南京大学学报（自然科学版）2024，Vol.60Issue(1)：130-140,11.DOI:10.13232/j.cnki.jnju.2024.01.013
场地有机污染物吸附行为多参数线性自由能模型研究
Poly-parameter linear free energy relationship models for organic pollutant sorption prediction at contaminated sites
摘要
Abstract
Accurate assessment of the sorption process of pollutants is an important step for the safe management and development of contaminated sites.We used a representative e-waste dismantling site in China as a research object to analyse the sorption properties of soil organic matter,developed a prediction model for the partition coefficient(KOC)of organic carbon,and revealed the sorption mechanism of organic pollutants on the soil organic matter in the site.The results show that the site soil organic matter has a strong sorption capacity for organic pollutants.Poly parameter linear free energy relationships(pp-LFER)show a better fit to the site sorption data than single parameter linear free energy relationships(sp-LFER)(R2=0.919).However,the validation of the established pp-LFER for soil organic matter from different areas shows a significant deviation(RMSE＞1.12).The performance of pp-LFERs is affected by the soil organic matter propreties.It may not be able to accurately predict the sorption behavior of soils in different sites.The sorption mechanism was further analysed through molecular structure descriptors and the physicochemical significance of the coefficients.It was found that hydrophobic effect and polarisation are important forces in the sorption process,with hydrogen bonding having a significant effect on strongly polar compounds.This study provides the high-precision sorption prediction model for the actual contaminated site.It provides the managers with reliable model options for risk assessment.关键词
场地污染/土壤有机质/有机污染物/有机碳标化分配系数/预测模型Key words
site contamination/soil organic matter/organic pollutants/organic carbon normalized partition coefficient/prediction model分类
资源环境引用本文复制引用
刘昆,南晨曦,孔令冉,刘慧婷,付翯云,瞿晓磊..场地有机污染物吸附行为多参数线性自由能模型研究[J].南京大学学报（自然科学版）,2024,60(1):130-140,11.基金项目
国家重点研发计划(2019YFC1804201,2020YFC1807002),污染控制与资源化研究国家重点实验室开放基金(PCRRF22034),国家自然科学基金青年基金(22206132) （2019YFC1804201,2020YFC1807002）
