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比较地表饮用水中模拟农药浓度与监测数据:对观测到的差异的解释和对新监管建模方法的建议。

Comparison of simulated pesticide concentrations in surface drinking water with monitoring data: explanations for observed differences and proposals for a new regulatory modeling approach.

机构信息

Stone Environmental, Inc. , 535 Stone Cutters Way, Montpelier, Vermont 05602, United States.

出版信息

J Agric Food Chem. 2014 Jan 15;62(2):348-59. doi: 10.1021/jf4036996. Epub 2014 Jan 2.

Abstract

A primary component to human health risk assessments required by the U.S. Environmental Protection Agency in the registration of pesticides is an estimation of concentrations in surface drinking water predicted by environmental models. The assumptions used in the current regulatory modeling approach are designed to be "conservative", resulting in higher predicted pesticide concentrations than would actually occur in the environment. This paper compiles previously reported modeling and monitoring comparisons and shows that current regulatory modeling methods result in predictions that universally exceed observed concentrations from the upper end of their distributions. In 50% of the modeling/monitoring comparisons, model predictions were more than 229 times greater than the observations, while, in 25% of the comparisons, model predictions were more than 4500 times greater than the observations. The causes for these overpredictions are identified, followed by suggestions for alternative modeling approaches that would result in predictions of pesticide concentrations closer to those observed.

摘要

美国环境保护署在注册农药时进行的人类健康风险评估的一个主要组成部分是通过环境模型预测地表饮用水中的浓度。目前监管建模方法中使用的假设旨在“保守”,导致预测的农药浓度高于实际环境中会出现的浓度。本文汇总了之前报告的建模和监测比较结果,表明当前的监管建模方法导致的预测结果普遍超过了其分布上限的观测浓度。在 50%的建模/监测比较中,模型预测值比观测值高出 229 倍以上,而在 25%的比较中,模型预测值比观测值高出 4500 倍以上。本文确定了这些过高预测的原因,并提出了替代建模方法的建议,这些方法将导致更接近观测到的农药浓度预测。

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