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Effects of energy structure differences on chemical compositions and respiratory health of PM2.5 during late autumn and winter in China.
Sun, Wenwen; Huo, Juntao; Li, Rui; Wang, Dongfang; Yao, Lan; Fu, Qingyan; Feng, Jialiang.
Affiliation
  • Sun W; Department of Research, Shanghai University of Medicine & Health Sciences Affiliated Zhoupu Hospital, Shanghai 201318, China; College of Medical Technology, Shanghai University of Medicine & Health Sciences, Shanghai 201318, China.
  • Huo J; Shanghai Environmental Monitoring Center, Shanghai 200235, China.
  • Li R; Department of Earth System Science, Tsinghua University, Beijing 100084, China.
  • Wang D; Shanghai Environmental Monitoring Center, Shanghai 200235, China.
  • Yao L; Department of Environmental Engineering, School of Environmental and Geographical Science, Shanghai Normal University, Shanghai 200234, China.
  • Fu Q; Shanghai Environmental Monitoring Center, Shanghai 200235, China.
  • Feng J; School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China. Electronic address: fengjialiang@shu.edu.cn.
Sci Total Environ ; 824: 153850, 2022 Jun 10.
Article in En | MEDLINE | ID: mdl-35176377
ABSTRACT
To understand the influence of the energy structure (including solid fuel and clean energy) on air pollution, two comprehensive measurement campaigns were conducted in Baoding and Shanghai in late autumn and winter during 2017-2018. The chemical compositions, driving factors, regional transport of pollutants, and potential respiratory disease (RD) health risks of PM2.5 for Baoding and Shanghai were analyzed. The results showed that the concentration of PM2.5 in Baoding (156.9 ± 139.8 µg m-3) was 2.6 times of that in Shanghai (60.9 ± 45.9 µg m-3). The most important contributor to PM2.5 in Baoding was organic matter (OM), while inorganic aerosols accounted for major fractions of PM2.5 in Shanghai. Positive matrix factorization (PMF) results indicated that coal combustion (CC; 39%) accounted for the most in Baoding, followed by secondary aerosols (21%), biomass burning (BB; 20%), industrial emissions (14%), dust (3%), and vehicle exhaust (2%). However, the average contribution in Shanghai followed the order secondary aerosols (44%), vehicle exhaust (36%), dust (11%), marine aerosols (6%), and BB (3%). The evolution of source contributions at different pollution levels revealed that haze episodes in Baoding and Shanghai were triggered by CC and secondary formation, respectively; however, the air quality on clean days in Baoding and Shanghai was affected mostly by BB and vehicle emissions, respectively. Potential source contribution function (PSCF) results suggested that CC in Baoding was primarily from local emissions, while BB was primarily from local and regional transport. Vehicle exhaust and secondary aerosols in Shanghai were mainly from local emissions and regional transport. The number of RD deaths related to haze episodes in Baoding and Shanghai were 215 (95% CI 109, 319) and 76 (95% CI 11, 135), respectively. This research also emphasized the importance of further attention to the usage of coal in Baoding and vehicle emissions in Shanghai.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Vehicle Emissions / Air Pollutants Country/Region as subject: Asia Language: En Journal: Sci Total Environ Year: 2022 Document type: Article Affiliation country: China

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Vehicle Emissions / Air Pollutants Country/Region as subject: Asia Language: En Journal: Sci Total Environ Year: 2022 Document type: Article Affiliation country: China