Impact of the COVID-19 induced lockdown measures on PM2.5 concentration in USA. (1st June 2021)

• Record Type: Journal Article
• Authors: Ghosal, Rahul; Saha, Enakshi
• Is Part Of: Atmospheric environment. Volume 254(2021)
• Publication Date: 2021-06-01
• Subjects: COVID-19 -- Lockdown -- PM2.5 -- Air quality -- Mixed effects model -- Functional regression; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2021.118388
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: In 2020, most countries around the world have observed varying degrees of public lockdown measures to mitigate the transmission of SARS-CoV-2. As an unintended consequence of reduced transportation and industrial activities, air quality has dramatically improved in many major cities around the world. In this paper, we analyze the environmental impact of the lockdown measures on P M 2.5 concentration levels in 48 core-based statistical areas (CBSA) of the United States, during the pre and post-lockdown period of January to June 2020. We model the effect of lockdown on the P M 2.5 concentration in different CBSAs while adjusting for various meteorological factors like temperature, wind-speed, precipitation and snow. Linear mixed effects models and functional regression methods with random intercepts are employed to capture the heterogeneity of the effect across different regions. Our analysis shows there is a statistically significant reduction in levels of P M 2.5 across most of the regions during the lock-down period, although interestingly, this effect is not uniform across all the CBSAs under consideration. Highlights: We examine the impact of the COVID-19 lockdown measures on the PM2.5 concentration level in USA. We also investigate if there is any spatio-temporal heterogeneity in the effect of lockdown on air quality. We use Linear mixed effects models and functional regression methods as primary tools of our analysis. We adjust for local meteorological variables to remove their confounding effect on PM2.5 concentration.