Variability Assessment ofGroundwater Exposure to Pesticidesand Its Consideration in Life-CycleAssessmentG E O R G G E I S L E R , * S T E F A N I E H E L L W E G ,S I M O N L I E C H T I , A N DK O N R A D H U N G E R B U H L E R

Swiss Federal Institute of Technology,ETH Honggerberg, HCI G-143, CH-8093 Zurich

Pesticide leaching from agricultural fields to groundwateris an environmentally relevant and highly variableprocess. In the present paper, leaching scenarios typicalin European agriculture are defined. These scenarios considerimportant sources of pesticide leaching variability,namely site factors, farming practice, and substanceproperties. The logic-tree method was used to structurethese scenarios. For each scenario, leached fractions ofpesticide applied in agriculture were calculated with dataand models used in the registration process of theEuropean Union (EU). Contributions of all parameters tovariability were calculated for 11 pesticides. Substanceproperties (Koc and DT50,soil) contributed the most to variability,followed by site, weather, season of application, crop,and macropore flow. The results of the variability assessmentmay be directly applied in policy making or they may beused in the environmental assessment of pesticides, e.g.with the life-cycle assessment (LCA) method. Severalapproaches are suggested for how the variability assessmentpresented in this paper may be incorporated in LCA. Theapplication of these approaches is illustrated by a case studyon atrazine.

IntroductionSeveral monitoring studies (1-4) have revealed groundwatercontamination by pesticides. Such pesticides in the ground-water may have adverse impacts on the environment:Humans may be exposed to pesticides by using groundwateras drinking water, and ecosystems may be exposed throughup-welling of groundwater. One important source of ground-water pollution is pesticide leaching after application inagriculture. We use the term pesticides here to denote activeingredients of plant protection products. Pesticide leachingdepends on several factors: (1) Substance properties thatinfluence leaching are the tendency of pesticides to sorb tosoil organic carbon and to be degraded (4-6) as well as tovolatilize from soil (7). (2) Site factors include soil texture,structure, and organic carbon content as well as climaticparameters such as precipitation and temperature. Thesefactors determine the water movement and balance in thesoil and thus the formation of percolate flow, which is amajor prerequisite for pesticide leaching (e.g. refs 5, 6, and8). (3) Farming practices determine the type of pesticide

applied, the timing of pesticide application, crops treated,and thus evapotranspiration and plant uptake; the use ofirrigation may also increase percolate flow (5, 6, 9).

Substance properties, site and farming factors were allfound to significantly influence pesticide leaching (10). Thisfinding emphasizes the need to take into account the spatialand temporal variability of pesticide leaching.

Due to the inherent toxicity of many pesticides, theapplication of pesticides in agriculture has been extensivelyassessed with tools such as risk assessment and, lessfrequently, with life-cycle assessment (LCA). Existing LCAmethods (11-13) consider various exposure pathways ofhumans and ecosystems to pesticides, such as volatilization,plant uptake, and leaching to groundwater. However, all thesemethods only consider one scenario for soil vulnerability,weather conditions, and other factors influencing leaching.Thus, none of the existing methods systematically accountsfor the manifold sources of pesticide leaching variability tothe groundwater. Moreover, no attempt has been made toobtain average estimates of pesticide leaching, e.g. for Europe,which would be desirable for use in LCA.

Numerous field-scale models exist to quantitativelysimulate the leaching of pesticides as a function of substanceproperties, site and farming factors. The greater part of thesemodels was reviewed by the FOCUS working group (14). Aframework was developed to reduce uncertainties resultingfrom modelers’ choices and different concepts of simulationmodels (9, 14). In the final reports (9, 14) the FOCUS workinggroup recommends models for the simulation of pesticideleaching, provides user-input guidance to these models, andsuggests harmonized leaching scenarios depicting EU ag-riculture. However, no assessment has been carried out forthe contribution of different parameters in the FOCUSframework to the variability of leaching.

The goal of this paper is (a) to assess the variability ofpesticide leaching to groundwater in EU agriculture, (b) toquantify the contribution of substance-property, site, andfarming-practice parameters to variability, thus identifyingthe most important parameters for consideration in theassessment of pesticide leaching, and (c) to suggest pos-sibilities for incorporating such variability assessment in LCA.To these ends, we use the recommendations of FOCUS todevelop scenarios and model pesticide leaching in thesescenarios. We structure the scenarios in a logic tree and assignprobabilities to all parameters. These probabilities representtypical conditions of EU agriculture. Finally, we aggregatethe scenarios probabilistically to calculate European averageleached fractions of any pesticide. Using these averageleached fractions of pesticides in LCA enables the consid-eration of variability, which is an important factor fordecision-making.

MethodsLogic Tree of Pesticide Leaching Scenarios. The logic treemethod ((15) Figure 1) was used to structure major scenariosof pesticide leaching in EU agriculture. The construction ofscenarios was based on combinations of substance propertiesof pesticides as well as site and farming factors. Fractions ofpesticides leached to 1 m depth were simulated for allscenarios (eq 1). This leached fraction of pesticide wasassumed to be relevant for groundwater exposure

where fj is the leached fraction (%) of pesticide in scenarioj, and m is the cumulated annual mass of pesticide (kg/ha)

* Corresponding author fax: +41-1-6321189; e-mail: georg.geisler@gmx.net.

fj ) mleached/mavailable‚100% (1)

Environ. Sci. Technol. 2004, 38, 4457-4464

10.1021/es0352707 CCC: $27.50 2004 American Chemical Society VOL. 38, NO. 16, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY 9 4457Published on Web 07/17/2004

leached or available for leaching, the latter specifying theapplied dose corrected for interception by plants (see below).Simulated leached fractions smaller than 0.1% were con-sidered to be of minor environmental importance and wereset to a value of 0.1%. This cutoff is in accordance with theEU drinking water limit of 0.1 µg/L, which implies leachingof less than 0.1% of average pesticide doses at sites with anaverage European infiltration rate (16). A single annual sprayapplication with a pesticide dose of 0.5 kg/ha was assumed.Plant interception ranges between 0% of pesticide doses forpreemergence applications and up to 90% for postemergenceapplications (9). Intercepted pesticide is considered here asunavailable for leaching. Hence, pesticide doses need to becorrected for interception using default values from inter-ception tables (9) in order to calculate mavailable (eq 1). Fromthe four models recommended by FOCUS, we used two: ThePELMO model (17) was utilized to simulate pesticidetransport via matrix flow in all scenarios. The MACRO model(18) was additionally used in selected scenarios to assesscombined effects of matrix and macropore flow. We did notuse the PRZM model because it is conceptually similar toPELMO (19). The PEARL model may have advantages overPELMO concerning the simulation of hydraulic behavior inthe soil system. Nevertheless, Vanclooster et al. (19) foundno large differences between the performance of PELMO,PEARL, and MACRO in a validation study. Therefore and tomaintain the study simple, we only performed the modelingwith PELMO and MACRO.

The contribution of each parameter in the logic tree tothe leached fractions’ variability was assessed with eq 2

where Q is a quotient of two leached fractions, i denotes aparameter in the logic tree, j is a scenario with a specificvalue of parameter i, and base is the scenario with the basevalue of parameter i. For the base scenario, the value ofparameter i resulting in lowest leached fractions was used.

With the exception of i, all other parameters are equal inscenarios j and base. Quotients Qi,j were calculated for allvalues of all parameters z, where z * i. Subsequently, themedian and various percentiles of Qi,j could be calculatedfrom all these scenarios (with fixed values j and base forparameter i).

The parameters in the logic tree (Figure 1) are individuallyexplained in the following. The first parameter to beconsidered is volatilization, as volatized pesticides are nolonger available for leaching. Volatilization depends onphysical-chemical characteristics of the pesticide (especiallyHenry’s law constant), environmental conditions, and farm-ing practice (7). In this study, volatile pesticides with a Henry’slaw constant (Kh) larger than 0.001 J/mol were excluded fromthe calculations (Figure 1), because we assumed that theenvironmental impact from volatilization would dominatethe impact from leaching in these cases in any LCA study.This is a simplification, e.g. volatile fumigant residues maybe trapped in soil micropores and leached to the groundwater(20). Two other processes that counteract leaching arepesticide sorption to soil organic carbon and degradation ofpesticides. The Groundwater Ubiquity Score (GUS) (4)aggregates the substance properties corresponding to theseprocesses, namely the organic carbon-water partition coef-ficient (Koc) and the soil degradation half-life (DT50,soil in d):

We calculated leached fractions for 11 pesticides fromthree different regions of the GUS space: one region withmedium Koc and DT50,soil and two regions with both very lowor both very high Koc and DT50,soil (Figure 2). These substanceswere assumed to be representative of a large range ofpesticides with differing substance properties and areexpressed as GUS ranges (Figure 1).

An important site factor affecting leaching is the presenceof artificial drainage. It was assumed that leaching of

FIGURE 1. Logic tree of leaching scenarios in EU agriculture. All parameters are shown, but only one arbitrary scenario. For dependenciesamong values of site and farming parameters see Supporting Information (Table S1). Stop signs indicate parameter values leading tononleaching. Leached fractions (fj) were calculated with PELMO (17) or MACRO (18). The values for the leached fractions in the right columnrefer to the scenario shown in the graph (Kh > 0.001 J/mol, 3.5 e GUS e 4.8, etc.). For definition of the GUS, see eq 3 and Figure 2.

Qi,j ) fj/fbase (2)

GUS ) log DT50,soil‚(4 - log Koc) (3)

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pesticides is insignificant when artificial drainage is present((6) Figure 1). To depict further site parameters, six of thenine sites from the FOCUS framework (9) were used (Figure1). These sites are realistic combinations of weather and soildata (Table S1) representative of major agricultural regionsin the EU (9). Two of the FOCUS sites, Porto and Jokioinen,were excluded because they were only representative of asmall fraction (3%) of the total agricultural area in the EUmember states (9). Additionally, we did not consider the Thivasite. As leaching was very low at both the Thiva and Sevillasites in test simulations, we found it to be unnecessary tomodel both. According to a recent validation study of theFOCUS scenarios (19), this low leaching of the scenarios forSouthern Europe is questionable. Weather variability isrepresented for each site by 20 years of meteorological data(9). A best, average, and worst-case of leached fractions ofpesticides are considered in this paper by the 20th, 50th, and80th percentile of leached fractions, respectively, from these20 simulation years (Figure 1). Concerning the crops treated,two crop cases were defined in the logic tree, describing aworst-case and a best-case with respect to the influence onpercolate flow and thus leaching. Finally, pesticide leachingin finely textured soils may be influenced by preferentialtransport (22). To assess the influence of macropore transport,we simulated scenarios in MACRO (18) and PELMO (17) forthe Chateaudun site (clayey topsoil structure, Table S1).Interdependencies between site and farming-practice pa-rameters were taken into account. For a detailed documen-tation concerning the definition of the parameter values inthe logic tree, consult the Supporting Information.

Leached Fractions for Any Single Pesticide and Vari-ability Assessment. The logic tree enables a site-dependentassessment of pesticide leaching. However, the spatial andtemporal resolution of LCA is generally low, and averagevalues are hence of interest (23). Therefore, we calculatedweighted-average leached fractions for specific pesticides.These weighted-average leached fractions aggregate scenarios

of the logic tree and take into account uncertainties ofsubstance properties.

To calculate such weighted-average leached fractions, first,conditional probabilities of occurrence were assigned to allparameter values in the logic tree (pi,k, dimensionless, wherei denotes the parameter in the logic tree and k the value ofthe parameter; Supporting Information). Probabilities ofoccurrence (pj) were then calculated for any scenario j in thelogic tree following Pate-Cornell (15). Second, these prob-abilities were used to calculate a weighted leached fraction(fws) for substances no. 1-5 (Figure 2)

where fws is the weighted leached fraction of substance s, fj

is the leached fraction in scenario j (in %, eq 1), pj is theprobability of occurrence of this scenario (dimensionless),and nj is the number of scenarios considered. Ranges ofsimilar GUS (Figure 1) were defined using the GUS ofsubstances no. 1-5 as maximum and minimum. For eachof these GUS ranges, a mean leached fraction (fwg) wasinterpolated

where fwSmin,g and fwSmax,g are weighted leached fractions ofsubstances determining the minimum and maximum valueof each GUS range (g).

Third, these mean leached fractions calculated fromsubstances no. 1-5 were used to interpolate leached fractionsfor specific pesticides. To take into account uncertainties insubstance properties of specific pesticides, log-normaldistributions were fitted to Koc and DT50,soil. The uncertaintywas propagated into the GUS (eq 3), using Monte CarloSimulation (@Risk (24) plug-in for Microsoft Excel, Latin-Hypercube sampling, 10 000 iterations per simulation). From

FIGURE 2. GUS ((4) eq 3) for around 300 pesticides (21). Bold marks and numbers denote substances assumed to be representative forother substances with equal GUS within the respective GUS region. The curves depict equal GUS and represent the borders of the GUSranges considered in this paper.

fws ) ∑j)1

nj

fj‚pj (4)

fwg ) 0.5(fwSmin,g + fwSmax,g) (5)

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this Monte Carlo Simulation we obtained the probability pg

of a pesticide to be in GUS range g. Subsequently, the meanleached fraction (fwg; eq 5) for each GUS range g was weightedwith this probability pg in eq 6

where fwpest is the total weighted leached fraction for a specificpesticide pest, ng is the number of GUS ranges considered,and fwpest,g is the mean leached fraction per GUS range g ofpesticide pest. The leached fraction for GUS below 1.8 isassumed zero, following ref 4. The leached fraction for GUSlarger than 6.4 is set to the same value as the leached fractionfor the GUS range between 4.8 and 6.4.

To take into account the uncertainty in the interpolationof weighted leached fractions from the values for substancesno. 1-5 (Figure 2), fwSmin,g and fwSmax,g (eq 5) were appliedas minimum and maximum, respectively, of a uniformdistribution. The uncertainty of these distributions waspropagated into the total weighted leached fraction of anysingle pesticide (fwpest; see eq 6) using Monte Carlo Simulationas described above.

Our method can be applied to the whole logic tree usingthe proposed probabilities (Table S2) or to subsets ofscenarios. Leached fractions for subsets of the scenarios inthe logic tree are aggregated by setting the probability ofspecific parameter values to zero. The corresponding pa-rameter values are thus canceled from the logic tree. Theprobabilities of occurrence of the remaining values of eachparameter need to be scaled to give a sum of one.

The mean leached fractions per GUS range (fwg; eq 5) canbe obtained for any combination of logic tree scenariosusing an Excel spreadsheet available at http://www.tech.chem.ethz.ch/hungerb/research/lifecycle/gg_download.html.

ResultsLeached Fractions in Scenarios of the Logic Tree. In total,1056 scenarios were simulated in PELMO (17). Of thesescenarios, 132 were also simulated in MACRO (18). For thesubstances no. 6-8 (Figure 2), steady state of leaching massflows was not attained in the simulations (20 years simulationtime, daily time steps). This is due to the strong sorption ofthese pesticides to soil organic carbon and their slowdegradation. The potential leaching of these pesticides maythus be underestimated. Owing to today’s rigid registrationprocedures (25), the use of such pesticides is not expectedin EU agriculture.

The total variability of leached fractions in all scenariosis a factor of 300 (Table 1), expressed as the quotient of the

95th to the 5th percentile. Scenarios with leached fractionssmaller than 0.1% of dose are most frequent (more than 97%of the scenarios) for the substances 1, 2, and 6. Therefore,pesticides of GUS below 2.5 are not likely to relevantly leachin any scenario of pesticide application in EU agriculture(with the exception of substance 9; see end of next paragraph).This is a considerable expansion of the “nonleachers” groupcompared to the classification of Gustafson (4) based ongroundwater monitoring studies in North America. Leachedfractions were larger than 30% in 5% of the scenarios. Thesevalues are considerably higher than leached fractions foundin field studies for various pesticides (e.g. refs 5, 6, and 8).Consequently, leached fractions higher than 30% were set toa cutoff value of 30%. Most of these leached fractions appearwith substance 5, which is the compound with the highestGUS analyzed in this work.

Table 1 shows that the ranking according to the leachedfraction is similar to the ranking according to GUS, takinginto account that in the GUS no cutoff value is used fornonleachers. However, differences are apparent in theinfluence of very low values of Koc and DT50,soil in the GUSas compared to the leaching models used. For instance,substance no. 9 shows the lowest GUS of all pesticidesanalyzed here, while it is leached stronger in the simulationsthan substances no. 1, 6, or 2, considering the 95th percentileof leached fractions. The expansion of the “nonleachers”group discussed above is therefore debatable for pesticideswith very low values of Koc and DT50,soil.

The range of leached fractions for a given substance isdepicted as the quotient of the 95th and 5th percentile (Table1). This range incorporates variability other than that due tosubstance properties. For substances that are relevantlyleached (fj > 0.1%) in more than 10% of the scenarios, thequotient of 95th and 5th percentile of leached fractions isbetween 280 and 8. This variability is caused by variationsof site and management factors in the logic tree scenariosonly, excluding the influence of substance properties (Figure1).

Contributions of Logic Tree Parameters to LeachingVariability. Quotients of leached fractions in comparablescenarios (eq 2) were calculated for each parameter in thelogic tree (Table 2). The GUS exhibits the highest medianquotients by far: up to a value of 220. The site follows withmedian quotients ranging from 71 to 14. Weather variability,crop case, season of application, and macropore flow showmedian quotients between 4.0 and 1.5. Even for the param-eters with median quotients below 4, 95th percentiles ofquotients range from 8 up to 100. These dispersions of thequotients indicate that all parameters studied here contributerelevantly to the total variability in at least some of thescenarios analyzed.

TABLE 1. Statistics of Leached Fractions (fj, %)a of All Scenarios and of Scenarios per Substance Analyzedb

GUSc all 2.0 2.1 2.2 2.5 2.7 3.5 3.7 3.9 4.8 5.3 6.4

substance no.b all 9 1 6 2 7 3 10 8 4 11 5ranked by

GUS 11 10 9 8 7 6 5 4 3 2 1median (fj) 7 7 7 7 7 5 6 3 2 4 1

percentiles of fjmedian e0.1 e0.1 e0.1 e0.1 e0.1 e0.1 0.35 0.13 2.8 10 0.70 2395th 30 0.75 e0.1 e0.1 e0.1 2.1 4.5 9.4 28 37 21 655th e0.1 e0.1 e0.1 e0.1 e0.1 e0.1 e0.1 e0.1 e0.1 0.26 e0.1 2.295th/5th percentile 300 7.5 1 1 1 21 45 94 280 140 210 30

fraction of scenarioswith fj < 0.1% 54 19 0 3 1 19 71 53 70 96 78 98with fj > 30% 5 0 0 0 0 0 0 0 5 9 1 40

total no. of scenarios 1188 108 108 108 108 108 108 108 108 108 108 108a Eq 1. b Figure 2. c Eq 3.

fwpest ) ∑g)1

ng

fwg*pg ) ∑g)1

ng

fwpest,g (6)

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Variability of leached fractions due to the GUS has alreadybeen discussed above (Table 1). Concerning sites, the Sevillasite (base parameter value in eq 2) combines high annualtemperatures and low annual precipitation (SupportingInformation). In contrast to other sites, irrigation at the Sevillasite does not produce increased percolate flow, as the irrigatedwater is mainly evapotranspirated according to ref 9. Thissite was thus considered as best-case regarding site-de-pendent vulnerability to leaching. The influence of site factorson the total variability of leached fractions is fairly similarthroughout most sites (median quotient around 45), withthe exception of the Chateaudun (not irrigated) site. However,it should be born in mind that the FOCUS scenario for theSevilla site may underestimate leaching (19). Therefore, theinfluence of site factors may be smaller than indicated inthis work.

No simple relation can be anticipated between the seasonsof application and the variability of leached fractions, becausethe weather varies with time differently at different sites.This is confirmed by 17% to 28% of the scenarios attainingquotients smaller than 1. Still, the median quotient forautumn compared to spring application is 2.9 in the median.For most sites, heaviest rainfalls are simulated in autumn(9). Moreover, evapotranspiration is lower in autumn thanin summer or spring. Thus, percolate flow and leaching are

generally stronger in autumn than in other seasons, as is thevariability of leaching.

Macropore flow was modeled for the Chateaudun siteonly (Table S1). In the simulations, macropore flow increasesleached fractions by a factor of 1.5 (median value of Q inTable 2). This is a relatively low increase in comparison toother studies. For instance, Larsson and Jarvis (26) found upto 4 orders of magnitude increase in leached fractionsresulting from the combined simulation of macropore andmatrix flow, as compared to simulations that took intoaccount only matrix flow. One explanation for the weakinfluence of macropore transport in the current work maybe the rather coarse soil texture and moderate precipitationat the Chateaudun site (Table S1) in comparison to (26).Another explanation may be that the modeling of preferentialflow is still limited by imperfect understanding and, mostimportant, difficulties in parametrization (19).

Case Study Results on Atrazine. Weighted leachedfractions (fwpest,g; eq 6) of atrazine were calculated in threescenarios (Table 3). In the scenario “European average” (EA),we considered all scenarios in the logic tree, while the scenario“spring application” (SA) encompasses only spring applica-tion to maize (crop case 1) and represents the main field ofapplication of atrazine. The scenario “spring application,loam soil” (SAL) is a further subset of the scenario SA,

TABLE 2. Contributions of Logic Tree Parameters to Leaching Variability, Expressed as Statistics of Quotients Q (Eq 2)

parameter GUSa site

value scenario jb 6.4 4.8 3.9 5.3 3.9 3.5 2.7 Hamburg Piacenza (notirrigated)

Okehampton

value base scenarioc 2.1 2.1 2.2 2.0 2.0 2.1 2.2 Sevilla Sevilla Sevillasubstances numbersd 5 and 1 4 and 1 8 and 6 11 and 9 10 and 9 3 and 1 7 and 6rank (median (Q)) 1 2 4 11 12 13 14 3 5 6median 220 100 71 7.9 6.9 6.5 4.3 71 54 4795th percentile 570 330 260 46 20 36 21 440 390 4505th percentile 9.8 28 1.1 1.1 1.0 1.8 2.7 1.8 2.5 1.495th/5th percentile 58 12 240 43 20 20 8.0 240 160 320fraction of quotients

with Q e 1, %0 0 0 0 0 0 0 0 0 0

parameter site continued weather variability season of application crop casemacropore

flow

value scenario jb Chateaudun(irrigated)

Kremsmunster Piacenza(irrigated)

Chateaudun(not

irrigated)

80th p. median autumn summer crop case 2 yes

value base scenarioc Sevilla Sevilla Sevilla Sevilla 20th p. 20th p. spring spring crop case 1 norank (median (Q)) 7 8 9 10 15 18 16 19 17 20median 41 39 23 14 4.0 2.2 2.9 2.1 2.3 1.595th percentile 250 220 260 66 100 50 65 8.4 36 9.35th percentile 2.2 1.1 1.0 1.1 1.5 1.3 0.72 0.52 1.0 0.7495th /5th percentile 110 210 260 61 70 38 91 16 36 13fraction of quotients

with Q e 1, %0 0 0 0 0 0 17 28 3 14e

a Only scenarios depicting substances from the same GUS region (Figure 2) are compared. b Eq 2; leached fractions below 0.1% were notconsidered. c Leached fractions below 0.01% were set equal to 0.01% to obtain quotients in meaningful orders of magnitude. d See Figure 2.e Thedecrease of leached fractions in 14% of the scenarios with macropore flow might be explained by the unavailability of adsorbed pesticide againsttransport by macropore flow (26).

TABLE 3. Scenarios for the Atrazine Case Study and Logic-Tree Parameters Taken into Account To Calculate Leached Fractionsfor These Scenarios

choice of substance property values logic tree parameters

scenario name abbreviation Koc DT50,soil season of application sites crop casea

European average EA all all all all crop case 1 and 2spring application SA all all spring only all crop case 1 onlyspring application,

loam soilSAL studies in loam soil only all spring only loam or silt/loam

topsoil textureonly

crop case 1 only

a Table S1.

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considering only Koc values determined in loam soils.Accordingly, only the sites with loam or loam/silt topsoiltexture were aggregated in the logic tree (Kremsmunster,Okehampton, Sevilla, and Piacenza; Supporting Information,Table S1). In all scenarios, all values of weather variabilitywere considered.

Substance properties were taken from Fenner (27) (Sup-porting Information). Atrazine exhibits the highest probabilityof having a GUS between 2.5 and 3.5 when all substance datavalues are considered (scenarios EA and SA; Table 4, firstrows of values). The probability of atrazine showing a GUSlarger than 4.8 or smaller than 2.1 is below 8. Concerningscenario SAL, GUS below 2.5 displays higher probabilitiesthan in scenarios EA and SA, while high GUS shows lowerprobabilities. This is due to the higher mean value and lowercoefficient of variation of the Koc in scenario SAL as comparedto scenarios EA and SA (Table S3).

Table 4 illustrates that leaching vulnerability due to siteand farming factors is higher in scenario EA than in scenarioSA. This is in line with the findings that leached fractions aregenerally lower in spring application scenarios than inautumn application scenarios and that crop case 1 leads tolower leached fractions than crop case 2 (see above). Thedifference in mean leached fractions between scenario SAand SAL is below a factor of 1.7 regarding GUS with relevantleached fractions.

The probably of atrazine to have a GUS smaller than 4.8is 95% in scenarios EA and SA and 100% in scenario SAL. Forthese GUS values, mean leached fractions (fwg, Table 4,second rows of values) range from 0% to 12%. In exceptionalcases (5% in scenarios EA and SA) the GUS is higher andleached fractions are as high as 25%. By comparison,measured leached fractions of atrazine ranged between 0.04and 17.5% in 20 field studies (Table S4). The general rangeof our simulated leached fractions is therefore in goodaccordance with leached fractions measured in field studies.

The total weighted leached fraction (fwpest; eq 6) is ofhighest interest for LCA studies. Concerning the scenarioEA, the median of this total weighted leached fraction ofatrazine is 2.8% (Table 5). This is the fraction of atrazine thatleaches to 1 m depth and is therefore considered relevant forgroundwater exposure. The total weighted leached fractionfor scenario SA is a factor of 2 lower than the correspondingvalue with regard to scenario EA. The difference in totalweighted leached fractions between the scenarios SA andSAL is a factor of 3.

The uncertainty due to the interpolation of mean leachedfractions (fwg eq 5) per GUS range from simulated leachedfractions of substances 1-5 (Figure 2) is expressed asquotients of the 95th and 5th percentile of the total weightedleached fraction (Table 5; for the calculation of this uncer-tainty, see Methods). The differences between the total

weighted leached fractions in the three atrazine scenariosare relevant in relation to this uncertainty. Hence, theinterpolation of leached fractions for specific pesticides fromthe substances no. 1-5 is a useful method in the context ofthis work.

DiscussionThe calculation of weighted leached fractions as presentedin this work, takes into account variability arising fromsubstance properties, site, and farming practices. The resultis therefore a more sophisticated value than leached fractionsestimated using only one soil/climate scenario, as is thecurrent practice in LCA (see Introduction). The probabilitiesfor the scenario parameters proposed here (Table S2) referto the European state of affairs. These values may be adaptedif further specific information is available (preferably aboutsite, as it contributes most to variability, see Results).Moreover, scenarios considering specific parameter valuesin the logic tree can be used in sensitivity or uncertaintyanalysis (see the scenarios in the atrazine case-study).Uncertainty analyses are increasingly incorporated in LCA(28), as they help to obtain more reliable results. Our approachcan be seen as a further methodological step toward enablinguncertainty and variability assessments in LCA.

The aggregation of subsystems of the logic tree enablesconsideration of dependencies between specific use patternsfor pesticide, site, and farming factors. Further, dependenciesbetween substance properties and sites can be accountedfor. The usefulness of considering such dependencies wasillustrated in the atrazine case study, in which total weightedleached fractions of the European average scenario differedconsiderably from scenarios where such dependencies weretaken into account.

The calculation of leached fractions needs to be adaptedto the constraints of data availability in specific LCA studies.The results of the atrazine case study indicate the importanceof specifying the season of application of a pesticide.

TABLE 4. Results of the Probabilistic Aggregation of Leached Fractions for the Case Study on Atrazine

GUS range GUS < 1.8 1.8 e GUS < 2.1 2.1 e GUS < 2.5 2.5 e GUS < 3.5 3.5 e GUS < 4.8 4.8 e GUS < 6.4 GUS g 6.4

Scenario “European Average” (EA)pg

a, % 3.2 4.8 12 47 28 5.0 0.040fwg

b, % 0 1.7E-06 3.5E-03 0.41 6.2 18 25fwpest,g

a, % 0 8.2E-08 4.2E-04 0.19 1.8 0.91 9.9E-03

Scenario “Spring Application” (SA)pg

a, % 3.2 4.8 12 47 28 5.0 0.040fwg

b, % 0 3.6E-08 7.4E-05 0.12 2.9 8.9 12fwpest,g

a, % 0 1.7E-09 8.9E-06 0.056 0.82 0.44 4.9E-03

Scenario “Spring Application, Loam Soil” (SAL)pg

a, % 9.7 13 24 45 7.9 0.12 0fwg

b, % 0 7.5E-08 1.6E-04 0.19 3.8 12 18fwpest,g

a, % 0 9.6E-09 3.8E-05 0.084 0.30 0.015 0a Eq 6. b Eq 5.

TABLE 5. Total Weighted Leached Fractiona for the ThreeScenarios of the Atrazine Case Study

scenariob

parameter EA SA SAL

median,c % 2.8 1.3 0.385th percentile,c % 2.0 0.93 0.2695th percentile,c % 4.0 1.8 0.6095th percentile/5th percentile 1.9 2.0 2.3a fwpest (eq 6). b Table 3. c Percentiles reflect the uncertainty due to

interpolation of mean leached fractions per GUS range from simulatedleached fractions of substances 1-5 (Figure 2; see Methods).

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Moreover, the uncertainty of substance properties shouldbe considered. Ideally, dependencies between substanceproperties and sites should also be taken into account. Formost pesticides, however, less substance data are availablethan for atrazine. In such cases, we recommend using adefault coefficient of variation of 80%, as derived from theatrazine case study (Table S3). However, dependenciesbetween substance properties, site, and farming parameterscannot be assessed using such generic uncertainties. Still, aslong as accurate data of substance properties are lacking,generic coefficients of variation may be a pragmatic solutionfor considering variability. Regarding specific pesticides, theuncertainty of substance data may differ from that observedfor atrazine. Considering the significant influence of sub-stance data on the uncertainty of leached fractions shownhere, further research on such uncertainties would bebeneficial.

One shortcoming in the present paper is the relativelysmall number of substances assumed to be representativefor any pesticide in the GUS space (Figure 2). To moreaccurately distinguish between different pesticides and toreflect the high contribution of the GUS to variability,scenarios could be calculated for more substances. Forpesticides with extremely low values of Koc and DT50,soil

(substances 9-11 in Figure 2), leached fractions should beinterpolated from substances in the corresponding region ofthe GUS space (see Results).

Further, certain deficiencies and uncertainties of theFOCUS framework (9) also affect this study. Some of themost prominent sources of uncertainty are the definition ofthe depth of 1 m as system boundary for the assessment ofgroundwater exposure to pesticides and the representationof the FOCUS sites for real soil and weather conditions in EUagriculture. The full spatial and temporal variability in EUagriculture is considerably more complex than described withthe six sites used here. However, the spatial and temporalresolution of LCA is generally low (23). Therefore, analysesthat quantify variability in relatively few scenarios repre-sentative for a larger system are appropriate for LCA. Thisis achieved with the method presented here. Anotherdrawback is that the validation status of the models used inthis paper is rather variable and depends on the scenariosimulated (19). For instance, PELMO cannot model fasttransport processes and therefore was found to underestimateleaching from clay soils. However, for sites where preferentialflow was not the dominant process, PELMO predictedleaching with acceptable precision, despite weaknesses inpredicting moisture in soil (19). The MACRO model used inthis paper has also been validated with measured data fromthree sites with clay soils (19). MACRO produced results ofacceptable accuracy. In their validation study, Vancloosteret al. (19) draw the overall conclusion that the models areacceptable if they are used as first tier of exposure modeling.Our work aims at an overview of leaching variability for LCAand policy-making, which corresponds to a first tier riskassessment in its relatively low demand for accuracy,precision, and site-specificity.

The total weighted leached fraction is applicable for theLCA of pesticides in several ways. First, it is useful in theestimation of emissions to groundwater in life-cycle inven-tories (LCIs) of agricultural systems, according to theapproach of Hauschild (13). Second, emissions to ground-water should be incorporated in the effect assessment oflife-cycle impact assessment. The leached fractions calculatedin this paper might be integrated in approaches such as thoseof ref 29. Furthermore, the total weighted leached fractioncould be used to improve modeling of pesticide leaching inmultimedia fate and exposure models, e.g. USES-LCA (11).However, it would not be possible to undertake further studieswith more detailed resolution, as in the current study, as

long as models such as USES-LCA assume only one homo-geneously mixed box for agricultural soil on the Europeancontinent. Finally, leached fractions may be used as a separateindicator for an assessment on the exposure level togetherwith pesticide emissions from the field via other pathways.This would ensure that potential effects not quantifiable inlife-cycle impact assessment today, such as mixture toxicityand endocrine disruption, would be represented in asimplified way (30). The outlined use of the method in LCAis illustrated in a case study on two plant-protection productselsewhere (31, 32).

AcknowledgmentsWe gratefully acknowledge the reviewing of the manuscriptby Martin Scheringer and the support of Ulrich Fischer. Weare also thankful to Scott Loren and Rene Ballerini forlanguage corrections and to three anonymous reviewers fortheir helpful comments.

Supporting Information AvailableInformation about the parameter values (see logic tree, Figure1) and their probabilities of occurrence and additionalinformation about the atrazine case study . This material isavailable free of charge via the Internet at http://pubs.acs.org.

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Received for review November 14, 2003. Revised manuscriptreceived May 21, 2004. Accepted June 3, 2004.

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