Don't Throw the Baby Out with the Bathwater—Sample Collection and Processing Issues Associated with Particulate Solids in Stormwater
Bibliographic record
Abstract
Particulate solids are the most common constituent examined during stormwater monitoring projects.They have also been used as the basis of many regulatory programs, mostly as a surrogate for particulate bound pollutants, such as metals and nutrients.There is controversy concerning older total suspended solids values compared to newer data observations (such as when comparing total suspended solids, TSS, values with suspended sediment concentration, SSC), especially when used in conjunction with the particle size distributions (PSDs).As part of this controversy, some have advocated not using the earlier TSS data as being unreliable.Some commonly used sampling and analytical procedures do not represent the complete range of particle sizes present in the water being sampled.If the PSD is mismatched with the particulate solids information, incorrect calculations will lead to misleading stormwater control calculations.Most stormwater controls preferentially remove large particles (faster settling in sedimentation devices, easier to trap in filtering facilities, easier to remove by conventional street cleaners).Therefore, if SSC PSDs are incorrectly paired with TSS concentration data, more of the TSS load would be calculated to be removed by stormwater controls than actually occurs.It is therefore important that the correct particle size distributions be matched with the particulate solids concentrations when conducting these modeling calculations for the most accurate performance and characterization predictions.This paper presents the results of several stormwater monitoring studies that have examined alternative sample collection and laboratory analyses options focusing on stormwater particulate solids and particle size distributions.Recommendations are made to assist in the selection of the most appropriate stormwater sampling and processing methods based on actual monitoring results and mass balance calculations.
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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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".