Measuring Spatial and Temporal PM_2.5 Variations in Sacramento, California, Communities Using a Network of Low-Cost Sensors

• Main Authors: Anondo Mukherjee, Steven G. Brown, Michael C. McCarthy, Nathan R. Pavlovic, Levi G. Stanton, Janice Lam Snyder, Stephen D’Andrea, Hilary R. Hafner
• Format: Article
• Language: English
• Published: MDPI AG 2019-10-01
• Series: Sensors
• Subjects: low-cost sensor; particulate matter; air quality; calibration strategies; network design
• Online Access: https://www.mdpi.com/1424-8220/19/21/4701

Low-cost sensors can provide insight on the spatio-temporal variability of air pollution, provided that sufficient efforts are made to ensure data quality. Here, 19 AirBeam particulate matter (PM) sensors were deployed from December 2016 to January 2017 to determine the spatial variability of PM_2.5 in Sacramento, California. Prior to, and after, the study, the 19 sensors were deployed and collocated at a regulatory air monitoring site. The sensors demonstrated a high degree of precision during all collocated measurement periods (Pearson R² = 0.98 &#8722; 0.99 across all sensors), with little drift. A sensor-specific correction factor was developed such that each sensor reported a comparable value. Sensors had a moderate degree of correlation with regulatory monitors during the study (R² = 0.60 &#8722; 0.68 at two sites). In a multi-linear regression model, the deviation between sensor and reference measurements of PM_2.5 had the highest correlation with dew point and relative humidity. Sensor measurements were used to estimate the PM_2.5 spatial variability, finding an average pairwise coefficient of divergence of 0.22 and a range of 0.14 to 0.33, indicating mostly homogeneous distributions. No significant difference in the average sensor PM concentrations between environmental justice (EJ) and non-EJ communities (<i>p</i> value = 0.24) was observed.