Air Quality Forecast by Statistical Methods: Application to Portugal and Macao - Mendes, Luísa; Monjardino, Joana; Ferreira, Francisco (Frontiers in Big Data, 2022)
Air pollution is a major concern issue for most countries in the world. In Portugal and Macao the values of nitrogen dioxide (NO2), particulate matter (PM) and ozone (O3) are frequently above the concentration thresholds accepted as “good air quality”. The work presented refers to the statistical forecast of air pollutants for three regions: Greater Lisbon Area, Madeira Autonomous Region (both located in Portugal), and Macao Special Administrative Region (in Southern China). The presented statistical approach combines Classification and Regression Trees (CART) and Multiple Regression (MR) analysis to obtain optimized regression models. This consolidated methodology is now in operation for more than a decade, in Portugal, and is subject to regular updates that reflect the ongoing research and the changes in the air quality monitoring network. Recently, the same methodology was applied to Macao in collaboration with the Macao Meteorological and Geophysical Bureau (SMG). The here described statistical approach to air quality forecasting has proven to be successful, being able to forecast PM10, PM2.5, NO2 and O3 concentrations, for the next day, with a good performance. In general, all the models have shown a good agreement between the observed and forecasted concentrations (with R2 from 0.50 to 0.89), and were able to follow the concentration evolution trend. For some cases there is a slight delay in the prediction trend. Moreover, the results obtained for pollution episodes have proven that statistical forecast can be an effective way of protecting public health.