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Air Quality Changes under COVID-19 Social Distancing in the United States: Observational Analysis and Modeling Sensitivity Study
Citation:
Kang, D., C. Hogrefe, B. Murphy, V. Isakov, R. Mathur, R. Gilliam, G. Pouliot, B. Henderson, F. Sidi, G. Sarwar, AND T. Spero. Air Quality Changes under COVID-19 Social Distancing in the United States: Observational Analysis and Modeling Sensitivity Study. UK-US Collaboration on Air-Quality Modelling & Exposure Science Virtual Workshop, Virtual, November 16 - 17, 2020.
Impact/Purpose:
The coronavirus disease 2019 (COVID-19) has prompted the national and local governments around the world to take unprecedented measures to restrict human activity such as lockdowns, social distancing, and strict travel bans. The dramatic and varying scales of responses to COVID-19 offer a rare opportunity to assess the impacts of real-world reductions in emissions on local, regional and global air quality and to investigate the relative contributions of different source sector emission perturbations. This study aims to assess the impact of these social events on air quality changes and evaluate how chemistry and transport models, such as CMAQ, respond to this real-world drastic anthropogenic emission changes.
Description:
The rapid spread of coronavirus disease 2019 (COVID-19), declared as a pandemic by the World Health Organization (WHO), prompted the national and local governments around the world to take unprecedented measures to restrict human activity such as lockdowns, social distancing, and strict travel bans. The dramatic and varying scales of responses to COVID-19 offer a rare opportunity to assess the impacts of real-world reductions in emissions on local, regional and global air quality and to investigate the relative contributions of different source sector emission perturbations. In this study, fine particulate matter (PM2.5) and ozone (O3) measured from U.S. EPA’s AIRNOW network before and after the start of social distancing are analyzed to assess the change in air quality during the period of social distancing. The temporal and spatial variations will be analyzed in response to publicly available mobility data collected since the start of social distancing. Another objective of this study is to examine how predictions of various air pollutant concentrations from the Community Multiscale Air Quality (CMAQ) modeling system simulates the estimated reduction of anthropogenic emissions induced as a result of changing activity patterns. Sensitivity studies based on the 2016 modeling platform are performed by incorporating real-time observed mobility data such as Google mobility data and vehicle miles traveled (VMT) data through CMAQ’s Detailed Emission Scaling Isolation and Diagnostic (DESID) module and analyzed to develop an initial estimate of the likely impacts. The strengths and weaknesses of the modeling system in response to quick and dramatic emission changes from different sectors will be identified and presented.