Evaluation of a new low-cost particle sensor as an internet-of-things device for outdoor air quality monitoring

被引:6
作者
Roberts, F. A. [1 ]
Van Valkinburgh, Kathryn [1 ]
Green, Austin [2 ]
Post, Christopher J. [2 ]
Mikhailova, Elena A. [2 ]
Commodore, Sarah [3 ]
Pearce, John L. [4 ]
Metcalf, Andrew R. [1 ]
机构
[1] Clemson Univ, Dept Environm Engn & Earth Sci, 342 Comp Court Anderson, Clemson, SC 29625 USA
[2] Clemson Univ, Dept Forestry & Environm Conservat, Clemson, SC 29625 USA
[3] Indiana Univ, Dept Environm & Occupat Hlth, Bloomington, IN USA
[4] Med Univ South Carolina, Dept Publ Hlth Sci, Charleston, SC 29425 USA
关键词
PARTICULATE MATTER; LABORATORY EVALUATION; NETWORK; URBAN; PM2.5;
D O I
10.1080/10962247.2022.2093293
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Many low-cost particle sensors are available for routine air quality monitoring of PM2.5, but there are concerns about the accuracy and precision of the reported data, particularly in humid conditions. The objectives of this study are to evaluate the Sensirion SPS30 particulate matter (PM) sensor against regulatory methods for measurement of real-time particulate matter concentrations and to evaluate the effectiveness of the Intelligent Air(TM) sensor pack for remote deployment and monitoring. To achieve this, we co-located the Intelligent Air(TM) sensor pack, developed at Clemson University and built around the Sensirion SPS30, to collect data from July 29, 2019, to December 12, 2019, at a regulatory site in Columbia, South Carolina. When compared to the Federal Equivalent Methods, the SPS30 showed an average bias adjusted R-2 = 0.75, mean bias error of -1.59, and a root mean square error of 2.10 for 24-hour average trimmed measurements over 93 days, and R-2 = 0.57, mean bias error of -1.61, and a root mean square error of 3.029, for 1-hr average trimmed measurements over 2300 hours when the central 99% of data was retained with a data completeness of 75% or greater. The Intelligent Air(TM) sensor pack is designed to promote long-term deployment and includes a solar panel and battery backup, protection from the elements, and the ability to upload data via a cellular network. Overall, we conclude that the SPS30 PM sensor and the Intelligent Air(TM) sensor pack have the potential for greatly increasing the spatial density of particulate matter measurements, but more work is needed to understand and calibrate sensor measurements. Implications: This work adds to the growing body of research that indicates that low-cost sensors of particulate matter (PM) for air quality monitoring has a promising future, and yet much work is left to be done. This work shows that the level of data processing and filtering effects how the low-cost sensors compare to existing federal reference and equivalence methods: more data filtering at low PM levels worsens the data comparison, while longer time averaging improves the measurement comparisons. Improvements must be made to how we handle, calibrate, and correct PM data from low-cost sensors before the data can be reliably used for air quality monitoring and attainment.
引用
收藏
页码:1219 / 1230
页数:12
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共 32 条
  • [1] Design and Evaluation of a Reliable Low-Cost Atmospheric Pollution Station in Urban Environment
    Astudillo, Galo D.
    Garza-Castanon, Luis E.
    Minchala Avila, Luis I.
    [J]. IEEE ACCESS, 2020, 8 : 51129 - 51144
  • [2] Preliminary research for low-cost particulate matter sensor network
    Bathory, Csongor
    Kiss, Marton L.
    Trohak, Attila
    Dobo, Zsolt
    Palotas, Arpad Bence
    [J]. 11TH CONFERENCE ON INTERDISCIPLINARY PROBLEMS IN ENVIRONMENTAL PROTECTION AND ENGINEERING (EKO-DOK 2019), 2019, 100
  • [3] Low-Cost Air Quality Sensors: One-Year Field Comparative Measurement of Different Gas Sensors and Particle Counters with Reference Monitors at Tusimice Observatory
    Bauerova, Petra
    Sindelarova, Adriana
    Rychlik, Stepan
    Novak, Zbynek
    Keder, Josef
    [J]. ATMOSPHERE, 2020, 11 (05)
  • [4] Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of PM2.5 and PM10 Sensors
    Cavaliere, Alice
    Carotenuto, Federico
    Di Gennaro, Filippo
    Gioli, Beniamino
    Gualtieri, Giovanni
    Martelli, Francesca
    Matese, Alessandro
    Toscano, Piero
    Vagnoli, Carolina
    Zaldei, Alessandro
    [J]. SENSORS, 2018, 18 (09)
  • [5] Reliability of Low-Cost, Sensor-Based Fine Dust Measurement Devices for Monitoring Atmospheric Particulate Matter Concentrations
    Cho, Eun-Min
    Jeon, Hyung Jin
    Yoon, Dan Ki
    Park, Si Hyun
    Hong, Hyung Jin
    Choi, Kil Yong
    Cho, Heun Woo
    Cheon, Hyo Chang
    Lee, Cheol Min
    [J]. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2019, 16 (08)
  • [6] Observed data quality concerns involving low-cost air sensors
    Clements, Andrea L.
    Reece, Stephen
    Conner, Teri
    Williams, Ron
    [J]. ATMOSPHERIC ENVIRONMENT-X, 2019, 3
  • [7] A Statistical Calibration Framework for Improving Non-Reference Method Particulate Matter Reporting: A Focus on Community Air Monitoring Settings
    Commodore, Sarah
    Metcalf, Andrew
    Post, Christopher
    Watts, Kevin
    Reynolds, Scott
    Pearce, John
    [J]. ATMOSPHERE, 2020, 11 (08)
  • [8] Performance assessment of low-cost environmental monitors and single sensors under variable indoor air quality and thermal conditions
    Demanega, Ingrid
    Mujan, Igor
    Singer, Brett C.
    Andelkovic, Aleksandar S.
    Babich, Francesco
    Licina, Dusan
    [J]. BUILDING AND ENVIRONMENT, 2021, 187
  • [9] Performance evaluation of twelve low-cost PM2.5 sensors at an ambient air monitoring site
    Feenstra, Brandon
    Papapostolou, Vasileios
    Hasheminassab, Sina
    Zhang, Hang
    Boghossian, Berj Der
    Cocker, David
    Polidori, Andrea
    [J]. ATMOSPHERIC ENVIRONMENT, 2019, 216
  • [10] A distributed network of low-cost continuous reading sensors to measure spatiotemporal variations of PM2.5 in Xi'an, China
    Gao, Meiling
    Cao, Junji
    Seto, Edmund
    [J]. ENVIRONMENTAL POLLUTION, 2015, 199 : 56 - 65