A Bayesian LSTM Model to Evaluate the Effects of Air Pollution Control Regulations in China

Rapid socio-economic development and urbanization have resulted in serious deterioration in air-quality in many world cities, including Beijing, China. This preliminary study is the first attempt to examine the effectiveness of air pollution control regulations implemented in Beijing during 2013 - 2017 through a data-driven regulatory intervention analysis. Our proposed machine-learning model utilizes proxy data including Aerosol Optical Depth (AOD) and meteorology; it can explain 80% of the PM _2.5 variability. Our preliminary results show that air pollution control regulatory measures introduced in China and Beijing have reduced PM _2.5 pollution in Beijing by 23% on average.