天然有机质疏水性定量方法及吸附预测模型  

The hydrophobicity quantification methods and sorption models for natural organic matter

作　　者：刘昆 付翯云 瞿晓磊 LIU Kun;FU Heyun;QU Xiaolei(State Key Laboratory of Pollution Control and Resource Reuse,School of the Environment,Nanjing University,Nanjing,210046,China)

机构地区：[1]污染控制与资源化研究国家重点实验室,南京大学环境学院,南京210046

出　　处：《环境化学》2023年第1期71-79,共9页Environmental Chemistry

基　　金：科技部“场地土壤污染成因与治理技术”重点专项(2019YFC1804201,2020YFC1807002);国家自然科学基金(21876075)资助.

摘　　要：天然有机质是有机污染物在土壤和水环境中的重要赋存相,显著影响有机污染物的传输、归趋和生物有效性.有机污染物的有机碳-水分配系数是污染物迁移积累模型的核心参数.如何准确、快速地评估有机污染物在天然有机质上的吸附行为是环境科学研究的一个重要科学问题.该方向的研究内容主要包括有机污染物和天然有机质性质定量描述、有机污染物-天然有机质作用机制解析和吸附预测模型构建三个环节.学界在有机污染物-天然有机质作用机制解析方向取得了丰硕的成果,在天然有机质性质定量描述和吸附预测模型构建方向仍有广阔的发展空间.本文系统评估了现有的天然有机质疏水性定量方法和天然有机质吸附预测模型.通过对方法和模型优缺点和适用范围的系统分析,揭示了现有研究的不足,展望了未来研究的主要方向.Natural organic matter(NOM)is one of the major partition phases of organic pollutants in the environment.The partition of organic pollutants to NOM is a key process controlling its environmental fate,transport and bioavailability.The organic carbon normalized sorption coefficient(KOC)is one of key parameters in the fate and transport model of organic pollutants.Accurate and efficient prediction of the sorption behavior of organic pollutants to NOM is one of the major challenges in the environmental science field.The solution includes the quantitative description of organic pollutants and NOM,the sorption mechanisms,and the prediction models.Currently,significant progresses have been made in revealing the sorption mechanisms.There are great needs for studies regarding the quantitative description of organic pollutants and NOM as well as the prediction models.In this review,we compared current methodologies for scaling NOM hydrophobicity and the current models for prediction of organic compound sorption to NOM.By systematic analysis of the advantages and limitations of current methods and models,we proposed future research needs for further advancing the prediction of organic pollutant sorption behavior.

关 键 词：天然有机质 有机污染物 吸附分配 疏水性 预测模型. 

分 类 号：X505[环境科学与工程—环境工程]