ELECTRONIC SUPPLEMENTARY MATERIAL 1
LCA FOR AGRICULTURE
PestLCI 2.0: A second generation model for estimating emissions of pesticides from arable land in LCA
Teunis J. Dijkman • Morten Birkved • Michael Z. Hauschild
Received: 23 December 2011 / Accepted 4 May 2012
© Springer-Verlag 2012
Responsible editor: Ivan Muñoz
T. J. Dijkman () • M. Birkved • M. Z. Hauschild
Department of Management Engineering, Technical University of Denmark, 2800 Lyngby, Denmark
e-mail: tedi@dtu.dk
() Corresponding author:
Teunis J. Dijkman
Tel.: (+45) 45 25 48 86
e-mail: tedi@dtu.dk
This document describes the equations used for the calculation of emissions of pesticides to air, surface water and
ground water for the new and updated processes in PestLCI 2.0. For the processes and equations that are unaltered,
compared to PestLCI 1.0, we refer to the paper by Birkved&Hauschild (2006).
S.1.1 Wind drift
The new wind drift curves generally take the following shape:
f ( x)  A0 e  x / B0  A1e  x / B1
(eq.1)
Where f(x) the fraction lost at distance x (m). A and B are fitting parameters. For the various crop types, the parameters
given in Table 1 are used (Holterman & Van de Zande, 2003).
Table 1: Parameters used in spray loss curves (Holterman & Van de Zande, 2003)
Spray boom
Crop type
A0
B0
A1
B1
Conventional boom
Flower bulb
84
1.30
1.79
0.153
Cross flow
Sugar beet
294
2.44
2.39
0.147
Cereals
39
2.28
0.90
0.147
Bare soil
25
1.54
1.50
0.133
Fruit trees, with leaves
48
2.7
0.45
0.091
Fruit trees, leafless
120
6.75
0.45
0.091
For potato crops, eq. 2 is used (Holterman & Van de Zande, 2003):
f ( x)  114  x 1.29
(eq.2)
S.2.1 Volatilization from plant leaves
By combining 3 leaf volatilization datasets for pesticides having vapour pressures varying between 1.0∙10-4 Pa and
2.2∙104 Pa, Van Wesenbeeck et al. (2008) presented how the evaporation rate of pesticides can be regressed as
ln [ER] = 12.2 + 0.933∙ln VP
(eq. 3)
Where ER is the evaporation rate (µg m-2h-1) and VP is the vapour pressure (Pa) at 25°C. Dividing the ER from eq. 3
with the applied pesticide dose, expressed in µg m-2, gives the rate constant of vaporization kTr (h-1) at 25°C. A
correction for the ambient air temperature in the month of application is done using eq. 4 (Schwarzenbach et al., 2003):
ln
k Ta  Ea  1
1
 

k Tr  R  Tr Ta



(eq. 4)
Where k(Ta) and k(Tr) are the rate constants of vaporization at actual and reference temperature (K), respectively, E a the
activation energy (kJ mol-1) and R the gas constant (8.3145 J K-1 mol-1). For pesticides a typical Ea of 100 kJ mol-1 is
assumed, based on the average value of 95 kJ mol-1 found by Leistra and Wolters (2004).
The fraction of pesticide emitted by volatilization from leaves is hereafter calculated using the temperature-adjusted rate
constant kTa , assuming first-order evaporation kinetics.
S.2.2 Uptake in leaves
Since most pesticides are lipophilic molecules, leaf uptake could be assumed to occur dominantly through cuticles
(Korte et al., 2000). The first-order uptake of pesticides can then be calculated from the Arrhenius function (Baur and
Schönherr, 1995):
log k lu  a  bV 
 ED
R  2.3T  273.15
(eq. 5)
Where klu is the first-order rate constant (s-1), a and b are plant-species dependent constants, V is the characteristic
volume (cm3 mol-1), ED is the activation energy for diffusion (kJ mol -1). Following the same procedure as described by
Birkved and Hauschild (2006), re-evaluating the data, the following 2 equations were derived for citrus- and pear-type
leaves:
Citrus:

78.875  e 3.470MV / 1000 
  3600
k lu    5.31  5.84  10 3 0.945  MV  2.772 
R  2.303  Ta


(eq. 6)
Pear:

63.222  e 2.874MV / 1000 
3

  3600


k lu    4.93  5.30  10 0.945  MV  2.772 
R

2
.
303

T
a


(eq. 7)
With MV the molecular volume (cm3 mol-1).
Citrus and pear leaves have cuticles with quite different thicknesses and compositions, with the citrus leaves being
waxier and having a thicker cuticle. Each of the crop types in PestLCI 2.0’s database has been assigned to one of the 2
types, depending on the crop leave properties.
S.2.3 Degradation on leaves
The approach to model pesticide degradation on leaves has not been changed from PestLCI 1.0, but a new regression of
the OH• radical concentration has been established as the previous regression was found to considerably underestimate
the radical concentration at higher light intensities:
log [OH•] = (4∙10-4 ∙Il) + 4.7783
(eq. 8)
Where Il the monthly average light intensity (Wh m-2 day-1). The first-order rate constant for pesticide degradation on
leaves hence takes the following form:
k ld  k OH  10 410
4

 I l  4.7783  dl / 24 
(eq. 9)
Where kld is the first-order photodegradation rate constant (h-1), kOH is the overall OH• oxidation rate constant (cm3
molecules-1 h-1), and dl/24 term corrects eq. 9 for the latitude and day-of-year dependent daylight length. This equation
hence works under the assumption that degradation does not take place in the absence of light. Dl is calculated as
follows (Math forum, 2011):
0.8333
L


sin
 sin
sin P 

24
180
180

dl 
 arccos
L



cos
 cos P


180


(eq. 10)
Where
P  arcsin 0.39795  cos0.2163180  2  arctan 0.9671396  tan 0.00860  DOY  186
Where L is the latitude (in degrees) and DOY is the day number of the application day in the year.
(eq. 11)
S.3.1 Soil volatilization
The approach for calculation of pesticide volatilization from soil is based on a fugacity level 3 model by Mackay
(2001). The rate constant for soil volatilization is calculated using equation 12:
k s ,v 
DV
VT Z T
(eq. 12)
Where DV is the total D value of the volatilization process (mol Pa-1 h-1) and VTZT is the product of the VxZx values of
the phases in soil (x=air, water, or solid matter), with V x the volume of phase x (m3) and Zx the phase’s fugacity
capacity (mol m-3 Pa-1).
D-values are a transport parameter used for fugacity calculations. The overall D-value is calculated from the D values of
diffusion through air (DA) and water (DW) and the boundary air layer (DBL). Diffusion through air and water are parallel
processes, followed by diffusion trough the boundary air layer, resulting in the following overall transport parameter:
1
1
1


DV DBL D A  DW
(eq. 13)
with
DBL  A
Bia
ZA
BLT
(eq. 14)
In eq. 14, A is the surface area of the field under study (m2), Bia the diffusivity of organic molecules in air (cm2 s-1).
BLT, the boundary layer thickness, is set to 0.00475 m (Mackay, 2001) but can be user-adjusted. Bia is calculated using
a semi-empirical equation given by Schwarzenbach (2003):
T 1.75 1 M air   1 / M i 
1/ 2
Bia  10
7

1/ 3
p Vair
 Vi1 / 3

2
(eq. 15)
Where T is the absolute temperature (K), Mair and Mi are the molar masses of air and active ingredient i (g mol-1), p the
gas phase pressure (atm) and Vair and Vi are the molar volumes (cm3 mol-1) of air and the pesticide, respectively.
DA and DW are calculated in a comparable way, using
Dx 
BEx AZ x
Y
(eq. 16)
Where
B Ex 
/3
B x v 10
x
v a  v w 2
(eq. 17)
In eq. 16 and 17 subscript x can be either A (air) or W (water), depending on whether D A or DW is calculated. BEx is the
molecular diffusivity, calculated useing vx, the volume fractions of air and water in the total soil. Y is the diffusion path
length measured as the vertical distance of the pesticide to the soil surface. Y by default is set to half the depth of the
topsoil (0.005 m), thereby assuming the active ingredient is equally distributed over the topsoil.
The Vx and Zx values are calculated separately for soil solids, water and air:
Vx 
Vx
Vs  Vw  Va
ZS 
K OC , pH f OC  b Z W
1  S
(eq. 18)
(eq. 19)
ZW 
1000  S
VP  MW
(eq. 20)
ZA 
1
R T
(eq. 21)
In eq. 19-21, Vx the volume of phase x (m3), where s is the soil solid matter, w is soil water, and a is soil air, KOC,pH is
the pH-dependent organic carbon-water partition coefficient (L kg-1), fOC is the fraction of organic carbon in the topsoil
(dimensionless), ρb is the soil bulk density (kg m-3), ϕS is the soil porosity (dimensionless), calculated by summing the
volume fractions of air and water in soil, S is the water solubility of the active ingredients (kg m -3), VP is the vapour
pressure (Pa), MW is the molecular weight of the active ingredient (g mol-1).
As is evident does the vapour pressure of an active ingredient depend on the ambient temperature. Based on the
measurement reference temperature, the vapour pressure at the ambient temperature in the month of pesticide
application is determined by assuming that the vapour pressure of the active ingredient increases exponentially with
increasing temperature following a tenfold increase per 10 degrees Celsius.
For many non-ionic pesticides, the organic carbon-water partitioning depends on the pH of the topsoil. This was not
taken into account for the top soil layer in PestLCI 1.0. The pH-dependent KOC,pH is calculated as follows:
K OC , pH
K OC  10  pH
  pH
10
 10  pKa
(eq. 22)
With pKa the first dissociation constant of the pesticide.
S.3.2 Topsoil degradation
In contrast to the approach applied in PestLCI 1.0, the new calculation approach relies on 1 equation for the entire
temperature range. It is further assumed that the soil degradation rate will increase with a fixed factor per 10 degrees,
resulting in correction factor Fs,T:
Fs,t = Q(T-Tref)/10
(eq. 23)
Where Q is the increase in degradation rate per 10 degrees, T is the ambient soil temperature, T ref is the reference
temperature at which the soil degradation rate was determined. Q is set to 2.1 by default as recommended by the
FOCUS soil modelling workgroup (Boesten et al., 1996), but can be adjusted by the model user if desired. The soil
temperature T is assumed to be related to the air temperature T air through eq. 24, based on Zheng et al. (1993):
Tref = 1.05∙Tair – 1.5
(eq. 24)
Where both temperatures are expressed in degrees Celcius.
S.3.3 Top soil runoff
The runoff fraction is calculated using the approach presented by Berenzen et al. (2005):
f sr 
Q
1
 F  f s ,t 
P
1 KD
(eq. 25)
Where fsr is the fraction of applied pesticide that is emitted to surface water, Q/P is the ratio of runoff amount (mm) and
precipitation amount (mm). F is a slope dependent correction factor, f s,t is the fraction of applied pesticide that is present
at the moment of precipitation. KD is the ratio of dissolved and sorbed pesticide, calculated by multiplying the pHdependent Koc and the fraction of organic matter in the top soil.
In eq. 25, the latter 2 terms together determine the fraction of pesticide available for runoff, the first 2 terms determine
which fraction will actually run off. It thus assumed that the rainfall and runoff occurs within such a short time span that
the sorbed fraction of pesticide does not have time to desorb. The sorbed fraction of pesticide fraction is therefore
assumed unavailable for runoff.
In eq. 25, Q further depends on the soil type. For sandy soils (fsand > 0.50):
Q = -0.016427 - 0.011377∙P + 0.0026284∙P2 – 5.8564∙10-6∙P3
(eq. 26)
For all loamy soils, which were here assumed to be all soils with fsand < 0.50:
Q = -0.061108 - 0.0041626∙P + 0.0040395∙P2 – 9.0361∙10-6∙P3
(eq. 27)
The correction factor F in eq. 25 is the product of 2 terms, accounting for the slope of the field and the width of the nospray bufferzone:
Fslope = 0.02153∙S + 0.001423∙S2
if S < 20%
(eq. 28)
Fslope = 1
if S ≥ 20%
(eq. 29)
Fbuffer = 0.83WBZ
(eq. 30)
Where S is the slope of the field (%), WBZ is the width of the bufferzone (m). In order for eq. 30 to be valid, it is
assumed that the bufferzone is densely covered with vegetation (i.e. crop and/or wild flora) and hence acts a “run-off
resistance” beyond the field borders.
S.3.4 Macropore flow
In the PestLCI 2.0 approach, the pore water is divided in ‘mobile’ and ‘immobile’ water, based on the approach from
Hall (1993). The first class can flow, the second does not flow through the soil. The ‘mobile’ water is divided in a slowflowing (0.35 m day-1) and a fast-flowing domain. The fast-flowing domain represents the macropores. It is assumed
that the pesticides dissolved in the water flowing through the macropores will quickly reach the groundwater and is
hence considered to be emitted to ground water without undergoing degradation.
The total pore volume is calculated as the sum of the volume fractions of air and water in soil. Based on Hall (1993), it
is assumed that the mobile pore fraction depends on the composition of the solid soil fraction:
fpore,mobile = 0.72∙fsand + 0.35∙fsilt + 0.14∙fclay
(eq. 31)
The macropore fraction of the total mobile pore volume is set to 0.30 by default, based on Hall (1993), but can be
adjusted by the model user. Following Hall (1993), precipitation water will only flow into macropores when both the
immobile pores in the topsoil and the slow-flowing pores are water filled. Using a flow rate of 0.35 m day-1 for the latter
group of pores, the volume of water that can be taken up by the topsoil in 1 hour is calculated:
0.35 

C  f pore,immobile  0.01   1  0.30  f pore,mobile 

24 

(eq. 32)
Where C is the water storage capacity of the soil (m3), fpore, immobile is the total pore fraction minus the mobile pore
fraction. Since the soil water content prior to a precipitation event is known, the minimum hourly precipitation rate
needed to initiate macropore can be calculated from the average rain intensity.The probability density function of
rainfall can be approximated as an exponential function depending on the rainfall intensity (Eagleson, 1972):
f i     e   i
(eq. 33)
In this equation, β is a constant, and i is the rain intensity (mm hour -1). The constant β is calculated from the mean of
this distribution, and therefore equals the mean rain intensity:
i
1


iday
l precip
(eq. 34)
Where iday is the average precipitation on a rainy day, and lprecip the average length of a rain event which here is set at 5
hours by default. The constant β, and hence the shape of the probability density function depends on the local climatic
circumstances. The average recommended rain event duration has been derived from precipitation data for Danish
circumstances (Harremoës and Mikkelsen, 1995) and can in PestLCI 2.0 be adjusted according to user preferences if
necessary.
Integration of eq. 33 up to the rainfall intensity where no macropore flow occurs, yields the fraction of precipitation
events without macropore flow. The fraction of precipitation events with macropore flow is then easily calculated. As
the rainfall intensity distribution depends on local climatic circumstances, the rainfall intensity at which macropore flow
will start, is also dependent on local climate. As LCA aims at calculating impacts from average scenarios, it can be
assumed that the macropore rainfall fraction is the average fraction of rain in a rain event that flows into macropores.
Knowing the fraction of pesticide molecules that is dissolved in the leaching water, the fraction of dissolved pesticide
that enters macropores and flows to towards the ground water is calculated.
References
Baur P, Schönherr J (1995) Temperature dependence of organic compounds across plant cuticles. Chemosphere
30:1331-1340
Berenzen N, Lentzen-Godding A, Probst M, Schulz H, Schulz R, Liess M (2005) A comparison of predicted and
measured levels of runoff-related pesticide concentrations in small lowland streams on a landscape level.
Chemosphere 58:683-691
Birkved M, Hauschild MZ (2006) PestLCI: A model for estimating field emissions of pesticides in agricultural LCA.
Ecol Model 198:433-451
Boesten J, Helweg A, Businelli M, Bergstrom L, Schäfer H, Delmas A, Kloskowski R, Walker A, Travis K, Smeets L,
Jones R, Vanderbroeck V, Van der Linden A, Broerse S, Klein M, Layton R, Jacobsen O-S, Yon D (1996)
FOCUS Report - Soil persistence and EU registration - EU document 7617/VI/96, EU, Brussels
Eagleson PS (1972) Dynamics of flood frequency. Water Resour Res 8:878-899
Hall DGM (1993). An amended functional leaching model applicable to structured soils: I: Model description. J Soil Sci
44:579-588
Harremoes P, Mikkelsen PS (1995) Properties of extreme point rainfall I: Results from a rain gauge system in Denmark.
Atmos Res 37:277-286
Holterman HJ, Van de Zande JC (2003) IMAG drift calculator v1.1: User manual
Korte F, Kvesitadze G, Ugrehelidze D, Gordeziani M, Khatisashvili G, Buadze O, Zaalishvili G, Coulston F (2000)
Organic toxicants and plants. Ecotoxicol Environ Saf 47:1-47
Leistra M, Wolters A (2004) Computations on the volatilization of the fungicide fenpropimorph from plants in a wind
tunnel. Water Air Soil Poll 157:133-148
Mackay D (2001) Multimedia environmental models: The fugacity approach. Second edition. Taylor and Francis, Boca
Raton
Math forum (2011). Latitude and longitude and daylight hours. http://mathforum.org/library/drmath/view/56478.html.
Accessed on 29 July, 2011
Schwarzenbach RP, Gschwend PM, Imboden DM (2003) Environmental organic chemistry. Second edition. John Wiley
and Sons, Hoboken
Van Wesenbeeck I, Driver J, Ross J (2008) Relationship between the evaporation rate and vapour pressure of
moderately and highly volatile chemicals. Bull Environ Contam Toxicol 80:315-318
Zheng D, Hunt Jr ER, Running SW (1993) A daily soil temperature model based on air temperature and precipitation
for continental applications. Clim Res 2:183-191
ELECTRONIC SUPPLEMENTARY MATERIAL 2
Table S1: Input data for PestLCI 2.0, SWASH 3.1 and PEARL 4.4 used for the validation study
PestLCI 2.0 SWASH PEARL
Pesticide
x
x
x
MCPA
Molar mass (g mol-1)
x
x
x
200.61
3
-1
Molecular volume (cm mol )
x
152.71
pKa
x
3.731
Saturated vapour pressure (Pa)
x
x
x
4.0∙10-4 1
-1
Solubility in water at 25°C (mg L )
x
x
x
293901
Molar enthalpy of vaporization at 25°C (J mol-1)
x
x
950002
-1
Molar enthalpy of dissolution (J mol )
x
x
270002
2 -1
Diffusion coefficient in water (m d )
x
x
5.27E-053
Diffusion coefficient in air (m2 d-1)
x
x
0.5383
Freundlich exponent (-)
x
x
0.92
-3
Reference concentration in liquid phase (g m )
x
x
12
Factor for the uptake by plant in soil (-)
x
x
0.52
Wash-off factor from crop MACRO (mm-1)
x
0.052
-1
Wash-off factor from crop PRZM (cm )
x
0.52
-1
Wash-off factor from crop (m )
x
504
t½ in soil at 20°C (d)
x
x
x
241
t½ in water at 20°C (d)
x
13.55
t½ in sediment at 20°C (d)
x
175
t½ in crop at 20°C (d)
x
x
105
-1
Activation energy (J mol )
x
x
654002
-1
Exponent (K )
x
0.09482
Q10fac (-)
x
2.582
-1
Koc (L kg )
x
741
-1
Kom at 20°C (L kg )
x
376
log Kow
x
-0.811
Molar enthalpy of sorption (kJ mol-1)
x
-37
-1
Desorption rate coefficient at 20°C (d )
x
02
Factor relating CofFreNeq and CofFreEql
x
02
Exponent for the effect of liquid
x
0.72
Canopy process options
x
Lumped2
3
-1 -1
Atmospheric OH rate (days)(cm molecules s )
x
1.26∙10-11 1
Width no sprayzone (m)
x
101
1: from PestLCI database; 2: model default values; 3: Schwarzenbach et al. (2003); 4: base don
SWASH default; 5: PPDB (2011); 6: Kom = ½ Koc, Koc from PestLCI database; 7: Estimate based on
Hiller et al. (2008)
Table S2: Characteristics of soils used in validation (‘average’ soil) and case study (average, high sand and high clay)
Soil #
average
high clay
content
high sand
content
layer
Ap
B
Bt1
Bt2
Ah
Bcs
(B)Cg
Cgx
A1
Bw11
Bw12
Bw13
C
thickness (m)
0.33
0.22
0.25
0.20
0.15
0.20
0.27
0.38
0.10
0.20
0.30
0.35
0.05
fclay
0.18
0.22
0.27
0.27
0.41
0.35
0.40
0.40
0.10
0.10
0.11
0.10
0.09
fsilt
0.36
0.30
0.28
0.34
0.33
0.41
0.49
0.53
0.20
0.20
0.21
0.21
0.21
fsand
0.46
0.48
0.45
0.39
0.26
0.24
0.11
0.07
0.70
0.70
0.68
0.69
0.70
fOC
1.54
0.25
0.16
0.11
2.8
0.7
0.0
0.0
3.8
1.1
0.4
0.2
0.3
pH
4.4
3.9
3.7
3.6
7.4
7.7
7.8
7.8
4.6
4.6
4.6
4.6
4.8
Table S3: Climate data used for case study (month: May)
Location
Latitude
Longitude
Elevation (m)
Taverage (°C)
Tmin (°C)
Tmax (°C)
Rainfall (mm)
Rain days (>1mm) (mm)
Average rainfall on rainy day (mm)
Rain frequency (day-1)
Annual potential evaporation (mm)
Solar irradiation (Wh m-2 day-1)
Temperate maritime Continental 2
Mediterranian 1
Tranebjerg (DK)
Gyor (HU)
Thessaloniki (GR)
55°50’ N
47°41’ N
40°38’ N
10°36’ E
17°38’ E
22°56’ E
11
115
32
13.5
16.8
19.6
8.2
10.9
14.3
16.5
22.6
25
49,4
67.2
43.6
8.6
7.9
5.3
5.7
8.5
8.3
3.6
3.9
5.9
1067
1466
1833
5040
5310
5580
Table S4: Results for the comparision of PEARL and PestLCI 2.0
Location
Chateaudun
Date of application
9 mar
Application dose (kg/ha)
1
1
Annual emissions (kg/ha)
4.10E-06
2.40E-06
1.35E-05
2.47E-05
1.10E-04
8.97E-05
1.00E-04
5.60E-05
2.86E-05
3.23E-05
5.69E-05
7.51E-05
3.62E-05
5.22E-05
1.26E-05
0
0
0
0
1.20E-06
Average (kg/ha)
4.35E-05
Location
Date of application
Application dose (kg/ha)
Annual emissions (kg/ha)1
Tours
PEARL
Hamburg
Joikionen
31 mar
17 may
1
1
2.40E-04
1.12E-04
0
6.72E-05
3.16E-04
8.61E-05
1.08E-03
1.89E-04
1.30E-03
2.45E-04
5.96E-03
3.36E-04
1.24E-02
7.69E-04
1.34E-03
3.99E-04
1.07E-03
5.59E-04
9.55E-04
7.16E-04
7.89E-04
4.10E-04
5.99E-04
2.23E-04
1.31E-03
3.21E-05
2.47E-03
1.27E-05
2.16E-04
2.70E-06
0
7.80E-06
4.49E-04
4.80E-05
5.87E-04
2.25E-04
1.56E-03
3.25E-04
4.80E-03
1.68E-04
2.08E-03
2.47E-04
PestLCI 2.0
Tranebjerg
Birzai
Kremsmünster
31 mar
1
4.97E-04
6.93E-04
1.69E-03
4.60E-03
5.43E-03
1.71E-03
2.37E-03
2.17E-03
8.12E-04
5.19E-04
9.27E-04
1.27E-03
1.80E-03
1.48E-03
6.69E-04
0
0
2.14E-04
2.85E-04
4.16E-04
1.53E-03
Okehampton
31 mar
1
2.39E-03
3.48E-04
8.15E-04
1.40E-03
2.92E-03
3.41E-03
5.58E-03
2.96E-03
1.46E-03
1.82E-03
2.39E-03
2.48E-03
4.34E-03
6.14E-03
1.20E-03
8.29E-04
3.57E-03
5.40E-03
2.24E-03
2.65E-03
2.72E-03
Porto
9 mar
1
8.41E-05
1.01E-04
4.03E-03
8.74E-04
8.91E-04
5.65E-04
1.52E-04
2.64E-04
2.09E-04
7.67E-04
9.11E-04
3.52E-04
6.04E-04
1.35E-03
4.30E-05
9.47E-05
2.75E-04
2.16E-05
4.00E-07
1.65E-05
5.80E-04
Kremsmünster
Gogerddan
La Coruna
March
April
May
April
April
March
1
3.90E-03
1
2.10E-03
1
4.40E-03
1
6.10E-03
1
3.80E-03
1
6.10E-03
References
Hiller E, Jurkovic L, Bartal M (2008) effect of temperature on the distribution of polycyclic aromatic hydrocarbons in
soil and sediment. Soil Water Res 4:231-240
PPDB (2011) Pesticide properties database. http://sitem.herts.ac.uk/aeru/footprint/index2.htm. Accessed on 29 July,
2011
Schwarzenbach RP, Gschwend PM, Imboden DM (2003) Environmental organic chemistry. Second edition. John Wiley
and Sons, Hoboken