Hydrochemical evaluation and risk assessment of the Danube river, Hungary using Canadian indices, geochemical modeling, and simulation techniques
Bibliographic record
Abstract
Abstract This study investigates the metals risks in the Danube River, Hungary, and identifies the natural and anthropogenic sources using geochemical modeling. In total, 76 water samples were collected from seven sites along the river during 2018. Physicochemical and heavy metals have been analyzed. Statistical tools, including Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), were applied. Water quality was assessed using the Canadian Water Quality Index (CWQI), Metal Pollution Index (MPI), Nemerow Composite Index (NCI), Hazard Quotient (HQ), Hazard Index (HI), and Carcinogenic Risk (CR). A probabilistic approach using Monte Carlo simulation was applied to evaluate uncertainty and health risks. Geochemical modeling revealed that the river is undersaturated with minerals like gypsum, anhydrite, and halite, but supersaturated with aragonite, dolomite, and calcite. The average CWQI (44.8) and Weighted Arithmetic Water Quality Index (WAWQI, 60.1) indicate that the water is unsuitable for drinking. However, Sodium Adsorption Ratio (SAR = 0.5), Sodium Percentage (Na% = 15.4), and Kelly’s Ratio (KR = 0.2), suggest favorable conditions for agricultural use. The low ecological risk index (RI = 0.5) and MPI (< 0.3) indicate minimal contamination, while the NCI (1.2) flags the right bank of Dunaföldvár as nearing a critical pollution threshold. Although non-carcinogenic health risks (HQ, HI < 1) for chromium, copper, lead, and nitrate were minimal, Monte Carlo simulation showed elevated carcinogenic risk for lead and chromium in children at the 95th percentile. These findings highlight the need for ongoing monitoring and treatment of water and offer valuable insights for sustainable water management and policy planning in Hungary.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".