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<p>India, the most populous country in the world, ranks as the seventh largest by area. As per IQAir reports, in 2024, India was the fifth most polluted country, preceded by Chad, Congo, Bangladesh, and Pakistan, based on Air Quality Index (AQI) values. This study aims to predict air quality in Pune, Maharashtra, using an AI-driven data-centric approach. The dataset, obtained from sources such as Kaggle, CPCB, and WHO, comprises 3,170 records covering fifteen key factors influencing AQI, including SO₂, NOx, RSPM, precipitation, maximum and minimum temperature, sun hours, UV index, wind gust, humidity, pressure, average temperature, and wind speed. Data spanning nineteen years (2006–2024) is utilized to develop the predictive model, with records from 2006–2019 used for training and testing, while data from 2020–2024 is reserved for validation. This research proposes Linear Regression (LR) as a machine learning approach, achieving an R-value of 0.9611. The LR model's performance metrics include an RMSE of 21.4079, MAPE of 7.8945%, and MAE of 13.5884. The developed model can assist in forecasting air quality for urban residents, contributing to public health protection. Furthermore, it can aid in identifying effective mitigation strategies and operational measures to enhance air quality.</p>

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Assessing urban air quality of Pune city using AI-based predictive model: a data-driven approach for forecasting air quality index

  • 期刊论文

  • 发表日期：

    2025年4月23日

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  • Sushant Waghmare,
  • Gopi Ghadvir

摘要

India, the most populous country in the world, ranks as the seventh largest by area. As per IQAir reports, in 2024, India was the fifth most polluted country, preceded by Chad, Congo, Bangladesh, and Pakistan, based on Air Quality Index (AQI) values. This study aims to predict air quality in Pune, Maharashtra, using an AI-driven data-centric approach. The dataset, obtained from sources such as Kaggle, CPCB, and WHO, comprises 3,170 records covering fifteen key factors influencing AQI, including SO₂, NOx, RSPM, precipitation, maximum and minimum temperature, sun hours, UV index, wind gust, humidity, pressure, average temperature, and wind speed. Data spanning nineteen years (2006–2024) is utilized to develop the predictive model, with records from 2006–2019 used for training and testing, while data from 2020–2024 is reserved for validation. This research proposes Linear Regression (LR) as a machine learning approach, achieving an R-value of 0.9611. The LR model's performance metrics include an RMSE of 21.4079, MAPE of 7.8945%, and MAE of 13.5884. The developed model can assist in forecasting air quality for urban residents, contributing to public health protection. Furthermore, it can aid in identifying effective mitigation strategies and operational measures to enhance air quality.