Southeastern United States Observations of Stormwater Pollutant Strengths by Particle Size
Bibliographic record
Abstract
This paper summarizes the results of several stormwater research projects that investigated particulate pollutant strengths for different particle size ranges.This paper builds on the previous paper simultaneously published describing particle size distributions of stormwater particulates (Pitt et al. 2016).The pollutant strength information presented in this paper, along with the particle size distribution in the other paper, is critical when understanding the routing of stormwater particulates through urban systems and especially when calculating the expected performance of stormwater controls.The pollutant concentrations commonly have a bimodal distribution, with higher values for small particles (due to large surface areas) and sometimes for large particles (such as for polycyclic aromatic hydrocarbons, PAHs, that are strongly associated with large organic debris).In most cases, the majority of the stormwater pollutant masses at outfalls are associated with small and moderate-sized particulates (usually from ~10 µm to 200 µm) which are effectively transported through drainage systems.Stormwater controls that focus on larger particles (such as >300 µm) that are more abundant at source areas may have less effective benefits on discharged stormwater quality as they only contribute small fractions of the total particulate mass after being poorly transported through most drainage systems.Treatability tests show that effective removal of particulate-bound stormwater pollutants requires the control of the small particles, usually down to ~10 µm in size.Pre-treatment stormwater controls that focus on larger particles reduce maintenance issues and provide other benefits, but need to be supplemented with additional controls that are effective in the removal of small particles, usually in a treatment train arrangement.
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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 | 0.001 | 0.002 |
| 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) | 0.003 | 0.001 |
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".