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 (PM2.5, mgm–3).

Related Organizations

Universitat Politècnica de Catalunya
Spain

Keywords

Compositional Data; CoDa; Air Quality; Environmental statistics; Modelling

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	0
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average