Attribution of PM 2.5 exposure in Beijing–Tianjin–Hebei region to emissions: implication to control strategies

Journal Article · Sat Jul 01 00:00:00 EDT 2017 · Science Bulletin

DOI:https://doi.org/10.1016/j.scib.2017.06.005· OSTI ID:1478988

Li, Xin ^[1]; Zhang, Qiang ^[2]; Zhang, Yang ^[3]; Zhang, Lin ^[4]; Wang, Yuxuan ^[5]; Zhang, Qianqian ^[6]; Li, Meng ^[7]; Zheng, Yixuan ^[7]; Geng, Guannan ^[7]; Wallington, Timothy J. ^[8]; Han, Weijian ^[8]; Shen, Wei ^[9]; He, Kebin ^[10]

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1. Tsinghua Univ., Beijing (China). Dept, of Earth System Science; North Carolina State Univ., Raleigh, NC (United States). Dept. of Marine, Earth, and Atmospheric Sciences
2. Tsinghua Univ., Beijing (China). Dept, of Earth System Science; Collaborative Innovation Center of Quantum Matter, Beijing (China)
3. North Carolina State Univ., Raleigh, NC (United States). Dept. of Marine, Earth, and Atmospheric Sciences; Collaborative Innovation Center of Quantum Matter, Beijing (China)
4. Peking Univ., Beijing (China). Dept. of Atmospheric and Oceanic Sciences
5. Tsinghua Univ., Beijing (China). Dept, of Earth System Science; Texas A & M Univ., Galveston, TX (United States). Dept. of Marine Sciences and Dept. of Atmospheric Sciences
6. China Meteorological Administration, Beijing (China). Naitonal Satellite Meterological Center
7. Tsinghua Univ., Beijing (China). Dept, of Earth System Science
8. Ford Motor Co., Dearborn, MI (United States). Research and Advanced Engineering
9. Ford Motor Co., Beijing (China). Asia Pacific Research
10. Collaborative Innovation Center of Quantum Matter, Beijing (China); Tsinghua Univ., Beijing (China). School of Environment

The Beijing–Tianjin–Hebei (BTH) region is one of the most heavily polluted regions in China, with both high PM2.5 concentrations and a high population density. A quantitative source-receptor relationship can provide valuable insights that can inform effective emission control strategies. Both source apportionment (SA) and source sensitivity (SS) can provide such information from different perspectives. In this study, both methods are applied in northern China to identify the most significant emission categories and source regions for PM2.5 exposure in BTH in 2013. Despite their differences, both models show similar distribution patterns for population and simulated PM2.5 concentrations, resulting in overall high PM2.5 exposure values (approximately 110 μg/m3) and particularly high exposure values during the winter (approximately 200 μg/m3). Both methods show that local emissions play a dominant role (70%), with some contribution from surrounding provinces (e.g., Shandong) via regional transport. The two methods also agree on the priority of local emission controls: both identify industrial, residential, and agricultural emissions as the top three categories that should be controlled locally. In addition, the effect of controlling agricultural ammonia emissions is approximately doubled when the co-benefits of reducing nitrate are considered. The synthesis of SA and SS for addressing specific categories of emissions provides a quantitative basis for the development of emission control strategies and policies for controlling PM2.5 in China.

• Research Organization: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
• Sponsoring Organization: USDOE Office of Science (SC)
• DOE Contract Number: SC0006695; AC02-05CH11231
• OSTI ID: 1478988
• Journal Information: Science Bulletin, Vol. 62, Issue 13; ISSN 2095-9273
• Country of Publication: United States
• Language: English