Quantifying the Natural Variation of ‘Data Signatures’ from Aerosols Using Statistical Control Bands

Timothy M. Young, Edward Sobek and Faramarz Farahi
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Timothy M. Young: Center for Renewable Carbon, The University of Tennessee, 2506 Jacob Drive, Knoxville, TN 37996, USA
Edward Sobek: Global Plasma Solutions, 3101 Yorkmont Rd., Suite 400, Charlotte, NC 28208, USA
Faramarz Farahi: Center for Optics, The University of North Carolina at Charlotte, Charlotte, NC 28223, USA

Mathematics, 2022, vol. 10, issue 12, 1-17

Abstract: The natural variation of the data signatures of airborne aerosols from calibrated cigarette particles were quantified using enhanced Bonferroni methods. The significance of the problem of improving analytical methods for understanding the natural variation of airborne particles cannot be understated given the positive impact for mitigating harmful airborne particles. The data presented in this paper were obtained using experiments to examine the effect of a carbon-brush-based bipolar ionization on filtration efficiency of a MERV 10 filter in a recirculating HVAC system. Ionization technology is deployed throughout the world as a multilayered approach with filtration for improving indoor air quality. Despite its wide use, ionization is still considered an emerging technology due to a dearth of peer-reviewed literature. Poorly designed test protocols and a lack of robust statistical methods for analyzing experimental data are the primary reasons. Presented herein is a statistical groundwork for analyzing ionization-efficacy data from highly controlled and properly designed particulate-matter test trials. Results are presented for three experimental groups where bipolar ionization was used to study the behaviors of data signatures from cigarette-smoke aerosol particles ranging in size from 49.6 to 201.7 nm. Statistical control bands of the data from these experimental groups revealed that bipolar ionization had significant changes to the pdfs and reductions in the natural variation of the data signatures for the particle count (number of particles) across all particle sizes. Statistical control bands may provide enhanced quantitative knowledge of variation and provide expanded inference that goes beyond examination of percentiles only. The implications from this research are profound, as it lays the groundwork for the development of highly effective ionization-filtration layered strategies to mitigate the hazards of airborne particulates and is the first step towards creating robust efficacy test standards for the industry.

Keywords: data signatures; statistical control bands; natural variation; aerosols (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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