Predicting Pesticide Environmental Risk in Intensive Agricultural

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Environ. Sci. Technol. 2009, 43, 522–529

Predicting Pesticide Environmental Risk in Intensive Agricultural Areas. I: Screening Level Risk Assessment of Individual Chemicals in Surface Waters ROBERTO VERRO,† ANTONIO FINIZIO,† S T E F A N O T T O , ‡ A N D M A R C O V I G H I * ,† Department of Environmental Sciences, University of Milano Bicocca, Piazza della Scienza 1, 20126 Milano, Italy; National Council of Research, Institute of Agro-Environmental and Forest Biology, Agripolis, Viale dell’Universita` 16, 35020 Legnaro (PD), Italy

Received July 4, 2008. Revised manuscript received October 21, 2008. Accepted November 3, 2008.

A GIS-based procedure for assessing and mapping pesticide ecotoxicological risk for surface waters was applied to all active ingredients used in a catchment characterized by intensive agriculture.Chemicalconcentrationsinriverwaterwerecalculated for 54 chemicals in 25 drift and 21 runoff events that occurred during the growing season, from March to September. Screening level risk for the aquatic community was estimated using a risk index. The different role of drift and runoff processes, as well as the temporal trends of exposure and risk, were compared for the three classes of pesticides (herbicides, fungicides, and insecticides). High levels of risk are usually associated with runoff events for herbicides and to drift events for insecticides and fungicides. The described approach may serve as a powerful tool for a comparative evaluation of sitespecific pesticide risk for surface water. However, for largescale risk mapping, getting information on pesticide use with sufficient detail would be the major problem.

Introduction Registration schemes for plant protection products (PPPs) under the Directive 91/414/EEC require assessing the ecological risks through a tiered testing system. This risk assessment procedure consists of two steps: (i) effect assessment derived from ecotoxicological experiments on selected nontarget species and (ii) assessment of concentrations to which organisms will be exposed in the field after pesticide application. The FOCUS group (Forum for the Coordination of Pesticide Fate Models and Their Use) has developed a tiered approach to be used in the EU for assessing exposure of aquatic ecosystems (1) that takes into account entry of pesticides into surface water via drift, drainage, and runoff. Steps 1 and 2 are based on simple models and scenarios, whereas Step 3 uses more sophisticated models (2-5) applied to 10 scenarios representing “realistic worst case” of the major agricultural and climatic areas across the * Corresponding author phone: +39 0264482741; Fax: +39 0264482795; e-mail: [email protected]. † University of Milano Bicocca. ‡ National Council of Research, Institute of Agro-Environmental and Forest Biology. 522

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EU. In addition, FOCUS in 2005 (6) also developed an extensive list of modeling refinements and mitigation measures that led to FOCUS Step 4 scenarios. So, aquatic exposure assessment has become quite sophisticated. However, Directive 91/414/EEC mainly concentrates on the start and end of pesticide life cycle. More recently, the Sixth EU Environment Action Programme provided for the development of a “Thematic Strategy on the Sustainable Use of PPPs” (7) with the aim to complement the existing legislative framework by targeting the use phase of these chemicals. In the strategy, the need to develop tools (e.g., pesticide risk indicators) that should operate on different scales, from EU/ regional level, up to the catchment level (site-specific risk assessment) (8) is clearly indicated. Furthermore, the Water Framework Directive (WFD) has marked a change in community water policy toward an integrated framework for assessment, monitoring, and management of surface waters based on their ecological and chemical status. The targets and principles set out in Directive 91/414/EEC for pesticides and in the thematic strategy on the sustainable use of PPPs have been translated into objectives for all waters and will be implemented on a river basin scale. Again the need of site-specific tools for pesticide risk assessment becomes evident. The application of geographical information systems (GIS) as an effective spatial tool in pesticide transport modeling has become very promising for site-specific risk assessment. Various GIS-based procedures for predicting chemical distribution and fate have been reported in the literature (9, 10). Verro et al. also developed a GIS-based methodology for mapping pesticide risk for surface water (11). The method was experimentally validated in a pilot basin by Bonzini et al. (12). This study represents the application of the same procedure to a river catchment characterized by intensive agriculture. The implementation of the procedure will serve to highlight the complexity of the information required to conduct a relatively simple screening level assessment of the risk that agrochemicals pose to surface waters. Furthermore this exercise will also address the complexity and limitations of the data interpretation process. The risk assessment procedure implemented in this study takes into account the following processes: • spatial and temporal distribution of pesticide application; • combination of drift and runoff processes; • temporal trend due to pesticide dissipation; • risk due to complex mixtures. This paper presents the risk that individual chemicals pose to aquatic communities over time. The risk posed by mixtures of chemicals used throughout the basin will be described in a further publication (13).

Materials and Methods Description of the Field Site and Data Set. The study was conducted in the River Meolo basin, a small resurgence river in northeast Italy. The watercourse is 17 km in length, and is characterized by relatively flat terrain, ranging between 23 and 3 m asl (Supporting Information (SI) Figure 1). The catchment covers an area of 2817 ha, and the river flow rate ranges between 1.6 and 3.2 m3 s-1. The basin is hydraulically isolated, resulting in surface water runoff rapidly flowing into the river. Arable land covers about 80% of the watershed surface, equating to 2228 ha in the simulation year (2004), as described in SI Table 1. The landscape description database developed in this work, based on GIS vector and raster structure, contains 10.1021/es801855f CCC: $40.75

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information relating to land use, crop distributions, digital terrain model, hydrographic networks, soil properties, hedgerows or buffer strips, watershed boundaries, rain and river flow measurement stations, orthoimages, administrative boundaries, and pesticide application. These data sets were derived from a combination of field surveys, the digitalization of orthoimages or topographic maps, and other public spatial and relational database. Details of site description layers are given in SI Figures 1-4. Development of Scenarios. A field survey was performed to identify all active ingredients (a.i.) used in the 2004 growing season. 54 different a.i. were applied on the four main crop types covering 1689 ha. Wheat and barley were excluded from the main crops because they were not treated in the period relevant to this study, and furthermore, only 61 kg of a.i was applied in total. Four a.i. were applied on more then one crop: metolachlor and flufenacet were used to treat corn and soybean; glyphosate was used on corn, soybean, and vineyards; and quizalofop-C2H5 was used on soybean and sugar beet. The volume of chemicals applied and the area of crops treated in the basin were estimated from sales data and through interviews with the major farmers. Where sufficient information was available (e.g., terbuthylazine), chemical application volumes and application dates were spatially distributed in the basin, within a set of application windows. Where detailed information was unavailable, application dates were set in the middle of the application window, according with local agricultural practices. The same application date was assumed on all farms. The list of chemicals used and application data are shown in Table 1. Since the processes involved in pesticide transport to the river are drift and runoff, the contamination events correspond to the 25 application dates for drift and to 21 rain events for runoff. Physical-chemical properties and ecotoxicological data of the chemicals (SI Table 5) were stored in a relational database. Predicting Exposure. Predicted environmental concentrations (PECs), for all the a.i. applied in the Meolo river catchment, were calculated using the approach proposed by Verro et al. (11) improved by Bonzini et al. (12). The procedure allows the prediction of PECs in surface water as a result of both drift and runoff process. Drift processes occur immediately after chemical application, while runoff occurs after precipitation events. Details of the precipitation events and the river discharge used for the calculation (SI Tables 2 and 3). For the purpose of this study the Meolo watershed was divided into three sub-basins (Rovare`, Monastier, Castelletto), and the model simulations were performed for each section. PECs were calculated for each of the 25 pesticide application dates (PECs drift) and for all the 21 rain events (PECs runoff) that occurred during the studied period. As a result, 930 modeling simulations for each sub basin were performed. (see details in the SI). For reasons of space, this paper shows only the results for the Castelletto station, which receives the pesticide load from the whole watershed, are reported. The complete data set is available on request. Assessing Risk. The level of risk that pesticides pose to an aquatic ecosystem was estimated using the PRISW-1 index (short-term pesticide risk index for surface water) proposed by Finizio et al. (14). The index is a scoring system that requires the calculation of TERs (toxicological exposure ratio), which are obtained from the ratio between acute toxicity (EC50 or LC50) data for selected organisms (algae, Daphnia, fish) and the estimated PECs in surface waters. To each TER a score is assigned, which is then weighted dependent on the type of organism (SI Table 4). The index is calculated according to the following equation:

PRISW - 1 ) (A × Wa) + (B × Wb) + (C × Wc)

(1)

where A, B, C are the scores assigned to the TERs for algae, Daphnia, and fish respectively; Wa, Wb, and Wc are the weights. Input data for calculating the PRISW-1 scores are taken from the PECs and toxicity data gathered from the literature (SI Table 5). For calculating PRISW-1 only the base set of toxicity data required by European regulation is needed. Therefore, toxicological information must be available for all chemicals used in European agriculture. It makes the index particularly suitable for risk assessment on a large number of chemicals.

Results and Discussion Exposure. Calculated PECs for the basin outlets (Castelletto) are reported in SI Table 6. PECs were calculated down to a minimum chemical concentration of 0.0001 µg/L. The need for such low concentrations is due to the sensitivity of the PRISW-1 index. The index considers TERs as high as 10 000, which is important considering that some EC50 are lower than 1 µg/L (see Si Table 5). Considering that a PNEC (predicted no effect concentration), calculated according to the TGD (Technical Guidance Document) (15), by applying a factor of 1000 to those EC50, would be lower than 1 ng/L, the assumed threshold is not unrealistically low. At the basin outlet, the maximum calculated PEC was 6.6 µg/L (terbuthylazine, 30 April, corn, runoff process). The minimum calculated PEC value of 1 µg/L) throughout the growing season, while others, such as sulfonylureas, are rapidly lost from the system; • fungicide applications start late in May, with the exception of chlorotalonil. Calculated PECs for drift are generally higher than those resulting from runoff; • repeated applications of the same fungicide, combined with high application rates, may produce very high PEC drift values. Such values rapidly decline over time due VOL. 43, NO. 2, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 1. Data on Plant Protection Product Uses in the Meolo Basina chemicals

category

crop

application dates

treated ha (%)

amounts (kg)

maximum PEC (µg/L)

dicamba dimethenamid flufenacet glyphosate isoxaflutole mesotrione metolachlor nicosolfuron terbuthylazine chlorpyrifos cymoxanil cyprodinil dimethomorph fenitrothion fludioxonil flufenoxuron folpet fosetyl-Al glufosinate-NH4 glyphosate mancozeb metalaxyl metiram penconazole pirimethanil procymidone cycloxydim flufenacet glyphosate imazamox imazethapyr linuron metolachlor metribuzin oxasulfuron propargite quizalofop-C2H5 thifensulfuron-CH3 azoxystrobin chloridazon chlorothalonil clethodim clopyralid cyhalothrin cyproconazole deltamethrin desmedipham difenoconazole ethofumesate fenpropidin lenacil metamitron phenmedipham propaquizafop propiconazole quizalofop-C2H5 tetraconazole trifloxistrobin triflusulfuron-CH3

H H H H H H H H H I F F F I F I F F H H F F F F F F H H H H H H H H H I H H F H F H H I F I H F H F H H H H F H F F H

m m m m m m m m m v v v v v v v v v v v v v v v v v s s s s s s s s s s s s sb sb sb sb sb sb sb sb sb sb sb sb sb sb sb sb sb sb sb sb sb

May 18 April 9 April 9 April 9 April 9 May 18 April 9 May 18 April 8, 9, 18, 20 and 24 July 2 and 16 May 15 July 10, August 22 May 15 and 30 July 2 and 26 July 10, August 22 July 2 and 26 May 15, June 15, July 15, August 4 May 15 and 30 May 15 March 15 May 15, June 15, July 15, August 4 May 15 May 15, June 15, July 15, August 4 June 20, July 10 July 10, August 22 July 10, August 22 June 15 May 11 April 20 June 15 June 15 May 11 (2004) May 11 May 11 June 15 June 15 June 15 June 15 July 1 and 20, August 10 March 1 April 27 April 27 April 27 July 1 and 20, August 10 1 July 1 and 20, August 10 May 29 July 1 and 20, August 10 July 1 and 20, August 10 April 27 July 1 and 20, August 10 April 27 March 1 April 27 May 29 July 1 and 20, August 10 May 29 July 1 and 20, August 10 July 1 and 20, August 10 May 29

182 (23) 25 (3) 265 (33) 10 (1) 811 (100) 180 (22) 163 (20) 155 (19) 502 (62) 75 (24) 112 (36) 35 (11) 311 (100) 235 (75) 35 (11) 130 (42) 38(12) 97 (31) 68 (22) 120 (39) 311 (100) 30 (10) 20 (6) 8 (3) 147 (47) 110 (35) 30 (7) 134 (30) 98 (22) 86 (19) 57 (13) 3 (0,7) 53 (12) 184 (42) 181 (41) 94 (21) 214 (48) 292 (66) 56 (45) 67 (54) 42 (34) 26 (21) 10 (8) 25 (20) 26 (21) 56 (45) 92 (74) 124 (100) 124 (100) 72 (58) 92 (74) 113 (91) 123 (99) 10 (8) 31 (25) 31 (25) 42 (34) 26 (21) 31 (25)

24.75 22.50 76.32 6.20 50.51 17.50 218.50 6.70 399.33 20.70 19.28 9.66 223.80 82.20 7.00 6.50 135.84 282.96 38.78 149.90 3448.00 18.00 128.16 7.50 206.82 215.00 10.50 96.59 39.58 68.80 19.95 1.10 78.04 17.50 12.17 13.93 11.97 0.83 6.64 42.45 40.56 9.75 7.50 0.31 1.90 0.79 2.24 13.45 30.88 28.96 12.54 159.92 11.81 0.97 0.39 0.60 4.33 4.47 0.33

1.1662 0.5428 0.4528 0.0086 1.1974 0.3360 4.5938 0.2107 6.5721 0.1571 0.1769 0.0730 0.7063 0.6231 0.0519 0.0492 0.5151 1.2990 0.3107 1.8936 6.5063 0.3099 0.4844 0.0578 1.5689 1.6300 0.0195 0.4145 0.0649 3.5738 1.0929 0.0236 1.0475 0.8677 0.2507 0.0281 0.0242 0.0164 0.0541 0.3562 1.1697 0.5462 1.0268 0.0002 0.0030 0 in all the days of application (drift) and in runoff events. Sixteen active ingredients (12 herbicides, four fungicides, all crops) showed PRISW-1 ) 0 in all scenarios. Details of PRISW-1 values for all a.i. are presented in SI Table 7. Finizio et al. (14) proposed the following risk classification based on the PRISW-1 scores: 60: very high risk. The majority of the scores obtained present either a negligible or low risk (601 and 174 e.e. respectively). Approximately 9% of the PRISW-1 values can be classified as medium or high risk (64 and 11 e.e., respectively), which is likely to result in potential stress for the aquatic communities.

In Figure 2, PRISW-1 values for selected a.i., representative of the three different pesticide categories, are reported. Some general trends can be observed: • herbicides pose a negligible to medium level of risk across the entire growing season; • the ecological risk posed by fungicides is mainly associated with drift, even if the highest runoff risk event is posed by a fungicide (chlorothalonil); however, the level of risk relating to runoff risk is equal to zero in 77% of the e.e.; • insecticides show highest PRISW-1 values for drift, despite their runoff scenario values commonly being >0 (51% of the runoff emission events); • out of the three pesticide groups, insecticides appear to present the highest level of risk to the ecosystem for drift, fungicides for runoff for considerable periods within the growing season. Figure 3 reports the frequency distribution of PRISW-1 values, for the three pesticide groups. The distribution of PRISW-1 scores for both drift and runoff have been considered separately. The data indicates that medium and high risk levels (PRISW-1 values higher than 15) occur with high frequency for insecticides and fungicides, mainly during drift events. Furthermore, insecticide drift produce high level of risk with a frequency higher than 10%. More detailed analysis of Figure 3 suggests that. • For the herbicides the majority of PRISW-1 scores correspond to negligible or low risk: 61 and 53.8% of runoff and drift events respectively have shown a score of 0. Only in a small number runoff events (5%) herbicides have posed a medium level of risk. The highest calculated risk score was 33; however, it only occurred in 0.6% of the cases. • For the fungicides, more than 83% of the runoff PRISW-1 scores correspond to a negligible level of risk. The remaining values are 11% between 5 and 15 (low risk) and 5% between 15 and 40 (medium risk). The remaining 1% of runoff events have been classified in the higher risk category (value >40) and represent the highest runoff PRISW-1 (score 44), In contrast for drift processes, even if the distribution of the majority of PRISW-1 values fall into the negligible or low risk range (43 and 29% respectively), 28% of drift events are distributed between the classes of medium or high risk (20 and 8% respectively). • Insecticides revealed a more widespread frequency distribution of PRISW-1 values throughout the different classes of risk. 51% of runoff values are distributed in the range of 0-5 (negligible risk), 22% between 5 and 15 (low risk) and 27% between 15 and 40 (medium risk). The highest value is 24 in 5.1% of e.e. When considering the drift data, the majority of values (46%) falls within the low risk category. However 18 and 36% of drift events correspond to medium and high risk classes respectively. Value and Limitations of the Approach. The procedure applied may serve as a tool for predicting site-specific pesticide risk for surface water. It allows for the comparative evaluation of the potential risk that all pesticides used in an agricultural basin pose to the aquatic ecosystem. Although, in this study, data were reported for a single point in the river, the approach allows for the assessment of the spatial distribution of risk, resulting in the generation of GIS-based risk maps. It also allows the variation in the level of risk to be assessed as a function of time. This is of extreme importance for pesticides that are discontinuously applied and produce variable risk throughout the growing season. However, some limitations of the approach as well as its realistic applicability must be highlighted. The first problem is related to the uncertainty of the input data (environmental characteristics, properties and use of VOL. 43, NO. 2, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 1. PECs due to drift (d) and runoff (r) in Castelletto station (basin outlet) for selected herbicides (top), fungicides (middle), and insecticides (bottom). Numbers on the time axis represent days after March 1st, starting date of the procedure. pesticides). Several uncertainty factors (variability, heterogeneity, approximation, inaccuracy, etc.) can lead to a lack of confidence with regard to the results obtained. Dubus et al. (16) described the sources of uncertainty in pesticide fate modeling and demonstrated that the errors and uncertainties accumulate in various forms. Furthermore, they also pointed out that the techniques designed to account for uncertainties are themselves subject to significant uncertainty (results from Monte Carlo can be influenced by the selection of input parameters to be included in the analysis). Therefore, the authors claimed for further research for assessing the origin and magnitude of the sources of uncertainties and for integrating into probabilistic risk assessment. We are aware that in the proposed system the uncertainty has not been taken into account. On the other hand • The approach here proposed is not probabilistic but deterministic. By definition this kind of models im526

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plicitly assume that data on which the parametrization is based are error free and parameters and model structures are considered as completely known. • In this study, the pilot risk assessment procedure was applied to a small river basin, where all the required input data were collected at a high resolution. • The system has been developed as a screening tool for comparing different pesticides. This implies that all chemicals are modeled into the same environmental scenario. Consequently, at least the uncertainties related to the environmental parameters are the same in all simulations. • The results generated from this study are supported by experimental data from an exposure prediction study conducted by Bonzini et al. (12). The second controversial aspect of the proposed approach is related to its scale of applicability. When applying this

FIGURE 2. PRISW-1 values due to drift (d) and runoff (r) in Castelletto station (outlet) for selected herbicides (top), fungicides (middle), and insecticides (bottom). approach to larger scale systems, in addition to the increased uncertainties, it comes a problem about the availability of information. As underlined by Bonzini et al. (12), data for most of the input parameters (e.g., land use, geographical data, meteorological data) can be sourced at an acceptable level of detail within Europe. The most important data set for predicting pesticide exposure is information relating to the application of the a.i. Therefore, reliable data on the active ingredients used, the volumes applied, the application periods, and the spatial distribution of application are essential for producing accurate risk maps. Official data on national pesticide sales (e.g., Eurostat data), frequently used for large scale pesticide assessment, are unsuitable for this approach as more precise spatial and temporal distribution information is required. A possible solution could be the

adoption of a “stepwise zooming” approach which is based on the level of data available at a particular spatial scale. Each step may therefore provide a different type of output, at each scale, which could be used by decision makers to implement measures for environmental protection and agricultural management: Step 1: National or Continental Scale. This would result in the implementation of a large-scale mapping exercise for assessing areas where more detailed input data are required. In this case pesticide information could be estimated on the basis of sales data, crop distribution, and the most common agricultural practices in these areas. The output data would allow for the assessment of areas, which may be at particularly high level of risk. An example of such an approach is described in Schriever and Liess (17) VOL. 43, NO. 2, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 3. Frequency distribution of the PRISW-1 values for the three pesticide groups, calculated for drift and runoff. Frequencies are expressed as percentage of a given PRISW-1 score in relation to the total number of PRISW-1 values calculated for a given group of chemicals and emission type (e.g.: herbicide drift). Step 2: Large River Basins or Agricultural Comprensories. This approach can be used to further assess the risk in the potentially “hot areas” identified in Step 1. In this case, pesticide data should be collected for all of the medium and small hydrographic basins in the region. The temporal application window should also be reduced by using information detailing the typical agricultural practices in the geo-climatic area. The output data would allow a quantitative assessment of pesticide risk to be derived for different crops across river basins. Step 3: Local Scale. This approach can be applied to conduct a detailed risk mapping. In this case, the spatial and temporal distribution data for pesticide application should be at as high resolution as possible. The output data would provide detailed information for local pesticide and land use management. Detailed risk maps could account for the spatial heterogeneity and highlight hot spot situations. Finally, the last problematic aspect of the proposed approach refers to the ecological meaning of the obtained results. The PRISW-1 index applied in this exercise can be used as an effective comparative tool, but it lacks of ecological realism. The actual level of ecosystem health, represented by a scoring system that is based on a limited number of laboratory data, is highly unknown. The calibration and validation of standard risk assessment procedures, to assess their accuracy for predicting damage to the structure and function of natural communities, is a challenge for modern ecotoxicology. However, from the results obtained in the present work and considering that no one compound from this study spanning the whole range of the index (from 0 to 100), some consideration can be made: • a PRISW-1 score close to 100 indicates that the chemical is likely to cause acute toxicity at all levels of an aquatic ecosystem (algae, invertebrates, fish), being PECs close or higher than the LC/EC50s. It is highly unlikely that a pesticide is toxic to all aquatic organisms, as studies have shown that their toxicity is usually species-specific; • for herbicides, a value of 24 may indicate acute toxicity for primary producers; therefore, the maximum calculated value of 33 (terbuthylazine in late spring) is considered to represent a significant risk for this class of chemicals; 528

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• for insecticides and fungicides, values higher than 50 (chlorpyrifos and mancozeb after applications) indicate a potential risk for at least two out of three representative organisms of the community; • for many chemicals, the PRISW-1 score is zero throughout the study period, being the PECs at least 4 orders of magnitude lower than the lowest acute ecotoxicological end point. In conclusion, assessing the risk that individual chemicals pose to an ecosystem is a valuable process allowing comparisons between a.i. to be made. Unfortunately the relevance of the calculated risk scores for the different active ingredients is limited, as the major risk posed to ecosystems comes from the complex mixtures of chemicals, which are applied in the basin. The composition of chemical mixtures is highly variable in space and time and specific procedures, such as those described Verro et al. (13), are required to predict the risk that complex agricultural mixtures pose to aquatic ecosystems.

Acknowledgments This research was financially supported by the Italian Ministry of University and Research (Project: GIS-Based Assessment and Spatial Distribution of the Ecotoxicological Risk Deriving by Pesticide Use, COFIN 2002) and by the European Union (European Commission, FP6 Contract No. 506675, ALARM and Contract No. 003956, NoMiracle). We are grateful to Mr. Oddino Bin for supporting field data collection.

Supporting Information Available Figures S1-S4 and Tables S1-S7. This material is available free of charge via the Internet at http://pubs.acs.org.

Literature Cited (1) FOCUS. Surface Water Scenarios in the EU Evaluation Process under 91/414/EEC, Report of the FOCUS Working Group on Surface Water Scenarios, EC Document Reference SANCO/4802/ 2001-rev.2; EU Commission: Brussels, 2001; p 245. (2) BBA (Federal Biological Agency of Agriculture and Forestry, Germany). Bekanntmachung des Verzeichnisses Risikomidern der Anwendungsbedingungen fu ¨ r Nichtzielorganism. Bundesanzeiger; BBA: Berlin, 2000; Vol. 100, pp 9878-9880. (3) Larsbo, M.; Jarvis, N. J. MACRO 5.0. a Model of Water Flow and Solute Transport in Macroporous Soil Technical Description.

(4)

(5) (6)

(7)

(8) (9)

Studies on the Biogeophysical Environment, Emergo 2003:6; Department of Soil Sciences, Swedish University of Agricultural Sciences: Uppsala, Sweden, 2003. Carsel, R. F.; Imhoff, J. C.; Hummel, P. R.; Cheplick, J. M., Donigian, A. S., Jr. PRZM-3, Model for Predicting Pesticide and Nitrogen Fate in the Crop Root and Unsaturated Soil Zones: User’s Manual for Release 3.0; Environmental Protection Agency: Athens, GA, 1998. Adriaanse, P. I. Exposure assessment of pesticides in field ditches: the TOXSWA model. Pestic. Sci. 1997, 49, 210–212. FOCUS. Landscape and Mitigation Factors in Aquatic Risk Assessment. Volume 1. Extended Summary and Recommendations, Report of the FOCUS Working Group on Landscape and Mitigation Factors in Ecological Risk Assessment, EC Document Reference SANCO/10422/2005 v2.0; EU Commission: Brussels, 2007; p 169. Commission of the European Communities. Communication from the Commission to the Council, The European Parliament, the European Economic and Social Committee and the Committee of the Regions. A Thematic Strategy on the Sustainable Use of Pesticides, 12.7.2006 COM(2006) 372; EU Commission: Brussels, 2006. HAIR project (HArmonised environmental Indicators for pesticide Risk), European Commission Project SSPE-CT 2003-501997; EU Commission: Brussels, 2003. Sood, C.; Bhagat, R. M. Interfacing geographical information systems and pesticide models. Curr. Sci. 2005, 89, 1362–1370.

(10) Schriever, C. A.; Liess, M. Mapping ecological risk of agricultural pesticide runoff. Sci. Total Environ. 2007, 384, 264–279. (11) Verro, R.; Calliera, M.; Maffioli, G.; Auteri, D.; Sala, S.; Finizio, A.; Vighi, M. GIS based system for surface water risk assessment of agricultural chemicals. 1. Methodological approach. Environ. Sci. Technol. 2002, 36, 1532–1538. (12) Bonzini, S.; Verro, R.; Otto, S.; Lazzaro, L.; Finizio, A.; Vighi, M. Experimental validation of a GIS-based procedure for predicting pesticide exposure in surface water. Environ. Sci. Technol. 2006, 40, 7561–7569. (13) Verro, R.; Finizio, A.; Otto, S.; Vighi, M. Predicting pesticide environmental risk in intensive agricultural areas. II: screening level risk assessment of complex mixtures in surface waters Environ. Sci. Technol. 2009, 2, 530-537. (14) Finizio, A.; Calliera, M.; Vighi, M. Rating systems for pesticide risk classification on different ecosystems. Ecotoxicol. Environ. Saf. 2001, 49, 262–274. (15) Technical Guidance Document (TGD) on Risk Assessment of Chemical Substances, EUR 20418 EN/2, 2nd ed.; European Chemical Bureau, Joint Research Centre: Luxembourg, 2003. (16) Dubus, I. G.; Brown, C. D.; Beulke, S. Sources of uncertainty in pesticide fate modelling. Sci. Total Environ. 2003, 317, 53–72. (17) Schriever, C. A.; Liess, M. Mapping ecological risk of agricultural pesticide runoff. Sci. Total Environ. 2007, 384, 264–279.

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