Environmental Impacts of Selenium Contamination: A Review on Current-Issues and Remediation Strategies in an Aqueous System
Bibliographic record
Abstract
In both aquatic and terrestrial environment, selenium contamination may exist at concentrations above the micronutrient limit. Since there is such a narrow bandwidth between which selenium concentration is acceptable, the health of the public may be at risk of selenium toxicity once the concentration increases beyond a threshold. Selenium contamination in an aqueous environment can occur due to anthropogenic activities and/or from natural sources. This study presents a review of the forms of selenium, inorganic and organic selenium contamination, mobilization, analytical methods for various forms of selenium and remediation strategies. The review also provides recent advances in removal methods for selenium from water including bioremediation, precipitation, coagulation, electrocoagulation, adsorption, nano-zerovalent iron, iron co-precipitation and other methods. A review of selenomethionine and selenocysteine removal strategy from industrial wastewaters is presented. Selenium resource recovery from copper ore processing has been discussed. Various analytical methods used for selenium and heavy metal analysis were compared. Importantly, existing knowledge gaps were identified and prospective areas for further research were recommended.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".