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.

We examined windblown dust within the Imperial Valley (CA) during strong springtime west-southwesterly (WSW) wind events. Analysis of routine agency meteorological and ambient particulate matter (PM) measurements identified 165 high WSW wind events between March and June 2013 to 2019. The PM concentrations over these days are higher at northern valley monitoring sites, with daily PM mass concentration of particles less than 10 micrometers aerodynamic diameter (PM10) at these sites commonly greater than 100 μg/m³ and reaching around 400 μg/m³, and daily PM mass concentration of particles less than 2.5 micrometers aerodynamic diameter (PM2.5) commonly greater than 20 μg/m³ and reaching around 60 μg/m³. A detailed analysis utilizing 1 km resolution multi-angle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD), Identifying Violations Affecting Neighborhoods (IVAN) low-cost PM2.5 measurements and 500 m resolution sediment supply fields alongside routine ground PM observations identified an area of high AOD/PM during WSW events spanning the northwestern valley encompassing the Brawley/Westmorland through the Niland area. This area shows up most clearly once the average PM10 at northern valley routine sites during WSW events exceeds 100 μg/m³. The area is consistent with high soil sediment supply in the northwestern valley and upwind desert, suggesting local sources are primarily responsible. On the basis of this study, MAIAC AOD appears able to identify localized high PM areas during windblown dust events provided the PM levels are high enough. The use of the IVAN data in this study illustrates how a citizen science effort to collect more spatially refined air quality concentration data can help pinpoint episodic pollution patterns and possible sources important for PM exposure and adverse health effects.

We present FDS and AERMOD simulations of the Alaska Clean Seas (ACS) oil burn experiments to improve dispersion modeling of large, open-air fires relevant to industrial settings. We propose a method in which FDS smoke fields with available ground measurements are used to empirically calibrate AERMOD configured using volume sources to represent the fire source. FDS is first run for the three ACS experiments at high resolutions (~10 m) and verified against ground monitoring to provide detailed three-dimensional smoke fields. The fractional allocation of total fire emissions (weights, w_i) is then empirically specified for each volume source i so AERMOD smoke predictions fit both the ground level measurements and FDS simulations to acceptable accuracy. Runs for volumes at the surface (i = 1), 100 m AGL (i = 2) and 300 AGL (i = 3) and w_i = [0.01, 0.09, 0.9]–[0.04, 0.36, 0.6] accurately represent these data, suggesting this range as suitable for fire heat fluxes (~800–3000 kW/m²), wind speeds (5–10 m/s) and PBL depths (300–500 m with and without capping temperature inversions) of the three ACS experimental burns. Further work exploring the applicability of this AERMOD setup to a broader range of conditions is ongoing.