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Effect Modeling Quantifies the Difference Between the Toxicity of Average Pesticide Concentrations and Time-Variable Exposures from Water Quality Monitoring.
Environmental Toxicology and Chemistry ( IF 3.6 ) Pub Date : 2020-07-31 , DOI: 10.1002/etc.4838 Roman Ashauer 1 , Roland Kuhl 2 , Elke Zimmer 2 , Marion Junghans 3
Environmental Toxicology and Chemistry ( IF 3.6 ) Pub Date : 2020-07-31 , DOI: 10.1002/etc.4838 Roman Ashauer 1 , Roland Kuhl 2 , Elke Zimmer 2 , Marion Junghans 3
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Synthetic chemicals are frequently detected in water bodies, and their concentrations vary over time. Water monitoring programs typically employ either a sequence of grab samples or continuous sampling, followed by chemical analysis. Continuous time‐proportional sampling yields the time‐weighted average concentration, which is taken as proxy for the real, time‐variable exposure. However, we do not know how much the toxicity of the average concentration differs from the toxicity of the corresponding fluctuating exposure profile. We used toxicokinetic–toxicodynamic models (invertebrates, fish) and population growth models (algae, duckweed) to calculate the margin of safety in moving time windows across measured aquatic concentration time series (7 pesticides) in 5 streams. A longer sampling period (14 d) for time‐proportional sampling leads to more deviations from the real chemical stress than shorter sampling durations (3 d). The associated error is a factor of 4 or less in the margin of safety value toward underestimating and an error of factor 9 toward overestimating chemical stress in the most toxic time windows. Under‐ and overestimations occur with approximate equal frequency and are very small compared with the overall variation, which ranged from 0.027 to 2.4 × 1010 (margin of safety values). We conclude that continuous, time‐proportional sampling for a period of 3 and 14 d for acute and chronic assessment, respectively, yields sufficiently accurate average concentrations to assess ecotoxicological effects. Environ Toxicol Chem 2020;39:2158–2168. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
中文翻译：
效果模型可量化平均农药浓度的毒性与水质监测中随时间变化的暴露之间的差异。
合成化学物质经常在水体中被检测到，其浓度会随时间变化。水监控程序通常采用一系列采样或连续采样，然后进行化学分析。连续按时间比例采样可得出按时间加权的平均浓度，该浓度可作为真实，随时间变化的暴露量的代理。但是，我们不知道平均浓度的毒性与相应的波动暴露曲线的毒性相差多少。我们使用了毒物动力学-毒物动力学模型（无脊椎动物，鱼类）和种群增长模型（藻类，浮萍）来计算5种溪流中所测水生浓度时间序列（7种农药）在移动时间窗内的安全裕度。与时间比例较短的采样持续时间（3 d）相比，时间比例采样的较长采样周期（14 d）导致与实际化学应力的偏差更大。在最有毒的时间窗内，相关的误差是在安全值裕度中偏低4或小于4的因数，而在估计毒性最严重的时间窗中偏高估计化学应力的是9的误差。低估和高估的发生频率近似相等，与整体变化相比范围很小，范围为0.027至2.4×1010（安全值裕度）。我们得出的结论是，分别进行3 d和14 d的连续时间比例采样分别进行急性和慢性评估，可产生足够准确的平均浓度来评估生态毒理作用。Environ Toxicol Chem 2020； 39：2158-2168。©2020作者。Wiley Periodicals LLC代表SETAC发布的《环境毒理学和化学》。
更新日期：2020-07-31
中文翻译：
效果模型可量化平均农药浓度的毒性与水质监测中随时间变化的暴露之间的差异。
合成化学物质经常在水体中被检测到，其浓度会随时间变化。水监控程序通常采用一系列采样或连续采样，然后进行化学分析。连续按时间比例采样可得出按时间加权的平均浓度，该浓度可作为真实，随时间变化的暴露量的代理。但是，我们不知道平均浓度的毒性与相应的波动暴露曲线的毒性相差多少。我们使用了毒物动力学-毒物动力学模型（无脊椎动物，鱼类）和种群增长模型（藻类，浮萍）来计算5种溪流中所测水生浓度时间序列（7种农药）在移动时间窗内的安全裕度。与时间比例较短的采样持续时间（3 d）相比，时间比例采样的较长采样周期（14 d）导致与实际化学应力的偏差更大。在最有毒的时间窗内，相关的误差是在安全值裕度中偏低4或小于4的因数，而在估计毒性最严重的时间窗中偏高估计化学应力的是9的误差。低估和高估的发生频率近似相等，与整体变化相比范围很小，范围为0.027至2.4×1010（安全值裕度）。我们得出的结论是，分别进行3 d和14 d的连续时间比例采样分别进行急性和慢性评估，可产生足够准确的平均浓度来评估生态毒理作用。Environ Toxicol Chem 2020； 39：2158-2168。©2020作者。Wiley Periodicals LLC代表SETAC发布的《环境毒理学和化学》。
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