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Assessment of the regional source contributions to PM2.5 mass concentration in Beijing

Authors :
Xiao HAN
Mei-Gen ZHANG
Source :
Atmospheric and Oceanic Science Letters, Vol 11, Iss 2, Pp 143-149 (2018)
Publication Year :
2018
Publisher :
KeAi Communications Co., Ltd., 2018.

Abstract

A source apportionment tool, ISAM (Integrated Source Apportionment Method), coupled with a regional air quality modeling system, RAMS-CMAQ (Regional Atmospheric Modeling System and Community Multiscale Air Quality), was applied to simulate the major aerosol components (sulfate, nitrate, ammonium, black carbon, organic carbon, dust, and sea salt) and investigate the impact of local and regional sources on the PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 μm) mass concentration over the North China Plain (NCP) in July 2016. The simulation results indicated that the high mass burden of PM2.5 mainly appeared in the south part of Hebei and west part of Shandong. The PM2.5 particles emitted from Beijing and Tianjin were just concentrated in the local area, but Hebei and Shandong were the dominant source over the NCP. Focusing on Beijing, the local contribution was just 20%–30% due to the emissions control strategies executed in recent years in Beijing. The pollutants derived from Hebei and Shandong were major contributors, providing more than 25% and 10% to the PM2.5 mass concentration in Beijing, respectively. Moreover, the surrounding regions showed larger contributions against the pollution background. The contribution percentage from Hebei and Shandong increased in both cases when the air quality became worse. Thus, it is suggested that comprehensive control strategies should be implemented based on coordinated control of emissions at the regional scale in Beijing, especially during pollution periods.

Details

Language :
English
ISSN :
16742834 and 23766123
Volume :
11
Issue :
2
Database :
Directory of Open Access Journals
Journal :
Atmospheric and Oceanic Science Letters
Publication Type :
Academic Journal
Accession number :
edsdoj.0af01f4f991f45b19e8257b513018c8d
Document Type :
article
Full Text :
https://doi.org/10.1080/16742834.2018.1412796