in

Characterization of spatial and temporal variations of CO2 concentration on tropical Island and analysis of influencing factors


Abstract

In this study, the spatial and temporal variations and distribution characteristics of the carbon dioxide (CO2) concentration on Hainan Island are analyzed using GOSAT L3 data from 2011 to 2024, and the effects of various factors impacting the CO2 concentration on Hainan Island are discussed. The results indicate that from 2011 to 2024, the CO2 concentration on Hainan Island showed an increasing trend, with a fast growth rate in the early period and a slow growth rate in recent years with the implementation of the dual-carbon strategy. The spatial distribution is affected by anthropogenic activities, topography, vegetation and solar radiation, and the overall CO2 concentration pattern is high in the north and low in the south. Human activities are the most important source of carbon on Hainan Island, vegetation is the most important carbon sink, and elements such as surface temperature, precipitation, and total solar radiation play roles in suppressing CO2. The CO2 concentration on Hainan Island is expected to continue to increase at a slow rate and may display a decreasing trend in the future.

References

  1. The state of. Greenhouse gases in the atmosphere based on global observations through 2010. WMO Greenh. Gas Bull. 7(21) (2011).

  2. Zhang, S. et al. Policy recommendations for the zero energy building promotion towards carbon neutral in Asia-Pacific Region. Energy Policy https://doi.org/10.1016/j.enpol.2021.112661 (2021).

    Google Scholar 

  3. Gregg, J. S., Andres, R. J., Marland, G. & China Emissions pattern of the world leader in CO2 emissions from fossil fuel consumption and cement production. Geophys. Res. Lett. 35 (8), 135–157 (2008).

    Google Scholar 

  4. Etheridge, D. M. et al. Natural and anthropogenic changes in atmospheric CO2 over the last 1000 years froin air in Antarctic ice and firn. J. Geophys. Research: Atmos. 101(D2), 4115 (1996).

    Google Scholar 

  5. Li, C., Li, H. & Qin, X. Spatial heterogeneity of carbon emissions and its influencing factors in china: evidence from 286 prefecture-level cities. Int. J. Environ. Res. Public Health. 19 (3), 1226 (2022).

    Google Scholar 

  6. Zhang, M. N. et al. Elevated CO2 moderates the impact of climate change on future bamboo distribution in Madagascar. Sci. Total Environ. 810, 152235 (2022).

    Google Scholar 

  7. Jung, M. et al. Compensatory water effects link yearly global land CO2 sink changes to temperature. Nature 541 (7638), 516–520 (2017).

    Google Scholar 

  8. Yamagishi, H. et al. Role of nitrification and denitrification on the nitrous oxide cycle in the Eastern tropical North Pacific and Gulf of California. J. Geophys. Research:Biogeosciences. https://doi.org/10.1029/2006JG000227 (2007).

    Google Scholar 

  9. Humphrey, V. et al. Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage. Nature 560 (7720), 628–631 (2018).

    Google Scholar 

  10. Yi, L. I. U. et al. Advances in technologies and methods for satellite remote sensing of atmospheric CO2. Remote Sens. Technol. Application. 26 (2), 247–254 (2011).

    Google Scholar 

  11. Wang, Q., Chiu, Y. H. & Chiu, C. R. Driving factors behind carbon dioxide emissions in china: A modified production-theoretical decomposition analysis. Energy Econ. 51, 252–260 (2015).

    Google Scholar 

  12. Heyntann, J. et al. Consistent satellite X CO2 retrievals from SCIAMACHY and GOSAT using the BESD algorithm. Atmos. Meas. Tech. 8 (2), 2961–2980 (2015).

    Google Scholar 

  13. Takagi, H. et al. On the benefit of GOSAT observationsto the Estimation of regional CO2 fluxes. Sola 7, 161–164 (2011).

    Google Scholar 

  14. Buchwitz, M. et al. Atmospheric Methaneand carbon dioxide from SCIAMACHY satellite data: initial comparison with chemistry and transportmodels. Atmos. Chem. Phys. 5 (4), 941:962 (2005).

    Google Scholar 

  15. Yizhen, J. I. A. et al. Spatial and Temporal distribution of XCO2 and XCH4 in China based on satellite remote sensing. J. Atmospheric Environ. Opt. 17 (6), 679692 (2022).

    Google Scholar 

  16. Yokota, T. et al. Global concentrations of CO2 and CH4 retireved from GOSAT: first preliminary results. Sola 5, 160163 (2009).

    Google Scholar 

  17. Guo, M. et al. Assessment of Global Carbon Dioxide Concentration Using MODIS and GOSAT Data. Sensors https://doi.org/10.3390/s121216368 (2012).

    Google Scholar 

  18. Da-zhang, H. E. & Sheng-ling, Z. H. A. N. G. The Reginal climate division of Hainan Island. ACTA Geogr. SINACA. 40 (2), 169–178 (1985).

    Google Scholar 

  19. Bo-Sun, W. A. N. G. et al. Diversity of tropical forest landscape-type in Hainan Island, China. Acta Ecol. Sin. 27 (5), 1690–1695 (2007).

    Google Scholar 

  20. Long, J. Y. et al. Spatial autocorrelation analysis of Chinese provincial carbon dioxide emissions. Ecol. Econ. https://doi.org/10.1109/CSO.2009.147 (2011).

    Google Scholar 

  21. WEI, P. et al. Spatial and Temporal characteristics of vegetation resilience to drought in China. Sci. China Earth Sci. 68(07), 2310–2327 (2025).

    Google Scholar 

  22. Zhang, C., Li, S. & Wan, J. H. The warmest year 2015 in the instrumental record and its comparison with year 1998. Atmospheric Ocean. Sci. Lett. 9 (6), 487–494 (2016).

    Google Scholar 

  23. Niinistö & Kellomäki Silvola. Seasonality in a boreal forest ecosystem affects the use of soil temperature and moisture as predictors of soil CO2 efflux. Biogeosciences Discuss. 8 (8), 2811–2849 (2011).

    Google Scholar 

  24. Huimin, Y. et al. Multiple cropping intensity in China derived from Agro-meteorological observations and MODIS data. Chin. Geogra. Sci. 24 (02), 205–219 (2014).

    Google Scholar 

  25. Doughty, C. E. & Goulden, M. L. Are tropical forests near a high temperature threshold?. J. Geophys. Res. : Biogeosc 113 113, G00B07 (2008).

    Google Scholar 

  26. Cox, P. M. et al. Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature 494, 341–344 (2013).

    Google Scholar 

  27. Li, G. et al. Analysis and prediction of global vegetation dynamics:past variations and future perspectives. J. Forestry Res. 34 (02), 317–332 (2023).

    Google Scholar 

  28. Tadesse, K. B. & Dinka, M. O. .Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa. J. Water Land. Dev. https://doi.org/10.1515/jwld-2017-0088 (2017).

    Google Scholar 

  29. Aboelnour, M. A. et al. Leveraging ERA5-Land reanalysis precipitation data for urban flood vulnerability and water security assessments: A global perspective. Earth Syst. Environ. 9 (3), 2335–2353 (2025).

    Google Scholar 

  30. GoovaertsP Kriging interpolation. Geographic Inform. Sci. Technol. Body Knowl. https://doi.org/10.22224/gistbok/2019.4.4 (2019).

    Google Scholar 

  31. YU Yan-sheng, C. H. E. N. & Xing-wei Analysis of future trend characteristics of hydrological time series based on R/S and Mann-Kendall methods. J. Water Resour. Water Enigineering. 19 (3), 4144 (2008).

    Google Scholar 

Download references

Funding

This study is supported by the National Key Research and Development Program (2023YFC3008001);Natural science foundation of China(Grant No.42465006, Grant No.U21A6001).Hainan Provincial Natural Science Foundation of China(424QN364).

Data availability statement.

The Data used and during the current study are publicly available in repositories:

1. GOSAT data is available at https://data2.gosat.nies.go.jp/index_en.html.

2. EVI、PAR 、LST and GPP data are available at https://modis.gsfc.nasa.gov/.

3. Meteorological data are available at https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land-monthly-means.

4. Population, GDP, and energy data are available at https://en.hainan.gov.cn/hainan/tjnj/list3.shtml.

Author information

Authors and Affiliations

Authors

Contributions

Luo wrote the main manuscript text and prepared the figures. Han dowload the satellite and reanlysis data. Liu provided technical guidance and revised the article.All authors reviewed the manuscript.

Corresponding author

Correspondence to
Qi Luo.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

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

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Luo, Q., Han, J. & Liu, S. Characterization of spatial and temporal variations of CO2 concentration on tropical Island and analysis of influencing factors.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-32647-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41598-025-32647-x

Keywords

  • Tropical island
  • CO2 concentration
  • Influencing factors
  • Variation trend


Source: Ecology - nature.com

Foraging strategies and geographic factors jointly shape gut microbiota of spiders in the Sichuan and Guizhou regions of China

Wolbachia enhances ovarian development in the rice planthopper Laodelphax striatellus through elevated energy production

Back to Top