Incorporating uncertainty in a chemical leaching assessment

Pesticide leaching models are being used to assist in the regulation and management of pesticides by indicating their potential for leaching to groundwater. Uncertainty in model input data is not, regrettably, included in most pesticide leaching assessments. In the work described here, we use logarithmic transformations of the attenuation factor (AF), a simple process-based index model, to represent uncertainty in a pesticide leaching assessment. Characterization of a wide range of pesticides as `leachers' or `non-leachers' for a specific Hawaii hydrogeological setting is facilitated by comparing the log-transformed AF, designated AFR, for each chemical with two reference chemicals for which leaching behavior in Hawaii is known. Defining a mean and uncertainty interval for the AFR index of each chemical being ranked provides a practical method of incorporating data uncertainty into a regulatory protocol.     

Characterizing sources of nitrate leaching from an irrigated dairy farm in Merced County, California

Dairy farms comprise a complex landscape of groundwater pollution sources. The objective of our work is to develop a method to quantify nitrate leaching to shallow groundwater from different management units at dairy farms. Total nitrate loads are determined by the sequential calibration of a sub-regional scale and a farm-scale three-dimensional groundwater flow and transport model using observations at different spatial scales. These observations include local measurements of groundwater heads and nitrate concentrations in an extensive monitoring well network, providing data at a scale of a few meters and measurements of discharge rates and nitrate concentrations in a tile-drain network, providing data integrated across multiple farms. The various measurement scales are different from the spatial scales of the calibration parameters, which are the recharge and nitrogen leaching rates from individual management units. The calibration procedure offers a conceptual framework for using field measurements at different spatial scales to estimate recharge N concentrations at the management unit scale. It provides a map of spatially varying dairy farming impact on groundwater nitrogen. The method is applied to a dairy farm located in a relatively vulnerable hydrogeologic region in California. Potential sources within the dairy farm are divided into three categories, representing different manure management units: animal exercise yards and feeding areas (corrals), liquid manure holding ponds, and manure irrigated forage fields. Estimated average nitrogen leaching is 872 kg/ha/year, 807 kg/ha/year and 486 kg/ha/year for corrals, ponds and fields respectively. Results are applied to evaluate the accuracy of nitrogen mass balances often used by regulatory agencies to assess groundwater impacts. Calibrated leaching rates compare favorably to field and farm scale nitrogen mass balances. These data and interpretations provide a basis for developing improved management strategies.     

Evaluation of soil temperature effect on herbicide leaching potential into groundwater in the Brazilian Cerrado

The effect of annual variations in the daily average soil temperatures, at different depths, on the calculation of pesticide leaching potential indices is presented. This index can be applied to assess the risk of groundwater contamination by a pesticide. It considers the effects of water table depth, daily recharge net rate, pesticide sorption coefficient, and degradation rate of the pesticide in the soil. The leaching potential index is frequently used as a screening indicator in pesticide groundwater contamination studies, and the temperature effect involved in its calculation is usually not considered. It is well known that soil temperature affects pesticide degradation rates, air-water partition coefficient, and water-soil partition coefficient. These three parameters are components of the attenuation and retardation factors, as well as the leaching potential index, and contribute to determine pesticide behavior in the environment. The Arrhenius, van't Hoff, and Clausius-Clapeyron equations were used in this work to estimate the soil temperature effect on pesticide degradation rate, air-water partition coefficient, and water-soil partition coefficient, respectively. The relationship between leaching potential index and soil temperature at different depths is presented and aids in the understanding of how potential pesticide groundwater contamination varies on different climatic conditions. Numerical results will be presented for 31 herbicides known to be used in corn and soybean crops grown on the municipality of S?o Gabriel do Oeste, Mato Grosso do Sul State, Brazil.     

Dissolved plume attenuation with DNAPL source remediation, aqueous decay and volatilization--analytical solution, model calibration and prediction uncertainty

A vertically-integrated analytical model for dissolved phase transport is described that considers a time-dependent DNAPL source based on the upscaled dissolution kinetics model of Parker and Park with extensions to consider time-dependent source zone biodecay, partial source mass reduction, and remediation-enhanced source dissolution kinetics. The model also considers spatial variability in aqueous plume decay, which is treated as the sum of aqueous biodecay and volatilization due to diffusive transport and barometric pumping through the unsaturated zone. The model is implemented in Excel/VBA coupled with (1) an inverse solution that utilizes prior information on model parameters and their uncertainty to condition the solution, and (2) an error analysis module that computes parameter covariances and total prediction uncertainty due to regression error and parameter uncertainty. A hypothetical case study is presented to evaluate the feasibility of calibrating the model from limited noisy field data. The results indicate that prediction uncertainty increases significantly over time following calibration, primarily due to propagation of parameter uncertainty. However, differences between the predicted performance of source zone partial mass reduction and the known true performance were reasonably small. Furthermore, a clear difference is observed between the predicted performance for the remedial action scenario versus that for a no-action scenario, which is consistent with the true system behavior. The results suggest that the model formulation can be effectively utilized to assess monitored natural attenuation and source remediation options if careful attention is given to model calibration and prediction uncertainty issues.     

Sampled Monte Carlo uncertainty analysis for photochemical grid models

This study reports on the development and testing of a method of quantifying the uncertainties in concentration predictions by a complex photochemical grid model (PGM), using a modification of the basic Monte Carlo method (MCM). The computationally intensive aspects of applying a full MCM to hundreds of PGM inputs and model parameters is replaced by a highly restricted sampling approach that exploits the spatial persistence found in predicted concentration fields. The sampling approach to the MCM is being explored as an efficient approach to assess the uncertainty in the differences in predicted maximum ozone concentration between base case and control scenarios. The MCM is applied to several dozen surface cells, with the goal of sampling the spatial pattern of uncertainty in the PGM-predicted differences in surface ozone concentration fields between a pair of base and control scenarios. The uncertainty in model inputs and parameters is simulated using several types of stochastic models. These stochastic models are driven using Latin hypercube sampling (LHS) to generate a non-redundant ensemble of alternative model inputs. Preliminary testing of the sampled MCM approach was conducted using the UAM-IV PGM on the New York ozone attainment modeling domain for the 6–8 July 1988 ozone episode. One hundred alternative concentration estimates were generated for a base scenario and for control scenarios representing 50%, 10% and 5% reduction of NO_x emissions. The upper and lower bounds of the concentration difference ensemble that define a 95% confidence range were spatially interpolated from 27 monitoring sites to the full (surface) modeling domain, using the field of zero uncertainty (ZU) concentration differences. For the 50% NO_x control scenario, predicted increases in peak ozone concentration smaller than 20 ppb were generally not significant from zero. By contrast, predicted decreases in peak ozone greater than 10 ppb were usually significant. For a control scenario with a small 5% NO_x reduction, predicted concentration differences and confidence intervals were much smaller, but predicted changes in peak ozone were significant at a number of sample cells.     

Estimation of plant protection product application dates for environmental fate modeling based on phenological stages of crops

According to the EU directive 91/414/EEC potential environmental concentrations of pesticides have to be assessed with environmental fate models. For the calculation of pesticide concentrations it is necessary to provide an application date which has to match the specific Biologische Bundesanstalt, Bundesamt, Chemische Industrie (BBCH) stage at which the pesticide shall be applied. If these application dates are not available for a specific stage, crop and country they must be estimated, which adds an additional uncertainty to the predicted concentrations. In the present study, we therefore evaluate to which extent application dates can be derived from phenological data. For this analysis phenological data, converted to BBCH stages, of two field crops provided by the German Weather Service (DWD) were analyzed. We found a linear correlation between BBCH stages and the respective appearance dates, which can be used for interpolation of appearance dates of specific BBCH stages. Remarkably, when comparing BBCH stages from Germany and the Czech Republic almost identical correlations of appearance dates and BBCH stages were found. In the next step, soil and climate data from Joint Research Centre (JRC) were analyzed together with phenological data in order to evaluate if BBCH stages can be estimated for countries with other climate or soil conditions. This analysis revealed that temperature, global radiation and evaporation were the parameters with the strongest impact. These parameters were used for estimating appearance dates of BBCH stages for other countries. Exemplarily, appearance dates for maize BBCH were calculated for Italy. Estimated and observed appearance dates showed a high concordance (on average six days difference). Finally, the political of impact a variation of a few days on calculated pesticide concentration was analyzed. Exemplarily, the pesticide fate model FOCUS PEARL was used to estimate pesticide groundwater concentrations. When calculating concentrations for application dates varying by ± two weeks, concentrations in groundwater usually varied very little. The highest variation was found for application at BBCH 30 in maize (6.6 % variation over all scenarios). These results showed that the uncertainty included in the estimation of appearance dates of BBCH stages for other countries has a relatively small effect on the results of PEARL and consequentially on the decision of the pesticide risk assessment by changing only the application date.     

Leaching of seven pesticides currently used in cotton crop in Mato Grosso State-Brazil

This study aimed to evaluate the leaching of pesticides and the applicability of the Attenuation Factor (AF) Model to predict their leaching. The leaching of carbofuran, carbendazim, diuron, metolachlor, alpha and beta endosulfan and chlorpyrifos was studied in an Oxisol using a field experiment lysimeter located in Dom Aquino-Mato Grosso. The samples of percolated water were collected by rain event and analyzed. Chemical and physical soil attributes were determined before pesticide application to the plots. The results showed that carbofuran was the pesticide that presented a higher leaching rate in the studied soil, so was the one representing the highest contamination potential. From the total carbofuran applied in the soil surface, around 6% leached below 50 cm. The other pesticides showed lower mobility in the studied soil. The calculated values to AF were 7.06E-12 (carbendazim), 5.08E-03 (carbofuran), 3.12E-17 (diuron), 6.66E-345 (alpha-endosulfan), 1.47E-162 (beta-endosulfan), 1.50E-06 (metolachlor), 3.51E-155 (chlorpyrifos). AF Model was useful to classify the pesticides' potential for contamination; however, that model underestimated pesticide leaching.     

Small-scale variability of metals in soil and composite sampling

Soil pollution data is also strongly scattering at small scale. Sampling of composite samples, therefore, is recommended for pollution assessment. Different statistical methods are available to provide information about the accuracy of the sampling process. Autocorrelation and variogram analysis can be applied to investigate spatial relationships. Analysis of variance is a useful method for homogeneity testing. The main source of the total measurement uncertainty is the uncertainty arising from sampling. The sample mass required for analysis can also be estimated using an analysis of variance. The number of increments to be taken for a composite sample can be estimated by means of simple statistical formulae. Analytical results of composite samples obtained from different fusion procedures of increments can be compared by means of multiple mean comparison. The applicability of statistical methods and their advantages are demonstrated for a case study investigating metals in soil at a very small spatial scale. The paper describes important statistical tools for the quantitative assessment of the sampling process. Detailed results clearly depend on the purpose of sampling, the spatial scale of the object under investigation and the specific case study, and have to be determined for each particular case.     

Leaching of seven pesticides currently used in cotton crop in Mato Grosso State—Brazil

This study aimed to evaluate the leaching of pesticides and the applicability of the Attenuation Factor (AF) Model to predict their leaching. The leaching of carbofuran, carbendazim, diuron, metolachlor, α and β endosulfan and chlorpyrifos was studied in an Oxisol using a field experiment lysimeter located in Dom Aquino – Mato Grosso. The samples of percolated water were collected by rain event and analyzed. Chemical and physical soil attributes were determined before pesticide application to the plots. The results showed that carbofuran was the pesticide that presented a higher leaching rate in the studied soil, so was the one representing the highest contamination potential. From the total carbofuran applied in the soil surface, around 6 % leached below 50 cm. The other pesticides showed lower mobility in the studied soil. The calculated values to AF were 7.06E-12 (carbendazim), 5.08E-03 (carbofuran), 3.12E-17 (diuron), 6.66E-345 (α-endosulfan), 1.47E-162 (β-endosulfan), 1.50E-06 (metolachlor), 3.51E-155 (chlorpyrifos). AF Model was useful to classify the pesticides' potential for contamination; however, that model underestimated pesticide leaching.     

Uncertainty in Nonpoint Source Pollution Models and Associated Risks

The usefulness of water quality simulation models for environmental management is explored with a focus on prediction uncertainty. The specific objective is to demonstrate how the usability of a flow and transport model (here: MACRO) can be enhanced by developing and analyzing its output probability distributions based on input variability. This infiltration-based model was designed to investigate preferential flow effects on pollutant transport. A statistical sensitivity analysis is used to identify the most uncertain input parameters based on model outputs. Probability distribution functions of input variables were determined based on field-measured data obtained under alternative tillage treatments. Uncertainty of model outputs is investigated using a Latin hypercube sampling scheme (LHS) with restricted pairing for model input sampling. Probability density functions (pdfs) are constructed for water flow rate, atrazine leaching rate, total accumulated leaching, and atrazine concentration in percolation water. Results indicate that consideration of input parameter uncertainty produces a 20% higher mean flow rate along with two to three times larger atrazine leaching rate, accumulated leachate, and concentration than that obtained using mean input parameters. Uncertainty in predicted flow rate is small but that in solute transport is an order of magnitude larger than that of corresponding input parameters. Macropore flow is observed to contribute to the variability of atrazine transport results. Overall, the analysis provides a quantification of prediction uncertainty that is found to enhance a user's ability to assess risk levels associated with model predictions.     

Uncertainty in Nonpoint Source Pollution Models and Associated Risks

The usefulness of water quality simulation models for environmental management is explored with a focus on prediction uncertainty. The specific objective is to demonstrate how the usability of a flow and transport model (here: MACRO) can be enhanced by developing and analyzing its output probability distributions based on input variability. This infiltration-based model was designed to investigate preferential flow effects on pollutant transport. A statistical sensitivity analysis is used to identify the most uncertain input parameters based on model outputs. Probability distribution functions of input variables were determined based on field-measured data obtained under alternative tillage treatments. Uncertainty of model outputs is investigated using a Latin hypercube sampling scheme (LHS) with restricted pairing for model input sampling. Probability density functions (pdfs) are constructed for water flow rate, atrazine leaching rate, total accumulated leaching, and atrazine concentration in percolation water. Results indicate that consideration of input parameter uncertainty produces a 20% higher mean flow rate along with two to three times larger atrazine leaching rate, accumulated leachate, and concentration than that obtained using mean input parameters. Uncertainty in predicted flow rate is small but that in solute transport is an order of magnitude larger than that of corresponding input parameters. Macropore flow is observed to contribute to the variability of atrazine transport results. Overall, the analysis provides a quantification of prediction uncertainty that is found to enhance a user's ability to assess risk levels associated with model predictions.     

Dynamic modeling of organophosphate pesticide load in surface water in the northern San Joaquin Valley watershed of California

The hydrology, sediment, and pesticide transport components of the Soil and Water Assessment Tool (SWAT) were evaluated on the northern San Joaquin Valley watershed of California. The Nash-Sutcliffe coefficients for monthly stream flow and sediment load ranged from 0.49 to 0.99 over the watershed during the study period of 1992-2005. The calibrated SWAT model was applied to simulate fate and transport processes of two organophosphate pesticides of diazinon and chlorpyrifos at watershed scale. The model generated satisfactory predictions of dissolved pesticide loads relative to the monitoring data. The model also showed great success in capturing spatial patterns of dissolved diazinon and chlorpyrifos loads according to the soil properties and landscape morphology over the large agricultural watershed. This study indicated that curve number was the major factor influencing the hydrology while pesticide fate and transport were mainly affected by surface runoff and pesticide application and in the study area.     

A spatially-evaluated methodology for assessing risk to a population from contaminated land

A methodology is proposed which combines quantitative probabilistic human health risk assessment and spatial statistical methods (geostatistics) to produce an assessment of risks to human health from exposure to contaminated land, in a manner which preserves the spatial distribution of risks and provides a measure of uncertainty in the assessment. Maps of soil contaminant levels, which incorporate uncertainty, are produced from sparse sample data using sequential indicator simulation. A real, age-stratified population is mapped across the contaminated area, and intake of soil contaminants by individuals is calculated probabilistically using an adaptation of the Contaminated Land Exposure Assessment (CLEA) model. An abundance of information is contained in results which can be interrogated at the population and individual level, and mapped to provide a powerful visual tool for risk managers, enabling efficient targeting of risk reduction measures to different locations.     

Monod kinetics rather than a first-order degradation model explains atrazine fate in soil mini-columns: Implications for pesticide fate modelling

Pesticide transport models commonly assume first-order pesticide degradation kinetics for describing reactive transport in soil. This assumption was assessed in mini-column studies with associated batch degradation tests. Soil mini-columns were irrigated with atrazine in two intermittent steps of about 30 days separated by 161 days application of artificial rain water. Atrazine concentration in the effluent peaked to that of the influent concentration after initial break-through but sharply decreased while influx was sustained, suggesting a degradation lag phase. The same pattern was displayed in the second step but peak height and percentage of atrazine recovered in the effluent were lower. A Monod model with biomass decay was successfully calibrated to this data. The model was successfully evaluated against batch degradation data and mini-column experiments at lower flow rate. The study suggested that first-order degradation models may underestimate risk of pesticide leaching if the pesticide degradation potential needs amplification during degradation.     

Model comparison for risk assessment: a case study of contaminated groundwater

Many environmental multimedia risk assessment models have been developed and widely used along with increasing sophistication of the risk assessment method. Despite of the considerable improvement, uncertainty remains a primary threat to the credibility of and users' confidence in the model-based risk assessments. In particular, it has been indicated that scenario and model uncertainty may affect significantly the assessment outcome. Furthermore, the uncertainty resulting from choosing different models has been shown more important than that caused by parameter uncertainty. Based on the relationship between exposure pathways and estimated risk results, this study develops a screening procedure to compare the relative suitability between potential multimedia models, which would facilitate the reduction of uncertainty due to model selection. MEPAS, MMSOILS, and CalTOX models, combined with Monte Carlo simulation, are applied to a realistic groundwater-contaminated site to demonstrate the process. It is also shown that the identification of important parameters and exposure pathways, and implicitly, the subsequent design of uncertainty reduction and risk management measures, would be better-formed.     

Error propagation in pesticide residue measurements estimated by computational simulations and inter-laboratory sample analysis

An assessment of the error associated with conventional pesticide residue analysis has been conducted based on computer simulations and inter-laboratory residue analysis. Computational simulations were conducted based on (i) typical performance and regulatory acceptance criteria of analytical methods, and (ii) field residue distributions. In addition, field samples with incurred residues were sent to different private laboratories and the results compared. The relative difference in pesticide residues obtained when samples from the same field or produce lot are analyzed at separate laboratories was used to quantify the uncertainty associated with residue analyses performed using common analytical technology, and methods that are in compliance with current regulatory requirements. The study showed that differences of > 100% are common and should be expected when samples from the same crop are analyzed at different laboratories. The results also suggest that the error within residue measurements can be particularly detrimental when a result is reported near the maximum residue limit (MRL).     

Assessing the risk of N leaching from forest soils across a steep N deposition gradient in Sweden

Nitrogen leaching from boreal and temporal forests, where normally most of the nitrogen is retained, has the potential to increase acidification of soil and water and eutrophication of the Baltic Sea. In parts of Sweden, where the nitrogen deposition has been intermediate to high during recent decades, there are indications that the soils are close to nitrogen saturation. In this study, four different approaches were used to assess the risk of nitrogen leaching from forest soils in different parts of Sweden. Nitrate concentrations in soil water and C:N ratios in the humus layer where interpreted, together with model results from mass balance calculations and detailed dynamic modelling. All four approaches pointed at a risk of nitrogen leaching from forest soils in southern Sweden. However, there was a substantial variation on a local scale. Basing the assessment on four different approaches makes the assessment robust.     

Risk assessment and prioritisation of contaminated sites on the catchment scale

Contaminated sites pose a significant threat to groundwater resources worldwide. Due to limited available resources a risk-based prioritisation of the remediation efforts is essential. Existing risk assessment tools are unsuitable for this purpose, because they consider each contaminated site separately and on a local scale, which makes it difficult to compare the impact from different sites. Hence a modelling tool for risk assessment of contaminated sites on the catchment scale has been developed. The CatchRisk screening tool evaluates the risk associated with each site in terms of its ability to contaminate abstracted groundwater in the catchment. The tool considers both the local scale and the catchment scale. At the local scale, a flexible, site specific leaching model that can be adjusted to the actual data availability is used to estimate the mass flux over time from identified sites. At the catchment scale, a transport model that utilises the source flux and a groundwater model covering the catchment is used to estimate the transient impact on the supply well. The CatchRisk model was tested on a groundwater catchment for a waterworks north of Copenhagen, Denmark. Even though data scarcity limited the application of the model, the sites that most likely caused the observed contamination at the waterworks were identified. The method was found to be valuable as a basis for prioritising point sources according to their impact on groundwater quality. The tool can also be used as a framework for testing hypotheses on the origin of contamination in the catchment and for identification of unknown contaminant sources.