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Title:Particle-resolved modeling for evaluating assumptions in aerosol studies
Author(s):Gasparik, Jessica T.
Advisor(s):Riemer, Nicole
Department / Program:Atmospheric Sciences
Discipline:Atmospheric Sciences
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):aerosol modeling
mixing state
Abstract:In most aerosol models, simplifying assumptions must be made regarding aerosol size and composition. PartMC-MOSAIC, being particle-resolved, does not require such assumptions. Thereby, the model provides a useful tool to evaluate assumptions and limitations within aerosol studies. This thesis, I show two applications that highlight the potential of this modeling approach. The first application pertains to coarse aerosol particles (diameter > 1 micron), such as mineral dust or sea salt. Regional and global models frequently do not consider the interactions of the coarse mode aerosol with smaller particles and its interactions with the gas phase. This assumption might introduce errors in predictions of the size distribution, gas partitioning, and cloud condensation nuclei (CCN) concentrations. The objective of my study is to assess the interactions between the coarse mode with other particle size ranges and determine the conditions where it is acceptable to treat the coarse mode as non-interactive. Using the coupled model, PartMC-WRF, we designed four scenarios based on average global coarse mode concentrations and past studies of dust storms. In each scenario, we compared the aerosol properties when the coarse mode is present and interactive versus absent. Though many coagulation events occur between coarse mode and combustion particles, the black carbon mass fraction that is transferred to the coarse mode remained negligible over the course of the 2-day simulation. Up to 4% of all available nitrate partitioned onto the coarse mode, and negligible error occurred in CCN concentrations when the coarse mode is considered. The second application evaluates the error that is introduced in quantifying observed aerosol mixing states due to a limited particle sample size. We used the particle-resolved model PartMC-MOSAIC to generate a scenario library that encompasses a large number of reference particle populations with a wide range of mixing states, quantified by a mixing state metric. We stochastically sub-sampled these particle populations using sample sizes of 10 to 10 000 particles and recalculated the mixing state metric based on the sub-samples. The finite sample size lead to a consistent overestimation of the mixing state metric, with the 95% confidence intervals ranging from -70 to 40 percentage points for sample sizes of 10 particles, and decreasing to plus or minus 10 percentage points for sample sizes of 10 000 particles. These findings are experimentally confirmed using SP-AMS measurement data from the Pittsburgh area.
Issue Date:2019-12-11
Rights Information:Copyright 2019 Jessica Gasparik
Date Available in IDEALS:2020-03-02
Date Deposited:2019-12

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