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Record W4322210742 · doi:10.5194/egusphere-egu23-13991

Key role of iron oxyhydroxides in dust aerosol from high and low latitudes

2023· preprint· en· W4322210742 on OpenAlexaboutno aff
Zongbo Shi, Clarissa Baldo

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAeolian processes and effects
Canadian institutionsnot available
Fundersnot available
KeywordsMineral dustAtmosphere (unit)Environmental scienceAsian DustAerosolAtmospheric sciencesHematiteSnowAlbedo (alchemy)LatitudeGeologyGeographyMineralogyMeteorologyGeomorphology

Abstract

fetched live from OpenAlex

Millions of tonnes of dust are emitted into the atmosphere every year, a large proportion of which is transported and deposited to the oceans. Dust particles can directly affect the climate via dust–radiation interaction and indirectly via dust–cloud interaction, the snow/ice albedo effect and impacts on ocean biogeochemical cycles. Dust impacts on the climate and ecosystems depend on their mineralogical, chemical, microphysical, and optical properties. Over the past 20 years, important progress has been made in determining the properties of low-latitude dust and understanding how they change in the atmosphere.The mineralogical compositions, including iron mineralogy, of northern African and Asian dust are now better known and show a large variability depending on the source region. Distinctive patterns were found. For example, more calcium minerals (such as calcite) are found in dust from the Taklamakan Desert and palaeolakes in northern Africa than in dust from the Gobi and Sahara Desert; the contribution of iron oxides to the total iron in Saharan dust (25%-40%) is lower than in Sahel dust (ca. 60%), whereas dust from palaeolakes, including that from the Bodele depression, has lower iron oxide content (<25%). Most of the Fe oxide particles from the Sahara and Gobi Desert are as goethite, while more hematite is found in Sahel dust. These new data have allowed a much better modelling of the role of low-latitude dust in the Earth system.Only until recently, we started to study the properties of high-latitude dust, including from Iceland, Canada (Yukon), and Alaska. Icelandic dust particles are distinguished by the fact that most of them consist primarily of amorphous basaltic materials, up to 90 wt %. The total Fe content is usually very high (10%–13%), and hematite and goethite contribute only 1%–6% of the total Fe, which is significantly lower than in low-latitude dust (except in palaeolakes). Magnetite accounts for 7%–15% of the total Fe, which is orders of magnitude higher than in dust from northern Africa. Nevertheless, about 80%–90% of the Fe is contained in pyroxene and amorphous glass. Data from both low- and high-latitude dust showed that the iron mineralogy is associated with the degree of chemical weathering and the composition of the parent sediments.The spectral single scattering albedo (SSA) of Icelandic dust falls within the range of low-latitude dust. The complex refractive index of dust is highly dependent on its source region, with Sahel and Icelandic dust showing highest values of imaginary index - k(λ). This indicates that Sahel and Icelandic dust is likely to be more absorbing. The measured spectral optical properties of both low- and high- latitude dust in the short-wave spectrum are consistent with what was predicted from their iron mineralogy.The iron mineralogy in dust also determines the rate of dissolution during atmospheric processing, and thus its impact on ocean biogeochemical processes after dust deposition. For example, the high dissolution rate in the first few minutes in dust under acidic conditions is related to the content of amorphous Fe oxides.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.011
GPT teacher head0.202
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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