Use of phosphorus-sorbing materials to remove phosphate from greenhouse wastewater
Bibliographic record
Abstract
High phosphate content in wastewater is currently a major issue faced by the North American greenhouse industry. Phosphate-sorbing material filters could provide a means of removing phosphate from wastewater prior to discharge to the environment, but the characterization of economically viable materials and specific recommendations for greenhouse wastewater are not available. Batch and column experiments were used to examine the capacity of two calcium-based waste materials, basic oxygen furnace slag and a concrete waste material, to remove phosphate from greenhouse nutrient solution at varied operating conditions. Material columns operating at a hydraulic retention time (HRT) of 3 h consistently removed >99% of influent phosphate at a concentration of 60 mg/L over repeated applications and demonstrated high phosphate retention capacity (PRC) of 8.8 and 5.1 g P/kg for slag and concrete waste, respectively. Both materials also provided some removal of the micronutrients Fe, Mn and Zn. Increasing HRT to 24 h increased P retention capacity of slag to >10.5 g P/kg but did not improve retention by concrete waste. Decreasing influent phosphate concentration to 20 mg/L decreased PRC to 1.64 g P/kg in concrete waste columns, suggesting fluctuations in greenhouse wastewater composition will affect filter performance. The pH of filter effluent was closely correlated to final P concentration and can likely be used to monitor treatment effectiveness. This study demonstrated that calcium-based materials are promising for the removal of phosphate from greenhouse wastewater, and worthy of further research on scaling up the application to a full-sized system.
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 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 | 0.000 |
| 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) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".