Speciation of arsenic in freshwater biota
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,006 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».