Predicting Influence of Relative Humidity (RH) on Low-Cost Particulate Matter Sensors (LCPMSs) with Empirically Derived Single-Parameter for Hygroscopicity based on K-Kohler Theory
2022-12
Loading...
Persistent link to this item
Statistics
View StatisticsJournal Title
Journal ISSN
Volume Title
Title
Predicting Influence of Relative Humidity (RH) on Low-Cost Particulate Matter Sensors (LCPMSs) with Empirically Derived Single-Parameter for Hygroscopicity based on K-Kohler Theory
Authors
Published Date
2022-12
Publisher
Type
Thesis or Dissertation
Abstract
Low-cost particulate matter sensors (LCPMSs) could provide significant insight into air quality data with their ability to be placed virtually anywhere, short sampling time,
and cost to build. However, LCPMSs are also known to significantly overestimate
particle counts when the relative humidity (RH) is above 65%. It is widely considered that
the hygroscopic growth of aerosols is the cause. Hygroscopicity of PM can be described
by a single parameter, symbolized as K, and was used in a previous study (Di Antonio et
al., 2018) to correct LCPMS data with promising results. However, the study assumed
ambient PM to be a pure substance, however, it is often found to be a complex mixture
of organic and inorganic chemical species. This study tested if a statistically derived
empirical value of K, referred to as “ambient K”, could improve representing the RH
influence on LCPMSs. Ambient K is defined as the statistically best-fitting value for
several experimental observations of hygroscopy and makes no assumptions on the
number of species in ambient PM. Ambient K was graphically demonstrated to be more
representative of the experimentally observed RH error compared to assuming K, while
having the same statistical performance as conventionally assuming K. Varying
observations of hygroscopic behavior among multiple sensors provided strong evidence
of multiple chemical species in the observed ambient PM.
Description
University of Minnesota M.S. thesis. December 2022. Major: Chemical Engineering. Advisor: Steven Sternberg. 1 computer file (PDF); vi, 52 pages.
Related to
Replaces
License
Series/Report Number
Funding information
Isbn identifier
Doi identifier
Previously Published Citation
Other identifiers
Suggested citation
Tejada, Rayan. (2022). Predicting Influence of Relative Humidity (RH) on Low-Cost Particulate Matter Sensors (LCPMSs) with Empirically Derived Single-Parameter for Hygroscopicity based on K-Kohler Theory. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/252493.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.