Nanosized particles in North American snow: physicochemical properties of efficient ice nucleating particles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it