摘要:
污染物的被动采样材料-水分配系数(KPW)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KPW值,有必要发展其KPW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KPW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(R2adj介于0.806~0.989)、稳健性(Q2LOO和Q2BOOT分别介于0.786~0.988和0.773~0.801)和预测能力(R2ext和Q2ext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logKPW值。有机污染物的logKPW与分子McGowan体积(Vx)、氯原子个数(nCl)、环周长(Rperim)、多重键个数(nBM)、N, O极性贡献的拓扑极性表面积(TPSA(NO))、[-N(=)=]结构个数(NddsN)和羟基个数(nROH)等参数有关。
Abstract:
The partition coefficients for organic pollutants between sampling materials and water (KPW) are significant for designing passive sampling devices and calculating water concentrations from the samplers. However, it is difficult to measure KPW for all potential pollutants since experimental determination of KPW is generally laborious, time-consuming and expensive. Therefore, it is necessary to develop in silico models for predicting KPW values. In the present study, multiple linear regression analysis was employed to develop quantitative structure-activity relationship models for KPW of seven sampling materials. For the developed models, the adjusted correlation coefficient squares (R2adj) range from 0.806 to 0.989; the leave-one-out cross-validated Q2 (Q2LOO) and bootstrap method Q2BOOT range from 0.786 to 0.988 and from 0.773 to 0.801, respectively; the external explained R2ext and Q2ext 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 KPW values of diverse chemical species including alkanes, alkenes, aromatics, alcohols, ketones, esters and ethers. The dominant molecular structural factors on logKPW 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).
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