UC Merced

Accurate characterization of fine particulate matter (PM2.5) exposure remains a critical challenge in air quality science, particularly in regions with complex emission sources and limited regulatory monitoring coverage. Traditional regulatory monitoring networks may fail to capture local variability due to their limited number and distribution of monitors because of their higher cost. This dissertation addresses these limitations by integrating information from community-based low-cost air quality monitoring, advanced spatiotemporal statistical modeling, considering socioeconomic status demographics, and global fusion model evaluation to improve understanding of PM2.5 exposure patterns in California’s San Joaquin Valley. Chapter 2 evaluates a dense network of low-cost air quality sensors deployed across Fresno County by comparing observations from community monitors with regulatory monitors. Using spatial interpolation techniques, this analysis identifies periods of elevated PM2.5 concentrations that were not detected by nearby regulatory monitors, demonstrating that low-cost sensors can capture fine-scale variability in air pollution. Chapter 3 applies Bayesian hierarchical spatiotemporal models to examine the relationship between PM2.5 concentrations and socioeconomic and demographic characteristics across the San Joaquin Valley from 2021 to 2023. Results indicate that higher PM2.5 levels are disproportionately concentrated in census tracts with greater socioeconomic disadvantage and higher proportions of residents of color. Comparisons with communities designated under California’s Assembly Bill 617 reveal substantial overlap with identified pollution hotspots, while also identifying additional high-burden areas outside current boundaries used to define high-risk communities. These findings underscore the importance of high-resolution monitoring and modeling approaches for informing equitable air quality management and environmental justice policy. Chapter 4 investigates how low-cost sensor data compares to exposure modeling frameworks, influences estimates of PM2.5 concentrations, and the resulting Air Quality Index (AQI) across California. By comparing model outputs with community monitoring data, this study demonstrates that low-cost sensors improve the spatial representation of pollution. These improvements have important implications for public health communication, as more accurate AQI estimates can better inform individual and community-level decisions regarding air quality exposure. Together, these three studies demonstrate that community-based monitoring networks can serve as both a scientific tool and resource for impacted residents, enhancing exposure assessment, improving model performance, and describing environmental inequities.