Spatio-temporal learning in predicting ambient particulate matter concentration by multi-layer perceptron

In this work, a novel spatio-temporal air quality prediction framework is proposed, and its development and efficacy as a predictive tool are described. The framework exploits data from the suite of models of the Copernicus Atmosphere Monitoring Service (CAMS), fed to an artificial neural network for the removal of bias. The method inherently considers spatial and temporal correlations, because it is applied simultaneously to all monitoring stations of a given region, using past observations and past and future forecasts. The methodology is tested on twelve months of CAMS forecasts of daily surface particulate matter (PM10) in 2017 and is verified against observations measured at 413 monitoring stations from the Italian air quality network.