Performance of low-impact development best management practices: a critical review
Bibliographic record
Abstract
Low-impact development (LID), a land planning and engineering design approach for managing urban stormwater runoff, has been widely adopted across the globe. LID best management practices (BMPs) are man-made features that rely on natural processes to manage stormwater water quantity and quality. In this article, recent literature (published after the year 2008) related to nine BMPs was reviewed to highlight the ranges in treatment efficiencies for 21 of the most frequently investigated runoff parameters. The primary function, pros and cons, and factors affecting performance of each BMP were discussed. A frequency analysis of the reviewed parameters showed that total suspended solids, total phosphorous, total nitrogen, runoff reduction, and zinc concentrations were the most frequently investigated stormwater runoff parameters. Five recurring themes were observed with regards to knowledge gaps and conflicting objectives for research related to LID BMPs that include: (i) lack of consensus on which parameters to measure for effective LID adoption, (ii) BMP performance is highly variable, (iii) many BMPs are known exporters of nutrient pollutants, (iv) lack of cold weather performance-specific studies for individual BMPs, and (v) lack of human pathogen-related stormwater quality studies for individual BMPs. Suggestions for future research are discussed to address these knowledge gaps.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".