Finely Resolved On‐Road PM_2.5 and Estimated Premature Mortality in Central North Carolina

Abstract

To quantify the on-road PM -related premature mortality at a national scale, previous approaches to estimate concentrations at a 12-km × 12-km or larger grid cell resolution may not fully characterize concentration hotspots that occur near roadways and thus the areas of highest risk. Spatially resolved concentration estimates from on-road emissions to capture these hotspots may improve characterization of the associated risk, but are rarely used for estimating premature mortality. In this study, we compared the on-road PM -related premature mortality in central North Carolina with two different concentration estimation approaches-(i) using the Community Multiscale Air Quality (CMAQ) model to model concentration at a coarser resolution of a 36-km × 36-km grid resolution, and (ii) using a hybrid of a Gaussian dispersion model, CMAQ, and a space-time interpolation technique to provide annual average PM concentrations at a Census-block level (∼105,000 Census blocks). The hybrid modeling approach estimated 24% more on-road PM -related premature mortality than CMAQ. The major difference is from the primary on-road PM where the hybrid approach estimated 2.5 times more primary on-road PM -related premature mortality than CMAQ due to predicted exposure hotspots near roadways that coincide with high population areas. The results show that 72% of primary on-road PM premature mortality occurs within 1,000 m from roadways where 50% of the total population resides, highlighting the importance to characterize near-road primary PM and suggesting that previous studies may have underestimated premature mortality due to PM from traffic-related emissions.

FHIR PIT: an open software application for spatiotemporal integration of clinical data and environmental exposures data

“…Researchers and healthcare practitioners across fields of biomedicine acknowledge the tremendous impact that environmental exposures have on health and disease. For example, airborne pollutant exposures have been linked to diseases as diverse as asthma [1][2][3][4][5][6], diabetes [7][8][9], cardiovascular disease [10], dementia [11], mental health disorders [12], obesity [13], liver disease [14], and premature mortality [15]. Yet, informatics tools to study the interaction between environmental exposures and health outcomes at the level of the individual are largely non-existent.…”

“…Yet, informatics tools to study the interaction between environmental exposures and health outcomes at the level of the individual are largely non-existent. For instance, the fields of epidemiology and environmental health focus primarily on population-based correlations between trends in spatiotemporal exposures and populationlevel health outcomes [15]. Longitudinal clinical studies likewise are limited in their ability to collect subject-level data on environmental exposures, typically relying on survey-based self-report [5] or expensive personal monitors [6].…”

FHIR PIT: an open software application for spatiotemporal integration of clinical data and environmental exposures data

“…Researchers and healthcare practitioners across fields of biomedicine acknowledge the tremendous impact that environmental exposures have on health and disease. For example, airborne pollutant exposures have been linked to diseases as diverse as asthma [1-6], diabetes [7][8][9], cardiovascular disease [10], dementia [11], mental health disorders [12], obesity [13], liver disease [14], and premature mortality [15]. Yet, informatics tools to study the interaction between environmental exposures and health outcomes at the level of the individual are largely non-existent.…”

“…Yet, informatics tools to study the interaction between environmental exposures and health outcomes at the level of the individual are largely non-existent. For instance, the fields of epidemiology and environmental health focus primarily on population-based correlations between trends in spatiotemporal exposures and population-level health outcomes [15]. Longitudinal clinical studies likewise are limited in their ability to collect subject-level data on environmental exposures, typically relying on survey-based self-report [5] or expensive personal monitors [6].…”

FHIR PIT: an open software application for spatiotemporal integration of clinical data and environmental exposures data

“…Yet, informatics tools to study the interaction between environmental exposures and health outcomes at the level of the individual are largely non-existent. For instance, the fields of epidemiology and environmental health focus primarily on population-based correlations between trends in spatiotemporal exposures and populationlevel health outcomes [15]. Longitudinal clinical studies likewise are limited in their ability to collect subject-level data on environmental exposures, typically relying on survey-based selfreport [5] or expensive personal monitors [6].…”