Non-contact hyperspectral monitoring of urban wastewater quality: Optimization of model calibration and performance
Bibliographic record
Abstract
Monitoring pollution in urban drainage systems (UDS) is challenging due to their inaccessibility and harsh conditions. Reflectance spectrophotometry has emerged as a promising non-contact technique, but prior studies have largely focused on synthetic wastewaters or single sites. In this work, we evaluate visible and near-infrared hyperspectral imaging for monitoring of turbidity, dissolved organic carbon (DOC), and ammonium nitrogen (NH 4 -N) in raw urban wastewater. We collected samples from five locations, including a stormwater sewer, two foul sewers, and two combined sewers. We used partial least squares regression models to predict pollutants from hyperspectral data. For model calibration, we explored three approaches: local models trained with five to thirty samples from a single site, global models trained on data from all sites but one, and hybrid models combining global models with two to twenty site-specific samples to enhance accuracy. Model performance is in general best for foul sewers, followed by the stormwater sewer, while it is worst for more variable combined sewers. Local calibrations with thirty training samples perform best, with cross-validated median errors of 6.5 % for turbidity, 14.1 % for DOC, and 22.3 % for NH 4 -N. Global models perform satisfactorily only for turbidity, with a median error of 11.8 %. We also showed that model performance for turbidity can be explained by its correlation with reflected light intensity, while performance for DOC and NH 4 -N can mainly be explained by their correlations with turbidity. Overall, these findings demonstrate the potential of (hyper)spectral imaging for low-maintenance, non-contact monitoring of key pollutants in urban drainage systems. • Hyperspectral imaging and data-driven modeling are used to predict water pollutants. • Partial least squares models trained with 30 samples are optimal. • Best models have an accuracy of 6 %, 14 %, and 22 % for turbidity, DOC, and NH 4 -N. • Global models trained with data from four sites predict turbidity at new sites (12 %). • Model performances are higher for less variable foul sewers than combined sewers.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 teacher head, 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".