The Nexus Between COVID-19 Factors and Air Pollution

Abstract

Background and objective: There have been significant effects of the current coronavirus-19 (COVID-19) infection outbreak on many facets of everyday life, particularly the environment. Despite the fact that a number of studies have already been published on the topic, an analysis of those studies' findings on COVID-19's effects on environmental pollution is still lacking. The goal of the research is to look into greenhouse gas emissions and air pollution in Bangladesh when COVID-19 is under rigorous lockdown. The specific drivers of the asymmetric relationship between air pollution and COVID-19 are being investigated.

Methods: The nonlinear relationship between carbon dioxide ( $C{O}_2$ ) emissions, fine particulate matter $\left(P{M}_{2.5}\right),$ and COVID-19, as well as its precise components, are also being investigated. To examine the asymmetric link between COVID-19 factors on $C{O}_2$ emissions and $P{M}_{2.5},$ we employed the nonlinear autoregressive distributed lag (NARDL) model. Daily positive cases and daily confirmed death by COVID-19 are considered the factors of COVID-19, with lockdown as a dummy variable.

Results: The bound test confirmed the existence of long-run and short-run relationships between variables. Bangladesh's strict lockdown, enforced in reaction to a surge of COVID-19 cases, reduced air pollution and dangerous gas emissions, mainly $C{O}_2,$ according to the dynamic multipliers graph.

Keywords: Bangladesh; Bound test; Coronavirus-19; fine particulate matter; greenhouse gas emissions; nonlinear autoregressive distributed lag.

Figure 1.

COVID-19, lockdown and the effects on the world. Adapted from “Kumar et al.”

Figure 2.

Total scenario during and before (after) lockdown in Bangladesh. Adapted from “Pavel et al.”

Figure 3.

Carbon emission and $P{M}_{2.5}$ in Bangladesh during the pandemic.

Figure 4.

Contributions of all the study variables.

Figure 5.

Stability check for model 1.

Figure 6.

Stability check for model 2.

Figure 7.

Dynamic multiplier graph COVID-19 determinants on carbon emissions.

Figure 8.

Dynamic multiplier graph COVID-19 determinants on PM 2.5.