Characterizing lead-rich particles in Beijing's atmosphere following coal-to-gas conversion: insights from single-particle aerosol mass spectrometry
Year: 2025
Type: article
Abstract: . Coal-to-gas (CTG) policies are important energy transformation strategies to address air pollution issues, but how well it improves atmospheric Lead (Pb) pollution remains poorly understood. By the end of 2018, Beijing had achieved coal-free in urban and plain areas. The mixing state and atmospheric chemical processes of Pb-rich particles in Beijing were monitored using a single particle aerosol mass spectrometry (SPAMS) during 2019. Based on a large dataset of mass spectra, this study find that the number fractions of Pb-rich particles, as well as two types of Pb-rich particles (K-Na-EC and K-OC) related to coal combustion during heating period, show lower than those after the heating period. Based on concentration-weighted trajectory plots, the results indicate that lead aerosols mainly derive from the transmission of surrounding provinces. Lead nitrate is one of the important forms of lead in aerosol particles, most contributed by photo-chemical reactions in spring, fall, and winter. Due to the decomposition of nitrate during high temperatures, the aqueous reactions mechanism contributes more to lead nitrate in summer. These results improve our understanding of the seasonal distribution, formation mechanisms, and influencing factors of toxic Pb-containing particles after CTG.
Institutions: Jinan University, Taiyuan University of Technology, Xinjiang University, Hong Kong University of Science and Technology, The Hong Kong University of Science and Technology (Guangzhou), China National Environmental Monitoring Center, Chinese Academy of Sciences, Guangzhou Institute of Geochemistry, State Key Laboratory of Organic Geochemistry, Fudan University
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $1,725
OpenAlex ID: W4416702679
