Most evaluations of low-cost aerosol sensors have focused on their measurement bias compared to regulatory monitors. Few evaluations have applied fundamental principles of aerosol science to increase our understanding of how such sensors work and could be improved. We examined the Plantower PMS5003 sensor's internal geometry, laser properties, photodiode responses, microprocessor output, flow rates, and response to mono- and poly-disperse aerosols. We developed a physics-based model of particle light scattering within the sensor, which we used to predict counting and sizing efficiency for 0.30 to 10 μm particles. We found that the PMS5003 counts single particle scattering events, acting like an imperfect optical particle counter, rather than a nephelometer. As particle flow is not focused into the core of the laser beam, >99% of particles that flow through the PMS5003 miss the laser, and those that intercept the laser usually miss the focal point and are subsequently undersized, resulting in erroneous size distribution data. Our model predictions of PMS5003 response to varying particle diameters, aerosol compositions, and relative humidity were consistent with laboratory data. Computational fluid dynamics simulations of the PurpleAir monitor housing showed that for wind-speeds less than 3 m s^-1, fine and coarse particles were representatively aspired to the PMS5003 inlet. Our measurements and models explain why the PurpleAir overstates regulatory PM_2.5 in some locations but not others; why the PurpleAir PM₁₀ is unresponsive to windblown dust; and why it reports a similar particle size distribution for coarse particles as it does for smoke and ambient background aerosol.

Americans spend most of their time indoors at home, but comprehensive characterization of in-home air pollution is limited by the cost and size of reference-quality monitors. We assembled small “Home Health Boxes” (HHBs) to measure indoor PM_2.5, PM₁₀, CO₂, CO, NO₂, and O₃ concentrations using filter samplers and low-cost sensors. Nine HHBs were collocated with reference monitors in the kitchen of an occupied home in Fort Collins, Colorado, USA for 168 h while wildfire smoke impacted local air quality. When HHB data were interpreted using gas sensor manufacturers’ calibrations, HHBs and reference monitors (a) categorized the level of each gaseous pollutant similarly (as either low, elevated, or high relative to air quality standards) and (b) both indicated that gas cooking burners were the dominant source of CO and NO₂ pollution; however, HHB and reference O₃ data were not correlated. When HHB gas sensor data were interpreted using linear mixed calibration models derived via collocation with reference monitors, root-mean-square error decreased for CO₂ (from 408 to 58 ppm), CO (645 to 572 ppb), NO₂ (22 to 14 ppb), and O₃ (21 to 7 ppb); additionally, correlation between HHB and reference O₃ data improved (Pearson’s r increased from 0.02 to 0.75). Mean 168-h PM_2.5 and PM₁₀ concentrations derived from nine filter samples were 19.4 μg m⁻³ (6.1% relative standard deviation [RSD]) and 40.1 μg m⁻³ (7.6% RSD). The 168-h PM_2.5 concentration was overestimated by PMS5003 sensors (median sensor/filter ratio = 1.7) and underestimated slightly by SPS30 sensors (median sensor/filter ratio = 0.91).