CAREER: Leveraging Complex Variables to Refine Estimates of Air Pollution Emissions and their Impacts under Uncertainty

Drexel University, Philadelphia PA

Investigators

Abstract

Air pollution is one of the top ten health risks for Americans. We need to understand which actions result in the most pollution to effectively reduce these risks. This CAREER project will develop a new computational approach to understanding these relationships using satellite data. This technique holds great promise to provide results more rapidly, efficiently, and economically than existing techniques. The public will be able to access these estimates through an interactive website at science museums and in urban classrooms. The second aim of this project is to use these computer-based tools to improve estimates of where air pollution moves to better identify sources. Finally, this work aims to develop improved air pollution forecasts across the United States. Successful completion of this research will benefit the public by providing tools to identify pollution sources to help clean the air. The public will also benefit from rapid and accurate forecasts and warnings of pollution to protect populations vulnerable to the health effects of air pollution. The researcher will engage with regulators and other stakeholders to improve forecasts and partner with schools to teach students about the science of air pollution. Together, these efforts will increase the scientific literacy of the Nation. The goal of this CAREER project is to augment GEOS-Chem and the Community Multiscale Air Quality model to efficiently elucidate the relationship between pollutant emissions and atmospheric concentrations. By augmenting two of the most widely used atmospheric chemical transport models, the results will be available to environmental decisionmakers for use in designing emission control strategies. Satellite-based atmospheric composition observations will be assimilated with a novel ensemble-based four-dimensional variational framework. Observations of ammonia from the Cross-track Infrared Sensor will be used to refine the ammonia emissions estimates to allow comparison of this novel approach to existing techniques that have previously been used to refine ammonia emissions. The utility of the assimilation framework for improving air quality forecasting will be evaluated for particulate matter and ozone for which observations will be produced in approximately real-time from NASA’s geo-stationary satellite-based Tropospheric Emissions: Monitoring of Pollution (TEMPO) sensor. Successful completion of this research will benefit the scientific community long-term through the development of an easily extensible approach to sensitivity analysis in two widely used atmospheric chemistry models. As such, it has great potential to provide faster and more accurate predictions than presently used techniques using satellite-based observations of atmospheric composition. These results will provide tools for policy decision makers who are tasked to identify emission strategies to meet the National Ambient Air Quality Standards (NAAQS). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.