Source-tracking metal contamination using Cu isotopes in two tributaries in the Great Lakes region
Bibliographic record
Abstract
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".