Department of Soil and Environment, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Environ Toxicol Chem. 2018 Mar;37(3):755-761. doi: 10.1002/etc.4011. Epub 2017 Dec 13.
Models used to assess leaching risks generally use organic carbon partition coefficient (k ) values derived from batch experiments on topsoil samples to estimate pesticide sorption in subsoils of much smaller organic carbon contents. This can introduce significant errors in leaching risk calculations, because inorganic sorbents can play an important role for sorption in subsoil. The objectives of the present study were therefore to summarize the available literature data on pesticide sorption in subsoils and to test whether a simple alternative model could improve on the standard k approach used in risk assessment models for pesticide leaching. This model describes the sorption constant as a power law function of the organic carbon content. A database with the results of batch sorption experiments was collated from published studies that emphasized measurements in subsoils. This database contains 1029 data entries from 36 published studies with data for 29 active substances (11 nonionic compounds, 10 weak acids, 6 weak bases, one cation, and one zwitterion). The results show that whereas the constant k model proved to be an adequate model for 17 of the 63 individual datasets, the power law model gave acceptable fits (p < 0.05) for 60 of these cases. The exponent in the power law model varied over a wide range, from slightly negative to near unity. It also differed significantly (p = 0.015) for ionized and nonionized compounds, with median values of 0.25 and 0.55, respectively. It is concluded that the power law model could be used to parameterize subsoil sorption in regulatory leaching models, because it has widespread applicability and is simple enough for this purpose. Suitable ways of incorporating this approach in risk assessment procedures are discussed. Environ Toxicol Chem 2018;37:755-761. © 2017 SETAC.
用于评估浸出风险的模型通常使用从表层土壤样品的批量实验中得出的有机碳分配系数 (k) 值来估计有机碳含量低得多的底土中的农药吸附。这可能会在浸出风险计算中引入重大错误，因为无机吸附剂在底土中的吸附中可能起着重要作用。因此，本研究的目的是总结关于底土中农药吸附的现有文献数据，并测试简单的替代模型是否可以改进用于农药浸出风险评估模型的标准 k 方法。该模型将吸附常数描述为有机碳含量的幂函数。从强调底土测量的已发表研究中整理出具有批量吸附实验结果的数据库。该数据库包含 36 项已发表研究的 1029 个数据条目，涉及 29 种活性物质（11 种非离子化合物、10 种弱酸、6 种弱碱、一种阳离子和一种两性离子）。结果表明，尽管常数 k 模型被证明是 63 个单独数据集的 17 个数据集的合适模型，但幂律模型对于其中 60 个数据集的拟合情况可接受（p<0.05）。幂律模型中的指数变化范围很广，从略负到接近 1。对于离子化和非离子化化合物，它也有显著差异（p=0.015），分别为 0.25 和 0.55。结论是，幂律模型可用于法规浸出模型中的底土吸附参数化，因为它具有广泛的适用性，并且足够简单适用于此目的。讨论了在风险评估程序中纳入这种方法的合适方法。环境毒理化学 2018；37:755-761。©2017 SETAC。
