Dissolved organic carbon (DOC) and select aldehydes in cloud and fog water: the role of the aqueous phase in impacting trace gas budgets
Notice bibliographique
Résumé
Abstract. Cloud and fog droplets efficiently scavenge and process water-soluble compounds and, thus, modify the chemical composition of the gas and particle phases. The concentrations of dissolved organic carbon (DOC) in the aqueous phase reach concentrations on the order of ~ 10 mgC L−1 which is typically on the same order of magnitude as the sum of inorganic anions. Aldehydes and carboxylic acids typically comprise a large fraction of DOC because of their high solubility. The dissolution of species in the aqueous phase can lead to (i) the removal of species from the gas phase preventing their processing by gas phase reactions (e.g., photolysis of aldehydes) and (ii) the formation of unique products that do not have any efficient gas phase sources (e.g., dicarboxylic acids). We present measurements of DOC and select aldehydes in fog water at high elevation and intercepted clouds at a biogenically-impacted location (Whistler, Canada) and in fog water in a more polluted area (Davis, CA). Concentrations of formaldehyde, glyoxal and methylglyoxal were in the micromolar range and comprised ≤ 2% each individually of the DOC. Comparison of the DOC and aldehyde concentrations to those at other locations shows good agreement and reveals highest levels for both in anthropogenically impacted regions. Based on this overview, we conclude that the fraction of organic carbon (dissolved and insoluble inclusions) in the aqueous phase of clouds or fogs, respectively, comprises 2–~ 40% of total organic carbon. Higher values are observed to be associated with aged air masses where organics are expected to be more highly oxidised and, thus, more soluble. Accordingly, the aqueous/gas partitioning ratio expressed here as an effective Henry's law constant for DOC (KH*DOC) increases by an order of magnitude from 7 × 103 M atm−1 to 7 × 104 M atm−1 during the ageing of air masses. The measurements are accompanied by photochemical box model simulations. These simulations are used to contrast two scenarios, i.e., an anthropogenically vs. a more biogenically impacted one as being representative for Davis and Whistler, respectively. Since the simplicity of the box model prevents a fully quantitative prediction of the observed aldehyde concentrations, we rather use the model results to compare trends in aldehyde partitioning and ratios. They suggest that the scavenging of aldehydes by the aqueous phase can reduce HO2 gas phase levels significantly by two orders of magnitude due to a weaker net source of HO2 production from aldehyde photolysis in the gas phase. Despite the high solubility of dicarbonyl compounds (glyoxal, methylglyoxal), their impact on the HO2 budget by scavenging is < 10% of that of formaldehyde. The overview of DOC and aldehyde measurements presented here reveals that clouds and fogs can be efficient sinks for organics, with increasing importance in aged air masses. Even though aldehydes, specifically formaldehyde, only comprise ~ 1% of DOC, their scavenging and processing in the aqueous phase might translate into significant effects in the oxidation capacity of the atmosphere.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».