Defense Presentation Title: Spatial Predictive Modeling of Particulate Matter Related to Health Effects

Advisor: Dr. Jennifer Richmond-Bryant, faculty fellow and associate professor in the Department of Forestry and Environmental Resources

Abstract: Epidemiological studies have provided strong evidence that exposure to PM 2.5 , including Diesel Particulate Matter (DPM), is associated with adverse cardiovascular and respiratory health effects. Geospatial modeling incorporating transport models with surface data provides realistic, complex prediction of pollutant concentrations while reducing exposure measurement errors. First, we studied spatial associations between COVID-19 mortality and long-term past exposure DPM. Low-cost air pollutant sensors have been widely used as model inputs, but they are well- known to exhibit bias due to relative humidity and temperature. We developed bias correction models for use in the warm humid climate zones of the U.S. Last, we predicted spatiotemporal variation in PM 2.5 concentrations to inform potential health risks due to exposure within a rural community with an open burn-open detonation source.