A wind-blown dust simulation and forecast system for public health services

Dazhong Yin
University of Arizona


Wind-blown dust events contribute significantly to the air-borne particulate matter (PM) pollution in the southwestern United States. Besides health problems, such as allergies, eye-infections and asthma, which are commonly associated and/or aggravated by elevated PM in the atmosphere, valley fever (coccidioidomycosis) is a special public health concern in the Southwest and closely related to wind blown dust pollution. Our wind-blown dust modeling system is comprised of the following several major components. Currently, the Dust REgional Atmospheric Model (DREAM) is the backbone model of the system. It is Eta-model-based and has a dust transport module coupled online with the NCEP/Eta model. The development of an on-line dust transport module coupled with the WRF/NMM is ongoing. Once the latter work is done, the system will be able to use finer grid spacing, take more advantage of high resolution remote sensing data, and provide higher resolution forecasts. The NASA observing system remote sensing data is collected and/or retrieved to help identify/pinpoint desert dust sources or to be used to evaluate the models. The high temporal and spatial resolutions of the satellite data are important in capturing dust sources and source changes due to climate variability, agriculture activities, urban development, and construction site changes. One of the satellite-data-based products is temporally dynamic Aeolian erosion vulnerability maps for the Southwest. The satellite data sources include GOES, Landsat MMS, Landsat TM, WiFS, with different spectral, temporal and spatial resolutions. The EPA and other agencies’ ground based PM observations at scattered sites are used in the model evaluation. In the mean time, remote sensing data such as MODIS level 2, MODIS deep blue data and AERONET data are also used to evaluate spatial coverage of dust model results. A web-mapping system is developed to provide dust simulations and forecasts to public health clients and to deliver information to other public health decision systems.