A Bayesian LSTM Model to Evaluate the Effects of Air Pollution Control Regulations in China
- Resource Type
- Conference
- Authors
- Han, Yang; Lam, Jacqueline C.K.; Li, Victor O.K.
- Source
- 2018 IEEE International Conference on Big Data (Big Data) Big Data (Big Data), 2018 IEEE International Conference on. :4465-4468 Dec, 2018
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Transportation
Air pollution
Atmospheric modeling
Data models
Bayes methods
Machine learning
air pollution control regulation
effects of regulatory interventions
Bayesian LSTM
- Language
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.