CAREER: Ground-truthing Ozone and Particulate Matter Sensitivities to Emissions Trends

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The research will consider recent abatement efforts in the United States as a real-world experiment in emissions perturbations, developing innovative techniques to ground-truth pollutant sensitivities to emissions trends. An iterative inverse modeling method with four-dimensional data assimilation will be extended and applied to quantify long-term emissions trends and to evaluate ozone and particulate matter (PM) responsiveness to those trends. The inverse modeling will identify leading contributors to uncertainty in sensitivities and investigate potential adjustments to improve the accuracy of photochemical modeling. Scientific investigations and environmental decision-making rely on environmental models to accurately simulate not only pollutant concentrations but also their sensitivities to emissions. Simulating sensitivities is especially challenging for air pollutants such as ozone and PM that exhibit nonlinear and highly variable responses to multiple precursor emissions. Whereas models are routinely evaluated against observations for pollutant concentrations, much less is known about the accuracy of pollutant sensitivities that cannot be directly observed.Broader impacts will include an education plan that will engage a significant number of fifth-grade students in hands-on, inquiry-based learning about meteorology and air pollution. Students will develop hypotheses and conduct hands-on measurements to investigate how weather and pollutant conditions vary on their school campuses. Lesson plans will be developed and implemented at a pilot school and then used to train teachers from across the Houston region to teach scientific environmental inquiry at their own campuses. The principal investigator will also engage undergraduate and graduate students in scientific learning and research through vertically-integrated research experiences and the development of new curriculum and colloquia.