Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN, USA; U.S. Environmental Protection Agency, Office of Chemical Safety and Pollution Prevention, Office of Pesticide Programs, Arlington, VA, USA.
Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN, USA; U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, NC, USA.
Environ Pollut. 2020 Feb;257:113486. doi: 10.1016/j.envpol.2019.113486. Epub 2019 Oct 30.
Vernal pools are ephemeral wetlands that provide critical habitat to many listed species. Pesticide fate in vernal pools is poorly understood because of uncertainties in the amount of pesticide entering these ecosystems and their bioavailability throughout cycles of wet and dry periods. The Pesticide Water Calculator (PWC), a model used for the regulation of pesticides in the US, was used to predict surface water and sediment pore water pesticide concentrations in vernal pool habitats. The PWC model (version 1.59) was implemented with deterministic and probabilistic approaches and parameterized for three agricultural vernal pool watersheds located in the San Joaquin River basin in the Central Valley of California. Exposure concentrations for chlorpyrifos, diazinon and malathion were simulated. The deterministic approach used default values and professional judgment to calculate point values of estimated concentrations. In the probabilistic approach, Monte Carlo (MC) simulations were conducted across the full input parameter space with a sensitivity analysis that quantified the parameter contribution to model prediction uncertainty. Partial correlation coefficients were used as the primary sensitivity metric for analyzing model outputs. Conditioned daily sensitivity analysis indicates curve number (CN) and the universal soil loss equation (USLE) parameters as the most important environmental parameters. Therefore, exposure estimation can be improved efficiently by focusing parameterization efforts on these driving processes, and agricultural pesticide inputs in these critical habitats can be reduced by best management practices focused on runoff and sediment reductions.
春池是短暂存在的湿地,为许多列入清单的物种提供了关键的栖息地。由于进入这些生态系统的农药数量及其在干湿周期中的生物利用度存在不确定性,因此对春池中的农药命运了解甚少。农药水计算器(PWC)是一种用于监管美国农药的模型,用于预测春池栖息地中的地表水和沉积物孔隙水中的农药浓度。PWC 模型(版本 1.59)采用确定性和概率性方法实施,并针对加利福尼亚州中央山谷圣华金河流域的三个农业春池流域进行了参数化。模拟了毒死蜱、二嗪农和马拉硫磷的暴露浓度。确定性方法使用默认值和专业判断来计算估计浓度的点值。在概率性方法中,进行了蒙特卡罗(MC)模拟,跨越了整个输入参数空间,并进行了敏感性分析,量化了参数对模型预测不确定性的贡献。偏相关系数被用作分析模型输出的主要敏感性指标。条件日敏感性分析表明,曲线数(CN)和通用土壤流失方程(USLE)参数是最重要的环境参数。因此,通过专注于这些驱动过程的参数化工作,可以有效地提高暴露估计的准确性,并通过侧重于减少径流和泥沙的最佳管理实践来减少这些关键栖息地中的农业农药投入。