Assessment Report: Air quality in Europe for 2007

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Assessment Report: Air quality in

Europe for 2007

Edited by Laurence ROUÏL (INERIS)

With contribution from the MACC regional modelling teams:

CERFACS Sébastien Massart

CNRS/LISA Matthias Beekman, Gilles Foret

FMI Mikhail Sofiev, Julius Vira

KNMI Henk Eskes

INERIS Frédérik Meleux, Anthony Ung

Meteo France Vincent-Henri Peuch

Met.no Alvaro Aldebenito, Michael Gauss

RIUKK Hendrik Elbern, Elmar Friese, Achim Strunk

SMHI Lennart Robertson

TNO Martjin Schaap, Renske Timmermans, Lyana Curier

January 2011

Ref : D-R-EVA_3.1

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Table of content

1 Executive summary ................................................................................5

2 Glossary ..............................................................................................6

3 Rationale ............................................................................................7

4 General issues ......................................................................................9

5 Ozone concentrations in 2007 .................................................................. 10

5.1 Ozone indicators ............................................................................ 10

5.2 Uncertainty analysis ........................................................................ 14

6 Nitrogen dioxide indicators ..................................................................... 16

6.1 Annual and seasonal averages............................................................. 16


7 PM10 indicators ................................................................................... 19

7.1 Annual indicators ........................................................................... 19

7.2 Specific episodes ............................................................................ 20


8 PM2.5 indicators .................................................................................. 26

9 Conclusions ........................................................................................ 27

10 ANNEX: methodologies and assumptions ...................................................... 28

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List of figures

Figure 1. Global surface temperature anomalies for 2007 relative to the 1951-1980 mean. From the

Goddard Institute for Space Studies (http://www.giss.nasa.gov/research/news/20080116/ ) ____ 9

Figure 2. 36th highest 24-hour mean PM10 concentration observed at (sub)urban background stations, EEA

member countries, 1997-2008; source EEA : www.eea.europa.eu _________________________ 10

Figure 3. Reanalysis of the annual daily mean ozone concentrations in 2007. _______________________ 10

Figure 4. Ensemble re-analyses of SOMO35 in (µg/m3)·days (a) and AOT40 in (µg/m

3)·hours (b) indicators

for the year 2007 ________________________________________________________________ 11

Figure 5. Ensemble re-analyses of the number of days when 8-hours average exceeds 120 µg/m3 and of the

number of hours when hourly average exceeds 180 µg/m3· __________________________________ 12

Figure 6. Observed and simulated number of exceedances of the O3 information threshold, over Europe

(GMES domain) and for July episodes (a) and for the whole period (b) _____________________ 13

Figure 7. Observed number of exceedances of the ozone information threshold (180 µg/m3 hourly) ,

throughout Europe; EUW: Western Europe, EUC: central Europe, EUS: Southern Europe, EUN:

Northern Europe, EUE: Eastern Europe _______________________________________________ 13

Figure 8. Re-analyses of the number of exceedances , throughout Europe, of the ozone alert threshold (240

µg/m3 ). EUW: Western Europe, EUC: central Europe, EUS: Southern Europe, EUN: Northern

Europe, EUE: Eastern Europe ______________________________________________________ 14

Figure 9. Correlation coefficient between the MACC individual and ensemble models and the observation

for rural stations (a) and suburban stations (b). The indicator is calculated sub-region by sub-

region (on the x-axis) _____________________________________________________________ 14

Figure 10. Root-mean-square error of the MACC individual and ensemble models for rural stations. The

indicator is calculated sub-region by sub-region (on the x-axis) ___________________________ 15

Figure 11. Standard deviation of the MACC models results to the ensemble; (a) 7 models hindcasts (b) 3

models re-analyses ______________________________________________________________ 15

Figure 12. 2007 Annual mean concentrations of Nitrogen dioxide throughout Europe simulated by the

RIUUK/EURAD modelling system (a) NO2 annual concentrations hindcasts (b) NO2 concentrations

re-analyses assimilating AIRBASE data and satellite information __________________________ 16

Figure 13. Trends in NO2 annual mean concentrations observed in 2007 at the suburban and urban European

monitoring sites. Source : EEA ______________________________________________________ 17

Figure 14. 2007 seasonal mean concentrations of Nitrogen dioxide throughout Europe simulated by the

RIUUK/EURAD modelling system (a) Winter (b) Summer ________________________________ 17

Figure 15. NO2 averaged concentrations over the year 2007 computed by the various MACC/EVA models and

sorted by sub-regions: EUW: Western Europe, EUC: central Europe, EUS: Southern Europe, EUN:

Northern Europe, EUE: Eastern Europe. Black bars give the averaged concentrations levels

observed at the stations implemented in each sub-region. _______________________________ 18

Figure 16. Root means Square error (a) and correlation coefficient (b) of the MACC/EVA model results for the

year 2007, against the observations available at European urban stations _________________ 18

Figure 17. PM10 annual average for the year 2007, assessed with the Ensemble MACC/EVA system ______ 19

Figure 18. Number of days exceeding the 50 µg/m3 threshold (daily average) simulated by the MACC/EVA

system ________________________________________________________________________ 19

Figure 19. Number of days exceeding the 50 µg/m3 threshold (daily average) observed in European sub-

regions: EUW: Western, EUC: central, EUS: Southern, EUN: Northern, EUE: Eastern. __________ 20

Figure 20. Number of days exceeding the (a) 50 µg/m3 threshold (daily average) and (b) 80 µg/m

3 threshold

(daily average) observed in European sub-regions between 10th

march and 19th

April ________ 21

Figure 21. PM10: daily mean concentrations in spring 2007: Reanalyses from MACC/EVA (a) 15th

March,

(b)24th

March, (c) 29th

March, (d) 15th

April ___________________________________________ 22

Figure 22. Time series of observed hourly PM10 concentrations recorded on March 14 [00:00] – 27 [00:00],

2007 in Gravelines (France) and chemical analyses for K+, Ca2+, Mg2+ ____________________ 23

Figure 23. Vertical cross-section (Latitude-Height diagrams) of CALIOP data March 23, 2007, at about [10:50]

: satellite trajectory (top), backscatter signal (middle) and color ratio (bottom) ______________ 24

Figure 24. PM10 averaged concentrations over the year 2007 computed by the various MACC/EVA models

and sorted by sub-regions: EUW: Western, EUC: central, EUS: Southern, EUN: Northern, EUE:

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Eastern. Black bars give the averaged concentrations levels observed at the stations implemented

in each sub-region. ______________________________________________________________ 25

Figure 25. Root means Square error (top) and correlation coefficient (bottom) of the MACC/EVA model

results for the year 2007, against the observations available at European suburban (left) and

urban (right) stations ____________________________________________________________ 25

Figure 26. PM2.5 annual average concentrations for the year 2007, assessed with the Ensemble MACC/EVA

system ________________________________________________________________________ 26

Figure 27. Location of the NO2 AIRBASE stations (2007) selected for data assimilation (red dots) and

validation (green dots) processes ___________________________________________________ 30

Figure 28. Location of the O3 AIRBASE stations (2007) selected for data assimilation (red dots) and validation

(green dots) processes ____________________________________________________________ 31

Figure 29. Location of the PM10 AIRBASE stations (2007) selected for data assimilation (red dots) and

validation (green dots) processes ___________________________________________________ 32

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1 Executive summary This report is the first MACC/EVA assessment report delivered by the air quality re-analysis multi-model system implemented in the MACC project. Although re-analysed chains were not fully operational after the first year of the project, seven European regional air quality modelling systems were run to provide simulations of air pollutant concentrations over the year 2007, with an hourly resolution throughout Europe. In some cases, data assimilation procedures were activated to establish re-analyses of the air pollution fields, combining raw simulation outputs with validated observations available at the monitoring stations. Ensemble estimation of the concentrations had been built up mixing the available results in a median average. This is the very stage of the MACC/EVA operational system which aims at providing re-analysed air pollution fields (according to the regulatory and the usual impact indicators) based on the ensemble of re-analysed results provided by a set of individual models. The results presented in this report are very promising: they give for the first time an overview at the European scale of the 2007 of air pollution indicators issued from model simulation and observations merged in the same map. This offers very comprehensive information, which is considered as the “best estimate” of air pollution patterns. The systematic evaluation of the model and data assimilated results against a relevant set of dedicated observation data (not used for being assimilated in the models) shows satisfactory performances compared to the state of the art. It shows that there is still space for improvement within the next assessment operations (2008-2009) as well. The strength of the multi-model approach is the capacity of highlighting the most sensitive geographical areas where the re-analyses are the most uncertain, either because of model weaknesses (uncertainties on emission, complex topography, limit of the dynamic and chemical parametrizations...) or because of a lack of available representative observations. The year 2007 is particularly interested because of the distinction that can be made between ozone and particulate matter (PM) impacts. On the ozone point of view, at the European scale, 2007 can be considered as a rather “quiet” year, except in the Mediterranean areas (especially Greece, Balkans) where a severe heat wave held during summer. Conversely, on the PM point of view, 2007 is worrying with a significant increase (compared to the last years situation) of the level of highest PM10 concentration values and an increasing number of situations when the regulatory thresholds were exceeded. This document reports a number of indicators demonstrating this situation and brings elements for their interpretation. Nitrogen dioxide and fine particulate matter (PM2.5) concentration maps are also provided. Annual averages do not exceed the limit and target values respectively set by the European regulation for both of these pollutants. However the limited spatial resolution (25 km in the best case) of the model runs does not allow the proper consideration of local air pollution hot spots (near industrial sites or dense road traffic lanes for instance). Such situations are out of the scope of this work.

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2 Glossary AIRBASE European Air Quality database (http://air-

climate.eionet.europa.eu/databases/airbase/) AOT 40 Accumulated Ozone over the 40 ppb Threshold

AQD Air Quality Directive

AOT Accumulated dose over a threshold of 40 ppb

CERFACS European Centre for Research and Advanced Training in Scientific Computation (F)

CNRS National Research Center (F)

DA Data Assimilation

Ensemble Combination (median, weighted average) of model results to derive a new estimation of the targeted variables (concentrations or dynamical variables). It is generally expected that the “Ensemble model” gets better performances and skills than the individual one.

FMI Finnish Meteorological Institute (FI)

Hindcast Raw simulation of a past situation

INERIS National Institute for Industrial Environment and Risks (F)

KNMI Royal Netherlands Meteorological Institute (NL)

LISA Interuniversity Laboratory on Atmospheric Systems (F)

Météo France French Weather services (F)

Met.no Norwegian Weather services (N)

PM Particulate Matter

Reanalysis Hindcast of past period where validated observations have been assimilated to reduce the model uncertainty at the observation sites

RIU Rhenish Institute for Environmental Research at the University of Cologne

(DE) SMHI 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)

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3 Rationale The GMES/MACC project aims at delivering a number of services dedicated to global atmospheric composition and air quality monitoring in Europe. This later issues is covered by a set of subprojects belonging to the so-called “regional cluster”, RAQ (Regional air quality, see the annex ). Two operational services are available in 2010: those that are focussed on routine and near real time products (forecasts, Near Real Time analyses) and those related to detailed analysis of past situations thanks to validated material issued from observation networks and modelling. The first class of products is available through the MACC website on a daily basis: http://www.gmes-atmosphere.eu/services/raq/raq_nrt/. Up to two days forecasts of ozone, nitrogen dioxide and particulate matter concentrations throughout Europe are available every day. Daily analyses of the same variables (simulated fields are “corrected” with observations thanks to data assimilation techniques) are proposed as well. The MACC/R-EVA service (http://www.gmes-atmosphere.eu/services/raq/raq_reanalysis/) provides a posteriori validated air quality assessments for Europe, based on re-analysed air pollutant concentration fields. Simulations of past years are performed and “corrected” thanks to the assimilation of available validated in-situ and satellite observations. By the end of the MACC I project, operational production of such information should be implemented for the years 2007-2009. The so-called MACC regional air quality assessment reports should describe, with a yearly frequency, the state and the evolution of background concentrations of air pollutants in European countries. Special attention is given to pollutants characterised by the influence of long range transport, correctly caught by European scale modelling: ozone, nitrogen dioxide, particulate matter (PM10 and PM2.5). Focus on specific pollution episodes that happened during the year will be considered. The MACC 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 the state of air pollution. In particular, situations (or episodes) when ozone, nitrogen dioxide and particulate matter concentrations exceed regulatory thresholds must be carefully analysed (geographical extension, duration, intensity, population exposed...) and control plans to limit their impact and to avoid their future development must be proposed. Member states usually base their investigations on observations available from the national air quality monitoring network, modelling tools and national expertise. In its operational stage (foreseen in 2014) the GMES 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 scientists to elaborate the so-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 project this new generation of tool and information based on spacialized indicators fields set 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. Those set the basis of the MACC/EVA assessment reports, being considered as the “best available or most realistic” representations of air pollution

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patterns: more relevant than interpolation of observations and more accurate than raw simulations. Moreover the multi-model functionalities developed in the MACC “regional cluster” will allow to derived “ensemble” model estimations. They relate to the combination of 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 former option has been chosen for the present report, while the later one should be considered for future reports according to the developments realised in the sub-project dedicated to the “ensemble” production. The present document is the first MACC assessment report on European air Quality for the year 2007. It is established on model outputs provided by the seven modelling teams involved in the regional operational production, and the in-situ observations available from the AIRBASE database (from the European Environment Agency, http://air-climate.eionet.europa.eu/databases/airbase/). Notice As a first experiment, it should be noted that not all the expected capacities of the MACC program have been used for the 2007 assessment report working-out. The technical descriptions of the modelling and data assimilation systems that have been used at this first stage report are described in detail in annex 1. Therefore, in the following section, some results are issued exclusively from raw simulations of past situations (“hindcasts”), and others combine numerical data with observations according to various data assimilation approaches (“re-analyses”). Obviously re-analyses offer, by definition the most accurate approach. But because only a limited number of models provided analysed results, the benefits of the ensemble approach that is supposed to be derived in the MACC/EVA program, is limited. In some cases, individual models could give better results (for NO2 for instance with the EURAD system which is the most achieved in term of data assimilation). The performances of the models and data assimilation systems were systematically evaluated by INERIS and are reported in a dedicated MACC report called “Validation of the MACC assessment products for the year 2007”. 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 is related to the air quality situation in Europe during the year 2007. 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 2007 in terms of air quality exposure in Europe. These indicators are presented in the following sections for the various pollutants. Moreover, some periods corresponding to remarkable air pollution episodes are highlighted and interpreted the material available within MACC. Their description is proposed in the related sections. Whatever the considered indicators, the availability of various models1 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. This issue is considered for ozone in the report. 1 These models were acknowledged for their correct performances they showed during the preliminary GEMS project

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Finally it should be noted that all the results, maps, histograms, graphs presented in this report can be consulted and downloaded on the MACC website (http://www.gmes-atmosphere.eu/services/raq/raq_reanalysis/). Below the graphical displays, the model numerical results are available on demand. Please contact Laurence Rouïl the leader of the MACC/EVA subproject ([email protected]).

4 General issues European air quality in 2007 has been influenced by exceptional meteorological situations with temperatures temporally higher than the climatological average (Figure 1). In particular a persistent heat wave affected most of Southern Europe and the Balkans from June to end of August 2007. Such conditions made difficult situations when wildfires started in Greece by the end of June, which became critical by the end of August when fire intensity and spreading increased. Their effect on air quality had been difficult to evaluate but is far to be negligible, especially on Particulate Matter (PM) levels. Combined with high levels of ozone due to photochemical production, it can be reasonably considered that European population exposure to air pollution in summer 2007 should have been rather high in some European regions (Southern Europe, Balkans). Paradoxically, the European Environment Agency (EEA) reported in the report “Air Pollution by ozone across Europe during summer 2007”2 that averaged ozone levels in Europe were among the lowest in the past decade, which is confirmed by the results presented in the next sections. It reflects the located features of the summer heat wave effects, in space and time. In addition, remarkable high temperatures and drought situation developed in Europe in spring 2007 as well. They caused some significant PM pollution episodes in Western Europe in March and April 2007 which will be further commented. For 2007, the Member States reported rather high PM10 concentrations, the highest for a few years, (Figure 2 below), that will be commented in the present report.

Figure 1. Global surface temperature anomalies for 2007 relative to the 1951-1980 mean. From the

Goddard Institute for Space Studies (http://www.giss.nasa.gov/research/news/20080116/ )

2 “ Air Pollution by ozone across Europe during summer 2007”, EEA technical Report No 5/2008 - http://www.eea.europa.eu/publications/technical_report_2008_5

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Figure 2. 36th highest 24-hour mean PM10 concentration observed at (sub)urban background stations,

EEA member countries, 1997-2008; source EEA : www.eea.europa.eu

5 Ozone concentrations in 2007

5.1 Ozone indicators The average daily mean concentration over the year 2007 and throughout the MACC domain is presented on Figure 3. It ranged from 40 µg/m3 to more than 70 µg/m3 in Central and Southern Europe (Greece, Balkans, Italy, Spain, and Portugal). Summer heat wave that happened in these areas explains the gradient.

Figure 3. Reanalysis of the annual daily mean ozone concentrations in 2007.

Three MACC models provided re-analyses over the “warm” summer period (01/04/2007- 30/09/2007) which is the most favourable to ozone production through photochemical processes. Therefore it was possible to calculate ensemble estimation with these 3 members. Indicators generally used for regulatory reporting according to the Air Quality Directive can be mapped over the MACC domain. Ensemble re-analysed AOT40 and SOMO 35 are displayed on Figure 4. AOT 40 (Accumulated dose over a threshold of 40 ppb) is a European key indicator of the impact of ozone on vegetation. It 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

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each hour when the concentration exceeds 40 ppb, accumulated during daylight hours (8:00-20:00 UTC). AOT40 has a dimension of (µg/m3)�hours. In the AQD, the AOT40 calculated from May to July should not exceed 18.000 (µg/m3)�hours. It is interesting to note on Figure 4(b) that in the western part of Europe and Scandinavia, AOT40 was lower than this threshold for a 3 months longer period. This confirms the low level of ozone values that held in some parts of Europe in 2007. However in the most exposed geographical areas (Northern Italy, Mediterranean area, central Europe) the AOT40 exceeded 26.000 (µg/m3)�hours which is amongst the highest values recorded in the last years. 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/m3)�days. In 2007, SOMO35 ranged from 1000 to more than 8000 (µg/m3)�days, according to MACC/EVA analysis. As for AOT40, Northern Italy, Central Europe and Balkans were the most exposed areas in summer 2007. According to the AQD, the maximum daily eight-hours mean should not exceed the 120 threshold more than 25 days by calendar year. This is the target value for the protection of Human health. Moreover 180 µg/m3 as an hourly average ozone concentration corresponds to the information threshold which triggers public information in the Member States. The numbers of hours and days when these thresholds are exceeded are reported to the European Commission by the countries. These regulatory indicators are mapped on Figure 5 at the European level, with values issued from the ensemble MACC re-analyses. Target value of daily ozone 8-hours average was exceeded in a large part of Europe. The countries mainly concerned by exceeding values according to the MACC re-analyses were Austria, Belgium, Bulgaria, Czech Republic, France, Germany, Greece, Hungary, Italy, Poland, Portugal, Romania, Slovak Republic, Slovenia, Spain, Turkey. This list is consistent with the one established in the EEA’s report on summer ozone in 2007.

(a) (b) Figure 4. Ensemble re-analyses of SOMO35 in (µg/m3)�days (a)

and AOT40 in (µg/m3)�hours (b) indicators for the year 2007

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(a) (b) Figure 5. Ensemble re-analyses of the number of days when 8-hours average exceeds

120 µg/m3 and of the number of hours when hourly average exceeds 180 µg/m3�

However, the model evaluation process showed that despite the good performances of the models for simulating daily peaks, and average values, some improvements are still needed for the prediction of the highest concentrations and especially the exceedances of the ozone information and alert thresholds. Figure 6 presents the observed and simulated (by all the models, and various versions of these models) number of situations when the information threshold had been exceeded (on an hourly basis) in July (a) and in the “summer” period (b). The most important ozone episode that held in July 2007 (15th-22nd July) had been underestimated by all the models, even the DA systems. And some models overestimated spring events. Considering Figure 7 and Figure 8 helps in the interpretation of the model results. It details observed exceedance indicators for the ozone information and alert thresholds respectively, stored by European “sub-regions”. Five sub-regions are distinguished (Western, Eastern, Southern, Northern and Central Europe). The end-of-July episode (27th-29th July), which mainly concerned southern Europe, was correctly predicted by at least one model. But this one missed the previous episode that also largely concerned Central Europe. Therefore, one can reasonably suspect larger model uncertainties, shared by all the models, in this sub-region. Further investigations, especially regarding the stations that were considered for data assimilation in these countries will be conducted for the next reports.

(a)

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(b) Figure 6. Observed and simulated number of exceedances of the O3 information threshold, over

Europe (GMES domain) and for July episodes (a) and for the whole period (b)

Figure 7. Observed number of exceedances of the ozone information threshold (180 µg/m3 hourly) ,

throughout Europe; EUW: Western Europe, EUC: central Europe, EUS: Southern Europe, EUN: Northern Europe, EUE: Eastern Europe

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Figure 8. Re-analyses of the number of exceedances , throughout Europe,

of the ozone alert threshold (240 µg/m3 ). EUW: Western Europe, EUC: central Europe, EUS: Southern Europe, EUN: Northern Europe, EUE: Eastern Europe

5.2 Uncertainty analysis The twin report “Validation of the MACC assessment products for the year 2007” contains a number of scores that allow the qualification of skills and performances of the MACC products. In this paragraph, only main conclusions are reported to help in the interpretation of the previous results. The correlation coefficient between simulations and observation is rather high for the best estimations (larger than 0.9)

(a) (b) Figure 9. Correlation coefficient between the MACC individual and ensemble models and the observation for rural stations (a) and suburban stations (b). The indicator is calculated sub-region

by sub-region (on the x-axis)

The Root-Mean Square error (RMSE) indicator can differ largely from a sub-region to another. It is the lowest in Northern, Western and central Europe, ranging from 10 to less than 20 µg/m3 for the best models. RMSE is the highest in Southern Europe for almost all the models.

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Figure 10. Root-mean-square error of the MACC individual and ensemble models for rural stations.

The indicator is calculated sub-region by sub-region (on the x-axis)

Three models provided data assimilation results (orange, grey and red bars on the graphs). Two of them have close performances when the other one performed rather differently and better. Consequently, the ensemble (which the median of thee model results) performed in between. It is therefore recommended to consider the best individual model for policy-support use. Quantification of the uncertainty of this evaluation can be obtained considering the standard deviation of the model results to the ensemble. Figure 11 shows this indicator for the complete set of models (6 models) and for the data assimilation systems (3 over 6). Significant reduction of the standard deviation is obtained with the “data assimilated” results, compared to the “raw simulation” results. It is checked that, DA assimilation systems tend to tighten models’ dispersion. However, it can be still high in Germany, Poland, and Eastern countries, what must be further investigated.

(a) (b) Figure 11. Standard deviation of the MACC models results to the ensemble;

(a) 7 models hindcasts (b) 3 models re-analyses

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6 Nitrogen dioxide indicators

6.1 Annual and seasonal averages For the year 2007, only one team assimilated NO2 observations: RIU (Rhenish Institute for Environmental Research at the University of Cologne) assimilated NO2 ground-level observations from the AIRBASE database and also NO2 columns retrieved from satellites observations (OMI, GOME-2, SCHIAMACHI). Only the results provided by the EURAD modelling system (considering as the best one) performed by RIUUK are proposed below. The annual mean of nitrogen dioxide concentrations should not exceed the limit value of 40 µg/m3in compliance with the AQ Directive. This indicator mapped throughout Europe is presented below (Figure 12). The first map on the left (a) is issued from raw simulations forecasted by the EURAD system. The right one (b), results from a data assimilation procedure of in-situ and satellite observations available in 2007. Both are rather different with nitrogen dioxide concentration fields variability better captured by the DA system. In particular, concentrations are actually higher than those that the model alone forecasted in Germany, France, the Benelux, the UK and Northern Italy. The cities’ footprint is more marked, and the ship traffic influence as well, especially in the Mediterranean Sea and in the Channel. According to the MACC/EVA results, the annual limit value was close to be reached in 2007 only in the north part of Italy, but has not been exceeded. This result is consistent with the statistics of the European Environment Agency3 presented in Figure 13.

(a) (b)

Figure 12. 2007 Annual mean concentrations of Nitrogen dioxide throughout Europe simulated by the RIUUK/EURAD modelling system (a) NO2 annual concentrations hindcasts (b) NO2 concentrations re-

analyses assimilating AIRBASE data and satellite information

3 http://www.eea.europa.eu/data-and-maps/figures/annual-mean-no2-concentration-averaged-through-available-urban-background-stations-eea-member-countries-1997

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Figure 13. Trends in NO2 annual mean concentrations observed in 2007 at the suburban and urban

European monitoring sites. Source : EEA

(a) (b)

Figure 14. 2007 seasonal mean concentrations of Nitrogen dioxide throughout Europe simulated by the RIUUK/EURAD modelling system (a) Winter (b) Summer

Figure 14 shows the winter and summer nitrogen dioxide mean concentrations in Europe re-analysed by the EURAD system. Higher concentrations occurred in winter, as expected, because of stable meteorological conditions, and larger emissions due to fuel combustion and biomass burning. Western Europe and North part of Italy were particularly concerned by high winter concentrations. Another indicator to be considered as a regulatory flag is the limit value of 200 µg/m3 1 h 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. This characteristic is not compatible with the spatial resolution of the model re-analyses conducted in the MACC/EVA project (20km in the best case). Such a resolution allows the representation of rural and urban background concentrations throughout the whole Europe but is very uncertain for simulating local pollution effects, especially those that drive exceedances situations at traffic stations. Therefore this indicator has not been calculated within the MACC/EVA re-analyses.

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6.2 Uncertainty analysis More information is available on the model performance in the “Validation of the MACC assessment products for the year 2007”. However few elements are given below to help in the interpretation of the results. The correct behaviour of the EURAD model in assimilation mode (named “EURADa” in the graphs below) is demonstrated by the statistical performance indicators (root mean square error, correlation coefficient) calculated to confront model results to observations (Figure 16). Figure 15 shows a comparison of the NO2 concentration averaged values over the year 2007 calculated for the various models, discriminated by European sub-regions to get a more comprehensive picture. The observed concentrations averages for each sub-region are also given as reference levels (black bars). The huge variations of concentrations between the sub-regions are rather well-represented by the best models. However their performance varies significantly from a sub-region to another. They behave correctly in Northern, Central and even Eastern Europe and underestimate actual concentration levels in Western and above all Southern Europe. This region is the most uncertain in the modelling prospective and should be deeper investigated in the next assessment studies.

Figure 15. NO2 averaged concentrations over the year 2007 computed by the various MACC/EVA models

and sorted by sub-regions: EUW: Western Europe, EUC: central Europe, EUS: Southern Europe, EUN: Northern Europe, EUE: Eastern Europe. Black bars give the averaged concentrations levels observed

at the stations implemented in each sub-region.

(a) (b)

Figure 16. Root means Square error (a) and correlation coefficient (b) of the MACC/EVA model results for the year 2007, against the observations available at European urban stations

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7 PM10 indicators

7.1 Annual indicators The year 2007 can be considered as worrying on the Particulate Matter point of view. Figure 17 shows the annual average of PM10 concentrations which ranged from 10 to 33 µg/m3, with highest levels recorded in Southern Europe (Spain, Portugal, north part of Italy), Benelux ,and punctually in Central Europe. The 40 µg/m3 limit value (annual average) had not been exceeded according to the MACC/EVA system. However the number of days exceeding the 50 µg/m3 threshold (daily average) is rather significant in some areas Figure 18. According to the MACC/EVA assessment, the regulatory number of 35 days of exceedance had been reached in some parts of France, Italy, Spain, Benelux and Czech Republic. As for NO2 peaks assessment some situations could occurred only because of local sources (road traffic especially), and are underestimated by the modelling system which has a limited spatial resolution. However the overview proposed in Figure 18 gives an idea of the European areas which are the most sensitive to PM10 pollution.

Figure 17. PM10 annual average for the year 2007, assessed with the Ensemble MACC/EVA system

Figure 18. Number of days exceeding the 50 µg/m3 threshold (daily average) simulated by the

MACC/EVA system

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When compared to the observation data and the maps provided by the European Environment Agency (http://www.eea.europa.eu/themes/air/airbase/zones-in-relation-to-eu-air-quality-thresholds), it seems that the number of days exceeding the 50 µg/m3

threshold (daily average) is underestimated by the modelling system in some regions, especially in Greece and the Balkans region. This can be due to the fact that the impact of the forest fires that developed in these regions especially at the end of August 2007 are not correctly taken into account. In the future versions of the MACC/EVA system an operational link between the modelling platforms and the service in charge of forest fires emissions monitoring will allow the improvement of the assessment maps, better accounting for these exceptional sources of particulate matter and other pollutants.

7.2 Specific episodes A number of remarkable high intensity PM episodes occurred in 2007, especially during the spring and December (Figure 19). Several reasons can be invoked, among which one can quote natural sources or atypical meteorological patterns. The spring episodes are particularly interested because of their intensity. Central and Western Europe (Figure 20) were mainly concerned by these events which are briefly commented below.

Figure 19. Number of days exceeding the 50 µg/m3 threshold (daily average) observed in European sub-

regions: EUW: Western, EUC: central, EUS: Southern, EUN: Northern, EUE: Eastern.

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(a)

(b) Figure 20. Number of days exceeding the (a) 50 µg/m3 threshold (daily average) and (b) 80 µg/m3

threshold (daily average) observed in European sub-regions between 10th march and 19th April

(a) (b)

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Figure 21. PM10: daily mean concentrations in spring 2007: Reanalyses from MACC/EVA

(a) 15th March, (b)24th March, (c) 29th March, (d) 15th April

Warm and dry meteorological conditions, unusual in this season, held in Western and Central Europe for almost one month and allowed some air pollution exceptional events to develop. Although the geographical extension, the duration and the PM concentration levels were rather exceptional in spring 2007 (Figure 21), the reasons why high concentrations of PM are usually observed in spring in Europe are reasonably understood. Generally, a significant part of the PM composition is made of ammonium nitrate resulting from chemical reactions involving ammonia and nitric acid. In spring, with soil fertilizers spreading, the emissions in air of ammonia and nitrogen oxides are higher than in other periods, bringing precursors for secondary particles formation. But in spring 2007, the magnitude of this phenomenon was high: some chemical analyses of the PM monitored at the stations showed a large part of volatile compounds (ammonium nitrate) which reached 50 to 60% in some sites in Western Europe. This was certainly due to exceptional meteorological conditions. Indeed, abnormally high maximum temperatures were observed in Western Europe: near 20°C on March 13, 27 and 28 and near 30°C on April 15 throughout ammonia emission areas. It is well known that temperature is one of the most sensitive meteorological parameter influencing NH3 evaporation from soils. An increase of temperature induces a large increase of ammonia emissions. [Misselbrook et al.,2004]4 showed that a difference of 5oC could lead to a factor 3 in ammonia emission rate for the Ammonium Nitrate fertilizer and a factor of 2 for Urea. For Urea, ammonia emissions largely increase between 20oC and 29oC (for a given soil type), a factor of 2 to 3 was already observed [Liu et al., 2007]5. As a consequence, the high ammonium nitrate concentrations observed on April 15, with maximum temperatures close to 25oC during the afternoon suggest that ammonium nitrate probably produced during the night and morning was only partly evaporated during the afternoon. Actually, the chemical analyses available for these periods showed that the origin of the episode that occurred the 23rd -25th March was different than what happened the other days. The volatile fraction was very low, and the presence of mineral compounds had been

4 Misselbrook, T., M. Sutton, and D. Scholefield (2004), A simple process-based model for estimating ammonia emissions from

agricultural lan after fertilizer applications, Soil Use and Management, 20, 365-372, doi: 10.1079/SUM2004280. 5 Liu, G. D., Y. C. Li, and A. K. Alva (2007), Temperature quotients of ammonia emission of different nitrogen sources applies to

four agricultural soils, Soil Sci. Soc. Am., 71, 1482-1489.

(c) (d)

23

detected (Figure 22). The hourly concentrations monitored in some places were exceptionally high, in particular in Central Europe where 1000 µg/m3 had been exceeded. Such characteristics led the experts to diagnose a dust-event like episode. First it was attributed to one of the Saharan dust plumes which reach Europe rather often. But some retro-trajectory analyses challenged this hypothesis and other investigations had been realised, especially with earth-observations. And it was demonstrated that the CALIOP lidar embedded on the CALIPSO (Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite managed by the NASA (US) and the CNES (F) measured an abnormal dust cloud blown up by a storm blowing over Ukrainian dry agricultural areas, the Chernozems (”black soil” in Russian) (Figure 23). The fact that these soil areas were exceptionally dry, because of drought and naturally sensitive to wind erosion explained the magnitude of this dust event from the East part of Europe which impacted all the PM monitoring stations in Central and Western Europe.

Figure 22. Time series of observed hourly PM10 concentrations recorded on March 14 [00:00] – 27 [00:00], 2007 in Gravelines (France) and chemical analyses for K+, Ca2+, Mg2+, [Bessagnet et al, 2007]6

6 Bessagnet, B., L. Menut, G. Aymoz, H. Chepfer, and R. Vautard (2008), Modeling dust emissions and transport within Europe:

The Ukraine March 2007 event, J. Geophys. Res., 113, D15202, doi:10.1029/2007JD009541.

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Figure 23. Vertical cross-section (Latitude-Height diagrams) of CALIOP data March 23, 2007, at about

[10:50] : satellite trajectory (top), backscatter signal (middle) and color ratio (bottom) [Bessagnet et al, 2007]

7.3 Uncertainty analysis More information is available on the model performance in the “Validation of the MACC assessment products for the year 2007”. However few elements are given below to help in the interpretation of the results. The first important point related to PM re-analyses is the fact that whatever the model, 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). Figure 24 shows the PM10 averaged concentrations for the year 2007, simulated and/or re-analyzed by the various MACC/EVA models (including the Ensemble) and interpolated at the station sites’ location, and compared to the actual concentrations observed in different European sub-regions. Larger model underestimation is obtained for central and Southern Europe. Concentrations in the other parts of Europe are rather correctly represented by the data assimilation systems. The added-value brought by this process to correct a posteriori the PM10 concentrations is particularly significant in this case. The reasons why it is less efficient in Central and Southern Europe have to be considered more carefully for the next assessment reports. However the density of available PM10 monitoring stations in these sub-regions is lower than in Western and Northern Europe. This can partly explain the geographical differences in the performances. This is highlighted when statistical scores of the model results against the observations (root mean square errors or correlation coefficient) are considered, for different station typologies (Figure 25).

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Figure 24. PM10 averaged concentrations over the year 2007 computed by the various MACC/EVA

models and sorted by sub-regions: EUW: Western, EUC: central, EUS: Southern, EUN: Northern, EUE: Eastern. Black bars give the averaged concentrations levels observed at the stations

implemented in each sub-region.

Figure 25. Root means Square error (top) and correlation coefficient (bottom) of the MACC/EVA model

results for the year 2007, against the observations available at European suburban (left) and urban (right) stations

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8 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 PM2.5 concentration that should no longer be exceeded by the 1st January 2010, is fixed to 25 µg/m3. It will become a limit value in 2015, and will be reduced to 20 µg/m3 in 2020. Figure 26 shows the PM2.5 annual average concentrations simulated with the MACC/EVA ensemble system. Large European cities 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. However the 25 µg/m3 threshold is not exceeded in this estimation although it probably underestimates, like for NO2, local hot spots impact (road traffic influence for instance).

Figure 26. PM2.5 annual average concentrations for the year 2007, assessed with the Ensemble

MACC/EVA system

PM2.5 concentrations monitoring became mandatory according to new unified air quality Directive in 2008. Therefore very few PM2.5 monitoring stations were implemented in Europe in 2007. The set of available stations was not considered as representative enough neither for data assimilation, nor for validation. Consequently for the 2007 analysis, only raw simulations were provided and considered for interpretation. This point should improve significantly for the 2009 assessment report.

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9 Conclusions This document is the first MACC/EVA assessment report which presents a number of results related to ozone, nitrogen dioxide and particulate matter (PM10 and PM2.5) concentrations that held during the year 2007. They result from the “decentralised” regional air quality modelling systems implemented in the MACC project, the same that are routinely run for the provision of forecasts and near real time analyses. Some of them (but not all in this first stage) were used in a “re-analysis” mode, with a data assimilation procedure which allowed an accurate representation of the air pollution patterns “combining” simulation and observations. Because only a limited number of models provided analysed results, the benefits of the ensemble approach that is supposed to be derived in the MACC/EVA program, is limited. In some cases, individual models could give better results (for NO2 for instance with the EURAD system which is the most achieved in term of data assimilation). Those results, 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 2007” (report D_R-EVA_2.2 of the MACC work plan). The year 2007 is particularly interested because of the distinction that can be made between ozone and particulate matter (PM) impacts. On the ozone point of view, at the European scale, 2007 can be considered as a rather “quiet” year, except in the Mediterranean areas (especially Greece, Balkans) where a severe heat wave held during summer. Conversely, on the PM point of view, 2007 is worrying with a significant increase (compared to the last years situation) of the level of highest PM10 concentration values and an increasing number of situations when the regulatory thresholds were exceeded. This document reports a number of indicators demonstrating this situation and brings elements for their interpretation. Nitrogen dioxide and fine particulate matter (PM2.5) concentration maps are also provided. Annual averages do not exceed the limit and target values respectively set by the European regulation for both of these pollutants. However the limited spatial resolution (25 km in the best case) of the model runs does not allow the proper consideration of local air pollution hot spots (near industrial sites or dense road traffic lanes for instance). Such situations are out of the scope of this work. 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 is the European sub-region 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 this geographical area are well-known from the scientific communities and are still the subject of research project. In a lesser extend the situation is more or less the same for Central Europe. The potential improvement of the MACC/EVA re-analysis system in these regions will be further investigated in the next stages, at least for the 2008 and 2009 reports. The density of the observation data available for being assimilated in the modelling systems will be analysed for the various pollutants. Heterogeneities in the monitoring network can explain some data assimilated systems behaviour. Conclusions will be reported to the “observation” sub-project in MACC for future recommendations. Finally, feedback from the users and the experts from the Member States 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 demand.

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10 ANNEX: methodologies and assumptions Models: The models that provided raw simulations and data assimilated fields of air pollutant concentrations for the year 2007 are the ones involved in the MACC regional cluster dedicated to the provision of air quality information at the European scale. Seven models running operationally on their own modelling platform will perform re-analyses by 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 (http://www.gmes-atmosphere.eu/documents/deliverables/r-ens/). 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 achieved the development of their data assimilation system to use for the first 2007 assessment report 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 2007 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

AIRBAS

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

NO: in development,

evaluation not achieved

EURAD Intermittent 3DVAR O3, NO2,

NO, CO,

SO2, PM10

AIRBASE in situ

measurements

MOSAIC air borne in situ

measurements

NO2 tropospheric column

retrievals from OMI, GOME-2,

SCIAMACHY

MOPITT CO profiles

Yes

LOTOS-

EUROS

Ensemble Kalman

filter

O3

NO2,

PM10,

AOD, SO2,

SO4

Airbase

Under evaluation: OMI ,

observation operator

developed, evaluation studies

Yes, Significant

improvement

Not yet

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MATCH 3D-VAR with

transform into

spectral space

O3, NO2 NRT in-situ Not yet , evaluation not

achieved

MOCAGE 3D-VAR O3 Ozone in-situ data Yes since summer 2010,

online evaluation

available

SILAM 3D-VAR

4D-VAR

O3, NO2,

SO2

AIRBASE in situ Operational

In test

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 QA/QC dossier

reference

CHIMERE O3, PM10, with OI for the whole year and

the requested episodes


of the simulation results

D_R‐ENS_1.4.1

EMEP No reanalyses provided, DA under

development

Not applicable D_R‐ENS_1.2.2

EURAD O3, NO2, NO, CO, SO2, PM10 (surface obs) ,

MOZAIC, NO2 tropospheric column

retrievals from OMI, GOME-2, SCIAMACHI

and MOPITT CO profiles


of the simulation results

D_R‐ENS_1.3.2

LOTOS-

EUROS 1-4-2007 to 30-10-2007 – ozone analyses

on 30 km resolution Significant improvement

of the simulation results D_R‐ENS_1.4.2

MATCH No re-analyses provided Not applicable D_R‐ENS_1.5.2

MOCAGE No re-analyses provided Not applicable D_R‐ENS_1.6.2

SILAM No re-analyses provided Not applicable D_R‐ENS_1.9.2

Table 2: Brief summary of the model configurations used for the 2007 assessment report

Observation datasets: The AIRBASE database gathering all observations reported from the regulatory air quality monitoring networks is available for the year 2007 and had been used for both re-analysis and evaluation processes. However same data cannot be used for both (it would not make sense to evaluate re-analyses 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

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(rural, suburban, and urban) were the principles considered for the station classification. The table below presents the number of stations selected in each categories and their location is mapped on the following figures

Rural/ DA

Rural/ Validation

Suburban/ DA

Suburban/ Validation

Urban/ DA

Urban/ Validation

O3 213 104 250 119 448 211 Total O3: 1345

NO2 283 145 261 123 359 165 Total NO2: 1336

PM10 165 74 21à 93 454 202 Total PM10: 1198

Table3: Number of stations selected in the AIRBASE database for 2007 for DA and validation

Figure 27. Location of the NO2 AIRBASE stations (2007) selected for data assimilation (red dots) and

validation (green dots) processes

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Figure 28. Location of the O3 AIRBASE stations (2007) selected for data assimilation (red dots) and


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Figure 29. Location of the PM10 AIRBASE stations (2007) selected for data assimilation (red dots) and


Hypotheses on input data: Emission data, meteorological re-analyses and boundary conditions emissions have been provided by the other MACC sub-project or by the MACC partners. Indeed, meteorological re-analyses were provided by ECMWF. The emission inventory used for running the models is the high resolution one provided for the year 2007 by the TNO within the “Emissions” MACC subproject. Finally boundary conditions come, for the gaseous compounds from the Global “reactive gases” sub-project (MOZART re-analyses).

Documents

Assessment Report: Air quality in Europe for 2007