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-. 毒代动力学一毒效动力学(Toxicokinetic-Toxicodynamic, Tk-Td)和种群模型联用评价浓度随时间变化的农药的水生态风险[J]. 生态毒理学报, 2018, 13(5):
毒代动力学一毒效动力学(Toxicokinetic-Toxicodynamic, Tk-Td)和种群模型联用评价浓度随时间变化的农药的水生态风险
Coupling Toxicokinetic-Toxicodynamic (Tk-Td) and Population Models for Assessing Aquatic Ecological Risks to Time-Varying Pesticide Exposures
  
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中文摘要:
      利用种群模型评价农药暴露时间序列的方法与现有常用的风险评价方法进行了比较。美国环境保护局农药项目办公室(The US Environmental Protection Agency’s Office of Pesticide Programs)在近30年对水生生物农药日暴露值建立了模型,但并未对这些信息进行充分的利用。我们利用糠虾(Americamysis bahia)毒性数据和种群数量统计数据推演了毒代动力学一毒效动力学(Toxicokinetic-Toxicodynamic, Tk-Td)与一系列矩阵种群模型联用的数值。糠虾是一种沿岸浅海底的小型甲壳动物,通常用于常规毒性测试中。我们展示了这种联用方法如何仅用现有的标准常规毒性数据来优化已有的风险评价方法。我们创建了几种暴露场景,每种场景在基于生物的传统方法中有着相同的初始风险表征特征,而种群模型方法中则显示出不同的风险水平。这种TK-TD与种群模型联用的方法可以对不同的急性和慢性毒性数据的场景区分出不同的风险水平,而传统的风险评价方法则做不到。这种联用方法在风险评价方面独具优势,特别是针对污染物的暴露浓度随时间变化的情况。 精选自Glen Thursby, Keith Sappington, Matthew Etterson. Coupling Toxicokinetic-Toxicodynamic (Tk-Td) and Population Models for Assessing Aquatic Ecological Risks to Time-Varying Pesticide Exposures. Environmental Toxicology and Chemistry,2018,37:2633-2644.
详情请见 https://doi.org/10.1002/etc.4224
  
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Glen Thursby, Keith Sappington, Matthew Etterson
英文摘要:
      Population modeling evaluations of pesticide exposure time series were compared to aspects of a currently used risk assessment process. The US Environmental Protection Agency’s Office of Pesticide Programs models daily aquatic pesticide exposure values for 30 years in its risk assessments, but does not routinely make full use of the information in those time series. We used mysid shrimp Americamysis bahia toxicity and demographic data to demonstrate the value of a toxicokinetic-toxicodynamic model coupled with a series of matrix population models in risk assessment refinements. This species is a small epibenthic marine crustacean routinely used in regulatory toxicity tests. We demonstrate how the model coupling can refine current risk assessments using only existing standard regulatory toxicity test results. Several exposure scenarios (each with the same initial risk characterization as determined by a more traditional organismal-based approach) were created within which population modeling documented different risks than assessments based on the traditional approach. We also present different acute and chronic toxicity data scenarios where TK-TD coupled with population modeling can distinguish different responses; responses that tradition risk evaluations are not designed to detect. Our results reinforce the benefits of this type of modeling in risk evaluations, especially related to time-varying exposure concentrations.
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