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Estimation of urban AQI based on interpretable machine learning

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Abstract

Air pollution is an increasingly serious problem. Accurate and efficient prediction of air quality can effectively prevent air pollution and improve the quality of human life. The air quality index (AQI) is a dimensionless tool to describe air quality quantitatively. In this study, the machine learning (ML) method was used to estimate AQI for Shijiazhuang, China, as the research object, and pollutants and meteorological factors as data models. Specifically, eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF) models were used. The experimental results show that XGBoost model captures the AQI variation trend well, and the R2 of XGBoost model is 0.929, which is 0.3% and 2.3% higher than the R2 of RF model and LightGBM model, respectively. In addition, through the SHAP-based model interpretation method, the study reveals the key factors of AQI variation, that is PM2.5 and PM10, play positive roles in the variation of AQI and AQI is less sensitive to meteorological factors. Finally, Beijing, Shanghai, Xi’an, and Guangzhou were selected to test the model’s validity, and the model performance remained good. Our study shows that applying ML approach to air quality prediction is beneficial for efficiently assessing cities’ future air quality.

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Data availability

The datasets during the study are available at https://air.cnemc.cn:18014/ and https://power.larc.nasa.gov/data-access-viewer/.

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Funding

This work was supported by Yan’an City Science and Technology Development Program (No.203010096), Yan’an University Doctoral Program (No.20504306), Shaanxi Provincial Talent Program (No. YAU202305399), Yan’an University 14th Five-Year Major Research Program (YAU202313738), and Graduate Education Innovation Program of Yan'an University (No. YCX2023008).

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Authors and Affiliations

  1. School of Mathematics and Computer Science, Yan’an University, Yan’an, 716000, China

    Siyuan Wang, Ying Ren & Bisheng Xia

Authors
  1. Siyuan Wang
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  2. Ying Ren
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  3. Bisheng Xia
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Contributions

Siyuan Wang: conceptualization, methodology, data analysis, data collection, and writing—original draft. Ying Ren: formal analysis, investigation, and validation. Bisheng Xia: supervision, validation, writing—review and editing, and funding acquisition.

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Correspondence to Bisheng Xia.

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Wang, S., Ren, Y. & Xia, B. Estimation of urban AQI based on interpretable machine learning. Environ Sci Pollut Res 30, 96562–96574 (2023). https://doi.org/10.1007/s11356-023-29336-5

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