Assessing Human Imprints on Trace Element Fluxes in the Great Lakes
Notice bibliographique
Résumé
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.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 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 ».