Using Low-Cost Air Quality Sensor Networks to Improve the Spatial and Temporal Resolution of Concentration Maps

Faraz Enayati Ahangar, Frank R. Freedman and Akula Venkatram
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Faraz Enayati Ahangar: Department of Mechanical Engineering, University of California, Riverside, CA 92521, USA
Frank R. Freedman: Department of Meteorology and Climate Science, San Jose State University, San Jose, CA 95192, USA
Akula Venkatram: Department of Mechanical Engineering, University of California, Riverside, CA 92521, USA

IJERPH, 2019, vol. 16, issue 7, 1-17

Abstract: We present an approach to analyzing fine particulate matter (PM 2.5 ) data from a network of “low cost air quality monitors” (LCAQM) to obtain a finely resolved concentration map. In the approach, based on a dispersion model, we first identify the probable locations of the sources, and then estimate the magnitudes of the emissions from these sources by fitting model estimates of concentrations to corresponding measurements. The emissions are then used to estimate concentrations on a grid covering the domain of interest. The residuals between model estimates at the monitor locations and the measured concentrations are then interpolated to the grid points using Kriging. We illustrate this approach by applying it to a network of 20 LCAQMs located in the Imperial Valley of Southern California. Estimating the underlying mean concentration field with a dispersion model provides a more realistic estimate of the spatial distribution of PM 2.5 concentrations than that from the Kriging observations directly.

Keywords: LCAQM; dispersion modeling; spatial interpolation; Kriging; imperial valley; PM 2.5 (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
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