Environ. Sci. Technol. 2004, 38, 3239-3246
Landscape-Level Approach To Assess Aquatic Exposure via Spray Drift for Pesticides: A Case Study in a Mediterranean Area LAURA PADOVANI, ETTORE CAPRI,* AND MARCO TREVISAN Istituto di Chimica Agraria ed Ambientale, Universita` Cattolica del Sacro Cuore, 29100 Piacenza, Italy
The development of methods to extract information from landscape analysis to refine risk assessment is becoming increasingly important. This paper presents results from a pesticide surface water exposure assessment at the watershed scale, based on a combination of edge of field studies, large-scale monitoring studies, and modeling activities with GIS-based landscape analysis methodologies covering an area of approximately 3200 ha surrounding the Simeto River in Sicily (Italy). The dynamic behavior of the pesticide chlorpyrifos-methyl was modeled in two different steps: calculation of the fraction of the application rate that is deposited beyond the field edge and simulation of the fate and persistence of the pesticide in the aquatic environment. Drift loads showed high spatial variability. Considering spray drift deposition as a fraction of the pesticide application rate, 60% of the results were e0.02 (equal to 0.04 mg/m2). Only 8.5% of the results were above 0.5. The highly variability of the landscape factors was reflected in the results. More than 60% of the predicted pesticide concentrations were less than the limit of quantification (0.05 µg/L), affecting about 75% of the total length of the river tract analyzed. Predicted pesticide concentrations were higher than 0.1 µg/L in 23% of cases, but this corresponded to an insignificant portion of the river (1.2% of the total length). These results suggest that management options, such as increased no-spray zones, could provide further protection for surface water. These could be modeled to illustrate their overall impact. As an alternative, the introduction of a 20-m no-spray zone clearly reduced potential exposure, and 92% of the water body was protected. Estimated data are in agreement with data collected during a field monitoring study.
Introduction The goal of pesticide exposure assessment is to estimate chemical emissions, releases, fate, and distribution in order to determine the duration of environmental concentrations to which nontarget species may be exposed. Three potential nonpoint sources of pesticides in surface water are recognized in exposure assessment: atmospheric spray drift, subsurface drainflow or lateral flow, and surface runoff (1). These transport processes have been thoroughly studied at the edge of the field scale. Recently, great progress has been made in predicting spatial and temporal concentrations of pesticides * Corresponding author telephone: +00390523599218; fax: +00390523599217; e-mail:
[email protected]. 10.1021/es049699p CCC: $27.50 Published on Web 05/14/2004
2004 American Chemical Society
at the watershed level, using mathematical models combined with geographic information systems (GIS) (2-6). These approaches can manage vast amounts of heterogeneous data inherent with large-scale exposure assessment. Geographical and quantitative information critical to aquatic exposure modeling is gathered and processed through GIS in order to estimate the path of the chemical through the environment over time. However, making decisions to manage the potential risks from exposure to chemicals entails a clear understanding of the predictive models and of the standard assumptions and default values generally used in the absence of data. To overcome these limitations, an increasing number of scientists recognize the need for the development of methods to extract information from landscape analysis in order to refine risk assessment (7-10). Furthermore, the policy makers in the United States (11) and Europe (12) have stressed the importance of the development of tiered approaches to exposure assessment that produce increasingly “realistic” estimates of the likely range of predicted environmental concentrations (PEC) of pesticides at successively higher tiers of assessment. Depending on the properties of the compound, its pattern of use, and areas of potential concern identified in lower tier assessments, the higher tier exposure assessment step may include a number of refinement options of varying degrees of complexity including risk mitigation measures, generation of additional environmental fate (lab/field) data, refinement of fate input parameters, and regional and landscape-level approaches. This paper presents results from a pesticide surface water exposure assessment at the watershed scale, focusing on a case study involving chlorpyrifos-methyl (O,O-dimethyl O-3,5,6-trichloro-2-pyridyl phosphorothioate, RELDAN22) use in a citrus cropped area in southern Italy. Chlorpyrifosmethyl (CPM) is a broad-spectrum organophosphate insecticide used for the control of many pests infesting vineyard, apple, potato, tomato, strawberry, ornamental plants, maize, poplar, and citrus crops. The approach is based on a combination of edge of field studies (13), large-scale monitoring studies (14), and modeling activities using GIS-based landscape analysis methodologies. In particular, aerial image processing techniques are used to identify different spatially dependent categories that are critical to drift deposition over the surface water body. The major goals of this study were (i) to develop a pilot approach for pesticide aquatic exposure assessment arising from spray drift using reliable, quantitative information regarding the release and emission of the substance and detailed spatial data on the agricultural landscape to refine the assessment; (ii) to assess the reliability of the approach by comparing model outputs to measured pesticide concentrations in field samples; and (iii) to investigate possible mitigation options to reduce the impact of spray drift on pesticide concentrations in the “critical” areas identified in the studied watershed. This study demonstrates the effectiveness of integrating landscape analysis into environmental exposure assessment and should be one of the approaches considered in moving toward more realistic exposure assessment.
Materials and Methods Study Area. Sicily is the largest Italian region and the largest island in the Mediterranean Sea. The only wide valley on the island is the fertile plain of Catania (Figure 1), positioned between the Ionian Sea and Etna, Europe’s highest volcano. The plain is located along the lower Simeto River and is the most important agricultural area in the region. Citrus groves VOL. 38, NO. 12, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 1. Location of studied area. (108 097 ha) represent more than 65% of the permanent crops in the area, with 64 300 ha cultivated with oranges. The soils of the Catania plain are alluvial (Typic and/or Vertic Xerofluvent: Typic and/or Vertic Xerochrepts; and Eutric Fluvisols: Eutric and/or Vertic Cambisols) with mediumfine-coarse texture. The climatic conditions can be described as temperate continental, with an average annual temperature ranging from 15 to 18 °C and some rare ground frosts (15). Total rainfall is approximately 800-900 mm/yr but is not uniformly distributed during the seasons: 90% of the total rainfall is concentrated between October and March. The summer season (from June to September) is dry, with less than 50 mm of rainfall. Simeto is the largest river in Sicily, 130 km long and with a watershed area of 4,186 km2. The Simeto watershed has a complex morphology with considerable tributaries: the Salso, Dittaino, and Gornalunga Rivers flow into the Simeto River in the lower part of the valley, close to the Simeto outflow into the Ionian Sea. Several artificial reservoirs distributed in the watershed ensure the availability of water for irrigation purposes during the drought season. The present study focuses on the middle part of the watershed covering an area of approximately 320 km2 surrounding the Simeto River. Landscape Spatial Analyses. The data sources for the characterization of spatially dependent variables critical to aquatic exposure modeling were eight digital aerial photograph images at a scale of 1:10 000 (CTR Nos. 623120, 624090, 624100, 624060, 624140, 624150, 633030, and 633070) provided by Terraitaly, Compagnia Generale Ripreseaeree S.p.A, Parma (www.terraitaly.it). These 1-m spatial resolution orthophotos (254 dpi) covered a sub-watershed of the Simeto 3240
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River, meandering over the gentle flood plain with a slope less than 5%. The area is well defined by the Traversa Contrasto Dam located near the Salso River confluence in the upper part of the subbasin and is closed downstream with the Ponte La Barca Dam (Paterno` municipality), including a river length of approximately 19 km. The aerial images were acquired during the period May-September 1998/1999 and are georeferenced to the Gauss-Boaga projection. Images were stored and processed in an ArcView GIS environment (Version 3.0a ESRI). The ArcView Image Analysis Extension tools (Version 1.0, ERDAS) were used to (i) identify citrus crops and their delineation; (ii) characterize receiving waters (width and length); and (iii) identify the presence of riparian vegetation. Proximity analysis was designed to generate margins around the water course at different widths (10, 20, 30, 40, and 50 m) to reflect the potential for varying loads of pesticide spray drift from adjacent citrus fields. The maximum distance of 50 m was chosen in order to examine only those areas most likely to present cases of potentially high exposure. As the network of small or narrow streams that drain into the watershed was scarce and barely detected in the image, the analysis was performed for the main river (Simeto) only. The river is subdivided into sections (referred to as River Assessment Unit (RAU) in this paper) consistent with (i) the distance from the last row of crop to the water edge (influencing the percentage of compound reaching the water body), (ii) the river width, and (iii) the length of river adjacent to field crops (cross-sectional area of the receiving water perpendicular to the field). Width variations of the river were considered a critical factor in estimating the aquatic fate of
FIGURE 2. Experimental spray drift data: average of median values of fan-assisted trials and manual lance trials and calculated regression equation. the compound. River widths were subdivided into six categories: 25 m. Aquatic Exposure Modeling. Dynamic behavior of the pesticide was modeled in two steps: (i) calculation of the fraction of the application rate which is deposited beyond the field edge and (ii) simulation of the fate and persistence of the pesticide in the aquatic environment. Two models were used for each step, and the outputs of the first model were part of the input of the second. In both models, spatially dependent parameters critical to aquatic exposure modeling were derived from the landscape spatial analysis. Final results were expressed as the maximum PECs in water for each segment of the Simeto River. Modeling Pesticide Drift Loadings. Most EU countries use tables based on regression curves derived from experimental data that are generally “worst-case” to calculate drift loadings. The European Directive 91/4141/EEC (17), with reference to the authorization procedure for pesticides, requires the use of Ganzelmeier tables to calculate pesticide drift deposits (18). On this basis, the FOCUS (the Forum for the Coordination of pesticide environmental fate models and their Use) Surface Water Group developed a software tool to facilitate the calculation of aquatic drift loadings to estimate PECs in surface water and sediments (1). The drift deposition is a width-average number based on a 90th percentile probability of the drift occurrence curve, constructed from the German Biologische BundesAnstalt data (19). Field trials on CPM spray drift conducted in four different citrus crops in the Catania plain (13) showed that the “drift reference values” calculated with Ganzelmeier tables are unsuitable for the simulation of drift under the actual conditions of the study area. Thus, in the present study, a modified version of the FOCUS drift calculator was developed by replacing the original algorithm with a regression equation calculated from the average experimental data from field trials (Figure 2):
drift ) (6.7769 × (-1/0.817) exp(0.1817 × z2)) (6.7769 × (-1/0.1817 × z1))/[z2 - z1] (1)
where z1 is the distance from the edge of the treated field to the closest edge of water body (m) and z2 is the distance from the edge of the treated field to the farthest edge of water body (m). The equation is integrated over the width of the water body segment because drift is higher on the edge nearest to the field and lower on the edge farthest from the field. The replacement in the calculator of the experimental drift measures allowed us to tailor the model to the specific exposure scenario of interest. Key variable inputs to the calculator referring to each RAU included (i) distance from the edge of the field to the receiving water body; (ii) river width; (iii) length of the receiving water body section (RAU); and (iv) presence/ absence of riparian vegetation in the zone between the citrus field and the river. Some parameters were assumed to be constant in all the calculations, producing realistic worst-case estimations. A single application rate was considered with the maximum permitted label dose (1800 g/ha). A default water depth of 1 m was applied to every river section (a scouting study in the area indicated this value to be the mean water depth). As far as the riparian vegetation is concerned, it is wellrecognized that the type and structure of plants in the margin between the sprayed crop and the adjacent surface water can have a large influence on the rates of pesticide deposition to surface water (20). As these features were impossible to define at the landscape analyses level (they ranged from small bushes of 0.5 m up to 3 m plants, dominated by the Tamarix spp. species), an indicative 50% drift-reducing effect of riparian vegetation was adopted. This mitigation factor can be considered a conservative value in comparison with data from literature reporting a 45-90% reduction in drift (21). The model outputs, expressed as drift loadings in mg/m2, were calculated for each RAU identified in the landscape spatial analysis and were intended for use as input parameters to the aquatic fate model. Modeling Pesticide Aquatic Fate and Persistence. The use of mathematical models simulating the fate of pesticides in VOL. 38, NO. 12, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 3. Conceptual representation of geo-referenced citrus fields, River assessment units (RAU) and calculation for determining aquatic exposure via mathematical models (see text for details). freshwater ecosystems are useful to estimate aquatic concentrations in space and time. In particular, the TOXSWA (TOXic substances in Surface WAters) model is used for pesticide exposure assessment in the EU evaluation process (22, 23). The TOXSWA model calculates exposure in water and in sediment at the downstream end of a ditch, stream, or pond neighboring a treated field. Version 1.2, used in the present study, can only handle constant water depths and discharges. It is particularly suitable for simulating entries of pesticide to a water body system by spray drift only. The suitability of the selected model to the environmental conditions of the study area was assessed previously at the edge of the field scale (24). Simulations were performed for each RAU identified for the Simeto River in order to provide a spatial distribution of PECs at the watershed scale. The simulation design to calculate PECs is schematically represented in Figure 3. The following assumptions were used to produce a worst-case scenario: (i) The orientation of direct spray drift was perpendicular to surface water from every citrus crop. (ii) The drift loading to a unique RAU from a given citrus crop was calculated using the modified version of the drift calculator as reported above. (iii) The spray drift occurred over the total length of the RAU. (iv) CPM was applied at the same time in 68 citrus fields within the sub-watershed. (v) The sequence of simulations started from the most upstream RAUs at Traversa Contrasto and followed the flow direction of the river down to the last RAU at the Ponte La Barca Dam. (vi) As registered in the eight field trials conducted in the study area (13), wind direction can vary considerably during a single application; for this reason all fields within the watershed are considered to potentially contribute to drift loading. Where a RAU is surrounded by citrus crops on both sides of the river and/or there is more than one field present within 50 m, the sum of pesticide mass reaching the water body via drift was considered. 3242
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(vii) The initial pesticide concentration within each RAU resulted both from the drift component entering surface water and the amount of pesticide flowing into the RAU from the upstream part of the river. (viii) A default mean value of water flow rate of 0.083 m/s was used in every RAU, with the exception of the meandering sections of the river (100 m/d). (ix) A default mean value of 1 m was used for river depth. (x) The river was trapezoidal in shape (top/bottom ) 1.25). (xi) Aquatic vegetation (floating and submerged) was assumed absent. Tables 1 and 2 summarize input parameters used in TOXSWA simulations and the properties of CPM.
Results and Discussion Landscape Spatial Analyses. Citrus groves were identified in the digital aerial images, and the created polygons were classified. Digital buffers were drawn around the surface water body, and the aforementioned parameters, determining the RAU, were calculated. In the worst-case scenario (50-m margin around the water body), 176 RAUs were identified as candidates for drift. Of the total length of the river tract analyzed (19 324 m), 63.8% had no agricultural land in the 50-m margin. Citrus fields bordered the river for nearly 7 km. Most of the citrus crops (23.3% of the total fields delineated) fell within the 20-30-m margin, with approximately 1 km of the sum total lengths bordering the river. The remaining 18.2% of fields were within the 10-20-m margin (2 km sum total lengths), 15.9% within the 30-40-m margin (1.5 km sum total lengths), 15.3% within the 40-50-m margin (about 1.5 km total length), and less than 5% were within the 10-m margin and surrounded the river for less than 700 m. No crops were found directly adjacent to the river. Possible drift contributions derived mostly from the right bank of the river (62.8% of the total citrus crops analyzed). This information could be very useful when combined with maps of dominant wind directions in the study area to provide
TABLE 1. Input Parameters for TOXSWA Model Concerning Water Body Characteristics, Hydrology, Space Step, and Time Step for Calculations data category waterbody
hydrology
simulation
a
parameter
units
values
source/comments
segmentsa
water layer length waterbody segments geometry of waterbody bottom width depth def perimeter sediment segments thickness segments suspended solids concentration of suspended solids mass ratio organic matter flow velocity waterbody water depth waterbody temperature in water and sediment dispersion coefficient in water dispersion length in sediment upward seepage in incoming water upward concn pesticide in incoming water total time calculation time step time interval of output
m no.
landscape analyses landscape analyses
m m
0.8 0.01
field data and estimated field data and estimated
m no.
0.1 30
estd selected by user
g m-3
15 0.5 7171 0.1 25 7888 0.015 0 0 0.5 10 0.0067
field data field data field data and estimated field data and estimated field data and estimated field data and estimated field data and estimated selected by user selected by user selected by user selected by user selected by user
m d-1 m °C m2 d-1 m mm d-1 mg L-1 d s d
Specific values for single RAU derived from landscape analyses.
TABLE 2. Pesticide Input Parameters for TOXSWA Model data category transformation sorption
volatilization
parameter
units
half-life in water at 20 °C half-life in sediment at 20 °C activation energy suspended solids sorption coefficient, Kom Freundlich exponent sediment sorption coefficient, Kom Freundlich exponent saturated gas pressure saturated gas pressure (measd at temp) molar enthalpy of vaporization solubility solubility in water (measd at temp) molar enthalpy of solution exchange coefficient pesticide in liquid phase in gas phase
diffusion coefficient in water molecular mass
a detailed probabilistic assessment of spray drift impacting the Simeto River watershed. Spray drift deposition can be mitigated by the presence of natural buffers that separate water from agricultural land: vegetation was present on approximately 40% of the river banks surrounded by citrus crops. Buffer analysis could be improved in future work by the definition of the vegetation cover type (dense trees, sparse bushes, etc.), the average width of the buffers, and the inclusion in the modified version of the drift calculator of specific attenuation factors. Pesticide Drift Loading. A total of 143 simulations were performed using the modified version of the drift calculator. The CPM drift loads on the different RAUs of Simeto River ranged from 0 to 1.497 mg/m2. The median value was 0.026 mg/m2, and the standard deviation was 0.238 mg/m2. Drift loads were highly variable in space, as a consequence of the variability of the input landscape parameters considered in the drift calculator. Considering spray drift deposition as a fraction of the pesticide application rate,
values
source/comments
d d J mol-1
9 1 55 000
laboratory data (28) laboratory data (28) model’s user manual (23)
L kg-1
282 0.9
estd from laboratory data (28) model’s user manual (23)
L kg-1
282 0.9
laboratory data (28) model’s user manual (23)
Pa (°C)
0.0056 (25)
literature (29)
J mol-1
95 000
model’s user manual (23)
g L-1 (°C) J mol-1
0.004 (24) 27 000
literature (30) model’s user manual (23)
m d-1 m dmm2 d-1 g mol-1
1.7 163.1 40 322.5
model’s user manual (23) model’s user manual (23) model’s user manual (23) literature (31)
60% of the results were e0.02 (equal to 0.04 mg/m2). Only 8.5% of the results were greater than 0.5. Figure 4 illustrates an example of a citrus field (labeled R_07) with different “drift zone impact” to river segments (RAUs). Seven RAUs (named as R_07a, R_07b, ..., R_07g) can be identified. The predicted CPM loads via spray drift varied along the river tract according to the values of the specific input variables (Table 3). To evaluate the magnitude of inaccuracy that can occur in pesticide drift loading calculations, the effect of error in the measurement of landscape parameters was analyzed. Proximity analysis indicated the most sensitive parameter: a 5-m error in measuring the distance from the river to the edge of the field produced a constant 148% inaccuracy in the final output. Less significant was the error relating to the measurement of river width. A 1-m error in measurement resulted in a 9% inaccuracy in the calculation for narrow tracts of the river and a 4% inaccuracy where the width of the river was greater than 20 m. Errors in measuring both of VOL. 38, NO. 12, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 4. Example of predicted chlorpyrifos-methyl spray drift deposition from citrus field labeled R_07 onto different RAUs. Reprinted with permission from Digital Orthophoto; CTR Section No. 624140 (commessa n 20020012). Copyright 2004 Terraitaly, Compagnia Generale Ripreseaeree S.p.A.
TABLE 3. Example of Subdivision of a Tract of Simeto River in RAUs, Related Input Parameters for the Drift Calculator, and Resultsa
field label
RAU id
R_07 R_07a R_07b R_07c R_07d R_07e R_07f R_07g
distance between river and RAU (m)
RAU widthb (m)
25 24 15 20 30 45 48
37-40 31-37 31-40 40-42 20-40 16-19 11-16
drift RAU presence calculator length of natural output (m) vegetation (mg/m2) 13 42 12 50 30 12 15
yes yes yes yes no no no
0.009 0.013 0.062 0.022 0.010 0.001 0.001
a See text and Figure 3 for details. b Minimum and maximum width: the mean value as input in the drift calculator was used.
the parameters produced an inaccuracy in the results ranging from 20% to 50% with decreasing river width. Pesticide Aquatic Fate and Persistence. Chlorpyrifosmethyl exposure in the aquatic system was simulated for each RAU along the river. Exposure concentrations are defined as the CPM concentration at the downstream end of the RAU where pesticide input occurred and where the longest exposure duration is expected. The 176 results reflected the high variability of landscape factors and cannot be presented as a spatial distribution. Peak water concentrations were classified into five classes (Table 4). More than 60% of the PEC values were less than the limit of quantification (LOQ ) 0.05 µg/L), affecting about 75% of the total length of the river tract analyzed. Concentrations greater than 0.1 3244
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TABLE 4. Predicted Acute Exposure Concentrations of Chlorpyrifos-methyl in RAUs for Simeto River with the TOXSWA Model minimum
