Establishing relationships between chemical health stressors in urban traffic environments: Prediction of toluene concentration levels in European cities

Abstract Health can be impacted in many ways by exposure to chemical stressors in urban areas. Epidemiological research community has established consistent associations between traffic related air pollution and various health outcomes. Nevertheless, many urban environments, due to practical reasons (bulk of equipment) and mainly due to economical constraints, are characterised by the absence of the necessary monitoring infrastructure, for pollutants such as toluene. This chemical stressor is associated with numerous risks to human health, mainly with acute and chronic effects on the central nervous system. Due to the lack of monitoring data, it may be convenient to identify and establish a set of possible empirical relationships between health stressors in order to assess air quality trends of traffic related pollution in an urban area and support decision making. The use of environmental statistics can be meaningful towards this direction. This paper aims at developing and presenting a tractable approach, in order to reliably forecast toluene levels in EU urban environments. Multiple stepwise regression analysis is used for this purpose and a strong statistical relationship is detected mainly between toluene, benzene and CO. The adopted regression models are validated in order to depict their applicability and representativeness. In addition the models are applied to Thessaloniki, Greece, which is considered one of the most polluted cities within Europe. A comparison between available measurements, predictions based on the developed statistical models and air quality modelling output, provides discussion for transferability issues of such statistical relations between cities, but also interesting insights for the specific city. In general the presented results demonstrate that the adopted approach is capable of capturing toluene concentration trends and should be considered as complementary to air quality monitoring.