Mitigation of nutrient leaching from bioretention systems using amendments
Bibliographic record
Abstract
Bioretention systems have been showcased to be effective in reducing stormwater quantity and improving stormwater quality. However, the systems can also leach nutrients especially during their initial operation, which needs to be mitigated to optimize their benefits. To examine the efficiency of six amendments selected primarily for reducing phosphorus (P) leaching, six amended and two control cells were constructed and monitored soon after their construction in the field of the 2020 growing season. The effects of the amendments on both the hydrological performance (in terms of water retention rate (WRR)) and water quality performance (in terms of the event mean concentration and pollutant removal rate) were investigated. The results showed that all amendments had the capability of preventing or mitigating P leaching from bioretention systems to varying degrees, with the water treatment residual (WTR) outperforming all other amendments, followed by the activated aluminum (AA) and sorptiveMEDIA (SM) amendments. In addition, some of the amendments (i.e., drywall (DRY), WTR, and SM) were also found to be beneficial in reducing the nitrogen (N) leaching to a slight degree, whereas eggshell (EGG) introduced an extra source of N leached. Furthermore, the temporal evolution of the P leaching of the amendment cells was found to be different from that of the control cells. The same result was not observed for the temporal evolution of the N leaching, implying that the amendments (except EGG) did not largely affect the N leaching. Among the amendments’ effect on WRR, there was no obvious difference. Whereas the observed differences between the control cells and some amendment cells in vegetation growth and antecedent media moisture condition might imply their potential impacts on the hydrologic performance. Overall, other than P leaching, the use of amendments affected other functions of bioretention systems, which should be taken into consideration when selecting an amendment for practice.
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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 | 0.000 | 0.000 |
| 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".