International Journal of Remote Sensing
34(8): 2672–2688, 2013.
Analysis of ground-monitoring data from 2007 to 2008 in southern Israel revealed 67 DD, with more than 88% occurring during winter and spring. A Classification and Regression Tree CART model that was applied to a database containing ground monitoring the dependent variable and SRS aerosol product 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 us to distinguish between health, ecological, and environmental effects that result from exposure to these distinct particle populations.