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

Yilin Chen,Athanasios Nenes,Sergey L Napelenok,Gregory R Carmichael,Jaroslav Resler,Yongtao Hu,Martin Van Damme,Amir Hakami,Gertrude K Pavur,Jesse O Bash,Tianfeng Chai,Jennifer Kaiser,Lieven Clarisse,Shunliu Zhao,Armistead G Russell,Huizhong Shen,Kathleen M Fahey,Shannon L Capps,Jhih‐Shyang Shih ,Martin Turner ,Pierre‐François Coheur ,D K Henze

doi:10.5194/acp-21-2067-2021

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.

Highlights

Ammonia (NH3) emissions play a major role in ambient aerosol formation and reactive nitrogen deposition (Stevens, 2019: Houlton et al, 2013)
Several studies have utilized NH3 column density retrieved from the Infrared Atmospheric Sounding Interferometer (IASI) (Clarisse et al, 2009; Van Damme et al, 2015b) or the Atmospheric Infrared Sounder (AIRS; Warner et al, 2016) as well as the inferred surface mixing ratio of NH3 from the Crosstrack Infrared Sounder (CrIS; Shephard and Cady-Pereira, 2015; Shephard et al, 2020) to characterize the spatiotemporal distribution of NH3
A comparison between IASI-NH3 and airborne measurements indicated an underestimation in California, while the comparison between IASI-NH3 and ground observation from the Ammonia Monitoring Network (AMoN) indicated an overestimation (Van Damme et al, 2015a; National Atmospheric Deposition Program (NADP), 2014)

Summary

Introduction

Ammonia (NH3) emissions play a major role in ambient aerosol formation and reactive nitrogen deposition (Stevens, 2019: Houlton et al, 2013). Several studies have utilized NH3 column density retrieved from the Infrared Atmospheric Sounding Interferometer (IASI) (Clarisse et al, 2009; Van Damme et al, 2015b) or the Atmospheric Infrared Sounder (AIRS; Warner et al, 2016) as well as the inferred surface mixing ratio of NH3 from the Crosstrack Infrared Sounder (CrIS; Shephard and Cady-Pereira, 2015; Shephard et al, 2020) to characterize the spatiotemporal distribution of NH3 These satellite measurements are useful for supplementing emission inventories to identify and quantify underestimated or missing emission hotspots, especially in intensive agricultural zones (Van Damme et al, 2018; Dammers et al, 2019; Clarisse et al, 2019). The NH3 retrievals from satellites are subject to large uncertainties when the signal-to-noise ratio is low, which limits their ability to accurately measure NH3 columns in low-emission areas (Clarisse et al, 2010; Van Damme et al, 2015a)

Methods

Results

Conclusion