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DEVELOPING SEASONAL AMMONIA EMISSION ESTIMATES WITH AN INVERSE MODELING TECHNIQUE
Citation:
Gilliland, A B., R L. Dennis, S J. Roselle, T E. Pierce Jr., AND L. E. Bender. DEVELOPING SEASONAL AMMONIA EMISSION ESTIMATES WITH AN INVERSE MODELING TECHNIQUE. THE SCIENTIFIC WORLD JOURNAL 1((S2)):356-362, (2001).
Impact/Purpose:
To improve the accuracy of emissions and dry deposition algorithms in the Agency's regulatory air quality and multimedia simulation models. This effort requires developing process-oriented algorithms, assembling geographical data, evaluating algorithms against field data, and designing and collaborating on field experiments to collect the data needed to test these algorithms.
Description:
Significant uncertainty exists in magnitude and variability of ammonia (NH3) emissions, which are needed for air quality modeling of aerosols and deposition of nitrogen compounds. Approximately 85% of NH3 emissions are estimated to come from agricultural non-point sources. We suspect a strong seasonal pattern in NH3 emissions because of the volatility of ammonia, which is temperature dependent, and the seasonality of agricultural practices. However, current NH3 emission inventories' lack of intra-annual variability that could significantly affect model predicted concentrations and wet and dry deposition of nitrogen-containing compounds. We apply a Kalman filter inverse modeling technique to deduce monthly NH3 emissions for the eastern United States. Final products of this research will include monthly emissions estimates from each season. Results for January and June 1990 are currently available and are presented here. The U.S. Environmental Protection Agency (USEPA) Community Multiscale Air Quality (CMAQ) model and ammonium (NH4+) wet concentration data from the National Atmospheric Deposition Program (NADP) network are used. The inverse modeling technique estimates the emission adjustments that provide optimal modeled results with respect to wet NH4+ concentrations, observational data error, and emission uncertainty. Our results suggest that NH3 emissions estimates should be decreased by 64% for January 1990 and increased by 25% for June 1990. These results illustrate the strong differences that are anticipated for NH3 emissions.
This paper has been subjected to U.S. Environmental Protection Agency peer review and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.