The biogeochemical cycle of dissolved aluminium in the Atlantic Ocean
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".