Variations in pesticide leaching related to land use, pesticide properties, and unsaturated zone thickness

Pesticide leaching through variably thick soils beneath agricultural fields in Morgan Creek, Maryland was simulated for water years 1995 to 2004 using LEACHM (Leaching Estimation and Chemistry Model). Fifteen individual models were constructed to simulate five depths and three crop rotations with associated pesticide applications. Unsaturated zone thickness averaged 4.7 m but reached a maximum of 18.7 m. Average annual recharge to ground water decreased from 15.9 to 11.1 cm as the unsaturated zone increased in thickness from 1 to 10 m. These point estimates of recharge are at the lower end of previously published values, which used methods that integrate over larger areas capturing focused recharge in the numerous detention ponds in the watershed. The total amount of applied and leached masses for five parent pesticide compounds and seven metabolites were estimated for the 32-km2 Morgan Creek watershed by associating each hectare to the closest one-dimensional model analog of model depth and crop rotation scenario as determined from land-use surveys. LEACHM parameters were set such that branched, serial, first-order decay of pesticides and metabolites was realistically simulated. Leaching is predicted to be greatest for shallow soils and for persistent compounds with low sorptivity. Based on simulation results, percent parent compounds leached within the watershed can be described by a regression model of the form e(-depth) (a ln t1/2-b ln K OC) where t1/2 is the degradation half-life in aerobic soils, K OC is the organic carbon normalized sorption coefficient, and a and b are fitted coefficients (R2 = 0.86, p value = 7 x 10(-9)).     

Sensitivity and first-step uncertainty analyses for the preferential flow model MACRO

Sensitivity analyses for the preferential flow model MACRO were carried out using one-at-a-time and Monte Carlo sampling approaches. Four different scenarios were generated by simulating leaching to depth of two hypothetical pesticides in a sandy loam and a more structured clay loam soil. Sensitivity of the model was assessed using the predictions for accumulated water percolated at a 1-m depth and accumulated pesticide losses in percolation. Results for simulated percolation were similar for the two soils. Predictions of water volumes percolated were found to be only marginally affected by changes in input parameters and the most influential parameter was the water content defining the boundary between micropores and macropores in this dual-porosity model. In contrast, predictions of pesticide losses were found to be dependent on the scenarios considered and to be significantly affected by variations in input parameters. In most scenarios, predictions for pesticide losses by MACRO were most influenced by parameters related to sorption and degradation. Under specific circumstances, pesticide losses can be largely affected by changes in hydrological properties of the soil. Since parameters were varied within ranges that approximated their uncertainty, a first-step assessment of uncertainty for the predictions of pesticide losses was possible. Large uncertainties in the predictions were reported, although these are likely to have been overestimated by considering a large number of input parameters in the exercise. It appears desirable that a probabilistic framework accounting for uncertainty is integrated into the estimation of pesticide exposure for regulatory purposes.     

Regional assessment of herbicide sorption and degradation in two sampling years

Sorption and degradation of the herbicide 2,4-D [2,4-dichlorophenoxyacetic acid] were determined for 123 surface soils (0 to 15 cm) collected in 2002 and in 2004 between 49 degrees to 60 degrees north longitude and 110 degrees to 120 degrees west latitude in Alberta, Canada. The soils were characterized by soil organic carbon content (SOC), pH, electrical conductivity, soil texture, cation exchange capacity, carbonate content, and total soil microbial activity. The 2,4-D sorption coefficients, Kd and Koc, were highly variable with coefficients of variation of 89 and 59%, respectively, at the provincial scale. Both Kd and Koc were well described by regression models with SOC and soil pH as variables, regardless of scale. Surprisingly, variations in 2,4-D mineralization were much smaller than variations in sorption. Variability in total 2,4-D mineralization was particularly low, with a coefficient of variation of only 7% at the provincial scale. Average 2,4-D half-lives in ecoregions ranged from 1.7 to 3.5 d, much lower than the field dissipation half-life of 10 d reported for 2,4-D in general pesticide property databases. Regression models describing degradation parameters were generally poor or not significant because 2,4-D mineralization was only weakly associated with measured 2,4-D sorption parameters and soil properties. As such, regional variations in herbicide sorption coefficients should be measured or calculated based on soil properties, to assign distinct pesticide fate model input parameters when estimating 2,4-D off-site transport at the provincial scale. Spatial variations in herbicide degradation appear less important for Alberta as 2,4-D half-lives were similar in soils across the province. The rapid mineralization of 2,4-D is noteworthy because 2,4-D is widely used in Alberta and perhaps adaptation of soil microbial communities allowed for accelerated degradation regardless of soil properties or the extent of 2,4-D sorption by soil.     

Field-scale variation in microbial activity and soil properties in relation to mineralization and sorption of pesticides in a sandy soil

Pesticides applied to agricultural soils are subject to environmental concerns because leaching to groundwater reservoirs and aquatic habitats may occur. Knowledge of field variation of pesticide-related parameters is required to evaluate the vulnerability of pesticide leaching. The mineralization and sorption of the pesticides glyphosate and metribuzin and the pesticide degradation product triazinamin in a field were measured and compared with the field-scale variation of geochemical and microbiological parameters. We focused on the soil parameters clay and organic carbon (C) content and on soil respiratory and enzymatic processes and microbial biomass. These parameters were measured in soil samples taken at two depths (Ap and Bs horizon) in 51 sampling points from a 4-ha agricultural fine sandy soil field. The results indicated that the spatial variation of the soil parameters, and in particular the content of organic C, had a major influence on the variability of the microbial parameters and on sorption and pesticide mineralization in the soil. For glyphosate, with a co-metabolic pathway for degradation, the mineralization was increased in soils with high microbial activity. The spatial variability, expressed as the CV, was about five times higher in the Bs horizon than in the Ap horizon, and the local-scale variation within 100 m(2) areas were two to three times lower than the field-scale variation within the entire field of about 4 ha.     

Propagation of uncertainties in soil and pesticide properties to pesticide leaching

In the new Dutch decision tree for the evaluation of pesticide leaching to groundwater, spatially distributed soil data are used by the GeoPEARL model to calculate the 90th percentile of the spatial cumulative distribution function of the leaching concentration in the area of potential usage (SP90). Until now it was not known to what extent uncertainties in soil and pesticide properties propagate to spatially aggregated parameters like the SP90. A study was performed to quantify the uncertainties in soil and pesticide properties and to analyze their contribution to the uncertainty in SP90. First, uncertainties in the soil and pesticide properties were quantified. Next, a regular grid sample of points covering the whole of the agricultural area in the Netherlands was randomly selected. At the grid nodes, realizations from the probability distributions of the uncertain inputs were generated and used as input to a Monte Carlo uncertainty propagation analysis. The analysis showed that the uncertainty concerning the SP90 is 10 times smaller than the uncertainty about the leaching concentration at individual point locations. The parameters that contribute most to the uncertainty about the SP90 are, however, the same as the parameters that contribute most to uncertainty about the leaching concentration at individual point locations (e.g., the transformation half-life in soil and the coefficient of sorption on organic matter). Taking uncertainties in soil and pesticide properties into account further leads to a systematic increase of the predicted SP90. The important implication for pesticide regulation is that the leaching concentration is systematically underestimated when these uncertainties are ignored.     

Do lab-derived distribution coefficient values of pesticides match distribution coefficient values determined from column and field-scale experiments? A critical analysis of relevant literature

In this study, we analyzed sorption parameters for pesticides that were derived from batch and column or batch and field experiments. The batch experiments analyzed in this study were run with the same pesticide and soil as in the column and field experiments. We analyzed the relationship between the pore water velocity of the column and field experiments, solute residence times, and sorption parameters, such as the organic carbon normalized distribution coefficient ( ) and the mass exchange coefficient in kinetic models, as well as the predictability of sorption parameters from basic soil properties. The batch/column analysis included 38 studies with a total of 139 observations. The batch/field analysis included five studies, resulting in a dataset of 24 observations. For the batch/column data, power law relationships between pore water velocity, residence time, and sorption constants were derived. The unexplained variability in these equations was reduced, taking into account the saturation status and the packing status (disturbed-undisturbed) of the soil sample. A new regression equation was derived that allows estimating the values derived from column experiments using organic matter and bulk density with an value of 0.56. Regression analysis of the batch/column data showed that the relationship between batch- and column-derived values depends on the saturation status and packing of the soil column. Analysis of the batch/field data showed that as the batch-derived value becomes larger, field-derived values tend to be lower than the corresponding batch-derived values, and vice versa. The present dataset also showed that the variability in the ratio of batch- to column-derived value increases with increasing pore water velocity, with a maximum value approaching 3.5.     

Mapping ground water vulnerability to pesticide leaching with a process-based metamodel of EuroPEARL

To support EU policy, indicators of pesticide leaching at the European level are required. For this reason, a metamodel of the spatially distributed European pesticide leaching model EuroPEARL was developed. EuroPEARL considers transient flow and solute transport and assumes Freundlich adsorption, first-order degradation and passive plant uptake of pesticides. Physical parameters are depth dependent while (bio)-chemical parameters are depth, temperature, and moisture dependent. The metamodel is based on an analytical expression that describes the mass fraction of pesticide leached. The metamodel ignores vertical parameter variations and assumes steady flow. The calibration dataset was generated with EuroPEARL and consisted of approximately 60,000 simulations done for 56 pesticides with different half-lives and partitioning coefficients. The target variable was the 80th percentile of the annual average leaching concentration at 1-m depth from a time series of 20 yr. The metamodel explains over 90% of the variation of the original model with only four independent spatial attributes. These parameters are available in European soil and climate databases, so that the calibrated metamodel could be applied to generate maps of the predicted leaching concentration in the European Union. Maps generated with the metamodel showed a good similarity with the maps obtained with EuroPEARL, which was confirmed by means of quantitative performance indicators.     

A GIS-BASED MODELING APPROACH FOR EVALUATING GROUNDWATER VULNERABILITY TO PESTICIDES1

ABSTRACT: The Attenuation Factor (AF), a screening model, was used to evaluate the relative degree of vulnerability of groundwater to pesticide contamination in Louisa County, Virginia. For evaluating the contamination potential of pesticides, three scenarios of pesticide leaching represented by high, moderate, and low cases of degradation and sorption in the soil were considered. Data layers were overlaid within a Geographic Information System (GIS) for spatial computation of AF for the actual and 2m groundwater depths. This spatial database was divided into five contamination potential categories namely high, medium, low, very low, and unlikely, based on the numerical values of the AF for each cell (119 ha). The results for the three most mobile pesticides are presented in this paper. The performance of the AF model was evaluated by comparing its predicted results with the field data from an experimental watershed. The AF model was able to identify most of the frequently detected pesticides in the watershed. A sensitivity analysis was also performed. The results of this study provide information about the potential groundwater threat by pesticides to the citizens ahd decision-makers in the County and can be used for formulating an appropriate land use management plan to protect the groundwater quality.     

Leaching of glyphosate and amino-methylphosphonic acid from Danish agricultural field sites

Pesticide leaching is an important process with respect to contamination risk to the aquatic environment. The risk of leaching was thus evaluated for glyphosate (N-phosphonomethyl-glycine) and its degradation product AMPA (amino-methylphosphonic acid) under field conditions at one sandy and two loamy sites. Over a 2-yr period, tile-drainage water, ground water, and soil water were sampled and analyzed for pesticides. At a sandy site, the strong soil sorption capacity and lack of macropores seemed to prevent leaching of both glyphosate and AMPA. At one loamy site, which received low precipitation with little intensity, the residence time within the root zone seemed sufficient to prevent leaching of glyphosate, probably due to degradation and sorption. Minor leaching of AMPA was observed at this site, although the concentration was generally low, being on the order of 0.05 microg L(-1) or less. At another loamy site, however, glyphosate and AMPA leached from the root zone into the tile drains (1 m below ground surface [BGS]) in average concentrations exceeding 0.1 microg L(-1), which is the EU threshold value for drinking water. The leaching of glyphosate was mainly governed by pronounced macropore flow occurring within the first months after application. AMPA was frequently detected more than 1.5 yr after application, thus indicating a minor release and limited degradation capacity within the soil. Leaching has so far been confined to the depth of the tile drains, and the pesticides have rarely been detected in monitoring screens located at lower depths. This study suggests that as both glyphosate and AMPA can leach through structured soils, they thereby pose a potential risk to the aquatic environment.     

Seasonal leaching and biodegradation of dicamba in turfgrass

The leaching of surface-applied herbicides, such as dicamba (2methoxy-3,6-dichlorobenzoic acid), to ground water is an environmental concern. Seasonal changes in soil temperature and water content, affecting infiltration and biodegradation, may control leaching. The objectives of this study were to (i) investigate the leaching of dicamba applied to turfgrass, (ii) measure the degradation rate of dicamba in soil and thatch in the laboratory under simulated field conditions, and (iii) test the ability of the model EXPRES (containing LEACHM) to simulate the field transport and degradation processes. Four field lysimeters, packed with sandy loam soil and topped with Kentucky bluegrass (Poa pratensis L.) sod, were monitored after receiving three applications (May, September, November) of dicamba. Concentrations of dicamba greater than 1 mg L(-1) were detected in soil water. Although drying of the soil during the summer prevented deep transport, greater leaching occurred in late autumn due to increased infiltration. From the batch experiment, the degradation rate for dicamba in thatch was 5.9 to 8.4 times greater than for soil, with a calculated half-life as low as 5.5 d. Computer modeling indicated that the soil and climatic conditions would influence the effectiveness of greater degradation in thatch for reducing dicamba leaching. In general, EXPRES predictions were similar to observed concentration profiles, though peak dicamba concentrations at the 10-cm depth tended to be higher than predicted in May and November. Differences between predictions and observations are probably a result of minor inaccuracies in the water-flow simulation and the model's inability to modify degradation rates with changing climatic conditions.     

Modeling macropore flow effects on pesticide leaching: inverse parameter estimation using microlysimeters

Macropore flow is a key factor determining pesticide fate, but models accounting for this process need parameters that cannot be easily measured. This study was conducted to investigate the use of inverse techniques to estimate parameters controlling macropore flow and pesticide fate in the dual-permeability model MACRO. Undisturbed columns were sampled at three landscape positions (hilltop, slope, hollow) with contrasting texture and organic carbon content. Transient leaching experiments were performed for an anionic tracer and the herbicide MCPA (4-chloro-2methylphenoxy acetic acid) during a 4-mo period, first under natural rainfall, and then under controlled irrigation in the laboratory. The tracer breakthrough for the liner-textured soil from the hilltop showed strong evidence of macropore flow, resulting in a rapid leaching of MCPA, while leaching was minimal from the organic-rich hollow soil, since macropore flow was weaker and adsorption stronger. The MACRO model was linked to the inverse modeling program SUFI (Sequential Uncertainty Fitting) to enable calibration of nine key model parameters. Based on calculated model efficiencies, MACRO-SUFI gave generally good predictions of water movement and tracer and pesticide transport, although some errors were attributed to difficulties in simulating the effects of soil moisture on degradation and the timing of water outflows. Even after calibration, significant uncertainties remained for some key parameters controlling macropore flow. Nevertheless, the parameter estimates were significantly different between landscape positions and could also be related to basic soil properties. The posterior uncertainty ranges could probably be reduced with a more exhaustive sampling of the parameter space and improved experimental designs.     

MULTICOMPONENT GEOCHEMICAL TRANSPORT MODELING USING HYDRUS-1D AND HP11

Abstract: The transport of reactive contaminants in the subsurface is generally affected by a large number of nonlinear and often interactive physical, chemical, and biological processes. Simulating these processes requires a comprehensive reactive transport code that couples the physical processes of water flow and advective-dispersive transport with a range of biogeochemical processes. Two recently developed coupled geochemical models that are both based on the HYDRUS-1D software package for variably saturated flow and transport are summarized in this paper. One model resulted from coupling HYDRUS-1D with the UNSATCHEM module. While restricted to major ion chemistry, this program enables quantitative predictions of such problems as analyzing the effects of salinity on plant growth and the amount of water and amendments required to reclaim salt-affected soil profiles. The second model, HPI, resulted from coupling HYDRUS-1D with the PHREEQC biogeochemical code. The latter program accounts for a wide range of instantaneous or kinetic chemical and biological reactions, including complexation, cation exchange, surface complexation, precipitation dissolution and/or redox reactions. The versatility of HP1 is illustrated in this paper by means of two examples: the leaching of toxic trace elements and the transport of the explosive TNT and its degradation products.     

Phosphorus leaching in relation to soil type and soil phosphorus content

Phosphorus losses from arable soils contribute to eutrophication of freshwater systems. In addition to losses through surface runoff, leaching has lately gained increased attention as an important P transport pathway. Increased P levels in arable soils have highlighted the necessity of establishing a relationship between actual P leaching and soil P levels. In this study, we measured leaching of total phosphorus (TP) and dissolved reactive phosphorus (DRP) during three years in undisturbed soil columns of five soils. The soils were collected at sites, established between 1957 and 1966, included in a long-term Swedish fertility experiment with four P fertilization levels at each site. Total P losses varied between 0.03 and 1.09 kg ha(-1) yr(-1), but no general correlation could be found between P concentrations and soil test P (Olsen P and phosphorus content in ammonium lactate extract [P-AL]) or P sorption indices (single-point phosphorus sorption index [PSI] and P sorption saturation) of the topsoil. Instead, water transport mechanism through the soil and subsoil properties seemed to be more important for P leaching than soil test P value in the topsoil. In one soil, where preferential flow was the dominant water transport pathway, water and P bypassed the high sorption capacity of the subsoil, resulting in high losses. On the other hand, P leaching from some soils was low in spite of high P applications due to high P sorption capacity in the subsoil. Therefore, site-specific factors may serve as indicators for P leaching losses, but a single, general indicator for all soil types was not found in this study.     

Degradation and sorption of metribuzin and primary metabolites in a sandy soil

Leaching to the ground water of metabolites from the herbicide metribuzin [4-amino-6-(1,1-dimethylethyl)-3-(methylthio)-1,2,4-triazin-5-one] has been measured in a Danish field experiment in concentrations exceeding the European Union threshold limit for pesticides at 0.1 microg/L. In the present work, degradation and sorption of metribuzin and the metabolites desamino-metribuzin (DA), diketo-metribuzin (DK), and desamino-diketo-metribuzin (DADK) were studied in a Danish sandy loam topsoil and subsoil from the field in question, using accelerated solvent extraction and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Fast dissipation of metribuzin and the metabolites was observed in the topsoil, with 50% disappearance within 30 to 40 d. A two-compartment model described degradation of metribuzin and DA, whereas that of DADK could be described using first-order kinetics. Part of the dissipation was probably due to incorporation into soil organic matter. Degradation in subsoil occurred very slowly, with extrapolated half-lives of more than one year. Sorption in the topsoil followed the order DA > metribuzin > DK > DADK. Subsoil sorption was considerably lower, and was hardly measurable for metribuzin and DK. Abiotic degradation was considerably higher in the topsoil than the subsoil, especially concerning the de-amination step, indicating that organic matter may be related to the degradation process. The present results confirm observations of metribuzin and transformation product leaching made in the field experiment and demonstrate the need for knowledge on primary metabolites when assessing the risk for pesticide leaching.     

Simulating pesticide leaching and runoff in rice paddies with the RICEWQ-VADOFT model

There is a current need to simulate leaching and runoff of pesticide from rice (Oryza sativa L.) paddies for assessing environmental impacts on a valuable agricultural system. The objective of this study was to develop a model for determining predicted environmental concentration (PEC) in soil, runoff, and ground water through the linkage of two models, rice water quality model (RICEWQ) and vadose zone transport model (VADOFT), to simulate pesticide fate and transport within a rice paddy and underlying soil profile. Model performance was evaluated with a field data set obtained from a 2-yr field experiment in 1997 and 1998 in northern Italy. The predictions of amount of pesticide running off from the paddy field and accumulating in the paddy sediment were in agreement with measured values. Leaching into the vadose zone accounted for approximately 19% of the applied dose, but only a small amount of chemical (<0.1%) was predicted to reach ground water at a 5-m depth due to sorption and transformation in the soil. The permeability of the soil and the water management practices in the paddy field were shown to have a strong influence on pesticide fate. These factors need to be well characterized in the field if model predictions are to be successful. The combined model developed in this work is an effective tool for exposure assessments for soil, surface water, and ground water, in the particular conditions of rice cultivation.     

WATER QUALITY MODEL CALIBRATION: A COMPARISON OF INPUT AND OUTPUT ERROR CRITERIA1

ABSTRACT: A common framework for the analysis of water resources systems is the input-parameter-output representation. The system, described by its parameters, is driven by inputs and responds with outputs. To calibrate (estimate the parameters) models of these systems requires data on both inputs and outputs, both of which are subject to random errors. When one is uncertain as to whether the predominant source of error is associated with inputs or outputs, uncertainty also exists as to the correct specification of a calibration criterion. This paper develops and analyzes two alternative least squares criteria for calibrating a numerical water quality model. The first criterion assumes that errors are associated with inputs while the second assumes output errors. Statistical properties of the resulting estimators are examined under conditions of pure input or output error and mixed error conditions from a theoretical perspective and then using simulated results from a series of Monte Carlo experiments.     

Assessing and communicating data quality in policy-relevant research

The quality of scientific information in policy-relevant fields of research is difficult to assess, and quality control in these important areas is correspondingly difficult to maintain. Frequently there are insufficient high-quality measurements for the presentation of the statistical uncertainty in the numerical estimates that are crucial to policy decisions. We propose and develop a grading system for numerical estimates that can deal with the full range of data quality—from statistically valid estimates to informed guesses. By analyzing the underlying quality of numerical estimates, summarized as spread and grade, we are able to provide simple rules whereby input data can be coded for quality, and these codings carried through arithmetical calculations for assessing the quality of model results. For this we use the NUSAP (numeral, unit, spread, assessment, pedigree) notational system. It allows the more quantitative and the more qualitative aspects of data uncertainty to be managed separately. By way of example, we apply the system to an ecosystem valuation study that used several different models and data of widely varying quality to arrive at a single estimate of the economic value of wetlands. The NUSAP approach illustrates the major sources of uncertainty in this study and can guide new research aimed at the improvement of the quality of outputs and the efficiency of the procedures.     

AN INTERACTWE MODELING ENVIRONMENT FOR NON-POINT SOURCE POLLUTION CONTROL1

ABSTRACT: Non-point source pollution cuntinues to be an important environmental and water quality management problem. For the moat part, analysis of non-point source pollution in watersheds has depended on the use of distributed models to identify potential problem areas and to assess the effectiveness of alternative management practices. To effectively use these models for watershed water quality management, users depend on integrated geographic information systems (GIS)-based interfaces for input/output data management. However, existing interfaces are ad-hoc and the utility of GIS is limited to organization of input data and display of output data. A highly interactive water quality modeling interface that utilizes the functional components and analytical capability of GIS is highly desirable. This paper describes the tight coupling of the Agricultural Non-point Source (AGNPS) water quality model and ARC/INFO GIS software to provide an interactive hybrid modeling environment for evaluation of non-point source pollution in a watershed. The modeling environment is designed to generate AGNPS input parameters from user-specified GIS coverages, create AGNPS input data files, control AGNPS model simulations, and extract and organize AGNPS model output data for display. An example application involving the estimation of pesticide loading in a southern Iowa agricultural watershed demonstrates the capability of the modeling environment. Compared with traditional methods of watershed water quality modeling using the AGNPS model or other ad-hoc interfaces between a distributed model and GIS, the interactive modeling environment system is efficient and significantly reduces the task of watershed analysis using tightly coupled GIS databases and distributed models.