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Wind erosion of soil is a major concern of the agricultural community, as it removes the most fertile part of the soil and thus degrades soil productivity. Furthermore, dust emissions due to wind erosion degrade air quality, reduce visibility, and cause perturbations to regional radiation budgets. PM10 emitted from the soil surface can travel hundreds of kilometers downwind before being deposited back to the surface. Thus, it is necessary to address agricultural air pollutant sources within a regional air quality modeling system in order to forecast regional dust storms and to understand the impact of agricultural activities and land-management practices on air quality in a changing climate. The Wind Erosion Prediction System (WEPS) is a new tool in regional air quality modeling for simulating erosion from agricultural fields. WEPS represents a significant improvement, in comparison to existing empirical windblown dust modeling algorithms used for air quality simulations, by using a more process-based modeling approach. This is in contrast with the empirical approaches used in previous models, which could only be used reliably when soil, surface, and ambient conditions are similar to those from which the parameterizations were derived. WEPS was originally intended for soil conservation applications and designed to simulate conditions of a single field over multiple years. In this work, we used the EROSION submodel from WEPS as a PM10 emission module for regional modeling by extending it to cover a large region divided into Euclidean grid cells. The new PM10 emission module was then employed within a regional weather and chemical transport modeling framework commonly used for comprehensive simulations of a wide range of pollutants to evaluate overall air quality conditions. This framework employs the Weather Research and Forecasting (WRF) weather model along with the Community Multi-scale Air Quality (CMAQ) model to treat ozone, particulate matter, and other air pollutants. To demonstrate the capabilities of the WRF/EROSION/CMAQ dust modeling framework, we present here results from simulations of dust storms that occurred in central and eastern Washington during 4 October 2009 and 26 August 2010. Comparison of model results with observations indicates that the modeling framework performs well in predicting the onset and timing of the dust storms and the spatial extent of their dust plumes. The regional dust modeling framework is able to predict elevated PM10 concentrations hundreds of kilometers downwind of erosion source regions associated with the windblown dust, although the magnitude of the PM10 concentrations are extremely sensitive to the assumption of surface soil moisture and model wind speeds. Future work will include incorporating the full WEPS model into the regional modeling framework and targeting field measurements to evaluate the modeling framework more extensively. [ABSTRACT FROM AUTHOR]

Results from a regional air quality forecast model, AIRPACT-3, are compared to OMI tropospheric NO2 integrated column densities for an 18 month period over the Pacific Northwest. AIRPACT column densities were well correlated with cloud-free monthly averages of tropospheric NO2 (R=0.75) to NASA retrievals for months without wildfires, but were poorly correlated with significant model overpredictions (R=0.21) for months with wildfires when OMI and AIRPACT were compared over the entire domain. AIRPACT forecasted higher NO2 in some US urban areas, and lower NO2 in many Canadian urban areas, when compared to OMI. There are significant changes in results after spatially averaging model results to the daily OMI swath. Also, it is shown that applying the averaging kernel to model results in cloudy conditions has a large effect, but applying the averaging kernel in cloud free conditions has little effect. The KNMI and NASA retrievals of tropospheric NO2 from OMI (collection 3) are compared. The NASA product is shown to be significantly different than the KNMI tropospheric NO2 product, i.e. July 2007 (R=0.60) and January 2008 (R=0.69). [ABSTRACT FROM AUTHOR]