Speciation of arsenic in freshwater biota
Bibliographic record
Abstract
Arsenic can reach potentially concerning levels in fish and other aquatic biota, but the risk posed is strongly dependent on the element’s chemical speciation. However, the speciation of arsenic in biotic samples remains analytically challenging and freshwater fish, in particular, have not been extensively studied. The limited information available suggests that freshwater fish can have highly variable arsenic speciation patterns, both within and between populations. Based on these knowledge gaps, my thesis has two main goals: (1) to assess the current state of knowledge on arsenic speciation using a systematic literature review and (2) measure arsenic speciation in biota from boreal lakes to investigate drivers of variation among individual fish and invertebrates. My literature review focussed on arsenic speciation in freshwater fish muscle. I identified 39 studies that matched predefined criteria for inclusion based on a review of 1096 potential studies. I found considerable variability in the available literature; although less toxic organic species of arsenic typically dominated in fish muscle, there were reports of fish with high concentrations of the most toxic inorganic species. While studies modeling the drivers of this variation were limited, some suggest that waterbody characteristics, fish size, and trophic ecology may contribute. In my field study, I collected and analyzed fish and invertebrates for two common organic species of arsenic, arsenobetaine (AsB) and dimethylarsinic acid (DMA), in three lakes across a contamination gradient near Sudbury, Ontario. Concentrations of these arsenic species varied widely across fish and invertebrates, generally being found at higher concentrations in the most contaminated system, a lake associated with an abandoned gold mining site. Trophic ecology appeared to be a primary factor affecting arsenic speciation in aquatic food webs, with both AsB and DMA decreasing in concentration with increasing trophic position, inferred from stable nitrogen isotope values. To my knowledge, this is the first study to apply stable isotope techniques to assess how trophic ecology and diet influence arsenic speciation across whole freshwater food webs; where prior arsenic speciation studies have focused on fish alone and did not observe the same biodilution effect. I also identified other factors that may influence arsenic speciation. These included variation in fish size and age, diet, and interactions with co-occurring chemicals (e.g., selenium). However, considerable unexplained differences in arsenic species among taxa remains for further studies to address. Future avenues for research on arsenic speciation include continued improvements in analytical techniques and detection levels, deepening our molecular understanding of arsenic biotransformation and accumulation, broadening toxicological testing of various arsenic species, and assessing the behaviour of arsenic species across diverse food webs. Additionally, improving our understanding of arsenic speciation in freshwater environments is essential to accurately assess risk to consumers or the aquatic biota themselves. A refinement of environmental and human health risk assessments based on the results found herein and in future studies are warranted.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".