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 CO 2 (pCO 2 ), and CH 4 and N 2 O 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 pCO 2 and CH 4 were distinctively higher in polluted tributaries and affected reaches of the Ganges and Mekong than in the Yellow River. The highest levels of N 2 O were found in the Ganges, followed by the Yellow River and Mekong. Across these basins, dry‐season mean concentrations of CO 2 , CH 4 , and N 2 O were 1.6, 2, and 7 times higher than those measured in the monsoon season, respectively. This seasonality was consistent with that of δ 13 C‐CO 2 , while δ 13 C‐CH 4 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 δ 13 C‐CO 2 and δ 13 C‐CH 4 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".