Air Pollution Space-Time Analysis

The environmental decision-making process is preceded by data analysis, which is conditioned by a specific temporal and spatial scale. This paper aims to present a methodology that integrates the time-space framework of air quality data to infer the temporal pattern and spatial variability that could be interpreted for environmental decision purposes. Variograms that accommodate time and space lags were used for the analysis and proved to be effective. Temporal and spatial trends were found for data collected on an hourly and daily basis and its environmental meaning is discussed. Visualization of spatial patches of air pollution in Lisbon during a working day is performed through the use of an image processing technique, named morphing.