Quantitative insights into phosphorus loadings and speciation in urban catchments
Bibliographic record
Abstract
Phosphorus (P) loadings in stormwater runoff drained from urban landscapes causes eutrophication in aquatic ecosystems downstream of urban areas. Many recent research have addressed urban P dynamics to improve understanding about magnitudes and speciation of P in urban watersheds. We quantified P export and forms in four research sites including three urban sewersheds and a stormwater pond, all located within the drainage basin of Lake Ontario. P speciation laboratory analyses were conducted on water and sediment samples taken from our sites to measure a suite of P species, including total P (TP), total dissolved P (TDP), dissolved reactive P (DRP), dissolved unreactive P (DUP), particulate P (PP), and particulate reactive P (PRP). Using multiple linear regression (MLR) models, we quantified annual loadings of these P species, which appeared to be close to the lower limit of ranges reported in the literature. Average loadings among urban catchments were 0.54 kg ha-1 yr-1 for TP, 0.064 kg ha-1 yr-1 for TDP, 0.007 and 0.045 kg ha-1 yr-1 for DRP and DUP, 0.46 kg ha-1 yr-1 for PP, and 0.16 kg ha-1 yr-1 for PRP. Results indicated that larger catchment-scale loadings of reactive P species (DRP and PRP) were exported as residential development increased. We also found that the pond retained all P species significantly (77-94%), which, according to mass balance and sequential P extraction analyses, was attributed to both sedimentation and chemical precipitation of P with calcium mineral phases. Findings in our study imply that, due to loadings’ variability imposed by land-use characteristics, urban P management options need to vary from a catchment to another. Furthermore, enhancing the formation of calcium phosphate and other redox-stable mineral phases could be explored as a best management practice in existing and new ponds for improving P retention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".