Spatio-temporal air pollution modelling using a compositional approach (Dataset and R code)

These R files presents the dataset and code for 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 approach. The novel modelling approach was applied in an urban context (Quito-Ecuador, South America) for carbon monoxide (CO, mgm^–3), sulphur dioxide (SO₂, mgm^–3), ozone (O₃, mgm^–3), nitrogen dioxide (NO₂, mgm^–3), and particulate matter less than 2.5 mm in aerodynamic diameter (PM_2.5, mgm^–3).