Contribution of Saharan dust to chemical weathering fluxes and associated phosphate release in West Africa
Bibliographic record
Abstract
Huge amounts of mineral dust are produced in northern Africa, representing the largest source of aerosols worldwide. Transatlantic dust transport is known to fertilize soils as far as in the Amazon Basin. Yet, the influence of Saharan dust on chemical weathering fluxes and associated nutrient release in West Africa remains largely overlooked. To address this issue, we analysed clay fractions (<2 µm) of river sediments ( n = 37) from across the Niger River basin - the largest river system in West Africa - using neodymium and hafnium isotope compositions as proxies for provenance (ε Nd ) and chemical weathering (Δε Hf CLAY ). Compared to previously published data for corresponding sand fractions, measured ε Nd values indicate significant size-dependent decoupling for Nd isotopes in most samples, with ε Nd differences between clay and sand fractions yielding values as great as ∼26 ε-units. Using mixing models, we show that this discrepancy reflects the overwhelming presence in the studied clay fractions of Harmattan dust blown from the Bodélé Depression in Chad, which we estimate to account for about 40 % of the fine-grained sediment load exported to the Gulf of Guinea. Additionally, significant Δε Hf CLAY variability occurs across the Niger catchment, partly explained by the presence of zircon in clay-size fractions, but also by preferential alteration of dust-borne accessory phosphate minerals in the subtropical regions of the watershed. Based on these results, we propose that Saharan dust plays a major role in controlling regional patterns of chemical weathering in West Africa, suggesting that enhanced wet deposition of mineral dust in shield areas dominated by transport-limited weathering regime can result in a large increase in weatherability and associated release of phosphorus. These findings have general implications for the importance of mineral aerosols in controlling sediment yield and the supply of weathering-derived nutrients to continental areas bordering large subtropical deserts worldwide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".