Localized Pollution Impacts on Greenhouse Gas Dynamics in Three Anthropogenically Modified Asian River Systems
Bibliographic record
Abstract
Abstract Despite growing research on greenhouse gas (GHG) emissions from inland waters, few systematic efforts have been made to assess the regional‐scale GHG emissions from Asian rivers under increasing anthropogenic stress. We examined factors controlling longitudinal and seasonal variations in the partial pressure of CO2 (pCO2), and CH4 and N2O concentrations in the Ganges, Mekong, and Yellow River by simultaneously measuring gas concentrations and stable C isotopes, and optical properties of dissolved organic matter (DOM) from 2016 to 2019. The levels of pCO2 and CH4 were distinctively higher in polluted tributaries and affected reaches of the Ganges and Mekong than in the Yellow River. The highest levels of N2O were found in the Ganges, followed by the Yellow River and Mekong. Across these basins, dry‐season mean concentrations of CO2, CH4, and N2O were 1.6, 2, and 7 times higher than those measured in the monsoon season, respectively. This seasonality was consistent with that of δ13C‐CO2, while δ13C‐CH4 showed an opposite pattern. GHG concentrations exhibited significant positive relationships with DOM concentrations and optical properties including fluorescence index and protein‐like fluorescence, implying the contribution of anthropogenic, labile DOM to production of GHGs in the polluted reaches. Graphical mixing models of δ13C‐CO2 and δ13C‐CH4 support the stronger impact of wastewater on the Ganges and Mekong than on the Yellow River. The overall results suggest that neglecting localized pollution impacts on GHG emissions from increasingly urbanized river basins can result in a substantial underestimation of global riverine GHG emissions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".