On why the embankment matters when assessing greenhouse gas emissions from urban stormwater ponds
Bibliographic record
Abstract
Urban stormwater ponds (SWPs) are a common runoff control measure that can also have beneficial outcomes for water quality. However, pond emissions of greenhouse gases (GHGs), such as carbon dioxide (CO2) and methane (CH4), raise questions about the climate impact of SWPs. Here, we establish whole-system annual carbon budgets for two SWPs in the City of Kitchener, Ontario, Canada, to compare the open water CO2 and CH4 effluxes to other input and output fluxes of carbon. These include the fluxes of particulate and dissolved inorganic and organic carbon at the inlet and outlet points of the pond, plus those associated with the sediments accumulating in the ponds. In both SWPs, the open-water effluxes of CO2 and CH4 are small compared to the inflow, outflow, and burial carbon fluxes. The SWP sediment budgets further imply that a large fraction of the sediment accumulating in the ponds is supplied by erosion of the embankment. The accompanying delivery of soil organic matter, together with direct litter and organic detritus inputs from the vegetation surrounding the pond, serves as an important source of the open-water CO2 and CH4 emissions. The latter are therefore largely derived from atmospheric CO2 fixed by the ponds’ littoral and embankment vegetation. Consequently, although the SWPs open waters emit CO2 and CH4, the entire SWP engineered systems, including the embankment, act as net CO2 sinks. Overall, our results point to the potential to design and manage SWPs for enhanced climate change mitigation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".