Comment on acp-2021-518
Bibliographic record
Abstract
<strong class="journal-contentHeaderColor">Abstract.</strong> We present here the results obtained during an intensive field campaign conducted in the framework of the French âBIO-MAÃDOâ (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 MarchâApril 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 oceanâcloud exchanges. Indeed, <span class="inline-formula">Na<sup>+</sup></span> and <span class="inline-formula">Cl<sup>â</sup></span> dominate the inorganic composition contributing to 30â% and 27â%, respectively, to the average total ion content. The strong correlations between these species (<span class="inline-formula"><i>r</i><sup>2</sup></span>â<span class="inline-formula">=</span>â0.87, <span class="inline-formula"><i>p</i></span> value: <span class="inline-formula"><</span>â0.0001) 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 38â% and 91â%, 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 (25.5â<span class="inline-formula">±</span>â18.4â<span class="inline-formula">mgâCâL<sup>â1</sup></span>), with levels reaching up to 62â<span class="inline-formula">mgâCâL<sup>â1</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) (0.62â% and 0.06â%, respectively) has been observed, significant levels of biogenic VOC (20 to 180â<span class="inline-formula">nmolâL<sup>â1</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.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.004 |
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.
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; both teacher heads agree on what is shown here.
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".