Sensor selection to improve estimates of particulate matter concentration from a low-cost network

ECU Author/Contributor (non-ECU co-authors, if there are any, appear on document)

Alyson Gray (Creator)

Kirsten Koehler (Creator)

Thomas Peters (Creator)

Sinan Sousan (Creator)

Larissa Stebounova (Creator)

Geb Thomas (Creator)

Christopher Zuidema (Creator)

Institution

East Carolina University (ECU )

Web Site: http://www.ecu.edu/lib/

Abstract: Deployment of low-cost sensors in the field is increasingly popular. However , each sensor requires on-site calibration to increase the accuracy of the measurements. We established a laboratory method , the Average Slope Method , to select sensors with similar response so that a single , on-site calibration for one sensor can be used for all other sensors. The laboratory method was performed with aerosolized salt. Based on linear regression , we calculated slopes for 100 particulate matter (PM) sensors , and 50% of the PM sensors fell within ±14% of the average slope. We then compared our Average Slope Method with an Individual Slope Method and concluded that our first method balanced convenience and precision for our application. Laboratory selection was tested in the field , where we deployed 40 PM sensors inside a heavy-manufacturing site at spatially optimal locations and performed a field calibration to calculate a slope for three PM sensors with a reference instrument at one location. The average slope was applied to all PM sensors for mass concentration calculations. The calculated percent differences in the field were similar to the laboratory results. Therefore , we established a method that reduces the time and cost associated with calibration of low-cost sensors in the field.

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Sousan , S. , Gray , A. , Zuidema , C. , Stebounova , L. , Thomas , G. , Koehler , K. , & Peters , T. (2018). Sensor selection to improve estimates of particulate matter concentration from a low-cost network. Sensors (Basel , Switzerland) , 18(9) doi:10.3390/s18093008

Language: English

Date: 2018

Keywords

PM, aerosol exposure, low-cost sensors, low-cost wireless network, occupational monitoring, sensor calibration, sensor selection

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