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<p>This study extensively examines five distinct machine learning models used to predict hourly air particulate matter concentrations. The study used real-world data, including pollutant levels and various meteorological parameters, for model training and evaluation, making the study more reliable and effective. The study focused on capturing short-term trends in pollutant concentrations and meteorological conditions. Results showed varied model performances. The Ridge Regression model exhibited a moderate R<sup>2</sup> value of 0.44 for PM<sub>2.5</sub> prediction and an impressive R<sup>2</sup> of 0.91 for PM<sub>10</sub> prediction. Support Vector Regression showed strength in PM<sub>2.5</sub> prediction (R<sup>2</sup> = 0.83) but faced challenges in forecasting PM<sub>10</sub>. Random Forest and Extra Trees Regression demonstrated robust overall performance, particularly in PM<sub>10</sub> forecasting (R<sup>2</sup> = 0.75). Extreme Gradient Boosting displayed competitive results for both PM<sub>2.5</sub> and PM<sub>10</sub> (R<sup>2</sup> = 0.80 and 0.81). Each model's identified strengths and limitations provide valuable insights for air quality management, offering a foundation for future research and the development of machine learning models in the continuous pursuit of accurate and timely air quality predictions. The AirQ+ model was used to estimate the health effects of PM<sub>2.5</sub> exposure and predict the long-term mortality rates associated with PM<sub>2.5</sub>. The average estimated attributable proportion for all years is 10.2% (with a range of 6.5% to 13.2%). The results show differing trends in estimated mortality rates, underscoring the need for targeted interventions to reduce the public health risks associated with exposure to polluted air.</p>

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Comprehensive evaluation of machine learning models for real-world air quality prediction and health risk assessment by AirQ+

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月13日

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  • Ebru Koçak

摘要

This study extensively examines five distinct machine learning models used to predict hourly air particulate matter concentrations. The study used real-world data, including pollutant levels and various meteorological parameters, for model training and evaluation, making the study more reliable and effective. The study focused on capturing short-term trends in pollutant concentrations and meteorological conditions. Results showed varied model performances. The Ridge Regression model exhibited a moderate R2 value of 0.44 for PM2.5 prediction and an impressive R2 of 0.91 for PM10 prediction. Support Vector Regression showed strength in PM2.5 prediction (R2 = 0.83) but faced challenges in forecasting PM10. Random Forest and Extra Trees Regression demonstrated robust overall performance, particularly in PM10 forecasting (R2 = 0.75). Extreme Gradient Boosting displayed competitive results for both PM2.5 and PM10 (R2 = 0.80 and 0.81). Each model's identified strengths and limitations provide valuable insights for air quality management, offering a foundation for future research and the development of machine learning models in the continuous pursuit of accurate and timely air quality predictions. The AirQ+ model was used to estimate the health effects of PM2.5 exposure and predict the long-term mortality rates associated with PM2.5. The average estimated attributable proportion for all years is 10.2% (with a range of 6.5% to 13.2%). The results show differing trends in estimated mortality rates, underscoring the need for targeted interventions to reduce the public health risks associated with exposure to polluted air.