Abstract

Low-cost sensors (LCS), if used appropriately, are useful instruments to elucidate human exposure to particles suspended in the air (i.e. particulate matter, PM). Characterizing this exposure is crucial, as exposure to PM2.5 (i.e. particles with aerodynamic diameter ≤ 2.5 μm) negatively affects the respiratory, cardiovascular, and other organ systems and is the leading environmental burden for global mortality. Similarly, exposure to particles with aerodynamic diameters greater than 2.5 μm and less than or equal to 10 μm, here forth termed as PMCoarse, is known to cause health ailments for the upper respiratory system. This dissertation focuses on the MODULAIRTM-PM (MOD-PM), a device manufactured by QuantAQ that couples two light-scattering low-cost PM sensors–the Plantower PMS5003 and the Alphasense OPC-N3–to measure both PM2.5 and PMCoarse. QuantAQ purportedly uses the PMS5003 to estimate the mass concentration of particles below the detection limit of the OPC-N3; in this case, the MOD-PM uses both the PMS5003 and OPC-N3 to calculate PM2.5 estimates and uses only the OPC-N3 to calculate PMCoarse estimates. This brings to question how well the MOD-PM couples the various light-scattering measurements of these LCS to accurately estimate PM2.5 and PMCoarse mass concentrations. This dissertation presents research to evaluate and calibrate the coupling of the LCS used in the MOD-PM. The first chapter outlines the light-scattering principles underpinning the LCS used in the MOD-PM to examine the strengths and weaknesses of QuantAQ’s approach to coupling the PMS5003 and OPC-N3. A major limitation towards evaluating the PM estimates from the MOD-PM–the uncertainty of how the PMS5003 measurements are used to estimate the mass concentration of particles below the detection limit of the OPC-N3–is resolved by deriving an independent methodology to calibrate the PMS5003 to estimate PM mass concentrations for a size range of particles complementary to those detected by the OPC-N3. The second chapter utilizes this methodology in a locally weighted linear regression between PM estimates from MOD-PM and reference-grade devices to apportion error in PM2.5 and PM10 (i.e. PM2.5 + PMCoarse) estimates from the MOD-PM to the PMS5003 and OPC-N3. This regression demonstrates that the detection efficiency of the OPC-N3 causes this sensor to variably contribute to error in the PM2.5 and PM10 estimates of the MOD-PM. The third chapter utilizes gradient descent to calibrate the detection efficiency of the OPC-N3 by minimizing the error between MOD-PM and reference-grade estimates of PM2.5 and PMCoarse. This utilization of gradient descent lays the groundwork for future research attempting to infer calibration parameters of LCS through inverse modeling. Upon determining how to properly calibrate and couple the PMS5003 and OPC-N3, in the fourth chapter, these insights are used to measure the annual spatiotemporal trends of PM2.5 and PMCoarse from an outdoor network of fifteen MOD-PM devices in portions of the City of St. Louis and nearby north St. Louis County. This pragmatic use of the MOD-PM requires the methodologies derived to calibrate the PMS5003 and OPC-N3 to be repurposed to address practical issues affecting MOD-PM measurement error such as seasonal variations and sensor degradation. In addition to this research on coupled low-cost PM sensors, the fifth chapter describes work to improve and demonstrate the Multichannel Organics In situ enviRonmental Analyzer (MOIRA) for mobile platform measurements of volatile organic compounds.

Committee Chair

Jay Turner

Committee Members

Brent Williams

Degree

Doctor of Philosophy (PhD)

Author's Department

Energy, Environmental & Chemical Engineering

Author's School

McKelvey School of Engineering

Document Type

Dissertation

Date of Award

12-19-2025

Language

English (en)

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