Electrokinetic Remediation of Organic Soil Polluted with Petroleum Products in Temperate and Cold Regions
Bibliographic record
Abstract
Global warming leads to the thawing of ice caps in cold regions like Canada's northern territories, which are predominantly covered with permafrost. Its active top layer contains different fractions of organic matter and clay. More frequent use of shipping routes increases the risk of northern soil pollution. One group of common pollutants, originating from pipeline spills or various leakage, are light hydrocarbons. Soil pollution affects the sensitive northern environment. Effective soil remediation necessitates a comprehensive understanding of soil properties to implement an appropriate remediation method tailored to the specific soil characteristics and the challenging climatic conditions in northern territories. A series of laboratory tests were conducted to evaluate the feasibility of electrokinetic (EK) soil remediation on organic soils in cold-tempered regions. The study involved four distinct soil compositions to determine the efficacy of EK transport within organic matter (3%, 16%, and 35% w/w) containing soil. The soil was polluted with toluene and exposed to a constant low DC voltage gradient. To optimize electrokinetic remediation, a daily injection of surfactant was conducted. The tests were carried out continuously over a period of four days without anolyte, and one day including electroosmotic discharge collecting both anolyte and catholyte. The study was conducted in ambient (20°C) and cold temperatures (7°C) to simulate temperate and cold environments. The results showed the feasibility of light hydrocarbons removal from organic soils, while EK transport was more effective under low temperatures. Soil changed properties and pH gradient was observed between the anode and cathode. Furthermore, extracted liquids indicated some dissolutions of humic substances present in organic soil.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".