Overview of Approaches to Improve Rhizoremediation of Petroleum Hydrocarbon-Contaminated Soils
Bibliographic record
Abstract
Soil contamination with petroleum hydrocarbons (PHCs) has become a global concern and has resulted from the intensification of industrial activities. This has created a serious environmental issue; therefore, there is a need to find solutions, including application of efficient remediation technologies or improvement of current techniques. Rhizoremediation is a green technology that has received global attention as a cost-effective and possibly efficient remediation technique for PHC-polluted soil. Rhizoremediation refers to the use of plants and their associated microbiota to clean up contaminated soils, where plant roots stimulate soil microbes to mineralize organic contaminants to H2O and CO2. However, this multipartite interaction is complicated because many biotic and abiotic factors can influence microbial processes in the soil, making the efficiency of rhizoremediation unpredictable. This review reports the current knowledge of rhizoremediation approaches that can accelerate the remediation of PHC-contaminated soil. Recent approaches discussed in this review include (1) selecting plants with desired characteristics suitable for rhizoremediation; (2) exploiting and manipulating the plant microbiome by using inoculants containing plant growth-promoting rhizobacteria (PGPR) or hydrocarbon-degrading microbes, or a combination of both types of organisms; (3) enhancing the understanding of how the host–plant assembles a beneficial microbiome, and how it functions, under pollutant stress. A better understanding of plant–microbiome interactions could lead to successful use of rhizoremediation for PHC-contaminated soil in the future.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".