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Discussion papers
https://doi.org/10.5194/acp-2019-173
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2019-173
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 21 Feb 2019

Submitted as: research article | 21 Feb 2019

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This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Atmospheric Chemistry and Physics (ACP) and is expected to appear here in due course.

Assessing the impact of Clean Air Action Plan on Air Quality Trends in Beijing Megacity using a machine learning technique

Tuan V. Vu1, Zongbo Shi1,2, Jing Cheng3, Qiang Zhang3, Kebin He4,5, Shuxiao Wang4, and Roy M. Harrison1,6 Tuan V. Vu et al.
  • 1Division of Environmental Health & Risk Management, School of Geography, Earth & Environmental Sciences, University of Birmingham, Birmingham B1 52TT, United Kingdom
  • 2Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science,Tsinghua University, Beijing 100084, China
  • 3Institute of Earth Surface System Science, Tianjin University, Tianjin, 300072, China
  • 4State Key Joint Laboratory of Environment, Simulation and Pollution Control, School of Environment, Tsinghua University,Beijing 100084, China
  • 5State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
  • 6Department of Environmental Sciences/Center of Excellence in Environmental Studies, King Abdulaziz University, P.O. Box 80203, Jeddah, Saudi Arabia

Abstract. A five-year Clean Air Action Plan was implemented in 2013 to reduce air pollutant emissions and improve ambient air quality in Beijing. Assessments of this Action Plan is an essential part of the decision-making process to review the efficacy of the Plan and to develop new policies. Both statistical and chemical transport modelling were 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 improved a novel machine learning-based random forest technique to quantify the effectiveness of Beijing's Acton 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 analysis show that the favorable meteorological conditions in winter 2017 contributed to a lower PM2.5 mass concentration (58 μg m−3) than predicted from the random forest model (61 μg m−3), which is higher than the target of the Plan (2017 annual PM2.5 < 60 μg m−3). However, over the whole period (2013 to 2017), impact of meteorological conditions on the trend of ambient air quality are small. It is the primary emission control, because of the Action Plan, that has led to the significant reduction in PM2.5, PM10, NO2, SO2 and CO from 2013 to 2017, which are approximately 34 %, 24 %, 17 %, 68 %, and 33 % 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 is 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.

Tuan V. Vu et al.
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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Interactive discussion
Status: closed
Status: closed
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Printer-friendly Version - Printer-friendly version Supplement - Supplement
Tuan V. Vu et al.
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Short summary
A five-year Clean Air Action Plan was implemented in 2013 to improve ambient air quality in Beijing. Here, we developed a novel machine learning-based model to determine the real trend in air quality from 2013 to 2017 in Beijing to assess the efficacy of the Plan. We showed that the action plan led to a major reduction in primary emissions and significant improvement air quality. The marked decrease in PM2.5 and SO2 is largely attributable to a reduction in coal combustion.
A five-year Clean Air Action Plan was implemented in 2013 to improve ambient air quality in...
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