摘要
当污染地块中非水相液体(NAPL)相污染源位置未知时,空间上稀疏的钻孔和土壤采样常会漏判污染地块中NAPL相污染源的存在。针对该问题,提出一种同时基于土壤和地下水采样数据判别污染地块中NAPL相污染源是否存在的改进方法。与土壤中污染物浓度分布相比,地下水中污染物浓度分布较为连续,故综合考虑土壤-地下水采样数据可有效降低漏判污染地块中NAPL相污染源存在的风险,该改进方法首先基于土壤采样数据建立筛选模型,对土壤中检出的有机物进行相平衡分配,以估算NAPL相质量,从而判别土壤中NAPL相污染源是否存在;若土壤中未发现NAPL相污染源存在,则进一步分析地下水中溶解相有机物的检出浓度与有效溶解度之间的关系,以判定污染地块中NAPL相污染源是否存在。为便于场地实践,基于该改进方法开发了相应的界面可视化软件,并通过典型案例对该改进方法和软件可靠性进行验证。验证结果证明该改进方法和软件计算结果可靠,表明综合考虑地下水监测数据可有效减少仅基于钻孔揭露和土壤采样数据导致的漏判污染地块中NAPL相污染源存在的风险,可为污染地块调查提供有力支持。
Direct visual detection of Non-aqueous Phase Liquids(NAPLs) in contaminated sites is difficult in many contaminated sites due to the limited number of boreholes and soil samples.Compared with the distribution of contaminants in soil,the dissolved NAPL plume may spread over a larger area with the groundwater flow.Therefore,integrating soil and groundwater sampling data can effectively reduce the risk of missing NAPL phase.In this study,we improved the NAPL-identification method by utilizing the information from both soil and groundwater sampling data.In this work,we established a screening model based on the phase partitioning theory of organic compounds.The mass of NAPL phase was first estimated using the screening model for the soil sampling data.If NAPL phase is not detected from the soil samples,groundwater samples will be further used to determine the presence of NAPL phase based on the relationship between the concentration of dissolved NAPL and effective solubility.For practical use,we developed a visualization software.We evaluated the performance of the software by two reported cases to demonstrate the advantage of the improved method.The results show that both the improved method and the developed software are reliable.Therefore,the risk of missing NAPL is effectively reduced by integrating the soil and groundwater sampling data.
作者
杜方舟
施小清
康学远
DU Fangzhou;SHI Xiaoqing;KANG Xueyuan(School of Earth Science and Engineering,Nanjing University,Nanjing 210023,China)
出处
《安全与环境工程》
CAS
CSCD
北大核心
2022年第5期175-182,195,共9页
Safety and Environmental Engineering
基金
国家自然科学基金项目(41977157)
江苏省研究生实践创新计划项目(SJCX21_0016)。