Dataset associated with "Effects of aerosol type and simulated aging on performance of low-cost PM sensors"
Files
01_sensor_mass_concentrations.csv (1.35 MB) Time-averaged particulate matter mass concentrations reported by low-cost sensors 02_sensor_number_concentrations.csv (999.19 KB) Time-averaged particle number concentrations reported by low-cost sensors 03_SMPS.csv (1.27 MB) Time-averaged particle size distribution data measured using an Scanning Mobility Particle Sizer 04_APS.csv (543.71 KB) Time-averaged particle size distribution data measured using an Aerodynamic Particle Sizer 05_APS_aging.csv (305.52 KB) Real-time particle size distribution data measured using an Aerodynamic Particle Sizer during a simulated aging experiment
Date
2020
Authors
Tryner, Jessica
Mehaffy, John
Miller-Lionberg, Daniel
Volckens, John
Journal Title
Journal ISSN
Volume Title
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% to 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-hour mean total PM = 33000 μg m-3; 18-hour 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 R2 ≥ 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 either, 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 hours, 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.
Description
These data were collected during a study on the performance of low-cost particulate matter (PM) sensors. All data were collected in an indoor laboratory at Colorado State University in Fort Collins, Colorado, USA between 2019-07-02 and 2019-10-06. The files associated with this dataset include: (1) time-averaged PM mass concentrations reported by the low-cost sensors during each steady-state test point included in the study, (2) time-averaged particle number concentrations reported by the low-cost sensors during each steady-state test point included in the study, (3) time-averaged particle size distribution data measured using an Scanning Mobility Particle Sizer (SMPS) during each steady-state test point included in the study, (4) time-averaged particle size distribution data measured using an Aerodynamic Particle Sizer (APS) Spectrometer during each steady-state test point included in the study, (5) real-time particle size distribution data measured using an APS during an experiment in which the low-cost sensors were exposed to very high Arizona road dust concentrations for 18 hours, (6) PM2.5 concentrations recorded at one-minute intervals by a Tapered Element Oscillating Microbalance (TEOM) during all experiments conducted during the study, (7) PM concentrations recorded at one-minute intervals by a DustTrak during an experiment in which the low-cost sensors were exposed to very high Arizona road dust concentrations for 18 hours, (8) data associated with all gravimetric filter samples of PM collected during the study, (9) real-time data recorded by the low-cost PM sensors during an experiment in which the sensors were exposed to very high Arizona road dust concentrations for 18 hours, (10) all of the raw data recorded by the low-cost PM sensors during the study, and (11) all of the raw data recorded by a DustTrak DRX 8533 during the study.
Department of Mechanical Engineering
Department of Mechanical Engineering
Rights Access
Subject
Plantower
PMS5003
SPS30
aerosol light scattering
photometer
optical particle counter
nephelometer
Citation
Associated Publications
Tryner, J., Mehaffy, J., Miller-Lionberg, D., & Volckens, J. (2020). Effects of aerosol type and simulated aging on performance of low-cost PM sensors. Journal of Aerosol Science, 150, 105654. https://doi.org/10.1016/j.jaerosci.2020.105654