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

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 PM 2.5 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 PM 2.5 −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 PM 2.5 , PM 10 , NO 2 , SO 2 and CO from 2013 to 2017, which are approximately 34 %, 24 %, 17 %, 68 %, and 33 % after meteorological correction. The marked decrease in PM 2.5 and SO 2 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.