TNO (M. Schaap, R. Kranenburg, S. Jonkers, A. Segers, C. Hendriks) METNO (M. Schulz, A. Valdebenito, A. Mortier, M. Pommier, S.Tsyro, H.

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Damian Turner
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1 This document has been produced in the context of the Copernicus Atmosphere Monitoring Service (CAMS). The activities leading to these results have been contracted by the Source contributions to EU cities Description of episode January 2017 TNO (M. Schaap, R. Kranenburg, S. Jonkers, A. Segers, C. Hendriks) METNO (M. Schulz, A. Valdebenito, A. Mortier, M. Pommier, S.Tsyro, H.Fagerli) Date: 28/03/2017 For Methods and References see full report on Source Contributions to Pollution in EU cities available on policy.atmosphere.copernicus.eu

1.1 Summary In January 2017, Southern Germany, Switzerland, Austria, the Czech Republic, and some areas in France and Belgium, experienced polluted events with high PM concentrations, and the third

2 1.1 Summary In January 2017, Southern Germany, Switzerland, Austria, the Czech Republic, and some areas in France and Belgium, experienced polluted events with high PM concentrations, and the third activation was thus applied. The results from both models suggested that during the episode in Amsterdam and Brussels, the significance of German emissions decreased, whereas that of French emissions increased. Further, it was shown that Eastern cities as Vienna and Prague, were mostly impacted by the pollution coming from the Eastern countries. It is also worth noting both models may be missing the emissions from residential heating in central Europe, causing an underestimation of the contribution of local sources. 1.2 Introduction The third activation for the country source receptor service came in for the period January In this week, a high pressure system was situated over central Europe, bringing stable conditions and extreme frost over the influenced regions. 1.3 Source definition The service set-up was kept as it was for the previous two episodes. The countries for which the source contribution was quantified were: United Kingdom, France, Belgium, Netherlands, Germany, Denmark, Austria, Switzerland, Czech Republic, Poland. The receptors were chosen to be the capitals of these countries as well as Oslo. 1.4 Episode From January 19 to 24, levels of PM 10 were elevated in Central Europe. Especially on January 23 rd and 24 th, concentrations at measurement stations in Southern Germany, Switzerland, Austria and the Czech Republic, as well as some in France and Belgium, far exceeded the daily limit value of 50 µg/m 3. The episode was characterized by a high pressure system over central Europe, which moved in from the Atlantic ocean on January 16 th. During the following ten days stable conditions with low temperatures characterized the situation (Figure 1) until a front moved in from the west on January 27 th. Figure 1. Top: synoptic situation at the January 21 st (zamg.ac.at) and January temperatures for Vienna. Bright lines are actual temperatures, vague lines are climatic averages (accuweather.com). CAMS D Country source receptor calculation Jan

3 Figure 2 shows a snapshot of observed (dots) and forecasted concentrations of PM 10 on January 23 rd at noon, using EMEP and LOTOS-EUROS. Both models capture the development of the episode, although the gradients and maxima are underestimated. In Belgium and the North of France, simulated values are much closer to the observations than in Southern Germany and Central Europe in general, where the highest values are observed. The discrepancy between the observations and the simulations probably indicates a misrepresentation of local sources in Central European countries. CAMS D Country source receptor calculation Jan

4 Figure 2. Comparison of forecasted and observed PM 10 concentrations using the EMEP (top) and LOTOS-EUROS (bottom) model in CAMS-50 for 23 January 2017, noon. CAMS D Country source receptor calculation Jan

5 Due to the uncommonly low temperatures, the real emissions from residential heating and perhaps electricity production could significantly exceed the emissions used by the models, which do not take all actual meteorological variability impact on emissions into account. Figure 3 to Figure 7 show the source attribution results for the central part of the episode for Amsterdam, Brussels, Vienna and Prague, respectively. The explanation of the colors is given in Error! Reference source not found.. The extra layer of dark grey in the plots for EMEP denotes the local sources (e.g. Amsterdam sources if Amsterdam is the receptor, Paris sources if Paris is the receptor). Both EMEP and (to a lesser extent) LOTOS-EUROS model higher PM 10 -concentrations in Amsterdam and Brussels (Figure 3 and Figure 4, respectively) than in Prague and Vienna (Figure 5 and Figure 6, respectively), while the measurements point at the opposite. For Amsterdam, LOTOS- EUROS shows a buildup of concentration arriving at the peak at January 23 rd, with contributions from across the region; during the buildup, emissions from Germany are significant but its role is taken over by French emissions as the episode proceeds. Domestic emissions play a significant role throughout the episode. The contrast between the German contribution on January and more French and British influence later on January is even more prominent in the EMEP model, that also calculates a strong contribution from countries that are not labeled in this exercise. LOTOS-EUROS models slightly higher peak levels for Amsterdam but shows lower concentrations in the last days of the episode. For Brussels, EMEP models higher concentrations than LOTOS-EUROS, again with a high contribution of unlabeled sources. The shift from German to French contribution that was seen in Amsterdam, occurs in Brussels as well, especially with EMEP. Local buildup is mainly important in the second part of the episode. In the LOTOS-EUROS results, Belgian emissions are relatively more important than for EMEP, which is mainly due to a lower modelled contribution to total PM 10 from Germany, France and unlabeled sources. For Prague, both models agree that emissions from the Czech Republic is an important source, combined with unlabeled sources and, for the last days of the episode, Germany. According to the EMEP model, PM 10 in Vienna is mainly due to unlabeled sources until January 24 th, when more local buildup is seen (with contributions from Austria / Germany / Czech Republic). This contrast is less prominent in the LOTOS- EUROS model, but the general pattern is the same. However, taking note that the models could very well be missing emissions from residential heating in central Europe, the contribution from local sources could in reality well be higher than indicated by the models for Prague and Vienna. The higher contribution of unlabeled sources for these two cities might be explained by the fact that many eastern European countries emissions were not labeled separately, and their impact on concentrations in central Europe will be higher than the impact on PM levels in Western Europe. CAMS D Country source receptor calculation Jan

Figure 3 Source attribution of PM10 for Amsterdam as

PM10 for Prague as modelled by EMEP (top) and Figure 4

(top) and Figure 6 Source attribution of PM10 for Vienna as

The extra layer of dark grey in the EMEP plots represent the

6 Figure 3 Source attribution of PM10 for Amsterdam as modelled by EMEP (top) and Figure 5 Source attribution of PM10 for Prague as modelled by EMEP (top) and Figure 4 Source attribution of PM10 for Brussels as modelled by EMEP (top) and Figure 6 Source attribution of PM10 for Vienna as modelled by EMEP (top) and Figure 7 Legend of Figures The extra layer of dark grey in the EMEP plots represent the local sources CAMS D Country source receptor calculation Jan

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