title: Developing seasonal ammonia emission estimates with an inverse modeling technique.
abstract: 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 nonpoint sources. We suspect a strong seasonal pattern in NH 3 emissions; however, current NH3 emission inventories lack intra-annual variability. Annually averaged NH 3 emissions 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 U.S. 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 annual average NH 3 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.
authors.affiliation: NOAA Air Resources Laboratory, Research Triangle Park, NC 27709, USA. gilliland.alice@epa.gov
authors.forename: A B
authors.initials: AB
authors.lastname: Gilliland
authors.forename: R L
authors.initials: RL
authors.lastname: Dennis
authors.forename: S J
authors.initials: SJ
authors.lastname: Roselle
authors.forename: T E
authors.initials: TE
authors.lastname: Pierce
authors.forename: L E
authors.initials: LE
authors.lastname: Bender
mesh_terms: D000383:Agriculture / Q000379:methods / Q000706:statistics & numerical data / Q000639:trends; D000393:Air Pollutants / Q000378:metabolism; D000641:Ammonia / Q000378:metabolism*; D000818:Animals; D000829:Animals, Domestic / Q000378:metabolism; D004777:Environment; D004784:Environmental Monitoring / Q000379:methods / Q000706:statistics & numerical data; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D009511:New England; D000644:Quaternary Ammonium Compounds / Q000378:metabolism; D012621:Seasons; D015144:Southeastern United States; D014481:United States; D014484:United States Environmental Protection Agency / Q000706:statistics & numerical data
chemical_list: D000393:Air Pollutants; D000644:Quaternary Ammonium Compounds; D000641:Ammonia