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Record W4319019048 · doi:10.1016/j.jhydrol.2023.129182

Mitigation of nutrient leaching from bioretention systems using amendments

2023· article· en· W4319019048 on OpenAlexafffund
Y. Zhang, Anton Skorobogatov, Jianxun He, Caterina Valeo, Angus Chu, Bert van Duin, Leta van Duin

Bibliographic record

VenueJournal of Hydrology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsImpactUniversity of VictoriaUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBioretentionLeaching (pedology)Environmental scienceAmendmentStormwaterNutrientAlumWater qualitySurface runoffEnvironmental engineeringChemistrySoil waterEcologySoil scienceBiology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.024
GPT teacher head0.249
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2023
Admission routes2
Has abstractno

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