The Effects of Ferric Sulfate (Fe2(SO4)3) on the Removal of Cyanobacteria and Cyanotoxins: A Mesocosm Experiment
Bibliographic record
Abstract
Cyanobacterial blooms are a global concern. Chemical coagulants are used in water treatment to remove contaminants from the water column and could potentially be used in lakes and reservoirs. The aims of this study was to: 1) assess the efficiency of ferric sulfate (Fe2(SO4)3) coagulant in removing harmful cyanobacterial cells from lake water with cyanobacterial blooms on a short time scale, 2) determine whether some species of cyanobacteria can be selectively removed, and 3) determine the differential impact of coagulants on intra- and extra-cellular toxins. Our main results are: (i) more than 96% and 51% of total cyanobacterial cells were removed in mesocosms with applied doses of 35 mgFe/L and 20 mgFe/L, respectively. Significant differences in removing total cyanobacterial cells and several dominant cyanobacteria species were observed between the two applied doses; (ii) twelve microcystins, anatotoxin-a (ANA-a), cylindrospermopsin (CYN), anabaenopeptin A (APA) and anabaenopeptin B (APB) were identified. Ferric sulfate effectively removed the total intracellular microcystins (greater than 97% for both applied doses). Significant removal of extracellular toxins was not observed after coagulation with both doses. Indeed, the occasional increase in extracellular toxin concentration may be related to cells lysis during the coagulation process. No significant differential impact of dosages on intra- and extra-cellular toxin removal was observed which could be relevant to source water applications where optimal dosing is difficult to achieve.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".