CHAPTER 6

CHAPTER 6
University of Pretoria etd – Madyiwa, S (2006)
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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