A Review on Advanced Treatment Methods for Arsenic Contaminated Soils and Water
Bibliographic record
Abstract
Abstract A number of treatment methods have been investigated in the laboratory or full scale to remove arsenic from drinking water and to remediate arsenic-contaminated sites. This paper gives a review on the advanced technologies for the treatment of arsenic-contaminated soils and water. Treatment methods such as oxidation, anion exchange, membrane separation, and adsorption/precipitation have been developed to remove arsenic from drinking water or groundwater. However, further research is needed to find new and more efficient substitute materials for the ion exchange resins, membranes, and adsorbents to improve the treatment and cost efficiencies. A stabilization/solidification method has been demonstrated successfully to contain arsenic in contaminated soils, sediments, and solid wastes. Vitrification is also applicable but may be more expensive due to the high energy requirements. Electrochemical methods based on electrokinetics are emerging. Especially, electrokinetics and electrodialysis are suited for fine-grained soils. Chemical extraction, either in-situ or ex-situ, can be efficient to remove bulk arsenic from contaminated soils and solid wastes. Selection of proper extractants is the key to the success of this method. Bioremediation, phytoremediation, and natural attenuation show great potential for future developments because of their environmental compatibility and cost effectiveness. Generally, it is critical to recognize that no single specific technology may be considered as generally applicable. Combination of existing technologies may provide an efficient and cost-effective treatment alternative. Use of biodegradable and environmentally benign products to enhance the remediation processes should be further investigated.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".