Arsenic in the Tibetan Plateau’s geothermal systems: a detailed analysis of forms, sources, and geochemical behaviors
Bibliographic record
Abstract
Abstract In high-altitude tectonic regions, significant geothermal activity influences groundwater arsenic levels, presenting crucial resource and environmental challenges. The present study examines the Gonghe-Guide Basin located in the northeastern region of the Qinghai-Tibet Plateau. The study employs a comprehensive approach encompassing field sampling, hydrochemical analysis, thermodynamic modeling, and statistical analysis to ascertain the composition and origins of arsenic in geothermal groundwater. The research data indicates that the geothermal groundwater in the area displays weak alkalinity and medium to high mineralization, with the principal hydrochemical types being SO4−Cl·Na and Cl·Na. The concentration of arsenic has a notable inverse relationship with Cl− and a positive correlation with water temperature and DO. According to thermodynamic calculations, the most common kind of arsenic is As5+. The hydrochemical properties of the research area are shaped by rock weathering, evaporative concentration, and ion-exchange adsorption working together. These factors contribute to the favorable circumstances for the formation and migration of arsenic throughout the environment. Notably, the ion-exchange between sodium ions and calcium and magnesium ions significantly impacts the arsenic concentration. This study marks the first discovery of a unique arsenic contamination pattern in geothermal groundwater within the Gonghe-Guide Basin in the northeastern Tibetan Plateau, revealing a positive correlation between arsenic levels, water temperature, and dissolved oxygen content. This provides a new perspective on understanding arsenic pollution in geothermal groundwater in high-altitude regions.
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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 | 0.002 |
| 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 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".