Correlation Engine 2.0
Clear Search sequence regions

  • CH4 (4)
  • china (6)
  • forest (2)
  • past (1)
  • root (1)
  • Sizes of these terms reflect their relevance to your search.

    Methane is the second largest anthropogenic greenhouse gas, and changes in atmospheric methane concentrations can reflect the dynamic balance between its emissions and sinks. Therefore, the monitoring of CH4 concentration changes and the assessment of underlying driving factors can provide scientific basis for the government's policy making and evaluation. China is the world's largest emitter of anthropogenic methane. However, due to the lack of ground-based observation sites, little work has been done on the spatial-temporal variations for the past decades and influencing factors in China, especially for areas with high anthropogenic emissions as Central and Eastern China. Here to quantify atmospheric CH4 enhancements trends and its driving factors in Central and Eastern China, we combined the most up-to-date TROPOMI satellite-based column CH4 (xCH4) concentration from 2018 to 2022, anthropogenic and natural emissions, and a random forest-based machine learning approach, to simulate atmospheric xCH4 enhancements from 2001 to 2018. The results showed that (1) the random forest model was able to accurately establish the relationship between emission sources and xCH4 enhancement with a correlation coefficient (R²) of 0.89 and a root mean-square error (RMSE) of 11.98 ppb; (2)The xCH4 enhancement only increased from 48.21±2.02 ppb to 49.79±1.87 ppb from the year of 2001 to 2018, with a relative change of 3.27%±0.13%; (3) The simulation results showed that the energy activities and waste treatment were the main contributors to the increase in xCH4 enhancement, contributing 68.00% and 31.21%, respectively, and the decrease of animal ruminants contributed -6.70% of its enhancement trend. Copyright © 2023. Published by Elsevier B.V.


    Xinyue Ai, Cheng Hu, Yanrong Yang, Leying Zhang, Huili Liu, Junqing Zhang, Xin Chen, Guoqiang Bai, Wei Xiao. Quantification of Central and Eastern China's atmospheric CH4 enhancement changes and its contributions based on machine learning approach. Journal of environmental sciences (China). 2024 Apr;138:236-248

    Expand section icon Mesh Tags

    Expand section icon Substances

    PMID: 38135392

    View Full Text