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
Related authors
No articles found.
Ayesha Riaz, Kang Sun, Brian D. Baker, Brian Buma, Karen E. Cady-Pereira, Christopher Chan Miller, William C. Eddy III, Betsy M. Farris, Thomas U. Kampe, Eric A. Kort, Nathan P. Leisso, Robert Spurr, Emily R. Stuchiner, and Wendy H. Yang
Atmos. Meas. Tech., 19, 5169–5191, https://doi.org/10.5194/amt-19-5169-2026, https://doi.org/10.5194/amt-19-5169-2026, 2026
Short summary
Short summary
N2O is a powerful greenhouse gas mainly released from agricultural soils, but its emissions are difficult to track as they vary strongly in space and time. We tested whether combining two kind of infrared measurements in one instrument could improve ability of airborne and satellite instruments to observe these emissions. We found that this combined approach improves sensitivity to near-surface emissions with low measurement error and could guide the design of future N2O dedicated missions.
Zhan Zhang, Maryann Sargent, Ethan Manninen, 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, Manuel Perez-Carrasco, Kang Sun, James P. Williams, Katlyn MacKay, Mark Omara, Jia Chen, Luis Guanter, Ritesh Gautam, Jonathan Franklin, Xiong Liu, and Steven C. Wofsy
Atmos. Meas. Tech., 19, 4637–4651, https://doi.org/10.5194/amt-19-4637-2026, https://doi.org/10.5194/amt-19-4637-2026, 2026
Short summary
Short summary
Methane released into the atmosphere is often difficult to detect in satellite imagery because the signals can be weak and hidden by background noise. We developed an automated method that improves the visibility of methane plumes while reducing false detections, decreasing the need for time-consuming manual inspection. The method identifies more small emission sources across different instruments, helping build a more complete understanding of methane emissions and their impacts.
Christopher Chan Miller, Sébastien Roche, Jonas Wilzweski, Xiong Liu, Kelly V. Chance, Bingkun Luo, Kang Sun, Jonathan E. Franklin, Joshua S. Benmergui, Maya Nasr, David J. Miller, Sean Crowell, Nathan Leisso, Eleanor Walker, Ritesh Gautam, Nicholas LoFaso, Sasha Ayvazov, David Wells, Carlos Cervantes, Christopher Hairfield, and Steven Wofsy
EGUsphere, https://doi.org/10.5194/egusphere-2026-3365, https://doi.org/10.5194/egusphere-2026-3365, 2026
Short summary
Short summary
Reducing oil and gas methane emissions is among the fastest ways to slow near-term climate change, and satellites offer a transparent means of verifying progress. MethaneSAT fills a gap between existing instruments, which either measure methane accurately but at coarse scales, or detect large leaks at fine resolution without the accuracy to account for all sources. We present the first measurements and show they meet the accuracy requirements for regional emissions mapping at the 2–4 km2 scale.
David J. Miller, Kang Sun, Jonathan E. Franklin, Nathan Leisso, Sébastien Roche, Bingkun Luo, Christopher Chan Miller, Sasha Ayvazov, Sean Crowell, Nick LoFaso, Tom Kampe, Peter Spuhler, Betsy Farris, Eleanor Walker, Tom Melendez, Ritesh Gautam, Xiong Liu, and Steven C. Wofsy
EGUsphere, https://doi.org/10.5194/egusphere-2026-2675, https://doi.org/10.5194/egusphere-2026-2675, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
With a focus on mitigating climate change, the MethaneSAT satellite mission aims to catalyze methane emission reductions. MethaneSAT has provided high-resolution emissions products across global oil and gas basins. We present novel calibration methods of MethaneSAT's instrumentation that demonstrate stable on-orbit performance compared with ground-based calibration. Our results are important for similar satellite missions focused on monitoring climate-relevant emissions.
Ling Chen, Zhaonan Cai, Kang Sun, Yi Liu, Dongxu Yang, Mingming Li, and Lingyun Zhu
Earth Syst. Sci. Data, 18, 2749–2767, https://doi.org/10.5194/essd-18-2749-2026, https://doi.org/10.5194/essd-18-2749-2026, 2026
Short summary
Short summary
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 approach (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.
Ethan Manninen, Apisada Chulakadabba, Maryann Sargent, Zhan Zhang, Harshil Kamdar, Jack Warren, Sébastien Roche, Christopher Chan Miller, Ethan Kyzivat, Joshua Benmergui, Jasna Pittman, Eleanor Walker, Jacob Bushey, Jenna Samra, Jacob Hawthorne, Bingkun Luo, Maya Nasr, Kang Sun, Jonathan Franklin, Xiong Liu, Jia Chen, and Steven Wofsy
EGUsphere, https://doi.org/10.5194/egusphere-2026-115, https://doi.org/10.5194/egusphere-2026-115, 2026
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cited articles
Ayazpour, Z., Tao, S., Li, D., Scarino, A. J., Kuehn, R. E., and Sun, K.:
Estimates of the spatially complete, observational-data-driven planetary boundary layer height over the contiguous United States, Atmos. Meas. Tech., 16, 563–580, https://doi.org/10.5194/amt-16-563-2023, 2023. a
Barrington-Leigh, C. and Millard-Ball, A.:
A century of sprawl in the United States, P. Natl. Acad. Sci. USA, 112, 8244–8249, https://doi.org/10.1073/pnas.1504033112, 2015. 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, 2011. a
Beirle, S., Borger, C., Dörner, S., Li, A., Hu, Z., Liu, F., Wang, Y., and Wagner, T.:
Pinpointing nitrogen oxide emissions from space, Science Advances, 5, eaax9800, https://doi.org/10.1126/sciadv.aax9800, 2019. a, b, c, d
Chen, Y.-C., Chou, C. C. K., Liu, C.-Y., Chi, S.-Y., and Chuang, M.-T.:
Evaluation of the nitrogen oxide emission inventory with TROPOMI observations, Atmos. Environ., 298, 119639, https://doi.org/10.1016/j.atmosenv.2023.119639, 2023. a, b
Choo, G.-H., Lee, K., Hong, H., Jeong, U., Choi, W., and Janz, S. J.:
Highly resolved mapping of NO2 vertical column densities from GeoTASO measurements over a megacity and industrial area during the KORUS-AQ campaign, Atmos. Meas. Tech., 16, 625–644, https://doi.org/10.5194/amt-16-625-2023, 2023. a
Crippa, M., Guizzardi, D., Muntean, M., Schaaf, E., Dentener, F., van Aardenne, J. A., Monni, S., Doering, U., Olivier, J. G. J., Pagliari, V., and Janssens-Maenhout, G.:
Gridded emissions of air pollutants for the period 1970–2012 within EDGAR v4.3.2, Earth Syst. Sci. Data, 10, 1987–2013, https://doi.org/10.5194/essd-10-1987-2018, 2018. a
Dammers, E., Tokaya, J., Mielke, C., Hausmann, K., Griffin, D., McLinden, C., Eskes, H., and Timmermans, R.:
Can TROPOMI−NO2 satellite data be used to track the drop and resurgence of NOx emissions between 2019–2021 using the multi-source plume method (MSPM)?, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2022-292, in review, 2022. a, b
de Foy, B. and Schauer, J. J.:
An improved understanding of NOx emissions in South Asian megacities using TROPOMI NO2 retrievals, Environ. Res. Lett., 17, 24006, https://doi.org/10.1088/1748-9326/ac48b4, 2022. a, b, c, d
Ding, F., Iredell, L., Theobald, M., Wei, J., and Meyer, D.:
PBL Height From AIRS, GPS RO, and MERRA-2 Products in NASA GES DISC and Their 10-Year Seasonal Mean Intercomparison, Earth and Space Science, 8, e2021EA001859, https://doi.org/10.1029/2021EA001859, 2021. a
Ding, J., van der A, R. J., Eskes, H. J., Mijling, B., Stavrakou, T., van Geffen, J. H. G. M., and Veefkind, J. P.:
NOx Emissions Reduction and Rebound in China Due to the COVID-19 Crisis, Geophys. Res. Lett., 47, e2020GL089912, https://doi.org/10.1029/2020GL089912, 2020. a
Dix, B., Francoeur, C., Li, M., Serrano-Calvo, R., Levelt, P. F., Veefkind, J. P., McDonald, B. C., and de Gouw, J.:
Quantifying NOx Emissions from U. S. Oil and Gas Production Regions Using TROPOMI NO2, ACS Earth and Space Chemistry, 6, 403–414, https://doi.org/10.1021/acsearthspacechem.1c00387, 2022. a, b, c, d, e, f
European Space Agency (ESA): Copernicus Sentinel-5P: TROPOMI Level 2 Nitrogen Dioxide total column products, Version 01, ESA [data set], https://doi.org/10.5270/S5P-s4ljg54, 2018. a
Eskes, H., Van Geffen, J., Boersma, F., Eichmann, K.-U., Apituley, A., Pedergnana, M., Sneep, M., Veefkind, J., and Loyola, D.:
Sentinel-5 precursor/TROPOMI Level 2 Product User Manual Nitrogendioxide, Royal Netherlands Meteorological Institute, https://sentinel.esa.int/documents/247904/2474726/Sentinel-5P-Level-2-Product-User-Manual-Nitrogen-Dioxide.pdf (last access: 11 February 2022), 2022. a
Gkatzelis, G. I., Gilman, J. B., Brown, S. S., Eskes, H., Gomes, A. R., Lange, A. C., McDonald, B. C., Peischl, J., Petzold, A., Thompson, C. R., and Kiendler-Scharr, A.:
The global impacts of COVID-19 lockdowns on urban air pollution: A critical review and recommendations, Elementa: Science of the Anthropocene, 9, 176, https://doi.org/10.1525/elementa.2021.00176, 2021. a
Godłowska, J., Hajto, M. J., Lapeta, B., and Kaszowski, K.:
The attempt to estimate annual variability of NOx emission in Poland using Sentinel-5P/TROPOMI data, Atmos. Environ., 294, 119482, https://doi.org/10.1016/j.atmosenv.2022.119482, 2023. a
Goldberg, D. L., Lu, Z., Streets, D. G., de Foy, B., Griffin, D., McLinden, C. A., Lamsal, L. N., Krotkov, N. A., and Eskes, H.:
Enhanced Capabilities of TROPOMI NO2: Estimating NOx from North American Cities and Power Plants, Environ. Sci. Technol., 53, 12594–12601, https://doi.org/10.1021/acs.est.9b04488, 2019. a
Goldberg, D. L., Harkey, M., de Foy, B., Judd, L., Johnson, J., Yarwood, G., and Holloway, T.:
Evaluating NOx emissions and their effect on O3 production in Texas using TROPOMI NO2 and HCHO, Atmos. Chem. Phys., 22, 10875–10900, https://doi.org/10.5194/acp-22-10875-2022, 2022. a, b
Hernández, J.: World cities database, Kaggle, https://doi.org/10.34740/KAGGLE/DSV/3360046, 2022. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Mu noz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.:
The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
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., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023. a
Huang, G. and Sun, K.:
Non-negligible impacts of clean air regulations on the reduction of tropospheric NO2 over East China during the COVID-19 pandemic observed by OMI and TROPOMI, Science of The Total Environment, 745, 141023, https://doi.org/10.1016/j.scitotenv.2020.141023, 2020. a
Kang, M., Zhang, J., Cheng, Z., Guo, S., Su, F., Hu, J., Zhang, H., and Ying, Q.:
Assessment of Sectoral NOx Emission Reductions During COVID-19 Lockdown Using Combined Satellite and Surface Observations and Source-Oriented Model Simulations, Geophys. Res. Lett., 49, e2021GL095339, https://doi.org/10.1029/2021GL095339, 2022. a
Lange, K., Richter, A., and Burrows, J. P.:
Variability of nitrogen oxide emission fluxes and lifetimes estimated from Sentinel-5P TROPOMI observations, Atmos. Chem. Phys., 22, 2745–2767, https://doi.org/10.5194/acp-22-2745-2022, 2022. a, b
Laughner, J. L. and Cohen, R. C.:
Direct observation of changing NOx lifetime in North American cities, Science, 366, 723–727, https://doi.org/10.1126/science.aax6832, 2019. a, b
Levelt, P. F., Stein Zweers, D. C., Aben, I., Bauwens, M., Borsdorff, T., De Smedt, I., Eskes, H. J., Lerot, C., Loyola, D. G., Romahn, F., Stavrakou, T., Theys, N., Van Roozendael, M., Veefkind, J. P., and Verhoelst, T.:
Air quality impacts of COVID-19 lockdown measures detected from space using high spatial resolution observations of multiple trace gases from Sentinel-5P/TROPOMI, Atmos. Chem. Phys., 22, 10319–10351, https://doi.org/10.5194/acp-22-10319-2022, 2022. a
Likas, A., Vlassis, N., and Verbeek, J. J.:
The global k-means clustering algorithm, Pattern Recogn., 36, 451–461, 2003. 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
Liu, M., van der A, R., van Weele, M., Eskes, H., Lu, X., Veefkind, P., de Laat, J., Kong, H., Wang, J., Sun, J., Ding, J., Zhao, Y., and Weng, H.:
A New Divergence Method to Quantify Methane Emissions Using Observations of Sentinel-5P TROPOMI, Geophys. Res. Lett., 48, e2021GL094151, https://doi.org/10.1029/2021GL094151, 2021. a, b, c
Lorente, A., Boersma, K. F., Eskes, H. J., Veefkind, J. P., van Geffen, J. H. G. M., de Zeeuw, M. B., Denier van der Gon, H. A. C., Beirle, S., and Krol, M. C.:
Quantification of nitrogen oxides emissions from build-up of pollution over Paris with TROPOMI, Sci. Rep.-UK, 9, 20033, https://doi.org/10.1038/s41598-019-56428-5, 2019. a
McDuffie, E. E., Smith, S. J., O'Rourke, P., Tibrewal, K., Venkataraman, C., Marais, E. A., Zheng, B., Crippa, M., Brauer, M., and Martin, R. V.:
A global anthropogenic emission inventory of atmospheric pollutants from sector- and fuel-specific sources (1970–2017): an application of the Community Emissions Data System (CEDS), Earth Syst. Sci. Data, 12, 3413–3442, https://doi.org/10.5194/essd-12-3413-2020, 2020. a
Mijling, B. and Van Der A, R. J.:
Using daily satellite observations to estimate emissions of short-lived air pollutants on a mesoscopic scale, J. Geophys. Res.-Atmos., 117, D17302, https://doi.org/10.1029/2012JD017817, 2012. a
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, 2020. a
Rey-Pommier, A., Chevallier, F., Ciais, P., Broquet, G., Christoudias, T., Kushta, J., Hauglustaine, D., and Sciare, J.:
Quantifying NOx emissions in Egypt using TROPOMI observations, Atmos. Chem. Phys., 22, 11505–11527, https://doi.org/10.5194/acp-22-11505-2022, 2022. a, b
Riess, T. C. V. W., Boersma, K. F., van Vliet, J., Peters, W., Sneep, M., Eskes, H., and van Geffen, J.:
Improved monitoring of shipping NO2 with TROPOMI: decreasing NOx emissions in European seas during the COVID-19 pandemic, Atmos. Meas. Tech., 15, 1415–1438, https://doi.org/10.5194/amt-15-1415-2022, 2022. a
Sentinel-5P Product Algorithm Laboratory (S5P-PAL): S5P-PAL Data Portal, https://data-portal.s5p-pal.com/products/no2.html, last access: 11 February 2023. a
Shah, V., Jacob, D. J., Li, K., Silvern, R. F., Zhai, S., Liu, M., Lin, J., and Zhang, Q.:
Effect of changing NOx lifetime on the seasonality and long-term trends of satellite-observed tropospheric NO2 columns over China, Atmos. Chem. Phys., 20, 1483–1495, https://doi.org/10.5194/acp-20-1483-2020, 2020. 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
Smits, A. J.:
A physical introduction to fluid mechanics, John Wiley & Sons Incorporated, Hoboken, New Jersey, ISBN 10: 0471253499, ISBN 13: 9780471253495, 2000. a
Streets, D. G., Bond, T. C., Carmichael, G. R., Fernandes, S. D., Fu, Q., He, D., Klimont, Z., Nelson, S. M., Tsai, N. Y., Wang, M. Q., Woo, J.-H., and Yarber, K. F.:
An inventory of gaseous and primary aerosol emissions in Asia in the year 2000, J. Geophys. Res.-Atmos., 108, 8809, https://doi.org/10.1029/2002JD003093, 2003. a
Sun, K.: Kang-Sun-CfA/Oversampling_matlab: NOx city emission paper, Version v0.3, Zenodo [code], https://doi.org/10.5281/zenodo.7987812, 2023. a, b, c, d
Sun, K., Zhu, L., Cady-Pereira, K., Chan Miller, C., Chance, K., Clarisse, L., Coheur, P.-F., González Abad, G., Huang, G., Liu, X., Van Damme, M., Yang, K., and Zondlo, M.:
A physics-based approach to oversample multi-satellite, multispecies observations to a common grid, Atmos. Meas. Tech., 11, 6679–6701, https://doi.org/10.5194/amt-11-6679-2018, 2018. a
Sun, K., Li, L., Jagini, S., and Li, D.:
A satellite-data-driven framework to rapidly quantify air-basin-scale NOx emissions and its application to the Po Valley during the COVID-19 pandemic, Atmos. Chem. Phys., 21, 13311–13332, https://doi.org/10.5194/acp-21-13311-2021, 2021. a, b
Valin, L. C., Russell, A. R., and Cohen, R. C.:
Variations of OH radical in an urban plume inferred from NO2 column measurements, Geophys. Res. Lett., 40, 1856–1860, https://doi.org/10.1002/grl.50267, 2013. a, b
van Geffen, J., Eskes, H., Compernolle, S., Pinardi, G., Verhoelst, T., Lambert, J.-C., Sneep, M., ter Linden, M., Ludewig, A., Boersma, K. F., and Veefkind, J. P.:
Sentinel-5P TROPOMI NO2 retrieval: impact of version v2.2 improvements and comparisons with OMI and ground-based data, Atmos. Meas. Tech., 15, 2037–2060, https://doi.org/10.5194/amt-15-2037-2022, 2022a. a
van Geffen, J., Eskes, H., Veefkind, J., and Boersma, K.:
TROPOMI ATBD of the total and tropospheric NO2 data products, Royal Netherlands Meteorological Institute, https://sentinel.esa.int/documents/247904/2476257/Sentinel-5P-TROPOMI-ATBD-NO2-data-products (last access: 11 February 2023), 2022b. a
Veefkind, J. P., Serrano-Calvo, R., de Gouw, J., Dix, B., Schneising, O., Buchwitz, M., Barré, J., van der A, R. J., Liu, M., and Levelt, P. F.:
Widespread Frequent Methane Emissions From the Oil and Gas Industry in the Permian Basin, J. Geophys. Res.-Atmos., 128, e2022JD037479, https://doi.org/10.1029/2022JD037479, 2023. a, b, c
Wang, P., Huang, C., Brown de Colstoun, E. C., Tilton, J. C., and Tan, B.:
Global Human Built-up And Settlement Extent (HBASE) Dataset From Landsat, NASA Socioeconomic Data and Applications Center (SEDAC) [dat set], Palisades, New York, https://doi.org/10.7927/H4DN434S, 2017. a
Xue, R., Wang, S., Zhang, S., He, S., Liu, J., Tanvir, A., and Zhou, B.:
Estimating city NOx emissions from TROPOMI high spatial resolution observations – A case study on Yangtze River Delta, China, Urban Climate, 43, 101150, https://doi.org/10.1016/j.uclim.2022.101150, 2022.
a
Zhang, L., Jacob, D. J., Knipping, E. M., Kumar, N., Munger, J. W., Carouge, C. C., van Donkelaar, A., Wang, Y. X., and Chen, D.:
Nitrogen deposition to the United States: distribution, sources, and processes, Atmos. Chem. Phys., 12, 4539–4554, https://doi.org/10.5194/acp-12-4539-2012, 2012. a
Zhang, L., Wang, L., Wang, R., Chen, N., Yang, Y., Li, K., Sun, J., Yao, D., Wang, Y., Tao, M., and Sun, Y.:
Exploring formation mechanism and source attribution of ozone during the 2019 Wuhan Military World Games: Implications for ozone control strategies, J. Environ. Sci., 136, 400–411, https://doi.org/10.1016/j.jes.2022.12.009, 2022. a
Zhang, Q., Boersma, K. F., Zhao, B., Eskes, H., Chen, C., Zheng, H., and Zhang, X.:
Quantifying daily NOx and CO2 emissions from Wuhan using satellite observations from TROPOMI and OCO-2, Atmos. Chem. Phys., 23, 551–563, https://doi.org/10.5194/acp-23-551-2023, 2023. a, b
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
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...
Altmetrics
Final-revised paper
Preprint