Graduate Program in Agricultural Engineering (PGEAGRI), Western Paraná State University (Universidade Estadual do Oeste do Paraná, UNIOESTE), Cascavel, Paraná, Brazil.
Graduate Program in Agricultural Engineering (PGEAGRI), Western Paraná State University (Universidade Estadual do Oeste do Paraná, UNIOESTE), Cascavel, Paraná, Brazil.
Water Res. 2014 Apr 15;53:191-9. doi: 10.1016/j.watres.2014.01.023. Epub 2014 Jan 23.
The collection of data to study the damage caused by pesticides to the environment and its ecosystems is slowly acquired and costly. Large incentives have been established to encourage research projects aimed at building mathematical models for predicting physical, chemical or biological properties of environmental interest. The organic carbon normalized soil sorption coefficient (K(oc)) is an important physicochemical property used in environmental risk assessments for compounds released into the environment. Many models for predicting logK(oc) that have used the parameters logP or logS as descriptors have been published in recent decades. The strong correlation between these properties (logP and logS) prevents them from being used together in multiple linear regressions. Because the sorption of a chemical compound in soil depends on both its water solubility and its water/organic matter partitioning, we assume that models capable of combining these two properties can generate more realistic results. Therefore, the objective of this study was to propose an alternative approach for modeling logK(oc), using a simple descriptor of solubility, here designated as the logarithm of solubility corrected by octanol/water partitioning (logS(P)). Thus, different models were built with this descriptor and with the conventional descriptors logP and logS, alone or associated with other explanatory variables representing easy-to-interpret physicochemical properties. The obtained models were validated according to current recommendations in the literature, and they were compared with other previously published models. The results showed that the use of logS(p) instead of conventional descriptors led to simple models with greater statistical quality and predictive power than other more complex models found in the literature. Therefore, logS(P) can be a good alternative to consider for the modeling of logK(oc) and other properties that relate to both solubility and water/organic matter partitioning.
收集研究农药对环境及其生态系统造成的损害的数据是缓慢且昂贵的。已经建立了大量激励措施，以鼓励旨在构建预测环境中感兴趣的物理、化学或生物性质的数学模型的研究项目。有机碳归一化土壤吸附系数（K(oc)）是用于对释放到环境中的化合物进行环境风险评估的重要物理化学性质。近几十年来，已经发表了许多使用参数 logP 或 logS 作为描述符来预测 logK(oc)的模型。这些性质（logP 和 logS）之间的强相关性使得它们不能在多元线性回归中一起使用。由于化合物在土壤中的吸附取决于其水溶性和水/有机物质分配，我们假设能够结合这两个性质的模型可以产生更现实的结果。因此，本研究的目的是提出一种替代方法来建模 logK(oc)，使用一个简单的溶解度描述符，这里指定为通过辛醇/水分配修正的溶解度的对数（logS(P)）。因此，使用该描述符和传统描述符 logP 和 logS 单独或与代表易于解释的物理化学性质的其他解释变量一起构建了不同的模型。根据文献中的当前建议对获得的模型进行了验证，并将其与其他先前发表的模型进行了比较。结果表明，使用 logS(p)代替传统描述符可以得到具有更大统计质量和预测能力的简单模型，优于文献中发现的其他更复杂的模型。因此，logS(P)可以作为建模 logK(oc)和其他与溶解度和水/有机物质分配都相关的性质的一个很好的替代方案。
