Impact of air pollution control policies on future PM2.5 concentrations and their source contributions in China

Siyi Cai; Qiao Ma; Shuxiao Wang; Bin Zhao; Michael Brauer; Aaron Cohen; Randall V Martin; Qianqian Zhang; Qinbin Li; Yuxuan Wang; Jiming Hao; Joseph Frostad; Mohammad H Forouzanfar; Richard T Burnett

doi:10.1016/j.jenvman.2018.08.052

Impact of air pollution control policies on future PM_2.5 concentrations and their source contributions in China

J Environ Manage. 2018 Dec 1:227:124-133. doi: 10.1016/j.jenvman.2018.08.052. Epub 2018 Aug 31.

Authors

Siyi Cai¹, Qiao Ma², Shuxiao Wang³, Bin Zhao⁴, Michael Brauer⁵, Aaron Cohen⁶, Randall V Martin⁷, Qianqian Zhang⁸, Qinbin Li⁴, Yuxuan Wang⁹, Jiming Hao¹⁰, Joseph Frostad¹¹, Mohammad H Forouzanfar¹¹, Richard T Burnett¹²

Affiliations

¹ State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China.
² State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; School of Energy and Power Engineering, Shandong University, Jinan, 250061, China.
³ State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China. Electronic address: shxwang@tsinghua.edu.cn.
⁴ Joint Institute for Regional Earth System Science and Engineering, Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA, 90095, USA.
⁵ School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia V6T1Z3, Canada.
⁶ Health Effects Institute, Boston, MA, 02110, USA; Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, 98195, USA.
⁷ Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, B3H 4R2, Canada.
⁸ National Satellite Meteorological Center, Beijing, 100089, China.
⁹ Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX, USA.
¹⁰ State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China.
¹¹ Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, 98195, USA.
¹² Health Canada, Ottawa, ON, K1A 0K9, Canada.

PMID: 30172931
DOI: 10.1016/j.jenvman.2018.08.052

Abstract

To investigate the impact of air pollutant control policies on future PM_2.5 concentrations and their source contributions in China, we developed four future scenarios for 2030 based on a 2013 emission inventory, and conducted air quality simulations for each scenario using the chemical transport model GEOS-Chem (version 9.1.3). Two energy scenarios i.e., current legislation (CLE) and with additional measures (WAM), were developed to project future energy consumption, reflecting, respectively, existing legislation and implementation status as of the end of 2012, and new energy-saving policies that would be released and enforced more stringently. Two end-of-pipe control strategies, i.e., current control technologies (until 2017) and more stringent control technologies (until 2030), were also developed. The combinations of energy scenarios and end-of-pipe control strategies constitute four emission scenarios (2017-CLE, 2030-CLE, 2017-WAM, and 2030-WAM) evaluated in simulations. PM_2.5 concentrations at national level were estimated to be 57 μg/m³ in the base year 2013, and 58 μg/m³, 42 μg/m³, 42 μg/m³, and 30 μg/m³ under the 2017-CLE, 2030-CLE, 2017-WAM, and 2030-WAM scenarios in 2030, respectively. Large PM_2.5 reductions between 2013 and 2030 were estimated for heavily polluted regions (Sichuan Basin, Middle Yangtze River, North China). The energy-saving policies show similar effects to the end-of-pipe emission control measures, but the relative importance of these two groups of policies varies in different regions. Absolute contributions to PM_2.5 concentrations from most major sources declined from 2017-CLE to 2030-WAM. With respect to fractional contributions, most coal-burning sectors (including power plant, industrial and residential coal burning) increased from 2017-CLE to 2030-WAM, due to larger reductions from non-coal sources, including transportation and biomass open burning. Residential combustion and open burning had much lower fractional contribution to ambient PM_2.5 concentrations in the 2017-WAM/2030-WAM compared to the 2017-CLE/2030-CLE scenarios. Fractional contributions from transportation were reduced dramatically in 2030-CLE and 2030-WAM compared to 2017-CLE/2017-WAM, due to the enforcement of stringent end-of-pipe emission controls. Across all scenarios, coal combustion remained the single largest contributor to PM_2.5 concentrations in 2030. Reducing PM_2.5 emissions from coal combustion remains a strategic priority for air quality management in China.

Keywords: China; Emission scenario; GEOS-Chem simulation; PM(2.5) concentration; Source contribution.

MeSH terms

Air Pollutants
Air Pollution*
China
Environmental Monitoring*
Particulate Matter
Power Plants

Substances

Air Pollutants
Particulate Matter