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. 2021 Oct 29;21(21):7206.
doi: 10.3390/s21217206.

Performance and Deployment of Low-Cost Particle Sensor Units to Monitor Biomass Burning Events and Their Application in an Educational Initiative

Fabienne Reisen  1 , Jacinta Cooper  1 , Jennifer C Powell  1 , Christopher Roulston  1 , Amanda J Wheeler  2   3
Affiliations

Performance and Deployment of Low-Cost Particle Sensor Units to Monitor Biomass Burning Events and Their Application in an Educational Initiative

Fabienne Reisen et al. Sensors (Basel). .

Abstract

Biomass burning smoke is often a significant source of airborne fine particles in regional areas where air quality monitoring is scarce. Emerging sensor technology provides opportunities to monitor air quality on a much larger geographical scale with much finer spatial resolution. It can also engage communities in the conversation around local pollution sources. The SMoke Observation Gadget (SMOG), a unit with a Plantower dust sensor PMS3003, was designed as part of a school-based Science, Technology, Engineering and Mathematics (STEM) project looking at smoke impacts in regional areas of Victoria, Australia. A smoke-specific calibration curve between the SMOG units and a standard regulatory instrument was developed using an hourly data set collected during a peat fire. The calibration curve was applied to the SMOG units during all field-based validation measurements at several locations and during different seasons. The results showed strong associations between individual SMOG units for PM2.5 concentrations (r2 = 0.93-0.99) and good accuracy (mean absolute error (MAE) < 2 μg m-3). Correlations of the SMOG units to reference instruments also demonstrated strong associations (r2 = 0.87-95) and good accuracy (MAE of 2.5-3.0 μg m-3). The PM2.5 concentrations tracked by the SMOG units had a similar response time as those measured by collocated reference instruments. Overall, the study has shown that the SMOG units provide relevant information about ambient PM2.5 concentrations in an airshed impacted predominantly by biomass burning, provided that an adequate adjustment factor is applied.

Keywords: STEM; air quality; particulate matter; sensors; smoke; validation.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Assembled Smoke Observation Gadget (SMOG).
Figure 2
Figure 2
Fitted calibration curves for the SMOG units against the TEOM.
Figure 3
Figure 3
Scatterplot of hourly PM2.5 concentrations measured with the SMOG units compared to (a) the E-sampler and (b) Fidas_CF (both of which have been corrected against gravimetric mass measurements). Blue lines show linear least-squares fit; red line represents 1:1 line.
Figure 4
Figure 4
Bland-Altman plots of hourly PM2.5 concentrations measured with the SMOG units compared to (a) the E-sampler and (b) Fidas, both corrected against the gravimetric mass measurements.
Figure 5
Figure 5
Scatterplot of hourly PM2.5 concentrations measured with the SMOG units compared to the E-sampler and Fidas_CF (both of which have been corrected against gravimetric mass measurements) by season and location. Blue lines show linear least-squares fit; red line represents 1:1 line.
Figure 6
Figure 6
Hourly time series of ambient PM2.5 concentrations at the four measurement locations in different seasons: (a) Aspendale autumn, (b) Aspendale winter, (c) Rutherglen, (d) Alexandra summer, (e) Alexandra autumn-winter.
Figure 7
Figure 7
Time series plots of hourly PM2.5 concentrations measured at monitoring sites in NE Victoria in 2019. Shaded polygons represent the smoke plume events.
See this image and copyright information in PMC

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