Removal of algal taste and odour compounds by granular and biological activated carbon in full-scale water treatment plants
Bibliographic record
Abstract
Abstract The occurrence and severity of cyanobacterial and algal blooms in water supplies has been increasing due to the effects of eutrophication and climate change, resulting in more frequent taste and odour (T&O) events. Conventional treatment processes have been found to be inefficient in removing the two most commonly detected algal T&O compounds, geosmin and 2-methylisoborneol (MIB), though granular activated carbon (GAC) and biological activated carbon (BAC) contactors have achieved high T&O removal rates. Literature on the performance of GAC and BAC for T&O removal in full-scale treatment plants, however, is limited. This review collates and assesses pilot-scale and full-scale studies which focus on removal of geosmin and MIB, with the aim of understanding the factors which influence T&O removal and determining knowledge gaps in the use of GAC and BAC. Age and empty bed contact time (EBCT) were found to have a significant impact on GAC performance, with removal efficiency decreasing with increased age and increasing with longer EBCTs. BAC contactors have achieved higher removal rates than non-biologically active GAC contactors and were not impacted by age, EBCT and/or carbon type. From these observations, implementation of BAC for T&O removal would be favourable; however, further investigations are required to understand full-scale performance of BAC and service life modelling.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".