Coupling of Anodic Oxidation and Soil Remediation Processes: A Review
Bibliographic record
Abstract
In recent years, due to industrial modernization and agricultural mechanization, several environmental consequences have been observed, which make sustainable development difficult. Soil, as an important component of ecosystem and a key resource for the survival of human and animals, has been under constant contamination from different human activities. Contaminated soils and sites require remediation not only because of the hazardous threat it possess to the environment but also due to the shortage of fresh land for both agriculture and urbanization. Combined or coupled remediation technologies are one of the efficient processes for the treatment of contaminated soils. In these technologies, two or more soil remediation techniques are applied simultaneously or sequentially, in which one technique complements the other, making the treatment very efficient. Coupling anodic oxidation (AO) and soil remediation for the treatment of soil contaminated with organics has been studied via two configurations: (i) soil remediation, ex situ AO, where AO is used as a post-treatment stage for the treatment of effluents from soil remediation process and (ii) soil remediation, in situ AO, where both processes are applied simultaneously. The former is the most widely investigated configuration of the combined processes, while the latter is less common due to the greater diffusion dependency of AO as an electrode process. In this review, the concept of soil washing (SW)/soil flushing (SF) and electrokinetic as soil remediation techniques are briefly explained followed by a discussion of different configurations of combined AO and soil remediation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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 | 0.002 |
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".