The impact of pond depth and environmental conditions on sunlight inactivation of Escherichia coli and enterococci in wastewater in a warm climate
Bibliographic record
Abstract
Microcosm experiments were carried out under dark and real sunlight conditions in Ouagadougou (Burkina Faso) to investigate the survival of faecal indicators (Escherichia coli and enterococci) in secondary wastewater. Light damage was estimated by loss of bacterial culturability. The results clearly show that sunlight has a deleterious effect on the survival of both indicators. The mean dark inactivation coefficients for E. coli and enterococci were 0.045 and 0.047 h(-1), respectively, whereas inactivation coefficients in the shallowest microcosm (0.1 m) in illuminated conditions were 0.796 and 0.559 h(-1), respectively. No significant effect of pond depth (0.1-0.9 m) on the inactivation of both indicators was observed in the dark. However, the effect of depth was significant in the microcosms exposed to sunlight, probably because of attenuation. In illuminated conditions, enterococci were broadly inactivated more rapidly than E. coli (T90 = 26.81 h for E. coli and 15.67 h for enterococci in the 0.4 m microcosm). However, E. coli presented greater variability in the survival capabilities, suggesting difficulties in interpreting data using only E. coli as an indicator. Therefore, the use of both indicators together should be advisable for the assessment of effluent quality from waste stabilization ponds in the Sahelian region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".