Optimizing the PM2.5 Tradeoffs: The Case of Taiwan
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 co...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2022-07-01
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| Series: | Aerosol and Air Quality Research |
| Subjects: | |
| Online Access: | https://doi.org/10.4209/aaqr.210315 |
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| _version_ | 1823862917028642816 |
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| author | Shihping Kevin Huang Sin-Yao Chen Kuei-Lan Chou Wei Chung Hsu Kang-Hua Lai Tung-Hung Chueh Lopin Kuo William Lu |
| author_facet | Shihping Kevin Huang Sin-Yao Chen Kuei-Lan Chou Wei Chung Hsu Kang-Hua Lai Tung-Hung Chueh Lopin Kuo William Lu |
| author_sort | Shihping Kevin Huang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a96004f041e94d1d90c9bdc151c4f2d2 |
| institution | Kabale University |
| issn | 1680-8584 2071-1409 |
| language | English |
| publishDate | 2022-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Aerosol and Air Quality Research |
| spelling | doaj-art-a96004f041e94d1d90c9bdc151c4f2d22025-02-09T12:18:02ZengSpringerAerosol and Air Quality Research1680-85842071-14092022-07-01221011310.4209/aaqr.210315Optimizing the PM2.5 Tradeoffs: The Case of TaiwanShihping Kevin Huang0Sin-Yao Chen1Kuei-Lan Chou2Wei Chung Hsu3Kang-Hua Lai4Tung-Hung Chueh5Lopin Kuo6William Lu7Institute of Management of Technology, National Yang Ming Chiao Tung UniversityInstitute of Management of Technology, National Yang Ming Chiao Tung UniversityGreen Energy and Environment Research Laboratories, Industrial Technology Research InstituteGreen Energy and Environment Research Laboratories, Industrial Technology Research InstituteGreen Energy and Environment Research Laboratories, Industrial Technology Research InstituteGreen Energy and Environment Research Laboratories, Industrial Technology Research InstituteTamKang UniversityInstitute of Management of Technology, National Yang Ming Chiao Tung UniversityAbstract 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.https://doi.org/10.4209/aaqr.210315Air pollutionMachine learningPM2.5Forecasting |
| spellingShingle | Shihping Kevin Huang Sin-Yao Chen Kuei-Lan Chou Wei Chung Hsu Kang-Hua Lai Tung-Hung Chueh Lopin Kuo William Lu Optimizing the PM2.5 Tradeoffs: The Case of Taiwan Aerosol and Air Quality Research Air pollution Machine learning PM2.5 Forecasting |
| title | Optimizing the PM2.5 Tradeoffs: The Case of Taiwan |
| title_full | Optimizing the PM2.5 Tradeoffs: The Case of Taiwan |
| title_fullStr | Optimizing the PM2.5 Tradeoffs: The Case of Taiwan |
| title_full_unstemmed | Optimizing the PM2.5 Tradeoffs: The Case of Taiwan |
| title_short | Optimizing the PM2.5 Tradeoffs: The Case of Taiwan |
| title_sort | optimizing the pm2 5 tradeoffs the case of taiwan |
| topic | Air pollution Machine learning PM2.5 Forecasting |
| url | https://doi.org/10.4209/aaqr.210315 |
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