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.
Highlights
Several studies indicate that exposure to fine particulate matter (PM2.5 ) concentrations is associated with several adverse health oucomes that include cardiovascular diseases [1], lung cancer, cardiopulmonary mortality, and asthma [2,3]
As an alternative that offers advantages mentioned in that section, we propose a method that combines dispersion modeling with Kriging to construct spatially section, we propose a method that combines dispersion modeling with Kriging to construct spatially continuous concentration fields [23,24]
That provides much better spatial resolution than that from the sparser monitors” (LCAQMs) that provides much better spatial resolution than that from the sparsernetwork network of it to a network of Low-Cost Air Quality Monitors (LCAQM) located in ofFRM/FEM
Summary
Several studies indicate that exposure to fine particulate matter (PM2.5 ) concentrations is associated with several adverse health oucomes that include cardiovascular diseases [1], lung cancer, cardiopulmonary mortality, and asthma [2,3]. The data gathered by these sensors can enhance the information provided by traditional networks, in particular by helping to detect local hot spots in concentration patterns. This will provide significantly improved information for air quality management purposes [9,10,11]
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