Abstract
Since 2002, Environment and Climate Change Canada (ECCC) has been producing hourly surface analyses of pollutants covering North America which have been used in numerous health impact studies. The analyses are produced using an optimum interpolation scheme that combines the operational air quality forecast model GEM-MACH outputs (CHRONOS model outputs were used prior to 2009) with real-time hourly observations of O3, PM2.5, PM10, NO2, and SO2. We examine how passive observations, that are not used to create the analysis (i.e. cross-validation), can be used to evaluate the analysis error and to optimize the input error statistics.
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