Laura Buckley

Nitrogen dioxide (NO ₂ ) affects a range of population health outcomes. However, comprehensive assessment of exposure patterns and associated health burdens has often been constrained by limited geographic coverage of air monitoring networks, particularly in low- and middle-income countries. To calculate the health impacts of NO ₂ exposure in Mexico using multiple newly available models of ground-level concentrations, assessing how the magnitude, geographic distribution, and sociodemographic distribution of health risks differ across alternative datasets. We analyzed exposures and health risks using two globally modeled ground-level NO ₂ datasets, and additionally used TROPOMI tropospheric NO ₂ column densities to compare spatial patterns across datasets. For both ground-level data sets, we investigated differences in population-weighted exposures among various sociodemographic and geographic subgroups, examined spatial variations of NO ₂ concentrations across Mexico, and calculated health impacts attributable to NO ₂ . Across Mexico, we estimated between 24 000 (95% CI: 12 000–36 000) and 33 000 (95% CI: 17 000–50 000) annual premature deaths attributable to NO ₂ exposure among adults aged 25 years and older, depending on the exposure dataset. In both models, most of the health burden was concentrated in Mexico City, though the relative contribution of each state varied by dataset. When mortality was modeled at the state level, the proportion of NO ₂ -attributable premature deaths occurring in Mexico City and the State of Mexico—together comprising most of the metropolitan area—ranged from 52% to 81%, depending on the exposure dataset. Our analysis demonstrates substantial NO ₂ -related health burdens across Mexico, including both urban and rural areas. We highlight the variation in results associated with exposure modeling, particularly for subnational and local analyses. These results indicate the need to select exposure datasets that are fit for the purpose of the research questions and test the sensitivity of health impact estimates to dataset choice.

Background: Vehicle electrification is a key component of many climate action plans, however, its benefits and impacts vary by implementation location and strategy. Given limited governmental resources, understanding how targeting emission reductions in specific sources and regions affects overall outcomes is important for effective planning. Objective: This study examines how place-specific emission reductions in different vehicle classes across regions of the Boston metropolitan area influence pollutant exposures, health benefits, and related inequities. Methods: Using the Community Multiscale Air Quality model, we analyzed PM2.5, NO2, and O3 concentration sensitivities to on-road vehicle emissions at 1.33 km x 1.33 km resolution. We integrated the air pollutant estimates with area-level health data to assess the benefits and impacts of changing on-road vehicle emissions from five vehicle classes (light-duty autos; light-duty, medium-duty, and heavy-duty trucks; and buses) across six inner core and suburban source regions within the metropolitan area. We modeled emission reduction scenarios to evaluate effects on all-cause mortality, adult asthma emergency department visits, and health inequities among racial and ethnic groups. Results: Our analysis revealed tradeoffs between total health benefits and reductions in health inequities. Emission reductions from larger suburban fleets led to greater overall health benefits but primarily benefited non-Hispanic White communities, which, on average, had lower exposure and health burdens at baseline. Targeting heavy-duty trucks in inner core regions maximized health benefits and reduced inequities when considered per vehicle. NO2 was identified as the primary driver of health impacts and inequities. Discussion: This study provides policymakers with insights for assessing climate action plans and vehicle electrification policies. We highlight the importance of considering multiple pollutants and using high-resolution health data. This framework helps evaluate strategies balancing overall health benefits with reductions in health inequities, offering valuable guidance for resource-constrained environments.