Microscopic Characteristics and Source identification of Ambient Particles Based on Electron Microscopy: A Case Study of Steel City

Abstract

Advances in Computer-Controlled Scanning Electron Microscopy (CCSEM) technology allow large volumes of single-particle microscopic characteristic data to be acquired. Combined with big data analysis, this enables refined source apportionment of ambient particulate matter (PM). To meet particulate pollution prevention and control needs while evaluating this method's performance in source identification, this study selected Benxi, a typical steel industrial city, for ambient PM collection and single-particle source apportionment. Passive samplers collected samples at two sites (CT: Caitun; XH: Xihu) in winter, summer, and autumn 2023. Concentration monitoring showed PM was higher in winter and lower in summer, and PM2.5 at CT was generally higher than at XH. The particle size distribution presents obvious seasonal differences and the diurnal variation of particle concentration shows a bimodal pattern closely related to human activities and the evolution of the atmospheric boundary layer. By obtaining single-particle elemental compositions and microscopic image data through CCSEM technology, combined with methods such as clustering analysis, 10 major pollution source types were ultimately identified. Among these, soil dust (31.5%) and construction dust (17.4%) were the primary local pollution sources, while organic particles (20.4%) and coal-fired ash (8.2%) highlighted the significant roles of industrial emissions, combustion processes, and secondary formation. Size distribution characteristics indicated that particles associated with combustion, industrial emissions, and secondary formation (e.g., biomass particles, carbonaceous particles, and metal particles) were mainly enriched in the fine particle fraction (PM2.5), whereas fugitive dust sources (soil dust, construction dust, and salt particles) were dominated by coarse particles (PM2.5-10). Significant differences in source contributions exist between the CT site affected by industry and residential activities and the XH site affected by mining activities. Influenced by steel mills, coking industries, and residential activities, the CT site showed prominent contributions from industrial and combustion sources to PM2.5. In contrast, driven by mining and transportation activities, the XH site had higher contributions from fugitive dust. This study verified the effectiveness of CCSEM technology and big data analysis in the source identification of ambient PM in urban area, providing a scientific basis for formulating targeted pollution control strategies in Benxi and similar cities.