Arsenic Removal by Adsorbents from Water for Small Communities’ Decentralized Systems: Performance, Characterization, and Effective Parameters
Bibliographic record
Abstract
Arsenic (As), a poisonous and carcinogenic heavy metal, affects human health and the environment. Numerous technologies can remove As from drinking water. Adsorption is the most appealing option for decentralized water treatment systems (DWTS) for small communities and household applications because it is reliable, affordable, and environmentally acceptable. Sustainable low-cost adsorbents make adsorption more appealing for DWTS to address some of the small communities’ water-related issues. This review contains in-depth information on the classification and toxicity of As species and different treatment options, including ion exchange, membrane technologies, coagulation-flocculation, oxidation, and adsorption, and their effectiveness under various process parameters. Specifically, different kinetic and isotherm models were compared for As adsorption. The characterization techniques that determine various adsorbents’ chemical and physical characteristics were investigated. This review discusses the parameters that impact adsorption, such as solution pH, temperature, initial As concentration, adsorbent dosage, and contact time. Finally, low-cost adsorbents application for the removal of As was discussed. Adsorption was found to be a suitable, cost-effective, and reliable technology for DWTS for small and isolated communities. New locally developed and low-cost adsorbents are promising and could support sustainable adsorption applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".