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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Main Authors: Ravindiran G., Rajamanickam S., Kanagarathinam K., Hayder G., Janardhan G., Arunkumar P., Arunachalam S., AlObaid A.A., Warad I., Muniasamy S.K.
Other Authors: 57226345669
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Published: Academic Press Inc. 2024
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spelling 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
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic 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
spellingShingle 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
description 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.
author2 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.
format 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
_version_ 1814061028078518272
score 13.214268