A remote sensing based study of tropospheric ozone concentration amid COVID-19 lockdown over India using Sentinel-5P satellite data

Abstract

Amid the COVID-19 crisis, governments all over the world, and not excluding India, took to lockdown measures to deaccelerate the spread of the COVID-19 virus. This led to reduction of atmospheric pollution by declining the harmful Nitrogen and Sulphur Oxide (NOX and SOX) concentrations. However, one hand while the stratospheric Ozone (O3) showed repair, the lower atmospheric O3 concentrations demonstrated a remarkable increase during lockdown phase over India. This study aims to estimate the O3 concentration during the Covid-19 lockdown over Pune city in India using freely available Sentinel-5P satellite datasets. The study makes use of the Ordinary Least Squares (OLS) and Random Forest (RF) regressions and compares the findings of the two algorithms based on estimation results. This study utilizes lower atmospheric O3 concentration data from Sentinel-5P satellite of the European Space Agency (ESA) over the Indian mainland for a month of lockdown scenario (March 22nd, 2020, to April 25th, 2020) and shows the remarkable increase in concentration of O3 gas as a pollutant. Despite the complete lockdown over India during this given time frame, there has been enough emission of O3 precursors from other sources such as stubble burning. The estimates of tropospheric O3 concentration for May 2020 for Pune city, using OLS and RF Regressions, have been validated with May 2020 data. The results have provided a RMSE of 1.05 and 1.23 with R2 -statistics of 0.90 and 0.857 in training and testing phases for OLS and RMSE of 0.98 and MAE of 1.07 with R2 -statistics of 0.968 and 0.895 in training and testing phases of the RF. The outcome of this study has proven that O3 gas concentrations in the atmosphere depends upon various other causative factors apart from the precursor gases. The study also shows that the remotely sensed Sentinel-5P datasets, supplemented with ground-based sensor data can help in time and cost saving estimation of O3 concentrations in the troposphere with considerable accuracy.