Unveiling the potential of olive oil production residues as adsorbent materials for water treatment: A literature review
Bibliographic record
Abstract
Olive oil is a nutritionally and economically valuable product whose global production has steadily increased, alongside the generation of large volumes of solid and liquid waste. Olive oil mill wastewater and solid residues such as olive pomace and olive stones have become major environmental concerns due to their high pollutant load. At the same time, these byproducts offer an opportunity: their valorization as low-cost, sustainable adsorbents for water treatment. Addressing this dual environmental challenge, this review provides a comprehensive and systematized synthesis of the current state of research on the use of olive oil production residues for water decontamination via adsorption. Specifically, the study maps the types of byproducts used, their target pollutants, removal efficiencies, and adsorption capacities. Unlike previous reviews, this work emphasizes studies that apply raw or minimally processed residues, as well as experiments conducted with real wastewater or under environmentally relevant conditions. The data are presented in a structured and comparative format, highlighting promising results and underexplored combinations. By identifying trends, gaps, and practical applications, this review contributes to advancing the development of circular economy-based, eco-friendly solutions for water pollution control and provides a valuable resource for future research and implementation.
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 | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".