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. 2019 Oct 29;19(21):4701.
doi: 10.3390/s19214701.

Measuring Spatial and Temporal PM2.5 Variations in Sacramento, California, Communities Using a Network of Low-Cost Sensors

Anondo Mukherjee  1   2 , Steven G Brown  3 , Michael C McCarthy  4 , Nathan R Pavlovic  5 , Levi G Stanton  6 , Janice Lam Snyder  7 , Stephen D'Andrea  8 , Hilary R Hafner  9
Affiliations

Measuring Spatial and Temporal PM2.5 Variations in Sacramento, California, Communities Using a Network of Low-Cost Sensors

Anondo Mukherjee et al. Sensors (Basel). .

Abstract

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 PM2.5 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 - 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 - 0.68 at two sites). In a multi-linear regression model, the deviation between sensor and reference measurements of PM2.5 had the highest correlation with dew point and relative humidity. Sensor measurements were used to estimate the PM2.5 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 (p value = 0.24) was observed.

Keywords: air quality; calibration strategies; low-cost sensor; network design; particulate matter.

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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
Map of Sacramento study domain showing AirBeam network stations and the two regulatory stations. Community boundaries for EJ and non-EJ communities are shown. Areas shaded yellow, orange and red have a higher EJ Index percentile. Meteorology stations are collocated at the Del Paso Manor and ARB T street sites.
Figure 2
Figure 2
Hourly measurements of PM2.5 from the AirBeam compared to the BAM at Del Paso Manor during December 2016-January 2017. The data are color-coded to (top left) relative humidity, (top right) dew point, (bottom left) temperature, and (bottom right) wind speed. 1:1 and 3:1 lines are shown for reference.
Figure 3
Figure 3
Daily average measurements of PM2.5 from the Del Paso Manor AirBeam compared to the FRM. The data are color-coded to (top left) relative humidity, (top right) dew point, (bottom left) temperature, and (bottom right) wind speed.
Figure 4
Figure 4
Hourly AirBeam PM2.5 measurements from the six communities (top), difference from median AirBeam concentration across the six communities (middle), and wind speed and wind direction measurements measured at Del Paso Manor (bottom) from December 15, 2016, to January 05, 2017. Colonial Heights, Del Paso Manor, and T Street are non-EJ areas, while Arden, South Sacramento, and South Natomas are EJ areas.
Figure 5
Figure 5
Temporal distribution of two BAM monitors and corrected AirBeam PM2.5 measurements from the six communities in the study.
Figure 6
Figure 6
Distribution of corrected AirBeam PM2.5 measurements from six communities. The center of the boxplot represents the median value, with 95% confidence interval at the notches. The box cutoffs are the inter-quantile ranges (IQRs), the whiskers represent 1.5 × IQR, and the remaining points in the distribution are plotted individually.
Figure 7
Figure 7
Comparison of gridded winter PM2.5 emissions and average AirBeam PM2.5 concentrations in the grid cell from December 2016 through January 2017 for weekdays (left) and weekends (right).
See this image and copyright information in PMC

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