Light-Assisted Advanced Oxidation Processes for the Elimination of Chemical and Microbiological Pollution of Wastewaters in Developed and Developing Countries
Bibliographic record
Abstract
In this work, the issue of hospital and urban wastewater treatment is studied in two different contexts, in Switzerland and in developing countries (Ivory Coast and Colombia). For this purpose, the treatment of municipal wastewater effluents is studied, simulating the developed countries' context, while cheap and sustainable solutions are proposed for the developing countries, to form a barrier between effluents and receiving water bodies. In order to propose proper methods for each case, the characteristics of the matrices and the targets are described here in detail. In both contexts, the use of Advanced Oxidation Processes (AOPs) is implemented, focusing on UV-based and solar-supported ones, in the respective target areas. A list of emerging contaminants and bacteria are firstly studied to provide operational and engineering details on their removal by AOPs. Fundamental mechanistic insights are also provided on the degradation of the effluent wastewater organic matter. The use of viruses and yeasts as potential model pathogens is also accounted for, treated by the photo-Fenton process. In addition, two pharmaceutically active compound (PhAC) models of hospital and/or industrial origin are studied in wastewater and urine, treated by all accounted AOPs, as a proposed method to effectively control concentrated point-source pollution from hospital wastewaters. Their elimination was modeled and the degradation pathway was elucidated by the use of state-of-the-art analytical techniques. In conclusion, the use of light-supported AOPs was proven to be effective in degrading the respective target and further insights were provided by each application, which could facilitate their divulgation and potential application in the field.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".