Air quality managers and forecasters need accurate emissions estimates of PM2.5 precursors, such as ammonia (NH3), to better predict and study how these emissions affect human health and air quality. However, current emission inventories are too uncertain to provide reliable estimates of NH3. Observations from the Cross-track Infrared Spectrometer (CrIS) present an opportunity to address this problem and improve NH3 emissions estimates using inversion-based modeling techniques. Moreover, as new CrIS instruments are expected to be launched over the next two decades as part of the JPSS series, designing an infrastructure and methodology to use these observations in air quality modeling and policymaking will provide benefits extended through 2030, and possibly beyond. In the current study, CrIS NH3 observations are used in a finite-difference mass-balance approach to constrain NH3 emissions in the Community Multiscale Air Quality (CMAQ) model. CMAQ is run over the continental United States using 12 km grid spacing for June 2015. A baseline simulation is made with unperturbed NH3 emissions. Then, a second simulation is performed with NH3 emissions perturbed by 20%. The resulting total column concentrations of NH3 are compared to CrIS observations to derive a monthly-mean scaling factor for the a priori NH3 emissions. This scaling factor accounts for the relationship of NH3 concentrations to NH3 emissions in the baseline model run. It is applied to derive updated NH3 emissions, which are then used in a final CMAQ simulation. This finite-difference inversion method has been incorporated into Amazon Web Services, and the resulting emissions data will be made publicly available. Altogether, this project will allow air quality managers and other stakeholders to obtain more accurate NH3 emissions estimates that can be implemented directly into their air quality modeling.
