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.

Fig 1

Spatial map of 2016 annual PM_2.5 model concentrations for (a) CMAQ 12 km x 12 km grid resolution for a subdomain in central North Carolina (b) CMAQ + R-LINE Hybrid, and (d) RAMP Hybrid at census block centroids. Each map includes measurements represented as circles. The gray lines represent the major roads. The gradient bar in figures a, b, and d represents concentration levels in μg/m³. Also shown is one of the RAMP curves (c) used to correct biases from the Hybrid model. The grey dots represent paired observed and modeled daily PM_2.5 concentrations for all of 2016 consisting of the 50 closest AQS measurement stations to the centroid of the CMAQ grid cell G(p). The dashed vertical lines represent the 10 equally divided bins used to stratify all the paired data where each bin includes one decile of all the paired points. The solid black line is the one-to-one line between model and observed. The red dots in each bin identify $\mathit{\lambda }{}_{\mathit{l}}({\tilde{\mathit{x}}}_{\mathit{l}},\mathit{G}(\mathit{p}\left)\right)$ the average of paired observed values within each decile bin. The red dots are linearly interpolated to obtain λ₁(p) corresponding to the hybrid modeled data $\tilde{\mathit{x}}\left(\mathit{p}\right)$ with G(p).

Fig 2. Spatial map of 2016 annual PM_2.5 and NO₂ model concentrations for CMAQ, CMAQ R-LINE Hybrid, and RAMP Hybrid at census block centroids at Boston, Massachusetts and Chicago, Illinois.

The gradient bar represents concentration levels in μg/m³ that range from the smallest 10^th percentile of the 3 three models to the highest 90^th percentile of the three models for the corresponding domain.

Fig 3

Premature mortality difference between RAMP Hybrid and CMAQ attributable to PM_2.5 (left) and NO₂ (right) aggregated by county. The Top Panel Show spatial differences across the continental United States. In the Bottom Panel, the blue bars represent the top 20 counties where RAMP Hybrid shows less premature mortality than CMAQ. The red bars represent the top 20 counties where RAMP Hybrid shows more premature mortality than CMAQ. The lines in each bar correspond to the 95% confidence intervals.

Fig 4. Net difference in premature mortality for PM_2.5 and NO₂ between models with varying resolution vs distance from primary road.

Net premature mortality was aggregated at every 25 m from a primary road. The blue line represents the population aggregated at every 25 m from a primary road and the red line corresponds to the cumulative sum of the population.

Fig 5. Exposure inequity ratio (EIR) at Madera County, CA.

Top panel shows Exposure Inequity Ratio (EIR) at Madera County, CA (highlighted) and surrounding counties for PM_2.5 and NO₂ at County (left), Census Tract (middle), and Census Block Group level (right). Bottom panel shows RAMP Hybrid concentration as circles for PM_2.5 (left) and NO₂ (right) at census block centroids where the size of the census block centroid is proportional to population, as well as the percent of Minority population (middle) at census block centroids where size of census block is proportional to percent Minority at Madera County, CA.

Fig 6

Population-Weighted Exposure using RAMP (top) for PM_2.5 (left) and NO₂ (right) and Exposure Inequity ratio (bottom) aggregated at 10m from major roads across the continental U.S. In the bottom two frames, EIRs has been blurred at distances greater than 2 km from the road to convey that there is high noise in the EIR results.