Total staphylococci as performance surrogate for greywater treatment
Bibliographic record
Abstract
Faecal indicator bacteria (FIB) are commonly used as water quality indicators; implying faecal contamination and therefore the potential presence of pathogenic enteric bacteria, viruses, and protozoa. Hence in wastewater treatment, the most commonly used treatment process measures (surrogates) are total coliforms, faecal coliforms, Escherichia coli ( E. c oli ), and enterococci. However, greywater potentially contains skin pathogens unrelated to faecal load, and E. coli and other FIB may grow within greywater unrelated to pathogens. Overall, FIB occurs at fluctuating and relatively low concentrations compared to other endogenous greywater bacteria affecting their ability as surrogates for pathogen reduction. Therefore, unlike municipal sewage, FIB provides a very limited and unreliable log-reduction surrogate measure for on-site greywater treatment systems. Based on our recent metagenomic study of laundry greywater, skin-associated bacteria such as Staphylococcus , Corynebacterium, and Propionibacterium spp. dominate and may result in more consistent treatment surrogates than traditional FIB. Here, we investigated various Staphylococcus spp. as potential surrogates to reliably assay over 4-log 10 reduction by the final-stage UV disinfection step commonly used for on-site greywater reuse, and compare them to various FIB/phage surrogates. A collimated UV beam was used to determine the efficacy of UV inactivation (255, 265 and 285 nm) against E. coli , Enterococcus faecalis , E. faecium , E. casseliflavus , Staphylococcus aureus, and S. epidermidis . Staphylococcus spp. was estimated by combining the bi-linear dose-response curves for S. aureus and S. epidermidis and was shown to be less resistant to UV irradiation than the other surrogates examined. Hence, a relative inactivation credit is suggested; whereas, the doses required to achieve a 4 and 5-log 10 reduction of Staphylococcus spp. (13.0 and 20.9 mJ cm −2 , respectively) were used to determine the relative inactivation of the other microorganisms investigated. The doses required to achieve a 4 and 5-log 10 reduction of Staphylococcus spp. resulted in a log 10 reduction of 1.4 and 4.1 for E. coli , 0.8 and 2.8 for E. faecalis , 0.8 and 3.6 for E. casseliflavus and 0.8 and 1.2 for MS2 coliphage, respectively. Given the concentration difference of Staphylococcus spp. and FIB (3 to 5-log 10 higher), we propose the use of Staphylococcus spp. as a novel endogenous performance surrogate to demonstrate greywater treatment performance given its relatively high and consistent concentration and therefore ability to demonstrate over 5-log 10 reductions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".