Evaluating Data Product Exposure Metrics for Use in Epidemiologic Studies of Dust Storms

Dust storms are increasing in frequency and correlate with adverse health outcomes but remain understudied in the United States (U.S.), partially due to the limited spatio-temporal coverage, resolution, and accuracy of current data sets. In this work, dust-related metrics from four public areal data products were compared to a monitor-based "gold standard" dust data set. The data products included the National Weather Service (NWS) storm event database, the Modern-Era Retrospective analysis for Research and Applications-Version 2, the EPA's Air QUAlity TimE Series (EQUATES) Project using the Community Multiscale Air Quality Modeling System (CMAQ), and the Copernicus Atmosphere Monitoring Service global reanalysis product. California, Nevada, Utah, and Arizona, which account for most dust storms reported in the U.S., were examined. Dichotomous and continuous metrics based on reported dust storms, particulate matter concentrations (PM10 and PM2.5), and aerosol-type variables were extracted or derived from the data products. Associations between these metrics and a validated dust storm detection method utilizing Interagency Monitoring of Protected Visual Environments monitors were estimated via quasi-binomial regression. In general, metrics from CAMS yielded the strongest associations with the "gold standard," followed by the NWS storm database metric. Dust aerosol (0.9-20 mu m) mixing ratio, vertically integrated mass of dust aerosol (9-20 mu m), and dust aerosol optical depth at 550 nm from CAMS generated the highest standardized odds ratios among all metrics. Future work will apply machine-learning methods to the best-performing metrics to create a public dust storm database suitable for long-term epidemiologic studies. Plain Language Summary Health studies of dust storms are limited by the kinds of dust data that are available over large areas and long periods of time. Our study compares four publicly available data products to determine which is most suitable for large-scale population studies of dust storms in the southwestern U.S. Using dust-related variables from these products, the study evaluated relationships with a previously validated "gold standard" data set that identifies dust storms only on certain days and at certain locations. The study found that the dust-related variables from the Copernicus Atmosphere Monitoring Service (CAMS) product had the strongest associations with the "gold standard," followed by the National Weather Service storm events database, a long-term model run from one of the Environmental Protection Agency's air quality models (CMAQ-EQUATES), and one of the National Aeronautics and Space Administration's data sets (MERRA-2). Specifically, variables from CAMS that were based on daily maximum values of dust particles in the air performed the best. Future work will apply complex computer-based methods to the best-performing exposure metrics to create a public dust storm database suitable for use in long-term population health studies.