Abstract
A hybrid model for forecasting PM10 surface concentrations in the Moscow region, consisting of a chemical transport model (CTM) and an artificial neural network (ANN), with the use of PM10 automatic measurements data was developed and tested. The ANN was trained to predict PM10 concentrations based on the forecasts of CTM concentrations and meteorological parameters on a 2 km grid. The results of testing the ANN based on independent samples, including the episodes of high PM10 pollution, are discussed.
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