The biogeochemical cycle of dissolved aluminium in the Atlantic Ocean
Notice bibliographique
Résumé
Dissolved aluminium (dAl) is the most abundant metal in the Earth’s crust and has not known biological function. Dissolved aluminium is supplied to the ocean through several sources which include atmospheric deposition, rivers, sedimentary, and hydrothermal sources. The major removal of dAl from seawater is via adsorption onto particles with subsequent sinking of the particles which are finally buried at the seafloor. Dissolved aluminium concentrations in surface waters can be converted into atmospheric deposition fluxes. Atmospheric deposition fluxes to the surface of the ocean are challenging to determine and present large uncertainties due to the inter-seasonal variability in the mechanisms and sources supplying aerosols to the ocean. Therefore it is important to study the distribution and understand the mechanisms that supply and remove dAl to and from the seawater. The work presented in this thesis has focused on the biogeochemical cycling of dAl in surface waters and water column of the Atlantic Ocean. Chapter 3 presents the largest highresolution vertical and lateral dataset of dAl that exists in the North Atlantic Ocean (>40°N) and in the Labrador Sea. The latter regions present large phytoplankton blooms and during this study it was found that diatoms directly influence the transfer of the dAl phase into the particulate aluminium phase. In the North Atlantic Ocean (>40°N) and in the Labrador Sea dAl displayed, in general, a recycled type distribution which differs from other regions in the Atlantic Ocean and seems to be coupled with surface uptake and the dissolution of diatoms frustules at depth. In chapter 4 the potential use of dAl as an atmospheric deposition tracer was studied over four different regions along the Atlantic Ocean. The studied regions showed marked regional differences in the concentration of dAl in surface waters as a consequence of varying degrees of aluminium sources and sinks. The datasets presented in chapter 4 have now filled in gaps for regions were no, or limited, dAl data and atmospheric deposition fluxes were available. These new datasets provide a baseline for future modelling studies to test and improve the mechanisms that influence the biogeochemical cycling of dAl in surface waters. The atmospheric deposition fluxes determined in this study from the concentration of dAl in surface waters show, in general, a good agreement with modelling studies. However, in regions affected by enhanced aluminium inputs from non-mineral dust sources or enhanced removal by suspended particles the atmospheric deposition fluxes calculated show low agreement with previous studies. In chapter 5 the distribution of dAl within the Congo River Plume in the Southeast Atlantic Ocean was studied. Prior to this study, the latter region was largely under sampled and this study represents the largest dataset of dAl in this region and it is the first dataset that has traced the influence that the Congo River Plume has on dAl concentrations, which extends as far as 1300 km from the river mouth. The input of dAl from the Congo River Plume showed a conservative behaviour, as a strong correlation was found between salinity and dAl. In this study the flux of dAl from the Congo River into the Southeast Atlantic was calculated and determined that the Congo River accounts for ca. 7.5% of the global world river to ocean dAl flux. Overall, the results presented in this thesis have identified processes which control the distribution of dAl in the North Atlantic Ocean, tropical Atlantic, Southeast Atlantic, South Atlantic, and it has determined atmospheric deposition and riverine fluxes of dAl to the ocean.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».