Design and Testing of a Low-Cost Sensor and Sampling Platform for Indoor Air Quality

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).