Atmospheric Pollution

Air quality control has become a major issue for the environment in general and the public health in particular. In recent decades Europe has significantly cut emissions of several air pollutants, greatly reducing exposure to several pollutants (e.g. SO2, CO, C6H6, Pb). However, particulate matter, ozone, reactive nitrogen substances and some organic compounds still pose a significant threat (EEA, 2013). Despite improvements over several decades, air pollution continues to damage human health and the environment (EEA, 2013). According to a recent report by the Organization for Economic Cooperation and Development (OECD), by the year 2050, outdoor air pollution is projected to be the world’s top environmental cause of mortality, ahead of dirty water and lack of sanitation (OECD, 2012). Facing this direct impact of air pollution on human mortality and morbidity, exposure to pollutants is currently a key environment-related health concern (EEA, 2013).

Air pollution is determined by the combination between different factors, namely, emissions, physical constrains and meteorological conditions (e.g. Demuzere et al., 2009; Pearce et al., 2011). Meteorological conditions importance in constraining the atmospheric processes of dilution, transformation, transport and removal of pollutants has been referred previously by several authors (e.g. Dayan and Levy, 2002; Pearce et al., 2011). In order to better understand the atmospheric factors which are responsible for poor air quality, the relationships between driving atmospheric synoptic patterns, meteorological variables and surface air pollution have become an important research area. Particularly, a number of authors have investigated the relationships between weather conditions and air pollution in Portugal (Barros et al., 2003; Carvalho et al., 2006; Evtyugina et al., 2006; Borrego et al., 2013). The tools used in the studies for Portugal and for other geographical regions include atmospheric and air pollution models (e.g. Carvalho et al., 2006; Pearce et al., 2011; Borrego et al., 2013) or synoptic patterns and back-trajectories approaches (e.g. Dayan and Levy, 2002, 2004; Carvalho et al., 2006; Demuzere et al., 2009; Saavedra et al., 2012; Russo et al., 2014), and were applied to historical data (e.g. Pearce et al., 2011; Saavedra et al., 2012), or to climate scenarios (e.g. Dias et al., 2012).

The climatology and climate change group at IDL has been developing work on the assessment and characterization of links between the interannual variability of daily air quality and meteorology (Russo et al., 2012; Russo et al., 2013a), and particularly with interannual variability of major Weather Types (Demuzere et al., 2009; Russo et al., 2014). Additionally, the climatology and climate change group has been working on the identification of relationships between weather and environmental factors and mortality (Días et al, 2004). The climatology and climate change group was also involved on the development of statistical models for the characterization and forecast of air pollution (Russo et al., 2008; Russo et al., 2010; Russo et al., 2012; Russo et al., 2013b).

Figure 1. Episodes hourly concentration time series (a) O3 e August 1st, 2003; (b) PM10 e August 5th, 2005 (Russo et al., 2014).


  • Barros, N., Borrego, C., Toll, I., Soriano, C., Jiménez, P., Baldasano, J.M., 2003. Urban photochemical pollution in the Iberian Peninsula: Lisboa and Barcelona airsheds. Air &Waste Management Association 5, 347-359.
  • Borrego, C., Souto, J.A., Monteiro, A., Dios, M., Rodriguez, A., Ferreira, J., Saavedra, S., Casares, J.J., Miranda, A.I., 2013. The role of transboundary air pollution over Galicia and North Portugal area. Environmental Science and Pollution Research 20 (5), 2924-2936.
  • Carvalho, A.C., Carvalho, A., Gelpi, I., Barreiro, M., Borrego, C., Miranda, A.I., Pérez-Muñuzuri, V., 2006. Influence of topography and land use on pollutants dispersion in the Atlantic coast of Iberian Peninsula. Atmospheric Environment 40, 3969-3982.
  • Dayan, U., Levy, I., 2002. Relationship between synoptic-scale atmospheric circulation and ozone concentrations over Israel. Journal of Geophysical Research 107 (D24), 4813.
  • Demuzere, M., Trigo, R.M., Vila-Guerau de Arellano, van Lipzig, N.P.M., 2009. The impact of weather and atmospheric circulation on O3 and PM10 levels at a rural mid-latitude site. Atmospheric Chemistry and Physics 9, 2695-2714.
  • Dias, D., Tchepel, O., Carvalho, A., Miranda, A.I., Borrego, C., 2012. Particulate matter and health risk under a changing climate: assessment for Portugal. ScientificWorldJournal 2012, 409546.
  • Díaz, J., Linares, C., López, C., García-Herrera, R., Trigo, R.M., 2004. Relationship between environmental factors and infant mortality in Madrid, 1986e1997. Journal of Occupational and Environmental Medicine 6 (8), 768-774.
  • EEA-European Environment Agency, 2013. Air quality in Europe — 2013 report. EEA Report No 9/2013. doi:10.2800/92843
  • Evtyugina, M., Nunes, T., Alves, C., Marques, M.C., 2009. Photochemical pollution in a rural mountainous area in the northeast of Portugal. Atmospheric Research 92, 151-158.
  • Evtyugina, M., Nunes, T., Pio, C., Costa, C., 2006. Photochemical pollution under sea breeze conditions, during summer, at the Portuguese West Coast. Atmospheric Enironment. ISSN: 1352-2310 40 (33), 6277-6293.
  • OECD, 2012. OECD Environmental Outlook to 2050: the Consequences of Inaction, p. 350.
  • Raischel F., Russo A., Haase M., Kleinhans D., Lind P.G., 2012. Searching for optimal variables in real multivariate stochastic data. Physics Letters A 376, 2081-2089.
  • Russo A., Raischel F., Lind P.G., 2013a. Air quality prediction using optimal neural networks with stochastic variables Atmospheric Environment, 79, 822-830.
  • Russo A., Soares A., Pereira M.J., Trigo R.M., 2010. Joint Space-Time Geostatistical Model for Air Quality Surveillance/Monitoring System. Geoenv Vii - Geostatistics For Environmental Applications Volume: 16 Pages: 173-185. DOI: 10.1007/978-90-481-2322-3_16
  • Russo A., Soares A.O., 2013b. Hybrid Model for Urban Air Pollution Forecasting: A Stochastic Spatio-Temporal Approach. Mathematical Geosciences46, Issue 1, 75-93.
  • Russo A., Trigo R., Soares A., 2008. Stochastic modelling applied to air quality space-time characterization. geoENV VI-Geostatistics for Environmental Applications. Soares A., Pereira M.J., Dimitrakopoulos R. (Eds.). Springer, 83-93.
  • Russo A., Trigo R.M., Martins H., Mendes M.T., 2014. NO2, PM10 and O3 urban concentrations and its association with circulation weather types in Portugal. Atmospheric Environment, 89, 768-785. DOI:10.1016/j.atmosenv.2014.02.010