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Title |
Greenhouse Gas Emission Characteristics and Comparison of Measurement Methods in Inland Waters
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Authors |
장수헌(Su-Heon Jang) ; 박성직(Seong-Jik Park) |
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DOI |
https://doi.org/10.15681/KSWE.2026.42.1.92 |
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Keywords |
Biogeochemical hotspots; Greenhouse gas; Modeling-based estimation; Monitoring; Reporting and Verification (MRV); Sampling methods |
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Abstract |
Inland waters constitute dynamic biogeochemical hotspots within the global carbon and nitrogen cycles and are now recognized as substantial sources of greenhouse gas emissions (GHG). This review synthesizes current knowledge of the production, transformation, and emission pathways of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) across diverse aquatic environments including rivers, lakes, reservoirs, ponds, wetlands, and estuaries. CO2 emissions are primarily driven by microbial respiration, organic matter decomposition, and external inputs of dissolved inorganic and organic carbon. CH4 arises predominantly from anaerobic methanogenesis and is partly offset by aerobic or anaerobic oxidation, while its efflux occurs via diffusion, ebullition, and plant-mediated transport. N2O is generated through nitrification, nitrifier denitrification, and incomplete denitrification, though under specific physicochemical conditions inland waters may also function as net sinks. Emission magnitudes are strongly modulated by climatic forcing, hydrological dynamics, nutrient enrichment, oxygen availability, and land-use practices. A critical appraisal of monitoring techniques is also presented. The headspace method provides cost-effective and scalable estimates but underrepresents transient fluxes. Chambers directly capture both diffusive and ebullitive emissions yet remain spatially constrained. Bubble traps are particularly suited to quantifying ebullition-dominated CH4 fluxes, though their irregularity limits extrapolation. Eddy covariance offers continuous, ecosystem-scale flux measurements but entails high technical and financial demands. Modeling approaches facilitate spatially explicit and long-term assessments, but estimates remain sensitive to input data and underlying assumptions. Integrated methodological frameworks combining field observations, process-based modeling, and remote sensing are essential for advancing Monitoring?Reporting?Verification (MRV) systems.
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