Model implementation: A major element of the PM3 project is the use of simulation modeling to analyse sources and contributions to observed and predicted particulate concentrations. On the basis if a validated model system, we can then optimize control strategies (a dust management plan) for the control of local particulate emission sources. The models used are building on the existing tools and models of the EUREKA project E!3266 WEBAIR. The first set of project activities and milestones of the preparatory phase concentrates on the compilation and analysis of the basic input and validation data: Data availability and analysis: Meteorology Air quality monitoring Reference data sets Emission inventory Extreme events Gravimetric data analysis Temporal patterns PM10 Compliance Calibration runs The modelling of regional, national and local dust (PM10/PM2.5) is based on a system of cascading nested grid dynamic and 3D models and their associated data bases, that basically include: Meteorological modelling, using (national) numerical weather forcasts (providing daily updates of multi-day forecasts with input data in Netcdf format) or using the results of the non-hydrostatic prognostic MM5 model system; Emission modelling, that combines pyrogenic emissions (starting with the European scale EMEP emission data base) including shipping, and wind erosion, wind driven entrainment of soils or secondary, deposited particles, including their long range transport (e.g., Saharan dust); Particulates transport/dispersion modelling, using

On-line demo EUREKA WEBAIR

3D Eulerian model CAMx for particulates transport from continental to peri-urban scale; Alternative dispersion models for specific sources and areas of interest include: INPUFF for major indivual point and area sources from power or cement plants to mobile sources (ships, traffic on unpaved roads) local street canyon modeling for inter city hot spots (AUT); Beyond and in support of the basic numerical modeling and scenario analysis, the project involves: Impact assessment that keeps track of population exposure with emphasis opn sensitive zones, groups, and individulas. Public information on dust episodes from operational forecasting, emphasis on sensitive population groups; Dust sampling and mineralogical/chemical analysis to support source apportionment. Remote sensing that provide large scale synoptic observation for model validation.




Model domains:   Nested grids

Spatial data:   DEM, GIS

Simulation models:   Modeling tools

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Model System Implementation