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Record W4321996503 · doi:10.5194/egusphere-egu23-10753

Modeling urban phosphorus export to receiving waters: magnitudes, speciation, and management implications

2023· preprint· en· W4321996503 on OpenAlexaffabout
Mahyar Shafii, Stephanie Slowinski, Yubraj Bhusal, Md Abdus Sabur, Calvin Hitch, William Withers, Fereidoun Rezanezhad, Philippe Van Cappellen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsYork Central HospitalToronto and Region Conservation AuthorityUniversity of Waterloo
Fundersnot available
KeywordsEutrophicationPhosphorusDrainage basinEnvironmental scienceHydrology (agriculture)Surface runoffSedimentPrecipitationAquatic ecosystemDrainageEnvironmental chemistryNutrientEcologyChemistryGeographyBiologyGeology

Abstract

fetched live from OpenAlex

Understanding phosphorus (P) dynamics in urban landscapes is an emerging research topic as P export from urban landscapes towards aquatic ecosystems causes eutrophication-related challenges in these environments. We investigated P export and forms in four research sites in Ontario, Canada, including three urban catchments and a stormwater pond, all located within the Great Toronto Area in the drainage basin of Lake Ontario. We conducted P speciation laboratory analyses on runoff and suspended sediment samples to measure total P (TP), total dissolved P (TDP), dissolved reactive P (DRP), dissolved unreactive P (TDP–DRP), and PP (TP–TDP). Multiple linear regression (MLR) models were also developed to quantify annual loadings of these P species. Models indicated that P loadings in our sites were close to the lower limit of values reported in the literature, with the simulated range of 0.2—0.46 kg ha-1 yr-1 for TP export, 0.06—0.168 kg ha-1 yr-1 for TDP, 0.011—0.073 kg ha-1 yr-1 for DRP, 0.026—0.095 kg ha-1 yr-1 for DUP, and 0.163—0.288 kg ha-1 yr-1 for PP. In our MLR models, precipitation explained a large fraction of variability in loadings with the median of 58% across all models. Moreover, we realized that as the proportion of residential land within the drainage area increased, larger amounts of P loadings were exported at the catchment scale. Results also implied that pond served as a major P sink, with annual retention of 82, 93, 91, 94, and 77% for TP, TDP, DRP, DUP, and PP, respectively. Mass balance analyses based on sequential P extraction in the sediment core samples revealed that P retention was attributed to sedimentation in the ponds, as well as chemical precipitation of P with calcium mineral phases. In terms of P composition, most of P export in our sites (72—88%) were in particulate form. Besides, the ratio between dissolved forms and TP were the highest in the catchment with the largest amount green spaces. This study demonstrates that, as land-use characteristics impose variations in constituent loadings, urban P management options also have to be varying from a catchment to another. However, sediment removing practices such as the use of ponds will certainly be a reliable P retention approach as most of urban P could be sediment-bound. Furthermore, enhancing the formation of calcium phosphate and other redox-stable mineral phases could be explored as a best management practice in existing and new ponds for improving P retention.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.809
Threshold uncertainty score0.383

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.029
GPT teacher head0.245
Teacher spread0.217 · 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 designSimulation or modeling
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

Citations0
Published2023
Admission routes2
Has abstractyes

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