High-resolution hybrid inversion of IASI ammonia columns to constrain US ammonia emissions using the CMAQ adjoint model

Abstract

Abstract. Ammonia (NH3) emissions have large impacts on air quality and nitrogen deposition, influencing human health and the well-being of sensitive ecosystems. Large uncertainties exist in the “bottom-up” NH3 emission inventories due to limited source information and a historical lack of measurements, hindering the assessment of NH3-related environmental impacts. The increasing capability of satellites to measure NH3 abundance and the development of modeling tools enable us to better constrain NH3 emission estimates at high spatial resolution. In this study, we constrain the NH3 emission estimates from the widely used 2011 National Emissions Inventory (2011 NEI) in the US using Infrared Atmospheric Sounding Interferometer NH3 column density measurements (IASI-NH3) gridded at a 36 km by 36 km horizontal resolution. With a hybrid inverse modeling approach, we use the Community Multiscale Air Quality Modeling System (CMAQ) and its multiphase adjoint model to optimize NH3 emission estimates in April, July, and October. Our optimized emission estimates suggest that the total NH3 emissions are biased low by 26 % in 2011 NEI in April with overestimation in the Midwest and underestimation in the Southern States. In July and October, the estimates from NEI agree well with the optimized emission estimates, despite a low bias in hotspot regions. Evaluation of the inversion performance using independent observations shows reduced underestimation in simulated ambient NH3 concentration in all 3 months and reduced underestimation in NH4+ wet deposition in April. Implementing the optimized NH3 emission estimates improves the model performance in simulating PM2.5 concentration in the Midwest in April. The model results suggest that the estimated contribution of ammonium nitrate would be biased high in a priori NEI-based assessments. The higher emission estimates in this study also imply a higher ecological impact of nitrogen deposition originating from NH3 emissions.

UK Ammonia Emissions Estimated With Satellite Observations and GEOS‐Chem

“…These products have also seen extensive use in characterizing NH 3 emissions. This includes detecting global and regional NH 3 emission hotspots (Cady‐Pereira et al., 2017; Clarisse et al., 2019; Dammers et al., 2019; Shephard et al., 2020; Van Damme et al., 2018), constraining NH 3 emissions from biomass burning (Adams et al., 2019; Whitburn, Van Damme, et al., 2016), evaluating regional emission inventories (Chen et al., 2021; Fortems‐Cheiney et al., 2020), identifying underestimated or missing NH 3 sources in widely used global and regional emission inventories and models (Heald et al., 2012; Hickman et al., 2018; Van Damme, Kruit, et al., 2014), and determining long‐term local and regional trends and variability in NH 3 (Hickman et al., 2020; Van Damme, Eriman, et al., 2015; 2021; Vohra, Marais, et al., 2021).…”

The Diel Cycle of NH3 Observed From the FY‐4A Geostationary Interferometric Infrared Sounder (GIIRS)

“…As we have shown in this letter, its 10 overpasses provide an unprecedented opportunity to study atmospheric NH 3 and its evolution throughout the day. The availability of so many overpasses per day could also greatly benefit NH 3 modeling and model assimilation (Chen et al., 2021; Z. Wang et al., 2020). Finally, compared to polar orbits, GIIRS has the advantage of revisiting the same footprint every day, which will be most useful for tracking seasonal variations over particular locations, and for validation and intercomparison.…”

Evaluation of interactive and prescribed agricultural ammonia emissions for simulating atmospheric composition in CAM-chem

“…Due to the scarcity of in situ data, the emissions in these regions remain poorly constrained. Satellite retrievals of NH 3 offer an alternative data source for emission assessment (e.g., Chen et al, 2021); however, since only the gaseous ammonia is observable from satellites, this approach is sensitive to assumptions or model errors in the lifetime and gas-aerosol partitioning of atmospheric NH 3 .…”