Literature DB >> 28012666

Ambient and laboratory evaluation of a low-cost particulate matter sensor.

K E Kelly1, J Whitaker2, A Petty3, C Widmer3, A Dybwad4, D Sleeth5, R Martin6, A Butterfield3.   

Abstract

Low-cost, light-scattering-based particulate matter (PM) sensors are becoming more widely available and are being increasingly deployed in ambient and indoor environments because of their low cost and ability to provide high spatial and temporal resolution PM information. Researchers have begun to evaluate some of these sensors under laboratory and environmental conditions. In this study, a low-cost, particulate matter sensor (Plantower PMS 1003/3003) used by a community air-quality network is evaluated in a controlled wind-tunnel environment and in the ambient environment during several winter-time, cold-pool events that are associated with high ambient levels of PM. In the wind-tunnel, the PMS sensor performance is compared to two research-grade, light-scattering instruments, and in the ambient tests, the sensor performance is compared to two federal equivalent (one tapered element oscillating microbalance and one beta attenuation monitor) and gravimetric federal reference methods (FEMs/FRMs) as well as one research-grade instrument (GRIMM). The PMS sensor response correlates well with research-grade instruments in the wind-tunnel tests, and its response is linear over the concentration range tested (200-850 μg/m3). In the ambient tests, this PM sensor correlates better with gravimetric methods than previous studies with correlation coefficients of 0.88. However additional measurements under a variety of ambient conditions are needed. Although the PMS sensor correlated as well as the research-grade instrument to the FRM/FEMs in ambient conditions, its response varies with particle properties to a much greater degree than the research-grade instrument. In addition, the PMS sensors overestimate ambient PM concentrations and begin to exhibit a non-linear response when PM2.5 concentrations exceed 40 μg/m3. These results have important implications for communicating results from low-cost sensor networks, and they highlight the importance of using an appropriate correction factor for the target environmental conditions if the user wants to compare the results to FEM/FRMs.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Air quality; Cold-air pool; Low-cost sensors; Particulate matter

Mesh:

Substances:

Year:  2016        PMID: 28012666     DOI: 10.1016/j.envpol.2016.12.039

Source DB:  PubMed          Journal:  Environ Pollut        ISSN: 0269-7491            Impact factor:   8.071


  32 in total

1.  Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea.

Authors:  Chris C Lim; Ho Kim; M J Ruzmyn Vilcassim; George D Thurston; Terry Gordon; Lung-Chi Chen; Kiyoung Lee; Michael Heimbinder; Sun-Young Kim
Journal:  Environ Int       Date:  2019-07-27       Impact factor: 9.621

2.  One Year Evaluation of Three Low-Cost PM2.5 Monitors.

Authors:  Misti Levy Zamora; Jessica Rice; Kirsten Koehler
Journal:  Atmos Environ (1994)       Date:  2020-05-31       Impact factor: 4.798

3.  Field Test of Several Low-Cost Particulate Matter Sensors in High and Low Concentration Urban Environments.

Authors:  Karoline K Johnson; Michael H Bergin; Armistead G Russell; Gayle S W Hagler
Journal:  Aerosol Air Qual Res       Date:  2018       Impact factor: 3.063

4.  Use of low-cost PM monitors and a multi-wavelength aethalometer to characterize PM2.5 in the Yakama Nation Reservation.

Authors:  Orly Stampfer; Elena Austin; Terry Ganuelas; Tremain Fiander; Edmund Seto; Catherine Karr
Journal:  Atmos Environ (1994)       Date:  2020-01-20       Impact factor: 4.798

5.  Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA.

Authors:  Jianzhao Bi; Jennifer Stowell; Edmund Y W Seto; Paul B English; Mohammad Z Al-Hamdan; Patrick L Kinney; Frank R Freedman; Yang Liu
Journal:  Environ Res       Date:  2019-10-10       Impact factor: 6.498

6.  Variation in gravimetric correction factors for nephelometer-derived estimates of personal exposure to PM2.5.

Authors:  Jessica Tryner; Nicholas Good; Ander Wilson; Maggie L Clark; Jennifer L Peel; John Volckens
Journal:  Environ Pollut       Date:  2019-04-05       Impact factor: 8.071

7.  Evaluating the Performance of Using Low-Cost Sensors to Calibrate for Cross-Sensitivities in a Multipollutant Network.

Authors:  Misti Levy Zamora; Colby Buehler; Hao Lei; Abhirup Datta; Fulizi Xiong; Drew R Gentner; Kirsten Koehler
Journal:  ACS ES T Eng       Date:  2022-04-11

8.  Calibration of low-cost particulate matter sensors: Model development for a multi-city epidemiological study.

Authors:  Marina Zusman; Cooper S Schumacher; Amanda J Gassett; Elizabeth W Spalt; Elena Austin; Timothy V Larson; Graeme Carvlin; Edmund Seto; Joel D Kaufman; Lianne Sheppard
Journal:  Environ Int       Date:  2019-11-26       Impact factor: 9.621

9.  Ultra-Light Airborne Measurement System for Investigation of Urban Boundary Layer Dynamics.

Authors:  Piotr Sekula; Miroslaw Zimnoch; Jakub Bartyzel; Anita Bokwa; Michal Kud; Jaroslaw Necki
Journal:  Sensors (Basel)       Date:  2021-04-21       Impact factor: 3.576

10.  Publicly available low-cost sensor measurements for PM2.5 exposure modeling: Guidance for monitor deployment and data selection.

Authors:  Jianzhao Bi; Nancy Carmona; Magali N Blanco; Amanda J Gassett; Edmund Seto; Adam A Szpiro; Timothy V Larson; Paul D Sampson; Joel D Kaufman; Lianne Sheppard
Journal:  Environ Int       Date:  2021-09-30       Impact factor: 9.621

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