Environmental Pollution 157 (2009) 463–473

Contents lists available at ScienceDirect

Environmental Pollution
journal homepage: www.elsevier.com/locate/envpol

Characterizing pesticide sorption and degradation in microscale
biopuriﬁcation systems using column displacement experiments
Tineke De Wilde a, *, Jan Mertens b, Jirka Šimunek c, Kristel Sniegowksi b,
Jaak Ryckeboer b, Peter Jaeken d,1, Dirk Springael b, Pieter Spanoghe aaLaboratory of Crop Protection Chemistry, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, B-9000 Ghent, Belgium
Division Soil and Water Management, Faculty of Bioscience Engineering, K.U. Leuven, Kasteelpark Arenberg 20, B-3001 Heverlee, Belgium
Department of Environmental Sciences, University of California, Riverside, CA, USAd
PCF-Royal Research Station of Gorsem, De Brede Akker 13, 3800 Sint-Truiden, BelgiumbcTransport of pesticides in column experiments containing organic substrates.

a r t i c l e i n f o

a b s t r a c t

Article history:
Received 19 June 2008
Received in revised form 2 September 2008
Accepted 14 September 2008

Biopuriﬁcation systems treating pesticide contaminated water are very efﬁcient, however they operate asa black box. Processes inside the system are not yet characterized. To optimize the performance,
knowledge of degradation and retention processes needs to be generated. Therefore, displacement
experiments were carried out for four pesticides (isoproturon, bentazone, metalaxyl, linuron) in columns
containing different organic mixtures. Bromide, isoproturon and bentazone breakthrough curves (BTCs)
were well described using the convection–dispersion equation (CDE) and a ﬁrst-order degradation
kinetic approach. Metalaxyl and linuron BTCs were well described using the CDE model expanded with
Monod-type kinetics. Freundlich sorption, ﬁrst-order degradation and Monod kinetics coefﬁcients wereﬁtted to the BTCs. Fitted values of the distribution coefﬁcient Kf,column were much lower than thosedetermined from batch experiments. Based on mobility, pesticides were ranked as: bentazone > metalaxyl – isoproturon > linuron. Based on degradability, pesticides were ranked as: linuron > metalaxyl – isoproturon > bentazone.
Ó 2008 Elsevier Ltd. All rights reserved.

Keywords:
Pesticide
HYDRUS-1D
Breakthrough curve
Degradation
Monod kinetic

1. Introduction
Organic contaminants in general, and organic pesticides inparticular, are widespread in both surface and ground waters(Dousset et al., 2007; Kamra et al., 2001; Rodriguez-Cruz et al.,2007; Shaw et al., 2000). Several ﬁeld surveys and measurement
campaigns have demonstrated that 40–90% of surface watercontamination is attributable to point sources or direct losses(Carter, 2000; Decoin, 2003; Kreuger and Nilsson, 2001; Mason
et al., 1999). Point source pollution results, for example, from spills
at ﬁlling operations, leakages of spray equipment, spray leftovers,
spills of rinsing water from internal and external cleaning of thespraying equipment (Isensee and Sadeghi, 1996; Jaeken and
Debaer, 2005; Ramwell et al., 2004; Shepherd and Heather, 1999;
Torstensson and Castillo, 1997). These practices occur mainly at theﬁlling and cleaning areas of farms, where the ground is often* Corresponding author. Tel.: þ32 9 264 60 09; fax: þ32 9 264 62 49.
E-mail address: tineke.dewilde@UGent.be (T. De Wilde).
1
Present address: essenscia, A. Reyerslaan 80, 1030 Brussels, Belgium.
0269-7491/$ – see front matter Ó 2008 Elsevier Ltd. All rights reserved.
doi:10.1016/j.envpol.2008.09.008

covered with concrete, gravel or sand and where the ability toadsorb and degrade pesticides is relatively low, creating high risk
for contamination (Helweg et al., 1998; Wiren-Lehr et al., 2001).
Even small quantities of the concentrated spray solution may
present a serious contamination risk. To meet European standards
for drinking water (98/93/EEC), efforts are required to improvewater treatment and prevent contamination. On-farm water
treatment systems for treatment of pesticide contaminated waste
water need to be cheap, reliable, and labor efﬁcient, with low waste
disposal costs.
On-farm biopuriﬁcation systems, introduced by Torstensson
and Castillo (1997) in 1997 in Sweden, have been developed for thispurpose. Typical overall clean-up efﬁciency exceeds 95%, often
removing more than 99% of the pesticide contamination (De Wildeet al., 2007). These systems consist of a biologically active matrix
that retains pesticides into the organic matter and enhances theirmicrobial degradation. The composition and the type of the organic
material present in the biobed are crucial for retention of chemicals, as well as for the amount and activity of microorganisms
responsible for their degradation (Castillo and Torstensson, 2007).

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T. De Wilde et al. / Environmental Pollution 157 (2009) 463–473

Retention of pesticides in these systems does not depend solely on
the structure and composition of the organic substrates, but also on
the physicochemical characteristics of the pesticide. The efﬁciencyof these systems is expected to be much lower in retaining anddegrading mobile pesticides, such as bentazone (Koc ¼ 13 L kg1),
andmecoprop
metsulfuron-methyl(Koc ¼ 39.5 L kg1),(Koc ¼ 31.5 L kg1), than less mobile pesticides such as linuron(Koc ¼ 410 L kg1) (De Wilde et al., 2007). However, at present,
biopuriﬁcation systems operate as a black box, since the researchcarried out to sufﬁciently understand processes taking place insideof these systems is limited. In order to optimize the efﬁciency ofthese systems for a broad range of pesticides and organic
substrates, the fate of pesticides and the effects of degradation andretention processes inside these systems need to be wellcharacterized.
This study focuses on the fate of four pesticides, i.e., linuron,bentazone, metalaxyl and isoproturon, in small-scale biopuriﬁcation systems, focusing mainly on degradation and retention(sorption) processes. A comparison is carried out between sorption
in batch experiments and in column displacement experiments.
Degradation of pesticides is evaluated using the ﬁrst-order orMonod kinetics, the latter to describe the possible occurrence ofdelayed degradation. While the transport of pesticides has beenfrequently evaluated in soil columns, this study uses different
mixtures of organic materials, which have not previously beencharacterized in detail. The one-dimensional transport model
HYDRUS-1D (Simunek et al., 2005) was used to identify andquantify solute transport and hydraulic parameters.

2. Materials and methods
2.1. Experimental
2.1.1. Pesticide properties
The pesticides used in this study were selected based on their physicochemical
characteristics, solubility, frequency of detection in surface and ground waters, andfrequency of use. The mobility of a pesticide, which we assumed to be characterized
by the organic carbon partition coefﬁcient Koc, was an important selection parameter, as it largely determines pesticide retention. Linuron (3,3,4 dichlorophenyl-1methoxy-1-methyl-urea) (Koc ¼ 410 L kg1) was selected as a rather immobile
pesticide, metalaxyl (methyl N-(2,6-dimethylphenyl)-N-(methoxyacetyl)-DL-alaniand
isoproturon(N,N-dimethyl-N0 -[4-(1-methylnate)(Koc ¼ 47 L kg1)
ethyl)phenyl]urea) (Koc ¼ 36 L kg1) as intermediately mobile pesticides, andbentazone(3-isopropyl-1H-2,1,3-benzothiadiazin-4(3H)-one
2,2-dioxide)(Koc ¼ 13 L kg1) as a mobile pesticide. None of the four selected pesticides was
highly persistent, because their DT50soil values (time, during which the pesticideconcentration decreases by 50%) were lower than 50 days (Vercruysse and Steurbaut, 2002). Analytical standard grades (99%) of metalaxyl, isoproturon, linuron andbentazone were purchased from Riedel-de Haen, Seelze, Germany. Technical grade
metalaxyl (95.5% purity) was kindly supplied by Syngenta (Basel, Switzerland),technical grade linuron (97.7% purity) by Dupont de Nemours (Hamburg, Germany),technical grade isoproturon (98% purity) by Bayer Crop Science (Monheim, Germany), and technical grade bentazone (98.4% purity) by BASF (Limburgerhof, Germany). Methanol, acetonitrile, and water were of A.R. grade (VWR, Leuven,
Belgium).
The sorption properties of the four pesticides on various organic substrates usedin the column microcosm experiments (described below) were previously determined by De Wilde et al. (2008b) in batch experiments conducted according theOECD guideline 106. The Freundlich sorption parameters (i.e., Kf – the distribution
coefﬁcient, and n – the exponent) calculated in that study for all pesticide–substrate
combinations are presented in Table 1.
2.1.2. Matrix substrate description
The following organic substrates were selected for inclusion in the matrix of thelaboratory columns: peat mix, garden waste compost, straw, sandy loam soil, dried
cow manure and coco chips. The characteristics of these substrates were discussed
in detail by De Wilde et al. (2008a). Their main physicochemical properties arepresented in Table 2. The substrates peat mix, garden waste compost, dried cow
manure and coco chips were used in the form which is commercially available. Straw
was cut to obtain fractions of 3–6 cm. Before its use, the sandy loam soil (33% sand,
56% loam and 11% clay) was ﬁrst air-dried and crushed to pass a 2 mm sieve. Only
the fraction smaller than 2 mm was used.

Table 1
Freundlich sorption parameters for various combinations of pesticides and organic
substrates determined in batch experiments.
Substrate

Sorptionparameter

Metalaxyl

Isoproturon

Linuron

Bentazone

Coco chips

Kf,batch (L kg1)
nbatch

10.66
0.98

9.22
1.17

51.01
0.85

7.08
0.79

Gardenwaste
compostKf,batch (L kg1)
nbatch

16.59
0.81

27.08
0.61

80.55
0.58

2.45
0.78

Sandy
loam soil

Kf,batch (L kg1)
nbatch

6.91
0.74

2.12
1.09

2.86
0.86

0.57
0.91

Cow manure

Kf,batch (L kg1)
nbatch

4.25
1.20

34.87
0.71

47.67
0.60

4.36
0.79

Peat mix

Kf,batch (L kg1)
nbatch

58.83
0.74

57.27
0.77

192.57
0.63

9.27
0.76

Straw

Kf,batch (L kg1)
nbatch

6.58
1.01

7.91
1.08

95.28
0.81

2.54
0.89

2.1.3. Column microcosm set-up
Column microcosms were packed in triplicates with ﬁve different mixtures of
air-dried organic substrates and sandy loam soil as indicated in Table 3. Substrate
amounts were measured volumetrically, manually mixed in a bucket for about 5–
10 min to form homogeneous mixtures, and then packed into glass columns.
Compaction of the matrix was carried out by placing a weight of 5 kg on top of thecolumn. The columns were 15 cm high and had an inner diameter of 10 cm. The
bottom of the columns was lined with a glass ﬁlter, so that seepage face conditions
could develop there.
2.1.4. Displacement experiments
Displacement experiments were conducted under unsaturated, steady-state
ﬂow conditions. Steady-state water ﬂow conditions were established prior to theapplication of the solute step input. A CaCl2 solution (0.001 M CaCl2) was supplied tothe column surface using PTFE (Polytetraﬂuoroethylene) tubes. A peristaltic pump(Type 205S/CA, Watson Marlow, Zwijnaarde, Belgium) delivered a constant Darcy
ﬂux of 1.74 cm d1. Droplets fell on a paper ﬁlter placed on the organic substrates inorder to provide a homogenous distribution of the solution. The top of each column
was covered with paraﬁlm to avoid evaporation. It was assumed that steady-state
conditions had been reached once the mass of the soil column remained constant intime. When steady-state conditions were achieved, pesticides were applied to thecolumn, initially together with bromide solution (0.1 mM Br). The pesticide solution pumped onto the column contained 0.001 M CaCl2 and 10 mg L1 linuron,isoproturon, metalaxyl and bentazone. While pesticides were added continuously asa step input, the bromide solution was applied as a pulse with duration of 18.3 h. The
efﬂuent was collected in a fraction collector at the bottom every 2–3 days (theefﬂuent was the total amount of solution eluted during two consecutive samplingdays) and both outﬂow volumes and pesticide concentrations were measured.
Bromide in the form of KBr was used as a non-reactive tracer in the column
experiments to determine physical transport parameters. Bromide concentrations
were determined using ion chromatography (Dionex ICS 2000), containing an AS15
column and KOH efﬂuent. Bromide detection was performed by conductivity witha detection limit of 0.001 mM. The experiments lasted for about 100 d until the
efﬂuent concentrations of most pesticides reached a constant value.
Pesticide efﬂuent concentrations were determined using solid-phase extraction
followed by HPLC-DAD UV analysis performed on a Finnigan Surveyor HPLC
(Thermo Electron Corporation; Waltham, MA, USA) equipped with a gradient pump,a degasser, an autosampler, and a diode array detector (DAD), as described in detailby De Wilde et al. (2008a). The analytical column used in the analysis was an Alltima
HP C18 EPS 3 mm 150 mm  3.0 mm (Alltech Associates Inc. Deerﬁeld, IL, USA).
2.2. Transport models
The HYDRUS-1D model for simulating one-dimensional water ﬂow and transport of solutes in soils was used to describe the transport of pesticides in the column(Simunek et al., 2005). It was assumed that experimental breakthrough curves(BTCs) could be described using the classical convection–dispersion transport modelthat neglects both physical and chemical non-equilibrium (Lapidus and Amundson,1952). Chemical non-equilibrium (Cameron and Klute, 1977) was not consideredsince the sorption process for the pesticide–substrate combinations was sufﬁciently
fast under the applied low ﬂow rates to establish local equilibrium (De Wilde et al.,2008a).
Assuming that decay is only active in the liquid phase, and since a nonlinear
relationship between liquid and solid-phase concentrations can be described using
the Freundlich adsorption isotherm: s ¼ Kf cn, where c [M L3] and s [M M1] areT. De Wilde et al. / Environmental Pollution 157 (2009) 463–473

465

Table 2
Physicochemical characteristics of the substrates used in the matrix of the column experiment.

Garden waste
compostStraw
Coco chips
Peat mix
Sandy loam soil
Cow manure

pH

OCa
(%)

P2O5
(%)

K2O
(%)

CaO
(%)

MgO (%)

Na(mg 100 g1)

CECc
(meq 100 g1)

Speciﬁc density(106 g m3)

7.7

27.84

NDb

ND

ND

ND

ND

ND

1.84

6.6
6.7
6.4
6.9
6.9

42.34
44.69
47.61
0.91
37.59

0.69
0.98
0.45
0.03
9.87

7.72
12.39
0.8
0.42
11.37

0.38
0.08
67
0.19
1.8

0.36
0.1
0.79
0.08
4.92

0.57
2.52
0.42
0.21
1.41

25.21
58.98
129.42
13.87
53.22

1.56
1.67
1.58
2.81
1.68

a

OC – organic carbon.
ND – not determined.
c
CEC – cation exchange capacity.

bconcentrations in the liquid and solid phases, respectively, and Kf and n are
Freundlich parameters, the transport of a reactive solute for steady-state water ﬂow
conditions can be written asvc
v2 cvc r vs¼ D 2 n  b ml c
vt
vz
q vt
vz

(1)

where D is the dispersion coefﬁcient [L2 T1], n is the pore water velocity [L T1],
n ¼ q/q, in which q is the Darcian water ﬂux [L T1] and q is the volumetric watercontent [L3 L3], rb is the bulk density [M L3], ml is the ﬁrst-order degradation
constant for the solute in the liquid phase [T1], and t [T] and z [L] are the temporaland spatial coordinates, respectively. The change in the sorbed concentration can be
written as follows

rb vs rb ds vc rb
vc¼¼
K ncn1
q vt
q dc vt
q f
vt

(2)

Finally, incorporating (2) into (1) leads to
vc
v2 c
vc
R ¼ D 2  n  ml c
vt
vz
vz

(3)

dX
¼
dt



mm;b
Ks




c X  kdecay;b X ¼ m*m;b c X  kdecay;b X





mm;b
dc
¼ 
X c ¼  m*b X c
dt
YKs

(7)

(8)

1
*
where mm,b/Ks is the modiﬁed mass growth rate [L3w M1p T ], and mb has units of
1[L3w M1b T ]. Incorporating Eq. (8) into the one-dimensional transport Eq. (3),results in

vc
v2 c
vc 
R ¼ D 2  n  m*b X c
vt
vz
vz

(9)

These equations (referred to below as the Monod model) have been implemented in HYDRUS-1D. The ﬁtting parameters for this model are m*b , m*m;b (yield Y)
and kdecay,b.

3. Results and discussion

where R is the retardation factor ():R ¼ 1þ

rb
K ncn1
q f(4)

In the model described above (referred to below as the CDE model), pesticidedegradation is assumed to be a ﬁrst-order process depending only on pesticideconcentration. However, this is in contrast with a lag phase (a start-up period,
during which degradation is not detected) commonly observed in laboratory
mineralization experiments (Mertens et al., 2008). Therefore, we incorporated thesimpliﬁed version of the Monod kinetics (Guimont et al., 2005) to describe BTCs
where a lag phase was clearly present (e.g., metalaxyl BTCs) into HYDRUS-1D. In
such a model, bacterial growth can be described using the following Monod kinetics:
dX
¼
dt



mm;b


c
X  kdecay;b X
Ks þ c(5)where X is the pesticide-degrading biomass concentration [Mb L3w ], c is the liquid
1pesticide concentration [Mp L3w ], mm,b is the mass growth rate [T ], Ks is the half
1saturation constant [Mp L3w ], and kdecay,b is the decay rate [T ] (subscripts b, p andw refer to biomass, pesticide and water, respectively). The bacterial Monod growth
function can be related to the pesticide consumption utilizing a yield coefﬁcient, Y,deﬁned as a mass ratio of the organisms formed per pesticide utilized [Mb M1p ]. The
local change in pesticide concentration when neglecting the effects of the transport
can then be expressed as follows


dc
1
c
¼  mm;bX
dtYKs þ c(6)

If we assume that the pesticide concentration (c) is signiﬁcantly lower than thehalf saturation constant (c  Ks), then Eqs. (5) and (6) simplify as follows

Table 3Composition of the matrix in the column microcosms set-up (g).

Garden waste
compostStraw
Coco chips
Peat mix
Sandy loam soil
Cow manure

Mix 1

Mix 2

Mix 3

Mix 4

Mix 5

–

159.93

–

–

–

27.33
–
105.20641.07–28.43––
643.27
–

12.97
52.43
104.77642.33
–

13.00
52.57
83.87
639.67
64.27

27.27
–
87.03
640.6364.27

Physical transport was ﬁrst evaluated by analyzing the bromide
BTCs. The volumetric water content, q, and the longitudinal dispersivity, l (D ¼ l$v), were ﬁtted to the observed Br BTCs. Once thephysical transport was fully characterized using bromide BTCs,
chemical processes for isoproturon, metalaxyl, linuron, and bentazone were analyzed by inverting the pesticide BTCs with theLevenberg–Marquardt algorithm in HYDRUS-1D, using the physicaltransport parameters estimated for bromide transport. In all
inversions of the pesticide BTCs, the soil water content, q, the waterﬂux, q, the bulk density, r, and the dispersivity, l, were ﬁxed.
Estimated reaction parameters included the Freundlich parameters
Kf,column and ncolumn, and the liquid degradation constant ml.
3.1. Bromide BTCs
BTCs are plotted in Fig. 1 as Br concentrations versus thenumber of pore volumes (PV) eluted (one pore volume represents
time needed for the volume of water that ﬁlls the voids of thecolumn to be eluted: PV ¼ qt/(qL)). The HYDRUS-1D model ﬁtted
the experimental data well with R2 varying between 0.70 and 0.92.
However, the ﬁt to the Br BTCs of mixtures 5A and B is poorer due
to the presence of a plateau in the experimental data. High values
before and after the peak could be caused by analytical errors. The
experimental BTCs did not show any signiﬁcant asymmetry or
long-tailing that would indicate physical non-equilibrium. The
mass balance of the amount of Br entering and leaving the column
showed an overall average mass recovery of 96%, therefore it could
be concluded that physical equilibrium prevailed. Fitted transportparameters are presented in Table 4, together with bulk densities of
the column microcosm. High water contents (in comparison tosoils), likely caused by the high water sorbing capacity of organic
substrates such as peat mix and coco chips, were found for allcolumns. The dispersivity length was quite similar for all mixtures,with a slightly higher dispersion for mixtures 3 and 4. Only slight466

T. De Wilde et al. / Environmental Pollution 157 (2009) 463–473

0.025

0.025

1

A simulated
A observed
B simulated
B observed
C simulated
C observed

0.020
0.015

0.020

0.010

0.005

0.005

0.5

1.0

1.5

2.0

2.5

A simulated
A observed
B simulated
B observed
C simulated
C observed

0.015

0.010

0.000
0.0

2

3.0

0.000
0.0

0.5

1.0

PV
0.025
0.020

0.025
A simulated
A observed
B simulated
B observed
C simulated
C observed

0.015

0.020

0.010

0.005

0.005

1.0

1.5

2.0

2.5

4

3.0

0.000
0.0

0.5

1.0

PV
0.025
0.020

2.5

3.0

A simulated
A observed
B simulated
B observed
C simulated
C observed

0.015

0.010

0.5

2.0

PV

3

0.000
0.0

1.5

1.5

2.0

2.5

3.0

PV

5

A simulated
A observed
B simulated
B observed
C simulated
C observed

0.015
0.010
0.005
0.000
0.0

0.5

1.0

1.5

2.0

2.5

3.0

PV
Fig. 1. Observed and simulated BTCs of Br in 5 experimental column set-ups (replicates A, B, and C) containing different types of organic substrate. (1) Mixture 1, (2) mixture 2, (3)mixture 3, (4) mixture 4 and (5) mixture 5. Symbols represent the observed data, full, dashed and dotted lines the model ﬁts. Absolute concentrations are plotted against the
number of pore volumes (PV).

Table 4
Estimated physical transport parameters for Br in the 5 microcosms using the CDEtransport model (95% conﬁdence interval).
Mixql (cm)R2

r (g cm3)

1A
1B
1C

0.68  0.03
0.53  0.02
0.54  0.02

2.20  0.34
1.61  0.28
1.56  0.20

0.86
0.87
0.91

0.66
0.66
0.66

2A
2B
2C

0.51  0.02
0.52  0.02
0.55  0.02

1.88  0.31
1.14  1.14
1.12  0.23

0.81
0.85
0.78

0.70
0.70
0.71

3A
3B
3C

0.69  0.03
0.57  0.04
0.63  0.02

3.04  0.51
3.33  0.76
1.97  0.29

0.84
0.91
0.87

0.69
0.70
0.70

4A
4B
4C

0.53  0.05
0.47  0.02
0.57  0.02

2.99  0.36
4.28  0.72
3.82  0.49

0.90
0.87
0.87

0.69
0.69
0.69

5A
5B
5C

0.57  0.04
0.53  0.03
0.83  0.04

1.86  0.56
2.50  0.55
2.13  0.42

0.70
0.75
0.80

0.73
0.72
0.72

differences existed in transport parameters between replicatedcolumns. These differences could be explained by the spatial
heterogeneity of the structure of the organic substrates and packingof the columns.
3.2. Isoproturon BTCs
Isoproturon BTCs are shown in Fig. 2 in terms of relative
concentrations (i.e., measured concentrations relative to the inlet
concentration) versus the number of pore volumes (PV) eluted. The
ﬁtted solute reaction parameters are presented in Table 5. The CDEmodel described the isoproturon BTCs well, as reﬂected in highvalues of the determination coefﬁcient R2 (0.95–1.00) (Table 5). The
BTCs shifted signiﬁcantly more to the right than bromide (Fig. 1),
which indicates that the pesticide is strongly adsorbed into theorganic substrates, which is also reﬂected by the retardation coefﬁcient R (Table 5). However, R which is calculated using the initialconcentration as input concentration, is slightly overestimated if
degradation occurs and if n > 1. Mixtures 1 and 2 retained isoproturon the most, with averages R of 12 and 15, respectively.

T. De Wilde et al. / Environmental Pollution 157 (2009) 463–473

1.0

1.0
A simulated
A observed
B simulated
B observed
C simulated
C observed

0.8
0.6

A simulated
A observed
B simulated
B observed
C simulated
C observed

0.8
0.6

0.4

0.4

0.2
0.0

0.2

1
0

5

10

15

20

0.0

2
0

5

PV
1.0

0.8

0.8

0.6

0.6

0.4

A simulated
A observed
B simulated
B observed
C simulated
C observed

0.2

0

5

10

10

15

20

PV

1.0

0.0

467

0.4
0.2

3

15

A simulated
A observed
B simulated
B observed
C simulated
C observed

20

PV

0.0

0

5

10

15

4
20

PV

1.0
0.8
0.6
0.4

A simulated
A observed
B simulated
B observed
C simulated
C observed

0.2
0.0

0

5

10

15

5
20

PV
Fig. 2. Observed and simulated BTCs of isoproturon in 5 experimental column set-ups (replicates A, B, and C) containing different types of organic substrate. (1) Mixture 1, (2)mixture 2, (3) mixture 3, (4) mixture 4 and (5) mixture 5. Symbols represent the observed data, full, dashed and dotted lines the model ﬁts. Relative concentrations are plottedagainst the number of pore volumes (PV).

Mixtures 3, 4 and 5 retained isoproturon in a similar way, withaverages R of 7, 8 and 7, respectively. This indicates that the classicalbiomix composition as in mixtures 1 and 2 with peat mix or garden
waste compost retains isoproturon the most. Decreasing theamount of peat or straw appears to decrease the retention of thispesticide. The value of the Freundlich exponent ncolumn is an indicator of the curvature of the sorption isotherm. Most sorption
isotherms are of the S-type (n > 1), which indicates a strong
competition by water molecules for adsorption sites at low pesticide concentrations (Giles et al., 1960). However, the sorption of
isoproturon on mixtures 1, 3 and, 4 was almost linear. Also note thatthe uncertainty of this parameter was relatively low (i.e., narrow
conﬁdence intervals, see Table 5). However, this was true only
when sorption was almost linear. Notice below that when sorption
was nonlinear, the uncertainty increased signiﬁcantly (Table 6). A
comparison of the Freundlich parameters obtained using column
transport experiments with those obtained in batch experiments(Table 5) showed that Kf,column values were about 2.4–6.9 times
smaller for the column experiments. This means that isoproturon ismore strongly sorbed by organic substrates in batch experiments,

and that using coefﬁcients based on such studies in transportmodels would overestimate sorption. This was reported previously
by Meyer-Windel et al. (1999) for atrazine and isoproturon, andKookana et al. (1992) for fenamiphos, linuron and simazine. In thepresent study initial analysis of the BTCs were performed with theFreundlich parameters obtained in batch experiments, nonetheless
optimizations with chemical equilibrium and non-equilibrium
assumptions did not improve the ﬁt of the BTC. Batch sorption
experiments are not always representative of the sorption process
occurring in column studies or in the ﬁeld, since the solid/liquidratio is much higher in percolate column experiments than in batch
experiments, there could also be an increase in surface area
resulting from the disaggregation of clusters during shaking andmoreover, the pesticide–substrate solution is continuously shaken,
which improves contact between pesticide and substrate, which
could enhance sorption (Fesch et al., 1998). However, there also
exist an alternative opinion that is supported by a number ofexperiments and resulting publications that show that results frombatch experiments can be used to describe the transport behaviourof pesticides in soil columns and outdoor if the essential processes

468

T. De Wilde et al. / Environmental Pollution 157 (2009) 463–473

Table 5
Solute reaction parameters ﬁtted to isoproturon BTCs (95% conﬁdence interval).
Mix

Kf,column (L kg1)

ncolumn

Kf,batch (L kg1)

nbatchml (103) (h1)

R2R

DT50 (d)1A
1B
1C

9.30  0.39
7.81  0.44
5.60  0.63

1.12  0.02
1.03  0.04
1.09  0.05

18.28

0.89

1.24  0.18
2.74  0.18
1.75  0.38

1.00
0.99
0.98

14.19
11.84
10.18

331
125
168

2A
2B
2C

3.27  0.40
3.47  0.29
2.11  0.10

1.43  0.07
1.26  0.04
1.56  0.02

11.72

0.81

1.15  0.17
3.64  0.12
2.43  0.12

0.98
0.99
0.99

18.06
11.60
16.50

452
92
196

3A
3B
3C

5.93  0.31
4.32  0.38
5.65  0.59

1.01  0.05
1.09  0.05
0.95  0.06

17.15

0.90

1.45  0.09
2.10  0.09
1.46  0.16

0.99
0.99
0.99

7.09
8.10
6.40

141
112
126

4A
4B
4C

3.98  0.40
6.19  0.07
5.89  0.49

1.17  0.05
0.90  0.01
0.92  0.05

18.37

0.93

0.98  0.04
2.72  0.12
2.76  0.10

0.99
0.98
0.99

9.87
7.52
6.53

292
80
68,3

5A
5B
5C

4.07  0.43
1.44  0.43
1.98  0.45

0.94  0.05
1.47  0.16
1.41  0.12

17.25

0.93

2.37  0.28
2.78  0.19
1.03  0.00

0.99
0.95
0.95

5.26
9.53
7.30

64
99
205

such as non-equilibrium sorption are considered (e.g., Boesten
et al., 1989; Streck et al., 1995; Streck and Richter, 1999; Altfelder
et al., 2001; Ma and Selim, 2005).
Since the inlet concentration of isoproturon is not reached at theoutlet, degradation needs to occur inside the columns, as is also
reﬂected by the positive values of the ﬁrst-order degradation rate ml(Table 5). Differences in degradation potential between the
mixtures were minimal, all degradation constants (ml) were of thesame magnitude. Thus, the composition of the matrix appears tohave little inﬂuence on degradation of isoproturon. As degradationis only considered in the liquid phase, half-life values (DT50) need
to be calculated instead of from ml from ml/R for sorbing chemicals.
Half-life (DT50) values of isoproturon were calculated and presented in Table 5. According to the DT50 value, degradation wasslightly higher in mixtures 3, 4 and 5, while it was lower in
mixtures 1 and 2. This is much higher than the reported values inliterature (DT50soil ¼ 6–20 days) (Roberts, 1998; Tomlin, 2006).
Sorption to organic substrates can decrease the bioavailability andtherefore decrease biodegradation.
3.3. Bentazone BTCs
Experimental bentazone BTCs were also analyzed using the CDEmodel (Fig. 3, Table 6). The determination coefﬁcient R2 ranged
from 0.51 to 0.91. The arrival of bentazone, which is a very mobile

pesticide, was very similar to that of the inert tracer bromide,
showing that this pesticide is hardly retained in the organic
substrates. Since the value of the Freundlich exponent ncolumn has
been proven to be highly uncertain (high standard deviations inTable 6), the retardation factor was calculated assuming linear
adsorption (n ¼ 1). Reported retardation factors are therefore
underestimated because the n exponent is larger than 1. In general,
the retardation factors R were close to 1, indicating that hardly any
retention occurred for all mixtures, and conﬁrming Gaston andLocke’s (1996) results in columns of Dundee silty clay loam soil. The
ﬁtted ncolumn values for most mixtures were relatively high, ascompared to n values for bentazone reported in the literature,
which ranged from 0.88 to 1.061 for Mississippi and coastal plain
soils (Gaston et al., 1996; Grey et al., 1996). However, as discussed
above, these values are highly uncertain. The ncolumn values are
determined by the changing slope of the part of the BTC curve
where C/C0 increases from 0 to equilibrium conditions. Since bentazone breakthrough occurs very fast, the number of data points inthis part of the curve is limited. This results in a poor estimation of
the n value. Calculated Kf,column values for bentazone are muchsmaller than those obtained from the batch experiments (Table 6),
which conﬁrms the similar results obtained for isoproturon. Whilebentazone Kf,column values were much lower than for isoproturon,
the ratio of Koc (isoproturon)/Koc (bentazone) was much smaller
than Kf,column (isoproturon)/Kf,column (bentazone). This could

Table 6
Solute reaction parameters ﬁtted to bentazone BTCs (95% conﬁdence interval).
Mix

Kf,column (L kg1)

ncolumn

Kf,batch (L kg1)

nbatchml (103) (h1)

R2Ra

DT50 (d)1A
1B
1C

1.35  103  2.95  103
1.41  101  1.06  1011.75  105  6.03  102

3.62  0.96
1.66  0.31
5.86  1.57

3.42

0.81

3.51  1.02
4.84  0.54
2.79  0.00

0.92
0.90
0.71

1.00
1.29
1.00

14,032
10,516
18,9322A
2B
2C

5.45  106  1.26  1051.42  104  1.83  104
9.07  102  1.13  10þ1

6.12  0.99
4.74  0.57
0.00  4.55

2.93

0.80

3.45  1.28
5.67  1.11
6.21  1.07

0.88
0.92
0.81

1.00
1.00
1.00

14,427
8398
7828

3A
3B
3C

6.11  103  1.48  1022.70  103  7.03  103
1.54  102  4.10  102

3.39  1.12
3.79  1.22
2.84  1.15

3.40

0.81

1.02  0.54
1.61  1.07
3.55  0.91

0.70
0.69
0.53

1.02
1.01
1.05

55,61928,84914,0284A
4B
4C

1.00  1.03
1.31  1.34
4.75  103  8.59  1030.12  0.26
0.33  0.55
3.23  0.82

4.10

0.81

4.10  1.25
6.12  2.54
3.76  1.62

0.82
0.51
0.85

1.16
1.64
1.02

49,89231,51851,7495A
5B
5C

3.12  106  9.91  1065.84  102  5.75  1022.92  104  1.04  103

6.28  1.39
2.15  0.42
4.25  1.51

4.08

0.81

1.96  1.71
0
0

0.84
0.91
0.87

1.00
1.17
1.00

24,249––aRetardation coefﬁcient was calculated assuming linear sorption (n ¼ 1).

T. De Wilde et al. / Environmental Pollution 157 (2009) 463–473

1.2

1.2

1

1.0

0.8

0.6

0.6

0.4

0.4

A simulated
A observed
B simulated
B observed
C simulated
C observed

0.2
0.0

0

2

1.0

0.8

5

10

15

A simulated
A observed
B simulated
B observed
C simulated
C observed

0.2

20

0.0

0

5

PV
1.2

1.2

0.8

0.6

0.6

0.4

0.4

A simulated
A observed
B simulated
B observed
C simulated
C observed

0.2

0

5

10

20

15

A simulated
A observed
B simulated
B observed
C simulated
C observed

0.2

20

0.0

0

5

10

15

20

PV

PV
1.2

15

4

1.0

0.8

0.0

10

PV

3

1.0

469

5

1.0
0.8
0.6
0.4

A simulated
A observed
B simulated
B observed
C simulated
C observed

0.2
0.0

0

5

10

15

20

PV
Fig. 3. Observed and simulated BTCs of bentazone in 5 experimental column set-ups (replicates A, B, and C) containing different types of organic substrate. (1) Mixture 1, (2)mixture 2, (3) mixture 3, (4) mixture 4 and (5) mixture 5. Symbols represent the observed data, full, dashed and dotted lines the model ﬁts. Relative concentrations are plottedagainst the number of pore volumes (PV).

indicate that bentazone is sorbed to an even lesser extent thanisoproturon in column experiments compared to batch experiments. However, it should be noted that Kf,column and ncolumn values
were also very uncertain as a result of the limited number of data
points in the early part of BTCs. Correlations between the twosorption parameters were as high as 0.99. Note that the uncertainty of sorption parameters and their correlation is reﬂected in
the relatively large conﬁdence intervals. Because of this uncertainty, it is difﬁcult to compare various mixtures with respect totheir bentazone retention capacity.
Degradation of bentazone was about one magnitude lower thandegradation of isoproturon, making it almost negligible. Outﬂow
bentazone concentrations quickly approached the input concentration, which indicated very limited degradation. This is also
conﬁrmed by the values of the half-life of bentazone which werevery high (>7828 days). This is in contrast to the literature, wherebentazone was described as a very low persistent molecule witha half-life in soil of less than two weeks (Wauchope et al., 1992).
However, low degradation of bentazone in our column studies
could be expected because of the very short residence time in thecolumns. Finally, as for isoproturon, composition of the matrix wasof little importance because small differences in degradation efﬁciency between the organic mixtures were present and values wereof the same order of magnitude.
3.4. Metalaxyl BTCs
Transport of metalaxyl (both observed, as well as simulatedusing the CDE model) through the experimental columns is shownin Fig. 4. Collected experimental metalaxyl BTCs for mixtures 2, 3,
and 5 have a very different pattern than those obtained for isoproturon and bentazone. Although a step input was applied, the
BTCs for mixtures 2, 3, and 5 showed a pattern that is characteristic
for a pulse input (Fig. 4). Efﬂuent concentrations increased up to

470

T. De Wilde et al. / Environmental Pollution 157 (2009) 463–473

1.0

1.0

1

0.8
0.6

0.6

0.4

0.4

A simulated
A observed
B simulated
B observed
C simulated
C observed

0.2
0.0

2

0.8

0

5

10

A simulated
A observed
B simulated
B observed
C simulated
C observed

0.2

15

20

0.0

0

5

10

PV
1.0

1.0

3

0.8

20

4

0.8

0.6

0.6

0.4

0.4
A simulated
A observed
B simulated
B observed
C simulated
C observed

0.2
0.0

15

PV

0

5

10

0.2

15

20

PV
0.8

A simulated
A observed
B simulated
B observed
C simulated
C observed

0.0

0

5

10

15

20

PV

5

A simulated
A observed
B simulated
B observed
C simulated
C observed

0.6

0.4

0.2

0.0

0

5

10

15

20

PV
Fig. 4. Observed and simulated BTCs of metalaxyl in 5 experimental column set-ups (replicates A, B, and C) containing different types of organic substrate. (1) Mixture 1, (2) mixture
2, (3) mixture 3, (4) mixture 4 and (5) mixture 5. Symbols represent the observed data, full, dashed and dotted lines the model ﬁts. Relative concentrations are plotted against the
number of pore volumes (PV).

a certain value, and then began decreasing. A ﬁrst-order degradation process could not be used to describe the observed efﬂuent
metalaxyl concentrations in these mixtures. It was hypothesizedthat these BTCs could be described using a Monod-type degradation model (Eqs. (7) and (9)) that considers bacterial growth anddecay.
The classical CDE model (3) was ﬁrst used to ﬁt the ﬁrst part ofthe BTCs up to the maximum efﬂuent concentration for mixtures 2,
3, and 5, in order to obtain the Freundlich parameters of this initialpart of the BTCs. Although we realize that the Monod-type degradation is involved already during this ﬁrst part of the BTC, its effects
are relatively limited, and in this way we can obtain initial estimates of these reaction parameters. The determination coefﬁcient
R2 for the CDE model ranged from 0.64 to 0.99 (Table 7). Since theretardation coefﬁcient R for metalaxyl is higher than one, retentionof this pesticide into the organic matrix occurs (Table 7). Retentionof metalaxyl (average R ¼ 8.51) is similar to that of isoproturon(average R ¼ 7.97) in mixture 4, but is slightly lower in mixtures 1, 2,
3, and 5. This is in agreement with more or less similar Koc values ofmetalaxyl (Koc ¼ 47 L kg1) and isoproturon (Koc ¼ 36 L kg1).

Retardation factors of 2.3–3.8 were observed by Fernandes et al.
(2003) for agricultural soils with a lower organic matter content
than in the mixtures evaluated in this study. This likely explains thehigher retention found in our experiments.
The Kf,column values of metalaxyl (Table 7) (average
Kf,column ¼ 2.84 L kg1) were very similar to those of isoproturon(Table 4) (average Kf,column ¼ 4.74 L kg1). However, these values
were again much lower than those obtained from batch experiments (Kf,batch ¼ 15.39 L kg1) (Table 7). This conﬁrms that values
obtained from batch experiments cannot be used directly intransport models as they would lead to a signiﬁcant underestimation of the leaching potential of the pesticide.
First-order degradation constants for metalaxyl in mixtures 1
and 4 are presented in Table 7. The obtained values were of thesame magnitude as those recorded for isoproturon, and close to
those reported by Kookana et al. (1992). Overall, the lowest ﬁrstorder degradation was found in mixtures 1 and 4. These are also the
mixtures in which no delayed degradation occurred (Fig. 4). The
DT50 values are very high for mix 2A and 4A because the efﬂuent
concentration in these mixtures reached the inlet concentration.

T. De Wilde et al. / Environmental Pollution 157 (2009) 463–473

471

Table 7Predicted solute parameters for metalaxyl (95% conﬁdence interval).
Mix

Kf,column (L kg1)

ncolumn

Kf,batch (L kg1)

nbatchml (103) (h1)

R2

R

1A
1B
1C

5.83  0.48
2.86  0.33
2.22  0.59

0.95  0.04
1.26  0.06
1.28  0.13

17.54

0.85

1.36  0.08
1.31  0.14
1.53  0.26

0.99
0.98
0.94

5.75
9.27
7.60

2A
2B
2C

0.69  0.25
1.88  0.40
1.94  0.32

1.78  0.16
1.08  0.10
1.07  0.08

9.46

0.89

0.01  0.09
–
–

0.90
0.96
0.97

11.10
4.27
4.19

22,607
–
–

3A
3B
3C

4.58  0.47
2.68  0.55
2.95  0.30

0.77  0.06
0.94  0.07
1.00  0.07

15.17

0.87

–
–
–

0.92
0.96
0.92

3.09
3.68
4.29

–
–
–

4A
4B
4C

1.62  0.36
3.74  1.09
2.39  0.46

1.47  0.10
1.04  0.14
1.28  0.10

18.58

0.85

0
1.36  0.32
1.06  0.21

0.96
0.96
0.97

10.15
7.25
8.12

1,006,514
154
220

5A
5B
5C

3.11  0.61
2.77  0.57
3.22  0.26

0.89  0.11
0.76  0.12
0.67  0.05

16.20

0.87

–
–
–

0.64
0.68
0.93

3.72
2.70
1.87

–
–
–

Nonetheless, the overall DT50 value in the organic mixtures issimilar or slightly lower to the values described in literature(DT50 > 200 days) (Tomlin, 2006). Evidence of delayed degradation
can be clearly seen in mixtures 2, 3, and 5 (Fig. 4). Two out of three
replicated BTCs for mixture 2 (B and C) showed a slight decrease ofpesticide concentrations after reaching a maximum value. The
decrease of metalaxyl concentrations, compared to the maximum
value, was about 20% for replicate B and 31% for replicate C.
However, a much more pronounced decrease in efﬂuent concentrations was observed in mixtures 3 and 5. The decrease of efﬂuent
concentrations in mixture 3 was between 45% and 50% for different
replicates, and in mixture 5 it was about 85%. The presence of cow
manure is the only difference in the composition of mixtures 3 and5, compared to mixtures 1, 2, and 4. Cow manure represents
a source of nitrogen, which could stimulate the growth of metalaxyl
degrading bacteria. Although no speciﬁc results have been reported
in the literature on the inﬂuence of manure on metalaxyl degradation, the effects of manure on degradation of other pesticides arewell documented. For example, Dolaptsoglou et al. (2007) foundthat the incorporation of composted animal manure accelerated
the degradation of 1,3-D. Moreover, atrazine, metolachlor, andtriﬂuralin degradation also improved in the presence of manure(Moorman et al., 2001). Finally, the dealkylation reactions of striazines and phenylureas were also enhanced by manure (Doyleet al., 1978).
The Monod kinetic parameters and the yield coefﬁcientsdetermined by ﬁtting the entire experimental data curves arepresented in Table 8. Determination coefﬁcients of the ﬁt using theCDE model (9) with the Monod kinetics (7) ranged between 0.86
and 0.96, indicating that the model described the experimental
data very well. Since reaction coefﬁcients of Eq. (7) are the same

DT50 (d)
122
204
144

whether (7) was formulated in absolute or relative biomass
concentrations, and the biomass concentration (X) in Eq. (8) is
divided by the yield (Y), the actual absolute values of biomass
cannot be determined from the pesticide (c) breakthrough curvealone. To determine the biomass, at least one measured value of thebiomass concentration is needed (e.g., initial, ﬁnal, or any other).
This is because the biomass concentration and yield are fully
correlated. Higher values of biomass concentrations and thecorrespondingly lower yield will produce identical results. For thatreason, we carried out simulations in relative biomass concentrations with the initial value being equal to one. The yield coefﬁcient
Y was then calculated as the ratio between m*m;b =m*b . This yield
coefﬁcient showed high values for mixture 5, indicating that morebiomass was generated in this mixture per unit of degradedpesticide compared to mixtures 2 and 3. Parameters m*m;b and kdecay,b are strongly related, and determine the amount of biomass inthe system. Although similar values of m*m;b were observed for
mixtures 3 and 5, much lower values of kdecay,b were obtained for
mixture 5. This means that the biomass will reside longer in
mixture 5 and will be less susceptible to decay, which may lead toa higher pesticide degradation capacity of this mixture. Mixture 5
contains 25% less straw than mixture 3, however in mixture 5 straw

0.12
A simulated
A observed
B simulated
B observed
C simulated
C observed

0.10
0.08
0.06

Table 8
Monod kinetic parameters and the yield coefﬁcient for the degradation of metalaxyl(95% conﬁdence interval).

0.04

m*b (107)(L kg1 h1)m*m;b (104)

kdecay,b (h1)

R2

Y ()

0.02

2B
2C

9.15  14.02
13.43  2.92

0.18  8.21
2.11  1.93

3.15  106  7.01  103
1.08  103  1.37  103

0.96
0.96

20.20
156.89

0.00

3A
3B
3C

9.99  0.89
10.95  3.96
4.62  0.95

5.27  1.09
0.98  3.23
2.42  1.41

2.57  103  6.22  1042.30  105  2.01  103
6.26  104  1.06  103

0.96
0.86
0.98

527.3989.66
524.84

5A
5B
5C

3.38  0.65
4.43  0.70
1.99  0.25

3.95  0.99
2.69  0.54
2.51  0.93

5.27  104  4.88  1044.90  108  2.14  104
1.12  107  4.30  104

0.93
0.91
0.90

1168.18
607.711266.12Mix(L kg1 h1)

0

5

10

15

20

25

30

PV
Fig. 5. Observed and simulated BTC of linuron for mixture 4 (in triplicate: A, B, C)
containing 25% sandy loam soil, 25% peat mix, 25% coco chips, 25% straw. Symbols are
the observed data, full, dashed and dotted lines the model ﬁts given by the convection–
dispersion equation. Relative concentrations are presented against the number of porevolumes (PV).

472

T. De Wilde et al. / Environmental Pollution 157 (2009) 463–473

Table 9Predicted solute parameters of linuron (95% conﬁdence interval).
Mix Kf,column(L kg1)
4A
4B
4A

ncolumn

Kf,batch nbatch ml (102)(L kg1)(h1)29.52  0.42 0.46  0.03 88.35
27.85  0.72 0.78  0.07
28.05  2.18 0.83  0.09

0.75

R2

R

DT50 (d)2.98  0.03 0.93 6.12 142–
0.91 20.15 ––0.90 20.10 –is replaced by coco chips, which is slower mineralized than straw
and thus could provide nutrients longer which could decrease thedecay of the biomass.
3.5. Linuron BTCs
Linuron is a pesticide with a higher Koc value than isoproturon,
metalaxyl, and bentazone, thus classifying it as immobile. This low
mobility was previously reported by Guzzella et al. (2006), Hance(1965) and MacNamar and Toth (1970). No breakthrough of thispesticide was observed in 4 mixtures (out of 5), even after about 20
pore volumes. Breakthrough occurred only in mixture 4 (Fig. 5).
Replicate A did not show clear evidence of the delayed degradation,
which was apparent for replicates B and C. However, from the ﬁnal
point of the BTC there was an indication that linuron efﬂuent
concentrations will also decrease for replicate A. As for metalaxyl,
the CDE model was used for the ﬁrst part (up to the maximum
efﬂuent concentration) of the BTCs to determine the Freundlichparameters (Table 9) (R2 ¼ 0.90–0.93). The Kf,column constants weremuch higher than for the other pesticides, which was expected
because of the late arrival and the high Koc value of linuron. Again,
Kf,column values obtained from column experiments (average
Kf,column ¼ 29.08 L kg1) were lower than those from the batch
experiments (average Kf,batch ¼ 88.35 L kg1) (Table 9). The retardation coefﬁcient R conﬁrms the high retention of linuron on theorganic substrate. However, a lower retardation coefﬁcient could beobserved for replicate A due to the very low n value, which led toa decrease in sorption with increasing concentration. First-orderdegradation constant of mix 4A is one order of magnitude higher
than those for metalaxyl and isoproturon, likely explaining why nobreakthrough occurred in the other mixtures (Table 9). The DT50
value of mix A was 142 days which is much higher than the value
reported by Fava et al. (2006) of 14 days in soil. A combination of
the strong adsorption, and a correspondingly long resident time oflinuron in the column, along with high degradation probably
resulted into a complete degradation of linuron before it reached
the bottom of the column. Linuron has previously been reported asa substrate for bacterial growth by Cullington and Walker (1999)
and by Widehem et al. (2002).
The BTCs of replicates B and C were ﬁtted with the CDE model
combined with the Monod kinetics (Table 10). However, very low
efﬂuent concentrations and incomplete breakthrough prevented us
from obtaining ﬁtted parameters with sufﬁcient conﬁdence. Also,model ﬁts were relatively poor with R2 ranging from 0.47 to 0.50.
4. Summary and conclusions
Pesticide displacement experiments were carried out for isoproturon, bentazone, metalaxyl, and linuron on columns ﬁlled with
Table 10
Monod kinetic parameters and the yield coefﬁcient for the degradation of linuron(95% conﬁdence interval).
Mix
4B
4C

m*b (107)(L kg1 h1)m*m;b (104)

kdecay,b(h1)

R2(L kg1 h1)Y()2.04  18.36
4.15  21.71

6.65  1.39
7.04  26.4

5.80  107  1.52  1056.65  107  1.90  105

0.50
0.47

32.67
16.96

ﬁve different types of organic mixtures. The experimental bromide,
isoproturon, and bentazone BTCs were well described using thetransport model based on the convection–dispersion equation andﬁrst-order degradation. The transport model had to be enhancedwith the Monod kinetics to describe experimental BTCs of metalaxyl and linuron that displayed a decrease of outﬂow concentrations after reaching an intermediate maximum. Freundlich
adsorption parameters and ﬁrst-order degradation constants wereﬁtted to the observed BTCs. Kf,column values ﬁtted to transport
experiments were much lower than those determined by previous
batch studies, conﬁrming the idea that sorption coefﬁcientsobtained from batch experiments are often not suitable fordescribing solute transport at the column or ﬁeld scale. The Koc
value is a very good indicator of the mobility of the pesticide.
Bentazone was the most mobile pesticide, followed by metalaxyl,
isoproturon, and linuron. From the point of view of their degradability, pesticides can be ranked as follows: linuron > metalaxyl –isoproturon > bentazone. In the case of linuron, a combination ofa strong sorbing capacity with a high degradation rate led to no
breakthrough for 4 out of 5 mixtures. Finally, the composition of theorganic matrix had a minor inﬂuence on retention and degradationof isoproturon, bentazone and linuron. However, it did appear tohave an inﬂuence on the degradation of metalaxyl, where thepresence of cow manure stimulated degradation.
Acknowledgement
This research was ﬁnanced by IWT-Vlaanderen, project nr. IWT40727 (Instituut voor de aanmoediging van Innovatie door
wetenschap en technologie in Vlaanderen). Support from FWO(Fonds voor Wetenschappelijk Onderzoek) and Prof. Jirka Simunek
for the stay of Tineke De Wilde at University of California, Riverside(UCR) was greatly appreciated.
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