Articles | Volume 25, issue 13
https://doi.org/10.5194/acp-25-6725-2025
© Author(s) 2025. 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-25-6725-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Estimation of diurnal emissions of CO2 from thermal power plants using spaceborne integrated path differential absorption (IPDA) lidar
Xuanye Zhang
State Key Laboratory of Environment Characteristics and Effects for Near-space, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Hailong Yang
Shanghai Satellite Engineering Research Institute, Shanghai, 201109, China
Lingbing Bu
CORRESPONDING AUTHOR
State Key Laboratory of Environment Characteristics and Effects for Near-space, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Zengchang Fan
State Key Laboratory of Environment Characteristics and Effects for Near-space, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Yale-NUIST Center on Atmospheric Environment, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Binglong Chen
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Innovation Center for FengYun Meteorological Satellite (FYSIC), Beijing, 100081, China
Lu Zhang
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Innovation Center for FengYun Meteorological Satellite (FYSIC), Beijing, 100081, China
Sihan Liu
Satellite Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing, China
Zhongting Wang
Satellite Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing, China
Jiqiao Liu
Key Laboratory of Space Laser Communication and Detection Technology, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy Of Sciences, Shanghai, China
Weibiao Chen
Key Laboratory of Space Laser Communication and Detection Technology, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy Of Sciences, Shanghai, China
Xuhui Lee
School of the Environment, Yale University, New Haven, CT, USA
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Pingyi Dong, Xingwen Jiang, Xingbing Zhao, Yuanchang Dong, Jiafeng Zheng, Chun Hu, Guolu Gao, Lei Liu, Shulei Li, and Lingbing Bu
EGUsphere, https://doi.org/10.5194/egusphere-2025-2523, https://doi.org/10.5194/egusphere-2025-2523, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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A method is developed and validated for retrieving vertical profiles of DSD parameters from a single-frequency Ka-band radar in this study. Some unique characteristics of the vertical profiles of DSD parameters in the eastern Tibetan Plateau are found. The empirical relationships for quantitative precipitation estimates and attenuation correction in the eastern Tibetan Plateau with Ka-band radar are derived.
Fanqian Meng, Junwu Tang, Guangyao Dai, Wenrui Long, Kangwen Sun, Zhiyu Zhang, Xiaoquan Song, Jiqiao Liu, Weibiao Chen, and Songhua Wu
Atmos. Meas. Tech., 18, 2021–2039, https://doi.org/10.5194/amt-18-2021-2025, https://doi.org/10.5194/amt-18-2021-2025, 2025
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This paper presents a comprehensive calibration procedure for the first spaceborne high-spectral-resolution lidar with an iodine vapor absorption filter Aerosol and Carbon Detection Lidar (ACDL) on board DQ-1 by utilizing nighttime 532 nm multi-channel data. We analyzed the error sources of the multi-channel calibration coefficients and assessed the results. The results indicate that the uncertainty of the clear-air scattering ratio was within the anticipated range of 7.9 %.
Mengyuan Wang, Min Min, Jun Li, Han Lin, Yongen Liang, Binlong Chen, Zhigang Yao, Na Xu, and Miao Zhang
Atmos. Chem. Phys., 24, 14239–14256, https://doi.org/10.5194/acp-24-14239-2024, https://doi.org/10.5194/acp-24-14239-2024, 2024
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Although machine learning technology is advanced in the field of satellite remote sensing, the physical inversion algorithm based on cloud base height can better capture the daily variation in the characteristics of the cloud base.
Chenxing Zha, Lingbing Bu, Zhi Li, Qin Wang, Ahmad Mubarak, Pasindu Liyanage, Jiqiao Liu, and Weibiao Chen
Atmos. Meas. Tech., 17, 4425–4443, https://doi.org/10.5194/amt-17-4425-2024, https://doi.org/10.5194/amt-17-4425-2024, 2024
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China has launched the atmospheric environment monitoring satellite DQ-1, which consists of an advanced lidar system. Our research presents a retrieval algorithm of the DQ-1 lidar system, and the retrieval results are consistent with other datasets. We also use the DQ-1 dataset to investigate dust and volcanic aerosols. This research shows that the DQ-1 lidar system can accurately measure the Earth's atmosphere and has potential for scientific applications.
Rong Mao, Xin Luo, Jiu Jimmy Jiao, Xiaoyan Shi, and Wei Xiao
EGUsphere, https://doi.org/10.5194/egusphere-2024-1513, https://doi.org/10.5194/egusphere-2024-1513, 2024
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Lake Taihu is the largest eutrophic lake in China that is shallow with a dense river network. Eutrophication is frequently observed in the lake due to excess pollutant loadings. Understanding water transport is essential for solving the problem. We developed an age-tracking rainfall mixing model to calculate residence time of rain and river water using isotope data. The variation of mixing ratio of rainwater is also estimated. The isotope data indicates the control factors of mixing in the lake.
Kangwen Sun, Guangyao Dai, Songhua Wu, Oliver Reitebuch, Holger Baars, Jiqiao Liu, and Suping Zhang
Atmos. Chem. Phys., 24, 4389–4409, https://doi.org/10.5194/acp-24-4389-2024, https://doi.org/10.5194/acp-24-4389-2024, 2024
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This paper investigates the correlation between marine aerosol optical properties and wind speeds over remote oceans using the spaceborne lidars ALADIN and CALIOP. Three remote ocean areas are selected. Pure marine aerosol optical properties at 355 nm are derived from ALADIN. The relationships between marine aerosol optical properties and wind speeds are analyzed within and above the marine atmospheric boundary layer, revealing the effect of wind speed on marine aerosols over remote oceans.
Guangyao Dai, Songhua Wu, Wenrui Long, Jiqiao Liu, Yuan Xie, Kangwen Sun, Fanqian Meng, Xiaoquan Song, Zhongwei Huang, and Weibiao Chen
Atmos. Meas. Tech., 17, 1879–1890, https://doi.org/10.5194/amt-17-1879-2024, https://doi.org/10.5194/amt-17-1879-2024, 2024
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An overview is given of the main algorithms applied to derive the aerosol and cloud optical property product of the Aerosol and Carbon Detection Lidar (ACDL), which is capable of globally profiling aerosol and cloud optical properties with high accuracy. The paper demonstrates the observational capabilities of ACDL for aerosol and cloud vertical structure and global distribution through two optical property product measurement cases and global aerosol optical depth profile observations.
Qiantao Liu, Zhongwei Huang, Jiqiao Liu, Weibiao Chen, Qingqing Dong, Songhua Wu, Guangyao Dai, Meishi Li, Wuren Li, Ze Li, Xiaodong Song, and Yuan Xie
Atmos. Meas. Tech., 17, 1403–1417, https://doi.org/10.5194/amt-17-1403-2024, https://doi.org/10.5194/amt-17-1403-2024, 2024
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The achieved results revealed that the ACDL observations were in good agreement with the ground-based lidar measurements during dust events. The heights of cloud top and bottom from these two measurements were well matched and comparable. This study proves that the ACDL provides reliable observations of aerosol and cloud in the presence of various climatic conditions, which helps to further evaluate the impacts of aerosol on climate and the environment, as well as on the ecosystem in the future.
Fanqian Meng, Junwu Tang, Guangyao Dai, Wenrui Long, Kangwen Sun, Zhiyu Zhang, Xiaoquan Song, Jiqiao Liu, Weibiao Chen, and Songhua Wu
EGUsphere, https://doi.org/10.5194/egusphere-2024-588, https://doi.org/10.5194/egusphere-2024-588, 2024
Preprint archived
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This paper presents a comprehensive calibration procedure for the first spaceborne high-spectral-resolution lidar with an iodine vapor absorption filter ACDL on board DQ-1 by utilizing nighttime 532 nm multi-channel data. And analyzed the error sources of the multi-channel calibration coefficients and assessed the results. The results shows that the ACDL polarization channel calibration is reliable and operates within the expected error range of approximately 5 %.
Mengyuan Wang, Min Min, Jun Li, Han Lin, Yongen Liang, Binlong Chen, Zhigang Yao, Na Xu, and Miao Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2023-2843, https://doi.org/10.5194/egusphere-2023-2843, 2023
Preprint archived
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Our study primarily addresses the feasibility of employing advanced machine learning and physics-based algorithms to capture the diurnal variations in cloud base height parameters using geostationary meteorological satellite remote sensing. The results indicated that the caution is warranted when utilizing cloud base property products trained on satellite and laser radar data for climate research. Fixed training samples might obscure the pronounced diurnal variations in cloud base heights.
Farhan Mustafa, Lingbing Bu, Qin Wang, Na Yao, Muhammad Shahzaman, Muhammad Bilal, Rana Waqar Aslam, and Rashid Iqbal
Atmos. Meas. Tech., 14, 7277–7290, https://doi.org/10.5194/amt-14-7277-2021, https://doi.org/10.5194/amt-14-7277-2021, 2021
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A neural-network-based approach was suggested to estimate CO2 emissions using satellite-based net primary productivity (NPP) and XCO2 retrievals. XCO2 anomalies were calculated for each year using OCO-2 retrievals. A Generalized Regression Neural Network (GRNN) model was then built; NPP, XCO2 anomalies, and ODIAC CO2 emissions from 2015 to 2018 were used as a training dataset; and, finally, CO2 emissions were predicted for 2019 based on the NPP and XCO2 anomalies calculated for the same year.
Qin Wang, Farhan Mustafa, Lingbing Bu, Shouzheng Zhu, Jiqiao Liu, and Weibiao Chen
Atmos. Meas. Tech., 14, 6601–6617, https://doi.org/10.5194/amt-14-6601-2021, https://doi.org/10.5194/amt-14-6601-2021, 2021
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In this work, an airborne experiment was carried out to validate a newly developed CO2 monitoring IPDA lidar against the in situ measurements obtained from a commercial CO2 monitoring instrument installed on an aircraft. The XCO2 values calculated with the IPDA lidar measurements were compared with the dry-air CO2 mole fraction measurements obtained from the in situ instruments, and the results showed a good agreement between the two datasets.
Tao Tang, Drew Shindell, Yuqiang Zhang, Apostolos Voulgarakis, Jean-Francois Lamarque, Gunnar Myhre, Gregory Faluvegi, Bjørn H. Samset, Timothy Andrews, Dirk Olivié, Toshihiko Takemura, and Xuhui Lee
Atmos. Chem. Phys., 21, 13797–13809, https://doi.org/10.5194/acp-21-13797-2021, https://doi.org/10.5194/acp-21-13797-2021, 2021
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Previous studies showed that black carbon (BC) could warm the surface with decreased incoming radiation. With climate models, we found that the surface energy redistribution plays a more crucial role in surface temperature compared with other forcing agents. Though BC could reduce the surface heating, the energy dissipates less efficiently, which is manifested by reduced convective and evaporative cooling, thereby warming the surface.
Cheng Hu, Jiaping Xu, Cheng Liu, Yan Chen, Dong Yang, Wenjing Huang, Lichen Deng, Shoudong Liu, Timothy J. Griffis, and Xuhui Lee
Atmos. Chem. Phys., 21, 10015–10037, https://doi.org/10.5194/acp-21-10015-2021, https://doi.org/10.5194/acp-21-10015-2021, 2021
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Seventy percent of global CO2 emissions were emitted from urban landscapes. The Yangtze River delta (YRD) ranks as one of the most densely populated regions in the world and is an anthropogenic CO2 hotspot. Besides anthropogenic factors, natural ecosystems and croplands act as significant CO2 sinks and sources. Independent quantification of the fossil and cement CO2 emission and assessment of their impact on atmospheric δ13C-CO2 have potential to improve our understanding of urban CO2 cycling.
Xiaodan Ma, Jianping Huang, Tianliang Zhao, Cheng Liu, Kaihui Zhao, Jia Xing, and Wei Xiao
Atmos. Chem. Phys., 21, 1–16, https://doi.org/10.5194/acp-21-1-2021, https://doi.org/10.5194/acp-21-1-2021, 2021
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The present work aims at identifying and quantifying the relative contributions of the key factors in driving a rapid increase in summertime surface O3 over the North China Plain during 2013–2019. In addition to anthropogenic emission reduction and meteorological variabilities, our study highlights the importance of inclusion of aerosol absorption and scattering properties rather than aerosol abundance only in accurate assessment of aerosol radiative effect on surface O3 formation and change.
Zhen Zhang, Mi Zhang, Chang Cao, Wei Wang, Wei Xiao, Chengyu Xie, Haoran Chu, Jiao Wang, Jiayu Zhao, Lei Jia, Qiang Liu, Wenjing Huang, Wenqing Zhang, Yang Lu, Yanhong Xie, Yi Wang, Yini Pu, Yongbo Hu, Zheng Chen, Zhihao Qin, and Xuhui Lee
Earth Syst. Sci. Data, 12, 2635–2645, https://doi.org/10.5194/essd-12-2635-2020, https://doi.org/10.5194/essd-12-2635-2020, 2020
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Inland lakes play an important role in regulating local climate. In this paper, we describe a dataset on microclimate and eddy covariance variables measured at a network of sites across Lake Taihu. The dataset, which appears to be the first of its kind for lake systems, can be used for validation of lake–air flux parameterizations, investigation of climatic controls on lake evaporation, evaluation of remote-sensing surface data products and global synthesis on lake–air interactions.
Cited articles
Ahn, D., Hansford, J. R., Howe, S. T., Ren, X. R., Salawitch, R. J., Zeng, N., Cohen, M. D., Stunder, B., Salmon, O. E., and Shepson, P. B.: Fluxes of atmospheric greenhouse-gases in Maryland (FLAGG-MD): Emissions of carbon dioxide in the Baltimore, MD-Washington, DC area, J. Geophys. Res.-Atmos., 125, e2019JD032004, https://doi.org/10.1029/2019JD032004, 2020.
Amediek, A., Ehret, G., Fix, A., Wirth, M., Büdenbender, C., Quatrevalet, M., Kiemle, C., and Gerbig, C.: CHARM-F – a new airborne integrated-path differential-absorption lidar for carbon dioxide and methane observations: measurement performance and quantification of strong point source emissions, Appl. Optics, 56, 5182–5197, 2017.
Arias, P., Bellouin, N., Coppola, E., Jones, R., Krinner, G., Marotzke, J., Naik, V., Palmer, M., Plattner, G.-K., and Rogelj, J.: Climate Change 2021: the physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; technical summary, Cambridge University Press, https://doi.org/10.1017/9781009157896.002, 2021.
Ashrafi, K. and Hoshyaripour, G. A.: A model to determine atmospheric stability and its correlation with CO concentration, International Journal of Civil and Environmental Engineering, 2, 82–88, 2010.
Beals, G. A.: Guide to local diffusion of air pollutants, Air Weather Service (MAC), US Air Force, 1971.
Brunner, D., Kuhlmann, G., Marshall, J., Clément, V., Fuhrer, O., Broquet, G., Löscher, A., and Meijer, Y.: Accounting for the vertical distribution of emissions in atmospheric CO2 simulations, Atmos. Chem. Phys., 19, 4541–4559, https://doi.org/10.5194/acp-19-4541-2019, 2019.
Brunner, D., Kuhlmann, G., Henne, S., Koene, E., Kern, B., Wolff, S., Voigt, C., Jöckel, P., Kiemle, C., Roiger, A., Fiehn, A., Krautwurst, S., Gerilowski, K., Bovensmann, H., Borchardt, J., Galkowski, M., Gerbig, C., Marshall, J., Klonecki, A., Prunet, P., Hanfland, R., Pattantyús-Ábrahám, M., Wyszogrodzki, A., and Fix, A.: Evaluation of simulated CO2 power plant plumes from six high-resolution atmospheric transport models, Atmos. Chem. Phys., 23, 2699–2728, https://doi.org/10.5194/acp-23-2699-2023, 2023.
Brusca, S., Famoso, F., Lanzafame, R., Mauro, S., Garrano, A. M. C., and Monforte, P.: Theoretical and experimental study of Gaussian Plume model in small scale system, Enrgy. Proced., 101, 58–65, 2016.
Cai, M., Han, G., Ma, X., Pei, Z., and Gong, W.: Active–passive collaborative approach for XCO2 retrieval using spaceborne sensors, Opt. Lett., 47, 4211–4214, 2022.
Carbon Brief: Global coal power, Carbon Brief [data set], https://www.carbonbrief.org/mapped-worlds-coal-power-plants/, last access: 12 December 2024.
Climate TRACE: Greenhouse Gas Emissions Data – Climate TRACE, Climate TRACE, United States of America, https://coilink.org/20.500.12592/qjq2f9h (last access: 30 June 2025), 2023.
Crisp, D., Pollock, H. R., Rosenberg, R., Chapsky, L., Lee, R. A. M., Oyafuso, F. A., Frankenberg, C., O'Dell, C. W., Bruegge, C. J., Doran, G. B., Eldering, A., Fisher, B. M., Fu, D., Gunson, M. R., Mandrake, L., Osterman, G. B., Schwandner, F. M., Sun, K., Taylor, T. E., Wennberg, P. O., and Wunch, D.: The on-orbit performance of the Orbiting Carbon Observatory-2 (OCO-2) instrument and its radiometrically calibrated products, Atmos. Meas. Tech., 10, 59–81, https://doi.org/10.5194/amt-10-59-2017, 2017.
Ehret, G., Kiemle, C., Wirth, M., Amediek, A., Fix, A., and Houweling, S.: Space-borne remote sensing of CO2, CH4, and N2O by integrated path differential absorption lidar: a sensitivity analysis, Appl. Phys. B, 90, 593–608, 2008.
Eldering, A., Taylor, T. E., O'Dell, C. W., and Pavlick, R.: The OCO-3 mission: measurement objectives and expected performance based on 1 year of simulated data, Atmos. Meas. Tech., 12, 2341–2370, https://doi.org/10.5194/amt-12-2341-2019, 2019.
Fan, C., Chen, C., Liu, J., Xie, Y., Li, K., Zhu, X., Zhang, L., Cao, X., Han, G., and Huang, Y.: Preliminary analysis of global column-averaged CO2 concentration data from the spaceborne aerosol and carbon dioxide detection lidar onboard AEMS, Opt. Express, 32, 21870–21886, 2024.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., and Reichle, R.: The modern-era retrospective analysis for research and applications, version 2 (MERRA-2), J. Climate, 30, 5419–5454, 2017.
Global Modeling and Assimilation Office (GMAO): MERRA-2 tavg3_3d_asm_Nv: 3d,3-Hourly,Time-Averaged,Model-Level,Assimilation,Assimilated Meteorological Fields V5.12.4, Goddard Earth Sciences Data and Information Services Center (GES DISC), Greenbelt, MD, USA [data set], https://doi.org/10.5067/SUOQESM06LPK, 2015.
Guo, W., Shi, Y., Liu, Y., and Su, M.: CO2 emissions retrieval from coal-fired power plants based on OCO-2/3 satellite observations and a Gaussian plume model, J. Clean. Prod., 397, 136525, https://doi.org/10.1016/j.jclepro.2023.136525, 2023.
Gurney, K. R., Liang, J., Patarasuk, R., O'Keeffe, D., Huang, J., Hutchins, M., Lauvaux, T., Turnbull, J. C., and Shepson, P. B.: Reconciling the differences between a bottom-up and inverse-estimated FFCO2 emissions estimate in a large US urban area, Elem. Sci. Anth., 5, 44, https://doi.org/10.1525/elementa.137, 2017.
Han, G., Huang, Y., Shi, T., Zhang, H., Li, S., Zhang, H., Chen, W., Liu, J., and Gong, W.: Quantifying CO2 emissions of power plants with Aerosols and Carbon Dioxide Lidar onboard DQ-1, Remote Sens. Environ., 313, 114368, https://doi.org/10.1016/j.rse.2024.114368, 2024.
Hendriks, C.: Carbon dioxide removal from coal-fired power plants, Springer Science & Business Media, ISBN 9780792332695, 2012.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., and Schepers, D.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, 2020.
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 pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023.
Hu, C., Griffis, T. J., Xia, L., Xiao, W., Liu, C., Xiao, Q., Huang, X., Yang, Y., Zhang, L., and Hou, B.: Anthropogenic CO2 emission reduction during the COVID-19 pandemic in Nanchang City, China, Environ. Pollut., 309, 119767, https://doi.org/10.1016/j.envpol.2022.119767, 2022.
Hu, Y. and Shi, Y.: Estimating CO2 emissions from large scale coal-fired power plants using OCO-2 observations and emission inventories, Atmosphere, 12, 811, https://doi.org/10.3390/atmos12070811, 2021.
Kiemle, C., Ehret, G., Amediek, A., Fix, A., Quatrevalet, M., and Wirth, M.: Potential of spaceborne lidar measurements of carbon dioxide and methane emissions from strong point sources, Remote Sens., 9, 1137, https://doi.org/10.3390/rs9111137, 2017.
Krings, T., Gerilowski, K., Buchwitz, M., Reuter, M., Tretner, A., Erzinger, J., Heinze, D., Pflüger, U., Burrows, J. P., and Bovensmann, H.: MAMAP – a new spectrometer system for column-averaged methane and carbon dioxide observations from aircraft: retrieval algorithm and first inversions for point source emission rates, Atmos. Meas. Tech., 4, 1735–1758, https://doi.org/10.5194/amt-4-1735-2011, 2011.
Krings, T., Neininger, B., Gerilowski, K., Krautwurst, S., Buchwitz, M., Burrows, J. P., Lindemann, C., Ruhtz, T., Schüttemeyer, D., and Bovensmann, H.: Airborne remote sensing and in situ measurements of atmospheric CO2 to quantify point source emissions, Atmos. Meas. Tech., 11, 721–739, https://doi.org/10.5194/amt-11-721-2018, 2018.
Kyoto Protocol: United Nations framework convention on climate change, Kyoto Protocol, Kyoto, 19, 1–21, http://scholar.google.com/scholar_lookup?&title=United%20Nations%20framework%20convention%20on%20climate%20change&journal=Kyoto%20Protocol%2C%20Kyoto&volume=19&issue=8&pages=1-21&publication_year=1997&author=Protocol%2CK (last access: 1 July 2025), 1997.
Lauvaux, T., Miles, N. L., Deng, A., Richardson, S. J., Cambaliza, M. O., Davis, K. J., Gaudet, B., Gurney, K. R., Huang, J., and O'Keefe, D.: High-resolution atmospheric inversion of urban CO2 emissions during the dormant season of the Indianapolis Flux Experiment (INFLUX), J. Geophys. Res.-Atmos., 121, 5213–5236, 2016.
Letu, H., Nakajima, T. Y., and Nishio, F.: Regional-scale estimation of electric power and power plant CO2 emissions using defense meteorological satellite program operational linescan system nighttime satellite data, Environ. Sci. Technol. Lett., 1, 259–265, 2014.
Li, W., Zhang, S., and Lu, C.: Exploration of China's net CO2 emissions evolutionary pathways by 2060 in the context of carbon neutrality, Sci. Total Environ., 831, 154909, https://doi.org/10.1016/j.scitotenv.2022.154909, 2022.
Luther, A., Kleinschek, R., Scheidweiler, L., Defratyka, S., Stanisavljevic, M., Forstmaier, A., Dandocsi, A., Wolff, S., Dubravica, D., Wildmann, N., Kostinek, J., Jöckel, P., Nickl, A.-L., Klausner, T., Hase, F., Frey, M., Chen, J., Dietrich, F., Nȩcki, J., Swolkień, J., Fix, A., Roiger, A., and Butz, A.: Quantifying CH4 emissions from hard coal mines using mobile sun-viewing Fourier transform spectrometry, Atmos. Meas. Tech., 12, 5217–5230, https://doi.org/10.5194/amt-12-5217-2019, 2019.
Menzies, R. T., Spiers, G. D., and Jacob, J.: Airborne laser absorption spectrometer measurements of atmospheric CO2 column mole fractions: Source and sink detection and environmental impacts on retrievals, J. Atmos. Ocean. Tech., 31, 404–421, 2014.
Miller, C., Crisp, D., DeCola, P., Olsen, S., Randerson, J., Michalak, A., Alkhaled, A., Rayner, P., Jacob, D. J., and Suntharalingam, P.: Precision requirements for space-based data, J. Geophys. Res.-Atmos., 112, D10314, https://doi.org/10.1029/2006JD007659, 2007.
Nassar, R., Hill, T. G., McLinden, C. A., Wunch, D., Jones, D. B., and Crisp, D.: Quantifying CO2 emissions from individual power plants from space, Geophys. Res. Lett., 44, 10045–10053, 2017.
Nassar, R., Mastrogiacomo, J.-P., Bateman-Hemphill, W., McCracken, C., MacDonald, C. G., Hill, T., O'Dell, C. W., Kiel, M., and Crisp, D.: Advances in quantifying power plant CO2 emissions with OCO-2, Remote Sens. Environ., 264, 112579, https://doi.org/10.1016/j.rse.2021.112579, 2021.
Ohyama, H., Shiomi, K., Kikuchi, N., Morino, I., and Matsunaga, T.: Quantifying CO2 emissions from a thermal power plant based on CO2 column measurements by portable Fourier transform spectrometers, Remote Sens. Environ., 267, 112714, https://doi.org/10.1016/j.rse.2021.112714, 2021.
Panofsky, H. A. and Dutton, J. A.: Atmospheric turbulence. Models and methods for engineering applications, John Wiley & Sons, New York, ISBN 0471057142, 1984.
Pasquill, F.: The estimation of the dispersion of windborne material, Meteor. Mag., 90, 33–49, 1961.
Pasquill, F. and Smith, F. B.: Atmospheric diffusion, vol. 437, E. Horwood, New York, NY, USA, ISBN 9780130513359, 1983.
Peters, G. P., Marland, G., Le Quéré, C., Boden, T., Canadell, J. G., and Raupach, M. R.: Rapid growth in CO2 emissions after the 2008–2009 global financial crisis, Nat. Clim. Change, 2, 2–4, 2012.
Pillai, D., Gerbig, C., Kretschmer, R., Beck, V., Karstens, U., Neininger, B., and Heimann, M.: Comparing Lagrangian and Eulerian models for CO2 transport – a step towards Bayesian inverse modeling using WRF/STILT-VPRM, Atmos. Chem. Phys., 12, 8979–8991, https://doi.org/10.5194/acp-12-8979-2012, 2012.
Reuter, M., Buchwitz, M., Schneising, O., Krautwurst, S., O'Dell, C. W., Richter, A., Bovensmann, H., and Burrows, J. P.: Towards monitoring localized CO2 emissions from space: co-located regional CO2 and NO2 enhancements observed by the OCO-2 and S5P satellites, Atmos. Chem. Phys., 19, 9371–9383, https://doi.org/10.5194/acp-19-9371-2019, 2019.
Savić, S., Selakov, A., and Milošević, D.: Cold and warm air temperature spells during the winter and summer seasons and their impact on energy consumption in urban areas, Nat. Hazards, 73, 373–387, 2014.
Schwandner, F. M., Gunson, M. R., Miller, C. E., Carn, S. A., Eldering, A., Krings, T., Verhulst, K. R., Schimel, D. S., Nguyen, H. M., and Crisp, D.: Spaceborne detection of localized carbon dioxide sources, Science, 358, eaam5782, https://doi.org/10.1126/science.aam5782, 2017.
Searchinger, T. D., Wirsenius, S., Beringer, T., and Dumas, P.: Assessing the efficiency of changes in land use for mitigating climate change, Nature, 564, 249–253, 2018.
Sheng, M., Lei, L., Zeng, Z.-C., Rao, W., Song, H., and Wu, C.: Global land 1° mapping dataset of XCO2 from satellite observations of GOSAT and OCO-2 from 2009 to 2020, Big Earth Data, 7, 170–190, 2023.
Shi, T., Han, G., Ma, X., Pei, Z., Chen, W., Liu, J., Zhang, X., Li, S., and Gong, W.: Quantifying strong point sources emissions of CO2 using spaceborne LiDAR: Method development and potential analysis, Energ. Convers. Manage., 292, 117346, https://doi.org/10.1016/j.enconman.2023.117346, 2023.
Toja-Silva, F., Chen, J., Hachinger, S., and Hase, F.: CFD simulation of CO2 dispersion from urban thermal power plant: Analysis of turbulent Schmidt number and comparison with Gaussian plume model and measurements, J. Wind Eng. Ind. Aerod., 169, 177–193, 2017.
Tubiello, F. N., Salvatore, M., Ferrara, A. F., House, J., Federici, S., Rossi, S., Biancalani, R., Condor Golec, R. D., Jacobs, H., and Flammini, A.: The contribution of agriculture, forestry and other land use activities to global warming, 1990–2012, Glob. Change Biol., 21, 2655–2660, 2015.
Turnbull, J. C., Karion, A., Davis, K. J., Lauvaux, T., Miles, N. L., Richardson, S. J., Sweeney, C., McKain, K., Lehman, S. J., and Gurney, K. R.: Synthesis of urban CO2 emission estimates from multiple methods from the Indianapolis Flux Project (INFLUX), Environ. Sci. Technol., 53, 287–295, 2018.
Turner, A. J., Kim, J., Fitzmaurice, H., Newman, C., Worthington, K., Chan, K., Wooldridge, P. J., Köehler, P., Frankenberg, C., and Cohen, R. C.: Observed impacts of COVID-19 on urban CO2 emissions, Geophys. Res. Lett., 47, e2020GL090037, https://doi.org/10.1029/2020GL090037, 2020.
Waite, M., Cohen, E., Torbey, H., Piccirilli, M., Tian, Y., and Modi, V.: Global trends in urban electricity demands for cooling and heating, Energy, 127, 786–802, 2017.
Wolff, S., Ehret, G., Kiemle, C., Amediek, A., Quatrevalet, M., Wirth, M., and Fix, A.: Determination of the emission rates of CO2 point sources with airborne lidar, Atmos. Meas. Tech., 14, 2717–2736, https://doi.org/10.5194/amt-14-2717-2021, 2021.
Wu, D., Lin, J. C., Fasoli, B., Oda, T., Ye, X., Lauvaux, T., Yang, E. G., and Kort, E. A.: A Lagrangian approach towards extracting signals of urban CO2 emissions from satellite observations of atmospheric column CO2 (XCO2): X-Stochastic Time-Inverted Lagrangian Transport model (“X-STILT v1”), Geosci. Model Dev., 11, 4843–4871, https://doi.org/10.5194/gmd-11-4843-2018, 2018.
Ye, X., Lauvaux, T., Kort, E. A., Oda, T., Feng, S., Lin, J. C., Yang, E. G., and Wu, D.: Constraining fossil fuel CO2 emissions from urban area using OCO-2 observations of total column CO2, J. Geophys. Res.-Atmos., 125, e2019JD030528, https://doi.org/10.1029/2019JD030528, 2020.
Zhang, H., Han, G., Ma, X., Chen, W., Zhang, X., Liu, J., and Gong, W.: Robust algorithm for precise X CO2 retrieval using single observation of IPDA LIDAR, Opt. Express, 31, 11846–11863, 2023.
Zhang, H., Han, G., Chen, W., Pei, Z., Liu, B., Liu, J., Zhang, T., Li, S., and Gong, W.: Validation Method for Spaceborne IPDA LIDAR X co 2 Products via TCCON, IEEE J. Sel. Top. Appl., 17, 16984–16992, https://doi.org/10.1109/JSTARS.2024.3418028, 2024.
Zhang, T., Zhang, W., Yang, R., Liu, Y., and Jafari, M.: CO2 capture and storage monitoring based on remote sensing techniques: A review, J. Clean. Prod., 281, 124409, https://doi.org/10.1016/j.jclepro.2020.124409, 2021.
Zheng, B., Chevallier, F., Ciais, P., Broquet, G., Wang, Y., Lian, J., and Zhao, Y.: Observing carbon dioxide emissions over China's cities and industrial areas with the Orbiting Carbon Observatory-2, Atmos. Chem. Phys., 20, 8501–8510, https://doi.org/10.5194/acp-20-8501-2020, 2020.
Zhu, Y., Yang, J., Zhang, X., Liu, J., Zhu, X., Zang, H., Xia, T., Fan, C., Chen, X., and Sun, Y.: Performance improvement of spaceborne carbon dioxide detection IPDA LIDAR using linearty optimized amplifier of photo-detector, Remote Sens., 13, 2007, https://doi.org/10.3390/rs13102007, 2021.
Short summary
This study utilized the IPDA (integrated path differential absorption) lidar on board the DQ-1 satellite to monitor emissions from localized strong point sources and, for the first time, observed the diurnal variation in CO2 emissions from a high-latitude power plant. Overall, power plant CO2 emissions were largely consistent with local electricity consumption patterns, with most plants emitting less at night than during the day and with higher emissions in winter compared to spring and autumn.
This study utilized the IPDA (integrated path differential absorption) lidar on board the DQ-1...
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