Public Abstract

DE-SC0025197: Generalizing aerosol mixing state: synthesis from observations and connection to models

Award Status: Active

Institution: Regents of the University of Michigan, Ann Arbor, MI
UEI: GNJ7BBP73WE9
DUNS: 073133571

Most Recent Award Date: 08/27/2024
Number of Support Periods: 1
PM: Stehr, Jeffrey

Current Budget Period: 09/01/2024 - 02/28/2026
Current Project Period: 09/01/2024 - 08/31/2027
PI: O'Brien, Rachel

Supplement Budget Period: N/A

Public Abstract

Generalizing aerosol mixing state: synthesis from observations and connection to models Principal Investigator: R. O’Brien, University of Michigan Co-Investigators: A. Ault, University of Michigan; N. Riemer, University of Illinois at Urbana-Champaign Aerosol particles play important roles in the Earth’s climate by serving as cloud condensation nuclei (CCN), ice nuclei, or interacting directly with solar radiation. These processes depend on the chemical composition, the size, and the morphology of the individual particles. The aerosol mixing state is a description of the distribution of these properties across a particle population. Previous work has characterized aerosol mixing states based on chemical (or elemental) composition with either microspectroscopic or mass spectrometric methods that can measure on a single particle level. Mathematical representations of the extent that a population is internally vs. externally mixed have also been developed. However, there is no single instrument that can determine the complete aerosol mixing state, and substantial effort is needed to synthesize a complete picture. In addition, aerosol physical and chemical properties like form, shape, surface tension, and viscosity will influence aerosol behavior, but gaps exist in both the models and single particle measurements for these properties. Thus, continued work is needed to improve our ability to predict the role aerosols will play in our climate. To achieve these goals, the team has three specific objectives for this study: To fully characterize the aerosol particle mixing state and composition by collecting aerosol particle samples at the third Atmospheric radiation measurement Mobile Facility (AMF3) and using a range of microspectroscopic and mass spectrometric analyses to characterize their properties. To advance model representations of the mixing state, and compare model outputs to experimental results as well as in-situ online measurements of aerosol characteristics. To develop the ability to infer the most likely aerosol mixing state based on bulk measurements by applying machine learning and statistical methods to online measurements of aerosol characteristics at the Southeast U.S. site. This project involves sample collection at AFM3, and will advance experimental methods and theory, and will integrate theory with offline and online data from AFM3. This work will advance the understanding of aerosol processes related to cloud formation and light scattering and it will integrate modeling efforts with measurements to improve the ability to predict particle level properties from online data sets and bulk information.

Generalizing aerosol mixing state: synthesis from observations and connection to models

Principal Investigator: R. O’Brien, University of Michigan
Co-Investigators: A. Ault, University of Michigan; N. Riemer, University of Illinois at Urbana-Champaign

Aerosol particles play important roles in the Earth’s climate by serving as cloud condensation nuclei (CCN), ice nuclei, or interacting directly with solar radiation. These processes depend on the chemical composition, the size, and the morphology of the individual particles. The aerosol mixing state is a description of the distribution of these properties across a particle population. Previous work has characterized aerosol mixing states based on chemical (or elemental) composition with either microspectroscopic or mass spectrometric methods that can measure on a single particle level. Mathematical representations of the extent that a population is internally vs. externally mixed have also been developed. However, there is no single instrument that can determine the complete aerosol mixing state, and substantial effort is needed to synthesize a complete picture. In addition, aerosol physical and chemical properties like form, shape, surface tension, and viscosity will influence aerosol behavior, but gaps exist in both the models and single particle measurements for these properties. Thus, continued work is needed to improve our ability to predict the role aerosols will play in our climate. To achieve these goals, the team has three specific objectives for this study:

To fully characterize the aerosol particle mixing state and composition by collecting aerosol particle samples at the third Atmospheric radiation measurement Mobile Facility (AMF3) and using a range of microspectroscopic and mass spectrometric analyses to characterize their properties.
To advance model representations of the mixing state, and compare model outputs to experimental results as well as in-situ online measurements of aerosol characteristics.
To develop the ability to infer the most likely aerosol mixing state based on bulk measurements by applying machine learning and statistical methods to online measurements of aerosol characteristics at the Southeast U.S. site.

This project involves sample collection at AFM3, and will advance experimental methods and theory, and will integrate theory with offline and online data from AFM3. This work will advance the understanding of aerosol processes related to cloud formation and light scattering and it will integrate modeling efforts with measurements to improve the ability to predict particle level properties from online data sets and bulk information.