Cyanobacteria Monitoring and Cyanotoxin Management in Drinking Water Treatment Plants
Bibliographic record
Abstract
This thesis examines three aspects of toxic cyanobacteria with respect to drinking water treatment plants: risk, monitoring, and treatment. Previous work shows that cyanobacteria cells can accumulate to high concentrations inside treatment plants, potentially rupture, and release their toxins. Therefore, accumulation in four Great Lakes region treatment plants was assessed, revealing that accumulation was generally minimal for plants experiencing low levels of cyanobacteria but could be significant for plants where cyanobacteria blooms occur in their source water. Drinking water utilities susceptible to harmful algal blooms need to be able to detect the onset of a bloom to implement management strategies. One way to monitor cyanobacterial activity is to use fluorescence-based probes that measure phycocyanin, a pigment specific to cyanobacteria. The conventional approach in the literature is to correlate phycocyanin fluorescence measurements to independent measures of cyanobacteria cell counts to use fluorescence to estimate the influx of cells, but most utilities do not measure cell counts, so this approach cannot be applied at all sites. This research explored machine learning for anomaly detection in phycocyanin fluorescence data without the need for corresponding cell counts. Two models were identified with an average accuracy of 86% to correctly predict elevated cyanobacterial activity in four data sites when trained on 2014-2018 data and tested on 2019 monitoring data. Therefore, this research has resulted in a potentially useful tool for drinking water utilities to use to improve their cyanobacteria monitoring strategies. When a utility is faced with cyanotoxins in their raw water, various treatment methods may be used to degrade them, each with its own advantages and limitations. Peracetic acid has recently emerged as a potential oxidant for water treatment, but its ability to degrade cyanotoxins has not been assessed with or without ultraviolet (UV) light for advanced oxidation. This research evaluates the degradation of two microcystins by UV only, peracetic acid only, and UV with peracetic acid. The results show that the combined process is as effective as or better than conventional advanced oxidation processes under typical water quality conditions. These findings support the potential future adoption of peracetic acid in water treatment plants.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".