Nutrient Removal Of Tropical Bioretention System In Treating Polluted Runoff A Pilot Study
Bibliographic record
Abstract
Poor water quality is a common problem nowadays due to the increase in pollution from human activities. Urban runoff comes from residential areas, industrial areas, and agriculture areas containing nitrogen (N) and phosphorus (P), leading to nitrification and eutrophication. In this study, a pilot-scale bioretention system will be used as stormwater Best Management Practices (BMPs) to solve water quality issues in tropical climates. This study included two bioretention pilot sites, a vegetated site with tropical plants, which is Red Hot Hibiscus (Hibiscus rosa-sinensis), Amaryllis (Hippeastrum), Singapore Daisy (Sphagneticola trilobata), Lobster claw (Heliconia rostrata), Alternanthera (Alternanthera cultivar) and a non-vegetated control site. The field study investigated the pollutant removal efficiency between 2 pilot sites in treating polluted runoff and infiltration rate using two methods, single ring infiltration test and Guelph permeameter test. The site uptake from the polluted runoff will be observed by testing the effluent with TSS, TN and TP test for three weeks at 30 mins, 2 hours, 4 hours and 8 hours after the runoff being released. The results showed pollutant removal efficiency for TSS (76%), TN (78%), and TP (71%) for the vegetated site, which is slightly better compared to control site (TSS (75%), TN (76%) and TP (54%)). The infiltration rate at the vegetated site (36-48 cm/hr) shows lower results than the control site (60-108 cm/hr). However, both pilot sites did not meet the requirement by MSMA (5 to 20 cm/hr). This study concluded that the vegetated site has slightly better performance on nutrient removal efficiency, but the infiltration rate did not achieve the MSMA minimum requirement.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".