Urban PM and the integrated assessment. Jean-Marc BRIGNON Institut National de l ’Environnement Industriel et des Risques Direction des Risques Chroniques Unité Modélisation et Analyse Économique pour la Gestion des Risques Tél. 03 44 55 61 29 jean-marc.brignon@ineris.fr

Urban PM exposure and integrated assessment Important benefits expected from reduction in urban PM exposure. IAM will help set priorities for the repartition of efforts between regional and local policies for PM. => Necessity to take account of the diversity of urban situations regarding PM in the IAM work : high number of cities to be taken into account. => Underestimation of PM by regional-scale dispersion model : how can we compute correction factors for many cities in Europe ? 2

Two similar approaches are possible IIASA (Rains Review 2004 report) : correction factor based on PM emission density in cities. This approach : correction factors based on NOx concentrations in cities and SO4 concentrations in rural background around the city. 2

PM for regional background Origin of PM in cities PM Concentration PM for regional background Local PM Distance from city centre 2

Statistical assessment of origin of PM in cities Data from local monitoring networks (from City-Delta database for this trial) PM10 = A * [NOx urban] (surrogate for PM from local combustion) + B * [SO4 rural] (surrogate for regional PM) + C (other PM : crustal material, marine aerosol, resuspended) Data from EMEP network 2

An example of detailed results : Milan 2

An example of detailed results : Milan 2

Higher share for PM2.5 than for PM 10 2

Qualitative use of PM apportionment results in the Integrated Assessment. => build a typology and range european cities in categories according to the strenght of the share of local sources in PM concentration. => test scenarios with RAINS in which emission controls on mobile sources (PM filter, fuel switches in public transports,...) and other local sources are differentiated in each city type ? 2

Hybrid model to compute PM in cities. Rigorously, for each city, the regional contribution should be computed zeroing the local urban emissions in the EMEP model Observed PM concentration in urban background Regional contribution computed with EMEP model Local contribution computed as % of regional The % factor is given by the statistical PM apportionment models Assumes that the % factor is not dependant from the scenario being analysed EMEP Model Hybrid model 2

Interannual variability of the share of local emissions. 2

Conclusions. Contribution of local emissions is highly variable between european cities : need to adopt a method able to include a high number of cities : statistical models combined with dispersion models can be part of a solution. Data availability problems : local PM emissions, PM 2.5 concentrations. Uncertainties : variability of the “urban signal “ over time and under different emission scenarios. What can be done for urban ozone ? 2