Statistical assessment of spatio-temporal impact of lockdown on air pollution using different modelling approaches in India

TL;DR

This paper evaluates the impact of COVID-19 lockdowns on air pollution levels in Indian cities using advanced statistical models that account for complex data patterns and seasonal effects.

Contribution

It introduces a comprehensive statistical framework that captures non-linear relationships and seasonal variations in air pollution data during lockdown periods.

Findings

Lockdown significantly reduced PM2.5 levels in metropolitan cities.

Seasonal factors influence air pollution trends during the pandemic.

Advanced models outperform traditional linear approaches in analyzing pollution data.

Abstract

One of the main contributors to air pollution is particulate matter (PMxy), which causes several COVID-19 related diseases such as respiratory problems and cardiovascular disorders. Therefore, the spatial and temporal trend analysis of particulate matter and the mass concentration of all aerosol particles less than 2.5 m in diameter (PM2.5) has become critical to control the risk factors of co-morbidity of a patient. Lockdown plays a significant role in maintaining COVID-19 cases as well as air pollution, including particulate matter. This study aims to analyse the effect of the lockdown on controlling air pollution in metropolitan cities in India through various statistical modelling approaches. Most research articles in the literature assume a linear relationship between responses and covariates and take independent and identically distributed error terms in the model, which may not be appropriate for analysing such air pollution data. In this study, we performed a pattern analysis of daily PM2.5 emissions in various major activity zones during 2019 and 2020. By measuring the lockdown effect, we also considered seasonal influence.