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Application of a machine learning methodology for data implementation
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1  Climate and Climatic Change Group, Section of Environmental Physics and Meteorology, Department of Physics, National and Kapodistrian University of Athens, Greece


An important aspect in environmental sciences is the study of air quality, using statistical methods (environmental statistics) which utilize large datasets of climatic parameters. The air quality monitoring networks that operate in urban areas provide data of the most important pollutants, which via environmental statistics can be used for the development of continuous surfaces of pollutants concentrations. Generating ambient air quality maps can help guide policy makers and researchers to formulate measures to minimize the adverse effects. The information needed for a mapping application can be obtained by employing spatial interpolation methods to the available data, for generating estimations of air quality distributions. This study uses point monitoring data from the network of stations that operates in Athens. A machine learning scheme will be applied as a method to spatially estimate pollutants’ concentrations and the results can be effectively used to implement missing values and provide representative data for statistical analyses purposes.

Keywords: artificial neural networks; shallow neural networks; machine learning; spatial interpolation; data implementation; air quality