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

1
Department of Mechanical Engineering, University of California, Riverside, CA 92521, USA
2
Department of Meteorology and Climate Science, San Jose State University, San Jose, CA 95192, USA
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(7), 1252; https://doi.org/10.3390/ijerph16071252
Received: 8 February 2019 / Revised: 23 March 2019 / Accepted: 26 March 2019 / Published: 8 April 2019
(This article belongs to the Special Issue Near-Source Air Pollution)
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Abstract

We present an approach to analyzing fine particulate matter (PM2.5) 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 PM2.5 concentrations than that from the Kriging observations directly. View Full-Text
Keywords: LCAQM; dispersion modeling; spatial interpolation; Kriging; imperial valley; PM2.5 LCAQM; dispersion modeling; spatial interpolation; Kriging; imperial valley; PM2.5
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Ahangar, F.E.; Freedman, F.R.; Venkatram, A. Using Low-Cost Air Quality Sensor Networks to Improve the Spatial and Temporal Resolution of Concentration Maps. Int. J. Environ. Res. Public Health 2019, 16, 1252.

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