Disinfection processes and mechanisms in wastewater stabilization ponds: a review
Bibliographic record
Abstract
Wastewater stabilization ponds (WSPs) have been proven to be economical alternatives to conventional wastewater treatment technologies due to their unique advantages including ease of operation, minimal energy input, and minimal maintenance requirements. Their reported high pathogen removal efficiencies have made WSPs a popular choice for wastewater treatment, especially as tertiary lagoons. This paper provides a critical overview of the various disinfection processes and mechanisms that occur in WSPs. A thorough review of the removal or attenuation mechanisms for bacterial, viral, protozoan, and helminthic pathogens is presented. Factors that impact the removal efficiency of pathogenic organisms may include sunlight, pH, dissolved oxygen, temperature, sedimentation, attachment, hydraulic retention time, pond depth, predation and nutrient availability; the relationship between these factors is also discussed. The purpose of this review paper is to utilize the current understanding of pathogen removal mechanisms in pond systems to improve the operation and design of WSPs, and more importantly, to provide guidance for the definition of regulations with respect to pathogen removal in eco-engineered wastewater treatment systems such as WSPs.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".