An Improved Indirect Procedure for Estimating Pesticide Volatility from Field Trials.

Conventional indirect approaches for estimating pesticide volatility from agricultural fields require an air dispersion model and near-field, temporal air concentration measurements. The model pesticide flux profile is chosen such that field observations are recovered. Ross et al. (1996) first proposed a back-calculation method (BCM) using a single iteration of the Industrial Source Complex Short Term (ISCST) parameterized by a unit source flux. The unit flux is updated by scaling with the slope of a linear regression line between model predictions and actual field observations at each measured time point and location, yielding an estimate for the field flux that occurred over the sampling period. The BCM is expanded using a downhill simplex optimization procedure requiring many ISCST iterations to consecutively adjust the volatility flux rate such that the sum of the squared residuals between predicted and measured air concentrations is minimized (denoted as BCMO). The BCMO is ideally suited for comparing the volatility of different pesticide formulations of the same active from field studies performed simultaneously. Comparison of the BCM and BCMO from field trials containing single (Texas) and multiple simultaneously treated fields (Indiana) are provided for pesticides ranging from low to high volatility. The advanced BCMO is a better alternative than the original BCM, as shown by closer model predictions to measured air concentrations. A major advantage of the BCMO is the ability to extract unique flux source strengths for each field when multiple fields are present and treated consecutively and contiguously with each field emitting pesticide mass at different rates.

Development of a Monte Carlo sampling shell for the pesticide root zone model and its application by the Federal Insecticide, Fungicide, and Rodenticide Act Environmental Modeling Validation Task Force.

A user interface to the U.S. Environmental Protection Agency pesticide root zone model (PRZM) was constructed to allow Monte Carlo sampling of input parameter distributions. The interface was constructed employing the Visual Basic for Applications development environment, along with the functionality of the Crystal Ball Professional forecasting and risk analysis package. The tool has been utilized by the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) Environmental Model Validation Task Force to perform detailed statistical analyses of model input parameter uncertainty and the propagation of this uncertainty on the model outputs as well as comparisons of modeled and field-measured data.

Using Monte Carlo techniques to judge model prediction accuracy: validation of the pesticide root zone model 3.12.

Individuals from the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) Environmental Model Validation Task Force (FEMVTF) Statistics Committee periodically met to discuss the mechanism for conducting an uncertainty analysis of Version 3.12 of the pesticide root zone model (PRZM 3.12) and to identify those model input parameters that most contribute to model prediction error. This activity was part of a larger project evaluating PRZM 3.12. The goal of the uncertainty analysis was to compare site-specific model predictions and field measurements using the variability in each as a basis of comparison. Monte Carlo analysis was used as an integral tool for judging the model's ability to predict accurately. The model was judged on how well it predicts measured values, taking into account the uncertainty in the model predictions. Monte Carlo analysis provides the tool for inferring model prediction uncertainty. We argue that this is a fairer test of the model than a simple one-to-one comparison between predictions and measurements. Because models are known to be imperfect predictors prior to running the model, the inaccuracy in model predictions should be considered when models are judged for their predictive ability. Otherwise, complex models can easily fail a validation test. Few complex models, such as PRZM 3.12, would pass a typical model validation exercise. This paper describes the approaches to the validation of PRZM 3.12 used by the committee and discusses issues in sampling distribution selection and appropriate statistics for interpreting the model validation results.

Validation of pesticide root zone model 3.12: employing uncertainty analysis.

Computer models are being increasingly used to provide an efficient cost-effective means of evaluating the fate and behavior of chemicals in the environment. These models can be used in lieu of or in conjunction with field studies. Because of the increasing reliance on models for critical regulatory decision making, the need arose to assess the validity of regulatory models via an analysis of the correlation of model response estimates with measured data. In conjunction with the evaluation of the correlation of model response estimates and measured field data, a rigorous statistically based validation was also warranted. Because of the unique nature of the correlative exercise using modeled and measured data, standard statistical analyses, while informative, failed to encompass factors associated with the uncertainty of measured environmental fate data and potential model inputs. In an effort to evaluate this uncertainty, an initial sensitivity analysis was performed where key model input parameters for runoff and leaching simulations were identified. Once the sensitive input parameters were identified, a Monte Carlo-based preprocessor was developed whereby the sampling distributions of these parameters were used to propagate uncertainty in the input parameters into error in model predictions. Importantly, assumptions about parameter distributions for input into the Monte Carlo tool were made only after a formal detailed site-specific analysis of measured field data. Employing the functionality of the Crystal Ball Pro development environment, the pesticide root zone model (PRZM) 3.12 was run iteratively for 500 trials, and model output was collated and analyzed. The model predictions were considered reasonably accurate for most regulatory requirements, and the model prediction error was considered acceptable.