Effects of Road Traffic on the Accuracy and Bias of Low-Cost Particulate Matter Sensor Measurements in Houston, Texas

Temitope Oluwadairo, Lawrence Whitehead, Elaine Symanski, Cici Bauer, Arch Carson and Inkyu Han
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Temitope Oluwadairo: Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
Lawrence Whitehead: Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
Elaine Symanski: Center for Precision Environmental Health, Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
Cici Bauer: Department of Biostatistics, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
Arch Carson: Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
Inkyu Han: Department of Epidemiology and Biostatistics, Temple University College of Public Health, Philadelphia, PA 19122, USA

IJERPH, 2022, vol. 19, issue 3, 1-14

Abstract: Although PM 2.5 measurements of low-cost particulate matter sensors (LCPMS) generally show moderate and strong correlations with those from research-grade air monitors, the data quality of LCPMS has not been fully assessed in urban environments with different road traffic conditions. We examined the linear relationships between PM 2.5 measurements taken by an LCPMS (Dylos DC1700) and two research grade monitors, a personal environmental monitor (PEM) and the GRIMM 11R, in three different urban environments, and compared the accuracy (slope) and bias of these environments. PM 2.5 measurements were carried out at three locations in Houston, Texas (Clinton Drive largely with diesel trucks, US-59 mostly with gasoline vehicles, and a residential home with no major sources of traffic emissions nearby). The slopes of the regressions of the PEM on Dylos and Grimm measurements varied by location (e.g., PEM/Dylos slope at Clinton Drive = 0.98 ( R 2 = 0.77), at US-59 = 0.63 ( R 2 = 0.42), and at the residence = 0.29 ( R 2 = 0.31)). Although the regression slopes and coefficients differed across the three urban environments, the mean percent bias was not significantly different. Using the correct slope for LCPMS measurements is key for accurately estimating ambient PM 2.5 mass in urban environments.

Keywords: low-cost sensors; road traffic; particulate matter (PM); PM monitoring; PM sensor calibration (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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