Developing a conceptual model for the assessment of personal exposure to air pollution

Human exposure to environmental pathogens and specifically air pollutants is a highly topical issue. Clean air to breathe is a basic requirement of life and the quality of air both outdoors and indoors is a crucial determinant of health (WHO, 2010). Air is however affected by pollutants such as Nitrogen Oxides (NO_x), Particulate Matter (PM), ground level Ozone (O ₃) and Carbon Monoxide (CO) which can have adverse effects on public health. Air pollutants are ubiquitous and their concentrations are typically subject to a high spatial and temporal variability. For risk and impact assessments and for the design of effective air pollution control policies as well as public health advice, it is necessary to quantify human exposure to air pollutants. This is a challenging task as human exposure is based on complex relationships and interactions between environmental and human systems. Traditionally human exposure has been assessed based on concentrations from static monitors. Now technology is available to enable us to monitor personal exposure to air pollutants. The work described here is conducted in the frame of a joint PhD studentship between the Centre for Ecology & Hydrology and the University of Exeter. It focuses on the application of methods for personal exposure monitoring and the integration of measured data with existing pollution and contextual data in a combined approach. The aims are to understand more about potential associations between air pollution, human exposure to it and health effects in Scotland which is strongly influenced by activity patterns and a person's general activity-space. For this purpose, an experimental design with a wearable personal monitoring device to derive personal time-activity patterns and pollutant concentrations is currently devised. Resulting personal exposure profiles will be integrated with modelled pollution concentrations and contextual data such as socioeconomic, population and health indicators. The work presented here will focus on the development of a conceptual model integrating monitored, modelled and contextual data.