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Record W4213117816 · doi:10.5194/acp-2021-518-rc2

Comment on acp-2021-518

2021· peer-review· en· W4213117816 on OpenAlexaff
Pamela Dominutti, Pascal Renard, Angelica Bianco, Jean‐Luc Baray, Thierry Bourianne, Anne‐Marie Delort, Valentin Duflot, Stéphan Houdier, Jean‐Luc Jaffrezo, Muriel Joly, Martin Leremboure, Jean‐Marc Metzger, Jean‐Marc Pichon, Manon Rocco, Pierre Tulet, Anthony Vella, Maud Leriche, Laurent Deguillaume

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldEnvironmental Science
TopicAir Quality Monitoring and Forecasting
Canadian institutionsUniversité du Québec à Montréal
FundersAgence Nationale de la Recherche
KeywordsSpan (engineering)Life spanOrganic matterSolubilityChlorideChemistryEnvironmental chemistryOrganic chemistryBiology

Abstract

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<strong class="journal-contentHeaderColor">Abstract.</strong> We present here the results obtained during an intensive field campaign conducted in the framework of the French “BIO-MAÏDO” (Bio-physico-chemistry of tropical clouds at Maïdo (Réunion Island): processes and impacts on secondary organic aerosols' formation) project. This study integrates an exhaustive chemical and microphysical characterization of cloud water obtained in March–April 2019 in Réunion (Indian Ocean). Fourteen cloud samples have been collected along the slope of this mountainous island. Comprehensive chemical characterization of these samples is performed, including inorganic ions, metals, oxidants, and organic matter (organic acids, sugars, amino acids, carbonyls, and low-solubility volatile organic compounds, VOCs). Cloud water presents high molecular complexity with elevated water-soluble organic matter content partly modulated by microphysical cloud properties. <span id="page506"/>As expected, our findings show the presence of compounds of marine origin in cloud water samples (e.g. chloride, sodium) demonstrating ocean–cloud exchanges. Indeed, <span class="inline-formula">Na<sup>+</sup></span> and <span class="inline-formula">Cl<sup>−</sup></span> dominate the inorganic composition contributing to 30 % and 27 %, respectively, to the average total ion content. The strong correlations between these species (<span class="inline-formula"><i>r</i><sup>2</sup></span> <span class="inline-formula">=</span> 0.87, <span class="inline-formula"><i>p</i></span> value: <span class="inline-formula">&lt;</span> 0.0001) suggest similar air mass origins. However, the average molar <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M7" display="inline" overflow="scroll" dspmath="mathml"><mrow><mrow class="chem"><msup><mi mathvariant="normal">Cl</mi><mo>-</mo></msup></mrow><mo>/</mo><mrow class="chem"><msup><mi mathvariant="normal">Na</mi><mo>+</mo></msup></mrow></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="44pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="92f6831eb037b2b43c8446dbbfee4dac"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-22-505-2022-ie00001.svg" width="44pt" height="14pt" src="acp-22-505-2022-ie00001.png"/></svg:svg></span></span> ratio (0.85) is lower than the sea-salt one, reflecting a chloride depletion possibly associated with strong acids such as <span class="inline-formula">HNO<sub>3</sub></span> and <span class="inline-formula">H<sub>2</sub>SO<sub>4</sub></span>. Additionally, the non-sea-salt fraction of sulfate varies between 38 % and 91 %, indicating the presence of other sources. Also, the presence of amino acids and for the first time in cloud waters of sugars clearly indicates that biological activities contribute to the cloud water chemical composition. A significant variability between events is observed in the dissolved organic content (25.5 <span class="inline-formula">±</span> 18.4 <span class="inline-formula">mg C L<sup>−1</sup></span>), with levels reaching up to 62 <span class="inline-formula">mg C L<sup>−1</sup></span>. This variability was not similar for all the measured compounds, suggesting the presence of dissimilar emission sources or production mechanisms. For that, a statistical analysis is performed based on back-trajectory calculations using the CAT (Computing Atmospheric Trajectory Tool) model associated with the land cover registry. These investigations reveal that air mass origins and microphysical variables do not fully explain the variability observed in cloud chemical composition, highlighting the complexity of emission sources, multiphasic transfer, and chemical processing in clouds. Even though a minor contribution of VOCs (oxygenated and low-solubility VOCs) to the total dissolved organic carbon (DOC) (0.62 % and 0.06 %, respectively) has been observed, significant levels of biogenic VOC (20 to 180 <span class="inline-formula">nmol L<sup>−1</sup></span>) were detected in the aqueous phase, indicating the cloud-terrestrial vegetation exchange. Cloud scavenging of VOCs is assessed by measurements obtained in both the gas and aqueous phases and deduced experimental gas-/aqueous-phase partitioning was compared with Henry's law equilibrium to evaluate potential supersaturation or unsaturation conditions. The evaluation reveals the supersaturation of low-solubility VOCs from both natural and anthropogenic sources. Our results depict even higher supersaturation of terpenoids, evidencing a deviation from thermodynamically expected partitioning in the aqueous-phase chemistry in this highly impacted tropical area.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.357
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0370.004

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 head0.069
GPT teacher head0.326
Teacher spread0.257 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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