Identifying global trends and drivers of freshwater aluminium concentrations using GloFAD (Global Freshwater Acidification Database)
Notice bibliographique
Résumé
Aluminum is toxic to most aquatic and terrestrial organisms. Increased Al concentrations in soils and freshwaters are a direct result of human activity, via increases in acid deposition. Elevated Al concentrations pose a wide variety of threats to ecosystems and society, from causing human neurotoxicity, reducing carbon sequestration in forests, threatening biodiversity, and increasing the cost of water treatment. Freshwater aluminium concentrations increased across Europe and North America between the 1960s and 1990s, predominantly due to ecosystem acidification. Following acidic deposition reduction legislation enacted in the 1990s, the problems of acidification and increased freshwater aluminium concentrations were considered solved. However, recently and unexpectedly, Sterling et al. identified aluminum concentrations to be increasing across North America and Scandinavia. Sterling et al. proposed a conceptual model suggesting these widespread increases in freshwater aluminium concentrations resulted from a hysteresis of base cation and dissolved organic carbon (DOC) response to decreasing acidic deposition, where base cation increase is slow compared to that of DOC, resulting in elevated freshwater aluminium concentrations. This process can be exacerbated by further increases in DOC due to increasing global surface temperatures. The Sterling et al. conceptual model is supported by prior work by Weyhenmeyer et al. (2019, Scientific Reports) and Monteith et al., 2007, Nature) who identified widespread decreasing calcium and increasing DOC concentrations. In this study, we aim to validate the Sterling et al. model and identify if it is generalizable to other regions with decreasing calcium and increasing DOC trends, irrespective acidification status. Additionally, we aim to characterize other regions across the globe which are at risk of elevated aluminium concentrations. To fulfill these research goals, we compiled a large-sample water chemistry database from existing national and global datasets – GloFAD (Global Freshwater Acidification Database). The database is comprised of over 11 million unique samples spanning nearly 286,000 sites located between Antarctica and Russia, 18 years (2000 to 2019), and 40 water chemistry parameters. Preliminary analysis shows that aluminium is increasing in 27% to 71% of sites, dependent on the species, base cations are decreasing for 62% to 70% of sites, freshwater organic carbon is increasing for 58% to 64% of sites, and water temperature is increasing in 61% of sites. Increasing dissolved aluminium trends are strongly significantly correlated with decreasing base cation trends (calcium τ = -0.71 and magnesium τ = -0.59, α < 0.05) but not with DOC concentrations (τ = -0.08). The lack of correlation with DOC indicates that drivers of increasing aluminium trends may differ based on the acidification status of the watershed and that regional models of freshwater aluminium chemistry may not be globally applicable. The widespread decreasing base cation trends and strong correlation between decreasing base cation and increasing aluminium trends indicates that increasing aluminium concentrations may become more widespread, posing a threat to aquatic and terrestrial organisms, potentially including humans, reducing carbon sequestration in forests, threatening biodiversity, and increasing water treatment costs.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».