Sensor selection to improve estimates of particulate matter concentration from a low-cost network
Sousan, Sinan; Gray, Alyson; Zuidema, Christopher; Stebounova, Larissa; Thomas, Geb; Koehler, Kirsten; Peters, Thomas
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.
Sousan, Sinan, & Gray, Alyson, & Zuidema, Christopher, & Stebounova, Larissa, & Thomas, Geb, & Koehler, Kirsten, & Peters, Thomas. (September 2018). Sensor selection to improve estimates of particulate matter concentration from a low-cost network. , (), - cost network. Sensors (Basel, Switzerland), 18(9) doi:10.3390/s18093008. Retrieved from http://hdl.handle.net/10342/7358
Sousan, Sinan, and Gray, Alyson, and Zuidema, Christopher, and Stebounova, Larissa, and Thomas, Geb, and Koehler, Kirsten, and Peters, Thomas. "Sensor selection to improve estimates of particulate matter concentration from a low-cost network". . . (), September 2018. September 21, 2023. http://hdl.handle.net/10342/7358.
Sousan, Sinan and Gray, Alyson and Zuidema, Christopher and Stebounova, Larissa and Thomas, Geb and Koehler, Kirsten and Peters, Thomas, "Sensor selection to improve estimates of particulate matter concentration from a low-cost network," , no. (September 2018), http://hdl.handle.net/10342/7358 (accessed September 21, 2023).
Sousan, Sinan, Gray, Alyson, Zuidema, Christopher, Stebounova, Larissa, Thomas, Geb, Koehler, Kirsten, Peters, Thomas. Sensor selection to improve estimates of particulate matter concentration from a low-cost network. . September 2018; (): . http://hdl.handle.net/10342/7358. Accessed September 21, 2023.