Mitigation of Cyanobacterial Harmful Algal Blooms by Electrochemical Ozonation: From Bench-scale Studies to Field Applications
Bibliographic record
Abstract
Cyanobacterial harmful algal blooms (HABs) are an emerging threat to ecosystems, drinking water safety, and the recreational industry. As an environmental challenge intertwined with climate change and excessive nutrient discharge, HAB events occur more frequently and irregularly. This dilemma calls for fast-response treatment strategies. This study developed an electrochemical ozonation (ECO) process, which uses a Ni-Sb-SnO2 anode to produce locally concentrated ozone (O3) on electrodes to inactivate cyanobacteria and destroy microcystins within minutes. More importantly, the proof-of-concept was evolved into a full-scale boat-mounted completely mixed flow reactor for the treatment of HAB-impacted lake water at a treatment capacity of 544 m3/d and energy consumption of <1 Wh/L. Both lab-scale and full-scale investigations show that the byproducts (e.g., chlorate, bromate, trihalomethanes, and haloacetic acids) in the ECO-treated lake water were below the regulatory limits for drinking water. The whole effluent toxicity tests suggest that ECO treatment at 10 mA/cm2 posed certain chronic toxicity to the model invertebrate (Ceriodaphnia dubia). However, the treatment at 7 mA/cm2 (identified as the optimum condition) did not increase toxicity to model invertebrate and fish (Pimephales promelas) species. This study is a successful example of leveraging fundamental innovations in electrocatalysis to solve real-world problems.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".