有机污染物的被动采样材料-水分配系数的QSAR研究

发布时间：2018-05-19 04:20

本文选题：被动采样材料-水分配系数 + 聚乙烯　；参考：《生态毒理学报》2016年06期

【摘要】：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。
[Abstract]:The passive sampling materials-water distribution coefficient (KSP) is an important index to measure the performance and optimize the performance of the passive sampler. It is necessary to develop its KP W prediction method because it is difficult for the experimental method to measure the Kapp PWs of many pollutants one by one. In this study, seven kinds of passive sampling materials, such as PEP, PAA) and SRX, were used to construct QSAR model, which can be used to predict Kapp PWs by multiple linear regression analysis. The established QSAR model has a good goodness of fit: R2a dj is between 0.806 and 0.989, robustness of Q2O and Q2B O O T are between 0.786 and 0.801, respectively) and prediction ability is 0.7690.989 and 0.7570.982C, respectively. It can be used to predict alkanes, olefins, aromatics, alcohols, ketones, alkanes, alkenes, aromatics, alcohols and ketones, respectively, and to predict alkanes, alkenes, aromatics, alcohols, ketones, and to predict alkanes, alkenes, aromatics, alcohols and ketones, respectively. Log KP W values of esters, ethers and other organic pollutants. The log KP W of organic pollutants is related to the parameters such as the volume of Mc Gowan, the number of chlorine atoms, the circumference of the ring, the number of multiple bonds, the topologic polarity surface area of the contribution of polarity of Mc Gowan, the number of the structure of [-N], the number of Ndds N and the number of hydroxyl groups (nROH).
【作者单位】：工业生态与环境工程教育部重点实验室大连理工大学环境学院;
【基金】：国家自然科学基金(21325729;21661142001)
【分类号】：X832

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