Ammonia (NH3) emissions can exert adverse impacts on air quality and ecosystem well-being. NH3 emission inventories are viewed as highly uncertain. Here we optimize the NH3 emission estimates in the U.S. using an air quality model and NH3 measurements from the IASI satellite instruments. The optimized NH3 emissions are much higher than the National Emission Inventory estimates in April. The optimized NH3 emissions improved model performance when evaluated against independent observation.
Ammonia (NH3) emissions can exert adverse impacts on air quality and ecosystem well-being. NH3...
Review status: a revised version of this preprint was accepted for the journal ACP and is expected to appear here in due course.
High-resolution Hybrid Inversion of IASI Ammonia Columns to Constrain U.S. Ammonia Emissions Using the CMAQ Adjoint Model
Yilin Chen1,Huizhong Shen1,Jennifer Kaiser1,2,Yongtao Hu1,Shannon L. Capps3,Shunliu Zhao4,Amir Hakami4,Jhih-Shyang Shih5,Gertrude K. Pavur1,Matthew D. Turner6,Daven K. Henze7,Jaroslav Resler8,Athanasios Nenes9,10,Sergey L. Napelenok11,Jesse O. Bash11,Kathleen M. Fahey11,Gregory R. Carmichael12,Tianfeng Chai13,Lieven Clarisse14,Pierre-François Coheur14,Martin Van Damme14,and Armistead G. Russell1Yilin Chen et al.Yilin Chen1,Huizhong Shen1,Jennifer Kaiser1,2,Yongtao Hu1,Shannon L. Capps3,Shunliu Zhao4,Amir Hakami4,Jhih-Shyang Shih5,Gertrude K. Pavur1,Matthew D. Turner6,Daven K. Henze7,Jaroslav Resler8,Athanasios Nenes9,10,Sergey L. Napelenok11,Jesse O. Bash11,Kathleen M. Fahey11,Gregory R. Carmichael12,Tianfeng Chai13,Lieven Clarisse14,Pierre-François Coheur14,Martin Van Damme14,and Armistead G. Russell1
1School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
2School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
3Department of Civil, Architectural, and Environmental Engineering, Drexel University, Philadelphia, Pennsylvania 19104, United States
4Department of Civil and Environmental Engineering, Carleton University, Ottawa, Ontario K1S5B6, Canada
5Resources for the Future, Washington, D.C. 20036, USA
6SAIC, Stennis Space Center, MS 39529, USA
7Mechanical Engineering Department, University of Colorado, Boulder, CO 80309, USA
8Institute of Computer Science of the Czech Academy of Sciences, Prague, 182 07, Czech Republic
9Institute for Chemical Engineering Sciences, Foundation for Research and Technology Hellas, Patras, GR-26504, Greece
10School of Architecture, Civil & Environmental Engineering, Ecole polytechnique fédérale de Lausanne, CH-1015, Lausanne, Switzerland
11Atmospheric & Environmental Systems Modeling Division, U.S. EPA, Research Triangle Park, NC 27711, USA
12Department of Chemical and Biochemical Engineering, University of Iowa, Iowa City, IA 52242, USA
13NOAA Air Resources Laboratory (ARL), Cooperative Institute for Satellites Earth System Studies (CISESS), University of Maryland, College Park, MD 20740, USA
14Université libre de Bruxelles (ULB), Spectroscopy, Quantum Chemistry and Atmospheric Remote Sensing (SQUARES), Brussels, Belgium
1School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
2School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
3Department of Civil, Architectural, and Environmental Engineering, Drexel University, Philadelphia, Pennsylvania 19104, United States
4Department of Civil and Environmental Engineering, Carleton University, Ottawa, Ontario K1S5B6, Canada
5Resources for the Future, Washington, D.C. 20036, USA
6SAIC, Stennis Space Center, MS 39529, USA
7Mechanical Engineering Department, University of Colorado, Boulder, CO 80309, USA
8Institute of Computer Science of the Czech Academy of Sciences, Prague, 182 07, Czech Republic
9Institute for Chemical Engineering Sciences, Foundation for Research and Technology Hellas, Patras, GR-26504, Greece
10School of Architecture, Civil & Environmental Engineering, Ecole polytechnique fédérale de Lausanne, CH-1015, Lausanne, Switzerland
11Atmospheric & Environmental Systems Modeling Division, U.S. EPA, Research Triangle Park, NC 27711, USA
12Department of Chemical and Biochemical Engineering, University of Iowa, Iowa City, IA 52242, USA
13NOAA Air Resources Laboratory (ARL), Cooperative Institute for Satellites Earth System Studies (CISESS), University of Maryland, College Park, MD 20740, USA
14Université libre de Bruxelles (ULB), Spectroscopy, Quantum Chemistry and Atmospheric Remote Sensing (SQUARES), Brussels, Belgium
Received: 28 May 2020 – Accepted for review: 26 Jun 2020 – Discussion started: 29 Jun 2020
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 national emission inventory for 2011 (2011 NEI) in the U.S. 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 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 32 % in 2011 NEI in April with overestimation in 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 three 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 NEI-based assessments. The higher emission estimates in this study also imply a higher ecological impact of nitrogen deposition originating from NH3 emissions.
Ammonia (NH3) emissions can exert adverse impacts on air quality and ecosystem well-being. NH3 emission inventories are viewed as highly uncertain. Here we optimize the NH3 emission estimates in the U.S. using an air quality model and NH3 measurements from the IASI satellite instruments. The optimized NH3 emissions are much higher than the National Emission Inventory estimates in April. The optimized NH3 emissions improved model performance when evaluated against independent observation.
Ammonia (NH3) emissions can exert adverse impacts on air quality and ecosystem well-being. NH3...
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