Hypoxic conditions in stormwater retention ponds: potential for hydrogen sulfide emission
Bibliographic record
Abstract
Improper design and maintenance of stormwater ponds (SWPs) may lead to hypoxic conditions, poor water quality and the production of hydrogen sulfide (H2S). The objective of this study is to develop a comprehensive understanding of hypoxic conditions of SWPs, with a focus on the potential for H2S production and emission. This study was conducted at two retention SWPs in Ottawa, Canada; a problematic pond with the propensity for H2S emission and a reference pond that did not demonstrate H2S emission. The investigation illustrated a significant impact of low dissolved oxygen (DO) concentrations, hypoxic conditions, on the concentration of total sulfides in the water column. Both ponds were shown to periodically experience hypoxic conditions at depth, especially during summer periods with less precipitation and across longer periods of winter, ice-covered conditions. The problem pond, however, was shown to experience lower DO and longer hypoxic conditions than the reference pond in both non-ice-covered and ice-covered conditions due to greater depth and a longer hydraulic retention time. Hypoxic conditions were initiated at the deepest locations in the problem pond and subsequently were spread across the entirety of the pond under winter, ice-covered conditions. Algal biomass (Chlorophyll-a) and soluble biochemical oxygen demand concentrations were shown to not likely be significant factors in the development of hypoxia in the H2S-generating pond. Algal blooms of colonial Chrysophyceae, Synura, a known mixotroph, were observed during ice-covered conditions in the problem pond possibly due to stress-coping mechanisms of algae.
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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 | 0.001 | 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) | 0.001 | 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".