Environmental Science and Engineering Seminar

Advancements in computational efficiency and accessibility have invigorated a renaissance within air quality modeling. After decades of dutiful observational data collection, modelers now have access to robust records of chemical and meteorological information for use in data-driven predictive algorithms. This has enabled scientists to draw multi-dimensionally informed conclusions regarding future air quality in the face of rapidly changing climate. Computational hardware advancements also offer a new avenue to improve the operational efficiency of full-scale air quality models. In this talk, I will discuss my group's recent efforts to leverage the abovesaid tools in the support of determining multiscale drivers of long-term air quality trends and improving process-specific computational efficiency.