Repercussions of the COVID-19 Pandemic on the Air Quality of Chennai, India
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School of Civil Engineering, SASTRA Deemed to be University, Thanjavur, Tamil Nadu, India
Department of Civil Engineering, Mohamed Sathak Engineering College, Kilakarai, Ramanathapuram, Tamil Nadu, India
Submission date: 2022-10-13
Final revision date: 2022-12-02
Acceptance date: 2022-12-17
Online publication date: 2023-06-26
Publication date: 2023-07-21
Corresponding author
Vidya R   

vidyarajesh123@gmail.com School Of Civil Engineering, SASTRA Deemed to be University,Thanjavur, India
Pol. J. Environ. Stud. 2023;32(4):3739–3753
COVID 19 Pandemic in India had demanded an imposition of lockdown for three weeks initially and was extended further. This has drastic effect on air quality making it better because of control of vehicle emissions. This study analyzed the air quality in Chennai city using the parameters of pollution (NH3, PM2.5, NO2, SO2, O3 and CO) for air quality data for monitoring stations (three) spread over the city. National Air Quality Index (NAQI) has been used to present the quality of air spatially during lockdown and before lockdown. The concentrations of PM2.5 among the pollutants selected showed a maximum reduction (-61%) compared to the pre-lockdown process. NO2 (−40%) and CO (−32%) have also gone down when lockdown was in place, among other contaminants. In the different sections of the city, about 53% reduction in NAQI has been observed. Deep learning short-term predictions of various air pollutants are made in this study. The correlation between AQI and Pollutants (NH3, PM2.5, NO2, SO2, O3 and CO) in the study area modelled in deep learning using PYTHON. The classification of AQI class has been created in python with AQI values of Good (0-50), Satisfactory (51-100), Moderate (101-200), Poor (201-300), Very poor (301-400) and severe (>401). The study shows the level of co-relation of PM2.5 being the highest. A linear regression model was performed and metrics such mean absolute error, r2 to check the model performance for training and testing data are calculated. These results can be coupled with social, economic and Cultural factors that could have common emission patterns and air quality especially in metropolitan cities. The present study would aid authorities as it clearly shows that the quality can be made better if sources of emission can be diminished. This will pave way to protect and make the surroundings and environment better.