An integrated forecasting system for air quality control

Atmospheric air pollution is one of the main environmental problems that our society is facing. Moreover, according to the World Health Organization it is a major worldwide environmental risk to health. Due to these facts, Decision Support Systems (DSSs) have been developed to help Environmental Authorities in designing short and long terms air quality plans to cost-efficiently control the impacts of atmospheric pollution. A key component of the DSSs is the air quality forecasting system, needed to compute the pollutant concentrations in advance with respect to the occurrence of critical events. These models can adopt either a deterministic or a statistical approach. In both cases, the resulting models are characterized by intrinsic strengths and weaknesses. This work proposes an approach to develop and implement air quality forecasting models by integrating these two approaches to reap their benefits while avoiding or minimizing the disadvantages, focusing on the often neglected field of short term air pollution. This integration is done by implementing a reanalysis algorithm allowing to rely on the complexity and accuracy of deterministic models and on the performances of statistical models, limiting, at the same time, the frequent concentration underestimation of deterministic models and the statistical models spatial limitations. Such approach has been tested by identifying models to reproduce the daily mean concentrations of particulate matter on Lombardy region, a highly polluted area in Northern Italy