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Published July 2020 | Version v1
Journal article

Optimized methods for diffusive greenhouse gas flux analyses in inland waters

  • 1. Nanjing University of Information Science & Technology. School of Environmental Science and Engineering (China)
  • 2. Helmholtz-Zentrum Geesthacht (HZG). Institute of Coastal Research (Germany)
  • 3. Nanjing Hydraulic Research Institute. Center for Eco-Environment Research (China)
  • 4. Chinese Academy of Sciences. Nanjing Institute of Geography and Limnology (China)

Description

Inland waters are considered hotspots of greenhouse gas (GHG) emissions and have been extensively researched. Static chamber (STAT) and thin boundary layer (BLE) are two commonly used methods for analyzing diffusive GHG emissions from inland waters. However, the STAT method is often disturbed by GHG bubbles; meanwhile, many kinds of headspace gas are used in the BLE method, but the differences between their diffusive GHG emission analysis results are not understood. In this study, the chamber in the STAT method was modified to combat the disturbances from GHG bubbles, and the typically used gases for the BLE method, namely, pure nitrogen, air, and filtered air, were comparatively studied. Results demonstrated that the modified chamber could effectively prevent the invasion of GHG bubbles; it increased the success rate from 67 to 90% in the field test, with no obvious impacts on the results of the GHG emission analyses. The use of air and filtered air in the BLE method yielded the lower values of GHG emissions relative to pure nitrogen, and this finding was potentially attributed to the inhibition effects of the residual GHGs and high humidity in air and filtered air on the extraction of diffusive GHGs from the surface water. This study improved the commonly used methods for diffusive GHG emission analysis, and the current findings are beneficial to the study of GHG emissions from inland waters.

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Publishing Information

Journal Title
Environmental Science and Pollution Research International
Journal Volume
27
Journal Issue
21
Journal Page Range
p. 25870-25876
ISSN
0944-1344
CODEN
ESPLEC

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Copyright (c) 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019