Unraveling Health Risk and Speciation of Arsenic from Groundwater in Rural Areas of Punjab, Pakistan
Bibliographic record
Abstract
This study determined the total and speciated arsenic (As) concentrations and other health-related water quality parameters for unraveling the health risk of As from drinking water to humans. Groundwater samples (n = 62) were collected from three previously unexplored rural areas (Chichawatni, Vehari, Rahim Yar Khan) of Punjab in Pakistan. The mean and median As concentrations in groundwater were 37.9 and 12.7 µg·L(-1) (range = 1.5-201 µg·L(-1)). Fifty three percent groundwater samples showed higher As value than WHO safe limit of 10 µg·L(-1). Speciation of As in groundwater samples (n = 13) showed the presence of inorganic As only; arsenite (As(III)) constituted 13%-67% of total As and arsenate (As(V)) ranged from 33% to 100%. For As health risk assessment, the hazard quotient and cancer risk values were 11-18 and 46-600 times higher than the recommended values of US-EPA (i.e., 1.00 and 10(-6), respectively). In addition to As, various water quality parameters (e.g., electrical conductivity, Na, Ca, Cl(-), NO₃(-), SO₄(2-), Fe, Mn, Pb) also enhanced the health risk. The results show that consumption of As-contaminated groundwater poses an emerging health threat to the communities in the study area, and hence needs urgent remedial and management measures.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".