摘要
准确评估污染物在污染场地土壤中的吸附分配过程是污染场地安全管理和再利用开发的重要环节.以我国典型电子垃圾拆解场地为研究对象,解析了场地土壤中有机质的吸附特征,建立了适用于场地有机污染物分配评估的有机碳标化分配系数(K_(OC))预测模型,揭示了有机污染物在场地土壤中有机质上的吸附机制.该场地土壤有机质对有机污染物具有较强的吸附能力.构建了预测场地土壤中有机质吸附有机污染物的K_(OC)的多参数线性自由能模型(pp-LFER).ppLFER模型相较于常用的单参数线性自由能模型(sp-LFER)展现出更好的场地吸附数据的预测能力(R^(2)=0.919).同时,采用其他文献报道的pp-LFER模型对该场地污染物K_(OC)进行预测,发现预测偏差较高(RMSE>1.12),这表明pp-LFER模型的预测效果受场地土壤中有机质的性质影响较大,其跨区域应用性仍待提升.进一步结合pp-LFER模型中的分子结构描述符和各项系数解析了土壤中有机质的吸附机制,发现疏水作用和极化作用是吸附过程的重要作用力,氢键作用显著影响极性化合物的吸附过程.本研究基于实际污染场地土壤构建了高精度吸附预测模型,为场地污染风险评估和修复利用提供了更准确的技术手段.
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(K_(OC))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)(R^(2)=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.
作者
刘昆
南晨曦
孔令冉
刘慧婷
付翯云
瞿晓磊
Liu Kun;Nan Chenxi;Kong Lingran;Liu Huiting;Fu Heyun;Qu Xiaolei(State Key Laboratory of Pollution Control and Resource Reuse,School of Environment,Nanjing University,Nanjing,210023,China;Jiangsu Environmental Engineering Technology Co.,Ltd.,Nanjing,210023,China;Key Laboratory for Technology of Wastewater Treatment in Liaoning Province,School of Environmental and Chemical Engineering,Shenyang Ligong University,Shenyang,110159,China)
出处
《南京大学学报（自然科学版）》
CAS
CSCD
北大核心
2024年第1期130-140,共11页
Journal of Nanjing University（Natural Science）
基金
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)
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
site contamination
soil organic matter
organic pollutants
organic carbon normalized partition coefficient
prediction model
