Publication: Impact of air pollutants on climate change and prediction of air quality index using machine learning models
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Date
2023
Authors
Ravindiran G.
Rajamanickam S.
Kanagarathinam K.
Hayder G.
Janardhan G.
Arunkumar P.
Arunachalam S.
AlObaid A.A.
Warad I.
Muniasamy S.K.
Journal Title
Journal ISSN
Volume Title
Publisher
Academic Press Inc.
Abstract
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.
Description
Keywords
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