Skip to main content
Log in

Spatiotemporal distribution patterns of atmospheric methane using GOSAT data in Iran

  • Published:
Environment, Development and Sustainability Aims and scope Submit manuscript

Abstract

Methane (CH4) is the simplest hydrocarbon in the atmosphere and is the second most important greenhouse gas (GHG) after carbon dioxide (CO2) whose concentration is changing due to human activities. The main objective of this study is to examine the spatial distribution of CH4 concentration for Iran in 2013 based on the level 2 GOSAT data using the ordinary kriging technique. For this purpose, first, the relationship between CH4 concentration and environmental variables such as land surface temperature (LST), normalized difference vegetation index (NDVI), air temperature, and humidity was determined. The results showed that CH4 concentration changes gradually with latitude and longitude across Iran. The spatial distribution of CH4 concentration presents the high concentration of this gas in the southern hemisphere and in the east of the study area throughout the year. The correlation of CH4 concentration with LST and temperature was positive, and its correlation with NDVI and humidity was negative in different seasons of 2013. This implies that with the decline of temperature and LST and rise of humidity and NDVI, CH4 concentration has decreased in the study area. It is possible to transfer the CH4 gas from the south to the southeast of Iran according to the location of flaring gas, and wind speed and direction in different seasons. These findings can help decision makers for better management of the sinks and sources of GHGs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Alexe, M., Bergamaschi, P., Segers, A., Detmers, R., Butz, A., Hasekamp, O., et al. (2014). Inverse modeling of CH4 emissions for 2010–2011 using different satellite retrieval products from GOSAT and SCIAMACHY. Atmospheric Chemistry and Physics Discussions,14, 11493–11539.

    Google Scholar 

  • Andrews, A. E., Boering, K. A., Daube, B. C., Wofsy, S. C., Loewenstein, M., Jost, H., et al. (2001). Mean ages of stratospheric air derived from in situ observations of CO2, CH4, and N2O. Journal of Geophysical Research: Atmospheres,106(D23), 32295–32314.

    CAS  Google Scholar 

  • Anonymous. (2017). Emissions database for global atmospheric research. http://edgar.jrc.ec.europa.eu/. Accessed February 8, 2017.

  • BR. (2017). British petroleum. http://www.bp.com/en/global/corporate/about-bp/energy-economics/energy-outlook. Accessed September 12, 2017.

  • Cambardella, C. A., Moorman, T. B., Parkin, T. B., Karlen, D. L., Novak, J. M., Turco, R. F., et al. (1994). Field-scale variability of soil properties in central Iowa soils. Soil Science Society of America Journal,58(5), 1501–1511.

    Google Scholar 

  • Chhabra, A., Manjunath, K. R., Panigrahy, S., & Parihar, J. S. (2013). Greenhouse gas emissions from Indian livestock. Climatic Change,117(1–2), 329–344.

    CAS  Google Scholar 

  • Cicerone, R. J., & Oremland, R. S. (1988). Biogeochemical aspects of atmospheric methane. Global Biogeochemical Cycles,2(4), 299–327.

    CAS  Google Scholar 

  • Crutzen, P. J., & Gidel, L. T. (1983). A two-dimensional photochemical model of the atmosphere: 2. The tropospheric budgets of the anthropogenic chlorocarbons CO, CH4, CH3Cl and the effect of various NOx sources on tropospheric ozone. Journal of Geophysical Research: Oceans,88(C11), 6641–6661.

    CAS  Google Scholar 

  • Dai, L., Jia, J., Yu, D., Lewis, B. J., Zhou, L., Zhou, W., et al. (2013). Effects of climate change on biomass carbon sequestration in old-growth forest ecosystems on Changbai Mountain in Northeast China. Forest Ecology and Management,300, 106–116.

    Google Scholar 

  • Deng, F., Jones, D. B. A., Henze, D. K., Bousserez, N., Bowman, K. W., Fisher, J. B., et al. (2014). Inferring regional sources and sinks of atmospheric CO2 from GOSAT XCO2 data. Atmospheric Chemistry and Physics,14(7), 3703–3727.

    Google Scholar 

  • Deng, S., Shi, Y., Jin, Y., & Wang, L. (2011). A GIS-based approach for quantifying and mapping carbon sink and stock values of forest ecosystem: A case study. Energy Procedia,5, 1535–1545.

    Google Scholar 

  • Eisele, F. L., Mount, G. H., Tanner, D., Jefferson, A., Shetter, R., Harder, J. W., et al. (1997). Understanding the production and interconversion of the hydroxyl radical during the Tropospheric OH Photochemistry Experiment. Journal of Geophysical Research,102, 6457–6465.

    CAS  Google Scholar 

  • Englund, E., Weber, D., & Leviant, N. (1992). The effects of sampling design parameters on block selection. Mathematical Geology,24(3), 329–343.

    Google Scholar 

  • ENI. (2016). Encyclopaedia Iranica. http://www.iranicaonline.org. Accessed December 6, 2017.

  • Ericson, K. (2014). A crude awakening: The relationship between petroleum exploration and environmental conservation in western Uganda. https://digitalcollections.sit.edu/isp_collection/1924. Accessed October 12, 2017.

  • 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), 627.

    Google Scholar 

  • Fu, L., Zhao, Y., Xu, Z., & Wu, B. (2015). Spatial and temporal dynamics of forest aboveground carbon stocks in response to climate and environmental changes. Journal of Soils and Sediments,15(2), 249–259.

    CAS  Google Scholar 

  • Gao, C. Z., Wang, J., & Zhang, F. S. (2013). Collision of H++ CH4 at 30 eV: A simulation study. Nuclear Instruments & Methods in Physics Research, Section B: Beam Interactions with Materials and Atoms,307, 225–228.

    CAS  Google Scholar 

  • Gavrilov, N. M., Makarova, M. V., Poberovskii, A. V., & Timofeyev, Y. M. (2014). Comparisons of CH4 ground-based FTIR measurements near Saint Petersburg with GOSAT observations. Atmospheric Measurement Techniques,7(4), 1003–1010.

    Google Scholar 

  • Gloor, M., Fan, S. M., Pacala, S., & Sarmiento, J. (2000). Optimal sampling of the atmosphere for purpose of inverse modeling: A model study. Global Biogeochemical Cycles,14(1), 407–428.

    CAS  Google Scholar 

  • Guo, M., Wang, X., Li, J., Yi, K., Zhong, G., & Tani, H. (2012). Assessment of global carbon dioxide concentration using MODIS and GOSAT data. Sensors,12(12), 16368–16389.

    CAS  Google Scholar 

  • Guo, M., Wang, X., Li, J., Wang, H., & Tani, H. (2013a). Examining the relationships between land cover and greenhouse gas concentrations using remote-sensing data in East Asia. International Journal of Remote Sensing,34(12), 4281–4303.

    Google Scholar 

  • Guo, M., Wang, X. F., Li, J., Yi, K. P., Zhong, G. S., Wang, H. M., et al. (2013b). Spatial distribution of greenhouse gas concentrations in arid and semi-arid regions: A case study in East Asia. Journal of Arid Environments,91, 119–128.

    Google Scholar 

  • IMO. (2016). Iran Meteorological Organization. http://www.irimo.ir/far/. Accessed December 18, 2017.

  • Inoue, M., Morino, I., Uchino, O., Miyamoto, Y., Saeki, T., Yoshida, Y., et al. (2014). Validation of XCH4 derived from SWIR spectra of GOSAT TANSO-FTS with aircraft measurement data. Atmospheric Measurement Techniques,7(9), 2987–3005.

    Google Scholar 

  • Isaaks, E. H., & Srivastava R. M. (1989). Applied geostatistics. No. 551.72 I86. Oxford: Oxford University Press.

  • Janssens-Maenhout, G., Petrescu, A. M. R., Muntean, M., & Blujdea, V. (2011). Verifying greenhouse gas emissions: Methods to support international climate agreements. Greenhouse Gas Measurement and Management,1, 132–133.

    Google Scholar 

  • Journel, A. G., & Huijbregts, C. J. (1978). Mining geostatistics. Caldwell: Blackburn Press.

    Google Scholar 

  • Kavitha, M., & Nair, P. R. (2016a). Non-homogeneous vertical distribution of methane over Indian region using surface, aircraft and satellite-based data. Atmospheric Environment,141, 174–185.

    CAS  Google Scholar 

  • Kavitha, M., & Nair, P. R. (2016b). Region-dependent seasonal pattern of methane over Indian region as observed by SCIAMACHY. Atmospheric Environment,131, 316–325.

    CAS  Google Scholar 

  • Keppler, F., Hamilton, J. T., Braß, M., & Röckmann, T. (2006). Methane emissions from terrestrial plants under aerobic conditions. Nature,439(7073), 187.

    CAS  Google Scholar 

  • Kim, H. S., Chung, Y. S., Tans, P. P., & Dlugokencky, E. J. (2015). Decadal trends of atmospheric methane in East Asia from 1991 to 2013. Air Quality, Atmosphere and Health,8(3), 293–298.

    CAS  Google Scholar 

  • Kirschke, S., Bousquet, P., Ciais, P., Saunois, M., Canadell, J. G., Dlugokencky, E. J., et al. (2013). Three decades of global methane sources and sinks. Nature Geoscience,6(10), 813.

    CAS  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.

    CAS  Google Scholar 

  • Laslett, G. M. (1994). Kriging and splines: An empirical comparison of their predictive performance in some applications. Journal of the American Statistical Association,89(426), 391–400.

    Google Scholar 

  • Li, J., & Heap, A. D. (2011). A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors. Ecological Informatics,6(3–4), 228–241.

    Google Scholar 

  • Li, J., & Heap, A. D. (2014). Spatial interpolation methods applied in the environmental sciences: A review. Environmental Modelling and Software,53, 173–189.

    Google Scholar 

  • Liu, Y., Wang, X., Guo, M., & Tani, H. (2012). Mapping the FTS SWIR L2 product of XCO2 and XCH4 data from the GOSAT by the Kriging method—A case study in East Asia. International Journal of Remote Sensing,33(10), 3004–3025.

    Google Scholar 

  • Miao, R., Lu, N., Yao, L., Zhu, Y., Wang, J., & 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.

    Google Scholar 

  • Mindas, J., & SNvareninova, J. (2016). Calculation of methane emissions from wetlands in Slovakia via IPCC methodology. World Academy of Science, Engineering and Technology, International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering,10(7), 792–795.

    Google Scholar 

  • Modarres, R., & da Silva, V. D. P. R. (2007). Rainfall trends in arid and semi-arid regions of Iran. Journal of Arid Environments,70(2), 344–355.

    Google Scholar 

  • Morino, I., Uchino, O., Inoue, M., Yoshida, Y., Yokota, T., Wennberg, P. O., et al. (2011). Preliminary validation of column-averaged volume mixing ratios of carbon dioxide and methane retrieved from GOSAT short-wavelength infrared spectra. Atmospheric Measurement Techniques,4(6), 1061–1076.

    CAS  Google Scholar 

  • Mousavi, S. M., Falahatkar, S., & Farajzadeh, M. (2017). Assessment of seasonal variations of carbon dioxide concentration in Iran using GOSAT data. Natural ResourcesForum,41, 83–91.

    Google Scholar 

  • Nayak, R. K., Deepthi, E. N., Dadhwal, V. K., Rao, K. H., & Dutt, C. B. S. (2014). Evaluation of NOAA carbon tracker global carbon dioxide products. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences,40(8), 287–290.

    Google Scholar 

  • Portmann, R. W., Daniel, J. S., & Ravishankara, A. R. (2012). Stratospheric ozone depletion due to nitrous oxide: Influences of other gases. Philosophical Transactions of the Royal Society B,367(1593), 1256–1264.

    CAS  Google Scholar 

  • Prasad, P., Rastogi, S., & Singh, R. P. (2016). Study of CO2variability over India using data from satellites. Paper presented at the conference of the international society for optics and photonics, May 10–14, 2016. https://doi.org/10.1117/12.2228029.

  • Robertson, G. P., Klingensmith, K. M., Klug, M. J., Paul, E. A., Crum, J. R., & Ellis, B. G. (1997). Soil resources, microbial activity, and primary production across an agricultural ecosystem. Ecological Applications,7(1), 158–170.

    Google Scholar 

  • Sasakawa, M., Shimoyama, K., Machida, T., Tsuda, N., Suto, H., Arshinov, M., et al. (2010). Continuous measurements of methane from a tower network over Siberia. Tellus B: Chemical and Physical Meteorology,62(5), 403–416.

    Google Scholar 

  • Shim, C., Lee, J., & Wang, Y. (2013). Effect of continental sources and sinks on the seasonal and latitudinal gradient of atmospheric carbon dioxide over East Asia. Atmospheric Environment,79, 853–860.

    CAS  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.

    CAS  Google Scholar 

  • Sun, B., Zhou, S., & Zhao, Q. (2003). Evaluation of spatial and temporal changes of soil quality based on geostatistical analysis in the hill region of subtropical China. Geoderma, 115, 85–99.

    Google Scholar 

  • Sun, Z., Wang, X., Tani, H., Zhong, G., & Yin, S. (2016). Spatial distribution of CO2 concentration over South America during ENSO episodes by using GOSAT data. American Journal of Climate Change,5, 77–87.

    Google Scholar 

  • Terao, Y., Mukai, H., Nojiri, Y., Machida, T., Tohjima, Y., Saeki, T., & Maksyutov, S. (2011). Interannual variability and trends in atmospheric methane over the western Pacific from 1994 to 2010. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1029/2010JD015467.

    Article  Google Scholar 

  • Vaghjiani, G. L., & Ravishankara, A. R. (1991). New measurement of the rate coefficient for the reaction of OH with methane. Nature,350, 406–409.

    CAS  Google Scholar 

  • Wada, A., Matsueda, H., Sawa, Y., Tsuboi, K., & Okubo, S. (2011). Seasonal variation of enhancement ratios of trace gases observed over 10 years in the western North Pacific. Atmospheric Environment,45(12), 2129–2137.

    CAS  Google Scholar 

  • Wang, H., Liu, G., & Gong, P. (2005). Use of cokriging to improve estimates of soil salt solute spatial distribution in the Yellow River delta. Acta Geographica Sinica,60(3), 511–518.

    Google Scholar 

  • Wang, T., Shi, J., Jing, Y., Zhao, T., Ji, D., & Xiong, C. (2016). Correction: Combining XCO2 measurements derived from SCIAMACHY and GOSAT for potentially generating global CO2 maps with high spatiotemporal resolution. PLoS ONE,11(1), e0148152.

    Google Scholar 

  • Watanabe, H., Hayashi, K., Saeki, T., Maksyutov, S., Nasuno, I., Shimono, Y., et al. (2015). Global mapping of greenhouse gases retrieved from GOSAT Level 2 products by using a kriging method. International Journal of Remote Sensing,36(6), 1509–1528.

    Google Scholar 

  • Yokota, T., Yoshida, Y., Eguchi, N., Ota, Y., Tanaka, T., Watanabe, H., et al. (2009). Global concentrations of CO2 and CH4 retrieved from GOSAT: First preliminary results. Sola,5, 160–163.

    Google Scholar 

  • Yoshida, Y., Ota, Y., Eguchi, N., Kikuchi, N., Nobuta, K., Tran, H., et al. (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.

    CAS  Google Scholar 

  • Zeng, Z., Lei, L., Guo, L., Zhang, L., & Zhang, B. (2013). Incorporating temporal variability to improve geostatistical analysis of satellite-observed CO2 in China. Chinese Science Bulletin,58(16), 1948–1954.

    CAS  Google Scholar 

  • Zhang, Y., Xu, M., Chen, H., & Adams, J. (2009). Global pattern of NPP to GPP ratio derived from MODIS data: Effects of ecosystem type, geographical location and climate. Global Ecology and Biogeography,18(3), 280–290.

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank the GOSAT Project of Japan, the Islamic Republic of Iran Meteorological Organization, ECMWF-ERA, and NASA for allowing us to use their data in this research.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Seyed Mohsen Mousavi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mousavi, S.M., Falahatkar, S. Spatiotemporal distribution patterns of atmospheric methane using GOSAT data in Iran. Environ Dev Sustain 22, 4191–4207 (2020). https://doi.org/10.1007/s10668-019-00378-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10668-019-00378-5

Keywords

Navigation