December 10, 2024

Once again, the Air Resources Laboratory (ARL) is well-represented at the 2024 AGU annual meeting happening this week in Baltimore, MD. Sixteen ARL scientists are presenting their work with a total of 22 posters and talks. 

Chris Lougher, Praveena Krishnan and Barry Baker took time to give talks at the OAR booth in addition to their AGU presentations. Additionally, Xinrong Ren and Phillip Stratton represented ARL with their work on the Air Resources Car at a Maryland Earth Sciences booth sponsored by the Maryland Chamber of Commerce with other NOAA groups and organizations such as NASA Goddard Space Flight Center, the University of Maryland and several companies. 

Additionally, ARL Director Ariel Stein had the unexpected opportunity to meet with Maryland Lt. Governor Aruna Miller while attending the conference. 

Available Session Links:

• Development of the Urban Greenhouse Gas Emissions Measurement and Monitoring System (Urban-GEMMS)
• Evaluation of Turbulent Mixing in Dispersion Modeling Using Lidar and Tower Measurements
• Multi-Scale Assessment of Wildfire Smoke Impact on Air Quality and Public Health in North America during 2020-2023
• Long-Term (2015-2023) Assessment of Methane Emission Trends in the Baltimore-DC Metropolitan Area from Aerial Measurements
• Constraining the clumped isotope budget of atmospheric methane using firn air measurements
• Development and Evaluation of Vertically Resolved Biogenic Emission Parameterizations for Modeling Across Scales
• Optimization of the CarbonTracker-Lagrange (CT-L) atmospheric inversion model for Washington, DC, and Baltimore, MD, as part of the Urban Greenhouse Gas Emissions Measurement and Monitoring System (Urban-GEMMS)
• Landscape fires, nitrogen emissions, and deposition: Implications for downwind ecosystems in the U.S.
• UrbanNet observations in the Washington DC metro area: Variability of fluxes and turbulence characteristics
• Comparative Analysis of Biogenic VOC Emissions from MEGAN2.1 implemented in HEMCO Driven by Various Meteorological Inputs
• Fire Intensity and spRead forecAst (FIRA): A machine learning based fire spread prediction model for air quality forecasting application
• Use of AEROMMA Aircraft Data to Verify the NOAA’s Unified Forecast System Air Quality Model (UFS-AQM): A Multi-Scenario Comparison
• Improving wildfire smoke predictions by assimilating satellite observations and in-situ PM2.5 measurements with the HYSPLIT model
• Methane Emissions from the Denver-Julesburg and Uinta Basins based on Aircraft Mass Balance Approach