ABSTRACT
This chapter shows how to fit a spatio-temporal model to predict fine particulate air pollution levels (PM2.5) in Spain over the years 2015 to 2017 using the stochastic partial differential equation approach and the R-integrated nested Laplace approximation (INLA) package. PM2.5 are a mixture of solid particles and liquid droplets less than 2.5 micrometers in diameter that are floating in the air. These particles come from various sources including motor vehicles, power plants and forest fires. Air pollution measurements recorded at monitoring stations in Spain and other European countries can be obtained from the European Environment Agency. A CSV file called dataPM25.csv that contains these data can be downloaded from the book webpage. The chapter also shows how to specify a spatio-temporal model to predict PM2.5 and the steps required to fit the model using R-INLA.
