The World Health Organization (WHO) reported that, “air pollution must be recognized as a major threat to human well-being”. According to the WHO, there is a direct link between air pollution and human health (such as high blood pressure, stress related illnesses, sleep disruption, and hearing loss, to cite a few). About 93% of all population live in environments with air pollution levels above the WHO guidelines.
Although the potential health effects of exposure to air pollution are the same everywhere, there are considerable variation in the exposure levels depending on the demographic, socio-economic, ethnic, as well as the environmental context. Only a few recent epidemiological studies are available concerning the relationship of exposure to air pollution with the demographic, socio-economic, ethnic and, environmental context, and the consequent potential physical and/or mental comorbidities. For instance, it has been recognized that for a number of reasons the elderly, low-income individuals and ethnic groups are more exposed to air pollution, and that these vulnerable groups suffer from more health problems than the younger, wealthier, and white citizens, respectively. Consequently, it seems relevant to assess the exposure of demographic, socio-economic, and ethnic sub-populations to air pollution, considering their particular environmental settings like land use and greenness (degree of naturalness), and further evaluate if there exists a relationship with their physical and/or mental comorbidity patterns. Therefore, to cope with these and related challenges, the objective of this session is to present research and review papers in order to synthesize the discussion on the application of the latest advances in exposure/exposome which is a growing in importance in health research. In this session, we are looking for contributions that quantify air pollution and its distribution at fine temporal and spatial resolutions, individuals/population exposure in a dynamic environment and related inequality and health effect. Further, this special session aims to stimulate the development of novel algorithms using advanced technologies in the broadest sense in the era of Big Data, and machine learning. We encourage both theoretical as well as application-oriented papers dealing with these emerging issues. Our interest is in papers that cover a wide spectrum of methodological and domain-specific topics.