Optimizing the PM2.5 Tradeoffs: The Case of Taiwan

Shihping Kevin Huang*, Sin Yao Chen, Kuei Lan Chou, Wei Chung Hsu, Kang Hua Lai, Tung Hung Chueh, Lopin Kuo, William Lu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish
Article number210315
JournalAerosol and Air Quality Research
Issue number10
StatePublished - Oct 2022


  • Air pollution
  • Forecasting
  • Machine learning
  • PM2.5


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