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. 2020 Dec 24;21(1):63.
doi: 10.3390/s21010063.

Validating and Comparing Highly Resolved Commercial "Off the Shelf" PM Monitoring Sensors with Satellite Based Hybrid Models, for Improved Environmental Exposure Assessment

Dan Lesser  1 , Itzhak Katra  1 , Michael Dorman  1 , Homero Harari  2 , Itai Kloog  1
Affiliations

Validating and Comparing Highly Resolved Commercial "Off the Shelf" PM Monitoring Sensors with Satellite Based Hybrid Models, for Improved Environmental Exposure Assessment

Dan Lesser et al. Sensors (Basel). .

Abstract

Particulate matter is a common health hazard, and under certain conditions, an ecological threat. While many studies were conducted in regard to air pollution and potential effects, this paper serves as a pilot scale investigation into the spatial and temporal variability of particulate matter (PM) pollution in arid urban environments in general, and Beer-Sheva, Israel as a case study. We explore the use of commercially off the shelf (COTS) sensors, which provide an economical solution for spatio-temporal measurements. We started with a comparison process against an A-grade meteorological station, where it was shown that under specific climatic conditions, a number of COTS sensors were able to produce robust agreement (mean R2=0.93, average SD=17.5). The second stage examined the COTS sensors that were proven accurate in a mobile measurement campaign. Finally, data collected was compared to a validated satellite prediction model. We present how these tests and COTS sensor-kits could then be used to further explain the continuity and dispersion of particulate matter in similar areas.

Keywords: Brompton bicycle; bike; dust sensors; micro-controllers; mobile measurements; particulate matter.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
A map of the study site and the 28 mobile measurement points, along with the location of the monitoring station.
Figure 2
Figure 2
The COTS sensors that were used for the measurements: (a) Sharp-GP2Y1030AU0F; (b) OPC-N3; (c) Dylos DC1700; (d) Honeywell HPMA115S0-XXX.
Figure 3
Figure 3
Brompton bicycles used for mobile measurements: (a) equipped with the COTS sensors and GPS and (b) in the field campaign.
Figure 4
Figure 4
Lab measurement correlation matrix between four COTS sensors, for two particle size classes (PM.25 and PM10) and six measurements lag intervals (10, 15, 30, 45, 60 s and “original”). (H = Honeywell, D = Dylos, O = OPC, S = Sharp). Astrix denotes the signifiance level: * < 0.05, *** < 0.001, ** < 0.001.
Figure 5
Figure 5
COTS Sensor readings over time under different exposure levels to wind.
Figure 6
Figure 6
Difference between COTS sensor readings and tapered element oscillating microbalances (TEOM) station measurements, as function of TEOM measurements, per COTS sensor and particle type (D = Dylos, H = Honeywell, O = OPC-N3, S = Sharp). Dashed times are +1 SD around the y = 0 line, to emphasize the variability and bias in each case (T = Top roof, G = Ground level, BS = Bottom roof-shaded, BE = Bottom roof-exposed to sun). The shaded area represents a convex hull.
Figure 7
Figure 7
Mobile measurements campaign and spatial layout.
Figure 8
Figure 8
Average particulate matter (PM) per 1 km2 pixel in September of 2005–2015 in the city of Beer Sheva based on satellite model predictions (Top: PM2.5, bottom: PM10).
Figure 9
Figure 9
Mobile measurements comparison to satellite data, categorized by number (date of measurement).
Figure 10
Figure 10
Statistical correlation between ground measurements and satellite-based PM data from September of 2005–2015.
Figure 11
Figure 11
Scatter plot linearity test between the Dylos, OPC-N3 sensors and the satellite model, along with environmental parameters (PM_D = Dylos readings, PM_O = OPC-N3 readings, PM_M = TEOM readings, RH = Relative humidity).
See this image and copyright information in PMC

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