Fine particulate matter (PM2.5), with a diameter of 2.5 micrometers or smaller, presents a significant health risk due to its ability to penetrate deeply into the lungs and enter the bloodstream. Conventional exposure assessments often overlook critical factors such as individual movement patterns and spatial variability in pollution, leading to less accurate exposure estimates and masking disparities in vulnerable populations. This study introduces an innovative spatial-temporal agent-based modeling (ABM) approach to capture detailed exposure dynamics within urban airsheds, using the Pleasant Run Airshed in Indianapolis, IN, as a case study. By integrating data from 23 PM2.5 sensors, meteorological variables, and land use data, we modeled PM2.5 concentrations over 50 weeks and simulated exposure for 10,000 virtual agents grouped by susceptibility, reflecting varying levels of health vulnerability. Our results reveal marked exposure disparities across sociodemographic groups, with high-susceptibility agents experiencing significantly greater health impacts. The spatial analysis identifies high-exposure zones near industrial areas and transportation corridors, underscoring the urgent need for targeted environmental justice interventions. This study demonstrates ABM’s potential to capture spatial-temporal exposure variability and illuminate inequities in pollutant exposure, offering critical insights for public health policy to reduce environmental health risks. Future research should explore combining ABM with multi-pollutant analysis to comprehensively address complex urban air quality challenges and promote equitable health outcomes.

This study introduces the results from fitting a Bayesian hierarchical spatiotemporal model to COVID-19 cases and deaths at the county-level in the United States for the year 2020. Two models were created, one for cases and one for deaths, utilizing a scaled Besag, York, Mollié model with Type I spatial-temporal interaction. Each model accounts for 16 social vulnerability variables and 7 environmental measurements as fixed effects. The spatial structure of COVID-19 infections is heavily focused in the southern U.S. and the states of Indiana, Iowa, and New Mexico. The spatial structure of COVID-19 deaths covers less of the same area but also encompasses a cluster in the Northeast. The spatiotemporal trend of the pandemic in the U.S. illustrates a shift out of many of the major metropolitan areas into the U.S. Southeast and Southwest during the summer months and into the upper Midwest beginning in autumn. Analysis of the major social vulnerability predictors of COVID-19 infection and death found that counties with higher percentages of those not having a high school diploma and having minority status to be significant. Age 65 and over was a significant factor in deaths but not in cases. Among the environmental variables, above ground level (AGL) temperature had the strongest effect on relative risk to both cases and deaths. Hot and cold spots of COVID-19 cases and deaths derived from the convolutional spatial effect show that areas with a high probability of above average relative risk have significantly higher SVI composite scores. Hot and cold spot analysis utilizing the spatiotemporal interaction term exemplifies a more complex relationship between social vulnerability, environmental measurements, and cases/deaths.