Geochemistry of Rare Earth Elements in Simulated Natural Waters: Experiments and modelling of precipitation, complexation, and bioavailability.
Bibliographic record
Abstract
Rare earth elements (REEs) are an important part of modern technology and used for many applications. Mining, agriculture, improper recycling and medical applications can increase the concentration of these elements in surface waters. Metals in the environment including REEs can be found naturally but understanding the implications increase concentrations from anthropogenic inputs is important. REE mobility, persistence and toxicity is imperative to risk assessment as Canada begins to mine REE. Ultimately, as with other metals, the impacts of REEs in the environment should be dependent on the specific physical and chemical forms of the elements. Fractionation provides a practical method to understand speciation of metals. In this research, it was possible using syringe filtration and ultrafiltration to measure various fractions of europium, dysprosium and praseodymium for defined water chemistries. Fractionation results indicate that various factors, such as precipitation, organic complexation, and inorganic complexation play a role in which size fraction the REEs exist in and make interpretation complicated. The presence of DOM did increase the concentration of REE in the dissolved phase by chelating the metal and preventing precipitation, even at elevated pH. Total REE concentration influenced the fractionation trends, where increased concentrations would increase the differences between fractions (i.e. greater difference between precipitated and dissolved with increasing total REE concentration. Similar to the chemical equilibrium explored through size fractionation, bioaccumulation can also provide information about the equilibrium between chemical reactions and reactions at the biotic ligand. Including bioaccumulation experiments with Daphnia magna in the context of size fractionation can help elucidate the fractions of total metal that are available for uptake. Overall conclusions include the following; (i) the presence of DOM decreases the uptake of REE, (ii) precipitation, traditionally avoided in tests of this kind, is hard to quantify and various hypotheses are explored. Bioaccumulation experiments when compared to fractionation results do not provide clear relationships and I provide some rationalizations. Both bioaccumulation and fractionation experiments can be compared to geochemical models, in programs such as Chemical Equilibria in Aquatic Systems (CHEAQS). Finally, geochemical modelling provides another avenue to exploring the legitimacy of all of these methods aimed at assessing appropriate and accurate water quality guidelines.
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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 | 0.001 |
| 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 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".