Methane Emissions Driven by Adding a Gradient of Ethanol as Carbon Source in Integrated Vertical-Flow Constructed Wetlands
Bibliographic record
Abstract
This work aims to investigate the methane emissions from integrated vertical-flow constructed wetlands (IVCWs) when ethanol is added as an external carbon source. In this study, a gradient of ethanol (0, 2, 4, 8, 16 and 32 mmol/L) was added as the carbon source in an IVCW planted with Cyperus alternifolius L. The results showed that the methane emission flux at an ethanol concentration of 32 mmol/L was 32.34 g CH4 m−2 day−1 less than that of the control experiment (0 mmol/L) and that the methane emission flux at an ethanol concentration of 16 mmol/L was 5.53 g CH4 m−2 day−1 less than that at 0 mmol/L. In addition, variations in the water quality driven by the different ethanol concentrations were found, with a redox potential range of −64 mV to +30 mV, a pH range of 6.6–6.9, a chemical oxygen demand (COD) removal rate range of 41% to 78%, and an ammonia nitrogen removal rate range of 59% to 82% after the ethanol addition. With the average CH4-C/TOC (%) value of 35% driven by ethanol, it will be beneficial to understand that CH4-C/TOC can be considered an ecological indicator of anthropogenic methanogenesis from treatment wetlands when driven by carbon sources or carbon loading. It can be concluded that adding ethanol as an external carbon source can not only meet the water quality demand of the IVCW treatment system but also stimulate and increase the average CH4 emissions from IVCWs by 23% compared with the control experiment. This finding indicates that an external carbon source can stimulate more CH4 emissions from IVCWs and shows the importance of carbon sources during sewage treatment processes when considering greenhouse emissions from treated wetlands.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".