Source-tracking metal contamination using Cu isotopes in two tributaries in the Great Lakes region
Notice bibliographique
Résumé
The Great Lakes basin is one of the world’s most important freshwater resources, critical not only to public water supply but also for agriculture, transportation, hydroelectric power, and as an ecosystem. Anthropogenic contamination in all Great Lakes has been causally linked to ecosystem deterioration since the start of the industrial revolution, and it has been pervasive and cumulative. A major anthropogenic contaminant in the Great Lakes is copper [(Cu): a trace metal that has been a concern for decades. Point-sources for Cu include industrial activities such as metal mining, smelting, and chemical industries. However, Cu is also introduced to surface waters from diffuse sources, such as fertilizer application or urban runoff, as well as by atmospheric deposition and natural weathering processes. The importance of these geogenic versus anthropogenic sources is spatiotemporally variable and there are a multitude of sources and processes controlling the environmental fate of Cu in the Great Lakes region that remain poorly quantified (Bentley et al., 2022). Nontraditional stable isotopes have proven useful as environmental tracers for metal contaminants in human-impacted areas and served as an excellent tool to quantify a variety of biogeochemical processes (i.e., adsorption to mineral and organic surfaces, biological uptake). To understand the impacts of anthropogenic activities on Cu concentrations in the environment, background Cu isotope compositions of relatively pristine environments must first be determined. However, Cu isotopic analyses of baseline conditions in the Great Lakes are extremely scarce. In this work, we explore the use of Cu isotope analyses to quantify the baselines and sources of Cu in two tributaries in the Great Lakes. Surface water samples were collected from 44 locations along the Spanish River (Lake Huron) and Trent River (Lake Ontario) in August 2021, together with samples of probable endmember phases that include (agricultural) soils, municipal wastewater effluents and mine waste materials in the respective catchments. Water quality in the studied catchments was variable (6.6 < pH < 9.1; 58.7 mg/L < alkalinity < 216.7 mg/L), with recorded Cu concentrations in the river water samples ranging between 0.79 to 4.88 ng/ml, tending towards higher concentrations upstream compared to downstream, and presenting peaks in specific locations, suggesting anomalous Cu input in these areas. δ65Cu in the rivers analyzed (−1.02 to 0.09‰) present values above the natural average of upper continental crust (0.07 ± 0.10‰) and uncontaminated sedimentary materials from estuaries (−0.04 ± 0.18‰), revealing distinct mixing of two or more sources (including geogenic, mine waste and agriculture fertilizers). We contextualize the Cu compositions observed in surface water samples to those in endmember materials with mixing models and geospatial analysis of the catchments to quantify possible sources. Our results may help distinguish historic versus new contaminant sources and geogenic versus anthropogenic contributions, as well as major pathways by which metals are loaded into the Great Lakes, besides facilitating the protection of this critical freshwater resource from legacy and emerging metal pollution.
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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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 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 ».