ARTICLE IN PRESS
Soil Biology & Biochemistry 38 (2006) 2910–2918
www.elsevier.com/locate/soilbio
Field-scale study of the variability in pesticide biodegradation with soil
depth and its relationship with soil characteristics
M. Sonia Rodrı́guez-Cruz, Julie E. Jones, Gary D. Bending
Warwick HRI, University of Warwick, Wellesbourne, Warwick CV35 9EF, UK
Received 16 December 2005; received in revised form 19 April 2006; accepted 30 April 2006
Available online 2 June 2006
Abstract
The extent of within-field spatial variability of pesticide degradation was characterised in topsoil and subsoil, using the compounds
isoproturon, bentazone and mecoprop, which are major contaminants of groundwater and surface freshwater in Europe. Twenty topsoil
samples from 0 to 15 cm depth and twenty subsoil samples from 50 to 60 cm depth were collected from a single agricultural field within a
160 90 grid. It was shown that degradation rates of all compounds declined with soil depth. Variability of pesticide degradation rates,
pesticide sorption and formation of non-extractable pesticide residues was higher in subsoil relative to topsoil. Furthermore, in the
subsoil, there was variation in large scale soil physicochemical composition, which did not occur in topsoil. The greater variability in
pesticide degradation rates in subsoil relative to topsoil could be the result of a greater range of degradation kinetics, which could reflect
greater spatial variability in the distribution and/or activities of pesticide metabolising communities.
r 2006 Elsevier Ltd. All rights reserved.
Keywords: Spatial variability; Pesticide biodegradation; Soil depth; Sorption; Mineralisation; Isoproturon; Bentazone; Mecoprop
1. Introduction
The extent to which pesticides are susceptible to
transport through and from soil, and contribute to nonpoint source pollution, is dependant on the processes of
biodegradation and sorption which determine the longevity
and mobility of the pesticide in the soil, respectively
(Kookana et al., 1998).
The subsoil represents a very different physical, chemical
and biological environment to the topsoil. In particular,
organic matter declines with increasing depth, resulting in
significantly reduced microbial population sizes (Vinther et
al., 2001). Most studies show that pesticide biodegradation
rates decline with soil depth (Fomsgaard, 1995), reflecting
reduced catabolic potential as the amount of microbial
biomass declines. However, there are some reports of
Corresponding author. Present address: Institute of Natural Resources
and Agrobiology (CSIC), Department of Environmental Chemistry and
Geochemistry, C/ Cordel de Merinas 40-52, Salamanca 37008, Spain.
Tel.: +34 923 219606; fax: +34 923 219609.
E-mail address: sorocruz@usal.es (M. Sonia Rodrı́guez-Cruz).
0038-0717/$ - see front matter r 2006 Elsevier Ltd. All rights reserved.
doi:10.1016/j.soilbio.2006.04.051
pesticide degradation rates increasing with soil depth,
despite a reduction in organic matter content and amounts
of microbial biomass (Di et al., 1998; Karpouzas et al.,
2001), although this appears to be the exception rather
than the rule. Enhanced degradation rates in subsoil have
been attributed to increases in pesticide bioavailability
down the soil profile, linked to a decline in amounts of
organic matter (Di et al., 1998). However, the movement of
specific catabolic bacteria through the soil profile to sites
with smaller microbial communities and hence less
competition could also play a role.
Within the topsoil, there can be significant within-field
spatial variability in pesticide degradation rates, associated
with variation in soil properties controlling degradation
processes or the localisation of specific pesticide degrading
populations (Walker et al., 2001; Bending et al., 2003,
2006). Variability in pesticide catabolism rate within
topsoil occurs at a range of spatial scales (Gonod et al.,
2003), and has implications for both pesticide performance
and assessment of environmental fate (Walker and Brown,
1983; Beck et al., 1996). In particular, areas of low
catabolic activity may contribute disproportionately to
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facilitated transport, or preferential flow, of pesticides to
groundwater. The scale and patterns of subsoil variability
in degradation rates compared to those in topsoil are not
known, although there are indications that degradation of
pesticides in subsoil may be highly variable (Issa and
Wood, 1999).
Pesticide degradation can occur through growth-linked
metabolism, in which catabolic organisms proliferate
during degradation, and cometabolism, in which there is
no proliferation of catabolic organism during degradation,
and in which degradation is relatively slow. Studies in
topsoil have demonstrated that communities contributing
to growth-linked metabolism can show high spatial
variability (Bending et al., 2003), although the distribution
of such organisms within subsoil is not known. The relative
extent to which organisms contributing to cometabolic and
growth-linked degradation of pesticides exhibit spatial
variability in their distribution in subsoil, relative to
topsoil, is not clear.
Pesticide sorption can also show significant spatial
variability at both the catchment and field level (Walker
et al., 2001; Coquet, 2003). Several authors have shown
that sorption of pesticides decreases with soil depth, with
the pattern governed by OM content (Clay and Koskinen,
2003). Since sorption can be an indicator of bioavailability
(Jensen et al., 2004), change in the amount or variability of
sorption with depth could affect patterns of biodegradation, although the significance of any changes would be
expected to depend on the strength of sorption of
individual compounds.
Degradation and sorption of pesticides in soils evidently
show three-dimensional variability with highly complex
relationships between bioavailability and biodegradation in
both subsoil and topsoils. Understanding of the extent of
this three-dimensional variation, the scale at which it
occurs, and the factors that control it, is essential to risk
assessment of environmental exposure, and is particularly
important within the context of probabilistic modelling of
pesticide fate (Beulke et al., 2004).
The aims of this study were: (1) To compare the extent of
variability in pesticide biodegradation rate and sorption
within, and between, topsoil and subsoil; (2) To determine
whether variability in pesticide degradation and sorption
within and between topsoil and subsoil was related to
variability in gross soil microbial, chemical and physical
properties. Pesticides with contrasting modes of degradation were used in order to compare the environmental
interactions and distribution of organisms contributing to
growth-linked and cometabolic means of degradation.
2. Materials and methods
2.1. Pesticides
Three herbicides were used in this study, the moderately
mobile compound isoproturon (3-(4-isopropylphenyl)-1,1dimethylurea), and the more mobile compounds bentazone
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(3-isopropyl-1H-2,1,3-benzothiadiazin-4(3H)-one-2,2-dioxide) and mecoprop (2-(4-chloro-2-methylphenoxy) propanoic acid). These pesticides are among the most frequently
found contaminants of surface freshwater and groundwater in the UK (Eke, 1996; Environment Agency, 2003).
The pure compounds were supplied by Chem Service Inc.
(West Chester, USA). [ring-U-14C]Isoproturon (499%
purity), [ring-U-14C]mecoprop (potassium salt; 495%
purity) and [carbonyl-14C]bentazone (495% purity) were
supplied by Bayer Corp. (Germany), International Isotopes (Germany), and Izotop Co. (Hungary), respectively.
2.2. Soil
Soil samples were collected from different sites in Long
Close field at Warwick-HRI, Wellesbourne, UK, in April
2003. In general, soil taken from this field is a sandy clay
loam of the Wick series, described by Whitfield (1974):
0–30 cm, friable sandy clay loam with weakly developed
medium and coarse subangular blocky structure;
30–100 cm, loose sandy clay loam with occasional small
rounded Bunter pebbles, occasional band of calcareous
clay. Isoproturon had been regularly applied to the field
over the preceding 20 years, with an application 2 months
prior to soil collection. There had been no application of
mecoprop or bentazone over at least the previous 10 years.
A previous study (Bending et al., 2006) mapped gross soil
properties in the same field. This study showed a gradient
of pH and organic matter in the field, which have been
shown previously to be important properties controlling
pesticide degradation rates (Walker et al., 2001). In the
current study, the sampling regime was designed to
encompass the range of variability in pH and organic
matter shown in the field. Twenty holes were excavated
using a mechanical digger to 1 m depth. The holes were
located within a 160 90 m grid at intervals of 40 m (north/
south) and 30 m (east/west). From every hole, 2 kg of soil
were collected from 0 to 15 cm depth (topsoil) and
50–60 cm depth (subsoil). Before collecting each sample,
the wall was excavated with a trowel, which was washed
with 70% ethanol between samples. Soil was sieved
(o3 mm) with the sieve surface sterilised with ethanol
between samples.
2.3. Laboratory incubation experiments
Suspensions of the commercial formulation of bentazone
(BASF, Ludwigshafen, Germany), isoproturon (Atlas
Crop Protection Ltd., Doncaster, UK) or mecoprop-p
(Mirfield Sales Services Ltd., Doncaster, UK) were
prepared in distilled water and the 14C labelled analogue
were added to 300 g fresh weight of soil from each sampling
location to give a concentration of 5 mg kg1 and an
activity of 200 Bq g1 with soil water potential at 33 kPa.
The procedure to prepare soil samples has been described
previously by Walker et al. (2001). A CO2 trap, consisting
of a scintillation vial containing 1 M NaOH (1 ml) was
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M. Sonia Rodrı´guez-Cruz et al. / Soil Biology & Biochemistry 38 (2006) 2910–2918
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attached to the lid via a stainless steel clip as described by
Reid et al. (2001). Soil moisture content was maintained by
addition of sterile distilled water as necessary.
ship between Cs and Ce, were considered as a measure of
pesticide adsorption capacity by soil. All determinations
were carried out in duplicate.
2.4. Pesticide extraction and analysis
2.7. Statistical analysis of the data
After pesticide addition, duplicate samples were taken
from each bottle in order to check variability associated
with pesticide extraction and analysis. Thereafter, soils
were repeatedly sampled at regular intervals depending on
the pesticide. The method to extract and analyse pesticide
residues has been described previously (Reid et al., 2001;
Walker et al., 2001). The HPLC mobile phase used to elute
pesticides was acetonitrile/water/orthophosphoric acid
(75:25:0.25 by volume for isoproturon and mecoprop-p;
65:35:0.25 by volume for bentazone), at a flow rate of
1 ml min1. Detection was by UV absorbance at 220, 230
and 240 nm for bentazone, mecoprop-p and isoproturon,
respectively. Pesticide recoveries from the soil in the range
from 1.0 to 5.0 mg kg1 varied from 97.0% to 96.4% for
bentazone, from 101% to 100% for isoproturon and from
98.2% to 98.8% for mecoprop. Duplicate samples at each
concentration were used to assess recovery values. After
the incubation period the soil samples were analysed for
residual 14C-pesticide residues as described by RodriguezCruz et al. (2006).
pH, total organic carbon and dehydrogenase activity
were determined according to Bending et al. (2006).
Time to 50% degradation (DT50) values were used to
characterise the decay curves, and to compare horizontal
and vertical variations in the degradation rates. GenStat
(7th edition, VSN International Ltd.) software was used to
calculate this parameter by fitting the data of degradation
kinetics to the model of best fit. The degradation kinetics
were fitted to different models (linear, exponential and
Gompertz) and DT50 values calculated. Standard errors
(SE) were used to compare DT50 values obtained in order
to choose the model to provide the best fit DT50 value. Also
visual inspection was used to assess whether the best fit
model provided an acceptable description of the data. DT50
data for isoproturon were not normally distributed, and
were square root transformed prior to calculation of
coefficient of variation in order to confer normality.
GenStat software was also used to perform a multivariate
analysis of the data in order to obtain correlations between
variables.
To compare variability of different parameters between
topsoil and subsoil the coefficients of variation (%CV) was
determined. The percentage of total variability attributable
to within sample variability (technical error associated with
sampling and pesticide analysis, and fit lack of model
during calculation of DT50) was determined using a firstorder Taylor series approximation (Bending et al., 2006).
2.6. Pesticide adsorption
3. Results
Adsorption of the pesticides in the soils was determined
by treating 5 g of dried soil (sieved o3 mm) with 10 ml of a
5 mg ml1 solution of the commercial pesticide formulation
in 0.01 M CaCl2. Suspensions were shaken for 24 h at
25 1C. After shaking, suspensions were centrifuged at
6000 rpm for 5 min and the concentration of pesticide
was determined in the equilibrium solution, as described
above. The amount of herbicide adsorbed (Cs) was
calculated by determining the difference between the initial
and the equilibrium concentrations (Ce). Adsorption
distribution coefficients (Kd), calculated from the relation-
3.1. Chemical and biological analysis of soils
2.5. Chemical and biological analyses of soils
The characteristics of the samples are shown in Table 1.
Dehydrogenase activity was ten fold higher in topsoil
compared with subsoil, ranging from 22.0 to 76.8 mg
triphenyl formazan (TPF) g1 in the topsoil and from
2.62 to 7.50 mg TPF g1 in the subsoil. However, there was
little difference in the %CV between topsoil and subsoil.
pH was significantly lower in the topsoil samples, ranging
between 5.78 and 7.56, compared to the subsoil, in which it
ranged from 6.49 to 8.09. %CV for pH was higher in
Table 1
Chemical, physicochemical and biological properties (mean values) and coefficient of variation (CV) of topsoil and subsoil samples
Topsoil
CV (%)
Subsoil
CV (%)
Pb
a
Dehydrogenase (mg TPFa g1)
pH
Organic matter (%)
Sand (%)
Silt (%)
Clay (%)
44.3
36.3
4.59
32.2
o0.001
6.69
6.80
7.42
7.24
o0.001
2.88
26.9
1.59
36.5
o0.001
68.7
5.69
65.3
26.4
NS
8.09
18.8
8.30
69.7
NS
23.4
13.5
26.5
48.5
NS
Triphenyl formazan.
P indicates significance of difference in soil properties between topsoil and subsoil at level indicated. NS: not significantly different.
b
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subsoil relative to topsoil. Soil organic matter (SOM)
content significantly decreased with depth, ranging between
2.24% and 5.89% in topsoil, and from 0.78% to 2.70% at
50–60 cm depth. %CV for SOM was 26.9 and 36.5 in
topsoil and subsoil, respectively. There was no significant
difference in average soil textural characteristics between
topsoil and subsoil. However there was considerably
greater variability in sand, silt and clay content in subsoil
compared to topsoil (Table 1).
3.2. Degradation
DT50 of all pesticides was significantly higher in subsoil
than topsoil (Table 2). The degradation of isoproturon in
the topsoil samples was very rapid (Fig 1a). Degradation
began shortly after the start of the incubations and was
complete within 20 days. Degradation kinetics in 18 of the
topsoil samples were fitted to an exponential model
(SEDT50p1.56), with the remaining samples fitted to linear
and Gompertz models (SEDT50p0.28 and SEDT50p1.63,
respectively). DT50 in the topsoil varied between 1.4 and
12.9 days. In 15 subsoil samples, degradation kinetics were
fitted to a Gompertz model where an initial exponential
Table 2
Average values of pesticide DT50, 14C-ring mineralised and nonextractable residue amounts, distribution coefficients (Kd) and coefficients
of variation (CV) in topsoil and subsoil
Bentazone
Isoproturon
Mecoprop-p
66.9
16.2
238
44.2
o0.001
3.27
42.4a
44.0
54.4a
o0.01
15.0
22.1
42.3b
50.6b
o0.001
C-ring mineralized
Average
6.17
CV (%)
30.0
Average
2.62
CV (%)
54.6
o0.001
22.7
31.8
14.6
50.2
o0.01
19.3
40.4
13.1
41.6
o0.01
60.1
15.7
57.0
21.1
NS
52.7
12.8
38.9
53.7
o0.01
1.32
6.71
0.34
50.3
o0.001
0.26
25.9
0.07
24.3
o0.001
DT50 (days)
Topsoil
Average
CV (%)
Average
CV (%)
Subsoil
Pc
% Pesticide
Topsoil
Subsoil
Pc
14
% Non-extractable pesticide residue
Topsoil
Average
76.0
CV (%)
13.2
Subsoil
Average
5.50
CV (%)
18.2
Pc
o0.001
Kd (ml g1)
Topsoil
Subsoil
Pc
a
Average
CV (%)
Average
CV (%)
0.06
26.1
0.11
43.6
o0.05
For isoproturon DT50, data were square root transformed prior to
calculation of CV.
b
Mecoprop subsoil DT50 excludes 2 samples in which degradation was
too slow to allow calculation of DT50 values.
c
P indicates significance of difference in parameter between top- and
sub- soil at level indicated. NS: not significantly different.
2913
decline in residue concentration lasting for between 4.9 and
46.1 days (Fig 1b) was followed by more rapid rates of loss
with DT50 values varying from 7.3 to 48.8 days (SEp1.79).
In the remaining 5 subsoil samples there was very slow
progressive linear decrease in concentration over time, with
the slowest degrading soils showing DT50 of 119 and 292
days (SEp19.8).
For mecoprop, 16 topsoil samples showed a lag phase of
between 7.2 and 18.4 days, which was followed by a rapid
phase of degradation, with kinetics fitted to the Gompertz
model (SEDT50p1.62) (Fig 1a). In the remaining 4 topsoil
samples, degradation showed a progressive linear decline in
residue concentration over time. DT50 values in topsoil
ranged between 7.4 and 19.7 days (SEDT50p1.24). In 2
subsoil samples, there had been no degradation of the
compound by the end of the experiment. In 15 subsoil
samples degradation had a lag period of between 22.4 and
59.7 days before a period of rapid degradation (Fig 1b)
with degradation kinetics fitted to the Gompertz model
(SEDT50p3.54). The remaining 3 sites showed progressive
linear decrease in concentration over time and DT50 values
ranged from 27.9 to 114 days (SEDT50p11.3). DT50 values
could not be calculated for two subsoil samples showing no
degradation (Table 2).
Degradation of bentazone in top and subsoil followed
linear kinetic (SEDT50p15.0) (Fig 1a,b) with the data
showing a progressive decrease in concentration over time.
Only one of the topsoil samples showed non-linear
degradation of bentazone and degradation kinetics were
explained better using the Gompertz double exponential
equation (SEDT50p4.70). DT50 varied from 54.4 to 98.8
days in the topsoil and from 85.0 to 521 days in the subsoil
(Table 2).
The coefficient of variation of DT50 was higher in subsoil
than in topsoil for the three pesticides (Table 2). Following
transformation, %CV for isoproturon DT50 was 42.4 in
topsoil and 54.4 in subsoil. %CV for mecoprop DT50 was
22.1 in topsoil and 50.6 in subsoil. However, since DT50
values could not be calculated for those subsoil samples
showing no degradation of mecoprop, actual variability in
subsoil DT50 was higher. %CV of bentazone DT50 was
16.2 in topsoil and 44.2 in subsoil. Percentages of
variability in DT50 accounted for by within-sample
variability were 59.8% and 26.9% for isoproturon,
40.2% and 21.8% for mecoprop and 26.6% and 14.8%
for bentazone, in topsoil and subsoil, respectively.
3.3. Mineralisation of pesticide to CO2
Mineralisation of all pesticides was significantly lower in
subsoil relative to topsoil (Table 2). For isoproturon, in
both topsoil and subsoil there was a linear increase in
14
CO2 evolution, which reached a plateau after approximately 20 days (Fig 2a,b). In 2 subsoil samples there was
very little mineralisation, with no change in the rate of
14
CO2 evolution over time. The amount of isoproturon ring
ARTICLE IN PRESS
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2914
Topsoil
120
120
Isoproturon
100
80
60
40
80
60
40
20
20
0
5
10
15
20
10
20
40
30
40
50
0
30
Time (Days)
Time (Days)
(a)
60
0
0
25
80
20
0
0
Bentazone
100
Residue (% applied)
Residue (% applied)
100
Residue (% applied)
120
Mecoprop-p
60
90
120
150
120 160
200
Time (Days)
Subsoil
120
Isoproturon
Residue (% applied)
Residue (% applied)
100
80
60
40
20
100
100
80
80
60
40
20
0
0
0
(b)
120
Mecoprop-p
Residue (% applied)
120
20
40
60
80
100
Bentazone
60
40
20
0
0
20
Time (Days)
40
60
80
100
0
Time (Days)
40
80
Time (Days)
Fig. 1. Degradation of bentazone, isoproturon and mecoprop-p in topsoils and subsoils. Average of the 20 samples with bars indicating standard errors
(~), and single samples with the shortest (m) and longest (’) DT50 values are shown.
C mineralised to CO2 after 160 days ranged from 10.1% to
38.7% in topsoil, and from 0.52% to 30.7% in subsoil.
In topsoil samples, the kinetics of 14CO2 evolution from
mecoprop was similar to that of isoproturon (Fig 2a), with
amounts of mecoprop ring C mineralised after 153 days
ranging from 6.8% to 39.9%. In the subsoil most samples
showed the same pattern of mineralisation as topsoil
samples (Fig 2b). In 2 subsoil samples there was very little
mineralisation, with no change in the rate of 14CO2
evolution over time. In subsoil, mineralisation after 153
days ranged from 1.10% to 21.5%.
In the case of bentazone, there was a linear increase in
14
CO2 evolution over time (Figs. 2a and b), and after 180
days between 3.4% and 10.1% ring C had been mineralised
in topsoil, and between 0.4% and 5.8% mineralised in
subsoil.
The coefficient of variation of the total amount of 14CO2
evolved was higher in subsoil than in topsoil for the three
pesticides (Table 2). %CV of the total amount of 14CO2
evolved was 31.8 and 50.2 for isoproturon, 40.4 and 41.6
for mecoprop, and 30.0 and 54.6 for bentazone, in the
topsoil and the subsoil, respectively.
In most samples the total amount of 14CO2 evolved from
mecoprop and isoproturon exceeded the level of radiochemical impurities in the 14C-ring labelled compounds.
However, for bentazone in most subsoil samples and in five
topsoil samples, and for mecoprop and isoproturon in two
subsoil samples, amount of 14C evolved was not above the
impurity level, so actual mineralisation of the compound
may be lower than indicated, or may even not have
occurred.
3.4. Non-extractable residues
For mecoprop and bentazone, a significantly greater
proportion of the pesticide was converted to non-extractable forms in topsoil relative to subsoil (Table 2). For
mecoprop, amounts of compound added remaining as nonextractable residues after 153 days ranged between 42.8%
and 68.7% in topsoil and between 0% and 63.2% in
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Topsoil
50
20
10
(% of applied 14C)
30
12
Mecoprop-p
40
30
20
14CO
2
(% applied 14C)
Isoproturon
14CO
2
14CO
2
(% applied 14C)
40
10
0
0
0
40
80
120
160
9
6
3
0
0
40
Time (Days)
(a)
Bentazone
80
120
0
160
50
100
150
200
150
200
Time (Days)
Time (Days)
Subsoil
50
10
0
20
(%of applied 14C)
20
10
40
30
40
(b)
80
Time(Days)
120
160
9
6
3
0
0
0
Bentazone
2
30
12
Mecoprop-p
14CO
(% applied 14C)
Isoproturon
14CO
2
14CO
2
(% applied 14C)
40
0
40
80
Time (Days)
120
160
0
50
100
Time (Days)
Fig. 2. Mineralisation of [carbonyl-14C]bentazone, [ring-U-14C]isoproturon and [ring-U-14C]mecoprop-p in topsoils and subsoils. Average of the 20
samples with bars indicating standard errors (m), and single samples with the shortest (’) and longest (~) total 14CO2 evolution values are shown.
subsoil. For bentazone, amounts of compound remaining
as non-extractable residues after 182 days ranged from
68.8% to 82.5% in topsoil and 9.56–17.7% in subsoil.
After 160 days non-extractable isoproturon residues
accounted for between 29.9% and 71.2% of compound
added in topsoil, and between 24.6% and 70.6% in subsoil.
The coefficient of variation of pesticide converted to
bound residues was higher in subsoil than topsoil for
bentazone, isoproturon and mecoprop, with %CV of 13.2,
15.7 and 12.8 in topsoil, and 18.2, 21.1 and 53.7 in subsoil,
respectively (Table 2).
3.5. Adsorption
Sorption of isoproturon and mecoprop was significantly
higher in topsoil than subsoil, while the reverse was true for
bentazone (Table 2). Kd coefficients varied from 1.19 to
1.48 ml g1 in the topsoil and from 0.13 to 0.73 ml g1 in
the subsoil for isoproturon, from 0.12 to 0.39 ml g1 in the
topsoil and from 0.04 to 0.09 ml g1 in the subsoil for
mecoprop, and from 0.04 to 0.09 ml g1 in the topsoil and
from 0.04 to 0.19 ml g1 in the subsoil for bentazone. %CV
of Kd for isoproturon and bentazone were 50.3 and 43.6 in
topsoil respectively, which were significantly higher than in
subsoil, in which %CV of Kd were 6.71 and 26.1
respectively, while %CV of mecoprop Kd was not
significantly different in topsoil (25.9) and subsoil (24.3)
(Table 2).
3.6. Relationships between pesticide biodegradation and
sorption parameters and soil characteristics
A multivariate analysis of the data was carried out to
establish relationships between biodegradation and sorption parameters and soil characteristics in order to find the
key parameters controlling these processes. DT50 of all
pesticides was correlated with OM content and dehydrogenase, with the strongest correlation shown with dehydrogenase for bentazone and mecoprop (r ¼ 0:668,
Po0:001 and r ¼ 0:634, Po0:001, respectively) and with
OM for isoproturon (r ¼ 0:457, Po0:001). Bentazone
DT50 was also correlated with pH (r ¼ 0:731, Po0:001).
Pesticide Kd was correlated with pH, OM and dehydrogenase, with the strongest correlations with pH for
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bentazone (r ¼ 0:493, Po0:01), and with dehydrogenase
for isoproturon and mecoprop (r ¼ 0:838, Po0:001, and
r ¼ 0:752, Po0:001 respectively). The amount of 14CO2
evolved was correlated with OM content for all pesticides,
although for bentazone and isoproturon, the strongest
correlation was with dehydrogenase (r ¼ 0:714, Po0:001,
r ¼ 0:547, Po0:001, respectively). Amounts of bound
pesticide residues were most strongly correlated with
dehydrogenase for bentazone and mecoprop (r ¼ 0:882,
Po0:001 and r ¼ 0:415, Po0:01, respectively).
When topsoil samples were dealt with separately,
mecoprop DT50 was significantly correlated with dehydrogenase (r ¼ 0:655, Po0:01) and pH (r ¼ 0:539,
Po0:05). Mecoprop Kd was correlated with OM content
(r ¼ 0:693, Po0:05). For subsoil, bentazone DT50 and
amount of 14CO2 evolved were correlated with pH
(r ¼ 0:711, Po0:001 and r ¼ 0:456, Po0:05, respectively).
Sand, silt and clay contents were significantly correlated
with DT50, total amount of 14CO2 evolved, and bound
residue amounts for isoproturon when topsoil and subsoil
samples were considered together. When subsoil samples
were considered separately, sand, silt and clay were
significantly correlated with isoproturon DT50, Kd, total
amount of 14CO2 evolved and bound residues. The
strongest relationship between isoproturon DT50 and Kd,
were with silt (r ¼ 0:872, Po0:001, r ¼ 0:850, Po0:001,
respectively), while amount of 14CO2 evolved and bound
residues were more strongly correlated with sand
(r ¼ 0:755, Po0:001 and r ¼ 0:721, Po0:001, respectively). Furthermore those sites at which less than 5%
degradation of mecoprop was recorded, and for which
DT50 could not be calculated, were those sites with sand
contents of less than 23% and clay contents of more than
48%.
4. Discussion
For all pesticides biodegradation rates declined with soil
depth, agreeing with most previous studies (Fomsgaard,
1995; Soulas and Lagacherie, 2001). For isoproturon and
mecoprop, sorption was lower in subsoil than in topsoil,
and communities capable of growth linked pesticide
metabolism of these compounds occurred in most subsoil
locations. However, average degradation rates of mecoprop and isoproturon, measured by loss of parent
compound and evolution of pesticide derived CO2, were
higher in topsoil relative to subsoil. Further, at every
sampling location, degradation of all pesticides was higher
in the topsoil sample relative to that taken from the
corresponding subsoil location.
In the study of Di et al. (1998), those pesticides which
showed enhanced degradation in subsoil relative to topsoil
had high Koc values (400–6070), and therefore bind tightly
to organic matter. Given their sorption characteristics,
these compounds are unlikely to leach to subsoil. In
common with our findings, those compounds with Koc
lower than 400, showed greater degradation in topsoil
compared to subsoil. However, in the studies of Di et al.
(2001) and Karpouzas et al. (2001) the pesticides that
showed a faster degradation in subsoil relative to topsoil
had similar Koc to the compounds investigated in the
present study. Rather than being solely related to sorption,
enhanced degradation in subsoil relative to topsoil in these
studies must additionally reflect either the size of the
pesticide degrading community or its competitive interactions with other components of the microbial community
at the particular site being studied.
The variability of pesticide degradation rates was
considerably higher in subsoil relative to topsoil in our
study. Furthermore, since the percentages of total variability accounted for by within sample variability was
greater in topsoil than subsoil, the actual differences in
environmental (between sample) variability in topsoil and
subsoil would be greater than suggested by the CV. Results
from a number of previous studies, in which there was
limited replication of samples across surface and subsurface profiles, have also indicated that variability in
degradation rates in subsoil and aquifer materials could
be higher than that in topsoils (Issa and Wood, 1999;
Karpouzas et al., 2001).
Within sample variability arising from technical error
associated with sampling, pesticide analysis and model lack
of fit during derivation of DT50 accounted for between
21.8% and 59.8% of the total variability. It is clear that in
order to understand the true extent of spatial variability in
environmental processes, experimental variability must be
accounted for. However, this has rarely been done in
studies of environmental variability in pesticide fate and
behaviour (Bending et al., 2006).
The kinetics of mecoprop and isoproturon degradation
in topsoil showed two general patterns. Either there was
rapid degradation immediately following application, or
following a lag phase which had variable duration. The
variability associated with degradation in topsoil largely
reflected the length of the lag phase prior to the phase of
rapid degradation. In the subsoil, some sites also showed
rapid degradation immediately following pesticide application. Other sites showed a lag phase prior to a period of
rapid degradation, with the lag phase typically longer than
that in topsoil samples. Similarly, Jensen et al. (2004) found
that for the herbicide 2,4-D, the duration of the lag phase
prior to a period of rapid degradation was longer and more
variable in subsoil relative to topsoil. Furthermore, in
contrast to the topsoil, some subsoil sites showed no rapid
phase of degradation. The greater range of pesticide
degradation kinetics could be the primary cause of the
greater variability of degradation rates in subsoil relative to
topsoil.
In the case of isoproturon and mecoprop, degradation
kinetics in those soil samples showing an immediate rapid
rate of degradation following application, or a lag phase
followed by a rapid phase of degradation, reflect the
dynamics of organisms able to utilise the pesticide as an
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M. Sonia Rodrı´guez-Cruz et al. / Soil Biology & Biochemistry 38 (2006) 2910–2918
energy source (Soulas and Lagacherie, 2001; Bending et al.,
2003). The variability in the length of the lag phase prior to
a rapid phase of degradation reflects either differences in
the time taken for the microbial community to adapt to
degrade the pesticide, or differences in the initial size or
rate of growth of the degrader community.
With the exception of a single topsoil site, bentazone
degradation showed first-order kinetics, reflecting a constant degradation rate over time, indicative of cometabolism, in which there is no net proliferation of the
degradative community (Soulas and Lagacherie, 2001). In
previous studies, bentazone has typically shown first order
degradation kinetics (Piutti et al., 2002), and growth-linked
metabolism of the compound has not previously been
reported. In subsoil, which had a higher pH, bentazone
degradation was very slow. This difference in degradation
rates could be explained by the strong correlations found
in this study between bentazone DT50 values and OM
and pH.
While OM and dehydrogenase were correlated with
overall DT50 rates within the combined topsoil and subsoil
dataset for all pesticides, these parameters were generally
poor predictors of variability in degradation rates within
the surface or subsurface profiles individually. Evidently
the distribution of the specific microbial communities
involved in both growth-linked and cometabolic pesticide
degradation are more variable in subsoil relative to topsoil,
and this cannot be predicted by analysis of variability in
broad scale microbial metabolic activity, as measured by
dehydrogenase activity.
Our data confirms other studies which show that the size
and activity of the microbial biomass declines with depth
(Fomsgaard, 1995). Studies of the micro-architecture of
soil have revealed that the spatial distribution of bacteria in
subsoil is more variable than in topsoil, showing a
randomly distributed mosaic pattern of high and low
densities, with bacteria aggregated in smaller patches in
subsoil relative to topsoil (Nunan et al., 2001, 2003). Low
numbers of active organisms, combined with uneven
distribution, could account for the greater variability in
pesticide degradation kinetics within the subsoil. Furthermore, Nunan et al. (2001) found that whereas the scale of
spatial structure of bacterial communities in topsoil is
influenced solely by microscale properties, in subsoil it is
controlled by both microscale and large scale properties.
Similarly in our study, the variability in mineralogical
composition of the soil was far higher in subsoil than
topsoil. In particular, the presence of clay and silt rich
regions in subsoil represented variability in large scale
properties which was not present in topsoil. Such
variability in large scale soil properties is presumably lost
in topsoil through ploughing, which acts to homogenise the
surface soil.
Within topsoil, previous studies have shown that
degradation rates of isoproturon can be highly variable
within single fields, with pH controlling the dynamics of
organisms responsible for growth linked metabolism of the
2917
compound (Bending et al., 2003). In the current study, for
isoproturon and mecoprop, the sand, silt and clay content
of the subsoil was critical to determining degradation rates,
with those locations possessing high clay and silt contents
showing lower degradation rates relative to sites with a
higher proportion of sand. For isoproturon but not
mecoprop, physicochemical soil properties influenced
sorption, which may have influenced availability and
thereby degradation rate (Jensen et al., 2004). However,
the texture parameters of soil can influence a range of soil
environmental factors, including redox potential and
moisture content, which may also have influenced degradation rates (Vink and van der Zee, 1997; Jurado-Exposito
and Walker, 1998).
Relative to isoproturon and mecoprop, mineralisation of
bentazone to 14CO2 was quite limited, due to the high
percentage of non-extractable residues formed, particularly
in the topsoil. Piutti et al. (2002) suggested that hydroxylated bentazone metabolites are rapidly incorporated
into soil OM, limiting the mineralisation of this herbicide.
Our data supports this, with OM content significantly
correlated with the formation of non-extractable bentazone
residues.
The data presented represents comparison of potential
degradation and mineralisation rates across the subsoil and
topsoil samples under standard conditions. However, in the
field, differences in environmental factors such as moisture
content and temperature will interact with inherent
differences in catabolic potential to influence actual
dissipation rates.
Currently models dealing with the environmental fate of
pesticides take no account of within field spatial variability.
Our data shows clearly that pesticide fate in soil shows
considerable three-dimensional variability, and accurate
assessment of risks associated with pesticide use will
require consideration of the extent of this variability.
Probabilistic modelling provides a promising approach to
incorporate spatial variability in the environmental fate of
pesticides into the risk assessment process (Beulke et al.,
2004).
Acknowledgements
This work was funded in the UK by the Deparment for
Environment, Food and Rural Affairs (project PL0550).
M.S. Rodriguez-Cruz thanks the Spanish Ministry of
Education and Science for her postdoctoral fellowship.
We thank Mrs. Suzanne Lincoln for technical assistance,
and Dr. Sebastian Sorensen, GEUS, Denmark, for
providing 14C labelled mecoprop.
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