Review of environmental performance of permeable pavement systems: state of the knowledge
Bibliographic record
Abstract
Permeable pavement (PP) systems provide opportunities to mitigate the impacts of urbanization on receiving water systems by providing at source treatment and management of stormwater. However, they do not receive mainstream use throughout much of Canada and the USA because of a lack of local guidance documents, demonstration projects and performance data. Studies have repeatedly shown that PPs attenuate stormwater flows by reducing volume and frequency of stormwater flows, reducing and delaying peak flow rates, and increasing flow durations. PP systems have been shown to improve stormwater quality by reducing stormwater temperature, pollutant concentrations and pollutant loadings of suspended solids, heavy metals, polyaromatic hydrocarbons, and some nutrients. This review is intended as a comprehensive summary of the current state of knowledge of the environmental performance of PP systems. Published research is synthesized to examine the hydrologic performance, impacts to water quality, longevity and functionality and maintenance needs of PP systems. Where appropriate, the limitations of current knowledge are discussed and emerging and future research needs are presented. The intent of this review is to provide stakeholders in stormwater management with the critical information that is needed to foster acceptance of PPs as a viable alternative to traditional systems.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".