Stormwater quality control for sustainable urban drainage systems
Bibliographic record
Abstract
The paper deals with the performance assessment of stormwater facilities in mitigating the negative effects of urban runoff pollutants carried both to the receiving environment and to the urban drainage system.Experimental campaigns and numerical simulations have been carried out on a residential catchment of Pavia, Lombardia, Italy.The rainfall-runoff process and the pollutant dynamics in the catchment and combined sewer network were simulated using a conceptual model based on an instantaneous unit hydrograph of a single linear reservoir system for individual events as well as for a continuous run of events and inter-event periods over one year.Several design confi gurations and operating conditions of fl ow regulators (FRs) and stormwater detention tanks (SWDTs) have been evaluated by defi ning performance indicators that simultaneously account for a large number of technical, environmental, and socio-economic constraints.On many occasions, wet-weather pollutant concentrations substantially exceeded what might normally be expected of raw wastewater, which thus required stormwater treatment facilities for the environmental and ecological protection of the receiving water.FR was unable to control the acute pollution impact on the river, thereby causing severe ecological deterioration.SWDT was very appropriate to safeguard both the quantity and the quality of the entire chain, consisting of the sewer system, the treatment plant, and the receiving water.The results of this research provide information about a key issue related to the implementation of environmental policies in large urban areas for the recovery of impacted receiving water bodies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".