We propose an exploratory statistical tool that combines functional data analysis (FDA) with unsupervised learning algorithms and spatial statistics to extract meaningful information about the main spatiotemporal patterns underlying air pollutant exceedances in mainland Portugal. Firstly, we describe the temporal evolution of air pollutant concentrations by CAMS grid node as a function of time and outline the main temporal patterns of variability using a functional principal component analysis. Then, CAMS grid nodes are classified according to their spatiotemporal similarities through hierarchical clustering adapted to spatially correlated functional data. The proposed methodology provides an automated and robust approach for quantifying temporal trends in PM10 time series data to support subsequent air quality classification.

The amount of territory in square kilometers that Ukraine has to liberate to free the temporarily occupied territory in one year. Slava Ukraini!

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