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Examining the Role of the Main Terrestrial Factors Won the Seasonal Distribution of Atmospheric Carbon Dioxide Concentration over Iran

Abstract

Global warming is one of the most important environmental issues from the last few decades and is causing climate change due to carbon dioxide (CO2) emissions and other greenhouse gases (GHG) emissions. Therefore, examination of atmospheric CO2 concentration (XCO2) variations on the local and global scale is critical. The purpose of this research was to analyze the role of environmental variables in the seasonal distribution of XCO2 from 2015 to 2021 across Iran. Initially, by using XCO2 data acquired from the OCO-2 satellite, yearly and seasonal Spatio-temporal XCO2 distribution maps were developed. Subsequently, to understand the role of environmental variables on the Spatio-temporal variations of XCO2, the correlation of XCO2 with vegetation cover, precipitation and temperature were examined in different seasons. The annual increase of XCO2 can be observed, the concentrations in each year in all the areas were higher than those in the previous year. There was a different pattern of the spatial distribution of XCO2 in different seasons. In the growing seasons (spring and summer), the critical role of environmental variables in the north and west of Iran caused reaching XCO2 to the minimum amount. While in the cold seasons (autumn and winter) due to the decrease of vegetation cover, the XCO2 did not decrease in these areas. Furthermore, finding the correlation of XCO2 with environmental variables revealed that XCO2 had a significant correlation with these variables in the growing seasons. These correlations showed that the region with low vegetation coverage and precipitation and high temperature, which is situated in the south and east of Iran had the maximum amount of XCO2. Our results suggested the potential of satellite data for producing Spatio-temporal distribution maps that will help us to a better understanding of the carbon cycle at a regional scale.

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References

  • Barbosa, H. A., & Kumar, T. L. (2016). Influence of rainfall variability on the vegetation dynamics over Northeastern Brazil. Journal of Arid Environments, 124, 377–387.

    Article  Google Scholar 

  • Borhani, F., Shafiepour Motlagh, M., Ehsani, A. H., Rashidi, Y., Maddah, S., & Mousavi, S. M. (2022). 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, 1513–1526.

    Article  Google Scholar 

  • Borhani, F., Shafiepour Motlagh, M., Stohl, A., Rashidi, Y., & Ehsani, A. H. (2021). Changes in short-lived climate pollutants during the COVID-19 pandemic in Tehran Iran. Environmental Monitoring and Assessment, 193(6), 1–12.

    Article  Google Scholar 

  • Buschmann, M., Deutscher, N. M., Sherlock, V., Palm, M., Warneke, T., & Notholt, J. (2016). Retrieval of xCO2 from ground-based mid-infrared (NDACC) solar absorption spectra and comparison to TCCON. Atmospheric Measurement Techniques, 9(2), 577–585.

    Article  Google Scholar 

  • Cao, L., Chen, X., Zhang, C., Kurban, A., Qian, J., Pan, T., Yin, Z., Qin, X., Ochege, F. U., & 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.

    Article  Google Scholar 

  • Chu, X., Han, G., Xing, Q., Xia, J., Sun, B., Li, X., Yu, J., Li, D., & Song, W. (2019). Changes in plant biomass induced by soil moisture variability drive interannual variation in the net ecosystem CO2 exchange over a reclaimed coastal wetland. Agricultural and Forest Meteorology, 264, 138–148.

    Article  Google Scholar 

  • Crisp, 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., Miller, C. E., O’Brien, D., Pawson, S., Randerson, J. T., Rayner, P., Salawitch, R. J., Sander, S. P., Sen, B., Stephens, G. L., … Schroll, S. (2004). The orbiting carbon observatory (OCO) mission. Advances in Space Research, 34(4), 700–709.

    Article  Google Scholar 

  • Crisp, D., Pollock, H. R., Rosenberg, R., Chapsky, L., Lee, R. A., 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., … Wunch, D. (2017). The on-orbit performance of the Orbiting Carbon Observatory-2 (OCO-2) instrument and its radiometrically calibrated products. Atmospheric Measurement Techniques, 10(1), 59–81.

    Article  Google Scholar 

  • Darand, M., Amanollahi, J., & Zandkarimi, S. (2017). Evaluation of the performance of TRMM multi-satellite precipitation analysis (TMPA) estimation over Iran. Atmospheric Research, 190, 121–127.

    Article  Google Scholar 

  • Darvishi, A., Yousefi, M., Marull, J., & Dinan, N. M. (2022). Modelling ecological scarcity considering the longterm interaction between human and nature in dry agricultural landscapes. Application in Qazvin (Iran). Ecological Modelling, 472, 110106.

    Article  Google Scholar 

  • Delfani, S., Pasdarshahri, H., & Karami, M. (2010). Experimental investigation of dehumidification process in cooling coil by utilizing air-to-air heat exchanger in humid climate of Iran. Energy and Buildings, 42(6), 822–827.

    Article  Google Scholar 

  • Didan, K., Munoz, A. B., Solano, R., & Huete, A. (2015). MODIS vegetation index user’s guide (MOD13 series). University of Arizona.

    Google Scholar 

  • Ezimand, K., & Kakroodi, A. A. (2019). Prediction and spatio–temporal analysis of ozone concentration in a metropolitan area. Ecological Indicators, 103, 589–598.

    Article  Google Scholar 

  • Falahatkar, S., Mousavi, S. M., & Farajzadeh, M. (2017). Spatial and temporal distribution of carbon dioxide gas using GOSAT data over IRAN. Environmental Monitoring and Assessment, 189(12), 1–13.

    Article  Google Scholar 

  • Friend, A. D., Lucht, W., Rademacher, T. T., Keribin, R., Betts, R., Cadule, P., Ciais, P., Clark, D. B., Dankers, R., Falloon, P. D., & Woodward, F. I. (2014). Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2. Proceedings of the National Academy of Sciences, 111(9), 3280–3285.

    Article  Google Scholar 

  • Ghyoumi, R., Ebrahimi, E., & Mousavi, S. M. (2022). Dynamics of mangrove forest distribution changes in Iran. Journal of Water and Climate Change., 13(6), 2479–2489.

    Article  Google Scholar 

  • Golkar, F., Al-Wardy, M., Saffari, S. F., Al-Aufi, K., & Al-Rawas, G. (2020). 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.

    Article  Google Scholar 

  • Golkar, F., & Mousavi, S. M. (2022). Variation of XCO2 anomaly patterns in the middle east from OCO2 satellite data. International Journal of Digital Earth, 15(1), 1218–1234.

    Article  Google Scholar 

  • Guo, L., Lei, L., Zeng, Z. C., Zou, P., Liu, D., & Zhang, B. (2014). Evaluation of spatio-temporal variogram models for mapping Xco2 using satelclite observations: A case study in China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(1), 376–385.

    Article  Google Scholar 

  • He, Z., Lei, L., Zhang, Y., Sheng, M., Wu, C., Li, L., Zeng, Z. C., & Welp, L. R. (2020). Spatio-temporal mapping of multi-satellite observed column atmospheric CO2 using precision-weighted kriging method. Remote Sensing, 12(3), 576.

    Article  Google Scholar 

  • Huang, N., Gu, L., & Niu, Z. (2014). Estimating soil respiration using spatial data products: A case study in a deciduous broadleaf forest in the Midwest USA. Journal of Geophysical Research: Atmospheres, 119(11), 6393–6408.

    Article  Google Scholar 

  • Javanbakht, M., Saghafipour, A., Ezimand, K., Hamta, A., Farahani, L. Z., & 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), 40.

    Article  Google Scholar 

  • Johnson, S. J., Stockdale, T. N., Ferranti, L., Balmaseda, M. A., Molteni, F., Magnusson, L., Tietsche, S., Decremer, D., Weisheimer, A., Balsamo, G., Keeley, S. P. E., Mogensen, K., Hao Zuo, B. M., & Monge-Sanz,. (2019). SEAS5: The new ECMWF seasonal forecast system. Geoscientific Model Development, 12(3), 1087–1117.

    Article  Google Scholar 

  • Jung, M., Reichstein, M., Schwalm, C. R., Huntingford, C., Sitch, S., Ahlström, A., Arneth, A., Camps-Valls, G., Ciais, P., Friedlingstein, P., Gans, F., Ichii, K., Jain, A. K., Kato, E., Papale, D., Poulter, B., Raduly, B., Rödenbeck, C., Tramontana, G., … Zeng, N. (2017). Compensatory water effects link yearly global land CO2 sink changes to temperature. Nature, 541(7638), 516–520.

    Article  Google Scholar 

  • Kong, Y., Chen, B., & Measho, S. (2019). Spatio-temporal consistency evaluation of XCO2 retrievals from GOSAT and OCO-2 based on TCCON and model data for joint utilization in carbon cycle research. Atmosphere, 10(7), 354.

    Article  Google Scholar 

  • Kuze, A., Suto, H., Nakajima, M., & 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.

    Article  Google Scholar 

  • Lal, R., Kimble, J., & Follett, R. F. (2018). Pedospheric processes and the carbon cycle. In Rattan Lal, John M. Kimble, Ronald F. Follett, & Bobby A. Stewart (Eds.), Soil processes and the carbon cycle (pp. 1–8). CRC Press.

    Chapter  Google Scholar 

  • Liang, A., Gong, W., Han, G., & Xiang, C. (2017). Comparison of satellite-observed XCO2 from GOSAT, OCO-2, and ground-based TCCON. Remote Sensing, 9(10), 1033.

    Article  Google Scholar 

  • Liu, M., Lei, L., Liu, D., & Zeng, Z. C. (2016). Geostatistical analysis of CH4 columns over Monsoon Asia using five years of GOSAT observations. Remote Sensing, 8(5), 361.

    Article  Google Scholar 

  • Lv, Z., Shi, Y., Zang, S., & Sun, L. (2020). Spatial and temporal variations of atmospheric CO2 concentration in China and Its influencing factors. Atmosphere, 11(3), 231.

    Article  Google Scholar 

  • Morais Filho, L. F. F., de Meneses, K. C., de Araújo Santos, G. A., da Silva Bicalho, E., de Souza Rolim, G., & La Scala Jr, N. (2021). xCO2 temporal variability above Brazilian agroecosystems: A remote sensing approach. Journal of Environmental Management, 288, 112433.

    Article  Google Scholar 

  • Mousavi, S. M., Falahatkar, S., & Farajzadeh, M. (2017a). Assessment of seasonal variations of carbon dioxide concentration in I ran using GOSAT data. Natural Resources Forum (Vol. 41, No. 2, pp. 83–91). Blackwell Publishing Ltd.

  • Mousavi, S. M., Darvishi, G., Mobarghaee Dinan, N., & 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.

    Article  Google Scholar 

  • Mousavi, S. M., Dinan, N. M., Ansarifard, S., & Sonnentag, O. (2022b). Analyzing spatio-temporal patterns in atmospheric carbon dioxide concentration across Iran from 2003 to 2020. Atmospheric Environment: X, 14, 100163.

    Article  Google Scholar 

  • Mousavi, S. M., & Falahatkar, S. (2020). Spatiotemporal distribution patterns of atmospheric methane using GOSAT data in Iran. Environment, Development and Sustainability, 22(5), 4191–4207.

    Article  Google Scholar 

  • Mousavi, S. M., Falahatkar, S., & Farajzadeh, M. (2017b). Monitoring of monthly and seasonal methane amplitude in Iran using GOSAT data. Physical Geography Research Quarterly, 49(2), 327–340.

    Google Scholar 

  • Mousavi, S. M., Falahatkar, S., & 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.

    Google Scholar 

  • Mousavi, S. M., Falahatkar, S., & 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.

    Google Scholar 

  • Muntean, M., Guizzardi, D., Schaaf, E., Crippa, M., Solazzo, E., Olivier, J., & Vignati, E. (2018). Fossil CO2 emissions of all world countries (p. 2). Publications Office of the European Union.

    Google Scholar 

  • Mustafa, F., Bu, L., Wang, Q., Ali, M., Bilal, M., Shahzaman, M., & 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), 2498.

    Article  Google Scholar 

  • NOAA (2021) National Oceanic and Atmospheric Administration. (Retrieved 17 March 2021). https://gml.noaa.gov/ccgg/trends/

  • Ohyama, H., Morino, I., Nagahama, T., Machida, T., Suto, H., Oguma, H., Sawa, Y., Matsueda, H., Sugimoto, N., Nakane, H., & Nakagawa, K. (2009). Column-averaged volume mixing ratio of CO2 measured with ground-based Fourier transform spectrometer at Tsukuba. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1029/2008JD011465

    Article  Google Scholar 

  • Parker, R., Boesch, H., Cogan, A., Fraser, A., Feng, L., Palmer, P. I., Messerschmidt, J., Deutscher, N., Griffith, D. W., Notholt, J., & Wunch, D. (2011). Methane observations from the greenhouse gases observing SATellite: Comparison to ground-based TCCON data and model calculations. Geophysical Research Letters. https://doi.org/10.1029/2011GL047871

    Article  Google Scholar 

  • Peng, K. F., Jiang, W. G., Hou, P., Sun, C. X., Zhao, X., & Xiao, R. L. (2020). Spatiotemporal variation of vegetation coverage and its affecting factors in the three-river-source National Park. Chinese Journal of Ecology, 39(10), 3388–3396.

    Google Scholar 

  • Pollock, R., Haring, R. E., Holden, J. R., Johnson, D. L., Kapitanoff, A., Mohlman, D., Phillips, C., Randall, D., Rechsteiner, D., Rivera, J. and Rodriguez, J.I., Sutin, B. M. (2010, October). The Orbiting Carbon Observatory instrument: performance of the OCO instrument and plans for the OCO-2 instrument. In Sensors, Systems, and Next-Generation Satellites XIV . International Society for Optics and Photonics Vol. 7826, p. 78260W.

  • Razmi, R., Balyani, S., & Mansouri Daneshvar, M. R. (2017). Geo-statistical modeling of mean annual rainfall over the Iran using ECMWF database. Spatial Information Research, 25(2), 219–227.

    Article  Google Scholar 

  • Schimel, D., Stephens, B. B., & Fisher, J. B. (2015). Effect of increasing CO2 on the terrestrial carbon cycle. Proceedings of the National Academy of Sciences, 112(2), 436–441.

    Article  Google Scholar 

  • Siabi, Z., Falahatkar, S., & Alavi, S. J. (2019). Spatial distribution of XCO2 using OCO-2 data in growing seasons. Journal of Environmental Management, 244, 110–118.

    Article  Google Scholar 

  • Sreenivas, G., Mahesh, P., Subin, J., Kanchana, A. L., Rao, P. V. N., & Dadhwal, V. K. (2016). Influence of meteorology and interrelationship with greenhouse gases (CO2 and CH4) at a suburban site of India. Atmospheric Chemistry and Physics, 16(6), 3953–3967.

    Article  Google Scholar 

  • Sun, W., & Liu, X. (2020). Review on carbon storage estimation of forest ecosystem and applications in China. Forest Ecosystems, 7(1), 1–14.

    Article  Google Scholar 

  • Szulejko, J. E., Kumar, P., Deep, A., & Kim, K. H. (2017). Global warming projections to 2100 using simple CO2 greenhouse gas modeling and comments on CO2 climate sensitivity factor. Atmospheric Pollution Research, 8(1), 136–140.

    Article  Google Scholar 

  • Tarek, M., Brissette, F. P., & Arsenault, R. (2020). Evaluation of the ERA5 reanalysis as a potential reference dataset for hydrological modelling over North America. Hydrology and Earth System Sciences, 24(5), 2527–2544.

    Article  Google Scholar 

  • Tucker, C. J., Pinzon, J. E., Brown, M. E., Slayback, D. A., Pak, E. W., Mahoney, R., Vermote, E. F., & El Saleous, N. (2005). An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. International journal of remote sensing, 26(20), 4485–4498.

    Article  Google Scholar 

  • Yang, X., Guo, B., Han, B. M., Chen, S. T., Yang, F., Fan, Y. W., He, T. L., & Liu, Y. (2019). Analysis of the spatial-temporal evolution patterns of NPP and its driving mechanisms in the Qinghai-Tibet Plateau. Resour Environ Yangtze Basin, 28(12), 3038–50.

    Google Scholar 

  • Yin, S., Wang, X., Tani, H., Zhang, X., Zhong, G., Sun, Z., & Chittenden, A. R. (2018). 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.

    Article  Google Scholar 

  • Yoshida, Y., Ota, Y., Eguchi, N., Kikuchi, N., Nobuta, K., Tran, H., Morino, I., & Yokota, T. (2011). Retrieval algorithm for CO2 and CH4 column abundances from short-wavelength infrared spectral observations by the Greenhouse gases observing satellite. Atmospheric Measurement Techniques, 4(4), 717–734.

    Article  Google Scholar 

  • Zeng, Z., Lei, L., Hou, S., Ru, F., Guan, X., & Zhang, B. (2013). A regional gap-filling method based on spatiotemporal variogram model of CO2 Columns. IEEE Transactions on Geoscience and Remote Sensing, 52(6), 3594–3603.

    Article  Google Scholar 

  • Zeng, Z. C., Lei, L., Strong, K., Jones, D. B., Guo, L., Liu, M., Deng, F., Deutscher, N. M., Dubey, M. K., Griffith, D. W., & Hase, F. (2017). Global land mapping of satellite-observed CO2 total columns using spatio-temporal geostatistics. International Journal of Digital Earth, 10(4), 426–456.

    Article  Google Scholar 

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Acknowledgements

This work was supported by Iran National Science Foundation (Grant Number: 98027555). The researchers would like to extend their sincere gratitude to the OCO-2 Project of NASA, ECMWF-ERA, for providing us with access to their data. Also, this research is supported by the MECW research program and the Centre for Advanced Middle Eastern Studies, Lund University.

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Mousavi, S.M., Dinan, N.M., Ansarifard, S. et al. Examining the Role of the Main Terrestrial Factors Won the Seasonal Distribution of Atmospheric Carbon Dioxide Concentration over Iran. J Indian Soc Remote Sens 51, 865–875 (2023). https://doi.org/10.1007/s12524-022-01650-4

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