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Record W4392466268 · doi:10.1007/s12665-024-11431-6

Evaluation of the impact of coal mining on surface water in the Boesmanspruit, Mpumalanga, South Africa

2024· article· en· W4392466268 on OpenAlexaboutno aff
Thandi R. Dzhangi, Ernestine Atangana

Bibliographic record

VenueEnvironmental Earth Sciences · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
FundersUniversiteit van die Vrystaat
KeywordsTotal dissolved solidsWater qualityEnvironmental sciencePollutionSulfateSurface waterWater resourcesEnvironmental engineeringMetallurgyMaterials science

Abstract

fetched live from OpenAlex

Abstract Surface water quality has major environmental and socioeconomic consequences, notably in terms of the country’s long-term fresh water supply. This study aimed at assessing the current state of water quality and status of the Boesmanspruit in a coal mining environment. The study used historical water quality data for a period of five years from 2017 to 2021. Aluminum, calcium, iron, manganese, magnesium, sodium, sulfate, electrical conductivity, pH, and total dissolved solids were the water quality variables selected for the study; the chosen variables were chosen based on the available secondary data. The water quality was evaluated against South African resource quality objectives, the South African water quality guidelines, and the Canadian Council of Ministers of the Environment water quality index (CCME-WQI). The data were analyzed using such as the CCME-WQI, the comprehensive pollution index (CPI), and multivariate statistics. The following parameters were above the prescribed thresholds: pH, total dissolved solids, electrical conductivity, sulfate, manganese, and iron. The CCME-WQI results showed that monitoring locations GR S26 and GR S21 near mining activities had poor water quality (40–44), whereas comprehensive pollution index (CPI) also had similar category results for the monitoring points, indicating that they were heavily polluted (2.4–4.8). The WQI showed that if certain variables, such as aluminum, iron, magnesium, sulfate, electrical conductivity, and total dissolved solids, exceed the permissible range, the water quality would deteriorate in accordance with the CPI classification. Therefore, the CPI was the best way to categorize the water quality. The principal component analysis and cluster analysis identified two primary sources of pollution which are anthropogenic and natural. The utilization of statistical analysis proved to be effective in determining the ideal quantity of significant variables within the study area. The study recommends low-cost options for reducing the effects of acid mine drainage, which includes passive mine water treatment methods using artificial wetlands.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.575
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.062
GPT teacher head0.309
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2024
Admission routes1
Has abstractyes

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