气候变化领域集成服务门户(CCPortal): Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique

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DOI	10.5194/acp-19-11303-2019
	Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique
	V. Vu T.; Shi Z.; Cheng J.; Zhang Q.; He K.; Wang S.; M. Harrison R.
发表日期	2019
ISSN	16807316
起始页码	11303
结束页码	11314
卷号	19 期号:17
英文摘要	A 5-year Clean Air Action Plan was implemented in 2013 to reduce air pollutant emissions and improve ambient air quality in Beijing. Assessment of this action plan is an essential part of the decision-making process to review its efficacy and to develop new policies. Both statistical and chemical transport modelling have been previously applied to assess the efficacy of this action plan. However, inherent uncertainties in these methods mean that new and independent methods are required to support the assessment process. Here, we applied a machine-learning-based random forest technique to quantify the effectiveness of Beijing's action plan by decoupling the impact of meteorology on ambient air quality. Our results demonstrate that meteorological conditions have an important impact on the year-to-year variations in ambient air quality. Further analyses show that the PM 2.5 mass concentration would have broken the target of the plan (2017 annual PM2.5 <60 μg m-3) were it not for the meteorological conditions in winter 2017 favouring the dispersion of air pollutants. However, over the whole period (2013-2017), the primary emission controls required by the action plan have led to significant reductions in PM2.5/span PM10/NO2, SO2, and CO from 2013 to 2017 of approximately 34 %, 24 %, 17 %, 68 %, and 33 %, respectively, after meteorological correction. The marked decrease in PM2.5 and SO2 is largely attributable to a reduction in coal combustion. Our results indicate that the action plan has been highly effective in reducing the primary pollution emissions and improving air quality in Beijing. The action plan offers a successful example for developing air quality policies in other regions of China and other developing countries. © 2019 Copernicus GmbH. All rights reserved.
语种	英语
来源期刊	Atmospheric Chemistry and Physics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/144166
作者单位	Division of Environmental Health and Risk Management, School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, B1 52TT, United Kingdom; Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China; 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; Department of Environmental Sciences/Center of Excellence in Environmental Studies, King Abdulaziz University, P.O. Box 80203, Jeddah, Saudi Arabia
推荐引用方式 GB/T 7714	V. Vu T.,Shi Z.,Cheng J.,et al. Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique[J],2019,19(17).
APA	V. Vu T..,Shi Z..,Cheng J..,Zhang Q..,He K..,...&M. Harrison R..(2019).Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique.Atmospheric Chemistry and Physics,19(17).
MLA	V. Vu T.,et al."Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique".Atmospheric Chemistry and Physics 19.17(2019).