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- Publisher Website: 10.1109/BigData.2018.8622417
- Scopus: eid_2-s2.0-85062621748
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Conference Paper: A Bayesian LSTM Model to Evaluate the Effects of Air Pollution Control Regulations in China
Title | A Bayesian LSTM Model to Evaluate the Effects of Air Pollution Control Regulations in China |
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Authors | |
Keywords | air pollution control regulation Bayesian LSTM effects of regulatory interventions |
Issue Date | 2018 |
Publisher | IEEE. 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? |
Abstract | 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 2.5variability. 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 Identifier | http://hdl.handle.net/10722/277813 |
ISBN |
DC Field | Value | Language |
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dc.contributor.author | Han, Y | - |
dc.contributor.author | Lam, JCK | - |
dc.contributor.author | Li, VOK | - |
dc.date.accessioned | 2019-10-04T08:01:51Z | - |
dc.date.available | 2019-10-04T08:01:51Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 10-13 December 2018, p. 4465-4468 | - |
dc.identifier.isbn | 978-1-5386-5036-3 | - |
dc.identifier.uri | http://hdl.handle.net/10722/277813 | - |
dc.description.abstract | 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 2.5variability. 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.language | eng | - |
dc.publisher | IEEE. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1802964 | - |
dc.relation.ispartof | IEEE International Conference on Big Data (Big Data) | - |
dc.rights | IEEE International Conference on Big Data (Big Data). Copyright © IEEE. | - |
dc.subject | air pollution control regulation | - |
dc.subject | Bayesian LSTM | - |
dc.subject | effects of regulatory interventions | - |
dc.title | A Bayesian LSTM Model to Evaluate the Effects of Air Pollution Control Regulations in China | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Lam, JCK: h9992013@hkucc.hku.hk | - |
dc.identifier.email | Li, VOK: vli@eee.hku.hk | - |
dc.identifier.authority | Lam, JCK=rp00864 | - |
dc.identifier.authority | Li, VOK=rp00150 | - |
dc.identifier.doi | 10.1109/BigData.2018.8622417 | - |
dc.identifier.scopus | eid_2-s2.0-85062621748 | - |
dc.identifier.hkuros | 306527 | - |
dc.identifier.spage | 4465 | - |
dc.identifier.epage | 4468 | - |
dc.publisher.place | United States | - |