Measuring the Impact of COVID-19 Lockdowns on Air Quality Using Crowdsourced PurpleAir Sensors

Abstract:

To hinder transmission of COVID-19, state and local governments have enacted stay-at-home policies and supported other social distancing efforts. These policies and efforts have altered human transportation and behavioral patterns and produced changes in air quality. Satellite observations have shown record low aerosol optical thickness over India and reductions in NOx column concentrations over China and northern Italy in connection to COVID-19 lockdown measures. We used crowdsourced PurpleAir sensors to detect the changes in air quality following the implementation of stay-at-home policies. PurpleAir is a crowdsourced air pollution monitoring network made of low-cost air quality sensors. Their plug-and-play design lowers the barrier for entry for interested citizen scientists while maintaining a good correlation to EPA standards, thus improving the spatial and temporal resolution of air quality data in urban areas. We look at crowdsourced PurpleAir sensors in urban areas to analyze the change in particulate matter (PM2.5) concentrations related to COVID-19 and related social distancing efforts. Preliminary results for the city of Los Angeles, California show a decrease in PM2.5 concentrations and diurnal variability between late February to early May when compared to previous years. We will extend this analysis to four other US cities (New York City, Pittsburg, Durham, and Salt Lake City) chosen for the density of sensors, and correlate the changes to cell phone mobility data from SafeGraph.