AAAR 37th Annual Conference October 14 - October 18, 2019 Oregon Convention Center Portland, Oregon, USA
Abstract View
Quantifying Errors in Aerosol Mixing State Metrics due to Limited Particle Sample Size
Jessica Gasparik, NICOLE RIEMER, Matthew West, Qing Ye, Ryan Sullivan, Albert Presto, University of Illinois at Urbana-Champaign
Abstract Number: 324 Working Group: Instrumentation and Methods
Abstract Atmospheric aerosols are evolving mixtures of different chemical species. The term "aerosol mixing state" is commonly used to describe how different chemical species are distributed throughout a particle population. A population is "fully internally mixed" if each individual particle consists of same species mixtures, whereas it is "fully externally mixed" if each particle only contains one species. Mixing state matters for climate-relevant aerosol properties, such as the particles’ propensity to form cloud droplets or the aerosol optical properties.
The mixing state metric χ quantifies the degree of internal or external mixing and can be calculated based on the particles’ species mass fractions. Several field studies have used this metric to quantify mixing states for different ambient environments using sophisticated single-particle measurement techniques. Inherent to these methods is that a finite number of particles is used to estimate the mixing state metric, ranging from a few hundred to several thousand particles.
This study evaluates the error that is introduced in calculating χ 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 and that represents a wide range of mixing states. We stochastically sub-sampled these particle populations using sample sizes of 10 to 10,000 particles and recalculated χ based on the sub-samples.
The errors generated in χ from limited sample sizes were greater for external mixtures (maximum error of 324% for 10-particle sample), indicating that these mixtures require larger particle sample sizes to accurately represent the mixing state. Mean errors decrease for larger sample sizes, ranging from 76% to 3% for 10 and 10,000 particle samples, respectively. The finite sample size further leads to a consistent overestimation of χ. These findings are experimentally confirmed using SP-AMS measurement data from the Pittsburgh area.