Assessment Report: Air quality in Europe in 2010

Transcription

1 MACC-II Deliverable D_113.2 Assessment Report: Air quality in Europe in 2010 Date: 05/2013 Lead Beneficiary: INERIS (#17) Nature: R Dissemination level: PU Grant agreement n

2 File: MACCII_EVA_DEL_D_113.2_AQ2010_May2013_INERIS.docx/.pdf Work-package 113 (EVA, Assessment reports production and routine validation) Deliverable D_113.2 Title European Air quality evaluation report for 2010 Nature R Dissemination PU Lead Beneficiary INERIS (#17) Date 05/2013 Status Final version Authors L. Rouïl et al. (INERIS) Approved by V.-H. Peuch (ECMWF) Contact info@gmes-atmosphere.eu This document has been produced in the context of the MACC-II project (Monitoring Atmospheric Composition and Climate - Interim Implementation). The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7 THEME [SPA ]) under grant agreement n All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and liability. For the avoidance of all doubts, the European Commission has no liability in respect of this document, which is merely representing the authors view. 2 / 57

3 Assessment Report: Air quality in Europe in 2010 Edited by Laurence ROUÏL (INERIS) With contribution from the MACC regional modelling teams: CERFACS CNRS/LISA FMI KNMI INERIS Meteo France Met.no RIUKK SMHI TNO Emmanuele Emili Matthias Beekman, Gilles Foret Mikhail Sofiev, Julius Vira Henk Eskes Frédérik Meleux, Anthony Ung Virginie Marécal Alvaro Aldebenito, Anna Carlin-Benedictow Hendrik Elbern, Elmar Friese, Achim Strunk Lennart Robertson Arjo Segers March

4 Table of content 1 Executive summary Rationale Ozone concentrations in Ozone indicators Uncertainty analysis Nitrogen dioxide indicators Annual and seasonal averages Uncertainty analysis PM10 indicators Annual and daily indicators compared to limit values Uncertainty analysis PM2.5 indicators Pollen indicators Birch pollen distribution in Performance of SILAM model in the birch pollen re-analysis in Conclusions

5 List of figures Figure 1. Reanalysis of the annual daily mean ozone concentrations in (Ensemble data assimilated chain of the MACC-II /EVA systems) 13 Figure 2. Reanalysis of the annual daily mean ozone concentrations in (Ensemble data assimilated chain of the MACC/EVA systems) 13 Figure 3. Reanalysis of the summer daily mean ozone concentrations in 2010 (Ensemble data assimilated chain of the MACC-II/EVA systems) 14 Figure 4. Number of days in 2010 when the 8-hour daily average exceeded 120 µg/m 3 (Ensemble data assimilated chain of the MACC-II/EVA systems) 15 Figure 5. Number of days in 2009 when the 8-hour daily average exceeded 120 µg/m 3 (Ensemble data assimilated chain of the MACC/EVA systems) 15 Figure 6. Re-analyses of SOMO35 in (µg/m 3 ) days(right column)and AOT40 in (µg/m 3 ) hours(left column) indicators for the year 2010 (top) and the year 2009 (below) 17 Figure 7. Observed number of exceedances in 2010 of the Ozone information threshold (180 µg/m 3 hourly); EUW: Western Europe, EUC: central Europe, EUS: Southern Europe, EUN: Northern Europe, EUE: Eastern Europe 18 Figure 8. Observed number of exceedances in 2010 of the Ozone alert threshold 19 (240 µg/m 3 hourly); EUW: Western Europe, EUC: central Europe, EUS: Southern Europe, EUN: Northern Europe, EUE: Eastern Europe 19 Figure 9. Simulated number of exceedances in 2010 of the Ozone information threshold (180 µg/m 3 hourly), throughout Europe; results issued from the MACC-II/EVA ensemble system 19 Figure 10. Correlation coefficient comparing MACC-II/EVA reanalyses of ozone daily maximum concentrations to observations (AIRBASE)- period: summer Figure 11. Root mean square Error comparing MACC-II/EVA reanalyses of ozone daily maximum concentrations to observations (AIRBASE)- period: summer Figure 12. Standard deviation (in µg/m 3 ) of model responses to simulate annual daily ozone averages in 2010 (left) and 2009 (right) 22 Figure re-analysed annual mean concentrations of Nitrogen dioxide throughout Europe simulated by the RIUUK/EURAD modelling system assimilating AIRBASE data and satellite information 24 Figure 14. Distance-to-target graphs for the annual NO2 limit value, for different station types, 2010; source : 2012 EEA air Quality report in Europe 25 Figure re-analysed winter mean concentrations of Nitrogen dioxide throughout Europe simulated by the RIUUK/EURAD modelling system assimilating AIRBASE data and satellite information 25 Figure 16. RMSE for 2010 daily mean concentrations calculated at urban stations with all the MACC-II regional modelling suites. The a index refers to data assimilated results. EUW: Western Europe, EUC: central Europe, EUS: Southern Europe, EUN: Northern Europe, EUE: Eastern Europe 26 Figure 17. Correlation coefficient for 2010 daily mean concentrations calculated at suburban sites with all the MACC-II regional modelling suites. The a index refers to data assimilated results. EUW: Western Europe, EUC: central Europe, EUS: Southern Europe, EUN: Northern Europe, EUE: Eastern Europe 27 Figure Reanalysis of the PM10 annual average in Europe (Ensemble data assimilated chain of the MACC-II/EVA systems) 29 Figure Reanalysis of the PM10 annual average in Europe (Ensemble data assimilated chain of the MACC/EVA systems) 29 Figure 20. Annual PM10 mean concentrations measured at the European monitoring sites in Source: EEA 30 Figure Reanalysis of the PM10 winter average in Europe; (Ensemble data assimilated chain of the MACC-II/EVA systems). 30 3

6 Figure 22. Number of days exceeding the 50 µg/m 3 threshold (daily average) simulated by the MACC- II/EVA system in Figure 23. Number of days of exceedances of the PM10 regulatory threshold (50 µg/m 3 ) observed in Europe in 2010, sorted by regions 31 Figure 24. Bias for daily means of PM10 concentrations at urban stations over the year 2010 computed by the various MACC-II/EVA models and sorted by sub-regions: EUW: Western, EUC: central, EUS: Southern, EUN: Northern, EUE: Eastern. 33 Figure 25. Correlation coefficients for daily means of PM10 concentrations at urban stations over the year 2010 computed by the various MACC-II/EVA models and sorted by sub-regions: EUW: Western, EUC: central, EUS: Southern, EUN: Northern, EUE: Eastern. 33 Figure 26. Comparison between the observed and ensemble modelled number of days when the 2010 PM10 daily regulatory threshold value was exceeded; Ensemble re-analyses provided by the MACC-II/EVA ensemble system. 34 Figure 27. Number of days of exceedances of the PM10 regulatory threshold (50 µg/m 3 ) modelled in Europe in 2010 by the ensemble re-analysis system, sorted by regions 34 Figure 28. PM2.5 annual average concentrations for the year 2010, assessed with the data assimilation EURAD system (re-analysed fields) 35 Figure 29. Annual PM 2.5 mean concentrations measured at the European monitoring sites in Source: EEA 36 Figure 30. Propagation of the flowering season in 2010: 10-days mean concentrations from till [pollen m - 38 Figure 31. Total pollen load (cumulative daily count over the whole season), [pollen day m -3 ], left-hand panel, and the number of hours when the pollen concentrations exceeded 50 pollen m Figure 32. Season start, end and duration following the 2.5%-97.5% criterion 40 Figure 33. Correlation coefficient and RMSE of the daily time series for birch pollen 42 Figure 34. Model-predicted mean pollen concentrations over with calibration method of data assimilation (panel a); bias of 4D-VAR-based prediction (panel b) and calibration-based prediction (panel c). [pollen m -3 ] 43 Figure 35. Location of the NO 2 AIRBASE stations selected for data assimilation (red dots) and validation (green dots) processes 50 Figure 36. Location of the O3 AIRBASE stations selected for data assimilation (red dots) and validation (green dots) processes 51 Figure 37. Location of the PM10 AIRBASE stations selected for data assimilation (red dots) and validation (green dots) processes 52 Figure 38. Location of the PM2.5 AIRBASE stations proposed for data assimilation (red dots) and validation (green dots) processes 53 4

7 Glossary AIRBASE European Air Quality database ( Analyses Maps of air pollutant concentrations fields issued from numerical model results combined with up-to-date available observation data to improve their accuracy in the vicinity of measurement points. In MACC-II, they are produced routinely on a daily basis. AOT 40 Accumulated Ozone over the 40 ppb Threshold AQD Assessments CERFACS Air Quality Directive Quantitative evaluation of air quality fields based on validated data and numerical model results Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique (France) Data assimilation Mathematical process to incorporate observations in a numerical model of physical systems EAN: European Aeroallergen Network EDA Air Quality data assimilation sub-project in the MACC and MACC-II projects EEA European Environment Agency ENS Air quality forecasting and analysis sub-project in the MACC and MACC-II projects Ensemble Model Combination of various results from various models. This can be a simple average (median), or a weighted average resulting from analysis of models behaviour over past periods. The models building-up the ensemble can correspond to different systems (multi-model approach) or to the same modelling system fed with different input datasets. EVA: Air quality validated assessments sub-project in the MACC and MACC-II projects FMI Finnish meteorological Institute KNMI Royal Netherlands Meteorological Institute LISA Laboratoire Interuniversitaire des Systèmes Atmosphérique (France) Météo France French Weather Services Met.no Norwegian Meteorological institute (Norway) Raw model data Model results directly issued from the modelling chain, without any post-treatment process Re-analyses Maps of air pollutant concentrations fields issued from numerical model results combined with validated observation data to improve their accuracy in the vicinity of measurement points RIUKK Rhenish Institute for Environmental research at the University of Cologne (Germany) 5

8 RMSE SMHI Root Mean Square Error. It gives the standard deviation of the model prediction error. A smaller value indicates better model performance. Swedish Meteorological and Hydrological Institute (S) SOMO35 Ozone concentrations accumulated dose over a threshold of 35 ppb TNO Netherlands Organisation for applied Scientific Research (NL) VOC WHO WMO Volatile Organic Compound World Health Organization World Meteorological Organization 6

9 1 Executive summary This report is the MACC-II/EVA assessment report for the year 2010 delivered by the air quality re-analysis multi-model system implemented in the MACC and MACC-II projects from the Copernicus program. It results from the regional air quality reanalysis chains that have been developed by the MACC-II regional air quality modelling teams. Actually, seven European regional air quality modelling systems were run to provide simulations of air pollutant concentrations over the year 2010, with an hourly resolution throughout Europe. When possible (depending on the pollutant and the maturity of the modelling chain), data assimilation procedures were activated to calculate re-analyses of the air pollution fields, combining raw simulation outputs with validated observations available at monitoring stations running in air quality networks. So-called Ensemble estimations of the concentrations had been built up mixing the available results from the various models in a median average. Operational production of ensemble re-analysed air pollutant concentration fields is the main objective of the EVA subproject within MACC-II, for deriving, on a yearly basis, air pollution regulatory and usual exposure indicators. It is expected that very comprehensive information on air pollution patterns and levels, considered as the best estimate can be derived. A systematic evaluation of the model and data assimilated results against a relevant set of dedicated observation data (not used for assimilation in the models) has been conducted. It showed very satisfactory performances compared to the state of the art. The strength of the multi-model approach is the capacity of highlighting the most sensitive geographical areas where re-analyses are the most uncertain, either because of model weaknesses (uncertainties on emissions, complex topography, and uncertainty in the dynamic and chemical parametrizations...) or because of a lack of available representative observations. Uncertainty is linked to the range of model responses. Therefore we hope to be able to provide policy-oriented users with high quality maps associated with spatio-temporal uncertainty linked to the variability of the model responses. MACC-II regional air quality re-analyses are relevant for assessing rural and urban background concentrations and areas where the limit values are exceeded 1. This information must be reported according to the air quality Directive of the European Commission (2008/50/EC). Because the overall European domain is considered in MACC-II, in countries where no mapping or modelling systems are available to simulate air pollution patterns, MACC-II products can be an alternative to get data to be reported, or to get assessments of pollution episodes. Beyond graphical maps, numerical data issued from model results with or without assimilation process can be delivered to interested users on request. 1 It should be noted that the MACC-II assessments are not suited for mapping local exceedances (street canyons, industrial sites). Model resolution is too coarse to simulate correctly such situations that must be considered with appropriate models. 7

10 The year 2010 revealed interesting issues in terms of air pollution. High temperatures in summer time (and even a heat wave that impacted Eastern Europe and Russia) and very cold temperatures combined with stable meteorological conditions in winter were responsible for air pollution episodes that impacted a large part of Europe over the whole year. Summer ozone levels rather high, especially in Mediterranean countries and in the Balkans, where averaged daily ozone concentrations were above µg/m 3. Belgium, a large part of France, Germany, Austria, Czech Republic, Croatia, Slovakia, and a part of Hungary and Poland were the most exposed countries considering indicators relevant for both human health and ecosystem impacts. A strong gradient between northern and southern countries developed. Annual PM 10 concentrations ranged in 2010 between 5 µg/m 3 (in Scandinavia) to 40 µg/m 3 which the limit annual average value set by the air quality Directive. The limit value was exceeded in Poland, Slovakia, Czech Republic, Turkey, Italy, the Balkans and in Paris area. PM10 high concentrations occurred during winter periods because of stable meteorological conditions combined with residential heating extra emissions (compared to the rest of the year). Concerning the number of days exceeding the 50 µg/m 3 limit value (daily average), it is significantly higher than in The MACC- II/EVA assessment reports the number days of exceedances of the PM 10 regulatory threshold for daily average at background locations. The model resolution is not sufficient to catch exceedances at traffic or industrial stations that are representative of local air pollution sources. However the MACC-II/EVA assessment work gives an idea of the European areas which are the most sensitive to PM 10 pollution. Poland, Slovakia, Czech Republic, the Pô Valley, Turkey and other specific locations in Western and Central Europe are the most concerned. The 35 limit number of days exceeding the daily limit value is likely to be exceeded in all these regions. In 2010, an unusual number of days exceeding the PM10 regulatory threshold for the daily value was noticeable in the East part of Europe and in western-northern Europe. It is interesting to note the good consistency between annual NO 2 and PM 2.5 concentration fields. The Pô Valley, Poland, Slovakia, Czech Republic, Benelux, Turkey and big cities in Europe had been concerned by worrying concentrations close to the limit value for NO 2 and the target threshold for PM 2.5. PM2.5 simulations reported areas where the target value of 20 µg/m 3 (annual average) for 2020 is already exceeded. Locations concerned are hot spot areas being driven by high anthropogenic activity. Thanks to the FMI expertise and effort, re-analyses and assessment of pollen concentration levels (from birches) to which European citizens were exposed in 2010 are also provided in this report. It is particularly interesting to note that the number of days when pollen concentrations lead to mass concentrations higher than the PM10 regulatory threshold for daily mean (50 µg/m 3 ) can be very high. This shows the potential contribution of this natural source to PM episodes depending on the season. Evaluation of the pollen re-analysis system developed by the FMI is provided to complete the information. This report synthesizes those results with a selection of illustrations. All numerical data are available for users on request to the MACC-II/EVA sub-project leader: Laurence.rouil@ineris.fr. 8

11 2 Rationale The Copernicus/MACC-II project aims at delivering a number of services dedicated to global atmospheric composition and air quality monitoring in Europe. Air quality issues are covered by two services ( : - The so-called ENS services are focussed on routine and near real time products (forecasts, Near Real Time analyses). Up to four days forecasts of ozone, nitrogen dioxide and particulate matter concentrations (PM 10 and PM 2.5 ) throughout Europe are available every day. Daily analyses of the same variables (simulated fields are improved with observations thanks to data assimilation techniques) are proposed as well. They are available on the Copernicus atmosphere services website ( ). - The so-called EVA services relate to detailed analysis of past situations thanks to validated material issued from observation networks and modelling. Therefore, a posteriori validated air quality assessments for Europe, based on re-analysed air pollutant concentration fields are proposed. Simulations of past years are performed and improved thanks to the assimilation of available validated in-situ and satellite observations. They are available on the Copernicus atmosphere services website ( The so-called MACC-II regional air quality assessment reports describe, with a yearly frequency, the state and the evolution of background concentrations of air pollutants in European countries. Special care is given to pollutants characterised by the influence of long range transport, correctly caught by European scale modelling systems: ozone, nitrogen dioxide, particulate matter (PM 10 and PM 2.5 ). Focus on specific pollution episodes that happened during the year will be considered. The MACC-II air quality assessment initiative is conceived to be an actual tool for European policy and decision makers in charge of air quality monitoring and reporting. Indeed, according to the Directive an Ambient Air quality and Cleaner Air for Europe (the CAFE Directive 2008/50/EC), the Member states have to report to the European Commission and to inform their citizens on air pollution they are exposed to. In particular, situations (or episodes) when nitrogen dioxide and particulate matter concentrations exceed regulatory limit values (or target values for ozone) must be carefully analysed (geographical extension, duration, intensity, population exposed...) and action plans to limit their impact and to avoid their future development must be proposed. Member states usually base their investigations on observations available from national air quality monitoring networks, modelling tools and national expertise. In its operational stage (foreseen in 2014) the Copernicus atmospheric services will propose complementary and comprehensive information established on the basis of state of art chemistry-transport models run operationally by modelling teams with a long experience in the field of air pollution, and extended in-situ and satellite observation sets gathered and made available. Both sources of information (modelling and measurement) are smartly mixed by the MACC-II scientists to elaborate the so- 9

12 called re-analyses of past periods and support national experts in the Member states in their interpretation of air pollution trends and events that touch their country. With the MACC-II project this new generation of tools and information based on indicators geographically distributed on comprehensive maps becomes available. It combines the capacities of chemistry transport models to represent air pollutant patterns and the accuracy of observations in re-analyses. They are compiled and presented in the MACC-II/EVA assessment reports, being considered as the best available or most realistic representations of air pollution patterns: more relevant than interpolation of observations and more accurate than raw simulations. Moreover the multi-model functionalities developed in the MACC-II services allow the provision of ensemble model estimations. They relate to the combination of the various model results to obtain an average with improved skills compared to the individual models ones. This combination can be a simple median average or more sophisticated averages, weighted by coefficients depending on the model, the geographical location, the simulated period... The 2010 ensemble assessments are built upon the median of the MACC-II models results. The present document is the fourth MACC-II assessment report on European air Quality for the year It is established on model outputs provided by the six modelling teams involved in the regional operational production, and the in-situ observations available from the AIRBASE database (from the European Environment Agency, 2. Notice The technical descriptions of the modelling and data assimilation systems that have been used for the 2010 assessment report are described in detail in annex 1. A specific report is edited to describe the model performances (individual models and ensemble). The performances of the models and data assimilation systems were systematically evaluated by INERIS. The maps and graphs proposed in the present report correspond to the best available estimation of the targeted indicators amongst the individual model results and the ensemble, according to the evaluation that have been conducted and reported in the validation report. Content of the report This report describes the air quality situation in Europe during the year A set of indicators characterizing the state of regulatory air pollutant concentrations is established. It includes annual and seasonal indicators: average values and indicators related to situations when regulatory thresholds for the considered pollutants are exceeded, are proposed, for a better qualification of the year 2010 in terms of air quality exposure in Europe. These indicators are presented in the following sections for the ozone, particulate matter and nitrogen dioxide. Another section is dedicated to pollen re-analyses. 2 Data being available on 10

13 All the indicators presented in the report have been calculated using the MACC-II data issued from the available assimilation chains that the regional modelling teams developed within the MACC and MACC-II projects. Observation data that were used both for building up the re-analyses and performing the score calculations were issued from the AIRBASE database maintained by the European Environment Agency (EEA). No satellite information was used at this stage of the project, except for the production of NO 2 concentration fields with the German EURAD model and the Dutch LOTOS- EUROS model. Moreover, the air quality indicators presented in this report result from data assimilated results from the individual model chains combined in a median average to process the MACC-II Ensemble. In the following section the Ensemble assimilated fields are referred as ENSa 3. Finally, whatever the considered indicators, the availability of various models 4 results allows to assess the range of variability of model responses which is considered as a quantification of the modelling uncertainty. So it is possible to consider the situations when the model results are the most uncertain (when the range of variability of the model responses is the highest) and should be taken with caution. It should be noted that all the results, maps and graphs presented in this report can be consulted and downloaded on the MACC website ( Below the graphical displays, the model numerical results are available on request. Please contact Laurence Rouïl the leader of the MACC-II/EVA subproject (Laurence.rouil@ineris.fr). 3 In comparison to the Ensemble results from the raw simulations that are simply referred as ENS 4 These models were acknowledged for their correct performances they showed during the preliminary GEMS ( ) and MACC ( ) precursor projects 11

14 3 Ozone concentrations in Ozone indicators The summer of 2010 was exceptionally warm in Eastern Europe and large parts of Russia, and high temperatures had been recorded in the whole of Europe (see annex 1). Therefore average daily mean concentrations over the year 2010 and throughout the MACC domain presented on Figure 2, show rather high ozone levels, especially in Mediterranean countries and in the Balkans, where averaged daily ozone concentrations were above µg/m 3. Figure 2 presents, as a reminder the 2009 situation which was quieter. Considering summer situation (Figure 3) highlights the exceptional nature of ozone levels in Europe. Daily mean ozone concentrations ranged from 50 µg/m 3 in very few areas (Benelux, United Kingdom, North Scandinavia), to µg/m 3 in the largest part of Europe. Higher levels exceeding 90 µg/m 3 were observed in areas along the Mediterranean coast. The most exposed areas appear clearly considering Figure 4 that maps the number of days when the 120 µg/m 3 threshold (8-hours average) had been exceeded. It must be noted that the target value set in the air quality Directive (2008/50/EC) is 25 days. It was exceeded in several places along the Mediterranean Sea (Italy, Portugal, Turkey, South of France and Greece) and about 20 (or more) days were recorded in the largest part of Europe. This is a remarkable difference with the 2009 situation: the places where the 25 days threshold was exceeded are more or less the same, but in 2010 the total area where this threshold was almost reached (between 20 and 25 days) is quite large. It covers Belgium, a large part of France, Germany, Austria, Czech Republic, Croatia, Slovakia, and a part of Hungary and Poland. But it is interesting to note that these high temperatures (and summer heat waves) did not impact ozone levels as it was the case in High ozone levels slightly increased compared to the previous years, but no unexpected high values had been reported. This is well illustrated in the trends analysis established by the European Environment Agency in its annual report 5. This point should be more investigated to check whether such a situation can result from the impact of the emissions reduction strategies of ozone precursors (NOx and Volatile Organic Compounds especially). This can also be the consequence of the economical crisis that touched Europe in According to IIASA, it was supposed to reduce anthropogenic air pollutant emissions by about 25% and 20 % of NOx and VOC emissions respectively between 2005 and Air Quality in Europe 2012 report, EEA report n 4/2012, 6 Greenhouse gases and air pollutants in the European Union: baseline projections up to 2030; EC4MACS interim report. 12

15 Figure 1. Reanalysis of the annual daily mean ozone concentrations in (Ensemble data assimilated chain of the MACC-II /EVA systems) Figure 2. Reanalysis of the annual daily mean ozone concentrations in (Ensemble data assimilated chain of the MACC/EVA systems) 13

16 Figure 3. Reanalysis of the summer daily mean ozone concentrations in 2010 (Ensemble data assimilated chain of the MACC-II/EVA systems) 14

17 Figure 4. Number of days in 2010 when the 8-hour daily average exceeded 120 µg/m 3 (Ensemble data assimilated chain of the MACC-II/EVA systems) Figure 5. Number of days in 2009 when the 8-hour daily average exceeded 120 µg/m 3 (Ensemble data assimilated chain of the MACC/EVA systems) The indicator generally used in regulatory reporting to assess ozone impact on vegetation according to the Air Quality Directive is the AOT40. 15

18 AOT 40 (Accumulated dose over a threshold of 40 ppb) requires for its calculation hourly ozone data. It is the sum of the differences between the hourly ozone concentration (in ppb) and 40 ppb, for each hour when the concentration exceeds 40 ppb, accumulated during daylight hours (8:00-20:00 UTC). AOT40 has a dimension of (µg/m 3 )hours. In the AQD, the AOT40 calculated from May to July should not exceed (µg/m 3 )hours, with a target value of (µg/m 3 )hours. For quantification of the ozone health impacts the World Health Organisation recommends the use of the SOMO35 (ozone concentrations accumulated dose over a threshold of 35 ppb) indicator. SOMO35 is the sum of the differences between maximum daily 8-hour running mean concentrations greater than 35 ppb and 35 ppb. SOMO35 has a dimension of (µg/m 3 )days. This indicator is used as an impact constraint in impact assessment modelling used to define national emission ceilings set in the NEC Directive (2001/81/EC). Ensemble re-analyses produced by the MACC-II platforms allow to map AOT40 and SOMO35 indicators Figure 6. Those maps show a significantly higher impact of ozone level on populations and ecosystems in 2010 than in A north-south gradient is strongly marked: In 2010, SOMO35 ranged from values lower that 1000 in the Northern Europe to more than 8000 (µg/m 3 )days in some punctual locations in the Mediterranean countries. In between SOMO35 ranged from 4000 to 5000 (µg/m 3 )days over most of the country. The situation is the same when AOT 40 exposure indicator is studied. The same North- South gradient developed. It should be noted that the total area where the (µg/m 3 )hours target value is reached or exceeded is quite large, covering Germany, Czech republic, Slovakia, Slovenia, Croatia, the Balkans, Italy, Portugal, and large parts of Austria, Switzerland, France and Spain. As usually, health and ecosystems exposure was higher in the Po Valley, south of Italy, South of France, South of Spain, Greece, Balkans and Turkey counter-balances clearly the slight improvement in terms of ozone exposure noted in See 2009 MACC air quality validated assessment report 16

19 Figure 6. Re-analyses of SOMO35 in (µg/m 3 )days (right column)and AOT40 in (µg/m 3 )hours (left column) indicators for the year 2010 (top) and the year 2009 (below) Figure 7 and Figure 8 show the number of situations when information threshold (180 µg/m 3 hourly) and alert threshold (240 µg/m 3 hourly) are exceeded, respectively. Those diagrams are based on data from the AIRBASE European database. Figures are sorted by geographical regions and southern Europe and central Europe appear as the most concerned areas. Only 6 hours exceeding the alert threshold were reported. They have been correctly caught by the ensemble modelling system. Figure 9 represents the cumulated number of exceedances of the information threshold simulated by the MACC-II Ensemble model. Although this simulated indicator fits rather well with observations, it should be noted that a number of exceedances have not been correctly caught. They mainly correspond to episodes that occurred in Central Europe and in the second half of July. However this conclusion should be moderated by the fact that evaluation against observed threshold indicators is extremely difficult, the response being right or wrong 17

20 whether the model answer is few micrograms/m 3 (in its range of uncertainty) below or above the limit value. Figure 7. Observed number of exceedances in 2010 of the Ozone information threshold (180 µg/m 3 hourly); EUW: Western Europe, EUC: central Europe, EUS: Southern Europe, EUN: Northern Europe, EUE: Eastern Europe 18

21 Figure 8. Observed number of exceedances in 2010 of the Ozone alert threshold (240 µg/m 3 hourly); EUW: Western Europe, EUC: central Europe, EUS: Southern Europe, EUN: Northern Europe, EUE: Eastern Europe Figure 9. Simulated number of exceedances in 2010 of the Ozone information threshold (180 µg/m 3 hourly), throughout Europe; results issued from the MACC- II/EVA ensemble system 19

22 3.2 Uncertainty analysis The twin report Validation of the MACC assessment products for the year 2010 contains a number of scores that allow the qualification of skills and performances of the MACC-II products. Indeed, to build up confidence in the Copernicus air quality regional modelling system, special care should be according to the evaluation of its performance in simulating right concentrations and in catching correctly situations when regulatory threshold values are exceeded. In this paragraph, only main conclusions are reported to help in the interpretation of the previous results. The correlation coefficient (Figure 10) between simulations and observations is high for the ensemble estimations (about 0.9) whatever the station typology. The model performances are rather consistent from a geographical area to another. In particular, good results for Southern Europe (rural stations) are a noticeable improvement of the MACC-II modelling system achieved over the period of the project. The Root mean square error (RMSE) gives the standard deviation of the model prediction error. A smaller value indicates better model performance. RMSE for the Ensemble results is displayed on Figure 11 for various station typologies. The results are very convincing as well with values lower than 15 µg/m 3 (and even 10 µg/m 3 ) in many locations. Few isolated stations show poor performance especially in the South of Europe where models have generally more difficulties to reproduce correctly dynamic and chemical complex processes. Finally it is important to note that the proposed simulations did not account for the impact of the huge forest fires that occurred in Russia in during the first half of August. The impact of forest fires on ozone and PM atmospheric concentrations is well-known and it is expected that ozone concentrations in Central and Eastern Europe could have been influenced by huge forest fire emissions. It will be interesting to investigate the actual impact of such events on ozone concentrations. Nevertheless, in the next reports the MACC-II modelling chains will account for forest fire emissions provided by the dedicated sub-project (FIRE). A significant improvement of the model performances is expected from this new functionality. 20

23 Figure 10. Correlation coefficient comparing MACC-II/EVA reanalyses of ozone daily maximum concentrations to observations (AIRBASE)- period: summer 2010 Figure 11. Root mean square Error comparing MACC-II/EVA reanalyses of ozone daily maximum concentrations to observations (AIRBASE)- period: summer 2010 For further information, the last map (Figure 12) presents the standard deviation of the various models responses to simulate daily ozone mean over the years 2010 and 21

24 2009. The most uncertain areas where the standard deviation is the highest are Italy, Croatia, Balkans, Greece, and Turkey. But the variability of model responses is higher for 2010 showing that ozone production regimes where more complex to simulate in It should be noted that uncertainty of modelling in Central Europe was a bit higher in 2010 than in However in the largest part of Europe this index remains reasonably low showing the relevance of the approach. Standard deviation 2010 Standard deviation 2009 Figure 12. Standard deviation (in µg/m 3 ) of model responses to simulate annual daily ozone averages in 2010 (left) and 2009 (right) 22

25 4 Nitrogen dioxide indicators Warning note: It should be noted that the MACC-II/EVA mapping system is not fitted to deal with local hot spot situations as those that develop near busy roads or on industrial sites. Actually, the model resolution is about km 2, and is not sufficient to catch actual NO 2 concentrations at traffic and industrial sites. For the year 2010, three teams over six assimilated in an operational way NO 2 observations: RIUUK (Rhenish Institute for Environmental Research at the University of Cologne) assimilated NO2 ground-level observations from the AIRBASE database and also NO 2 columns retrieved from satellites observations (OMI, GOME-2, SCHIAMACHI). The consortium KNMI/TNO assimilated ground level concentrations from AIRBASE, and satellite observations from IASI. The FMI assimilated NO 2 in-situ data from AIRBASE in its results. For a pollutant driven by local sources like NO 2, data assimilation improved significantly the simulation of concentration patterns (see warning note above) and regional model results with observed data assimilation should be preferred. Therefore, an ensemble based on the results of these three models has been built-up. The analysis of the scores will be discussed in detail in the 2010 validation report. It shows that because of various levels of maturity in the data assimilation chains and because the models do not assimilate the same sets of observations, differences of performance between models used in the ensemble are high. It impacts the quality of the ensemble which is not yet optimal. Because the results presented in this report should be based on the best estimates provided by the MACC-II suites, we have chosen to present NO 2 indicators simulated by the best individual model. The EURAD assimilated chain from the RIUUK performed the best results in that sense and are commented below. 4.1 Annual and seasonal averages The annual mean of nitrogen dioxide concentrations should not exceed the limit value 40 µg/m 3 to be in compliance with the 2008 AQ Directive. This indicator mapped throughout Europe is presented below (Figure 13). The cities footprint is clearly marked with annual background concentrations reaching or close to the limit value. Many countries were concerned. The EEA report on Air quality in Europe mentioned that (...) in 2010, 22 of the 27 EU Member States recorded exceedances of the limit value at one or more stations. The ship traffic influence is caught as well, especially in the Mediterranean Sea and in the Channel where NO 2 annual concentrations ranged between 20 and 30 µg/m 3. The Pô Valley, Germany, the UK, Czech Republic and the Benelux were the most exposed areas to NO 2 in Europe in The distance to the annual NO 2 concentrations limit is analysed with regard to station typology in EEA 2012 air quality report ( ). Figure 14 is issued from this report and synthesises this analyses. It shows that the exceedances are mainly due to traffic hot spots that are not in the scope of the MACC-II/EVA work. 23

26 In winter high concentration averages are clearly correlated with high emission zones like cities areas. Figure 15 shows winter nitrogen dioxide mean concentrations in Europe re-analysed by the EURAD system. As expected, annual high concentrations actually occurred in winter, because of stable and cold meteorological conditions that were observed at the beginning and the end of the year. Very cold weather induced larger emissions due to fuel combustion and biomass burning (traffic and residential heating are the main sources). The North part of Western Europe and North part of Italy were particularly concerned by high winter concentrations. In major cities of Western Europe, winter averaged NO 2 concentrations exceeded 40 µg/m 3. Figure re-analysed annual mean concentrations of Nitrogen dioxide throughout Europe simulated by the RIUUK/EURAD modelling system assimilating AIRBASE data and satellite information 24

27 Figure 14. Distance-to-target graphs for the annual NO2 limit value, for different station types, 2010; source : 2012 EEA air Quality report in Europe Figure re-analysed winter mean concentrations of Nitrogen dioxide throughout Europe simulated by the RIUUK/EURAD modelling system assimilating AIRBASE data and satellite information Another indicator to be considered as a regulatory flag is the limit value of 200 µg/m3 hourly average that should not to be exceeded more than 18 times a year. Generally this indicator is representative of hot spots situations near high emission sources (typically dense traffic areas), and refers to local pollution. As mentioned above, this 25

28 characteristic is not compatible with the spatial resolution of the model re-analyses conducted in the MACC-II/EVA project (20km in the best case). Therefore this indicator has not been calculated within the 2010 MACC-II/EVA re-analyses. 4.2 Uncertainty analysis More information is available on the model performance in the Validation of the MACC assessment products for the year However few elements are given below to help in the interpretation of the results. Figure 16 and Figure 17 give for each MACC-II/EVA models the RMSE at urban stations and the correlation coefficient at suburban sites respectively. Performances for raw model results and data assimilated ones are displayed on the same histogram graphs for information. The added-value of the data-assimilation process for the models that performed so is very clear. The excellent behaviour of the EURAD data assimilated chain is highlighted as well. This model performs even better than the Ensemble (which is the second best model). The results are discriminated region by region. Northern, Central and Western Europe are the regions where the all the models perform the best. Once more, Southern Europe is more difficult to simulate, because of the complexity of meteorological and dynamics patterns. Figure 16. RMSE for 2010 daily mean concentrations calculated at urban stations with all the MACC-II regional modelling suites. The a index refers to data assimilated results. EUW: Western Europe, EUC: central Europe, EUS: Southern Europe, EUN: Northern Europe, EUE: Eastern Europe 26

29 Figure 17. Correlation coefficient for 2010 daily mean concentrations calculated at suburban sites with all the MACC-II regional modelling suites. The a index refers to data assimilated results. EUW: Western Europe, EUC: central Europe, EUS: Southern Europe, EUN: Northern Europe, EUE: Eastern Europe 5 PM10 indicators For the year 2010, three teams over six assimilated in an operational way PM 10 observed concentrations: EURAD from RIU (Rhenish Institute for Environmental Research at the University of Cologne), CHIMERE from INERIS and LOTOS-EUROS from the KNMI/TNO consortium assimilated in their raw model results in-situ observation data gathered in the AIRABSE database. It is acknowledged that data assimilation process improves significantly the simulation of concentration patterns and regional model results with observed data assimilation should be preferred. Therefore, an ensemble based on the results of these three models has been built-up and the results are presenting in the report. 5.1 Annual and daily indicators compared to limit values Annual PM 10 concentrations ranged in 2010 between 5 µg/m 3 (in Scandinavia) to 40 µg/m 3 which the limit annual average value set by the air quality Directive (Figure 18). According to this map, the limit value was exceeded in Poland, Slovakia, Czech Republic, Turkey and in Paris area. Actually considering reported values in the AIRBASE database, Italy and the Balkans (Figure 20) were concerned as well ( However, MACC-II/EVA re-analysis systems give for PM10 average indicators results that are very consistent with the actual observed values reported to the EEA in AIRBASE. Figure 19 shows 2009 PM 10 annual averages, demonstrating that 27

30 concentration levels were lower than in 2010, especially in Central and Western Europe. Figure 21 shows winter averages map for the year It demonstrates that yearly averages are mainly driven by winter averages which developed in Europe due to very cold temperatures and stable atmospheric situations observed in this period. Locations with dense anthropogenic activities are, logically, the most concerned: large cities, industrial areas... It is not surprising that highest concentrations are generally observed in winter when meteorological stagnant conditions and inversion avoid dispersion. Concerning the number of days exceeding the 50 µg/m 3 limit value (daily average), it is still significantly higher than in 2009 (Figure 22). The MACC-II/EVA assessment reports whether the number of 35 days of exceedances of the PM 10 regulatory threshold for daily average is exceeded at background locations. The model resolution is not sufficient to catch exceedances at traffic or industrial stations that are representative of local air pollution sources. However the overview proposed in Figure 22 gives an idea of the European areas which are the most sensitive to PM 10 pollution. Poland, Slovakia, Czech Republic, the Pô Valley, Turkey and other specific locations in Western and Central Europe are the most concerned. The 35 limit number of days exceeding the daily limit value is likely to be exceeded in all these regions. This is confirmed by observation from national networks reported in the AIRBASE database (Figure 23). In 2010, an unusual number of days exceeding the PM10 regulatory threshold for the daily value was noticeable in the East part of Europe and in western- Northern Europe. Highest concentrations were found in winter periods, confirming that cold and stable meteorological conditions captured high emissions levels over emitted areas (mainly the cities) and avoided their dispersion. 28

31 Figure Reanalysis of the PM10 annual average in Europe (Ensemble data assimilated chain of the MACC-II/EVA systems) Figure Reanalysis of the PM10 annual average in Europe (Ensemble data assimilated chain of the MACC/EVA systems) 29

32 Figure 20. Annual PM10 mean concentrations measured at the European monitoring sites in Source: EEA Figure Reanalysis of the PM10 winter average in Europe; (Ensemble data assimilated chain of the MACC-II/EVA systems). 30

33 Figure 22. Number of days exceeding the 50 µg/m 3 threshold (daily average) simulated by the MACC-II/EVA system in 2010 Figure 23. Number of days of exceedances of the PM10 regulatory threshold (50 µg/m 3 ) observed in Europe in 2010, sorted by regions 31

34 5.2 Uncertainty analysis More information is available on the model performance in the Validation of the MACC assessment products for the year However few elements are given below to help in the interpretation of the results. Figure 24 and Figure 25 present bias and correlation coefficient, respectively for PM 10 daily mean simulated at urban AIRBASE stations by each MACC-II/EVA model. The situation is different from what was analysed for other pollutants. If the data assimilated results are always the best ones, models behave differently depending on the location. Therefore, the Ensemble model (ENSa) provides the most robust results. Western Europe and Southern Europe are the regions were the statistical scores are the best (for urban locations). Specific behaviour of data assimilated results over southern locations should be noted with a positive bias which means that model overestimate observations. This is rather unusual for PM modelling, and the reasons of such behaviour must be investigated. PM concentrations are generally underestimated. This is due to some missing sources that are not correctly integrated in the emission inventories (heavy metal sources, diffusive sources, natural sources like windblown dust, sea salts...). There are also large uncertainties in the parametrisation of some chemical reactions, especially those related to the formation of Secondary Organic Aerosols (SOA). However the performances obtained for data assimilated results are very satisfactory compared to the state of the art. The added-value brought by the data-assimilation process to improve PM 10 concentrations is particularly significant in this case for two models: CHIMERE and EURAD. The case of the model LOTOS-EUROS is reported for further investigation. The data assimilation process does not significantly improve the modelled PM 10 concentrations. This point will be reviewed as a priority point for future runs Finally Figure 26 shows the comparison between modelled (with the ensemble model) and observed numbers of exceedances of the daily limit value for PM10. Temporal correlation is quite good, but it seems that model systematically underestimates the observations. This is consistent with state of the art, higher uncertainties being generally observed than for other pollutants. Figure 27 should be compared to Figure 23. The number of days of exceedances of the daily limit value is displayed region by region. It shows that the model underestimates this figure especially in Central Europe and eastern Europe. 32

35 Figure 24. Bias for daily means of PM10 concentrations at urban stations over the year 2010 computed by the various MACC-II/EVA models and sorted by sub-regions: EUW: Western, EUC: central, EUS: Southern, EUN: Northern, EUE: Eastern. Figure 25. Correlation coefficients for daily means of PM10 concentrations at urban stations over the year 2010 computed by the various MACC-II/EVA models and sorted by sub-regions: EUW: Western, EUC: central, EUS: Southern, EUN: Northern, EUE: Eastern. 33

36 Figure 26. Comparison between the observed and ensemble modelled number of days when the 2010 PM10 daily regulatory threshold value was exceeded; Ensemble re-analyses provided by the MACC-II/EVA ensemble system. Figure 27. Number of days of exceedances of the PM10 regulatory threshold (50 µg/m 3 ) modelled in Europe in 2010 by the ensemble re-analysis system, sorted by regions 34

37 6 PM2.5 indicators PM2.5 concentrations should be monitored since the adoption of the Unified Air quality Directive (2008/50/EC), with specific recommendations in urban areas to estimate the so-called Average Exposure Indicator in populated areas. Indeed, this pollutant is not regulated like the other air pollutants because there is no identifiable threshold below which PM2.5 would not pose a [health] risk. A target value for the annual average of PM 2.5 concentrations over three years that should no longer be exceeded by the 1 st January 2010, is fixed to 25 µg/m 3. It will become a limit value in 2015, and will be reduced to 20 µg/m 3 in As for NO 2, only two models provided results assimilating PM 2.5 observed concentrations: RIUUK and TNO/KNMI. Considering the model performances, it becomes obvious that for PM 2.5 assessment, current regional chemistry-transport models need to assimilate observations even if only few stations are available. However the score analysis shows that results are significantly better for EURAD than for LOTOS-EUROS. Consequently, to provide the best estimate, the results presented in this report are issued from EURAD simulations. Figure 28 shows PM 2.5 annual average concentrations simulated with the EURAD assimilated ensemble system. Large European cities, North part of Italy; Turkey and local areas in Central and Eastern Europe appear as potential critical areas regarding the probability of exceedance of the current target value and a fortiori the 2020 limit value if the emissions do not decrease. This map is very consistent with observations gathered in the AIRBASE database (Figure 29). It shows that exposure to fine particulate matter in Europe remains a worrying issue in Europe with current and foreseen target values likely to be exceeded in many cities. Figure 28. PM2.5 annual average concentrations for the year 2010, assessed with the data assimilation EURAD system (re-analysed fields) 35

38 Figure 29. Annual PM 2.5 mean concentrations measured at the European monitoring sites in Source: EEA 7 Pollen indicators In addition to the chemical regulatory species studied in the previous paragraphs, the natural atmospheric allergens, such as pollen, were considered by the FMI as experimental developments within the scope of MACC, likely to become operational in for MACC-II. The present paragraph is the third MACC-II R-EVA re-analysis report for birch pollen (after the 2008 and 2009 reports). It is prepared as an initiative work paving the way for the pollen services within the scope of MACC-2. It provides the reader with a collection of the performance indicators, which allow the evaluation of the quality of the results used in the assessment. Building up the confidence in the pollen re-analysis system also helps to understand and interpret the model results. Re-analyses of birch concentrations in Europe in 2010 are presented below. The evaluation phase, which is exceptionally discussed in the present document, allows the establishment of objective and quantitative criteria to assess model skills and performances. Methodological aspects are detailed in annex Birch pollen distribution in 2010 Propagation of the flowering season predicted by SILAM is presented in Figure 30 which covers 5 months from March till June presenting 10-day average pollen concentrations. The season was quite different from years 2008 and 2009 computed in previous MACC reports. It started comparatively late in the third decade of March but emission picked 36

39 up almost at once over most of Central Europe. Within 2 weeks, pollination extended towards the east and only in the second part of April, when the flowering in France and south-western Germany was over, started the move towards the north. It also was quite late there: in the end of June the model still predicted noticeable pollen concentrations over large areas of Scandinavia and Finland. Important characteristics for sensitive people are the total number of hours with the pollen concentrations exceeding the 50 grain m -3 threshold and the total pollen load over the season for a specific location (Figure 31). With regard to these parameters, 2010 was not very high year. The total load stayed within 100,000 pollen day m -3 and the number of hours with the load exceeding 50 pollen m -3 did not exceed hours, with no clear areas with particularly high exposure. This is in contrast with previous years, where these two metrics were higher and showed clearly high exposure in Northern and Eastern Europe. 5a) 5b) 5c) 5d) 5e) 5f) 37

40 5g) 5h) 5i) 5j) 5k) 5l) Figure 30. Propagation of the flowering season in 2010: 10-days mean concentrations from till [pollen m - 38

41 Figure 31. Total pollen load (cumulative daily count over the whole season), [pollen day m -3 ], left-hand panel, and the number of hours when the pollen concentrations exceeded 50 pollen m -3 39

42 The pollen season duration is traditionally computed following the so-called 2.5% criterion: the season is considered started when the cumulative daily pollen count reaches 2.5% of the total seasonal count and finished when the cumulative count reaches 97.5% of the seasonal total. The EAN network, however, provides the season duration for 1%-97.5% basis. Still, in the current report we stick to 2.5% as a criterion for the season start. The seasonal total for 2010 is shown in Figure 31. Evidently, this criterion is applicable only in the re-analysis exercise because it requires the knowledge of the concentrations over the whole season to calculate its starting date. The Figure 32 presents the start and end dates (as the day number within 2010) of the main pollen season, as well as its duration in days. The season duration (Figure 32 panel c) should not be mixed with the length of the flowering season, which is shorter. The extension of the pollen season in comparison the flowering season is due to regional and long-range transport of grains emitted elsewhere. a) Pollen season start: the first day when cumulative count reaches 2.5% of the seasonal total Figure 31. Contour line highlights the 90- th day ( ). a) Pollen season end: the first day when cumulative count reaches 97.5% of the seasonal total Figure 31. Contour line highlights the 120-th day ( ) c) Pollen season duration: the difference between the last and the first days of the season (panel b and a, of this figure, respectively). Contour line highlights the 30-day season duration. Figure 32. Season start, end and duration following the 2.5%-97.5% criterion 40

43 7.2 Performance of SILAM model in the birch pollen re-analysis in 2010 Correlation of the daily observed and predicted time series is presented in Figure 33, left-hand panel, whereas a RMSE is shown in the right-hand panel. The correlation is generally close or above in the North-Eastern Europe and over the areas with large birch tree populations, such as Switzerland, Central and Eastern Germany, east of Austria, etc. Over the areas with poor birch stand information, the time series appear essentially uncorrelated but the level of pollen concentrations is very low. The mean seasonal concentrations (average daily count from till ) observed by EAN, predicted by SILAM, and their normalised difference are shown in Figure 34 When the seasonal mean load was used as a criterion in the data assimilation, the overall agreement was good. The bias at individual stations does not form any large-scale pattern, which would indicate problems in the data assimilation procedure. The data assimilation, however, had limited impact on the correlation between the time series, which highlight the problematic regions: mountains and the southern Europe. Notably, there is less birch towards the south of France and practically no birch in Spain, except for the northern-most mountain regions. The still-above-zero concentrations, which were both observed and predicted over both countries, are due to the long-range transport of the pollen grains. Comparing the efficiency of data assimilation approaches, one can see generally similar performance of both approaches. A few sites show somewhat higher correlation after the simple calibration rather than after 4D-VAR, which can be attributed to constraints on the correction level imposed to improve the convergence of solution of the ill-posed problem. The effect is even more pronounced in comparison of the model biases (Figure 33). Calibration-based correction was evidently more powerful in eliminating the initial model under-estimation of the concentrations. However, it also produced several strongly over-stated peaks in the southern part of the domain, which significantly reduced its value, finally bringing 4D-VAR approach as the more robust way of utilising the whole-season pollen counts. 41

44 4D-VAR assimilation. Emission-total calibration Figure 33. Correlation coefficient and RMSE of the daily time series for birch pollen

45 a) Mean modelled pollen concentration, calibration, [grains m -3 ] b) Mean model bias, 4D-VAR, [grains m -3 ] Figure 34. Model-predicted mean pollen concentrations over with calibration method of data assimilation (panel a); bias of 4D-VAR-based prediction (panel b) and calibration-based prediction (panel c). [pollen m - 3 ] c) Mean model bias, calibration, [grains m -3 ]. 43

46 8 Conclusions The 2010 MACC-II/EVA assessment report presents a number of results related to ozone, nitrogen dioxide and particulate matter (PM10 and PM2.5) concentrations that held during the year They result from the decentralised regional air quality modelling systems implemented in the Copernicus MACC-II project, the same that are routinely run for the provision of forecasts and near real time analyses in the ENS sub-project. Some of them (more than for the previous reports but not all at this stage) were used in a re-analysis mode, with data assimilation procedures which allowed an accurate representation of the air pollution patterns combining simulation and observations. Because only a limited number of models provided re-analysed results, the benefits of the ensemble approach that is supposed to be derived in the MACC-II/EVA program will be further improved by the end of the MACC-II project (July 2012). In some cases, individual models could give better results (for NO 2 for instance with the EURAD system which is the most achieved in term of data assimilation) or data assimilated results need to be investigated to detect potential inconsistencies. However, one should note an overall improvement of the quality of the model results compared to those presented in the previous assessment reports. In any case, the results considered as the best available ones are reported in this document. A second report focussing on the validation aspect and the models performances is edited under the name Validation of the MACC assessment products for the year The year 2010 revealed interesting issues in terms of air pollution. High temperatures in summer time (and even a heat wave that impacted Eastern Europe and Russia) and very cold temperatures combined with stable meteorological conditions in winter were responsible for air pollution episodes that impacted a large part of Europe over the whole year. Annex 1 provides some insights about the climate situation in 2010 compared to the previous years. Summer ozone levels increased compared to They were rather high, especially in Mediterranean countries and in the Balkans, where averaged daily ozone concentrations were above µg/m 3. Belgium, a large part of France, Germany, Austria, Czech Republic, Croatia, Slovakia, and a part of Hungary and Poland were the most exposed countries considering indicators relevant for both human health and ecosystem impacts. A strong gradient between northern and southern countries developed. It is interesting to note that high temperatures (and summer heat waves) did not impact ozone levels as it was the case in High ozone levels slightly increased compared to the previous years, but no unexpected high values had been reported. This point should be more investigated to check whether such a situation can result from the impact of the emissions reduction strategies of ozone precursors (NO x and Volatile Organic Compounds especially). This can also be the consequence of the economical crisis that touched Europe in Annual PM10 concentrations ranged in 2010 between 5 µg/m 3 (in Scandinavia) to 40 µg/m 3 which the limit annual average value set by the air quality Directive. The limit value was exceeded in Poland, Slovakia, Czech Republic, Turkey, Italy, and the Balkans and in Paris area. PM 10 high concentrations occurred during winter periods because of stable meteorological conditions combined with residential heating extra emissions (compared to the rest of the year). Concerning the number of days exceeding the 50 µg/m 3 limit value (daily average), it is still significantly higher than in The MACC-II/EVA assessment reports the number of days of exceedances of the PM 10 regulatory threshold for daily average at background locations. The model resolution is not sufficient to catch exceedances at traffic or industrial stations that are representative of local air pollution 44

47 sources. However the MACC-II/EVA assessment work gives an idea of the European areas which are the most sensitive to PM 10 pollution. Poland, Slovakia, Czech Republic, the Pô Valley, Turkey and other specific locations in Western and Central Europe are the most concerned. The 35 limit number of days exceeding the daily limit value is likely to be exceeded in all these regions. In 2010, an unusual number of days exceeding the PM 10 regulatory threshold for the daily value was noticeable in the East part of Europe and in western-northern Europe. Finally it is important to note that the proposed simulations did not account for the impact of the huge forest fires that occurred in Russia in during the first half of August. The impact of forest fires on ozone and PM atmospheric concentrations is well-known it is expected that summer ozone and PM10 concentrations in Central and Eastern Europe could have been influenced by huge forest fire emissions. This contribution is clearly missing in the proposed simulation and can explain the model discrepancies observed in the summer period. In the next reports the MACC-II modelling chains will account for forest fire emissions provided by the dedicated sub-project. A significant improvement of the model performances is expected from this new functionality. It is interesting to note the good consistency between annual NO 2 and PM 2.5 concentration fields. The Pô Valley, Poland, Slovakia, Czech Republic, Benelux, Turkey and big cities in Europe had been concerned by worrying concentrations close to the limit value for NO 2 and the target threshold for PM 2.5. PM 2.5 simulations reported areas where the target value of 20 µg/m 3 (annual average) for 2020 is already exceeded. Locations concerned are hot spot areas being driven by high anthropogenic activity. However, it is reminded that the MACC-II/EVA mapping system is not fitted to deal with local hot spot situations as those that develop near busy roads or on industrial sites. Actually, the model resolution is about km 2, and is not sufficient to catch actual NO 2 concentrations at traffic and industrial sites. The availability of a set of several model results allows a qualitative estimation of the model uncertainty which is linked to the variability of the model results. Southern Europe and Eastern Europe are the European sub-regions where the model responses are the most variable and uncertain, even with the data assimilation systems. This is not a surprising result; the difficulties for simulating the dynamic and chemical atmospheric processes that occur in these geographical areas are well-known from the scientific communities and are still the subject of research project. Moreover lack of measurements in Eastern Europe (compared to other geographical areas, see in annex 2) can also result in poor performance when modelled indicators are compared to observations. But this must be interpreted with caution. However the global uncertainty of the modelling systems is reduced compared to the one reported in the previous assessment reports. Thanks to the FMI expertise and effort, re-analyses and assessment of pollen concentration levels (from birches) to which European citizens were exposed in 2010 are also provided in this report. It is particularly interesting to note that the number of days when pollen concentrations lead to mass concentrations higher than the PM 10 regulatory threshold for daily mean (50 µg/m 3 ) can be very high. This shows the potential contribution of this natural source to PM episodes depending on the season. Evaluation of the pollen reanalysis system developed by the FMI is provided to complete the information. Finally, feedback from the users and the experts from the Member States on this report is expected and highly welcome. It will be the best way for improving the overall system. Therefore all the numerical values related to the results discussed in this report are available on request to the MACC/EVA subproject leader : Laurence.rouil@ineris.fr. 45

48 ANNEX 1: European and global climate in 2010 The weather over Europe and Asia during July and August 2010 has been rather unusual. In the first half of July several western European countries (Austria, Belgium, France, Germany, Switzerland, the UK..) recorded very high temperatures in their cities. The Iberian Peninsula was strongly concerned as well by a remarkably hot summer period. In Eastern and Central Europe, this situation stood from June to August. During the second half of July and beginning of August, a blocking anticyclone over Russia dominated the weather pattern in Europe. The abnormal heat wave experienced in Russia, caused important fires throughout the country, impacting the rest of Europe. The WMO summarizes the situation in its 2010 report 8 : The summer was unusually hot over most of Europe and was the hottest on record averaged over the continent, breaking the previous record set in 2003 by 0.62 C. The most extreme conditions were in the western Russian Federation, but summer temperatures were above average virtually throughout the continent. July was especially hot and broke the previous continental record by nearly a degree, with temperatures at least 1 C above normal almost everywhere except the United Kingdom, Ireland and parts of Bulgaria. Conversely some countries recorded their coldest winter temperatures at the beginning and the end of 2010: Ireland, the UK and several Northern (Finland, Sweden) and Central Europe countries were concerned. The four graphs above show seasonal-mean temperature anomalies relative to for the most recent two summers and winters; that is, they show how temperatures during the various seasons differ from the mean temperatures from , which serves as a reference period. Source: NASA/Goddart Institute for Space Studies and (Hansen al, 2010) 9. 8 WMO statement on the Status of climate in 2010, WMO report N 1074, 9 Hansen, J., R. Ruedy, Mki. Sato, and K. Lo, 2010: Global surface temperature change. Rev. Geophys., 48, RG4004, doi: /2010rg

49 ANNEX 2: methodologies and assumptions Models: The models that provided raw simulations and data assimilated fields of air pollutant concentrations for the year 2010 are the ones involved in the MACC-II regional cluster dedicated to the provision of air quality information (near-real time and in delayed mode) at the European scale. Seven models running operationally on their own modelling platform perform re-analyses since the end of the MACC project (October 2011) for establishing yearly assessment reports. These models are described in the QA/QC dossiers available and regularly updated on the MACC website ( It is important to note that differences can occur between the modelling chain run routinely for the provision of daily air quality forecasts and near real time analyses, and the modelling chain used for re-analyses calculations. The later requires larger computational resources (computational time and storage space) to deal with a whole year on an hourly basis. Some teams did not achieve the development of their data assimilation system and in this cases reported only raw simulation values. The tables below give an overview of each data assimilation system developed by the regional air quality modelling partners in MACC, and its status at the time when the 2010 runs have been performed. This synthesis can facilitate the interpretation of some results reports in this report and in the validation report. Model DA process Pollutants concerned Data sources Operational production CHIMERE Optimal interpolation : kriging observation data with CHIMERE as external drift Ensemble Kalman filter O3, PM10 O3 AIRBASE AIRBASE Under evaluation : O3 partial tropospheric columns (IASI) Yes Significant improvement Not yet; need for comparison with OI EMEP 3D-VAR NO2 OMI NO2 tropospheric column Yes but did not run its data assimilation chain (not yet operational) EURAD Intermittent 3DVAR O 3, NO 2, NO, CO, SO 2, PM 10 AIRBASE in situ measurements MOSAIC air borne in situ measurements NO 2 tropospheric column retrievals from OMI, GOME-2, SCIAMACHY MOPITT CO profiles Yes 47

50 LOTOS- EUROS Ensemble Kalman filter O3 NO2, PM10, AOD, SO2, SO4 Airbase OMI : observation operator developed and used for the 2009 report Yes, However further evaluation is needed MATCH 3D-VAR with transform into spectral space O3, NO2 Airbase System not fully operational which did not provide model outputs for the 2010 assessment report MOCAGE 3D-VAR O3 Ozone in-situ data (AIRBASE) Yes since summer 2010, online evaluation available SILAM 3D-VAR 4D-VAR O3, NO2, SO2 AIRBASE in situ Yes, Operational Table 1: Synthesis of the current data assimilation capacities developed in the regional air quality models involved in MACC. They must be operational by the end of the MACC project Model Data provided Quality checking CHIMERE O3, PM10, with OI for the whole year and the requested episodes ; raw simulation results for NO2 and PM2.5 Significant improvement of the simulation results EMEP Only raw simulations provided, DA under development Not applicable for DA chain (still under development) EURAD O 3, NO 2, NO, CO, SO 2, PM 10 (surface obs), MOZAIC, NO 2 tropospheric column retrievals from OMI, GOME-2, SCIAMACHI and MOPITT CO profiles Significant improvement of the simulation results LOTOS- EUROS MATCH MOCAGE ozone, PM10 and NO2, PM2.5 re-analyses on 30 km resolution No re-analyses nor raw simulations provided O3 re-analyses provided; raw simulation results for the other compounds Significant improvement of the simulation results Not applicable Significant improvement SILAM O3,NO2, PM10 re-analyses provided Significant improvement of the model results Table 2: Brief summary of the model configurations used for the 2010 assessment report 48

51 Observation datasets: The AIRBASE-v6 database gathering all observations reported from the regulatory air quality monitoring networks is available for the year 2010 and had been used for both reanalysis and evaluation processes ( However same data cannot be used for both (it would not make sense to evaluate reanalyses against the same observation dataset that was used for data assimilation in the models). So the set of observations available in AIRBASE had been split in two subsets, one for DA and the other for validation (about one third of the total number of stations). Randomness and homogeneous spatial coverage with respect with the station typology (rural, suburban, and urban) were the principles considered for the station classification. The table below presents the number of stations selected in each category and their location is mapped on the following figures Rural/ DA Rural/ Validation Suburban/ DA Suburban/ Validation Urban/ DA Urban/ Validation O Total O3: 1345 NO Total NO 2 : 1336 PM Total PM10: 1198 PM Total PM2.5: 226 Table3: Number of stations selected in the AIRBASE database for 2010 for DA and validation 49

52 Figure 35. Location of the NO 2 AIRBASE stations selected for data assimilation (red dots) and validation (green dots) processes 50

53 Figure 36. Location of the O3 AIRBASE stations selected for data assimilation (red dots) and validation (green dots) processes 51

54 Figure 37. Location of the PM10 AIRBASE stations selected for data assimilation (red dots) and validation (green dots) processes Although there is much more PM 2.5 observation data reported in AIRBASE than in the previous year, the number of PM 2.5 stations is still reduced in Europe. It will increase significantly in the coming year with the implementation of the air quality Directive. However PM 2.5 data from AIRBASE has been checked and processed for further used in the MACC/EVA systems. The figures below show the station locations according to their typology: rural, suburban, urban. In some countries, no rural stations are reported: this is not mandatory according to the Air Quality Directive that only requests data representative of human exposure to calculate the national average exposure indicator (AEI). 52