Ionization technologies such as GPS’s patented needlepoint bipolar ionization (NPBI™) have been widely used for decades to improve indoor air quality as part of a multilayered approach that also includes filtration and ventilation. Despite a long track record that, for GPS, includes 250,000-plus implementations since its founding in 2008, ionization is still considered an emerging technology due to a lack of peer-reviewed literature.

Peer-reviewed research has been hampered for at least two reasons:

  • Poorly designed test protocols

  • A lack of robust statistical methods for analyzing experimental data

For the latter, and in layman's terms, how does one go about making sense of vast reams of data from experiments, accounting for variations (such as false positives) that can and do occur?

Accounting for Variations in Data

A recent peer-reviewed article, “Quantifying the Natural Variation of Data Signatures from Aerosols Using Statistical Control Bands” in the scientific journal Mathematics, helps address this issue. Specifically, the study addresses the challenge of how to statistically quantify the natural variations of the data signatures of calibrated cigarette-smoke aerosols (ultrafine particles). By quantifying the natural variation of aerosol data signatures using analytical-based control bands, expanded inferences from experiments are possible.

Importantly, this peer-reviewed work provides a critical building block for analyzing ionization-efficacy data going forward. This, in turn, will support additional peer-reviewed research and literature going forward, especially for fine and ultrafine airborne particles including certain pathogens that impact human health.

The peer-reviewed article was authored by Dr. Timothy M. Young, Dr. Edward Sobek and Dr. Faramarz Farahi. Dr. Sobek is the chief science officer for GPS. Drs. Young and Farahi are professors at the University of Tennessee and the University of North Carolina at Charlotte, respectively, and scientific advisors to GPS.

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