Department of Civil and Environmental Engineering, University of Delaware, Newark, Delaware, USA.
Environ Toxicol Chem. 2011 Sep;30(9):2023-9. doi: 10.1002/etc.610.
Predicting the association of contaminants with both particulate and dissolved organic matter is critical in determining the fate and bioavailability of chemicals in environmental risk assessment. To date, the association of a contaminant to particulate organic matter is considered in many multimedia transport models, but the effect of dissolved organic matter is typically ignored due to a lack of either reliable models or experimental data. The partition coefficient to dissolved organic carbon (K(DOC)) may be used to estimate the fraction of a contaminant that is associated with dissolved organic matter. Models relating K(DOC) to the octanol-water partition coefficient (K(OW)) have not been successful for many types of dissolved organic carbon in the environment. Instead, linear solvation energy relationships are proposed to model the association of chemicals with dissolved organic matter. However, more chemically diverse K(DOC) data are needed to produce a more robust model. For humic acid dissolved organic carbon, the linear solvation energy relationship predicts log K(DOC) with a root mean square error of 0.43.
预测污染物与颗粒态和溶解态有机物的关联对于确定环境风险评估中化学物质的归宿和生物可利用性至关重要。迄今为止,许多多介质传输模型都考虑了污染物与颗粒态有机物的关联,但由于缺乏可靠的模型或实验数据,通常忽略了溶解态有机物的影响。分配系数到溶解有机碳(K(DOC))可用于估计与溶解有机物关联的污染物的分数。将 K(DOC)与辛醇-水分配系数(K(OW))相关联的模型对于环境中的许多类型的溶解有机碳并不成功。相反,提出线性溶剂化能关系来模拟化学物质与溶解有机物的关联。然而,需要更多化学性质多样化的 K(DOC)数据来生成更稳健的模型。对于腐殖酸溶解有机碳,线性溶剂化能关系预测的 log K(DOC)的均方根误差为 0.43。