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Impact of air pollutants on climate change and prediction of air quality index using machine learning models

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dc.citedby13
dc.contributor.authorRavindiran G.en_US
dc.contributor.authorRajamanickam S.en_US
dc.contributor.authorKanagarathinam K.en_US
dc.contributor.authorHayder G.en_US
dc.contributor.authorJanardhan G.en_US
dc.contributor.authorArunkumar P.en_US
dc.contributor.authorArunachalam S.en_US
dc.contributor.authorAlObaid A.A.en_US
dc.contributor.authorWarad I.en_US
dc.contributor.authorMuniasamy S.K.en_US
dc.contributor.authorid57226345669en_US
dc.contributor.authorid57190127095en_US
dc.contributor.authorid57203041846en_US
dc.contributor.authorid56239664100en_US
dc.contributor.authorid57217976806en_US
dc.contributor.authorid58498085600en_US
dc.contributor.authorid57784786600en_US
dc.contributor.authorid57223087505en_US
dc.contributor.authorid6506402060en_US
dc.contributor.authorid57214630614en_US
dc.date.accessioned2024-10-14T03:17:22Z
dc.date.available2024-10-14T03:17:22Z
dc.date.issued2023
dc.description.abstractThe 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo117354
dc.identifier.doi10.1016/j.envres.2023.117354
dc.identifier.scopus2-s2.0-85173824316
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85173824316&doi=10.1016%2fj.envres.2023.117354&partnerID=40&md5=858cc55e0a4f0988922bf96fdc53ce83
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/33868
dc.identifier.volume239
dc.publisherAcademic Press Inc.en_US
dc.sourceScopus
dc.sourcetitleEnvironmental Research
dc.subjectAir pollution
dc.subjectAir quality index
dc.subjectClimate action
dc.subjectMachine learning
dc.subjectAir Pollutants
dc.subjectAir Pollution
dc.subjectClimate Change
dc.subjectIndia
dc.subjectMachine Learning
dc.subjectChennai
dc.subjectIndia
dc.subjectTamil Nadu
dc.subjectAir quality
dc.subjectClimate change
dc.subjectClimate models
dc.subjectErrors
dc.subjectFog
dc.subjectForecasting
dc.subjectForestry
dc.subjectHigher order statistics
dc.subjectLearning algorithms
dc.subjectMean square error
dc.subjectQuality assurance
dc.subject'current
dc.subjectAir pollutants
dc.subjectAir quality indices
dc.subjectChennai
dc.subjectClimate action
dc.subjectCoastal cities
dc.subjectHistorical data
dc.subjectMachine learning models
dc.subjectMachine-learning
dc.subjectMetropolitan cities
dc.subjectaction plan
dc.subjectair quality
dc.subjectatmospheric pollution
dc.subjectclimate change
dc.subjecterror analysis
dc.subjectmachine learning
dc.subjectparticulate matter
dc.subjectair pollutant
dc.subjectair pollution
dc.subjectair quality
dc.subjectarticle
dc.subjectclimate change
dc.subjectcorrelation coefficient
dc.subjectheat
dc.subjectmachine learning
dc.subjectmean absolute error
dc.subjectmeteorology
dc.subjectparticulate matter
dc.subjectparticulate matter 2.5
dc.subjectprediction
dc.subjectrandom forest
dc.subjectroot mean squared error
dc.subjectclimate change
dc.subjectIndia
dc.subjectmachine learning
dc.subjectMachine learning
dc.titleImpact of air pollutants on climate change and prediction of air quality index using machine learning modelsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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