Evaluation of Groundwater Quality Using the Water Quality Index (WQI) and Human Health Risk (HHR) Assessment in West Bank, Palestine
Bibliographic record
Abstract
Access to clean and safe water is extremely important, not only in Palestine but also worldwide. In the West Bank, groundwater is particularly valuable because of its scarcity and inaccessibility, and, due to the nature of the area’s aquifers, is currently regarded as being at high risk of pollution. Moreover, the water quality in this area is also of wide concern, with its effects being directly linked to human health. Certain parts of the West Bank groundwater suffer from high concentrations of nitrate and potassium. In total, 38.8% of nitrate and 10% of potassium concentrations in well samples exceed the permissible limit set by the WHO and PSI, and, therefore, health problems arise as a limiting factor for life quality and welfare in this region. Moreover, 87.7% of samples are classified as having very hard water. To evaluate the well water in the study area, an assessment was conducted based on the WQI and HHR. Therefore, 49 samples were taken from a group of wells distributed across the study area during the year 2021. The physico-chemical parameters of each sample were analysed. The WQI values showed that 78% of the well samples were of good quality. Moreover, in the classification of the water based on a Piper diagram, 65% of the groundwater was determined to be calcium–magnesium–bicarbonate-type water. Likewise, health risk assessments were evaluated for fluoride and nitrate in drinking water for adults, children, and infants. The main values of the estimated total hazard index (THI) obtained from the analysed data on the health risk assessments revealed a diverse effect on the local population based on age category. The ranges of THI in all sampling locations varied considerably and extended from 0.093 to 3.01 for adults, 0.29 to 3.08 for children, and 0.302 to 3.21 for infants. These results widely indicate that infants are more exposed to health risks.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".