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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 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 
