Articles | Volume 23, issue 15
https://doi.org/10.5194/acp-23-8727-2023
© Author(s) 2023. 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-23-8727-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Aug 2023
Research article |
| 08 Aug 2023
| 08 Aug 2023
Nitrogen oxides emissions from selected cities in North America, Europe, and East Asia observed by the TROPOspheric Monitoring Instrument (TROPOMI) before and after the COVID-19 pandemic
Chantelle R. Lonsdale
Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, NY, USA
Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, NY, USA
Research and Education in Energy, Environment and Water Institute, University at Buffalo, Buffalo, NY, USA
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EGUsphere, https://doi.org/10.5194/egusphere-2026-115, https://doi.org/10.5194/egusphere-2026-115, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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In this work, my coauthors and I interpret methane emissions observed by remote sensing systems as plumes, or point sources. We model the probability that a general imaging spectrometer will detect a plume, and apply this framework to multiple remote sensing systems. We show how two recent systems- MethanAIR and MethaneSAT- provide enough sensitivity to facility scale point sources to effectively characterize most plume emissions in the Permian Basin.
Zhan Zhang, Maryann Sargent, Jack D. Warren, Apisada Chulakadabba, Marcus Russi, Sasha Ayvazov, Joshua Benmergui, Marvin Knapp, Ethan Kyzivat, Christopher C. Miller, Sébastien Roche, Bingkun Luo, David J. Miller, Maya Nasr, Kang Sun, James P. Williams, Katlyn MacKay, Mark Omara, Luis Guanter, Ritesh Gautam, Jonathan Franklin, Xiong Liu, and Steven C. Wofsy
EGUsphere, https://doi.org/10.5194/egusphere-2026-141, https://doi.org/10.5194/egusphere-2026-141, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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Accurate plume identification from satellite imagery is important for measuring methane emissions, but weak signals often require time-consuming human inspection. We developed an automatic plume masking method that enhances plume patterns while reducing background noise based on wavelet transform image processing. The method finds more small emission sources with fewer false detections and works efficiently across different instruments, helping build a more complete picture of methane emissions.
Zitong Li, Kang Sun, Kaiyu Guan, Sheng Wang, Bin Peng, Lieven Clarisse, Martin Van Damme, Pierre-François Coheur, Karen Cady-Pereira, Mark W. Shephard, Mark Zondlo, and Daniel Moore
Atmos. Chem. Phys., 26, 703–721, https://doi.org/10.5194/acp-26-703-2026, https://doi.org/10.5194/acp-26-703-2026, 2026
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We estimate ammonia fluxes over the contiguous U.S. from 2008 to 2022 using a directional derivative approach applied to satellite observations from the Infrared Atmospheric Sounding Interferometer (IASI) and Cross-track Infrared Sounder (CrIS). The resulting flux estimates reveal pronounced seasonal and spatial patterns driven by agricultural activities and indicate substantial local deposition to surrounding vegetation, underscoring the need for improved monitoring and management strategies.
Ling Chen, Zhaonan Cai, Kang Sun, Yi Liu, Dongxu Yang, Mingming Li, and Lingyun Zhu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-480, https://doi.org/10.5194/essd-2025-480, 2025
Preprint under review for ESSD
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An appropriate representation of the NOx/NO2 ratio and NOx lifetime is essential for satellite-based NOx emissions estimation. We introduce a lightweight approach that applies variable NOx/NO2 ratios and piecewise fitting based on the directional derivative framework (DDA) to estimate regional and point-source NOx emissions. Our method directly captures nonlinear NOx chemistry and serves as an efficient alternative to both bottom-up inventories and computationally demanding top-down models.
Christopher Chan Miller, Sébastien Roche, Jonas S. Wilzewski, Xiong Liu, Kelly Chance, Amir H. Souri, Eamon Conway, Bingkun Luo, Jenna Samra, Jacob Hawthorne, Kang Sun, Carly Staebell, Apisada Chulakadabba, Maryann Sargent, Joshua S. Benmergui, Jonathan E. Franklin, Bruce C. Daube, Yang Li, Joshua L. Laughner, Bianca C. Baier, Ritesh Gautam, Mark Omara, and Steven C. Wofsy
Atmos. Meas. Tech., 17, 5429–5454, https://doi.org/10.5194/amt-17-5429-2024, https://doi.org/10.5194/amt-17-5429-2024, 2024
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MethaneSAT is an upcoming satellite mission designed to monitor methane emissions from the oil and gas (O&G) industry globally. Here, we present observations from the first flight campaign of MethaneAIR, a MethaneSAT-like instrument mounted on an aircraft. MethaneAIR can map methane with high precision and accuracy over a typically sized oil and gas basin (~200 km2) in a single flight. This paper demonstrates the capability of the upcoming satellite to routinely track global O&G emissions.
Zhendong Lu, Jun Wang, Yi Wang, Daven K. Henze, Xi Chen, Tong Sha, and Kang Sun
Atmos. Chem. Phys., 24, 7793–7813, https://doi.org/10.5194/acp-24-7793-2024, https://doi.org/10.5194/acp-24-7793-2024, 2024
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In contrast with past work showing that the reduction of emissions was the dominant factor for the nationwide increase of surface O3 during the lockdown in China, this study finds that the variation in meteorology (temperature and other parameters) plays a more important role. This result is obtained through sensitivity simulations using a chemical transport model constrained by satellite (TROPOMI) data and calibrated with surface observations.
Eamon K. Conway, Amir H. Souri, Joshua Benmergui, Kang Sun, Xiong Liu, Carly Staebell, Christopher Chan Miller, Jonathan Franklin, Jenna Samra, Jonas Wilzewski, Sebastien Roche, Bingkun Luo, Apisada Chulakadabba, Maryann Sargent, Jacob Hohl, Bruce Daube, Iouli Gordon, Kelly Chance, and Steven Wofsy
Atmos. Meas. Tech., 17, 1347–1362, https://doi.org/10.5194/amt-17-1347-2024, https://doi.org/10.5194/amt-17-1347-2024, 2024
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The work presented here describes the processes required to convert raw sensor data for the MethaneAIR instrument to geometrically calibrated data. Each algorithm is described in detail. MethaneAIR is the airborne simulator for MethaneSAT, a new satellite under development by MethaneSAT LLC, a subsidiary of the EDF. MethaneSAT's goals are to precisely map over 80 % of the production sources of methane emissions from oil and gas fields across the globe to a high degree of accuracy.
Karen E. Cady-Pereira, Xuehui Guo, Rui Wang, April B. Leytem, Chase Calkins, Elizabeth Berry, Kang Sun, Markus Müller, Armin Wisthaler, Vivienne H. Payne, Mark W. Shephard, Mark A. Zondlo, and Valentin Kantchev
Atmos. Meas. Tech., 17, 15–36, https://doi.org/10.5194/amt-17-15-2024, https://doi.org/10.5194/amt-17-15-2024, 2024
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Ammonia is a significant precursor of PM2.5 particles and thus contributes to poor air quality in many regions. Furthermore, ammonia concentrations are rising due to the increase of large-scale, intensive agricultural activities. Here we evaluate satellite measurements of ammonia against aircraft and surface network data, and show that there are differences in magnitude, but the satellite data are spatially and temporally well correlated with the in situ data.
Apisada Chulakadabba, Maryann Sargent, Thomas Lauvaux, Joshua S. Benmergui, Jonathan E. Franklin, Christopher Chan Miller, Jonas S. Wilzewski, Sébastien Roche, Eamon Conway, Amir H. Souri, Kang Sun, Bingkun Luo, Jacob Hawthrone, Jenna Samra, Bruce C. Daube, Xiong Liu, Kelly Chance, Yang Li, Ritesh Gautam, Mark Omara, Jeff S. Rutherford, Evan D. Sherwin, Adam Brandt, and Steven C. Wofsy
Atmos. Meas. Tech., 16, 5771–5785, https://doi.org/10.5194/amt-16-5771-2023, https://doi.org/10.5194/amt-16-5771-2023, 2023
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We show that MethaneAIR, a precursor to the MethaneSAT satellite, demonstrates accurate point source quantification during controlled release experiments and regional observations in 2021 and 2022. Results from our two independent quantification methods suggest the accuracy of our sensor and algorithms is better than 25 % for sources emitting 200 kg h−1 or more. Insights from these measurements help establish the capabilities of MethaneSAT and MethaneAIR.
Rui Wang, Da Pan, Xuehui Guo, Kang Sun, Lieven Clarisse, Martin Van Damme, Pierre-François Coheur, Cathy Clerbaux, Melissa Puchalski, and Mark A. Zondlo
Atmos. Chem. Phys., 23, 13217–13234, https://doi.org/10.5194/acp-23-13217-2023, https://doi.org/10.5194/acp-23-13217-2023, 2023
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Ammonia (NH3) is a key precursor for fine particulate matter (PM2.5) and a primary form of reactive nitrogen, yet it has sparse ground measurements. We perform the first comprehensive comparison between ground observations and satellite retrievals in the US, demonstrating that satellite NH3 data can help fill spatial gaps in the current ground monitoring networks. Trend analyses using both datasets highlight increasing NH3 trends across the US, including the NH3 hotspots and urban areas.
Huiqun Wang, Gonzalo González Abad, Chris Chan Miller, Hyeong-Ahn Kwon, Caroline R. Nowlan, Zolal Ayazpour, Heesung Chong, Xiong Liu, Kelly Chance, Ewan O'Sullivan, Kang Sun, Robert Spurr, and Robert J. Hargreaves
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-66, https://doi.org/10.5194/amt-2023-66, 2023
Preprint withdrawn
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A pipeline for retrieving Total Column Water Vapor from satellite blue spectra is developed. New constraints are considered. Water-leaving radiance is important over the oceans. Results agree with reference datasets well under clear conditions. Due to high sensitivity to clouds, strict data filtering criteria are required. All-sky retrievals can be corrected using machine learning. GPS stations’ representation errors follow a power law relationship with grid resolutions.
Zolal Ayazpour, Shiqi Tao, Dan Li, Amy Jo Scarino, Ralph E. Kuehn, and Kang Sun
Atmos. Meas. Tech., 16, 563–580, https://doi.org/10.5194/amt-16-563-2023, https://doi.org/10.5194/amt-16-563-2023, 2023
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Accurate knowledge of the planetary boundary layer height (PBLH) is essential to study air pollution. However, PBLH observations are sparse in space and time, and PBLHs used in atmospheric models are often inaccurate. Using PBLH observations from the Aircraft Meteorological DAta Relay (AMDAR), we present a machine learning framework to produce a spatially complete PBLH product over the contiguous US that shows a better agreement with reference PBLH observations than commonly used PBLH products.
Kang Sun, Mahdi Yousefi, Christopher Chan Miller, Kelly Chance, Gonzalo González Abad, Iouli E. Gordon, Xiong Liu, Ewan O'Sullivan, Christopher E. Sioris, and Steven C. Wofsy
Atmos. Meas. Tech., 15, 3721–3745, https://doi.org/10.5194/amt-15-3721-2022, https://doi.org/10.5194/amt-15-3721-2022, 2022
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This study of upper atmospheric airglow from oxygen is motivated by the need to measure oxygen simultaneously with methane and CO2 in satellite remote sensing. We provide an accurate understanding of the spatial, temporal, and spectral distribution of airglow emissions, which will help in the satellite remote sensing of greenhouse gases and constraining the chemical and physical processes in the upper atmosphere.
Amir H. Souri, Kelly Chance, Kang Sun, Xiong Liu, and Matthew S. Johnson
Atmos. Meas. Tech., 15, 41–59, https://doi.org/10.5194/amt-15-41-2022, https://doi.org/10.5194/amt-15-41-2022, 2022
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The central component of satellite and model validation is pointwise measurements. A point is an element of space, whereas satellite (model) pixels represent an averaged area. These two datasets are inherently different. We leveraged some geostatistical tools to transform discrete points to gridded data with quantified uncertainty, comparable to satellite footprint (and response functions). This in part alleviated some complications concerning point–pixel comparisons.
Kang Sun, Lingbo Li, Shruti Jagini, and Dan Li
Atmos. Chem. Phys., 21, 13311–13332, https://doi.org/10.5194/acp-21-13311-2021, https://doi.org/10.5194/acp-21-13311-2021, 2021
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We bridge the gap between satellite column observations and emissions by accounting for the dynamic lifetime of pollutants due to wind dispersion and the chemical lifetime due to chemical reactions. Applying it to the Po Valley air basin, we derive the monthly emissions of nitrogen oxides using satellite nitrogen dioxide observations. We further quantify the COVID-19-driven decline of emissions and estimate a 22 % decrease in nitrogen oxide emissions due to the pandemic in 2020.
Carly Staebell, Kang Sun, Jenna Samra, Jonathan Franklin, Christopher Chan Miller, Xiong Liu, Eamon Conway, Kelly Chance, Scott Milligan, and Steven Wofsy
Atmos. Meas. Tech., 14, 3737–3753, https://doi.org/10.5194/amt-14-3737-2021, https://doi.org/10.5194/amt-14-3737-2021, 2021
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Given the high global warming potential of CH4, the identification and subsequent reduction of anthropogenic CH4 emissions presents a significant opportunity for climate change mitigation. Satellites are an integral piece of this puzzle, providing data to quantify emissions at a variety of spatial scales. This work presents the spectral calibration of MethaneAIR, the airborne instrument used as a test bed for the forthcoming MethaneSAT satellite.
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Short summary
The COVID-19 pandemic, which was caused by the SARS-CoV-2 virus, emerged in 2019, and its still evolving variants have resulted in unprecedented shifts in human activities and anthropogenic emissions into the Earth's atmosphere. We present monthly nitrogen oxide emissions over three major continents from May 2018 to January 2023 to capture variations before and after the COVID-19 pandemic. We focus on a diverse collection of 54 cities to quantify the post-COVID-19 perturbations.
The COVID-19 pandemic, which was caused by the SARS-CoV-2 virus, emerged in 2019, and its still...
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