Articles | Volume 22, issue 21
https://doi.org/10.5194/acp-22-14059-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-14059-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Inverse modelling of Chinese NOx emissions using deep learning: integrating in situ observations with a satellite-based chemical reanalysis
Department of Physics, University of Toronto, Toronto, ON, Canada
now at: Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA
Dylan B. A. Jones
Department of Physics, University of Toronto, Toronto, ON, Canada
Kazuyuki Miyazaki
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Kevin W. Bowman
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Zhe Jiang
School of Earth and Space Sciences, University of Science and Technology of China, Hefei, Anhui, China
Xiaokang Chen
School of Earth and Space Sciences, University of Science and Technology of China, Hefei, Anhui, China
Rui Li
School of Earth and Space Sciences, University of Science and Technology of China, Hefei, Anhui, China
Yuxiang Zhang
School of Earth and Space Sciences, University of Science and Technology of China, Hefei, Anhui, China
Kunna Li
Department of Physics, University of Toronto, Toronto, ON, Canada
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Yasin Elshorbany, Jerald R. Ziemke, Sarah Strode, Hervé Petetin, Kazuyuki Miyazaki, Isabelle De Smedt, Kenneth Pickering, Rodrigo J. Seguel, Helen Worden, Tamara Emmerichs, Domenico Taraborrelli, Maria Cazorla, Suvarna Fadnavis, Rebecca R. Buchholz, Benjamin Gaubert, Néstor Y. Rojas, Thiago Nogueira, Thérèse Salameh, and Min Huang
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Zhaojun Tang, Zhe Jiang, Jiaqi Chen, Panpan Yang, and Yanan Shen
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Rui Zhu, Zhaojun Tang, Xiaokang Chen, Xiong Liu, and Zhe Jiang
Geosci. Model Dev., 16, 6337–6354, https://doi.org/10.5194/gmd-16-6337-2023, https://doi.org/10.5194/gmd-16-6337-2023, 2023
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Drew C. Pendergrass, Daniel J. Jacob, Hannah Nesser, Daniel J. Varon, Melissa Sulprizio, Kazuyuki Miyazaki, and Kevin W. Bowman
Geosci. Model Dev., 16, 4793–4810, https://doi.org/10.5194/gmd-16-4793-2023, https://doi.org/10.5194/gmd-16-4793-2023, 2023
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Sachiko Okamoto, Juan Cuesta, Matthias Beekmann, Gaëlle Dufour, Maxim Eremenko, Kazuyuki Miyazaki, Cathy Boonne, Hiroshi Tanimoto, and Hajime Akimoto
Atmos. Chem. Phys., 23, 7399–7423, https://doi.org/10.5194/acp-23-7399-2023, https://doi.org/10.5194/acp-23-7399-2023, 2023
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We present a detailed analysis of the daily evolution of the lowermost tropospheric ozone documented by IASI+GOME2 multispectral satellite observations and that of its precursors from TCR-2 tropospheric chemistry reanalysis. It reveals that the ozone outbreak across Europe in July 2017 was produced during favorable condition for photochemical production of ozone and was associated with multiple sources of ozone precursors: biogenic, anthropogenic, and biomass burning emissions.
Jagat S. H. Bisht, Prabir K. Patra, Masayuki Takigawa, Takashi Sekiya, Yugo Kanaya, Naoko Saitoh, and Kazuyuki Miyazaki
Geosci. Model Dev., 16, 1823–1838, https://doi.org/10.5194/gmd-16-1823-2023, https://doi.org/10.5194/gmd-16-1823-2023, 2023
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Brendan Byrne, David F. Baker, Sourish Basu, Michael Bertolacci, Kevin W. Bowman, Dustin Carroll, Abhishek Chatterjee, Frédéric Chevallier, Philippe Ciais, Noel Cressie, David Crisp, Sean Crowell, Feng Deng, Zhu Deng, Nicholas M. Deutscher, Manvendra K. Dubey, Sha Feng, Omaira E. García, David W. T. Griffith, Benedikt Herkommer, Lei Hu, Andrew R. Jacobson, Rajesh Janardanan, Sujong Jeong, Matthew S. Johnson, Dylan B. A. Jones, Rigel Kivi, Junjie Liu, Zhiqiang Liu, Shamil Maksyutov, John B. Miller, Scot M. Miller, Isamu Morino, Justus Notholt, Tomohiro Oda, Christopher W. O'Dell, Young-Suk Oh, Hirofumi Ohyama, Prabir K. Patra, Hélène Peiro, Christof Petri, Sajeev Philip, David F. Pollard, Benjamin Poulter, Marine Remaud, Andrew Schuh, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Colm Sweeney, Yao Té, Hanqin Tian, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, John R. Worden, Debra Wunch, Yuanzhi Yao, Jeongmin Yun, Andrew Zammit-Mangion, and Ning Zeng
Earth Syst. Sci. Data, 15, 963–1004, https://doi.org/10.5194/essd-15-963-2023, https://doi.org/10.5194/essd-15-963-2023, 2023
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Changes in the carbon stocks of terrestrial ecosystems result in emissions and removals of CO2. These can be driven by anthropogenic activities (e.g., deforestation), natural processes (e.g., fires) or in response to rising CO2 (e.g., CO2 fertilization). This paper describes a dataset of CO2 emissions and removals derived from atmospheric CO2 observations. This pilot dataset informs current capabilities and future developments towards top-down monitoring and verification systems.
Madison J. Shogrin, Vivienne H. Payne, Susan S. Kulawik, Kazuyuki Miyazaki, and Emily V. Fischer
Atmos. Chem. Phys., 23, 2667–2682, https://doi.org/10.5194/acp-23-2667-2023, https://doi.org/10.5194/acp-23-2667-2023, 2023
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We evaluate the spatiotemporal variability of peroxy acyl nitrates (PANs), important photochemical pollutants, over Mexico City using satellite observations. PANs exhibit a seasonal cycle that maximizes in spring. Wildfires contribute to observed interannual variability, and the satellite indicates several areas of frequent outflow. Recent changes in NOx emissions are not accompanied by changes in PANs. This work demonstrates analysis approaches that can be applied to other megacities.
Hongxia Zhu, Rui Li, Shuping Yang, Chun Zhao, Zhe Jiang, and Chen Huang
Atmos. Chem. Phys., 23, 2421–2437, https://doi.org/10.5194/acp-23-2421-2023, https://doi.org/10.5194/acp-23-2421-2023, 2023
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Brendan Byrne, Junjie Liu, Yonghong Yi, Abhishek Chatterjee, Sourish Basu, Rui Cheng, Russell Doughty, Frédéric Chevallier, Kevin W. Bowman, Nicholas C. Parazoo, David Crisp, Xing Li, Jingfeng Xiao, Stephen Sitch, Bertrand Guenet, Feng Deng, Matthew S. Johnson, Sajeev Philip, Patrick C. McGuire, and Charles E. Miller
Biogeosciences, 19, 4779–4799, https://doi.org/10.5194/bg-19-4779-2022, https://doi.org/10.5194/bg-19-4779-2022, 2022
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Plants draw CO2 from the atmosphere during the growing season, while respiration releases CO2 to the atmosphere throughout the year, driving seasonal variations in atmospheric CO2 that can be observed by satellites, such as the Orbiting Carbon Observatory 2 (OCO-2). Using OCO-2 XCO2 data and space-based constraints on plant growth, we show that permafrost-rich northeast Eurasia has a strong seasonal release of CO2 during the autumn, hinting at an unexpectedly large respiration signal from soils.
Helen M. Worden, Gene L. Francis, Susan S. Kulawik, Kevin W. Bowman, Karen Cady-Pereira, Dejian Fu, Jennifer D. Hegarty, Valentin Kantchev, Ming Luo, Vivienne H. Payne, John R. Worden, Róisín Commane, and Kathryn McKain
Atmos. Meas. Tech., 15, 5383–5398, https://doi.org/10.5194/amt-15-5383-2022, https://doi.org/10.5194/amt-15-5383-2022, 2022
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Satellite observations of global carbon monoxide (CO) are essential for understanding atmospheric chemistry and pollution sources. This paper describes a new data product using radiance measurements from the Cross-track Infrared Sounder (CrIS) instrument on the Suomi National Polar-orbiting Partnership (SNPP) satellite that provides vertical profiles of CO from single-field-of-view observations. We show how these satellite CO profiles compare to aircraft observations and evaluate their biases.
Zhaojun Tang, Jiaqi Chen, and Zhe Jiang
Atmos. Chem. Phys., 22, 7815–7826, https://doi.org/10.5194/acp-22-7815-2022, https://doi.org/10.5194/acp-22-7815-2022, 2022
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We provide a comparative analysis to explore the effects of satellite and surface measurements on atmospheric CO in data assimilations in 2015–2020 over East Asia. We find possible overestimated enhancements of atmospheric CO by assimilating surface CO measurements due to model representation errors, and a large discrepancy in the derived trends of CO columns due to different vertical sensitivities of satellite and surface observations to lower and free troposphere.
Vivienne H. Payne, Susan S. Kulawik, Emily V. Fischer, Jared F. Brewer, L. Gregory Huey, Kazuyuki Miyazaki, John R. Worden, Kevin W. Bowman, Eric J. Hintsa, Fred Moore, James W. Elkins, and Julieta Juncosa Calahorrano
Atmos. Meas. Tech., 15, 3497–3511, https://doi.org/10.5194/amt-15-3497-2022, https://doi.org/10.5194/amt-15-3497-2022, 2022
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We compare new satellite measurements of peroxyacetyl nitrate (PAN) with reference aircraft measurements from two different instruments flown on the same platform. While there is a systematic difference between the two aircraft datasets, both show the same large-scale distribution of PAN and the discrepancy between aircraft datasets is small compared to the satellite uncertainties. The satellite measurements show skill in capturing large-scale variations in PAN.
Min Huang, James H. Crawford, Gregory R. Carmichael, Kevin W. Bowman, Sujay V. Kumar, and Colm Sweeney
Atmos. Chem. Phys., 22, 7461–7487, https://doi.org/10.5194/acp-22-7461-2022, https://doi.org/10.5194/acp-22-7461-2022, 2022
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This study demonstrates that ozone dry-deposition modeling can be improved by revising the model's dry-deposition parameterizations to better represent the effects of environmental conditions including the soil moisture fields. Applying satellite soil moisture data assimilation is shown to also have added value. Such advancements in coupled modeling and data assimilation can benefit the assessments of ozone impacts on human and vegetation health.
Weichao Han, Tai-Long He, Zhaojun Tang, Min Wang, Dylan Jones, and Zhe Jiang
Geosci. Model Dev., 15, 4225–4237, https://doi.org/10.5194/gmd-15-4225-2022, https://doi.org/10.5194/gmd-15-4225-2022, 2022
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We present an application of a hybrid deep learning (DL) model on prediction of surface CO in China from 2015 to 2020, which utilizes both convolutional neural networks and long short-term memory neural networks. The DL model performance is better than a Kalman filter (KF) system in the training period (2005–2018). Furthermore, the DL model demonstrates good temporal extensibility: the mean bias and correlation coefficients are 95.7 ppb and 0.93 in the test period (2019–2020) over eastern China.
Takashi Sekiya, Kazuyuki Miyazaki, Henk Eskes, Kengo Sudo, Masayuki Takigawa, and Yugo Kanaya
Atmos. Meas. Tech., 15, 1703–1728, https://doi.org/10.5194/amt-15-1703-2022, https://doi.org/10.5194/amt-15-1703-2022, 2022
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This study gives a systematic comparison of TROPOMI version 1.2 and OMI QA4ECV tropospheric NO2 column through global chemical data assimilation (DA) integration for April–May 2018. DA performance is controlled by measurement sensitivities, retrieval errors, and coverage. Due to reduced errors in TROPOMI, agreements against assimilated and independent observations were improved by TROPOMI DA compared to OMI DA. These results demonstrate that TROPOMI DA improves global analyses of NO2 and ozone.
Heba S. Marey, James R. Drummond, Dylan B. A. Jones, Helen Worden, Merritt N. Deeter, John Gille, and Debbie Mao
Atmos. Meas. Tech., 15, 701–719, https://doi.org/10.5194/amt-15-701-2022, https://doi.org/10.5194/amt-15-701-2022, 2022
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In this study, an analysis has been performed to understand the improvements in observational coverage over Canada in the new MOPITT V9 product. Temporal and spatial analysis of V9 indicates a general coverage gain of 15–20 % relative to V8, which varies regionally and seasonally; e.g., the number of successful MOPITT retrievals in V9 was doubled over Canada in winter. Also, comparison with the corresponding IASI instrument indicated generally good agreement, with about a 5–10 % positive bias.
Sabour Baray, Daniel J. Jacob, Joannes D. Maasakkers, Jian-Xiong Sheng, Melissa P. Sulprizio, Dylan B. A. Jones, A. Anthony Bloom, and Robert McLaren
Atmos. Chem. Phys., 21, 18101–18121, https://doi.org/10.5194/acp-21-18101-2021, https://doi.org/10.5194/acp-21-18101-2021, 2021
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We use 2010–2015 surface and satellite observations to disentangle methane from anthropogenic and natural sources in Canada. Using a chemical transport model (GEOS-Chem), the mismatch between modelled and observed methane concentrations can be used to infer emissions according to Bayesian statistics. Compared to prior knowledge, we show higher anthropogenic emissions attributed to energy and/or agriculture in Western Canada and lower natural emissions from Boreal wetlands.
Yuqiang Zhang, Drew Shindell, Karl Seltzer, Lu Shen, Jean-Francois Lamarque, Qiang Zhang, Bo Zheng, Jia Xing, Zhe Jiang, and Lei Zhang
Atmos. Chem. Phys., 21, 16051–16065, https://doi.org/10.5194/acp-21-16051-2021, https://doi.org/10.5194/acp-21-16051-2021, 2021
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In this study, we use a global chemical transport model to simulate the effects on global air quality and human health due to emission changes in China from 2010 to 2017. By performing sensitivity analysis, we found that the air pollution control policies not only decrease the air pollutant concentration but also bring significant co-benefits in air quality to downwind regions. The benefits for the improved air pollution are dominated by PM2.5.
Min Huang, James H. Crawford, Joshua P. DiGangi, Gregory R. Carmichael, Kevin W. Bowman, Sujay V. Kumar, and Xiwu Zhan
Atmos. Chem. Phys., 21, 11013–11040, https://doi.org/10.5194/acp-21-11013-2021, https://doi.org/10.5194/acp-21-11013-2021, 2021
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This study evaluates the impact of satellite soil moisture data assimilation on modeled weather and ozone fields at various altitudes above the southeastern US during the summer. It emphasizes the importance of soil moisture in the understanding of surface ozone pollution and upper tropospheric chemistry, as well as air pollutants’ source–receptor relationships between the US and its downwind areas.
Beata Bukosa, Jenny Fisher, Nicholas Deutscher, and Dylan Jones
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-173, https://doi.org/10.5194/gmd-2021-173, 2021
Revised manuscript not accepted
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Human activities led to rising levels of greenhouse gases (carbon dioxide (CO2), methane (CH4), carbon monoxide (CO)) in the atmosphere, threatening our future. We use models and measurements to predict and understand the climatological impact of these gases. Here, we describe a new simulation in the GEOS-Chem model that uses a more accurate method to simulate CO2, CH4 and CO, through their chemical dependence. Relative to the original simulations our results agree better with measurements.
Ilya Stanevich, Dylan B. A. Jones, Kimberly Strong, Martin Keller, Daven K. Henze, Robert J. Parker, Hartmut Boesch, Debra Wunch, Justus Notholt, Christof Petri, Thorsten Warneke, Ralf Sussmann, Matthias Schneider, Frank Hase, Rigel Kivi, Nicholas M. Deutscher, Voltaire A. Velazco, Kaley A. Walker, and Feng Deng
Atmos. Chem. Phys., 21, 9545–9572, https://doi.org/10.5194/acp-21-9545-2021, https://doi.org/10.5194/acp-21-9545-2021, 2021
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We explore the utility of a weak-constraint (WC) four-dimensional variational (4D-Var) data assimilation scheme for mitigating systematic errors in methane simulation in the GEOS-Chem model. We use data from the Greenhouse Gases Observing Satellite (GOSAT) and show that, compared to the traditional 4D-Var approach, the WC scheme improves the agreement between the model and independent observations. We find that the WC corrections to the model provide insight into the source of the errors.
Zhe Jiang, Hongrong Shi, Bin Zhao, Yu Gu, Yifang Zhu, Kazuyuki Miyazaki, Xin Lu, Yuqiang Zhang, Kevin W. Bowman, Takashi Sekiya, and Kuo-Nan Liou
Atmos. Chem. Phys., 21, 8693–8708, https://doi.org/10.5194/acp-21-8693-2021, https://doi.org/10.5194/acp-21-8693-2021, 2021
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We use the COVID-19 pandemic as a unique natural experiment to obtain a more robust understanding of the effectiveness of emission reductions toward air quality improvement by combining chemical transport simulations and observations. Our findings imply a shift from current control policies in California: a strengthened control on primary PM2.5 emissions and a well-balanced control on NOx and volatile organic compounds are needed to effectively and sustainably alleviate PM2.5 and O3 pollution.
Vikram Khade, Saroja M. Polavarapu, Michael Neish, Pieter L. Houtekamer, Dylan B. A. Jones, Seung-Jong Baek, Tai-Long He, and Sylvie Gravel
Geosci. Model Dev., 14, 2525–2544, https://doi.org/10.5194/gmd-14-2525-2021, https://doi.org/10.5194/gmd-14-2525-2021, 2021
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A new modeling system has been developed at Environment and Climate Change Canada to ingest observations of carbon monoxide (CO) into a coupled weather and constituent transport model. We show that accounting for the uncertainty in surface flux leads to a better estimate of CO distributions. The benefit of assimilating observations from different simulated networks varies with region. This is the first step towards developing a state and flux estimation system for greenhouse gases.
Joannes D. Maasakkers, Daniel J. Jacob, Melissa P. Sulprizio, Tia R. Scarpelli, Hannah Nesser, Jianxiong Sheng, Yuzhong Zhang, Xiao Lu, A. Anthony Bloom, Kevin W. Bowman, John R. Worden, and Robert J. Parker
Atmos. Chem. Phys., 21, 4339–4356, https://doi.org/10.5194/acp-21-4339-2021, https://doi.org/10.5194/acp-21-4339-2021, 2021
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We use 2010–2015 GOSAT satellite observations of atmospheric methane over North America in a high-resolution inversion to estimate methane emissions. We find general consistency with the gridded EPA inventory but higher oil and gas production emissions, with oil production emissions twice as large as in the latest EPA Greenhouse Gas Inventory. We find lower wetland emissions than predicted by WetCHARTs and a small increasing trend in the eastern US, apparently related to unconventional oil/gas.
Junjie Liu, Latha Baskaran, Kevin Bowman, David Schimel, A. Anthony Bloom, Nicholas C. Parazoo, Tomohiro Oda, Dustin Carroll, Dimitris Menemenlis, Joanna Joiner, Roisin Commane, Bruce Daube, Lucianna V. Gatti, Kathryn McKain, John Miller, Britton B. Stephens, Colm Sweeney, and Steven Wofsy
Earth Syst. Sci. Data, 13, 299–330, https://doi.org/10.5194/essd-13-299-2021, https://doi.org/10.5194/essd-13-299-2021, 2021
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On average, the terrestrial biosphere carbon sink is equivalent to ~ 20 % of fossil fuel emissions. Understanding where and why the terrestrial biosphere absorbs carbon from the atmosphere is pivotal to any mitigation policy. Here we present a regionally resolved satellite-constrained net biosphere exchange (NBE) dataset with corresponding uncertainties between 2010–2018: CMS-Flux NBE 2020. The dataset provides a unique perspective on monitoring regional contributions to the CO2 growth rate.
Shoma Yamanouchi, Camille Viatte, Kimberly Strong, Erik Lutsch, Dylan B. A. Jones, Cathy Clerbaux, Martin Van Damme, Lieven Clarisse, and Pierre-Francois Coheur
Atmos. Meas. Tech., 14, 905–921, https://doi.org/10.5194/amt-14-905-2021, https://doi.org/10.5194/amt-14-905-2021, 2021
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Ammonia (NH3) is a major source of pollution in the air. As such, there have been increasing efforts to measure the atmospheric abundance of NH3 and its spatial and temporal variability. In this study, long-term measurements of NH3 over Toronto, Canada, derived from multiscale datasets are examined. These NH3 datasets were compared to each other and to a model to better understand NH3 variability and to assess model performance.
A. Anthony Bloom, Kevin W. Bowman, Junjie Liu, Alexandra G. Konings, John R. Worden, Nicholas C. Parazoo, Victoria Meyer, John T. Reager, Helen M. Worden, Zhe Jiang, Gregory R. Quetin, T. Luke Smallman, Jean-François Exbrayat, Yi Yin, Sassan S. Saatchi, Mathew Williams, and David S. Schimel
Biogeosciences, 17, 6393–6422, https://doi.org/10.5194/bg-17-6393-2020, https://doi.org/10.5194/bg-17-6393-2020, 2020
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We use a model of the 2001–2015 tropical land carbon cycle, with satellite measurements of land and atmospheric carbon, to disentangle lagged and concurrent effects (due to past and concurrent meteorological events, respectively) on annual land–atmosphere carbon exchanges. The variability of lagged effects explains most 2001–2015 inter-annual carbon flux variations. We conclude that concurrent and lagged effects need to be accurately resolved to better predict the world's land carbon sink.
Benjamin Gaubert, Louisa K. Emmons, Kevin Raeder, Simone Tilmes, Kazuyuki Miyazaki, Avelino F. Arellano Jr., Nellie Elguindi, Claire Granier, Wenfu Tang, Jérôme Barré, Helen M. Worden, Rebecca R. Buchholz, David P. Edwards, Philipp Franke, Jeffrey L. Anderson, Marielle Saunois, Jason Schroeder, Jung-Hun Woo, Isobel J. Simpson, Donald R. Blake, Simone Meinardi, Paul O. Wennberg, John Crounse, Alex Teng, Michelle Kim, Russell R. Dickerson, Hao He, Xinrong Ren, Sally E. Pusede, and Glenn S. Diskin
Atmos. Chem. Phys., 20, 14617–14647, https://doi.org/10.5194/acp-20-14617-2020, https://doi.org/10.5194/acp-20-14617-2020, 2020
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This study investigates carbon monoxide pollution in East Asia during spring using a numerical model, satellite remote sensing, and aircraft measurements. We found an underestimation of emission sources. Correcting the emission bias can improve air quality forecasting of carbon monoxide and other species including ozone. Results also suggest that controlling VOC and CO emissions, in addition to widespread NOx controls, can improve ozone pollution over East Asia.
Erik Lutsch, Kimberly Strong, Dylan B. A. Jones, Thomas Blumenstock, Stephanie Conway, Jenny A. Fisher, James W. Hannigan, Frank Hase, Yasuko Kasai, Emmanuel Mahieu, Maria Makarova, Isamu Morino, Tomoo Nagahama, Justus Notholt, Ivan Ortega, Mathias Palm, Anatoly V. Poberovskii, Ralf Sussmann, and Thorsten Warneke
Atmos. Chem. Phys., 20, 12813–12851, https://doi.org/10.5194/acp-20-12813-2020, https://doi.org/10.5194/acp-20-12813-2020, 2020
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This paper describes the use of a network of 10 Arctic and midlatitude ground-based FTIR measurement sites to detect enhancements of the wildfire tracers carbon monoxide, hydrogen cyanide, and ethane from 2003 to 2018. A tagged CO GEOS-Chem simulation is used for source attribution and to evaluate the relative contribution of CO sources to the FTIR measurements. The use of FTIR measurements allowed for the emission ratios of hydrogen cyanide and ethane to be quantified.
Kazuyuki Miyazaki, Kevin Bowman, Takashi Sekiya, Henk Eskes, Folkert Boersma, Helen Worden, Nathaniel Livesey, Vivienne H. Payne, Kengo Sudo, Yugo Kanaya, Masayuki Takigawa, and Koji Ogochi
Earth Syst. Sci. Data, 12, 2223–2259, https://doi.org/10.5194/essd-12-2223-2020, https://doi.org/10.5194/essd-12-2223-2020, 2020
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This study presents the results from the Tropospheric Chemistry Reanalysis version 2 (TCR-2) for 2005–2018 obtained from the assimilation of multiple satellite measurements of ozone, CO, NO2, HNO3, and SO2 from the OMI, SCIAMACHY, GOME-2, TES, MLS, and MOPITT instruments. The evaluation results demonstrate the capability of the reanalysis products to improve understanding of the processes controlling variations in atmospheric composition, including long-term changes in air quality and emissions.
Ilya Stanevich, Dylan B. A. Jones, Kimberly Strong, Robert J. Parker, Hartmut Boesch, Debra Wunch, Justus Notholt, Christof Petri, Thorsten Warneke, Ralf Sussmann, Matthias Schneider, Frank Hase, Rigel Kivi, Nicholas M. Deutscher, Voltaire A. Velazco, Kaley A. Walker, and Feng Deng
Geosci. Model Dev., 13, 3839–3862, https://doi.org/10.5194/gmd-13-3839-2020, https://doi.org/10.5194/gmd-13-3839-2020, 2020
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Systematic errors in atmospheric models pose a challenge for inverse modeling studies of methane (CH4) emissions. We evaluated the CH4 simulation in the GEOS-Chem model at the horizontal resolutions of 4° × 5° and 2° × 2.5°. Our analysis identified resolution-dependent biases in the model, which we attributed to discrepancies between the two model resolutions in vertical transport in the troposphere and in stratosphere–troposphere exchange.
Cited articles
Bauwens, M., Compernolle, S., Stavrakou, T., Müller, J.-F., van Gent, J.,
Eskes, H., Levelt, P. F., van der A, R., Veefkind, J. P., Vlietinck, J., Yu,
H., and Zehner, C.: Impact of Coronavirus Outbreak on NO2 Pollution Assessed
Using TROPOMI and OMI Observations, Geophys. Res. Lett., 47,
e2020GL087978, https://doi.org/10.1029/2020GL087978, 2020. a
Beirle, S., Boersma, K. F., Platt, U., Lawrence, M. G., and Wagner, T.:
Megacity Emissions and Lifetimes of Nitrogen Oxides Probed from Space,
Science, 333, 1737–1739, https://doi.org/10.1126/science.1207824, 2011. a
Bertram, T. H., Heckel, A., Richter, A., Burrows, J. P., and Cohen, R. C.:
Satellite measurements of daily variations in soil NOx emissions,
Geophys. Res. Lett., 32, L24812, https://doi.org/10.1029/2005GL024640, 2005. a
Boersma, K. F., Jacob, D. J., Eskes, H. J., Pinder, R. W., Wang, J., and
van der A, R. J.: Intercomparison of SCIAMACHY and OMI tropospheric NO2
columns: Observing the diurnal evolution of chemistry and emissions from
space, J. Geophys. Res.-Atmos., 113, D16S26,
https://doi.org/10.1029/2007JD008816, 2008. a
Boersma, K. F., Vinken, G. C. M., and Eskes, H. J.: Representativeness errors in comparing chemistry transport and chemistry climate models with satellite UV–Vis tropospheric column retrievals, Geosci. Model Dev., 9, 875–898, https://doi.org/10.5194/gmd-9-875-2016, 2016. a
Boersma, K. F., Eskes, H. J., Richter, A., De Smedt, I., Lorente, A., Beirle, S., van Geffen, J. H. G. M., Zara, M., Peters, E., Van Roozendael, M., Wagner, T., Maasakkers, J. D., van der A, R. J., Nightingale, J., De Rudder, A., Irie, H., Pinardi, G., Lambert, J.-C., and Compernolle, S. C.: Improving algorithms and uncertainty estimates for satellite NO2 retrievals: results from the quality assurance for the essential climate variables (QA4ECV) project, Atmos. Meas. Tech., 11, 6651–6678, https://doi.org/10.5194/amt-11-6651-2018, 2018. a
Chai, T., Carmichael, G. R., Tang, Y., Sandu, A., Heckel, A., Richter, A., and
Burrows, J. P.: Regional NOx emission inversion through a four-dimensional
variational approach using SCIAMACHY tropospheric NO2 column
observations, Atmos. Environ., 43, 5046–5055,
https://doi.org/10.1016/j.atmosenv.2009.06.052, 2009. a
Choi, Y., Wang, Y., Zeng, T., Martin, R. V., Kurosu, T. P., and Chance, K.:
Evidence of lightning NOx and convective transport of pollutants in
satellite observations over North America, Geophys. Res. Lett.,
32, L02805, https://doi.org/10.1029/2004GL021436, 2005. a
de Foy, B., Wilkins, J. L., Lu, Z., Streets, D. G., and Duncan, B. N.: Model
evaluation of methods for estimating surface emissions and chemical lifetimes
from satellite data, Atmos. Environ., 98, 66–77,
https://doi.org/10.1016/j.atmosenv.2014.08.051, 2014. a
Han, W., He, T.-L., Tang, Z., Wang, M., Jones, D., and Jiang, Z.: A comparative analysis for a deep learning model (hyDL-CO v1.0) and Kalman filter to predict CO concentrations in China, Geosci. Model Dev., 15, 4225–4237, https://doi.org/10.5194/gmd-15-4225-2022, 2022. a
He, K., Zhang, X., Ren, S., and Sun, J.: Deep Residual Learning for Image
Recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016, 770–778, https://doi.org/10.1109/CVPR.2016.90, 2016. a
He, T.-L.: Unet_Chinese_NOx, v1.0.1, Zenodo [code], https://doi.org/10.5281/zenodo.7145714, 2022. a
He, T.-L., Jones, D. B. A., Miyazaki, K., Huang, B., Liu, Y., Jiang, Z., White,
E. C., Worden, H. M., and Worden, J. R.: Deep Learning to Evaluate US NOx
Emissions Using Surface Ozone Predictions, J. Geophys. Res.-Atmos., 127, e2021JD035597,
https://doi.org/10.1029/2021JD035597, 2022. a, b, c
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J.-N.: ERA5 hourly data on single levels from 1959 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2018. a
Hochreiter, S. and Schmidhuber, J.: Long Short-Term Memory, Neural Comput., 9,
1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735, 1997. a
Hoffmann, L., Günther, G., Li, D., Stein, O., Wu, X., Griessbach, S., Heng, Y., Konopka, P., Müller, R., Vogel, B., and Wright, J. S.: From ERA-Interim to ERA5: the considerable impact of ECMWF's next-generation reanalysis on Lagrangian transport simulations, Atmos. Chem. Phys., 19, 3097–3124, https://doi.org/10.5194/acp-19-3097-2019, 2019. a
Hunt, B., Kostelich, E., and Szunyogh, I.: Efficient data assimilation for
spatiotemporal chaos: A local ensemble transform Kalman filter, Physica D, 230, 112–126, https://doi.org/10.1016/j.physd.2006.11.008, 2007. a
Jiang, Z.: Model settings and surface measurements for air quality study, Zenodo [data set], https://doi.org/10.5281/zenodo.5030857, 2021. a
Keller, C. A. and Evans, M. J.: Application of random forest regression to the calculation of gas-phase chemistry within the GEOS-Chem chemistry model v10, Geosci. Model Dev., 12, 1209–1225, https://doi.org/10.5194/gmd-12-1209-2019, 2019. a
Kim, S.-W., Heckel, A., McKeen, S. A., Frost, G. J., Hsie, E.-Y., Trainer,
M. K., Richter, A., Burrows, J. P., Peckham, S. E., and Grell, G. A.:
Satellite-observed U.S. power plant NOx emission reductions and their
impact on air quality, Geophys. Res. Lett., 33, L22812,
https://doi.org/10.1029/2006GL027749, 2006. a
Kingma, D. P. and Ba, J.: Adam: A Method for Stochastic Optimization, in: 3rd International Conference on Learning Representations, ICLR, San Diego, CA, USA, 7–9 May 2015, edited by: Bengio, Y. and LeCun, Y., arXiv [preprint], https://doi.org/10.48550/arXiv.1412.6980, 22 December 2014. a
Konovalov, I. B., Beekmann, M., Richter, A., and Burrows, J. P.: Inverse modelling of the spatial distribution of NOx emissions on a continental scale using satellite data, Atmos. Chem. Phys., 6, 1747–1770, https://doi.org/10.5194/acp-6-1747-2006, 2006. a
Kraemer, M. U. G., Yang, C.-H., Gutierrez, B., Wu, C.-H., Klein, B., Pigott,
D. M., null null, du Plessis, L., Faria, N. R., Li, R., Hanage, W. P.,
Brownstein, J. S., Layan, M., Vespignani, A., Tian, H., Dye, C., Pybus,
O. G., and Scarpino, S. V.: The effect of human mobility and control measures
on the COVID-19 epidemic in China, Science, 368, 493–497,
https://doi.org/10.1126/science.abb4218, 2020. a, b, c
Kurokawa, J. i., Yumimoto, K., Uno, I., and Ohara, T.: Adjoint inverse modeling
of NOx emissions over eastern China using satellite observations of NO2
vertical column densities, Atmos. Environ., 43, 1878–1887,
https://doi.org/10.1016/j.atmosenv.2008.12.030, 2009. a
Lamsal, L. N., Martin, R. V., van Donkelaar, A., Steinbacher, M., Celarier,
E. A., Bucsela, E., Dunlea, E. J., and Pinto, J. P.: Ground-level nitrogen
dioxide concentrations inferred from the satellite-borne Ozone Monitoring
Instrument, J. Geophys. Res.-Atmos., 113, D16308,
https://doi.org/10.1029/2007JD009235, 2008. a, b
LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning, Nature, 521, 436–444, https://doi.org/10.1038/nature14539, 2015. a
Li, H., Xu, Z., Taylor, G., Studer, C., and Goldstein, T.: Visualizing the Loss
Landscape of Neural Nets, in: Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montréal, Canada, Curran Associates Inc., 6391–6401, 2018. a
Li, J. and Wang, Y.: Inferring the anthropogenic NOx emission trend over the United States during 2003–2017 from satellite observations: was there a flattening of the emission trend after the Great Recession?, Atmos. Chem. Phys., 19, 15339–15352, https://doi.org/10.5194/acp-19-15339-2019, 2019. a
Li, M., Zhang, Q., Kurokawa, J.-I., Woo, J.-H., He, K., Lu, Z., Ohara, T., Song, Y., Streets, D. G., Carmichael, G. R., Cheng, Y., Hong, C., Huo, H., Jiang, X., Kang, S., Liu, F., Su, H., and Zheng, B.: MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP, Atmos. Chem. Phys., 17, 935–963, https://doi.org/10.5194/acp-17-935-2017, 2017. a
Lin, J.-T. and McElroy, M. B.: Impacts of boundary layer mixing on pollutant
vertical profiles in the lower troposphere: Implications to satellite
remote sensing, Atmos. Environ., 44, 1726–1739,
https://doi.org/10.1016/j.atmosenv.2010.02.009, 2010. a
Liu, F., Beirle, S., Zhang, Q., Dörner, S., He, K., and Wagner, T.: NOx lifetimes and emissions of cities and power plants in polluted background estimated by satellite observations, Atmos. Chem. Phys., 16, 5283–5298, https://doi.org/10.5194/acp-16-5283-2016, 2016. a
Liu, F., Page, A., Strode, S. A., Yoshida, Y., Choi, S., Zheng, B., Lamsal,
L. N., Li, C., Krotkov, N. A., Eskes, H., van der A, R., Veefkind, P.,
Levelt, P. F., Hauser, O. P., and Joiner, J.: Abrupt decline in tropospheric
nitrogen dioxide over China after the outbreak of COVID-19, Science Advances,
6, eabc2992, https://doi.org/10.1126/sciadv.abc2992, 2020. a
Luo, Y., Wang, H., Zhang, R., Qian, W., and Luo, Z.: Comparison of Rainfall
Characteristics and Convective Properties of Monsoon Precipitation Systems
over South China and the Yangtze and Huai River Basin, J. Climate,
26, 110–132, https://doi.org/10.1175/JCLI-D-12-00100.1, 2013. a
Martin, R. V., Jacob, D. J., Chance, K., Kurosu, T. P., Palmer, P. I., and
Evans, M. J.: Global inventory of nitrogen oxide emissions constrained by
space-based observations of NO2 columns, J. Geophys. Res.-Atmos., 108, 4537, https://doi.org/10.1029/2003JD003453, 2003. a
Martin, R. V., Sioris, C. E., Chance, K., Ryerson, T. B., Bertram, T. H.,
Wooldridge, P. J., Cohen, R. C., Neuman, J. A., Swanson, A., and Flocke,
F. M.: Evaluation of space-based constraints on global nitrogen oxide
emissions with regional aircraft measurements over and downwind of eastern
North America, J. Geophys. Res.-Atmos., 111, D15308,
https://doi.org/10.1029/2005JD006680, 2006. a
Miyazaki, K., Bowman, K., Sekiya, T., Eskes, H., Boersma, F., Worden, H., Livesey, N., Payne, V. H., Sudo, K., Kanaya, Y., Takigawa, M., and Ogochi, K.: Chemical Reanalysis Products, NASA DOIMS [data set], https://doi.org/10.25966/9qgv-fe81, 2019. a
Miyazaki, K., Bowman, K., Sekiya, T., Eskes, H., Boersma, F., Worden, H., Livesey, N., Payne, V. H., Sudo, K., Kanaya, Y., Takigawa, M., and Ogochi, K.: Updated tropospheric chemistry reanalysis and emission estimates, TCR-2, for 2005–2018, Earth Syst. Sci. Data, 12, 2223–2259, https://doi.org/10.5194/essd-12-2223-2020, 2020a. a, b, c
Miyazaki, K., Bowman, K., Sekiya, T., Jiang, Z., Chen, X., Eskes, H., Ru, M.,
Zhang, Y., and Shindell, D.: Air Quality Response in China Linked to the 2019
Novel Coronavirus (COVID-19) Lockdown, Geophys. Res. Lett., 47,
e2020GL089252, https://doi.org/10.1029/2020GL089252, 2020b. a, b, c
Miyazaki, K., Bowman, K., Sekiya, T., Takigawa, M., Neu, J. L., Sudo, K.,
Osterman, G., and Eskes, H.: Global tropospheric ozone responses to reduced
NOx emissions linked to the COVID-19 worldwide lockdowns,
Science Advances, 7, eabf7460, https://doi.org/10.1126/sciadv.abf7460, 2021. a, b
Müller, J.-F. and Stavrakou, T.: Inversion of CO and NOx emissions using the adjoint of the IMAGES model, Atmos. Chem. Phys., 5, 1157–1186, https://doi.org/10.5194/acp-5-1157-2005, 2005. a
Murray, L. T.: Lightning NOx and Impacts on Air Quality, Current Pollution
Reports, 2, 115–133, https://doi.org/10.1007/s40726-016-0031-7, 2016. a
Napelenok, S. L., Pinder, R. W., Gilliland, A. B., and Martin, R. V.: A method for evaluating spatially-resolved NOx emissions using Kalman filter inversion, direct sensitivities, and space-based NO2 observations, Atmos. Chem. Phys., 8, 5603–5614, https://doi.org/10.5194/acp-8-5603-2008, 2008. a
Nault, B. A., Laughner, J. L., Wooldridge, P. J., Crounse, J. D., Dibb, J.,
Diskin, G., Peischl, J., Podolske, J. R., Pollack, I. B., Ryerson, T. B.,
Scheuer, E., Wennberg, P. O., and Cohen, R. C.: Lightning NOx Emissions:
Reconciling Measured and Modeled Estimates With Updated NOx
Chemistry, Geophys. Res. Lett., 44, 9479–9488,
https://doi.org/10.1002/2017GL074436, 2017. a
Qu, Z., Henze, D. K., Theys, N., Wang, J., and Wang, W.: Hybrid Mass
Balance/4D-Var Joint Inversion of NOx and SO2 Emissions in East Asia, J. Geophys. Res.-Atmos., 124, 8203–8224,
https://doi.org/10.1029/2018JD030240, 2019. a
Qu, Z., Jacob, D. J., Silvern, R. F., Shah, V., Campbell, P. C., Valin, L. C.,
and Murray, L. T.: US COVID-19 Shutdown Demonstrates Importance of Background
NO2 in Inferring NOx Emissions From Satellite NO2 Observations, Geophys.
Res. Lett., 48, e2021GL092783,
https://doi.org/10.1029/2021GL092783, 2021.
a
Rasp, S., Pritchard, M. S., and Gentine, P.: Deep learning to represent subgrid
processes in climate models, P. Natl. Acad. Sci. USA,
115, 9684–9689, https://doi.org/10.1073/pnas.1810286115, 2018. a, b
Sekiya, T., Miyazaki, K., Ogochi, K., Sudo, K., and Takigawa, M.: Global high-resolution simulations of tropospheric nitrogen dioxide using CHASER V4.0, Geosci. Model Dev., 11, 959–988, https://doi.org/10.5194/gmd-11-959-2018, 2018. a
Silvern, R. F., Jacob, D. J., Mickley, L. J., Sulprizio, M. P., Travis, K. R., Marais, E. A., Cohen, R. C., Laughner, J. L., Choi, S., Joiner, J., and Lamsal, L. N.: Using satellite observations of tropospheric NO2 columns to infer long-term trends in US NOx emissions: the importance of accounting for the free tropospheric NO2 background, Atmos. Chem. Phys., 19, 8863–8878, https://doi.org/10.5194/acp-19-8863-2019, 2019. a
Sudo, K., Takahashi, M., Kurokawa, J.-I., and Akimoto, H.: CHASER: A global
chemical model of the troposphere 1. Model description, J.
Geophys. Res.-Atmos., 107, 4339,
https://doi.org/10.1029/2001JD001113, 2002. a
Toenges-Schuller, N., Stein, O., Rohrer, F., Wahner, A., Richter, A., Burrows,
J. P., Beirle, S., Wagner, T., Platt, U., and Elvidge, C. D.: Global
distribution pattern of anthropogenic nitrogen oxide emissions: Correlation
analysis of satellite measurements and model calculations, J.
Geophys. Res.-Atmos., 111, D05312, https://doi.org/10.1029/2005JD006068, 2006. a
van Geffen, J., Boersma, K. F., Eskes, H., Sneep, M., ter Linden, M., Zara, M., and Veefkind, J. P.: S5P TROPOMI NO2 slant column retrieval: method, stability, uncertainties and comparisons with OMI, Atmos. Meas. Tech., 13, 1315–1335, https://doi.org/10.5194/amt-13-1315-2020, 2020. a
Wei, S. and Wang, L.: Examining the population flow network in China and its
implications for epidemic control based on Baidu migration data, Humanities
and Social Sciences Communications, 7, 145, https://doi.org/10.1057/s41599-020-00633-5,
2020. a
Wu, H., Tang, X., Wang, Z., Wu, L., Li, J., Wang, W., Yang, W., and Zhu, J.:
High-spatiotemporal-resolution inverse estimation of CO and NOx emission
reductions during emission control periods with a modified ensemble Kalman
filter, Atmos. Environ., 236, 117631,
https://doi.org/10.1016/j.atmosenv.2020.117631, 2020. a
Zhang, Y., Bo, H., Jiang, Z., Wang, Y., Fu, Y., Cao, B., Wang, X., Chen, J.,
and Li, R.: Untangling the contributions of meteorological conditions and
human mobility to tropospheric NO2 in Chinese mainland during the COVID-19
pandemic in early 2020, Natl. Sci. Rev., 8, nwab061,
https://doi.org/10.1093/nsr/nwab061, 2021. a, b, c, d, e, f
Zheng, B., Zhang, Q., Geng, G., Chen, C., Shi, Q., Cui, M., Lei, Y., and He, K.: Changes in China's anthropogenic emissions and air quality during the COVID-19 pandemic in 2020, Earth Syst. Sci. Data, 13, 2895–2907, https://doi.org/10.5194/essd-13-2895-2021, 2021. a, b
Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., and He, Q.:
A Comprehensive Survey on Transfer Learning, Proceedings of the IEEE, 109, 43–76, 2021. a
Short summary
We use a deep-learning (DL) model to estimate Chinese NOx emissions by combining satellite analysis and in situ measurements. Our results are consistent with conventional analyses of Chinese NOx emissions. Comparison with mobility data shows that the DL model has a better capability to capture changes in NOx. We analyse Chinese NOx emissions during the COVID-19 pandemic lockdown period. Our results illustrate the potential use of DL as a complementary tool for conventional air quality studies.
We use a deep-learning (DL) model to estimate Chinese NOx emissions by combining satellite...
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