Assessing Human Imprints on Trace Element Fluxes in the Great Lakes
Bibliographic record
Abstract
The extraction and use of rare earth elements, platinum group elements and other trace metals is growing exponentially around the world. The occurrence of these trace elements in anthropogenic waste streams is increasing correspondingly. Yet, conclusive data on trace element concentrations in urban runoff and wastewater is scarce as these elements are typically not part of governmental surveillance programs and barely environmentally regulated. The human imprints on natural trace element fluxes and their potential environmental impacts therefore remain poorly quantified. We are working to quantify natural and anthropogenic trace element fluxes in the Great Lakes basin. The Great Lakes basin provides a globally unique setting to investigate human imprints on large-scale elemental cycling because it houses >60 million people, contains >20% of the world’s freshwater, and is divided into serially connected sub-basins that facilitate environmental system analyses at various scales. First, we established baseline estimates of current (natural) trace element fluxes in the Great Lakes by aggregating hydrometric and water quality data in simplified black-box mass-balances and dynamic reactor models. These models were informed by >100,000 hydrometric and >50,000 water quality measurements collected across the Great Lakes between 1980-2020 and were calibrated to existing long-term water level and water chemistry records. The bulk of the incorporated data stems from Canadian and US federal and provincial and state monitoring programs, including publicly available datasets from NOAA, EPA, ECCC, Ontario and Michigan state, municipalities, and local conservation authorities. Mass-balance could be achieved up to 94% for conservative elements (Cl, Na), while our dynamic models reveal significantly different source/sink behavior across the upper and lower lakes for more reactive elements. We are currently expanding our models with new ultra-trace level analyses of recent freshwater samples from cruise expeditions, major tributary rivers, and precipitation, as well as sediment records. Second, we considered municipal and industrial wastewater as a proxy for human activity. We collected and analyzed wastewater effluent and digested sludge samples from >40 US and Canadian wastewater treatment facilities (WWTF) and estimated, for >20 trace elements, average discharge rates into the Great Lakes basin. We compared average wastewater-effluent loads with large-scale natural biogeochemical fluxes in the Great Lakes, allowing us to rank the analyzed trace elements as well as individual lakes and tributaries by their apparent human imprint. Our results show anomalously high loading rates for select rare earth elements and precious metals in several tributary systems. Geospatial attributes of the sampled sewersheds (demographics, land use, industrial activity) serve as independent variables in our ongoing effort to source-track these anomalous loads and establish human imprints on catchment tributaries further upstream.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".