Spatio-temporal air pollution modelling using a compositional approach

Air pollutant data are compositional in character because they describe quantitatively the parts of a whole (atmospheric composition). However, it is common to use air pollutant concentrations in statistical models without considering this characteristic of the data and, therefore, without control of common statistical problems, such as spurious correlations and subcompositional incoherence. This paper now proposes a daily multivariate spatio-temporal model with a compositional approach. The air pollution spatio-temporal model is based on a dynamic linear modelling framework with Bayesian inference. The novel modelling methodology was applied in an urban area for carbon monoxide (CO, mg·m(−3)), sulfur dioxide (SO(2), μg·m(−3)), ozone (O(3), μg·m(−3)), nitrogen dioxide (NO(2), μg·m(−3)), and particulate matter less than 2.5 μm in aerodynamic diameter (PM(2.5), μg·m(−3)). The proposal complemented and improved the conventional approach in air pollution modelling. The main improvements come from a fast multivariate data description, high spatial-correlation, and adequate modelling of air pollutants with high variability.