Publication
How many particles do we need to measure aerosol mixing state?
Publisher:
Copernicus GmbH
Date:
23-03-2020
DOI:
10.5194/EGUSPHERE-EGU2020-6132
Abstract: & & Atmospheric aerosols are evolving mixtures of different chemical species.& The term & #8220 aerosol mixing state& #8221 is commonly used to describe how different chemical species are distributed throughout a particle population.& A population is & #8220 fully internally mixed& #8221 if each in idual particle consists of same species mixtures, whereas it is fully externally mixed if each particle only contains one species. Mixing state matters for aerosol health impacts and for climate-relevant aerosol properties, such as the particles& #8217 propensity to form cloud droplets or the aerosol optical properties.& & & & The mixing state metric & #967 quantifies the degree of internal or external mixing and can be calculated based on the particles& #8217 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 a finite number of particles, ranging from a few hundred to several thousand particles, used to estimate the mixing state metric.& & & & & This study evaluates the error that is introduced in calculating & #967 due to a limited particle s le 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-s led these particle populations using s le sizes of 10 to 10,000 particles and recalculated & #967 based on the sub-s les. This procedure mimics the impact of only having a limited s le size as it is common in real-world applications. The finite s le size leads to a consistent overestimation of & #967 , meaning that the populations appear more internally mixed than they are in reality. These findings are experimentally confirmed using single-particle SP-AMS measurement data from the Pittsburgh area. We also determined confidence intervals of & #967 for our sub-s led populations. To determine & #967 within a range of & +/- 10 percentage points requires a s le size of at least 1000 particles.& & & & & & &