Environ. Sci. Technol. 2000, 34, 4425-4433
Coupling SoilFug Model and GIS for Predicting Pesticide Pollution of Surface Water at Watershed Level R I C A R D O B A R R A , † M A R C O V I G H I , * ,‡ GUIDO MAFFIOLI,‡ ANTONIO DI GUARDO,§ AND PAOLO FERRARIO# Environmental Sciences Center EULA-Chile, University of Concepcio´n, P.O. Box 160-C, Concepcio´n, Chile, Department of Environmental and Landscape Sciences, University of Milano-Bicocca, Milan, Italy, Environmental Research Group, Department of Structural and Functional Biology, University of Insubria, Varese, Italy, and Institute of Agricultural Engineering, University of Milan, Milan, Italy
A multimedia fugacity-based model (SoilFug) was applied to predict pesticide pollution of surface water in a river basin of about 400 km2 in northern Italy. To account for the heterogeneity of environmental characteristics of the territory, a detailed description of the environmental scenario (hydrological network, land use, rainfall, soil properties, etc.) was made by using a Geographical Information System (GIS). Fourteen Uniform Geographic Units (UGUs) were defined for applying the SoilFug model on a modular basis. UGUs were then combined to predict river concentrations. A validation of the approach, made by comparing predicted data with the results of experimental monitoring on selected chemicals, gave satisfying results. This approach, based on the combination of a multimedia fugacity model with GIS, proved to be a suitable tool for the site-specific prediction of environmental distribution and fate of chemicals on a river basin scale.
Introduction During the past decade, multimedia models have become one of the major tools for predicting the environmental fate of chemicals (1). Their role is not only recognized by the scientific community, but multimedia models are also increasingly used for administrative and regulatory purposes. Several international regulations on potentially hazardous chemicals require a preventive risk assessment for man and the environment, performed on the basis of environmental concentrations predicted by suitable evaluative models. The most widely recognized multimedia models are those based on the fugacity concept (2). Fugacity-based models have been developed to predict the behavior of organic chemicals in a variety of environmental systems and conditions, such as leaching to groundwater (3), runoff to surface water (4), distribution in lakes and streams (5, 6), and * Corresponding author phone: +39-0264474205; fax: +390264474200; e-mail:
[email protected]. Corresponding address: Dipartimento di Scienze dell’Ambiente e del Territorio Universita` degli Studi di Milano-Bicocca, Piazza della Scienza, 1 I-20126 Milan, Italy. † University of Concepcio ´ n. ‡ University of Milano-Bicocca. § University of Insubria. # University of Milan. 10.1021/es000986c CCC: $19.00 Published on Web 09/16/2000
2000 American Chemical Society
multicompartmental distribution on a regional basis. A stepwise modeling strategy has been recommended by Mackay and co-workers (7-9) as a tool to predict the fate of chemicals in diverse environmental conditions. The SoilFug model (4) was specifically developed for the prediction of pesticide pollution in surface water and experimentally validated at field scales (10) up to the scale of a small river basin of about 100 km2 (11). Many regional models do not take into account the spatial distribution of a chemical in the environment, assuming that the compartments (air, water, soil) are homogeneous in composition and properties. This results in a single soil or water environment for the entire region. However, the variability of the system, which results in different intensities of driving forces that causes spatially variable transport fluxes, must be considered. While there are no theoretical obstacles for applying SoilFug model to a larger area, one must be aware of spatial nonhomogeneity. A suitable solution to this problem is to link multimedia models and Geographical Information Systems (GIS) and to define Uniform Geographical Units (UGUs), which are areas fairly uniform in their characteristics such that they can be represented by a specific set of partition and transport parameters (1). The model is then applied separately for each UGU described in the area. In this paper, the results of the application of an integrated SoilFug-GIS approach on a pilot catchment area are described together with an experimental validation carried out on selected chemicals in order to compare predicted and measured results.
Materials and Methods The suggested methodology consists of five steps (Figure 1): data collection, definition of UGUs, definition of environmental scenarios, modeling, and validation. Step I: Data Collection. Selected Chemicals and Properties. Three herbicides (alachlor, metolachlor, and terbuthylazine), used in large amounts on corn and soybean, were selected as tracers. Their main physical-chemical properties are shown in Table 1. Data for degradation halflife in soil (t1/2) were selected from the range of values reported in the literature. The chosen values are considered reliable in relation to the environmental characteristics (climatic, agronomic, geopedologic) of the study area, on the basis of direct experience on agricultural fields (15). Landscape Description for UGUs. The selected pilot catchment area was the lower Lambro river basin (Northern Italy), an intensive agricultural basin of the Po valley. The total surface area is 386 km2, almost completely flat, with a complex network of natural and artificial channels. The catchment area was divided into three sub-basins. The main sampling stations for experimental monitoring are located at the outlet of the three sub-basins: Salerano (sub-basin A), Sant’Angelo (sub-basin B), S. Colombano (sub-basin C and total basin) (Figure 2). Land use data were obtained from a regional map where comprehensive land use categories are reported, such as “arable crops” or “built up areas” (16). These data were integrated and updated with information from national and regional statistical institutes (17, 18) on crop surfaces in the Lombardia provinces and municipalities as well as with detailed information on paddies provided by the national rice authority (Ente Nazionale Risi). A detailed ARC/INFO map of land use can be found in Figure S1 in the Supporting Information. VOL. 34, NO. 20, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 1. Scheme of the methodological approach. Soil Properties. The most important soil characteristics influencing the environmental distribution of non ionic organic molecules are the organic carbon content (OC) and the physical properties of the soil that determine the soil water fluxes. The SoilFug model requires as input data the mass fraction of organic carbon as well as the air and water volume fraction, which are used to calculate the volume of organic matter, water, and air in the soil in the available soil thickness (named “soil depth” in the input mask) for the considered basin area. Soil depth in a cultivated area depends on a number of factors: textural and structural properties of the soil, type of crop, and plowing depth. According to local agricultural practices, soils are usually plowed before seeding. So the soil depth value can be different in different types of soil for the same crop root system. For example, clayey and loamy soils are in general plowed more deeply than sandy soils (19). Soil texture can be classified according to Soil Taxonomy classification (20). Soil classification data produced by local soil survey service (ERSAL) (21) and pedological GIS map (22, 23) for the studied area were used. Experimental data on OC in the surface soil layer, up to a depth of about 30 cm (22, 23) were used for the development of a map of OC distribution (see Figure S2 in the Supporting Information). Rainfall Data. Rainfall data were collected from four meteorological stations, two inside the research area and the others close to the northern and western boundaries, respectively (Figure 2). Daily average rainfall data in the period from April to August 1996 were available for all the stations. For the first two stations, hourly rainfall data were also continuously recorded. The area studied was divided into four Thiessen polygons that were digitized and entered into a GIS. The evaluation of Thiessen polygons is a suitable method in flat regions to individuate an area belonging to a point source of data like a meteorological station (24). Daily rainfall data were aggregated into Rain Events (RE). For every RE the total rainfall volume and duration is calculated. 4426
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Irrigation must also be taken into account as a water input event. Detailed information on irrigation patterns was available from irrigation and land reclamation authorities in the study area. Each field is irrigated in turn according to the interval that the irrigation authority assigns to the whole field intake. The irrigation event is modeled like a RE that falls on the middle day of the 10 day (on average) irrigation period and of course regards only the irrigated area. Corn requires about 5000 m3 of water to produce 10 metric tons/ ha of harvest. Given that about 200 mm of water are provided by rainfall during the growth season, usually 3000 m3/ha must be provided with two to four irrigation events, about 70-80 mm/ha each, losses excluded (19). The most common irrigation system in the Po Valley is border dike irrigation, a method that enhances evaporation losses, so that even 4000 m3/ha of water and 100 mm/ha for each irrigation event are needed. Volumes of water distributed on soybean fields are substantially comparable to those described for corn, due to local irrigation practices (25). Hydrological Network and River Flow Data. The hydrological network is extremely developed and complex. Descriptions of the seasonal course of the main water bodies were taken from the literature (26-28). Continuous water flow measurements during the study period were available at the outlet of the research area, at San Colombano station (Figure 2). For an estimate of the Lambro river inflow into the study area, flow measurements at two stations located upstream, about 8 and 28 km, respectively (Figure 2) were used. Southern Lambro River (Lambro Meridionale) is formed by the confluence of two major streams (the Deviatore Olona and the Olona River), upstream from the study area. Flow measurements for both streams were available, even if some data were missing. When data were missing, the difference between the S. Colombano and upstream measurements on the Northern Lambro River (Lambro Settentrionale) was accepted as a rough (over)estimate. Step II: Definition of UGU and Their Characterization. UGU Map Definition. GIS tools were employed to acquire and manage territory information in order to develop the input scenarios required by the model. The GIS software PC ARC/INFO was used to produce thematic layers containing a selection of the cartographic information. Several ARC/INFO coverages were produced and imported in ARCVIEW 3.0 (ESRI Inc.) to be elaborated and printed. The most important layers are the following: hydrological network and sampling station localization; land use with the Regional Technical Map (CTR) categories; organic carbon content in the plowed layer of soils; boundaries of the subbasins draining to the sampling stations of the river; and Thiessen polygons each belonging to a single rainfall data measuring station. Moreover, different Landscape Units were identified according to the ERSAL Pedological Map (22, 23). A Landscape Unit is defined as the following: “Portion of the territory, geographically delimited, where the relief morphology, the hydrography, the forestry and the land use present uniform characteristics being the result of the univocal action by geomorphologic, biological and climatic agents on rocks and surface deposition materials” (29). Landscape Unit definition is a concept comparable to that of Uniform Geographic Unit (1); therefore the Landscape Unit map by ERSAL was considered a suitable base to process a UGU map. The ERSAL-Landscape-Units-coverage was overlaid with the previously described coverages and acquired into ARCVIEW. Queries were made to select the input parameters required by SoilFug and calculate their values. Thus, different scenarios were created for the main Landscape Units, with a surface larger than 10 km2. The characteristics of the
TABLE 1. Main Properties and Application Rate (AR) of the Selected Herbicidesa alachlor metolachlor terbuthylazine a
molecular wt
solubility, mg/L
vapor pressure, Pa
log Kow
t1/2 (soil) days
AR, kg/ha
269.8 283.8 229.7
148 (12) 530 (12) 8.5 (13)
0.0029 (13) 0.0042 (13) 0.00015 (13)
3.3 (14) 2.9 (13) 2.9 (14)
20 (15) 30 (15) 60 (15)
2.0 1.7b-1.9c 0.9
References are reported in parentheses.
b
Corn. c Soybean.
FIGURE 2. Map of the study area with the boundaries of the three main sub-basins (A, B, and C). Due to the complexity of the hydrographic network, only the main rivers are reported. Sampling stations of experimental monitoring are reported as well as the rainfall and water flow measurement stations. Landscape Units smaller than 10 km2 were neglected, but the total surface of them was calculated and reallocated into the main Landscape Units to keep the sum of the single areas equal to the entire studied area. Soil texture class and Hydrologic Soil Group (HSG) were available for every Landscape Unit. HSG were used to calculate Curve Numbers (CN) for all the different UGUs that are involved in water balance estimation. By means of PC ARC/INFO a grid with 9 km2 cells was built and used to calculate the drainage density, i.e., the average length of the hydrological network per unit of area (30). The grid was acquired into ARCVIEW to identify cells belonging to each UGU and to calculate drainage density for every UGU. For every cell of the grid the time (hours) that surface or subsurface runoff flow takes to reach the river or stream was estimated. This time was taken into account for estimating a dilution factor, specific for each UGU, to be multiplied by runoff water concentrations to estimate river water concentrations. Even if subsurface runoff flow is much
slower than surface runoff, it can be assumed that, in the study area, characterized by a high drainage density, water flow through a relatively thin soil layer (20 to 30 cm) would reach in short term the drainage system. Water Balance. To quantify every RE, SoilFug requires data on water input (rainfall) and output (percentage of outflowing water). An estimate of runoff water volume per unit of area must be provided to predict the fate of chemicals in a catchment area. Runoff water volumes were evaluated in three steps: the first step calculates a coefficient, RC, with a simple empirical equation in which only the registered rainfall data of each rain gauge station are taken into account. The second and third steps increase or reduce the RC value according to UGU specific conditions with respect to average whole catchment conditions. In particular this was achieved by calculating the UGU CN and considering the UGU drainage density. Must be noticed that the widely used Curve Number (CN) method developed by U.S. Soil Conservation Service (31) was not applied here to calculate runoff water VOL. 34, NO. 20, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 2. Main Characteristics of the 14 Uniform Geographic Units (UGU), of the Three Sub-Basins (SB), and of the Whole Basin (WB) soil volume fractions water air
OC (% w/w)
soil depth (m)
HSG
0.3 0.3 0.2
2 2.5 2
0.2 0.2 0.3
C C C
Landriano Caleppio Landriano
0.3 0.3 0.2
0.2 0.2 0.3
2 2.5 1
0.3 0.3 0.2
B B C
Landriano Caleppio Sant′Angelo
coarse silty coarse silty coarse loamy coarse silty
0.2 0.2 0.3 0.2
0.3 0.3 0.2 0.3
1.5 2 1 1.5
0.2 0.2 0.3 0.2
C C B C
Lodi Caleppio Sant′Angelo Landriano
1887.2 4935.1
coarse silty coarse loamy
0.2 0.3
0.3 0.2
1.5 1.5
0.2 0.3
C C
Sant’Angelo Landriano
1352.1 1647.3
coarse loamy coarse silty
0.3 0.2
0.2 0.3
2 2.5
0.3 0.15
C D
coarse loamy coarse loamy coarse silty coarse loamy
0.3 0.3 0.2 0.3
Sub-Basins (SB) 0.2 2.25 0.3 0.2 1.5 0.3 0.3 1.25 0.2 0.2 1.5 0.3
C C C C
surface (ha)
soil texture
1 2 3
2054.7 1991.3 4154.5
coarse silty coarse silty coarse loamy
0.2 0.2 0.3
4 5 6
1086.3 1417.3 7100.0
coarse loamy coarse loamy coarse silty
7 8 9 10
3369.3 1638.6 1115.0 2447.0
11 12 13 14
UGU
A: Salerano B: Sant’Angelo C: S.Colombano WB
13401 10126 15069 38596
volumes but only to have a synthetic index of different UGU conditions affecting runoff process, namely soil properties (HSG) and land cover. Details of the calculation method are found in the Supporting Information. Step III: Definition of Environmental Scenarios. Application Scenario. Data on loading and application patterns were estimated on the basis of the application rate (AR) of the considered active ingredients (a.i.) and the treated area of each crop. On the basis of the most common agricultural practices in the study area, a likely AR value for each a.i. was evaluated (Table 1), and, to simplify the application scenario, two application dates were established (the 15th and 28th of April). It was assumed that 60% of the fields had been treated on the first date and the remainder on the second. Obviously, this is only an approximation. Actual applications may be split, in different farms of the area, within a period of twothree weeks (19). This uncertainty may affect the runoff prediction if a rain event occurs during the application period. In the studied area there is a high number of rather small farms, so a direct measurement of treated surface for a given herbicide is very difficult. Consequently, treated surface was estimated by means of the Delphi technique (32). Details of the method as well as data on treated surfaces are found in the Supporting Information (Table S1). Soil Properties and Water Input-Output Scenarios. For each UGU the selected soil properties (organic carbon, air and water volumes, depth of the layer) are fed to the model in order to predict the concentration of the chemical in the rain events. The water input by rainfall and the corresponding runoff water volume for each RE were obtained by the GIS after UGU classification, as explained before. Step IV: Modeling. SoilFug Calculations. To predict runoff to surface water the SoilFug model (4) was used. SoilFug is an unsteady state but equilibrium model, based on the fugacity concept. The equilibrium events are the rain event simulations. The model calculates the partitioning among the various compartments of the system during each RE and the dissipation patterns (degradation, volatilization, runoff and leaching) during and between two successive rain 4428
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rain measuring station
sampling station sub-basin
sub-basin divided surface (ha)
Caleppio Landriano
Salerano Salerano Salerano Sant’Angelo Salerano Salerano S.Colombano Sant’Angelo S.Colombano S.Colombano S.Colombano Sant’Angelo S.Colombano Sant’Angelo Sant’Angelo S.Colombano Salerano Salerano
2054.7 1991.3 2532.0 1622.4 1086.3 1417.3 5802.7 1297.3 3369.3 1638.6 1115.0 1631.0 816.0 1887.2 3305.1 1630.0 1352.1 1647.3
Caleppio Landriano Sant’Angelo Landriano
Salerano Sant’Angelo S.Colombano S.Colombano
13401 10126 15069 38596
events. Besides molecular properties, the model requires the above-mentioned soil properties (soil depth, organic carbon, air and water volume fractions), a water input-output balance, and an application scenario as input data. Calculations were made by using the software SoilFug 1.01. Software is available on request. The model was applied at three different levels of detail of environmental description corresponding to three grouping scenarios for the model. (1) At the first level, 14 UGU were identified, and UGU environmental scenarios requested by SoilFug were developed by means of queries on GIS database. (2) At the second level, the three major sub-basins corresponding to the sampling stations were described by using representative values for the model input parameters. (3) At the third level, the entire study area was considered as a whole, and representative values were selected for the model simulation. Dilution of Runoff Water in River Water. To calculate pesticide concentration in river water, a dilution factor of runoff water must be estimated. Through a comparison between rainfall and river flow patterns at the different flow measuring stations, it was established that the river flow response occurs 6 to 24 h after the RE as a comparison between hydrographs and rainfall records shows. Thus, it was possible to assign to each UGU the time of 6 or 12 or 24 h needed for runoff water to reach the river, according to the distance from the sampling station and the drainage density. For each RE, the dilution factors were calculated for the water input-output scenario created for a given UGU, as the result of the division of the runoff flow volume (m3) of a RE by the total river flow during the RE, taking into account the delay corresponding to the UGU (6, 12, or 24 h, respectively). Similar approaches were set for modeling dilution coefficients of the sub-basins and dilution coefficients of the entire study area. For the irrigation events, a different procedure was used. During the irrigation event runoff flow from the treated surface is supposed to be diluted in the water volume flowing from the whole irrigated territory and then in the river flow
TABLE 3. Experimentally Measured Concentrations (µg/L) of the Three Herbicides in the Three Sampling Stationsa S. Colombano
Salerano
S. Angelo
date
ala
met
ter
ala
met
ter
ala
met
ter
23-26 April 30-April 3-May 10-15 May 20-23 May 4-6 June 17-19 June 22-25 June 8-10 July 21-23 August
0.065 0.169 0.086 0.012 0.007 0.012 0.028 0.009 nd
0.310 0.177 0.123 0.017 0.014 0.082 0.058 0.084 0.014
nd 0.294 0.185 0.050 0.064 0.243 0.070 0.056 0.047
0.538 0.253 0.074 0.052 0.006 0.017 0.039 nd nd
0.059 0.148 0.098 0.022 0.008 0.017 0.072 0.035 nd
0.099 0.374 0.159 0.100 0.073 0.182 0.098 0.057 0.014
0.046 0.158 0.081 0.238 0.007 0.024 0.023 0.016 nd
0.059 0.087 0.153 0.016 0.010 0.043 0.027 0.016 0.009
0.093 0.158 0.166 0.283 0.048 0.168 0.057 0.033 0.015
a Weighted averages representative of the different rain events are reported. Concentrations before the treatment period and in the inflowing stations, always below the detection limit (nd), are not reported in the table.
corresponding to the whole irrigation turn interval (10 days). The time it takes to reach the river from the irrigated area in an UGU or a sub-basin is the same as the RE runoff. Step V: Validation. Sampling Scheme. Water samples were collected in three sampling stations corresponding to the outlet of the three main sub-basins (A, B, and C). Moreover, to control the main external inputs into the experimental area, three additional sampling stations were positioned on the main water courses entering the basin (Figure 2). First samples were collected at the end of March, before herbicides were applied according to the agricultural practices of the study area. Starting from April to the end of August, samples were collected in the three main stations, in correspondence of each significant RE. Samples were taken daily for a period of 3-5 days, to cover, as far as possible, the hydrological response of the river to the RE. Herbicide predicted concentrations were averaged for each RE to obtain a single representative figure. Weights for single day values were calculated on the basis of daily river flow measures. Upstream from the study area, agricultural practices are negligible, thus, samples in the three inflow stations were collected monthly to verify the absence of herbicides. Analytical Methods. Details of the analytical procedure are found in the Supporting Information.
Results Scenario Descriptions and Calculation of Outflowing Water Concentrations. The three grouping scenarios previously described (14 UGU, 3 major sub-basins, whole area) were adopted in order to study model performances and needs for detail in the SoilFug simulation. The 14 UGU, about 1000 to 7000 ha each, are relatively homogeneous as to uses, water balance, landscape units, and soil characteristics. The main characteristics of the UGUs are reported in Table 2. A map of UGU boundaries is shown in the Supporting Information (Figure S3). It must be noticed that soil depth definition depends on soil texture; coarse-loamy soils have a soil depth of 0.3 m, coarse-silty, 0.2 m. There is only one case (14th UGU) where soil texture was defined as coarse-loamy and the HSG was D: for this case the soil depth was reduced to 0.15 m to consider the relative impermeability of the D class. The SoilFug model was applied to each UGU using the corresponding water balance and application scenarios. In the second step, the model was applied to the three main sub-basins, characterized by grouping the different UGU and assuming representative values for environmental properties (Table 2) equal to those of the widest UGU included in each sub-basin. Finally, the total study area was assumed as a whole, and a comprehensive scenario was set up. A detailed description of the model application (application scenarios,
FIGURE 3. Comparison between experimentally measured (exp) and SoilFug predicted herbicide concentrations in river water at the Salerano sampling station (sub-basin A). Predicted data obtained through the modular composition of the Uniform Geographic Units (2) are compared with those obtained from the whole sub-basin A (1) scenario. The analytical detection limit (D.L.) is reported. water input-output scenarios, herbicide concentrations in runoff water) is found in the Supporting Information (Tables S1-S4). Predicted River Water Concentrations and Comparison with Experimental Data. Weighted averages of experimentally measured concentrations at each rain event in the three sampling stations are shown in Table 3. Weights are given by the daily river flow recorded for each day of sampling. The comparison between predicted and observed concentrations, either in the sampling station corresponding to the two sub-basins or in the total basin outflow, is shown in Figures 3-5. Due to the variability over several orders of VOL. 34, NO. 20, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 4. Comparison between experimentally measured (exp) and SoilFug predicted herbicide concentrations in river water at the Sant’Angelo sampling station (sub-basin B). Predicted data obtained through the modular composition of the Uniform Geographic Units (2) are compared with those obtained from the whole subbasin B (1) scenario. The analytical detection limit (D.L.) is reported. magnitude, log scale is used for concentration in the figures. It must be underlined that the sequence of predicted values is not exactly the same in the three simulations due to differences in RE occurrence. The agreement between predicted and observed concentrations is generally satisfying. The most controversial data refer, in some cases, to the first sample after application (see alachlor in Figure 3 and terbuthylazine in Figure 5). This is probably related to the approximation in the application time scenario, as pointed out above. This hypothesis is suitable for alachlor in subbasin A, while the low concentration of terbuthylazine in the total basin is not confirmed by the results in the two subbasins. In this case, the most suitable explanation could be a sampling or analytical error. Even if differences among the three scenarios are relatively small, increased detail in information generally seems to improve the precision of the prediction. In all the sampling stations, and, particularly in the whole basin, predictions based on the 14 UGU produce, in most cases, a best fit with experimental data. To confirm the apparent visual comparison, the Nash and Sutcliffe index (33) was applied. The index (ranging from 1 to -∞) is generally used for assessing the reliability of water flow predictions, but it can be applied more generally for comparing series of predicted and observed data. Values of the index close to 1 indicate good predictive capability. The results of the comparison between whole basin and 14 UGUs data (Figure 5) are shown in the following scheme. For terbuthylazine, the first point, previ4430
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FIGURE 5. Comparison between experimentally measured (exp) and SoilFug predicted herbicide concentrations in river water at the S. Colombano sampling station (Whole basin). Predicted data obtained from the whole basin (1) scenario are compared with those obtained from the three sub-basins (2) and the modular composition of the 14 Uniform Geographic Units (3). ously indicated as an outlier, was not included in the calculation.
whole basin 14 UGUs
alachlor
metolachlor
terbuthylazine
0.42 0.95
-0.81 0.10
0.09 0.72
For the whole basin, index values are relatively low, even if differences between predicted and observed figures, always within a factor of 4, can be assumed as an acceptable predictive capability. Results for the 14 UGUs data are always higher and for alachlor and terbuthylazine seem very satisfying.
Discussion One of the major advantages of the SoilFug model and of other comparable fugacity-based multimedia models is that they require, as input data, information on a few parameters selected among the major driving forces needed for the description of the environmental fate of a chemical in a study site. Even if the predictive capability of SoilFug has been validated with satisfying results in relatively small and homogeneous basins, the limiting factor, for site-specific application in large areas, is the quality and detail of data and the variability of environmental conditions. The results obtained in this work demonstrate that, if the quality of input data is good and detailed enough, the SoilFug
FIGURE 6. Influence of various parameters on predicted alachlor concentrations in runoff water. In a fixed scenario, individual input data have been changed. I: half-life (20 and 40 days); II: organic carbon content (1 and 4%); III: soil depth (0.1 and 0.4 m); IV: soil texture (A ) 40% and B ) 60% porosity); V: applications (A ) single application before the first RE; B ) five repeated applications). model also produces good predictions of surface water concentrations of pesticides for a relatively large catchment area. Differences in predicted data obtained between the simulation performed by averaging the information on the total basin and those performed by combining results obtained in more homogeneous sectors (three sub-basins or fourteen UGUs) are relatively small. This is due to the moderate variability of most environmental factors in the experimental area. Indeed, the entire basin was in the flat Po valley, with little variability of soil properties, almost constant climate, and mainly agricultural land use, though with different crops. Yet even in this case, more detailed
information increases the precision of the prediction. It is reasonable to suppose that, in more complex and heterogeneous situations, the extra work using homogeneous UGUs could be justified by the better quality of the predictive results. In such a case, it could be important to know what parameters (chemical or environmental) must be described with more precision and detail. To evaluate the role of different input data and their influence in the variability of a response, a sensitivity evaluation was made. The results are reported in Figure 6, where the SoilFug predictions for alachlor, in runoff water from the entire basin, are reported for a fixed scenario by changing, step-by-step, one key VOL. 34, NO. 20, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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parameter. From the results of this evaluation, the following comments can be made on the role of different input data. Chemical Properties. At present, the availability and reliability of the physicochemical properties of environmentally relevant chemicals is adequate. The most controversial parameter is persistence (or degradation half-life), which can be greatly affected by a number of environmental characteristics. By doubling soil half-life, differences in runoff concentrations increase with time and, in the long run, may be higher than 1 order of magnitude. As mentioned before, soil half-life selected in this work can be assumed as reliable for the environmental characteristics typical of the study area. Thus, persistence values must be carefully evaluated and referred to site-specific conditions. Soil Properties. Obviously, high organic carbon content increases the soil retention capability of non polar organic chemicals. Chemical concentrations in runoff water predicted by SoilFug are inversely proportional on a log basis to organic carbon content, and the proportionality factor depends on the hydrophobicity of the chemical. In a large area significant differences in OC content (for example from 1 to 4%) are likely to occur. Therefore, a suitable map of organic carbon content of the study area is a basic requirement and shows the necessity of the definition of UGUs. The same kind of relationship exists between water concentration and the soil depth involved in exchanges with surface and subsurface runoff water. Soil depth depends on several factors, such as soil texture, slope, crop coverage, tillage, etc; thus it may be very variable in the territory. Suitable information on these factors should be collected and evaluated in order to develop a reliable map of soil depth. The direct role of soil textural data (air and water volume fraction) on the SoilFug prediction is less relevant. In the normal range of soil porosity of the Po Valley (usually from 40 to 60%), differences in the predictions are relatively small. The model takes into account the effects of soil texture on other relevant parameters such as soil depth or water balance, thus this last information is relatively redundant, and less detail is required for an acceptable performance of the model. Anyway, if a large range of geopedological characteristics is supposed, this property should also be accounted for. Applications. Besides the quantitative evaluation of the amount applied, obviously proportional to the runoff concentration, the application time schedule in the territory should be carefully evaluated. In Figure 6 the results corresponding to two different treatment scenarios are shown: in the first scenario (A) the total amount is applied before the first RE, in the second (B) the load was split into 5 portions (20% each) applied from April 15 to May 15. The collection of precise information on the treatment schedule in a large area needs a huge amount of work, particularly if many small agricultural farms are present. In these cases, only a reasonable scenario is possible, based on adequate knowledge of the usual agricultural practices in function of meteorological and agronomic conditions. Water Balance. The succession of rain events and the water input-output balance is a major key factor for SoilFug predictions. Rainfall records from different stations located in significant sites of the study area are essential, particularly in large areas characterized by relevant climatic differences. The amount of water output does not change dramatically the concentration of the runoff water at each field level predicted by SoilFug. Nevertheless, this parameter is essential for calculating the dilution of runoff water in natural water bodies. Several parameters affecting water output, such as soil properties, slope, density of hydrographic network, etc., must be carefully evaluated. It must be highlighted that, to show the level of variability of predicted concentrations in relation with big changes of the major driving forces, many of the examples of Figure 6 4432
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are quite unrealistic if compared with the variability of environmental parameters within the study area. In particular, application frequency and timing will largely affect predicted concentrations, mainly in the short time after or during the application period. Nevertheless, even if precise information on application is difficult to obtain, agricultural practices are relatively uniform in given agricultural areas, being related to climatic conditions, type of pests, and farmer’s custom. A further crucial point is the effect of transit distance between treated areas and surface water bodies. In the area studied in this paper, due to the high drainage density, the problem can be assumed as negligible. At every RE, runoff water will be transferred very quickly into surface water bodies. In different environmental conditions, the problem should be better evaluated, and losses in transfer should be taken into account. The key parameters, which are the major driving forces for the model, could vary greatly in large basins, showing heterogeneous characteristics in term of environmental properties, meteo-climatic conditions, land use, etc. It follows, that to ensure a good predictive capability of the model, the accuracy in defining UGUs and in quantifying major input data should be the more high the more variable are the conditions within the studied area. In conclusion, the approach used in the present work, based on the use of the Geographical Information System for the selection of relatively homogeneous sub-areas, and on their modular combination has proved to be an effective tool for the application of site-specific multimedia models on relatively large, heterogeneous territories. This may represent the first step of a strategy for the integration of environmental models (such as SoilFug) and GIS approaches. A better integration of the model on a regional scale, linked to an atmospheric sub-model in order to predict movement of pesticides in air, is one of the next goals in this strategy.
Acknowledgments The work has been done under the contract EEC No. CI1*CT94-0542. Authors are grateful to Prof. Claudio Gandolfi of the Institute of Agricultural Hydraulics - University of Milan, Italy for his contribution on hydrological evaluation. Authors are also grateful to the Regional Agricultural Authority (ERSAL) and to the Consorzio Agrario of Milano and of Pavia for the information provided.
Supporting Information Available Details on methodology, tables of calculated values for model parameters, and maps of the main characteristics of the studied area. This material is available free of charge via the Internet at http://pubs.acs.org.
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Received for review February 9, 2000. Revised manuscript received July 11, 2000. Accepted July 25, 2000. ES000986C
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