Real-time wastewater quality monitoring by fluorescence sensors: Validation for COD and CEC monitoring and implication for carbon footprint reduction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigated the applicability of a protein-like fluorescence sensor for wastewater quality monitoring. Several wastewater matrices, including raw, primary, secondary and tertiary effluents from three different wastewater treatment plants were used. Furthermore, the sensor was tested for the monitoring of quaternary effluent in a pilot scale plant installed downstream of a water reuse facility. The pilot plant involved advanced oxidation processes (AOPs) and granular activated carbon (GAC) adsorption. Corrections on excitation/emission matrices (EEMs), including Inner Filter Effect (IFE) and scattering, showed no effect on linear correlation ( R 2 = 0.99 ) between sensor measurement and either raw or corrected benchtop protein-like fluorescence data, suggesting that for this application the signal from the sensor might be interpreted without the need for further adjustments. Furthermore, the use of quenched, diluted and filtered samples did not affect such correlations. Overall, the fluorescence sensor showed a very high capability to monitor a wide range of wastewater matrices, including raw, primary, secondary, tertiary, and quaternary effluents, providing fast information on the efficiency of the processes. The protein-like fluorescence monitoring by the real-time sensor was validated online through 9 days of 24-hour continuous monitoring of tertiary wastewater effluents. The employed fluorescence sensor was validated for monitoring the removal of contaminants of emerging concern (CEC), including a wide range of pharmaceuticals, in different AOP systems (ozone and UV based). In view of the results reported in this study, possible environmental implications for the reduction of the carbon footprint have emerged: the use of fluorescence sensors may contribute to the optimization of processes and the reduction of secondary pollution. • Protein-like fluorescence sensor tracks process efficiency. • Fluorescence sensor allows the monitoring of tertiary and quaternary treatments. • Wastewater quality (e.g., COD) can be controlled by fluorescence sensor data. • Fluorescence sensor may be an innovative tool for the real-time monitoring of CEC.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it