Seasonal hydrochemical characteristics of spring water in Southern Poland: integrating geochemical modeling, health risk analysis and mitigation strategies
Bibliographic record
Abstract
Abstract This study investigates seasonal hydrochemical characteristics of spring water at six sites in southern Poland (Leśniów, Zygmunt, Halszka, Dobro Woda, Święto Woda, and Zimny Sztok) using integrated geochemical modeling, health risk assessment, and water quality indexing. Seasonal sampling revealed distinct temporal patterns in major ion concentrations. Calcium and magnesium concentrations were significantly higher during winter (Ca 2+ : 90–96 mg/L; Mg 2+ : 5.1–7.2 mg/L) compared to summer (Ca 2+ : 19–45 mg/L; Mg 2+ : 3–5 mg/L), attributed to reduced biological uptake and enhanced carbonate dissolution at lower temperatures. Conversely, sodium (2.5–11 mg/L) and chloride (13–28 mg/L) concentrations peaked during summer due to evaporative concentration and anthropogenic influences. Heavy metals (Fe, Mn, Hg) showed maximum concentrations in summer. Hydrogeochemical analysis identified two water types: Ca–Mg–HCO 2 and Ca–Mg–Cl/SO 4 . PHREEQC modeling revealed undersaturation in calcite (−4.27 to 0.1), dolomite (−9.08 to −1.25), and gypsum (−2.6 to −1.78). Canadian Water Quality Index (CWQI) values (84.57–96.52) classified all samples as “Good,” while Heavy Metal Pollution Index (HPI) values (20.27–120.10) and Metal Index (MI) values (0.58–2.35) indicated highest contamination at Zimny Sztok and Leśniów. Ecological Risk Index (ERI) values (0.18–4.17) suggested low ecological risk. Health risk assessment demonstrated children face 1.5−2× higher non-carcinogenic risks than adults, with maximum hazard index (HI) of 3.37 at Leśniów, primarily from mercury exposure with hazard quotient (HQ) = 3.01. Corrosion indices indicated predominantly corrosive conditions with minimal scaling potential.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".