The Role of Littoral Vegetation and Open Water Greenhouse Gas Fluxes on the Carbon Budget of Urban Stormwater Ponds
Bibliographic record
Abstract
Stormwater ponds (SWPs) are a common stormwater management technology in new urban developments and have been suggested to be significant sources of the greenhouse gases (GHGs) carbon dioxide (CO2) and methane (CH4). However, they also sequester organic carbon and reduce the surface runoff of nutrients, hence, altering nutrient limitation patterns, trophic conditions, and GHG exchanges. Although numerous studies have focused on estimating open water GHG emissions in artificial ponds, there are limited studies that evaluate net carbon budgets of urban SWP systems comprehensively. In this study, we assessed the relative contributions of the littoral vegetation and open water GHG fluxes to the carbon budgets in two SWPs located in the City of Kitchener, Ontario, Canada. CO2 and CH4 fluxes were measured in the forebay and main basin of two SWPs draining catchments with two different catchment land use (residential versus industrial). Using vegetation and floating chambers, CO2 and CH4 fluxes were measured bi-weekly across all seasons, capturing Net Ecosystem Exchange (NEE), Ecosystem Respiration (ER), and Gross Ecosystem Production (GEP) from both bank and submerged vegetation, plus the diffusive and ebullitive fluxes from the open water surface. Additionally, key parameters, including photosynthetically active radiation (PAR), air and soil temperature, water pH, conductivity, and dissolved gas concentrations, were also measured. We observed significant differences in the fluxes between the littoral vegetation and open water surfaces. Carbon gas emissions from the open water surface were dominated by ebullitive CH₄ fluxes, with the open water acting as a net carbon source. Ebullition events occurred more frequently and with greater intensity in the forebay areas of the SWPs, contributing the most to open water carbon emissions. In contrast, carbon gas emissions from the vegetation were largely driven by photosynthesis and soil respiration, with the vegetated littoral zone functioning as a net CO2 sink. Different vegetation types exhibited varied responses to meteorological conditions, but all showed clear seasonal trends, with higher gas fluxes in summer due to increased biological activity, and minimal fluxes during the frozen season. Unlike vegetation, open water fluxes did not display a distinct seasonal trend; instead, they were primarily influenced by precipitation events and inflow runoff. The forebay of the industrial pond received higher carbon inputs from contaminated stormwater runoff, leading to greater sediment accumulation and elevated GHG fluxes, with frequent and high-intensity CH4 ebullition events being a notable feature. Our findings highlight the critical influence of land use, hydrological events, and seasonal cycles on the carbon balance of SWPs and their potential role in urban carbon cycling.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".