Thursday, 11 January 2018: 1:30 PM
Room 12A (ACC) (Austin, Texas)
Increasing drought conditions in the western United States raise concerns of more extreme events such as wildfires and dust storms. New algorithm are developed for identifying windblown dust storm events using the U.S. EPA Air Quality System (AQS) to reconstruct a long-term dataset within the contiguous U.S. The algorithm from Baker et al. (2017) is a hybrid method developed using both the methods of Ganor et al. (2009) and the simplified Tong et al. (2012). PM10, PM2.5, CO and wind speed datasets are used to identify windblown dust storms. The long-term trend of dust storms is analyzed for major dust source regions in the Western U.S. Results are analyzed and compared to the IMPROVE dataset using the Tong et al. (2012) dataset. The results confirm the findings of Tong et al. (2017) that although anthropogenic emissions and overall PM10 concentrations decreases, the frequency of dust storms and their contribution to aerosol loading have increased considerably in the past decades, implying future challenges of dynamic air quality management in the Southwestern states under a changing climate. Comparisons with a dust model used within the U.S. National Air Quality Forecast is also performed.
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