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高校地质学报 ›› 2026, Vol. 32 ›› Issue (05): 659-669.DOI: 10.16108/j.issn1006-7493.2025053

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基于场界污染超标历时考虑模型不确定性的地下水DNAPL 污染风险评估

潘 玥1,2,3,杨沫涵2,罗建男1*,曾献奎3,吴吉春3   

  1. 1. 地下水资源与环境教育部重点实验室,吉林大学,长春 130021;
    2. 南京工业大学 交通运输工程学院,南京 211816;
    3. 南京大学 地球科学与工程学院,南京 210023
  • 出版日期:2026-10-20 发布日期:2026-10-20

Risk Assessment of Groundwater DNAPL Pollution Considering Model Uncertainty Based on the Duration of Field Pollution#br# Exceeding the Standard

PAN Yue1,2,3,YANG Mohan2,LUO Jiannan1*,ZENG Xiankui3,WU Jichun3   

  1. 1. Key Laboratory of Groundwater Resources and Environmental (Jilin University), Ministry of Education, Changchun 130021, China;
    2. College of Transportation Engineering, Nanjing Tech University, Nanjing 211816, China;
    3. School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China
  • Online:2026-10-20 Published:2026-10-20

摘要: 地下水污染风险评估是当前地下水污染防治的重要基础。重非水相(Dense Non-aqueous Phase Liquid,DNAPL)污染
物的理化性质特殊,在地下水中的迁移转化规律复杂,对环境与生态具有较大威胁,亟待开展重非水相污染物的地下水污染风险评估工作。文章提出一种基于场界污染超标历时的地下水DNAPL污染风险评估新框架。通过建立理想三维非均质土壤—地下水PCE运移模型,模拟污染物时空分布,并以场地边界浓度超过阈值的时间作为划分指标,构建风险分级体系。研究基于贝叶斯理论刻画模型不确定性,利用MCMC方法进行参数识别,分析了基于过程模拟方法的模型不确定性对风险评估结果的影响。结果表明,该评估方法能够合理反映不同修复情景下的风险等级,考虑模型不确定性可显著提高风险评估结果的准确性与可靠性。

关键词: 地下水污染风险评估, 地下水数值模拟, 模型不确定性, 重非水相液体

Abstract: Groundwater pollution risk assessment is a critical foundation for the prevention and control of groundwater
pollution. Dense Non-aqueous Phase Liquid ( DNAPL ) pollutants exhibit distinct physicochemical properties and complex migrationtransformation behaviors in groundwater, posing substantial threats to ecological and environmental integrity. Therefore, urgent efforts are required to conduct risk assessments for groundwater contaminated by DNAPLs. In this paper, a novel groundwater DNAPL pollution risk-assessment framework is proposed based on field-scale pollution exceedance duration. By constructing an ideal three-dimensional heterogeneous soil-groundwater model for PCE transport, the spatial-temporal distribution of contamination is simulated. Risk classification criteria are developed using site-boundary concentration exceedance time as the partitioning indicator. Bayesian theory is employed to quantify model uncertainty, and the MCMC algorithm is adopted for parameter calibration. Process-based numerical simulations are applied to analyze the impacts of model uncertainty on riskassessment outputs. The results indicate that the proposed evaluation method can reasonably chracterize risk levels under different remediation scenarios, and the inclusion of model uncertainty can markedly enhance the accuracy and reliability of the risk assessment results.

Key words: groundwater pollution risk assessment, groundwater numerical simulation, model uncertainty, dense non-aqueous
liquid

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