidence
interval (upper limit 1251.2 mmol kg -1 and lower limit 1153.2 mmol kg -1) indicating to be
significantly different from the measured mean (1202.2 mmol kg -1). Repopulating the data
readjusted the confidence interval (upper limit 1322.7 mmol kg -1 and lower limit 1204.0 mmol
kg-1) allowing the mean of the calculated values not to be significantly different. (Table 6.5) This
statistical verification provided evidence that the means of calculated Ca values in the mixtures
were not significantly different from the means of the measured Ca values.
Noticeably the measured Ca content of the gasification ash was more (1802.3 mmol kg -1) than
for fine ash (1283.3 mmol kg-1) and sludge (145.5 mmol kg -1) (Table 6.3). These findings were
similar in trend to results obtained in 2008 in the characterization of Sasol wastes; gasification
ash had the highest Ca content (1802.3 mmol kg-1 ) than fine ash (1283.3 mmol kg-1) and sludge
(87.3 mmol kg-1). Clearly the Ca content of the mixtures increased with increase in gasification
ash (Fig 6.5). The determination of the specific sources of Ca in the Sasol ashes was beyond the
88
scope of this study, however, Mahlaba et al. (2011) found through X-ray Diffraction (XRD) that
the secondary mineral, calcite (CaCO3), was the most abundant (2.2-6.3%) Ca containing
mineral and remained the main source of Ca followed by ettringite (Ca6Al2(SO4)3(OH)12.26H2O)
(0.8-4.5%).
Table 6.5: Measured and calculated means for Ca, in selected mixtures using microwave
digestion method
Mixture
Number
Measured Ca
(mmol kg-1)
Calculated Ca
(mmol kg-1)
Confidence interval
at 95 % (mmol kg-1)
Lower
Upper
limit
limit
Standard
deviation
(mmol kg-1)
CV %
1
6
11
26
46
51
SL
1283.3
1465.5
1802.3
1203.2
718.6
922.3
145.5
1283.3
1442.8
1802.3
1263.4
714.4
973.9
145.5
1166.1
1375.2
1281.0
1322.7
702.2
869.0
-
47.2
36.4
210
19.7
6.6
21.5
12.4
3.7
2.5
11.7
1.6
0.9
2.3
18.1
1400.6
1555.7
2323.6
1204.0
735.0
975.8
-
Note: mixtures 1 (100% fine ash), 6 (50% fine ash and 50% gasification ash), 11 (100%
gasification ash), 26 (40% fine ash, 40% sludge and 20% sludge), 46 (50% fine ash and 50%
gasification ash) and51 (50% gasification ash and 50% sludge) were exclusively analysed to
enable the estimation of total elements of the other mixtures. SL means sludge.
6.3.5 Calcium leaching from mixtures
Amending gasification ash with sludge, and increasing its content in the mixtures, increased Ca
released from the mixtures and therefore the net effect of sludge amendment was the increase in
the soluble Ca of the mixture (Fig.6.6, a and b). As a result mixture 43 (40% gasification ash,
20% fine ash 40% sludge) released the most cumulative Ca (52.6 mmol kg -1) than any other
mixture but with > 400 mSm-1 followed by mixtures 37, 40, 41, 44 and 48 (described in chapter
3) which also released more than 40 mmol kg -1 Ca and had salinity less than 400 mSm-1. Based
on the significantly higher Ca content of the gasification and fine ash, it is reasonable to expect
that it was the source of Ca. Most of the Ca in all these mixtures eluviated from both fine and
gasification ash than from sludge (Fig.6.5). Mixtures without sludge released the least Ca on
average compared to all mixture groupd. Increasing sludge increased dissolved organic carbon
(DOC) that enhanced the solubility of Ca from the solid phases (Li & Shuman, 1997). The
addition of soluble organic ligands has been found to decrease the sorption of elements on the
surfaces of clay minerals (Sposito et al., 1982). Sludge contributes organic ligands that form
89
soluble complexes with the elements like Ca enhancing their leachability (Li & Shuman, 1997).
Subjecting the mixtures to weathering (wetting and drying) and the formation of carbonic acid
(resulting from the reaction of CO2 and H2O enhanced by microbial activities) increased the
dissolution of calcite (CaCO3) and ettringite in ash that led to an increase Ca release (Sparks,
2003 & Kolahchi & Jalali, 2007) by the dissociation of CaHCO3 (from the CaCO3) produced H+
which resulted in mineral weathering (Mengel et al., 2001). Fine ash alone (mixture 1) released
the least cumulative Ca (7.8 mmol kg-1) than any other mixture due to the high pH that remained
high even after the tenth eluviations cycle (Fig.6.1 c).
60
40 % sludge
30 % sludge
Ca (mmol kg-1)
50
50 % sludge
40
No sludge
30
10 % sludge
20 % sludge
20
10
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Number of mixtures
a
40 % sludge 50 % sludge
6
Soluble Ca (%)
5
30 % sludge
4
20 % sludge
3
2
No sludge
10 % sludge
1
0
b
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Number of mixtures
Fig.6. 6: a) Cumulative amount of soluble Ca (mmol kg -1) released after 10 eluviation cycles and
b) Cumulative soluble Ca (%) in mixtures released after 10 eluviation cycles. The arrows in a
and b indicate the direction of increasing gasification ash content of each sludge treatment group.
90
The release of Ca should also be viewed in context of the other major cations and anions that
potentially can play role in controlling the solubility. The solubility of Mg and P was favored
over the solubility of Ca after the addition of sludge. This was revealed by the molar ratios of Ca:
Mg and Ca:P in Fig. 6.7, a and d respectively. The molar ratio of Ca:Mg indicated that solid
phases containing Ca were more soluble than solid phases containing Mg in the mixtures without
sludge but the trend changed, the Mg solubility was favored over Ca solubility in mixtures with
10 to 50% sludge. The trend presented by molar ratio Ca:P was similar to the trend that was
presented by the molar ratio Ca:Mg, Ca solubility was more that the solubility of P in mixtures
without sludge and drastically dropped after the addition of sludge.
The trend presented by molar ratio Ca:K differed, K solubility was more than Ca solubility in
mixtures without sludge. However, the liberation of Ca was favoured over that of K in mixtures
with sludge sludge. The solubility of Ca was favored in mixtures with 40% sludge and rapidly
reduced in mixtures with 50% sludge favoring K. At this stage more K was complexed by
soluble organic ligands improving its mobility. The trend presented by the molar ratio of Ca:Na
was similar to the trend presented by the molar ratio of Ca:K. Sludge increased the solubility of
Ca in mixtures with sludge (Fig.6.7, c).
The decrease of Ca release relative to that of Ca and P could be attributable to several factors. Ca
could have been adsorbed by organic functional groups (from sludge) that increased the cation
exchange capacity (CEC) from 5.5 to 19.0 cmolc kg-1 (chapter 7 Fig. 7.1 a) The sorption of
organic ligands on mineral surfaces creating new sorption surfaces for Ca could lower the Ca
release (Li & Shuman, 1997). The increase in sludge content in the mixtures increased CEC that
also slowed down the leaching of soluble Ca (Mulder & Cresser, 1994). This led to the
displacement of other cations on the surfaces of clay minerals (present in Sasol fine and
gasification ashes as characterized by Mahlaba et al., (2011) and Ginster & Matjie, 2005 &
Matjie et al., in chapter 2) like K, Mg and Na (with lower selectivity) by Ca; this subsequently
reduced the amount of Ca available for leaching (Wang, Brusseau & Artiola et al., 1997,
Messenger, Menge, Amrhein & Faber 1997). A high pH and the presence of Ca favours the
formation of soluble Ca-ligand complexes increasing the solubility of Ca shown by millimolar
ratios of Ca:K and Ca:Na in mixtures with sludge (Mengel et al 1987). The more soluble P
91
exhibited by the molar ratio Ca:P could be attributable to the more soluble P contained in sludge
in organic form.
Calcium is an essential element without which plants cannot complete their life cycle,
irreplaceable by other elements, and directly involved in plant metabolism (Fageria et al., 2002).
This element therefore is indispensible in soil fertility and is required by plants in large quantities
(Brady & Weil, 2008). An increase in soluble Ca as a result of sludge addition is arguably
beneficial as the sludge has an indirect effect, that is, by the dissolution of solid phases. The
soluble Ca released by mixtures without sludge and mixtures with 20 to 30 % sludge was quite
low and need to be supplemented for the betterment of plant growth.
92
1.6
30 % SL
1.2
No SL
1.4
No SL
0.8
10 % SL
0.6
20 % SL
0.4
30 % SL
0.2
40 % SL
50 % SL
0.6
0.4
0.2
0
-0.2
10 % SL
0.8
Log molar Ca:K ratio
Log molar Ca:Mg ratio
1
50 %
SL
20 % SL
1
1.2
40 % SL
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
0
-0.4
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
-0.6
-0.2
a
0.8
Number of mixtures
b
Number of mixtures
30 % SL
40 % SL
4.5
50 % SL
No SL
4
No SL
10 % SL
Log molar Ca:P ratio
0.6
20 % SL
Log molar Ca:Na ratio
0.4
0.2
0
-0.2
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
-0.6
3
10 % SL
20 % SL
2.5
30 % SL
40 % SL
50 % SL
2
1.5
1
0.5
0
-0.4
c
3.5
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
d
Number of mixtures
Number of mixtures
Fig.6. 7: a) Log molar Ca:Mg ratio based on the cumulative calcium and magnesium released after ten eluviation cycles, b) Log molar
Ca:K ratio based on the cumulative calcium and potassium released after ten eluviation cycles, c) Log molar Ca:Na ratio based on the
cumulative calcium and sodium released after ten eluviation cycles, and d) Log molar Ca:P ratio based on the cumulative calcium and
phosphorus released after ten eluviation cycles. The SL in a, b, c and d means sludge.
93
6.3.6 Magnesium content of Sasol sludge, fine and gasification ashes measured in 2006,
2007, 2008 and 2011
Mg analysis of these industrial wastes over the five year period indicated that Mg content in
sludge was significantly lower with a mean of 0.2% than the Mg content of fine (1.3%) and
gasification (mean 1.4%) ashes at 5% level (Fig.6.8). However, fine and gasification ashes were
not significantly different from each other at both 1 and 5% level. The least significant difference
was 0.3% while the coefficient of variation was 23.0%. The variability in magnesium content of
both fine and gasification ash were high (as indicated by the long error bars). This was due to the
low Mg content which was obtained for the fine ash in 2006 and gasification ash in 2008 of 0.95
and 0.97% respectively compared to the higher Mg content (1.3 to 1.6%) measured in 2011.
However, the Mg content of the fine ash was slightly more variable with a minimum, maximum
and median of 0.95, 1.57 and 1.3% respectively than gasification ash with a minimum, maximum
and median 1.0, 1.5 and 1.5% respectively. Sludge showed a narrower variability indicated by
shorter error bars with minimum, maximum and median 0.1, 0.3 and 0.3% respectively.
1.8
1.6
1.4
A
A
Mg (%)
1.2
1
0.8
0.6
0.4
B
0.2
0
Fine ash
Gasification ash
Sludge
Industrial wastes
Fig.6. 8: The variation in magnesium content of the Sasol sludge, fine and gasification ashes
based on analysis performed in 2006, 2007, 2008 and 2011 as determined by digestion method.
94
Table 6.6: Magnesium content in Sasol fine and gasification ashes as determined by X-ray
Fluorescence Spectroscopy (XRF) measured in 2008 and 2011.
Year
2011
2011
2011
2008
Mean
Standard deviation
Coefficient of variation(%)
Mg (%) in Gasification ash
1.5
1.2
1.5
1.5
1.4
0.1
8.9
Mg (%) in Fine ash
1.5
0.8
1.1
0.4
41.4
Magnesium content analysed using X-ray Fluorescence Spectroscopy (XRF) in 2008 and 2011
indicated high variability in fine ash than in gasification ash (Table 6.6). Fine ash showed a
higher coefficient of variation (41.4%) than gasification ash with a lower coefficient of variation
(8.9%). Noticeably, the Mg content of gasification ash was higher (mean 1.4%) than in fine ash
(1.1%). Sasol coal ashes contain periclase (MgO) (Mahlaba et al., 2011) which could be higher
in gasification ash than in fine ash. There was a significant difference in the Mg determined by
the two methods (XRF and acid digestion) in gasification as indicated by the overlapping 95%
confidence intervals. However, in fine ash the 95 % confidence interval given by acid digestion
was narrower and fitted in the wider 95% confidence interval given by the XRF method (Table
6.7). This was an indication that there was no significant difference in the two methods in
determining Mg in fine ash.
Table 6.7: Comparison of total Mg content determined using acid digestion and XRF in fine and
gasification ashes based on 95% confidence intervals
Ash material
Acid digestion
Mean Lower
XRF
Upper
limit
limit
Stdev
CV (%)
Mean
Lower
Upper
limit
limit
Stdev
CV (%)
Fine
1.3
0.8
1.9
0.3
19.6
1.2
0.3
2.6
0.5
41.4
Gasification
1.4
0.8
1.9
0.3
19.4
1.4
1.1
1.7
0.1
8.9
95
6.3.7 Magnesium content of mixtures
The Mg proved to be the second most abundant after Ca in all mixtures (Table 6.8). Similar to
Ca content, Mg was more abundant in gasification ash (619.9 mmol kg -1) than in fine ash (582.9
mmol kg-1) and sludge (107.7 mmol kg -1). The Mg content in Sasol gasification and fine ashes
were 0.97% and 0.95% respectively in 2008 showing to be significantly lower than the Mg
content of gasification (1.51%) and fine ash (1.46%) of the current study. The Mg content of
sludge was much lower compared to the Mg content of both fine and gasification ash. In both the
current study and in 2008 the Mg content of sludge was 0.30 and 0.12% respectively. Evidently
the contribution of gasification ash to Mg content was higher than the contribution of fine ash
and sludge (Fig.6.9). In characterizing Sasol fine ash, Mahlaba et al. (2011) found that Mg was
localized in periclase (MgO) that ranged between 0.3-1.3% in abundance. The abundance of
MgO was much lower than Ca bearing minerals hence Ca content was higher than Mg content in
the mixtures.
700
Mg (mmol kg-1)
600
500
400
300
200
100
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Number of mixtures
Sludge
Gasification ash
Fine ash
Fig.6. 9: Contribution of sludge, fine and gasification ash to Mg content of mixtures
96
Table 6.8: Measured and calculated means for Mg, in selected mixtures using microwave
digestion method
Mixture
Number
Measured Mg
(mmol kg-1)
Calculated Mg
(mmol kg-1)
Confidence interval
at 95 % (mmol kg-1)
Lower
Upper
limit
limit
Standard
deviation
(mmol kg-1)
CV %
1
6
11
26
46
51
SL
582.9
588.4
619.9
483.4
320.9
345.6
107.7
582.9
601.4
619.9
502.6
345.3
363.8
107.7
446.8
561.3
608.1
487.5
257.1
288.7
-
54.8
10.9
4.8
5.4
25.7
22.9
7.2
9.4
1.9
0.8
1.1
8.0
6.6
6.7
588.9
615.4
631.7
517.8
384.7
402.5
-
Note: mixtures 1 (100% fine ash), 6 (50% fine ash and 50% gasification ash), 11 (100%
gasification ash), 26 (40% fine ash, 40% sludge and 20 % sludge), 46 (50% fine ash and 50%
gasification ash) and51 (50% gasification ash and 50% sludge) were exclusively analysed to
enable the estimation of total elements of the other mixtures. SL means sludge.
The Mg content measured in the selected mixtures (mixture 1, 6, 11, 26, 46 and 51) was
compared to the calculated Mg content. This was made possible by calculating a 95% confidence
interval based on the measured Mg values and the mean. The calculated meansfitted well in the
respective 95% confidence intervals. The calculated mean(502.6 mmol kg-1) for mixture 26 was
significantly different from the measured mean(483.4 mmol kg-1) since it did not fit in the
confidence interval (upper limit 497.0 and lower limit 469.9). Repopulating the data readjusted
the confidence interval to an upper limit of 517.8 mmol kg -1 and a lower limit 487.5 mmol kg -1
that fitted the calculated mean (502.6 mmol kg -1).
6.3.8 Magnesium leaching from mixtures
Cumulatively mixtures without sludge (mixtures 1 to 11) released the least Mg (1.0 to 7.8 mmol
kg-1) than any other group mixtures (Fig.6.10 a). Similarly the same mixtures (mixtures 1 to 11)
released the least percent soluble Mg fraction (0.2 to 1.3%) than any other group mixture
(Fig.6.10 b). Mixture 43 (with >400 mSm-1)released the most Mg (68.0 mmol kg -1) followed by
mixtures 29, 37 40, 41, 44, 47 and 48 (with <400 mSm-1) that released Mg above 50 mmol kg -1.
Fine ash (mixture 1) released the least Mg (1.0 mmol kg -1) than any other mixture.
97
The release of Mg was further viewed in relation to other major cations and anions that
potentially can play role in controlling the solubility. Magnesium solubility was less favoured
over the solubility of K, Ca and Na in mixtures without sludge and this was indicated by
millimolar ratios Mg:K, Ca:Mg and Mg:Na in Fig.6.11 a, Fig.6.7 a and Fig.6.11 b respectively.
Adding sludge enhanced in solubilizing Mg containing solid phases and the solubility dominated
over the solubility of K, Ca and Na containing solid phases (Fig.6.11 a, Fig.6.8 a and Fig.6.11 b).
The addition of sludge further created new negatively charged adsorption sites for Mg reducing
its solubility in mixtures without sludge, concurrently increasing the solubility of P in mixtures
with sludge (Fig.6.11 c millimolar ratio Mg:P).
Magnesium generally increases the availability of other cations (with one positive charge) to
plants by displacing them from the exchange site (Essington, 2004 &Maiti et al., 1990).
Magnesium is an essential plant nutrient required in large amounts by plants (Brady and Weil,
2008) thus high solubility of Mg containing minerals influenced by the addition of sludge and its
release is desired in improving the fertility status of the mixtures. Mixtures without sludge
(mixtures 1 to 11) and mixtures with 10% sludge may need to be supplemented with soluble Mg
to fulfill plant requirements.
98
80
40 % sludge 50 % sludge
30 % sludge
70
20 % sludge
Mg (mmol kg-1)
60
50
10 % sludge
40
30
20
No sludge
10
0
1
a
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Number of mixtures
50 % sludge
40 % sludge
20
18
30 % sludge
Mg soluble fraction (%)
16
14
20 % sludge
12
10
10 % sludge
8
6
4
No sludge
2
0
1
b
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Number of mixtures
Fig.6. 10: a) Cumulative amount of Mg (mmol kg-1) released after 10 eluviation cycles and b)
Cumulative soluble Mg fraction (%) in mixtures released after 10 eluviation cycles. The arrows
in a and b indicate the direction of increasing gasification ash content of each sludge treatment
group.
99
Log molar Mg:K ratio
1.5
10 % sludge
1
0.5
30 % sludge40 % sludge 50 % sludge
No sludge
0
-0.5
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
-1
a
Number of mixtures
1
Log molar Mg:Na ratio
20 % sludge
10 % sludge
20 % sludge
30 % sludge 40 % sludge 50 % sludge
0.5
0
-0.5
No sludge
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
-1
-1.5
Log molar Mg:P
b
-2
Number of mixtures
4
3
No sludge
10 % sludge
20 % sludge
30 % sludge 40 % sludge
50 % sludge
2
1
0
c
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Number of mixtures
Fig.6. 11: a) Log molar Mg:K ratio based on the cumulative magnesium and potassium released
after ten eluviation cycles, b) Log molar Mg:Na ratio based on the cumulative magnesium and
sodium released after ten eluviation cycles and c) Log molar Mg:P ratio based on the cumulative
magnesium and phosphorus released after ten eluviation cycles.
100
6.3.9 Potassium content of Sasol sludge, fine and gasification ashes measured in 2006, 2007,
2008 and 2011
Statistically there was no significant difference at both 1 and 5% level in K content amongst the
means 0.2, 0.2 and 0.1% for sludge, fine and gasification ashes respectively (Fig.6.12). The least
significant difference was calculated as 0.3. The coefficient of variation was very high (122.2%)
due to the high variability in K content for gasification ash as indicated by longer error bars with
minimum, maximum and median of 0.02, 0.9 and 0.02% respectively. Fine ash had the least
variability in K content over the five year period with minimum, maximum and median of 0.02,
0.2 and 0.02% respectively. Potassium content in sludge varied slightly with minimum,
maximum and median of 0.1, 0.3 and 0.4% respectively.
1
0.9
0.8
0.7
K (%)
0.6
0.5
0.4
A
0.3
A
0.2
0.1
A
0
Fine ash
Gasification ash
Industrial wastes
Sludge
Fig.6. 12: Potassium content in Sasol sludge, fine and gasification ashes measured in 2006, 2007,
2008 and 2011 as determined by digestion method
101
Table 6.9: Potassium content in Sasol fine and gasification ashes as determined by X-ray
Fluorescence Spectroscopy (XRF) measured in 2008 and 2011.
Year
2011
2011
2011
2008
Mean
Standard deviation
Coefficient of variation (%)
K (%) in Gasification ash
0.5
0.6
0.5
0.5
K(%) in Fine ash
0.8
0.8
0.8
0.0
2.8
0.6
0.04
7.5
Over the two years the K content in both fine and gasification ash differed, the meas for
gasification and fine asheswere 0.6 and 0.8% respectively (Table 6.9). However, the K content in
gasification ash showed to be more variable over the two year period with a coefficient of
variation of 7.5%. The K content in fine ash was less variable with a coefficient of variation of
2.8% and a standard deviation of 0. In determining K both methods (XRF and acid digestion)
gave different results with 95% confidence interval indicating significant differences in the two
methods in determining K for fine and gasification ashes (Table 6.10).
Table 6.10: Comparison of total K content determined using acid digestion and XRF in fine and
gasification ashes based on 95% confidence intervals
Ash material
Acid digestion
Mean Lower
XRF
Upper
limit
limit
Stdev
CV (%)
Mean
Lower
Upper
limit
limit
Stdev
CV (%)
Fine
0.1
-0.2
0.3
0.1
150.4
0.6
0.5
0.6
0.0
2.7
Gasification
0.2
-0.7
1.2
0.4
185.7
0.6
0.5
0.7
0.0
7.5
6.3.10 Potassium content in mixtures
Measured and calculated K content was less abundant than Ca, Mg and Na in the mixtures
(Table 6.9) Fine and gasification ash total K content was 44.8 and 43.8 mmol kg-1 respectively
higher than the K content of sludge (26.5 mmol kg-1). Therefore, fine ash contributed the most to
K content in the mixtures followed by gasification ash and sludge respectively (Fig.6.13). The K
content in sludge was low because most K compounds are water soluble and remain in the
aqueous fraction during sludge dewatering (Rechcigl, 1995). While the K in both ash is located
in the interior glassy matrix and in minerals present in ash (Jala & Goyal, 2006).
102
Table 6.11: Measured and calculated means for K, in selected mixtures using microwave
digestion method
Mixture
Number
Measured K
(mmol kg-1)
Calculated K
(mmol kg-1)
Confidence interval at
95 % (mmol kg-1)
Lower
Upper
limit
limit
Standard
deviation
(mmol kg-1)
CV %
1
44.8
44.8
42.5
47.1
0.9
2.1
6
47.1
44.3
43.9
44.7
0.5
1.1
11
43.8
43.8
42.2
45.4
0.6
1.5
26
42.5
40.7
37.4
47.5
2.0
4.8
46
66.5
35.7
32.4
38.9
2.5
3.8
51
68.2
35.2
32.0
38.4
8.2
12.1
SL
26.5
26.5
9.4
35.4
Note: mixtures 1 (100% fine ash), 6 (50% fine ash and 50% gasification ash), 11 (100% gasification ash),
26 (40% fine ash, 40% sludge and 20% sludge), 46 (50 % fine ash and 50% gasification ash) and51 (50%
gasification ash and 50% sludge) were exclusively analysed to enable the estimation of total elements of
the other mixtures. SL means sludge.
To establish the significance level of the calculated means for the mixtures 1, 11, 26, 46 and 51 a
95% confidence interval was computed based on the measured means of the same mixtures
(Table.6.11). The calculated means 44.8, 43.8 and 40.8 mmol kg -1 for mixtures 1, 11 and 26
fitted into their respective confidence intervals 47.1 – 42.5, 45.4 – 42.2 and 47.5 – 37.4 mmol kg1
. However, the calculated means 44.3, 35.7, 35.2 mmol kg -1 for mixtures 6, 46 and 51
respectively could not fit in the corresponding confidence intervals showing to be significantly
different from the measured means. Repopulating the measured data adjusted the confidence
interval to 44.7 - 43.9, 38.9 – 32.4 and 38.4 – 32.0 mmol kg-1 for mixtures 6, 46 and 51 allowing
K (mmol kg-1)
the calculated means to fit respectively.
50
45
40
35
30
25
20
15
10
5
0
1
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Sludge
Number of mixtures
Gasification ash
Fine ash
Fig.6. 13: Contribution of sludge, fine and gasification ash to total K content of mixtures
103
12
No sludge
K (mmol kg-1)
10
50 % sludge
8
10 % sludge
6
20 % sludge
30 % sludge
40 % sludge
4
2
0
1
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Number of mixtures
a
K soluble fraction (%)
25
50 % sludge
No sludge
20
15
10 % sludge
20 % sludge
30 % sludge 40 % sludge
10
5
0
1
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
b
Number of mixtures
Fig.6. 14: a) Cumulative amount of K (mmol kg-1)released after 10 eluviation cycles and b)
Cumulative soluble K fraction (%) in mixtures released after 10 eluviation cycles. The arrows in
a and b indicate the direction of increasing gasification ash content of each sludge treatment
group.
6.3.11 Potassium leaching from mixtures
After the tenth eluviation cycle, cumulatively mixture 7 released the most K (9.5 mmol kg -1) than
any other mixture followed by mixtures 6 (8.2 mmol kg -1) and 47 (8.2 mmol kg-1). Both ashes
remain as the main source of K. Mixture 31 released the least K (3.2 mmol kg -1) (Fig.6.14 a).
However, mixture 46 released the most soluble percent K fraction and mixture 19 released the
least (Fig.6.14 b).
Potasium released was also compared to the release of other major nutrients. Potassium release
was favoured over the solubility of Ca, Mg and P in mixtures without sludge as exposed by
millimolar ratios Ca:K (Fig.6.8 b), Mg:K (Fig.6.12 a) and K:P (Fig.6.16 b). The same millimolar
104
ratios Ca:K, Mg:K and K:P showed that most of the soluble K was released from both fine and
gasification ash than from sludge. Therefore, increasing sludge could not enhance K solubility
which was continuously decreasing with decrease in both fine and gasification ash. New negative
adsorption sites for K developed as a result of the organic matter from increasing K retention in
the mixtures with sludge. The millimollar ratio K:Na indicated that Na solubility was favoured
over the solubility of K in mixtures without sludge and in mixtures with 20 to 30% sludge.
Increasing the sludge content of the mixtures from 40 to 50% solubilized most of the solid
phases containing Na. The increase in Na concentration contributed to the replacement of K by
Na from the exchange sites increasing K release (Fig.6.15 a).
The deficiency of potassium could result to a plant not completing its life cycle (Brady and Weil,
2008) therefore K released is essential for the improvement of soil fertility. Adding sludge
increases the retention of K through adsorption, as such, mixtures with sludge require soluble K
supplement.
Log molar K:Na
0
Log molar K:P
a
b
50 % sludge
1 3
-0.2
5 7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
No sludge
10 % sludge
20 % sludge
30 % sludge 40 % sludge
-0.4
-0.6
-0.8
Number of mixtures
No sludge
3.5
3
2.5
2
1.5
1
0.5
0
10 % sludge
1
3
5
7
20 % sludge
30 % sludge 40 % sludge
50 % sludge
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Number of mixtures
Fig.6. 15: a) Log molar K:Na ratio based on the cumulative potassium and sodium released after ten eluviation
cycles and b Log molar K:P ratio based on the cumulative potassium and phosphorus released after ten eluviation
cycles
105
6.3.12 Sodium content of Sasol sludge, fine and gasification ashes measured in 2006, 2007,
2008 and 2011
The measured Na content (with a mean of 0.1%) in sludge was significantly different and lower
than the Na content of both fine (with a mean of 0.3 %) and gasification ash (with a mean of
0.3%) over the five year period (Fig.6.16). However, the Na content in fine ash was not
significantly different from the Na content of gasification ash. The calculated least significant
difference was 0.09 and the coefficient of variation was slightly high (30.2%). The high
coefficient of variation was due to the high variability in the Na content in fine ash as indicated
by the long error bars having the minimum, maximum and median of 0.1, 0.4 and 0.4%
respectively. Gasification ash had the lowest variability in Na content with minimum, maximum
and median of 0.3, 0.4 and 0.3% respectively. Unexpectedly, Na content in sludge was more
variable than in gasification ash this was because the measured Na content was high in 2007
(0.1%) and for two replications in 2008 (0.13 and 0.16%). The Na contents for some sludge
replications were lower (0.05 to 0.1%) in 2008 and 2011 compared to 2007 and 2008 (for two
replications).
0.45
0.4
A
A
0.35
Na (%)
0.3
0.25
0.2
0.15
B
0.1
0.05
0
Fine ash
Gasification ash
Sludge
Industrial wastes
Fig.6. 16: Sodium content in Sasol sludge, fine and gasification ashes measured in 2006, 2007,
2008 and 2011 as determined by digestion method
106
Table 6.12: Sodium content in Sasol fine and gasification ashes as determined by X-ray
Fluorescence Spectroscopy (XRF) measured in 2008 and 2011.
Year
2011
2011
2011
2008
Mean
Standard deviation
Coefficient of variation (%)
Na (%) in Gasification ash
0.3
0.3
0.4
0.4
Na(%) in Fine ash
0.6
3.2
1.9
1.8
96.4
0.4
0.07
18.7
The Na content was less variable in gasification ash with a coefficient of variation of 18.7% than
fine ash with an extremely high coefficient of variation of 96.4% (Table 6.12). The high
variability in the Na content for fine ash was because of the high Na content (4.48%) which was
measured in 2011. However, the Na content in fine ash was higher (with a mean of 1.9%) than in
gasification ash (with a mean of 0.4%). The 95% confidence intervals given by XRF for fine and
gasification were wider than those given by acid digestion indicating no significant differences
between the two methods in determining Na (Table 6.13).
Table 6.13: Comparison of total Na content determined using acid digestion and XRF in fine and
gasification ashes based on 95 % confidence intervals
Ash material
Acid digestion
Mean Lower
XRF
Upper
limit
limit
Stdev
CV (%)
Mean
Lower
Upper
limit
limit
Stdev
CV (%)
Fine
0.3
0.1
0.6
0.1
38.3
1.9
-3.7
7.5
1.8
96.4
Gasification
0.3
0.3
0.4
0.0
8.3
0.4
0.2
0.5
1.1
18.7
6.3.13 Sodium content of mixtures
Measured and calculated Na was the third abundant element after Ca and Mg in the mixtures
(Table 6.14). Fine ash remained the main contributor of Na (171.1 mmol kg-1) in the mixtures
followed by gasification (146.4 mmol kg-1) and sludge contributed the least (37.8 mmol kg-1)
(Fig.6.17 and Table 6.12). The main sources of Na in ash are sodium silicate (Na2SiO3) and
halite (NaCl)(Dijkistra et al., 2006). Sasol fine ash as characterized by Mahlaba et al. (2011) was
107
found to contain analcime (NaAlSi2 O6.H2O) ranging between 0.5 to 1.6% as Na source and the
percent Na2O ranged between 0.98 to 3.51%. However, in 2008, gasification ash was found to
contain more Na (0.38%) than fine ash (0.14%). Noticeably the Na content for fine ash (0.39%)
of the current study was significantly above the Na content of fine ash obtained in 2008, while
the Na content for gasification ash (0.34%) in the current study was found to be significantly
low. However, fine ash remained the main contributor of Na than gasification ash and sludge in
Na (mmol kg-1)
the current study (Fig.6.17).
180
160
140
120
100
80
60
40
20
0
1
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Number of mixtures
Sludge
Gasification ash
Fine ash
Fig.6. 17: Contribution of sludge, fine and gasification ash to total Na content of mixtures
Table 6.14: Measured and calculated means for Na, in selected mixtures using microwave
digestion method
Mixture
Number
Measured Na
(mmol kg-1)
Calculated Na
(mmol kg-1)
Confidence interval at
95 % (mmol kg-1)
Lower
Upper
limit
limit
Standard
deviation
(mmol kg-1)
CV %
(mmol kg-1)
1
171.1
171.1
143.9
198.3
10.9
6.4
6
161.7
158.8
144.3
179.0
7.0
4.3
11
146.4
146.4
124.0
168.9
9.19
6.2
26
135.6
134.6
122.0
149.2
5.5
4.0
46
116.0
104.4
93.5
138.5
9.19
7.8
51
89.2
92.1
79.8
98.5
3.8
4.2
SL
37.8
37.8
12.6
33.4
Note: mixtures 1 (100% fine ash), 6 (50% fine ash and 50% gasification ash), 11 (100% gasification ash),
26 (40% fine ash, 40% sludge and 20% sludge), 46 (50% fine ash and 50% gasification ash) and51 (50%
gasification ash and 50% sludge) were exclusively analysed to enable the estimation of total elements of
the other mixtures. SL means sludge.
108
To enable the estimation of Na content for the rest of the mixtures Na was measured in mixtures
1, 6, 11, 26, 46 and 51. The calculated Na contents were then compared to the measured Na
mean values. To verify the significance of the calculated Na values a 95% confidence interval for
each of the mixtures was calculated based on the measured Na means to fit in the means of the
calculated values. It was found that the calculated means; 171.1, 158.8, 146.4, 134.6, 104.4 and
92.1 mmol kg-1 for mixtures 1, 6, 11, 26, 46 and 51 fitted in the confidence intervals; 198.3 143.9, 179.0 - 144.3, 168.9 - 124.0, 149.2 - 122, 138.5 - 93.5 and 98.5 - 79.8 mmol kg-1
respectively. This clearly showed that there was no significant difference between the measured
means and calculated means.
6.3.14 Soluble sodium released
The release of Na was also viewed in relation to the other major nutrients. Evidently the release
of Na was controlled by the concentration of other cations that also compete for the exchange
sites, Ca being the most competitor. Calcium ions have a larger interaction with mineral surfaces
than Na+ and Ca2+ forms monodentate inner sphere complex. Sodium ions have a weak
interaction with the mineral surfaces forming outer sphere complexes (Rahnemaie et al., 2006).
This was evident from molar ratio Ca:Na (Fig.6.7 a), where Na released was favoured over Ca
released in mixtures without sludge. Adding sludge reduced Na released through the
development of new adsorption sites for Na further reducing the dissolution of less soluble Na
containing mineral phases (Fig.6.18, a and b) (Dijkistra et al., 2006).
Cumulatively mixture 7 released the most Na (25.4 mmol kg -1) than any other mixture as the
ashes remain as the main source of Na (they contain minerals with Na) and mixture 51 released
the least Na (8.0 mmol kg-1).Most mixtures without sludge (mixtures; 1, 3, 4, 5, 6 and 10) and
mixtures that received 10 % sludge (mixtures; 17, 20 and 21) were capable of releasing Na above
20 mmol kg-1 but less than 25 mmol kg-1. Molar ratios; Ca:Na, Mg:Na and K:Na in Fig.6.7 a,
Fig.6.11 b and Fig.6.15 a respectively indicated that solubility favoured Na over Ca, Mg and K
in mixtures without sludge. The addition of sludge favours the solubility of Ca, Mg and K than
Na in all mixtures with sludge.
109
30
No sludge
10 % sludge
25
Na (mmol kg-1)
20 % sludge
20
30 %
sludge
15
50 % sludge
40 %
sludge
10
5
0
1
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
a
Number of mixtures
18
No sludge
Na soluble fraction (%)
16
10 % sludge
20 % sludge
14
50 % sludge
30 % sludge 40 % sludge
12
10
8
6
4
2
0
1
b
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Number of mixtures
Fig.6. 18: a) Cumulative amount of Na (mmol kg-1) released after 10 eluviation cycles and b)
Cumulative soluble Na fraction (%) in mixtures released after 10 eluviation cycles. The arrows
in a and b indicate the direction of increasing gasification ash content of each sludge treatment
group.
110
6.3.15 Phosphorus content of Sasol sludge, fine and gasification ashes measured in 2006,
2007, 2008 and 2011
As expected the measured P in sludge was high (with a mean of 0.6%) and significantly different
from the P content of both fine (with a mean of 0.3%) and gasification ashes (with a mean of
0.3%) at both 1 and 5% levels (Fig.6.19). But the P contents of both fine and gasification ashes
were not significantly different from each other. The least significant difference was computed as
0.2 and the coefficient of variation was calculated as 28.8%. Gasification ash showed to have the
least variability in P content as indicated by short error bars than fine ash and sludge. The P
content in gasification ash had minimum, maximum and median of 0.3, 0.4 and 0.3%
respectively while sludge had minimum, maximum and median of 0.4, 0.8 and 0.6%
respectively. Fine ash had similar error bars as sludge with minimum, maximum and median of
0.2, 0.4 and 0.3% respectively.
0.9
0.8
A
0.7
P (%)
0.6
0.5
0.4
0.3
B
B
0.2
0.1
0
Fine ash
Gasification ash
Sludge
Industrial wastes
Fig.6. 19: Phosphorus content in Sasol sludge, fine and gasification ashes measured in 2006,
2007, 2008 and 2011 as determined by digestion method
111
Table 6.15: Phosphoruscontent in Sasol fine and gasification ashes as determined by X-ray
Fluorescence Spectroscopy (XRF) measured in 2008 and 2011.
Year
2011
2011
2011
2008
Mean
Standard deviation
Coefficient of variation (%)
P(%) in Gasification ash
0.5
0.5
0.5
0.6
0.5
0.05
10.2
P(%) in Fine ash
0.5
0.6
0.6
0.08
14.9
The mean P content in gasification ash was 0.1 units lower than the P content on fine ash when
averaged over a period of two years (Table 6.15). However, the variability in P content for fine
ash was higher with a coefficient of variation of 14.9% than the variability P content in
gasification ash with a coefficient of variation of 10.9%. In determining total P both methods
(acid digestion and XRF) gave overlapping 95% confidence intervals for both fine and
gasification ashes indicating significant differences in the two methods in determining P (Table
6.16)
Table 6.16: Comparison of total P content determined using acid digestion and XRF in fine and
gasification ashes based on 95 % confidence intervals
Ash material
Acid digestion
Mean Lower
XRF
Upper
limit
limit
Stdev
CV (%)
Mean
Lower
Upper
limit
limit
Stdev
CV (%)
Fine
0.3
0.1
0.4
0.1
25.5
0.5
0.3
0.8
0.1
14.9
Gasification
0.3
0.2
0.4
1.2
18.1
0.5
0.4
0.6
0.1
10.2
6.3.16 Phosphorus content in mixtures
Sludge contained the most measured P content (149.6 mmol kg -1) than fine (106.0 mmol kg -1)
and gasification ashes (106.5 mmol kg -1) (Table 6.17). In 2008 the P content of Sasol sludge
(0.45%), fine (0.23%) and gasification ash (0.25%) was significantly lower than the P content of
sludge (0.46%), fine ash (0.32%) and gasification ash (0.33%) of the current study. The
contribution of sludge to P content of the mixtures was therefore more evident in Fig.6.20. Most
112
of the P is contained in calcium compounds such as carbonate apatite (Ca5(PO4)3(OH,F,Cl) in
alkaline conditions like in coal ash (Brady and Weil, 2008).
160
140
120
P (mmol kg-1)
100
80
60
40
20
0
1 3 5 7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Number of mixtures
Sludge
Gasification ash
Fine ash
Fig.6. 20: Contribution of sludge, fine and gasification ash to total P content of mixtures
Table 6.17: Measured and calculated means for P, in selected mixtures using microwave
digestion method
Mixture
Number
Measured P
(mmol kg-1)
Calculated P
(mmol kg-1)
Confidence interval
at 95 % (mmol kg-1)
Lower
Upper
limit
limit
Standard
deviation
(mmol kg-1)
CV % (mmol
kg-1)
1
6
11
26
46
51
SL
106.0
135.6
106.5
113.0
102.2
99.0
149.6
106.0
106.3
106.5
114.9
127.8
128.1
149.6
47.6
17.5
61.9
78.3
71.9
119.2
-
23.5
47.6
18.0
14.0
12.2
10.4
18.4
22.2
35.1
16.9
12.4
12.0
10.5
12.3
164.4
253.7
151.2
147.7
132.6
136.9
-
Note: mixtures 1 (100% fine ash), 6 (50% fine ash and 50% gasification ash), 11 (100%
gasification ash), 26 (40% fine ash, 40% sludge and 20% sludge), 46 (50% fine ash and 50%
gasification ash) and51 (50% gasification ash and 50% sludge) were exclusively analysed to
enable the estimation of total elements of the other mixtures. SL means sludge.
Based on measured P values for mixtures 1, 6, 11, 26, 46 and 51 a 95% confidence interval was
calculated to verify the significance of the calculated P values. It was found that the calculated
means for mixtures 1, 6, 11, 26, and 46 fitted well in the respective confidence intervals
113
indicating not to be significantly different from the measured means. However, the calculated
mean (128.1 mmol kg-1) for mixture 51 did not fit in the confidence interval (124.8 - 73.2 mmol
kg-1) showing to be significantly different from the measured mean (99.0 mmol kg -1).
Repopulating the data readjusted the confidence interval to an upper limit of 136.9 mmol kg -1 to
a lower limit of 119.2 mmol kg-1 that fitted the calculated mean (128.1 mmol kg -1).
6.3.17 Soluble phosphorus released
The release of P is generally controlled by pH and an increase in pH desorbs the P from the Ca-P
compounds by increasing competition between hydroxyl ions and the adsorbed P (Jin et al.,
2006). The initial P release in ash is generally rapid until equilibrium is reached. Fast,
intermediate and slow P released is attributed to; the dissolution of poorly crystalline metastable
calcium phosphates converting to hydroxyapatite. A combination of desorption and diffusiondissolution reactions control the initial fast and final slow release of P in ash (Shariatmadari et
al., 2006).
Cumulatively mixture 51 released the highest P (1.1 mmol kg -1) followed by mixtures 45, 49, 50,
47, 48, 38 and 44 respectively with more than 0.4 mmol kg -1. In these mixtures sludge remained
as the main source of P. Mixture 21 (fine ash 90%, gasification ash 0% and sludge 10%) released
the least P (0.05 mmol kg-1) amongst mixtures with sludge. Mixtures without sludge released the
least P that ranged from 0.01 to 0.004 mmol kg -1 (Fig.6.21 a). The same trend was also indicated
in the release of soluble P fraction (%) in Fig.6.21 b where P released increased with increase in
sludge. The increase in P release as influenced by sludge has been showed by millimolar ratios;
K:P, Mg:P and Ca:P in Fig.6.15 b, Fig.6.11 c and Fig.6.7 d respectively.Humified compounds
such as fulvic acid (FA) and humic acid (HA) resulted from the decomposition of sludge and
contained functional groups (carboxyl and phenolic hydroxyl) that normally deprotonate at
certain pH levels. In these mixtures the dissociation of the functional groups was possible
because the pH ranged from 7.6 to 11.7 sinceat this pH (pH>3) Ha and FA behave as negatively
charged poly-electrolytes.The dissociation of carboxyl groups (3<pH<9) and phenolic hydroxyl
groups (pH>9) increased P release (Mulder & Cresser, 1994).
114
1.2
50 % sludge
1
P (mmol kg-1)
0.8
40 % sludge
0.6
30 % sludge
0.4
20 % sludge
10 % sludge
0.2
No sludge
0
1 3 5
7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
a
Number of mixtures
50 % sludge
0.9
0.8
P soluble fraction (%)
0.7
0.6
40 % sludge
0.5
30 % sludge
0.4
20 % sludge
0.3
0.2
No sludge
0.1
10 % sludge
0
1
b
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Number of mixtures
Fig.6. 21: a) Cumulative amount of P (mmol kg-1) released after 10 eluviation cycles and b)
Cumulative soluble P fraction (%) in mixtures released after 10 eluviation cycles. The arrows in
a and b indicate the direction of increasing gasification ash content of each sludge treatment
group.
115
6.3.18 Micronutrients
Essential micronutrients (Zn, Cu, Mn, Mo, Fe, Cl and B) described as trace elements and
required by plants in extremely small quantities (Fageria et al. 2002, Gupta et al., 2008 & Brady
& Weil, 2008) were also determined in the leachates as soluble elements including; S, Al and
Co. However, the discussion was limited to Zn, Cu, Mn, Mo, Fe and B, but it should be noted
that total elemental analysis on fresh samples was done for Fe, Zn, Mn and Cu amongst the
micronutrients (Table 6.18). The analysis was performed on selected mixtures (mixtures 1, 6, 11,
26, 46, 51 and sludge). The reason behind analyzing mixture 1 (fine ash), 11 (gasification ash)
and sludge was to enable calculation of the total elements in the other mixtures. Mixtures 6 (50%
fine ash and 50% gasification ash), 26 (40% fine ash, 40% gasification ash and 20% sludge), 46
(50% fine ash and 50% sludge) and 51 (50% gasification ash and 50% sludge) were included in
the analysis for verification purposes. Generally, the total measured and calculated content of
micronutrients in the mixtures were in the following order; Fe>Mn>Zn>Cu (Table 6.18). The
percentage difference between the measured and calculated total elements was narrow and
ranged between 0 to 23%.
6.3.19 Iron and manganese in mixtures
The trend in Fe and Mn indicated that fine ash had both elements in abundance compared to
gasification ash and sludge (Table 6.18). Fine ash, gasification ash and sludge contained 2145.2,
515.7 and 109.2 mmol kg -1 Fe respectively. With respect to Mn; fine ash, gasification ash and
sludge contained 14.93, 5.10 and 1.49 mmol kg -1 respectively. It was also evident in Fig.6.22, a
and c that fine ash was the main contributor of both Fe and Mn in the mixtures while sludge
contributed the least. In comparison with results obtained in 2008, fine ash contained less Fe
(0.56%) than fine ash Fe content (12.0%) of the current study. Similarly, in 2008 fine ash
contained less Mn (0.032%) than the Mn content (0.082 %) of the same material for the current
study. Sasol fine ash contains magnetite (FeFe2O4), hematite (Fe2O3) and pyrrhotite (Fe9S10) that
range from 0.1 to 14.1%, 0.75 to 2.0% and 0.3 to 0.8% respectively that remain as main sources
of Fe (Mahlaba et al., 2011). Manganese occurs in carbonates (rhodochrosite), silicates
(rhodanate), simple oxides (manganite) and complex oxides (braunite) (Fageria et al. 2002) that
may also be present in coal ash.
116
Table 6.18: Measured and calculated Cu, Mn, Zn and Fe in selected mixtures
Mix. Meas.
Calc.
Meas.
Calc.
Meas.
Calc.
Meas.
No. Cu
Cu
Mn
Mn
Zn
Zn
Fe
mmol kg-1
1
6
11
26
46
51
SL
0.83
0.79
0.92
0.70
0.61
0.45
0.27
0.83
0.87
0.92
0.75
0.55
0.59
0.27
14.93
10.27
5.10
8.19
7.60
2.89
1.49
14.93
10.01
5.10
8.31
8.21
3.30
1.49
0.40
0.33
0.19
0.77
1.49
1.43
2.32
0.40
0.29
0.19
0.70
1.36
1.26
2.32
2145.22
1328.68
515.71
1077.98
1088.73
333.06
109.23
Calc.
Fe
2145.22
1330.47
515.71
1086.22
1127.23
312.47
109.23
Note: mixtures 1 (100% fine ash), 6 (50% fine ash and 50% gasification ash), 11 (100% gasification ash),
26 (40% fine ash, 40% sludge and 20% sludge), 46 (50% fine ash and 50% gasification ash) and 51 (50%
gasification ash and 50% sludge) were exclusively analyzed to enable the estimation of total elements of
the other mixtures
6.3.20 Soluble Fe and Mn
In both ashes it was expected that processes such as dissolution of the solid phases and
desorption of Fe and Mn could occur as initiated by weathering, but seemingly they occurred at
very slow rate in mixtures without sludge because the minerals containing Fe and Mn have
limited solubility in nature (Essington, 2004). However, the addition of sludge liberated more of
the Fe and Mn by increasing the solubility of the solid phases and through humic substances
(Fig.6.22 b and d). Humified compounds such as fulvic acid (FA) and humic acid (HA) that
result from the decomposition of organic matter contain functional groups (carboxyl and
phenolic hydroxyl) that form complexes with metals through chelation, the chalation further
promotes the dissolution of metals from the minerals (Mulder & Cresser, 1994). This was
possible in the mixtures because pH ranged from 7.6 to 11.7 which is a parameter that
determines the charge characteristics of the humic substances. At this pH (pH>3) Ha and FA
behave as negatively charged poly-electrolytes due to the dissociation of carboxyl groups
(3<pH<9) and phenolic hydroxyl groups (pH>9) (Mulder & Cresser, 1994).
Cumulatively, the contribution of sludge to the solubility of solid phases and subsequent release
of Fe and Mn was more pronounced. Mixture 47 (40% fine ash, 10% gasification ash and 50%
sludge) released the most Fe (0.04 mmol kg -1) than any other mixture and mixture 21 (90% fine
ash, 0% gasification ash and 10% sludge) released the least (0.2 µmol kg -1). Mixture group
117
without sludge (mixture 1 to 11) and the group with 10% sludge (mixture 12 to 21) released the
least Fe (0.2 to 0.9 µmol kg -1) compared to all other mixture groups that released Fe in the range
between 2.0 to 40.0 µmol kg -1) (Fig.6.22 b). With Mn, mixtures without sludge and the mixture
group with 10% sludge released the least Mn (5.9 x 10 -4 mmol kg-1 to 1.2 x 10-3 mmol kg-1) than
any other mixture group, while mixture 43 (20% fine ash, 40 % gasification ash and 40% sludge)
released the most Mn (0.013 mmol kg -1) than any other mixture and mixture 1 (100% fine ash)
released the least Mn (5.9 x 10-4 mmol kg-1) (Fig.6.22 d).
Critical concentration ranges of available Fe and Mn for most plants are 2.5-5 mg kg-1 (as
extracted by NH4HCO3-DTPA and DTPA-TEA) and 4-8 mg kg-1 (as extracted by NH4HCO3DTPA and Mehlich-1, 2 and 3) respectively (Fageria, et al., 2002). Seemingly the ranges 0.0112.2 mg kg-1 (Fe) and 0.03-0.7 mg kg-1 (Mn) released from the mixtures were far below the
critical concentrations in literature. According to Fageria, et al. (2002) pH influences both
solubility and mobility of micronutrients. Iron solubility decreases approximately 1000 fold for
each unit increase of pH in the range between 4 to 9. At this pH Fe2+ is oxidized to Fe3+ that
precipitates. Manganese exists as Mn2+ and decreases approximately 100 fold for each unit
increase in pH and increases the organic fraction of Mn. The high pH (7.6 to 11.7) in the
mixtures could be the reason for the low soluble Fe and Mn. Both elements may therefore need
to be supplemented in all the mixtures.
118
2500
16
14
2000
Mn (mmol kg-1)
Fe (mmol kg-1)
12
1500
1000
10
8
6
4
500
2
0
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
a
Sludge
Number of mixtures
Gasification ash
1
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Number of mixtures
c
Fine ash
50 % sludge
0.045
3
Sludge
Gasification ash
Fine ash
40 % sludge
0.014
50 % sludge
0.04
0.012
0.035
30 % sludge
40 % sludge
0.01
Mn (mmol kg-1)
Fe (mmol kg-1)
0.03
0.025
30 % sludge
0.02
20 % sludge
0.015
0.01
0.008
20 % sludge
0.006
0.004
10 % sludge
No sludge
No sludge
10 % sludge
0.002
0.005
0
0
b
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Number of mixtures
d
Number of mixtures
Fig.6. 22: a) Contribution of sludge, fine and gasification ash to total Fe content of mixtures, b) Cumulative amount of Fe (mmol kg-1)
released after 10 eluviation cycles, c) Contribution of sludge, fine and gasification ash to total Mn content of mixtures and d)
Cumulative amount of Mn (mmol kg-1) released after 10 eluviation cycles. The arrows in b and d indicate the direction of increasing
gasification ash content of each sludge treatment group.
119
6.3.21 Zinc and copper
Measured and calculated Zn indicated to be more abundant in sludge (2.3 mmol kg -1) than in fine
(0.4 mmol kg-1) and gasification ash (0.19 mmol kg-1) (Table 6.18). The dominance of sludge
contribution to Zn content was evident in all mixtures with sludge; similarly the dominance of
fine ash contribution to Zn content was clearly depicted in mixtures without sludge (Fig.6.23 a).
However, in 2008 gasification ash dominated in Zn content (3.36 mmol kg -1) over fine ash (0.31
mmol kg-1 Zn) and sludge (1.7 mmol kg -1). In the current study, Cu was more abundant in the
ashes (0.9 mmol kg-1 Cu in gasification ash and 0.8 mmol kg-1 Cu in fine ash) than in sludge (0.3
mmol kg-1) (Table 6.18). The dominance of both ashes in Cu contribution was evident in all
mixtures and the minimal contribution of sludge to Cu in mixtures with sludge was clearly
shown in Fig.6.23 c. Copper content in 2008 was dominant in gasification ash (3.9 mmol kg -1)
than in fine ash (0.3 mmol kg -1) and sludge (1.6 mmol kg -1). Generally, in alkaline conditions Zn
and Cu are constituents of carbonates which are abundant in alkaline coal ashes (Fageria et al.
2002).
6.3.22 Solubility of zinc and copper in mixtures
Cumulatively, mixtures containing 50% sludge dominated in Zn released. Mixture 51 released
the most zinc (4.0 µmol kg-1) compared to any other mixture and mixture 11 released the least
(0.2 µmol kg-1) (Fig.6.23 b). Copper released after eluviation cycle 10 increased with increase in
sludge and fine ash. Mixture 21 (90% fine ash, 0% gasification ash and 10% sludge) released the
highest Cu (1.0 µmol kg-1) than any other mixture and mixture 5 (60% fine ash, 40% gasification
ash and 0% sludge) released the least (0.1 µmol kg-1). Clearly most of the copper was released
from both fine and gasification ashes than from sludge (Fig.6.23 c).
The generally low quantities of Zn and Cu released could result from the fact that Cu adsorption
increases at pH 4 to 7 on exchange sites and occluded by hydroxides and oxides. pH levels above
6 generally induce hydrolysis of hydrated Cu which then increases its adsorption to clay minerals
and organic matter (Fageria et al. 2002). Copper also precipitates as carbonate of hydroxides at
higher pH and forms strong bonds with soil organic matter (Wei et al., 2006). The adsorption of
Zn on hydrous oxides of Al, Fe and Mn increases as pH increases above 5.5. But at pH above 7,
120
Zn solubility increases due to solubilization of organic matter (Fageria et al. 2002). The high pH
(7.6 to 11.7) levels in the mixtures therefore favoured the precipitation of both Zn and Cu.
Zinc and Cu critical concentrations for most plants ranges from 0.25 to 10 mg kg-1 (as extracted
by NH4HCO3-DTPA, DTPA-TEA, Mehlich-1, 0.1 M HCl and 0.05 M HCl) and 0.1 to 10 mg kg 1
(as extracted by NH4HCO3-DTPA, Mehlich-1 and 3, 0.05 M EDTA and 0.05 M HCl)
respectively (Fageria, et al., 2002). It was clear that the ranges 0.0008-0.06 mg kg-1 Cu and 0.010.26 mg kg-1 Zn released from the mixtures were below the critical concentrations presented by
Fageria, et al. (2002). Both elements therefore need to be supplemented in all the mixtures.
However, it should be noted that EDTA results will always be higher than results obtained with
dionised water.
121
1
1.6
0.9
1.4
0.8
1.2
Cu (mmol kg-1)
Zn (mmol kg-1)
0.7
1
0.8
0.6
0.6
0.5
0.4
0.3
0.4
0.2
0.2
0.1
0
0
1
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Sludge
a
Number of mixtures
Gasification ash
1
Fine ash
0.0045
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Sludge
c
10 % sludge
0.004
Zn (mmol kg-1)
0.0025
0.0008
20 % sludge
0.002
10 % sludge
0.0015
0.001
50 % sludge
40 % sludge
40 % sludge
Cu (mmol kg-1)
30 % sludge
30 % sludge
20 % sludge
0.001
0.003
Fine ash
0.0012
50 % sludge
0.0035
Number of mixtures
Gasification ash
0.0006
0.0004
No sludge
No sludge
0.0002
0.0005
0
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
b
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
d
Number of mixtures
Number of mixture
Fig.6. 23: a) Contribution of sludge, fine and gasification ash to total Zn content of mixtures, b) Cumulative amount of Zn (mmol kg1
)released after 10 eluviation cycles, c) Contribution of sludge, fine and gasification ash to total Cu content of mixtures and d)
Cumulative amount of Cu released after 10 eluviation cycles. The arrows in b and d indicate the direction of increasing gasification
ash content of each sludge treatment group.
122
6.3.23 Boron and molybdenum in mixtures
During elemental analysis B and Mo were excluded, thus the following discussion was based on
release only. After the first to the tenth eluviation cycles B increased with increase in sludge and
fine ash. Group mixtures with 50% sludge dominated in B release. Cumulatively, the same trend
was observed, mixture 46 (50% fine ash and 50% sludge) releasing the most B (5.6 mmol kg-1)
and mixture 7 (40% fine ash and 60% gasification ash) releasing the least (0.6 mmol kg -1)
(Fig.6.24, a and b). Cumulatively, Mo released increased with increase in sludge content.
Mixture 49 (20% fine ash, 30 % gasification ash and 50% sludge) released the most Mo (0.032
mmol kg-1) and mixture 11 (100% gasification ash) released the least (0.008 mmol kg -1).
Boron in coal ash occurs in borax, Mg hydroxides and in Ca carbonates (Rahnemaie et al., 2006)
while Mo is a constituent of oxides, molybdates (Fageria et al. 2002). The dissolution of the
solid phases containing B and Mo occurred at a very slow rate in mixtures without sludge,
because the minerals containing them generally have a low solubility in nature (Essington,
2004). However, the addition of sludge liberated more of the B and Mo by increasing the
solubility of the solid phases and through humic substances (Mulder & Cresser, 1994).
Boron and Mo released were in the range 6.5 to 60.4 mg kg -1 and 0.8 to 2.9 mg kg -1 far above the
critical available concentration for B (0.1-2 mg kg-1 as extracted by hot water) and Mo (0.1-0.3
mg kg-1 as extracted by NH4-oxalate) in soils (Fageria et al. 2002) and such levels may induce
toxicity to plants.
123
6
40 % sludge
20 % sludge
5
B (mmol kg-1)
4
50 % sludge
30 % sludge
10 % sludge
3
No sludge
2
1
0
1
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Number of mixtures
a
0.035
50 % sludge
0.03
40 % sludge
Mo (mmol kg-1)
0.025
10 % sludge
20 % sludge 30 % sludge
0.02
No sludge
0.015
0.01
0.005
0
1
b
3
5
7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Number of mixtures
Fig.6. 24: a) Cumulative amount of B (mmol kg-1) released after 10 eluviation cycles and b)
Cumulative amount of Mo (mmol kg-1) released after 10 eluviation cycles. The arrows in a and b
indicate the direction of increasing gasification ash content of each sludge treatment group.
124
6.4 Conclusions
The main contributors to the high pH of the mixtures were fine and gasification ashes with pH
values of 11.3 and 11.7 respectively, while sludge alone had a pH of 6.8. Wetting and drying
cycles gradually reduced the pH for the mixtures. Initially (after the first eluviation cycle), the
pH for the mixtures was between 8.3 to 11.7 and this dropped to between 7.6 to 10.3 after the
tenth eluviation cycle. The incorporation of sludge in mixtures 12 to 51 (described in chapter 3)
abruptly reduced pH while mixtures without sludge retained a pH above 8.4. As a result, mixture
7 (40% fine ash, 60% gasification ash and 0 % sludge) maintained the highest pH of 10.3 while
mixture 49 (20% fine ash, 30% gasification ash and 50% sludge) showed the lowest pH value of
7.6. Mixtures 14 (20% fine ash, 70% gasification ash and 10% sludge), 19 (70% fine ash, 20 %
gasification ash and 10% sludge), 20 (80% fine ash, 10% gasification ash and 10% sludge), 24
(60% fine ash, 20% gasification ash and 20% sludge), 25 (50% fine ash, 30% gasification ash
and 20% sludge), 28 (20% fine ash, 60% gasification ash and 20% sludge), 29 (10% fine ash,
70% gasification ash and 20% sludge) and from 31 to 51 (with 30% sludge) maintained a pH of
less than 8 but greater than 7.6 after ten eluviation cycles. All other mixtures had pH more than
8. The reduction in pH could be caused by the removal of soluble alkalinity and various reactions
that released protons into the solution. In this case dissolved organic carbon upon degradation
released fulvic and humic acids containing carboxylic groups that deprotonated and ionized
under alkaline conditions. Other processes such as carbonation, hydrolysis, nitrification and
precipitation released H+ that reduced the pH. Clearly, the sludge reduced the pH in the ashes but
not low enough to be accommodated in optimum pH range (5.5-7.5) for a functional growth
medium.
In terms of salinity gasification ash with 527 mSm-1 EC and fine ash with 580 mSm-1 EC
remained as the main contributors to the mixtures. The addition of sludge gradually decreased
the salinity of all the mixtures to a mean of 500 mSm-1 after the first eluviation cycle. However,
wetting and drying coupled with functional groups resulting from dissolved organic carbon that
complexed the salts enhanced solubility and leaching of the salts reducing the salinity of the
mixtures after the tenth eluviation cycle. Mixture 11 (100% gasification ash) retained the
lowestEC (88 mSm-1) and mixture 43 (20% fine ash, 40% gasification ash and 40% sludge)
retained the highest EC of 415 mSm-1. It was only mixtures 40 (50% fine ash, 10% gasification
125
ash and 40% sludge) and 43 (20% fine ash, 40% gasification ash and 40% sludge) that had their
EC beyond the range (70 to 400 mSm-1) suggested by Handreck and Black (1984) and Brady and
Weil (2008) as optimum for plant growth after ten eluviation cycles.
Measured elements in all the mixtures in order of abundance were as follows; Ca>Mg>Na>P>K.
Gasification ash remained the main contributor of Ca and Mg while fine ash contributed mostly
K and Na. Sludge remained the main source of P as its contribution to these elements were in
this order; P>Ca>Mg>Na>K. The release of each element was further viewed in relation to the
release of other major elements. The solubility of Ca was favoured over the solubility of Mg and
P in mixtures without sludge (mixtures 1 to 11) but the solubility of Na and K were favoured
over the solubility of Ca in these mixtures giving a different order of abundance
(Na>K>Ca>Mg>P). This indicated that solid phases containing Ca were more soluble than solid
phases containing Mg and P but less soluble than solid phases containing Na and K. In mixtures
with sludge (mixtures 12 to 51) Ca solubility was favoured over Na and K solubility but the
solubility, of P and Mg were favoured over the solubility of Ca in these mixtures and the order of
abundance was as follows; P>Mg>Ca>Na>K. Sludge increased the solubility of solid phases
containing Ca more than the solid phases containing Na and K.
Mixture 43 (20% fine ash, 40% gasification ash and 40% sludge) released the highest Ca (52.6
mmol kg-1) and Mg (68.0 mmol kg -1), mixture 7 (40% fine ash, 60% gasification ash and 0%
sludge) released the highest K (9.7 mmol kg -1) and Na (25.4 mmol kg-1) while mixture 51(0%
fine ash, 50% gasification ash and 50 % sludge) released the highest P (1.1 mmol kg -1).
Generally it was clear that increasing sludge increased the solubility of most of the solid phases
containing the elements but at different rates. Increasing sludge content in the mixtures increased
P release overtime.A functional growth medium should be able to provide all the major elements
in sufficient quantities and reduced quantities of Na (high concentrations are toxic to plants).
Mixtures with 20 to 50% sludge (mixtures 22 to 51) are such desired functional growth media.
The order of abundance of measured trace elements contents in the mixtures was as follows;
Fe>Mn>Cu>Zn. Gasification ash was the main contributor of Cu and Mn while fine ash
contributed mostly Zn and Fe. Sludge contributed trace elements in this order; Fe>Mn>Zn>Cu.
126
Mixture 47 (40% fine ash, 10% gasification ash and 50% sludge)released more Fe (0.04 mmol
kg-1) than any other mixture while mixture 43(20% fine ash, 40% gasification ash and 40%
sludge) released the most Mn (0.01 mmol kg -1). Mixture 51(0% fine ash, 50% gasification ash
and 50% sludge) released more Zn (0.004 mmol kg-1) than any other mixture, while mixture 21
(90% fine ash, 0% gasification ash and 10% sludge)released the most Cu (0.001 mmol kg -1).
Boron (5.6 mmol kg-1) was released the most by mixture 46(50% fine ash, 0% gasification ash
and 50% sludge), while mixture 49 (20% fine ash, 30 % gasification ash and 50%
sludge)released the highest Mo (0.03 mmol kg -1), more than any other mixture. It was evident
that the solubility of Fe, Zn, B and Mo increased with increase in sludge and mixtures that
supplied abundant plant available trace elements included mixtures from 22 to 51 (described in
chapter 3).
127
CHAPTER 7: ASSESSING THE CATION EXCHANGE CAPACITY PROPERTIES OF
THE VARIOUS MIXTURES
7.1 Introduction
The mineralogy of Sasol fine ash mainly consists of; amorphous phase (a phase with non fixed
elemental proportions and has no ordered crystalline structure), mullite (Al6Si2O13) and quartz
(SiO2) and gasification ash mainly consists of; SiO2, Al6Si2O13 and anorthite (CaAl2Si2 O8)
(Ginster & Matjie, 2005 & Matjie et al., 2008, Mahlaba et al., 2011). The fine particles result
from fused clay minerals mainly comprising aluminium-silicate (AlO)2SiO3) and Sasol fly ash is
dominated by 59% of the silt-sized particles (Ahmaruzzaman, 2010 & Mahlaba et al., 2011).
The SiO2 and Al6Si2O13 in ash provide a ready source of Al and Si and the ratio (Al/Si) of these
elements is important for the development of cation exchange capacity (CEC). In this case a net
negative charge can develop through isomorphous substitution where structural cations of higher
valence replace cations of lower valence forming a permanent negative surface charge (Mulder
& Cresser, 1994).The CEC in ash is a result of the formation of zeolites (crystalline aluminiumsilicates) that consists of structure made of [SiO4]4- and [AlO4]5- tetrahedral linked by oxygen
(O2-) with lots of voids and spaces. The substitution of Si (IV) by Al (III) in the tetrahedra
accounts for a negative charge of the structure which gives rise to high CEC (Querol, et al.,
2002). The development of zeolites depends on the dissolution of the Al-Si bearing mineral
phases and a high Al/Si ratio results to a high CEC (Woolard et al., 2000). Chemical weathering
over time may induce alterations in the aluminium-silicate property to non-crystalline clay
minerals and the formation of such minerals is generally indicated by an increase in CEC
(Zevenbergen et al., 1999). Gitari et al., (2009) suggested that the increase in CEC is attributable
to the Al-Si rich phases that form during mineral transformation.
Sasol biological sludge is an unavoidable byproduct of the aerobic activated biosolid treatment
process and it contains 82% organic matter (OM) content (Sasol Synfuels, 2008). Organic matter
increases CEC through its deprotonated functional groups carboxylic and phenolic groups that
form during OM decomposition (Essington, 2004). Variable charges formwith an increase in pH
and ionic strength that allows the dissociation of H+ from organic functional groups. Carboxyl
and phenolic hydroxyl groups have a pKa < 5 and deprotonate at pH values below their
128
respective pKa values (Sparks, 2003). Carboxyl and phenolic hydroxyl groups deprotonate at 3 <
pH < 9 and at pH > 9 respectively, increasing negative charges (Mulder and Cresser, 1994 &
Sparks, 2003).
Basically, CEC is important for the retention of adequate quantities of plant available cations
such as calcium (Ca), magnesium (Mg) and potassium (K) (Brady & Weil, 2008, Rashidi &
Seilsepour, 2008 & Ross & Ketterings, 2011). It was envisaged that the incorporation of sludge
will transform to humic compounds, (during OM decomposition) with functional groups capable
of deprotonating under the alkaline conditions provided by the ash increasing the CEC. It was
also expected that subjecting the ash to prolonged weathering will promote the dissolution of AlSi bearing minerals and enhance the formation of secondary minerals with high and permanent
CEC. The characteristics of ash and sludge provided the necessary motivation to measure the
CEC of these materials.
Determination of CEC at pH 7.0 using ammonium acetate is a widely accepted and well adopted
method that buffers the soil pH at pH 7.0. The principle behind this method is that the exchange
sites are saturated with 1 M ammonium acetate (NH4OAc) extracting cations, equilibrated
overnight, reducing the 1 M NH4OAc concentration in the pore spaces by washing the soil with
0.1 M NH4OAc and replacement and leaching of exchangeable NH4 with 1 M KCl (Jackson,
1958, Chapman, 1965 and Horneck et al., 1989). The NH4 is then determined by Kjeldahl
procedure described by Bremner (1965). The disadvantage with this method is that it has the
potential of over estimating the CEC of acid soils by buffering the pH and is not suitable for soils
with pH >7.5 containing significant amounts of calcite (CaCO3). Under these conditions the
extracting solution dissolves the CaCO3 and reacts with NH4OAc generating Ca2+ that competes
with NH4+ for the exchange sites resulting in an under estimation of the CEC (Reiner, 2006 &
Ross & Ketterings, 2011). In view of these disadvantages un-buffer methods without a
conjugated basis of a weak organic acid with strong comlexing ability have been developed. An
example is the lithium chloride (LiCl) method.
The basic principle of this method as described by the Soil Science Society of South Africa,
1990 is that a 1 M LiCl solution is used as an extractant by saturating the exchange sites
129
simultaneously extracting the naturally existing cations. To reduce the 1 M LiCl concentration in
the pore spaces 95% ethanol is used to wash the sample. The Li+ that is adsorbed on the
exchange sites is displaced with 1 M CaCl and further determined using Inductively Coupled
Plasma Optical Emission Spectrometry (ICP-OES). The disadvantage is that Li at low electrolyte
concentration increases electrostatic repulsion of soil components like sodium (Na), K and NH4
(Ahmad & Mermut, 1996).
The hypothesis in this chapter was that the incorporated sludge in the mixtures and weathering
induced by eluviation cycles will increase the CEC of the mixtures through the deprotonation of
functional groups forming variable charges and formation of secondary minerals with permanent
charges. It was also hypothesized that the pH 7.0 buffer method will give a higher CEC than the
non-pH buffer methods. The aims of this study were to: 1) Determine the CEC of the leached
and unweathered alkaline coal ash – sludge artificial growth media;2) Compare buffered and
unbuffered CEC methods to establish the most applicable method for determining CEC of these
mixtures.
7.2 Materials and methods
7.2.1 Ammonium acetate procedure for the measurement of CEC for unleached and
leached mixtures
Unleached material refers to unweathered alkaline coal ash – sludge mixtures and leached
material refers to coal ash – sludge mixtures that have been subjected to wetting and drying
cycles for a year.
Step 1: Following the ammonium acetate procedure as was described by Jackson (1958), 40 g of
each mixture was replicated three times, transferred in preweighed 100 ml Schott bottles and
then saturated with 80 ml of 1 M NH4OAc. The purpose of saturating the sample is to ensure the
exchange of the cations (Ca2+, Mg2+, and K+) electrostatically adsorbed by negatively surface
charges by ammonium (NH4+). After the addition of NH4OAc,the sample was shaken on a
reciprocal shaker for 60 min and then let to stand overnight for equilibration purposes. The
equilibration allowed more time for the NH4 to replace the cations. After equilibration the
sample was centrifuged for 10 min and the resulting supernatant was filtered using a No. 2
Whatman filter paper with 110 mm diameter into a 250 ml volumetric flask. The samples were
130
again treated with a 1 M NH4OAc solution, shaken in the mechanical shaker for 30 min,
centrifuged for 10 min and then filtered through a new Whatman filter paper into the same 250
ml volumetric flask. The flask was then filled to the mark with 1 M NH4OAc solution. The
extract in the volumetric flask was filtered through a 0.45 µm membrane to reduce colloid
interference and then analysed using anInductively Coupled Plasma Optical Emission
Spectrometry (ICP-OES) to determine the 1 M NH4OAcextractable cations. Because of the high
concentration of soluble cations and a myriad of possible sources these cations extracted by the 1
M NH4OAc is only refered to as NH4OAcextractable cations.
Step 2: To reducethe 1 M NH4OAc concentration in the pore volume to about 0.1 M NH4OAc, the
sample was washed with 80 ml of 0.1 M NH4OAc three times. This was done reduce the carry
over concentration of the NH4OAc. After each addition the sample was shaken with a reciprocal
shaker for 30 min and centrifuged for 10 min. Afterwards the supernatant was filtered through a
new No. 2 Whatman filter paper into a new 250 ml volumetric flask which was afterwards filled
to the mark using the same 0.1 M NH4OAc. The extract in the volumetric flask was analyzed for
NH4+ using the Kjeldahl procedure described by Bremner (1965). In order to calculate and
correct later on for the amount of NH4 in the entrained solution, it was important to determine the
volume of the entrained solution after this step.
Step 3:The samples from step 2 were treated with 80 ml of 1 M KCl twice. After each addition,
the sample was shaken with a reciprocal shaker for 30 min and centrifuged for 10 min. The
supernatant was filtered the supernatant through a new No. 2 Whatman filter paper into a new
250 ml volumetric flask which was further filled to the mark using the 1 M KCl solution. The
extract in the volumetric flask was analyzed for NH4+ using the Kjeldahl procedure.
Step 4:These methods were developed for soil, a stable product subjected to millennia of
weathering. It is not expected that the intensive leaching with extractants during the CEC
determination will result in much further weathering of it. However, in the case of ash the
extractants may induce weathering. This was a minus step (intended to remove naturallyexisting
NH4 in the mixtures) where 40 g of each fresh mixture was treated with 80 ml of 1 M KCl twice
to leach out all the NH4 that was naturally in the mixtures. This step was necessary to subtract
131
from the NH4 obtained in step 3 all the NH4 that was not added. After each addition, the sample
was shaken with a reciprocal shaker for 30 min and centrifuged for 10 min. The supernatant was
filtered the supernatant through a new No. 2 Whatman filter paper into a new 250 ml volumetric
flask which was further filled to the mark using the 1 M KCl solution.The extract in the
volumetric flask was analyzed for NH4+ using the Kjeldahl procedure described.
Step 5: To determine NH4+ in the extracts from the 0.1 M NH4OAc and 1 M KCl treatments, 10
ml was pipetted into separate distillation flasks and then added 10ml de-ionized water in each
flask increasing the contents to a final volume of 20 ml. In each flask 2.5 g of magnesium oxide
(MgO) was added and immediately connected to a distiller and distilled to a final volume of 50
ml into a 50ml conical flask containing 10 ml boric acid indicator. To determine NH4+the
Kjeldahl procedure described in chapter 5was used.The CEC was calculated based on the dry
mass of the mixtures.
7.2.2 Lithium chloride procedure for the measurement of CEC for leached mixtures
Step 1: 40 g of each mixture was replicated three times, transferred into preweighed 100 ml
Schott bottles and then added 80 ml of 1 M LiCl solution. The saturated sample was shaken on a
reciprocal shaker for 60 min and then let to stand overnight for equilibration purposes. After
equilibration the sample was centrifuged for 10 min and the resulting supernatant was filtered
using a No. 2 Whatman filter paper into a 250 ml volumetric flask.The samples were again
treated with a 1 M LiCl, solution, shaken on a reciprocal shaker for 30 min, centrifuged for 10
min and then filtered through a new Whatman filter paper into the same 250 ml volumetric flask.
The flask was then filled to the mark with1 M LiCl solution. The extract in the volumetric flask
was filtered through a 0.45 µm membrane to reduce colloid interference and then analysed for Li
using ICP-OES.
Step 2: The sample was again treated with a diluted 80 ml of 0.001 M LiCl three times. After
each addition the sample was shaken with a reciprocal shaker for 30 min and centrifuged for 10
min, filtered the supernatant through a new No. 2 Whatman filter paper into a new preweighed
250 ml volumetric flask. The volumetric flask together with its contents was reweighed and
recorded the mass. The extract in the volumetric flask was filtered through a 0.45 µm membrane
132
and then analyzed for Li using the ICP-OES. Before the next step the Schott bottles with the
samples inside were reweighed and recorded the mass to enable final calculations.
Step 3: The same sample was treated with 80 ml of 1 M MgNO3 three times. After each addition
the sample was shaken with a reciprocal shaker for 30 min and centrifuged for 10 min, filtered
the supernatant through a new No. 2 Whatman filter paper into a new preweighed 250 ml
volumetric flask. The volumetric flask together with its contents was reweighed and recorded the
mass. The extract in the volumetric flask was filtered through a 0.45 µm membrane to reduce
colloid interference and then analysed using for Li using ICP-OES.
7.2.3 Potassium chloride (KCl) method for the measurement of CEC for leached mixtures
This method was similar to the LiCl method except that the 1 M LiCl was replaced with 1 M KCl
and the 0.001 M LiCl was replaced with 0.001 M KCl.
Step 1: 40 g sample of each mixture replicated three times and transferred into preweighed 100
ml schott bottles and then added 80 ml of 1 M KCl. The saturated sample was shaken on a
reciprocal shaker for 60 min and then let to stand overnight for equilibration purposes.After
equilibration the sample was centrifuged for 10 min and the resulting supernatant was filtered
using a No. 2 Whatman filter paper into a 250 ml volumetric flask. The samples were again
treated with a 1 M KClsolution shaken on a reciprocal shaker for 30 min, centrifuged for 10 min
and then filtered through a new Whatman filter paper into the same 250 ml volumetric flask. The
flask was filled to the mark with 1 M KCl solution. The extract in the volumetric flask was
filtered through a 0.45 µm membrane to reduce colloid interference and then analysed using for
K using ICP-OES.
Step 2: The same sample was again treated with a diluted 80 ml of 0.001 M KCl three times.
After each addition the sample was shaken with a reciprocal shaker for 30 min and centrifuged
for 10 min, filtered the supernatant through a new No. 2 Whatman filter paper into a new
preweighed 250 ml volumetric flask. The volumetric flask together with its contents was
reweighed and recorded the mass. The extract in the volumetric flask was filtered through a 0.45
133
µm membrane and then analyesed for K using the ICP-OES. Before the step 3 the Schott bottle
with the sample inside was reweighed and recorded the mass to enable final calculations.
Step 3: The same sample was treated with 80 ml of 1 M MgNO3 three times. After each addition
the sample was shaken with a reciprocal shaker for 30 min and centrifuged for 10 min, filtered
the supernatant through a new No. 2 Whatman filter paper into a new preweighed 250 ml
volumetric flask. The volumetric flask together with its contents was reweighed and recorded the
mass. The extract in the volumetric flask was filtered through a 0.45 µm membrane and then
analysed for K using the ICP-OES.
The”minus step” for the lithium chloride and potassium chloride methods
The Li+ and K+ obtained using the ICP-OES in steps 2 for the lithium chloride and potassium
chloride methods include Li+ and K+ naturally existing in the pore spaces and on the exchange
sites of the sample. The ash is very weatherable and the process of leaching will result the release
of Li and K from the ash matrix and can therefore not be refered to as “exchangeable” cations.
Not all of the cations resided from the exchange complex. It was important to establish how
much was released by this. Similarly, to correct or separate exchangeable K from K that was
released from the ash as a result of weathering induced by the leaching with the LiCl solution
and to correct or separate exchangeable Li from Li that was released from the ash as a result of
weathering induced by the leaching with the KCl solution.
7.3 Results and discussion
7.3.1 Cation exchange capacity (CEC) determination of selected unleached and leached
mixtures using ammonium acetate (NH4OAc) procedure
The incorporation and increase in sludge content from 10 to 50% in mixtures 12 to 51 generally
increased the CEC for these mixtures (Fig.7.1 a). The same trend was maintained even after 10
eluviation cycles (Fig.7.1 b). This phenomenon was expected because the decomposition of
organic matter (OM) from sludge releases humified compounds such as humic acids (HA) and
fulvic acids (FA) that contain functional groups especially carboxyl and phenolic hydroxyl
groups (Mulder & Cresser, 1994, Sparks, 2003 & Essington, 2005).Humic acids are dissolved in
the solution and will as much to CEC as dissolved anions like chloride and sulphate. It is the
134
humified solid phase organic material that formed as a result of sludge breakdown that will
contribute to CEC. The contribution of these functional groups to CEC relies on their charge
which also depends on their dissociation and ionisation.
Mixtures; 1 (with 100% fine ash), 6 (with 50% fine ash and 50% gasification ash) and 11 (with
100% gasification ash) was a group without sludge and maintained the lowest CEC of all the
mixtures even after ten eluviation cycles. Mixture 11 maintained the lowest CEC mean (2.4
cmolc kg-1 ± 0.1) before and after ten eluviation cycles. However, after ten eluviation cycles
mixture 30 (with 0% fine ash, 80% gasification ash and 20% sludge) had a CEC mean (2.2 cmolc
kg-1) which was not significantly different from the CEC mean (1.7 cmolc kg-1) for mixture 11.
This was an indication that sludge contributed very little. Mixture 48 (with 30% fine ash, 20%
gasification ash and 50% sludge) had the highest averaged mean CEC (19.04 cmolc kg-1± 1.1)
than any other mixture but this mean was not significantly different from the CEC means of
19.04, 18.2 and 17.9 cmolc kg-1 for mixtures 46 (with 50% fine ash and 50% sludge), 51 (with
50% gasification ash and 50% sludge)and 39 (with 60% fine ash and 40% sludge) respectively
(Fig.7.1 a). These CEC values could be comparable to CEC values of soils dominated by
kaolinite (2 to 15 cmolc kg-1)(Sparks, 2004). After ten eluviation cycles mixture 46 had the
highest mean CEC (4.5 cmolc kg-1± 0.3) standard deviation) but was not significantly different
from CEC means; 4.4 and 4.2 cmolc kg-1 for mixtures 48 and 39 respectively. It should be noted
that eluviation cycles drastically reduced the CEC of all the mixtures (Fig.7.2 b).
The general reduction in CEC in all the mixtures after the tenth eluviation cycle was due to
acidification by the dissociation and ionisation of HA an FA produced during OM decomposition
(Ross & Ketterings, 2011). Deprotonation increased the concentration of H+ reducing the pH of
the system that in turn limited the chances of developing negative charges thus reduced the CEC
(Mulder and Cresser, 1994 & Sparks, 2003 & Essington 2005). Under alkaline conditions
carboxyl and phenolic hydroxyl groups increase in solubility because of deprotonation and
ionisation that occurs at pH conditions greater than their respective pKa values. This makes the
organic molecule more polar and thus water soluble (Kleber & Johnson, 2010). The increase in
solubility of the functional groups could also result in the reduction of CEC in the mixtures. It
could be possible that most of the NH4 leached over time.The fixation of NH4 used to replace the
135
base cations naturally in the samples could result in the reduction of CEC (Tan, 1996). The
fixation of NH4 is result of aluminosilicate minerals which are dominant in the ash. It is possible
that the ash release K as well and this also blocked adsorption sites (include minus data)
These results differed from findings by Zevebergen et al. (1999) who found that the CEC for fly
ash weathered for 8 to 12 years was higher (8.1 cmolc kg-1) than the CEC for a fresh fly ash (2.9
cmolc kg-1). This author used NH4OAc method and did not apply sludge. The CEC results for the
fresh fly ash (2.9 cmolc kg-1)by Zevebergen et al. (1999) could only be comparable to the CEC
for leached fine ash of the current study with a CEC of 2.98 cmolc kg-1. The fresh fine ash of the
current study had a higher CEC (6.98 cmolc kg-1) compared to the CEC of the fly ash by
Zevebergen et al. (1999).The reason they gave for the high CEC in weathered fly ash was that
weathering reactions rapidly modified the surface of the glass matrix they also observed higher
levels of Alox and Siox. This was an indication that there was formation of non-crystalline
hydrous aluminosilicates that provided new phases for cation exchange. This phenomenon was
expected in the mixtures but could not occur because wetting and drying cycles took only a year.
Unfortunately, no work has been published dealing with the characterization of Sasol fine and
gasification ashes including the CEC parameter. However, work that was done by Woolard et al.
(2002) in characterizing South African fresh fly ash reveal that it has a CEC of 2.1 cmolc kg1
.Weathered data for the mixtures without sludge (especially fine ash) was in similar order for
the current study.These authors used sodium acetate as a saturating solution, washed the sample
with ethanol and used ammonium acetate as a displacing solution. This CEC value could be
exaggerated because fly ash generally contains substantial amounts of sodium. Cation exchange
capacity values determined using ammonium acetate in fresh fly ash ranging from 0.05 to 7.9
cmolc kg-1appear in the literature (Querol, et al., 2002, Veeresh, et al., 2003, Gupta & Sinha,
2006 & Nur Hanani et al., 2010). However an inference to both fine and gasification ash can be
made since Sasol fine ash is a combination of approximately 83% power station fly ash and 17%
made up of both gasification ash and bottom ash fines with particles less than 250 µm (Mahlaba,
et al., 2011). It can be concluded that the CEC for both fine and gasification ashes fell within the
range stated by literature.
136
7.3.2 Statistical comparison of CEC (NH4OAc) of unleached and leached mixtures
Statistically, there was a no significant difference between the CEC means of the unleached and
leached mixtures. Some unleached mixtures showed CEC means which were not significantly
different from CEC means of the same or different leached mixtures. The results suggest that the
CEC of mixtures unamended with sludge did not change. This was an indication that weathering
reactions could not effectively or significantly increase the CEC of such mixtures. For example,
unleached mixture 11 (with 100% gasification ash) had a mean CEC (2.4 cmolc kg-1) which was
not significantly different from the CEC mean (1.7 cmolc kg-1) of the same mixture but leached.
The CEC of theunleached mixture 11 was also not significantly different from the CEC means of
leached mixtures; 6, 12, 17, 30, 38, 21 and 26 (described in chapter 3). The interaction indicated
that the time frame of 1 year was too short to cause measurable and statistically significant
differences between the CEC’s of unleached and leached mixtures without sludge. The silica
base cations that were needed to accumulate and involved in the formation of secondary minerals
were leached (McBride, 1994).
There is evidence that the sludge in the mixtures added NH4 that increased the CEC in the
unleached mixtures.Ammonium determinedby the minus step increased with increase in sludge
content (Table 7.3). In the first step NH4 was able to replace cations from mineral surfaces
(Table 7.3). Interestingly, the concentrations of these cations (Ca, K, Mg, and Na) replaced by
NH4 in this step (for unleached mixtures) were lower than the concentrations of cumulative
cations obtained in ten eluviation cycles (chapter 6) confirming that weathering did occur.
137
Table 7.1:Cations replaced by NH4 in step 1 and NH4 replaced by K in the minus step.
Mixtures
1
6
11
12
17
21
22
26
30
31
35
38
39
42
45
46
48
51
Ca(mmol kg-1)
33.9
30.1
24.1
26.6
27.0
28.9
25.6
25.0
21.0
22.6
27.5
21.6
19.8
20.3
15.3
20.2
18.6
16.3
K(mmol kg-1)
1.3
1.0
0.6
1.5
1.6
1.8
2.2
2.1
1.8
2.6
3.1
3.5
3.0
2.9
2.6
3.9
3.5
3.1
Mg(mmol kg-1)
7.7
6.0
2.7
4.3
6.3
7.5
7.5
6.5
4.3
7.3
7.2
6.7
7.0
6.0
4.3
10.6
6.7
4.9
Na(mmol kg-1)
1.6
6.4
2.0
4.2
7.0
2.1
2.2
7.1
4.3
2.3
8.9
6.0
8.9
7.4
5.1
9.7
8.4
6.2
NH4 (mmol kg-1)
1.7
1.2
1.0
9.2
9.3
9.3
17.2
15.4
16.0
23.9
23.1
23.7
29.9
29.9
30.2
32.0
34.2
36.2
7.3.3 Cation exchange capacity (KCl) of leached mixtures
The CEC(KCl) and the CEC (LiCl) methods were used for comparison purposes since the CEC
(NH4OAc) method could be interfered by NH4 from sludge hence exaggerating the CEC at the
end. The same general trend were seen for CEC(KCl) as in CEC(NH4OAc). However, the CEC
values in general were lower.To evaluate the effectiveness of CEC (KCl)method in determining
CEC in leached material, mixtures; 1, 6, 11, 23, 37, 46 and 51 (described in chapter 3) were
used. Even with this method CEC increased with increase in sludge content (Fig.7.1 d).
Similarly, the mean CEC (KCl)for mixture 46 (6.1 cmolc kg-1) was aswell significantly different
from any other CEC mean of any other mixture. As expected mixture 6 had the lowest CEC
mean (1.5 cmolc kg-1) than any other mixture but this was not significantly different from the
mean CEC of 1.7 cmolc kg-1shown by mixture 11. Mixtures 23 and 51 with CEC means; 3.8 and
4.2 respectively were not significantly different from each other.
7.3.4 Cation exchange capacity (LiCl) of leached mixtures
This method was applied on leached material for mixtures; 1, 6, 11, 23 (with 70% fine ash, 10%
gasification ash and 20% sludge), 37 (with 10% fine ash, 60% gasification ash and 30% sludge),
46 and 51. It was evident that CEC increased with increase in sludge content (Fig.7.1c). The
138
CEC (LiCl) method also indicated that mixture 46 had the highest CEC. Statistically, mixture 46
was significantly different from any other mixture and had the highest CEC mean (6.8 cmolc kg1
). The high CEC in mixture 46 was a major contribution of the 50% sludge (Table 7.3) content
and a minor contribution of the 50% fine ash content. Mixtures 23, 37 and 51 with CEC means;
3.2, 3.8 and 4.8 cmolc kg-1respectively were not significantly different from each other but were
significantly different and lower than the CEC mean given by mixture 46. Mixture 6 had the
lowest CEC (0.6 cmolc kg-1) than any other mixture but was not significantly different from the
CEC; 1.2 and 1.1 cmolc kg-1 that were shown by mixtures 1 and 11 respectively. It was expected
that the CEC would be low because these mixtures (1, 6 and 11) contained no sludge.
139
Coefficient of Varience
Least significant difference
Cation exchange capacity (cmolc kg-1)
20
18
= 6.6 %
=1.3
CD
14
A
C
D
D
E
12
8
A
B
16
10
A
A
Cation exchange capacity (cmolc kg-1)
22
F
H
F
FG
GH
I
I
6
J
4
2
22
Coefficient of Varience
= 11.6 %
20
Least
significant
difference
= 0.6
18
16
14
12
10
8
6
A AB
ABCCDE
BCD
DEF
4 FGHI
EGH
EFG HIJ
FGH
HIJ HIJ GHIJ
HIJ
IJ
JK
K
2
0
1
0
1
a
6
b
22
Coefficient of Varience
Least significant difference
Cation exchange capacity (cmolc kg-1)
Cation exchange capacity (cmolc kg-1)
18
= 23.0 %
= 1.9
16
14
12
10
A
8
6
4
2
C
D
D
AB
AB
D
20
18
Coefficient of Varience
Least significant difference
= 16.3 %
= 0.9
16
14
12
10
A
8
BC
6
4
DE
F
B
CD
EF
2
0
0
1
c
11 12 17 21 22 26 30 31 35 38 39 42 45 46 48 51
Selected mixtures
11 12 17 21 22 Selected
26 30 mixtures
31 35 38 39 42 45 46 48 51
22
20
6
6
11
23
Selected mixtures
37
46
1
51
d
6
11
23
37
Selected mixtures
46
51
Fig.7. 1: a)Cation exchange capacity means for selected unleached mixtures determined by ammonium acetate (NH4OAc) procedure, b) Cation exchange capacity
means for selected leached mixtures determined by NH4OAc procedure, c) Cation exchange capacity of leached mixtures determined by lithium chloride (LiCl)
method and d) cation exchange capacity of leached mixtures determined by potassium chloride (KCl) method. Means with the same letter are not significantly
different from each other and means with different letters are significantly different from each other.
140
7.3.5 Statistical comparison CEC (NH4OAc), CEC (LiCl) and CEC (KCl) procedures
The comparison of these procedures was based on their efficienmcy in determining CEC in
leached material for mixtures; 1, 6, 11, 46, and 51 (Fig.7.2). Statistically the CEC (KCl) were
lower from CEC(NH4OAc) and CEC(LiCl). Both the NH4OAc and LiCl methods showed CEC
means that were higher (than those given by KCl) but not significantly different from each other.
Cation exchange capacity
(Cmolc kg-1)
The least significant difference was 0.38 and coefficient of variance (CV) of 15.6%.
8
7
6
5
4
3
2
1
0
KCl
LiCl
NH4OAc
1
6
11
46
51
Mixtures
Fig.7. 2: Comparison of the CEC of selected leached mixtures as measured by NH4OAc, LiCl
and KCl methods
For example, mixtures without sludge; 1, 6 and 11 for the LiCl, KCl andNH4OAc with CEC
means of 1.2, 1.5, and 1.7 cmolc kg-1respectively were not significantly different from eachother.
Mixture 46 (NH4OAc method), 51 (LiCl method) and 51 (KCl method) with CEC means; 4.5, 4.8
and 4.2 cmolc kg-1respectively were also not significantly different from each other.
Each method had disadvantages in measuring CEC in coal ash-sludge mixtures because these
methods are standard for soils. The NH4OAc method had the possibility of overestimating the
CEC of mixtures with sludge and underestimating the CEC for mixtures without sludge. The
sludge in the mixtures acted as a source of nutrients particularly nitrogen (N-7.9%). Therefore, it
was evident that using NH4OAc as a saturating solution increased the N content of the mixtures
and eventually increased the concentration of NH4 displaced by KCl overestimating the CEC
(the minus step revealed it Table 7.3). In mixtures without sludge the NH4OAc method under
estimated the CEC. Under alkaline conditions the NH4OAc reacted and dissolved the CaCO3
contained in the ash generating Ca2+ that further competed with NH4+ for the exchange sites
141
leading to an under estimation of the CEC (Reiner, 2006 & Ross & Ketterings, 2011). Both fine
and gasification ashes contained significant amounts of K (fine ash = 1.3 and gasification ash =
0.6 mmol kg-1shown in Table 7.3) and Li (fine ash = 0.2 and gasification ash = 0.1 mmol kg 1
).However, the ICP has a narrow range for Li and that it also dispersed the mixtures.The
leaching could have induced more weathering and released K and Li from the ash, which can
interfere with the method and resulting in high background concentrations. Similarly, saturation
the mixtures with KCl increased the concentration of K displaced by MgNO3 thus over
estimating the CEC. Dispersion of samples occurred during step 2 (samples treated with 0.001 M
LiCl and 0.001 M KCl) of both the LiCl and KCl methods increasing colloidal particles in the
filtered supernatant that could interfere with readings given by the ICP-OES.
Reference samples of kaolinite and illite were included in the analysis to test the reliability of the
methods. In the literature the CEC for kaolinite is narrower (2 and 15 cmolc kg-1) than the CEC
for illite that range between 10 and 40 cmolc kg-1(Sparks, 2003 & Essington, 2005). For kaolinite
the CEC measured by all the methods fell within the range reported in literature (2 – 15 cmolc
kg-1) (Table 7.4). However, the NH4OAc method measured the highest CEC (8.7 cmolc kg-1) and
the KCl method measured the least (3.2 cmolc kg-1 ). The LiCl method measured the highest CEC
in illite (18.1 cmolc kg-1) and KCl method measured the lowest (7.8 cmolc kg-1). The CEC
measured by both NH4OAc and LiCl methods fell within the CEC range (10 – 40 cmolc kg-1) for
illite while the CEC measured by KCl (7.8 cmolc kg-1) fell below the this range. Based on the
CEC determined in kaolinite all the three methods can be used to determine CEC in coal ashsludge mixtures. But the KCl method may not be an appropriate technique because it under
estimated the CEC of illite and it is possible that it can also under estimate the CEC in coal ashsludge mixtures. According to Tan (1996) the under estimation of CEC by the KCl method could
result from the fixation of K by the clay minerals especially illite. Illite is a 2:1 clay mineral that
has K+ as the predominant interlayer ion along with divalent ions such as Ca2+ and Mg2+ and
NH4+ can also occur (Sparks, 2003). Therefore, the fixation of K is the result of entrapment of
K+ ions between the layers (Sharma et al., 2010). The entrapped K+ can be displaced by Ca2+
and Mg2+ ions that expand the layers but cannot be effectively displaced by NH4+ because it
collapses mineral. However, potassium on the edge-interlayer sites can easily be replaced by
142
NH4+. Hydrated Mg2+ and Ca2+ are not selectively sorbed by these sites thus they are not
effective in replacing the K+ (Sawhney, 1972 & Tan, 1996).
Table 7.2: Comparing mean CEC of the reference material (kaolinite and illite) included
determined by the NH4OAc, LiCl and KCl procedures to that reported in literature.
Sample
Kaolinite
Illite
From literature (cmolc kg-1)
NH4OAc (cmolc kg-1)
Methods
LiCl (cmolc kg-1)
KCl (cmolc kg-1)
8.67 ± 0.69
11.83 ± 1.36
5.48 ± 0.3
18.05 ± 1.6
3.18 ± 0.54
7.77 ± 0.22
2 – 15 (Sparks, 2004)
10 – 40 (Sparks, 2004)
A t-test was computed for statistical inference of the differences CEC’s. For kaolinite the
calculated t values were 5.7 (comparing CEC means for KCl and LiCl methods), 18.5
(comparing CEC means for LiCl and NH4OAc methods) and 17.6 (comparing CEC means for
KCl and NH4OAc methods). It was found that t> tcritical (4.3) at 95% probability and thus the
means were significantly different from each other meaning that the methods did not give the
same results. For illite the calculated t values were 5.2(comparing CEC means for KCl and LiCl
methods), 6.9 (comparing CEC means for LiCl and NH4OAc methods) and 81.5 (comparing
CEC means for KCl and NH4OAc methods). It was found that t> tcritical (4.3) at 95% probability
and thus the means were significantly different from each other and the conclusion was that the
procedures give different results. The chemical component of the sample to be analysed must be
known in advance to aid in the selection of one of the methods to analyse CEC. This is because a
material may release an element that can interfere with the element being used to determine the
CEC when the material is subjected to intense weathering.
7.3.6 The contribution of sludge, fine and gasification ashes to cation exchange capacity of
the mixtures
Cation exchange capacity generally increased with increase in sludge content of the selected
mixtures (Fig.7.1 a, b, c and d) as indicated by all the CEC methods used. To verify sludge and
fine ash effects on CEC, mixtures; 1, 21, 22, 31, 39 and 46 were selected (Fig.7.3 a). The fine
ash content of these mixtures was decreasing from 100 to 50%, gasification ash content
maintained at 0% and sludge content increased from 0 to 50%. The CEC means for each mixture
obtained from both unleached and leached material were plotted against sludge increase (Fig.7.3
a). The effects of sludge and gasification ash to CEC were assessed by selecting mixtures; 11,
143
12, 30, 38, 45 and 51 that had 0% fine ash content, decreasing gasification ash content (100 to
50%) and increasing sludge content (0 to 50%)(Fig.7.3 b). Similarly, the CEC means for each
mixture obtained from both unleached and leached material were plotted against sludge increase
(Fig.7.3 b). In both situations it was clear that increasing sludge content in the mixtures gradually
increased CEC. A reduction in either fine and or gasification ash content did not counter the
CEC increasing trend associated with increasing sludge amendment. The CEC rate of increase
for unleached material increased following the incorporation and increase in sludge content.
However, weathering reactions reduced the CEC rate of increase in leached material for both
situations (Fig.7.3 a & b).
Theunleached mixtures had pH values that ranged between 8.3 to 11.7 way above pKa 4-6 for
carboxyl and phenolic hydroxyl groups from sludge. Such pH values allowed deprotonation of
the functional groups creating negative charges thus rapidly increased CEC (Sparks, 2003). The
reduction in pH over time limited the rate of deprotonation and ionization of the functional
groups hence reduced the CEC in the leached mixtures.
To assess the contribution of fine and gasification ash mixtures without sludge; 1, 6 and 11 were
selected. In these mixtures fine ash content decreased from 100 to 0% and gasification ash
content increased from 0 to 100%. The CEC means for each mixture obtained from both
unleached and leached material were plotted against gasification ash increase (Fig.7.3 c). It was
evident that CEC increased with increase in fine ash for both leached and unleached material.
Sasol fine and gasification ashes are generally low in organic matter content but contain
numerous minerals that can contribute to CEC. Mahlaba et al. (2011) characterized weathered
coal fine ash and found that it contained minerals such as; mullite (Al6Si2O13), quartz (SiO2),
calcite
(CaO3),
periclase
(Ca6Al2(SO4)3(OH)12.26H2O),
(MgO),
magnetite
sillimanite
(FeFe2O4),
(Al2 SiO5),
hematite
pyrrhotite
(Fe2O3),
(Fe9S10),
and
ettringite
analcime
(NaAlSi2O6.H2O) that in combination can significantly contribute to CEC by weathering and
form minerals with charges.
144
Cation exchange capacity (cmolc kg-1)
20
18
16
14
12
10
8
6
4
2
0
Before leaching
After leaching
0
10
Cation exchange capacity
(cmolc kg-1)
a
20
30
40
50
Sludge content in mixtures (%)
20
15
Before leaching
10
5
After leaching
0
0
10
Cation exchange capacity
(cmolc kg-1)
b
20
18
16
14
12
10
8
6
4
2
0
40
50
before leaching
After leaching
0
c
20
30
Sludge content in mixtures (%)
50
Gasification ash content in mixtures (%)
100
Fig.7. 3: a) Sludge and fine ash contribution to CEC for selected mixtures (1, 21, 22, 31, 39 and
46) with sludge content increasing (0 to 50%), fine ash decreasing (100 to 50%), gasification ash
content 0%, b) Sludge and gasification ash contribution to CEC for selected mixtures (11, 12, 30,
38, 45 and 51) with sludge content increasing and c) fine and gasification ash contribution to
CEC for selected mixtures (1, 6 and 11) without sludge.
145
7.4 Conclusions
The mean cation exchange capacities; 17.9, 19.04, 19.05 and 18.2 cmolc kg-1 for unleached
mixtures; 39(60% fine ash, 0% gasification ash and 40% sludge), 46 (50% fine ash, 0%
gasification ash and 50% sludge), 48(30% fine ash, 20% gasification ash and 50% sludge) and
51(0% fine ash, 50% gasification ash and 50% sludge) respectively were not statistically
different from each other but more than those of any other non-leached mixtures when
determined using the NH4OAc method and mixture 11(0% fine ash, 100% gasification ash and
0% sludge) had the lowest (2.4 cmolc kg-1). Mixture 46 retained the highest mean cation
exchange capacities; 4.5, 6.8 and 6.1 as determined by the NH4OAc, LiCl and KCl methods
respectively. Mixture 11 had the lowest mean CEC (1.7) of any other mixture determined by the
NH4OAc method for the leached material, while mixture 6 (50% fine ash, 50% gasification ash
and 0% sludge)retained the lowest mean cation exchange capacities (mean CEC of 0.6 and 1.5
cmolc kg-1 for the LiCl and KCl methods respectively) of any other mixture.
In the determination of CEC for coal ash-sludge mixtures (leached or non-leached), caution
should be taken that the concentration of LiCl and KCl should be > 0.001 M in step two to avoid
dispersion and < 0.1 M to effectively reduce the 1 M concentration in the pore spaces. Any LiCl
and KCl concentration ≥ 0.1 M in the second step increases the concentration of the carry over
that needs to be subtracted from the last step of the method. The dispersion can increase colloidal
particles that may interfere with the background of the element in the ICP-OES resulting in
erroneous data or under estimating CEC (making filtration more difficult). Sludge in the
mixtures has the potential of increasing the concentration of NH4+ added by the saturating
solution when using the NH4OAc method hence, overestimating CEC. The NH4OAc method in
mixtures without sludge could underestimate the CEC because the ash contains CaCO3 that
reacts with the NH4OAc generating Ca2+ that competes with the NH4 for the exchange sites.
146
CHAPTER 8: GENERAL DISCUSSION
Based on particle size distribution gasification ash was found to be macroporous and dominated
by particle sizes greater than 1mm in diameter as a result mixtures with 50 to 100% gasification
content were dominated by particle sizes greater than 8mm. Therefore, increasing gasification
ash content reduced the water holding capacities of mixtures like; 11(0% fine ash, 100%
gasification ash and 0% sludge), 12 (0% fine ash, 90% gasification ash and 10% sludge), 30 (0%
fine ash, 80% gasification ash and 20% sludge), 38 (0% fine ash, 70% gasification ash and 30%
sludge), 45 (0% fine ash, 60% gasification ash and 40% sludge) and 51(0% fine ash, 50%
gasification ash and 50% sludge) when compared to mixtures with 50 to 100% fine ash content
such as; 22 (80% fine ash, 0% gasification ash and 20% sludge), 31(70% fine ash, 0%
gasification ash and 30% sludge), 39 (60% fine ash, 0% gasification ash and 40% sludge), and
46 (50% fine ash, 0% gasification ash and 50% sludge).However, the incorporation of sludge at
from 10 to 50% in mixtures such as; 12, 30, 38, 45, and 51 significantly increased their water
holding capacities when compared to mixtures with the same content of gasification ash (50 to
100%) but without sludge (mixtures 6 to 11 described in chapter 3) for the first eluviation cycle.
But continuous weathering reduced the water holding capacities of these mixtures (mixtures; 12,
30, 38, 45, and 51) to even below capacities shown by mixtures with 50 to 100% gasification ash
and without sludge (mixtures 6 to 11).
Fine ash was found to be dominated by particle sizes between 100 to 250 µm in diameter and
this particle size range dominated in mixtures with 50 to 100% fine ash content. This particle
size range together with the incorporation of sludge from 20 to 50% increased the water holding
capacity of mixtures; 22, 31, 39 and 46 for the first eluviation cycle. However, the water holding
capacities of these mixtures were drastically reduced by weathering after the the tenth eluviation
cycle to levels similar to mixtures 1 to 5 with 50 to 100% fine ash content but without sludge
(described in chapter 3). Mixtures 31 and 39 maintained the highest water holding capacity (0.65
kg kg-1) than any other mixture for the tenth eluviation cycle followed by mixtures; 24 (60% fine
ash, 20% gasification ash and 20% sludge), 32 (60% fine ash, 10% gasification ash and 30%
sludge), 40 (50% fine ash, 10% gasification ash and 40% sludge) and 46 with 0.52, 0.60, 0.62
and 0.63 kg kg-1 respectively.
147
The pozzolanic properties of fine ash and the hydration of minerals present in mixtures with 50
to 100% fine ash content and without sludge increased the water holding capacity and this was
evident by the swelling of the mixtures 1 (100% fine ash, 0% gasification ash and 0% sludge)
and 2 (90% fine ash, 10% gasification ash and 0% sludge). Such mixtures are not likely to
provide plant available water and aeration required by essential processes such as nitrogen
mineralization. Only mixtures with 50 to 90% gasification ash can actually provide the necessary
aeration and plant available water.
The addition of sludge reduced the pH and salinity of all mixtures with 20 to 50% sludgeand
increased N mineralization. The resultant lower pH range (7.6-10.3) and salinity (88-415 mSm-1)
provided a conducive environment for the oxidation of NO2 -to NO3-. The NO3 - and NH4 - species
were mostly contributed by sludge while fine ash contributed more to the NO2- species. The
production of NO2 -by fine ash was because of the NH3purposely added by Sasol to remove fly
ash from the processing plant that was converted to NO2- and the oxidation rate of this species to
NO3- was minimal due to the extreme conditions caused by high pH (8.3-11.7) and salinity (622
mSm-1). The extreme conditions caused by high pH and salinity of mixtures with 0 and 10%
sludge negatively affected the nitrifying bacteria (Nitrobacter) as a result only NH4+ and NO2 species were detected in mixtures without sludge. Mixtures 35(30% fine ash, 40% gasification
ash and 30% sludge), 42 (30% fine ash, 30% gasification ash and 40% sludge), 46, 48 (30% fine
ash, 20% gasification ash and 50% sludge) and 51 exhibited the most amounts of NH4+ and NO3 species and lower NO2- compared to all other mixtures. Mixture 48 had the most mineralized
total inorganic N (24.4 mg kg-1) compared to all mixtures and in all eluviation cycles, while
mixture 12 exhibited the lowest (10.5 mg kg-1). It was clear that the reduction of pH and
salinityenhanced the N mineralization process in mixtures with sludge.
Fine and gasification ashes contributed to the high pH, with initial pH values of 11.3 and 11.7
respectively.The pH values for mixtures without sludge ranged between 10.8 and 11.7 for the
first eluviation cycle and reduced to between 8.4 and 10.3 for the tenth eluviation cycle due to
carbonation process. While the pH values for mixtures with sludge ranged between 8.3 and 10.2
for the first eluviation cycle and reduced to the range 7.6 to 8.2 for the tenth eluviation cycle.
Mixture 7 (40% fine ash, 60% gasification ash and 0% sludge)maintained the highest pH of 10.3
148
while mixture 49 (20% fine ash, 30% gasification ash and 50% sludge) showed the lowest pH
(7.6) for the tenth eluviation cycle. The pH was reduced by dissolved organic carbon upon
degradation of sludge released fulvic and humic acids containing carboxylic groups that
deprotonated and ionized under alkaline conditions. Other processes such as carbonation,
hydrolysis and nitrificationreleased H+ that reduced the pH. As a result mixtures; 14 (20% fine
ash, 70% gasification ash and 10% sludge), 19 (70% fine ash, 20% gasification ash and 10%
sludge), 20 (80% fine ash, 10% gasification ash and 10 %sludge), 24 (60% fine ash, 20%
gasification ash and 20% sludge), 25 (50% fine ash, 30% gasification ash and 20% sludge), 28
(20% fine ash, 60% gasification ash and 20% sludge), 29 (10% fine ash, 70% gasification ash
and 20% sludge) and from 31 to 51 (with 30% sludge) retained a pH of less than 8 but greater
than 7.6 after ten eluviation cycles while, all other mixtures had pH more than 8. Clearly, the
sludge reduced the pH in the ashes but not low enough to be accommodated in optimum pH
range (5.5-7.5) for a functional growth medium.
Gasification and fine ashes also contributed to high salinity, with EC of 527 mSm-1and 580
mSm-1repsectively. The EC values for mixtures without sludge ranged between 335 and 606
mSm-1 for the first eluviation cycle and reduced to the range 102 to 160 mSm-1 for the tenth
eluviation cycle. While the EC values for mixtures with sludge ranged between 95 to 622 mSm-1
for the first eluviation cycle and reduced to the range 116 to 415 mSm-1 for the tenth eluviation
cycle.Weathering coupled with functional groups resulting from dissolved organic carbon that
complexed the salts enhanced solubility and leaching of the salts reducing the salinity of the
mixtures for the tenth eluviation cycle. Mixture 11exhibited the lowest EC (88 mSm-1) and
mixture 43(20% fine ash, 40% gasification ash and 40% sludge)maintained the highest ECof 415
mSm-1 for the tenth eluviation cycle. It was only mixtures 40 and 43 that had their EC beyond
the range (70 to 400 mSm-1) suggested by Handreck and Black (1984) and Brady and Weil
(2008) as optimum for plant growth after ten eluviation cycles.
Gasification ash remained the main contributor of Ca and Mg while fine ash contributed K and
Na mostly. The order of abundance of these elements in the mixtures wasas follows;
Ca>Mg>Na>P>K. Sludge remained the main source of P as its contribution to these elements
were in this order; P>Ca>Mg>Na>K. The solubility of Ca was favoured over the solubility of
149
Mg and P in mixtures without sludge (mixtures 1 to 11described in chapter 3) but the solubility
of Na and K were favoured over the solubility of Ca in these mixtures giving a different order of
abundance as follows; Na>K>Ca>Mg>P. In mixtures with sludge (mixtures 12 to 51described
in chapter 3) Ca solubility was favoured over Na and K solubility but the solubility of P and Mg
were favoured over the solubility of Ca in these mixtures and the order of abundance was as
follows; P>Mg>Ca>Na>K. Mixture 43 released the highest Ca (52.6 mmol kg -1) and Mg (68.0
mmol kg-1), mixture 7 released the highest K (9.7 mmol kg -1) and Na (25.4 mmol kg-1) while
mixture 51 released the highest P (1.1 mmol kg -1). Generally it was clear that increasing sludge
increased the solubility of most of the solid phases containing the elements but at different rates.
Increasing sludge content in the mixtures increased P release overtime. A functional growth
medium should be able to provide all the major elements in sufficient quantities and reduced
quantities of Na (high concentrations are toxic to plants). Mixtures with 20 to 50% sludge
(mixtures 22 to 51) are such desired functional growth media.
Gasification ash was the main contributor of Cu and Mn while fine ash contributed mostly Zn
and Fe. The order of abundance of measured trace elements contents in the mixtures was as
follows; Fe>Mn>Cu>Zn. Sludge contributed trace elements in this order; Fe>Mn>Zn>Cu.
Mixture 47 (40 % fine ash, 10 % gasification ash and 50 % sludge) released the highest Fe (0.04
mmol kg-1) than any other mixture while mixture 43 released the most Mn (0.01 mmol kg -1).
Mixture 51 released the highest Zn (0.004 mmol kg-1) than any other mixture while mixture 21
(90 % fine ash, 0% gasification ash and 10% sludge)released the most Cu (0.001 mmol kg -1).
Boron (5.6 mmol kg-1) was released the most by mixture 46 while mixture 49 (20% fine ash,
30% gasification ash and 50% sludge)released the highest Mo (0.03 mmol kg -1) than any other
mixture. It was evident that the solubility of Fe, Zn, B and Mo increased with increase in sludge
content of the mixtures.It was evident that the solubility of Fe, Zn, B and Mo increased with
increase in sludge and mixtures that supplied abundant plant available trace elements included
mixtures from 22 to 51 (described in chapter 3).
The addition of sludge generally increased the CEC of the mixtures. When using the NH4OAc
method the CEC means; 17.9, 19.04, 19.05 and 18.2 cmolc kg-1 for unleached mixtureswith 40 to
50 % sludge; 39, 46, 48 and 51 respectively were not statistically different from each other
150
andhigherwhen compared to those of any other non-leached mixtures.Mixture 11 exhibited the
lowestCEC mean (2.4 cmolc kg-1). Within leached mixtures mixture 46 maintained the
highestCEC means; 4.5, 6.8 and 6.1 cmolc kg-1corresponding to NH4OAc, LiCl and KCl methods
respectively. Mixture 11showed the lowestCEC mean (1.7cmolc kg-1) than any other leached
mixture when determined by the NH4OAc method and mixture 6 (50% fine ash, 50% gasification
ash and 0% sludge)exhibited the lowest CEC means; 0.6 and 1.5 cmolc kg-1 corresponding to
LiCl and KCl methods respectively than any other leached mixture.When statistically comparing
these methods they did not give significantly different results for the leached mixtures as was
expected. Though the methods were did not differ in their results but the LiCl remainedas the
best method in the determination of CEC in all the mixtures since the ashes contained abundant
K and NH4+ that affected the KCL and NH4OAc methods.
151
CHAPTER 9: CONCLUSIONS AND RECOMMENDATIONS
9.1 Conclusions
A good growth medium should have: 20% of its particle size distribution between 100 and
250µm to provide a good balance between airfilled porosity and supply readily avilable water; be
able to provide plant available inorganic N (NH4+ and NO3-) and reduced supply of NO2 - toxic to
plants; with pH ranging between 5.5 and 8.0 to supply adequate macro and macronutrients; have
a salinity of less than 400mSm-1 ; have high CEC to enhance availability of nutrients to plants and
reduce their leaching; and provide plant available macro and micronutrients and reduced release
of Na which is toxic to plants.
Physically, the incorporation of fine ash and sludge generally increased the water holding
capacities of the mixtures by providing at least 20% particle size between 100 and 250µm. But
continuous weathering reduced the water holding capacities of all mixtures. As expected the
sludge incorporated moderated the extreme conditions of the mixtures by reducing the pH of the
mixtures to between 7.6 and 8.2 and salinity to less than 400 mSm-1. The reduction in pH and
salinity enhanced N mineralization and the release of both macro and micronutrients.
In terms of macronutrients gasification ash remained the main contributor of Ca and Mg while
fine ash contributed K and Na mostly. Sludge remained the main source of P. In terms of
micronutrients gasification ash was the main contributor of Cu and Mn while fine ash
contributed mostly Zn and Fe. Sludge contributed mostly Fe.
The addition of sludge generally increased the CEC of the mixtures. It was worth noting that
statistically the methods did not give significantly different results as was expected. But the LiCl
remained as the best method in the determination of CEC in all the mixtures since the ashes
contained abundant K and NH4+ that affected the KCL and NH4OAc methods.
In conclusion the main objective was achieved since mixtures: 31, 35, 39, 42, 43, 46, 47, 48, 49
and 51 appeared to be the best growth media since they provided available water, adequate
aeration, high CEC, sufficient plant available nutrients reduced salinity and optimum pH levels.
152
9.2 Recommendations

A thorough mineralogical characterization of the ashes is necessary to aid in explaining
their behaviour when used as artificial growth media and when subjected to weathering.

Redox status of the mixtures needs to be determined to help explain the species of
elements likely to be released.

Alkalinity measurement in the mixtures needs to be carried out to quantify bases.

The dissolution/solubility kinetics of the various minerals needs to be determined to help
explain the concentration levels of the elements in the leachates/solutions.

The adsorption and desorption reactions of essential plant nutrients need to be determined
using modeling techniques.

The next phase should include direct evaluation using plants. There is a need to match
vegetation to the growing media and climate/water balance and carry out bio assays to
validate the laboratory findings.

Characterisation of the dissolved organic compounds in the sludge treated mixtures.
153
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11 Appendices
Appendix A: Pore solution pH
Mixture
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
Eluv.
cycle 1
11.32
11.52
11.58
11.47
11.01
11.55
11.64
10.84
11.43
11.67
11.73
9.42
9.55
8.26
8.34
9.83
9.62
9.35
9.5
9
9.49
9.76
9.79
9.79
9.54
9.79
9.96
9.98
9.76
9.93
9.93
10.18
9.96
9.93
9.93
9.75
9.76
9.54
10.01
9.96
9.88
9.8
9.55
9.67
9.59
9.76
9.59
9.58
9.59
9.49
9.36
Eluv.
cycle 2
10.31
10.31
9.4
9.41
8.96
10.16
11.13
8
10.47
10.31
10.97
8.61
8.38
8.11
7.83
8
7.77
8.1
8.02
8.02
8.18
8.02
8
7.89
8.09
8.03
8.08
7.84
8.18
8.02
7.73
8.04
7.89
7.92
8.96
7.82
8.21
8.35
8.6
8.48
7.93
7.89
8.33
8.1
8.27
8.38
7.85
7.98
8.2
8.21
7.87
Eluv.
cycle 3
10.44
10.13
9.91
10.69
9.88
11.2
11.3
8.99
9.64
9.71
10.11
8.88
8.87
9.11
9.22
8.56
8.56
8.45
8.25
8.51
8.72
8.3
8.47
8.42
8.47
8.56
8.56
8.64
8.66
8.86
8.4
8.38
8.47
8.47
8.64
8.69
8.75
8.86
8.47
8.5
8.56
8.62
8.67
8.74
8.81
8.64
8.25
8.37
8.3
8.39
7.99
Eluv.
cycle 4
10.31
10.07
10.02
10.91
9.91
11.48
11.47
9.07
9.57
9.66
10.03
8.75
8.87
8.83
9.16
8.58
8.45
8.55
8.41
8.25
8.88
8.41
8.45
8.46
8.53
8.58
8.6
8.68
8.67
8.82
8.5
8.58
8.75
8.67
8.82
8.82
7.74
8.82
8.62
6.92
8.77
8.78
7.4
8.63
8.89
8.72
8.58
8.67
8.63
8.61
8.53
Eluv.
cycle 5
10.9
10.55
10.25
10.85
10.21
11.1
11.32
9.14
9.98
10.06
10.28
8.74
8.87
9.13
9.14
8.55
8.58
8.82
8.79
8.79
8.84
8.28
7.44
8.55
6.95
8.55
8.55
8.63
8.63
8.78
8.6
8.63
8.72
8.74
8.85
8.83
7.7
8.79
8.63
7.6
6.95
8.87
7.72
7.79
8.55
8.87
8.72
8.51
8.69
8.73
8.72
167
Eluv.
cycle 6
7.94
7.82
7.92
7.79
7.83
7.82
7.82
7.65
7.9
7.57
8.05
7.99
7.82
7.8
7.82
7.99
7.89
7.89
8.41
8
7.78
7.73
7.82
8.24
7.65
6.66
8.24
8.35
8.24
8.3
8.34
8.46
8.48
8.49
8.57
8.54
7.89
8.5
8.61
7.91
7.63
8.69
7.9
7.87
7.9
8.66
8.53
7.56
8.5
8.69
8.74
Eluv.
cycle 7
9.2
8.34
9.12
7.72
7.63
7.88
7.97
7.7
8.92
7.55
9.79
7.8
7.72
7.72
8.7
7.68
7.7
7.68
772
7.63
7.63
7.63
7.63
8.19
7.72
7.73
8.34
8.32
7.72
8.29
8.3
8.59
8.47
8.48
8.79
8.55
7.7
8.33
8.34
7.72
7.55
8.65
7.72
7.7
7.93
8.64
8.47
7.46
8.64
8.39
8.71
Eluv.
cycle 8
7.68
7.68
7.76
7.68
7.81
7.68
7.76
7.6
7.68
8.87
7.52
7.95
7.76
7.63
7.7
7.75
7.74
7.75
7.78
7.7
7.67
7.82
7.77
8.08
7.75
7.75
8.18
7.65
7.81
8.27
8.33
8.21
7.64
8.34
8.36
8.38
7.68
7.52
7.58
7.76
7.67
7.48
7.74
7.76
7.93
8.39
8.54
7.73
8.34
7.98
8.56
Eluv.
cycle 9
9.16
9.33
9.34
9.58
9.26
10.65
10.57
8.78
9.01
9.15
9.45
7.96
8.06
7.96
8.53
8.03
7.88
7.56
7.86
7.88
7.97
7.88
7.93
7.62
7.8
7.87
8.31
7.88
7.88
7.95
8.32
8.39
7.74
8.41
8.66
8.66
7.71
7.82
7.57
7.52
7.7
7.64
7.61
7.4
7.54
7.49
7.71
7.47
7.5
7.42
8.74
Eluv.
cycle 10
9.59
9.23
9.04
9.42
9.01
10.26
10.32
8.35
8.84
8.96
8.95
8.22
7.94
8.03
8.21
8.2
8.26
7.94
7.87
7.88
8.12
8.08
8.04
7.98
7.89
8.08
7.96
7.96
7.8
8.07
7.95
7.8
7.81
7.71
8.04
7.79
7.71
7.99
7.67
7.71
7.71
7.66
7.59
7.63
7.83
7.83
7.85
7.66
7.55
7.71
7.79
Appendix B: Pore solution electrical conductivity (EC) in mSm-1
Mixture
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
Eluv.
cycle 1
580
570
606
557
533
596
585
335
470
537
527
319
95
309
339
505
520
526
535
549
621
455
451
458
402
427
504
471
535
540
475
486
474
526
505
443
499
463
511
504
527
551
573
570
533
536
549
549
559
590
622
Eluv.
cycle 2
505
504
509
492
477
350
325
446
427
378
435
605
633
557
487
580
516
336
359
488
361
672
602
550
622
681
741
678
903
865
580
499
545
614
653
708
711
868
544
542
580
589
563
637
717
238
844
604
500
640
723
Eluv.
cycle 3
366
411
401
332
328
281
261
336
362
335
309
531
486
425
429
503
490
312
325
446
458
677
636
482
467
599
643
818
717
770
753
742
815
875
1107
1015
1000
960
752
923
815
935
932
903
916
622
744
901
847
751
528
Eluv.
cycle 4
318
354
320
269
293
259
241
289
307
254
186
423
426
353
319
403
389
297
303
366
302
702
630
631
535
587
583
667
579
552
723
775
736
878
920
813
915
764
821
925
793
848
858
829
832
805
602
818
806
760
497
Eluv.
cycle 5
282
272
249
227
223
197
210
223
221
174
145
305
293
246
226
298
303
239
243
283
265
606
617
509
566
483
466
501
432
358
657
687
586
730
721
622
681
601
725
756
868
749
617
799
664
697
543
708
715
623
445
168
Eluv.
cycle 6
134
127
111
105
127
98
95
117
103
85
78
146
158
135
140
167
194
197
173
133
158
425
335
368
396
335
133
249
259
193
397
396
333
337
341
284
322
359
408
428
415
366
316
411
345
350
253
507
423
321
270
Eluv.
cycle 7
138
145
123
115
123
114
114
121
125
107
95
166
174
172
153
192
204
207
173
191
212
424
381
348
384
389
289
257
368
172
338
331
331
376
311
312
279
341
403
419
444
365
338
432
315
373
242
465
346
346
282
Eluv.
cycle 8
106
98
99
92
113
95
76
114
104
84
71
120
163
145
144
138
174
165
155
165
159
308
291
250
248
256
235
256
252
128
254
295
391
288
260
228
239
377
523
351
364
518
275
370
255
260
249
342
323
296
204
Eluv.
cycle 9
100
112
92
102
102
97
97
99
90.4
75
60
101
129
108
107
118
133
155
131
147
140
253
224
241
214
247
184
209
191
148
255
230
332
243
206
178
221
258
383
349
312
330
231
298
248
435
338
255
373
266
157
Eluv.
cycle 10
118
122
105
111
124
160
137
122
117
102
88
140
212
135
132
116
122
139
134
146
203
222
209
224
214
178
194
154
324
148
268
306
294
306
227
302
197
191
319
410
294
302
415
274
243
284
287
329
307
340
220
Appendix C:Measured total elements from MINTEK in fine ash (FA), gasification ash
(GA) and sludge (SL) using acid digestion
Mixtures
Cu
(ppm)
Reps
Mn
(ppm)
Zn
(ppm)
Mg
(%)
Ca
(%)
Fe (%)
P(%)
N (%)
K (%)
Na (%)
FA
1
56
838
27.5
1.57
5.36
12
0.305
2.7123
0.01765
0.37
FA
2
51
830
25
1.35
5.06
12
0.27
4.0609
0.0171
0.39
FA
3
48
774
25
1.33
5.01
11.9
0.41
2.9467
0.0178
0.42
FA50%GA50 %
1
55
549
19
1.41
5.81
7.45
0.58
5.2948
0.0184
0.36
FA50%GA50 %
2
50
561
19
1.46
6.04
7.31
0.39
3.9595
0.0186
0.39
FA50%GA50 %
3
48
573
24
1.42
5.77
7.455
0.29
4.5789
0.0182
0.365
GA
1
65
290
16
1.52
8.17
2.92
0.34
6.264
0.0169
0.33
GA
2
59
274
10
1.5
6.56
2.83
0.27
2.8771
0.0171
0.32
GA
3
51
277
11
1.5
6.94
2.88
0.38
3.3438
0.0174
0.36
FA40%GA40%SL20%
1
43
486
53
1.17
4.75
5.99
0.4
9.8399
0.016
0.3
FA40%GA40%SL20%
2
43.5
454
49.5
1.19
4.8
6.155
0.325
7.08105
0.0163
0.31
FA40%GA40%SL20%
3
46
427.5
50
1.165
4.905
5.905
0.325
1.1823
0.375
0.325
FA50%SL50%
1
47
420
100
0.83
2.87
6.16
0.29
2.0767
0.27
0.29
FA50%SL50%
2
33
420
100
0.71
2.91
6.02
0.3
3.8165
0.26
0.26
FA50%SL50%
3
36
412
93
0.8
2.86
6.07
0.36
4.8036
0.25
0.25
GA50%SL50%
1
30
161
95
0.78
3.62
1.86
0.27
8.9584
0.29
0.21
GA50%SL50%
2
28
157
90
0.85
3.79
1.78
0.32
9.8416
0.23
0.195
GA50%SL50%
3
28
159
96
0.89
3.68
1.94
0.33
6.9969
0.28
0.21
SL
1
29
161
99
0.26
0.62
0.6
0.4
5.9584
0.12
0.0962
SL
2
12
54
173
0.28
0.63
0.59
0.48
8.1896
0.13
0.11
SL
3
13.5
56
168
0.245
0.5
0.615
0.51
8.16865
0.0618
0.0543
169
Appendix D: Total cumulative soluble elements measured using ICP-OES in mixtures for
all 10 eluviation cycles
Mixture
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
Ca
mmol
kg-1
7.8060
8.1490
10.0784
9.6932
12.2579
10.6593
8.2095
29.5518
27.0021
30.4592
26.8558
22.6861
29.9250
18.2036
19.7985
25.0143
25.2106
19.9113
16.4921
17.3789
18.2060
25.1530
28.6469
18.5004
28.6074
18.9340
21.2327
24.4149
28.6317
24.6137
15.3847
16.2019
26.9659
20.3186
17.9292
21.4116
46.8447
25.9789
26.9304
48.5279
41.9942
26.0284
52.5558
41.1304
30.3292
26.1032
29.5110
39.2006
24.7601
27.2636
21.8647
Cu
mmol
kg-1
0.0004
0.0003
0.0002
0.0001
0.0001
0.0002
0.0002
0.0002
0.0002
0.0002
0.0002
0.0002
0.0002
0.0003
0.0008
0.0005
0.0009
0.0006
0.0006
0.0008
0.0010
0.0008
0.0007
0.0006
0.0006
0.0003
0.0006
0.0005
0.0004
0.0004
0.0006
0.0004
0.0006
0.0006
0.0010
0.0006
0.0003
0.0004
0.0004
0.0005
0.0004
0.0009
0.0004
0.0003
0.0004
0.0006
0.0009
0.0006
0.0007
0.0004
0.0010
Fe
mmol
kg-1
0.0004
0.0004
0.0004
0.0005
0.0005
0.0005
0.0005
0.0006
0.0005
0.0005
0.0006
0.0009
0.0009
0.0007
0.0003
0.0003
0.0003
0.0002
0.0002
0.0003
0.0002
0.0019
0.0032
0.0021
0.0024
0.0031
0.0036
0.0044
0.0047
0.0073
0.0042
0.0037
0.0066
0.0063
0.0132
0.0106
0.0058
0.0175
0.0072
0.0049
0.0075
0.0172
0.0072
0.0113
0.0289
0.0184
0.0392
0.0265
0.0333
0.0255
0.0373
K
mmol
kg-1
4.8087
4.4760
4.8843
5.4503
6.2407
8.1559
9.5356
4.8209
5.4760
6.3106
6.5022
4.3261
4.8471
4.2585
5.2658
4.7150
5.0341
4.5722
3.9379
4.1518
4.3628
3.5742
3.9783
3.9534
3.8310
3.8645
4.9599
4.7680
4.7437
5.3147
3.1560
3.6653
4.2845
4.2468
4.6873
5.3163
3.4205
4.5379
4.0387
3.9034
4.2638
4.6123
4.0158
4.1790
4.1110
5.4416
8.1936
5.0918
5.1982
4.4057
5.2589
Mg
mmol
kg-1
0.9919
6.9701
5.7425
4.3449
3.3251
3.3461
3.6612
7.8070
5.9381
1.3830
1.6808
34.1464
24.1297
13.5624
10.3249
23.5449
13.9890
11.2375
7.7588
10.5319
7.8534
41.6879
44.3898
33.5109
49.2721
36.9345
40.8249
44.5508
54.8229
48.1938
31.7401
35.1804
44.4135
37.0575
34.4285
42.3056
63.8068
46.9731
38.3111
58.1855
55.2771
41.6713
68.0054
54.4596
44.9439
46.9974
53.0028
61.4238
43.8102
45.2009
38.2857
170
Mn
mmol
kg-1
0.0006
0.0006
0.0006
0.0007
0.0007
0.0008
0.0008
0.0008
0.0008
0.0007
0.0008
0.0008
0.0010
0.0011
0.0008
0.0012
0.0008
0.0007
0.0006
0.0006
0.0006
0.0050
0.0054
0.0032
0.0051
0.0028
0.0029
0.0030
0.0035
0.0025
0.0039
0.0041
0.0057
0.0039
0.0042
0.0038
0.0091
0.0036
0.0059
0.0122
0.0089
0.0060
0.0127
0.0065
0.0050
0.0073
0.0099
0.0102
0.0073
0.0084
0.0063
Na
mmol
kg-1
24.6897
19.8204
20.9455
20.2649
22.4576
23.9983
25.3891
15.4367
16.8466
20.2098
18.8935
13.9323
16.7079
15.3399
18.3980
19.6904
21.5164
20.0312
17.6981
20.4814
22.3534
15.7962
17.3006
14.6307
15.2291
11.4130
16.4055
16.3095
15.0943
13.6926
9.8192
10.7307
12.9906
10.9439
11.5263
12.5686
11.3501
10.5724
11.0843
11.4782
11.0996
10.5840
10.7409
10.0263
8.7818
12.4823
12.0956
11.1673
9.8222
8.4366
7.9605
P
mmol
kg-1
0.0104
0.0190
0.0083
0.0072
0.0069
0.0076
0.0088
0.0033
0.0047
0.0051
0.0049
0.1054
0.0857
0.0566
0.0654
0.0917
0.0656
0.0627
0.0556
0.0620
0.0546
0.1398
0.1398
0.1357
0.1649
0.1454
0.1758
0.1881
0.1986
0.3274
0.1389
0.1547
0.2140
0.2189
0.2974
0.3207
0.2823
0.4380
0.1938
0.2149
0.2316
0.3066
0.3200
0.4101
0.6600
0.3706
0.5282
0.4512
0.5931
0.5815
1.0574
Zn
Mmol
kg-1
0.0002
0.0003
0.0003
0.0002
0.0002
0.0002
0.0006
0.0004
0.0002
0.0003
0.0002
0.0005
0.0006
0.0007
0.0011
0.0006
0.0004
0.0007
0.0003
0.0003
0.0002
0.0006
0.0007
0.0007
0.0008
0.0010
0.0017
0.0013
0.0011
0.0012
0.0007
0.0014
0.0016
0.0014
0.0014
0.0024
0.0010
0.0017
0.0015
0.0024
0.0013
0.0021
0.0023
0.0011
0.0027
0.0015
0.0030
0.0031
0.0037
0.0031
0.0039
Appendix E: Total inorganic N in pore solution for ten eluviation cycles
Selected
mixtures
1
6
11
12
17
21
22
26
30
31
35
38
39
42
45
46
48
51
Ammonium – N
mg kg-1
4.79
4.10
3.60
3.98
4.57
5.25
5.88
5.06
4.51
6.66
5.78
5.17
7.59
6.73
5.83
8.76
7.76
6.76
Nitrate – N
mg kg-1
0.00
0.00
0.00
0.00
0.00
0.00
1.12
0.49
2.32
4.66
8.53
3.33
4.48
8.48
4.03
7.65
8.56
6.67
Nitrite - N
mg kg-1
9.43
8.12
7.11
6.50
8.14
9.24
7.63
6.93
4.16
3.76
2.25
4.12
5.85
4.59
6.10
5.03
8.06
1.91
171
Appendix F: CEC determination in leached and fresh mixtures using KCl, LiCl and
NH4OAc methods
Mixture
Replication
1
6
11
23
37
46
51
POA
NOA
KIOLINITE
ILLITE
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
KCl on
leached
material
cmolc kg-1
2.37
2.46
2.68
1.12
1.92
1.59
1.76
1.78
1.70
3.64
3.87
3.79
2.99
3.22
3.20
6.29
6.26
5.65
4.81
3.97
3.76
2.16
2.12
2.06
1.23
1.27
0.98
2.83
3.61
3.09
8.99
8.34
5.99
LiCl on
leached
material
cmolc kg-1
1.13
1.27
1.30
0.54
0.45
0.74
1.14
1.19
1.09
3.56
4.10
1.79
3.84
5.44
2.10
6.14
6.62
7.66
2.57
7.11
4.76
4.19
4.31
4.39
3.49
3.08
3.15
5.65
5.14
5.65
19.38
18.74
16.04
Mixture
Replication
1
6
11
12
17
21
22
26
30
31
35
38
39
42
45
46
48
172
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
NH4OAc
leached
material
cmolc kg-1
2.65
3.28
3.02
1.88
2.58
2.74
1.33
2.15
1.70
2.67
2.30
2.32
2.28
2.66
2.74
2.74
3.54
1.65
3.69
3.32
3.49
2.05
2.20
2.42
2.63
2.20
2.73
3.51
4.29
3.78
2.98
3.32
3.30
2.52
2.58
2.42
4.13
4.46
4.12
3.78
3.37
3.86
3.09
2.70
3.25
4.19
4.64
4.74
4.56
4.46
4.06
NH4OAc
fresh
material
cmolc kg-1
5.91
7.40
7.64
5.39
5.51
5.97
2.43
2.27
2.49
5.49
5.57
5.59
6.97
7.89
6.86
8.01
9.19
9.23
10.37
11.11
10.75
8.18
9.80
9.62
8.99
8.31
7.60
13.96
11.31
13.50
12.89
11.03
13.22
12.29
11.60
12.83
16.92
18.79
18.09
15.27
15.15
15.18
13.40
13.18
14.62
19.63
19.28
18.20
19.40
19.91
17.83
Appendix F(continued)
Mixture
Replication
KCl on
leached
material
LiCl on
leached
material
Mixture
Replication
51
POA
NOA
KAOLINITE
ILLITE
173
NH4OAc
leached
material
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
3.57
3.11
2.85
5.88
5.77
5.77
4.70
4.81
4.78
8.91
8.63
8.46
11.69
12.43
11.38
NH4OAc
fresh
material
17.85
18.36
18.36
Appendix G: ICP – OES theoretical and actual analytical ranges for each element and
wavelengths used in the CEC analysis (Essington, 2004)
Element
Li
K
Theoretical
Wavelength (nm)
670.8
766.5
Theoretical Analytical
ranges (mg L-1)
0.0 – 3.0
0.4 – 100
Actual Wavelengths
(nm)
670.8
766.5
Actual Analytical
ranges (mg L-1)
0.0 – 10
0.0 – 150
MERCK chemicals used, their grades, concentrations and traceability in the determination
of CEC
Chemical
Li (standard)
Grade
CertiPUR®
Concentration/quantity
1000 mg L-1
Catalog number
170223
K (standard)
Lithium Chloride (LiCl)
CertiPUR®
GR for analysis ACS,
Reag. Ph Eur
GR for analysis ACS,
Reag. Ph Eur
GR for analysis ACS,
Reag. Ph Eur
1000 mg L-1
0.1 kg
170230
105679
0.5 kg
104933
0.5 kg
105853
Potassium Chloride (KCl)
Magnesium Nitrate (MgNO3)
174
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