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SHERPA Philippe Thunis TFMM, Prague May 2017

SHERPA is a tool designed to help local and regional authorities in creating and evaluating air quality plans, identifying key sectors and pollutants, and collaborating effectively. The tool provides insights on emissions, sectoral allocation, and source-receptor relationships, aiding in the design of effective air quality strategies. SHERPA offers detailed analysis on PM2.5, PM10, NO2, and O3 levels, enabling authorities to assess and improve air quality in their regions with accuracy.

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SHERPA Philippe Thunis TFMM, Prague May 2017

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  1. SHERPAPhilippe ThunisTFMM, PragueMay 2017

  2. SHERPA SHERPA is a tool developed by the JRC to support local and regional authorities in the design and assessment of their air quality plans. In particular, it aims at providing insight on the following points NO As a local Authority, can I do something to abate air pollution in my city/region? To which level? With whom should I collaborate? YES Which sector? Which pollutant is priority? Design and assessment of my air quality plan

  3. The SHERPA screening tool AQ model Simplified SR model SHERPA ScreeningforHigh EmissionReduction Potentials on Air quality

  4. The SHERPA screening tool • Sherpa mimics the behavior of a CTM for emission scenarios over any given geographical area and activity sector with an accuracy of 5 to 10%. • SHERPA can be configured with any CTM but the quality of the results obviously depends the chosen CTM. SHERPA Current configuration Soon available • Domain: Europe • Meteorology: ECMWF 2012 • AQ model: CHIMERE • Emissions: EC4MACS • Resolution: 7km • Urban & rural background • Yearly PM2.5, PM10, NO2 • Domain: Europe • Meteorology: ECMWF 2014 • AQ model: EMEP • Emissions: JRC • Resolution: 10km • Urban & rural background • Yearly/Seasonal PM2.5, PM10, • NO2, O3

  5. SHERPA example output: annual average PM2.5 in Paris Sectoral allocation Traffic Resident Industry Agri Total City Commuting area Spatial allocation Country Europe 0 20 40 60 80 100 Mass percentage (%) • This type of analysis can be produced for any location in the EU with two spatial allocation hierarchies are currently proposed: • Core city, Functional Urban Area, Country, Transboundary • Province (NUTS3), Region (NUTS2), Country (NUTS0), Transboundary

  6. Possible contribution to the TFMM exercise Two different aspects • Comparison with the twin-sites measurements • CTM base case vs. measurements • Comparison of source receptor relationships • Inter-comparison of CTM scenario • ! The evaluation of SHERPA should be made against CTM scenarios, not measurements.

  7. Comparison with the twin-sites measurements The contribution from SHERPA is here limited, but work in FAIRMODE WGs might be relevant • The twin sites measurements can deliver very useful information to perform a “spatial” evaluation… • But the base case simulations of the CTMs (CHIMERE, EMEP, …) are sufficient to perform such an analysis: • Test the ability to reproduce spatial gradients of total mass, composition… for different time averages (year, season, day…). • If comparison with receptor models (connection with FAIRMODE WG3?) • Differences between CTMs and twin sites will depend on many factors, among which emissions (connection with FAIRMODE WG2?) • In this context, SHERPA could be used to test the impacts of the improvements made to the CTMs (or associated input) when applying them for specific urban scenarios

  8. Comparison of source receptor relationships • SHERPA and EMEP/CAMS source-receptor relationships could be inter-compared for some cities. This comparison will however end up in a direct comparison between CHIMERE (CTM underlying SHERPA) and EMEP when used to assess the impact of urban emission scenarios. However, how will such a comparison made us closer to establish the actual SR in the city area? • To perform this comparison, we should agree on a common city definition but the impact of other factors (grid resolution, emissions, meteorological year, time average…) on model responses to emission changes should also be assessed. Additional simulations with CHIMERE or EMEP are probably needed to support this analysis (connection with FAIRMODE WG4?).

  9. Note: The comparison between CTM based increments (based on scenarios) on one side and “Lenschowincrements” derived from the twin measurements is tricky (and probably not meaningful) given the different assumptions made in the two approaches. The same holds for Receptor models for which the Lenschow approach may compromise the source contribution (urban vs. regional) quantification (Belis et al., 2013) Conclusion: We are interested in contributing to the comparison but we need to define the scope and discuss further the methodological aspects.

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