Articles | Volume 21, issue 17
https://doi.org/10.5194/acp-21-13311-2021
© Author(s) 2021. 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-21-13311-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Sep 2021
Research article |
| 08 Sep 2021
| 08 Sep 2021
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
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
Lingbo Li
Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, NY, USA
Shruti Jagini
Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, NY, USA
Department of Earth and Environment, Boston University, Boston, MA, USA
Related authors
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-725, https://doi.org/10.5194/egusphere-2025-725, 2025
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 IASI and CrIS. Satellite-based flux estimates reveal that ammonia emissions deposit in nearby vegetation, with pronounced seasonal and spatial variability driven by agricultural activities, 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.
Chantelle R. Lonsdale and Kang Sun
Atmos. Chem. Phys., 23, 8727–8748, https://doi.org/10.5194/acp-23-8727-2023, https://doi.org/10.5194/acp-23-8727-2023, 2023
Short summary
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.
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.
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
Short summary
Short summary
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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-725, https://doi.org/10.5194/egusphere-2025-725, 2025
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 IASI and CrIS. Satellite-based flux estimates reveal that ammonia emissions deposit in nearby vegetation, with pronounced seasonal and spatial variability driven by agricultural activities, 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.
Zhongming Gao, Heping Liu, Dan Li, Bai Yang, Von Walden, Lei Li, and Ivan Bogoev
Atmos. Meas. Tech., 17, 4109–4120, https://doi.org/10.5194/amt-17-4109-2024, https://doi.org/10.5194/amt-17-4109-2024, 2024
Short summary
Short summary
Using data collected from three levels of a 62 m tower, we found that both the temperature variances and sensible heat flux obtained from sonic anemometers are consistently lower, by a few percent, compared to those from fine-wire thermocouples.
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.
Chantelle R. Lonsdale and Kang Sun
Atmos. Chem. Phys., 23, 8727–8748, https://doi.org/10.5194/acp-23-8727-2023, https://doi.org/10.5194/acp-23-8727-2023, 2023
Short summary
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.
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.
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
Short summary
Short summary
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.
Cited articles
Akaike, H.: A new look at the statistical model identification,
IEEE T. Automat. Contr., 19, 716–723,
https://doi.org/10.1109/TAC.1974.1100705, 1974. a
Apple: COVID-19 Mobility Trends Reports,
available at: https://covid19.apple.com/mobility/, last access: 12 July 2021. a
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, b
Beirle, S., Borger, C., Dörner, S., Li, A., Hu, Z., Liu, F., Wang, Y.,
and Wagner, T.: Pinpointing nitrogen oxide emissions from space, Sci. Adv., 5, eaax9800, https://doi.org/10.1126/sciadv.aax9800, 2019. a
Bösch, H., Brown, L., Castano, R., Christi, M., Crisp, D., Eldering, A., Fisher, B., Frankenberg, C., Gunson, M., Granat, R., McDuffie, J., Miller, C., Natraj, V., O'Brien, D., O'Dell, C., Osterman, G., Oyafuso, F., Payne, V., Polonski, I., Smyth, M., Spurr, R., Thompson, D., and Toon, G.: Orbiting
Carbon Observatory (OCO)-2 Level 2 Full Physics Retrieval Algorithm
Theoretical Basis Document, NASA JPL, Pasadena, CA, USA, 2015. a
Brasseur, G. P. and Jacob, D. J.: Modeling of atmospheric chemistry, Cambridge
University Press, Cambridge, UK, https://doi.org/10.1017/9781316544754, 2017. a, b
Buchwitz, M., Schneising, O., Reuter, M., Heymann, J., Krautwurst, S., Bovensmann, H., Burrows, J. P., Boesch, H., Parker, R. J., Somkuti, P., Detmers, R. G., Hasekamp, O. P., Aben, I., Butz, A., Frankenberg, C., and Turner, A. J.: Satellite-derived methane hotspot emission estimates using a fast data-driven method, Atmos. Chem. Phys., 17, 5751–5774, https://doi.org/10.5194/acp-17-5751-2017, 2017. a
Castellanos, P. and Boersma, K. F.: Reductions in nitrogen oxides over Europe
driven by environmental policy and economic recession, Sci. Rep.,
2, 265, https://doi.org/10.1038/srep00265, 2012. a, b
Choi, S., Lamsal, L. N., Follette-Cook, M., Joiner, J., Krotkov, N. A., Swartz, W. H., Pickering, K. E., Loughner, C. P., Appel, W., Pfister, G., Saide, P. E., Cohen, R. C., Weinheimer, A. J., and Herman, J. R.: Assessment of NO2 observations during DISCOVER-AQ and KORUS-AQ field campaigns, Atmos. Meas. Tech., 13, 2523–2546, https://doi.org/10.5194/amt-13-2523-2020, 2020. 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., McLinden, C. A., Griffin, D., Shephard, M. W., Van Der Graaf, S., Lutsch, E., Schaap, M., Gainairu-Matz, Y., Fioletov, V., Van Damme, M., Whitburn, S., Clarisse, L., Cady-Pereira, K., Clerbaux, C., Coheur, P. F., and Erisman, J. W.: NH3 emissions from large point sources derived from CrIS and IASI satellite observations, Atmos. Chem. Phys., 19, 12261–12293, https://doi.org/10.5194/acp-19-12261-2019, 2019. 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
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, b
Dong, E., Du, H., and Gardner, L.: An interactive web-based dashboard to track
COVID-19 in real time, Lancet Infect. Dis., 20, 533–534,
https://doi.org/10.1016/S1473-3099(20)30120-1, 2020. a
Duncan, B. N., Lamsal, L. N., Thompson, A. M., Yoshida, Y., Lu, Z., Streets,
D. G., Hurwitz, M. M., and Pickering, K. E.: A space-based, high-resolution
view of notable changes in urban NOx pollution around the world
(2005–2014), J. Geophys. Res.-Atmos., 121, 976–996,
https://doi.org/10.1002/2015JD024121, 2016. a, b
EEA: EEA: Air Quality e-Reporting,
available at: https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm, last access: 8 July 2021. a
ESA: TROPOMI Level 2 Nitrogen Dioxide total column products. Version 01,
https://doi.org/10.5270/S5P-s4ljg54, 2018. a
Filippini, T., Rothman, K. J., Goffi, A., Ferrari, F., Maffeis, G., Orsini, N.,
and Vinceti, M.: Satellite-detected tropospheric nitrogen dioxide and spread
of SARS-CoV-2 infection in Northern Italy,
Sci. Total Environ.,
739, 140278, https://doi.org/10.1016/j.scitotenv.2020.140278, 2020. a
Fioletov, V., McLinden, C. A., Kharol, S. K., Krotkov, N. A., Li, C., Joiner, J., Moran, M. D., Vet, R., Visschedijk, A. J. H., and Denier van der Gon, H. A. C.: Multi-source SO2 emission retrievals and consistency of satellite and surface measurements with reported emissions, Atmos. Chem. Phys., 17, 12597–12616, https://doi.org/10.5194/acp-17-12597-2017, 2017. a
GES DISC: Copernicus Sentinel data processed by ESA, Koninklijk Nederlands
Meteorologisch Instituut (KNMI), Sentinel-5P TROPOMI Tropospheric NO2 1-Orbit
L2 5.5 km × 3.5 km, https://doi.org/10.5270/S5P-s4ljg54, 2021. 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, 2019a. a, b, c
Goldberg, D. L., Saide, P. E., Lamsal, L. N., de Foy, B., Lu, Z., Woo, J.-H., Kim, Y., Kim, J., Gao, M., Carmichael, G., and Streets, D. G.: A top-down assessment using OMI NO2 suggests an underestimate in the NOx emissions inventory in Seoul, South Korea, during KORUS-AQ, Atmos. Chem. Phys., 19, 1801–1818, https://doi.org/10.5194/acp-19-1801-2019, 2019b. a
Goldberg, D. L., Anenberg, S. C., Griffin, D., McLinden, C. A., Lu, Z., and
Streets, D. G.: Disentangling the Impact of the COVID-19 Lockdowns on Urban
NO2 From Natural Variability, Geophys. Res. Lett., 47,
e2020GL089269, https://doi.org/10.1029/2020GL089269, 2020. a, b, c
Google LLC: Google COVID-19 Community Mobility Reports,
available at: https://www.google.com/covid19/mobility/, last access: 12 July 2021. a
Hansen, P. C. and O’Leary, D. P.: The use of the L-curve in the
regularization of discrete ill-posed problems,
SIAM J. Sci. Stat. Comp., 14, 1487–1503, 1993. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-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
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, Sci. Total Environ., 745,
141023, https://doi.org/10.1016/j.scitotenv.2020.141023, 2020. a
Huang, T., Zhu, X., Zhong, Q., Yun, X., Meng, W., Li, B., Ma, J., Zeng, E. Y.,
and Tao, S.: Spatial and Temporal Trends in Global Emissions of Nitrogen
Oxides from 1960 to 2014, Environ. Sci. Technol., 51,
7992–8000, https://doi.org/10.1021/acs.est.7b02235, 2017. a
Judd, L. M., Al-Saadi, J. A., Szykman, J. J., Valin, L. C., Janz, S. J., Kowalewski, M. G., Eskes, H. J., Veefkind, J. P., Cede, A., Mueller, M., Gebetsberger, M., Swap, R., Pierce, R. B., Nowlan, C. R., Abad, G. G., Nehrir, A., and Williams, D.: Evaluating Sentinel-5P TROPOMI tropospheric NO2 column densities with airborne and Pandora spectrometers near New York City and Long Island Sound, Atmos. Meas. Tech., 13, 6113–6140, https://doi.org/10.5194/amt-13-6113-2020, 2020. a
Keller, C. A., Evans, M. J., Knowland, K. E., Hasenkopf, C. A., Modekurty, S., Lucchesi, R. A., Oda, T., Franca, B. B., Mandarino, F. C., Díaz Suárez, M. V., Ryan, R. G., Fakes, L. H., and Pawson, S.: Global impact of COVID-19 restrictions on the surface concentrations of nitrogen dioxide and ozone, Atmos. Chem. Phys., 21, 3555–3592, https://doi.org/10.5194/acp-21-3555-2021, 2021. a
Kroll, J. H., Heald, C. L., Cappa, C. D., Farmer, D. K., Fry, J. L., Murphy,
J. G., and Steiner, A. L.: The complex chemical effects of COVID-19
shutdowns on air quality, Nat. Chem., 12, 777–779,
https://doi.org/10.1038/s41557-020-0535-z, 2020. a
Lamsal, L. N., Martin, R. V., Padmanabhan, A., Van Donkelaar, A., Zhang, Q.,
Sioris, C. E., Chance, K., Kurosu, T. P., and Newchurch, M. J.: Application
of satellite observations for timely updates to global anthropogenic NOx
emission inventories, Geophys. Res. Lett., 38, https://doi.org/10.1029/2010GL046476, 2011. a
Lamsal, L. N., Krotkov, N. A., Vasilkov, A., Marchenko, S., Qin, W., Yang, E.-S., Fasnacht, Z., Joiner, J., Choi, S., Haffner, D., Swartz, W. H., Fisher, B., and Bucsela, E.: Ozone Monitoring Instrument (OMI) Aura nitrogen dioxide standard product version 4.0 with improved surface and cloud treatments, Atmos. Meas. Tech., 14, 455–479, https://doi.org/10.5194/amt-14-455-2021, 2021. a
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, c
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, Sci. Adv., 6, eabc2992, https://doi.org/10.1126/sciadv.abc2992, 2020. a, b
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., 9, 20033,
https://doi.org/10.1038/s41598-019-56428-5, 2019. a
Lu, Z., Streets, D. G., de Foy, B., Lamsal, L. N., Duncan, B. N., and Xing, J.: Emissions of nitrogen oxides from US urban areas: estimation from Ozone Monitoring Instrument retrievals for 2005–2014, Atmos. Chem. Phys., 15, 10367–10383, https://doi.org/10.5194/acp-15-10367-2015, 2015. a, b
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, 2003. a
Martin, R. V., Fiore, A. M., and Van Donkelaar, A.: Space-based diagnosis of
surface ozone sensitivity to anthropogenic emissions,
Geophys. Res. Lett., 31, https://doi.org/10.1029/2004GL019416, 2004. 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, https://doi.org/10.1029/2012JD017817, 2012. a, b
Mijling, B., van der A, R. J., Boersma, K. F., Van Roozendael, M., De
Smedt, I., and Kelder, H. M.: Reductions of NO2 detected from space
during the 2008 Beijing Olympic Games, Geophys. Res. Lett., 36,
https://doi.org/10.1029/2009GL038943, 2009. 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, Jet Propulsion Laboratory [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
Krotkov, N. A., Lamsal, L. N., Marchenko, S. V., Bucsela, E. J., Swartz, W. H., Joiner, J., and the OMI core team: OMI/Aura Nitrogen Dioxide (NO2) Total and Tropospheric Column 1-orbit L2 Swath 13×24 km V003, available at: https://disc.gsfc.nasa.gov/datasets/OMNO2_003/summary, last access: 23 June 2021.
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
Rodgers, C. D.: Inverse methods for atmospheric sounding: theory and
practice, vol. 2, World Scientific, Singapore, https://doi.org/10.1142/3171, 2000. a, b
Russell, A. R., Valin, L. C., and Cohen, R. C.: Trends in OMI NO2 observations over the United States: effects of emission control technology and the economic recession, Atmos. Chem. Phys., 12, 12197–12209, https://doi.org/10.5194/acp-12-12197-2012, 2012. a
Savitzky, A. and Golay, M. J. E.: Smoothing and Differentiation of Data by
Simplified Least Squares Procedures, Anal. Chem., 36, 1627–1639,
https://doi.org/10.1021/ac60214a047, 1964. a
Schenkeveld, V. M. E., Jaross, G., Marchenko, S., Haffner, D., Kleipool, Q. L., Rozemeijer, N. C., Veefkind, J. P., and Levelt, P. F.: In-flight performance of the Ozone Monitoring Instrument, Atmos. Meas. Tech., 10, 1957–1986, https://doi.org/10.5194/amt-10-1957-2017, 2017. a, b
Seinfeld, J. H. and Pandis, S. N.: Atmospheric chemistry and physics: from air
pollution to climate change, 2nd edn., J. Wiley, New York, 2006. 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, b
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, b
Stavrakou, T., Müller, J.-F., Boersma, K. F., van der A, R. J., Kurokawa, J., Ohara, T., and Zhang, Q.: Key chemical NOx sink uncertainties and how they influence top-down emissions of nitrogen oxides, Atmos. Chem. Phys., 13, 9057–9082, https://doi.org/10.5194/acp-13-9057-2013, 2013. a
Sun, K.: Physical Oversampling code for ACP RRNES paper, v0.1, Zenodo [code], 2021.
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, 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, c, d
Valin, L. C., Russell, A. R., and Cohen, R. C.: Chemical feedback effects on the spatial patterns of the NOx weekend effect: a sensitivity analysis, Atmos. Chem. Phys., 14, 1–9, https://doi.org/10.5194/acp-14-1-2014, 2014. a
Van Damme, M., Clarisse, L., Whitburn, S., Hadji-Lazaro, J., Hurtmans, D.,
Clerbaux, C., and Coheur, P.-F.: Industrial and agricultural ammonia point
sources exposed, Nature, 564, 99, https://doi.org/10.1038/s41586-018-0747-1, 2018. a, b
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
Varon, D. J., Jacob, D. J., McKeever, J., Jervis, D., Durak, B. O. A., Xia, Y., and Huang, Y.: Quantifying methane point sources from fine-scale satellite observations of atmospheric methane plumes, Atmos. Meas. Tech., 11, 5673–5686, https://doi.org/10.5194/amt-11-5673-2018, 2018. a
Verhoelst, T., Compernolle, S., Pinardi, G., Lambert, J.-C., Eskes, H. J., Eichmann, K.-U., Fjæraa, A. M., Granville, J., Niemeijer, S., Cede, A., Tiefengraber, M., Hendrick, F., Pazmiño, A., Bais, A., Bazureau, A., Boersma, K. F., Bognar, K., Dehn, A., Donner, S., Elokhov, A., Gebetsberger, M., Goutail, F., Grutter de la Mora, M., Gruzdev, A., Gratsea, M., Hansen, G. H., Irie, H., Jepsen, N., Kanaya, Y., Karagkiozidis, D., Kivi, R., Kreher, K., Levelt, P. F., Liu, C., Müller, M., Navarro Comas, M., Piters, A. J. M., Pommereau, J.-P., Portafaix, T., Prados-Roman, C., Puentedura, O., Querel, R., Remmers, J., Richter, A., Rimmer, J., Rivera Cárdenas, C., Saavedra de Miguel, L., Sinyakov, V. P., Stremme, W., Strong, K., Van Roozendael, M., Veefkind, J. P., Wagner, T., Wittrock, F., Yela González, M., and Zehner, C.: Ground-based validation of the Copernicus Sentinel-5P TROPOMI NO2 measurements with the NDACC ZSL-DOAS, MAX-DOAS and Pandonia global networks, Atmos. Meas. Tech., 14, 481–510, https://doi.org/10.5194/amt-14-481-2021, 2021. a
Visser, A. J., Boersma, K. F., Ganzeveld, L. N., and Krol, M. C.: European NOx emissions in WRF-Chem derived from OMI: impacts on summertime surface ozone, Atmos. Chem. Phys., 19, 11821–11841, https://doi.org/10.5194/acp-19-11821-2019, 2019.
a, b
Wang, Y., Wang, J., Xu, X., Henze, D. K., Qu, Z., and Yang, K.: Inverse modeling of SO2 and NOx emissions over China using multisensor satellite data – Part 1: Formulation and sensitivity analysis, Atmos. Chem. Phys., 20, 6631–6650, https://doi.org/10.5194/acp-20-6631-2020, 2020. a
Witte, J. C., Schoeberl, M. R., Douglass, A. R., Gleason, J. F., Krotkov,
N. A., Gille, J. C., Pickering, K. E., and Livesey, N.: Satellite
observations of changes in air quality during the 2008 Beijing Olympics and
Paralympics, Geophys. Res. Lett., 36,
https://doi.org/10.1029/2009GL039236, 2009. a
Zara, M., Boersma, K. F., Eskes, H., Denier van der Gon, H.,
Vilà-Guerau de Arellano, J., Krol, M., van der Swaluw, E., Schuch,
W., and Velders, G. J. M.: Reductions in nitrogen oxides over the
Netherlands between 2005 and 2018 observed from space and on the ground:
Decreasing emissions and increasing O3 indicate changing NOx chemistry,
Atmos. Environ., 9, 100104,
https://doi.org/10.1016/j.aeaoa.2021.100104, 2021. a, b
Zhang, Y., Gautam, R., Zavala-Araiza, D., Jacob, D. J., Zhang, R., Zhu, L.,
Sheng, J.-X., and Scarpelli, T.: Satellite-Observed Changes in Mexico's
Offshore Gas Flaring Activity Linked to Oil/Gas Regulations, Geophys. Res. Lett., 46, 1879–1888, https://doi.org/10.1029/2018GL081145,
2019. a
Zheng, B., Geng, G., Ciais, P., Davis, S. J., Martin, R. V., Meng, J., Wu, N.,
Chevallier, F., Broquet, G., Boersma, F., van der A, R., Lin, J., Guan, D.,
Lei, Y., He, K., and Zhang, Q.: Satellite-based estimates of decline and
rebound in China's CO2 emissions during COVID-19 pandemic, Sci. Adv., 6, eabd4998, https://doi.org/10.1126/sciadv.abd4998, 2020. a
Zhu, L., Jacob, D. J., Keutsch, F. N., Mickley, L. J., Scheffe, R., Strum, M.,
González Abad, G., Chance, K., Yang, K., Rappenglück, B.,
Millet, D. B., Baasandorj, M., Jaeglé, L., and Shah, V.: Formaldehyde
(HCHO) as a hazardous air pollutant: mapping surface air concentrations from
satellite and inferring cancer risks in the United States, Environ. Sci. Technol., 51, 5650–5657, https://doi.org/10.1021/acs.est.7b01356, 2017. a
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.
We bridge the gap between satellite column observations and emissions by accounting for the...
Altmetrics
Final-revised paper
Preprint