Abstract View
Green Heart Louisville: Community-Level Assessment of Exposure to Air Pollution
PRADEEP S. PRATHIBHA, Eben Cross, Richard Strehl, Ray Yeager, Aruni Bhatnagar, Jay R. Turner, Washington University in St. Louis
Abstract Number: 489
Working Group: Aerosol Exposure
Abstract
Green Heart Louisville (greenheartlouisville.com) is a prospective cohort study examining linkages between urban vegetation and cardiovascular health, an association potentially mediated by the effect of vegetation on local air quality. We monitor long-term air quality and greenness in a 12 km2 study area in Louisville, KY, in tandem with a clinical study assessing cardiovascular function and risk factors in area residents. A 2.5 km2 contiguous section serves as the intervention area where mature vegetation is installed beginning in Fall 2019; the remaining area serves as the control.
We monitor high time-resolution ambient particulate pollution through (1) periodic PM2.5 measurements (1min) using PurpleAir and Alphasense OPCN2 particulate monitors—the latter mounted in QuantAQ air quality nodes—at four fixed locations and (2) recurring ultrafine particle (UFP) number concentrations (1s) on a mobile-platform. Log-transformed daily mean PM2.5 mass concentrations from collocated PurpleAir and OPCN2 sensors are well-correlated (R=0.72, p≪0.001), but the mean of ratios (OPC/PA, 0.19±0.10) of absolute PM2.5 reveals large bias. Contemporaneous PM2.5 from QuantAQ nodes are highly correlated (0.77<r<0.83) at locations with varying proximities to an interstate highway, an international airport, and commercial/industrial facilities.
Mobile-platform UFP measurements show strong dependence on wind direction: for winds parallel to an interstate highway with a 10ft sound wall, near-road (10-50m) concentrations remain at background levels during rush hour traffic conditions; under crosswinds, however, 3-5-fold elevated concentrations extend 750m downwind of the sound wall even at night. These measurements show that air pollutants have distinct and persistent spatiotemporal patterns within the study domain. Future work will integrate advanced GIS techniques like hyperlocal landuse regression models to estimate residential-level exposure to air pollution before and after planting to inform the clinical study.