Urban areas of high population density are important research areas for the exposure of the public to pathogenic environmental factors, namely atmospheric pollutants. To assess the risks and impacts of air pollution and to design control policies, it is necessary to accurately quantify the daily exposure of citizens. The object of the present study is to assess urban population exposure to air pollutants by measuring each subject’s exposure, combining location, activity and air pollution data in various microenvironments that each volunteer moves, using new, low-cost static and portable sensors. The experimental process has been a combination of personal and static monitoring sensors of high-technology, as well as, low cost. The PhD thesis aims to create a high spatial and temporal analysis sampling methodology in order to, accurately, estimate population exposure to air pollution through personal exposure, by setting a network of technologically upgraded, low cost, portable and static, real-time, exposure monitoring sensors, combined with qualitative data to be obtained through questionnaires. The development of a predictive computational model based on direct, real-time, exposure measurements, which will assess the risk to human health as a result of changing conditions, such as socio-economic parameters, health parameters, etc. is the main research outcome of this dissertation. The above mentioned model / tool, will comprise a decision support system for policy makers to develop strategies to improve air quality. This will be due to the fact that they will have valid procedures for assessing the exposure of citizens to gaseous pollution and its health effects. The long-term perspective of the dissertation’s results is the promotion of this research methodology to be used in studies of integrated population exposure assessment, in large urban centers.