Phosphorus Removal from Lake Water by Combined Dosage of Ferrous Iron and Diatomite
Bibliographic record
Abstract
Effective technology and approaches for reducing phosphorus concentration in the lake water are essential to control cyanobacterial bloom and lake eutrophication. This study investigated the effectiveness of the combined usage of ferrous iron (FeCl2) and diatomite for the phosphorus removal. Several combinations of FeCl2 and diatomite in various ratios were tested. Higher amounts of phosphorus were removed from water by the combined usage of FeCl2 and diatomite than that by FeCl2 alone. The optimum diatomite to FeCl2 ratio resulting in the highest efficiency was 0.8. Alkalinity and pH of lake water treated with the combined usage of FeCl2 and diatomite decreased less than that treated by FeCl2. The higher P removal efficiency achieved by the combined usage of FeCl2 and diatomite can be explained as that dissolved silica provided by diatomite was incorporated into the crystalline structure of iron oxide during the ferrous oxidation and thus silica-stabilized ferrihydrite was formed. Silica-stabilized ferrihydrite was an effective phosphorus adsorbent and long-term phosphorus sink. However, ferric oxide formed without dissolved silica was transformed into more crystalline ferric oxide and then phosphorus would be released back to the lake water during this transformation. Diatom growth was stimulated by the increased dissolved Fe and dissolved Si which was provided by diatomite. The stimulated diatom growth can further uptake phosphorus following the phosphorus adsorption by the silicate stabilized ferrihydrite. It was concluded that the combined usage of FeCl2 and diatomite is an effective approach for improving the chemical precipitation of phosphorus by FeCl2.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".