Sources of rare earth elements in the environment and their impact on living organisms
Bibliographic record
Abstract
This review paper aims at understanding natural and anthropogenic sources of rare earth elements (REEs) in the environment, their mechanisms of action, individual toxicity of REEs, and individual toxicity of their mixtures, including with other toxicants. We also summarized the scientific knowledge on REE bioaccumulation by plants, animals, and fungi. The purpose of the work was to analyze published scientific information and identify unsolved scientific issues in the selected areas. At present, both natural positive geochemical anomalies of REEs and “spots” of anthropogenic origin are discovered. There is a relationship between natural and anthropogenic sources of REEs in the environment. Increasing REE mining leads to the exposure of rock layers rich in target elements, which are then leached from natural and waste rocks. As a result, the total content of REEs in soils of their extraction areas reaches 2.3 µg/kg, and in the snow of cities it ranges from 0.411 to 1.463 µg/kg, which is comparable with natural anomalies of elements. Most often such levels of REE accumulation are not dangerous for biota. REEs, like many trace elements, have an essential effect in low concentrations and have harmful effects only when the dose is increased. However, stimulation effects proven for agricultural plants and animals in the natural environment can influence the change in population and community structures. The effects of REE compounds have many uncertainties under the conditions of their joint action with each other and with other substances containing biogenic or potentially toxic elements. The article provides examples of additive, synergistic, and antagonistic actions of a mixture of elements. Further, it is shown that the bioaccumulation of REEs is comparable to the accumulation of heavy metals in living organisms. Data on the transfer of REEs along trophic chains are single and contradictory. Thus, REEs contribute to the toxic load on living organisms. The main “blank spots” in knowledge about the action of REEs that require further research are identification of the most sensitive and vulnerable species, establishment of long-term effects and clarification of the joint action of REEs with other substances.
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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".