The negative health impacts of air pollution are well documented. Not as well-documented, however, is how particulate matter varies at the hyper-local scale, and the role that proximal sources play in influencing neighborhood-scale patterns. We examined PM2.5 variations in one airshed within Indianapolis (Indianapolis, IN, USA) by utilizing data from 25 active PurpleAir (PA) sensors involving citizen scientists who hosted all but one unit (the control), as well as one EPA monitor. PA sensors report live measurements of PM2.5 on a crowd sourced map. After calibrating the data utilizing relative humidity and testing it against a mobile air-quality unit and an EPA monitor, we analyzed PM2.5 with meteorological data, tree canopy coverage, land use, and various census variables. Greater proximal tree canopy coverage was related to lower PM2.5 concentrations, which translates to greater health benefits. A 1% increase in tree canopy at the census tract level, a boundary delineated by the US Census Bureau, results in a ~0.12 µg/m3 decrease in PM2.5, and a 1% increase in “heavy industry” results in a 0.07 µg/m3 increase in PM2.5 concentrations. Although the overall results from these 25 sites are within the annual ranges established by the EPA, they reveal substantial variations that reinforce the value of hyper-local sensing technologies as a powerful surveillance tool.

A substantial reduction in global transport and industrial processes stemming from the novel SARS-CoV-2 coronavirus and subsequent pandemic resulted in sharp declines in emissions, including for NO2. This has implications for human health, given the role that this gas plays in pulmonary disease and the findings that past exposure to air pollutants has been linked to the most adverse outcomes from COVID-19 disease, likely via various co-morbidities. To explore how much COVID-19 shutdown policies impacted urban air quality, we examined ground-based NO2 sensor data from 11 U.S. cities from a two-month window (March–April) during shutdown in 2020, controlling for natural seasonal variability by using average changes in NO2 over the previous five years for these cities. Levels of NO2 and VMT reduction in March and April compared to January 2020 ranged between 11–65% and 11–89%, consistent with a sharp drop in vehicular traffic from shutdown-related travel restrictions. To explore this link closely, we gathered detailed traffic count data in one city—Indianapolis, Indiana—and found a strong correlation (0.90) between traffic counts/classification and vehicle miles travelled, a moderate correlation (0.54) between NO2 and traffic related data, and an average reduction of 1.11 ppb of NO2 linked to vehicular data. This finding indicates that targeted reduction in pollutants like NO2 can be made by manipulating traffic patterns, thus potentially leading to more population-level health resilience in the future.

The air pollutant NO2 is derived largely from transportation sources and is known to cause respiratory disease. A substantial reduction in transport and industrial processes around the globe from the novel SARS-CoV-2 coronavirus and subsequent pandemic resulted in sharp declines in emissions, including for NO2. Additionally, the COVID-19 disease that results from the coronavirus may present in its most severe form in those who have been exposed to high levels of air pollution. To explore these links, we compared ground-based NO2 sensor data from 11 US cities from a two-month window (March-April) over the previous five years versus the same window during 2020 shutdowns. NO2 declined roughly 12-41% in the 11 cities. This decreased coincided with a sharp drop in vehicular traffic from shutdown-related travel restrictions. To explore this link more closely, we gathered more detailed traffic count data in one city, Indianapolis, Indiana, and found a strong correlation between traffic counts/classification and vehicle miles travelled, and a moderate correlation between NO2 and traffic related data. This finding indicates that we can use such analysis in targeting reduction in pollutants like NO2 by examining and manipulating traffic patterns, thus potentially leading to more population-level health resilience in the future.

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