EGU23-12311
https://doi.org/10.5194/egusphere-egu23-12311
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Applying space-time activity data and socio-economic profiles to assess the variation of personal air pollution exposures

Oliver Schmitz1, Kees de Hoogh2,3, Nicole Probst-Hensch2,3, Ayoung Jeong2,3, Benjamin Flückiger2,3, Danielle Vienneau2,3, Gerard Hoek4, Kalliopi Kyriakou4, Roel C. H. Vermeulen4,5, and Derek Karssenberg1
Oliver Schmitz et al.
  • 1Utrecht University, Faculty of Geosciences, Physical Geography, Utrecht, Netherlands
  • 2Swiss Tropical and Public Health Institute, Allschwil, Switzerland
  • 3University of Basel, Switzerland
  • 4Utrecht University, Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht, Netherlands
  • 5University Medical Centre Utrecht, Utrecht, Netherlands

Long-term personal air pollution exposures estimates from nationwide cohorts are useful in studies of the relationship between air pollution exposure and chronic diseases such as diabetes or cardiovascular disease. Ignoring space-time activity patterns and neglecting mobility in exposure assessment may lead to incorrect exposure distributions and bias in downstream exposure health relations. In our study we estimate personal air pollution exposures nationwide and across socio-economic and age profiles to identify the relevance of location or profiles on exposure analysis.

We developed a set of characteristic diurnal activity profiles that we use to calculate exposures for each home address in Switzerland. The profiles are specified by different characteristics such as age group, social economic status, or commute type (e.g. by car, bicycle, on foot). Potential working locations are retrieved from origin-destination matrices for a particular profile, derived from the annual Structural Survey data from the Swiss Population Census (https://www.bfs.admin.ch/bfs/en/home/statistics/population/surveys/se.html), at the level of municipalities. Commute trips between residential and work location are then calculated using the shortest route on OpenStreetMap data. For each profile and each residential address, we run an agent-based model in Monte Carlo mode, generating a database of personal long-term exposures to NO2 and PM2.5 for further epidemiological analysis.

Our activity-based mobility simulation provides a representative description of space-time activities of individuals. We present the model results at all unique 1.8 million residential address locations in Switzerland. We compare the exposure assigned from residential address alone to the exposures derived from 20 different activity profiles and present the differences between profiles. We also demonstrate the spatial variability of exposures per profile and the associated uncertainty.

The generated exposure database can be used for epidemiological analysis of large-scale cohorts, and enables follow-up studies to evaluate whether including commuting and other activities and therefore more detailed estimates of individual exposure results in more accurate risk estimates in health studies.

How to cite: Schmitz, O., de Hoogh, K., Probst-Hensch, N., Jeong, A., Flückiger, B., Vienneau, D., Hoek, G., Kyriakou, K., Vermeulen, R. C. H., and Karssenberg, D.: Applying space-time activity data and socio-economic profiles to assess the variation of personal air pollution exposures, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12311, https://doi.org/10.5194/egusphere-egu23-12311, 2023.