Chemical Removal of Total Phosphorus from Wastewater to Low Levels and Its Analysis
Bibliographic record
Abstract
Numerous studies have been conducted on the removal of inorganic phosphorus (P) from wastewater, but a push towards lower effluent targets necessitates the additional removal of organic phosphorus as well. This study tested the ability of manganese oxide nanoparticles and iron oxide as potential catalysts for conversion of organic P into more readily removable inorganic forms, as well as the role of iron(III) chloride as coagulant to subsequently allow P to be removed by solids/liquid separation. Removals of 99-101% were obtained for model compounds at pH 5-7, 0.05-0.5 M H2O2, and Fe:P molar ratio of 5:1. Presence of H2O2 was found necessary to remove phosphonates in particular, increasing removal from 17 to 101%. Tests in real wastewaters containing organic P also showed higher removal with peroxide addition. Due to interference from H2O2, the standard method for P analysis in wastewater, colorimetry, could not be used as the primary analytical tool. An accurate and sensitive protocol using Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES) capable of low-level P detection was developed instead and compared to colorimetry using model organic P compounds and real wastewater samples. Detection limits for colorimetry and ICP-OES were 0.002 and 0.09 mg P/L respectively. ICP-OES gave analytical recoveries closest to 100% for model organic P compounds, but both methods gave highly variable data at concentrations below 0.15 mg P/L. ICP-OES seems promising for TP measurements given its high recoveries for model compounds, but more work is needed to improve its detection limit and sensitivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".