A hyperlocal hybrid data fusion near-road PM2.5 and NO2 annual risk and environmental justice assessment across the United States

Abstract

Exposure to traffic-related air pollutants (TRAPs) has been associated with numerous adverse health effects. TRAP concentrations are highest meters away from major roads, and disproportionately affect minority (i.e., non-white) populations often considered the most vulnerable to TRAP exposure. To demonstrate an improved assessment of on-road emissions and to quantify exposure inequity in this population, we develop and apply a hybrid data fusion approach that utilizes the combined strength of air quality observations and regional/local scale models to estimate air pollution exposures at census block resolution for the entire U.S. We use the regional photochemical grid model CMAQ (Community Multiscale Air Quality) to predict the spatiotemporal impacts at local/regional scales, and the local scale dispersion model, R-LINE (Research LINE source) to estimate concentrations that capture the sharp TRAP gradients from roads. We further apply the Regionalized Air quality Model Performance (RAMP) Hybrid data fusion technique to consider the model’s nonhomogeneous, nonlinear performance to not only improve exposure estimates, but also achieve significant model performance improvement. With a R2 of 0.51 for PM2.5 and 0.81 for NO2, the RAMP hybrid method improved R2 by ~0.2 for both pollutants (an increase of up to ~70% for PM2.5 and ~31% NO2). Using the RAMP Hybrid method, we estimate 264,516 [95% confidence interval [CI], 223,506–307,577] premature deaths attributable to PM2.5 from all sources, a ~1% overall decrease in CMAQ-estimated premature mortality compared to RAMP Hybrid, despite increases and decreases in some locations. For NO2, RAMP Hybrid estimates 138,550 [69,275–207,826] premature deaths, a ~19% increase (22,576 [11,288 – 33,864]) compared to CMAQ. Finally, using our RAMP hybrid method to estimate exposure inequity across the U.S., we estimate that Minorities within 100 m from major roads are exposed to up to 15% more PM2.5 and up to 35% more NO2 than their White counterparts.

Air Pollution Inequality in the Denver Metroplex and its Relationship to Historical Redlining

“…31−33 National and multicity studies of pollution disparities demonstrate that certain minoritized groups tend to be affected more than others, particularly people identifying as Black and African American, Hispanic or Latino, Asian and Asian American, and American Indian and Alaska Native. 19,34,35 Studies of individual cities demonstrate similar results. 18,36,37 Large-scale studies that report national or multicity averages often discuss the range of values for pollution disparities between the cities as being quite large but lack deeper insight into this phenomenon.…”

“…National and multicity studies of pollution disparities demonstrate that certain minoritized groups tend to be affected more than others, particularly people identifying as Black and African American, Hispanic or Latino, Asian and Asian American, and American Indian and Alaska Native. ,, Studies of individual cities demonstrate similar results. ,, Large-scale studies that report national or multicity averages often discuss the range of values for pollution disparities between the cities as being quite large but lack deeper insight into this phenomenon. Sociology researchers have also identified this disparity in disparities and have attributed it to the settlement patterns and urban development of individual cities, noting that residential segregation could increase or decrease a racial or ethnic group’s proximity to an environmental hazard .…”

2024 United States Elections: Air Pollution, Neurodegeneration, Neuropsychiatric, and Neurodevelopmental Disorders. Who Cares?

“…Bramble et al [ 34 ] showed that higher exposures to multiple air pollutants and cumulative effects are disproportionately impacting historically marginalized communities, i.e., UFPM are higher than average for Black, 15%; Hispanic, 6%; Native American, 8%; and Pacific Islanders 11%. Valencia et al [ 35 ] estimated that minorities within 100 m from major roads are exposed to up to 15% more PM 2.5 and up to 35% more NO 2 than their white counterparts.…”

Capturing Exposure Disparities with Chemical Transport Models: Evaluating the Suitability of Downscaling Using Land Use Regression

“…Previous CTM downscaling approaches have been conducted by running a full CTM simulation and then running local scale dispersion models for a certain sector. 13,31,32 This approach is computationally demanding and requires major meteorological and terrain assumptions, as well as a simplified representation of the relevant chemical processes (i.e., assumptions regarding the conversion of NO x emissions to NO 2 ). These local factors begin to have a large impact for high-resolution modeling.…”