Nanosized particles in North American snow: physicochemical properties of efficient ice nucleating particles
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Notice bibliographique
Résumé
Aerosols interact with clouds and affect climate through absorption and scattering of radiation. However, aerosol-cloud interactions are complex, making radiative forcing predictions hard to calculate accurately. The most important uncertainty is the role of aerosols in the formation and dissipation of clouds, which are controlled by nucleation processes. In the lower troposphere, ice and mix-phase clouds are common. In these types of clouds, ice formation is primarily catalyzed by aerosols through heterogeneous ice nucleation. The conditions at which this process occurs depends on the properties of aerosols. Some aerosols are more efficient than other, but due to the complexity of aerosol-cloud interactions, models only focus on the contribution of aerosols that are efficient and abundant in the atmosphere. Even if an aerosol is very efficient, if its abundance in the atmosphere is low, its relevance as a global ice nucleating particle is minimal. This thesis presents the particle size distributions in snow from four different locations as well as their physical and chemical properties to find which particles sizes are the most abundant. It also presents their ice nucleation behavior to determine their potential as relevant ice nucleating particles. Sampling was done in two remote locations, one urban, and one highly contaminated by oil sands activities. The remote locations were Barrow in Alaska, USA and Alert in Nunavut, Canada. The urban location was Montreal, Quebec, Canada and the highly polluted area was the Athabasca Oil Sands Region (AOSR) in Alberta, Canada. The first part of the thesis presents the development of a system for the real-time measurement of aerosol size distributions in melted snow. This system brings particles suspended in melted snow into the airborne state. Collection of the generated particles onto electron microscopy grids is also possible. Samples are dialyzed before analysis to remove interferences from salts and other dissolved substances. Analysis of snow samples revealed that particles of 30 nm dominated the particle size distribution in Montreal snow and particles of 15 nm dominated the distribution in Alert and Barrow snow. Results suggest low particle size aggregation during the aerosolization process when compared to similar techniques. This developed technique had a high resolution of particle size in the range of 10-100 nm. Using this technique, it was also found that nanosized particles (<200 nm) are the most abundant (38-71 %) in the snow sampled from Alert, Barrow and Montreal. It was also found that nanoparticles represent 11-19% of all particles. Nanosized particles also exhibited high ice nucleation efficiencies, with average freezing temperatures of 19.6 ± 2.4 to 8.1 ± 2.6 °C. Chemical analysis of this size fraction revealed that these particles are composed by biological material such as amino acids and possibly cell debris as well as inorganic materials such as mineral dust.In snow from the AOSR, nanosized particles dominated the size distributions as well, but their concentrations were as high as 2 orders of magnitude higher than Montreal. Additionally, these particles were much more efficient at nucleating ice with average freezing temperatures of -7.1 ± 1.8 °C. Analysis of these particles (even for samples collected 7-25 km away from major bitumen upgrading facilities) revealed the presence of anthropogenic nanostructures such as carbon nanotubes and trace metals with concentration up to 72 mg/L.This thesis contributes to the understanding of the distribution of environmental particles and nanoparticles in northern locations and provided results that will help understand their effect on climate. With an increase in the release of chemicals by anthropogenic sources, understanding the properties of particles will help to predict atmospheric phenomena more accurately.
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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 | 0,001 |
| 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 découle