Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique

Abstract

Abstract. 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 PM2.5 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, 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.

Climate variability or anthropogenic emissions: which caused Beijing Haze?

“…A recent study noted that the number of winter haze days in most of eastern China during 1973-2012 was mainly controlled by meteorological and climate variations and did not exhibit any significant increasing trend despite the 2.5-fold increase in the anthropogenic emissions of particulate matter and its precursors (PM emissions) in the same period (Mao et al 2019). In contrast, an assessment of the reduction in PM 2.5 concentrations in China during 2013-2017 suggested that the Clean Air Action was the dominant factor for improvement in air quality in recent years (Vu et al 2019, Zhai et al 2019. This discrepancy was partly caused by the different indices and definitions of haze used in the studies.…”

Environmental Effective Assessment of Control Measures Implemented by Clean Air Action Plan (2013–2017) in Beijing, China

“…In order to reduce coal-fired power production, the use of controlling automotive vehicle, pollutant emissions and dust emissions, a series of legal, economic, technical, and administrative actions were pursued for the scientific, comprehensive, and profound management of air pollution. These actions significantly improved the air quality in Beijing [15][16][17][18]. Compared with the PM 2.5 concentration in 2013, the value in 2017 was 58 µg/m 3 [19], with a reduction of 35.6%.…”

Mutual promotion between aerosol particle liquid water and particulate nitrate enhancement leads to severe nitrate-dominated particulate matter pollution and low visibility

“…Aerosol particle hygroscopicity plays an important role in air quality deterioration and cloud formation (Yu and Luo, 2009;Fitzgerald, 1973;Kreidenweis and Asa-Awuku, 2014;Wang and Chen, 2019;McFiggans et al, 2006) and can also directly influence aerosol measurements (Chen et al, 2018a). In atmospheric environments influenced by anthropogenic activities, particulate secondary inorganic compounds are often dominated by particulate sulfate and nitrate (Heintzenberg, 1989), which originate from the oxidation of sulfur dioxide (SO 2 ) and nitrogen oxides (NO x ) via multiple chemical pathways (Calvert et al, 1985;Cheng et al, 2016;Wang et al, 2016;Gen et al, 2019a, b). The abundance of secondary inorganic components is one of the most important factors determining particle hygroscopicity (Swietlicki et al, 2008), thereby governing the aerosol liquid water content under ambient moist conditions.…”

“…The abundance of secondary inorganic components is one of the most important factors determining particle hygroscopicity (Swietlicki et al, 2008), thereby governing the aerosol liquid water content under ambient moist conditions. Increased aerosol particle liquid water could accelerate secondary inorganic and organic aerosol formation by decreasing the kinetic limitation of mass transfer of gaseous precursors and providing more of a medium for multiphase reactions (Mozurkewich and Calvert, 1988;Cheng et al, 2016;Wang et al, 2016;Ervens et al, 2011;Kolb et al, 2010).…”