Laboratory weathering studies to evaluate the water quality impact of a lithium mining in Portugal
Bibliographic record
Abstract
Abstract Lithium mining driven by the growing demand for lithium-ion batteries, has environmental consequences linked to soil and water pollution. Nevertheless, research on the environmental impacts of lithium extraction still needs to be improved, highlighting the imperative for additional research. The article addresses the potential impact of the C57 lithiniferous feldspar mine on water quality, specifically focusing on surface, groundwater and spring water samples collected at the mining site and surrounding area in Gonçalo (Guarda, Portugal). The objective is to evaluate the environmental consequences of mining activities, with particular emphasis on mineral leaching. This study aims to evaluate the water quality around the C57 mine and the potential environmental impacts of mining operations. Water samples were collected from different sources, such as surface, underground, and spring waters, and chemical analyses were carried out to determine concentrations of different parameters, which were later compared with national and international reference guidelines. In addition to analysing the water samples, weathering tests were carried out using the Soxhlet extractor method to simulate the leaching of minerals over a shorter period (about 125 days). The concentrations of the analysed elements by atomic absorption spectroscopy (Al, Ca, Cd, Cr, Cu, Fe, K, Li, Mg, Mn, Na, Ni, Pb and Zn) in the weathering solutions were generally low and decreased throughout the testing period, with significant concentrations of aluminium and chromium exceed Canadian environmental quality guidelines for surface waters. The detected lithium concentrations are quite different, ranging from 8.7 to 19.8 μg/L in surface waters, from 6.9 to 74.1 μg/L in groundwater, and from 25.6 to 35.4 μg/L in spring waters, but are all below the US EPA (2021) recommendations threshold of 0.7 mg/L. Based on the findings, the article concludes that there is currently no clear evidence to indicate the environmental impact of mining activities on water quality in the analysed samples. However, weathering tests suggest potential long-term implications regarding the leaching of specific chemical elements, particularly aluminium and chromium.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".