Green Heart Louisville: intra-urban, hyperlocal land-use regression modeling of nitrogen oxides and ozone

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Abstract
Exposure to urban air pollution is linked to increased mortality from cardiopulmonary causes. Urban areas juxtapose large numbers of residences and workplaces with near-road environments, exacerbating traffic-related air pollution (TRAP) exposure. TRAP is the primary source of variability in intraurban air quality, but continuous regulatory monitoring stations lack the spatial resolution to detect fine-scale pollutant patterns that recent studies using long-term, resource-intensive mobile measurements have established as persistent and associated with higher risk of cardiovascular events. This work evaluates a low-cost, fixed-site approach to characterizinglong-term, hyperlocal exposure to oxides of nitrogen (including NO 2 , a common surrogate for TRAP) as part of Green Heart Louisville, a prospective cohort study examining linkages between urban vegetation, local air quality, and cardiovascular health. We used a fixed 60-site network of Ogawa passive samplers in a 12 km 2 section of Louisville, KY, to measure two-week integrated NO 2 , NO x (NO + NO 2 ), and O 3 mixing ratios nominally every two months between May 2018-March 2021. Seasonal NO x averages were 2.5-fold higher during winter than in summer, and annual average NO (calculated by difference in NO x and NO 2 ) and NO 2 ranged from 4-21 ppb and 5-12 ppb, respectively. NO increased 3-to-5-fold within 150 m of highways or major arterial roads and 2-to-3-fold near parking lots. While both NO and NO 2 were elevated in near-road environments, the corresponding O 3 was depressed, consistent with titration by NO. We developed land-use regression models for annual average NO, NO 2 , and NO x using parameters of proximity (distance to nearest road type, restaurant, traffic signal), cumulative occurrence (length of roads, number of restaurants and traffic lights, all in buffers of up to 500 m in 50-m increments), and greenness (normalized difference vegetative index (NDVI)). Adjusted spatial variability explained by the models were 70% (p2 , and NO x , respectively. Common predictors were distances to the nearest restaurant and road as well as total length of roads within 350 m. Only one greenness metric was significant: mean NDVI within 50 m was negatively associated (p=0.02) with NO 2 . We plan to use these hyperlocal models to estimate residential-level exposures of the clinical study participants.

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