{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T02:19:20Z","timestamp":1793326760134,"version":"4.5.0"},"reference-count":94,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,4]],"date-time":"2025-01-04T00:00:00Z","timestamp":1735948800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,1,4]],"date-time":"2025-01-04T00:00:00Z","timestamp":1735948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-024-84593-9","type":"journal-article","created":{"date-parts":[[2025,1,4]],"date-time":"2025-01-04T08:28:50Z","timestamp":1735979330000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Improved estimation of carbon dioxide and methane using machine learning with satellite observations over the Arabian Peninsula"],"prefix":"10.1038","volume":"15","author":[{"given":"Mariam","family":"Alcibahy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fahim Abdul","family":"Gafoor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farhan","family":"Mustafa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mutasem","family":"El Fadel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamed","family":"Al Hashemi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Al Hammadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maryam R.","family":"Al Shehhi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,4]]},"reference":[{"issue":"1","key":"84593_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/srep20281","volume":"6","author":"G Althor","year":"2016","unstructured":"Althor, G., Watson, J. E. M. & Fuller, R. A. Global mismatch between greenhouse gas emissions and the burden of climate change. Sci. Rep. 6(1), 1\u20136. https:\/\/doi.org\/10.1038\/srep20281 (2016).","journal-title":"Sci. Rep."},{"key":"84593_CR2","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1016\/J.JCLEPRO.2019.06.140","volume":"234","author":"X Zheng","year":"2019","unstructured":"Zheng, X. et al. A review of greenhouse gas emission profiles, dynamics, and climate change mitigation efforts across the key climate change players. J. Clean Prod. 234, 1113\u20131133. https:\/\/doi.org\/10.1016\/J.JCLEPRO.2019.06.140 (2019).","journal-title":"J. Clean Prod."},{"key":"84593_CR3","doi-asserted-by":"publisher","first-page":"750","DOI":"10.1016\/J.SCITOTENV.2019.01.197","volume":"661","author":"D Liu","year":"2019","unstructured":"Liu, D., Guo, X. & Xiao, B. What causes growth of global greenhouse gas emissions? evidence from 40 countries. Sci. Total Environ. 661, 750\u2013766. https:\/\/doi.org\/10.1016\/J.SCITOTENV.2019.01.197 (2019).","journal-title":"Sci. Total Environ."},{"issue":"31","key":"84593_CR4","doi-asserted-by":"publisher","first-page":"7860","DOI":"10.1073\/PNAS.1716613115\/ASSET\/9C0859DE-6CAE-4469-A2C2-30D6EEB78517\/ASSETS\/GRAPHIC\/PNAS.1716613115FIG04.JPEG","volume":"115","author":"PJ Sellers","year":"2018","unstructured":"Sellers, P. J., Schimel, D. S., Moore, B., Liu, J. & Eldering, A. Observing carbon cycle\u2013climate feedbacks from space. Proc. Natl. Acad. Sci. U S A 115(31), 7860\u20137868. https:\/\/doi.org\/10.1073\/PNAS.1716613115\/ASSET\/9C0859DE-6CAE-4469-A2C2-30D6EEB78517\/ASSETS\/GRAPHIC\/PNAS.1716613115FIG04.JPEG (2018).","journal-title":"Proc. Natl. Acad. Sci. U S A"},{"issue":"35","key":"84593_CR5","doi-asserted-by":"publisher","first-page":"13116","DOI":"10.1073\/PNAS.0601816103\/SUPPL_FILE\/01816FIG6.PDF","volume":"103","author":"M Scholze","year":"2006","unstructured":"Scholze, M., Knorr, W., Arnell, N. W. & Prentice, I. C. A climate-change risk analysis for world ecosystems. Proc. Natl. Acad. Sci. U S A 103(35), 13116\u201313120. https:\/\/doi.org\/10.1073\/PNAS.0601816103\/SUPPL_FILE\/01816FIG6.PDF (2006).","journal-title":"Proc. Natl. Acad. Sci. U S A"},{"issue":"6","key":"84593_CR6","doi-asserted-by":"publisher","first-page":"1704","DOI":"10.1073\/PNAS.0812721106\/SUPPL_FILE\/0812721106SI.PDF","volume":"106","author":"S Solomon","year":"2009","unstructured":"Solomon, S., Plattner, G. K., Knutti, R. & Friedlingstein, P. Irreversible climate change due to carbon dioxide emissions. Proc. Natl. Acad. Sci. U S A 106(6), 1704\u20131709. https:\/\/doi.org\/10.1073\/PNAS.0812721106\/SUPPL_FILE\/0812721106SI.PDF (2009).","journal-title":"Proc. Natl. Acad. Sci. U S A"},{"key":"84593_CR7","doi-asserted-by":"publisher","unstructured":"Kumar, A. Global Warming, Climate Change and Greenhouse Gas Mitigation. in Biofuels: Greenhouse Gas Mitigation and Global Warming: Next Generation Biofuels and Role of Biotechnology 1\u201316 (Springer, New Delhi, 2018). https:\/\/doi.org\/10.1007\/978-81-322-3763-1_1.","DOI":"10.1007\/978-81-322-3763-1_1"},{"issue":"6","key":"84593_CR8","doi-asserted-by":"publisher","first-page":"4639","DOI":"10.1002\/AGJ2.20844","volume":"113","author":"H Kwon","year":"2021","unstructured":"Kwon, H., Liu, X., Xu, H. & Wang, M. Greenhouse gas mitigation strategies and opportunities for agriculture. Agron. J. 113(6), 4639\u20134647. https:\/\/doi.org\/10.1002\/AGJ2.20844 (2021).","journal-title":"Agron. J."},{"issue":"8","key":"84593_CR9","doi-asserted-by":"publisher","first-page":"1339","DOI":"10.3390\/SU9081339","volume":"9","author":"A Balafoutis","year":"2017","unstructured":"Balafoutis, A. et al. Precision agriculture technologies positively contributing to ghg emissions mitigation, farm productivity and economics. Sustainability 9(8), 1339. https:\/\/doi.org\/10.3390\/SU9081339 (2017).","journal-title":"Sustainability"},{"key":"84593_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/J.SCCA.2023.100012","volume":"2","author":"G Rothenberg","year":"2023","unstructured":"Rothenberg, G. A realistic look at CO2 emissions, climate change and the role of sustainable chemistry. Sustain. Chem. Clim. Action 2, 100012. https:\/\/doi.org\/10.1016\/J.SCCA.2023.100012 (2023).","journal-title":"Sustain. Chem. Clim. Action"},{"issue":"18","key":"84593_CR11","doi-asserted-by":"publisher","first-page":"5261","DOI":"10.5194\/AMT-15-5261-2022","volume":"15","author":"E Potier","year":"2022","unstructured":"Potier, E. et al. Complementing XCO2 imagery with ground-based CO2 and 14CO2 measurements to monitor CO2 emissions from fossil fuels on a regional to local scale. Atmos. Meas. Tech. 15(18), 5261\u20135288. https:\/\/doi.org\/10.5194\/AMT-15-5261-2022 (2022).","journal-title":"Atmos. Meas. Tech."},{"issue":"21","key":"84593_CR12","doi-asserted-by":"publisher","first-page":"11621","DOI":"10.3390\/SU132111621","volume":"13","author":"IV Ion","year":"2021","unstructured":"Ion, I. V. & Ene, A. Evaluation of greenhouse gas emissions from reservoirs: a review. Sustainability 13(21), 11621. https:\/\/doi.org\/10.3390\/SU132111621 (2021).","journal-title":"Sustainability"},{"key":"84593_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.12952\/JOURNAL.ELEMENTA.000067\/112741","volume":"3","author":"MG Schultz","year":"2015","unstructured":"Schultz, M. G. et al. The global atmosphere watch reactive gases measurement network. Elementa 3, 1\u201323. https:\/\/doi.org\/10.12952\/JOURNAL.ELEMENTA.000067\/112741 (2015).","journal-title":"Elementa"},{"issue":"4","key":"84593_CR14","doi-asserted-by":"publisher","first-page":"3015","DOI":"10.5194\/AMT-14-3015-2021","volume":"14","author":"BD Hall","year":"2021","unstructured":"Hall, B. D. et al. Revision of the world meteorological organization global atmosphere watch (WMO\/GAW) CO2 calibration scale. Atmos. Meas. Tech. 14(4), 3015\u20133032. https:\/\/doi.org\/10.5194\/AMT-14-3015-2021 (2021).","journal-title":"Atmos. Meas. Tech."},{"issue":"5","key":"84593_CR15","doi-asserted-by":"publisher","first-page":"2223","DOI":"10.5194\/AMT-9-2223-2016","volume":"9","author":"M Kiel","year":"2016","unstructured":"Kiel, M., Hase, F., Blumenstock, T. & Kirner, O. Comparison of XCO abundances from the total carbon column observing network and the network for the detection of atmospheric composition change measured in karlsruhe. Atmos. Meas. Tech. 9(5), 2223\u20132239. https:\/\/doi.org\/10.5194\/AMT-9-2223-2016 (2016).","journal-title":"Atmos. Meas. Tech."},{"issue":"1943","key":"84593_CR16","doi-asserted-by":"publisher","first-page":"2087","DOI":"10.1098\/RSTA.2010.0240","volume":"369","author":"D Wunch","year":"2011","unstructured":"Wunch, D. et al. The total carbon column observing network. Philos. Trans. R. Soc. A: Math. Phys. Eng. Sci. 369(1943), 2087\u20132112. https:\/\/doi.org\/10.1098\/RSTA.2010.0240 (2011).","journal-title":"Philos. Trans. R. Soc. A: Math. Phys. Eng. Sci."},{"key":"84593_CR17","doi-asserted-by":"publisher","DOI":"10.3389\/FMARS.2019.00544\/BIBTEX","volume":"6","author":"T Steinhoff","year":"2019","unstructured":"Steinhoff, T. et al. Constraining the oceanic uptake and fluxes of greenhouse gases by building an ocean network of certified stations: the ocean component of the integrated carbon observation system, ICOS-oceans. Front. Mar. Sci. 6, 435598. https:\/\/doi.org\/10.3389\/FMARS.2019.00544\/BIBTEX (2019).","journal-title":"Front. Mar. Sci."},{"issue":"3","key":"84593_CR18","doi-asserted-by":"publisher","first-page":"E855","DOI":"10.1175\/BAMS-D-19-0364.1","volume":"103","author":"J Heiskanen","year":"2022","unstructured":"Heiskanen, J. et al. The integrated carbon observation system in Europe. Bull. Am. Meteorol. Soc. 103(3), E855\u2013E872. https:\/\/doi.org\/10.1175\/BAMS-D-19-0364.1 (2022).","journal-title":"Bull. Am. Meteorol. Soc."},{"issue":"10","key":"84593_CR19","doi-asserted-by":"publisher","first-page":"5155","DOI":"10.1002\/2014JD022591","volume":"120","author":"C Sweeney","year":"2015","unstructured":"Sweeney, C. et al. Seasonal climatology of CO2 across North America from aircraft measurements in the NOAA\/ESRL global greenhouse gas reference network. J. Geophys. Res.: Atmos. 120(10), 5155\u20135190. https:\/\/doi.org\/10.1002\/2014JD022591 (2015).","journal-title":"J. Geophys. Res.: Atmos."},{"issue":"4","key":"84593_CR20","doi-asserted-by":"publisher","first-page":"2007","DOI":"10.1002\/JGRD.50168","volume":"118","author":"YP Shiga","year":"2013","unstructured":"Shiga, Y. P., Michalak, A. M., Randolph Kawa, S. & Engelen, R. J. In-situ CO2 monitoring network evaluation and design: A criterion based on atmospheric CO2 variability. J. Geophys. Res.: Atmos. 118(4), 2007\u20132018. https:\/\/doi.org\/10.1002\/JGRD.50168 (2013).","journal-title":"J. Geophys. Res.: Atmos."},{"issue":"12","key":"84593_CR21","doi-asserted-by":"publisher","first-page":"3207","DOI":"10.3390\/RS15123207","volume":"15","author":"Y Wang","year":"2023","unstructured":"Wang, Y., Wang, M., Teng, F. & Ji, Y. Remote sensing monitoring and analysis of spatiotemporal changes in china\u2019s anthropogenic carbon emissions based on XCO2 data. Remote Sens. (Basel) 15(12), 3207. https:\/\/doi.org\/10.3390\/RS15123207 (2023).","journal-title":"Remote Sens. (Basel)"},{"key":"84593_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/J.JENVMAN.2020.111423","volume":"277","author":"G Pan","year":"2021","unstructured":"Pan, G., Xu, Y. & Ma, J. The potential of CO2 satellite monitoring for climate governance: A review. J. Environ. Manage. 277, 111423. https:\/\/doi.org\/10.1016\/J.JENVMAN.2020.111423 (2021).","journal-title":"J. Environ. Manage."},{"key":"84593_CR23","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/J.JENVMAN.2019.05.049","volume":"244","author":"Z Siabi","year":"2019","unstructured":"Siabi, Z., Falahatkar, S. & Alavi, S. J. Spatial distribution of XCO2 using OCO-2 data in growing seasons. J. Environ. Manage. 244, 110\u2013118. https:\/\/doi.org\/10.1016\/J.JENVMAN.2019.05.049 (2019).","journal-title":"J. Environ. Manage."},{"issue":"5","key":"84593_CR24","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/AB7B99","volume":"15","author":"DH Cusworth","year":"2020","unstructured":"Cusworth, D. H. et al. Using remote sensing to detect, validate, and quantify methane emissions from California solid waste operations. Environ. Res. Lett. 15(5), 054012. https:\/\/doi.org\/10.1088\/1748-9326\/AB7B99 (2020).","journal-title":"Environ. Res. Lett."},{"key":"84593_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/J.SCITOTENV.2023.164701","volume":"893","author":"S Zhang","year":"2023","unstructured":"Zhang, S., Ma, J., Zhang, X. & Guo, C. Atmospheric remote sensing for anthropogenic methane emissions: Applications and research opportunities. Sci. Total Environ. 893, 164701. https:\/\/doi.org\/10.1016\/J.SCITOTENV.2023.164701 (2023).","journal-title":"Sci. Total Environ."},{"key":"84593_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/J.SCITOTENV.2023.166820","volume":"904","author":"S Zhao","year":"2023","unstructured":"Zhao, S. et al. The role of satellite remote sensing in mitigating and adapting to global climate change. Sci. Total Environ. 904, 166820. https:\/\/doi.org\/10.1016\/J.SCITOTENV.2023.166820 (2023).","journal-title":"Sci. Total Environ."},{"key":"84593_CR27","doi-asserted-by":"publisher","first-page":"1177877","DOI":"10.3389\/FENVS.2023.1177877\/BIBTEX","volume":"11","author":"D Francis","year":"2023","unstructured":"Francis, D., Weston, M., Fonseca, R., Temimi, M. & Alsuwaidi, A. Trends and variability in methane concentrations over the Southeastern Arabian Peninsula. Front. Environ. Sci. 11, 1177877. https:\/\/doi.org\/10.3389\/FENVS.2023.1177877\/BIBTEX (2023).","journal-title":"Front. Environ. Sci."},{"issue":"11","key":"84593_CR28","doi-asserted-by":"publisher","first-page":"2622","DOI":"10.3390\/RS14112622","volume":"14","author":"FPA Lopez","year":"2022","unstructured":"Lopez, F. P. A., Zhou, G., Jing, G., Zhang, K. & Tan, Y. XCO2 and XCH4 reconstruction using GOSAT satellite data based on EOF-algorithm. Remote Sens. (Basel) 14(11), 2622. https:\/\/doi.org\/10.3390\/RS14112622 (2022).","journal-title":"Remote Sens. (Basel)"},{"issue":"4","key":"84593_CR29","doi-asserted-by":"publisher","first-page":"440","DOI":"10.3390\/ATMOS15040440","volume":"15","author":"X Li","year":"2024","unstructured":"Li, X. et al. XCO2 Super-resolution reconstruction based on spatial extreme random trees. Atmosphere (Basel) 15(4), 440. https:\/\/doi.org\/10.3390\/ATMOS15040440 (2024).","journal-title":"Atmosphere (Basel)"},{"key":"84593_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/J.AEAOA.2022.100163","volume":"14","author":"SM Mousavi","year":"2022","unstructured":"Mousavi, S. M., Dinan, N. M., Ansarifard, S. & Sonnentag, O. Analyzing spatio-temporal patterns in atmospheric carbon dioxide concentration across Iran from 2003 to 2020. Atmos. Environ. X. 14, 100163. https:\/\/doi.org\/10.1016\/J.AEAOA.2022.100163 (2022).","journal-title":"Atmos. Environ. X."},{"issue":"4","key":"84593_CR31","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1080\/17538947.2016.1156777","volume":"10","author":"ZC Zeng","year":"2017","unstructured":"Zeng, Z. C. et al. Global land mapping of satellite-observed CO2 total columns using spatio-temporal geostatistics. Int. J. Digit. Earth 10(4), 426\u2013456. https:\/\/doi.org\/10.1080\/17538947.2016.1156777 (2017).","journal-title":"Int. J. Digit. Earth"},{"issue":"4","key":"84593_CR32","doi-asserted-by":"publisher","first-page":"870","DOI":"10.3390\/ATMOS5040870","volume":"5","author":"Y Jing","year":"2014","unstructured":"Jing, Y., Shi, J., Wang, T. & Sussmann, R. Mapping global atmospheric CO2 concentration at high spatiotemporal resolution. Atmosphere (Basel) 5(4), 870\u2013888. https:\/\/doi.org\/10.3390\/ATMOS5040870 (2014).","journal-title":"Atmosphere (Basel)"},{"issue":"2","key":"84593_CR33","doi-asserted-by":"publisher","DOI":"10.1117\/1.JRS.18.028502","volume":"18","author":"Y Huang","year":"2024","unstructured":"Huang, Y., Wang, R., Ju, M., Zhu, X. & Xie, Y. Reconstructing global daily XCO2 at 1\u00b0 \u00d7 1\u00b0 spatial resolution from 2016 to 2019 with multisource satellite observation data. J. Appl. Remote Sens. 18(2), 028502. https:\/\/doi.org\/10.1117\/1.JRS.18.028502 (2024).","journal-title":"J. Appl. Remote Sens."},{"issue":"3","key":"84593_CR34","doi-asserted-by":"publisher","first-page":"654","DOI":"10.3390\/RS14030654","volume":"14","author":"L Li","year":"2022","unstructured":"Li, L., Lei, L., Song, H., Zeng, Z. & He, Z. Spatiotemporal geostatistical analysis and global mapping of CH4 columns from GOSAT observations. Remote Sens. (Basel) 14(3), 654. https:\/\/doi.org\/10.3390\/RS14030654 (2022).","journal-title":"Remote Sens. (Basel)"},{"issue":"9","key":"84593_CR35","doi-asserted-by":"publisher","first-page":"1335","DOI":"10.3390\/ATMOS14091335","volume":"14","author":"A Liang","year":"2023","unstructured":"Liang, A., Pang, R., Chen, C. & Xiang, C. XCO2 Fusion algorithm based on multi-source greenhouse gas satellites and carbontracker. Atmosphere (Basel) 14(9), 1335. https:\/\/doi.org\/10.3390\/ATMOS14091335 (2023).","journal-title":"Atmosphere (Basel)"},{"key":"84593_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/J.JQSRT.2021.108006","volume":"278","author":"Z Zhao","year":"2022","unstructured":"Zhao, Z., Xie, F., Ren, T. & Zhao, C. Atmospheric CO2 retrieval from satellite spectral measurements by a two-step machine learning approach. J. Quant. Spectrosc. Radiat. Transf. 278, 108006. https:\/\/doi.org\/10.1016\/J.JQSRT.2021.108006 (2022).","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"issue":"17","key":"84593_CR37","doi-asserted-by":"publisher","first-page":"10853","DOI":"10.3390\/IJERPH191710853","volume":"19","author":"W Wang","year":"2022","unstructured":"Wang, W., He, J., Feng, H. & Jin, Z. High-Coverage reconstruction of xco2 using multisource satellite remote sensing data in beijing\u2013tianjin\u2013hebei region. Int. J. Environ. Res. Public Health 19(17), 10853. https:\/\/doi.org\/10.3390\/IJERPH191710853 (2022).","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"84593_CR38","doi-asserted-by":"publisher","DOI":"10.1029\/2022GL098435","author":"C He","year":"2022","unstructured":"He, C. et al. Deriving full-coverage and fine-scale XCO2 across china based on OCO-2 satellite retrievals and carbontracker output. Geophys. Res. Lett. https:\/\/doi.org\/10.1029\/2022GL098435 (2022).","journal-title":"Geophys. Res. Lett."},{"issue":"3","key":"84593_CR39","doi-asserted-by":"publisher","first-page":"436","DOI":"10.3390\/ATMOS14030436","volume":"14","author":"S He","year":"2023","unstructured":"He, S. et al. Machine learning model-based estimation of XCO2 with high spatiotemporal resolution in china. Atmosphere (Basel) 14(3), 436. https:\/\/doi.org\/10.3390\/ATMOS14030436 (2023).","journal-title":"Atmosphere (Basel)"},{"issue":"11","key":"84593_CR40","doi-asserted-by":"publisher","first-page":"7277","DOI":"10.5194\/AMT-14-7277-2021","volume":"14","author":"F Mustafa","year":"2021","unstructured":"Mustafa, F. et al. Neural-network-based estimation of regional-scale anthropogenic CO2 emissions using an orbiting carbon observatory-2 (OCO-2) dataset over east and west Asia. Atmos. Meas. Tech. 14(11), 7277\u20137290. https:\/\/doi.org\/10.5194\/AMT-14-7277-2021 (2021).","journal-title":"Atmos. Meas. Tech."},{"key":"84593_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/J.RSE.2024.114039","volume":"304","author":"K Li","year":"2024","unstructured":"Li, K. et al. Developing unbiased estimation of atmospheric methane via machine learning and multiobjective programming based on TROPOMI and GOSAT data. Remote Sens. Environ. 304, 114039. https:\/\/doi.org\/10.1016\/J.RSE.2024.114039 (2024).","journal-title":"Remote Sens. Environ."},{"key":"84593_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/J.ENVRES.2023.116866","volume":"236","author":"C Wu","year":"2023","unstructured":"Wu, C., Ju, Y., Yang, S., Zhang, Z. & Chen, Y. Reconstructing annual XCO2 at a 1 km\u00d71 km spatial resolution across China from 2012 to 2019 based on a spatial catboost method. Environ. Res. 236, 116866. https:\/\/doi.org\/10.1016\/J.ENVRES.2023.116866 (2023).","journal-title":"Environ. Res."},{"issue":"4","key":"84593_CR43","doi-asserted-by":"publisher","first-page":"220","DOI":"10.3390\/ATMOS10040220","volume":"10","author":"P Patlakas","year":"2019","unstructured":"Patlakas, P., Stathopoulos, C., Flocas, H., Kalogeri, C. & Kallos, G. Regional climatic features of the Arabian Peninsula. Atmosphere (Basel) 10(4), 220. https:\/\/doi.org\/10.3390\/ATMOS10040220 (2019).","journal-title":"Atmosphere (Basel)"},{"key":"84593_CR44","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/J.ATMOSRES.2012.02.013","volume":"111","author":"M Almazroui","year":"2012","unstructured":"Almazroui, M., Islam, M. N., Jones, P. D., Athar, H. & Rahman, M. A. Recent climate change in the Arabian Peninsula: Seasonal rainfall and temperature climatology of Saudi Arabia for 1979\u20132009. Atmos. Res. 111, 29\u201345. https:\/\/doi.org\/10.1016\/J.ATMOSRES.2012.02.013 (2012).","journal-title":"Atmos. Res."},{"issue":"11\u201312","key":"84593_CR45","doi-asserted-by":"publisher","first-page":"3113","DOI":"10.1007\/S00382-021-05858-X\/FIGURES\/14","volume":"57","author":"KH Cook","year":"2021","unstructured":"Cook, K. H., Vizy, E. K., Liu, Y. & Liu, W. Greenhouse-gas induced warming amplification over the Arabian Peninsula with implications for Ethiopian rainfall. Clim. Dyn. 57(11\u201312), 3113\u20133133. https:\/\/doi.org\/10.1007\/S00382-021-05858-X\/FIGURES\/14 (2021).","journal-title":"Clim. Dyn."},{"issue":"2","key":"84593_CR46","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1093\/CE\/ZKAB015","volume":"5","author":"A Alatiq","year":"2021","unstructured":"Alatiq, A. et al. Assessment of the carbon abatement and removal opportunities of the Arabian Gulf Countries. Clean Energy 5(2), 340\u2013353. https:\/\/doi.org\/10.1093\/CE\/ZKAB015 (2021).","journal-title":"Clean Energy"},{"issue":"5","key":"84593_CR47","doi-asserted-by":"publisher","first-page":"1329","DOI":"10.1002\/JOC.3772","volume":"34","author":"SH Alsarmi","year":"2014","unstructured":"Alsarmi, S. H. & Washington, R. Changes in climate extremes in the Arabian Peninsula: Analysis of daily data. Int. J. Climatol. 34(5), 1329\u20131345. https:\/\/doi.org\/10.1002\/JOC.3772 (2014).","journal-title":"Int. J. Climatol."},{"issue":"4","key":"84593_CR48","doi-asserted-by":"publisher","first-page":"1783","DOI":"10.5194\/ESSD-11-1783-2019","volume":"11","author":"P Friedlingstein","year":"2019","unstructured":"Friedlingstein, P. et al. Global carbon budget 2019. Earth Syst. Sci. Data 11(4), 1783\u20131838. https:\/\/doi.org\/10.5194\/ESSD-11-1783-2019 (2019).","journal-title":"Earth Syst. Sci. Data"},{"issue":"1","key":"84593_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-022-28989-z","volume":"13","author":"L Feng","year":"2022","unstructured":"Feng, L., Palmer, P. I., Zhu, S., Parker, R. J. & Liu, Y. Tropical methane emissions explain large fraction of recent changes in global atmospheric methane growth rate. Nat. Commun. 13(1), 1\u20138. https:\/\/doi.org\/10.1038\/s41467-022-28989-z (2022).","journal-title":"Nat. Commun."},{"key":"84593_CR50","doi-asserted-by":"publisher","unstructured":"X. Lan, K. W. Thoning, & E. J. Dlugokencky. Trends in globally-averaged CH4, N2O, SF6 determined from NOAA Global Monitoring Laboratory measurements. https:\/\/doi.org\/10.15138\/P8XG-AA10. (2024).","DOI":"10.15138\/P8XG-AA10"},{"issue":"3","key":"84593_CR51","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/S00374-015-1075-3\/FIGURES\/4","volume":"52","author":"S Huang","year":"2016","unstructured":"Huang, S., Sun, Y., Yu, X. & Zhang, W. Interactive effects of temperature and moisture on CO2 and CH4 production in a paddy soil under long-term different fertilization regimes. Biol. Fertil. Soils 52(3), 285\u2013294. https:\/\/doi.org\/10.1007\/S00374-015-1075-3\/FIGURES\/4 (2016).","journal-title":"Biol. Fertil. Soils"},{"issue":"7","key":"84593_CR52","doi-asserted-by":"publisher","first-page":"1587","DOI":"10.1007\/S10021-023-00852-2\/FIGURES\/6","volume":"26","author":"FV Jevon","year":"2023","unstructured":"Jevon, F. V., Gewirtzman, J., Lang, A. K., Ayres, M. P. & Matthes, J. H. Tree species effects on soil CO2 and CH4 fluxes in a mixed temperate forest. Ecosystems 26(7), 1587\u20131602. https:\/\/doi.org\/10.1007\/S10021-023-00852-2\/FIGURES\/6 (2023).","journal-title":"Ecosystems"},{"key":"84593_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/J.SCITOTENV.2024.171311","volume":"922","author":"MA Khaliq","year":"2024","unstructured":"Khaliq, M. A. et al. Spatiotemporal investigation of near-surface CH4 and factors influencing CH4 over South, East, and Southeast Asia. Sci. Total Environ. 922, 171311. https:\/\/doi.org\/10.1016\/J.SCITOTENV.2024.171311 (2024).","journal-title":"Sci. Total Environ."},{"issue":"16","key":"84593_CR54","doi-asserted-by":"publisher","first-page":"8397","DOI":"10.3390\/IJERPH18168397","volume":"18","author":"IA P\u00e9rez","year":"2021","unstructured":"P\u00e9rez, I. A., de los \u00c1. Garc\u00eda, M., S\u00e1nchez, M. L. & Pardo, N. Influence of wind speed on CO2 and CH4 concentrations at a rural site. Int. J. Environ. Res. Public Health 18(16), 8397. https:\/\/doi.org\/10.3390\/IJERPH18168397 (2021).","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"84593_CR55","unstructured":"Copernicus Climate Change Service (C3S), \u201cERA5-Land monthly averaged data from 1950 to present.\u201d Accessed: Apr. 01, 2023. [Online]. Available: https:\/\/cds.climate.copernicus.eu\/cdsapp#!\/dataset\/reanalysis-era5-land-monthly-means?tab=overview"},{"key":"84593_CR56","doi-asserted-by":"publisher","unstructured":"Crisp, D. Measuring atmospheric carbon dioxide from space with the Orbiting Carbon Observatory-2 (OCO-2). in SPIE Optical Engineering + Applications 960702 (SPIE, 2015). https:\/\/doi.org\/10.1117\/12.2187291.","DOI":"10.1117\/12.2187291"},{"key":"84593_CR57","unstructured":"Kramer, H. OCO-2 (Orbiting Carbon Observatory-2). eoPortal. Accessed: Jul. 16, 2024. [Online]. Available: https:\/\/www.eoportal.org\/satellite-missions\/oco-2"},{"issue":"1","key":"84593_CR58","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1080\/20964471.2022.2033149","volume":"7","author":"M Sheng","year":"2023","unstructured":"Sheng, M. et al. Global land 1\u00b0 mapping dataset of XCO2 from satellite observations of GOSAT and OCO-2 from 2009 to 2020. Big Earth Data 7(1), 180\u2013200. https:\/\/doi.org\/10.1080\/20964471.2022.2033149 (2023).","journal-title":"Big Earth Data"},{"issue":"12","key":"84593_CR59","doi-asserted-by":"publisher","first-page":"6539","DOI":"10.5194\/AMT-11-6539-2018","volume":"11","author":"CW O\u2019Dell","year":"2018","unstructured":"O\u2019Dell, C. W. et al. Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm. Atmos. Meas. Tech. 11(12), 6539\u20136576. https:\/\/doi.org\/10.5194\/AMT-11-6539-2018 (2018).","journal-title":"Atmos. Meas. Tech."},{"issue":"D23","key":"84593_CR60","doi-asserted-by":"publisher","first-page":"23302","DOI":"10.1029\/2006JD007080","volume":"111","author":"H B\u00f6sch","year":"2006","unstructured":"B\u00f6sch, H. et al. Space-based near-infrared CO2 measurements: testing the orbiting carbon observatory retrieval algorithm and validation concept using SCIAMACHY observations over park falls, Wisconsin. J. Geophys. Res.: Atmos. 111(D23), 23302. https:\/\/doi.org\/10.1029\/2006JD007080 (2006).","journal-title":"J. Geophys. Res.: Atmos."},{"issue":"2","key":"84593_CR61","doi-asserted-by":"publisher","first-page":"270","DOI":"10.3390\/RS3020270","volume":"3","author":"H Boesch","year":"2011","unstructured":"Boesch, H., Baker, D., Connor, B., Crisp, D. & Miller, C. Global characterization of CO2 column retrievals from shortwave-infrared satellite observations of the orbiting carbon observatory-2 mission. Remote Sens. (Basel) 3(2), 270\u2013304. https:\/\/doi.org\/10.3390\/RS3020270 (2011).","journal-title":"Remote Sens. (Basel)"},{"issue":"5","key":"84593_CR62","doi-asserted-by":"publisher","first-page":"3111","DOI":"10.5194\/AMT-11-3111-2018","volume":"11","author":"L Wu","year":"2018","unstructured":"Wu, L. et al. Carbon dioxide retrieval from OCO-2 satellite observations using the RemoTeC algorithm and validation with TCCON measurements. Atmos. Meas. Tech. 11(5), 3111\u20133130. https:\/\/doi.org\/10.5194\/AMT-11-3111-2018 (2018).","journal-title":"Atmos. Meas. Tech."},{"key":"84593_CR63","unstructured":"Jacobson, A. R. et al. CarbonTracker CT2022. NOAA."},{"issue":"15","key":"84593_CR64","doi-asserted-by":"publisher","first-page":"2498","DOI":"10.3390\/RS12152498","volume":"12","author":"F Mustafa","year":"2020","unstructured":"Mustafa, F. et al. Multi-Year Comparison of CO2 Concentration from NOAA carbon tracker reanalysis model with data from GOSAT and OCO-2 over Asia. Remote Sens. (Basel) 12(15), 2498. https:\/\/doi.org\/10.3390\/RS12152498 (2020).","journal-title":"Remote Sens. (Basel)"},{"key":"84593_CR65","unstructured":"Copernicus Sentinel-5P (processed by ESA). TROPOMI Level 2 Methane Total Column products. European Space Agency. Accessed: Jul. 16, 2024. [Online]. Available: https:\/\/sentinels.copernicus.eu\/web\/sentinel\/data-products\/-\/asset_publisher\/fp37fc19FN8F\/content\/tropomi-level-2-methane"},{"key":"84593_CR66","unstructured":"Kramer, H. Copernicus: Sentinel-5P. eoPortal. Accessed: Jul. 16, 2024. [Online]. Available: https:\/\/www.eoportal.org\/satellite-missions\/copernicus-sentinel-5p"},{"issue":"11","key":"84593_CR67","doi-asserted-by":"publisher","first-page":"5423","DOI":"10.5194\/AMT-9-5423-2016","volume":"9","author":"H Hu","year":"2016","unstructured":"Hu, H. et al. The operational methane retrieval algorithm for TROPOMI. Atmos. Meas. Tech. 9(11), 5423\u20135440. https:\/\/doi.org\/10.5194\/AMT-9-5423-2016 (2016).","journal-title":"Atmos. Meas. Tech."},{"issue":"4","key":"84593_CR68","doi-asserted-by":"publisher","first-page":"449","DOI":"10.3390\/ATMOS15040449","volume":"15","author":"Y Jiang","year":"2024","unstructured":"Jiang, Y., Zhang, L., Zhang, X. & Cao, X. Methane retrieval algorithms based on satellite: A review. Atmosphere (Basel) 15(4), 449. https:\/\/doi.org\/10.3390\/ATMOS15040449 (2024).","journal-title":"Atmosphere (Basel)"},{"key":"84593_CR69","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/J.RSE.2011.05.030","volume":"120","author":"A Butz","year":"2012","unstructured":"Butz, A. et al. TROPOMI aboard sentinel-5 precursor: prospective performance of CH4 retrievals for aerosol and cirrus loaded atmospheres. Remote Sens. Environ. 120, 267\u2013276. https:\/\/doi.org\/10.1016\/J.RSE.2011.05.030 (2012).","journal-title":"Remote Sens. Environ."},{"key":"84593_CR70","unstructured":"Kramer, H. Terra (EOS\/AM-1) - eoPortal. Accessed: Jan. 04, 2024. [Online]. Available: https:\/\/www.eoportal.org\/satellite-missions\/terra"},{"key":"84593_CR71","unstructured":"Ferrario, F. M. et al. EDGAR v6.0 Greenhouse Gas Emissions. European Commission, Joint Research Centre (JRC). Accessed: Jul. 18, 2024. [Online]. Available: https:\/\/data.jrc.ec.europa.eu\/dataset\/97a67d67-c62e-4826-b873-9d972c4f670b"},{"key":"84593_CR72","doi-asserted-by":"publisher","unstructured":"Chen, T., & Guestrin, C. XGBoost: A Scalable Tree Boosting System. in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785\u2013794 (Association for Computing Machinery, 2016). https:\/\/doi.org\/10.1145\/2939672.2939785.","DOI":"10.1145\/2939672.2939785"},{"key":"84593_CR73","doi-asserted-by":"publisher","DOI":"10.1016\/J.ATMOSENV.2023.119694","volume":"301","author":"Y Wan","year":"2023","unstructured":"Wan, Y. et al. Conversion of surface CH4 concentrations from GOSAT satellite observations using XGBoost algorithm. Atmos. Environ. 301, 119694. https:\/\/doi.org\/10.1016\/J.ATMOSENV.2023.119694 (2023).","journal-title":"Atmos. Environ."},{"key":"84593_CR74","doi-asserted-by":"publisher","first-page":"1384","DOI":"10.1016\/J.IJHYDENE.2023.12.298","volume":"56","author":"B Pan","year":"2024","unstructured":"Pan, B. et al. Machine learning - based shale wettability prediction: Implications for H2, CH4 and CO2 geo-storage. Int. J. Hydrog. Energy 56, 1384\u20131390. https:\/\/doi.org\/10.1016\/J.IJHYDENE.2023.12.298 (2024).","journal-title":"Int. J. Hydrog. Energy"},{"issue":"5","key":"84593_CR75","doi-asserted-by":"publisher","first-page":"3325","DOI":"10.5194\/ACP-23-3325-2023","volume":"23","author":"X Yu","year":"2023","unstructured":"Yu, X. et al. A high-resolution satellite-based map of global methane emissions reveals missing wetland, fossil fuel, and monsoon sources. Atmos. Chem. Phys. 23(5), 3325\u20133346. https:\/\/doi.org\/10.5194\/ACP-23-3325-2023 (2023).","journal-title":"Atmos. Chem. Phys."},{"key":"84593_CR76","doi-asserted-by":"publisher","DOI":"10.1016\/J.ATMOSENV.2024.120636","volume":"333","author":"L Zhang","year":"2024","unstructured":"Zhang, L., Cheng, T., Yue, T., Li, S. & Wilson, J. P. Quantitative analysis of spatiotemporal coverage and uncertainty decomposition in OCO-2\/3 XCO2 across China. Atmos. Environ. 333, 120636. https:\/\/doi.org\/10.1016\/J.ATMOSENV.2024.120636 (2024).","journal-title":"Atmos. Environ."},{"issue":"9","key":"84593_CR77","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/AB9CFE","volume":"15","author":"A Shekhar","year":"2020","unstructured":"Shekhar, A. et al. Anthropogenic CO2 emissions assessment of Nile Delta using XCO2 and SIF data from OCO-2 satellite. Environ. Res. Lett. 15(9), 095010. https:\/\/doi.org\/10.1088\/1748-9326\/AB9CFE (2020).","journal-title":"Environ. Res. Lett."},{"issue":"19","key":"84593_CR78","doi-asserted-by":"publisher","first-page":"5024","DOI":"10.3390\/RS14195024","volume":"14","author":"S Zhang","year":"2022","unstructured":"Zhang, S. et al. Evaluating anthropogenic CO2 bottom-up emission inventories using satellite observations from GOSAT and OCO-2. Remote Sens. (Basel) 14(19), 5024. https:\/\/doi.org\/10.3390\/RS14195024 (2022).","journal-title":"Remote Sens. (Basel)"},{"key":"84593_CR79","doi-asserted-by":"publisher","DOI":"10.1029\/2021GL097540","author":"F Chevallier","year":"2022","unstructured":"Chevallier, F., Broquet, G., Zheng, B., Ciais, P. & Eldering, A. Large CO2 emitters as seen from satellite: Comparison to a gridded global emission inventory. Geophys. Res. Lett. https:\/\/doi.org\/10.1029\/2021GL097540 (2022).","journal-title":"Geophys. Res. Lett."},{"key":"84593_CR80","doi-asserted-by":"publisher","DOI":"10.1016\/J.JCLEPRO.2024.141418","volume":"448","author":"C Jin","year":"2024","unstructured":"Jin, C. et al. Retrieval anthropogenic CO2 emissions from OCO-2 and comparison with gridded emission inventories. J. Clean Prod. 448, 141418. https:\/\/doi.org\/10.1016\/J.JCLEPRO.2024.141418 (2024).","journal-title":"J. Clean Prod."},{"key":"84593_CR81","unstructured":"International Energy Agency (IEA). Countries & Regions. Accessed: Aug. 04, 2024. [Online]. Available: https:\/\/www.iea.org\/countries"},{"issue":"9","key":"84593_CR82","doi-asserted-by":"publisher","first-page":"1356","DOI":"10.1002\/2016GB005406","volume":"30","author":"EG Nisbet","year":"2016","unstructured":"Nisbet, E. G. et al. Rising atmospheric methane: 2007\u20132014 growth and isotopic shift. Global Biogeochem. Cycles 30(9), 1356\u20131370. https:\/\/doi.org\/10.1002\/2016GB005406 (2016).","journal-title":"Global Biogeochem. Cycles"},{"issue":"1","key":"84593_CR83","doi-asserted-by":"publisher","first-page":"11","DOI":"10.3390\/ATMOS13010011","volume":"13","author":"A Farahat","year":"2021","unstructured":"Farahat, A. The impact of the 2020 oil production fluctuations on methane emissions over the gulf cooperation council (GCC) countries: A satellite approach. Atmosphere (Basel) 13(1), 11. https:\/\/doi.org\/10.3390\/ATMOS13010011 (2021).","journal-title":"Atmosphere (Basel)"},{"issue":"8","key":"84593_CR84","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/S13201-019-1067-9\/FIGURES\/13","volume":"9","author":"S Javadinejad","year":"2019","unstructured":"Javadinejad, S., Eslamian, S. & Ostad-Ali-Askari, K. Investigation of monthly and seasonal changes of methane gas with respect to climate change using satellite data. Appl. Water. Sci. 9(8), 1\u20138. https:\/\/doi.org\/10.1007\/S13201-019-1067-9\/FIGURES\/13 (2019).","journal-title":"Appl. Water. Sci."},{"issue":"4","key":"84593_CR85","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/ABEB36","volume":"16","author":"M Peacock","year":"2021","unstructured":"Peacock, M. et al. Global importance of methane emissions from drainage ditches and canals. Environ. Res. Lett. 16(4), 044010. https:\/\/doi.org\/10.1088\/1748-9326\/ABEB36 (2021).","journal-title":"Environ. Res. Lett."},{"issue":"6","key":"84593_CR86","doi-asserted-by":"publisher","first-page":"3953","DOI":"10.5194\/ACP-16-3953-2016","volume":"16","author":"G Sreenivas","year":"2016","unstructured":"Sreenivas, G. et al. Influence of meteorology and interrelationship with greenhouse gases (CO2 and CH4) at a suburban site of India. Atmos. Chem. Phys. 16(6), 3953\u20133967. https:\/\/doi.org\/10.5194\/ACP-16-3953-2016 (2016).","journal-title":"Atmos. Chem. Phys."},{"issue":"12","key":"84593_CR87","doi-asserted-by":"publisher","first-page":"15424","DOI":"10.1007\/S11356-020-11760-6\/TABLES\/4","volume":"28","author":"A Elmi","year":"2021","unstructured":"Elmi, A., Al-Harbi, M., Yassin, M. F. & Al-Awadhi, M. M. Modeling gaseous emissions and dispersion of two major greenhouse gases from landfill sites in arid hot environment. Environ. Sci. Pollut. Res. 28(12), 15424\u201315434. https:\/\/doi.org\/10.1007\/S11356-020-11760-6\/TABLES\/4 (2021).","journal-title":"Environ. Sci. Pollut. Res."},{"key":"84593_CR88","doi-asserted-by":"publisher","DOI":"10.1016\/J.SCITOTENV.2022.156721","volume":"842","author":"A Metya","year":"2022","unstructured":"Metya, A. et al. Methane sources from waste and natural gas sectors detected in Pune, India, by concentration and isotopic analysis. Sci. Total Environ. 842, 156721. https:\/\/doi.org\/10.1016\/J.SCITOTENV.2022.156721 (2022).","journal-title":"Sci. Total Environ."},{"issue":"5","key":"84593_CR89","doi-asserted-by":"publisher","first-page":"2988","DOI":"10.1111\/GCB.15046","volume":"26","author":"AN Al-Haj","year":"2020","unstructured":"Al-Haj, A. N. & Fulweiler, R. W. A synthesis of methane emissions from shallow vegetated coastal ecosystems. Glob. Chang Biol. 26(5), 2988\u20133005. https:\/\/doi.org\/10.1111\/GCB.15046 (2020).","journal-title":"Glob. Chang Biol."},{"key":"84593_CR90","doi-asserted-by":"publisher","unstructured":"Puliafito, S. E., Berna, L., Lopez-Norena, A., Pascual, R., & Bolano-Ortiz, T. Atmospheric Methane Emissions for Argentina: Comparison with TROPOMI Satellite Measurements. in IEEE Latin American GRSS and ISPRS Remote Sensing Conference LAGIRS  Institute of Electrical and Electronics Engineers Inc. https:\/\/doi.org\/10.1109\/LAGIRS48042.2020.9165602. (2020)","DOI":"10.1109\/LAGIRS48042.2020.9165602"},{"key":"84593_CR91","doi-asserted-by":"publisher","DOI":"10.1029\/2021GL094151","author":"M Liu","year":"2021","unstructured":"Liu, M. et al. A new divergence method to quantify methane emissions using observations of sentinel-5P TROPOMI. Geophys. Res. Lett. https:\/\/doi.org\/10.1029\/2021GL094151 (2021).","journal-title":"Geophys. Res. Lett."},{"issue":"7795","key":"84593_CR92","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1038\/s41586-020-1991-8","volume":"578","author":"B Hmiel","year":"2020","unstructured":"Hmiel, B. et al. Preindustrial 14CH4 indicates greater anthropogenic fossil CH4 emissions. Nature 578(7795), 409\u2013412. https:\/\/doi.org\/10.1038\/s41586-020-1991-8 (2020).","journal-title":"Nature"},{"key":"84593_CR93","doi-asserted-by":"publisher","DOI":"10.1016\/J.AEAOA.2021.100114","volume":"11","author":"I Ialongo","year":"2021","unstructured":"Ialongo, I., Stepanova, N., Hakkarainen, J., Virta, H. & Gritsenko, D. Satellite-based estimates of nitrogen oxide and methane emissions from gas flaring and oil production activities in Sakha Republic, Russia. Atmos. Environ. X 11, 100114. https:\/\/doi.org\/10.1016\/J.AEAOA.2021.100114 (2021).","journal-title":"Atmos. Environ. X"},{"key":"84593_CR94","doi-asserted-by":"publisher","DOI":"10.1126\/SCIADV.ABN9683\/SUPPL_FILE\/SCIADV.ABN9683_SM.PDF","author":"JD Maasakkers","year":"2022","unstructured":"Maasakkers, J. D. et al. Using satellites to uncover large methane emissions from landfills. Sci. Adv. https:\/\/doi.org\/10.1126\/SCIADV.ABN9683\/SUPPL_FILE\/SCIADV.ABN9683_SM.PDF (2022).","journal-title":"Sci. Adv."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-84593-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-84593-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-84593-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,4]],"date-time":"2025-01-04T09:02:17Z","timestamp":1735981337000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-84593-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,4]]},"references-count":94,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["84593"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-84593-9","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,4]]},"assertion":[{"value":"23 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"766"}}