ISSN 1000-3665 CN 11-2202/P

    有机污染场地精细刻画的研究进展与趋势

    Progress and trends in high-resolution characterization of organic-contaminated sites: a review

    • 摘要: 含水层介质的非均质性使得有机污染场地普遍呈现污染源区复杂、污染物反向扩散及浓度反弹等问题。传统依赖稀疏钻孔取样的低分辨率调查难以精确刻画污染物迁移分布,已成为制约场地精准治理的关键瓶颈。场地精细刻画,也称为高分辨率场地刻画(high-resolution site characterization,HRSC)已逐渐成为有机污染场地调查与修复实践的核心技术。本文系统梳理了HRSC的发展历程与研究进展。从关键非均质尺度与采样体积等角度界定了HRSC的基本原理,重点阐述了其“实时监测技术-动态采样策略-数据驱动决策”的3大核心步骤,总结了HRSC在污染物相态精准识别、源区划定和优势通道辨识中的应用进展。现有研究表明,基于直推探测、原位传感与地球物理成像等实时技术,可在厘米到米级关键尺度上显著提升对残留非水相液体分布、污染羽范围以及低渗区反向扩散等的刻画能力,结合动态采样与多源证据可有效降低概念模型的不确定性并提高靶区修复决策的可靠性。HRSC正由“以数据获取为主”向“多源数据融合与智能化决策支撑”演进,以支撑风险管控导向的高效调查与精准修复决策。

       

      Abstract: Heterogeneity of subsurface aquifer causes organic-contaminated sites to commonly exhibit complex source zones, contaminant back diffusion, and concentration rebound. Traditional low-resolution site investigations relying on sparse borehole sampling are inadequate for accurately characterizing contaminant migration and distribution, which has become a key constraint on precise site remediation. High-resolution site characterization (HRSC) has gradually emerged as a core technology in the investigation and remediation of organic-contaminated sites. This paper systematically reviews the development history and research progress of HRSC. First, the fundamental principles of HRSC are defined from the perspectives of key heterogeneity scales and sampling volumes, with emphasis on its three core advantages: real-time monitoring technologies, dynamic sampling strategies, and data-driven decision-making. Accumulated evidence indicates that real-time direct-push sensing, in situ tools, and hydrogeophysical imaging can substantially improve the delineation of residual NAPL distribution, plume boundaries, and low-permeability mass-transfer behaviors, while iterative integration of multi-source data reduces key uncertainties in the conceptual site model (CSM) and improves decision relevance. HRSC is transitioning from “data acquisition” toward “data fusion and intelligent decision support” to enable risk-informed and efficient remediation decisions.

       

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