University of Pretoria etd – Madyiwa, S (2006)
MODELLING LEAD AND CADMIUM UPTAKE BY STAR GRASS UNDER
IRRIGATION WITH TREATED WASTEWATER
BY
SIMON MADYIWA
Submitted in partial fulfilment of the requirements for the degree of
PHILOSOPHIAE DOCTOR
In the Faculty of Engineering, Built Environment and Information Technology
UNIVERSITY OF PRETORIA
March 2006
University of Pretoria etd – Madyiwa, S (2006)
ABSTRACT
MODELLING LEAD AND CADMIUM UPTAKE BY STAR GRASS UNDER
IRRIGATION WITH TREATED WASTEWATER
by
Simon Madyiwa
Supervisor
Department
Degree
:
:
:
Professor C. F. Schutte
Chemical Engineering
Philosophiae Doctor
This study was conducted to investigate the capacity of Cynodon nlemfuensis (star grass) to
accumulate lead (Pb) and cadmium (Cd) and develop metal uptake models for sandy soils
receiving treated sewage from domestic and industrial sources. The study area comprised a nonpolluted area and an adjacent area that received treated sewage from Harare’s Firle Wastewater
Treatment Plant for over 30 years.
Measured soil properties, total Pb and Cd in soils and grass and past records of Pb and Cd in
treated sewage were analysed. Growing grass in a greenhouse in pots with previously nonpolluted soils amended by single and mixed Pb and Cd salts and irrigated with treated sewage
tested the uptake capacity of star grass. Yields, soil bio-available and grass Pb and Cd levels were
measured and used to develop models for estimating critical soil and grass concentrations at
which productivity declines. In the field, star grass grown in 10m x 10m plots in the non-irrigated
and irrigated areas, received varying amounts of treated sewage over 11 months. Soil bioavailable and grass metal contents were measured and used to develop field-based models to
predict Pb and Cd content in star grass.
Star grass had a high Pb and Cd extraction capacity, making it unsuitable for pasture if grown on
polluted soils. Correlation between total Pb and Cd in soils and grass was insignificant (p<0.05).
Logarithm-based models of log10 bio-available soil levels and log10 grass metal levels provided
the best-fit regression models for Pb and Cd predictions in grass. Toxicity levels of Pb and Cd
that were derived for star grass from pot-based models were higher than levels recommended for
pasture. Toxicity occurred without visible signs on grass, making it difficult to recognise toxicity
without testing. The field-based uptake models predicted safe bio-available limits for pasture on
sandy soils. The co-presence of Pb and Cd resulted in increased Cd uptake but did not
significantly affect Pb uptake. Star grass can accumulate more than 1 mg/kg of Cd at total soil Cd
levels of less than 1 mg/kg, suggesting that the soil limit may be too high for a sandy soil.
Key words: Modelling Pb and Cd; Cynodon nlemfuensis; Sandy soil; Treated sewage
University of Pretoria etd – Madyiwa, S (2006)
EXECUTIVE SUMMARY
MODELLING LEAD AND CADMIUM UPTAKE BY STAR GRASS UNDER
IRRIGATION WITH TREATED WASTEWATER
Supervisor
:
Professor C. F. Schutte
Department
:
Chemical Engineering
University
:
University of Pretoria
Degree
:
Philosophiae Doctor
This study was conducted to investigate the capacity of Cynodon nlemfuensis (star grass) to
accumulate lead (Pb) and cadmium (Cd) from a sandy soil irrigated with treated sewage. It also
aimed to develop soil-vegetative tissue uptake models for predicting Pb and Cd levels in star
grass using measured soil concentrations.
By growing star grass in pots with sandy soils amended using different levels of single and mixed
inorganic salts of Pb and Cd and applying treated sewage, this study established that star grass is
a high accumulator of Pb and Cd. It also established that the co-presence of Pb and Cd in the soil
leads to increased uptake of Cd but does not significantly affect uptake of Pb by star grass. Star
grass accumulated 8 times and 18 times the maximum levels of 40 mg/kg Pb and 1 mg/kg Cd
recommended for pasture (United Kingdom Statutory Instrument No. 1412, 1995), respectively.
The co-presence of Pb and Cd led to a 2.6-fold increase in uptake of Cd but did not significantly
affect Pb bio-available soil levels and uptake by star grass.
Using the pot experiment, this study established that soil bio-available metal levels significantly
(p≤0.05) correlate with plant metal levels through logarithm-based single-factor linear regression
models of log10 (above-ground tissue metal concentrations) versus log10 (soil bio-available metal
concentrations). The models predict toxicity in star grass to occur at 53.7 mg/kg Pb and 3.2
mg/kg Cd, corresponding to soil bio-available levels of 186.2 mg/kg Pb and 8.3 mg/kg Cd. Since
toxicity occurred at metal levels higher than recommended for pasture without visible signs
showing, the study recommends that visual signs of toxicity should not be used to decide when to
stop grazing animals. Regular monitoring of bio-available levels of Pb and Cd is recommended.
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University of Pretoria etd – Madyiwa, S (2006)
In the field experiment where Pb and Cd levels in field plots were varied among treatments by
applying different quantities of treated sewage, this study produced a significant (p≤0.05) model:
log10 (above-ground tissue Pb concentration) = 0.3949log10 (soil bio-available Pb concentration)
+ 0.7880 for Pb and a strong (but marginally insignificant) model: log10 (above-ground tissue Cd
concentration) = 0.363log10 (soil bio-available Cd concentration) + 0.2987 for Cd. The models
predict that, to maintain Pb and Cd levels in star grass below recommended limits, soil bioavailable levels should not exceed 115.2 mg/kg Pb and 0.20 mg/kg Cd. Therefore this study
recommends management of soil bio-available Pb and Cd in sandy soils below 115.2 mg/kg and
0.20 mg/kg respectively, to ensure that star grass pasture is safe for animal consumption. The
field-based models are considered suitable where animals graze regularly, facilitating re-growth
of star grass over time.
Other results from this study suggest that the recommended limit of 1 mg/kg total Cd in soils may
be too high for sandy soils under repeated disposal of treated sewage. In this study, some samples
of mixed kikuyu and star grass from a sandy soil exposed to 29 years of treated sewage disposal
tested up to 1.2 mg/kg despite the soil having a total Cd of 0.65 mg/kg.
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University of Pretoria etd – Madyiwa, S (2006)
THESIS CONTRIBUTION TO KNOWLEDGE
A comparison of the capacity of the Cynodon nlemfuensis (star grass) to accumulate Pb and Cd,
obtained from this study, and that of other plants contributes vital information towards the search
for hyper-accumulators. By absorbing 4 592 mg/kg Pb, star grass ranks as a strong Pb
accumulator among grasses, considering that hyper-accumulating grasses such as Lolium perenne
(rye grass) accumulated 5 390 mg/kg Pb (US Department of Energy, 1998). However overall, star
grass ranks as a medium accumulator of Pb when compared to hyper-accumulating plants such as
Ipomea which accumulated 15 000 mg/kg in shoot tissue (Rhyne and Gosh, 2002). Given that
grasses within a species have similar uptake characteristics (McDonald et al., 1995), these
findings suggest that the Cynodon species of grasses has uptake capacities close to 4 592 mg/kg,
accompanied by very low yields. This implies that the Cynodon species may be a medium Pb
extractor whose use in phyto-remedying polluted soils may be limited.
Prior to this study, Pb and Cd uptake characteristics that are critical to the growth and monitoring
of suitability of star grass pasture, growing on soils polluted with Pb and Cd were not known. No
known models were available for estimating Pb and Cd levels in star grass growing on sandy soils
on which treated sewage is disposed. This study contributed to the development of soil-plant
metal uptake models by combining the use of bio-available concentrations in soils and the
concept of log-transforming soil and metal concentrations in grass to produce single-factor
regression models for estimating Pb and Cd levels in grass based on bio-available soil levels.
Using the models, the study estimates that toxicity of Pb and Cd in star grass occurs at 53.7
mg/kg Pb and 3.2 mg/kg Cd corresponding to critical soil bio-available levels (extracted using 1
M ammonium acetate) of 186.2 mg/kg Pb and 8.3 mg/kg Cd.
Furthermore, the study provides an indication of the critical levels of soil concentrations that
should not be exceeded in order to ensure that levels in star grass are below recommended
maximum levels. Using regression models:
(1) log10 (above-ground tissue Pb concentration) = 0.3949log10 (soil bio-available Pb
concentration) + 0.7880
(2) and log10 (above-ground tissue Cd concentration) = 0.363log10 (soil bio-available Cd
concentration) + 0.2987,
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developed under field conditions, the study estimated that soil bio-available levels should be
maintained below 115.2 mg/kg Pb and 0.20 mg/kg Cd to ensure compliance of star grass metal
content with recommended limits of 40 mg/kg Pb and 1 g/kg Cd (United Kingdom Statutory
Instrument No. 1412, 1995) for pasture grass.
Literature presents what appears to be conflicting evidence on the influence of Pb on Cd and vice
versa on uptake by plants. By assessing the effect of the co-presence of Pb and Cd in the soil on
uptake of the metals by star grass, this study contributes towards increasing available information
on interactions of the metals in plants. This study found that the addition of Pb and Cd to the soil
increased uptake of Cd 2.6-fold over uptake observed with single metals added to the soil, while
uptake of Pb was not affected significantly in star grass. Therefore available information on
interactions of Pb and Cd may not be conflicting but an indication of different uptake
characteristics of plants. It may also be argued that besides reducing Cd levels in treated sewage,
reduction of Pb levels can contribute towards reducing uptake of Cd.
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University of Pretoria etd – Madyiwa, S (2006)
ACKNOWLEDGEMENT
I wish to express my appreciation to the following organisations and persons who made this
thesis possible:
(a) This thesis is based on a bigger research project entitled Pollution Implications of Using
Wastewater for Irrigating Pasturelands, that was undertaken from 2001 to 2003. Permission
to use the material is gratefully acknowledged. The opinions expressed are those of the author
and do not necessarily represent the policy of the Water Research Fund for Southern Africa
(WAFSAR) or the University of Pretoria.
(b) WAFSAR for sponsoring a large part of this study, the Institute of Water and Sanitation
Development (IWSD) and University Lake Kariba Research Station (ULKRS) for jointly
administering funding from WARFSA, National Testing Laboratory, then Blair Research
Institute for hosting workshops on the study, the University of Zimbabwe (UZ) for providing
greenhouses for laboratory experiments, the Soil Research Institute of the Department of
Agricultural Research and Extension (AREX) for providing laboratory facilities and
assistance in carrying out chemical tests, Harare City Council for provision of data and access
to the study site and Blair Research Institute for provision of camping equipment for field
studies and conference facilities during the course of this study.
(c) The following persons are gratefully acknowledged for their assistance during the course of
the study:
(1) The late Dr. N. Ndamba
(2) Dr. J. Nyamangara
(3) Mr. C. Bangira
(4) Dr. S. Mukaratirwa
(d) Professor C. F. Schutte, my supervisor, for his guidance and support.
(e) Professor. M. Chimbari for guidance and supervision on practical field work and support
throughout this study.
(f) My wife Regina Madyiwa and two daughters, Sandra and Millicent for their encouragement
and support during the study.
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University of Pretoria etd – Madyiwa, S (2006)
TABLE OF CONTENTS
CHAPTER
PAGE
EXECUTIVE SUMMARY
i
THESIS CONTRIBUTION TO KNOWLEDGE
iii
ACKNOWLEDGEMENT
v
1.0 INTRODUCTION
1
1.1 Environmental and human health concerns of Pb and Cd
1
1.2 Metal pollution from wastewater
1
1.3 Paucity of data on accumulation of Pb and Cd in star grass
2
1.4 Challenges in modelling plant metal uptake from soils
3
1.4.1 Soil metal concentrations and sampling depth
4
1.4.2 Differences in uptake characteristics of plants
4
1.4.3 Influence of uptake by other metals
5
1.5 Objectives of study
6
1.6 Scope of study
6
1.7 Organisation of thesis
7
2.0 LITERATURE REVIEW
9
2.1 Essential and non-essential heavy metals for plants
9
2.2 Sources of Pb and Cd
9
2.2.1
Lead
10
2.2.2
Cadmium
10
2.3 Treated wastewater as source of Pb and Cd
11
2.4 Chemistry of Pb and Cd
13
2.4.1
Lead
13
2.4.2
Cadmium
13
2.5 Metal contamination and toxicity
14
2.5.1
Lead
16
2.5.2
Cadmium
16
2.6 Bio-availability of heavy metals
17
2.7 Lead and cadmium health hazards to humans
18
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2.8 Plants as soil cleaners and pathway of Pb and Cd to food chain
19
2.9 Treated sewage as source of Pb and Cd hazard to grazing animals
via plants
20
2.10
Potential of grasses to accumulate Pb and Cd
21
2.11
Cynodon nlemfuensis
21
2.12
Reliability of standard permissible toxic metal guidelines
22
2.13
Reliability of guidelines of loading rates for wastewater on soils
23
2.14
On land sewage disposal methods
26
2.15
Influence of plant and other chemical species on metal uptake
26
2.16
Models for heavy metal content prediction
27
2.16.1 Mass balance approach
27
2.16.2 Use of soil-plant system models for metal prediction
28
2.17
Metal uptake in sewage amended soils
30
2.18
Review of methods of measuring bio-available metal
concentrations
2.19
30
Review of some findings of pot and field methods for determining metal
Uptake
32
2.20
Review of sewage treatment systems in Zimbabwe
33
2.21
Problem statement and hypotheses
35
2.21.1 Problem statement
35
2.21.2 Potential benefits of study
36
2.21.3 Hypotheses
37
3.0 METHODOLOGY
38
3.1 Introduction
38
3.2 Background of study area
38
3.2.1
Location of study area
39
3.2.2
Sources of pollutants for study area
40
3.2.3
Treatment plants
41
3.3 Study design
41
3.3.1
Baseline assessment of Pb and Cd levels in study area
3.3.2
Greenhouse Pb and Cd uptake by star grass under
3.3.3
43
treated sewage application
45
Field assessment of Pb and Cd uptake
47
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3.3.4
Data analysis
48
4.0 BASELINE ASSESSMENT OF LEAD AND CADMIUM LEVELS IN
STUDY AREA
51
4.1 Introduction
51
4.2 Objectives
51
4.3 Detailed methods and materials
51
4.3.1
Analysis of past records on levels Pb and Cd in treated
sewage
4.3.2
51
Baseline assessment of chemical characteristics of study
area
52
4.4 Results
4.4.1
4.4.2
54
Analysis of past records on levels of Pb and Cd in treated
sewage
54
Chemical characteristics of study area
55
4.5 Discussion
4.5.1
59
Analysis of past records on levels of Pb and Cd in treated
sewage
59
4.5.2
Pb and Cd accumulation in soils and grasses
60
4.5.3
Implications of findings
63
5.0 ASSESSMENT OF LEAD AND CADMIUM UPTAKE BY
Cynodon nlemfuensis UNDER REPEATED APPLICATION OF
TREATED WATER
66
5.1 Introduction
66
5.2 Objectives
67
5.3 Detailed methods and materials
67
5.3.1
Experimental set-up
67
5.3.2
Grass establishment
68
5.3.3
Soil treatment and irrigation application
69
5.3.4
Soil sampling and testing
70
5.3.5
Grass sampling and testing
70
5.3.6
Sewage effluent and sludge collection and testing
70
5.3.7
Data analysis
71
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5.4 Results
72
5.4.1
Bio-available Pb and Cd content of soils
72
5.4.2
Extraction capacity of star grass
73
5.4.3
Grass metal content response to bio-available soil metal
content in single treatments
5.4.4
73
Yield response to Pb and Cd content of grass in single
treatments
75
5.4.5
Interactions of Pb and Cd in mixed treatments
78
5.4.6
Correlations of Pb and Cd in grass
82
5.4.7
Yield response to combined Pb and Cd
82
5.4.8
Yield, grass and soil metal content models and critical
5.4.9
limits of Pb and Cd
84
Pb and Cd levels in effluent and sludge mixture
87
5.5 Discussion
87
5.5.1
Extraction capacity of star grass
87
5.5.2
Grass yield response to Pb and Cd
89
5.5.3
Metal uptake models and critical metal limits
89
5.5.4
Implications of findings
93
6.0 FIELD ASSSESSMENT OF LEAD AND CADMIUM UPTAKE BY
Cynodon nlemfuensis UNDER REPEATED APPLICATION OF
TREATED WASTEWATER
94
6.1 Introduction
94
6.2 Objectives
95
6.3 Detailed methods and materials
95
6.3.1
Estimated irrigation requirements of star grass
95
6.3.2
Experimental set-up
96
6.3.3
Preparation of field plots
97
6.3.4
Irrigation of grass
98
6.3.5
Soil sampling and testing
99
6.3.6
Grass sampling and testing
100
6.3.7
Sewage effluent and sludge sampling and testing
100
6.3.8
Data analysis
100
6.4 Results
101
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6.4.1
Soil pH, cation exchange capacity and clay content
101
6.4.2
Bio-available Pb and Cd content of soils and grass
103
6.4.3
Soil bio-available Pb and Cd response to treatment
106
6.4.4
Grass Pb and Cd content response to treatment
107
6.4.5
Correlations between bio-available and grass Pb and Cd
contents for each grass crop
6.4.6
6.4.7
108
Correlation between average bio-available Pb and Cd in
soils and average Pb and Cd contents in grass
110
Rate of metal application from treated sewage
112
6.5 Discussion
113
7.0 GENERAL DISCUSSION
116
7.1 Long-term Pb and Cd accumulation in soils and bio-available levels
116
7.2 Capacity of star grass to absorb Pb and Cd
117
7.3 Yield responses to increasing bio-available Pb and Cd
118
7.4 Yield-metal uptake models for Pb and Cd and toxic limits in grass
118
7.5 Soil bio-available-grass metal uptake models and critical metal limits
118
7.6 Co-presence of Pb and Cd
120
7.7 Appropriate Pb and Cd levels in effluent and digested sludge
120
8.0 CONCLUSIONS AND RECOMMENDATIONS
122
8.1 Main conclusions
122
8.2 Recommendations
125
TABLES
Table 2.1
Sewage type, loading rates and soil type (Source: Chatterjee,
1987)
Table 2.2
24
German standards for heavy metals in soil and sludge
(Pescod et al, 1985)
Table 2.3
24
Recommended maximum concentrations of trace elements in
irrigation Water (adapted from Pescod, 1992)
Table 4.1
Average (range) concentration (mg/l) of heavy metals in samples
x
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University of Pretoria etd – Madyiwa, S (2006)
of digested sludge and effluent (Source: Harare City Council
records, 1991-1994)
Table 4.2
55
Selected properties of a sandy soil in the irrigated and control
areas
Table 4.3
56
Average total soil metal concentrations in horizons of soil
profile of the irrigated and control areas
Table 4.4
Average total metal levels (mg/kg) in 0-20cm soil depth and
mixed grass
Table 5.1
58
Soil metal and grass concentrations, yields and metal extraction
levels
Table 5.2
57
74
Pb concentrations in samples of treated effluent and sludge
mixture
87
Table 6.1
Estimated crop water and irrigation requirements of star grass
96
Table 6.2
Mean soil properties and soil depth
102
Table 6.3
Correlation coefficients for pH, cation exchange capacity
and clay content versus soil depth
103
Table 6.4
Mean soil profile bio-available metal and grass concentrations
104
Table 6.5
Correlation coefficients for soil depth and bio-available soil
metal concentration
105
Table 6.6
Average bio-available Pb and Cd levels in soils and grass (mg/kg)
106
Table 6.7
Quantities of treated sewage and computed average metal
concentrations applied to plots
Table 6.8
112
Average increase in profile Pb and Cd levels above levels in the
control (mg/kg)
113
LIST OF FIGURES
Figure 2.1
Generalised dose-response curve for nutrient metals
15
Figure 3.1
Schematic diagram of study area
39
Figure 5.1
Log10 soil bio-available level versus log10 Pb level in grass in
single treatments
Figure 5.2
75
Log10 bio-available Cd level versus log10 Cd levels in grass in
single treatments
Figure 5.3
76
Log10 Pb level (mg/kg) in grass versus log10 grass yield (g/pot)
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in Pb single treatments
Figure 5.4
77
Log10 Cd level (mg/kg) in grass versus log10 yield of grass
(g/pot) in single Cd treatments
Figure 5.5
78
Effect of treatment on bio-available levels of Pb in single and
mixed treatments
Figure 5.6
79
Effect of treatment on levels of Pb in grass in single and
mixed treatments
Figure 5.7
80
Log10 bio-available soil Pb levels (mg/kg) versus log10 Pb levels
in grass re-growth (mg/kg) in mixed treatments
Figure 5.8
Effect of treatment on bio-available levels of Cd in single and
mixed treatments
Figure 5.9
81
Effect of treatment on bio-available Cd levels in grass in single
and mixed treatments
Figure 5.10
81
Log10 bio-available Cd soil levels (mg/kg) versus log10 Cd levels
in grass re-growth in mixed treatments
Figure 5.11
82
Correlation of metal contents of Pb and Cd in grass in single and
mixed treatments
Figure 5.12
80
83
Yield response to concentrations of Pb and Cd in mixed Pb and
Cd treatments
83
Figure 6.1
Plot layout at Churu farm
98
Figure 6.2
Treatment versus log10 bio-available soil Pb concentration
106
Figure 6.3
Treatment versus log10 bio-available Cd soil concentration
106
Figure 6.4
Treatment versus log10 grass Pb concentration
107
Figure 6.5
Treatment versus log10 grass Cd concentration
108
Figure 6.6
Log10 bio-available soil Pb versus log10 Pb level in grass in field
experiment
109
Figure 6.7
Log10 bio-available soil Cd level versus log10 Cd level in grass
110
Figure 6.8
Log10 mean bio-available soil Pb versus log10 mean Pb level in
grass
Figure 6.9
110
Log10 mean bio-available soil Cd versus log10 mean Cd level in
grass
111
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LIST OF REFERENCES
127
APPENDICES
Appendix 1
Sewage treatment processes at Firle Wastewater Treatment Plant
135
Appendix 2
Randomised block design layout of pots in greenhouse
137
Appendix 3
Quantities of treated sewage and metals applied to field plots
138
Appendix 4
Mean soil bio-available concentrations (standard deviations),
mg/kg and soil depth
140
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CHAPTER 1
INTRODUCTION
1.1 Health concerns of Pb and Cd in humans and the environment
Lead (Pb) and cadmium (Cd) are toxic metals whose contribution to environmental
contamination is becoming a serious concern as they enter the air, food and water in
increasingly significant amounts fed by continuous mining and use of metals (Elson and
Haas, 2003). Besides Pb and Cd, other metals, such as zinc (Zn), copper (Cu), nickel (Ni),
chromium (Cr), iron (Fe), silver (Ag) and mercury (Hg) are of great concern to the
environment and human health. Pb and Cd are cumulative toxins that are indestructible and
can only be eliminated through excretion (Moolenar and Lexmond, 1999). When they
accumulate in the human body, Pb and Cd may cause health problems that include damage to
the central nervous system and reduced intellectual capabilities (Wildlife, 2000) and
hypertension (Staessen, 2002).
The major pathways of exposure to Pb and Cd in the non-smoking human population are:
food and water for Pb and food via the addition of cadmium to agricultural soils and uptake
by food and fodder crops, in the case of Cd (Scottish Executive Environment and Rural
Affairs Department, 2002). Plants can take up Pb and Cd in high concentrations from the soil
(Bazzaz, 1977; Johnston and Hones, 1995; Khan and Frankland, 1983) and hence provide a
major pathway to the human food chain. Thus, a good understanding of uptake of Pb and Cd
is critical in designing strategies for predicting uptake of the metals into the food chain.
1.2 Metal pollution from wastewater
Wastewater disposal on soils is a major source of metals to plants. The use of wastewater for
irrigation is justified on the need to dispose of the water, utilize the scarce water resource,
take advantage of the high nutrient content of wastewater and reduce the need for commercial
fertilizers (Bayer et al, 1972). It is also a low cost method for sanitary disposal of municipal
wastewater. However, disposal of wastewater on land has been widely reported to increase
soil metal content, because wastewater contains heavy metals from domestic and industrial
sources. Department for Environment, Food and Rural Affairs (DEFRA) and Environmental
Agency (2002) noted that disposal of sewage sludge to land increased Cd concentration in
soils. Janeic et al (1995) noted that Cd poses the greatest concern with respect to land
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application of sewage because ingestion of plants that contain large concentrations of the
metal by humans and animals may result in Cd accumulation in livers and kidneys. Treated
sewage is therefore a potential source of soil contamination that increases the possibility of
uptake of Pb and Cd by plants that grow on the soils on which it is disposed.
In Zimbabwe, municipal wastewater is used for irrigation in many peri-urban areas and the
practice is expected to increase with the expansion of the existing and creation of new urban
centers. One of Harare's largest treatment plants, Firle Wastewater Treatment Plant, processes
sewage coming from industrial and domestic sources and disposes mixed treated effluent and
sludge on pasturelands at Firle farm. The pastureland consists of sandy soil on which mixed
Cynodon nlemfuensis (star grass) and Pennisetum clandestium Chiov. (kikuyu grass) pasture
is irrigated. Firle farm employs 32 farm workers and supports 3 000 beef cattle that are born
and bred on the farm. The farm workers and animals may be subjected to hazards emanating
from exposure to Pb and Cd. In addition, any hazards that may exist could spread wider, since
the population at large consumes beef from animals bred on Firle farm.
1.3 Paucity of data on accumulation of Pb and Cd in star grass
Disposing treated sewage on pastures started over 30 years ago at Firle farm. It was
considered to be a cheap method for secondary treatment of wastewater, unfit to be
discharged directly into natural watercourses. Although the potential of Pb and Cd to
accumulate in soil is known, their accumulation in soils has not been monitored at Firle farm.
While there has been limited and inconsistent monitoring of heavy metal content in treated
sewage no attempt has been made to quantify Pb and Cd uptake by grass or animals at Firle
farm to ascertain compliance of metal content of grass with acceptable levels for grazing
pastures. Therefore the health hazards posed by heavy metals to animals that feed on the
grasses are not well documented.
To date, only a few short-term studies on the impact of sewage sludge disposal on soils have
been carried out in Zimbabwe. One such study by Nyamangara and Mzezewa (1999)
investigated the long-term effect of sewage sludge application on Pb, Zn, Cu and Ni levels in
a clay loam soil. The study, which was carried out at Crowborough Sewage Treatment Works
(one of Harare’s treatment works) concluded that sewage sludge significantly increased the
levels of Pb, Zn, Cu and Ni in the soil. The results of the study raised questions regarding the
potential uptake of large amounts of metals by pasture grass.
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Although uptake of some heavy metals by grasses such as, Lolium perenne (rye grass),
Pennisetum purpureur (elephant grass), Agrotis stolonifera (red top) and Medicago sativa
(alfalfa) has been studied, limited research has been conducted on the genus Cynodon to
which star grass belongs. No known study has determined Pb and Cd uptake characteristics of
star grass. The absence of studies on Pb and Cd in star grass pastures represents a gaping hole
in vital scientific information, considering that the grass is a widespread pasture grass in East,
Central and Southern Africa and is grown in Zimbabwe using wastewater which potentially
contains high levels of Pb and Cd.
The paucity of data on heavy metal pollution is not unique to Zimbabwe, but spread across
the developing world. World Health Organisation (WHO) Working Group on Cd,
(http://www.icsu-scope.org/cdmeeting/cdwgreport.htm) noted that while the developed world
is more concerned about food quality and public health, the developing world and tropical
areas in particular face persistent challenges of malnutrition and food security that take
precedence over food quality and public health. It further confirmed that tropical areas have
relatively few data on Cd accumulation in tropical soils and crops despite them covering a
large part of the globe in which two thirds of the world’s population lives. Such data would be
important for both local public health and international trade. Recognition of the potential
hazards caused by heavy metals and the need to protect the environment has resulted in
greater investment into research, legislating and enforcing permissible limits of the metals by
developed countries. This recognition has spread to developing countries and hence many
scientists have called for more research on heavy metal pollution in the developing world.
1.4 Challenges in modelling plant metal uptake from soils
Researchers face many challenges in generating data and analysing it to develop tools for use
in minimising environmental and health hazards associated with heavy metals. Many
countries worldwide have legislated maximum permissible heavy metal levels (guideline
values) in soils, some plants, irrigation water and food for human consumption. However
legislated metal limits differ from one country to another, depending on the context in which
they were developed. DEFRA and Environmental Agency (2002) stated that soil guideline
values may differ from one country to another depending on the conceptual models behind
the guidelines, reasons why the assessment criteria were developed, management context,
legislation, policy and differences in site conditions, such as soil pH and soil type. Therefore a
generic heavy metal permissible limit in soils may not be applicable to all countries and
situations.
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The soil-plant pathway has attracted research attention since it is a major contributor to
transmission of metal pollutants to animals and humans. Efforts have been made to develop
soil-plant tissue metal uptake models for predicting plant metal concentrations. Soil-plant
tissue uptake models have been used in soil-plant nutrient analysis for a long time. The
models have been extended to heavy metal analysis in soil-plant systems and used for
predicting levels of pollutant metals in plants on the basis of metal levels in soils. Soil metal
levels and plant metal content are central to the development of these models. Factors that
affect these two parameters have to be taken into account in developing soil-plant tissue
uptake models.
1.4.1 Soil metal concentrations and soil sampling depth
Total soil metal concentrations are widely used in the soil-plant tissue metal uptake models.
One major advantage of their use is that standard methods of measuring total metal
concentrations in soils are available. However the challenge is that total metal concentrations
are increasingly being regarded as inadequate for predicting plant metal content and for public
health assessments (Bak and Jensen, 1998). Like-wise, soil-vegetative tissue metal uptake
factors (Baes et al 1984) vary with total metal concentrations and can over- or under-predict
concentrations of some metals in plants, because they are based on total metal concentration
(US Department of Energy, 1998).
The depth of soil from which soils are sampled to determine soil concentrations may
introduce errors in relating concentrations of metals in soils and plants because concentrations
vary with soil depth. According to the US Department of Energy (1998), the depth interval at
which various plants in different environments obtain water and nutrients and the relative
biomass of feeder roots at different depths are unknown. Therefore the challenge is what
depth one should use in modelling so that the concentrations of metals in that depth reflects
uptake of metals by a particular plant.
Soil-plant tissue metal uptake models have been developed from existing data measured at
different sites across the world. This approach has presented challenges to modelling plant
uptake. US Department of Energy (1998) noted that non-uniformity of soil sampling depth,
scarcity of data and variations in methods used to measure soil metal concentration presented
constraints to modelling soil-plant metal uptake.
Suggestions have been made to overcome some of the challenges. Bio-available (also known
as plant available) levels of metals have been reported to correlate better with plant metal
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concentration. However, the absence of an agreed standard method for measuring bioavailable metal levels in soils (http://www.icsu-scope.org/cdmeeting/cdwgreport.htm) has
constrained their use. Another suggestion to improving soil-plant tissue metal uptake models
is to incorporate factors that influence availability of metals to plants, such as pH (Jesper and
Jensen, 1998). This approach has received considerable attention and although models were
developed, site-specific experiments were encouraged (Sample, 1998) to obtain site-specific
data for developing models.
1.4.2 Differences in uptake characteristics of plants
Research has also shown that different plant species and cultivars have different metal uptake
characteristics and capacities (Kabata-Pendias, 2001). Furthermore, different organs (leaves,
fruit, roots, stem) of the same plant have different metal uptake capacities. Therefore uptake
characteristics of a particular plant or its organs can only be known if an experiment is carried
out on a particular element. The absence of any known studies on Pb and Cd uptake by star
grass implies that soil-star grass uptake and growth characteristics, such as metal uptake and
yield response, are not known. In addition, the critical metal uptake levels of star grass, such
as toxicity levels, are not known and cannot be extrapolated from other grasses that have been
studied so far. Therefore, uptake of large quantities of Pb and/or Cd by animals grazing on the
treated sewage irrigated star grass pastures could not be ruled out on the basis of available
information.
1.4.3 Influence of uptake by other metals
Besides plant species and soil metal concentration, other chemicals in the soil influence
uptake of metals by plants (Moolenar and Lexmond, 1999). Other chemicals present in a soil
may interact with a particular metal causing an increase or reduction of uptake of the metal by
a plant. Khan and Frankland (1983), Miller (1977), Carlson and Rolfe (1979) and others
found different and sometimes conflicting results on the influence of Pb on Cd and vice versa,
where the two metals co-existed in the soil. Preliminary indications were that Pb and Cd were
present in treated sewage disposed on Firle farm. Therefore interactions of the two metals
could not be ruled out.
In view of the preceding arguments, a long-term study was considered necessary to determine
uptake of Pb and Cd by star grass growing on a sandy soil on which treated sewage is
disposed of. The following objectives were formulated to investigate the issues.
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1.5 Objectives of study
The general objective of this study was to establish the effect of irrigating pastures with a
mixture of sewage effluent and sludge from bio-filtration plants on contamination of pasture
grass by Pb and Cd. The specific objectives were:
1) To determine the long-term Pb and Cd accumulation in soils subjected to sewage effluent
and sludge mixture application
2) To evaluate changes in pasture grass yield level, response to Pb and Cd concentrations and
toxicity levels in grass under effluent and sewage sludge mixture application in combination
with different levels of added heavy metals
3) To determine Pb and Cd accumulation in pasture grass under effluent and sewage sludge
mixture application
4) To determine the maximum level of Pb and Cd concentrations in sewage effluent and
sludge mixture that would allow optimisation of yield of grass and prevent heavy metal
loading from exceeding acceptable limits
1.6 Scope of study
This study postulated that if star grass was exposed to very high levels of Pb and Cd, then
cattle could accumulate high levels of the metals in their body organs through consumption of
grass. This could lead to humans also accumulating high levels of the metals through
consumption of meat from those animals. To contribute to this wide area of research, this
study focused on accumulation of Pb and Cd in soils and star grass. In addition, it focused on
developing soil-vegetative metal uptake models that could be used to predict metal uptake in
grass using measured soil bio-available metal levels. Within this scope, the study was limited
to the main areas of focus described below.
A literature review was undertaken to gather detailed information on Pb and Cd hazards in the
environment and to identify gaps for research. This study is therefore based on the gaps
identified. The study was conducted on one study site only since the limited financial
resources only allowed a limited number of expensive chemical analyses. Recommended
levels of the metals in soils and pasture grass where extracted from literature.
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Data from chemical tests carried out by the City of Harare on the treatment process were
incorporated in the study to save on time and costs of obtaining similar data within the
context of this study. Interviews and visits to the treatment plant were undertaken to
familiarize the author with the sewage treatment and disposal systems.
A greenhouse pot experiment where soils and grasses were subjected to high levels of Pb and
Cd was undertaken to assess uptake of the metals and be able to define toxicity. This was
complemented by a field experiment meant to assess uptake under real life conditions and
determine what levels of Pb and Cd could be allowed in the soil to ensure that grass did not
exceed recommended levels. In the greenhouse experiment uptake was assessed on single
added Pb and Cd and the two metals combined. The latter was intended to investigate
interactions of Pb and Cd in soils and grasses. One method of extracting bio-available soil
metal levels was selected and used throughout the study to ensure consistency.
Since numerous soil and plant factors affect metal accumulation in soils and plants, only
selected soil factors, like soil pH, cation exchange capacity (CEC), clay content and organic
matter were investigated to assist in the interpretation of Pb and Cd uptake by grass. This
implies that other important factors such as plant physiology and interaction of Pb and Cd
with other chemical species like calcium (Ca) and zinc (Zn) were excluded from the study in
order to make it focused.
The focus of this study was to relate soil metal content and metal content in organs of star
grass that were consumed mostly by cattle. In this study, these organs were taken to be aboveground tissue of grass although it is acknowledged that animals sometimes consume roots and
even soils as they graze. This study defined above-ground plant tissue as all plant tissue
(stems and leaves) 5 cm above the ground on the assumption that cattle cut grass at 5 cm
above the ground as they graze. This is the plant tissue in which growth parameters of yield
and metal content were measured for use in modelling. The study therefore excluded belowground organs such as roots.
1.7 Organisation of thesis
This thesis is organized into 8 chapters. Chapter 2 consists of a literature review that brings
out gaps in knowledge that motivated this study. Chapter 3 presents the ‘General
Methodology’ of the study, in which the overall study design and components of the study are
discussed. In this chapter, the approaches and methods used in each component of the study
are discussed at a general level. Detailed methods and materials are presented in each
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component to facilitate a better understanding of the link between the detailed methods, the
results and discussions for that particular component of the study. Each component of the
study constitutes a chapter, from Chapter 4 to Chapter 6. An overall discussion is presented in
Chapter 7 while the conclusions and recommendations are presented in Chapter 8.
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CHAPTER 2
LITERATURE REVIEW
2.1 Essential and non- essential heavy metals for plants
Heavy metals are elements with a high relative atomic mass. They occur naturally in the
earth’s crust. The term "heavy metal" is used extensively in literature to refer to metals with
atomic numbers greater than 20 and is also associated with toxicity or pollution. According
to Malan (1999), the term is vague as some authors use it to refer to second and third row
transitional metals, others to all transitional metals while many use it to refer to metals not
normally found in biological tissue but are harmful. In this study the term heavy metal refers
to metals that have atomic numbers greater than 20 and may be harmful to plants and/or
animals. These metals include Fe, Zn, Ni, Cr, Cu, As, Hg, Pb and Cd. Pb and Cd have been
chosen for investigation in this study because they pose a much higher risk to the human food
chain than the rest. They enter the food chain and more easily accumulate to levels that cause
health problems to animals and humans.
Heavy metals such as Fe, Cu and Zn are essential for plant growth as they participate in
oxidation, electron transfer and various enzyme reactions (Polette et al, 1997). Others like Pb
and Cd are not known to have any metabolic roles in plants and animals and are therefore
non-essential (Johannesson, 2002; Elson and Haas, 2003). In general, essential elements may
be defined as metals that are necessary for a plant to complete its life cycle (Welch and Cary,
1987). Non-essential elements are metals with no known role in plant metabolism. Although
recent findings indicate that Cd may be essential to certain mushrooms (Johannessson, 2002)
the metal is still considered non-essential since its biological functions in plants are still not
known. Polette et al (1997) postulated that the mechanisms that allow uptake of nutrients by
plants could also facilitate uptake of heavy metals, as the latter are generally indistinguishable
from nutrients.
2.2 Sources of Pb and Cd
The major sources of heavy metals to the environment are direct deposition from mining and
industrial processes, atmospheric deposition from combustion processes and wastewater from
mining activities, industrial and domestic processes. The primary production and recycling of
Pb (which occurs in over 50 countries in the world) contributes to a total annual production of
6 million tonnes while that of Cd is estimated at 19 000 tonnes per year (Johannesson, 2002).
Heavy metals are emitted into the atmosphere as vapour or particulates (dust) or both from
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combustion processes (power generation, road transport), industrial sources (iron and steel
industry, non-ferrous metal industry) and waste incineration (Scottish Executive
Environmental and Rural Affairs Department, 2002). From these atmospheric emissions
heavy metals are then deposited onto the environment.
2.2.1 Lead
Pb is a mineral found deep within the earth and mined together with silver deposits (Elson
and Haas, 2003). It exists in nature as sulphate (PbSO4), carbonate (PbCO3) and sulphide
(PbS), which constitute the principal ore of Pb, known as galena. Impurities in the ore include
Ag and gold (Au). Pb ore produces oxides when heated.
Lead is a raw material in the manufacture of tetraethyl lead (Pb(C2H5)4), the additive in leaded
gasoline. It is used in the production of lead acid storage batteries, pigments and chemicals,
solder, other alloys and cables. It therefore becomes part of industrial waste from these
industrial activities. WHO (1993) stated that Pb is present in tap water primarily from
household plumping systems containing Pb in pipes, solder, fittings or service connections to
homes. This makes domestic waste a major source of Pb. The dissolved amount depends on
several factors including pH, temperature and water hardness. Wastewater consists of
domestic and industrial waste that is treated and may be disposed onto lands, including
pasturelands. In the process, treated wastewater may become a major source of Pb on
pasturelands.
Scottish Executive Environmental and Rural Affairs Department (2002) noted that average
human daily Pb intake for adults in the United Kingdom (UK) is estimated at 1.6 µg from air,
20 µg from drinking water and 28 µg from food. Food therefore constitutes a significant
proportion of the daily intake of human beings. Subhuti (2001) stated that meat was among
the top three main dietary sources of lead. The other two were grasses (mainly grains, such as
rice) and common vegetables. The same author noted that the two plants were particularly
vulnerable to taking up Pb deposited in the top layers of the soil due to their shallow rooting
depths.
2.2.2 Cadmium
Cadmium is present in the earth’s crust at an average of 0.2 mg/kg and usually occurs in
association with Zn, Pb and copper sulphide ore bodies. Cadmium is used in the steel and
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plastics industries and is released to the environment through wastewater (WHO, 1993). The
main sources of Cd in the environment are due to:
(1) air emission from Zn, Pb and copper smelters and industries involved in manufacturing
alloys, paints, batteries and plastics
(2) wastewater from mining
(3) agricultural use of sludge and fertilisers containing Cd
(4) burning of fossil fuels
(5) deterioration of galvanised materials and Cd-plated containers
Wastewater has been reported as a major source of Cd, although the metal is often not
detected in sludges (Lisk, 1972). Doyle (1978) reported Cd accumulations of over 1 mg/kg in
the soil, following high rates of application of sludges over a long time. The same author also
reported accumulation of 100mg/kg under furrow irrigation with sludge in some extreme
cases.
The average daily intake for humans is estimated at 0.15 µg from the air and 1 µg from water,
while smoking a 20-cigarette pack can lead to inhalation of around 2-4 µg of Cd (Scottish
Executive Environmental and Rural Affairs Department, 2002). Johnston and Jones (1995)
noted that plant-based foodstuffs were the largest source of dietary Cd and that the relative
contribution of soil Cd content in plants was important but largely unresolved.
2.3 Treated wastewater as source of Pb and Cd
Treated waste material from sewage treatment plants is disposed on land as effluent or liquid
sludge or dried sludge. Research has noted that most chemical pollutants are held by the
organic fraction of treated sewage, that is the sludge and not the effluent. Primary sludge
constitutes particulate organic material and secondary sludge consists mostly of microorganisms. However, WHO (1989) reported that conventional treatment processes, such as
the activated sludge and the bio-filtration systems have little effect on removing chemical
contaminants from wastewater. This suggests that chemical contaminants may also be present
in treated effluent. Junkins et al (1983) explains that during wastewater treatment, soluble
(dissolved) and insoluble suspended materials are adsorbed into microorganism cells where
they are broken down (digested). Digestion includes synthesis (reproduction of more cells)
and oxidation (formation of carbon dioxide (CO2), water (H2O) and energy. Junkins et al
(1983) described the process of activated sludge using the following equations:
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Conversion of organic matter
Bacteria
CO2 + H2O + Energy……………….. equation 1
Organic matter + O2
Enzyme
Reproduction of new cells
Bacteria
Organic matter + P+NH3+ O2
Protoplasm (new cell) + CO2 + H2O… equation 2
Energy
Degradation of other cells
Bacteria
Protoplasm + O2
CO2 + H2O + (NH3 or NO3) + Energy…….equation 3
Enzyme
As the microorganisms die, they break open, making nutrients and heavy metals available to
other microorganisms.
Liquid digested sludge differs from air-dried sludge in that during anaerobic digestion, much
of the organic nitrogen present in the sludge is mineralised to ammonia, thereby bringing the
ammonium ion into solution. When added to the soil, ammonium ions may volatilise, or
become adsorbed onto clay minerals or organic matter, absorbed by plants or nitrified (Doyle
1978). The mineralisation process also releases metals, including Pb and Cd into solution,
allowing for their adsorption onto clay minerals, hydroxides or uptake by plants.
The rate of decomposition of digested sludge was found to depend on soil moisture and
texture and most decomposition took place within one month of addition of sludge to soils
(Miller, 1974). This therefore suggests that liquid sludge has a higher proportion of readily
available metals in solution than dried sludge. As the soil dries after addition of liquid sludge,
decomposition decreases thereby reducing metal availability. Joffe (1955) attributed the
decrease in mineralisation upon drying of the soil to the retardation of microbial activity.
King and Morris (1972) reported decreases in soil pH and increase in cations available for
plant uptake in a sandy clay loam due to the application of liquid sludge to land. The same
authors also noted that large applications of sludge to soils have also been reported to create
anaerobic soil conditions that increase mineralisation of organic matter present in the sludge
as well as lower soil pH.
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Birley and Lock (2001) noted that nearly all Cd ions applied through irrigation water are
found in the topsoil due to strong sorption. However it has been observed that after filling all
available attachment sites, the soil particles gradually decrease the sorption rate (Christensen
1989a). Murray (2003) noted that metal behaviour in sewage sludge amended soils and plant
uptake is difficult to generalise because it strongly depends on nature of metal, sludge, soil
properties and crop.
2.4 Chemistry of Pb and Cd
2.4.1 Lead
Lead has an atomic number of 82 and atomic mass of 207. It is the heaviest non-radio-active
metal that naturally occurs in substantial quantities in the earth's crust (Subhuti, 2001). Pb is
the most common among the heavy metals and its most abundant isotope is
stable isotopes also exist. Lead has two oxidation states, Pb
2+
4+
and Pb . Pb
208
Pb. Other
2+
dominates
environmental chemistry. There is great similarity in the ionic sizes of Pb2+ and Ca2+, such
that Pb2+ may proxy for Ca2+ (Johannesson, 2002).
Plants differ widely in their ability to absorb, accumulate and tolerate Pb (Johannesson 2002).
Availability of Pb for plant uptake depends on total Pb in the soil, pH and organic matter.
Although organic matter immobilises lead, the metal becomes more available as
decomposition takes place. Doyle (1978) observed that Pb immobilisation from organic
matter was comparatively less than that of Cd.
2.4.2 Cadmium
Cadmium, a group IIb metal in the periodic table, is a mineral mined as part of Zn deposits
(Elson and Haas, 2003, Department for Environment, Food and Rural Affairs and
Environmental Agency, 2002). It is a relatively rare metal that is 67th in order of abundance.
Cd has an estimated half-life of between 15 and 1100 years implying that it is a long-term
problem (Johannesson, 2002). It occurs as an impurity in phosphate fertilizers, with which it
is applied to agricultural land. Cadmium is also added to agricultural land through treated
sewage. Its most common form in soils is the free Cd2+ (Department for Environment, Food
and Rural Affairs and Environmental Agency, 2002).
The chemistry of Cd in water is similar to that of Zn and to a lesser extent to Cu. Cadmium
interacts strongly with Zn due to chemical similarity between the two metals (Department for
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Environment, Food and Rural Affairs and Environmental Agency, 2002). Of all toxic metals
released in large quantities into the environment Cd is generally regarded as the one most
likely to accumulate in the human food chain (Johannesson, 2002). Adequate Zn intake tends
to provide partial protection against the toxic effects of Cd (Elson and Haas, 2003). The
presence of other metals may result in either synergistic or antagonistic interactions. The
presence of Cd and Hg may result in reduced toxic effects of both metals, while interaction of
Cu and Cd leads to a five-fold increase in the toxicity of each metal.
The toxicity of Cd in water is dependent upon the water’s hardness and chemical speciation,
which is influenced by pH, water temperature, ligands and co-existing metal cations present
in water. All these factors influence uptake and bio-concentration of cadmium by aquatic
organisms. In soils, Cd tends to be more mobile than many other heavy metals (Department
for Environment, Food and Rural Affairs and Environmental Agency, 2002) and its
adsorption has been shown to depend strongly on soil pH and to a lesser degree on hydrous
oxide and organic matter (Alloway, 1995).
Most of the Cd found in water up to pH 9.0 is in the divalent cation form (Cd2+). Cd is highly
soluble under acidic conditions, but its solubility decreases above pH 9.0 due to the formation
of cadmium hydroxide (Cd(OH)2). The presence of organic matter lowers the toxicity of Cd
as the metal is adsorbed onto exchange sites of organic matter (Doyle, 1978). Cd strongly
binds to sulphydryl (-SH) groups hence the pronounced tendency of Cd to bio-accumulate in
the food chain (Zambezi River Authority, 2001; www.agius.com/hew/resource/toxicol.htm).
Cadmium accumulates in the kidneys and has a long biological life in humans of 10-35 years
(WHO 1993).
2.5 Metal contamination and toxicity
All metals, including essential elements tend to be toxic to organisms at certain levels
(Breckle, 1991) with essential elements tending to be toxic at high concentrations while nonessential elements are toxic at relatively low concentrations. Any addition of a contaminant to
the soil is considered as contamination until it reaches a critical concentration when the
buffering capacity of the soil, that is its capacity to delay adverse effects, is exceeded. At this
point contamination becomes pollution (Moolenar and Lexmond, 1999). Pollution is the
malfunctioning of the soil due to abundant presence or availability of metals.
Figure 2.1 illustrates a generalised model of dose-response for plants exposed to nutrient
metals. When plants receive increasing input levels of essential elements like Cu and Zn the
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yield increases as metal dose increases. The supply and uptake reach a lower critical limit
where deficiency is eliminated. At this point the yield reaches a maximum. As the supply
increases beyond this limit, luxury consumption occurs and further increases in metal content
does not affect the crop or its yield within a range of metal doses, referred to as the tolerance
plateau in Figure 2.1.
Deficiency
Tolerance
plateau
Toxicity
Plant yield
100%
Non-essential
elements
Lethal
toxicity
Essential
elements
Critical level
for deficiency
Toxicity
threshold
Metal dose
Figure 2.1: Generalised dose-response curve for nutrient metals (Adapted from
Malan, 1999)
Luxury consumption (also known as tolerance) occurs when inactive complexes or storage
depots are formed in the case of certain metals (Clarkson, 1986) and the metals are deposited
there without toxicity occurring. Increasing metal dose beyond the upper limit of tolerance
induces adverse effects on soil flora and fauna and hence biological activity. This upper limit
represents the toxic level of the metal at which excessive uptake, whether of essential
elements like Cu and Zn or non-essential elements like Pb and Cd, results in adverse effects
on soil biota and plants as well as on mammals, birds and human consumers through the food
chain (Moolenar and Lexmond, 1999). It is not known at what level Pb and Cd become toxic
to star grass. If the dose-response curves of Pb and Cd in star grass are similar to Figure 2.1
and the toxic level is higher than the level recommended for pasture grass, then animals could
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graze on what appears to be healthy grass but would be exposed to metal hazard.
2.5.1 Lead
Lead is a bio-accumulative general poison. Birley and Lock (2001) note that industrial
pollutants, including Pb, may contaminate peri-urban crops and poison consumers. They note
that Pb can contaminate crops leading to neurological damage in humans. However,
Johannesson (2002) noted that the concentration of Pb in the soil had to be a minimum of 87
mg/kg before any effects on basic soil processes, such as microbial activity could be
observed. The same author states that uptake and accumulation of lead in tissue differed a lot
between species.
The toxicity of Pb is reduced by water hardiness. Ayers and Westcot (1985) recommend a
maximum of 5.0 mg/l Pb in irrigation water.
2.5.2 Cadmium
Cadmium is readily transported from the soil to the upper parts of the plants (Mengel and
Kirkby, 1982). Its transfer from soils to edible plant parts of agricultural crops is significantly
greater than for other heavy metals except Zn (Moolenar and Lexmond, 1999). Many studies
have shown that Cd concentration in crops is positively correlated to the content in soils
(IWMI, 1999).
The soil factors that play a direct role in controlling cadmium uptake by plants are the soil
type, through its CEC, organic matter and pH. Haghiri (1974) reported that organic matter
retained Cd through its cation exchange property. Doyle (1978) concluded that Cd adsorbed
by organic matter remained largely available for plant uptake and cadmium added as salts to
sludge, would possibly not exist in organic form when added to soils but would supplement a
reservoir of available Cd by being adsorbed on cation exchange sites of the soil components,
clay and organic matter.
John (1972) found the bonding energy for Cd to decrease in the following order: organic
matter>heavy clay>silt loam> sand. This suggests that compared to the mineral fraction of the
soil, Cd availability in sandy soils would be predominantly controlled by organic matter.
Doyle (1978) suggested that in low CEC soils or in soils that receive large amounts of sludge,
a portion of the Cd in the sludge would be leached while the rest would first fill the retention
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capacity of the soils (that is all exchange and adsorption sites) and then solubilise to become
available for plant uptake or leach.
World
Health
Organisation
(WHO)
Working
Groups
on
Cd
(http://www.icsu-
scope.org/cdmeeting/cdwgreport.htm (2000)) observed that compared with temperate soils
where data is much more available on Cd, tropical soils generally had:
(1) lower levels of organic matter
(2) lower pH
(3) higher variability in clay minerals and oxyhydroxides
(4) exposure to higher temperatures and fluctuations in soil moisture
These factors make Cd accumulation in tropical soils and crops less predictable on the basis
of the information already available and generated in the temperate regions of the earth. The
WHO groups recommended further investigation of Cd in tropical agro-ecosystems.
2.6 Bio-availability of heavy metals
The term bioavailability may differ among various research disciplines. Most literature refers
to bioavailability as the fraction of the total metal content that can be taken up by plants. This
fraction depends on total soil metal content, soil texture as influenced by the cation exchange
capacity, organic matter and pH. However the same term is also used to refer to availability of
metals to humans and animals from different food crops and sources as influenced by
physiological and nutritional factors (WHO Working Groups on Cd
(http://www.icsu-scope.org/cdmeeting/cdwgreport.htm)). Proponents of this definition prefer
to use the term phyto-availability to refer to metal availability to plants. In this study the term
bioavailability is used to refer to plant availability.
Plant availability of micronutrients in soils is related to the total amount of micronutrients in
various solid forms in equilibrium with the amount in the soil solution as dictated by the rate
at which the solution phase is renewed. Chaney (1988) noted that metals exist as a variety of
chemical species in a dynamic equilibrium governed by soil physical, chemical and biological
properties such that only a fraction of the soil metal is readily available for plant uptake. The
bulk of the metal fraction is in insoluble compounds unavailable for transport to the roots
(Lasat, 2000). Tandon (1993) stated that nutrients in the soil existed in several forms notably
(a) water soluble (b) exchangeable (c) specifically adsorbed, chelated or complexed (d)
secondary clay minerals or oxides and primary minerals. The first three forms are thought to
be important in supplying micronutrients for plant growth.
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The WHO Working Groups on Cd observed that the mass balance approach in determining
Cd pollution would indicate long term trends in Cd levels of the soil but would not be
adequate in assessing the risk. Bio-available Cd would be more applicable but labour and cost
intensive. However they noted the following constraints in the use of bio-available data:
(1) lack of generally applicable method for determining readily available Cd in soils
(2) changes in bio-available Cd with soil pH, clay content, organic matter, chloride
concentration and total Cd and therefore the need to interpret crop uptake risk, taking
these into account
(3) policy makers are reluctant to change from use of total concentration to bio-available
concentration until there is consistency on measuring of bio-available concentrations.
2.7 Lead and cadmium health hazards to humans
Evidence of negative effects of Pb and Cd on human health has been widely reported. Lead
levels in the human body have increased over time and it is estimated that the human body
can take up 1-2 mg daily up to a total content of 125-200mg, a level that is 500-1000 times
more than the levels detected in bones of very old human skeletons (Elson and Haas, 2003).
Subhuti (2001) noted that the WHO tolerable daily uptake of 0.2 mg/day has not yet been
attained in many parts of the world. According to Elson and Haas (2003), Cd increases in
content with age and is estimated to peak at 40mg in the human body at 50 years of age.
WHO (1993) set the maximum human intake of Cd and Pb at 1µg/kg and 3.5 µg/kg of body
weight per day, respectively.
In human beings, Staessen (2002) found 2-4% variance of urinary Cd (indicating mild renal
dysfunction and alterations in Zn and Cu homeostasis) related to consumption of vegetables
grown on an acidic sandy soil with 4.86 mg/kg total soil Cd and 2.43 mg/kg Cd in celery.
Lead poisoning in young children may cause permanent damage to the central nervous system
and reduce intellectual capabilities (Wildlife, 2000; WHO, 1993). It also causes high blood
pressure and hypertension in adults (Staessen, 2002). Though not very clear, Pb toxicity to
humans emanates from its interference with functions performed by essential elements such
as Ca, Fe, Cu and Zn in various enzymes (Elson and Haas 2003). It accumulates in the
skeleton, making the bones weaker (WHO, 1993; Elson and Hass, 2003). Placental transfer of
Pb occurs during gestation and throughout development (WHO, 1993). Young children may
lose up to 2 intelligence quotient (IQ) points for a rise in blood levels of Pb from 10 to 20
µg/dl (Scottish Executive Environmental and Rural Affairs Department, 2002).
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University of Pretoria etd – Madyiwa, S (2006)
2.8 Plants as soil cleaners and pathway of Pb and Cd to food chain
Plant uptake of Pb and Cd makes plants potential cleaners of contaminated soils but also
major sources of contamination for animals and human beings, if the plant is consumed.
Plants that tolerate relatively high concentrations of potentially hazardous metals are more
desirable for use in de-contaminating soils but create a greater risk to their consumers
compared to those that are more sensitive (Moolenar and Lexmond, 1999).
The removal of toxic elements from contaminated soils using plants, also known as phytoremediation, is among the most growing and exciting challenges for environmental research
and problem solving. Chaney et al (1997) categorises phyto-remediation into phyto-extraction
(use of plants to remove contaminants from soils), phyto-volatilisation (use of plants to make
volatile chemical species of soil elements), rhizo-filtration (use of plant roots to remove
contaminants from flowing water) and phyto-stabilisation (use of plants to transform soil
metals to less toxic forms, without removing the metal from the soil). About 400 metal
accumulating plants that take up high concentrations of heavy metals have been reported in
literature (Hoover, 2002; Salt and Kramer, 2000, McGrath et al, 2002). Several phytoremediation research studies are placing emphasis on the search for hyper-accumulating
plants, that can be used to de-contaminate soils in sites polluted by heavy metals from
industrial, mining and agricultural operations around the world.
Phyto-remediation can be cost effective in low- or medium-contaminated soils and does not
adversely affect soil fertility (Cunningham and Ow, 1996). However, there are limitations in
the use of plants for phyto-remediation (Cunningham et al, 1995; Cunningham and Ow, 1996;
Chaney et al, 1997). These include contamination of vegetation and food plants, difficulty of
establishing vegetation on contaminated sites and slow growth and small biomass of metal
hyper-accumulators. Plants used for phyto-remediation should be fast growing and be able to
accumulate large quantities of metal contaminants in their shoot (Cunningham and Ow,
1996).
Lasat (2002) defined hyper-accumulators as plant species that are capable of accumulating
metals at levels 100-fold greater than those typically measured in shoots of common
accumulator plants. The same author noted that hyper-accumulators will concentrate more
than 10 mg/kg Hg; 100 mg/kg Cd; 1000 mg/kg Co, Cr and Pb, levels that are way beyond
limits for animal and human consumption. Baker et al (2000) concluded that Thlaspi
caerulescens is the only known species to hyper-accumulate Cd in shoots. However, the
mechanism of Cd uptake in this hyper-accumulator plant is still not completely understood. It
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is often assumed that Cd, and other heavy metals without a biological function, are taken up
by transporters for essential elements because of a lack of specificity.
The plant pathway is therefore a major source of concern since most Cd consumed by humans
is obtained from the soil via food crops, while most Pb contamination through food crops is
obtained from surface pollution of crops (Elson and Haas, 2003). Johnston and Jones (1995)
noted that plant-based foodstuffs were the largest source of dietary Cd. They also noted that
the relationship between Cd in soils and Cd content in plants was important but largely
unresolved.
2.9 Treated sewage as source of Pb and Cd hazard to grazing animals via
plants
Secondary treatment of treated sewage through disposal on soils utilizes the soil as an
absorbent of contaminants and by default plants as contaminant extractors from soils. The
main concern of the public, regarding forage and other crops grown on sewage sludge
amended land is the potential uptake of trace elements by plants (Seaker, 1991). The other
concern regarding toxicity to animals grazing on pasturelands is the potential ingestion of
these elements from the soil under sewage sludge irrigation. Ingestion of large quantities of
soil by grazing animals is a rule rather than an exception (Fleming, 1986).
Concentrations of Pb and Cd were found to be higher in the liver and kidneys of animals
(Birley and Lock, 2001) exposed to the pollutants than those that were not. Roberts et al
(1994) reported that at soil Cd concentrations lower than the recommended sewage sludge
directive limit of 1 mg/kg (EEC, 1986) for use of sewage sludge in agriculture, grazing
livestock were found to accumulate Cd in their livers and kidneys, causing restrictions in the
growth of these body organs. Wilkinson et al (2001) reported a significant increase from 0.03
mg/kg to 2.57 mg/kg in the concentration of Cd in kidneys of lambs grazing on sewage
sludge-treated pasture compared to untreated pastures in the U.K.
FAO (1992) noted that the potential accumulation of certain toxic metals in plants and their
intake through eating of crops irrigated with contaminated water must be carefully assessed.
Plants can take up high levels of heavy metals until the levels become injurious. Although
DWAF (1996) reported that Pb and Cd interfere with metabolic processes, thereby affecting
plant growth and crop yields, the possibility of the reduction in yield coupled with high
increases in metal concentration in pasturelands going unnoticed cannot be ruled out. Where
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that happens cattle would continue to graze on pastures with high levels of metal
concentration posing a hazard.
2.10 Potential of grasses to accumulate Pb and Cd
Research shows that some grasses have a potential to be hyper-accumulators of heavy metals.
If such grasses were grown as pasture subjected to high concentrations of Pb and Cd, then
they would provide high levels of heavy metals to the human being through animals that
graze on the pastures. Gawronski et al (2002) who studied 21 varieties of grass (generas:
Festuca, Agrotis and Lolium) concluded that Agropyron repens L. Gramineae (quack grass)
was most promising for phyto-remediation purposes as its high biomass of 50 t/ha led to the
uptake of 20 kg of Pb from the soil.
The maximum recommended limit of Pb in grass on which animals feed is 40 mg/kg (United
Kingdom Statutory Instrument No. 1412, 1995), equivalent to 2 kg/ha for 50t/ha of grass.
These findings cannot, however, be directly translated to other grasses since the mineral
content of pasture is very variable depending on the species, stage of growth, soil type,
cultivation conditions and fertilizer application (McDonald et al, 1995).
Limited studies have been conducted on Cynodon grasses to date. Cynodon dactylon (couch
grass) reportedly accumulated high levels of Pb, after being grown on derelict mine dumps
with soil Pb of 340 mg/kg in eastern Zimbabwe (Jonnalagadda et al, 2002).
Cynodon
nlemfuesis grown on a soil with a total concentration of 15 mg/kg of Pb accumulated 0.1-2.0
mg/kg Pb and 0.2-0.5 mg/kg Cd (Simunyu et al, 2002). Although these studies confirm heavy
metal uptake by Cynodon grasses, they do not clarify the extent of pollutant uptake by the
grasses. According to Birley and Lock (2001), research is required to clarify the extent of
pollutant uptake by plants and the severity of adverse effects attributed to pollutant uptake.
2.11 Cynodon nlemfuensis
Cynodon nlemfuensis Vanderyst, also known as star grass, is a tropical and sub-tropical
stoloniferous perennial grass that originated in East and Central Africa (from Ethiopia, Sudan
and Democratic Republic of Congo) and was introduced to other parts of the tropics as a
fodder grass (Hanna, 1992). Star grass is established by vegetative propagation and resists
weed infestation once established. It is a variable species with mainly two varieties, Var.
nlemfuensis and Var. robustus. The grass is used as forage grass and as a cover crop for
erosion
control
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(http://www.fao.org/WAICENT/FAOINFO/AGRICULT/AGP/AGPC/doc/pasture/Mainmenu.htm).
Star grass grows well where temperatures do not fall below –40C, the pH is 5-8 and rainfall is
500-2000 mm per year. The grass is harvested for hay or silage when it is 30-40 cm tall or
after every 4-6 weeks growth (Hanna, 1992). Despite the widespread nature of this pasture
grass in Eastern and Central Africa, there is no evidence from literature that Pb and Cd uptake
characteristics of the grass have been studied.
2.12 Reliability of standard permissible toxic metal guidelines
Guidelines on heavy metal pollution in soils have been produced and are widely used for
legislating against soil contamination as well as in ecological risk assessments. However,
their widespread adoption has been questioned on the grounds that they vary depending on
the country and purpose of origin. According to DEFRA and Environmental Agency (2002),
soil guideline values may differ from one country to another depending on the conceptual
models behind each set of guidelines, reasons why the assessment criteria was developed,
management context, legislation, policy and differences in site conditions such as soil pH and
soil type. The differences imply that soil guideline values determined in one country may not
be directly applicable in another country. Furthermore, total metal levels in soils are
considered unreliable in predicting plant uptake since research has shown weak correlations
between total metal content of soils and plant metal content. The following observations
confirm the disparities in international guidelines on permissible total soil concentrations and
their weaknesses in predicting plant metal content.
FAO (1992) states that the maximum permissible concentrations of Pb and Cd in a soil under
grass should not exceed 300 mg/kg and 3-5 mg/kg respectively for soil samples taken within a
depth of 7.5 cm and with a soil pH above 5. Birley and Lock (2001) suggested a Pb limit of
150 mg/kg while Ross et al (1992) suggested 100 mg/kg. The USEPA Soil Screening Level
(SSL) for Cd for plant uptake that is 24 mg/kg (and 78 mg/kg for human beings) is based on a
total daily intake (TDI) averaging 1 µg/kg body weight/day over the first 30 years of human
life (USEPA (1996). On the other hand, the Soil Code (MAFF, 1998) reported a maximum
permissible soil Cd concentration of 3 mg/kg. This value relates to application of sewage
sludge to agricultural land and is intended to protect human and animal health from
consumption of arable crops. However DEFRA and Environmental Agency (2002) report that
although plant uptake is specifically considered and precautionary advice is given for low pH
soils in the Cd limit of 3 mg/kg, there is little information available about the conceptual
models implicit in the value. The Dutch Integrated Intervention Value for all land uses,
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quoted as 12 mg/kg for soil Cd and 34.9 mg/kg for human beings over a life time, is derived
from pathways that include direct ingestion of soil, consumption of crops and inhalation of
dust from the soils. On the other hand, Australians indicated that they had problems with Cd
in animals at Cd concentrations of less than 2 mg/kg. This scenario indicates that it may not
be technically sound to transpose guidelines from one area to another (DEFRA and
Environmental Agency (2002).
The soil guidelines, based on total metal concentrations, are increasingly regarded as
insufficient for predicting plant metal content, especially for health risk assessments. They do
not take into account the differences in bio-availability and hence toxicity in different soil
types and the fact that plants do not assimilate metals from bulk concentrations in the soil
(Bak and Jensen, 1998). The maximum permissible concentrations of heavy metals in surface
soils amended with sewage sludge have also been based on total soil concentrations
(Department of Environment, 1989) and are prone to the same shortfalls.
2.13 Reliability of guidelines on loading rates for wastewater on soils
The reliability of existing guidelines on loading rates from wastewater on soils deserves
scrutiny. According to Pescod (1987b), wastewater treatment through disposal on soils
requires specific loading rates that depend on; (a) nature of soil with loading rates increasing
in the order clay to gravel (b) nature of sludge effluent where the more dilute the wastewater
the higher the loading can be (c) climatic conditions where loading rates in dry and hot
climates can be higher than in wet and cold climates and (d) crops grown where loading can
be higher for less sensitive crops than sensitive crops. Murray (2003) noted that permitted
agricultural loadings of toxic metals from sewage sludge are typically regulated using the soil
criterion of total metal loadings or concentrations in soils and cautioned that generalised
assumptions on behaviour of sludge-borne metals in soil-crop systems may under-estimate or
over-estimate risks. The author further argues that in the absence of a basic understanding of
metal behaviour in each specific situation, a more precautionary approach to toxic metal
addition to soils is warranted. The following analysis highlights some grey areas in
generalised recommendations to toxic metal addition to soils.
The generalised relationships between loading rates, nature of soil and type of wastewater
presented in Table 2.1 do not specify the metal species in the sewage, the levels of the metals
and the type of crop for each dosing regime. Although a reduction in dosage would reduce
metal deposition the safe levels would not necessarily be guaranteed. According to Birley and
Lock (2001), further research is needed to establish the safe heavy metal content of sewage
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waste since the risk posed by heavy metals (Cd, Cr, Cu, Pb, Zn and Hg) will depend on their
dilution and uptake pathways. The same authors noted that some heavy metals might
precipitate in sludge such that their concentrations in treated wastewater may be very low. In
order to derive acceptable heavy metal loadings rates, it is necessary to determine intake
through consumption of plants grown in contaminated soils.
Table 2.1: Sewage type, loading rates and soil type (Source: Chatterjee, 1987)
Nature of soil
Loams, clay
Sandy loam
Alluvial loam
Alluvial loam
Loams
Sandy
Clay, loam
Clay
Type of sewage
Dosing (litres/ hectare /
day
Primary treated, diluted
Raw
Raw, diluted
Raw
Diluted
Raw
Raw, diluted
Raw, diluted
430 000
190 000
245 000
100 000
115 000
170 000
150 000
90 000
Other guidelines have been developed to address the need to specify the metal and its
permissible level in soils. Table 2.2 presents examples of such guidelines, for areas that
receive sewage application. However, the permissible limits presented in the table may differ
from limits prescribed in other sources for the same reasons presented in section 2.12. As an
example, while the table presents 0.033 kg/ha/year as the maximum permissible annual
application rate of Cd from sludge to agricultural land, the EU recommends 0.15 kg/ha/year.
Besides being different, the guidelines do not specify the soil type and the organic content on
which the Cd sorption depends (Christensen 1989b). In addition, they do not provide
information on the types of crops or soils concerned. Other factors also affect metal sorption
and uptake. Birley and Lock (2001) noted that evaporation, which is high in arid areas,
increases salt concentration and should therefore increase plant uptake of Cd in semi-arid
areas. This therefore limits widespread applicability of the guidelines.
Table 2.2: German standards for heavy metals in soil and sludge (Pescod et al.,
1985)
Element
Cd
Cr
Cu
Ni
Pb
Zn
Maximum permissible levels
in soil (mg/kg dry soil)
Maximum annual application rate
(kg/ha/year)
3
100
100
50
100
300
0.033
2.0
2.0
0.33
2.0
5.0
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Guidelines specifying metal content in sewage have been produced but have also differed
depending on the country of origin. According to Johannesson (2002), the maximum
allowable concentrations of Cd in sewage sludge in some countries are; Denmark (0.8
mg/kg), Finland (1.5 mg/kg), Sweden (2.0 mg/kg) and USA (8.5 mg/kg) and the differences
in the accepted levels resulted from the different risk management approaches adopted in each
country.
Pescod (1992) provided generalised guidelines of metal pollutant levels in wastewater (Table
2.3) that take into account the type of plant, type of metal and some soil parameters, such as
pH. However, other important soil characteristics that have a bearing on plant availability,
such as bioavailability were not addressed hence the same authors recommended assessments
of the potential for toxicity under local conditions. Metcaff (1992) noted that Cd could
accumulate in plants to levels that are toxic to humans and animals but are not toxic to plants.
Table 2.3: Recommended maximum concentrations of trace elements in
irrigation waters (adapted from Pescod, 1992)
Recommended
maximum
concentration in
water (mg/l)
Remarks
As (arsenic)
0.1
Cd
0.01
Cr
0.10
Cu
Fe
0.2
5.0
Mn
0.20
Ni
0.20
Pb
Se
(selenium)
5.0
0.02
Sn (tin)
Zn
2.0
Toxicity to plants varies widely, ranging from 12mg/l for Sudan grass to less
than 0.05 mg/l for rice
Toxicity to beans, beets, and turnips at concentrations as low as 0.1 mg/l in
nutrient solutions. Conservative limits recommended because of its potential
for accumulation in plants and soils to concentrations that may be harmful to
humans
Not generally recognised as essential growth element. Conservative limits
recommended because of lack of knowledge on toxicity
Toxic to a number of plants at 0.1 to 1.0 mg/l in nutrient solution
Not toxic to plants in aerated soils but can contribute to soil acidification and
reduced availability of phosphorus and molybdenum
Toxic to a number of crops at a few tenths of mg/l to a few mg/l, but only in
acid soils.
Toxic to a number of plants at 0.5 to 1.0 mg/l; reduced toxicity at neutral or
alkaline pH
Can inhibit plant cell growth at very high concentrations
Toxic to plants at concentrations as low as 0.025 mg/l and toxic to livestock.
Forage is grown in soils with relatively high levels of added selenium. An
essential element for animals but in very low concentrations
Effectively excluded by plants. Specific tolerance unknown.
Toxic to many plants at widely varying concentrations; reduced toxicity at
pH>6.0 and in fine-textured soils
Element
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2.14 On-land sewage disposal methods
There are different methods of disposal of effluent through irrigation. Some of the common
methods used worldwide are broad surface irrigation or flooding, sub-irrigation and ridge and
furrow irrigation. Broad surface irrigation involves the discharge of treated effluent to flow
overland onto the pastures or cultivated land. Sub-irrigation involves the use of surface drains
to distribute the sludge mixture onto the land. The mixture is allowed to stand in the drains
until it percolates and is subsequently collected by sub-surface drains. In the ridge and furrow
methods, the sludge mixture is distributed using furrows. Crops may be grown on the ridges
in such a manner that they do not have direct contact with the effluent.
2.15 Influence of plant and other chemical species on metal uptake
Plants and other chemical species influence uptake of metals by plants. Therefore, if bioavailable metal concentrations are used to improve reliability of critical limits, they would
have to be related to the plant species since uptake of Pb and Cd were observed to vary with
plant species (Haghiri, 1973; US Department of Energy, 1998). An example of the effect of
plant species on metal uptake is the wide range of the values of the transfer coefficient (metal
concentration in tissue above ground divided by total concentration in the soil) of 1-10 for Cd
and 0.01 - 0.1 for Pb (Johanneson, 2002). Alloway (1995) noted that due to numerous factors,
soil to plant transfer coefficients were not precise but indicative of accumulation differences.
A further complication is that root uptake also differs depending on the plant species and
other elements in the soil. According to Moolenar and Lexmond, (1999), actual plant uptake
in soil-crop ecosystems, not only depends on soil concentrations but also on the distribution
of a chemical element in relation to other chemical species in the soil (also known as
speciation) and mechanisms for root entry and translocation to aerial plant parts. Bak and
Jensen (1998) noted that while ecotoxicity tests were often conducted on single metals, toxic
responses to a mixture of metals could be antagonistic, synergistic or additive. Several
observations were made on this aspect.
Khan and Frankland (1983) observed that when Pb or Cd caused phytotoxicity Zn levels in
radish plants were close to deficient values. Cadmium and Pb have been shown to interact
positively or negatively in some plant species. Miller (1977) observed that accumulation of
Cd was increased by the addition of Pb while Cd in the soil reduced uptake of Pb in Zea mays
L. (corn). Similarly lead was observed to increase uptake of Cd in rye and fescue (Carlson
and Rolfe, 1979). The addition of both Pb and Cd increased the levels of both metals in
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American sycamore over uptake observed with single metals added (Carlson and Bazzaz,
1977). On the other hand, Miles and Parker (1979) found low level and inconsistent
synergistic and antagonistic effects among Pb, Cd and other heavy metals in uptake by
bluestem and black-eyed Susan.
The preceding observations, suggest that the level of interaction between Pb and Cd also
depends on the plant species. The interaction of these metals in star grass is not known. It
would however be beneficial if the interaction reduces plant uptake but detrimental if it
increases uptake.
2.16 Models for heavy metal content prediction
Researchers face many challenges in producing models that could be used to predict the
hazard of metal pollution. One major approach to studying heavy metal accumulation is the
mass balance approach in which inputs and outputs within the systems (area, field, region or
country) being polluted are determined and modelled for prediction of metal concentration
within environmental compartments. The mass balance approach produces long-term trends
in contamination and they incorporate total metal concentration in soils. However there are
major gaps in knowledge that militate against achieving mass balance calculations. It is
difficult to quantify outputs, such as aerial metal deposition, leaching, runoff and erosion and
the inadequacy of total soil metal concentrations in interpreting crop uptake risk.
The other approach involves setting legal criteria for limiting metal concentrations in the food
chain, in particular soil, crops and water. The major advantage of this system over the mass
balance approach is that the information required can be obtained by measurement and may
be available at local level. The challenge in this approach however lies in that the limits of
metals in soils are based on total metal concentration, which is regarded as being insufficient
in interpreting crop uptake risk. The following sections present examples on these
approaches.
2.16.1 Mass balance approach
Some effort currently underway towards modelling heavy metal pollution is called dynamic
modelling. Scottish Executive Environmental and Rural Affairs Department (2002) noted that
dynamic modelling of heavy metal pollutant concentrations in soils, was intended to predict
how heavy metal concentrations change over time in response to a given deposition scenario.
However the detailed information required, such as current and historic deposition,
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underlying geology, acidification status and rates of metal processing through soil layers on
site or catchment area is mostly unavailable. WHO Working Groups on Cd made similar
observations.
The
working
groups
on
Cd
(http://www.icsu-
scope.org/cdmeeting/cdwgreport.htm) noted that while it is possible to obtain values of inputs
such as aerial deposition, Cd in fertilisers and sewage sludge, there was a lack of knowledge
on the role Cd inputs play in entry into the food chain, lack of global and regional input data,
gaps in output information (leaching, runoff and erosion losses) and information on soil-plant
interactions that cause wide variations on Cd uptake. Scottish Executive Environmental and
Rural Affairs Department (2002) acknowledged the limitations in dynamic modelling by
suggesting that there is a need to develop models for predicting the bio-available
concentrations of metals in soils.
2.16.2 Use of soil-plant system models for metal prediction
The development of the simple model of soil-vegetative tissue uptake factors (Baes et al.
1984) often used for predicting plant metal concentration in health and ecological risk
assessments provides a basis on which plant metal content may be predicted from soil
concentrations. The Baes uptake factor is the ratio between the concentration of a chemical in
a plant and its total concentration in the soil. It is however known that uptake relationships
between soils and plants are considered to be valid only within a narrow range of chemical
concentrations in the relatively nontoxic range (Carson and Bazzaz 1977). This implies that
the uptake factors vary with total soil concentration (U.S. Department of Energy, 1998) and
could therefore lead to over-prediction or under-prediction of concentrations of some metals
in plants.
Another model, the Contamination Land Exposure Assessment (CLEA) model, developed
between 1992 and 1997 by the late Professor Colin Ferguson using soil-to-plant concentration
factors (as natural logarithms) for vegetable gardens, led to the development of soil guideline
values for Cd (Department for Environment, Food and Rural Affairs and the Environmental
Agency, 2002). The soil guideline values are used as screening tools or indicators that a given
soil concentration might present a health risk to users. They are used as a basis for
recommending further investigation and/or remediation.
Though the CLEA model takes into account different pathways of Cd such as ingestion from
the soil, soil plant uptake and inhalation, it reportedly over-estimates plant uptake in soils
where the pH is 6.5 and below. This happens because effects such as solution saturation, ionic
competition and plant tolerance to Cd were not considered in the model. Where pH is less
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University of Pretoria etd – Madyiwa, S (2006)
than 6.5, it is recommended that the bio-availability of Cd and plant Cd be determined on a
site-specific basis (Department for Environment, Food and Rural Affairs and the
Environmental Agency, 2002).
The preceding arguments suggest that the standard guidelines based on total permissible
metal levels in the soil may not be reliable for predicting plant metal content. In fact
bioavailability of metals has to be taken into account where the food chain is concerned since
plant uptake parallels bio-available fractions of metals in soils (Alloway, 1990). It is the bioavailable fraction of metals that poses a toxicological or environmental risk (Singh, 2002).
Birley and Lock (2001) also concurred and suggested that the tentative acceptable total
concentrations of various inorganic compounds in the soil should be regarded as first
approximations requiring further research, focused on determining uptake by plants grown in
contaminated soils, as a means of deriving acceptable heavy metal accumulation in the soil.
The use of bioavailable metal concentration to predict plant metal content from soil metal
content is undermined by the lack of consensus on a generally acceptable method to
determine soil metal levels. This causes policy makers to be reluctant to change from the use
of total metal concentrations to bio-available metal concentrations as indicators of
contamination (http://www.icsu-scope.org/cdmeeting/cdwgreport.htm).
Other researchers have made an effort to incorporate soil parameters that affect metal
availability in soils, such as pH, to improve accuracy of soil-plant metal concentration
models. On the strength of significant regressions for the uptake of inorganic elements by
earthworms using log-transformed concentrations that were obtained by Sample et al (1998a),
the US Department of Energy (1998) recommended the use of log-transformed total soil and
plant concentrations in regression models for predicting plant metal concentrations. After
Alsop et al (1996) demonstrated that Baes factors under-predicted or over-predicted uptake of
Pb in oats, Sample (1998) concluded that non-linear models, based on single-variables of
metal concentrations in plants versus total metal concentrations in soils or multi-variable
regression of metal concentrations in plants versus total metal concentrations in soils and pH,
were generally more useful and therefore recommended for risk assessments.
Sample et al (1998) demonstrated, using data from field studies, greenhouse studies and pot
studies on various soil types and types of plants, that log-transforming soil and plant
concentrations could result in statistically significant relationships that could be used to
estimate plant metal concentrations from soil concentrations, including those of Pb and Cd.
They however stated that if samples from specific sites are used to develop site-specific
uptake relationships, such type of data could provide more precise and accurate estimates of
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concentrations of chemicals in plants compared to their models. It appears that the major
weakness in their models was that the data they used from the different sources in the world
was not standardized, and in some cases scarce and varied. However they considered the use
of the logarithmic function in the models to be better than the use of soil and plant
concentrations in the Baes factor model.
2.17 Metal uptake in sewage amended soils
According to Murray (1995), short-term field experiments have shown that adsorptive
properties of sludge prevent excessive uptake of many metals into crops largely due to added
organic matter that complexes metals. However based on data from old sludge sites, the same
author also noted that this protection can not be considered to be permanent or effective for
all toxic metals. There is also a chance that this protection may not be the same where treated
sewage is added continuously to soils. The level of uptake of heavy metals by plants growing
in sewage-amended soils will depend on the bio-available levels of the metal in the soil
(Nyamangara and Mzezewa, 1999). The bio-available levels in turn depend on the type of
soil, organic matter content, pH, other chemical species present in the soil and heavy metal
loading on the soil, among other factors (Johannesson, 2002; Scottish Executive
Environmental and Rural Affairs Department, 2002).
Jesper and Jensen (1998) noted two approaches to increase reliability of critical metal limits
in determining critical loads. These were; (1) relating critical soil metal limits to parameters
controlling concentrations in soils, such as pH, soil texture, CEC and organic matter or (2)
using critical limits for soil solution as a basis for deriving critical metal concentrations in
soils. The former approach is the one that was utilized by Sample (1998) in developing multivariable regression models, based on logarithm functions and incorporating other soil
parameters, such as pH. The latter approach advocates for the use of bio-available metal
concentrations in studies to predict plant concentration from soils amended by sewage.
Overall, the preceding arguments suggest the use of bio-available soil metal concentrations
instead of total metal concentrations in the prediction of hazard of heavy metals.
2.18 Review of methods of measuring bio-available metal concentrations
The problems associated with the use of bio-available metal concentrations in setting
guidelines is that there is no agreed standard method of metal extraction from the soil that
scientists would accept as reflecting root uptake. Readily available heavy metals are estimated
by first extracting the metal from the soil into soil solution. The dissolved elements in the
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extract are then measured by atomic absorption spectrometry. There are many methods
considered appropriate for extracting bio-available heavy metals. These methods range from
the use of chelating compounds, such as Diethylene Triamine Penta Acetic Acid (DPTA)
(Lindsay and Norvell, 1978); diethylene triamine-pentaacetic acid-triethanolamine (DPTATEA) (Lindsay and Norvell, 1978) and ethylene-diamine-tetraacetic acid (EDTA) to nonchelating compounds such as ammonium acetate (Soane and Saunder, 1959, Ernest, 1974,
Robinson, 1997); calcium chloride (Murray et al, 2003) and water. Each method of soil
extraction provides its own value of bio-available metal content in a given soil, depending on
the relative strength of the extracting agent to solubilise the metal in the soil. According to
Murray et al (2003), the relative ability of mild and aggressive metal extracting agents to
assess metal bio-availability in soils has rarely been compared.
In general, chelating agents have been used widely for assessing readily available
micronutrients since they combine with free metal ions in solution and ions on exchangeable
sites to form soluble complexes. Chelating agents induce desorption of the metals, including
Pb and Cd, from soil solids thereby increasing the metal content in soil solution and
ultimately plant uptake (Chen and Hong, 1995; Baylock et al, 1998; Anderson et al, 1999).
Diethylene Triamine Penta Acetic Acid (DPTA), is a mild chelating agent that was found to
be useful for separating soils into deficient and non-deficient categories for Zn, Cu, Mn and
Fe and was therefore considered appropriate for estimating bio-available metals (Lindsay and
Noverll, 1978). The same authors noted that the DPTA micronutrient extraction method
correlates well with crop response to Zn and Cu and is considered suitable for monitoring Pb,
Cd, and Ni in soils receiving sludge applications. However, O’Connor (1988) observes the
anomaly that very high DPTA-extractable metals may be harmless to the plant and
correlations between DPTA-extracted metals and plant concentrations may not be significant
enough to predict plant levels based on soil levels. The author concludes that such
correlations may require consideration of pH to be significant and recommends limiting its
use to the original purpose described by Lindsay and Norvell (1978). On the other hand,
EDTA is a relatively strong chelating agent that has great potential for use in phyto-extraction
of major contaminants through its ability to chelate with metal ions bound by the soil, thereby
bringing them into solution. The addition of EDTA to the soil increased accumulation of Pb
in maize (Baylock et al, 1998) because EDTA stimulated release of Pb from the soil into soil
solution. Kirkham (2000) noted that EDTA mobilised Pb associated with the ion exchange
and carbonate fractions. Cunningham et al (1997) and Lasat (2002) suggested that EDTA
might increase the risk of spreading contamination and groundwater pollution, due to the high
solubility of the Pb-chelate complex.
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Other researchers prefer the use of some inorganic compounds instead of chelates, on the
grounds that chelating agents may extract more than the plant available fraction of metals in
the soil. Murray and Evans (2002) recommended the use of 0.01 M CaCl2 in predicting plant
availability after they found strong correlation between brome grass metal content and CaCl2extracted Cu, Ni, Zn and Cd from a sludge amended soil. Murray et al (2003) proceeded to
recommend dilute CaCl2 as a universal soil extractant for estimating trace metal availability to
crops based on findings of linear regression analysis of heavy metals (including Pb and Cd)
they undertook to relate concentrations of heavy metals in Trifolium pratense L. (red clover)
and in fine and coarse textured soils amended by heavy application of sewage sludge.
Ammonium acetate, an inorganic compound, has been used widely over the years due to the
strong correlation of soil bio-available metal it extracts from soils and plant metal content
(McGrath and Cegarra, 1992). Soane and Saunder (1959) found strong correlation between
bio-available Ni and Cd content of soils and plant metal content. Robinson (1997)
recommended I M ammonium acetate as the most suitable method for estimating bioavailability after obtaining good correlation between bio-available Zn and Cd and plant metal
contents. On the basis of the fore-going evidence, it is important to specify the method one
selects to extract bio-available metals from the soil and also avoid using methods that may
encourage spreading of soil contamination.
2.19 Review of some findings of pot and field methods for determining metal
uptake
Different results on uptake of heavy metals are obtained depending on whether plants are
grown in pots or field and the method of growing the crop. de Vries (1980) states that results
from pot experiments in greenhouses can not be extrapolated to field conditions due to
differences in environmental conditions under which the two are conducted. Results are
affected by pot size, growing conditions in the greenhouse (micro-climate), watering and
fertilisation regimes, all of which affect the yields and chemical composition of the plant
differently from what happens in the field.
In general, experiments in which plants are grown in pots tend to give comparatively higher
levels of contaminants than field experiments. Schmidt (2003) noted that heavy metals
recorded in pot experiments are generally higher than those recorded under field conditions
due in part to the higher efficiency of soil amendments in pots and the fact that plant roots
explore potted soil intensely and are therefore always in contact with the soil amendments.
Kayser at al (2000) obtained three times more Cd in tobacco (Nicotiana tabacum L.) and
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seven times more Cd in Indian mustard in pot experiments compared to field experiments. Pot
studies allow for testing appropriate concentration levels, simplify measurements of relevant
parameters, including leaching and are useful for designing field experiments (Schmidt,
2003). The same author suggested that when pot experiments lead to field experiments,
transplants should be avoided and instead the plant should be germinated in the contaminated
soil. Wu at al (1999) found that Pb concentrations resulting from transplanted corn were 45fold the concentration in the control compared to 6-fold the concentration in the control,
obtained for corn germinated in the contaminated soil. This outcome suggested that Pb uptake
differed if seedlings were transplanted or germinated directly.
Contrasting results were found when soil amendments were added in a single dose compared
to several doses. Grcman et al (2001) observed a 105-fold increase in Pb accumulation in
cabbage compared to a 44-fold increase after adding the same amount of EDTA as a single
dose and four doses, respectively. Conversely, Puschenreiter et al (2001) observed a 18-fold
increase in Pb accumulation in corn compared to 8-fold increase for multiple application and
single dose, respectively, of the same quantity of EDTA.
2.20 Review of sewage treatment systems in Zimbabwe
There are 139 wastewater treatment plants in Zimbabwe and of these 101 are waste
stabilization ponds (Hungerbuehler, 1997). Others use the biological trickling filtration
system, also known as bio-filtration system and biological nutrient removal activated sludge
treatment systems. Water from wastewater stabilization pond systems and biological trickling
systems is considered unfit for disposal into river systems or dams and therefore is used to
irrigate pasturelands. There is, however, a tendency by local authorities to invest more into
biological nutrient removal systems. Although this system is expensive to run it produces
better quality effluent that is considered safe for disposal into river systems and lakes/dams so
that such water may be recycled.
The use of wastewater effluent from wastewater stabilization pond and bio-filtration systems
for irrigating pastures is a common practice among local authorities in Zimbabwe. Although
Zimbabwe has over 30 years experience in irrigating pastures using wastewater, no detailed
study has been carried out to determine the possible effects of the practice on the health of the
farm workers, those living near the farms or those who consume the beef from animals bred
on the pastures.
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Zimbabwe has relevant legal instruments in the Zimbabwe Water Act (1999) and a legal
supervising authority, the Zimbabwe National Water Authority (ZINWA) for controlling
pollution by wastewater effluents but these regulations are not being fully enforced (Magadza,
2003). Each polluter is required to have a permit and is responsible for carrying out
environmental audits for submission to the Pollution Control Unit of ZINWA (Zimbabwe
Water Act, 1999). The Pollution Control Unit is responsible for enforcing regulations and as
such it carries out inspection whenever and wherever it deems necessary. Earlier on the Public
Health (Effluent) Regulations (1972) that were a part of the Rhodesia Water Act (1977)
prohibited the use of raw or undigested sewage sludge and effluent waters on land used for
agricultural purposes. It further stated that no effluent liquid (discharged from sewage works)
could be used for irrigation and no digested sludge could be used for agricultural purposes
without the prior permission from the appropriate authority.
Municipal wastewater management is the responsibility of local authorities. The authorities
have laboratories that monitor the quality of effluent discharged from wastewater treatment
plants. Municipal authorities in Zimbabwe take steps to minimise environmental problems
emanating from the use of wastewater for irrigation. Besides the treatment of wastewater, the
measures include prohibiting dairy farming on wastewater-irrigated pasturelands for fear of
milk contamination and discouraging horticultural crop production using wastewater. City
Council and beef abattoirs check for pathogens and helminths, in beef from animals that are
bred on pastures irrigated using treated wastewater. However, they do not check for levels of
chemical pollutants. This implies that metals would not be detected prior to beef being
consumed by humans. In general, the wastewater treatment systems are fraught with problems
that compromise operational efficiency, hence the need to investigate the impact they have on
the environment.
Extreme contamination of soils with sewage sludge has been demonstrated around Harare,
since the early 1970's and much of the research has been concentrated on the Crowborough
farm, one of the farms that receive effluent and sludge application (Mangwayana, 1995).
Although efforts are made to remove heavy metals from the effluent, they could still find their
way to the pastures since liquid digested sludge is often mixed with effluent and disposed on
pastures. Even where only effluent waters were disposed on pasturelands, the concentration of
heavy metal in the soil were still high. Mangwayana (1995) reported 1.6 mg/kg total soil Cd.
The fate of Pb and Cd is unknown at Firle Wastewater Treatment Works (one of Harare and
Zimbabwe’s largest treatment plants) where low quality effluent produced from the biological
filtration system is mixed with liquid digested sewage sludge (to produce a slurry of 3-4%
solids) that is used for irrigating 860 hectares of pasture at Firle Farm (Nyamangara, 1999).
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Considering that only a few short-term studies have been done to date, it was not possible to
rule out the risk posed to animals and humans by Pb and Cd through waste disposal on
pasturelands. It is on the strength of this background that this study was considered necessary.
2.21 Problem statement and hypotheses
2.21.1 Problem statement
The discussion in the previous sections of this study raised a number of important issues that
motivated this study. In Zimbabwe, pasturelands consisting of star grass have been grown on
sandy soils on which treated sewage has been disposed for over 30 years. Despite the
knowledge that sewage from domestic and industrial sources in cities contains potentially
harmful heavy metals, there has not been any meaningful monitoring of heavy metal content
in the treated sewage and soils. Furthermore, there has not been any attempt to quantify Pb
and Cd uptake by grass or animals to ascertain compliance of metal content of grass with
acceptable levels for grazing pastures, despite the detection of the two metals in treated
sewage.
It has been noted from the literature review that transposing permissible metal content
guidelines from one country to another could lead to inaccurate predictions of the pollution
hazard. Even though most developing countries have adopted guidelines developed
elsewhere, the use of permissible total soil metal concentration has come under scrutiny as
research has shown that total soil metal concentrations do not predict metal content in plants
accurately enough for health assessments. Bio-available metal concentrations have of late
been considered as being more accurate, but limited in use because of lack of consensus on
the method of measurement.
One of the largest treatment plants in Zimbabwe, Fire Wastewater Treatment Plant disposes
of treated sewage on sandy soils with star grass pasture. The fact that bio-availability and
hence toxicity of heavy metals is higher in sandy soils than clayey soils (Bak and Jensen,
1998) suggests the possibility of higher plant uptake of heavy metals where a sandy soil is
involved. Given that different plant species have different uptake capacities (Johannesson,
2002), the absence of any known studies on uptake of Pb and Cd by star grass implies that
soil-star grass uptake characteristics, including critical metal uptake levels are not known and
cannot be extrapolated from other grasses that have been studied. The absence of any such
study also implies that there is no model that could be used to predict metal content in plants
on the basis of metal content in soils. This scenario denies municipalities vital information
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required for developing soil and star grass management practices and policies for sewage
disposal on pasturelands.
Since bio-available metal levels in soils depend on plant, soil properties and climate, among
other factors, local conditions have a bearing on acceptable limits of heavy metals. It has also
been noted that those who developed models for soil-plant ecosystems encourage site-specific
studies due to perceived deficiencies in the models. Therefore this study aimed to:
•
determine the extent of Pb and Cd accumulation in sandy soils and star grass under
irrigation with treated sewage,
•
produce dose-response models for predicting metal concentrations in star grass using
bio-available soil metal concentrations in sandy soils
Such information was postulated to permit the estimation of appropriate levels of the metals
that can be allowed in a sandy soil and star grass, so that the production of star grass could be
optimized, while minimizing potential heavy metal accumulation in beef animals that graze
on such pastures.
2.21.2 Potential benefits of study
The formulating of objectives and hypotheses for this study was based on whether the
answers from the study (if obtained) would be useful and to whom? It was considered that if
answers were to be obtained, a number of stakeholders would be expected to benefit from the
findings. At local level, consumers of beef or milk from animals bred on wastewater disposed
pastures and farm workers would be in a position to, either allay fears of chemical pollution
or improve wastewater monitoring system to reduce chances of pollution. Harare City
Council would use the findings to identify chemical pollution hazards associated with its
current wastewater management practices and institute mitigatory measures to safeguard the
health of the residents. The benefit would also be expected to extend to other local authorities
in Zimbabwe and potentially SADC region through dissemination of the findings. The study
seeks to address and link well with pertinent issues in the region, notably environmental and
health issues and integrated water management. Within this context, it also complements
efforts being made by the Institute of Water and Sanitation Development in developing water
quality monitoring standards in Zimbabwe and scientists studying phyto-remediation
worldwide.
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2.21.3 Hypotheses
This study was intended to test the following hypothesis:
(i) Star grass takes up Pb and Cd and it is a high accumulator of both metals
(ii) The disposal of treated mixed effluent and sludge emanating from industrial and domestic
sources onto sandy soils increases bio-available Pb and Cd in the soil
(iii) Star grass grown on sandy soils on which treated wastewater from industrial sources is
disposed of can take up Pb and Cd to levels that present a Pb and Cd hazard to grazing
cattle.
(iv) Bio-available Pb and Cd in sandy soils and Pb and Cd concentrations in star grass form a
relationship that can be used to predict metal concentration in the grass.
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CHAPTER 3
GENERAL METHODOLOGY
3.1 Introduction
This chapter presents the general methodology that was followed in this study. It provides
background information on the study area and discusses the major principles that were used in
designing the study. It also outlines the major components of the study and provides a brief
discussion of the methodology followed in each component of the study. The detailed methods
and materials used in the study are presented in each chapter. Data analysis techniques employed
in this study are presented in this chapter and mentioned in subsequent chapters for clarity of
discussions.
Firle Farm was selected to be the study site because treated sewage from industrial sources is
continuously disposed onto pasture grass on which cattle graze. Flood disposal and irrigation of
pastures at Firle farm has been practiced for over 30 years as a form of tertiary treatment of
sewage whereby grass takes up nutrients for metabolic purposes. Preliminary studies indicated
that the treated sewage had high enough levels of Pb and Cd to warrant investigation in the sandy
soil and pasture grass. The sandy nature of the soils was postulated to allow the metals to be
readily available for uptake by grass.
3.2 Background of study area
Firle farm employs 32 farm workers and supports 3000 beef cattle. The cattle are born and bred
on the farm. The cattle are sent to abattoirs for slaughter and sale of beef to the population at
large. The farm workers carry out all operations including disposal of treated sewage, tend to
cattle and use water from boreholes situated on the farm for domestic purposes. There were
reports of cattle dying on the farm during the study. Although chemical pollution has been
suspected in some cases no chemical tests were carried out to confirm the suspicions.
The pastures at Firle farm comprise star grass and kikuyu grass and both grass species are
perennial. While star grass reportedly grows faster than kikuyu, the former is considered to be
more resilient to droughts (interview with Farm staff, 2000). As a result, the two grasses have
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been grown together, in order to reduce the risk of failure of pasture. There is no data available on
the application rates of sewage sludge and effluent on land at Firle farm, but based on annual
sewage output and area irrigated, the application rate is about 48 megalitres/ha/year or 126-167 t
solids/ha/year (Nyamangara, 1999).
3.2.1 Location of study area
Firle and Churu farms are located on the outskirts of the City of Harare. The Municipality of
Harare owns Firle farm and Firle Wastewater Treatment Plant. Churu farm, which is adjacent to
the Firle farm, belongs to the Government of Zimbabwe. Firle Wastewater Treatment Plant is
located on the outskirts of Glen View residential area, south west of Harare City. The study area
receives an average annual rainfall
(over a 30 year period) of 800mm (Department of
Meteorological Services, 1977).
The study site comprised two adjacent sections, one section in Churu farm and the other in Firle
farm (Figure 3.1). The areas are 30 metres apart and are separated by a fence and road. The sites
have a general slope of less than 3% towards a nearby river.
To Pension
NON-IRRIGATED AREA
(Churu farm)
To Glen View3
Sewage pond
Farm road & Fence
Pipeline
IRRIGATED AREA
South
(Firle Farm)
NORTH
Slope 3%
River
Tarred road
Firle
Treatment Plant
Division box
IRRIGATED AREA
Figure 3.1: Schematic diagram of the study area
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3.2.2 Sources of pollutants for study area
Firle Wastewater Treatment Plant processes both industrial and domestic effluent. The plant
services the southern residential areas and many industries within the Firle catchment area. The
industrial areas and the specific factories within the catchment area are listed below.
A) Willowvale Industrial areas
•
Imponent Tanning
•
Aluminium Industry
•
Industrial Galvanising and Fabrication
•
Tube and Pipe Industry
•
Power Lines Central Africa
B) Southern Industrial Area
•
Radiator Clinic
•
Radiator and Tinning
C) Adbernie Industrial Area
•
Edison Products
D) Graniteside Industrial Area
•
Hardcrame Company
•
Clover Electroplaters
•
Mcdiarmid and Company
•
Capri Corporation
E) Msasa Industrial Area
•
Msasa Platers
•
Sobhair refrigeration
•
Msasa Game Skins
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3.2.3 Treatment plants
Firle Wastewater Treatment Plant comprises of six units with a capacity to process 144 megalitres
of effluent per day. Two of the units use the conventional wastewater biological trickling
filtration system that produces low quality water (ca. 48 megalitres) used for irrigation of
pastures. The other four units use the Biological Nutrient Removal System (BNRS) that produces
high quality water (ca. 96 megalitres), which is discharged into the natural river system. The low
quality water produced from the biological filtration system is mixed with digested sewage sludge
from all the processing units to produce a slurry (3-4% solids) used to irrigating 860 hectares of
pasture.
3.3 Study design
The study was divided into 3 major components: (1) baseline assessment of Pb and Cd levels in
the study area, prior to subsequent detailed experiments (2) greenhouse experiments to assess the
capacity of star grass to accumulate Pb and Cd and (3) experiments on Pb and Cd uptake by star
grass under field conditions. The key approaches and principles that guided the design of each
component of the study are presented in this section together with the study design of each
component.
In baseline assessment, two approaches were followed. The first approach was to analyse past
records of chemical analysis of treated sewage to confirm presence and levels of Pb and Cd in
treated water disposed onto pasturelands. The second approach was to test Pb and Cd in soils of
the study area to provide levels of Pb and Cd prior to carrying out components 2 and 3. Soil
texture and other soil properties, such as clay content and cation exchange capacity, were
determined to provide data to describe soils and assess any relationships to Pb and Cd levels in
the soil. The levels obtained from analysis of past records were compared to national and
international legislated limits to assess compliance. Pb and Cd levels obtained from tests were
used to derive long-term levels of accumulation.
In the greenhouse experiment, the approach involved exposing star grass to a range of levels of
Pb and Cd so that responses in metal content of the soil, yield of grass and metal content of grass
to Pb and Cd added to the soil could be obtained. To achieve this, inorganic salts of the metals
were added to the soil on which star grass was planted. Treated sewage was applied to the soil,
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instead of water for the following reasons: Some research findings indicate that metal uptake
from soils in which inorganic salts are added do not adequately simulate uptake under field
conditions. Among the reasons cited, is the absence of the effect of organic matter on availability
of metals, which would occur under normal soil conditions. Several researchers indicate that
organic matter in sewage substantially reduces metal availability to plants (Christensen, 1989a;
Murray, 1995). However, others indicate that added inorganic metals remained largely bioavailable to plants as they would be adsorbed on cation exchange sites of soil components, clay
and organic matter (King and Morris, 1972; Doyle, 1978). In this experiment, inorganic metal
salts and treated sewage were applied together to bring the experiment closer to field conditions
where organic matter in treated sewage could affect plant availability of Pb and Cd. This design
was assumed to simulate field situations where different Pb and Cd pollutant loads from the
sources could find their way into raw sewage and subsequently treated sewage and onto
pasturelands.
Given that the soils used in this experiment were non-polluted, a pot experiment in which star
grass was grown was considered appropriate for raising levels of Pb and Cd. Notwithstanding
weaknesses of pot experiments described in de Vries (1980), pots were used in this study because
they allowed for controlled variation of Pb and Cd added and required less chemicals than would
be the case with a field experiment in which chemicals are added to the soil. Schmidt (2003)
observed that pot experiments are a low-cost approach to testing different soils, crops and
combinations of different elements and they simplify measurements of relevant parameters, such
as element balance, compared to field experiments. The same author noted that in many pot
experiments involving plant responses to chelate addition to soils, plants stopped growing on
transplanting onto the contaminated soil, hindering assessment of the effect of the chelate on
growth. In this experiment therefore, the design was to establish the crop first, so that the root
system would develop, then add Pb and Cd in solution to facilitate faster uptake of Pb and Cd.
Considering that many researchers have reported that most Pb measured in plants in the
environment is a result of direct deposition (Johannesson, 2002), this study was designed to apply
inorganic Pb and Cd directly onto the soil surface to minimise chances of contaminating grass.
To improve predictions of plant responses to soil concentration, soil bio-available metal levels
were used in place of total soil concentrations to develop soil-vegetative tissue metal uptake
models for Pb and Cd in the greenhouse and field experiments. In addition, this study combined
the concept of log-transforming soil and plant concentrations (as recommended by US
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Department of Energy, 1998) and use of bio-available metal levels in soils to produce soilvegetative tissue metal uptake models of Pb and Cd for star grass on a sandy soil.
Since there are many methods of extracting the bio-available metals from soils one method was
selected and used in both the greenhouse and field experiments to ensure consistency.
Ammonium acetate was selected as the extracting agent because it has been used widely over the
years and the bio-available metal fraction it extracts strongly correlates with plant metal content
(McGrath and Cegarra, 1992). Its suitability for extracting Cd has been confirmed (Soane and
Saunder, 1959; Robinson, 1997).
In this study, de-ionised water and standard series of Pb and Cd were used for quality control on
measurements of levels of Pb and Cd. No certified reference materials were used due to logistical
problems in acquiring and using them in Zimbabwe. It is acknowledged that the use of
appropriate certified reference materials for quality control improves comparison of the results of
studies to statutory limits. It is further noted that since the matrix in reference material used for
calibration is never strictly identical to that of the samples being analysed (ILAC, 1996) this
variation could generate a bias in analytical results, if careful selection of the reference material is
not undertaken. The bias is slight if (1) similarities between the sample and reference material is
good, (2) the measuring instrument is robust enough to detect matrix differences and (3) if the
samples are properly treated before analysis. Most local laboratories are yet to adopt the use of
these reference materials in environmental work.
3.3.1 Baseline assessment of Pb and Cd levels in the study area
Analysis of past records on levels of Pb and Cd in treated sewage
The Municipality of Harare provided past records on chemical tests carried out on treatment of
sewage. Recommended levels of Pb and Cd in treated sewage were obtained from literature. Firle
Treatment works, sewage conveyance system, Firle Farm and paddocks for cattle were inspected
to familiarise the author with the treatment and disposal systems.
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Baseline characterisation of soils and grass in study area
The area selected for the study consisted of 2 sections: (1) one section for testing long-term
accumulation of Pb and Cd and (2) another section for: (a) sampling soils for the greenhouse
experiment and (b) laying out the field experiment. Each section was 1000 m2. The first section,
labelled “Irrigated area” in Figure 3.1, was located in Firle Farm and it had received sewage
application for the previous 29 years. The second section, labelled “Non-irrigated area” in Figure
3.1 was located in the adjacent Churu farm and it had not received sewage application before. The
design was to use previously non-polluted soil in the greenhouse experiments and locate
treatments for the field experiments in the same area and soil type.
Soil samples were taken from the two areas. The soils were sampled at 10 cm depth intervals to
provide data for describing soil properties and accumulation of Pb and Cd along the soil profile. It
was decided that total soil metal concentrations be measured to obtain total levels of
accumulation. Total soil concentrations of Pb and Cd were determined by extraction using the
aqua regia method and atomic absorption spectrometry (Department of Environment, 1989). Soil
texture, clay content, cation exchange capacity and pH were determined using standard methods
described in detail in Chapter 4. Land slopes were also measured.
The data obtained from soil tests on the irrigated and non-irrigated areas was used to further
select and demarcate two smaller portions, one from within each section, for detailed
experimental work. To eliminate variations in observations induced by differences in soil
properties (other than those induced by treated sewage disposal) in subsequent experiments, it
was decided that the two portions for detailed experiments should have similar soil properties,
particularly texture, soil depth and land slope. The areas with minimal differences in these soil
properties were marked for the greenhouse and field experiments.
To validate uptake of Pb and Cd by pasture grass, the levels of the metals were tested in samples
taken from mixed kikuyu and star grass pasture that was within the irrigated area at the time. The
samples were cut at 5 cm off the ground. Pb and Cd in grass were extracted using hydrochloric
acid (HCl) and nitric acid (HNO3) employing the standard procedures detailed in Chapter 4. The
levels of heavy metals were then determined by atomic absorption spectrometry (Department of
Environment, 1989). Since the individual grasses could not be separated during sampling, the
levels of the metals in the mixed grass were considered to reflect uptake by either or both grasses.
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The levels of Pb and Cd in the non-irrigated soil were assumed to reflect background levels of a
non-polluted soil. The levels in the irrigated area represented 29 years of pollution. Therefore the
difference between the levels in the two areas was considered to be long-term accumulation.
Total soil metal concentrations were also analysed to evaluate levels of correlations with
concentrations in pasture grass and to justify whether there was enough evidence to carry out
subsequent experiments on the basis of bio-available soil concentrations.
3.3.2 Greenhouse Pb and Cd uptake by star grass under treated sewage
application
To evaluate responses of yield and metal content in grass to metal levels in soils, star grass was
grown in soils with Pb and Cd levels ranging from non-toxic to toxic levels. Yield and bioavailable Pb and Cd levels in soils and star grass were measured using detailed procedures
presented in Chapter 5. The data obtained was considered useful in developing dose-response
models for (1) soil metal content (dose) and metal content in grass (response), (2) soil metal
content (dose) and yield of grass (response) and (3) metal content in grass (dose) and yield of
grass (response). The purpose of the models was to enable prediction of responses based on
measurement of one parameter, that is dose level.
A pot experiment was designed as described below to apply different levels of Pb and Cd. To
exclude rainfall, the experiment was set up in a greenhouse. Pots were filled using soil from the
non-polluted section of the study area and star grass was grown inside the pots. The soil in all
pots was uniform, with respect to Pb and Cd levels.
Five treatments, each with 3 replicates, were set up to raise Pb levels in the soil. The purpose of
these treatments was to establish the dose-response relationships of Pb added as a single metal.
The first two treatments (a control irrigated using water and another treatment irrigated using
treated sewage) did not receive inorganic Pb. The treatment irrigated with treated sewage was
meant to apply the lowest level of Pb using treated sewage. In the remaining treatments, the
concentration of Pb in the soil was raised by 3 levels of 300 mg/kg, 600 mg/kg and 1200 mg/kg
of soil through the addition of Pb(NO3)2. Measured volumes of treated sewage were used to
irrigate the treatments. Pb content was measured in the water and treated sewage that was used to
irrigate star grass.
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In the second set of treatments there were 7 treatments of Cd. The aim was to establish the doseresponse relationships of Cd added as a single metal. The two treatments that did not receive
inorganic Pb also served as treatments of Cd, since inorganic Cd was not added to the soil. The
treatment irrigated with treated sewage was included to represent the lowest level of Cd addition
to the soil. In the remaining Cd treatments, the concentration of soil Cd was raised by 5 levels of
10 mg/kg, 20 mg/kg, 40 mg/kg, 60 mg/kg and 80 mg/kg of soil through the addition of CdS. The
volumes of irrigation were the same as in the first set. Cadmium content was measured in the
water and treated sewage that was used to irrigate star grass.
The third set of treatments was intended to establish dose-response relationships of Pb and Cd
when both metals were added to the soil. This set of treatments was included in the study because
literature provided conflicting findings on Pb-Cd interactions in the soil. The aim was to
investigate this interaction in a sandy soil and star grass because interaction was postulated to
occur under field conditions. Of the five treatments set up two did not receive inorganic metal
addition. Therefore the data obtained for similar treatments in the first and second sets also served
the third set. The remaining treatments of combined Pb and Cd were: 300 mg/kg Pb combined
with 10 mg/kg Cd, 600 mg/kg Pb combined with 20 mg/kg Cd and 1 200 mg/kg Pb combined
with 40 mg/kg Cd. The treatments received the same irrigation applications as those in the first
set. The levels of Pb and Cd were determined in each treatment.
The following standard analytical procedures, described in detail in relevant chapters, were used
to determine Pb and Cd levels. The extraction of Pb and Cd in treated sewage, soils (for
determination of total concentration) and grass utilised concentrated acids, hydrochloric acid
(HCl) and nitric acid (HNO3) to dislodge Pb and Cd from samples of sewage, soils and grass into
solution. After filtration, total Pb and Cd in soils, treated sewage and grass leachates were
determined using atomic absorption spectrometry (Department of Environment, 1989). Total soil
Pb and Cd were determined during baseline assessment of the study area. Pb and Cd in treated
sewage were determined for the greenhouse and field experiments. Bio-available Pb and Cd in
the soil were extracted by 1 M ammonium acetate (CH3COONH4) using procedures
recommended by McGrath and Cegarra (1992). After filtration of the leachate, the levels of Pb
and Cd were determined by atomic absorption spectrometry.
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A 210 VGP atomic absorption (absorbance range: -0.0820 to 3.200; concentration: 5 significant
digits; reproducibility: <+/-5%) spectrometer was used to determine the concentrations of Pb and
Cd in leachates of treated sewage, soils and grasses. Deionised water, which was used as a blank
for calibrating the spectrometer, was subjected to the same extraction procedures as the samples.
This procedure was undertaken to ensure that the metal being extracted had a matrix similar to the
same metal in the sample. Standards of Pb and Cd were used to produce a standard linear graph
of absorbance and metal concentration. Where the graph was not linear, fresh standards were
prepared or the instrument was checked for problems such as lamp alignment and burner height.
Once a linear graph was obtained, the metal level in the blank (which was expected to be zero)
was measured and the instrument auto-zeroed (for background correction), prior to re-measuring
levels of standards to re-check performance of the instrument. Measurements from the samples
were then taken, while re-checking levels in standards after reading every 5 samples. Where low
levels of metal concentrations were encountered, standard solutions were diluted accordingly. In
this study, values of concentration were rounded off to two decimal places and those values lower
than this were considered to be non-detectable for purposes of analysis. Webster (2001) noted
that few measurements of soil properties are accurate to more than 3 significant figures, implying
that with typical laboratory errors of 2-5%, sampling fluctuations could swell the error, making
the first two figures more meaningful, thus significant.
3.3.3 Field assessment of Pb and Cd uptake
This component of the study was undertaken to evaluate levels of accumulation of Pb and Cd in
star grass in response to changes in bio-available metal levels in soils under field conditions. The
approach used in this part of the study was similar to that used in greenhouse experiment except
that Pb and Cd levels in the treatments were raised by increasing total quantities of treated sewage
applied to the soil, instead of adding inorganic Pb and Cd. It was assumed that the higher the
quantity of treated sewage added to the soil, the higher the total quantity of Pb and Cd added to
the soil, hence the higher the bio-available metal content of the soil.
Four treatment levels were set up on 12 field plots in the non-polluted area. One treatment level
was located in the area previously irrigated in order to assess accumulation in a soil already
polluted. Five levels of irrigation application were allocated to the plots randomly. The nonpolluted area consisted of the control (which did not receive treated sewage application) and the
following treatments: (1) Treatment 1, where half of the estimated water requirements of grass
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was to be applied (2) Treatment 2, where the estimated water requirements was to be satisfied (3)
Treatment 3, where twice the estimated water requirement was to be provided. Treatment 4,
located in the irrigated area received the same level of application as treatment 3.
The plots were prepared for border irrigation and star grass was grown in each treatment. Treated
sewage was supplied to treatments 1 to 3 using a pump and a conveyance pipeline and Treatment
4 using a furrow. During irrigation the discharge of the pump was measured volumetrically, using
a bucket and stop watch. The discharge in the furrow was measured using a flume. The average
water application per irrigation and for the whole period was computed from discharge data for
each replicate and treatment.
It was not possible to pre-determine the levels of Pb and Cd in treated sewage. Therefore the
period of application of treated sewage was deliberately lengthened (11 months), to even out any
variations in the concentrations of Pb and Cd between irrigation events. Samples of treated
sewage were collected during each irrigation event and tested for Pb and Cd. Levels of Pb and Cd
in star grass and bio-available levels in the soil from each treatment were tested on samples
collected on 5 occasions during the experiment. The methods used for testing Pb and Cd in
samples from the greenhouse experiments were applied in this component. Details are presented
in Chapter 6.
3.4 Data analysis
The following sections present the analyses that were carried out in each component of the study.
Statistical analysis was carried out using the Statistical Package for the Social Sciences (SPSS)
package, SPSS 8.0 for Windows (www.spss.com, 1997), to determine normality of data inputs,
means, ranges, and standard deviations of various data sets throughout the study. Techniques such
as Analysis of Variance (ANOVA), correlation analysis, regression analysis and Student’s t-tests
were used to test for significance of the effect of one set of data on another and regression models
developed. Significance levels were quoted at p≤0.001, p≤0.01 and p≤0.05, although Webster
(2001) noted that this was a matter of choice and normally p≤0.01 and p≤0.05 would suffice. The
specific areas in which different types of analysis were employed and the use of the outputs are
briefly discussed below and also specified in the subsequent chapters for clarity of discussions
presented.
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In the first component of the study, arithmetic mean levels and ranges of Pb and Cd in raw
sewage, treated effluent and digested sludge were determined. Arithmetic means, ranges and
standard deviations were also determined for soil chemical properties that were cited in literature
as influencing Pb and Cd levels in soils and grass. The computed mean levels in treated sewage
and soil properties were compared to values and limits quoted in literature to assess similarities or
differences and compliance to local and international legislated limits. The difference in the
average values of Pb and Cd in the control and irrigated areas was computed to determine longterm accumulation in the soil. In grasses average metal concentrations were determined for
comparison with national and international legislated values as well as for correlation with metal
concentrations in soils.
Arithmetic means of levels of Pb and Cd in the soil and star grass were computed from measured
levels in replicates of each treatment. The maximum level of accumulation of Pb and Cd in star
grass was considered together with evidence of toxicity (damage to the plant leaves and/or stems)
in grass to provide an indicator of the capacity of grass to absorb Pb and Cd. This level was
compared to levels of accumulation in other grasses and plants, cited in literature, to establish
whether star grass had a higher or lower capacity to accumulate Pb and Cd, relative to capacities
of other plants.
In the second component of the study measured data for soil bio-available metal content and
growth parameters of yield and metal content in grass, was first tested for normality and then
normalized using the log10 function. Analysis of variance and comparison of means, were used to
determine the levels of significance of (1) treatment on soil bio-available metal levels, (2) soil
metal content on (a) yields and (b) content of metal in grass and (3) metal content in grass on
yield.
Regression analysis on log10 (variables) was used to develop the following dose-response
relationships:
(1) Pb and Cd content in star grass versus yield
(2) soil bio-available Pb and Cd concentration versus yield of star grass
(3) soil bio-available Pb and Cd concentration versus metal content in star grass
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Correlation analysis was then used to select and test the strength of best-fit regression models of
the variables. The best-fit regression model, whether linear or non-linear, was considered to be
the regression model with the highest correlation coefficient (that is, Pearson product moment
correlation coefficient, r2 value). The correlation coefficients of the best-fit regression models
were then compared to the critical values for correlation coefficients for one independent
variable, in order to assess the significance of association of the variables in the regression
models. After confirming the strength of the regression models, toxicity levels in grass were
derived from log10 grass metal content versus log10 yield models of Pb and Cd and used to derive
corresponding soil bio-available metal levels using log10 soil bio-available metal concentration
versus log10 metal concentration in grass models. To test whether the regression models from
single and mixed treatments were statistically different, the t-test for comparison of regression
coefficients was used.
In the third component of this study, the levels of the metals in soil and grass samples from field
plots were determined as in the second component. Regression analysis on log10 (variables) was
used to develop the following dose-response relationships:
(1) soil bio-available Pb and Cd concentration versus yield of star grass
(2) soil bio-available Pb and Cd concentration versus metal content in star grass
Analytical techniques similar to those used in the second component of the study were employed
in the third component of the study.
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CHAPTER 4
BASELINE ASSESSMENT OF LEAD AND CADMIUM LEVELS IN STUDY AREA
4.1 Introduction
This chapter describes a baseline assessment of Pb and Cd levels in treated sewage, soils and
existing pasture as well as soil properties of the study area where detailed experiments described
in chapters 5 and 6 were eventually carried out. The purpose of the assessment was to establish
characteristics of the area and use them to select portions of the area suitable for detailed
experiments on Pb and Cd. This chapter also provides an assessment of the long-term total levels
of accumulation of Pb and Cd in the soils, using chemical tests on soil samples from the irrigated
area and non-irrigated area.
4.2 Objectives
The specific objectives of this component of the study were:
(1) to assess Pb and Cd levels in treated sewage using past chemical records
(2) to determine long-term accumulation and distribution of Pb and Cd in a sandy soil after
29 years of treated sewage disposal
(3) to establish chemical characteristics of the soil in the study area
(4) to determine the presence and levels of Pb and Cd in mixed star and kikuyu pasture
grown in the area of disposal of treated sewage
(5) to investigate relationships between total concentrations of Pb and Cd in the soil and
metal content in mixed star and kikuyu grass pasture.
4.3 Detailed methods and materials
4.3.1 Analysis of past records on levels of Pb and Cd in treated sewage
Available past records of chemical tests on heavy metals covered a short period of time (1991 to
1994), during which the City of Harare carried out heavy metal tests. Data on Pb and Cd levels in
raw sewage, treated effluent and digested sludge were extracted and analysed statistically using
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the SPSS 8.0 for Windows (www.spss.com, 1997) computer package to obtain averages and
ranges that were compared with legislated limits.
4.3.2 Baseline assessment of chemical characteristics of study area
Soil sampling and testing
Soil samples were taken at least 2 months after the last irrigation. The samples were taken from
portions of the control and treatment areas. Each of the portions was 200 m long and 100 m wide.
The portions were sub-divided into 4 plots that were 100m long and 50 m wide. From each plot, 4
auger samples located on a grid of 6.25 m x 12.5 m were taken and corresponding horizons mixed
to make a composite sample. Each horizon was 10 cm in depth. The maximum soil depth was 50
cm. Areas that had evidence of localized ponding, such as irrigation furrows were avoided.
Surface litter was removed prior to soil sampling using augers. Plant debris was removed from
soil samples, before they were air-dried and passed through a 0.15 mm sieve.
Soil texture was determined using the hydrometer method (Gee and Bauder, 1986). Calgon (37.5g
sodium hexametaphosphate (Na6O18P6) and 7.94g sodium carbonate (Na2CO3) dissolved in 1000
ml of water) was used to disperse the soil fractions in the soil sample. The dispersed suspension
was passed through a 180-µm sieve and collected in a measuring cylinder to separate the sand
from the clay and silt. The weight of the sand was measured after oven-drying it overnight. A
brass plunger was used to stir the suspension. A hydrometer was used to take measurements of
clay and silt after 60 seconds of inserting the plunger into the suspension. Two hours from the
commencement of sedimentation, a reading representing the clay content, was taken using the
hydrometer. Temperatures were measured in both cases. The hydrometer readings were corrected
for temperature by adding or subtracting 0.3 units for every degree above or below 200C,
respectively. The percentage silt plus clay and clay content were corrected for hygroscopic
moisture to obtain the percentages of silt and clay.
Soil pH was determined using a 1:5 soil suspension of 0.01M CaCl2. A standard buffer solution
(pH 7.0) of dry potassium di-hydrogen orthophosphate (KH2PO4) and di-sodium hydrogen
orthophosphate (Na2HPO4) was used to calibrate the pH meter. Records of pH were made when
reading on the pH meter stabilised for at least 0.1 units per 30 seconds.
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Cation exchange capacity (CEC) was determined by saturating the soil with 1M CH3COONH4
buffered at pH 5.2. A mechanical vacuum extractor (Model LT-800-8, Concept Engineering, Inc,
Nebraska, III, USA) was used for extracting exchangeable bases. Excess ammonium was washed
out using ethanol (CH2CH3OH) and the adsorbed ammonium was determined by steam
distillation and titration with sulphuric acid (H2SO4) in a distillation unit, Kjeltec Auto 1030
Analyser. A standard sample (Harare 5E.2) was included in every 48 samples for quality control.
Where the value of the check sample was outside the expected range the whole batch was redone.
Total heavy metals in soil were extracted with aqua regia (1:3 conc. HNO3 and conc. HCl) and
heated under reflux. After filtration, the extract was diluted with 2M HNO3 and Pb and Cd were
determined on the atomic absorption spectrometer.
Organic carbon (C) was determined by the modified Walkley and Black method (Houba et al.,
1989) with additional heat applied under reflux (at 1300C). Excess Potassium dichromate
(K2Cr2O7) was used to oxidise organic C. The excess dichromate was then titrated using ferrous
ammonium sulphate (Fe(NH4)2(SO4)2) to determine the amount used in oxidising organic C. A
conversion factor of 1.33 was applied to organic C to determine total organic C assuming 75%
recovery (Houba et al., 1989). A Total Organic Carbon (TOC) analyser was not available, hence
the method described was used.
Sampling and testing of grass
Grass samples from the irrigated area were taken from each plot to determine the metal content.
One sample, consisting of 10 sub-samples was taken from each of the 4 plots in the sewage
sludge treatment. The samples were taken on a grid of 6.25 m x 10 m, within each plot.
The grass was cut at 5cm height off the ground. No grass samples were taken from the control
area because the sparse and mixed grasses were different from those grown under irrigation. It
was therefore not possible to compare heavy metal plant uptake between the control and the
sewage sludge treatment. The samples were washed using deionised water and oven dried at 65
0
C to constant weight before being ground and sieved through a 0.1 mm sieve. The samples were
then ashed at 550 oC for 16 hours and digested with 25% HCl and concentrated HNO3. After
filtration, Pb and Cd were determined using atomic absorption spectrometry.
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To analyse the measured data, arithmetic means and standard deviations were calculated on clay
content, pH, CEC, organic carbon, Pb and Cd for each soil horizon. Data from tests of Pb and Cd
on grass was subjected to the same analysis for which means and ranges are presented. Analysis
of variance was used to determine the significance of differences in means of various data.
Simple correlation was used to measure the degree of association between any two soil
properties, since it was important to investigate whether soil properties like CEC, organic matter,
heavy metal content and soil depth were associated. Pearson’s correlation coefficients (r2 values)
were, therefore computed for pairs of variables, including soil depth to determine trends in the
relationships of the variables and likely implications on uptake and modelling of uptake of Pb and
Cd.
4.4 Results
4.4.1 Analysis of past records on levels of Pb and Cd in treated sewage
Firle Wastewater Treatment Plant utilises two types of sewage treatment technologies, namely
biological trickling filtration plants and biological nutrient removal activated sludge plants.
Appendix 1 summarises the treatment processes in the two technologies at Firle Treatment
Works. The effluent from biological trickling filtration plants is mixed with sludge (shown as
liquid digested sludge or farm compost material for farmland and humus sludge in Appendix 1)
and directed to irrigate pastures. This approach serves two purposes, disposal of sludge without
the need for landfill and secondary treatment of effluent through the soil, where the seepage is
expected to be of acceptable quality before it gets into Manyame River. Sometimes liquid sludge
on its own or effluent on its own is disposed on pastures.
Heavy metals in treated sewage
Table 4.1 presents results of an analysis of Pb and Cd levels and their prescribed limits according
to the Zimbabwe Water (Wastewater and Effluent Disposal) Regulation (Zimbabwe Statutory
Instrument 274, 2000). These regulations were derived from international guidelines, particularly
the average recommended maximum limits that were based on United States Environmental
Protection Agency guidelines for wastewater reuse enshrined in US EPA (1992) and also
included in the table.
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Table 4.1 shows the presence of Pb and Cd in both the effluent and digested sludge over the 4year period. The average concentrations of the metals in effluent were within the acceptable
limits for short-term irrigation. However the highest levels were above maximum recommended
levels in both effluent and digested sludge.
The average levels of Pb of 2.6 mg/l for digested sludge and 0.20 mg/l for effluent were below
the local legislated limits for both short and long-term irrigation and US EPA (1992) limits.
However the highest concentration of Pb (5.03 mg/l) was marginally higher than the
recommended long-term level in sludge.
The concentration of Cd in the effluent and digested sludge was not detectable in some samples,
hence it was within acceptable limits. However, the upper levels of Cd in digested sludge (0.5
mg/l) and effluent (0.03 mg/l) were 50 and 3 times the local recommended level of 0.01 mg/l, for
long-term irrigation, respectively. The upper level of Cd in sludge was 10 times the level
recommended for short-term irrigation. This indicates a wide range in metal content of both
effluent and sludge.
Table 4.1: Average (range) concentration (mg/l) of heavy metals in samples of
digested sewage sludge and effluent (Source: Harare City Council
file records, 1991-1994)
Metal
Pb
Cd
Digested
sewage
sludge
2.6 (0.13-5.03)
ND (ND - 0.5)
Effluent
Current local legislated
levels in wastewater
suitable for irrigation*
0.20 (0.05-0.48)
ND (ND - 0.03)
Long term
Short term
5
0.01
20
0.05
Recommended
maximum
concentration **
5
0.01
ND - not detectable
*Zimbabwe Statutory Instrument 274 (2000).
** Source : United States Environmental Protection Agency (US EPA), 1992.
4.4.2 Chemical characteristics of study area
Type of soil, soil pH, cation exchange capacity and organic carbon
Table 4.2 presents selected soil properties of clay content, soil pH, organic carbon and CEC. The
control and treatment were situated on predominantly granitic sandy soil classified as Haplic
Arenosol.
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Table 4.2: Selected properties of a sandy soil in the irrigated and control areas
Sampled
area
Treatment
Control
Soil depth
(cm)
Clay content
(%)
Soil pH
(CaCl2)
Organic
carbon (%)
Cation exchange
capacity (cmolckg-1)
0-10
10-20
20-30
30-40
40-50
0-10
10-20
20-30
30-40
40-50
<3
5.7 (4.7)
8.5 (4.4)
7.3 (0.6)
6.0 (1.4)
6.3 (0.6)
6.0 (1.0)
5.0 (1.0)
5.7 (0.6)
10.0 (0.0)
5.4 (0.5)
5.5 (0.6)
5.9 (0.8)
5.4 (0.2)
5.5 (0.1)
4.6 (0.8)
4.4 (1.1)
4.6 (1.1)
4.5 (1.0)
4.9 (1.7)
3.3 (0.3)
1.7 (0.5)
0.9 (0.6)
1.1 (0.2)
1.0 (0.5)
1.3 (0.1)
0.9 (0.1)
1.0 (0.2)
0.9 (0.2)
0.7 (0.0)
29.3 (11.2)
8.1 (7.9)
4.6 (3.5)
2.9 (1.0)
3.2 (1.7)
1.6 (0.9)
1.5 (0.8)
1.4 (1.0)
1.4 (0.7)
1.3 (0.5)
() standard deviation
The soil consisted of a sandy top layer (0-30 cm) overlying a sandy-loam subsoil (30-50 cm). It
was generally acidic with the pH of the sewage sludge and effluent irrigated soils being on
average 1 pH unit higher than the corresponding horizon in the control. The very low CEC and
organic C levels were consistent with sandy soils. However addition of sewage sludge and
effluent resulted in a 2.6-fold and 15.5-fold increase in organic carbon and CEC, respectively in
the 0-20 cm soil layers, when compared to corresponding horizons in the control.
Total concentrations of Pb and Cd in soils
Table 4.3 presents the average total concentrations of Pb and Cd in the soil profile of the irrigated
and the control areas for every 10cm soil depth. Throughout the soil profiles of both the control
and the irrigated areas, Pb and Cd were present. The average levels of Pb and Cd within the 0-50
cm profile of the control were 18.4 mg/kg Pb and 0.40 mg/kg Cd, respectively. The average
levels of the metals in the area of disposal were 55.5 mg/kg Pb and 0.65 mg/kg Cd. Therefore the
level of accumulation over the 29 years were 37.1 mg/kg Pb and 0.25 mg/kg Cd.
The mean profile concentrations of Pb of 55.46 mg/kg and Cd, 0.65 mg/kg were 3.0 and 1.6 times
the mean profile levels in the control, respectively. Accumulation of the two metals
predominantly occurred within the 0-20 cm horizons, particularly in the 0-10cm horizons. In the
0-10 cm horizon of the irrigated area, Pb and Cd were 11.6 and 3.2 times the levels in the 0-10
cm horizon of the control, respectively. Otherwise the background levels in the 30-50 cm
horizons of the control were largely similar to the levels in the area of disposal.
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Analysis of variance showed that metal levels in the treatment were significantly (p≤0.01) higher
than in the control. The mean levels of the metals in the top 0-20 cm in the irrigated area were
significantly (p≤0.05) higher than the levels in the 20-50 cm and also significantly higher
(p≤0.05) than the levels in the 0-20 cm of the control.
Table 4.3: Average total soil metal concentrations in horizons of soil profile of
irrigated and control areas
Sampled area
Soil depth (cm)
Treatment
Control
0-10
10-20
20-30
30-40
40-50
0-10
10-20
20-30
30-40
40-50
Total metal concentration (mg/kg)
Pb
Cd
186.31 (120.92)
33.32 (26.61)
22.03 (5.70)
18.71 (5.01)
17.00 (9.92)
16.00 (1.63)
24.00 (13.47)
19.00 (7.39)
15.50 (1.00)
17.33 (1.15)
1.26 (0.35)
0.75 (0.24)
0.47 (0.11)
0.39 (0.03)
0.40 (0.25)
0.40 (0.14)
0.35 (0.09)
0.49 (0.13)
0.45 (0.19)
0.33 (0.20)
() standard deviation
Comparison of mean levels of metals in the 20-50 cm soil horizons showed that there were no
significant differences within the horizons in both the control and the treatment. Total metal
concentrations of the soil horizons correlated well with depth in the irrigated areas (r2 = -0.76 for
Pb and 0.89 for Cd) and poorly in the control (r2 = -0.27 for Pb and -0.09 for Cd).
Correlation of soil parameters and total metal concentrations
The pH correlated poorly with depth since r2 values for the irrigated area and control area were
0.08 and 0.59 respectively compared to an r2 critical value of 0.87. The correlation of the pH of
the soil and total metal concentration along the soil horizons was weak in both the irrigated and
the control areas for both Pb (r2 = -0.44 in both cases) and Cd (r2 = 0.35 in the irrigated area and
0.30 in the control area).
The clay content of the soils correlated strongly with total concentrations of both Pb and Cd in the
irrigated area (r2 = -0.85 for Pb and -0.87 for Cd) but relatively weakly in Pb of the control (r2 =
0.05). In the case of Cd correlation was strong in both cases (r2 = -0.87 in the irrigated area and
0.75 in the control area).
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Cation exchange capacity correlated strongly with total concentrations in the irrigated area (r2 =
0.99 for Pb and 0.97 for Cd). It however correlated weakly with total concentrations in the control
(r2 = 0.12 for Pb and 0.04 for Cd). Organic C strongly correlated with total metal concentrations
in the irrigated area (r2 = 0.97 for both Pb and Cd) but weakly with total metal concentrations in
the control (r2 = 0.25 for Pb and 0.34 for Cd).
Concentrations of Pb and Cd in soils and grasses
Table 4.4 presents the average total Pb and Cd concentrations in the 0-20 cm soil horizon and in
grass from sub-sampling areas within the irrigated area. Mixed star and kikuyu grasses took up Pb
and Cd. Cadmium was not detected in some samples. Using the Student’ t-test showed that there
was no significant difference in mean soil levels of Pb and Cd on sub-sampling points 1 and 4 and
in levels of Cd in the corresponding grasses. However soil Pb levels at these points were
significantly (p≤0.05) higher than on sub-sampling points 2 and 3 and the level for sub-sampling
point 3 was significantly (p≤0.05) higher than at sub-sampling point 2. The Pb level of grass at
sampling point 1 was significantly (p≤0.05) higher than on sub-sampling points 2 to 4. Subsampling point 2 had a significantly (p≤0.05) higher Cd level than the rest.
There was very weak correlation between total soil concentration of Pb and levels in the grass (r2
= 0.39). Similarly, weak correlation coefficients were obtained using log10-transformed total soil
concentration and log10-transformed concentration of the metals in grass.
Table 4.4: Average total metal levels (mg/kg) in 0-20 cm soil depth and mixed
grass
Pb
Sub-sampling
points
Soil
Grass
Soil
1
2
3
4
148.65 (15.32)
17.0 (11.31)
88.91 (8.22)
167.54 (17.68)
1.50 (0.04)
1.01 (0.02)
ND
1.0 (0.03)
0.66 (0.09)
1.19 (0.06)
1.21 (0.11)
0.95 (0.08)
() standard deviation
58
Cd
Grass
ND
1.2 (0.03)
0.17 (0.02)
ND
University of Pretoria etd – Madyiwa, S (2006)
4.5 DISCUSSION
4.5.1 Analysis of past records on levels of Pb and Cd in treated sewage
A comparison of mean levels of Pb and Cd in the treated wastewaters and legislated limits,
suggested that no hazard should be expected from the two metals. However, the upper limits of
Cd in treated sludge and effluent were 50 and 3 times the local legislated level of 0.01 mg/l,
respectively. The limitations in the data, described below and the probability that higher levels of
the metals could be discharged into the treatment system and onto the pasturelands, implied that
Pb and Cd hazard could not be ruled out on the basis of the data analysed in this component of the
study. This notion is supported by the fact that the upper levels of the metals in treated water were
very high, particularly in the case of Cd.
The data used in this analysis was scanty. Considering that on-the-spot or grab samples were
taken once every two months, it was not possible to determine the distribution of the levels within
each 2 month period, let alone the variation within the intervening period or within a production
cycle. Junkins et al (1983) recommended hourly sampling programs during some production
cycles and non-production cycles in order to capture variations in organic loading, including
detection of peak organic loads. Therefore it was not possible to ascertain the distribution of the
concentrations of the metals with respect to the upper or lower values, on the basis of the data
obtained from the City of Harare. If the systems tended to operate close to the upper limit, the
scenario presented considerable Pb and Cd hazard. Hofwegen and Veenstra (1995) noted that a
50% increase in total soil Cd from 0.5 mg/kg to 0.82 mg/kg caused a large increase in Cd content
in brown rice from 0.08 to 1 mg/kg (1200%).
The wide range of metal concentrations in both the effluent and digested sludge (Table 4.1)
implied large variations in levels of pollutants in raw sewage from the sources. This variation
could be a result of varying levels of dilution of the pollutants from sources or increases and
decreases of activities causing pollution at the sources or intermittent high levels of pollution.
Besides dilution, most variation in pollutant concentrations could be attributed to industrial
pollution since domestic pollution would not be expected to vary so widely. The variability in
strength and character of influent wastewater (which can cause organic shock loads to biological
treatment systems) is experienced by most treatment systems and is related to industrial and
commercial operation schedules, which tend to vary (Junkins et al 1983).
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There was insufficient disposal data on treated sewage application. The lack of data on the
proportions of mixes of sludge and effluent implied that the actual concentrations of the metals in
the mixture of effluent and sludge were unknown. The levels shown in Table 4.1 however suggest
that the concentration of the mixed effluent and sludge disposed on pastures was somewhere in
between the concentrations of the two. This implied that based on the 1991-1994 data, treated
sewage disposed on pastures had 0.20-2.60 mg/l Pb and <0.0-0.5 mg/l Cd, leaving open the
possibility of irrigation water having a concentration that could be higher than recommended
levels.
The fact that Harare City Council sometimes irrigated the land using effluent only or sludge only
or a mixture of the two also added uncertainty regarding heavy metal loading on the soils. In
addition the lack of proper irrigation schedules implied that some areas could receive more water
than others, suggesting that some areas could be exposed to higher metal concentrations from the
irrigation water if their irrigation coincided with a period when high loads were received by the
treatment plant.
4.5.2 Pb and Cd accumulation in soils
Variations of total soil metal concentrations between irrigated and control
areas
The higher levels of total Pb and Cd obtained in the soil profile of the irrigated area indicate
accumulation when compared with levels in profile of the control area. The average background
level of Pb of 18.4 mg/kg in the control area and the average soil profile Pb level of 55.5 mg/kg in
the irrigated area indicate accumulation of 37.1 mg/kg (1.3 mg/kg/year) in the soil over 29 years.
Both soils fell within the 10 mg/kg to 70 mg/kg background levels of Pb for normal soils
(Johannesson, 2002). Accumulation in the top soil layers translates to an average increase of 5.7
mg/kg per year in the top 10 cm and 0.3 mg/kg per year in the 10-20 cm depth.
The mean profile levels of Cd of 0.65 mg/kg obtained in the irrigated area against a background
level of 0.40 mg/kg in the control area indicates accumulation of 0.25 mg/kg in 29 years or 0.01
mg/kg/year in the irrigated area. Cadmium has been reported to be less than 1 mg/kg (Alloway,
1995), 0.2 mg/kg (WHO, 1993) and 0.1-0.4 mg/kg (Johannesson, 2002) in normal soils.
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Therefore the level obtained in this study was in agreement with levels quoted in these references.
McGrath and Loveland (1992) observed total Cd concentrations of 0.2 to 1.7 mg/kg in
agricultural soils in England and Wales, while Alloway (1995) reported up to 100 mg/kg Cd in
agricultural soils subjected to high application of phosphate fertilisers.
The preceding arguments confirm that the observed levels of all metals in the control area were
the base levels for an unpolluted soil. The fact that the two areas were just adjacent eliminates the
possibility of other sources of pollution, such as air pollution, being responsible for the increase
in metal concentration in the soils. Therefore the metals that were identified in treated sewage led
to a rise in the levels of the metals in the sandy soil.
The higher pH in the irrigated area (Table 4.3) was attributed to the alkalisation effect of basic
cations contained in sewage sludge and effluent. While some variations in metal levels are
expected due to inherent differences in soil chemistry between one sampling point and another,
the large variation in metal levels between sampling points was attributed to poor water
application and distribution associated with the flood disposal system.
In this study, the total metal concentrations of the Pb and Cd were strongly and positively
correlated to clay content, CEC and organic matter, but weakly correlated to pH in the sewageirrigated soil. In contrast the total metal concentrations of Pb and Cd in the control were generally
weakly correlated to clay content, CEC and organic matter but more strongly correlated to pH.
This confirms the influence of organic matter on soil parameters, including total metal content of
soils and suggests that high levels of organic matter is associated with high levels of total metal
content and vice versa. Since organic matter is largely retained in the topsoil (Birley and Lock,
2001), its correlation with CEC and total metal content along the soil profile suggests that where
a soil receives treated sewage, the extent of exposure of plant roots to metal ions depends on level
of organic matter and distribution of plant roots along the soil profile. This has implications on
plant metal uptake as discussed below.
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Distribution of metal concentrations along soil depth
There was a large variation of total metal concentration with depth. The top 20cm of the soil and
in particular the top 10cm had relatively higher metal concentrations than the lower horizons in
the irrigated area. Since Pb and Cd levels within the 30-50 cm horizons of the control and the area
of disposal were similar and the 0-20 cm horizons of the irrigated area had much higher levels
than the control, it can be argued that the metals were largely immobile.
Considering that the top 20cm had far less clay than the lower horizons, the high CEC could be
attributed to the high organic matter content of these layers, rather than the clay content. The
results suggest that organic matter held Pb and Cd in the top layers, making them immobile, and
thereby confirming their high affinity to organic matter (McGrath and Lane, 1989). This is also
confirmed by the high correlation between the metal levels and organic C (r2 = 0.97). This
outcome suggests that the metals accumulated in the top horizons where grass roots were
expected to grow.
The variation of total metal concentration with depth presents a potentially large source of error
in relating soil concentrations to acceptable limits and to plant concentrations as well as in
modeling soil-plant uptake. This is so if one considers that the depth interval at which various
plants in different environments obtain water and nutrients and the relative density of feeder roots
at different depths are unknown (US Department of Energy, 1998). When the average profile
content of Pb of 55.5 mg/kg is compared with 87 mg/kg stated by Johannesson (2002) as the
lower limit at which basic soil processes, such as microbial activity, are affected, Pb problems
would not be expected. However the opposite is true if the 109.8 mg/kg in the 0-20 cm depth is
considered. Similarly, the average level of Cd of 1.0 mg/kg in the 0-20cm horizon would be
regarded as being too high for a soil, considering that it is equal to the recommended sludge
directive limit of 1 mg/kg (EEC, 1986) for use of sewage in agriculture. However, the average
soil profile concentration of 0.65 mg/kg would be considered more acceptable.
Total soil concentration versus recommended guidelines
The maximum permissible concentrations of Pb in soil under grass, stated by different authorities
are: 300 mg/kg (Department of Environment, 1989), 100 mg/kg (Ross et al., 1992) and 150
mg/kg Birley (2001). A comparison of the results shown in Table 4.3 and these maximum
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permissible limits suggests that the levels obtained in this study would be acceptable according to
Department of Environment (1989) but unacceptable according to Ross et al (1992).
The disparities in the guidelines quoted here and other international literature emanate from the
different climatic, soil and crop conditions under which they were determined. Indeed most of
these guidelines do not specify the soil type and the organic content on which the heavy metal
sorption depends (Christensen 1989b). In addition they often do not specify the type of crop or
soil depth they apply to, both of which are important factors when considering the possibility of
plant uptake of the metals.
Heavy metal content in mixed grasses
Table 4.4 shows that the mixture of kikuyu and star grasses accumulated large quantities of Cd
and small amounts Pb compared to recommended limits. The average Pb level of 1.2 mg/kg in
grass was below the 10mg/kg tolerance level for agronomic crops (Seaker, 1991) and 40 mg/kg
recommended for pasture grass (U.K Statutory Instrument No. 1412, 1995). However, the fact
that Cd uptake varied from non-detectable to a level of 1.2 mg/kg indicates a potential for the
pasture grass to take up levels beyond the 1 mg/kg recommended for pasture for grazing animals
(U.K Statutory Instrument No. 1412, 1995).
Correlation of total soil metal content and metal content in mixed grasses
A comparison of mean total soil concentration of Pb and concentration of Pb in grass shows that
there was a weak correlation between the two (r2 = -0.03–0.4). This can partly be explained by the
fact plant uptake of metals is normally related to the bio-available metal concentration in the soil
(Nyamangara and Mzezewa, 1999). Organic matter, pH and CEC are the most important factors
that control the availability of heavy metals in the soils (Forbes et al, 1976).
4.5.3 Implications of findings
The findings of this component of the study could not be used to confirm or rule out the hazard
Pb and Cd contamination in soils and grasses due to a number of reasons. Although the average
level of Pb in the grass was relatively low (1.2 mg/kg), this did not necessarily confirm low
uptake of the metal by star or kikuyu grasses since the assessment was done on mixed kikuyu and
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star grass. Similarly, although grass accumulated up to 1.2 mg/kg of Cd this did not confirm high
uptake by star grass or kikuyu grasses for the same reason. The concentrations of Pb and Cd
relative to their total levels in the soil and levels in the grass left questions relating to whether
they interacted in the soil leading to increased or reduced uptake of one or the other or both. Bak
and Jensen (1998) noted that uptake of metals by plants could be antagonistic, synergistic or
additive.
The strong correlations between total soil concentration of Pb and Cd and soil properties of CEC,
clay content and organic matter confirms findings of previous research work on soils amended
with wastewater or sludge. While correlation provides an idea of the pattern distribution of the
metals within the soil profile the lack of information on distribution of plant roots within the
profile complicates selection of the soil depth on which to relate soil concentrations and plant
concentrations of metals.
The accumulation of up to 1.2 mg/kg Cd in mixed kikuyu and star grasses against a total soil
concentration of Cd of 0.65 mg/kg (35% less than 1 mg/kg recommended) confirmed the risk of
relying on total metal concentration for purposes of predicting hazard to animals. Roberts et al
(1994) reported restricted growth in livers and kidneys of animals grazing on pasture exposed to
total soil Cd concentrations lower than the recommended sludge directive limit of 1 mg/kg (EEC,
1986). The high Cd content of grass in this part of the study confirmed Bak and Jensen (1998)'s
observations that plants did not assimilate metals in direct proportion to total soil concentrations.
The weak correlation coefficients between total soil metal content and metal content of grass
were also consistent with Bak and Jensen (1998)'s observations. Indeed, Carson and Bazzaz
(1977) noted that plant uptake relationships to total soil concentrations were only valid within a
narrow range of chemical concentrations in the relatively non-toxic range. The data in this
component of the study was not sufficient to define such a range for the sandy soil and mixed
kikuyu and star grasses.
These findings suggested that the use of bio-available metal concentrations in the soil levels of
metal uptake by plants should be accorded more attention in research. Highiri (1973) and US
Department of Energy (1998) noted that if bio-available soil metal concentrations were to be used
to improve reliability of critical metal limits in soils, they would have to be related to the plant
species, since plant uptake of Pb and Cd were observed to vary with plant species.
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Given the unanswered questions that emanated from the findings of this component of the study,
it was considered logical to run an experiment under controlled conditions, using one of the
grasses, star grass, to clarify the issues.
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CHAPTER 5
ASSSESSMENT OF LEAD AND CADMIUM UPTAKE BY CYNODON
NLEMFUENSIS UNDER REPEATED APPLICATION OF TREATED
WASTEWATER
5.1 Introduction
This chapter describes responses in yield and metal content of star grass to increasing
concentrations of Pb and Cd added to a sandy soil as single and mixed inorganic metals in
combination with treated wastewater irrigation. This component of the study was intended to
determine whether star grass was a high accumulator of Pb or Cd or both and if so, the level
of the metal in a sandy soil and star grass, at which toxicity occurs. Therefore it sought to
determine the capacity of star grass to accumulate Pb and Cd under conditions where the
concentrations of single metals in the soil were raised and under conditions where the
concentrations of both metals in the soil were raised. In the latter case, the investigation was
focussed on determining interactions of Pb and Cd, in a sandy soil and in star grass because
these were unknown.
This chapter also presents soil-vegetative tissue metal uptake models that were developed for
predicting grass response to increases in Pb and Cd in the soil. Measured bio-available metal
levels in the soil, metal content in grass and yield of grass were the key inputs in the models.
The models were used to estimate: (1) the critical metal levels in the soil at which the yield of
grass declined (2) the toxicity level in grass (3) and also the critical bio-available metal
content of the soil at which the maximum recommended metal content of grass was reached.
The following assumptions were made in setting up the experiment for this component of the
study. Firstly, it was assumed that increasing the concentrations of Pb and Cd in the soil
would lead to: (1) increasingly higher metal uptake by grass and (2) negative effect on the
yield at some threshold concentrations of the metals in grass. It was assumed, therefore, that
the parameters of yield, soil metal content of grass and soil bio-available concentration would
form bi-variate relationships such that the responses of dependent parameters to independent
parameters could be predicted. Secondly, repeated application of treated sewage to soils
amended with inorganic Pb and Cd was assumed to simulate field conditions in which treated
wastewater containing occasional high doses of Pb and Cd would be disposed of on soils. The
situation where single metals were added to the soil was assumed to relate to occasions when
high loads of either metal would be released into the treatment system, while the situation in
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which mixed metals were added to the soil was assumed to represent high doses of both
metals.
5.2 Objectives
The specific objectives of this component of the study were: (1) to determine the extent to
which star grass accumulates Pb and Cd, (2) to develop yield and metal uptake response
models in relation to bio-available soil concentrations, (3) to estimate the critical grass metal
content (toxic level) at which Pb and Cd reduce yield of grass and the corresponding critical
bio-available levels in the soil, (4) to estimate soil Pb and Cd levels corresponding to
maximum recommended levels of Pb and Cd in pasture grass and (5) to determine
interactions of Pb and Cd in a sandy soil and star grass subjected to co-presence of high levels
of inorganic metals.
5.3 Detailed methods and materials
5.3.1 Experimental set-up
The experiment was carried out in a greenhouse at the University of Zimbabwe, using soils
taken from Churu farm, previously uncontaminated grass from Domboshava farm and low
quality treated effluent and sludge discharged from the Firle Treatment Plant. The grass from
Domboshava was tested for Pb and Cd and found to have undetected levels of the two metals.
Two approaches were used in the greenhouse experiment. The first approach was to
investigate the effect of each metal on levels in the soil and grass by elevating each metal in
the soil using its inorganic salt. This produced two sets of treatments, one for Pb and the other
for Cd. The second approach was to investigate interactions of the two metals in the soil and
grass by elevating their levels using combinations of the two salts in each treatment. Since the
soil concentrations of Pb and Cd at which the yield of star grass would be affected were not
known, guidelines on maximum permissible total soil concentrations were used to decide on
concentrations to add to the soil.
In single Pb treatments, five levels of Pb treatment were experimented with. The first level
was the control. It did not receive inorganic Pb. The second level (considered as the lowest
level of Pb addition) did not receive inorganic Pb but it received Pb through addition of
sewage. The treatment was denoted E&S and is referred likewise in the text of subsequent
sections and ES in graphs. The third level was pegged at the same level as the maximum
acceptable total soil concentration (300 mg/kg) from literature. The fourth level received
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double the maximum acceptable level while the last level received 4 times the acceptable Pb
level.
In single Cd treatments, seven levels of Cd were adopted to increase reliability of the results,
given that the CdS (which was the only Cd salt available in the country at the time) that was
used in the experiment was known to have low solubility. The first level was the control. It
did not receive inorganic Cd. The second level (considered as the lowest level of Cd addition)
did not receive inorganic Cd but it received Cd through addition of sewage. The control and
E&S treatments mentioned under single Pb treatments also served as part of single Cd
treatments. The added inorganic Cd levels were pegged at 10, 20, 40, 60 and 80 times the
maximum acceptable total concentration in the soil (1 mg/kg).
In mixed treatments, inorganic Pb and Cd were mixed in 3 more treatments to mimic field
conditions where Pb and Cd would be present together in sewage as confirmed by the metal
data from the City of Harare. Inorganic Pb and Cd were mixed in the order 300mg/kg Pb
combined with 10 mg/kg Cd up to 1 200 mg/kg combined with 40 mg/kg. The 3 lower levels
of inorganic Cd applied in single treatments were used in the combinations because, though
still very high, they were relatively closer to levels detected at Firle farm than the higher
levels. The control and E&S treatments mentioned above also served as part of mixed Pb and
Cd treatments.
Soil from the control site at Churu farm was excavated, mixed several times into one large
heap and passed through a 10mm sieve to remove large stones and grass debris. The soil was
excavated from the same area (described in chapter 4) where samples were taken and tested
for heavy metals. It was then packed into 79-litre pots. The packed pots were laid out
randomly (Appendix 2) inside a greenhouse to exclude rainfall from interfering with
irrigation applications. The randomised block design layout of the pots had one replicate of
each treatment in every one of the 3 blocks.
5.3.2 Grass establishment
In each pot, seven 15cm long stems of uncontaminated star grass, each with a node, were
planted. In order to eliminate nutrient deficiency, single applications of super phosphate
(Ca(H2PO4)2 + CaSO4), potassium sulphate (K2SO4) and ammonium nitrate (NH4NO3) were
added to each pot at 600, 100 and 100 kg/ha respectively. Water was applied to the grass for a
period of 3 weeks to establish the crop. The treatments were then allocated to the pots at
random, after which the soil was enriched with Pb(NO3)2 and CdS.
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5.3.3 Soil treatment and irrigation application
The single Pb treatments received 198.99, 397.98 and 663.3g of Pb(NO3)2 so as to elevate the
soil concentration by 300, 600 and 1 200 mg/kg Pb respectively. The treatments were referred
to as Pb300, Pb600 and Pb1200 respectively. Cadmium single treatments received 1.29, 2.58,
5.16, 7.74, 10.32 g of CdS, thereby elevating the soil concentration by 10, 20, 40, 60 and 80
mg/kg Cd respectively. These were denoted Cd10, Cd20, Cd40, Cd60 and Cd80. Combined Pb
and Cd treatments received 300 mg/kg Pb and 10 mg/kg Cd, 600 mg/kg Pb and 20 mg/kg Cd
and 1200 mg/kg Pb and 40 mg/kg Cd, and they were denoted Pb300Cd10, Pb600Cd20 and
Pb1200Cd40. The combined treatments were elevated using 300 mg/kg Pb combined with 10
mg/kg Cd, 600 mg/kg Pb combined with 20 mg/kg Cd and 1200 mg/kg combined with 40
mg/kg Cd. The treatments were denoted Pb300Cd10, Pb600Cd20 and Pb1200Cd40.
The metal treatments were added to the pots in a single dose 3 weeks after planting the
grasses. The inorganic chemicals were shaken in water and added to the soil surface together
with an irrigation application of 2.5 l of treated sewage and 2.5 l of water. Two squeeze
bottles, one for Pb and another for Cd, were used to apply Pb and Cd treatments directly onto
the soil surface and minimize chances of contamination of the grass shoot and inside walls of
the pots. The order of applying the metals was low concentrations to high concentrations.
After each treatment, the bottles were rinsed using de-ionised water, before applying the next
treatment.
Once established, 5.5 l of irrigation water (20 mm net depth) was applied per pot every 3.5
days in order to meet a peak water duty of 5.5 mm/day at an estimated 70% water application
efficiency. A total of 63.5 l (324 mm) of water were applied to each of the 3 control pots
while 23.5 l (120 mm) of treated effluent and sludge and 40 l (204 mm) were applied to each
of the remaining 36 pots over a period of 45 days.
Irrigation water was applied to the soil using a 5 l plastic container (graduated at 0.5 l
intervals for the purpose) via a 32 mm diameter flexible hose. During application, the
detachable hose covered the mouth of the container. It was used to direct the application onto
the soil surface and minimize human contact with treated sewage. The container was rinsed
with water after irrigation of all pots.
After harvesting the first crop, the re-growth was irrigated with the same quantity of water
over a 45-day period. The 45-day period before harvest was adopted following Miller’s
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(1974) observation that most decomposition of organic matter in sludge occurred within one
month of adding sludge to soils. It was assumed that most of the organic matter added to the
pots would have decomposed and released cations, including Pb and Cd into soil solution.
5.3.4 Soil sampling and testing
Soil samples were taken from the pots at depths of 0-10, 10-20, 20-30 and 30-40 cm, using a
soil auger, one day after harvesting the grass re-growth. After removing plant debris the
samples were air-dried and passed through a 2 mm sieve. Bio-available soil concentrations
were determined using procedures recommended by McGrath and Cegarra (1992). A 1 M
(CH3COONH4) solution was added to the soil sample and the suspension was shaken using a
mechanical shaker. The suspension was the filtered, after which levels of Pb and Cd were
measured on the atomic absorption spectrometer. No soil samples were taken at the time of
harvesting the first crop to avoid disturbing the soil.
5.3.5 Grass sampling and testing
Two grass harvests were made from each pot. The first crop was harvested 45 days after soil
enrichment with Pb and Cd and the re-growth 45 days after the first harvest. All the grass in
each pot was harvested to constitute a sample per harvest. The grass was cut at 5 cm height
off the soil surface, washed using de-ionised water, oven dried at 65 0C to constant weight.
After oven-drying above-ground tissue the yields of grass were measured by weighing and
samples were taken for metal testing. The samples were ground and sieved through a 0.1 mm
sieve, ashed at 550 oC for 16 hours and digested with 25% HCl and concentrated HNO3. After
filtration, Pb and Cd were determined using atomic absorption spectrometry. The sample of
the grass that was taken during planting was subjected to the same metal extraction process
prior to determination of levels of Pb and Cd using atomic absorption spectrometry.
5.3.6 Sewage effluent and sludge collection and testing
Effluent and sludge for irrigation and metal content testing was collected seven times at the
point of direct disposal onto the field. The levels of Pb and Cd in effluent and sludge were
determined by atomic absorption spectrometry (Department of Environment, 1989) after
extraction with HCl and concentrated HNO3.
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5.3.7 Data analysis
The data obtained in this experiment was quantitative continuous data (that is data that has
any of the infinitely real numbers). There were two groups of variables identified, one
associated with Pb and the other associated with Cd. In each group (1) soil bio-available
concentration, (2) grass metal level and (3) yields of grass were the key variables for this
analysis. All the variables involved were random continuous variables. Random continuous
variables are observations or measurements that can assume any of the infinitely many nonnegative real numbers (such as 0.1, 0.01 etc) and tend to vary in a haphazard manner due to
natural variation. Amongst these variables, soil bio-available metal concentration was
considered to be an independent/predictor/explanatory variable to grass metal content and
yield. Furthermore, metal content in grass was considered as a predictor variable to yield.
Given that the distribution of uptake of metals from the different soil horizons was not
known, the average soil profile concentration was used. Sample et al (1998) ignored metal
distribution within soil profiles in models to validate metal uptake by earthworms and
assumed that the data they used represented average profile concentrations and still obtained
significant regressions.
In single metal treatments of Pb and Cd the bi-variate relationship was considered to be
appropriate for analysing relationships among the continuous variables. A bi-variate
relationship is one in which two different continuous random variables have an association. In
mixed treatments, the multi-variate approach was considered appropriate for analysing the
influence of each of Pb and Cd on grass yield. Although there were 3 sources of total Pb and
Cd, namely background metals in soils, metals added through treated sewage and inorganic
metals added to soils, it was considered unnecessary to investigate the contribution of each
source since all the sources contributed to the bio-available soil metal levels measured.
Measured data on bio-available metal content of soils, metal content of grass and yields of
grass were tested for normality first. Since the data were not normally distributed, they were
transformed to log10, to normalise them, before analyses to determine correlation coefficients
and levels of significance of treatments on soil and grass metal contents and yields. The
Analysis of Variance (ANOVA) was used to test the significance of differences in metal
levels and yields amongst treatments. Soil bio-available metal levels were compared to the
levels of the metals in the re-growth since soils and grasses were sampled at the same time.
Regression techniques are generally used to draw up relationships between variables and to
estimate parameters in the regression function (model). Simple regression was therefore used
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to estimate and predict the responses of yield and metal concentrations to bio-available soil
concentrations and the response of yield to level of metal content in grass. Correlation is a
method used to measure the degree of linearity of a relationship between random variables,
which have a joint distribution. The statistic for measuring correlation is the product moment
correlation coefficient or simply correlation. It is also called Pearson's product moment
correlation and is denoted r2. Correlation and regression are important tools that show the
degree of relationship between two variables (Canhao and Keogh, 2001). Two continuous
random variables are correlated if the variables are related (associated) in such a way that the
value of one is indicative of the value of the other. Simple correlation was used to measure
the degree of strength of relationships between any two variables.
The different parameters (variables) were plotted against each other to provide the best-fit
regression lines. The parameters in the regression lines (such as slopes and intercepts) were
crosschecked using the Method of Least Squares (Canhao and Keogh, 2001). The standard
errors of each of the parameters were estimated, so as to establish the confidence intervals of
the parameters. The following equations were used for the purpose:
Confidence interval for a parameter = parameter estimate + t(s.e. parameter)…equation 4
Where: s.e = Mean Square value of the error (MSE)
t = the 97.5th percentile of a t-distribution with n-2 degrees of freedom
MSE = s2xy= 1/(n-2) *{SSyy-SPxy)2/SSxx….equation 5
Where n = sample size
SSyy = sum of squares of y
SSxx = sum of squares of x
SSxy = sum of products of x and y
5.4 Results
5.4.1 Bio-available Pb and Cd content of soils
Table 5.1 shows a general increase in mean bio-available levels of Pb and Cd with increase in
the level of treatment. Bio-available Pb increased from 2 mg/kg in the control to a maximum
of 343.7 mg/kg and Cd increased from 0.06 mg/kg to 0.47 mg/kg in single treatments. In
single metal treatments, analysis of variance showed that treatment of the soil with inorganic
Pb and Cd combined with applications of a mixture of sludge and effluent significantly
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increased the mean bio-available Pb (p ≤ 0.001) and Cd (p≤0.05) content of the soil profile.
Comparison of means of treatments showed that soil levels in Pb300, Pb600 and Pb1200 were
significantly (p≤0.1) different and higher than in the control and E&S. Differences in the later
were insignificant.
There was a significant difference (p≤0.1) between the mean levels of Cd60 and Cd80 on one
hand and the rest of the treatments on the other. The variation amongst the latter was
insignificant. Mean Pb levels in mixed treatments showed trends similar to those in single
treatments. The mean level of Cd in Pb1200Cd40 was significantly (p≤0.01) higher than in the
rest of the treatments.
5.4.2 Extraction capacity of star grass
Table 5.1 shows a general increase in uptake of Pb and Cd with increase in bio-available soil
metal concentration. There was a general decline in metal uptake from the first grass crop to
the re-growth. This component of the study found that the maximum uptake of Pb and Cd by
star were 4 592 mg/kg (13.66 kg/ha) and 17.67 mg/kg (0.13 kg/kg) Cd, respectively. The
maximum uptake of Pb was recorded in the treatment that received the highest level of added
Pb in the first crop of the single treatments. In contrast, the highest recorded uptake of Cd
(17.67 mg/kg) occurred in the re-growth of the mixed treatments. In single Cd treatments, the
highest recorded uptake of Cd was only 8.67 mg/kg (0.09 kg/ha), about half of the uptake in
mixed treatments.
In combined treatments, the highest uptake of Pb of 1 681.33 mg/kg that occurred in
Pb1200Cd40 in the first crop was far lower than the 4 592 mg/kg registered in the single Pb
treatments. This uptake of Pb was accompanied by a relatively higher uptake of Cd of 16
mg/kg Cd in the first crop.
5.4.3 Grass metal content response to bio-available soil metal content in single
metal treatments
Since soil samples were not taken at the time of harvesting the first crop of grass, it was
assumed that a better relationship between bio-available soil concentrations and levels of
metal in grass could be obtained using data for the re-growth crop. Soil samples were taken
for testing at the same time as grass samples of the re-growth crop.
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University of Pretoria etd – Madyiwa, S (2006)
Table 5.1: Soil metal and grass concentrations, yields and metal extraction
levels
Treatment
Mean bioavailable soil
profile metal
concentration
(mg/kg)
Mean grass concentration
(mg/kg)
Mean grass yield (g/pot)
First crop
Re-growth
First crop
Re-growth
11.33 (4.65)
12.02 (3.54)
140. 03 (10.11)
288.01 (4.24)
4 592 (155.89)
5.33 (3.54)
5.33 (1.41)
14.33 (2.2.1)
36.66 (1.41)
315.65 (47.34)
222.69 (9.19)
198.78 (26.41)
250.03 (33.20)
225.40 (14.92)
58.43 (1.91)
77.01 (13.29)
190.10 (20.18)
245.3 (35.29)
235.26 (0.49)
219.27 (13.68)
1.50 (0.04)
1.67 (0.57)
2.33 (1.15)
1.67 (0.57)
3.67 (1.08)
4.67 (1.52)
8.67 (2.14)
Nd
Nd
1.67 (1.14)
2.67 (1.15)
3.00 (0.95)
4.00 (1.00)
5.67 (2.08)
222.73 (7.51)
198.78 (17.21)
256.72 (26.46)
245.87 (21.01)
230.88 (9.03)
265.42 (1.35)
200.83 (17.44)
77.04 (12.63)
190.1 (16.35)
227.71 (15.19)
214.89 (9.76)
239.57 (23.56)
228.98 (13.27)
198.34 (7.77)
2.01 (0.21)
1.23 (0.08)
131.06 (1.72)
237.09 (22.43)
382.00 (24.99)
11.33 (4.65)
12.02 (3.54)
56.67 (16.56)
307.00 (23.94)
1681.3 (193.85)
5.33 (3.54)
5.33 (1.41)
8.67 (4.49)
43.67 (10.21)
93.67 (16.16)
222.69 (9.19)
198.78 (26.41)
224.94 (31.76)
160.10 (4.31)
48.3 (31.22)
77.01 (13.29
190.10 (20.18)
220.76 (30.19)
218.93 (10.39)
147.39 (24.08)
Nd
0.060 (0.02)
0.025 (0.020)
0.071 (0.054)
0.200 (0.012)
1.67 (0.04)
1.67 (0.57)
4.33 (2.31)
10.67 (1.53)
16.00 (1.73)
Nd
Nd
3.01 (0.07)
9.03 (3.11)
17.67 (2.51)
222.73 (7.51)
198.78 (26.41)
224.92 (30.87)
160.12 (4.31)
48.28 (41.22)
77.01 (13.29)
190.10 (20.18)
220.76 (22.57)
218.89 (10.39)
147.4 (34.08)
Lead single treatments
Control
2.01 (0.21)
E&S
1.23 (0.08)
Pb300
84.64 (0.16)
Pb600
180.84 (8.23)
Pb1200
343.7 (15.43)
Cadmium single treatments
Control
Nd
E&S
0.060 (0.02)
Cd10
0.026 (0.06)
Cd20
0.050 (0.023)
Cd40
0.068 (0.014)
Cd60
0.469 (0.051)
Cd80
0.200 (0.16)
Combined Pb and Cd treatments
Lead
Control
E&S
Pb300Cd10
Pb600Cd20
Pb1200Cd40
Cadmium
Control
E&S
Pb300Cd10
Pb600Cd20
Pb1200Cd40
Nd: not detectable
() standard deviation
Relationship of bio-available and grass Pb content in single Pb treatments
Overall, the levels of Pb in star grass increased significantly with increase in soil bio-available
Pb (p≤0.001 in the re-growth). Treatments that received inorganic Pb recorded a significant
(p≤0.001) increase in accumulation of Pb in grass beyond accumulations in the E&S
treatment and the control. There was no significant difference in Pb uptake between the
control and E&S treatment in both crops. Therefore the elevation of soil Pb levels by 300
mg/kg, 600 mg/kg and 1 200mg/kg significantly increased Pb uptake, well above the levels
taken up by the grass in the control and E&S treatment in both crops.
The relationship between soil bio-available Pb and grass Pb content is presented graphically
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University of Pretoria etd – Madyiwa, S (2006)
in Figure 5.1. The best-fit trend line for Pb fitted a linear relationship with regression model
shown in the figure. The correlation between log10 (soil bio-available Pb) and log10 (grass Pb
concentrations in the re-growth) fitted a computed Pearson's r2 value of 0.87 and a trend-line
Log(10) Pb concentration
(mg/kg) in grass re-growth
r2 value of 0.75 against r2critical = 0.87.
2.5
y = 0.5246x + 0.5389
R2 = 0.754
2
1.5
1
0.5
0
0
0.5
1
1.5
2
2.5
Log(10) soil bio-available Pb concentration (mg/kg)
Pb
Linear (Pb)
Figure 5.1: Log(10) soil bio-available level versus log(10) Pb level in
grass in single treatments
Relationship of bio-available and grass Cd content in single Cd treatments
Cadmium content of grass re-growth increased significantly (p≤0.001) with increases in soil
bio-available levels. There was a strong correlation between log10 (soil bio-available Cd) and
log10 (grass Cd concentrations in the re-growth) with the computed Pearson's r2 being 0.88
and r2 trend-line being 0.78 against an r2critical of 0.75. The trend-line fitted a linear relationship
with the regression model shown in figure 5.2.
5.4.4 Yield response to Pb and Cd content of grass in single metal
treatments
Yield response to Pb content of grass in single metal treatments
Figure 5.3 presents dose-response relationships between metal level in star grass and yields
for the 2 harvests. The figure shows that there was an initial increase in yield followed by a
gradual decline at higher concentrations of Pb. In the first crop, analysis of variance showed
that yields of grass were significantly (p≤0.001) reduced due to the increase in the level of Pb
in the grass. Comparison of means showed that this decrease was most significant (p≤0.01) in
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Log(10) Cd concentration
(mg/kg) in grass re-growth
Pb1200 compared to the rest of the treatments.
-1.8
-1.6
-1.4
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
-0.1 0
-0.2
y = 0.451x + 0.0837
R2 = 0.779
-0.3
-0.4
-0.5
-0.6
-0.7
-0.8
-0.9
Log(10) soil bio-available Cd concentration (mg/kg)
Cd
Linear (Cd)
Figure 5.2: Log(10) bio-available Cd levels versus log(10) Cd levels in
grass in single treatments
The differences in mean yields in the latter were insignificant. In the re-growth, yields also
significantly (p≤0.01) declined as Pb content in grass increased. Comparison of means
showed that the re-growth of the control had a significantly (p≤0.01) lower yield than the rest
of the treatments. The differences between the mean yields of the first crop and re-growth in
treatments E&S, Pb300 and Pb600 were not significant. However, the yield from control of the
re-growth declined by 65.4% compared to the yield from the control of the first crop while the
yield of the re-growth increased by 275.5% compared to that of the first crop in treatment
Pb1200.
The best-fit regression model (Figure 5.3), is a non-linear (curvilinear) model, that provides
for drawing tangents at the points where the yields started to drop to locate the critical
concentrations of Pb in grass at which metal content starts to reduce yield. This point is the
critical toxicity limit or toxicity threshold referred to in Figure 2.1, and discussed in detail in
section 5.4.8. The Pb content strongly correlated with the yield of the first crop (computed
Pearson's r2 = -0.74 and r2 = - 0.99 for the trend-line, while r2critical = 0.87) and weakly with the
re-growth (computed Pearson r2 = 0.52 and r2 = 0.55 for the trend-line).
Yield response to Cd content of grass in single metal treatments
Cadmium content in grass did not significantly affect the yields of the first crop but it
significantly (p≤0.001) reduced the yield of the re-growth. Analysis of variance amongst
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treatments that received inorganic Cd showed that the yield of Cd80 significantly declined in
both crops as a result of increase in the content of Cd in grass. Comparison of means showed
that in both crops, Cd80 was significantly lower (p≤0.01) than in the rest of the treatments. In
the re-growth, the control had a significantly (p≤0.05) lower yield than the rest of the
treatments while Cd40 and Cd60 had significantly higher yields than other treatments. There
was a 65% reduction in yield of the control from the first crop to the re-growth.
Log(10) grass yield (g/pot)
3
2
y = -0.2079x + 0.7373x + 1.7727
2
R = 0.991
2.5
2
y = -0.342x2 + 1.1831x + 1.4232
R2 = 0.5482
1.5
1
0.5
0
0
0.5
1
1.5
2
2.5
3
3.5
4
Log(10) Pb concentration (mg/kg) in grass re-growth
First crop
Re-growth
Poly. (Re-growth)
Poly. (First crop)
Figure 5.3: Log(10) Pb level (mg/kg) in grass versus Log(10) grass
yield (g/pot) in Pb single treatments
A weak correlation (computed Pearson's r2 = 0.25 and r2 = 0.20 for trend-line, r2critical = 0.75)
existed between the Cd content in grass and the yield of the first crop but there was a slightly
stronger, but overall weak correlation (computed Pearson's r2 = 0.54 and r2 = 0.51 for trendline, r2critical = 0.75) in the re-growth. Figure 5.4 presents a curving dose-response relationship
of Cd concentration in grass and yield of grass. The threshold concentration of Cd in grass is
located at the point (discussed in detail in section 5.4.8) of the curvature representing the
highest yield. This is also the point at which yield starts to decline.
Yield response to soil bio-available Pb and Cd concentrations in single metal
treatments
The soil bio-available levels of Pb and Cd were weakly correlated (r2 = 0.26 for Pb and 0.47
for Cd) to the yields of the re-growths.
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5.4.5 Interactions of Pb and Cd in mixed treatments
In order to analyse interactions of Pb and Cd, data from corresponding single and mixed
treatments of Pb and Cd were included in the following analysis of yield response to
combined Pb and Cd, bio-available metal level against treatment, levels in grass against
treatment and bio-available metal level against metal level in grass.
Yield response to combined Pb and Cd
Analysis of variance for the effect of two independent variables on yield showed that
combined Pb and Cd simultaneously and significantly (p≤0.001) reduced the yield of the first
crop but had no significant effect on the yield of the re-growth.
y = -0.655x2 + 0.6533x + 2.2138
R2 = 0.2035
Log(10) grass yield(g/pot)
3
2.5
2
y = -1.3832x2 + 1.2619x + 2.0992
R2 = 0.5149
1.5
1
0.5
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Log(10) Cd concentration (mg/kg) in grass re-growth
First crop
Regrowth
Poly. (First crop)
Poly. (Regrowth)
Figure 5.4: Log(10) Cd level (mg/kg) in grass versus log(10) yield of
grass (g/pot) in single Cd treatments
Mixed Pb versus single Pb
Figure 5.5 presents the relationship between log10 (soil bio-available Pb levels) in single and
mixed treatments and level of treatment. There was a significant (p≤0.05) increase in bioavailable levels of Pb due to treatment in both the mixed treatments and single treatments that
received the same Pb dose levels as mixed treatments. There was no statistical difference
(p≤0.05) in the mean bio-available levels of Pb in mixed treatments compared to single
treatments.
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Figure 5.6 presents the relationship between log10 (Pb levels) in single and mixed treatments
in grass and the level of treatment. There was no significant difference (p≤0.05) in Pb uptake
between single and mixed treatments in the first crop and the re-growth that received the same
level of Pb enrichment.
Figure 5.7 presents the relationship of bio-available Pb levels and Pb levels in grass regrowth. Unlike in single Pb treatments, the correlation between log10 (soil bio-available Pb)
and log10 (Pb concentrations in grass in the re-growth) in mixed treatments was marginally
weak (computed Pearson's r2 = 0.84 r2 trend-line = 0.70 and r2critical = 0.87).
Mixed Cd versus single Cd
Figure 5.8 suggests a gradual increase in bio-available soil Cd level with increase in the level
of soil enrichment. However statistically, there was no increase (p≤0.05) in Cd in single
treatments but there was a significant (p≤0.05) increase in bio-available Cd in mixed Pb and
Cd treatments.
Log(10) soil bio-available Pb
concentrations (mg/kg)
3.5
3
2.5
2
1.5
1
0.5
0
WO
ES
Pb300
Pb600
Single and mixed Pb treatments
Soil single trt
Soil mixed trt
Figure 5.5: Effect of treatment on bio-available levels of Pb in single
and mixed treatments
79
Pb1200
Log(10) Pb concentrations (mg/kg) in grass regrowth
University of Pretoria etd – Madyiwa, S (2006)
4
3.5
3
2.5
2
1.5
1
0.5
0
WO
ES
Pb300
Pb600
Pb1200
Single and mixed Pb treatments
Re-growth, single trt
Re-growth, mixed trt
1st crop, single trt
1st crop, mixed trt
Expon. (1st crop, mixed trt)
Expon. (1st crop, single trt)
Expon. (Re-growth, single trt)
Expon. (Re-growth, mixed trt)
Log(10) Pb concentration
(mg/kg) in grass re-growth
Figure 5.6: Effect of treatment on levels of Pb in grass in single
and mixed treatments
2.5
y = 0.3981x + 0.6072
R2 = 0.7006
2
1.5
1
0.5
0
0
0.5
1
1.5
2
2.5
3
Log(10) soil bio-available Pb concentration (mg/kg)
Pb
Linear (Pb)
Figure 5.7: Log(10) bio-available soil Pb levels (mg/kg) versus
log(10) Pb levels in grass re-growth (mg/kg) in mixed treatments
There was a significant (p≤0.05) increase in Cd level in grass with increase in soil enrichment
level (Figure 5.9) in both single and mixed treatments and the two grasses.
Mixed treatments had significantly (p≤0.05) higher Cd levels than single treatments for the
same doses of Cd. This is confirmed by the increasing divergence in levels of Cd in single
treatments compared to mixed treatments (Figure 5.9), as treatment level increased.
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University of Pretoria etd – Madyiwa, S (2006)
0.16
Log(10) soil bio-available Cd
concentrations (mg/kg)
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
WO
ES
Cd10
Cd20
Cd40
Single and mixed Cadmium treatments
Soil single trt
Soil mixed trt
Figure 5.8: Effect of treatment on bio-available levels of Cd in single
and mixed treatments
Log(10) Cd concentrations (mg/kg) in grass
re-growth
1.4
1.2
1
0.8
0.6
0.4
0.2
0
WO
ES
Cd10
Cd20
Cd40
-0.2
Single and mixed Cadmium treatments
-0.4
Re-growth mixed trt
Re-growth, single trt
Log. (Re-growth, single trt)
1st crop, single trt
Log. (1st crop, mixed trt)
Log. (Re-growth mixed trt)
1st crop, mixed trt
Log. (1st crop, single trt)
Figure 5.9: Effect of treatment on bio-available Cd levels in grass in single
and mixed treatments
Figure 5.10 presents the regression model for log10 (soil bio-available concentration) versus
log10 (Cd concentrations in the re-growth) for the points where Cd was detectable. The plot
shows that there was a stronger correlation between log10 (soil bio-available Cd) and log10
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University of Pretoria etd – Madyiwa, S (2006)
(grass Cd concentrations in the re-growth) in mixed treatments than in corresponding single
Log(10) Cd concentration
(mg/kg) in grass re-growth
treatments (computed Pearson's r2 = 0.99, r2 trend-line = 0.98 and r2critical = 0.75).
1.4
1.2
1
y = 0.853x + 1.8738
R2 = 0.9819
0.8
0.6
0.4
0.2
0
-1.8
-1.6
-1.4
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
Log(10) soil bio-available Cd concentration (mg/kg)
Cd
Linear (Cd)
Figure 5.10: Log(10) bio-available Cd soil levels (mg/kg) versus
log(10) Cd levels in grass re-growth in mixed treatments
5.4.6 Correlation of Pb and Cd in grass
Figure 5.11 presents the relative uptake of Pb and Cd in the re-growths of the single and
mixed metal treatments. Correlation coefficients presented in this figure show that Pb and Cd
uptake by the grass re-growths were more strongly correlated in mixed treatments than in
single treatments (Pearson's r2 = 0.87 and 0.76 for the mixed and single treatments,
respectively). The regression coefficient increased from 0.39 in single treatments to 1.01 in
mixed treatments signifying that the rate of uptake of Cd was higher, due to co-presence of Pb
and Cd in mixed treatments.
5.4.7 Yield response to combined Pb and Cd
Combined Pb and Cd significantly (p≤0.001) reduced the yield of the first crop but not that of
the re-growth. Analysis of variance showed that grass content of Pb and Cd simultaneously
and significantly reduced the yield of the first crop (P≤0.001) but the effect was weaker on the
re-growth. This is confirmed (Figure 5.12) by the strong correlation of the first crop (r2 = 0.99
for Pb and 0.89 for Cd) and weaker correlation of the re-growth (r2 = 0.45 for Pb and 0.51 for
Cd). The models for the re-growth were considered weak due to the poor correlation between
the yield and the metal content in grass.
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Log(10) Cd concentration (mg/kg) in
grass re-growth
1.6
1.4
y = 1.0128x - 0.6376
2
R = 0.8715
1.2
1
y = 0.3879x - 0.2058
2
R = 0.762
0.8
0.6
0.4
0.2
0
0
0.5
1
1.5
2
2.5
Log(10) soil bio-available Pb concentration (mg/kg)
Re-growth single metals
Linear (Re-growth single metals)
Re-growth mixed metals
Linear (Re-growth mixed metals)
Figure 5.11: Correlation of metal contents of Pb and Cd in grass in single and
mixed treatments
Log(10) grass yield(g/pot)
3
y = -0.9079x2 + 2.3691x + 0.8979
R2 = 0.449
2.5
y = -0.336x2 + 1.0481x + 1.5481
R2 = 0.9986
2
y = -0.7516x2 + 0.9607x + 2.0741
R2 = 0.5073
1.5
1
y = -1.5667x2 + 1.604x + 2.0398
R2 = 0.8912
0.5
0
0
0.5
1
1.5
2
2.5
3
3.5
Log(10) grass metal concentration
First crop Pb
First crop Cd
Regrowth Pb
Regrowth Cd
Poly. (First crop Pb)
Poly. (Regrowth Pb)
Poly. (Regrowth Cd)
Poly. (First crop Cd)
Figure 5.12: Yield response to concentrations of Pb and Cd in mixed Pb
and Cd treatments
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University of Pretoria etd – Madyiwa, S (2006)
5.4.8 Yield, grass and soil metal content models and critical limits of Pb and Cd
Grass metal content versus grass yield models were used specifically to estimate toxic levels
of metals in grass. For this purpose, data from single metal treatments were used. Figure 6.3
presents the following non-linear models of yield versus grass Pb concentrations:
y = -0.2079x2 + 0.7373x + 1.7727 for the first crop and
y = -0.342x2 + 1.1831x + 1.4232 for the re-growth.
The concentration of Pb at the peak yield (also the point when toxicity starts to reduce the
yield) was obtained by equating the first derivative of log10 (grass yield) (y) with respect to
log10 (Pb concentration) (x) to zero. This point occurred when x was 1.77 (equivalent to 58.88
mg/kg) and 1.73 (equivalent to 53.70 mg/kg) respectively. Substituting the values of x into the
respective models gave predicted maximum yields of 266.69 and 279.25 g/pot respectively or
an average of 272.97 g/pot for both crops.
Similarly, using the yield versus grass Cd content models of y = -1.38819x2 + 1.2609x +
2.0992 and y = -0.655x2 + 0.6533x + 2.2138 derived from Figure 5.4, the maximum yields
occurred when x was 0.454 (equivalent to 2.84 mg/kg) and 0.499 (equivalent to 3.16 mg/kg)
respectively for the first and second crops. The computed peak yields of 243.22 g/pot and
238.23 g/pot for the first crop and re-growth respectively, were close to each other in value,
despite the fact that both models were not significant.
The model for metal uptake response to soil bio-available concentration in single Pb
treatments was:
y = 0.5246x + 0.5389, where:
y = response variable = log10 (grass Pb concentration, mg/kg)
0.5246 = slope = regression co-efficient
x = log10 (soil bio-available Pb concentration, mg/kg)
0.5389 = regression intercept (y-intercept which tells the value of the dependent variable
when the independent variable is zero).
Since grass uptake of Pb and Cd was a central aspect of this research, it was necessary to
subject grass uptake models to further analysis to assess the strengths of the models. The
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University of Pretoria etd – Madyiwa, S (2006)
following section presents the analysis of the strengths of these models for the re-growth
crops. The mean square value of error used to compute standard errors for the slope, intercept
and y value was 0.194. Using this value, the standard errors for the slope and intercept of this
regression line were calculated to be 0.192 and 0.337 respectively. The confidence interval of
y forms a confidence band on either side of the yield response regression line, since y values
have different distributions at various values of x. The standard error for obtaining the
confidence interval of y at any point along the regression line was obtained using the equation
shown below:
Standard error of y = sqrt{S2xy[1/n + (xi - mean x)2/SSxx]….equation 6
Substituting the critical value of y of 1.73 (equivalent to 53.70 mg/kg metal concentration for
the grass re-growth) in the regression model (Figure 5.1) of y = 0.5246x + 0.5389 gave an x
value of 2.27, equivalent to a critical soil bio-available metal level of 186.21 mg/kg for Pb.
Substituting the critical value of x of 2.27 and a mean x value (mean bio-available soil
concentration of all treatments) of 1.42 in equation 6 gave a standard error of 0.207 in y at the
critical point. This translates to a confidence interval of y of 0.42 (or 2.63 mg/kg) at that point
(using a t of 2.03 at 95% confidence level). Therefore based on this model, the grass Pb
concentration at which yield started to decrease was 53.70 ± 2.63 mg/kg and the soil bioavailable concentration of Pb at that point was 186.21 mg/kg.
A similar calculation using single Cd treatments and the resulting model for Cd produced the
following. Substituting the critical value of y of 0.499 (equivalent to 3.16 mg/kg content in
grass) in the model, y = 0.451x + 0.0837, gave a soil bio-available metal level (x) of 0.92
(equivalent to 8.33 mg/kg of Cd). Substituting a value of 0.92 and a mean for all treatments
of -1.051 gave a standard error of y of 0.11 at the critical point. Using a t-value of 2.014, the
confidence interval of y (at 95% confidence level) was 0.22 (or 1.67 mg/kg). Therefore based
on this model, the grass Cd concentration at which yield started to decrease was 3.16 ± 1.67
mg/kg Cd, while the bio-available soil Cd concentration was estimated to be 8.33 mg/kg.
The model for metal content response to soil bio-available soil concentration in combined
treatments was y = 0.3981x + 0.6072 for Pb and y = 0.853x + 1.8738 for Cd. It was important
to test whether the regression equations from single and mixed treatments were statistically
different. The t-test for comparison of regression coefficients and the y-intercept was
employed using the following equations:
ts = (M1 - M2)/s.edifference (slope) .………..Equation 7
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University of Pretoria etd – Madyiwa, S (2006)
ts = (S1 - S2)/s.edifference (intercept) ………..Equation 8
where:
ts is the t statistic for comparison of means given with the general equation ts = difference
between two means divided by the standard error of the difference
M1 - is the slope of the model for single treatments
M2 - is the slope of the model for mixed treatments
S1 - is the y-intercept for single metal treatments
S2 - is the y-intercept for mixed metal treatments
s.edifference = standard error of the difference between two means given as:
s.edifference = sqrt (s2 (pooled) * {1/n1 + 1/n2})…..equation 9, where:
s2 (pooled) = {(n1-1)s12 + (n2-1)s22}/(n1+n2 - 2), in which n1 and n2 are sample sizes for single and
mixed metals respectively and n1+n2 - 2 is the pooled degrees of freedom
It was hypothesised that no difference existed between the slopes of the regression equation
of the single Pb treatment and the mixed Pb treatments. This hypothesis would stand, if 2.306 < ts < +2.306 at 95% level of significance (two tail). The pooled degrees of freedom
were 8. Calculations of s.edifference for the slope gave a value of 0.02 and s.edifference for
intercepts gave 0.06 for the regression models of single Pb and mixed Pb.
Using equation 7, the ts for the slope was calculated to be 6.3. Similarly ts for the intercept
was calculated to be - 1.14. Therefore the null hypothesis was rejected for the slopes but
accepted for the intercepts. This outcome implied that there was a statistical difference
between the slopes but there was no statistical difference between the intercepts of the
regression equations of single and mixed Pb treatments. Therefore the regression equations
predicted statistically the same concentration of Pb when log10 (soil bio-available
concentration) was zero but different values elsewhere along the regression line.
To compare the regression relationships between single and mixed treatment, the null
hypothesis, that no difference in slopes and intercepts of regression equations of the two
existed, was adopted. Using an s.edifference of 0.003 for the slope and 0.004 for the intercept
gave a ts for the slope of 139.0 and a ts for the intercept of -453.8. Since tcritical for 10 degrees
of freedom was ± 2.228 at 95% level of significance (two tailed), the null hypothesis was
rejected in both cases. Therefore there was a difference in the regression coefficients and
intercepts, hence regression equations. This confirms the positive impact of co-presence of Pb
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on uptake of Cd in mixed treatments. The impact is also confirmed by the increase in the
regression coefficient from 0.39 to 1.01 (Figure 5.11) associated with relative Pb and Cd
uptake from single treatments to mixed treatments.
5.4.9 Pb and Cd levels in effluent and sludge mixture
The overall mean concentration of Pb in treated sewage during the study period was 1.2 mg/l,
comprising means of 1.32 mg/l for treated sewage applied to the first crop and 1.04 mg/l for
treated sewage applied the re-growth (Table 5.2).
Table 5.2: Pb concentrations in samples of treated effluent and sludge mixture
Irrigation event
1
2
3
4
5
First crop
Volume applied/pot (l)
Concentration (mg/l)
Quantity of Pb (mg)
5.5
0.6
3.30
5
1.96
9.80
3
1.96
5.88
5
2.26
11.3
5
0.16
0.80
Second crop
Volume applied/pot (l)
Concentration (mg/l)
Quantity of Pb (mg)
5.5
0.04
0.22
0.9
0.04
0.04
5.5
0.12
0.66
2.1
0.12
0.25
5.5
2.46
13.5
6
Total
Mean (mg/l)
23.5
1.32
31.1
4
2.46
9.84
23.5
1.04
24.5
The levels exceeded the limit of 0.5 mg/l recommended for irrigation water (Table 4.1) but
were below the maximum limit of 5.0 mg/l recommended for irrigation water (Ayers and
Westcot, 1985). Cd levels in treated wastewater and Pb and Cd in samples taken from water
used to supplement effluent and sludge mixture application were not detectable.
5.5 Discussion
5.5.1 Extraction capacity of star grass
This component of the study established that not only did star grass take up both Pb and Cd as
does C. dactylon (Jonnalagadda et al, 2002), one of the few Cynodon grasses studied so far,
but it did so in large quantities after exposure to high concentrations in soils. The study also
showed that star grass could be ranked as one of the strongest accumulators of Pb among
grasses. It extracted 4 592 mg/kg, from sandy soil that had a total soil concentration of
approximately 1 200 mg/kg, equivalent to 343.7 mg/kg soil bio-available concentration of in
this experiment. This extraction capacity was comparable to hyper-accumulating grasses like
Lolium perenne (rye grass). Rye grass clippings from grass grown on a silt loam with a total
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Pb soil concentration of 10006 mg/kg extracted 5 390 mg/kg while those from the same soil
with a total Pb soil concentration of 5006 mg/kg extracted 2280 mg/kg (US Department of
Energy, 1998). However the low yield at high concentrations limited its capacity for phytoextraction. Baker, at al (2000) noted that hyper-accumulators should be high yielding.
Since it has been established that strong Pb hyper-accumulating plants such as Ipomea take up
larger quantities of Pb than star grass, the results of this component of the study suggested
that star grass could be classified as a medium Pb extractor. Ipomea accumulated 15 000
mg/kg in shoot tissue and 20 000 mg/kg in root tissue at Pb solutions of 500 mg/l and 1 000
mg/l respectively (Rhyne and Gosh, 2002). The fact that growth of the grass was severely
retarded at 343.7mg/kg bio-available metal concentration suggested that the uptake of 4 592
mg/kg was close to the maximum uptake capacity of star grass. It should be noted that the
high uptake of Pb by grass is chemically induced (due to addition of high levels of readily
available Pb) and should not be confused with natural hyper-accumulation reported in most
literature, quoting plants exposed to contaminated soil without artificially increasing bioavailability of Pb. McGrath et al (2002) noted that Pb hyper-accumulation is rare primarily
because Pb is very insoluble in the soil, but Pb hyper-accumulates in plant shoots once its
solubility is enhanced with synthetic chelates, such as EDTA.
The maximum uptake of 8.67 mg/kg Cd in single treatments and 17.67 mg/kg in mixed
treatments suggests that star grass is a relatively small Cd hyper-accumulator when compared
to hyper-accumulating plants such as maize. Maize accumulated 116.5 mg/kg at 125 mg/kg
total soil concentration on a clay loam amended with Cd(NO3)2, (US Department of Energy,
1998). Notwithstanding the relatively small solubility of CdS that was used to enrich the soil
in this component of the study, maximum uptake of Cd was significantly higher than the
uptake capacities of other grasses reported in the database of bio-accumulators (US
Department of Energy, 1998). The absence of clear signs of growth retardation in Cd
treatments in this experiment suggested that the maximum extraction capacity of star grass
was significantly higher than 17.67 mg/kg, implying that star grass was a strong accumulator
of Cd among grasses.
Since the results of this study confirmed star grass has relatively good Pb and Cd extracting
capacity they also implied the incompatibility of growing the grass for pasture on highly
contaminated soils.
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5.5.2 Grass yield response to Pb and Cd
The initial increase in the yield of star grass as Pb and Cd uptake increased was unusual,
given that Pb and Cd are not plant nutrients. However, it could partly be attributed to uptake
of sufficient vital elements (nutrients) available, largely from added fertilizers and treated
sewage. Since Pb and Cd are non-essential (Johannesson, 2002; Elson and Haas, 2003)
elements, the initial increase in yield cannot be attributed to them. The increase in uptake of
Pb and Cd could be associated with a general increase in uptake of nutrients associated with
higher growth. Polette et al (1997) postulated that the mechanisms that allow uptake of
nutrients by plants could facilitate uptake of heavy metals, since the latter are generally
indistinguishable from nutrients. These mechanisms are not yet well understood (Moolenar
and Lexmond, 1999) although it is reported that Pb2+ may proxy for Ca2+ (Johannesson (2002)
and Cd may be taken up in place of Zn (Elson and Haas, 2003). The weak correlations in the
models suggest that factors other than Pb concentration influence the yield.
Yields obtained at low concentrations of Pb and Cd suggest accumulation of the metals at
non-toxic levels. According to Clarkson (1986), accumulation of a heavy metal in tissue does
not necessarily imply toxicity because inactive or storage depots in the plant are formed in the
case of some metals. The decrease in yield with increasing uptake of Pb and Cd could be
attributed to toxicity of the metals possibly due to substitution of vital nutrients and their
metabolic functions because of the relative abundance of bio-available Pb and Cd compared
to other ions in the soil. The drop in the yield of the control, from the first crop to the second
crop, could be attributed to nutrient deficiency, while the increases in yields of the maximum
treatments of Pb and Cd from the first crop to the second crop could be attributed to reduced
toxicity resulting from the immobilization of the two metals by organic matter.
5.5.3 Metal uptake models and critical metal limits
Using the models generated in this study, the toxicity levels of Pb and Cd in grass and the
corresponding bio-available levels in a sandy soil were estimated using data from the regrowth crop. The critical Pb and Cd concentrations in star grass at peak growth were
computed to be 53.7 mg/kg and 3.2 mg/kg respectively. The corresponding critical bioavailable metal levels were 186.2 and 8.3 mg/kg respectively. These limits refer to metal
contents at which toxicity started to reduce yield and not the metal content limit desirable for
field grazing by animals. The latter would necessitate taking account of the levels of metals
taken up by animals through soil consumption during grazing, in addition to the levels in the
grass. However the critical limits are considered applicable where the grass is cut and fed to
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animals directly.
Considering that the prescribed Pb and Cd limits in pasture grass are 40 mg/kg and 1 mg/kg,
respectively (UK. Statutory Instrument No. 1412, 1995), this component of the study found
the critical metal limits of star grass at peak growth to be far in excess of the prescribed limits.
Substituting the logarithm of the prescribed grass Pb metal concentration of 40 mg/kg into the
soil-grass concentration model gives a corresponding bio-available limit of 106.32 mg/kg.
Similarly, for a limit of 1 mg/kg in grass the corresponding soil bio-available metal limit
would be 0.65 mg/kg.
The study also found that star grass appeared healthy at the critical Cd concentrations and
even beyond, making it difficult to recognize toxicity without testing for the metals. The lack
of visible signs of toxicity at peak productivity implies that animals may graze on highly
contaminated pastures that do not show obvious signs of pollution. The findings suggest that
bio-available Pb and Cd limits would have to be managed at below 106 mg/kg Pb and 0.65
mg/kg Cd, respectively, so that the limits in pasture do not exceed the UK Statutory
Instrument No. 1412 (1995) limits.
The relatively higher uptake of Pb and Cd in the first crop compared to the re-growth in single
metal treatments suggested a corresponding higher bio-available concentration in the former.
Although it was not possible to compare the bio-available metal concentrations of the soil for
each crop because of the absence of the bio-available data corresponding to the first crop, this
potential reduction in soil bio-available concentration could partly be attributed to the
increase in organic matter and equilibration of the metals with the soil. Bak and Jensen (1998)
attributed variations in bio-availability of metals, a common phenomenon in soils, to the
existence of different binding sites that control sorption and de-sorption processes in the soil.
The addition of organic matter through wastewater application could have increased binding
sites thereby reducing bio-available metals in the soil and hence reducing uptake levels in the
re-growth. It is also acknowledged that though necessary precautions were taken to minimise
contamination of grasses during application of inorganic metals, any contamination that
would have occurred unnoticed would have had the effect of increasing the levels of Pb and
Cd in the first crop compared to the re-growth and it would be difficult to quantify and
differentiate from uptake.
The bio-available Pb in the effluent and sludge treatment was lower than that in the control,
despite addition of Pb through effluent and sludge. The apparent reduction in background bioavailable Pb could be attributed to the sorption of Pb onto unsaturated binding sites of organic
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matter, leaving less in solution than in the control. However despite the differences in bioavailable Pb between the control and effluent and sludge treatment, grass uptake remained
similar in each harvest. According to Moolenar and Lexmond (1999), actual plant uptake in
soil-crop ecosystems, not only depends on soil concentrations but also on the distribution of a
chemical element in relation to other chemical species in the soil (also known as speciation)
and mechanisms for root entry and translocation to aerial parts of the plant.
The absence of a significant difference in bio-available Pb levels between single and mixed
treatments, that received the same dose of metal combined with treated wastewater, suggests
that Cd did not influence the bio-available level of Pb in soils. The insignificance of the
differences in Pb levels between the single metal and the mixed Pb and Cd treatments in star
grass suggests that Cd does not influence uptake of Pb by star grass. This finding is in
agreement with what Carlson and Rolfe (1979) found out in rye and fescue but contradicts the
finding by Miller (1977) that Cd in the soil reduced uptake of Pb in Zea mays L. (corn). It also
contradicts the finding by Carlson and Bazzaz (1977) that the uptake of Pb by plants
(American sycamore in their case) increased due to raised concentrations of Cd.
The increase in grass levels of Cd in mixed treatments beyond the levels in single treatments
and the strong correlation between Pb and Cd levels in the mixed treatments suggested
increasing accumulation of Cd in star grass due to the co-presence of Pb. This finding is
consistent with what Carlson and Rolfe (1979) found in rye and fescue and Miller et al (1977)
found in corn. These findings on Pb and Cd therefore confirmed that different plant species
accumulated different metal species to different levels. This component of the study, therefore
established that co-presence of Pb and Cd did not affect the levels of Pb in the sandy soil and
star grass but caused an increase in bio-available Cd in soils and Cd levels in star grass.
Regression of log10 (concentrations of the chemical in grass) versus the log10 (yield of grass)
produced non-linear model fits with r2 values that showed varying degrees of association
depending on the metal and the crop (whether first crop or re-growth). In general the levels of
association of log10 (yield) and log10 (grass metal concentration) in single treatments of Pb
were stronger in the first crop compared to the re-growth, while the reverse was true for single
Cd treatments. The higher r2 value of 0.99 for the regression y = - 0.2079x2 + 0.737x +
1.7727 (first crop) compared to 0.55 in the regression y = -0.342x2 + 1.1831x + 1.4232 (regrowth) suggested a much stronger association in the first crop and a weaker relationship in
the re-growth. When compared against an r2critical value of 0.87 at p≤0.05, the regression of the
first crop was significant while that of the re-growth was not. The stronger correlation in the
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first crop could be attributed to the higher levels of Pb that the grass absorbed.
A different trend to that of Pb was obtained for Cd single treatments where the regression of
Cd of y = -1.3832x2 + 1.2619x + 2.0992 for the re-growth was stronger (r2 = 0.51) than that
of the first crop y = -0.655x2 + 0.6533x + 2.2138 (r2 = 0.20). However both regression
models were not significant when compared with r2critical of 0.75 at p≤0.05. Therefore
generally, the association of yield and Cd content in grass became stronger in the single
treatments from the first crop to the re-growth. In the mixed Pb treatments, where the
regression relationship of the first crop was y = - 0.336x2 + 1.048x + 1.548 (r2 =1.0) and that
of the re-growth was y = - 0.908x2 + 2.369x + 0.898 (r2 = 0.45), the trend in which the regrowth was more weakly correlated in the re-growth persisted. The regression model of the
first crop was significant (p≤0.05).
A comparison of correlation coefficients of single and mixed treatments of the same metal
showed that the regression model of the single Pb treatments had the same level of association
as the regression model of the mixed Pb treatments. In the mixed treatments of Cd, the first
crop had a much stronger and significant (p≤0.05) regression model y = -1.557x2 + 1.604x +
2.040 (r2 = 0.89) compared to the re-growth regression model of y = - 0.752x2 + - 0.961x +
2.074 (r2 = 0.51). This suggests influence of co-presence of Pb and Cd on yield in the mixed
treatments, particularly as it relates to Cd. The intercepts of the regression models for each of
the metals were close in value to each other.
Regression of log10 (bio-available chemical concentration in the soil) versus log10 (chemical
concentrations in grass) produced linear model fits with positive slopes and r2 values. A
comparison of r2 values of regression models of log10 (metal content in the re-growth) and
log10 (bio-available metal concentration) in the soil to r2critical of 0.87 for Pb and 0.75 for Cd
(at p≤0.05) showed that the regression model for Pb in the single treatment and regression
models of Cd in single and mixed treatments were significant (p≤0.05). In addition, the
regression model for the single treatments of Pb y = 0.525x + 0.539 (trend-line r2 = 0.75) was
stronger than the model for the mixed Pb treatments, y = 0.398x + 0.607 (trend-line r2 =
0.70). The reverse was true for the single and mixed Cd treatments, where y = 0.451x + 0.084
in single Cd treatments and y = 0.853x + 1.874 in mixed treatments had significantly (p≤0.05)
high r2 values of 0.78 and 0.98, respectively, compared to an r2critical value of 0.75.
The fact that the slopes of the regression models of single and mixed Pb treatments were
statistically different and the intercepts were statistically the same, suggests that the two
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regression models were statistically different but closely associated. The higher r2 value in
mixed Cd treatments compared to single Cd treatments was consistent with the higher slope
of the mixed Cd regression model of 1.01 compared to the slope of 0.39 for the model of the
single Cd treatment. The higher slope implied a 2.6-fold increase in the rate of uptake of Cd
in mixed treatment compared with single treatments. This indicates the influence of copresence of Pb on Cd. The regression models of Pb in single and mixed treatment indicate
that the concentration of Pb in grass increased at rates of 0.52 and 0.40 times (respectively)
the level of Pb in the soil. Similar models for Cd suggested that the concentration in grass
increased by 0.45 and 0.85 times the bio-available levels in single and mixed treatments
respectively.
5.5.4 Implications of findings
This component of the study provided answers relating to the capacity of star grass to take up
high levels of Pb and Cd. However it was not clear if such levels could be reached under field
conditions where levels of Pb and Cd in treated sewage were much lower than the levels
added through inorganic salts and treated sewage. This component also confirmed that the
uptake of the metals could be described using significant models of log10-transformed
variables of measured parameters, allowing for the estimation of toxicity levels in soils and
grass and threshold bio-available levels that would have to be maintained so as not to exceed
allowable metal levels in star grass. Given that the conditions under which the threshold
allowable limits were derived were different from field conditions, these estimates had to be
re-confirmed through a field experiment. Therefore the logical step was to take the study
further and investigate uptake of Pb and Cd under field conditions.
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CHAPTER 6
FIELD ASSSESSMENT OF LEAD AND CADMIUM UPTAKE BY Cynodon
Nlemfuensis UNDER REPEATED APPLICATION OF TREATED WASTEWATER
6.1 Introduction
This chapter presents a field assessment and models of accumulation of Pb and Cd in star
grass under irrigation with treated sewage. Models produced under greenhouse conditions
(Chapter 5) estimate that star grass can absorb more than 40 mg/kg Pb and 1 mg/kg Cd
recommended for pasture, if bio-available levels in the soil are more than 106.3 mg/kg and
0.63 mg/kg, respectively. However considering that conditions for availability of the metals
from the soil in the pot experiment are different from those in the field, it was decided to
extend the investigation of Pb and Cd uptake to the field to reflect real life conditions and
develop models appropriate for these conditions. Therefore, the purpose of this component of
the study was to develop soil-vegetative metal uptake models for predicting Pb and Cd uptake
in star grass under field conditions where sandy soils were subjected to continuous disposal of
treated sewage. The models were postulated to be useful for estimating grass metal content
and providing an indication of suitability of using star grass grown under similar conditions as
pasture.
Unlike in the greenhouse experiment where the concentrations of Pb and Cd were varied
using inorganic salts of Pb and Cd, in the field the concentrations of the metals varied
depending on the metal content strength of influent wastewater. The strength of influent was
related to industrial and commercial operations (Junkins et al, 1983). To develop models
representative of field situations, it was necessary to vary quantities of Pb and Cd applied
amongst different experimental units (treatments) so as to vary the levels of Pb and Cd
applied. To vary the quantities of Pb and Cd applied to treatments, using incoming treated
sewage, it was necessary to vary the total volumes of treated wastewater applied to the
treatments over a long time. It was assumed that the quantity of Pb and Cd would vary
proportionally to the quantity of treated wastewater applied. Therefore the levels of the metals
in treated water used for irrigating star grass had to be determined for each irrigation event, so
as to determine the quantities of the metals added to the soils over time.
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6.2 Objectives
This component of the study was aimed at developing Pb and Cd uptake models for star grass
on sandy soil subjected to varying quantities of treated wastewater disposal under field
conditions.
The specific objectives of this chapter were to:
(1) develop Pb and Cd uptake models based on soil bio-available metal content and metal
content in grass under field conditions
(2) estimate the allowable limit of bio-available soil Pb and Cd content for a sandy soil on
which star grass pasture grows under field conditions
(3) establish the effect of rate of accumulation of Pb and Cd in a sandy soil and on uptake by
star grass under field conditions
6.3 Detailed methods and materials
6.3.1 Estimated irrigation requirements of star grass
In setting up the field experiment, it was important to estimate irrigation requirements of star
grass so as to decide on the quantities of treated sewage to apply to the soil. The irrigation
requirements were estimated using the modified Penman method described in the Food and
Agricultural Organisation (FAO), Irrigation and Drainage paper number 24 together with 30year climatic data from the nearest meteorological station to the study area. The nearest
meteorological station, Belvedere in Harare is located at an altitude of 1 471 m above sea
level at a latitude of 170 50' S and longitude of 310 01' E (Department of Agricultural
Technical and Extension Services and Department of Meteorological Services, 1989). The
area has a mean annual rainfall of 800 mm/annum and it lies in Agro-ecological Region IIA.
Table 6.1 presents the potential evapo-transpiration and estimated irrigation water
requirements of star grass for a full year, covering the period January to November, during
which the experiment was run. The months with excess water have theoretical negative water
requirements, which are however not carried forward to the next month since that water is
normally lost as run-off, deep percolation losses or evaporation. The data in Table 6.1 shows
that for optimum growth, grass would require a net of 765.4 mm of irrigation per year to
supplement rainfall.
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Table 6.1: Estimated crop water and irrigation requirements of star grass.
Month
Irrigation
80%
Potential evapo- Monthly Rainfall
total
(mm/month) dependable requirement
transpiration
mm/month
(mm)
Belvedere
rainfall
mm
(mm/month)
10 day periods
1
2
3
26
July
34
Aug
46
Sep
56
Oct
50
Nov
42
Dec
40
Jan
38
Feb
31
Mar
33
Apr
30
May
24
Jun
Total Jan-Nov
24
36
52
54
48
42
40
37
35
32
25
24
28
40
52
56
44
38
40
35
36
36
26
24
78
110
150
166
142
122
120
110
102
101
81
72
2.5
3.2
10.3
37.6
93.2
190.5
172.5
178.5
94
40.5
9.5
5
2
2.56
8.24
30.08
74.56
152.4
138
142.8
75.2
32.4
7.6
4
76
107.44
141.76
135.92
67.44
-30.4
-18
-32.8
26.8
68.6
73.4
68
765.36
Mean
irrigation
requirement
mm/day
2.53
3.58
4.73
4.53
2.25
-1.01
-0.60
-1.09
0.89
2.29
2.45
2.27
6.3.2 Experimental set-up
The field experiment was set up in the portions of Firle farm and Churu farm that had been set
aside for field experiments. The area in Churu farm was located 2m down-slope of the
position where soils for the greenhouse experiment were taken from. The portion in Firle farm
that was selected for this experiment lay within the area that was studied during soil
characterisation. The two areas were 30m apart.
It was assumed that the area not previously irrigated had a higher chance of showing marked
changes in soil bio-available levels of Pb and Cd added through treated sewage during the 11month period of the experiment, than the area that had been irrigated for 30 years. This
assumption was based on the fact that the unpolluted area had less organic matter and CEC
(Table 4.2 in Chapter 4) to immobilise Pb and Cd. Therefore in addition to the control, 3
treatments of Pb and Cd were set up in this area, while the fourth treatment was located in
Firle farm. The fourth treatment was included in the study to investigate Pb and Cd uptake by
star grass from a soil that has been receiving treated sewage for a long time.
All treatments were set on field plots measuring 10m x 10m. Each treatment had 3 replicates.
The control did not receive any treated sewage application. The 3 treatments in Churu farm
were planned to receive the following amounts of supplementary irrigation:
(1) treatment 1: half of the estimated water requirement
(2) treatment 2: the estimated water requirement
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(3) treatment 3: twice the estimated water requirement of grass was provided
Treatment 4 had to receive the same application as treatment 3.
To provide water to the plots in Churu farm, a 150 m long, 90mm diameter polyethylene
pipeline was installed from an outlet box, along the pipeline from Firle Treatment Plant to the
sewage pond (Figure 3.1) and finally to the top 3 plots (X1, Y1 and Z1 in Figure 6.1). Inside
the outlet box (on the main pipeline) there was a 200 mm pipe outlet joined to the main
pipeline through a t-piece on which there was a valve. The outlet box had outlets on 3 sides,
through which treated sewage was released for disposal onto the farm by flooding.
A potable, 8 horsepower pump and petrol engine, were used to pump treated wastewater from
the outlet box to the plots in Churu farm. Three hydrants were installed at the middle of the
top plots, labelled X1, Y1 and Z1 along the top edges of the plots. Each hydrant served four
downstream plots, through a 40 m flexible hose provided to deliver water from the hydrant to
the rest of the plots served by that hydrant. As an example the hydrant at X1 served X1 to X4.
One outlet was used to irrigate the 3 plots of treatment 4. The plots in treatment 4 were set up
side-by-side in the same manner as plots X1, Y1, and Z1. Water was supplied to treatment 4,
from the outlet box through a small 10m long clay-lined earth channel that brought treated
wastewater towards the plot located at the middle and directed it along the top edges of the
plots at a distance of 2 m from the plots into 2 m long channels that joined the plots. The
channel was run at 2 m away from the edges of the plots to minimize seepage of water from
the channels into the plots. A portable flume was installed at the upper ridge of the plot during
each irrigation event so as to measure the water supplied to each plot.
6.3.3 Preparation of field plots
The boundaries of the areas in which the plots were set up were marked. The areas were then
ploughed and ripped. After removing plant materials, roots and debris, the areas were
manually levelled in the direction perpendicular to the direction of irrigation and at a slope of
3 % in the direction of flow of irrigation water. Thus the land sloped uniformly in the
direction X1 to X4, to ensure uniform water application (Figure 6.1).
The boundaries of the plots were marked, leaving a 2m buffer zone between any two plots.
The buffer zones served four purposes. The first purpose was to minimise surface and ground
water flow from one plot to the next. The second purpose was to provide soil for forming
ridges around each plot so as not to disturb the area levelled inside the plot. The third purpose
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was to provide pathways for movement of people. The fourth purpose in the case of
treatments 1 to 3 was to provide an area in which water could spill without causing direct
application to a particular plot during water measurement.
III 2,3
II 1,3
Z1
III 2,1
Y1
III 2,2
II 1,1
III 1,3
C 1, 1
II 3,3
X2
C 1,2
C 2, 1
I 2, 1
I 3,3
X3
I 2,2
III 3, 1
II 2,3
Z4
II 2, 1
III 3,2
C 3,3
Y4
I 3,2
C 2,2
III 3,3
Y3
II 3,2
I 1,2
C 2,3
I 2,3
Z3
I 3, 1
I 1, 1
Y2
III 1,2
II 3,1
X1
II 1,2
C 1,3
Z2
III 1,1
I 1,3
X4
II 2,2
C 3, 1
C 3,2
C – Control; I, II, III –Treatments 1, 2 and 3 respectively; 1,1 to 3,3 – soil and grass sampling positions
Figure 6.1: Plot layout at Churu farm
Thirty centimetre high ridges (bunds) were made around each border. Each plot therefore
formed an irrigation border. An irrigation border is an area that is level along one axis and
slopes in the direction of flow of irrigation water. In order to increase uniformity of water
application across the level sides of the border and along the direction of water flow, a small
furrow was made within the border (on the inside of the top ridge) to catch the water as it
came out of the flexible hose or flume. This allowed the water to fill the furrow first before
over-flowing down the plot at all points along the furrow at the same time.
After preparing the plots, 5cm deep furrows spaced at 20 cm were made in the plots along the
direction of flow of water, for planting star grass. Strands of star grass with several nodes on
them were placed in the furrows and covered with soil. Small amounts of water were applied
to the grass for a period of 3 weeks to establish it. The rainfall that fell at the time was also
useful in assisting the establishment of grass. The plots in Churu farm were randomly
assigned to the control and treatments 1-3 (Figure 6.1) prior to administration of the
treatments.
6.3.4 Irrigation of grass
At the beginning of the experiment, the pump was tested in order to determine the point to
which the gate valve had to be opened in order to provide sufficient water for irrigation and at
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a relatively constant head above the suction pipe. The flow was directed away from the plots
during this process. During irrigation, the discharge of the pump was measured 3 times at the
beginning and 3 times at the end of irrigation of each plot. A 20-litre container and a
stopwatch were used for the purpose. The mean discharge was used in computations of
quantities of irrigation water applied and concentrations of the metals (Appendix 3). Samples
of water were also taken at the beginning and end of each irrigation event for determining
metal content. Since complete irrigation required 2 days, 4 samples were collected during
each irrigation event.
In order to measure the volume of water applied to treatment 4, the level of flow was set on
the scale of the flume while adjusting the amount of water coming from the outlet box.
During the set up, the potable flume was installed at 2 m away from the plots and the channel
was breached to direct the flow away from the plots. After set up, the flume was installed at
the ridge of the border and the breach was closed to direct the flow into the borders. The flow
was set as close as possible to the average discharge of the pump and application time was
recorded.
Irrigation of grass in treatments 1 to 4 commenced 3 weeks after planting. Eight irrigation
applications were undertaken during the period of the experiment with treatment 1 receiving
25.7 m3, treatment 2, 49.4 m3, treatment 3, 97.8 m3 and treatment 4, 85.9 m3.
6.3.5 Soil sampling and testing
Soil samples were taken using soil augers from field plots on 3 occasions during the
experiment. The soil was sampled from 3 points within each plot. Figure 6.1 shows the points
from which the samples were collected in each plot. As an example, in plot X1 the samples
were collected at points I 1,1, I 1,2 and I 1,3. The soil samples were collected from depths of
0-10, 10-20, 20-30 and 30-40 cm using a soil auger. For each horizon the soil from the 3
sampling points was mixed to form one sample for each depth for that plot. After removing
plant debris the samples were air-dried and passed through a 2mm sieve.
Soil depth and soil properties of clay content, pH and cation exchange capacity were
determined for use in interpreting Pb and Cd in soils. Soil texture, from which clay content
was derived, was determined using the hydrometer method (Gee and Bauder, 1986). Soil pH
was determined using a 1:5 soil suspension of 0.01M CaCl2. Cation exchange capacity was
determined by saturating the soil with 1M CH3COONH4 buffered at pH 5.2. Bio-available
soil concentrations were determined using procedures recommended by McGrath and Cegarra
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(1992). A 1 M (CH3COONH4) solution was added to the soil sample and the suspension was
shaken using a mechanical shaker. The suspension was the filtered, after which levels of Pb
and Cd were measured on the atomic absorption spectrometer.
6.3.6 Grass sampling and testing
Grass samples were taken from each plot on 5 out of a planned 6 occasions during the field
experiment. The sixth sample had to be foregone due to a limited budget. On 3 of these
occasions, the samples were taken at the same time as soil samples for purposes of comparing
soil and grass levels of Pb and Cd. The first crop was harvested 45 days after the start of
irrigation and the re-growth samples were collected at an interval of 51 days thereafter. The
grass samples were taken next to the position where soil samples were taken. Thus, from each
plot 3 replicates of grass samples were tested.
The grass was cut at 5 cm height off the soil surface, washed using de-ionised water, oven
dried at 65 0C, ground and sieved through a 0.1 mm sieve. The samples were then ashed at
550 oC for 16 hours and digested with 25% HCl and concentrated HNO3. After filtration, Pb
and Cd were determined using atomic absorption spectrometry. Three samples of the grass
that were taken during planting were subjected to the same metal extraction procedure prior to
determination of levels of Pb and Cd.
6.3.7 Sewage effluent and sludge sampling and testing
During each irrigation event, 4 samples of treated wastewater were collected for testing Pb
and Cd. The levels of Pb and Cd in the mixed effluent and sludge were determined by atomic
absorption spectrometry (Department of Environment, 1989) after extraction with HCl and
concentrated HNO3.
6.3.8 Data analysis
Means and standard deviations were calculated using measured data on clay content, soil pH
and CEC. Analysis of variance was performed on measured values of pH, CEC and clay
content of the soil profiles to determine whether the application of different volumes of
treated sewage on the soil parameters was significant at 95% confidence level. Correlation
analysis was used to test the strength of association of soil pH versus depth, CEC versus depth
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and clay content versus depth. The significance of the association was determined by
comparing r2 values with the Pearson r2critical value of 0.87 (p≤0.05).
For statistical analysis and development of dose-response relationships, measured data on bioavailable metal concentrations and concentration of metals in grasses was first tested for
normality and then transformed to log10 values. To assess Pb and Cd accumulation in the soil
profile, correlation analysis was used to relate soil depth and log10 (metal concentration).
Analysis of variance was used to test the significance of treatment on (1) bio-available Pb and
Cd and on (2) levels of the metals in grass.
To develop the best-fit models for uptake of Pb and Cd under field conditions two approaches
were used to analyse the data obtained. In the first approach, the data for each of the 3 sample
sets of bio-available metal levels and grass metal levels was used to draw up a model for each
harvest and test its strength. This was done to assess whether any of the models of individual
harvests could be representative of multiple harvests.
In the second approach, dose-response models of average bio-available soil levels and
average levels of the metals star grass throughout the life of the experiment were drawn up, to
assess whether they could represent multiple harvesting of grass. The assumption was that in
the field, a grass crop is planted and animals continue to feed on the crop until the old crop is
removed and a new crop is planted. Therefore the regular grazing of animals could be
regarded as being synonymous with regular harvesting of the grass crop. To develop models
representative of this situation, it was decided to analyse each harvest and each soil-sampling
event as a replicate of the average situation that prevails under field conditions over a long
time.
The best-fit models were tested for strength by comparing the computed correlation
coefficients and the critical t values from the t-test for comparison of regression coefficients.
6.4 Results
6.4.1 Soil pH, cation exchange capacity and clay content
Table 6.2 presents data on selected soil parameters of CEC, pH and clay content. Soil pH
varied from 4.9 to 5.5 in some horizons of the control to a maximum of 5.35 to 7.4 in some
horizons of the previously irrigated area. There was a gradual increase in CEC with increase
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in the level of treatment. Treatment 4 had a very high CEC, particularly in the top horizons.
Clay content decreased from the control and treatments 1 to 3 to treatment 4. Analysis of
variance on data (Table 6.2) from soil profiles shows that pH, CEC and clay content increased
significantly (p≤0.05) with treatment.
Depth
(cm)
Table 6.2: Mean soil properties (standard deviations) and soil depth
Control
Treatment 1
Treatment 2
Treatment 3
Har 1
0-10
0-20
20-30
30-40
0-10
10-20
20-30
30-40
0-10
10-20
20-30
30-40
Re-g3
Re-g4
Har 1
Re-g3
5.50
(0.53)
4.90
(0.49)
5.33
(0.33)
4.90
(0.21)
5.47
(0.49)
5.23
(0.35)
5.07
(0.51)
5.23
(0.76)
4.87
(0.29)
5.03
(0.12)
4.93
(0.31)
4.97
(0.06)
5.47
(0.36)
5.55
(0.88)
5.35
(0.25)
6.00
(1.00)
5.53
(0.70)
5.67
(0.91)
5.60
(0.95)
5.63
(1.05)
1.61
(0.09)
2.07
(0.19)
1.83
(0.18)
1.98
(0.17)
1.95
(0.32)
1.83
(0.23)
1.9
(0.00)
1.69
(0.04)
1.42
(0.13)
1.81
(0.12)
1.90
(0.28)
1.79
(0.13)
2.63
(0.35)
2.67
(0.55)
2.17
(0.23)
2.4
(1.11)
2.53
(0.45)
2.2
(0.1)
2.5
(0.87)
2.3
(0.44)
4.67
(0.58)
5.33
(0.58)
6.33
(0.58
7.00
(1.00)
5.33
(1.53)
6.00
(2.00)
6.00
(0.00)
6.67
(1.53)
4.67
(1.16)
4.33
(1.16)
6.50
(2.13)
6.5
(1.19)
3.43
(1.40)
2.93
(0.97)
3.73
(2.84)
4.6
(3.03)
4.00
(0.00)
5.33
(0.58)
6.67
(2.31)
6.67
(1.53)
Re-g4
Har 1
PH
5.60
(0.76)
5.51
(0.67)
5.56
(0.51)
5.54
(0.19)
Re-g3
Re-g4
Har 1
Re-g3
Re-g4
Treatment 4
Har 1
Re-g3
Re-g4
5.37
(0.40)
5.77
(1.00)
5.40
(0.60)
5.33
(0.72)
6.21
(0.30)
6.15
(0.48)
6.33
(0.99)
6.20
(0.87)
5.63
(0.31)
5.73
(0.59)
6.07
(1.24)
6.03
(1.10)
6.13
(1.21)
6.10
(1.06)
5.25
(0.35)
5.25
(0.21)
5.90
(0.31)
6.10
(0.44)
5.43
(0.10)
5.70
(0.01)
5.70
(0.46)
5.93
(0.50)
5.40
(0.14)
5.35
(0.07)
7.40
(0.10)
6.53
(0.81)
6.90
(0.57)
6.35
(0.35)
Cation exchange capacity (cmolckg-1)
3.18
3.17
2.57
2.73
(0.25)
(0.15)
(0.21)
(0.38)
2.52
3.00
2.53
2.47
(0.37)
(0.14)
(0.49)
(0.32)
2.20
2.30
2.53
2.10
(0.42)
(1.15)
(0.25)
(0.28)
2.25
2.57
2.15
2.90
(0.07)
(0.04)
(0.07)
(0.99)
2.93
(0.35)
2.53
(0.25)
2.67
(0.35)
2.70
(0.56)
2.73
(0.52)
2.47
(0.38)
2.70
(0.61)
3.07
(1.46)
3.51
(1.05)
2.47
(0.38)
2.70
(0.61)
1.91
(0.20)
31.17
(6.84)
11.37
(4.43)
4.30
(2.76)
-
31.78
(11.4)
12.2
(8.17)
5.33
(3.16)
-
29.11
(6.05)
9.56
(2.76)
4.58
(0.99)
Clay content (%)
4.67
4.33
(1.53)
(0.58)
7.00
5.67
(1.00)
(1.53)
8.33
7.33
(2.52)
(2.52)
7.33
5.5
(1.16)
(0.71)
5. 00
(0.00)
6.67
(0.58)
7.33
(1.16)
8.33
(2.89)
5.33
(1.53)
7.00
(1.00)
7.00
(1.00)
10.00
(2.65)
5.00
(1.00)
6.66
(0.58)
6.66
(1.50)
10.33
(3.05)
4.00
(1.41)
4.5
(2.12)
4.5
(3.54)
-
3.00
(1.00)
2.67
(0.58)
4.0
(1.00)
-
2.25
(0.35)
2.50
(0.71)
3.00
(0.00)
-
5.40
(0.20)
5.60
(1.05)
5.63
(1.57)
5.63
(1.31)
5.66
(1.52)
6.33
(1.15)
8.56
(2.52)
7.00
(1.01)
5.80
(0.46)
5.67
(0.76)
5.73
(1.10)
5.15
(0.78)
5.00
(1.00)
6.66
(0.58)
6.66
(1.16)
10.33
(3.05)
Har - Harvest 1 (i.e. first crop); Reg - re-growth; - missing values
Comparison of means of treatments showed that the pH in treatments 3 and 4 were
significantly (p≤0.05) higher than in the control. The pH in treatment 4 was also significantly
(p≤0.05) higher than in the rest of the treatments except treatment 3. The CEC was
significantly (p≤0.05) higher in treatment 4 than in the rest of the treatments and the control.
There was no significant difference in CEC in the latter. The CEC was significantly (p≤0.05)
higher in treatment 4 than in the rest of the treatments and the control. There was no
significant difference in CEC in the latter. Comparison of means of pH, CEC and clay content
within each depth showed that there was no significant difference in pH and CEC with
increase in depth, but there was a significant difference (p≤0.05) in clay content with depth.
The 30-40 cm horizon had significantly (p≤0.05) clay content that the 0-10 cm horizon.
Comparing Pearson’s correlation coefficient (r2critical of 0.87) to the computed correlation
coefficients in Table 6.3 shows that there was no association between pH and soil depth
except in treatment 3 (re-growths 3 and 4). Generally, there was no significant (p≤0.05)
association between CEC and soil depth except in treatment 4 and treatments 2 and 3 (re-
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growth 4). In these cases CEC was negatively correlated to soil depth implying that the top
soil layers had a higher CEC than lower soil horizons. Generally, clay content positively
correlated with soil depth.
Table 6.3: Correlation coefficients for pH, cation exchange capacity and clay
content versus soil depth
Treatment
Control
Treatment 1
Treatment 2
Treatment 3
Treatment 4
pH
CEC
Harvest
1
Re-growth
3
Re-growth 4
-0.58
0.63
-0.44
0.25
-0.57
-0.68
0.53
-0.82
0.92
-0.75
0.37
0.84
-0.30
-0.90
-0.78
Harvest
1
0.48
-0.67
-0.82
-0.44
-0.96
Clay content
Re-growth
3
Re-growth
4
Harvest
1
Re-growth
3
Re-growth
4
-0.44
-0.32
-0.81
0.64
-0.96
0.73
0.04
-0.89
-0.89
-0.95
1.00
0.79
0.54
0.98
0.99
0.94
0.61
0.92
0.93
0.87
0.87
0.85
0.72
0.91
0.98
6.4.2 Bio-available Pb and Cd content of soils and grass
Soil and grass Pb and Cd levels per harvest
Table 6.4 presents data on bio-available metal levels from soil samples taken at the same time
as grass samples of the first crop, 3rd and 4th re-growth crops as well as concentrations in star
grass obtained from the first crop, and 1st, 2nd, 3rd and 4th re-growth crops. This data is the
basis on which the dose-response relationships were derived for each harvest. Details on mean
bio-available metal concentrations of the soil profile along the 10 cm soil horizons are
presented in Appendix 4.
Soil bio-available Pb and Cd levels
Table 6.4 shows that the maximum levels of bio-available Pb and Cd in the soil profile were
12.55 mg/kg and 0.90 mg/kg respectively. Comparison of means between treatments and
among sampling events showed that treatment 4 had significantly (p≤0.05) higher levels of Pb
and Cd. Treatments 2 and 3 had significantly higher levels of Pb than the control. The mean
level of Cd in the control was significantly (p≤0.05) lower than the rest of the treatments.
However there were no significant differences in the latter.
Grass Pb and Cd levels
In grass the maximum levels attained were 16 mg/kg Pb and 2.17 mg/kg Cd. Comparison of
means showed that Cd in the 3rd and 4th re-growth crops was significantly higher (p≤0.05)
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than the rest of the re-growths and the first crop. Differences in mean levels of Pb between
harvests were not statistically significant.
Correlation of bio-available Pb and Cd and soil depth for each harvest
Comparing Pearson’s correlation coefficient (r2critical of 0.87) to the computed correlation
coefficients in Table 6.5 shows that bio-available Pb was strongly correlated to depth in the
control and in treatment 4. A similar trend also existed for Cd. In the control bio-available Pb
showed positive correlation while in treatment 4 the association between soil depth and Pb
concentration was negative. The correlation coefficients of both Pb and Cd changed from a
positive trend (in the control) to a progressively negative trend with increase in treatment
level.
Table 6.4: Mean soil profile bio-available metal and grass concentrations
Treatment
Sample
Mean bio-available soil
profile concentration
(mg/kg)
First crop
3
Re-growth
4
Mean grass concentration (mg/kg)
First crop
Re-growth
3
1
2
4
2.89
(0.84)
3.44
(0.69)
3.89
(0.19)
4.44
(1.02)
16.00
(1.31)
2.67
(1.53)
5.33
(1.46)
6.00
(1.67)
7.11
(2.83)
16.00
(3.00)
6.0
(3.46)
9.33
(2.19)
10.56
2.01)
9.89
(2.91)
15.00
(2.77)
6.33
(2.08)
8.00
(0.67)
10.11
(2.36)
10.11
(1.39)
15.00
(4.37)
0.30
(0.28)
0.11
(0.09)
0.11
(0.09)
0.33
(0.33)
2.00
(0.34)
0.39
(0.35)
1.0
(0.00)
1.17
(0.29)
1.06
(0.10)
2.17
(0.40)
0.56
(0.51)
1.22
(0.38)
1.28
(0.25)
1.67
(0.58)
1.47
(0.50)
0.44
(0.19)
1.22
(0.69)
1.44
(0.51)
1.44
(0.19)
1.54
(0.47)
Lead (Pb)
Control
Treatment 1
Treatment 2
Treatment 3
Treatment 4
Control
Treatment 1
Treatment 2
Treatment 3
Treatment 4
0.389
(0.069)
1.19
(0.018)
1.34
(0.185)
1.76
(0.117)
12.55
(1.050)
0.339
(0.059)
0.733
(0.227)
0.947
(0.528)
1.175
(0.238)
9.00
(1.400)
0.011
(0.007)
0.022
(0.03)
0.04
0.007)
0.065
0.022)
0.9
(0.020)
0.02
(0.021)
0.019
(0.007)
0.019
(0.028)
0.033
(0.025)
0.9
(0.400)
0.67
2.56
(0.208)
(0.694)
0.72
3.33
(0.194)
(0.333)
1.3
3.67
(1.201)
(0.882)
0.67
4.89
(0.355)
(0.385)
12.4
14
(0.50)
(1.50)
Cadmium (Cd)
0.03
0.33
(0.006)
(0.05)
0.24
0.11
0.183)
(0.09)
0.02
0.11
(0.015)
(0.100)
0.03
0.11
(0.014)
(0.017)
0.1
1.33
(0.020)
(0.14)
() standard deviation
Combined data for soils and grass for experimental period
Table 6.6 presents the average bio-available levels and average grass concentrations of Pb and
Cd for all samples taken over the 11-month period of the experiment. Each harvest was
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treated as a replicate. This data was log10-transformed and used to develop dose-response
relationships representing the average bio-available and grass metal concentrations in this
experiment.
Table 6.5: Correlation coefficients for soil depth and bio-available soil metal
concentration
Treatment
Lead
Cadmium
Control
Treatment 1
Treatment 2
Treatment 3
Treatment 4
Harvest 1
Re-growth 3
Re-growth 4
Harvest 1
Re-growth 3
Re-growth 4
0.87
-0.84
0.68
-0.77
-1.00
0.94
-0.13
0.87
0.88
-0.88
0.96
0.05
0.93
-0.40
-0.90
0.65
0.77
0.00
0.00
-0.99
-0.87
0.85
0.24
0.24
-0.96
-0.87
-0.40
-0.70
-0.97
-0.69
The data shows a gradual increases in Pb and Cd concentrations in both soils and grasses
from the control to treatment 3 and a sharp increase in treatment 4. Analysis of variance
showed that there was a significant (p≤0.001) increase in the level of bio-available Pb and a
significant (p≤0.05) increase in the level of Cd corresponding to each harvest with increase in
treatment. Comparison of mean levels between treatments showed that there was no
significant difference in bio-available Pb from the control to treatment 3. However treatment
4 had significant (p≤0.05) higher levels of bio-available Pb than all other treatments. There
was no significant difference in bio-available Cd levels amongst all treatments.
Table 6.6: Average bio-available Pb and Cd levels in soils and grass (mg/kg)
Treatment
Control
Treatment 1
Treatment 2
Treatment 3
Treatment 4
Pb concentrations
Soil
0.466 (0.178)
0.881 (0.267)
1.196 (0.216)
1.202 (0.545)
11.317 (2.007)
Grass
4.090 (1.902)
5.886 (2.699)
6.846 (3.316)
7.288 (2.675)
15.330 (0.836)
Cd concentrations
Soil
0.020 (0.009)
0.094 (0.009)
0.086 (0.010)
0.042 (0.019)
0.633 (0.046)
Grass
0.404 (0.102)
0.710 (0.574)
0.822 (0.657)
0.922 (0.681)
1.702 (0.362)
Grass Pb and Cd levels
Analysis of variance showed that there was a significant (p≤0.001 for Pb and p≤0.05 for Cd)
increase in levels in grass with treatment. Comparison of means between treatments showed
that the differences in means in the control and treatments 1 to 3 were not significant.
Treatment 4 had significantly (p≤0.05) higher grass levels of Pb than the rest. Treatment 4 had
significantly (p≤0.05) higher levels of Cd in grass than the Control. The differences in the rest
of the Cd treatments were not significant.
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6.4.3 Soil bio-available Pb and Cd response to treatment
The effect of the treatment on bio-available concentrations of Pb and Cd in the soil profile is
presented in Figures 6.2 and 6.3, using log10-transformed data from Table 6.6. Figure 6.2
presents what appears to be a general increase in bio-available Pb with increase in quantity of
treated sewage applied to the soil. However analysis of variance shows that statistically, there
was no significant (p≤0.05) increase in Pb with treatment.
Log(10) bio-available Pb
concentration (mg/kg)
1.2
1
0.8
0.6
0.4
0.2
0
-0.2
Control
T1
T2
T3
T4
-0.4
-0.6
Treatment
Figure 6.2 Treatment versus log(10) bio-available soil Pb
concentration
Although there appears to be a general increase in Cd content of soils with increase in
treatment level (Figure 6.3) analysis of variance showed that the rise in Pb levels was
statistically insignificant (p≤0.05). Treatment 3 had a lower bio-available level than expected.
Log(10) soil bio-available Cd
concentration (mg/kg)
0
-0.2
Control
T1
T2
T3
T4
-0.4
-0.6
-0.8
-1
-1.2
-1.4
-1.6
-1.8
Treatment
Figure 6.3: Treatment versus Log(10) bio-available soil Cd concentration
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6.4.4 Grass Pb and Cd content response to treatment
Figure 6.4 shows a general increase in Pb levels in grass with increase in treatment level. Soil
bio-available levels significantly (p≤0.05) increased Pb uptake by star grass. Levels of Pb
increased by 375% from a minimum of 4.09 mg/kg to a maximum uptake of 15.33 mg/kg.
Log(10) Pb concentration (mg/kg)
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Control
T1
T2
T3
T4
Treatment
Figure 6.4 : Treatment versus Log(10) grass Pb concentration
Similarly Figure 6.5 presents a general rise in Cd concentration with increase in level of
treatment. Soil bio-available levels significantly (p≤0.05) increased Cd uptake by star grass.
Overall, Cd uptake increased 425% from an average of 0.40 mg/kg to an average of 1.70
mg/kg (Table 6.4). The sharp increase in Cd uptake, followed a pattern observed by
Hofwegen and Veenstra (1995) where a 50% increase in total soil Cd from 0.5 mg/kg to 0.82
mg/kg led to a large increase (1200%) from 0.08 mg/kg to 1 mg/kg in brown rice.
Whereas Pb levels in grass were within the 40 mg/kg limit recommended for pasture grass,
Cd levels were above the recommended 1 mg/kg maximum limit in treatments 2 to 4.
According to Johannesson (2002) plant uptake of Cd ions is generally considerably higher
than that of Pb ions.
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6.4.5 Correlation between bio-available and grass Pb and Cd contents for each
grass crop
The regression models of log10-transformed bio-available concentrations in soils versus log10transformed concentrations in individual grass harvests are presented in Figures 6.6 and 6.7
for Pb and Cd, respectively. The models are based on data from soils and grass samples that
were taken at the same time. These are referred to as Harvest 1, Re-growth 3 and Re-growth 4
in Figures 6.6 and 6.7.
Log(10) Cd concentration (mg/kg)
in grass
0.3
0.2
0.1
0
-0.1
Control
T1
T2
T3
T4
-0.2
-0.3
-0.4
-0.5
Treatment
Figure 6.5: Treatment versus Log(10) grass Cd concentration
Figure 6.6 shows positive correlation between log10 (bio-available Pb) and log10 (grass Pb
concentration) represented by the following models:
y = 0.5162x + 0.5493 ……( r2 value = 0.96)
y = 0.2491x + 0.9688 ……( r2 value = 0.86)
y = 0.2159x + 0.9473 ……( r2 value = 0.72), respectively, where:
y = log10 grass Pb concentration (mg/kg)
x = log10 soil bio-available Pb concentration (mg/kg)
The correlation coefficients of 0.96, 0.86 and 0.72, confirmed that while the 3 models had
strong correlation (compared to a critical r2 value of 0.87) only the regression model for the
first crop had a strong enough association of x and y to be used for predictions only in the first
crop.
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1.4
y = 0.2491x + 0.9688
R2 = 0.8606
1.2
Log(10) Pb concentration (mg/kg) in grass
y = 0.2159x + 0.9473
R2 = 0.7165
1
0.8
y = 0.5162x + 0.5493
R2 = 0.9608
0.6
0.4
0.2
0
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
Log(10) soil bio-available Pb concentration(mg/kg)
Harvest 1
Re-growth 3
Re-growth 4
Linear (Harvest 1)
Linear (Re-growth 3)
Linear (Re-growth 4)
Figure 6.6: Log(10) bio-available soil Pb versus log(10) Pb level in grass
The three Pb models were tested to establish whether they were statistically different against
the hypothesis that no difference existed between the slopes and no differences existed
between the intercepts of the regression equations. This condition would be satisfied if -2.306
≤ ts ≤ +2.306 at 95% confidence level, ts being the computed t-statistic.
Using s2 (pooled) = {(n1-1)s12 + (n2-1)s22}/(n1+n2 - 2), in which n1 and n2 are sample sizes for
samples under comparison respectively and n1+n2 - 2 is the pooled degrees of freedom, the
three equations were compared as follows:
Models y = 0.5162x + 0.5493 versus y = 0.2491x + 0.9688: ts was 592 for slopes and 9.76
for intercepts. Models y = 0.2491x + 0.9688 versus y = 0.2159x + 0.9473: ts was 6.93 for
slopes and 10.75 for intercepts. Therefore in all the cases, the models of Pb grass content
response to soil bio-available concentration, were statistically different.
The regression models relating Cd content in grass to soil bio-available concentrations (Figure
6.7) had low correlation coefficients (r2 = 0.59 for the first crop, 0.20 for the third re-growth
and 0.008 for the fourth re-growth compared to a critical r2 of 0.87).
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Log(10) Cd concentrations (mg/kg) in grass
0.40
0.30
y = 0.1227x + 0.1832
2
R = 0.2032
0.20
0.10
0.00
-2.5
-2
-1.5
-1
-0.5
y = 0.0461x + 0.068
2
R = 0.0077
y = 0.6207x + 0.8448
R2 = 0.587
0
-0.10
-0.20
-0.30
-0.40
-0.50
Log(10) bio-available soil Cd concentrations(mg/kg)
Harvest 1
Re-growth 3
Re-growth 4
Linear (Harvest 1)
Linear (Re-growth 3)
Linear (Re-growth 4)
Figure 6.7: Log(10) bio-available soil Cd level versus log(10) Cd level in grass
6.4.6 Correlation between average bio-available Pb and Cd in soils and average
Pb and Cd contents in grass
The best-fit regression models, Figures 6.8 and 6.9 were obtained based on log10 values of the
concentrations in Table 6.6.
Log(10) Pb concentration (mg/kg)
in grass
1.4
y = 0.3949x + 0.788
R2 = 0.9741
1.2
1
0.8
0.6
0.4
0.2
0
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
Log(10) soil bio-available Pb concentration (mg/kg)
Figure 6.8: Log(10) mean bio-available soil Pb versus log(10) mean
Pb level in grass
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Log(10) mean Cd concentration
(mg/kg) in grass
y = 0.363x + 0.2987
2
R = 0.8228
0.3
0.2
0.1
0
-2
-1.5
-1
-0.5
0
-0.1
-0.2
-0.3
-0.4
-0.5
Log(10) mean bio-available soil Cd concentration (mg/kg)
Figure 6.9 : Log(10) mean bio-available soil Cd versus log(10) mean
Cd level in grass
Figure 6.8 presents a regression model that has a significantly strong correlation of r2 of 0.97
against r2critical of 0.87 (p≤0.05). Figure 6.9 presents a regression model for Cd, with a
correlation of r2 of 0.82, which is marginally weak when compared to against r2critical of 0.87
(p≤0.05).
The regression models representing the average situation in which all grass crops are
considered as replicates are:
y = 0.3949x + 0.788 for Pb,
where: y = log10 (concentration of Pb in grass, mg/kg) and x is log10 (soil bio-available
concentration of Pb, mg/kg) and
y = 0.363x + 0.2987 for Cd
where: y = log10 (concentration of Cd in grass, mg/kg) and x is log10 (soil bio-available
concentration of Cd, mg/kg).
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Although the regression model for Cd was much stronger than the models for individual crop
harvests, it marginally fell short of being sufficient for predicting grass concentrations on the
basis of bio-available concentrations.
Using the model for Pb predicts a bio-available soil concentration of 115.2 mg/kg when the
concentration of Pb in the grass is 40mg/kg. Similarly the model for Cd predicts 0.2 mg/kg
when the bio-available soil concentration is 1 mg/kg. However the latter should be considered
as a rough estimate.
6.4.7 Rate of metal application from treated sewage
Table 6.7 presents the quantities of treated wastewater that were applied to treatments 1-4 and
the average concentrations of the metals added to the plots. The latter were computed using
the volume of water applied and the concentration of the irrigation water derived from
detailed information provided in Appendix 3.
Table 6.7: Quantities of treated sewage and computed average metal
concentrations (standard deviation) applied to field plots
Plot
number
T 1.1
T 1.2
T 1.3
T 2.1
T 2.2
T 2.3
T 3.1
T 3.2
T 3.3
T 4.1
T 4.2
T 4.3
Volume of irrigation
(m3/plot)
Mean Pb concentration
applied to plots (mg/l)
Mean Cd concentration
applied to plots (mg/l)
25.70
25.05
26.42
48.68
49.88
49.59
100.46
98.60
94.25
99.30
95.20
93.25
0.42 (0.21)
0.43 (0.19)
0.43 (0.16)
0.45 (0.18)
0.43 (0.18)
0.41 (0.19)
0.44 (0.16)
0.44 (0.16)
0.46 (0.21)
0.45 (0.15)
0.43 (0.17)
0.43 (0.14)
0.18 (0.07)
0.18 (0.08)
0.14 (0.06)
0.16 (0.06)
0.13 (0.07)
0.17 (0.09)
0.20 (0.07)
0.19 (0.07)
0.20 (0.06)
0.18 (0.05)
0.17 (0.07)
0.16 (0.06)
Appendix 3 shows that the concentrations of Pb and Cd ranged from undetectable levels
(rounded off to zero) to 0.9 mg/l and 0.30 mg/l, respectively. From Table 6.7, the mean
concentrations of Pb and Cd applied to the plots were 0.44 mg/l and 0.17 mg/l respectively. A
comparison of the mean values of Pb and Cd in the table and the legislated limits (0.01 and
0.05 for Cd and 5 and 20 mg/l for Pb for long-term irrigation and short-term irrigation
respectively (Table 4.1) shows that Pb levels were within legislated limits. However Cd levels
were predominantly higher than the legislated limits and the mean value was 3.4 times the
long-term legislated level.
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Average increases in metal concentrations in soil and grass above the levels in the control are
presented in Table 6.8.
Table 6.8: Average increase in profile Pb and Cd levels above levels in the
control (mg/kg)
Treatment
Control
Treatment 1
Treatment 2
Treatment 3
Treatment 4
Soil Pb
concentrations
Average
0.466
0.881
1.196
1.202
11.317
Increase
0.000
0.415
0.730
0.736
10.851
Pb
concentrations in
grass
Average
4.090
5.890
6.850
7.290
15.330
Increase
0.000
1.800
2.760
3.200
11.240
Soil Cd
concentrations
Average
0.020
0.094
0.086
0.042
0.633
Increase
0.000
0.074
0.066
0.022
0.613
Cd
concentrations in
grass
Average
0.404
0.710
0.800
0.900
1.700
Increase
0.000
0.306
0.396
0.496
1.296
Average depths of irrigation: Treatment 1: 257.2 mm, Treatment 2: 493.8 mm, Treatment 3: 977.7 mm,
Treatment 4: 960.2 mm
There was a progressive increase in bio-available Pb and Cd with treatment, up to 0.74 mg/kg
Pb and 0.07 mg/kg Cd for treatments on previously unpolluted soil. For these treatments,
maximum increases in levels of the metals in grass were 3.2 mg/kg Pb and 0.5 mg/kg Cd.
6.5 Discussion
The maximum accumulation of Pb of 15.33 mg/kg in grass was below the 40 mg/kg legislated
in U.K for pasture grass. Although the concentration of Pb in treated sewage over the 30 years
of disposal could not be ascertained, the 2.6 mg/l, maximum level determined from Harare
City Council data, 1.2 mg/l average level for the greenhouse experiment and 0.44 mg/l
average level for the field experiment, were below the recommended level of 5.0 mg/l and
therefore acceptable. These levels resulted in star grass accumulating non-toxic levels of Pb.
It may be concluded that treated sewage disposal practices at Firle farm do not pose a hazard
to cattle through accumulation of Pb in star grass. In contrast, the increase in soil bioavailable Cd concentration caused by application of treated sewage with 0.17 mg/l (17 times
the legislated level for long-term irrigation) led to accumulation of Pb to levels higher than
recommended.
In the field the only significant regression models for Pb were the model for the first crop, y =
0.5162x + 0.5493 and the model representing average conditions over an 11-month period, y
= 0.3949x + 0.788. Therefore, the single variable regression model of Pb for the first crop
was not applicable to re-growths while the latter was. Thus the model y = 0.3949x + 0.788,
which was strongly significant (p≤0.05) could be considered for use in predicting the
concentration of Pb in star grass on the basis of bio-available soil concentrations extracted
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using the procedures recommended by McGrath and Cegarra (1992). The use of this model
could approximate field conditions where animals are grazing and therefore harvesting grass
periodically, leading to new re-growths each time. The model predicts a bio-available soil
concentration limit of 115.2 mg/kg for a concentration of 40 mg/kg in star grass.
In this component of the study, a significant single variable regression model for Cd based on
field data for each grass harvest could not be obtained. This outcome suggests that under field
conditions of this study, there were other factors that needed to be incorporated into the model
to improve the Cd models in order for prediction of metal concentration in grass to be
possible for each harvest.
Sample et al (1998) obtained significant model fits of ln (total soil concentration) and ln
(above ground plant concentrations) after including pH and calcium (Ca). Similarly the
decline in the strength of correlation for both Pb and Cd with the re-growths was probably
caused by other soil factors that needed to be incorporated in the models. US Department of
Energy (1998) improved single variable regression models of natural log10 (above-ground
plant tissue concentration of Pb) versus total metal concentration in soils by incorporating pH
in a multiple regression model for Pb. In this study an improvement in the model fit was
obtained by using average values of bio-available soil concentrations from all soil samples
and average grass concentrations for all grass samples harvested over a period of 11 months.
Using this approach, a model in which the correlation between x and y was strong but fell
marginally short of being significant was obtained. If the strength of correlation in the model
y = 0.363x + 0.2987 would be considered high enough to permit rough predictions, then for a
Cd limit of 1 mg/kg in grass the soil bio-available limit would be 0.20 mg/kg.
The general lack of clear relationship between soil pH and depth in this component of the
study was similar to the findings in section 4.4.2 of chapter 4 where there was also no clear
association between pH and total metal concentration. The marginal increase in the pH of
treatments receiving treated sewage, above that of the control was also consistent with the
findings of soil charaterisation (chapter 4). These findings were also in agreement with
observations by Nyamangara and Mzezewa (1999) that indicated increases in pH of surface
horizons of treatments that received sludge over treatments that did not receive from 6.8 to
8.0 respectively. The increase in pH and the stronger correlations between pH and bioavailable Pb and Cd are attributed to organic matter added to the soil through treated sewage.
This observation was also noted in chapter 4 and is in agreement with observations by
MacGrath and Lane (1989).
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The CEC of the control in this component of the study fell between 1 and 2 cmolckg-1. This is
consistent with the base levels of CEC obtained in the control during soil characterisation
(Table 4.3). In treatments 1 to 3 CEC increased slightly to about twice the base levels in the
control suggesting that the metal ions that were being added to the soil through treated sewage
could be responsible for the increase. The CEC of treatment 4 had the same order of
magnitude as the values presented in Table 4.3, suggesting a high association between CEC
and years of application of treated sewage.
There was a general lack of clear and consistent association between soil depth and CEC in all
treatments except treatment 4 and the 4th re-growths of treatments 2 and 3. Since the r2 values
of treatment 4 and 4th re-growths of treatments 2 and 3 were negative, their top soil horizons
had higher CEC than the lower layers. This suggested that there was accumulation of cations
in the top horizons. This argument is supported by the fact that these treatments received the
highest quantity of treated sewage, and therefore had a chance of accumulating more cations
from treated sewage. The gradual increase in CEC suggests an association in the amount of
treated sewage added to a treatment and the CEC. The stronger correlation coefficients of
CEC and bio-available Pb and Cd in treatment 4 compared to all other treatments could be
attributed to the higher CEC shown in Table 6.2. The stronger negative correlation of soil
depth and CEC in treatment 4 suggests that the cations including Pb and Cd were largely
located in the top soil horizons where the grass roots could easily access them. The correlation
of clay content and soil Pb and Cd was generally weak except for treatment 4 where clay
content negatively correlated to soil Pb, possibly due to accumulation of clay in top soil layers
over time.
This study offered some lessons relating to field experiments. Practical limitations,
particularly pump breakdowns at the Firle Wastewater Treatment Plant, reduced the total
amount of irrigation application. In this experiment, Treatments 1 to 4 received on average
257.2 mm, 493.8 mm and 977.7 mm and 960.2 mm respectively, although higher amounts
could have been applied. However the results obtained after applications of these amounts
suggest that the amounts of treated sewage did not limit the adequacy of the data for
modelling. Furthermore, the concentrations of the metals could not be pre-determined prior to
irrigation under field conditions. However measuring the metal levels in treated sewage at
each irrigation event and running the field experiment for a long time circumvented this
limitation. It was assumed that this would even out variation in levels of metals amongst
applications to provide an average situation representative of reality.
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CHAPTER 7
GENERAL DISCUSSION
This chapter discusses the results obtained from all the components of the study, bearing in
mind the main areas in which the study was focused. The major findings are as follows.
7.1 Long-term Pb and Cd accumulation in soils
The study found that after 29 years of disposal of treated sewage, Pb and Cd accumulation in
the sandy soils occurred predominantly in the top 20 cm, particularly the top 10 cm of the
soil. This is confirmed by the large difference in metal accumulation between the top and
lower layers of the soil and similarity of metal levels in lower layers of irrigated and nonirrigated soils. This outcome was largely expected since organic matter, which concentrates in
the top soil, has a high affinity for metals (McGrath and Lane, 1989).
At a total soil metal concentration of 186.3 mg/kg in the top 0-10 cm horizon and 33.3 mg/kg
in the 10-20 cm horizon, long-term accumulation of Pb was largely below the recommended
limit of 300 mg/kg in all horizons, suggesting that Pb was unlikely to be a hazard. This is
confirmed by the fact that after 29 years of disposal of treated sewage on soils, mixed kikuyu
and star grasses only absorbed a maximum of 1.5 mg/kg Pb. In addition, results from the field
experiment show that star grass accumulated 15.33 mg/kg Pb, a figure lower than 40 mg/kg
recommended for pasture grass and 53.70 mg/kg toxic limit obtained from the pot
experiment. At an average annual accumulation of 5.7 mg/kg and sewage disposal regime
similar to the previous 29 years, it would take another 20 years for Pb in the top 10 cm depth
to reach the 300 mg/kg total Pb limit. This implies a longer period of disposal.
At 1.26 mg/kg in the 0-10 cm horizon and 0.75 mg/kg in the 10-20 cm horizon, total Cd
exceeded the recommended 1 mg/kg in the top 10cm and was just below the limit in the 10-20
cm depth. This outcome suggested that Cd hazard was likely and is confirmed by uptake of up
to 1.2 g/kg by mixed star and kikuyu grasses and 1.70 mg/kg by star grass in the field
experiment.
The variation of metal levels with soil depth observed on total concentrations was also
observed on bio-available levels in pots. The same trend was observed in the field. In all
cases, this trend is attributed to the high affinity of organic matter for metals (McGrath and
Lane, 1989). The variation of bio-available metal concentration with soil depth presents a
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potentially large source of error in relating soil concentrations to plant concentrations and in
modelling soil-plant uptake. The depth interval at which various plants in different
environments obtain water and nutrients and the relative biomass of feeder roots at different
depths are unknown (US Department of Energy, 1998). Therefore, although the full 40 cm
profile was assumed to be the depth from which grass roots took up nutrients as well as Pb
and Cd in this study, this area may need further investigations.
7.2 Capacity of star grass to absorb Pb and Cd
A major feature of this study was to establish the capacity of star grass to take up Pb and Cd,
given that the grass is grown for pasture and is irrigated using treated wastewater. In this
respect the study found star grass to be a medium Pb and Cd extractor among plants, in
general, and a high accumulator among grasses. In the pot experiment, it was capable of
taking up as much as 4 592 mg/kg Pb and 316 mg Pb in the first crop and re-growth
respectively and 16 mg/kg Cd and 18 mg/kg Cd in the first crop and re-growth respectively,
in aerial plant parts. Severely retardation of growth at 4 592 mg/kg implies that this level was
close to the maximum uptake capacity of star grass. Given that grasses within a species are
known to have similar uptake characteristics (McDonald, 1995) the findings suggest that the
Cynodon species of grasses possibly had a maximum Pb uptake capacity close to 4 592
mg/kg, implying that the Cynodon species may be a medium extractor of Pb. Phyto-extractors
should combine high yields and high metal uptake (Baker et al, 2000). The absence of clear
signs of growth retardation in Cd treatments in the greenhouse experiment suggests that the
maximum extraction capacity of star grass was above 18 mg/kg attained in the experiment.
It should be noted that star grass was exposed to highly soluble Pb. High uptake of Pb is
generally limited by insolubility of Pb in soils and hyper-accumulation occurs in
contaminated soils when bio-availability is improved by chelates (McGrath et al, 2002).
However, any unintentional contact between the grass in the first crop and added Pb during
application of inorganic Pb may also have contributed to the large differences in levels
between the first crop and re-growth in pot experiments. Using the results from re-growths, of
from the pot study, star grass took up 8-fold and 18-fold the levels of 40 mg/kg and 1 mg/kg
Cd recommended for pasture grass (United Kingdom Statutory Instrument No. 1412, 1995).
The lack of Pb and Cd toxicity signs at toxic concentrations suggests that animals could
continue grazing on grass with Cd concentrations higher than the maximum limit of 1 mg/kg,
unless the grass is tested or the soil is tested for bio-availability of the metal. This and the
high extraction capacity of star grass imply that growing the grass for pasture in Pb and Cd
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polluted soils should be discouraged.
Despite total soil Cd being 0.65 mg/kg (35% lower than the 1 mg/kg maximum limit
recommended for soils on which pasture grows), mixed kikuyu and star grasses accumulated
up to 1.2 mg/kg Cd (20% more than the recommended limit in pasture grass). This outcome
confirms the risk of relying on total soil metal concentrations when predicting hazard to
animals. It also re-affirms the need to use bio-available soil metal levels instead of total
concentrations in predictions. In the field experiment, star grass accumulated up to 1.70
mg/kg Cd, against a bio-available level of 0.63 mg/kg Cd (Table 6.4). Both results suggest
that the limit of 1 mg/kg total Cd in the soil may be too high for star grass growing on a sandy
soil under conditions of repeated application of treated sewage.
7.3 Yield responses to increasing bio-available Pb and Cd
Another key feature of this study was to examine relationships between soil bio-available
levels and yield so as to predict the yield on the basis of soil bio-available concentration. The
study found that the strength of this relationship was insignificant at 95% confidence level.
There was very weak correlation between bio-available Pb and Cd and yield of star grass.
Therefore, under the conditions of this study, log10 (yield of above ground tissue) versus log10
(bio-available soil concentration) of Pb and Cd models were not significant enough for
accurate prediction of yield on the basis of soil bio-available concentrations. This implies that
there were other factors not incorporated into these models, such as nutrients, that had
influence on yield.
7.4 Yield-metal uptake models for Pb and Cd and toxic limits in grass
Single factor regression models for yield versus metal content in grass, based on log10 (yield
of above ground tissue) and log10 (metal concentration) of Pb and Cd in the greenhouse
experiment, were largely not significant (p≤0.05). Since Pb and Cd do not have known roles
in metabolism, their effect below the toxic level is not clear but may be one of the reasons
why the models are weak. Despite that, the models provide points where yield starts to
decline allowing for estimation of the toxicity limit or toxicity threshold. The study estimated
through dose-response models, that the toxic levels of Pb and Cd in star grass are 53.7 mg/kg
and 3.2 mg/kg, respectively. It is further noted that these toxic levels of star grass are way
above the recommended levels of Pb and Cd of 40 mg/kg and 1 mg/kg for pasture grass,
respectively.
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7.5 Soil bio-available-grass metal uptake models and critical metal limits
The development of soil-vegetative tissue metal uptake models on the basis of above-ground
star grass tissue and soil bio-available metal concentration was another major feature of this
study. Earlier findings confirmed that total soil metal levels were poorly correlated to metal
concentrations in star grass. The best-fit models for soil bio-available and grass metal content
data fitted into log10 function. Data from the greenhouse and field experiments fitted into this
model, despite differences between the conditions under which the two experiments were
conducted. Sample et al (1998) obtained significant model fits of ln (total soil concentration)
and ln (above ground plant concentrations) using data from experiments carried out around
the world.
The significant single variable regression models of log10 (above-ground grass tissue Pb
concentration) = 0.525log10 (bio-available soil Pb concentration) + 0.539 and log10 (aboveground grass tissue Cd concentration) = 0.451log10 (bio-available soil Cd concentration) +
0.087 produced using single metals are considered suitable where single metals are added to
the soil and for estimating toxicity limits. Bak and Jensen (1998) noted that eco-toxicity tests
were often conducted on single metals. Therefore these models are not appropriate under field
conditions, where other metals are also present in normal concentrations in the soil.
Under field conditions, the significant model log10 (above-ground grass tissue Pb
concentration) = 0.395log10 (bio-available soil Pb concentration) + 0.788, representing the
average situation over 11 months, predicts the bio-available Pb level in soils to be 115.2
mg/kg for the recommended Pb limit in grass of 40 mg/kg. The model developed in the
greenhouse predicts a bio-available Pb of 106.3 mg/kg for a limit of 40 mg/kg. This happens
to be close to the value predicted in the field. Although the differences may be explained by
the different conditions under which the 2 models were developed, the closeness of the two
figures may be a result of the lack of influence of other metals on uptake of Pb, under
experimental conditions
Under field conditions, none of the single variable regression models for Cd for individual
crops was significant at 95% confidence level. This suggests that the numerous soil factors
present under field conditions could have distorted the single variable relationship for each
harvest. In that case, a multiple regression model could be more appropriate for Cd in the first
crop and subsequent re-growths. The strength of the regression model for Cd under field
conditions was vastly improved by treating each harvest as a replicate, with the resulting
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model falling just short of being significant. The model developed was log10 (above-ground
grass tissues concentration) = 0.363log10 (bio-available soil concentration) + 0.2987. This
model predicts a soil bio-available Cd limit of 0.20 mg/kg as the concentration that would
cause an accumulation of 1 mg/kg Cd in grass. This figure is different from the 0.65 mg/kg
bio-available Cd predicted by the model log10 (above-ground grass tissues concentration) =
0.451log10 (bio-available soil concentration) + 0.087, produced under greenhouse conditions.
The differences are partly attributed to the different conditions under which the two models
were developed and inadequate strength of the field-based model.
In general, it should be noted that under field conditions of this study, there were other factors
that needed to be incorporated into the models to improve strength of Cd models. This may
serve to explain the decline in the strength of correlation of both Pb and Cd with re-growths.
US Department of Energy (1998) improved single variable regression models of natural log10
(above-ground plant tissue concentration) of Pb versus log10 (total metal concentration in
soils) by incorporating pH in multiple regression models for Pb. Under the experimental
conditions, the model, log10 (above-ground grass tissues concentration) = 0.363log10 (bioavailable soil concentration)
+ 0.2987, though not significant could be considered as
indicative of the probable relationship between soil bio-available Cd and concentration of the
metal in star grass under field conditions.
7.6 Co-presence of Pb and Cd
The results of this study suggest that co-presence of Pb and Cd does not significantly affect
the levels of Pb in the sandy soil and star grass, a finding that is in agreement with what
Carlson and Rolfe (1979) found in rye and fescue. The evidence is that there were no
significant differences in the uptake of Pb in single and mixed treatments in the greenhouse
experiment. The closeness in the predictions of bio-available Pb levels for a grass Pb content
of 40 mg/kg between pot-based and field-based models is also consistent with this
observation. Co-presence of Pb and Cd caused a 2.6 increase in the rate of uptake of Cd levels
in star grass above the uptake in single treatments in the greenhouse experiment. It is
therefore postulated that, besides the high levels of Cd in the treated sewage, co-presence of
Cd and Pb contributed to the high uptake of Cd under field conditions.
7.7 Appropriate Pb and Cd levels in effluent and digested sludge
One of the objectives of this study was to determine appropriate levels of Pb and Cd in treated
sewage to apply on pasturelands. Using the data obtained in this study it was not possible to
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determine appropriate levels in treated sewage since the levels of metals in the wastewater
could not be varied. However some indications were derived from the data obtained in the
study.
The levels of Pb and Cd in treated sewage fluctuated considerably. Pb in treated sewage was
below legislated levels. The average Pb levels of 2.6 mg/l in digested sludge and 0.2 mg/l in
effluent (Table 4.1), 1.2 mg/l determined during the greenhouse experiment and 0.4 mg/l
determined during field experiment were all below the limit of 5.0 mg/l recommended for
irrigation water (Ayers and Westcot, 1985). Therefore the low levels of Pb accumulation in
grasses can partly be attributed the low levels in treated sewage.
In contrast to Pb, the average Cd level of 0.17 mg/kg in treated sewage applied during the
field experiment, was above legislated limits, while the levels applied in the greenhouse
experiment were generally below. The excessive Cd levels in treated sewage amounting to
0.17 mg/l (17 times the recommended long-term limit of 0.01 mg/l) appears to have been the
determinant factor in causing high accumulation of Cd in grass. Therefore the uptake of Cd to
levels higher than the recommended limits within a period of only 160 days after planting
grass can be partly attributed to the high concentrations in treated sewage during the field
experiment. This outcome suggests that the use of volume-based loading rates for deciding
the application rate on a sandy soils and star grass would be of limited applicability unless the
concentration of the metal is known. It also implies that even in the presence of organic
matter, which is expected to immobilise Cd, bio-availability of Cd is still high. Doyle (1978)
noted that Cd adsorbed by organic matter largely remained available for plant uptake. Mengel
and Kirkby (1982) observed that Cd is readily transported to upper parts of plants leading to
high uptake by plants.
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CHAPTER 8
CONCLUSIONS AND RECOMMENDATIONS
8.1 Main conclusions
The main conclusions from this study with reference to the main objectives (Section 1.2) are
as follows:
Objective: To determine Pb and Cd accumulation, toxicity levels and yield of pasture
grass under treated sewage application.
1. The study found that star grass is a high accumulator of both Pb and Cd. In this study, it
accumulated 8 times the recommended level of 40 mg/kg Pb (United Kingdom Statutory
Instrument No. 1412, 1995) and 18 times the recommended level of 1 mg/kg Cd (United
Kingdom Statutory Instrument No. 1412, 1995) under conditions of high levels of added
inorganic metals combined with repeated applications of treated sewage in the soil.
Therefore growing star grass in a sandy soil for pasture under conditions of high levels of
Pb and Cd application is not advisable.
2. Using models produced in this study, the toxicity levels of Pb and Cd in grass were
established to be 53.7 mg/kg and 3.2 mg/kg, respectively. These levels corresponded to
soil bio-available concentrations of Pb and Cd of 186.2 mg/kg and 8.3 mg/kg,
respectively. The toxicity levels in grass are higher than the levels recommended in
pasture grass. By absorbing more than the recommended limits of Pb and Cd at the
threshold toxicity levels without showing visible signs of toxicity, star grass poses a risk
to animals if bio-available soil metal levels are not regularly measured. This risk arises
because animals may be grazed on star grass with Pb and Cd levels higher than
recommended (if the grass or soil is not tested), since the point at which toxicity starts
coincides with the highest level of productivity/yield.
3. Co-presence of Pb and Cd did not significantly affect uptake of Pb. It however caused a
260% increase in the rate of uptake of Cd by star grass subjected to high metal doses of
mixed inorganic Pb and Cd compared to high doses of single inorganic Cd combined with
treated sewage application. It is postulated that this effect of Pb on Cd also occurred
under field conditions, leading to high uptake of Cd. Therefore besides reducing Cd
levels, reduction of Pb levels in treated sewage may reduce uptake of Cd by star grass.
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4. The study established that there was some correlation between yield of star grass and bioavailable metal concentration. Though not statistically significant, the regression models
of log10 (above-ground tissue) versus log10 (soil bio-available metal concentration) were
sufficient to derive toxicity levels of Pb and Cd in grass.
Objective: To determine Pb and Cd accumulation in pasture grass under effluent and
sewage sludge mixture application.
5. Using the models produced in this study, the critical bio-available levels of Pb and Cd at
which metal uptake by star grass would not exceed recommended levels of 40 mg/kg Pb
and 1 mg/kg Cd, were estimated to be 115.2 mg/kg and 0.20 mg/kg, respectively. The
models:
log10 (above-ground grass tissue Pb concentration) = 0.3949 log10 (bio-available soil Pb
concentration) + 0.788
and
log10 (above-ground grass tissue Cd concentration) = 0.363 log10 (bio-available soil Cd
concentration) + 0.2987,
produced for predicting metal content in grass based on bio-available metal content in
soils, are considered to be representative of the situation where grazing animals continue
to graze on pasture thereby causing new re-growths. Though still to be field tested, these
models could form a basis for estimating accumulation of Pb and Cd in grass on the basis
of bio-available soil concentrations, at least as indicators of potential hazard to be
validated by detailed tests on plant tissue.
This study recommends the management of bio-available Pb and Cd below 115.2 mg/kg
and 0.20 mg/kg, respectively, in order to avoid accumulating critical pasture levels of 40
mg/kg Pb and 1 mg/kg Cd in star grass. This would ensure that pasture is safe for animal
consumption.
6. Under conditions of repeated applications of treated sewage the recommended maximum
total concentration of Cd of 1 mg/kg (sewage sludge directive limit for use of sewage
sludge in agriculture (EEC, 1986) may be too high for a sandy soil. Uptake of Cd beyond
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the recommended 1 mg/kg in mixed kikuyu and star grass pasture occurred despite the
soil having an average total soil Cd concentration of 0.65 mg/kg, a value lower than the
recommended 1 mg/kg. Absorption of 1.70 mg/kg by star grass against a soil bioavailable concentration of 0.63 mg/kg under field conditions pointed to a similar
conclusion.
7. The application of treated sewage at 17 times the recommended long-term concentration
of 0.01 mg/l Cd caused star grass to accumulate Cd to levels beyond 1mg/kg, within a
short period of 5 months. Therefore where concentrations of Cd in treated sewage are
high, short-term accumulation of the metal in a sandy soil and star grass are important to
consider.
Objective: To determine long-term Pb and Cd accumulation in soils subjected to treated
sewage application.
8. Long-term accumulation of Pb and Cd occurred predominantly in the 0-20 cm depth of
the sandy soil. The lower horizons of the irrigated soil had metal levels similar to
background levels in non-contaminated soil. This pattern of accumulation of Pb and Cd
suggests uncertainty in modelling soil-plant uptake since the depth from which the roots
took up nutrients and Pb and Cd cannot be ascertained.
9. After 29 years of continuous disposal of treated sewage, with Pb concentrations of
between 0.40 mg/l and 2.6 mg/l as determined in this study, total Pb was below the
recommended level of 300 mg/kg for a soil. Pb accumulated in the sandy soil at an annual
average of 5.7 mg/kg total concentration in the 0-10 cm depth and 0.3 mg/kg total
concentration in the 10-20 cm depth of the soil. These concentrations in treated sewage
caused a maximum accumulation in pasture grass of 40% of the recommended limit of 40
mg/kg in pasture. Therefore, the long-term accumulation of Pb from repeated application
of Pb at less than 2.6 mg/l did not constitute a threat to the sandy soil, star grass and
animals grazing on the pastures.
10. At a total soil concentration of 1.26 mg/kg in the 0-10 cm soil horizon and 0.75 mg/kg in
the 10-20 cm horizon after 30 years of treated sewage disposal, total soil Cd in the top 10
cm was above the 1 mg/kg total Cd limit recommended for growing food crops. The
annual accumulation rate of total Cd was 0.03 mg/kg in the top 10 cm and 0.01 mg/kg in
the 10-20 cm layer. Since mixed kikuyu and star grass absorbed up to 1.2 mg/kg Cd at
these soil levels, Cd poses a threat to the soil, star grass and animals grazing on the
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pastures. The study recommends that under conditions of repeated applications of treated
sewage with high levels of Cd, long-term limits of total Cd on sandy soils should be set
lower than 1 mg/kg.
11. There was weak correlation between total Pb and Cd and the levels of the metals in mixed
kikuyu and star grass, such that metal levels in grass could not be predicted on the basis
of total soil metal concentrations.
Objective: To determine appropriate levels of Pb and Cd concentrations in sewage
effluent and sludge mixture that would optimise yield of grass and minimise heavy
metals in beef animals.
12. The study noted that although it was not possible to determine the actual appropriate
levels under the conditions of the experiment where treated sewage had varying levels of
metals when it was disposed onto pasturelands, the Pb level of 2.6 mg/l and below, did
not present a hazard to star grass and animals. However, Cd presented a hazard at total
soil concentrations lower than 1 mg/kg in the soil. The hazard, as measured by the
concentration in the plant was evident after only 5 months of repeated application of
treated sewage with a concentration 17 times the recommended rate.
8.2 Recommendations
The main findings of this study provide scope for further research in related areas suggested
below:
1. There is a lack of knowledge on the depth from which grasses absorb metals. Given
that the level of accumulation of Pb and Cd is related to soil depth and soil
concentrations are inputs into soil-plant uptake models, a study to establish the depth
of uptake would provide information on the depth to consider in relating metal
content in grass and soil bio-available metal content.
2. Further research on uptake of the metals by re-growths of grass under the same
conditions and multiple variable analysis of uptake could improve regression models
established in this study. The models could consider incorporating pH or other
chemicals species, especially where interaction of the metal under study with other
chemical species is anticipated.
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3. There is scope to carry out similar research on Pb and Cd hazard in other soil types,
species of grass and other plants like agricultural crops, since these have not yet been
studied locally.
4. A study in which accumulation of Pb and Cd in animal organs are investigated and
related to those in grass and possibly soils would assist policy makers to draw up
management practices and policies on risk assessment of Pb and Cd.
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134
University of Pretoria etd – Madyiwa, S (2006)
Appendix 1: Sewage treatment processes at Firle Wastewater treatment
Plant
SCREENS
To remove large solids
and prevent blockages.
Disposed by burial
Raw
sewage
AIR LIFT PUMP
GRIT
REMOVAL
TANKS
To remove
heavy
inorganic
solids i.e.
PRIMARY SETTLING
TANK
Removes organic solids
i.e. vegetables and
faecal matter
HUMUS
SLUDGE
to primary
tanks
Tank
effluent
BOILER
Sludge
Sludge
warmed
BIOLOGICAL
FILTER
Sludge
layer
Aerobic
bacteria
oxidises the
sewage &
clarifies it.
Effluent
contains solids
HUMUS TANK
Removes final solids
Gases
given
off
CH4
&
CO2
Effluent
ANAEROBIC
DIGESTER
Offensive solids
broken down by
anaerobic bacteria to
inoffensive state
HUMUS
SLUDGE
PONDS Final effluent to
irrigate farmland
135
LIQUID
DIGESTED
SLUDGE to
irrigate pastures
OR COMPOST
MATERIAL for
farmland
University of Pretoria etd – Madyiwa, S (2006)
Types of treatment technologies used at Firle Wastewater Treatment Plant
Firle Wastewater Treatment Plant utilises two types of sewage treatment technologies, namely
biological trickling filtration plants and biological nutrient removal activated sludge plants.
Sewage treatment or wastewater processing normally comprises unit operations and processes
that provide various levels of treatment. The processes are commonly referred to as
preliminary, primary, secondary and tertiary. The term preliminary and/or primary refers to
physical processes, where coarse suspended materials are removed. Secondary treatment
refers to chemical and biological processes whereas tertiary treatment is a combination of the
three. The diagram shown above presents the treatment processes in the two technologies at
Firle Treatment Works and these are briefly described below.
In primary treatment a portion of suspended solids and organic matter is removed using
physical operations such as screening and sedimentation. As such effluent from primary
treatment normally has a considerable amount of organic matter and a relatively high BOD.
Conventional secondary treatment is targeted at the removal of biodegradable organic
materials and suspended solids. It includes biological treatment by activated sludge, fixedfilm reactors and sedimentation. Tertiary treatment is a level of treatment that goes beyond
conventional treatment to remove constituents of concern including increased amounts of
organic matter and solids, toxic compounds and nutrients.
Biological trickling filtration system
In trickling filter treatment (shown as biological filter in diagram), raw sewage is directed to
screens where large objects and grit are removed using grit removal tanks, before the sewage
flows to primary settling tanks. In the tanks the sewage is separated into settled sewage and
primary sewage. The settled sewage then flows to bio-filters where the trickling effluent goes
to secondary sedimentation tanks in which secondary sludge (humus) is removed. The
effluent from the secondary sedimentation tanks then goes to maturation tanks/ponds. This
effluent does not meet Zimbabwe’s effluent discharge standards, therefore it cannot be
discharged into rivers. Instead, in the case of Firle Treatment Works it is mixed with sludge
and directed to irrigated pastures.
Biological nutrient removal sludge activated system
In the biological nutrient removal activated sludge treatment system, the preliminary stage
comprises screening and grit removal. The primary stage physically separates primary sludge
and effluent. After that the sludge is sent to digesters (shown as anaerobic digesters in
diagram) and the settled sewage is directed to the activated sludge aeration tank.
Bacteria in the aerated tank use up most phosphates and nitrates in the sewage. Once the
sludge has settled in final settling tanks (clarifiers) some of it is sent out as waste activated
sludge while the other is returned to the head of the aeration basin to enrich the incoming
sewage with degrading bacteria. The effluent from the biological nutrient removal treatment
plants generally meets the Zimbabwe effluent standards and is therefore discharged into
Manyame river
136
University of Pretoria etd – Madyiwa, S (2006)
Appendix 2: Randomised block design layout of pots in greenhouse
Block 3
Water
Block 2
Cd80
Block 1
Cd10
Cd20
Cd10
Cd20
Cd20 +
Pb600
Pb1200
Cd80
Pb1200
Cd80
Efflue
nt &
sludge
Cd10
Cd40
Efflue
nt &
sludge
Cd10 +
Pb300
Cd60
Water
Pb1200
Water
Pb300
Pb300
Cd20 +
Pb600
Cd40 +
Pb1200
Pb300
Cd10 +
Pb300
Cd20 +
Pb600
Pb600
Cd40 +
Pb1200
Cd60
Cd10 +
Pb300
Cd20
Cd60
Cd40
Efflue
nt &
sludge
Cd40
Pb600
Pb600
Cd40 +
Pb1200
137
University of Pretoria etd – Madyiwa, S (2006)
Appendix 3: Quantities of treated sewage and metals applied to field plots
Irrigation
event
Plot
number
1
2
3
4
5
6
7
8
Mean
1
2
3
4
5
6
7
8
Mean
1
2
3
4
5
6
7
8
9
Mean
1
2
3
4
5
6
7
8
Mean
T 1.1
T 1.1
T 1.1
T 1.1
T 1.1
T 1.1
T 1.1
T 1.1
T 1.2
T 1.2
T 1.2
T 1.2
T 1.2
T 1.2
T 1.2
T 1.2
T 1.3
T 1.3
T 1.3
T 1.3
T 1.3
T 1.3
T 1.3
T 1.3
T 1.3
T 2.1
T 2.1
T 2.1
T 2.1
T 2.1
T 2.1
T 2.1
T 2.1
Volume of
irrigation
(m3/plot)
3.32
2.53
3.30
3.32
3.33
3.30
3.30
3.31
25.70
3.32
3.36
1.79
3.36
3.31
3.29
3.29
3.32
25.05
1.61
3.29
3.29
3.30
3.31
3.29
3.30
3.32
1.72
26.42
3.33
5.78
6.57
6.63
6.60
6.57
6.58
6.61
48.68
Pb applied to plots
(mg/l)
(mg)
0.3
0
0.1
0.40
0.9
0.6
0.55
0.4
0.42
0.3
0
0.25
0.40
0.9
0.6
0.55
0.4
0.43
0.3
0.25
0.40
0.9
0.55
0.55
0.25
0.4
995.40
0.00
329.82
1326.06
2993.14
1982.50
1812.26
1323.25
10762.43
996.35
0.00
448.63
1344.67
2982.35
1976.93
1807.04
1326.87
10882.84
0.00
986.46
822.20
1319.05
2977.83
1807.71
1814.96
830.98
687.66
11246.84
998.15
578.14
2629.66
5970.30
3630.35
3614.36
1645.28
2643.62
21709.86
0.43
0.3
0.1
0.40
0.9
0.55
0.55
0.25
0.4
0.45
138
Cd applied to plots
(mg/l)
(mg)
0.1
0.3
0.10
0.30
0.2
0.1
0.15
0.2
0.18
0.1
0.3
0.05
0.30
0.2
0.1
0.15
0.2
0.18
0.1
0.05
0.30
0.2
0.15
0.15
0.05
0.2
0.14
0.1
0.10
0.30
0.2
0.15
0.15
0.05
0.2
0.16
331.80
760.09
329.82
994.54
665.14
330.42
494.25
661.62
4567.69
332.12
1008.01
89.73
1008.50
662.74
329.49
492.83
663.43
4586.85
0.00
328.82
164.44
989.29
661.74
493.01
494.99
166.20
343.83
3642.31
332.72
578.14
1972.24
1326.73
990.10
985.73
329.06
1321.81
7836.53
University of Pretoria etd – Madyiwa, S (2006)
Appendix 3: cont'd
Irrigation
number
Plot
number
1
2
3
4
5
6
7
8
Mean
1
2
3
4
5
6
7
8
Mean
1
2
3
4
5
6
7
8
Mean
1
2
3
4
5
6
7
8
Mean
1
2
3
4
5
6
7
8
Mean
T 2.2
T 2.2
T 2.2
T 2.2
T 2.2
T 2.2
T 2.2
T 2.2
T 2.3
T 2.3
T 2.3
T 2.3
T 2.3
T 2.3
T 2.3
T 2.3
T 3.1
T 3.1
T 3.1
T 3.1
T 3.1
T 3.1
T 3.1
T 3.1
T 3.2
T 3.2
T 3.2
T 3.2
T 3.2
T 3.2
T 3.2
T 3.2
T 3.3
T 3.3
T 3.3
T 3.3
T 3.3
T 3.3
T 3.3
T 3.3
Volume of
irrigation
(m3/plot)
6.60
6.57
0.90
6.60
6.62
6.59
6.60
6.56
49.88
6.61
6.58
6.62
6.61
3.35
6.57
6.66
6.59
49.59
13.27
13.17
13.15
13.15
8.83
12.47
13.21
13.21
100.46
13.23
6.26
13.19
13.15
13.20
13.22
13.18
13.17
98.60
13.17
8.82
5.43
13.16
14.17
13.17
13.18
13.17
94.25
Pb applied to plots
(mg/l)
(mg)
0.3
0.25
0.2
0.40
0.9
0.55
0.55
0.25
0.43
0
0.25
0.40
0.9
0.6
0.45
0.25
0.5
0.41
0.3
0
0.40
0.9
0.6
0.45
0.45
0.5
0.44
0.3
0
0.9
0.40
0.3
0.45
0.45
0.5
0.44
0
0.40
0.9
0.9
0.3
0.45
0.45
0.5
0.46
1980.56
1643.35
179.71
2639.77
5957.68
3623.49
3630.43
1638.88
21293.87
0.00
1645.02
2646.93
5952.30
2011.04
2958.68
1664.10
3295.39
20173.46
3979.97
0.00
5259.65
11838.99
5297.54
5612.36
5943.74
6605.56
44537.81
3969.85
0.00
11871.46
5259.05
3959.38
5947.58
5931.27
6586.33
43524.93
0.00
3526.35
4888.54
11845.62
4249.63
5924.35
5932.16
6583.36
42950.01
139
Cd applied to plots
(mg/l)
(mg)
0.1
0.05
0.05
0.30
0.2
0.15
0.15
0.05
0.13
0.3
0.05
0.30
0.2
0.1
0.1
0.05
0.2
0.17
0.1
0.3
0.30
0.2
0.1
0.1
0.25
0.2
0.20
0.1
0.3
0.2
0.30
0.1
0.1
0.25
0.2
0.19
0.3
0.30
0.2
0.2
0.1
0.1
0.25
0.2
0.20
660.19
328.67
44.93
1979.83
1323.93
988.23
990.12
327.78
6643.66
1982.94
329.00
1985.20
1322.73
335.17
657.49
332.82
1318.16
8263.51
1326.66
3950.85
3944.74
2630.89
882.92
1247.19
3302.08
2642.23
19927.55
1323.28
1878.03
2638.10
3944.29
1319.79
1321.68
3295.15
2634.53
18354.87
3949.55
2644.77
1086.34
2632.36
1416.54
1316.52
3295.65
2633.34
18975.08
University of Pretoria etd – Madyiwa, S (2006)
Appendix 4: Mean soil bio-available concentrations (standard deviations), mg/kg and soil depth
Depth
Control
Har 1
Re-g3
Treatment 1
Re-g 4
Har 1
Re-g3
Treatment 2
Re-g 4
Har 1
Re-g3
Treatment 3
Re-g 4
Har 1
Re-g3
Treatment 4
Re-g 4
Har 1
Re-g3
Re-g 4
Lead
0-10
10-20
20-30
30-40
0-10
10-20
20-30
30-40
0.37
(0.12)
0.40
(0.17)
0.40
(0.10
-
0.01
(0.00)
0.01
(0.00)
0.02
(0.02)
0.01
(0.00)
0.23
(0.12)
0.28
(0.13)
0.50
(0.10)
-
0.04
(0.03)
0.01
(0.01)
0.01
(0.01)
0.02
(0.01)
0.50
(0.20)
0.60
(0.15)
0.90
(0.31)
-
0.04
(0.03)
0.03
(0.02)
0.03
(0.01)
0.01
(0.00)
1.28
(0.26)
1.34
(0.43)
1.21
(0.37)
0.94
(0.21)
0.02
(0.00)
0.02
(0.00)
0.02
(0.00)
0.03
(0.01)
0.80
(0.46)
0.83
(0..51)
0.43
(0.23)
0.87
(0.46)
0.01
(0.01)
0.01
(0.01)
0.02
(0.01)
0.03
(0.02)
0.80
1.31
(0.35)
(0.09)
0.60
1.30
(0.46)
(0.24)
0.73
1.39
(0.23)
(0.29)
0.77
1.35
(0.15)
(0.15)
Cadmium
0.12
0.04
(0.06)
(0.00)
0.13
0.04
(0.07)
(0.00)
0.13
0.04
(0.06)
(0.01
0.11
0.04
(0.01)
(0.02)
Har - Harvest 1 (i.e. first crop); Reg - re-growth; - missing values
140
0.90
(0.60)
0.87
(0.49)
1.00
(0.46)
1.35
(0.21)
0.04
(0.04)
0.04
(0.03)
0.69
(0.14)
0.03
(0.02)
0.16
(0.06)
0.75
(0.07)
1.30
(0.96)
1.27
(1.25)
0.06
(0.05)
0.00
(0.00)
0.01
(0.01)
0.01
(0.01)
1.82
(0.34)
1.85
(0.16)
1.82
(0.33)
1.56
(0.61)
0.07
(0.02)
0.07
(0.01)
0.07
(0.02)
0.07
(0.03)
1.00
(0.26)
1.10
(0.44)
1.33
(0.23)
1.27
(0.31)
0.02
(0.01)
0.05
(0.05)
0.02
(0.02)
0.04
(0.01)
0.47
(0.12)
0.93
(0.75)
0.90
(1.14)
0.05
(0.07)
0.03
(0.02)
0.03
(0.02)
0.02
(0.01)
0.02
(0.01)
17.92
(2.86)
7.18
(2.59)
-
1.2
(0.01)
1.0
(0.05)
0.80
(0.04)
0.70
(0.03)
16.91
(2.12)
8.65
(3.41)
4.79
(0.97)
5.6
(2.33)
1.2
(0.04)
0.93
(0.45)
0.77
(0.13)
0.72
(0.32)
19.30
(1.19)
12.71
(2.66)
8.45
(2.21)
9.30
(1.78)
0.15
(0.03)
0.07
(0.01)
0.1
(0.03)
0.07
(0.02)
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