Effects of aerosol type and simulated aging on performance of low-cost PM sensors. (December 2020)

• Record Type: Journal Article
• Authors: Tryner, Jessica; Mehaffy, John; Miller-Lionberg, Daniel; Volckens, John
• Is Part Of: Journal of aerosol science. Volume 150(2020)
• Publication Date: 2020-12
• Subjects: Plantower -- PMS5003 -- SPS30 -- Aerosol light scattering -- Photometer -- Nephelometer -- Optical particle counter; Aerosols -- Periodicals; Aerosols -- Periodicals; Aérosols -- Périodiques; 541.34515
• DOI: 10.1016/j.jaerosci.2020.105654
• Languages: English
• ISSNs: 0021-8502

Abstract

Abstract: Studies that characterize the performance of low-cost particulate matter (PM) sensors are needed to help practitioners understand the accuracy and precision of the mass and number concentrations reported by different models. We evaluated Plantower PMS5003, Sensirion SPS30, and Amphenol SM-UART-04L PM sensors in the laboratory by exposing them to: (1) four different polydisperse aerosols (ammonium sulfate, Arizona road dust, NIST Urban PM, and wood smoke) at concentrations ranging from 10 to 1000 μg m −3, (2) hygroscopic and hydrophobic aerosols (ammonium sulfate and oil) in an environment with varying relative humidity (15%–90%), (3) polystyrene latex spheres (PSL) ranging from 0.1 to 2.0 μm in diameter, and (4) extremely high concentrations of Arizona road dust (18-h mean total PM = 33, 000 μg m −3 ; 18-h mean PM2.5 = 7300 μg m −3 ). Linear models relating PMS5003- and SPS30-reported PM2.5 concentrations to TEOM-reported ammonium sulfate concentrations up to 1025 μg m −3, nebulized Arizona road dust concentrations up to 540 μg m −3, and NIST Urban PM concentrations up to 330 μg m −3 had R 2 ≥ 0.97; however, an F-test identified a significant lack of fit between the model and the data for each sensor/aerosol combination. Ratios of filter-derived to PMS5003-reported PM2.5 concentrations were 1.4, 1.7, 1.0, 0.4, and 4.3 for ammonium sulfate, nebulized Arizona road dust, NIST Urban PM, wood smoke, and oil mist, respectively. For SPS30 sensors, these ratios were 1.6, 2.1, 2.1, 0.6, and 2.2, respectively. Collocated PMS5003 sensors were less precise than collocated SPS30 sensors when measuring ammonium sulfate, nebulized Arizona road dust, NIST Urban PM, oil mist, or PSL. Our results indicated that particle count data reported by the PMS5003 were not reliable. The number size distribution reported by the PMS5003 (a) did not agree with APS data and (b) remained roughly constant whether the sensors were exposed to 0.1 μm PSL, 0.27 μm PSL, 0.72 μm PSL, 2.0 μm PSL, or any of the other laboratory-generated aerosols. The size distribution reported by the SPS30 did not always agree with APS data, but did shift towards larger particle sizes when the sensors were exposed to 0.72 PSL, 2.0 μm PSL, oil mist, or Arizona road dust from a fluidized bed generator. The proportions of PM mass assigned as PM1, PM2.5, and PM10 by all three sensor models shifted as the PSL size increased. After the sensors were exposed to high concentrations of Arizona road dust for 18 h, PM2.5 concentrations reported by SPS30 sensors remained consistent, whereas 3/8 PMS5003 sensors and 2/7 SM-UART-04L sensors began reporting erroneously high values. Graphical abstract: Image 1 Highlights: PMS5003 was less precise than SPS30. PMS5003 reported similar size distributions for all polydisperse aerosols and PSLs. Proportions of mass assigned to PM10-2.5, PM2.5-1 and PM1 changed with PSL size. SPS30 remained precise after 18-hr exposure to 7400 μg m −3 Arizona road dust PM2.5 PMS5003 and SM-UART-04L reported erroneous high values after accelerated aging.