Characteristics of single atmospheric particles in a heavily polluted urban area of China: size distributions and mixing states

To investigate the chemical composition, size distribution, and mixing state of aerosol particles on heavy pollution days, single-particle aerosol mass spectrometry was conducted during 9–26 October 2015 in Xi’an, China. The measured particles were classified into six major categories: biomass burni...

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Published inEnvironmental science and pollution research international Vol. 26; no. 12; pp. 11730 - 11742
Main Authors Li, Li, Wang, Qiyuan, Zhang, Xu, She, Yuanyuan, Zhou, Jiamao, Chen, Yang, Wang, Ping, Liu, Suixin, Zhang, Ting, Dai, Wenting, Han, Yongming, Cao, Junji
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2019
Springer Nature B.V
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Summary:To investigate the chemical composition, size distribution, and mixing state of aerosol particles on heavy pollution days, single-particle aerosol mass spectrometry was conducted during 9–26 October 2015 in Xi’an, China. The measured particles were classified into six major categories: biomass burning (BB) particles, K-secondary particles, elemental carbon (EC)–related particles, metal-containing particles, dust, and organic carbon (OC) particles. BB and EC-related particles were the dominant types during the study period and mainly originated from biomass burning, vehicle emissions, and coal combustion. According to the ambient air quality index, two typical episodes were defined: clean days (CDs) and polluted days (PDs). Accumulation of BB particles and EC-related particles was the main reason for the pollution in Xi’an. Most types of particle size were larger on PDs than CDs. Each particle type was mixed with secondary species to different degrees on CDs and PDs, indicating that atmospheric aging occurred. The mixing state results demonstrated that the primary tracers were oxidized or vanished and that the amount of secondary species was increased on PDs. This study provides valuable information and a dataset to help control air pollution in the urban areas of Xi’an. Graphical abstract
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ISSN:0944-1344
1614-7499
DOI:10.1007/s11356-019-04579-3