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

TitleA Bayesian LSTM Model to Evaluate the Effects of Air Pollution Control Regulations in China
Authors
Issue Date2018
PublisherIEEE. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1802964
Citation
2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 10-13 December 2018, p. 4465-4468 How to Cite?
AbstractRapid 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 2.5variability. Our preliminary results show that air pollution control regulatory measures introduced in China and Beijing have reduced PM 2.5pollution in Beijing by 23% on average.
Persistent Identifierhttp://hdl.handle.net/10722/277813
ISBN

 

DC FieldValueLanguage
dc.contributor.authorHan, Y-
dc.contributor.authorLam, JCK-
dc.contributor.authorLi, VOK-
dc.date.accessioned2019-10-04T08:01:51Z-
dc.date.available2019-10-04T08:01:51Z-
dc.date.issued2018-
dc.identifier.citation2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 10-13 December 2018, p. 4465-4468-
dc.identifier.isbn978-1-5386-5036-3-
dc.identifier.urihttp://hdl.handle.net/10722/277813-
dc.description.abstractRapid 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 2.5variability. Our preliminary results show that air pollution control regulatory measures introduced in China and Beijing have reduced PM 2.5pollution in Beijing by 23% on average.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1802964-
dc.relation.ispartofIEEE International Conference on Big Data (Big Data)-
dc.rightsIEEE International Conference on Big Data (Big Data). Copyright © IEEE.-
dc.titleA Bayesian LSTM Model to Evaluate the Effects of Air Pollution Control Regulations in China-
dc.typeConference_Paper-
dc.identifier.emailLam, JCK: h9992013@hkucc.hku.hk-
dc.identifier.emailLi, VOK: vli@eee.hku.hk-
dc.identifier.authorityLam, JCK=rp00864-
dc.identifier.authorityLi, VOK=rp00150-
dc.identifier.doi10.1109/BigData.2018.8622417-
dc.identifier.hkuros306527-
dc.identifier.spage4465-
dc.identifier.epage4468-
dc.publisher.placeUnited States-

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