Developing seasonal ammonia emission estimates with an inverse modeling technique

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Air Quality Modeling

Background:

• Ammonia (NH3) emissions are crucial for air quality modeling but current inventories lack seasonal variability.
• Agricultural nonpoint sources contribute approximately 85% of NH3 emissions, influencing aerosol formation and nitrogen deposition.
• Annually averaged NH3 emissions can significantly impact model predictions of nitrogen compound concentrations and deposition.

Purpose of the Study:

• To deduce monthly NH3 emissions for the eastern U.S. using a Kalman filter inverse modeling technique.
• To address the lack of intra-annual variability in current NH3 emission inventories.
• To provide monthly emission estimates for each season to improve air quality modeling.

Main Methods:

• Application of a Kalman filter inverse modeling technique.
• Utilizing the U.S. Environmental Protection Agency (USEPA) Community Multiscale Air Quality (CMAQ) model.
• Incorporating ammonium (NH4+) wet concentration data from the National Atmospheric Deposition Program (NADP) network.

Main Results:

• Estimated NH3 emissions decreased by 64% for January 1990.
• Estimated NH3 emissions increased by 25% for June 1990.
• Demonstrated strong seasonal differences in NH3 emissions.

Conclusions:

• Monthly NH3 emission estimates are essential for accurate air quality modeling.
• Inverse modeling effectively adjusts emission estimates based on observational data.
• The findings highlight the need to incorporate seasonal variability into NH3 emission inventories.