Performance characteristics of the low-cost plantower PMS optical sensor

被引:66
作者
He, Meilu [1 ]
Kuerbanjiang, Nueraili [1 ]
Dhaniyala, Suresh [1 ]
机构
[1] Clarkson Univ, Dept Mech & Aeronaut Engn, 8 Clarkson Ave, New York, NY 13699 USA
关键词
INVERSION; MOBILITY;
D O I
10.1080/02786826.2019.1696015
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Low-cost sensors have become very popular in recent years for monitoring air pollutants. Commonly, they are calibrated by correlating their signals with reference instrument measurements and using a machine learning model to account for the influence of air properties. As particle properties vary over location, such calibration models are only relevant to measurements made at the calibration location during a limited time period. For a more general operation of these sensors it is critical that their measurement performance is established using the calibration approaches commonly for research grade instruments. Without loss the generality, here we conducted an experimental study with size-classified, composition and concentration varied particles to determine the response function of a popular low-cost sensor, Plantower PMS5003. The sensor response in all the size channels is analyzed using Tikhonov regularization and quadratic programing method with the constraints of nonnegative and monotonic response with particle size. We show that the shape of the response function is closely related to the light scattering response, consistent with what might be expected for an optical sensor. The response function shows that signals in all size channels have a complex dependence on particle material and size distribution. Accurate determination of particle mass and number distributions from the sensor signals in different channels is, thus, not straightforward. The response function calculation is validated by comparing sensor measured and predicted signals using polydispersed particles. The obtained response functions provide critical insight into the operation of a popular low-cost sensor and guidance on interpretation of its results. Copyright (c) 2019 American Association for Aerosol Research
引用
收藏
页码:232 / 241
页数:10
相关论文
共 18 条
[1]   Long-term field comparison of multiple low-cost particulate matter sensors in an outdoor urban environment [J].
Bulot, Florentin M. J. ;
Johnston, Steven J. ;
Basford, Philip J. ;
Easton, Natasha H. C. ;
Apetroaie-Cristea, Mihaela ;
Foster, Gavin L. ;
Morris, Andrew K. R. ;
Cox, Simon J. ;
Loxham, Matthew .
SCIENTIFIC REPORTS, 2019, 9 (1)
[2]   Improved Inversion of Scanning Electrical Mobility Spectrometer Data Using a New Multiscale Expectation Maximization Algorithm [J].
Dubey, Praney ;
Dhaniyala, Suresh .
AEROSOL SCIENCE AND TECHNOLOGY, 2013, 47 (01) :69-80
[3]   TIKHONOV REGULARIZATION AND CONSTRAINED QUADRATIC PROGRAMMING FOR MAGNETIC COIL DESIGN PROBLEMS [J].
Garda, Bartlomiej ;
Galias, Zbigniew .
INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE, 2014, 24 (02) :249-257
[4]  
Hansen PC., 2008, Regularization Tools
[5]   Vertical and horizontal concentration distributions of ultrafine particles near a highway [J].
He, Meilu ;
Dhaniyala, Suresh .
ATMOSPHERIC ENVIRONMENT, 2012, 46 :225-236
[6]   The influence of humidity on the performance of a low-cost air particle mass sensor and the effect of atmospheric fog [J].
Jayaratne, Rohan ;
Liu, Xiaoting ;
Thai, Phong ;
Dunbabin, Matthew ;
Morawska, Lidia .
ATMOSPHERIC MEASUREMENT TECHNIQUES, 2018, 11 (08) :4883-4890
[7]   Ambient and laboratory evaluation of a low-cost particulate matter sensor [J].
Kelly, K. E. ;
Whitaker, J. ;
Petty, A. ;
Widmer, C. ;
Dybwad, A. ;
Sleeth, D. ;
Martin, R. ;
Butterfield, A. .
ENVIRONMENTAL POLLUTION, 2017, 221 :491-500
[8]  
Knutson E. O., 1975, Journal of Aerosol Science, V6, P443, DOI 10.1016/0021-8502(75)90060-9
[9]   The Parable of Google Flu: Traps in Big Data Analysis [J].
Lazer, David ;
Kennedy, Ryan ;
King, Gary ;
Vespignani, Alessandro .
SCIENCE, 2014, 343 (6176) :1203-1205
[10]  
MALINGS C, 2019, AEROSOL SCI TECH, P1