A comprehensive study of the COVID-19 impact on PM levels over the contiguous United States: A deep learning approach.

We investigate the impact of the COVID-19 outbreak on PM levels in eleven urban environments across the United States: Washington DC, New York, Boston, Chicago, Los Angeles, Houston, Dallas, Philadelphia, Detroit, Phoenix, and Seattle. We estimate daily PM levels over the contiguous U.S. in March-May 2019 and 2020, and leveraging a deep convolutional neural network, we find a correlation coefficient, an index of agreement, a mean absolute bias, and a root mean square error of 0.90 (0.90), 0.95 (0.95), 1.34 (1.24) μg/m, and 2.04 (1.87) μg/m, respectively. Results from Google Community Mobility Reports and estimated PM concentrations show a greater reduction of PM in regions with larger decreases in human mobility and those in which individuals remain in their residential areas longer. The relationship between vehicular PM (i.e., the ratio of vehicular PM to other sources of PM) emissions and PM reductions ( = 0.77) in various regions indicates that regions with higher emissions of vehicular PM generally experience greater decreases in PM. While most of the urban environments ⸺ Washington DC, New York, Boston, Chicago, Los Angeles, Houston, Dallas, Philadelphia, Detroit, and Seattle ⸺ show a decrease in PM levels by 21.1%, 20.7%, 18.5%, 8.05%, 3.29%, 3.63%, 6.71%, 4.82%, 13.5%, and 7.73%, respectively, between March-May of 2020 and 2019, Phoenix shows a 5.5% increase during the same period. Similar to their PM reductions, Washington DC, New York, and Boston, compared to other cities, exhibit the highest reductions in human mobility and the highest vehicular PM emissions, highlighting the great impact of human activity on PM changes in eleven regions. Moreover, compared to changes in meteorological factors, changes in pollutant concentrations, including those of black carbon, organic carbon, SO, SO, and especially NO, appear to have had a significantly greater impact on PM changes during the study period.