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Classification of dust days by satellite remotely sensed aerosol products

M. Sorek-Hamer, A. Cohen, R.C. Levy, B. Ziv, D.M. Broday

International Journal of Remote Sensing, In Print.

The last decade has shown a considerable progress in satellite remote sensing (SRS) of dust particles. From an environmental health perspective, such an event detection, after linking it to ground PM concentrations, can proxy acute exposure to respirable particles of certain properties (i.e. size, composition, toxicity). Being affected considerably by atmospheric dust, previous studies in the Eastern Mediterranean and in Israel in particular, focused on mechanistic and synoptic prediction, classification, and characterization of dust events. In particular, Ganor et al. (2009) suggested a scheme for identifying dust days (DD) in Israel based on ground PM10 measurements, which has been validated by compositional analysis. This scheme requires information about ground PM10 levels, which is naturally limited in places with sparse ground monitoring coverage. In such cases, satellite remote sensing may be an efficient and cost-effective alternative to ground measurements. This work demonstrates a new model for identifying DD and non-DD (NDD) over Israel, based on an integration of aerosol products from different satellite platforms (MODIS, OMI). Analysis of ground monitoring data from 2007-2008 in southern Israel revealed 67 DD, with more than 88% of them occurring during the winter and the spring. A CART model that was applied to a database that contained ground monitoring (the dependent variable) and SRS aerosol products (the independent variables) records revealed an optimal set of binary variables for the identification of DD. These variables are combinations of the following primary variables: the calendar month, ground level relative humidity (RH), the aerosol optical depth (AOD) from MODIS, and the aerosol absorbing index (AAI) from OMI. A logistic regression that uses these variables, coded as binary variables, demonstrated 93.2% correct classifications of DD and NDD. Evaluation of the combined CART-logistic regression scheme in an adjacent geographical region (Gush Dan) demonstrated good results. Using SRS aerosol products for DD and NDD identification may enable to distinguish between health, ecological, and environmental effects that result from exposure to these distinct particle populations.

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