Examination and Analysis of Annual, Seasonal, and Monthly Variations in Atmospheric Carbon Dioxide Concentration Across the Middle East Region Utilizing SCIAMACHY, GOSAT, and OCO-2 Satellite Data

Document Type : Original Article


1 Department of Environmental Planning and Design, Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran

2 School of Physics, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran


Introduction: Climate change and global warming, caused by the increase in the concentration
of greenhouse gases, have garnered significant attention across various national and international
sectors. The emission of greenhouse gases has long been recognized as one of the most pressing
environmental issues, sparking widespread concern. Since the Industrial Revolution, the demand
for energy and the consumption of fossil fuels have escalated, leading to increased greenhouse gas
emissions. While the input and output of atmospheric carbon dioxide have traditionally remained
in balance with nature, human activities and carbon dioxide emissions have disrupted this
equilibrium in recent decades, giving rise to climate change and global warming.
Material and Methods: There are various methods for measuring atmospheric carbon dioxide.
Remote sensing technology, in particular, has emerged as a solution, overcoming the limitations
of ground-based measurement methods by offering continuous monitoring and global coverage
of greenhouse gases. Despite the absence of ground stations for monitoring greenhouse gases in
the Middle East region, spanning 7,207,570 square kilometers, this study investigates the monthly,
seasonal, and annual atmospheric concentrations of carbon dioxide using data from satellites such
as SCIAMACHY, GOSAT, and OCO. The study period spans from 2003 to 2020.
Results and Discussion: The findings indicate a significant increase in atmospheric carbon
dioxide concentration over the 18 years examined by all three satellites. In addition to
examining annual changes, this study also investigated seasonal and monthly variations in
atmospheric CO2 concentration. The lowest concentrations of this greenhouse gas occurred
during the summer months, particularly in August and September, while the highest
concentrations were observed during the spring months, specifically in April and May.
Furthermore, the analysis of differences in atmospheric CO2 between seasons revealed the most
significant changes from spring to summer, with an average decrease of 6 ppm. Conversely,
the highest increases in atmospheric CO2 between seasons were observed from summer to
autumn, with a recorded average increase of approximately 4 ppm.
Conclusion: This research indicates a notable increase in atmospheric CO2 concentration in the
Middle East region from 2003 to 2020, accompanied by seasonal and monthly fluctuations
consistent with global trends of this greenhouse gas. This long-term rise in greenhouse gas
levels can lead to various detrimental effects in the region, including temperature escalation,
alterations in rainfall patterns, heightened drought severity, and damage to natural ecosystems.
Consequently, the socio-economic stability of the region could be jeopardized, impacting
agriculture, water resources, human health, and biodiversity. To effectively manage and
mitigate greenhouse gas emissions, immediate action is imperative at both national and
international levels. Such measures may involve promoting renewable energy sources,
enhancing energy efficiency, curbing industrial pollution, advancing emission reduction
technologies, and fostering regional and international collaboration in greenhouse gas reduction
efforts. Moreover, public awareness campaigns and policy interventions are essential to
mobilize stakeholders and facilitate the transition to a low-carbon economy in the Middle East,
ensuring sustainable development and climate resilience for future generations.


An, N., Mustafa, F., Bu, L., Xu, M., Wang, Q., Shahzaman, M., Bilal, M., Ullah, S. and Feng, Z., 2022. Monitoring of atmospheric carbon dioxide over Pakistan using satellite dataset. Remote Sensing. 14(22), p.5882. https://doi.org/10.3390/rs14225882Aslam, R.W., Shu, H., Tariq, A., Naz, I., Ahmad, M.N., Quddoos, A., Javid, K., Mustafa, F. and Aeman, H., 2024. Monitoring landuse change in Uchhali and Khabeki wetland lakes, Pakistan using remote sensing data. Gondwana Research. 16(5), p.928. https://doi.org/10.3390/rs16050928Borhani, F. and Noorpoor, A., 2020. Measurement of air pollution emissions from chimneys of production units moisture insulation (Isogam) Delijan. Journal of Environmental Science and Technology. 21 (12), 57-71. https://doi.org/10. 22034/jest.2020.25934.3488Borhani, F., Ehsani, A.H., McGuirk, S.L., Shafiepour Motlagh, M., Mousavi, S.M., Rashidi, Y. and Mirmazloumi, S.M., 2023b. Examining and predicting the influence of climatic and terrestrial factors on the seasonal distribution of ozone column depth over Tehran province using satellite observations. Acta Geophysica. 72, 1-36. https://doi.org/10.1007/s11600-023-01179-1Borhani, F., Shafiepour Motlagh, M., Ehsani, A.H. and Rashidi, Y., 2022. Evaluation of short-lived atmospheric fine particles in Tehran, Iran. Arabian Journal of Geosciences. 15(16), 1398. https://doi.org/10.1007/s12517-022-10667-5Borhani, F., Shafiepour Motlagh, M., Ehsani, A.H., Rashidi, Y., Maddah, S. and Mousavi, S.M., 2023a. On the predictability of short-lived particulate matter around a cement plant in Kerman, Iran: machine learning analysis. International Journal of Environmental Science and Technology. 20(2), 1513-1526. https://doi.org/10.1007/s13762-022-04645-3Buchwitz, M., Reuter, M., Schneising, O., Bovensmann, H., Burrows, J.P., Boesch, H., Anand, J., Parker, R., Detmers, R.G., Aben, I. and Hasekamp, O.P., 2018. Copernicus Climate Change Service (C3S) global satellite observations of atmospheric carbon dioxide and methane. Advances in Astronautics Science and Technology. 1, pp.57-60. https://doi.org/10.1007/s42423-018-0004-6Buchwitz, M., Schneising, O., Burrows, J.P., Bovensmann, H., Reuter, M. and Notholt, J., 2007. First direct observation of the atmospheric CO 2 year-to-year increase from space. Atmospheric Chemistry and Physics. 7(16), pp.4249-4256. https://doi.org/10.5194/acp-7-4249-2007Butz, A., Guerlet, S., Hasekamp, O., Schepers, D., Galli, A., Aben, I., Frankenberg, C., Hartmann, J.M., Tran, H., Kuze, A. and Keppel‐Aleks, G., 2011. Toward accurate CO2 and CH4 observations from GOSAT. Geophysical Research Letters. 38(14), L14812.  https://doi.org/10.1029/2011GL047888Canadell, J.G., Mooney, H.A., Baldocchi, D.D., Berry, J.A., Ehleringer, J.R., Field, C.B., Gower, S.T., Hollinger, D.Y., Hunt, J.E., Jackson, R.B. and Running, S.W., 2000. Commentary: Carbon metabolism of the terrestrial biosphere: A multitechnique approach for improved understanding. Ecosystems. 3, pp.115-130. https://doi.org/10.1007/s100210000014Cao, L., Chen, X., Zhang, C., Kurban, A., Qian, J., Pan, T., Yin, Z., Qin, X., Ochege, F.U. and Maeyer, P.D., 2019. The global spatiotemporal distribution of the mid-tropospheric CO2 concentration and analysis of the controlling factors. Remote Sensing. 11(1), 94. https://doi.org/10.3390/rs11010094Cao, L., Chen, X., Zhang, C., Kurban, A., Yuan, X., Pan, T. and De Maeyer, P., 2017. The temporal and spatial distributions of the near-surface CO2 concentrations in Central Asia and analysis of their controlling factors. Atmosphere. 8(5). p.85. https://doi.org/10.3390/atmos8050085Cheraghi, A. and Borhani, F., 2016. Evaluation of environmental and sustainable development of four pavements in Iran by four method of multi-criteria analysis. Journal of Environmental Science Studies. 1(2), 51-62. https://www.jess.ir/article_47113_en.htmlCrisp, D., Atlas, R.M., Breon, F.M., Brown, L.R., Burrows, J.P., Ciais, P., Connor, B.J., Doney, S.C., Fung, I.Y., Jacob, D.J. and Miller, C.E., 2004. The orbiting carbon observatory (OCO) mission. Advances in Space Research. 34(4), 700-709. https://doi.org/10.1016/j.asr.2003.08.062Da Costa, L.M., De Araújo Santos, G.A., Panosso, A.R., De Souza Rolim, G. and La Scala, N., 2022. An empirical model for estimating daily atmospheric column-averaged CO2 concentration above São Paulo state, Brazil. Carbon Balance and Management. 17(1), 9. https://doi.org/10.1186/s13021-022-00209-7Darvishi, A., Yousefi, M. and Mobargae, D.N., 2021. Evaluating the correlation between pollination ecosystem service and landscape pattern metrics (Case study: Qazvin province). Iranian Journal of Applied Ecology. 10 (1), 51-63. https://sid.ir/paper/1058635/enDarvishi, A., Yousefi, M., Marull, J. and Dinan, N.M., 2022. Modelling ecological scarcity considering the long-term interaction between human and nature in dry agricultural landscapes. Application in Qazvin (Iran). Ecological Modelling. 472, 110106. https://doi.org/10.1016/j.ecolmodel.2022.110106Darvishi, A., Yousefi, M., Schirrmann, M. and Ewert, F., 2024. Exploring biodiversity patterns at the landscape scale by linking landscape energy and land use/land cover heterogeneity. Science of The Total Environment. 916, 170163. https://doi.org/10.1016/j.scitotenv.2024.170163Dass, A., Mishra, A.K., de Araújo Santos, G.A. and Ranjan, R.K., 2024. Spatio-temporal variation of atmospheric CO2 and its association with anthropogenic, vegetation, and climate indices over the state of Bihar, India. Environmental Advances. 16, 100513. https://doi.org/10.1016/j.envadv.2024.100513De Araújo Santos, G.A., Morais Filho, L.F.F., de Meneses, K.C., da Silva Junior, C.A., de Souza Rolim, G. and La Scala Jr, N., 2022. Hot spots and anomalies of CO2 over eastern Amazonia, Brazil: A time series from 2015 to 2018. Environmental Research. 215 (2), 114379. https://doi.org/10.1016/j.envres.2022.114379De Lucena, W.B., Vicentini, M.E., Santos, G.A.D.A., Silva, B.D.O., Da Costa, D.V.M., Canteral, K.F.F., Román, J.A.N., Rolim, G.D.S., Panosso, A.R. and La Scala Jr, N., 2023. Temporal variability of the CO2 emission and the O2 influx in a tropical soil in contrasting coverage conditions. Journal of South American Earth Sciences. 121, 104120. https://doi.org/10.1016/j.jsames.2022.104120
Dlugokencky and Tans, 2024. E. Dlugokencky, P. Tans.Trends in Atmospheric Carbon Dioxide [WWW Document]. www.esrl.noaa.gov/gmd/ccgg/trends. (Accessed 15 January March 2024).
Doelling, D.R., Lukashin, C., Minnis, P., Scarino, B. and Morstad, D., 2011. Spectral reflectance corrections for satellite intercalibrations using SCIAMACHY data. IEEE Geoscience and Remote Sensing Letters. 9(1), 119-123. DOI: 10.1109/LGRS.2011.2161751.
Ezimand, K., Aghighi, H., Ashourloo, D. and Shakiba, A., 2024. The analysis of the spatio-temporal changes and prediction of built-up lands and urban heat islands using multi-temporal satellite imagery. Sustainable Cities and Society. 103, 105231. https://doi.org/10.1016/j.scs.2024.105231
Falahatkar, S., Mousavi, S.M. and Farajzadeh, M., 2017. Spatial and temporal distribution of carbon dioxide gas using GOSAT data over IRAN. Environmental Monitoring and Assessment. 189 (12), 1-13. https://doi.org/10.1007/s10661-017-6285-8Frankenberg, C., O'Dell, C., Berry, J., Guanter, L., Joiner, J., Köhler, P., Pollock, R. and Taylor, T.E., 2014. Prospects for chlorophyll fluorescence remote sensing from the Orbiting Carbon Observatory-2. Remote Sensing of Environment. 147, 1-12. https://doi.org/10.1016/j.rse.2014.02.007Ghayoumi, R., Charles, A. and Mousavi, S.M., 2023. A multi-level analysis of links between government institutions and community-based conservation: insights from Iran. Ecology and Society. 28(2), 33. https://doi.org/10.5751/ES-14217-280233Ghayoumi, R., Ebrahimi, E. and Mousavi, S.M., 2022. Dynamics of mangrove forest distribution changes in Iran. Journal of Water and Climate Change. 13(6), 2479-2489. https://doi.org/10.2166/wcc.2022.069Golkar, F. and Mousavi, S.M., 2022. Variation of XCO2 anomaly patterns in the Middle East from OCO-2 satellite data. International Journal of Digital Earth. 15(1), 1219-1235. https://doi.org/10.1080/17538947.2022.2096936Golkar, F. and Shirvani, A., 2020. Spatial and temporal distribution and seasonal prediction of satellite measurement of CO2 concentration over Iran. International Journal of Remote Sensing. 41(23), 8891-8909. https://doi.org/10.1080/01431161.2020.1788743Golkar, F., Al-Wardy, M., Saffari, S.F., Al-Aufi, K. and Al-Rawas, G., 2019. Using OCO-2 satellite data for investigating the variability of atmospheric CO2 concentration in relationship with precipitation, relative humidity, and vegetation over Oman. Water. 12(1), 101. https://doi.org/10.3390/w12010101Gupta, A., Dhaka, S.K., Matsumi, Y., Imasu, R., Hayashida, S. and Singh, V., 2019. Seasonal and annual variation of AIRS retrieved CO _ 2 CO 2 over India during 2003–2011. Journal of Earth System Science. 128, 1-12. https://doi.org/10.1007/s12040-019-1108-7Hoveidi, H., Aslemand, A., Borhani, F. and Naghadeh, S.F., 2017. Emission and health costs estimation for air pollutants from municipal solid waste management scenarios, case study: NOx and SOx pollutants, Urmia, Iran. Journal of Environmental Treatment Techniques. 5(1), 59-64. https://doi.org/10.1029/1999GB001137
Hatami, H., Khani, M., Rad, S.A.R. and Shokri, B., 2024. CO2 conversion in a dielectric barrier discharge plasma by argon dilution over MgO/HKUST-1 catalyst using response surface methodology. Heliyon. 10 (4), e26280. https://doi.org/10.1016/j.heliyon.2024.e26280.
 Huang, J., Zhang, G., Zhang, Y., Guan, X., Wei, Y. and Guo, R., 2020. Global desertification vulnerability to climate change and human activities. Land Degradation and Development. 31(11), 1380-1391. https://doi.org/10.1002/ldr.3556IPCC, 2019. Climate change and land. Geneva, Switzerland: The Intergovernmental Panel on Climate Chang.Iqbal, R., Raza, M.A.S., Rahman, M.H.U., Hyder, S., Israr, M., Aslam, M.U., Mustafa, F., Shahzaman, M., Ayaz, M., Toleikiene, M. and Hashemi, F., 2022. Effect of partial rhizosphere drying on plant photosynthetic, antioxidative and water related indicators in cotton. Communications in Soil Science and Plant Analysis. 53(16), pp.2125-2140. https://doi.org/10.1080/00103624.2022.2070633
Javanbakht, M., Saghafipour, A., Ezimand, K., Hamta, A., Farahani, L.Z. and Soltani, N., 2021. Identification of climatic and environmental factors associated with incidence of cutaneous leishmaniasis in Central Iran using satellite imagery. Asian Pacific Journal of Tropical Biomedicine. 11(1), pp.40-46. DOI: 10.4103/2221-1691.300730
Khaliq, M.A., Mustafa, F., Rehman, S.U., Shahzaman, M., Javed, Z., Sagir, M., Bashir, S. and Zuo, H., 2024. Spatiotemporal investigation of near-surface CH4 and factors influencing CH4 over South, East, and Southeast Asia. Science of The Total Environment. 922, p.171311. https://doi.org/10.1016/j.scitotenv.2024.171311Kuze, A., Suto, H., Nakajima, M. and Hamazaki, T., 2009. Thermal and near infrared sensor for carbon observation Fourier-transform spectrometer on the Greenhouse Gases Observing Satellite for greenhouse gases monitoring. Applied optics. 48(35), 6716-6733. https://doi.org/10.1364/AO.48.006716Mac Dowell, N., Fennell, P.S., Shah, N. and Maitland, G.C., 2017. The role of CO2 capture and utilization in mitigating climate change. Nature climate change. 7(4), 243-249. https://doi.org/10.1038/nclimate3231Magazzino, C. and Cerulli, G., 2019. The determinants of CO2 emissions in MENA countries: a responsiveness scores approach. International Journal of Sustainable Development & World Ecology. 26(6), 522-534. https://doi.org/10.1080/13504509.2019.1606863Magazzino, C., 2017. Stationarity of electricity series in MENA countries. The Electricity Journal. 30(10), 16-22. https://doi.org/10.1016/j.tej.2017.11.014Mai, B., Deng, X., Zhang, F., He, H., Luan, T., Li, F. and Liu, X., 2020. Background characteristics of atmospheric CO 2 and the potential source regions in the Pearl River Delta region of China. Advances in Atmospheric Sciences. 37, 557-568. https://doi.org/10.1007/s00376-020-9238-zMiao, R., Lu, N., Yao, L., Zhu, Y., Wang, J. and Sun, J., 2013. Multi-year comparison of carbon dioxide from satellite data with ground-based FTS measurements (2003–2011). Remote Sensing. 5(7), 3431-3456. https://doi.org/10.3390/rs5073431Morino, I., Uchino, O., Inoue, M., Yoshida, Y., Yokota, T., Wennberg, P.O., Toon, G.C., Wunch, D., Roehl, C.M., Notholt, J. and Warneke, T., 2010. Preliminary validation of column-averaged volume mixing ratios of carbon dioxide and methane retrieved from GOSAT short-wavelength infrared spectra. Atmospheric Measurement Techniques Discussions. 3(6), 5613-5643. https://doi.org/10.5194/amt-4-1061-2011
Mousavi, S.M., Falahatkar, S. and Farajzadeh, M., 2017a. Assessment of seasonal variations of carbon dioxide concentration in Iran using GOSAT data. Natural Resources Forum. 41 (2), 83–91.  https://doi.org/10.1111/1477-8947.12121
Mousavi, S.M. and Falahatkar, S., 2020. Spatiotemporal distribution patterns of atmospheric methane using GOSAT data in Iran. Environment, development and sustainability. 22, 4191-4207. https://doi.org/10.1007/s10668-019-00378-5Mousavi, S.M., Darvishi, G., Mobarghaee Dinan, N. and Naghibi, S.A., 2022a. Optimal landfill site selection for solid waste of three municipalities based on boolean and fuzzy methods: A case study in Kermanshah Province, Iran. Land. 11(10), 1779. https://doi.org/10.3390/land11101779Mousavi, S.M., Dinan, N.M., Ansarifard, S. and Sonnentag, O., 2022b. Analyzing spatio-temporal patterns in atmospheric carbon dioxide concentration across Iran from 2003 to 2020. Atmospheric Environment: X. 14, 100163. https://doi.org/10.1016/j.aeaoa.2022.100163Mousavi, S.M., Dinan, N.M., Ansarifard, S., Borhani, F., Ezimand, K. and Naghibi, A., 2023. Examining the role of the main terrestrial factors won the seasonal distribution of atmospheric carbon dioxide concentration over Iran. Journal of the Indian Society of Remote Sensing. 51(4), 865-875. https://doi.org/10.1007/s12524-022-01650-4
Mousavi, S.M., Falahatkar, S. and Farajzadeh, M., 2017b. Monitoring of monthly and seasonal methane amplitude in Iran using GOSAT data. Physical Geography Research. 49(2), 327-340. doi: 10.22059/jphgr.2017.62848
Mousavi, S.M., Falahatkar, S. and Farajzadeh, M., 2018. Concentration in changes of CO2 and CH4 greenhouse gases relation to environmental variable in Iran. Iranian Journal of Applied Ecology. 6(4), 65-79.  http://ijae.iut.ac.ir/article-1-781-en.html
Mousavi, S.M., Falahatkar, S. and Farajzadeh, M., 2020. The role of wind flow on sources of carbon dioxide concentration in the provincial scale. Journal of Environmental Science and Technology. 22(6), 147-160. https://sid.ir/paper/402606/en
Mousavi, S.M., Golkarian, A., Naghibi, S.A., Kalantar, B. and Pradhan, B., 2017c. GIS-based groundwater spring potential mapping using data mining boosted regression tree and probabilistic frequency ratio models in Iran. Aims Geosci. 3(1), 91-115. DOI: 10.3934/geosci.2017.1.91Mousavi, S.M., Mobarghaee Dinan, N., Ansarifard, S., Darvishi, G., Borhani, F. and Naghibi, A., 2024. Assessing the impact of global carbon dioxide changes on atmospheric fluctuations in Iran through satellite data analysis. Journal of Water and Climate Change. p.jwc2024702. https://doi.org/10.2166/wcc.2024.702Mustafa, F., Bu, L., Wang, Q., Ali, M.A., Bilal, M., Shahzaman, M. and Qiu, Z., 2020. Multi-year comparison of CO2 concentration from NOAA carbon tracker reanalysis model with data from GOSAT and OCO-2 over Asia. Remote Sensing. 12(15), p.2498. https://doi.org/10.3390/rs12152498Mustafa, F., Bu, L., Wang, Q., Shahzaman, M., Bilal, M., Aslam, R.W. and Dong, C., 2022. Spatiotemporal Investigation of Near-Surface CO 2 and Its Affecting Factors Over Asia. IEEE Transactions on Geoscience and Remote Sensing. 60, pp.1-16. DOI: 10.1109/TGRS.2022.3178125Mustafa, F., Wang, H., Bu, L., Wang, Q., Shahzaman, M., Bilal, M., Zhou, M., Iqbal, R., Aslam, R.W., Ali, M.A. and Qiu, Z., 2021. Validation of gosat and oco-2 against in situ aircraft measurements and comparison with carbontracker and geos-chem over Qinhuangdao, China. Remote Sensing. 13(5), 899. https://doi.org/10.3390/rs13050899
National Aeronautics and Space Administration., 2018. http://neo.sci.gsfc.nasa.gov/view .php?datasetId= MOD17A2_M_PSN&year. (accessed 15 December 2018).
NOAA, 2023. National Oceanic and Atmospheric Administration. https://www.esrl.noaa.gov/gmd/cc gg/trends/global.html (accessed 15 December 2023).
Noel, S., Bovensmann, H., Wuttke, M.W., Burrows, J.P., Gottwald, M., Krieg, E., Goede, A.P. and Muller, C., 2002. Nadir, limb, and occultation measurements with SCIAMACHY. Advances in Space Research. 29(11), 1819-1824. https://doi.org/10.1016/S0273-1177(02)00102-3O'dell, C.W., Eldering, A., Wennberg, P.O., Crisp, D., Gunson, M.R., Fisher, B., Frankenberg, C., Kiel, M., Lindqvist, H., Mandrake, L. and Merrelli, A., 2018. Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm. Atmospheric Measurement Techniques. 11(12), 6539-6576. https://doi.org/10.5194/amt-11-6539-2018Pan, G., Xu, Y. and Ma, J., 2021. The potential of CO2 satellite monitoring for climate governance: A review. Journal of Environmental Management. 277, 111423. https://doi.org/10.1016/j.jenvman.2020.111423Parker, R., Boesch, H., Cogan, A., Fraser, A., Feng, L., Palmer, P.I., Messerschmidt, J., Deutscher, N., Griffith, D.W., Notholt, J. and Wennberg, P.O., 2011. Methane observations from the Greenhouse Gases Observing SATellite: Comparison to ground‐based TCCON data and model calculations. Geophysical Research Letters. 38(15). L15807. https://doi.org/10.1029/2011GL047871Safaeian, S., Falahatkar, S. and Tourian, M.J., 2023. Satellite observation of atmospheric CO2 and water storage change over Iran. Scientific Reports. 13(1), 3036. https://doi.org/10.1038/s41598-023-28961-xSchneising, O., Buchwitz, M., Burrows, J.P., Bovensmann, H., Reuter, M., Notholt, J., Macatangay, R. and Warneke, T., 2008. Three years of greenhouse gas column-averaged dry air mole fractions retrieved from satellite–Part 1: Carbon dioxide. Atmospheric Chemistry and Physics. 8(14), 3827-3853. https://doi.org/10.5194/acp-8-3827-2008Soegaard, H., Nordstroem, C., Friborg, T., Hansen, B.U., Christensen, T.R. and Bay, C., 2000. Trace gas exchange in a high‐Arctic valley: 3. Integrating and scaling CO2 fluxes from canopy to landscape using flux data, footprint modeling, and remote sensing. Global Biogeochemical Cycles. 14(3), 725-744.  Wang, Q., Mustafa, F., Bu, L., Yang, J., Fan, C., Liu, J. and Chen, W., 2022. Monitoring of Atmospheric Carbon Dioxide over a Desert Site Using Airborne and Ground Measurements. Remote Sensing. 14(20), p.5224.https://doi.org/10.3390/rs14205224Wang, Q., Mustafa, F., Bu, L., Zhu, S., Liu, J. and Chen, W., 2021. Atmospheric carbon dioxide measurement from aircraft and comparison with OCO-2 and CarbonTracker model data. Atmospheric Measurement Techniques. 14(10), 6601-6617. DOI: 10.5194/amt-14-6601-2021Wang, T., Shi, J., Jing, Y., Zhao, T., Ji, D. and Xiong, C., 2014. Combining XCO2 measurements derived from SCIAMACHY and GOSAT for potentially generating global CO2 maps with high spatiotemporal resolution. PLoS One. 9(8), 105050. https://doi.org/10.1371/journal.pone.0105050Watham, T., Padalia, H., Srinet, R., Nandy, S., Verma, P.A. and Chauhan, P., 2021. Seasonal dynamics and impact factors of atmospheric CO 2 concentration over subtropical forest canopies: observation from eddy covariance tower and OCO-2 satellite in Northwest Himalaya, India. Environmental Monitoring and Assessment. 193, 1-15. https://doi.org/10.1007/s10661-021-08896-4Watson, A.J., Schuster, U., Shutler, J.D., Holding, T., Ashton, I.G., Landschützer, P., Woolf, D.K. and Goddijn-Murphy, L., 2020. Revised estimates of ocean-atmosphere CO2 flux are consistent with ocean carbon inventory. Nature communications. 11(1), 4422. https://doi.org/10.1038/s41467-020-18203-3
WDI (2024). World Development Indicators Databank, https://databank.worldbank.org/home.aspx#advanced DownloadOptions, (accessed 05 January 2023).
WRI (2023). World Resources Institute, https://www.wri.org/data/greenhouse-gas-emissions-over-165-years, (accessed 25 December 2023).
Wunch, D., Wennberg, P.O., Osterman, G., Fisher, B., Naylor, B., Roehl, C.M., O'Dell, C., Mandrake, L., Viatte, C., Kiel, M. and Griffith, D.W., 2017. Comparisons of the orbiting carbon observatory-2 (OCO-2) X CO 2 measurements with TCCON. Atmospheric Measurement Techniques. 10(6), 2209-2238.https://doi.org/10.5194/amt-10-2209-2017Xavier, C.V., Moitinho, M.R., Teixeira, D.D.B., de Araujo Santos, G.A., Cora, J.E. and La Scala Jr, N., 2020. Crop rotation and sequence effects on temporal variation of CO2 emissions after long-term no-till application. Science of the total environment. 709, p.136107. https://doi.org/10.1016/j.scitotenv.2019.136107Yin, S., Wang, X., Santoso, H., Tani, H., Zhong, G. and Sun, Z., 2018b. Analyzing CO2 concentration changes and their influencing factors in Indonesia by OCO-2 and other multi-sensor remote-sensing data. International journal of digital earth. 11(8), 825-844. https://doi.org/10.1080/17538947.2017.1359344Yin, S., Wang, X., Tani, H., Zhang, X., Zhong, G., Sun, Z. and Chittenden, A.R., 2018a. Analyzing temporo-spatial changes and the distribution of the CO2 concentration in Australia from 2009 to 2016 by greenhouse gas monitoring satellites. Atmospheric environment. 192, 1-12. https://doi.org/10.1016/j.atmosenv.2018.08.043Yoshida, Y., Ota, Y., Eguchi, N., Kikuchi, N., Nobuta, K., Tran, H., Morino, I. and Yokota, T., 2011. Retrieval algorithm for CO 2 and CH 4 column abundances from short-wavelength infrared spectral observations by the Greenhouse gases observing satellite. Atmospheric Measurement Techniques. 4(4), 717-734. https://doi.org/10.5194/amt-4-717-2011Yousefi, M., Darvishi, A., Tello, E., Barghjelveh, S., Dinan, N.M. and Marull, J., 2021. Comparison of two biophysical indicators under different landscape complexity. Ecological Indicators. 124, 107439. https://doi.org/10.1016/j.ecolind.2021.107439