Characterization of Aerosol Optical and Chemical Properties using In Situ, Remote Sensing, and Satellite Data
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".