有机污染物的被动采样材料-水分配系数的QSAR研究
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- 英文论文题名：QSAR Model for Predicting the Partition Coefficients of Organic Contaminants between Sampling Materials and Water
- 论文作者：杨蕾 ; 罗翔 ; 王雅 ; 陈景文
- 英文论文作者：Lei Yang ; Xiang Luo ; Ya Wang ; Jingwen Chen ; Key Laboratory of Industrial Ecology and Environmental Engineering (MOE) ; School of Environmental Science and Technology ; Dalian University of Technology
- 年：2016
- 作者机构：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;
- 论文关键词：被动采样材料-水分配系数 ; 聚乙烯 ; 聚丙烯酸酯 ; 硅橡胶 ; 定量构效关系
- 会议召开时间：2016-07-01
- 会议录名称：中国化学会第30届学术年会摘要集-第二十六分会：环境化学
- 语种：中文
- 分类号：X832
- 学会代码：ZGHY
- 会议名称：中国化学会第30届学术年会-第二十六分会 ; 环境化学
- 会议地点：中国辽宁大连
- 主办单位：中国化学会
- 学会名称：中国化学会
- 页数：1
- 文件大小：142k
- 原文格式：D
- 会议级别：全国
摘要
被动采样技术被广泛用于水中痕量有机污染物的采集。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[1],通常由实验测定获得。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log KPW值。模型结果表明,有机污染物的log KPW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(n ROH)等参数有关。
Passive sampling has been widely used to concentrate trace organic contaminants in aquatic environments.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.In general,KPW are obtained by experimental determination.However,it is difficult to measure KPW for all the pollutants.Thus,it is necessary to develop in silico methods for predicting KPW values.In the present study,multiple linear regression analysis was employed to develop quantitative structure-activity relationships(QSAR) models for predicting KPW values of seven sampling materials.The established models,with high goodness-of-fit,robustness and predictive ability,are capable for predicting the KPW values of diverse chemical species including alkanes,alkenes,aromatics,alcohols,ketones,esters and ethers.The main factors governing the log KPW values of organic contaminants are Mc Gowan volume(Vx),the number of chlorine atoms(n Cl),the ring perimeter(Rperim),the number of multiple bonds(n BM),the topological polar surface area using N,O polar contributions(TPSA(NO)),the number of-N(=)=(Ndds N) and the number of hydroxyl groups(n ROH).
Passive sampling has been widely used to concentrate trace organic contaminants in aquatic environments.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.In general,KPW are obtained by experimental determination.However,it is difficult to measure KPW for all the pollutants.Thus,it is necessary to develop in silico methods for predicting KPW values.In the present study,multiple linear regression analysis was employed to develop quantitative structure-activity relationships(QSAR) models for predicting KPW values of seven sampling materials.The established models,with high goodness-of-fit,robustness and predictive ability,are capable for predicting the KPW values of diverse chemical species including alkanes,alkenes,aromatics,alcohols,ketones,esters and ethers.The main factors governing the log KPW values of organic contaminants are Mc Gowan volume(Vx),the number of chlorine atoms(n Cl),the ring perimeter(Rperim),the number of multiple bonds(n BM),the topological polar surface area using N,O polar contributions(TPSA(NO)),the number of-N(=)=(Ndds N) and the number of hydroxyl groups(n ROH).
引文
[1]Endo,S.;Droge,S.T.J.;Goss,K.U.Anal.Chem.2011,83(4):1394.