Department of Civil and Environmental Engineering, University of Delaware, Newark, Delaware, USA.
Environ Toxicol Chem. 2011 Sep;30(9):2013-22. doi: 10.1002/etc.611.
Predicting the association of contaminants with particulate organic matter in the environment is critical in determining the fate and bioavailability of chemicals. A ubiquitous measure of contaminant association with soil and sediment particulate organic matter is the organic carbon partition coefficient K(OC) . Chemical class-specific models relating the K(OC) to the octanol-water partition coefficient K(OW) have been used to predict the partitioning to organic carbon in the water column and sediment for nonpolar hydrophobic pollutants and some polar pollutants. A single linear solvation energy relationship (LSER) is proposed as a simpler and chemically based alternative for predicting K(OC) for a more diverse set of compounds. A chemically diverse set of K(OC) data is used to obtain a more robust and more universally representative model of organic carbon partitioning than previously available LSER models. The resulting model has a root mean square error (RMSE) of prediction for log K(OC) of RMSE = 0.48 for the fitted data set and RMSE = 0.55 for an independent data set. An analysis of LSER coefficients highlights the relative importance of hydrogen bonding interactions.
预测环境中污染物与颗粒有机物质的关联对于确定化学物质的归宿和生物可利用性至关重要。一种普遍用于衡量土壤和沉积物颗粒有机物质中污染物关联程度的方法是有机碳分配系数 K(OC)。已有化学类别特异性模型将 K(OC)与辛醇-水分配系数 K(OW)相关联，用于预测非极性疏水性污染物和一些极性污染物在水柱和沉积物中的分配。本文提出了一种单一的线性溶剂化能关系（LSER），作为一种更简单、基于化学的替代方法，用于预测更多种类化合物的 K(OC)。利用一组化学性质多样的 K(OC)数据，获得了比以前可用的 LSER 模型更稳健、更具代表性的有机碳分配模型。该模型对拟合数据集的 log K(OC)的预测均方根误差（RMSE）为 0.48，对独立数据集的 RMSE 为 0.55。LSER 系数的分析强调了氢键相互作用的相对重要性。
