Long-term Indoor-outdoor PM2.5 Measurements at Three California Sites
LANCE WALLACE, Wayne Ott,
Stanford University Abstract Number: 468
Working Group: Indoor Aerosols
AbstractIndoor and outdoor PM2.5 measurements using low-cost PurpleAir monitors were made over extensive periods at three California homes. One objective was to compare a recently-developed algorithm for calculating PM2.5 to the proprietary algorithm used by the Plantower company that manufactures the sensors employed in the PurpleAir monitors. A second objective was to calculate and compare PM2.5 produced by indoor activities with PM2.5 due to particles of outdoor origin. The new algorithm (called ALT-CF3 and now available on the PurpleAir API) had better precision, lower limit of detection (LOD), and many more values above the LOD than the Plantower proprietary algorithms (CF1 and CF_ATM). At the low indoor PM2.5 levels found in many homes, this could result in the loss of more than half of all the indoor air data collected. The second objective of calculating PM2.5 concentrations due to indoor activities was approached by using the Random Component Superposition (RCS) model developed by Ott et al (2000). This regression model assumes a constant infiltration factor. If the assumption is correct, a line through the origin with the same slope as the regression defines a “forbidden zone” below the line where few observations should be found. This is a self-correcting feature, because if many observations lie in the forbidden zone, the assumption of a constant infiltration factor is violated. Then a search for a subset of values with a constant infiltration factor can sometimes identify such a subset and calculate the indoor-generated PM2.5. Our results for three homes identified indoor-generated PM2.5 meeting the forbidden-zone requirements for at least three seasons in all cases.