Determination of Primary Surface Water Pollution Indicators by Multivariate Statistical Techniques in an Industrialized Basin
Bibliographic record
Abstract
This study aimed to investigate the point and diffuse pollution sources of high total phosphorus (TP) pollution detected in the dry and wet seasons of the industrialized and urbanized Saz-Cayirova Basin through field observation and multivariate statistical techniques. In this context, nineteen water quality parameters were analyzed in surface water samples collected monthly between June 2020 and July 2021 from nineteen different sites along the Saz-Cayirova stream. Firstly, two reference sites representing a better surface water status were determined and assessed the water quality on the stream tributaries affecting the wastewater treatment plant in the two Organized Industrial Zones (OIZ). Secondly, hierarchical cluster analysis (HCA) and principal component analysis (PCA) were performed to evaluate the complex water quality dataset and reveal the latent sources of TP pollution. The results showed that the tributaries in the pressure of OIZ discharges were highly enriched in COD, TOC, NO3-N, NH3-N, and TP concentrations compared to the reference level. Compared with reference sites, the concentrations of the four heavy metals examined were at a plausible level. Besides, the pollution sources of TP were industrial processes wastewater such as dyeing, washing, phosphating, domestic wastewater from OIZ, urban diffuse waters with organic character, and continuous discharges of undefined sources. The seasonal variation of TP values is observed relatively low, indicating that the stream network is greatly affected by point source pollution. Our observation and analysis imply that the treatment technologies adopted by the OIZ wastewaters plant is sufficient to treat heavy metals. However, measures need to be formulated to remove TP and organic pollution from the secondary production process.
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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".