Insights into the impact of COVID-19 lockdowns on USEPA PMF-5 derived PM2.5 source contributions at Bhopal, India

Fine particulate matter (PM_2.5) is one of the major atmospheric components that is responsible for poor air quality and adverse health and climate effects. An identification of both primary emission sources as well as secondary formation mechanisms of PM_2.5 is important to develop effective and efficient strategies to control and mitigate these adverse effects.

The COVID-19 pandemic had a significant impact on air quality across the globe through reduction in source emissions. This study examines the impact of the lockdown measures on PM_2.5 and its chemical composition in Bhopal, India by comparisons with pre-lockdown period. Positive Matrix Factorization (PMF) (Paatero and Tapper, 1994) is the most widely used approach for factor analysis-based source apportionment studies. In this study the EPA PMF program version 5.0, was used to solve the PMF model. A comprehensive suite of instruments was used to measure the 24-hour integrated PM_2.5 mass and its chemical composition collected onto various filter substrates every other day for two years (2019-2020) at Bhopal. This period coincides with the pre-lockdown, lockdown, and post-lockdown phases in India. The mass concentrations during the study period ranged between 54.7 µg m^-3 during pre-lockdown and 45.1 µg m^-3 in lockdown phase. PMF5 was applied to a dataset of organic and elemental carbon fractions (OC1, OC2, OC3, OC4, OP, EC1, EC2, EC3), nine major water-soluble inorganic components namely F^-,Cl^-, NO₃^- ,SO₄^-2,Na⁺, NH₄⁺,Mg⁺², K⁺, Ca⁺², and elements (Al, Mg, Ca, Si, P, K, V, Ti, Co, Ni, Cu, As, Cr, Cd, Fe, Ni, Zn, Se, Sb, Ba, Pb) were used in the analysis.

Overall, the combined datasets (2019-2020) approach helped in better model resolution as several zeroes were present in both the loading and score matrices compared to a model run with 2019 data alone. An 8-factor solution was resolved with factors identified as coal and gasoline combustion, biomass burning, secondary sulfate, secondary nitrate, re-suspended crustal dust, diesel emissions, brick kiln emissions and mixed industrial emissions. Further, assessment of the pre-COVID and lockdown scenarios revealed a decreased in the mass contribution of diesel emissions (21.3%), mixed industrial emissions (13.7%), secondary sulfate (10.6%) and secondary nitrate (4.7%) during the lockdown phase compared to the pre-lockdown period at the study site. However, there was no decrease in the biomass burning source contribution due to no curbs on agricultural activities during the lockdown period in India. Overall, this study provides key insight into the source composition and contribution variations due to the reduction of specific anthropogenic source emissions due to COVID-19 lockdowns. Further, it is an added impetus for policymakers to implement targeted strategies and regulations, to reduce local and regional air pollution.