Publication: Predictive modelling of nitrogen dioxide using soft computing techniques in the Agra, Uttar Pradesh, India
Date
2024
Authors
Sihag P.
Mehta T.
Sammen S.S.
Pande C.B.
Puri D.
Radwan N.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Ltd
Abstract
Nitrogen dioxide (NO2) is one of the air pollutants which aggravates the human health as well as causes environmental issues. It is more causes respiratory problems due to acid rains. Agra is a major tourist destination spot in India also similarly air pollution also increased growing urbanization and traffic reflux. The current study aims to predicted the NO2 episodes in the Agra city using soft computing models namely, M5P, Random Forest (RF), Group method of data handling (GMDH), Multivariate adaptive regression (MARS), Reduced error pruning tree (REP Tree) and Random tree (RT). The models were generated using 1116 observations, from 2015 to 2020 with input parameters such as Particulate matter (PM2.5), Nitrogen monoxide (NO), Oxides of nitrogen (NOX), Sulphur dioxide (SO2), Carbon monoxide (CO), Ozone (O), Benzene (Be), Toluene, Relative humidity (RH), Wind speed (WS), Wind direction (WD), Solar radiation (SR), Barometric pressure (BP) and Xylene. The performance of each model was evaluated based on the six statistical indices, namely correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), normalized root of mean squared relative error (NRMSE), Willmott's Index (WI), and Legates and McCabe's Index (LMI). The performance evaluation models results of study area, Box plot and Taylor's diagram indicated that M5P is the outperforming model among others with testing CC = 0.9543, RMSE = 5.8006, MAE = 3,9204, NRMSE = 0.1512, WI = 0.9744, and LMI = 0.7549. Based on the sensitivity analysis indicated that NOx is the most influential parameter followed by WD and CO. These results of study area can be helpful to understanding the air pollution causes, health issues, and future NO2 levels around the study area with useful results for air pollution monitoring policy and development. ? 2024 Elsevier Ltd
Description
Keywords
Agra , India , Uttar Pradesh , Acid rain , Air pollution , Atmospheric pressure , Carbon monoxide , Data handling , Errors , Forestry , Linear matrix inequalities , Mean square error , Nitrogen oxides , Recurrent neural networks , Sensitivity analysis , Soft computing , Wind , Group method of data handling , M5P , Multivariate adaptive regression , Nitrogen monoxide , Nitrogen monoxide2 , Random forests , Random tree , Reduced error pruning tree , Reduced-error pruning , Study areas , atmospheric chemistry , atmospheric pollution , computer simulation , multivariate analysis , nitrogen dioxide , regression analysis , relative humidity , Sulfur dioxide