Reassessing air pollution exposure: How daily mobility and activities shape individual risk in greater Paris

Understanding individual exposure to air pollution is essential for tackling environmental inequalities and informing public policies aimed at reducing disparities. Traditional approaches often focus on residential locations, but exposure is intrinsically linked to daily mobility, activities and socio-economic profiles. This study presents new results based on a dynamic exposure modelling approach that takes these dimensions into account, offering a more realistic assessment of air pollution risk. By integrating high-resolution air quality data with detailed information on individual mobility, activities and socio-economic characteristics, we quantify the exposure of 400,000 individuals in the Île-de-France region. Our approach takes into account all the environments that individuals visit during the day and the time spent in each of them, going beyond static exposure assessments based on residential location. We compare this dynamic model with traditional exposure calculations, revealing significant differences in the spatial distributions of PM10 and NO2 exposure. Our analysis highlights how mobility patterns and daily activities contribute to total exposure, demonstrating that place of residence is only one part of reality. For example, commuting, workplaces and leisure activities play a key role in determining individual exposure levels. The results of our study show that dynamic exposure calculation provides a better understanding of exposure factors and offers a framework for understanding environmental inequalities. By shifting the focus from home-based to person-based exposure, our approach makes it possible to identify levers for action to reduce disparities and support targeted public health action. Our study redefines the way in which we assess the risks associated with air pollution, by highlighting the need to take into account mobility behaviour and individual trajectories. • Development of a dynamic exposure calculation model in an urban modelling platform. • High-resolution data integration combining air quality, mobility, and socio-economic data. • Evidence that commuting and work significantly shape individual air pollution exposure. • Understanding exposure factors and inequalities to better target public health policies.