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.
Highlights
In recent decades, China has achieved rapid economic growth and become the world’s second largest economy
To implement the national action plan and further improve air quality, the Beijing municipal government (BMG) formulated and released the “Beijing 2013–2017 Clean Air Action Plan”, which set a target for the mean concentration of fine particles (PM2.5, particulate matter with aerodynamic diameter less than 2.5 μm) to be below 60 μg m−3 by 2017 (BMG, 2013)
The results were compared with the latest emission inventory as well as results from previous study which used a chemical transport model – the Weather Research and Forecasting (WRF) – Community Multiscale Air Quality (CMAQ) model (Wong et al, 2012; Xiu and Pleim, 2001)
Summary
China has achieved rapid economic growth and become the world’s second largest economy. New approaches based on regression decision trees are being developed, which are suitable for air quality weather detrending, including the boosted regression tree (BRT) and random forest (RF) algorithms (Carslaw and Taylor, 2009; Grange et al, 2018) These machine-learning-based techniques have a better performance than the traditional statistical and air quality models by reducing variance/bias and error in highly dimensional data sets (Grange et al, 2018). We applied a machine learning technique based upon the random forest algorithm and the latest R packages to quantify the role of meteorological conditions in air quality and evaluate the effectiveness of the action plan in reducing air pollution levels in Beijing. The results were compared with the latest emission inventory as well as results from previous study which used a chemical transport model – the Weather Research and Forecasting (WRF) – Community Multiscale Air Quality (CMAQ) model (Wong et al, 2012; Xiu and Pleim, 2001)
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