引用本文:刘宏波,瞿明凯,张健琳等.土壤污染物源解析技术研究进展[J].环境监控与预警,2021,13(1):1-6
LIU Hong-bo, QU Ming-kai, ZHANG Jian-linet ai.Research Progress in Source Apportionment of Soil Pollutants[J].Environmental Monitoring and Forewarning,2021,13(1):1-6
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土壤污染物源解析技术研究进展
刘宏波,瞿明凯,张健琳等1,2
1. 中国科学院南京土壤研究所土壤环境与污染修复重点实验室,江苏 南京 210008;2. 江西理工大学土木与测绘工程学院,江西 赣州 341000
摘要:
简述了土壤污染物源解析技术的发展历程及土壤污染物的主要类型与来源。指出,定性源识别技术主要包括特征比值法、多元统计法、空间分析法等;定量源解析技术主要包括源清单法、扩散模型法、化学质量平衡模型法、正定矩阵因子分解法、UNMIX模型法、同位素法等。重点总结了这些技术方法的原理及其在应用上的优势与局限。从解析对象、解析方法和软件开发角度,提出了土壤污染物源解析技术的未来发展方向。
关键词:  土壤污染物  定性源识别  定量源解析  扩散模型  受体模型
DOI:
分类号:X53
文献标识码:A
基金项目:国家自然科学基金资助项目(41771249);国家重点研发计划资助项目(2018YFC1800104);中国科学院青年创新促进会资助项目(2018348)
Research Progress in Source Apportionment of Soil Pollutants
LIU Hong-bo, QU Ming-kai, ZHANG Jian-linet ai1,2
1.Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing,Jiangsu 210008, China;2. School of Civil and Surveying & Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou,Jiangxi 341000, China
Abstract:
This paper briefly introduced the development process of the source apportionment techniques for soil pollutants and their main types and sources. This study pointed out that qualitative source identification techniques mainly include diagnostic ratios (DRs), multivariate statistical methods, spatial analysis methods, and so on. While quantitative source apportionment techniques mainly include source emission inventory method, diffusion model, chemical mass balance (CMB), positive matrix factorization (PMF), UNMIX model, stable isotopic technique, and so on. Moreover, the principle of the above methods and their advantages and disadvantages in the application were summarized. The future development direction of the source apportionment techniques for soil pollutants was proposed from the perspective of apportionment objects, apportionment methods, and software development.
Key words:  Soil pollutants  Qualitative source identification  Quantitative source apportionment  Diffusion model  Receptor model