基于特征指纹的化工园区地下水污染物溯源研究进展

Research Progress on Groundwater Contaminants Source Tracking in Chemical Industrial Parks Using Fingerprinting Technology

  • 摘要: 地下水是全球水资源的重要组成部分, 随着工业化及城镇化发展, 地下水污染问题日益严重, 尤其是化工园区周边, 污染物呈现无机物-重金属-有机物复合污染的特征。由于化工园区地下水污染物与企业生产所采用的原料及加工工艺、产品有密切关系, 基于特征指纹的污染物溯源技术对于厘清场地污染责任、制定合理修复管控方案具有重要指导意义。本文详细介绍了用于化工园区场地溯源的常用仪器设备、特征指纹技术和源解析方法, 重点介绍了同位素技术、光谱技术、色谱质谱技术在特征指纹数据库构建及溯源方面的应用, 聚焦各技术的优缺点及适用场景, 并就其未来发展趋势进行了展望。未来应丰富不同污染来源的特征指纹及其指示意义, 建立化工园区多级特征指纹库, 细分为行业级、企业级及装置单元级, 同时开发低成本原位监测技术及智能溯源系统, 构建高精度、无扰动的化工园区地下水污染溯源技术。

     

    Abstract: Groundwater is a vital component of global water resources. With the advancement of industrialization and urbanization, groundwater pollution has become increasingly severe, particularly around chemical industrial parks, where contaminants often include complex mixtures of inorganic compounds, heavy metals, and organic pollutants. Due to the intrinsic connections between groundwater contaminants and the raw materials, processing techniques, and products of associated enterprises within these parks, fingerprinting-based source tracking technologies are essential for clarifying pollution liability and guiding effective remediation strategies. This paper provides a comprehensive reviews in instrumentation, fingerprinting techniques, and source apportionment methodologies for groundwater contamination tracing in chemical industrial parks. It highlights the application of isotopic analysis, spectroscopic techniques, and chromatography-mass spectrometry in the development of fingerprint database and source identification. The echnical strengths, limitations, operational contexts, and future development directions of these approaches are critically assessed. In the future, the efforts should be made to enrich multi-level characteristic fingerprint databases including park-wide signatures, sector-specific profiles, facility-level fingerprints, and process-unit identifiers, while also advancing integrated low-cost in-situ monitoring technologies and AI-driven intelligent tracing systems. These innovations will support real-time contamination tracking, precise source identification, and informed decision-making for pollution management across operational scales, from individual production units to entire industrial complexes.

     

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