Characterization of Aerosol Optical and Chemical Properties using In Situ, Remote Sensing, and Satellite Data
Notice bibliographique
Résumé
The major uncertainty in calculating worldwide anthropogenic radiative forcing is related directly to the aerosol interactions with clouds (IPCC, 2013), that is partially determined by the measurement of composition and transport of the aerosols. The influence of aerosol particles on radiative forcing is challenging which can be solved by improving how models treat aerosol properties such as size, composition, hygroscopicity, and refractive index. In this dissertation, these particle attributes are characterized and presented in three studies. First, this dissertation reports a characterization of the real part of dry particle refractive index (n) at 532 nm based on airborne measurements over the United States, Canada, the Pacific Ocean, and the Gulf of Mexico from the 2012 Deep Convective Clouds and Chemistry (DC3) and 2013 Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaigns. Effective n values are reported, with the limitations and uncertainties discussed. Eight air mass types were identified based on criteria related to gas-phase tracer concentrations, location, and altitude. Average values of n for these air types ranged from 1.50 to 1.53. Values of n for the organic aerosol (OA) fraction (nOA) were calculated using a linear mixing rule for each air mass type, with 1.52 shown to be a good approximation for all OA. Second, this dissertation examines 14 years (2004-2017) of surface aerosol composition data from the EPA IMPROVE network with a focus on the monthly profile, sources, and chemical nature of extreme dust events (>92nd percentile of fine soil concentration each month) impacting ten sites along the United States East Coast ranging in latitude from Florida to Maine. Based on trajectory, remote sensing, and reanalysis data, dust events were categorized into four source categories: African, Asian, Mix (African + Asian), and Other (anything other than African and Asian). The results reveal that extreme dust events account for between 3.3% and 4.6% of total available days depending on the site. March-April-May (MAM) had the most (174) dust events, followed by June-July-August (JJA) with 172, and then by September-October-November (SON) with 160 and December-January-February (DFJ) with 150. There is a variability in the predominant dust sources based on latitude, with African and Other sources more influential from North Carolina to the south, while Asian and Other were most important from New Jersey to the north. The Mix category is consistently the least frequent dust category at all sites. Third, this dissertation presents a study on the formation of sulfate and secondary organic aerosol mass in the aqueous phase (aqSOA) of cloud and fog droplets, which can significantly contribute to ambient aerosol mass. While tracer compounds give evidence that aqueous-phase processing occurred, they do not reveal the extent to which particle properties have been modified in terms of mass, chemical composition, hygroscopicity and oxidation state. We analyze data from several field experiments and model studies for six air mass types (urban, biogenic, marine, wildfire biomass burning, agricultural biomass burning and background air) using aerosol size and composition measurements for particles of 13 – 850 nm in diameter. We focus on the trends of changes in mass, hygroscopicity parameter kappa (κ), and oxygen-to-carbon (O/C) ratio due to chemical cloud processing. We find that the modification of these parameters upon cloud-processing is most evident in urban, marine and biogenic air masses, i.e. air masses that are more polluted than very clean air (background air) but cleaner than heavily polluted plumes as encountered during biomass burning.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 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) | 0,000 | 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 tête enseignante, 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 ».