Impact of air pollutants on climate change and prediction of air quality index using machine learning models
The impact of air pollution in Chennai metropolitan city, a southern Indian coastal city was examined to predict the Air Quality Index (AQI). Regular monitoring and prediction of the Air Quality Index (AQI) are critical for combating air pollution. The current study created machine learning models s...
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my.uniten.dspace-338682024-10-14T11:17:22Z Impact of air pollutants on climate change and prediction of air quality index using machine learning models Ravindiran G. Rajamanickam S. Kanagarathinam K. Hayder G. Janardhan G. Arunkumar P. Arunachalam S. AlObaid A.A. Warad I. Muniasamy S.K. 57226345669 57190127095 57203041846 56239664100 57217976806 58498085600 57784786600 57223087505 6506402060 57214630614 Air pollution Air quality index Climate action Machine learning Air Pollutants Air Pollution Climate Change India Machine Learning Chennai India Tamil Nadu Air quality Climate change Climate models Errors Fog Forecasting Forestry Higher order statistics Learning algorithms Mean square error Quality assurance 'current Air pollutants Air quality indices Chennai Climate action Coastal cities Historical data Machine learning models Machine-learning Metropolitan cities action plan air quality atmospheric pollution climate change error analysis machine learning particulate matter air pollutant air pollution air quality article climate change correlation coefficient heat machine learning mean absolute error meteorology particulate matter particulate matter 2.5 prediction random forest root mean squared error climate change India machine learning Machine learning The impact of air pollution in Chennai metropolitan city, a southern Indian coastal city was examined to predict the Air Quality Index (AQI). Regular monitoring and prediction of the Air Quality Index (AQI) are critical for combating air pollution. The current study created machine learning models such as XGBoost, Random Forest, BaggingRegressor, and LGBMRegressor for the prediction of the AQI using the historical data available from 2017 to 2022. According to historical data, the AQI is highest in January, with a mean value of 104.6 g/gm, and the lowest in August, with a mean AQI value of 63.87 g/gm. Particulate matter, gaseous pollutants, and meteorological parameters were used to predict AQI, and the heat map generated showed that of all the parameters, PM2.5 has the greatest impact on AQI, with a value of 0.91. The log transformation method is used to normalize datasets and determine skewness and kurtosis. The XGBoost model demonstrated strong performance, achieving an R2 (correlation coefficient) of 0.9935, a mean absolute error (MAE) of 0.02, a mean square error (MSE) of 0.001, and a root mean square error (RMSE) of 0.04. In comparison, the LightGBM model's prediction was less effective, as it attained an R2 of 0.9748. According to the study, the AQI in Chennai has been increasing over the last two years, and if the same conditions persist, the city's air pollution will worsen in the future. Furthermore, accurate future air quality level predictions can be made using historical data and advanced machine learning algorithms. � 2023 Elsevier Inc. Final 2024-10-14T03:17:22Z 2024-10-14T03:17:22Z 2023 Article 10.1016/j.envres.2023.117354 2-s2.0-85173824316 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173824316&doi=10.1016%2fj.envres.2023.117354&partnerID=40&md5=858cc55e0a4f0988922bf96fdc53ce83 https://irepository.uniten.edu.my/handle/123456789/33868 239 117354 Academic Press Inc. Scopus |
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Air pollution Air quality index Climate action Machine learning Air Pollutants Air Pollution Climate Change India Machine Learning Chennai India Tamil Nadu Air quality Climate change Climate models Errors Fog Forecasting Forestry Higher order statistics Learning algorithms Mean square error Quality assurance 'current Air pollutants Air quality indices Chennai Climate action Coastal cities Historical data Machine learning models Machine-learning Metropolitan cities action plan air quality atmospheric pollution climate change error analysis machine learning particulate matter air pollutant air pollution air quality article climate change correlation coefficient heat machine learning mean absolute error meteorology particulate matter particulate matter 2.5 prediction random forest root mean squared error climate change India machine learning Machine learning |
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Air pollution Air quality index Climate action Machine learning Air Pollutants Air Pollution Climate Change India Machine Learning Chennai India Tamil Nadu Air quality Climate change Climate models Errors Fog Forecasting Forestry Higher order statistics Learning algorithms Mean square error Quality assurance 'current Air pollutants Air quality indices Chennai Climate action Coastal cities Historical data Machine learning models Machine-learning Metropolitan cities action plan air quality atmospheric pollution climate change error analysis machine learning particulate matter air pollutant air pollution air quality article climate change correlation coefficient heat machine learning mean absolute error meteorology particulate matter particulate matter 2.5 prediction random forest root mean squared error climate change India machine learning Machine learning Ravindiran G. Rajamanickam S. Kanagarathinam K. Hayder G. Janardhan G. Arunkumar P. Arunachalam S. AlObaid A.A. Warad I. Muniasamy S.K. Impact of air pollutants on climate change and prediction of air quality index using machine learning models |
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The impact of air pollution in Chennai metropolitan city, a southern Indian coastal city was examined to predict the Air Quality Index (AQI). Regular monitoring and prediction of the Air Quality Index (AQI) are critical for combating air pollution. The current study created machine learning models such as XGBoost, Random Forest, BaggingRegressor, and LGBMRegressor for the prediction of the AQI using the historical data available from 2017 to 2022. According to historical data, the AQI is highest in January, with a mean value of 104.6 g/gm, and the lowest in August, with a mean AQI value of 63.87 g/gm. Particulate matter, gaseous pollutants, and meteorological parameters were used to predict AQI, and the heat map generated showed that of all the parameters, PM2.5 has the greatest impact on AQI, with a value of 0.91. The log transformation method is used to normalize datasets and determine skewness and kurtosis. The XGBoost model demonstrated strong performance, achieving an R2 (correlation coefficient) of 0.9935, a mean absolute error (MAE) of 0.02, a mean square error (MSE) of 0.001, and a root mean square error (RMSE) of 0.04. In comparison, the LightGBM model's prediction was less effective, as it attained an R2 of 0.9748. According to the study, the AQI in Chennai has been increasing over the last two years, and if the same conditions persist, the city's air pollution will worsen in the future. Furthermore, accurate future air quality level predictions can be made using historical data and advanced machine learning algorithms. � 2023 Elsevier Inc. |
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57226345669 |
author_facet |
57226345669 Ravindiran G. Rajamanickam S. Kanagarathinam K. Hayder G. Janardhan G. Arunkumar P. Arunachalam S. AlObaid A.A. Warad I. Muniasamy S.K. |
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Article |
author |
Ravindiran G. Rajamanickam S. Kanagarathinam K. Hayder G. Janardhan G. Arunkumar P. Arunachalam S. AlObaid A.A. Warad I. Muniasamy S.K. |
author_sort |
Ravindiran G. |
title |
Impact of air pollutants on climate change and prediction of air quality index using machine learning models |
title_short |
Impact of air pollutants on climate change and prediction of air quality index using machine learning models |
title_full |
Impact of air pollutants on climate change and prediction of air quality index using machine learning models |
title_fullStr |
Impact of air pollutants on climate change and prediction of air quality index using machine learning models |
title_full_unstemmed |
Impact of air pollutants on climate change and prediction of air quality index using machine learning models |
title_sort |
impact of air pollutants on climate change and prediction of air quality index using machine learning models |
publisher |
Academic Press Inc. |
publishDate |
2024 |
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1814061028078518272 |
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13.214268 |