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Representativeness of the Measurement Network from the German Federal Environmental Agency -

Representativeness of the Measurement Network from the German Federal Environmental Agency - Evaluation of Pollutant Measurements and Atmospheric Model Results Heiko Pfeiffer with Günter Baumbach, Markus Wallasch and Rainer Friedrich TFMM in Bordeaux 23-25 April 2008.

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Representativeness of the Measurement Network from the German Federal Environmental Agency -

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  1. Representativeness of the Measurement Network from the German Federal Environmental Agency - Evaluation of Pollutant Measurements and Atmospheric Model Results Heiko Pfeiffer with Günter Baumbach, Markus Wallasch and Rainer Friedrich TFMM in Bordeaux 23-25 April 2008

  2. The aim of the project was to answer the following questions: • 1. Are the measurement sites suitably located to fulfil international obligations? • 2. Is the number of stations appropriate to fulfil international obligations? • 3. Are there any local conditions that affect the representativity? • 4. How far can the measurements be spatially extrapolated? • 5. For which air masses the measurement sites are representative? • 6. How would an ideal network look like? • 7. Is there need for action?

  3. STEPS taken to determine the representativity of the German measurement network: • - Definition of requirements • - Integration of German network into the EMEP network • - On-site inspection • - Evaluation of dispersion model results • - Evalutation of backward trajectories • - Evaluation of emissions inventory • - Evaluation of pollutant measurements and meteorology

  4. German measurement network (EMEP) (+OSPAR) (+HELCOM) (+GAW) (+GAW)

  5. Definition of requirements: EMEP siting criteria • 1. Distances to emission sources: • Source strength dependend distances • Source type dependend distances • 2. Site density: • Site density depends on concentration gradients • At EMEP level 2: 1-2 sites per 100.000 km² • Representativity: Larger than resolution of EMEP-grid (50 x 50 km) • 3. Avoid: • Coastal areas, mountain tops, sheltering, stagnant air

  6. EMEP measurement network 2004 • Red circles with r = 178 km according to the EMEP requirement „1-2 stations in 100.000 km²“ • No complete database available at the moment • Overall good distribution with a few gaps • German network integrates well but has gaps in the NW and SE

  7. Height above sea level [m] Distribution of altitudes in the EMEP network

  8. STATION VISIT: • Questionaire based on the defined EMEP criteria • Carry out the visits in an objective way • Investigate local conditions • How measurements are carried out • Local emission sources • Local topography • Obstacles

  9. RESULTS OF STATION VISIT (I) NG: Neuglobsow / SC: Schauinsland / SM: Schmücke WA: Waldhof / WL: Westerland / ZI: Zingst m.E.: limited agreement

  10. RESULTS OF STATION VISIT (II) NG: Neuglobsow / SC: Schauinsland / SM: Schmücke WA: Waldhof / WL: Westerland / ZI: Zingst m.E.: limited agreement

  11. SO2 Concentration field (EURAD) • Mostly uniform distribution in Germany • Uniform green areas around most stations – good spatial representativenesss • Hot spot located close to Zingst, caused by shipping traffic • Ruhr area in Germany • Cleary visible are hot spots in Poland, Czech Republic, Netherlands and Belgium

  12. NO2 Concentration field (EURAD) • Heterogenous distribution in Germany • Hot spots located in and around major cities and the Ruhr area • Schauinsland and Zingst are apparently representative only for a small area • Measurements of other stations can be extrapolated to a larger area

  13. Compareability model – measurements • Problematic for stations in mountainous areas • Significant devations of actual heights and model heights • Actual inversion layers cannot be reproduced by model • High deviations in Schauinsland and Schmücke

  14. Number of measurements Month • Inversion statistics Schauinsland • Schauinsland at 1205 m a.s.l. • At about 50 % of the year station is located in mixing layer • Stable conditions in October • Local influence only if inversion layer is above station

  15. Backward trajectories • Determine which air masses influence the station • 10 minute steps • 72 hours back in time • Density maps -> in winter stations are influenced by air masses coming from a larger area Winter Summer

  16. Backward trajectories (continued): • - Intersection with geographic layers– percentage of „residence“ of air masses over a certain country or sea • The geographic location obviously determines the most important influences: • + Neuglobsow: Germany, North/Baltic Sea, Poland • + Schauinsland: France, Germany, Italy and Switzerland • + Schmücke: Germany, France and North Sea • + Waldhof: Germany, North / Baltic Sea, Poland, France • + Westerland: North Sea and Germany • + Zingst: North / Baltic Sea, Germany, Poland, Sweden

  17. Schauinsland NO2 Westerland NO2 • Wind roses and pollutant roses • Determination of predominantly polluted sectors • Determination of influence of local sources • Identification of transboundary sources Schmücke SO2

  18. Month • Annual course of daily averages: • Compare with other stations and pollutants from the same station to identify long-range transport or individual sources • Example: SO2 pollution episode February 2005 result from long range transport from Eastern Europe

  19. NO/NO2 ratio to determine influence of local sources: • High ratio if measurements are influenced by a local source • At traffic stations, this ratio may be higher than 1 • Overall very low ratios indicating no major influences

  20. SO2 emissions inventory: • EMEP requirement: Minimum distance of 20 km to sources >1000kg SO2/year • For sources >100kg SO2/year the minimum distance is 2 km • Germany covered with sources >1000kg --- criterion cannot be met • Criterion itself is not applicable for Germany! • Same is true for NOx

  21. Mon Tue Wen Thu Fr Sat Sun • Average weekly course of measured NO2 concentrations: • -Local sources with weekly time paternsmay be revealed • Accumulation during the week • Only slight drop in the weekend – no indication for strong local influences • Highest variations in Schmücke – influence from Suhl only a few km away

  22. Average daily course of measured NO concentrations: • Characteristic course influenced by traffic • Slight delay of NO rise in Schauinsland due to depletion of inversion layer • All concentrations at a very low level • Maybe influence by natural sources

  23. Summary and conclusions: • Most EMEP siting criteria are fulfiled in Germany • Due to the structure of the topography and emission sources all requirements are not applicable to German conditions • The representativeness has to be assessed in a differentiated way • All stations are representative for a certain natural unit (e.g. Westerland for the North Sea, Schmücke for mountain ranges around 1000m) • There are local sources and obstacles influencing wind measurements. • In some of the stations certain wind directions have to be filtered to exclude local influences • All measurements are of high quality standard and can be assigned to EMEP Level 2 • An extension with two stations is recommended to fill spatial gaps and the missing height level at 500 m

  24. FIN • Merci pour votre attention

  25. PM10 Concentration field (EURAD) • Mostly uniform distribution in Germany • Hot spot located in Ruhr area • Hot spots in Poland, Czech Republic, Netherlands, Belgium • Good spatial representativeness with the exception of Zingst and Schauinsland

  26. NH3 Concentration field (EURAD) • Heterogenous distribution in Germany • Highest ammonia loads in NW and SE Germany • Overall good representativeness except for Schauinsland and Westerland

  27. Average measured concentrations: • Low concentrations of NO and SO2 --- measurements are not influenced by strong local sources • Inversion layer is crucial for stations in mountainous area --- lowest NO and NO2 concentrations in Schauinsland • Ozone in Zingst comparably low --- influence of shipping traffic • Significant drop of pollutant concentrations in Westerland when locally influenced wind sectors are left out (240°-360°)

  28. NOx emissions inventory: • Circles at 2km, 20km and 50 km • Lowest source density around Neuglobsow, no major source within 50 km • Both Schauinsland and Schmücke are surrounded by many sources and influenced by a large city in a distance less than 50 km Schauinsland Schmücke Neuglobsow

  29. Average daily course of measured CO2 concentrations: • Constant level in Schmücke and Schauinsland • Siginificant rise during night in Neuglobsow, which is located in a forest • -> concentrations stem from plants in combination with surface inversion

  30. Answers to the questions posed in the beginning: • Are the measurement sites suitably located to fulfil international obligations? • Yes, provided that existing local influences are removed. • Is the number of stations appropriate to fulfil international obligations? • An extension with two stations is recommended to fill spatial gaps and the missing height level at 500 m • Are there any local conditions that affect the representativity? • Yes, local sources and obstacles influencing wind measurements. • How far can the measurements be spatially extrapolated? • Due to the high density of sources in Germany and other influences like inversion layers the area of extrapolation is limited at some stations.

  31. Answers to the questions posed in the beginning: • 5. For which air masses the measurement sites are representative? • Significant differences between winter and summer, details have been shown earlier. • 6. How would an ideal network look like? • An ideal network would cover the whole area of Germany and it’s natural units (coasts, mountains, planes etc.) and also the height distribution. • Firstly: Localise sites on maps (pollutant conc., emissions, land use, DEM) • Secondly: Visit chosen sites to exclude local influences and verify economic feasibility • Is there need for action? • At 3 stations specific wind directions have to be filtered and wind measurements at 2 stations have to be installed at a greater height.

  32. Agreement of annual averages model - measurements • Red positive deviation, green negative deviation of the model compared with measurements • High deviations for NO and SO2 the other pollutants differ ~ 30% • Most shown deviations are on a relatively low concentration level • NO difference can be explained by an underestimated NO – NO2 conversion • Schauinsland located at 1205 m a.s.l. - high deviation due to inversion layers

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