Air Pollution

To date, the majority of evidence about the health effects of air pollution from epidemiology and controlled exposure studies is based on particle mass as the measure of exposure. Consequently, regulatory agencies have adopted mass-based ambient air quality standards. Yet particulate matter (PM) is a complex mixture, and particles of different size and composition are likely to have different toxic effects. The EU-funded ESCAPE study has comprehensively characterized sources of outdoor air pollution and developed ambient LUR models for PM10, PM2.5 and NO2. Models are currently in development for elemental composition (XRF; X-ray fluorescence), EC/OC (elemental carbon/organic carbon) and PAHs (polycyclic aromatic hydrocarbons). There is, however, a need to develop models for ultrafine particles for which the long-term health effects have been poorly studied because of difficulties in exposure assessment. This is now possible using an innovative mobile monitoring design that has been shown to be reliable and cost-effective in recent studies.


One of the properties of particles likely to reflect toxicity is their oxidative potential (OP). By analyzing the spatial and temporal variability of the OP of particulate matter collected on filters, the determinants of that variation are characterized, and new, spatially resolved air pollution models for OP have been developed. The air pollution models alone, however, only provide information on ambient outdoor pollutant concentrations. Recent advances in GIS (e.g. route modeling) and micro-environmental models (e.g. indoor-to-outdoor), have led to the development of more detailed, personal exposure models which can be fed by rich data sources on detailed population time-activity patterns.

EXPOsOMICS provides an innovative framework for air pollution external exposome, via the following steps:

  1. The scientific partners have integrated instruments  from equipment manufacturers (e.g. DiSCmini for UFP and BGI pump for PM2.5) to develop a novel integrated personal monitoring system comprising UFP and PM2.5 personal air monitors with smartphone technology aimed at characterization of micro-environments, activity patterns, and inhalation rates. One of the main technological innovations is the collection of various measurements and the ability to access them via a single source (i.e. smartphone). 
  2. Deploy the new personal monitoring system among cohort members in a subset of five study areas, covering different sites (e.g. city centre, suburban, industrial, and rural), to collect the largest series of detailed personal exposure measurements of UFP, PM2.5 and activity data in Europe to date (with simultaneously collected blood samples for omics). 
  3. Develop models in longitudinal studies such as ESCAPE (, enriched for all the cohorts in the study, and develop methods to transfer these models of air pollution concentrations back in time.
  4. Undertake an UFP air pollution “mobile monitoring campaign” in the study areas where we also perform PEM to develop and validate the new land-use regression models for UFP. 
  5. Apply the OP depletion analysis technique to extend PM metrics to look at OP. PM2.5 filters collected during fixed-site outdoor monitoring in selected ESCAPE areas have been analysed to detect the spatial and temporal variability in PM2.5-related OP and subsequently create new land-use regression models for OP. We compare and assess the spatio-temporal differences in exposure estimates between OP with those from traditional particulate metrics (e.g. PM10, PM2.5).
  6. Compare PEMs in 3) above with those from residential address locations to quantify the correlation between the two. We further quantify the spatial and temporal micro-environmental contributions to total exposures.
  7. Assess the potential for exposure variability (or misclassification) in exposures, e.g. for UFP and PM2.5 where we have both LUR models and measured exposures. Compare traditional (i.e. residential address) and new methods to inform the omic studies (see below) and health risk assessment.
  8. Develop a new Europe-wide air pollution model for PM2.5 and methods to apply exposure models for the other pollutants (OP, UFP) to other cohorts and across Europe for health risk assessment. In order to develop exposure models for the pollutants being studied in the external exposome, which can be applied to health risk assessment, the transferability of the new LUR models and hybrid models to other countries/regions and time periods is investigated. Develop models which incorporate satellite data either as variables to enhance existing approaches or as a means of calibrating/validating models in areas where routine monitoring data are sparse or do not exist.
  9. Provide model estimates for single and multiple exposures (i.e. mixtures) to feed into risk models (e.g. partial least-square regression, ridge regression, Bayesian mixture methods) to study the contribution of single compounds and combination of compounds to adverse health effects in children and adults.