Quantifying errors in the aerosol mixing-state index based on limited particle sample size

• Published in: Aerosol science and technology Vol. 54; no. 12; pp. 1527 - 1541
• Main Authors: Gasparik, J. T., Ye, Q., Curtis, J. H., Presto, A. A., Donahue, N. M., Sullivan, R. C., West, M., Riemer, N.
• Format: Journal Article
• Language: English
• Published: New York Taylor & Francis 01.12.2020; Taylor & Francis Ltd; American Association for Aerosol Research
• Subjects: Aerosol research; Aerosols; Confidence intervals; Engineering; Environmental Sciences & Ecology; Error analysis; Kihong Park; Mass spectrometry; Meteorology & Atmospheric Sciences; Populations; Soot
• ISSN: 0278-6826; 1521-7388
• DOI: 10.1080/02786826.2020.1804523

This study 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 the mixing-state index χ. 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 finite sample size led to a consistent overestimation of χ, with the 95% confidence intervals ranging from −70 to 30 percentage points for sample sizes of 10 particles, and decreasing to ±10 percentage points for sample sizes of 10,000 particles. These findings were experimentally confirmed with single-particle measurements from the Pittsburgh area using a soot-particle aerosol mass spectrometer. Copyright © 2020 American Association for Aerosol Research