Optimizing the PM2.5 Tradeoffs: The Case of Taiwan

The causes of PM 2.5 is dynamic and systematic. However, many studies approach the PM 2.5 problem by focusing only on either socioeconomic factors or geo-meteorological factors in isolation such data insufficiency might undermine the effort to control PM 2.5 . We propose a LSTM-XGBoost model composi...

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Bibliographic Details
Published in:Aerosol and air quality research 2022-10, Vol.22 (10), p.210315-13
Main Authors: Huang, Shihping Kevin, Chen, Sin-Yao, Chou, Kuei-Lan, Hsu, Wei Chung, Lai, Kang-Hua, Chueh, Tung-Hung, Kuo, Lopin, Lu, William
Format: Article
Language:English
Subjects:
Agricultural equipment
Air pollution
Forecasting
Fossil fuels
Machine learning
Original Research
Particulate matter
Passengers
PM2.5
Social factors
Socioeconomic factors
Socioeconomics
Tractor trailers
Tractors
Trucks
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Summary:The causes of PM 2.5 is dynamic and systematic. However, many studies approach the PM 2.5 problem by focusing only on either socioeconomic factors or geo-meteorological factors in isolation such data insufficiency might undermine the effort to control PM 2.5 . We propose a LSTM-XGBoost model composing both socioeconomic and geo-meteorological factors together to improve the PM 2.5 monitoring system. We forecast the weekly PM 2.5 concentrations in five regions in Taiwan based on machine learning training data. The results indicate that overall small trucks usage should be reduced by 80% while maintaining semi-trucks and passenger cars at current level. In addition, coal and IPP Gas power have no impact on PM 2.5 concentrations in central Taiwan while usage in passenger cars, small tracks and tractor trailers should be reduced by 80% in central Taiwan. Overall, central Taiwan and Chiayi regions have the highest PM 2.5 projections at XGBoost output of 68.5 and 59.1 level. Finally, our model indicates that the use of fossil fuel based small tracks and tractor trailers should be reduced by 80% to maintain a reasonable level of PM 2.5 .
ISSN:1680-8584
2071-1409
DOI:10.4209/aaqr.210315