Data Science and Environmental Systems: Applications of Deterministic Models, Optimization, and Machine Learning to Address Multi-scale Air Quality Challenges
Prof. Cesunica Ivey, Department of Chemical and Environmental Engineering, UCRGlobally, human exposure to air pollution is a known risk factor for increased morbidity and mortality, and its chemical composition can vary significantly by region and season. Variabilities are largely driven by topography, meteorology, land cover, and human activities. State-of-the-science air quality modeling systems, such as the U.S. EPA’s Community Multiscale Air Quality (CMAQ) model, parameterize or directly resolve many important land-atmosphere interactions. CMAQ’s
direct sensitivity tool enables the investigation of the model response to emissions or boundary conditions. Further, optimization and predictive modeling are useful for bias correction of deterministic environment models and identifying natural or anomalous patterns from long-term observation records, respectively. I will discuss applications of these approaches to investigate regional source variability over the continental U.S. and troubleshoot local air pollution challenges in Salt Lake City, Utah. I will also discuss ongoing convergence research that addresses air quality and exposure disparities in inland Southern California.