Analysis of the effect of COVID-19 lockdown on air pollutants using multi-source pollution data and meteorological variables for the state of Uttar Pradesh, India

Abstract

The present study, conducted in the most populous state of India, i.e., Uttar Pradesh, estimates the variation of air quality for the period between 2019 and 2021, taking into account the extraordinary situation of COVID-19. The Government of India imposed the four-phased complete lockdown on 25^th March, 2020, which lasted until 31^st May, 2020. The study deals with pollution data during these phases with the help of ground station-based pollution data as well as available satellite data. Since ground data is available at limited stations, an Inverse Distance Weighted (IDW) interpolation technique is used for the generation of phase-wise pollution maps for the whole state during the timeline of 2020. The generated maps show a sharp decline in pollution levels for PM_2.5, PM₁₀, NO₂, NOx and NO, and an increase in the level of SO₂ and Ozone in Phase-I (P1), justifying the effectiveness of the lockdown. Further, for station-wise analysis, a six-phase timeline for the years 2019, 2020 and 2021 has been devised to calculate mean pollution levels as well as pollution level changes. In comparison to 2019 and 2021, the mean and standard deviation in the year 2020 through P1-P4 is the least, emphasising the least spread of pollution level in 2020 due to the lockdown. The analysis is also accompanied by Sentinel-5P TROPOMI satellite data, giving similar observations for NO₂. Regarding correlation, data from ground stations and satellites correlate most for NO₂ and least for SO₂. In addition, empirical relations between pollution data (dependent) and meteorological data (independent) are generated, which reveal that the power to explain the pollution level variability has further increased by using binary lockdown variables along with meteorological data.