Prediction and Potential Treatment of Underground Contaminated Water Based on Monitoring of pH and Salinity in a Coal Mine Waste Heap, Southern Poland
Bibliographic record
Abstract
This study presents a potential treatment method for contaminated groundwater from a waste heap in southern Poland. The method is based on the continuous monitoring of two parameters: pH and electrical conductivity (EC). Four years of historical monitoring data (2007–2010) were modeled using a Visual MODFLOW based numerical model of groundwater flow and migration of pollutants for low and high precipitation periods. Mapping the natural conditions in the numerical model allowed for the direction of contaminant migration in the aquifer to be identified. Groundwater treatment via injection of a nano zerovalent iron (nZVI) suspension into the aquifer was then designed; the target contaminant was Zn(II) because of its high concentrations and relatively high toxicity. Online monitoring for mining waste heaps is proposed to record sudden outflows of contaminants into the groundwater related to climate change and to take remedial action via nZVI injection. EC is the variable to be introduced into the contaminant migration model, which allows to determine the nZVI injection locations. As a result, the aquatic environment would be effectively and economically protected. The optimal use of nZVI, without intensively interfering with the environment, is ensured. This method is a convenient tool when making decisions to treat mining-impacted groundwater.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".