Intercomparison of PurpleAir Sensor Performance over Three Years Indoors and Outdoors at a Home: Bias, Precision, and Limit of Detection Using an Improved Algorithm for Calculating PM2.5

Abstract

Low-cost particle sensors are now used worldwide to monitor outdoor air quality. However, they have only been in wide use for a few years. Are they reliable? Does their performance deteriorate over time? Are the algorithms for calculating PM2.5 concentrations provided by the sensor manufacturers accurate? We investigate these questions using continuous measurements of four PurpleAir monitors (8 sensors) under normal conditions inside and outside a home for 1.5–3 years. A recently developed algorithm (called ALT-CF3) is compared to the two existing algorithms (CF1 and CF_ATM) provided by the Plantower manufacturer of the PMS 5003 sensors used in PurpleAir PA-II monitors. Results. The Plantower CF1 algorithm lost 25–50% of all indoor data due in part to the practice of assigning zero to all concentrations below a threshold. None of these data were lost using the ALT-CF3 algorithm. Approximately 92% of all data showed precision better than 20% using the ALT-CF3 algorithm, but only approximately 45–75% of data achieved that level using the Plantower CF1 algorithm. The limits of detection (LODs) using the ALT-CF3 algorithm were mostly under 1 µg/m3, compared to approximately 3–10 µg/m3 using the Plantower CF1 algorithm. The percentage of observations exceeding the LOD was 53–92% for the ALT-CF3 algorithm, but only 16–44% for the Plantower CF1 algorithm. At the low indoor PM2.5 concentrations found in many homes, the Plantower algorithms appear poorly suited.

Indoor contribution to PM 2 .5 exposure using all PurpleAir sites in Washington, Oregon, and California

“…This would be quite difficult to do in a field study such as ours; a chamber study with complete control over tested concentrations may be required. One study compared four PA‐II monitors in 6 locations (indoors or outdoors) in a single home for evidence of changing precision over time during an 18‐month or 3 years period 22 . Three monitor‐locations showed increased precision and three showed decreased precision over the times tested.…”

“…One study compared four PA-II monitors in 6 locations (indoors or outdoors) in a single home for evidence of changing precision over time during an 18-month or 3 years period. 22 Three monitor-locations showed increased precision and three showed decreased precision over the times tested. However, we are not aware of large-scale studies of a sufficiently large sample of PA-I or PA-II monitors to allow robust estimation of the effect of aging.…”

Long-Term Indoor-Outdoor PM2.5 Measurements Using PurpleAir Sensors: An Improved Method of Calculating Indoor-Generated and Outdoor-Infiltrated Contributions to Potential Indoor Exposure

“…The ALT-CF3 method has been found to provide less bias, better precision, and a lower limit of detection than either the CF1 or CF_ATM algorithms [ 47 , 48 ]. This method is presently available on the PurpleAir mapping page at the PurpleAir Website ( , accessed on 1 January 2023) as an alternative to the proprietary algorithms offered by Plantower.…”

Long-Term Indoor-Outdoor PM2.5 Measurements Using PurpleAir Sensors: An Improved Method of Calculating Indoor-Generated and Outdoor-Infiltrated Contributions to Potential Indoor Exposure

“…The total PM2.5 mass is then determined in two steps: first a density is assigned (in this case the density of water) and then a calibration factor is determined by comparison to nearby regulatory monitors employing Federal Reference or Federal Equivalent Methods (FRM/FEM). The original calibration factor for 33 PurpleAir PA-II monitors within 0.5 km of 27 regulatory monitors was 3.0, leading to naming this alternative algorithm ALT-CF3 (48). The equation giving the ALT-CF3 estimate of PM2.5 is PM2.5 = 3(0.00030418 N1 + 0.0018512 N2 + 0.2069706 N3) (5) where N1, N2, and N3 are the number of particles per deciliter in the three smallest size categories.…”

“…The ALT-CF3 method has been found to provide less bias, better precision, and a lower limit of detection than either the CF1 or CF_ATM algorithms [47][48]. This method is presently available on the PurpleAir mapping page at the PurpleAir Website (https://www2.purpleair.com/) as an alternative to the proprietary algorithms offered by Plantower.…”