Kyan Kuo Shlipak

Emissions from on-road vehicles are a pervasive source of air pollution, contributing to inequitable pollutant exposure and adverse health outcomes. Numerical air quality models are used to characterize traffic-related pollutant concentrations and impacts. However, model outputs rely on underlying emissions inventories whose formulations can differ substantially. Here, we leverage the widely used Intervention Model for Air Pollution (InMAP) to examine how four different on-road emissions inventories used at three different spatial resolutions (1, 1.3, and 4 km) influence simulated fine particulate matter (PM _2.5 ) population exposure, health impacts, and equity outcomes in a freight-heavy urban setting—the Greater Chicago Region. We find that population-weighted exposure and attributable mortality differ up to 72% and 69% across inventories, compared to only 14%–17% across resolutions. While all inventories result in higher estimated PM _2.5 exposure and mortality among Black, Hispanic, and Asian populations compared to non-Hispanic White populations, the magnitude and relative ordering of disparities across racial groups vary with both inventory choice and spatial resolution. Furthermore, we demonstrate that characterizing exposure inequities using emissions colocation can yield substantially different disparity estimates (up to 20%) from pollutant concentration-based estimates due to atmospheric transport and chemistry— a divergence that likely represents a lower bound given InMAP’s reduced-complexity parameterizations. This work builds on recent studies showing that model resolution influences air quality, health, and equity assessments by demonstrating that on-road emissions inventory choice introduces even greater differences than spatial resolution.

Ambient fine particulate matter pollution (PM2.5) is the leading global environmental risk factor for human health. This study leverages three years (April 2022-March 2025) of continuous, in situ ambient PM2.5 and black carbon (BC) measurements from the Multi-Angle Imager for Aerosols (MAIA) investigation’s surface monitoring network to characterize particulate air pollution in Addis Ababa, Ethiopia. Continuous PM2.5 data were collected from 10 PurpleAir sensors deployed across Addis Ababa and calibrated using collocated Beta Attenuation Monitors at two contrasting U.S. Embassy sites (Central and Jacros). Continuous BC data were collected with AethLabs MA350 microAethalometers operating at these two sites. During the study period, the average PM2.5 concentration across all 10 sites was 30 μg/m3, and the average BC concentrations were 3.7 μg/m3 and 7.7 μg/m3 at Central and Jacros, respectively. Both PM2.5 and BC concentrations exhibited strong diurnal and seasonal variation, with peaks during morning traffic and nighttime inversion, especially during the wet season. PM2.5 levels were highest at sites west of the city center and exceeded the WHO guideline for daily PM2.5 of 15 μg/m3 on 99% of days. The Aethalometer Model for source apportionment estimated that, on average, 94% of total BC in Addis Ababa was attributable to fossil fuel combustion, while 6% was associated with biomass burning. Directional and trajectory analyses indicate distinct local and regional sources of biomass and fossil fuel BC, including potential long-range transport of shipping emissions from the Gulf of Aden. This study presents, to our knowledge, the first long-term, multisite characterization of continuous PM2.5 and source-apportioned BC in Ethiopia. Findings from this study establish a pollution baseline for this MAIA target area ahead of the satellite’s launch, improving understanding of particulate pollution levels and sources and supporting future assessment of air quality trends in this region.

Residential wood combustion (RWC) is a primary heating fuel in just 2% of US homes. However, the 2023 release of the US Environmental Protection Agency’s National Emissions Inventory (NEI) found that RWC contributes ~28% of total wintertime fine particulate matter (PM _2.5 ) emissions, suggesting that ambient PM _2.5 concentrations could be substantially reduced if RWC were curtailed. Despite its contribution to PM _2.5 emissions, an assessment of the air quality, health, and distributional impacts of RWC using the updated NEI has not been performed. Here, we use a high-resolution (4 kilometers) air quality model and the updated NEI to evaluate wintertime RWC impacts over the contiguous United States. We find that RWC contributes 2.43 micrograms per cubic meter (21.9%) of winter population-weighted mean PM _2.5 concentrations, leading to ~8600 (confidence interval: 6500 to 9600) premature deaths annually. Moreover, nonwhite communities are disproportionately affected by RWC-related PM _2.5 and associated mortality, especially in urban areas. We suggest that policies targeting RWC could substantially reduce air pollution, improve health, and address distributional disparities.

Outdoor air pollution poses a major global health risk, yet monitoring remains insufficient, especially in regions with limited infrastructure. Solar-powered monitors could allow for increased coverage in regions lacking robust connectivity. However, reliable sample collection can be challenging with these systems due to extreme temperatures and insufficient solar energy. Proper planning can help overcome these challenges. Air Sampler Solar and Thermal Optimization for Reliable Monitoring (Air-STORM) is an open-source tool that uses meteorological and solar radiation data to identify temperature and solar charging risks for air pollution monitors based on the target deployment area. The model was validated experimentally, and its utility was demonstrated through illustrative case studies. Air-STORM simulations can be customized for specific locations, seasons, and monitor configurations. This capability enables the early detection of potential sampling risks and provides opportunities to optimize monitor design, proactively mitigate temperature and power failures, and increase the likelihood of successful sample collection. Ultimately, improving sampling success will help increase the availability of high-quality outdoor air pollution data necessary to reduce global air pollution exposure.