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Forecasting air quality index considering socioeconomic indicators and meteorological factors: A data granularity perspective

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  • Chih‐Hsuan Wang
  • Chia‐Rong Chang

Abstract

Forecasting air quality index (AQI) is critically important to provide a basis for government policy makers, especially in public health, smart transportation, energy management, economic development, and sustainable environments. In reality, AQI consists of various components, such as PM2.5, PM10, CO, NO2, and SO2. Although numerous methods have been presented, few studies concurrently considered the causalities of socioeconomic indicators and meteorological factors and different data granularities. The aggregate AQI of Taiwan comprises five representative cities: Taipei, Hsinchu, Taichung, Tainan, and Kaohsiung. Research findings identify seasonal factors, carbon power generation, steel and metal production, highway cargo load, the number of registered cars, and retail and manufacturing employment population as the key indicators to predict the monthly AQI of Taiwan. For the daily AQI of Hsinchu and the hourly AQI of Kaohsiung, PM2.5, PM10, O3, ambient temperature, humidity, wind speed, wind direction, and pollutants (CO, NO2, and SO2) are recognized. Deep learning significantly outperforms machine learning in the hourly AQI while it performs slightly better in the daily AQI. With the presented framework, governments can balance the trade‐offs between economic development and environmental sustainability.

Suggested Citation

  • Chih‐Hsuan Wang & Chia‐Rong Chang, 2023. "Forecasting air quality index considering socioeconomic indicators and meteorological factors: A data granularity perspective," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1261-1274, August.
    • Handle: RePEc:wly:jforec:v:42:y:2023:i:5:p:1261-1274
    DOI: 10.1002/for.2962
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    1. Lu Bai & Jianzhou Wang & Xuejiao Ma & Haiyan Lu, 2018. "Air Pollution Forecasts: An Overview," IJERPH, MDPI, vol. 15(4), pages 1-44, April.
    2. Bing-Chun Liu & Arihant Binaykia & Pei-Chann Chang & Manoj Kumar Tiwari & Cheng-Chin Tsao, 2017. "Urban air quality forecasting based on multi-dimensional collaborative Support Vector Regression (SVR): A case study of Beijing-Tianjin-Shijiazhuang," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-17, July.
    3. Thanongsak Xayasouk & HwaMin Lee & Giyeol Lee, 2020. "Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models," Sustainability, MDPI, vol. 12(6), pages 1-17, March.
    4. Buehn, Andreas & Farzanegan, Mohammad Reza, 2013. "Hold your breath: A new index of air pollution," Energy Economics, Elsevier, vol. 37(C), pages 104-113.
    5. Mauro Castelli & Fabiana Martins Clemente & Aleš Popovič & Sara Silva & Leonardo Vanneschi, 2020. "A Machine Learning Approach to Predict Air Quality in California," Complexity, Hindawi, vol. 2020, pages 1-23, August.
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