Air pollution is one of the most actual environmental problems in the world. Increasing traffic density and energy consumption lead to increased pollution causing substances emissions in ambient air. It is a significant risk factor for multiple health conditions including lung cancer, respiratory and heart diseases. Therefore it is important to keep up with latest data about actual air quality to react timely and initiate appropriate environment management procedures when pollution levels rise to high (Snyder et. al, 2013).
For this purposes, air quality monitoring is carried out. It is regular and continuous collecting of information about air quality to prevent with pollution associated hazards. Usually as monitoring technical solution automated measurement stations are used. They measure concentrations of various substances in air in nonstop mode – ranging from gaseous pollutants, like nitrogen oxides (NOx) and ozone, to particulate matter. Results are compared to according normative to determine if actual pollution is threat or not. However, one thing is to control individual substances – other is actual impact, caused by synergy of different pollutants and environment factors. Latest research shows that there are hidden threats called cumulative effect – synergy between different pollutants and environmental factors which produce greater impact on living organisms than same substances in separate action. For example, ozone mixing with other pollutants leads to increased effect on human health (Mauderly and Samet, 2009). Such cumulative effects are very complex and depend on many factors – weather, seasonality, exposure duration, etc. (Stylianou and Nicolich, 2009; Su et al., 2012). Therefore, it is hard to evaluate.
As cumulative risk assessment to real-world mixtures is hindered by a lack of verified analytical frameworks (Callahan and Sexton, 2007), there are only few methods for cumulative pollution evaluation. They are relatively simple and use statistical models with small fixed number of pollutants in association with different factors. Example of such methods is Su et al. (2009) cumulative environmental hazard inequality index (CEHII) which assesses exposure to multiple air pollutants within different racial-ethnic groups and socioeconomic positions in Los Angeles. Another approach is definition of cumulative pollution as difference of living organism health and measurement results in same pollution level as it is done in Cumulative Pollution Index (CPI) method – solution designed for cumulative effect calculation from bioindication and air quality measurement data (Kalniņš, 2012).
Bioindication is pollution evaluation method which use living organisms as indicators of pollution level and environment quality. By applying methods of bioindication, it is not possible to make measurements of air pollutant concentrations like with sensors, though it is an effective tool to evaluate exposure, dose and bioaccumulation, –...