计算机控制扫描电镜技术(CCSEM)在大气颗粒物表征及源解析中的应用

近年来，我国环境空气质量不断改善，在此背景下准确掌握大气颗粒物的理化性质及其来源是精准治污的重要基础. 计算机控制扫描电镜技术(CCSEM)的快速发展大幅提高了单颗粒分析效率，为实现颗粒物精细化源解析提供新的技术手段. 本文介绍了CCSEM技术的原理、特点、测试流程及技术发展，梳理了CCSEM在大气颗粒物的理化性质、来源解析及健康效应中的研究成果，总结了CCSEM的发展前景及局限性. 结果表明:CCSEM可通过自动化测试快速获取更全面的颗粒物信息，后处理功能便于快速掌握颗粒物污染源精细化信息，寻找部分隐匿的污染源，对比不同区域颗粒物类型的差异，并获取颗粒物精细化源解析结果. CCSEM对重金属等有潜在健康危害的高原子序数元素有较高的识别效率，可应用于颗粒物健康效应研究. 因此，CCSEM在大气颗粒物精细化源解析及健康效应研究等方面有较好的应用前景. 但是，CCSEM在颗粒物识别、分类标准及分析时效性等方面有一定的局限性，在未来应通过加强CCSEM形貌识别提高颗粒物的识别效率，结合单颗粒源谱数据库制定更科学的颗粒物分类规则，以及加强采样、测试及分析的连贯性以提高分析时效性. 

Application of Computer-Controlled Scanning Electron Microscope (CCSEM) in Characterization and Source Apportionment of Atmospheric Particulate Matter

In recent years, ambient air quality has been continuously improved in China. Under such situation, accurate understanding of the physicochemical properties and sources of atmospheric particulate matter has become an important basis for targeted control of air pollution. The rapid development of computer-controlled scanning electron microscopy (CCSEM) provides a new technical method for refined source apportionment of atmospheric particulate matter by improving the efficiency of individual particle analysis. This study introduces the principle, characteristics, testing process and technology development of CCSEM, and summarizes the application in the physicochemical properties, source apportionment, and health effect of atmospheric particulate matter, as well as the development prospect and limitation of CCSEM. The results show that CCSEM can efficiently obtain more comprehensive information of atmospheric particles through automated analysis. The post-processing function is beneficial to rapidly grasping the refined information of particle sources, discovering some hidden pollution source, comparing the differences in particle types in different regions, and obtaining refined source apportionment of particles. Since CCSEM has high identification efficiency for high atomic number elements (such as heavy metals) with potential harm to human health, it can also be applied to the health effect analysis of atmospheric particles. Therefore, CCSEM has a good application prospect in the refined source apportionment and health effect analysis of atmospheric particles. However, CCSEM has some limitations in particle recognition, classification standard, and analysis timeliness. In the future, the morphology recognition of CCSEM should be strengthened to improve the identification efficiency, a more scientific particle classification rule should be formulated based on the individual particle source spectrum database, and the consistency of sampling, testing and analysis should be strengthened to improve the analysis timeliness.