有机污染物的被动采样材料-水分配系数的QSAR研究

污染物的被动采样材料-水分配系数(K_(PW))是衡量被动采样器性能和进行优化的一个重要指标,由于实验方法难以逐个测定众多污染物的K_(PW)值,有必要发展其KP W预测方法。本研究选取聚乙烯(PE)、聚丙烯酸酯(PA)和硅橡胶(SR)3类常用的被动采样材料共7种,采用多元线性回归分析方法构建可用于K_(PW)预测的定量构效关系(QSAR)模型。所构建的QSAR模型具有良好的拟合优度(R2a dj介于0.806~0.989)、稳健性(Q2L O O和Q2B O O T分别介于0.786~0.988和0.773~0.801)和预测能力(R_(ext)~2和Q_(ext)~2分别介于0.769~0.989和0.757~0.982),可以用于预测烷烃、烯烃、芳香类、醇类、酮类、酯类、醚类等多种有机污染物的log KP W值。有机污染物的log KP W与分子Mc Gowan体积(Vx)、氯原子个数(n Cl)、环周长(Rperim)、多重键个数(n BM)、N,O极性贡献的拓扑极性表面积(TPSA(NO))、-N(=)=]结构个数(Ndds N)和羟基个数(nROH)等参数有关。

QSAR Models for Predicting Partition Coefficients of Organic Pollutants between Passive Sampling Materials and Water

The partition coefficients for organic pollutants between sampling materials and water (K_PW) are significant for designing passive sampling devices and calculating water concentrations from the samplers. However, it is difficult to measure K_PW for all potential pollutants since experimental determination of K_PW is generally laborious, time-consuming and expensive. Therefore, it is necessary to develop in silico models for predicting K_PW values. In the present study, multiple linear regression analysis was employed to develop quantitative structure-activity relationship models for K_PW of seven sampling materials. For the developed models, the adjusted correlation coefficient squares (R²_adj) range from 0.806 to 0.989; the leave-one-out cross-validated Q² (Q²_LOO) and bootstrap method Q²_BOOT range from 0.786 to 0.988 and from 0.773 to 0.801, respectively; the external explained R²_ext and Q²_ext range from 0.769 to 0.989 and from 0.757 to 0.982, respectively. The established models, with high goodness-of-fit, robustness and predictive ability, are capable of predicting K_PW values of diverse chemical species including alkanes, alkenes, aromatics, alcohols, ketones, esters and ethers. The dominant molecular structural factors on logK_PW of pollutants include McGowan volume (Vx/), the number of chlorine atoms (nCl), the ring perimeter (Rperim), the number of multiple bonds (nBM), the topological polar surface area with N and O polar contributions (TPSA(NO)), the number of -N(=)= (NddsN), and the number of hydroxyl groups (nROH).