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While most countries have networks of stations for monitoring pollutant concentrations, they do not cover the whole territory continuously. Therefore, to be able to carry out a spatial and temporal study, the predictions for air pollution from unmeasured sites and time periods need to be used. The objective of this study is to predict the air pollutant concentrations of PM10, O3, NO2, SO2 and CO in sites throughout Catalonia (Spain) and time periods without a monitoring station. Compositional data (CoDa) studies the relative importance of pollutants. A novel feature in this article is combining CoDa with an indicator of total pollution. Predictions are then made using a combination of spatio-temporal models and the Bayesian Laplace Integrated Approach (INLA) inference method. The most relevant results obtained indicate that the log-ratio between NO2 and O3 has the highest variance and the best predictive accuracy in time and space. Total pollution levels are second in variance but have low spatial predictive accuracy. Third in variance is the low temporal accuracy found in the log-ratio between SO2 and the remaining pollutants. Globally, the combination of CoDa and the INLA method is suitable for making effective spatio-temporal predictions of air pollutants This work was partially financed by the SUPERA COVID19 Fund, from SAUN: Santander Universidades, CRUE and CSIC; by the COVID-19 Competitive Grant Program from Pfizer Global Medical Grants; by the Spanish Ministry of Science, Innovation and Universities/FEDER (grant number RTI2018–095518–B–C21); and the Government of Catalonia (grant number 2017SGR656) Open Access funding provided thanks to the CRUE-CSIC agreement with Elsevier