Application of tryptophan-like fluorescence index to quantify the trace organic compounds removal in wastewater ozonation
Bibliographic record
Abstract
The effectiveness of ozonation, one of the techniques known for destroying organic contaminants from wastewater, depends on the composition of the wastewater matrix. The required ozone (O3) dose is determined based on the target compounds during ozonation. Hydroxyl radicals are quantified using a probe compound. The para-chlorobenzoic acid (pCBA) is typically used as a probe compound to measure hydroxyl radicals. However, real-time measurement is impossible, as the analysis process consumes time and resources. This study aimed to evaluate the spectroscopic characteristics of various organic substances in wastewater ozonation through fluorescence excitation-emission matrix and parallel factor analysis. The study also demonstrated that real-time analyzable tryptophan-like fluorescence (TLF) can be used as a hydroxyl radical index. Importantly, the correlation between para-chlorobenzoic acid and TLF was derived, and the results showed a high correlation (R2 = 0.91), confirming the reliability of our findings. Seven trace organic compounds, classified based on their reactivity with O3 and hydroxyl radicals, were selected as target compounds and treated with O3. The TLF index was used as a model factor for the removal rate of the target compounds. The experimental and model values matched when the O3 dose was below 1.0 g O3/g DOC (RMSE: 0.0445–0.0895).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".