Environ. Sci. Technol. 2007, 41, 4669-4676
Selecting Scenarios to Assess Exposure of Surface Waters to Veterinary Medicines in Europe M A N U E L K . S C H N E I D E R , * ,† CHRISTIAN STAMM,† AND K A T H R I N F E N N E R †,‡ Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Du ¨ bendorf, Switzerland, and Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, 8092 Zurich, Switzerland
Registering a veterinary medicinal product (VMP) in the European Union requires assessing its potential to contaminate surface waters (SW) on a European scale. VMP are spread to land in manure or excreted during grazing and may enter SW through runoff, erosion, or leaching. Since the factors driving these processes vary largely across Europe, it is necessary to identify characteristic conditions, so-called scenarios, under which VMP enter SW. These scenarios may guide the parameterization of mechanistic fate models to predict environmental concentrations for environmental risk assessment. A number of such scenarios for pesticides and VMP have been developed rather pragmatically. Here, we describe how a geo-referenced European database of driving factors was used to divide the European environment into groups with similar conditions for SW contamination by VMP. Out of these groups, relevant exposure scenarios in Europe were selected by a simple scoring system. Comparing these to the existing scenarios showed that a number of situations are not well covered. The newly identified scenarios are primarily located in hilly areas of Central Europe and the Mediterranean, and in Eastern European plains with a continental climate. We recommend that they are included in the technical guidelines for higher-tier assessment of VMP.
Introduction Veterinary medicinal products (VMP) are widely used in animal production to treat diseases and protect animal health. The global animal health market had a volume of nearly $15 billion in 2005 (1) and the veterinary usage of antibiotics in the European Union (∼5000 t in 1997) lies in the same range as human consumption (2). VMP are administered to animals internally and externally, and the majority of the product is excreted or washed off in unaltered form or as metabolites. VMP enter the aquatic environment either directly when used in aquaculture or via the terrestrial environment when treated animals graze or their manure is spread. In grab samples of manure from Swiss farms, concentrations of sulfonamide antibiotics were up to 20 mg L-1 (3). Given maximum application rates of manure of 50 m3 ha-1 yr-1, up to 1 kg * Corresponding author phone: +41 44 823 51 18; fax: +41 44 823 54 71; e-mail:
[email protected]. † Eawag, Swiss Federal Institute of Aquatic Science and Technology. ‡ Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich. 10.1021/es062486a CCC: $37.00 Published on Web 05/24/2007
2007 American Chemical Society
ha-1 of sulfonamide may be spread on agricultural fields, a rate comparable to herbicide applications. VMP enter surface waters (SW) primarily by fast transport processes. These include (i) surface runoff of substances dissolved in water or bound to manure particles during strong precipitation events (4, 5), (ii) soil erosion and the associated transport of bound substances, or (iii) leaching of substances through preferential flow paths to drainage systems or shallow groundwater with subsequent recharge to SW (6). Because VMP are designed to have an effect on treated animals or harmful organisms, they likely affect nontarget organisms in the aquatic and terrestrial environment. To assess their environmental risk, predicted environmental concentrations (PEC) are compared to predicted no-effect concentrations (PNEC). The current guideline on environmental risk assessment (ERA) for VMP (7) suggests a tiered approach. In phase 1, a decision tree is followed based on the characteristics and use pattern of the product. If PECs exceed trigger values or if the product is directly applied to the aquatic environment, it enters phase 2, where the PEC is refined and effects are assessed. The refinement in phase 2 is based on generic assumptions on administered doses, manure production and application, but does not make use of mechanistic exposure modeling. A new technical guideline (8), which comes into effect November 1, 2007, recommends the use of mechanistic exposure models in higher tiers of ERA. A number of models have been developed for this purpose and have been applied in regulatory contexts, e.g., MACRO (9), PRZM (10), or LEACHP (11). All of these models require detailed input to be run, such as daily meteorological data and in-depth soil informationsdata that can usually only be provided for specific locations. In addition, some of the models (e.g., MACRO) are computationally intensive and the number of modeled situations is limited. Predicting exposure to VMP in large and diverse areas such as Europe therefore requires a reduction of the task by the selection of characteristic situations, so-called scenarios, under which VMP enter the environment. The new technical guideline for the ERA of VMP (8) recommends the use of FOCUS scenarios (12) or allows using scenarios defined in the VetCalc tool (13). The FOCUS scenarios were defined by the FOrum for the Co-ordination of pesticide fate models and their USe to calculate PECs for pesticides in surface waters and groundwaters (12). The scenarios were selected pragmatically by classifying climate, slope, and soils according to their worst-case nature. However, FOCUS scenarios, as they were developed for pesticides, are restricted to arable land. In contrast, VetCalc scenarios were specifically developed for VMP and also cover grasslands. They were selected based on regional livestock numbers in three climatic zones (13), while largely neglecting the crucial influence of soil properties and hydrology on VMP fluxes to SW. While pesticides are authorized by the individual countries once they have obtained Annex I listing by passing one out of the ten FOCUS scenarios (12), new active substances for VMP are registered in Europe in a more centralized way. VMP can be authorized on a national level, but there is a clear tendency toward European or multi-national authorization procedures. The EMEA has the responsibility for European procedures. In multi-national procedures one rapporteur Member State prepares an extensive risk assessment report which is mutually accepted by the others or not. Hence, it is crucial to understand which scenarios are most critical and how representative they are in a European context. VOL. 41, NO. 13, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 1. Variables Used for the Partitioning and the Subsequent Selection of Scenarios for Environmental Risk Assessment of Veterinary Pharmaceuticalsa Load variables name a b c d e
unit
manure load dairy and other cows manure load other cattle manure load swine manure load poultry manure load sheep, goats and heifers
kg P km-2 kg P km-2 kg P km-2 kg P km-2 kg P km-2
Environmental Variables relationship with name f g h i j k l m n o p q r
organic carbon in topsoil clay in topsoil sand in topsoil grassland on total agricultural land mean annual temperature precipitation during vegetation period (VP) precipitation intensity during VP extreme rainfall (days >10 mm rain) duration of longest dry spell slope distance to natural water base-flow index shallow groundwater
unit % % % % °C mm mm d-1 d yr-1 d % m . %
runoff
erosion
leaching
-
-
+
+ + + + + -
+ + + + -
+ + +
Use of variables to calculate tendency scores for VMP load, runoff, erosion, and leaching is described by the + sign, which indicates a positive relationship of the variable to contamination potential, i.e., larger values increase the contamination potential, and the - sign, which indicates a negative relationship (see Supporting Information SI-3 for underlying assumptions). a
Our aim has been to select exposure scenarios for VMP in Europe based on an analysis of the relevant transport processes and their drivers, while relying on statistical tools as much as possible. Selecting scenarios in a geographical sense means to look for areas with similar environmental conditions regarding their potential for SW contamination by VMP. Our approach consists in compiling a spatially referenced matrix of variables on (i) the potential loads of VMP excreted by livestock, and (ii) environmental conditions influencing substance availability and transport to SW. The matrix cells are clustered into groups with similar characteristics and the groups are ranked according to VMP load and the tendency for runoff, erosion, and leaching processes. Finally, groups with a high contamination potential and a reasonable size are selected. Ultimately, this work should improve the European ERA process for VMP. We complement the rather pragmatically selected scenarios from FOCUS (12) and VetCalc (13) by a set of scenarios developed under a rigorous statistical framework based on up-to-date geo-referenced data.
Materials and Methods A flow chart of the procedure used to select the scenarios is available in the Supporting Information, SI-1. Variables Affecting the Potential for Surface Water Contamination. All included variables had to be available for all of Europe and on a continuous scale for the statistical analyses (Table 1). In addition, they needed to have a known monotonic relationship with contamination, because a simple scoring system and not a mechanistic model was used to select scenarios. Data availability allowed the evaluation for the 27 EU member countries excluding Cyprus but including Norway and Switzerland. Because very little information on the prospective spatial distribution of usage for a given VMP is available at the registration stage, we approximated the potential load of VMP to agricultural land by manure loads (Table 1, variables a-e) for five manure categories. These were estimated by spatially disaggregating census data with information on land 4670
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use and manure management (for details see SI-2). Phosphorus excretion was used as a proxy for manure loads because it is not influenced by dry matter content of the manure or losses during manure management. Environmental factors affect the potential for SW contamination by VMP in two ways: First, certain factors influence the availability of VMP for transport (Table 1, variables f-j). The variables are (i) soil properties (% organic carbon (OC), clay, and sand in the topsoil), providing information on the sorptive capacity of the soil, its infiltration capacity, and erodibility; (ii) the proportion of grassland on agricultural land as a proxy for the incorporation of manure into the soil and its infiltration capacity (4); and (iii) mean annual temperature as a main driver of degradation kinetics. Second, certain environmental factors influence the tendency of the available fraction of VMP to be actually dislocated from the top soil layer into surrounding water bodies. Variables in this second group (Table 1, variables k-r) are: (i) precipitation data, including average precipitation during the vegetation period, the frequency of extreme precipitation, rainfall intensity, and the duration of the longest dry spell to place special emphasis on fast-flow processes; (ii) slope, which has a crucial effect on the velocity of surface runoff; (iii) the distance to rivers as a descriptor for the probability of VMP loads of reaching SW; (iv) the base-flow index (BFI; the proportion of base flow on total catchment discharge) as an indicator of runoff occurrence; and (v) information on the presence of shallow groundwater. Details on data sources and preparation are given in SI-3. Statistical Analyses. The geo-referenced dataset at 10 km resolution was partitioned into a pre-assigned number of 40 groups so that members of the same group were more alike than members of different groups. A number of 40 clusters was chosen as a good compromise between only a few, but too general clusters, and many, but too small and spatially incoherent clusters. We used an algorithm called clara (14), which seeks a set of k representative objects (the medoids)
minimizing the sum of dissimilarities of cells to their closest medoid (more details are given in SI-4). The partitioning was based on the environmental variables only (Table 1, variables f-r) and groups were, thus, characterized by similar environmental conditions but independent of livestock densities. The reasons for this choice were that (i) livestock numbers are artificially aggregated into administrative units and might not correctly reflect local density; (ii) on a European scale, animal numbers cannot be related to VMP usage because medication data are only available for a few countries; and (iii) livestock densities in Europe vary with time and a partition based on these densities would need regular updates. We aimed at minimizing the influence of correlated variables by calculating principal components (PCs). These are linear combinations of the original variables which explain the highest percentage of the variation. In order to reduce complexity, subsequent analyses were based on the first seven PCs only, which all explained more than 5% of the variance and 83% in total. The robustness of our procedure was assessed by comparing results obtained (i) with different numbers of PC; (ii) using the original environmental variables instead of the principle components; and (iii) using a different clustering algorithm. Details on the robustness analysis are given in SI-4. Selection of Scenarios. A number of 10-15 exposure scenarios can realistically be run with available detailed fate models and this number of clusters was selected based on two criteria: contamination potential and size. The contamination potential was captured by tendency scores T for VMP load m and for the occurrence of runoff r, erosion e, and leaching l events.
Ti )
∑P
v+
+
∑ (1 - P
v-),
for i ) m, r, e, l
(1)
Ti were calculated based on percentiles P for each variable, at which values of individual cells or median values of all cells in a group lie with regard to the whole dataset. Table 1 shows the variables used for each Ti and whether they were assumed to have a positive (v+) or negative (v-) effect. Percentiles are robust against outliers because even large changes in extreme values result in a small change of the percentile value. In a second step, scores R for runoff, erosion, and leaching were calculated as
Rj ) Tm‚Tj,
for j ) r, e, l
(2)
The assumption underlying the multiplication of tendency scores for VMP loads with those for the three processes erosion, leaching, and runoff was that if there are no animals present in an area, SW contamination is unlikely even if runoff, erosion, or leaching are likely to occur. Selecting the groups with a score R for runoff, erosion, or leaching in the top 30% and a size in the top 80%, was found most suitable to select 10-15 scenarios with high potential and reasonable size. Our selected scenarios were compared with each FOCUS (12) and VetCalc (13) scenario (hereafter called existing scenarios), by calculating a distance measure D. N
D)
|vsi - vei |
∑v i)1
s i
+ vei
(3)
where vei and vsi are median values of variable i in existing
and selected scenarios. Values of D < 4 represent an average difference between scenarios below 40% and were considered small.
Results Structures in the Dataset. Strong correlations between some variables in the dataset were observed. Strongest correlations, as expressed by Spearman rank correlation coefficients, rSP, were found between manure loads (variables a,b: rSP ) 0.97; c,d: rSP ) 0.77; b,c: rSP ) 0.71; a,c: rSP ) 0.70) and are the result of general patterns in livestock production intensities in Europe. Strong correlations were also found for various variables in association with temperature (n,j: rSP ) 0.79; f,j: rSP ) -0.76; e,j: rSP ) 0.70; j,o: rSP ) -0.69), which is apparently an important driver for other processes, such as precipitation or soil formation. The first principal component (PC 1) explained 25% of the variance and was associated with mountainous areas with low temperatures, short dry spells, extreme rainfall, and a high percentage of grassland cover (SI-5, Table S5). PC 2 was primarily connected to a wet climate and extreme rainfall and explained 20% of the variance. PC 3 was associated with soil texture and the BFI as an indicator of soil permeability. PC 4 and 5 were related to precipitation, the presence of shallow groundwater, and the distance to natural waters. PC 6 was associated with BFI and distance to natural waters, and PC 7 was associated with OC content in soil and average annual temperature. Robustness of Partitioning. The number and type of variables included in the partitioning had the strongest effects on the results, whereas the algorithm and the number of groups had less impact. This may be concluded from calculated similarity measures (see SI-4 for detailed information and Table S4 for data), which were lowest between partitions based on different numbers of PC. Highest similarities were found between analyses which only differed in the algorithm used or where the number of groups was different. All partitions evaluated were clearly different from a random attribution of cells to groups. Tendency Scores for VMP Load, Runoff, Erosion, and Leaching. Maps of the tendency scores for VMP load Tm, runoff Tr, erosion Te, and leaching Tl for individual cells are displayed in Figure 1 a-d. Figure 1a shows a high Tm in areas with high livestock density (Western England and Wales, Ireland, Brittany, Benelux countries, Po valley, and Northern Spain). It must be noted that Tm was calculated with all five animal classes receiving equal weights. Summing up effective amounts of manure would assume a direct dependence of administered drugs on manure production, which, e.g., does not exist between poultry and cows. Tr was highest in wet areas with steep slopes and grasslands (Figure 1b). Our calculations indicated that Te was important in large areas of Southern and Central Europe, especially where precipitation is intense and slopes are steep (Figure 1c). For the same reasons, Te was high in certain regions of Northern Europe. Tl was highest in lowlands with sandy, permeable soils, but also in regions with intense precipitation (Figure 1d). Risk Scores and Selection of Scenarios. Figure 2a shows the coverage of existing scenarios from FOCUS and VetCalc. Selected scenarios for runoff, erosion, or leaching are shown in Figure 2b-d. They are characterized in Table 2, ordered by the average of all three R scores. Scenarios 1-5 appear in all three maps and are, thus, representative for all three processes. Scenarios 7, 8, 12, and 13 have relevance for runoff and erosion, 6 for runoff and leaching, 9 for erosion and leaching, 10 and 11 for leaching only, 14 for runoff only, and 15-18 for leaching only. We found that the first twelve scenarios are robust against reasonable changes in the scoring method (data not shown). Median values for each of the VOL. 41, NO. 13, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 1. Distribution of tendency scores for (a) VMP loads Tm, (b) runoff Tr, (c) erosion Te, and (d) leaching Tl. variables a-r in selected and existing scenarios are given in Tables S6 and S7. The scenarios cover a relatively large area of Europe, with some being geographically rather diffuse (Figure 2, raster files of the scenario maps can be downloaded from http://www.eawag.ch/vetscen/). They show only a partial overlap with existing scenarios from the FOCUS project (12) and the VetCalc tool (13) displayed in Figure 2a. Still, most of our scenarios approximately correspond to an existing scenario, with the exception of scenarios 6, 7, and 12, which are not represented by existing scenarios (Table 2). The qualitative assessment in Table 2 is confirmed by the calculated distances D between scenarios (Table S8). Scenarios 6, 7, and 12 have no existing scenarios with an average distance below 40%. On the other hand, there are a number of FOCUS and VetCalc scenarios without correspondence in our analysis, in particular the scenarios in Southern Finland in FOCUS and VetCalc (Figure 2a). According to the authors of VetCalc, the inclusion of this scenario was suggested during the consultation process to achieve a larger geographical stratification (Neil Mackay, personal communication). In general, distances of our selected scenarios to FOCUS scenarios are smaller than those to VetCalc scenarios (Table S8). This is 4672
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mainly due to the smaller geographic extent of the VetCalc scenarios.
Discussion This paper demonstrates the usefulness of statistical classification tools for the selection of exposure scenarios for the ERA of VMP. Groups of areas with similar conditions, but not necessarily geographically coherent, were formed. A simple scoring procedure was employed to select groups relevant for runoff, erosion, or leaching of VMP to SW. Identification of New Scenarios for the ERA of VMP. The main outcome of the study is that the scenarios selected by our analysis only partially overlap with existing scenarios from FOCUS (12) and VetCalc (13). This may be explained on the one hand by differences in targeted chemicals and spatial extent, and on the other hand by the data used and its analysis. FOCUS scenarios were selected for pesticides and in the area of EU-15 only (12). Consequently, they mostly cover situations in areas with intensive arable production in Northern and Western Europe. However, livestock is often located in areas where climate, soils, or topography limit
FIGURE 2. Geographic coverage of existing scenarios (a) and of selected scenarios for runoff (b), erosion (c), and leaching (d). In (a), red color indicates scenarios from the FOCUS framework, blue indicates scenarios from the VetCalc tool, and purple indicates areas covered by both. In (b) to (d), only selected scenarios, which are described in Table 2, are displayed using random colors shown at the bottom. arable cropping. Animal production in these less favorable areas may not be the most intensive, but local concentrations of animals may be high and ecosystems may be more diverse and species-rich than in intensive cropping areas (15). The scenarios in VetCalc were selected by summing ranks of animal numbers in EU regions for the categories of cattle, swine, sheep, and poultry (13). The evaluation was also limited to EU-15 countries and, more importantly, did consider only animal heads but not their density. This increases the probability for larger regions to be selected (e.g., Andalucia or Brandenburg). Smaller regions or regions with little agricultural land (e.g., Cantabria or Vorarlberg) may not be selected even if animal densities on agricultural land are high. In our analysis, however, animal numbers were spatially disaggregated using information on land use and manuring
practice. To select scenarios, median values of the animal numbers were calculated for regions which are homogeneous with regard to the environmental tendency of SW contamination. Our analysis has shown that three situations are not sufficiently covered by scenarios in the existing frameworks of FOCUS and VetCalc. These are (i) hilly areas with a cool, wet climate stretching from the Massiv Central to the Bavarian Forest (scenario 6); (ii) foothills of mid-altitude mountain ranges stretching from Belgium to Slovakia (scenario 7); and (iii) plains in central Spain, Hungary, and Romania with a continental climate and heavy soils (scenario 12). We recommend that these three scenarios are further developed to be applicable in the risk assessment process. Due to the general east-west stratification of the European environVOL. 41, NO. 13, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 2. Geographic Coverage and Characteristics of Selected Scenarios and Their Overlap with Existing Scenarios coverage 1 2 3 4
5
6 7
8 9 10
11 12 13 14
15 16 17 18
Po valley, Central France, Northern Netherlands Bretagne and Normandy, Ireland, Belgium , and Southern Germany Western England & Wales, Western Ireland, Galicia, West coast of Norway Southern France, southern foothills of Pyrenees, Alps and Apennine, Styria and Epirus Netherlands, Northern Germany, Denmark, Central France and Eastern England Massiv Central, Vosges, Bavarian Alps and Forest, Northern and Southern Norway Wallonia, Rhineland, central German mountain ranges, foot-hills of mountain ranges in the Czech Republic Central Spain, Italy, Greece, Bulgarian and Romanian lowlands Northern France, Belgium, Southern England and Poland Northern France, Central Germany, Czech Republic and Slovakia, Hungary, Southern Sweden Lorraine, Central Germany, Hungary Central and Eastern Spain, lowlands in Hungary, Romania and Bulgaria Central and Eastern Spain, Romanian lowlands Northern Portugal, South-Western foothills of Alps, Pyrenees and Apennine, Slovenia Northern Germany, Eastern Denmark, Poland and Czech Republik Western Denmark, Brandenburg, Eastern Lithuania Eastern England, Northern Germany, Poland and Baltic states Northern Germany, Poland
characteristics Lowlands with permeable soils over shallow groundwater. Intensive livestock production. Mild and humid climate. Hilly areas with loamy soils, mixed arable and grassland use. Intensive animal production of cows, cattle and pigs. Cool and wet climate, exlusively grassland on impermeable soils with high OC content, relatively close to waters, intensive animal production of dairy cows, cattle and sheep on grassland. Areas with wet and mild climate and high precipitation intensity. Permeable soils on moderate slopes. Intensive livestock production.
FOCUS D5 and potentially R3, VetCalc Emilia Romagna VetCalc Bretagne, potentially FOCUS D5 and D2 VetCalc Wales
Flat areas with humid climate and sandy soils. Intensive arable cropping and animal production, mainly pigs and dairy cows.
FOCUS D4, VetCalc Noord Brabant, VetCalc Denmark
9
VetCalc Midi-Pyrenees, FOCUS R4
Hilly areas with cool climate, high frequency of extreme rainfall, sandy soils with high OC content. Dairy production on grassland. Hilly areas with moderate climate but relatively high precipitation intensity. High OC content in soils. Moderate livestock production, mainly cows and cattle. Riverbeds and coastal areas with Mediterranean climate and permeable soils influenced by groundwater. Swine, poultry and sheep production. Plains dominated by arable soils with high content of sand and silt. Pig and poultry production. Similar to 9. Plains with primarily arable use of light, silty soils. Moderate climate. Important pig production.
FOCUS D6 and potentially R4
Related to 9 and 10, but soils with higher clay content. Intensive pig production. Lowlands with clay-loamy soils with a dry and mild climate. Important pig and poultry production
FOCUS R1 and D5, VetCalc Brandenburg
Related to 12, but hilly areas with steeper slopes,less clay in soil and a dry climate. Intensive sheep production. Mild and wet climate, high occurrence of extreme rainfall on steep slopes mainly covered by grassland. Moderate livestock production
FOCUS D6
Plains with sandy soils and mainly arable use. Important production of pig, poultry and dairy cows.
VetCalc Brandenburg FOCUS D4
Similar to 15, but with sandier soils and slightly less arable use and shorter distance to rivers. Related to 16, but heavier soils over shallow groundwater. Intensive poultry and pig production
VetCalc Brandenburg FOCUS D4 VetCalc Brandenburg FOCUS D2
Similar to 16, with higher permeability of soils.
VetCalc Brandenburg
ment, Eastern Europe is relatively well covered by groups associated with some existing scenarios. Uncertainties in the Scenario Selection Process. Scenario selection has traditionally been an expert process, making it vulnerable to bias arising from the composition and dynamics of the expert group. This bias may hamper the acceptance of the risk assessment framework in the regulatory process and intensifies the need for using quantitative techniques in scenario selection. However, despite successful application of statistical classification in eco-regionalization (e.g., 16), landscape typology (e.g., 17) and habitat description (18), there has been no application of these techniques to select scenarios for the ERA of chemicals. Here, we derived scenarios for VMP by dividing Europe into groups with similar conditions for SW contamination by VMP based on a purely statistical approach. The only subjectivity involved was the choice of the resolution of the analysis, the algorithm, and the selection and preparation of variables. The type and number of variables involved was identified as the factor with most effect on the results and would thus allow for most subjectivity. However, on a European scale, the use of variables is very much limited by their availability and there is little choice in this respect. It might be possible to include a large number of related data, e.g., monthly average temperatures. However, these would be highly correlated and result in partitions dominated by 4674
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FOCUS R1 and D4 FOCUS R1 and D4
FOCUS R2
temperature or be cancelled out by the principal component analysis. Therefore, not subjectivity but data availability and the need for a monotonic relationship with contamination potential led to the selection of variables used for this analysis. Expert knowledge as well as subjectivity come in where variables are interpreted with regard to their influence on the three processes of runoff, erosion, and leaching of VMP to SW. Ideally, data on measurements of VMP fluxes to SW would allow inferring relationships between processes and influencing factors. A number of tools have been developed for this purpose, e.g., regression, fuzzy sets (19) or neural networks (20). Unfortunately, fluxes of VMP to SW have only been investigated in a handful of experiments, too little to derive quantitative relationships between environmental conditions and the loss processes involved. We therefore had to base the interpretation of the environmental factors on scientific evidence derived from studies with other substances, e.g., pesticides and nutrients (e.g., 21). A first step in the validation of our results may be the comparison with existing estimates of soil erosion, runoff, and leaching. The literature provides estimates of runoff risk of pesticides from arable fields (22), soil loss by erosion (23), and predicted pesticide losses by drainage (24). These assessments or extensions of it cover at least a major part of the area considered in this analysis. Estimates for erosion agreed best and indicated highest values for Southern Europe,
especially in mountainous areas. However, our Re values (Figure 2c) seem to overestimate erosion in high-rainfall areas of Northern Europe. Reasons may be the different treatment of rainfall erosivity in the process-based model used by Pesera (23) and the under-representation of soil texture and surface coverage in our simple scoring approach. A comparison with the predictions for runoff and leaching of pesticides is more difficult because both existing studies (22, 24) did not distinguish between the environmental tendency for runoff and leaching and the actual load of substances on a particular area. Tiktak et al. (24) modeled PEC of a number of substances used in maize or wheat. Consequently, these concentrations rely heavily on the geographical distribution of the crops. The runoff of pesticides from arable land was modeled as a function of arable land cover, pesticide use, and other factors (22). Since VMP use is regionally different from pesticide use, these risk maps are incomparable. Besides comparing different risk maps, further validation may be achieved through comparison with measured response variables, here the concentration of VMP in runoff, drainage, and surface waters of different geographical regions. However, researchers have started only recently to systematically analyze VMP in surface waters (25). As a consequence, there is currently not enough data to corroborate our estimates, but the present analysis may guide researchers in the selection of locations for future experiments. Significance for the ERA Process of VMP. The application of classification algorithms and a simple scoring system to geo-referenced data has provided us with a set of scenarios for modeling the environmental risk of VMP to SW in Europe. Selected scenarios not covered by existing scenarios are primarily located in hilly areas of Central Europe and the Mediterranean, and in Eastern European plains with a continental climate. We recommend that these missing scenarios be included in the technical guidelines for higher tier assessment of VMP (8). To do so, they will need to be further elaborated and targeted field studies need to be carried out in order to calibrate mechanistic fate models (e.g., the FOCUS models) for these situations. Analyzing results for the FOCUS scenarios showed that certain scenarios, especially D2, D6, R2, and R3, tend to regularly predict highest maximum fluxes to SW (8, 12). Similarly, our own preliminary calculations with VetCalc showed, in line with evaluations by Alistair Boxall (written communication), that scenarios Emilia Romagna, Wales, and Devon predicted highest concentrations for a number of VMP. This indicates that not all scenarios have equal worst-case nature, but that there are systematic differences independent of substance properties. Similar trends can be observed from the order of scenarios in Table 2. The first scenarios in the table are those with the highest average R score (see Table S6 for details) and they correspond well with some of the consistently high-ranking scenarios from FOCUS and VetCalc. It must be noted that the present evaluation focuses on the transfer of VMP to SW by agriculture and does not consider aquaculture nor the unsafe disposal of unused or obsolete VMP. The assessment of exposure of SW by these paths would require additional scenarios not covered by this study. Also note that the load of VMP is approximated by a manure score over all animal classes in the current scenario selection procedure. In reality, however, certain active substances might only be applied to particular animal species and probably also be marketed in selected areas only. Through the separation of the environmental tendency for a contamination process to occur and the manure load in our analysis, we are providing a flexible framework to accommodate such information if available. In that case,
the environmental tendency scores can simply be multiplied with a more realistic estimate of the use of a particular substance in terms of its geographical distribution and its prescription for use in a particular animal species only.
Acknowledgments We thank Tom Holt, Zhibinew Klimont, Harald Menzi, and Beat Reidy for making unpublished data available, Ewenn Helary for technical assistance, and John Hollis for supporting the hydrological reclassification of the SGDBE. Comments by Janine Illian, Annette Johnson, Jan Koschorreck, Jo¨rg Ro¨mbke, and Anne Wehrhan and three anonymous reviewers are acknowledged. This research was funded by the EU Sixth Framework program through the project ERAPharm, Environmental Risk Assessment of Pharmaceuticals (Contract SSPI-CT-2003-511135).
Supporting Information Available 1. Flow chart of the scenario selection procedure; 2. Description of the disaggregation of census data for the calculation of manure loads; 3. Information on sources, transformations and interpretation of environmental variable data; 4. Evaluation of the robustness of partitioning; 5. Detailed results of PCA; 6. Median variable values for selected scenarios and existing FOCUS and VetCalc scenarios. This material is available free of charge via the Internet at http://pubs.acs.org.
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Received for review October 17, 2006. Revised manuscript received April 4, 2007. Accepted April 6, 2007. ES062486A
