Phosphorus event mean concentrations and first flush from urban catchments in continental climates
Bibliographic record
Abstract
• TP and SRP EMCs are significantly different between urban land use and seasons. • TP EMCs exceed hypereutrophic thresholds for all urban land uses. • No consistent relationships between EMCs and rainfall characteristics. • Occurrence of first flush varies considerably depending on definition used. • First flush strength varies seasonally with no first flush observed in winter. Urban stormwater is an important source of phosphorus (P) to surface waters. While factors influencing stormwater quality have been extensively analyzed for temperate and tropical climates, they are poorly quantified for cold continental climates. This study aggregated total suspended solids (TSS), total P (TP) and soluble reactive P (SRP) event mean concentration (EMC) and high resolution event data from the literature for urban catchments in continental climates and analyzed data to assess the influence of urban land use types, rainfall characteristics and seasons on EMCs and first flush occurrence and strength. The published data were supplemented with data collected in London, Ontario, Canada. TP EMCs exceeded the hypereutrophic threshold for 88 % of monitored events (n = 117). Pollutant EMCs were generally higher in residential compared to commercial and mixed urban catchments. TSS EMCs were highest in summer, while TP and SRP EMCs were higher in fall and summer. In contrast to temperate and tropical climates, no consistent significant relationships between pollutant EMCs and rainfall characteristics were found. First flush occurrence varied between pollutants and based on the different definitions applied. First flush strength varied seasonally, with first flush strongest in summer for TSS, and in fall and summer for TP and SRP. First flush did not occur winter for any pollutant possibly due to the more gradual runoff generated by snowmelt and pollutant accumulation in snowpack. Study findings provide new insights needed to improve water quality predictions and stormwater management approaches targeting P reductions in continental climates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".