PM2.5源解析方法讨论
PM2.5 Source Apportionment Method Discussion
DOI: 10.12677/aep.2026.164068, PDF,   
作者: 范 颖, 廖升珲, 景春菊：重庆市南川区生态环境监测站，重庆；付春芳：广东省韶关市生态环境监测站，广东 韶关
关键词: PM2.5；源解析；受体模型；PM2.5； Source Apportionment； Receptor Mode
摘要: 大气细颗粒物(PM2.5)对环境、气候及人类健康危害显著，中国虽通过治理降低其浓度但整体水平仍需改善，2023年《空气质量持续改善行动计划》明确2025年削减目标，源解析是污染防控的科学基础，本文通过梳理当前应用最广泛的PM2.5源解析方法，为不同区域PM2.5的研究分析提供参考。源解析以受体模型为核心，PMF应用最广、PCA适合作预处理、CMB适合固定源区域、Unmix适配短期数据、HYSPLIT追踪传输路径，多模型联用可提升解析可靠性。当前研究存在组分分析精度不足、二次源解析能力弱及区域协同缺失等局限，未来需推动技术融合、构建多模型一体化框架并统一源谱标准，为PM2.5污染联防联控提供更精准支撑。
Abstract: Atmospheric fine particulate matter (PM2.5) poses significant hazards to the environment, climate, and human health. Although China has reduced its concentration through governance efforts, the overall level still requires improvement. The 2023 “Action Plan for Continuous Improvement of Air Quality” has set clear reduction targets for 2025. Source apportionment serves as the scientific foundation for pollution prevention and control. This paper provides a reference for the research and analysis of PM2.5 in different regions by reviewing the most widely used PM2.5 source apportionment methods. Source apportionment centers around receptor models, with PMF being the most widely applied, PCA suitable for preprocessing, CMB appropriate for stationary source areas, Unmix adaptable to short-term data, and HYSPLIT tracking transmission paths. The combined use of multiple models can enhance the reliability of apportionment. Current research faces limitations such as insufficient precision in component analysis, weak secondary source apportionment capabilities, and a lack of regional collaboration. In the future, it is necessary to promote technological integration, construct a multi-model integrated framework, and unify source spectral standards to provide more precise support for joint prevention and control of PM2.5 pollution.
文章引用：范颖, 付春芳, 廖升珲, 景春菊. PM2.5源解析方法讨论[J]. 环境保护前沿, 2026, 16(4): 682-690. https://doi.org/10.12677/aep.2026.164068

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