Shihping Kevin Huang This email address is being protected from spambots. You need JavaScript enabled to view it.1, Sin-Yao Chen1, Kuei-Lan Chou2, Wei Chung Hsu2, Kang-Hung Lai2, Tung-Hung Chueh2, Lopin Kuo3, William Lu1 

1 Institute of Management of Technology, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
2 Green Energy and Environment Research Laboratories, Industrial Technology Research Institute, Hsinchu 310401, Taiwan
3 TamKang University, New Taipei 251301, Taiwan


Received: November 28, 2021
Revised: June 7, 2022
Accepted: July 18, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.210315  


Cite this article:

Huang, S.K., Chen, S.Y., Chou, K.L., Hsu, W.C., Lai, K.H., Chueh, T.H., Kuo, L., Lu, W. (2022). Optimizing the PM2.5 Tradeoffs: The Case of Taiwan. Aerosol Air Qual. Res. https://doi.org/10.4209/aaqr.210315


HIGHLIGHTS

  • A proper analysis of PM2.5 concentration should include socioeconomic and environmental factors.
  • Tradeoff between transportation and power generation is different in every district.
  • Machine learning is an effect tool in forecasting PM2.5 concentration
  • Real time and real scenario data might help to alleviate problem of PM2.5.
 

ABSTRACT


The causes of PM2.5 is dynamic and systematic. However, many studies approach the PM2.5 problem by focusing only on either socioeconomic factors or geo-meteorological factors in isolation such data insufficiency might undermine the effort to control PM2.5. We propose a LSTM-XGBoost model composing both socioeconomic and geo-meteorological factors together to improve the PM2.5 monitoring system. We forecast the weekly PM2.5 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 PM2.5 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 PM2.5 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 PM2.5.


Keywords: Air pollution, Machine learning, PM2.5, Forecasting




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