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

Abstract

<p><strong>Abstract.</strong> 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<sub>2.5</sub> mass concentration (58 μg m<sup>−3</sup>) than predicted from the random forest model (61 μg m<sup>−3</sup>), which is higher than the target of the Plan (2017 annual PM<sub>2.5</sub> < 60 μg m<sup>−3</sup>). 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<sub>2.5</sub>, PM<sub>10</sub>, NO<sub>2</sub>, SO<sub>2</sub> and CO from 2013 to 2017, which are approximately 34 %, 24 %, 17 %, 68 %, and 33 % after meteorological correction. The marked decrease in PM<sub>2.5</sub> and SO<sub>2</sub> 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.</p>

Heterogeneous effects of COVID-19 lockdown measures on air quality in Northern China

“…There are massive pollution outbreak events in northern China, especially in the BTH region, such as winter fog haze pollution. Extreme air pollution harms human health and the social economy, especially in densely populated and economically developed urban agglomerations [19] , [40] . Therefore, high-intensity restrictions on population, traffic, and economic activities should be taken to reduce its harm to deal with extreme urban pollution.…”

“…The Beijing-Tianjin-Hebei (BTH) region is one of the major economic zones in northern China. Air pollution in this region has always been a huge concern, especially in winter [19] . When meteorological conditions are unfavourable, air pollution usually becomes more serious than in other areas due to the unique topography [20] .…”

Air quality improvement in response to intensified control strategies in Beijing during 2013–2019

“…Statistical models, such as the deep neural network model, convergent cross-mapping (CCM) method, and the difference-in-difference (DID) model are other methods to decouple the influencing factors such as meteorology, pollutant emissions (Cobourn et al, 2010;Chen et al, 2018b;Wang et al, 2019a). Vu et al (2019) found the primary emission controls have led to reductions in PM2.5, PM10, NO2, SO2, and CO of about 34%, 24%, 17%, 68%, and 33% during 2013-2017 in Beijing, after meteorological correction.…”

Trends in Air Pollutant Concentrations and the Impact of Meteorology in Shandong Province, Coastal China, during 2013-2019

“…For example, severe air pollution events occurred in North China during January 2020, with the daily average concentration of PM2.5 exceeding 200 μg/m 3 , although the pollutant emissions decreased due to the Spring Festival holiday and coronavirus disease outbreak (Wang et al, 2020d;Zhang et al, 2020a). Several studies have explored the contribution of inter-annual meteorological changes to air pollutant concentration reductions (Hong et al, 2019;Vu et al, 2019;Xue et al, 2020;Zhang et al, 2020b). The decrease in the PM2.5 concentration due to emissions reduction increased annually from 2013-2017 in key regions of China, especially in…”