Predicting Air Quality from Measured and Forecast Meteorological Data: A Case Study in Southern Italy

Abstract

A great deal of attention has been devoted to the analysis of particulate matter (PM) concentrations in various scenarios because of their negative effects on human health. Here, we investigate how meteorological conditions can affect PM concentrations in the peculiar case of the district of the city of Lecce in the Apulia region (Southern Italy), which is characterized by the highest tumor rate of the whole region despite the absence of nearby heavy industries. We present a unified machine learning framework which combines air quality and meteorological data, either measured on ground or forecast. Our findings show that the concentrations of PM10, PM2.5, NO2 and CO are significantly associated with the meteorological conditions and suggest that it is possible to predict air quality using either ground weather observations or weather forecasts.