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ECMWF COPERNICUS REPORT

Copernicus Atmosphere Monitoring Service

Interim Annual Assessment Report for 2018

European air quality in 2018

Issued by: NILU /Leonor TARRASON

Date: 19/08/2019

Ref: CAMS71_2019SC1_D1.1.1.-2018_201908_IAR2018V2

This document has been produced in the context of the Copernicus Atmosphere Monitoring Service (CAMS). The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of CAMS on behalf of the European Union (Delegation Agreement signed on 11/11/2014). 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 and the European Centre for Medium-Range Weather Forecasts has no liability in respect of this document, which is merely representing the authors view.


CAMS71_2019SC1_D1.1.1._2018 – Interim Annual Assessment Report-2018 of 66

Contributors

NILU Leonor Tarrasón Paul David Hamer Cristina Guerreiro

INERIS Frederik Meleux Laurence Rouïl



Table of Contents

1. Introduction 9

2. Air Quality Indicators in 2018 12

2.1 Ozone in 2018 12 2.1.1 Meteorological Characterisation 12 2.1.2 Ozone Health Indicators 14 2.1.3 Ozone Vegetation Indicator 17

2.2 Nitrogen Dioxide in 2018 19 2.2.1 Seasonal Variations 19 2.2.2 Nitrogen Dioxide Health Indicators 19

2.3 PM10 in 2018 22 2.3.1 Meteorological Characterization 22 2.3.2 PM10 Health Indicators 24

2.4 PM2.5 in 2018 26 2.4.1 Meteorological Characterization 26 2.4.2 PM2.5 Health indicators 27

3. Pollution Episodes in 2018 29

3.1 Rationale for Episode Identification 29

3.2 Identified Pollution Events in 2018 29

3.3 Origin of Pollution Episodes 32 3.3.1 Ozone Episode in July and August 2018 34 3.3.2 PM Episodes in February and October 2018 39 3.3.3 Dust Episodes in April, August and October 2018 52 3.3.4 Forest Fires in July 2018 60

4. Conclusions 61

5. References 64



Executive Summary This Interim Annual Assessment Report (IAAR) documents the status of air quality in Europe during 2018. It presents timely reference information on air pollution indicators for background air quality for the main regulatory pollutants (ozone, NO2, PM10 and PM2.5). The report characterizes the year climatologically with respect to previous years, and gives details on the occurrence and origin of nine (9) selected large-scale episodes. The report is elaborated on the basis of non-validated up-to-date observations gathered by the European Environment Agency (EEA) and modelled data from the Copernicus Atmospheric Monitoring CAMS Services (CAMS). The year 2018 was a new ozone year and the recorded number of ozone exceedances in 2018 was higher than in previous years. This is related to the meteorological conditions predominant over Europe and also to the spatial density and distribution of monitoring stations reporting exceedances. What is exceptional for 2018 is that the area affected by high ozone levels is further north than in previous years, extending from Central Europe to the north of Germany and southern Scandinavia. According to the Copernicus European State of Climate 2018, 2018 was the third warmest year on record in Europe. It follows the clear warming trend that temperatures in Europe have shown over the last forty years, with the past four years (2015, 2016, 2017 and 2018) recorded as the four warmest. The year began with weak La Niña conditions, which are typically associated with lower temperatures, but, after March, Europe experienced the warmest late spring, summer and autumn temperatures on record. Consequently, Europe experienced high ozone values in 2018 with ozone health indicator values well above previous years and comparable only to the extreme years of 2015 and 2010. For SOMO35, the elevated ozone levels over all seasons resulted in higher values of the SOMO35 indicator than in any of the previous years. Europe experienced also high AOT40 values in 2018, well over the target value of 18 000 μg.m-3.hours. These record high ozone values in Central Europe caused crop damages and severely affected husbandry, even in southern Scandinavia. The year 2018 was a dry year over northern Europe and wetter than average year in southern Europe. The Northern Mediterranean region experienced many heavy rainfall events in 2018. Southwest Europe experienced one of the two wettest springs since 1950 while Southeast Europe had one of the six wettest summers of the last 70 years. Still, large parts of Europe (in particular Northern and Central Europe) also experienced exceptional heat and drought conditions through late spring and summer of 2018. There was an extensive period of drought in northern Europe, with 80% less than normal precipitation during the main growing and harvesting periods. These high temperatures and dry conditions increased the risk for vegetation damage and forest fires. A series of wildfires in Greece, during the 2018 European heat wave, began in the coastal areas of Attica in July 2018 with as many as 102 people confirmed dead. The fires were the second-deadliest wildfire event in the 21st century. Wildfires reached also an unprecedented extent in Sweden, with over 25 000 hectares burned, in what was considered to be the most serious wildfire event in the country’s modern history. While 2018 was an extreme year for ozone in northern Europe, for other pollution components and regions, the 2018 levels were similar to previous years. The annual mean concentrations of



background PM10 and PM2.5 over most regions in Europe were similar to those of 2016, 2015 and 2014. The background values for nitrogen dioxide in 2018 were not significantly different from those of previous years. We have studied the evolution and origin of nine (9) different air pollution events in 2018: one for ozone, three for PM, three associated to Saharan dust intrusions and two with wildfires. All the episodes are characterised by a high number of exceedances of EU quality objective and limit values for ozone and PM10 and of WHO guidance value for PM2.5. This year we have focused on explaining the capabilities of two recently added policy products, namely the CAMS Air Control Tool and the chemical speciation in the CAMS Source-Receptor (SR) calculations. These two new policy relevant products are particularly appropriate to characterise the origin of pollution and identify the long-range transport components. They can support the analysis of the extent of different natural and anthropogenic contributions to background air concentrations over Europe, which can be a key issue when exceedance of the PM10 limit values are reported by the member States according to the Ambient Air quality Directive 2008/50/EC (AAQD). There were a significant number of ozone episodes in 2018. We have selected the one that took place from 30th July to 9th August because it is the event that registered the highest number of exceedances, due to its persistence over 12 days and its broad spatial extent. Traffic and industrial emissions were as expected the main anthropogenic contributors to this ozone episode. The extent of the biogenic contribution and boundary condition and existence of titration effects from local sources are also well illustrated in the results from the CAMS SR calculations. We also can trace the influence of the various chemical regimes that characterize atmosphere chemistry in Europe when analyzing how the emission reductions planned under the NEC2030 scenario would affect the ozone episode. The three selected PM episodes are very different in nature. The first selected PM episode from 7th to 10th February began in Central Europe, with the highest values over Poland and developed over time to include also large areas in Germany, Denmark and the Baltic region. At the same time, a Saharan dust intrusion took place over the Eastern Mediterranean region reaching Turkey and the Black Sea. This episode had a significant long-range component, but it had also significant local contributions, as indicated by the fact that emissions from residential heating, including wood and coal combustion dominated the PM levels, especially in Southern and Eastern Europe. Examples for Warsaw and Ljubljana from the CAMS SR service show the balance between the long-range and local contributions to background air and helps identify the origin of the long-range transport contribution from different countries in these cities. The second PM episode studied occurred from 21st to 28th February and was mainly driven by residential heating emissions which are always very difficult to quantify. The third selected PM episode took place from 21st to 26th October 2018 and was characterized by a combination of a sea-salt episode over the Atlantic Coast and a Saharan dust intrusion over the Mediterranean area and southeastern Europe. In 2018, there were three major dust storm events affecting the European continent: from 22nd to 27th April affecting the Iberian Peninsula and the western Mediterranean basin, from 1st to 4th August, also over the Iberian Peninsula and from 16th to 20th October in an event affecting the central and eastern Mediterranean basin. We also studied the forest fire events of 23rd and 24th July in Greece



and the wildfires from 17th to 18th July in Sweden, because both contributed to elevated PM2.5

concentrations in their surrounding areas. The combination of data from the CAMS services on dust provides useful information to determine the timing, extent and magnitude of dust episodes. While the dust product from the CAMS global service serves to identify the timing of the dust events, the combination of regional CAMS products, with the ACT tool and the chemical speciation products allow a better quantification of the magnitude of the dust events. However, the CAMS policy products are more limited for the evaluation of forest fires contribution as it is illustrated in the report.



1. Introduction This report is the fourth CAMS Interim Annual Assessment Report (IAAR) and provides timely information on the air pollution situation in Europe in 2018. Maps and information on the status of air quality indicators for the main regulatory pollutants are provided here as well as a meteorological characterization of the year 2018 in terms of air pollution and compared with previous years. The report is part of a series of policy reports and products developed by the Copernicus Atmosphere Monitoring Service and is available on-line at (CAMS http://atmosphere.copernicus.eu/). The interim air quality indicators for ozone, NO2, PM10 and PM2.5 are elaborated following the criteria given by EU legislation and WHO guidelines and include both health and ecosystem impact indicators. The indicators presented here are based on the interim re-analyses of regional air quality that are produced by the CAMS regional system. The CAMS interim re-analyses for 2018 use non-validated up-to-date observation data from the EEA/EIONET network and state-of-the-art data assimilation techniques to combine them with air quality model results. The interim re-analyses are a robust estimate, based on the mean of an ensemble of state-of-the-art air quality models. The CAMS ensemble model runs are based on meteorological fields, provided by ECMWF and TNO-MACC-III emission inventories for European countries as input data. The emission inventory has not been updated since 2011, which must be considered in the interpretation of the results. However, the data assimilation processes implemented in the re-analyses production chain partially compensate this source of uncertainty in relation to the indicator results. This data assimilation approach aims at providing best possible estimates of air pollution patterns and maps throughout the European domain as documented regularly by the CAMS regional service in its quarterly validation reports, available at https://atmosphere.copernicus.eu/validation-regional-systems. Therefore, the CAMS interim air quality indicators are currently the best estimate of air quality situation in Europe at regional scale for 2018. The meteorological characterisation of the year 2018 in terms of air quality is based on calculated anomalies relative to the last 10–year mean meteorology (2007-2017) from the ERA-Interim daily rea-analysis dataset, produced at the European Centre for Medium-Range Weather Forecasts (ECMWF) and made available by the Copernicus Climate Change Service (C3S). The interim air quality indicators are then compared to previous years of validated CAMS indicator results to establish qualitative trends in air pollution patterns over the last 10 previous years. We have identified nine (9) significant air quality episodes that occurred in European background air during the year 2018, on the basis of observed exceedances of daily EU limit or target values or WHO guidelines. The observed exceedances are up-to-date non-validated data from monitoring stations from the regulatory EEA/EIONET network. For each selected episode, the report includes a characterisation of the origin and the main drivers behind.

https://atmosphere.copernicus.eu/validation-regional-systems



The episode characterisation draws from an extensive use of CAMS products and modelled data. As in previous IAAR reports (Tarrasón et al, 20161; Hamer et al, 20172; Tarrasón et al., 20183) the report makes use the following CAMS modelling products and data:

a) pollutant concentrations from the CAMS regional interim re-analyses to trace the temporal evolution and assess the spatial extent of the episodes;

b) results from the CAMS global aerosol production to determine whether there is a

contribution from desert dust, sea-salt and wildfires in the pollution episodes and identification of situations when they are the main driver as derived from the CAMS global validation reports;

c) results from the CAMS regional green-scenario calculations to determine what can be the

main anthropogenic emission sectors responsible for specific episodes;

d) results from the CAMS regional runs for future policy scenarios, related to future 2020 and 2030 EU legislation, to determine the impact that European scale future emission reductions planned under legislation would have had on the pollution episode;

e) results from the CAMS source-receptor calculations for major European cities to determine

the possible extent of long-range background contributions over the city areas and the new chemical speciation capability introduced since 15th May 2018.

For the first time, this year’s report makes use of the recently developed Air Control Toolbox (ACT), which is an on-line toolbox that allows to simulate the impact on ambient PM and ozone concentrations of sectoral (industry, road traffic, residential heating and agriculture) emission reductions defined by the users themselves. We have also made extensive use of new visualization and the chemical speciation products under the CAMS source-receptor calculations. From the basis of all the above CAMS products and data, we evaluate the temporal and spatial extension of pollution episodes over European background air in 2018 and provide information on areas where episodes have a significant natural dust, sea salt, or wildfire contribution, identifying also the mean sources behind the long-range transport pollution contribution of the specific episodes.

1 IAAR 2015 - http://policy.atmosphere.copernicus.eu/reports/CAMS71_2016SC1_D71.1.1.2_201609_final.pdf 2 IAAR 2016 http://policy.atmosphere.copernicus.eu/reports/CAMS-71_2016SC2_D71.1.1.6_201707_2016IAR_V4.pdf 3 IAAR 2017 https://policy.atmosphere.copernicus.eu/reports/CAMS71_D71.1.1.10_201807_IAAR2017_final.pdf

http://policy.atmosphere.copernicus.eu/reports/CAMS71_2016SC1_D71.1.1.2_201609_final.pdf

http://policy.atmosphere.copernicus.eu/reports/CAMS-71_2016SC2_D71.1.1.6_201707_2016IAR_V4.pdf


https://policy.atmosphere.copernicus.eu/reports/CAMS71_D71.1.1.10_201807_IAAR2017_final.pdf



The indicator maps included in this year’s report have a different colour scale that in previous years. This is in order to be consistent with the mapping guidelines at the European Environment Agency (EEA), applying until 2019, in an effort to streamline the information and respond to user requirements. We are aware that the EEA is considered to change again their colour scale, so possible new scales will be re-considered for our next year’s report.



2. Air Quality Indicators in 2018 This chapter provides a timely overview of the main air quality indicators for ozone (O3), nitrogen dioxide (NO2), particulate matter (PM10) and fine particulates (PM2.5) in Europe in 2018, and a comparison with respect to the 10 previous years. The air pollution levels are presented in the form of air quality indicator maps. The maps rely on interim re-analyses of regional air quality for the years 2018 and 2017 and validated re-analyses for all previous years, as produced by the CAMS regional service component4. Health and ecosystem impact indicators maps are also presented and are further characterised in terms of their meteorological variability with respect to previous years. The meteorological characterisation of 2018 is based on calculated anomalies relative to the 2008-2017 average meteorology from the ERA-Interim daily re-analysis dataset, produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) and made available by the Copernicus Climate Change Service (C3S) (Dee et al., 2011, Berrisford et al., 2011)5. Both the interim air quality indicators and their meteorological characterisation are intended to support the annual reporting of air quality by Member States by providing valuable up-to-date information on background European levels in 2018.

2.1 Ozone in 2018

2.1.1 Meteorological Characterisation The year 2018 is again recorded as a very warm year. Globally, 2018 is the fourth warmest year on record, according to the World Meteorological Organisation Statement on the State of the Global Climate in 2018 (WMO, 2019). In fact, the past four years (2015, 2016, 2017 and 2018) are the four warmest years in the WMO series. In Europe, 2018 is the third warmest year on record as reported in the Copernicus European State of Climate 2018 summary6, following the clear warming trend that temperatures in Europe have shown over the last forty years. The year began with weak La Niña conditions, which are typically associated with lower temperatures, but, after March, Europe experienced the warmest late spring, summer and autumn temperatures on record. Large parts of Europe (in particular Northern and Central Europe) experienced exceptional heat and drought conditions through late spring and summer of 2018. These high temperatures and dry conditions increase the risk for vegetation damage and forest fires. Wildfires reached an unprecedented extent

4All yearly CAMS regional re-analyses are freely available to Member States from http://macc-raq-op.meteo.fr/index.php?category=eva_access. 5The ERA-Interim daily re-analyses are available freely to Member States from http://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/ 6 Copernicus Climate Change service has elaborated a European State of Climate 2018 summary available at

https://climate.copernicus.eu/sites/default/files/2019-04/Brochure_Final_Interactive.pdf

http://macc-raq-op.meteo.fr/index.php?category=eva_access

http://macc-raq-op.meteo.fr/index.php?category=eva_access

http://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/




in Sweden, with over 25 000 hectares burned, in what was considered to be the most serious wildfire event in the country’s modern history. This is illustrated in Figure 1 below. The figure shows the seasonal anomalies of temperature in 2018 with respect to the mean of the past decade, 2008-2017. This last 10-year average analysis provides enough sampling of the current climate’s variability and is representative for the present climatic conditions. The average temperatures in winter, spring, summer, and autumn during 2007-2016 have been subtracted from the mean seasonal values 2018 calculated from the C3S ERA interim re-analyses.

Figure 1. Seasonal mean temperature anomalies over Europe in 2018 relative to a 10-year mean (2008-2017) baseline derived from the C3S/ECMWF ERA-Interim re-analyses. Upper left pane is winter; upper right panel is spring; lower left panel is summer and lower right panel is autumn. Units: [°C]

As indicated in Figure 1, 2018 is characterized by temperatures warmer than average over the whole of Europe, with colder values than average only during winter over Northern Scandinavia and Northern Russia. In Northern and central Europe, temperatures in late spring and summer are between 4 to 8°C above average. Also the autumn season is also generally warmer that average varying from 2 to 4°C above average over the whole of Europe. The consistently warmer situation over most of the year has important consequences for the status of air pollution in 2018. Atmospheric temperature has a strong influence on background ozone concentrations due to key photochemical reactions that are indirectly responsible for the formation of ozone. Higher atmospheric temperatures typically lead to enhanced ozone formation and higher background ozone levels. In 2018, the higher temperature anomalies took place over most of the year, but especially



during spring and summer, coinciding with the time when ozone production is at its highest. Consequently, Europe experienced record high ozone values in 2018 in late spring and summer, as illustrated in Figure 2. The figure shows also the elevated values of ozone in 2018 for all seasons. In particular, over Central Europe, the elevated ozone levels lead to generally high ozone indicators, well above other previous years and comparable only to the very high values experienced in 2015.

Figure 2. Ozone seasonal average concentrations for 2018. Upper left pane is winter; upper right panel is spring; lower left panel is summer and lower right panel is autumn. Units: [μg.m-3]

2.1.2 Ozone Health Indicators The European Union's Air Quality Directive (EU, 2008) sets four metrics to monitor air pollution by ozone and its impacts on health:

• an information threshold: 1-hour average ozone concentration of 180 μg.m-3, • an alert threshold: 1-hour average ozone concentration of 240 μg.m-3, • a long-term objective: the maximum daily 8-hour mean concentration of ozone should not

exceed 120 μg.m-3, and • a target value: long-term objective of 120 μg.m-3 should not be exceeded on more than 25

days per year, averaged over 3 years.



The World Health Organization also has set a guideline for O3: the maximum daily 8-hour mean concentration of ozone should not exceed 100 μg.m-3 (WHO, 2005). In addition, the Task Force on Health has also defined an ozone health impact indicator called SOMO35, which is the sum of daily maximum 8-hour running mean averages above 35 ppb (70 μg.m-3). SOMO35 is used to help quantification of health hazards from ozone and is used in health impact assessment modelling (WHO, 2008). Maps showing the different ozone health indicators in 2018 from the CAMS interim re-analyses are presented below, and to provide its meteorological characterization, we compare them with the same indicators for the 2007-2016 period. Please note, however, that the 2018 and the 2017 are results from the interim re-analyses from the regional CAMS system, which are produced assimilating non-validated observations whereas the 2007-2016 re-analyses are produced using validated data. Note also that the re-analyses maps for the years 2007-2010 use a smaller model domain (without the grey area) than the calculations for the rest of the years. Despite these small inconsistencies, the comparison does still provide a useful preview for how 2018 fits into the context of previous years’ ozone health impacts indicators. Three ozone health indicators are characterized in the figures below.

Number of days with 8 hour daily max over 120 µg m-3

Figure 3. Number of days when the maximum 8-hour daily mean of ozone exceeds the long-term objective value of 120 μg.m-3. The different figures show results for different meteorological years (from left to right descending: 2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, and 2007). Note that the 2018 and 2017 figures show interim re-analyses and the 2007 to 2016 figures show re-analyses, and also that the regional system domain increased in size in 2011 and onwards until present. Unit: [Number of days]



Figure 3 shows the number of days in 2018 when the maximum daily 8-hourly ozone means were above the long-term objective of 120 μg.m-3. It also shows the same information for the years 2007-2017. The warm late spring and summer of 2018 result in a relatively high number of long-term objective exceedances, more in line with the extreme year of 2015. Figure 4 shows the number of days in 2018 when the 1-hour information threshold of 180 μg.m-3 was exceeded. There were few exceedances of the information threshold in 2018, and most of them took place in Belgium, France, Germany and the Po region in Italy. Again, the situation in 2018 is comparable to the one experienced in the “ozone-extreme” years of 2015 and 2010.

Number of hours above the information threshold of 180 µg m-3

Figure 4. Number of days with ozone concentrations above the 1-hour information threshold of 180 μg.m-3. The different figures show results for different meteorological years (from left to right descending: 2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, and 2007). Note that the 2018 and 2017 figures show interim re-analyses and the 2007 to 2016 figures show re-analyses, and also that the regional system domain increased in size in 2011 and onwards until present. Unit: [Number of hours].

Figure 5 shows the situation for the last ozone health indicator, namely WHO’s SOMO35. Also for this indicator, values in 2018 are higher than in previous years and extend over large areas, covering most of central and southern Europe, reaching even to southern Sweden. Particularly high exceedances of the SOMO35 health indicator are shown in Spain, France, Italy, Spain, Germany, Austria, Czech Republic, Greece and Turkey. In addition, we can see again the effect of the higher values of ozone in 2018 over Northern Africa, Turkey and the Middle East, as we also saw in 2017. While the ozone



information threshold and the long-term objective are indicators targeting at the summer periods, the SOMO35 indicator is being calculated over the whole year. The situation in 2018, with elevated ozone levels over all seasons, leads to high values of the SOMO35 indicator. This is consistent with the reported trends of an even increase in background ozone levels (EEA, 2014) of which SOMO35 is a good indicator. It is interesting to note that the meteorological situation in 2018 lead to higher ozone levels over Northern Europe, in an area with good AQ station coverage, so that the number of recorded exceedances was somewhat higher in 2018 than in previous years.

SOMO35 Indicator / µg m-3.d

Figure 5. WHO’s health indicator SOMO35. This is the sum of maximum daily 8-hour running mean of ozone above 35ppb (70μg.m-3). The different figures in the panel show results for different meteorological years (from left to right descending: 2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008 and 2007). Note that the 2018 and 2017 figures show interim re-analyses and the 2007 to 2016 figures show re-analyses, and also that the regional system domain increased in size in 2011 and onwards until present. Unit: [μg.m-3.day]

2.1.3 Ozone Vegetation Indicator The indicator generally used in regulatory reporting to assess ozone impact on vegetation according to the Air Quality Directive (EU, 2008) is the accumulated dose over a threshold of 40 ppb (AOT40). AOT40 is the sum of the differences between the hourly ozone concentration (in ppb) and 40 ppb, calculated for each hour when the concentration exceeds 40 ppb, accumulated during daylight hours



(8:00-20:00 UTC). In the Air Quality Directive (EU, 2008), the target value of AOT40 calculated from May to July is 18 0007 (μg.m-3.hours), with a long-term objective of 6 000 (μg.m-3.hours). Figure 6 shows the calculated AOT40 indicator for 2018 as derived by the CAMS interim re-analysis and comparison with calculations from previous years. Again, the meteorological conditions of 2018, with elevated temperatures in late spring and summer over most of Europe, result in high AOT40 values. Over large areas in Central Europe, we can see AOT40 values over the target value of 18 000 μg.m-3.hours. These record high ozone values in Central Europe caused crop damages in 2018 and severely affected husbandry even in southern Scandinavia (WMO, 2019).

AOT40 Indicator / µg m-3.h

Figure 6. AOT40 indicator for protection of crops and vegetation. The different figures in the panel show results for different meteorological years (from left to right descending: 2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008 and 2007). Note that the 2018 and 2017 figures show interim re-analyses and the 2007 to 2016 figures show re-analyses, and also that the regional system domain increased in size in 2011 and onwards until present. Unit: [μg.m-3.hour]

Elevated ozone levels in Northern Africa, Egypt, Turkey and the Middle East are also prominent in the AOT40 maps for 2018.

7 Averaged over 5 years. In the current assessment only yearly AOT40 concentrations are considered, i.e. no average over 5 years is

presented.



2.2 Nitrogen Dioxide in 2018 The concentrations of nitrogen dioxide presented in this section are background values. The CAMS re-analyses products provide valuable information on background nitrogen dioxide concentrations over Europe, not on the highest levels of NO2 concentrations at “hot spots”. This is an important clarification, and it should be noted that the CAMS NO2 values are not directly comparable with observations in urban areas, especially the highest levels of NO2 pollution that occur close to traffic sources or near industrial emission sources. These very high levels of NO2 are distributed over relatively fine spatial scales (i.e., from tens of meters to a few kilometers) compared to the spatial resolution of the CAMS re-analyses (horizontal resolution of around 10 km). However, information about the variations in time of background NO2 is essential to understand exceedances of limit values close to source areas. While a model with finer spatial resolution would be required to simulate NO2 concentrations near traffic and industrial sources, the NO2 background concentrations are valuable information when considering exceedances of limit values under European legislation.

2.2.1 Seasonal Variations Figure 7 shows the calculated seasonal variation of background nitrogen dioxide (NO2) in Europe in 2018, as calculated by the CAMS interim re-analyses. The concentrations of NO2 in background air clearly reproduces the location of its main emission sources, so that the seasonal maps in Figure 7 would reflect the locations of European urban and industrial areas, major road traffic networks as well as maritime traffic routes where NOx emissions are known to be significant. However, given the new color scale adopted in this report, only main urban areas are visible currently in Figure 7, a reminder of the fact that the CAMS NO2 concentrations are only background values, lower than those usually observed in hotspots. Winter and autumn show the highest surface concentrations of NO2 throughout the year. Background NO2 concentrations peak at the surface during these seasons because of the increased prevalence of temperature inversions in the lower layers of the atmosphere that enhance the accumulation of NO2. In addition, the lower levels of sunlight during these seasons reduce the photochemical removal for NO2 compared to spring and summer. Thus, a reduced sink and higher levels of accumulation both contribute to the maxima in autumn and winter.

2.2.2 Nitrogen Dioxide Health Indicators The EU Air Quality Directive (EU, 2008) sets two standards for the control of NO2. These are an hourly limit value of 200 μg.m-3 not to be exceeded more than 18 times a year and a limit value of 40 μg.m-3

on the annual mean concentration of NO2.



Figure 7. Seasonal averages of background nitrogen dioxide in 2018. Upper left pane is winter; upper right panel is spring; lower left panel is summer and lower right panel is autumn. Unit: [μg.m-3].

The hourly limit value aims at controlling NO2 concentrations close to emission sources. It is an episode-related health indicator and applies at hotspots. As explained above, it cannot be properly addressed without local scale modelling. Therefore, we do not present the hourly indicator in this report and focus instead on the second indicator about annual means. Figure 8 shows the annual mean nitrogen dioxide concentrations in 2018 along with results from previous years. The figure compiles the annual CAMS re-analyses that are run every year with the actual meteorological conditions of the year in question, but the emission data remains fairly constant. The NO2 concentrations from 2009 to 2018 in Figure 8 are all based on TNO-MACC-III emissions. These emissions are an update of the TNO-MACC-II emission dataset (Kuenen et al, 2014) and cover up to 2011. As a consequence, the maps for 2014 to 2018 in Figure 8 are all based on the same emission data, namely the TNO-MACC-III emissions valid for the year 2011. This is the case for all the time-series provided in this chapter, but while other pollutants are also sensitive to meteorological variability, the inter-annual variability of NO2 is mainly determined by the year-to-year changes in emission sources. This should be kept in mind when interpreting the results presented in Figure 8.



The fact that there is little difference between 2017 and previous years indicates that meteorological variability plays a smaller role with respect to the annual mean concentrations of nitrogen dioxide. In order to reproduce the actual inter-annual variability of the NO2 background concentrations, the calculations would need to take into account the year-to-year evolution of the NOx emission sources, although extended use of data assimilation with satellite and in-situ observations are expected to compensate for uncertainties in the emission inventories.

NO2 Concentration / µg m-3

Figure 8. Annual mean value of nitrogen dioxide. The different figures in the panel show results for different meteorological years (from left to right descending: 2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010 and 2009. Note that the 2018 and 2017 figures show interim re-analyses and the 2009 to 2016 figures show re-analyses, and also that the regional system domain increased in size in 2011 and onwards until present. In 2016, a different plotting routine was used hampering the comparison between different years. Unit [μg.m-3]

It is recognized however that measures to curb NO2 emissions carried out at the urban level may have resulted in significant reductions of the annual nitrogen dioxide concentration. For instance, in Norway, 2018 was the first year in record with observed annual NO2 concentrations below threshold values8.

8 https://www.miljodirektoratet.no/aktuelt/nyheter/2019/juni-2019/historisk-god-luftkvalitet/

https://www.miljodirektoratet.no/aktuelt/nyheter/2019/juni-2019/historisk-god-luftkvalitet/



2.3 PM10 in 2018

2.3.1 Meteorological Characterization The Copernicus European State of Climate for 2018 reports no clear trend for precipitation as a whole in Europe during 2018. However, this is because there was a north-south divide in precipitation from spring onwards9. Spatial differences were significant in 2018. According to the reports from ECMWF, there was an extended period of drought in northern Europe and parts of central Europe in spring, summer and autumn, while wet conditions were present over southern Europe for most of the year.

Figure 9. Seasonal precipitation rate anomalies for 2018 over the CAMS regional domain relative to a 2008-2017 baseline derived from C3S/ECMWF ERA interim re-analyses. Upper left pane is winter; upper right panel is spring; lower left panel is summer and lower right panel is autumn. Units: [mm day-1].

Figure 9 shows the calculated seasonal precipitation rate anomalies in 2018 with respect the average from 2008 to 2017, based on the C3S/ECMWF ERA interim re-analyses data. Red colors indicate drier than average, while blue colors indicate situations wetter than average. The figure shows that 2018 was a dry year over northern Europe and a wetter than average year in western and southern Europe. In fact, there was an extensive period of drought northern Europe and with 80% less than normal

9 See the Copernicus European State of Climate 2018 summary available at https://climate.copernicus.eu/sites/default/files/2019-04/Brochure_Final_Interactive.pdf




precipitation in the area during the main growing and harvesting periods. The northern Mediterranean region experienced many heavy rainfall events in 2018. Southwest Europe experienced one of the two wettest springs since 1950 while Southeast Europe had one of the six wettest summers of the last 70 years (Copernicus Climate Change Service10, 2019).

Figure 10. Seasonal averages of background PM10 in 2018. Upper left pane is winter; upper right panel is spring; lower left panel is summer and lower right panel is autumn. Units: [μg.m-3]

How these meteorological conditions affected particulate matter concentrations in 2018 can be seen in Figure 10. The figure shows the 2018 PM10 average background concentrations in winter, spring, summer, and autumn as calculated by the CAMS regional interim re-analyses. Background PM10 concentrations are linked to meteorological parameters such as precipitation, temperature and stability. These meteorological conditions have a strong influence upon PM10 concentrations at the regional scale due to their effects on the emissions, formation, loss, and atmospheric accumulation and persistence of PM10. Precipitation leads to wet scavenging of PM10, and so represents an important driver of PM10 loss. Warmer temperatures can cause evaporative mass loss of the volatile

10 The Copernicus Climate Change service has elaborated a European State of Climate 2018 summary available at https://climate.copernicus.eu/sites/default/files/2019-04/Brochure_Final_Interactive.pdf




components of PM10 leading to lower concentrations, but they can also increase emissions of ammonia from fertilized soil leading to the formation of secondary inorganic aerosols and higher PM10 concentrations. The impacts of precipitation on PM10 are more noticeable under cooler conditions, when evaporative mass loss plays a smaller role in PM10 removal. Stability affects the accumulation of PM10 in the boundary layer. Finally, lower temperatures can lead to increased usage of home heating (especially wood burning) and resulting in combustion emissions of particulates, thus contributing positively to an increase of PM10 sources to the atmosphere. Generally, the winter and autumn PM10 concentrations are higher than those in spring and summer, which is due to the larger emissions in both seasons compared to the other half of the year. The increased prevalence of stable conditions during winter that favors the accumulation of PM10 also contributes to the higher concentrations during this season as compared to autumn. In 2018, the highest background PM10 concentration are seen over Poland.

2.3.2 PM10 Health Indicators The Air Quality Directive (EU, 2008) sets two metrics to monitor air pollution by PM10 and its impacts on health:

• a daily PM10 concentration limit value of 50 μg.m-3, not to be exceeded more than 35 times per year

• an annual PM10 concentration limit value of 40 μg.m-3

Figure 11. Number of days with background PM10 daily mean above 50 μg.m-3for 2018. Unit [Number of days]

Figure 11 shows the number of days with background PM10 concentrations exceeding the daily limit value of 50 μg.m-3. Similarly to NO2, PM10 has a significant primary component and therefore this pollutant is more sensitive to local source influences than ozone or fine particulates. The highest levels of PM10 pollution occur in “hot-spot” areas close to traffic, industrial or residential sources, or in connection to natural dust, sea-salt or forest fire events. Except when in connection to natural events (resulting from dust, sea salt or wild fires), these very high levels of PM10 are distributed over



relatively fine spatial scales as compared to the spatial resolution of the CAMS re-analyses. The identification of PM10 in “hot-spots” is not within the scope of CAMS and therefore, the values in Figure 11 do not capture the actual number of exceedances of the daily PM10 limit value. Still, the figures are useful to identify areas where the regional background exceedances were located and where background concentrations could have contributed substantially to exceedances in 2018. Figure 12 presents the yearly averaged background PM10 concentrations for 2018 compared with annual averages from earlier years (2008 to 2017). In Poland and parts of central Europe, the annual mean concentrations of PM10 in 2018 are generally higher than over most regions in Europe, similar to the levels in 2016, 2015 and 2014. Higher than normal precipitation amounts in western and southern Europe seem to have affected the air pollution levels in 2018, generally reducing the background PM10.

PM10 Concentration / µg m-3

Figure 12. Annual mean value of background PM10 for 2018. The different figures in the panel show results for different meteorological years (from left to right descending: 2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008 and 2007). Note that the 2018 and 2017 figures show interim re-analyses and the 2007 to 2016 figures show re-analyses, and also that the regional system domain increased in size in 2011 and onwards until present. Unit [μg.m-3]



2.4 PM2.5 in 2018

2.4.1 Meteorological Characterization As for coarse particulate matter (PM10), fine particles (PM2.5) concentrations in air are influenced by meteorological parameters such as wind, precipitation, temperature and stability. Other factors, such as atmospheric chemistry background values and emission sources are also important to determine the actual PM2.5 concentrations. 2018 was a warm record year, especially in central and northern Europe. This implies that the emission of fine particles related to residential combustion might have been lower in 2018. It also suggests that when average temperatures are higher, the number of situations with inversion may have been fewer in 2018 than in other years over most parts of Europe. The two effects combined indicate that 2018 may have had lower concentrations of PM2.5 than average. In addition, warmer temperatures can cause mass loss of volatile components to evaporate, again adding to the reduction of PM2.5 values in 2018 as compared to other years. However, 2018 was a year drier than average in northern and central Europe which implies that the particle pollution levels may have been similar to previous years in those areas. In southern Europe, however, 2018 was wetter than average which suggests that wet removal of PM2.5 may have been effective in 2018, especially in southwestern Europe. Both analysis of meteorological parameters align in the same conclusion in Southern Europe, indicating that that the year 2018 may have had lower than average background PM2.5 concentration in air in southern Europe, while close to average in northern and central Europe. Figure 13 shows the seasonal averages of background PM2.5 concentrations over Europe, as calculated by the CAMS interim re-analyses. The highest values of fine particles in 2018 took place during the winter, when emissions are high due to residential heating sources and dispersion conditions reduced due to cold stable situations. In winter 2018, there were a series of areas where winter-averaged values exceeded 30 μg.m-3 in particular over large areas in the Po Valley and Poland. However, these values were for the seasonal averages, not annual averages as referred to by the EU limit values. The yearly averages are considerably lower than the winter seasonal averages.



Figure 13. Seasonal averages of background PM2.5 in 2018. Upper left pane is winter; upper right panel is spring; lower left panel is summer and lower right panel is autumn. Unit: [μg.m-3].

2.4.2 PM2.5 Health indicators The PM2.5 health indicators are related to annual mean concentrations. The European Union’s Air Quality Directive (EU, 2008) sets an annual metric to monitor air pollution by PM2.5 and its impacts on health. The annual PM2.5 concentration limit value is 25μg.m-3 and has been in force since 2015. In addition, WHO has a guidance value for PM2.5 of 25μg.m-3 as a daily average which we use as a reference in this report as part of the episode characterization analysis. Figure 14 shows the annual mean value of background PM2.5 concentrations for 2018 as calculated by the CAMS interim re-analyses and compares these values with the annual means for previous years since 2010. The time-series for PM2.5 begins in 2010 when the CAMS regional products implemented data assimilation routines for PM2.5.



PM2.5 Concentration / µg m-3

Figure 14. Annual mean value of background PM2.5 for 2018. The different figures in the panel show results for different meteorological years (from left to right descending: 2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011 and 2010. Note that the 2018 and 2017 figures show interim re-analyses and the 2010 to 2016 figures show re-analyses, and also that the regional system domain increased in size in 2011 and onwards until present. Unit [μg.m-3].

As indicated above, the annual mean concentrations of PM2.5 in the year 2018 were similar to those from previous years, significantly below the concentration levels in 2016 and 2017 and more in line with the concentrations experienced in 2011, 2012 and 2013.



3. Pollution Episodes in 2018

3.1 Rationale for Episode Identification An episode is a pollution event that is persistent both in space and in time. Concretely, for the IAAR reports, air pollution episodes are characterised by elevated levels of air pollution covering a large area and lasting for a period of a few days up to 2-3 weeks. Pollution episodes arise due to the interaction between pollutant emissions and meteorology that favour either the production or the accumulation of pollutants in the atmosphere. Episodes vary in frequency throughout the year depending on the prevailing meteorological conditions that affect production, accumulation and removal of pollution. We identify the different air pollution episodes using exceedances of the short-term pollution thresholds for the various pollutants. We derive the occurrence of exceedances using the (un-validated) up-to-date observations from the large number of stations compiled by the European Environment Agency’s (EEA’s) European Environment Information and Observation Network (EIONET). These are the same observations that are used within the production of the CAMS ensemble re-analyses for both assimilation and validation. The spatial resolution of the CAMS products means they are useful for interpreting and understanding background pollutant concentrations at the rural and urban scales and are not relevant for examining pollution “hot-spots” near to traffic or industrial sources. For this reason, only rural, suburban, and urban stations from the EOINET network are used here for episode identification. As a result, neither episodes of NO2 related to traffic stations, nor SO2 episodes related to pollution at industrial sites are included in the evaluation. Episodes are identified for PM10 and ozone based on exceedances above the short-term EU limit or target values. PM10 exceedances are identified using the EU directive daily mean limit value of 50 μg.m-3 (EU, 2008). In the case of ozone, we use the EU directive 8-hour daily maximum mean limit of 120 μg.m-3 to define the episodes (EU, 2008). The episodes of PM2.5 are identified using the WHO guideline daily mean concentration 25 μg.m-3 (WHO, 2006). The use of these daily mean indicators allows us capture and characterize the temporal persistence of pollution episodes, which is consistent with our definition of an episode in terms of persistence both in space and time.

3.2 Identified Pollution Events in 2018 Up-to-date observations from the EEA/EIONET are analyzed to identify pollution episode events in 2018. Station data used to validate the CAMS interim re-analyses and data that is assimilated to make the interim re-analyses are both used for the identification of background air episodes. For ozone, this involves data from 553 rural, 421 suburban, and 1133 urban stations. For PM10, it involved data from 255 rural, 273 suburban, and 830 urban stations. For the PM2.5, we used 114 rural, 116 suburban, and 404 urban stations for episode identification.



Figure 15. Episode identification in 2018. The upper left panel shows the number of incidences of observed ozone concentrations exceeding the long-term objective of 8-hour daily maximum ozone average concentrations of 120 μg.m-3in different regions in Europe. The upper right panel shows the number of incidences of observed ozone concentrations above the information threshold of 180 μg.m-3for hourly means in different European regions. The lower left panel shows the number of incidences of daily mean PM10 values above the daily mean limit value of 50 μg.m-3in different European areas. The lower right panel shows the number of incidences of PM2.5 values above the WHO daily mean threshold value of 25 μg.m-3in different European areas.

Figure 15 shows the basis for episode identification in 2018. It shows four panels with time-series indicating the number of incidences of observed exceedances for the different pollutants. The panels in Figure 15 show the number of incidences with observations above threshold values in five different areas. These areas correspond to the country selection displayed in Figure 16 and include western Europe (EUW), central Europe (EUC), southern Europe (EUS), northern Europe (EUN) and eastern Europe (EUE). There is a notable bias towards identifying episodes that take place in central and western Europe. This is because the spatial coverage of the EIONET stations is higher in these regions. A scarcity in the number of stations in eastern and southern Europe implies the possibility that a number of episodes would remain unregistered in these areas. The time-series in the top panels for ozone covers the summer period from 1st May to 30th September, while in the lower panels, for particulate matter, the time-series covers the whole year. Ozone episodes occur under special meteorological situations characterized by stagnant high-pressure systems. Since the formation of ozone requires sunlight, ozone episodes tend to occur mainly during summer. Therefore, the ozone time-series covers the summer period. As explained



before, 2018 is a record high ozone year. Therefore, the number of exceedances in 2018 is much higher than in previous years. Also, the ozone exceedances occur regularly all through the late spring and summer season, with significant episodes taking place in central and southern Europe. PM episodes are usually related to stable dry conditions and, due to the seasonal variations of their main emission sources, PM episodes tend to occur in winter, spring and autumn. In 2018, there were a similar number of PM episodes in spring than in previous years. This similar number of episode occurrences is consistent with the meteorological dispersion conditions in 2018.

Figure 16: European countries included in the classification of European regions used throughout this report.

We have selected 5 different episodes in 2018 for further analysis: one for ozone and three for PM. For ozone, we have chosen the episode with the largest number of registered exceedances in 2018. This is an episode in two parts that took place in late summer.

• 30th July to 7thAugust when the largest exceedances of both the hourly information threshold and the long-term ozone threshold were measured in central, southern and western Europe.

For PM10 and PM2.5 the episodes with largest number of reported exceedances over limit values took place on winter. In addition to these, we have also selected one episode in the autumn when the number of reported exceedances of limit values were large.

• 7th to 10th February when concentrations over daily threshold were measured in central and southeastern Europe, also associated to a dust event.

• 21st to 28th February when a high number of exceedances over the daily threshold were measured in central western Europe.

• 21st to 26th October when concentrations over the daily WHO guideline value were measured in western Europe.

In addition, we will investigate 5 additional events caused by dust storms and wildfires. In 2018, high temperatures, low precipitation and dry vegetation meant an increased risk for fire events. Wildfires



reached an unprecedented extent in Sweden, with over 25 000 hectares burned, in what was considered to be the most serious wildfire event in the country’s modern history (WMO, 2019). We will look at 2 of these events, the wildfires from 18th to 19th July in Sweden and the ones in Greece from 23rd to 24th July. Following the CAMS global validation results and our own record tracking of events, there were also 3 different relevant dust events. We will focus on the dust episodes of 22nd

to 27th April, 1th to 4th August and the one from 16th to 20th October because these events coincided with reported exceedances of limit or guideline values in Europe.

3.3 Origin of Pollution Episodes To characterize the origin the identified pollution episodes, we make use of the following CAMS products. These CAMS products are, except for the re-analyses, modelling results. Thus, for the identification of pollution episodes we rely on observed data, for the characterization of sources and their relative contributions to pollution levels in background air we rely on CAMS modelled data.

The CAMS regional interim re-analyses are used to visualize the spatial extent and temporal evolution of the pollution episodes. The interim re-analyses data were downloaded from http://atmosphere.copernicus.eu/services/air-quality-atmospheric-composition, and post-processed to provide daily mean values to map the evolution of the episodes.

The green scenario forecasts from the CAMS policy products https://policy.atmosphere.copernicus.eu/ControlScenarios.php are post-processed to derive daily mean estimates of the contributions from each sector for each day of the episode. In this way, the green scenario data is used to provide information on the contributions of emissions from four sectors (traffic, industrial, agricultural, and residential heating). The raw data is available on-demand from INERIS.

This year, for the first time, we have also been able to use the interactive CAMS Air Control Toolbox available at https://policy.atmosphere.copernicus.eu/CAMS_ACT.php

The CAMS source-receptor calculations for background city air available at https://policy.atmosphere.copernicus.eu/SourceContribution.php are used to identify the relevance of long-range transported air pollution during the episode. This year we have also made use for the first time of the new “chemical speciation” capability to study background city air.

The CAMS forecast for the policy scenarios at 2020 and 2030 available at http://policy.atmosphere.copernicus.eu/GothenburgScenario.php are used to identify whether emission reductions agreed upon and expected in the future (2020 and 2030) could have avoided the occurrence of the episode under consideration. The raw data is available on-demand from INERIS.

http://atmosphere.copernicus.eu/services/air-quality-atmospheric-composition

http://atmosphere.copernicus.eu/services/air-quality-atmospheric-composition

https://policy.atmosphere.copernicus.eu/ControlScenarios.php

https://policy.atmosphere.copernicus.eu/CAMS_ACT.php

https://policy.atmosphere.copernicus.eu/SourceContribution.php

http://policy.atmosphere.copernicus.eu/GothenburgScenario.php



The CAMS global near-real-time dust forecast products available at http://apps.ecmwf.int/datasets/data/cams-nrealtime/levtype=ml/ (Morcrette et al., 2008) are post-processed to derive the modelled dust contributions to PM2.5 and PM10. The PM2.5 and PM10 global dust results give us an indication of when dust makes a contribution to the background PM2.5 and PM10 levels derived by the CAMS regional interim re-analyses.

The CAMS global near-real-time sea-salt aerosol forecast (Morcrette et al., 2008) were post-processed in a similar way as for dust to derive the global sea-salt contributions to PM2.5 and PM10. These sea-salt PM2.5 and PM10 products help determine if sea salt contributes to the background PM2.5 and PM10 pollution episodes. The raw data is available at http://apps.ecmwf.int/datasets/data/cams-nrealtime/levtype=ml/.

The CAMS global fire assimilation system provides estimates of fire emissions available at http://apps.ecmwf.int/datasets/data/cams-gfas/, based on satellite observations of fires. The fire emission data were used in the CAMS regional system, and the emission data may provide an indication of whether wildfires contribute to a particular pollution event.

The CAMS global validation system provides quarterly validation reports on an operational basis. The reports compare global results with both in-situ and satellite observations, and identify significant dust and wildfire events all over the globe. These reports were used here to complement the identification of dust storms and wildfires events affecting Europe. The validation reports are available at https://atmosphere.copernicus.eu/quarterly_validation_reports.

We make use of all the above CAMS modelling products to analyse the pollution episodes identified in section 3.2. The analysis presented below is intended as a guide for the type of evaluation one could perform using the different available CAMS products and data. We hope that policy users become aware of these products and use them to identify the origin of pollution episodes relevant to their own needs.

http://apps.ecmwf.int/datasets/data/cams-nrealtime/levtype=ml/

http://apps.ecmwf.int/datasets/data/cams-nrealtime/levtype=ml/

http://apps.ecmwf.int/datasets/data/cams-gfas/

https://atmosphere.copernicus.eu/quarterly_validation_reports



3.3.1 Ozone Episode in July and August 2018 The warmer temperatures in 2018 resulted in record high ozone levels, especially over Central Europe, both in terms of health and ecosystem indicators as well as in the number of registered exceedances of threshold values. The ozone episode that took place from 30th July to 9th August is the one that registered the highest number of exceedances, due to its persistence over 12 days and is broad spatial extent, covering large areas over Europe.

Figure 17. Evolution of the ozone maximum daily 8-hour mean as derived from the CAMS regional ensemble interim re-analyses for the ozone pollution episode from 30th July to 9th August. Each plot represents a different day: 30th July, 31st July, 1st August, 2nd August, 3rd August, 4th August, 5th August, 6th August, 7th August, 8th August and 9th August. Units: [μg.m-3].



Figure 17 shows the average CAMS surface ozone from the interim re-analyses over these days. It provides an illustration of the time evolution of the episode and the spatial extension of the areas affected by it. The ozone averages are 8-hourly mean values from 11:00 to 19:00 GMT, considered as representative of the maximum values during the day. The illustration shows how the ozone episode covers most of central Europe and evolves to the west with maximum hourly means well above the long-term threshold of 120 μg.m-3. We used the CAMS Air Control Toolbox (ACT) for the period to derive the contributions from each sector for each day of the episode. Figure 18 presents the contribution of the main four emission sectors (agriculture, industry, traffic and residential combustion) to the daily mean ozone levels during the four days of the episode from 3rd to 7th August. As for the green scenarios, the values in Figure 18 are given as concentration in μg.m-3, but represent differences (and not absolute concentration like in Figure 17) between a reference run with current emission levels and a scenario run with 100% reduction of the sectoral emission. Results from the ACT Toolbox are useful to rank the influence of the different contributions to the episode in different European areas. The results, as shown in Figure 18 are as expected. Traffic and industrial emissions are the main anthropogenic contributors to this ozone episode.

August 3rd August 4th August 5th August 6th August 7th

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Figure 18. Simulated differences in daily maximum ozone from the Air Control Toolbox between different emission scenarios and the reference run from 3rd to 7th August. Emission scenarios represent a 100% reduction in a sector’s emissions. Each row is a different green scenario, from top to bottom: agricultural, industrial, traffic and residential heating. Units: [μg.m-3].

We have also used the “Gothenburg scenario data” from the CAMS scenario forecast runs to investigate whether this episode in 2018 could have been avoided if the emission reductions agreed under the Gothenburg Protocol of the Convention for Long-range Transboundary Air Pollution



(CLRTAP) and under the National Emission Ceilings Directive (NEC, 2016) would have been in place. Differences between the Gothenburg scenario runs and the reference 2018 runs can be used to determine the effect of the planned emission reductions at European level. Figure 19 shows the differences in ozone daily mean concentration between the CAMS NEC scenario simulations for 2020 and 2030, and the reference run for the ozone episode from 3rd to 7th August. Except over a limited area over the Netherlands and southern United Kingdom at the English Channel, the planned emission reductions lead to significant reductions of daily mean ozone. The reductions planned under the NEC2030 scenario, especially over central and southern Europe reach up to 8-10 μg.m-3, representing about a 10-20% reduction in the levels experienced under this ozone episode. The percentage differences in daily mean ozone concentrations are presented in Figure 20.

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Figure 19. Differences in ozone daily mean concentration between the CAMS NEC scenario simulations and the reference run from 3rd to 7th August. The top row is the NEC 2020 scenario and the bottom row is the NEC 2030 scenario. Units: [μg.m-3].

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Figure 20. Percentage differences in ozone daily mean concentration between the CAMS NEC scenario simulations and the reference run from 3rd to 7th August. The top row is the NEC 2020 scenario and the bottom row is the NEC 2030 scenario. Units: [%].



Figure 20 shows, as expected, how reductions in NEC2030 are significantly larger than in those prescribed in NEC2020. The increase of ozone levels due to emission reductions over the English Channel is a consequence of the non-linear chemical behavior of ozone. In areas with high emission levels of nitrogen oxides (NOx), ozone can react with nitrogen monoxide to create nitrogen dioxide in the so-called titration effect. In such areas, like over the Netherlands and Southern UK, a reduction of NOx can then result in an increase of ozone as the emission reduction hampers the titration effect from taking place. The non-linear contribution of different sources to ozone concentrations over city areas is also seen from the CAMS Source-Receptor calculations of daily forecast. The CAMS Source-Receptor calculation webpage11 has refined its graphical presentation to better explain the effect of different source contributions to pollution over EU cities. Figure 21 shows the results in two different cities, Frankfurt and Milan, during the ozone episode 3rd and 6th August. In both cities, we see how the dominant contribution to ozone levels comes from emission sources outside the city, while the contribution of local city area sources decreases the ozone levels over the city. This decrease is again the result of the titration effect and implies the reduction of ozone levels by nitrogen monoxide over the city area. Figure 21 also shows the different contributions to ozone from outside the city area. In Frankfurt, the contribution of sources from the rest of Germany to the city ozone levels is significant but small. In Milan, the contribution from contribution of sources from the rest of Italy to the city ozone levels is also significant but small. In both cases, there is a significant contribution to ozone from sources denominated “Others” which in this case refers mainly to the contribution of natural biogenic sources of non-methane volatile organic compounds (NMVOC), and the effect of boundary conditions.

11 https://policy.atmosphere.copernicus.eu/DailySourceAllocation.php

https://policy.atmosphere.copernicus.eu/DailySourceAllocation.php



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Milan

Figure 21. Results from the CAMS Source-Receptor daily forecast calculations using the EMEP model between 3rd and 6th August for two different cities: Frankfurt and Milan. First panel for each city shows a time-series with contribution to ozone levels from emission sources a) located within the city (“Local”, dark grey), b) located outside of the city (“Rest of Europe”, blue), and c) originating from other sources including natural sources (“Others”, light grey). Second panel for each city shows the contribution to ozone concentrations as daily averages for each of the given 3 source types. Note that the contribution of local emission sources may be negative sometimes, implying that a reduction of local emission sources may result in an increase of the urban ozone levels over the city area. Units: [μg.m-3].



3.3.2 PM Episodes in February and October 2018 Episode from 7th to 10th February Figure 22 shows the temporal and spatial evolution of background PM10 for this February episode as derived from the CAMS interim re-analyses. The PM10 episode began in central Europe, with highest values over Poland and developed over time to include also large areas in Germany, Denmark and the Baltic region. At the same time, a Saharan dust intrusion took place over the eastern Mediterranean region reaching Turkey and the Black Sea. Note that the background PM10 values during this episode exceed the EU daily average threshold of 50 μg.m-3 over large areas, which suggest even higher PM10 local daily averages in the associated urban areas.

Figure 22. Daily mean PM10 concentrations derived from the CAMS regional ensemble re-analyses for the episode from 7th to 10th

February. Each plot represents a different day. Units: [μg.m-3].

Figure 23 shows the temporal and spatial evolution of PM2.5 during the same episode. Both PM components (PM10 and PM2.5) evolved similarly during the episode, which was also one with the largest number of exceedances in 2018 for PM2.5. This indicates that the PM10 episode is driven by the PM2.5 concentrations, which may be an indication of a significant long-range transport (LRT) contribution.



Figure 23. Daily mean PM2.5 concentrations derived from the CAMS regional ensemble re-analyses for the same episode from 7th to 10th February. Each plot represents a different day. Units: [μg.m-3].

The daily average WHO guidance value of 25 μg.m-3 for PM2.5 is exceeded more often and more widely than the EU daily threshold value for PM10. This is generally the case for PM episodes and it was also seen in Figure 15 where the number of exceedances recorded in observations were generally larger for PM2.5 than for PM10. The reason for this is that the WHO guidance value is more restrictive than the EU threshold value. It also indicates that fine particulates are generally dominant over coarse particles in the particulate mass concentrations of background PM10, so that exceedances of the WHO guidance value of 25 μg.m-3 do not necessarily imply exceedances of the EU daily mean threshold of 50 μg.m-3. The concurrence of the concentration fields of PM10 and PM2.5 during this particular episode shows that the background PM10 values had a stronger contribution from PM2.5 than from the coarser particles. The high concentrations over the eastern Mediterranean area deserve further analysis. Figure 24 shows the results from the CAMS global service on the contribution of Saharan dust contribution to PM2.5 levels over Europe for the days of this episode. The figure shows the evolution of the Saharan dust intrusion crossing over France and reaching the English Channel with significant contributions to European background PM2.5 levels especially on the 9th February. The CAMS global dust calculation is used as boundary condition in the regional CAMS service. In this particular episode, the CAMS global simulation predicts a larger intrusion over the western Mediterranean basin than what is included in the regional simulation. Still, the intrusion on the 9th February can be significant and is worth checking



whether the Saharan dust contribution can be confirmed with ground-based speciation measurements.

Figure 24. Daily mean PM2.5 dust concentrations derived from the CAMS global aerosol system for the same episode from 7th to 10th February. Each plot represents a different day. Units: [μg.m-3].

To determine the anthropogenic source contributions to this background PM episode we rely on the CAMS green scenario data. PM originates from a complex mix of emission sources and it is often difficult to assign episodes to a single source. It is possible, however, to compare the emission sector contributions against each other and rank their relative importance during the episode. Figure 25 shows the contributions from each sector to mean daily PM10 levels for the three last days of the episode and Figure 26 shows the same but for PM2.5. The contribution of the main four emission sectors, agriculture, industry, traffic and residential combustion is calculated as the difference between the reference run with current emission levels and the CAMS green scenarios with sectoral emission reductions of 30%. Conclusions are the same both for PM10 and PM2.5. Emissions from residential heating, including wood and coal combustion dominate the PM10 pollution levels, especially in southern and eastern Europe, followed closely by the contribution of ammonia emissions from agriculture. The influence of agriculture emissions in the PM levels of this winter episode is larger over central and eastern Europe, where it even dominates as a source over the residential emission sector. The contribution from



traffic and industrial emissions is smaller at a European-wide scale. This is consistent with a significant long-range transport contribution under this episode. The fact that the dominant contribution over most city areas is from residential heating indicates that this episode has also a significant local contribution over urban areas.

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Figure 25. Daily mean differences in PM10 between each CAMS green scenario simulation and the reference run from February 8th to 10th. Each row is a different green scenario; from top to bottom: agricultural, industrial, traffic, and residential heating. Each column is a different day of the pollution episode. Units: [μg.m-3].



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Figure 26. Daily mean differences in PM2.5 between each CAMS green scenario simulation and the reference run from February 8th to 10th. Each row is a different green scenario; from top to bottom: agricultural, industrial, traffic, and residential heating. Each column is a different day of the pollution episode. Units: [μg.m-3].

Still, the significant contribution of agricultural emissions as early as February is a bit surprising. It might be related to the temporal variability of emission sources (and there are active sources in this period like livestock farming, pig farms for instance) in the CAMS regional re-analysis, to secondary aerosol chemistry processes or even the underestimation of residential heating emissions in urban areas. The sector contribution results from the CAMS green scenarios and the ACT tool generally show a significant contribution from agriculture in PM background air, which is justified due to the importance of ammonia emissions for PM concentrations, but this is probably an overestimation, especially when other local sources are underestimated. In particular, we know that the 2011 TNO-MACC-III emissions used in the CAMS re-analysis most likely underestimate residential heating emissions (Denier van der Gon et al., 2015).



Over urban areas, the CAMS Source-Receptor (SR) calculations provides valuable information on the origin of background air. The CAMS SR calculations are carried out with just one of the models in the CAMS ensemble, namely the EMEP model, and do not rely on data assimilation to provide highly accurate concentration data. This implies that SR results for PM10 concentrations do not correspond exactly to (and are generally lower than) the CAMS regional interim results in city areas. Although the EMEP model results have a relatively coarse resolution (0.25 x 0.125 degrees longitude/latitude), the SR results from the EMEP model rely on an appropriate methodology for source allocation and consequently these results for background air provide valuable information for the downstream services responsible for modelling urban air pollution. The results of the SR runs are also valuable to evaluate the relative fractions of the long-range transport component and of the city local component in background air, although they are always constrained by the emissions used as basis for their calculations.

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Figure 27. Results from CAMS Source-Receptor forecast calculations between 8th and 11th February for two different cities: Warsaw and Ljubljana. First panel for each city shows the contribution to PM10 from emission sources a) located within the city (“Local”, dark grey), b) located outside of the city (“Rest of Europe”, blue), and c) originating from other sources including natural (“Others”, light grey). Second panel for each city shows the contribution to PM10 concentrations from sources located in different countries with the colour legend to the right giving the name of the country. Note that the country contributions are ranked from top to bottom in terms of their size of the contribution. “Others” in this case refers to “the sum of all other countries”. Units: [μg.m-3].



Figure 27 shows the contributions to background air over two cities affected by the episode: Warsaw and Ljubljana. In both cities, PM background levels have a significant long-range contribution, as indicated by the light blue named as “Rest of Europe”. It is also interesting to note that the local contribution from sources inside the city area to their PM10 levels is also considerable. In some cases the local contribution can be dominant, as it is on Sunday 11th February in Warsaw or Saturday 10th February in Ljubljana. During this winter episode, both local sources and long-range transport contributed to the background PM values over these two cities. Further in Figure 27, we can identify the origin of the long-range transport to different countries during this episode. While the PM background air in Warsaw has mostly contributions from Warsaw itself and the rest of Poland, the air over Ljubljana is affected by emissions in the rest of Slovenia, Austria and Germany, at least during the 11th February. Note that the analyses of air quality in city areas by CAMS products refers always to background PM10 concentrations and not to PM10 concentrations near “hot-spots”. The CAMS results refer always to background PM values, so that actually, in urban air, we can expect the local contribution to PM levels to be larger than the one calculated by CAMS for the background city air. Episode from 21th to 28th February This second episode of background PM in February was studied as part of an activation program for CAMS Source-Receptor calculations (Schaap et al., 2018). The advantage of activating the episode SR service is that it allows an in-depth study of the contributions to specific pollution episodes, including a characterization of the meteorological situation given rise to the episode and an evaluation of the model performance with respect to observation. During the last week of February 2018 there was a high pressure system located over Scandinavia and a low pressure system over south-eastern Europe, resulting in very strong eastern winds with very low temperatures in north-western Europe. The meteorological situation depicted in Figure 28 for 25th February is representative for the situation during the rest of the week. Partly caused by this meteorological situation, PM levels exceeded the daily limit values during the last days of February. However, the CAMS ensemble modelling system did not manage to reproduce the observed PM values and consistently underestimated the observations. This is also depicted in Figure 28 where the negative bias for PM10 concentrations modelled by all 7 models in the CAMS Ensemble is presented. According to Schaap et al. (2018), an important reason for this underestimation is the use of a default profile for domestic heating emissions. During very cold episodes, the heating demand is larger than usual and normally leads to an increase of emissions that is not currently considered in the CAMS modelled calculations. These missing local emissions can explain the general underestimation by the background PM model results and should also be taken into consideration when evaluating the different contributions to the pollution episode.



Figure 28. Mean bias for PM10 concentrations modelled by all 7 models in the CAMS Ensemble and pressure map to characterise the meteorological situation on 25th February. During the episode from 21st to 28th February, all models consistently underestimate the observed PM10 values at surface levels (adapted from Schaap et al., 2018).

The modelled CAMS PM10 and PM2.5 background air concentrations during the first days of the episode from 21st to 26th February are given, respectively, in Figures 29 and 30. Note that the episode is characterized by a strong easterly wind system that helps transport pollution from central Europe on to the Atlantic Ocean.

Figure 29. Daily mean PM10 concentrations derived from the CAMS regional ensemble re-analyses for the episode from 21st to 26th February. Each plot represents a different day. Units: [μg.m-3].



Figure 30. Daily mean PM2.5 concentrations derived from the CAMS regional ensemble re-analyses for the same episode from 21st to 26th February. Each plot represents a different day. Units: [μg.m-3].

Figure 31. Results from CAMS Source-Receptor calculations for Amsterdam from 21st and 24th February. First panel shows the contribution to PM10 from emission sources a) located within the city (“Local”, dark grey), b) located outside of the city (“Rest of Europe”, blue), and c) originating from other sources including natural (“Others”, light grey). Second panel shows the contribution to PM10 concentrations from sources located in different countries with the colour legend to the right giving the name of the country. Note that the country contributions are ranked from top to bottom in terms of their size of the contribution. “Others” in this case refers to “the sum of all other countries”. Units: [μg.m-3].



The contribution of different sources to background PM10 over Amsterdam is provided in Figure 31 as an example of this easterly transport of pollution. From the beginning of the episode, Amsterdam had an important inflow of long-range transported pollution from Germany, Poland and even Denmark, consistent with the meteorological situation depicted in Figure 28. The results in Figure 31 show that background PM10 pollution levels over Amsterdam are caused by the long-range transport component (“Rest of Europe”) and originate mainly from transboundary sources. However, this is not necessarily the case, because the local component is probably underestimated in the CAMS SR calculations. This is because the calculations are based on a probable underestimation of residential heating sources. The same bias affects the CAMS green scenario results for this episode. As seen in Figure 32, the contribution of residential heating sources is small over central and western Europe where the episode reached higher values. Instead, the CAMS green scenario results suggests again that agricultural emissions are the most significant contribution during this winter episode. This is probably a bias of the system that tends to overestimate the contribution of agricultural emissions especially where there is an underestimation of other local sources like domestic heating or traffic.

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Figure 32. Daily mean differences in PM10 between each CAMS green scenario simulation and the reference run from February 21st to

25th. Each row is a different green scenario; from top to bottom: agricultural, industrial, traffic, and residential heating. Each column is a different day of the pollution episode. Units: [μg.m-3].



Episode from 21st to 26th October From 15th May 2018, the CAMS Source-Receptor calculation web-services included an additional feature to allow identification of the chemical speciation of the background air over European cities. The new feature is also available retrospectively, which makes it especially useful for episode characterization purposes. The PM episode that took place from 21st to 26th October 2018, is characterized by a combination of a sea-salt episode over the Atlantic Coast and a Saharan dust intrusion over the Mediterranean area and southeastern Europe. This is clearly seen in Figure 33 that shows the temporal and spatial evolution of the background PM10 for each day of the episode as calculated by the CAMS re-analyses.

Figure 33. Daily mean PM10 concentrations derived from the CAMS regional ensemble re-analyses for the episode from 21st to 26th October. Each plot represents a different day. Units: [μg.m-3].

Figure 34. Daily mean PM2.5 concentrations derived from the CAMS regional ensemble re-analyses for the episode from 21st to 26th October. Each plot represents a different day. Units: [μg.m-3].



Figure 34 shows the PM2.5 values during this October episode. The evolution of the episode is very similar for both PM components, indicating a significant long-range transport (LRT) component. By 21st October a LRT sea-salt episode reaches the west coast of Europe and evolves over the area until the 26th October. The evolution of the sea-salt contribution to PM levels in European cities is also seen from the CAMS Source-receptor service on the chemical speciation of PM10. Figure 35 shows the episode chemical speciation from 21st to 24th October, for Copenhagen where the contribution of sea-salt is calculated by the EMEP model in the CAMS ensemble to be about 32% of PM10 mass by 21st October, and then went up to 85-87% the following days. The sea-salt contribution is calculated on the basis of the global CAMS aerosol concentrations, where a revised sea-salt parametrization introduced by June 2018 is documented to give good results (Wagner et al., 2019). The results in Figure 35 are therefore a good indicator of the presence of sea salt during this PM10 and PM2.5

episode.

Figure 35. Chemical speciation of the PM10 levels over Copenhagen calculated by the EMEP model from the CAMS Source-Receptor calculations. Upper panel is a time series of concentration levels for different species during the episode from 21st-24th October. Units: [μg.m-3]. Lower panel are daily averaged pie charts with the different contributions from the species given in the legend. Units: [%]

Figure 36. Chemical speciation of the PM10 levels over Nicosia calculated by the EMEP model from the CAMS Source-Receptor calculations. Upper panel is a time series of concentration levels for different species during the episode from 21st-24th October. Units: [μg.m-3]. Lower panel are daily averaged pie charts with the different contributions from the species given in the legend. Units: [%]



By contrast, Figure 36 shows the chemical speciation of PM10 in Nicosia, where there is an intrusion of Saharan dust from the beginning of the episode. The contribution from Saharan dust to PM10 mass over Nicosia reaches about 63% on the 21st October, then decreases to 34% the next day and is larger after the 23rd October reaching up to 72-73% of the concentrations. Note also that while nitrate is the next largest component in Copenhagen for this episode, sulphate is the next largest component in Nicosia. This is a result of the main sectoral emissions affecting PM levels over these two very different city areas. Figure 37 shows the results from the Air Control ToolBox for PM10 during some days of the episode. Again, agricultural ammonium sources dominate the background values of PM10 over Europe. However, this time, industrial and traffic sources also play an important role in the long-range transport component of background PM10. Nitrogen oxides sources from industrial sources affect PM10 levels in The Netherlands, while sulphur oxides sources are more relevant over Greece.

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Figure 37. Simulated differences in daily mean PM10 from the Air Control Toolbox (ACT) between different emission scenarios and the reference run from October 21st to 25th. Emission scenarios represent a 100% reduction in a sector’s emissions. Each row is a different green scenario, from top to bottom: agricultural, industrial, traffic and residential heating. Units: [μg.m-3].



3.3.3 Dust Episodes in April, August and October 2018 The global CAMS global aerosol system has identified three major dust storms events in 2018 affecting the European continent. They took place from 22nd to 27th April affecting the Iberian Peninsula and the western Mediterranean basin, from 1st to 4th August, also over the Iberian Peninsula and from 16th to 20th October in an event affecting the central and eastern Mediterranean basin. Satellite images for these dust events from MODIS (Moderate Resolution Imaging Spectroradiometer) can be found in the CAMS 2018 global validation reports of Eskes et al. (2018) and Warner et al. (2018; 2019). Data from the global CAMS system is very valuable to identify dust and sea-salt intrusions in European air. The CAMS global products are derived with the IFS meteorological model and assimilate a full range of satellite data, including observations from the MODIS instrument on NASA’s Aqua satellite. The global products have a coarser spatial resolution than the regional products (about 40 km resolution, versus the 10 km of the regional products) and provide boundary conditions for the regional system. An important difference between the two systems is that the global system uses EIONET surface observations only for validation purposes, not for data assimilation. In the regional CAMS systems, surface observations from the EIONET network are assimilated. Therefore, we can expect differences between the results from the global and the regional system under dust and sea-salt events. For quantification purposes, the data from the regional CAMS system are to be preferred because they have finer resolution and use surface observations in the results, while the data from the global CAMS system is useful to pinpoint events of natural origin. In the following section we will demonstrate how the combination of data from the CAMS services on dust provides useful information to determine the timing, extent and magnitude of dust episodes. While the dust product from the CAMS global service serves to identify the timing of the dust events, new products available now from the policy product service allow a better quantification of the magnitude of the dust events. Particularly useful products are the chemical speciation from the CAMS Source- Receptor (SR) calculations that includes dust and is available since 15th May 2018 and the new ACT tool that has an option to identify natural and background concentrations. Although the current version of the ACT does not have access to historical data, such data is available on demand at INERIS. Dust episode from 22nd to 27th April As reported in Eskes et al. (2018), in late April 2018, some hot spots of dust were observed by MODIS in Libya (on 22-23 April) and Algeria (on 25th April). These events affected the Iberian Peninsula leading to one week of high surface values of PM10/PM2.5. Figure 38 shows in the upper panel the evolution of the PM10 dust event according to global CAMS operational-suite while in the lower panel the figure shows the evolution of PM10 concentrations according to the CAMS regional service. Note that both dust and anthropogenic contributions are included in the calculations from the regional service, while in the global service calculations, only the dust contribution to PM10 are considered.



Still, as we can see from Figure 38, the dust PM10 concentrations from the global service are higher than the PM10 concentrations from the regional service.

a) Global

a) Regional

Figure 38. Evolution of the dust episode from 22nd to 27th April. Comparison of a) daily mean PM10 dust concentrations from the CAMS global system and (upper panels) b) daily mean PM10 concentrations from the CAMS regional system (lower panels). Each plot represents a different day. Units: [μg.m-3].

The CAMS global service reproduces the spatial distribution of the dust plume over Iberian Peninsula but overestimates its magnitude in comparison with the observations from both AERONET and EIONET networks (Eskes et al., 2018). The global validation service explains this dust concentration overestimation as related to an overestimation of the magnitude of gusts of winds during the spring season that consequently causes an enhancement of the dust emission over desert dust source



regions and explains the overestimation of the long-range transported dust over the European continent. The results from the regional service are in better agreement with surface level observations of PM10 over the Iberian Peninsula as these observations are included in the interim analysis results that are the basis for the calculations in the lower panel of Figure 38. For this episode, additional products from the CAMS SR calculations and from the ACT tool were not yet available. Therefore, the current analysis only establishes the timing and extension of the dust event but it is difficult to determine its magnitude since the PM10 data from the regional service includes all anthropogenic and natural sources and does not distinguish among these. Dust episode from 1st to 4th August In early-August 2018, MODIS satellite detected a North Atlantic dust plume with origin in Northern Algeria on 1st August. The dust plume moved in an arc shape over the Atlantic and arrived in Southern Portugal on 2nd August, to North-western Spain on early 3rd August and Central Spain on late 3rd August. According to Wagner et al. (2018), this dust trajectory was nicely tracked by the AERONET sun photometers in the Iberian Peninsula. The CAMS global simulation reproduced the spatial distribution of the dust plume Aerosol Optical Depth (AOD) over the Iberian Peninsula in comparison with the AERONET observations. Still, the global model underestimated the observed maximum values at surface levels from the EIONET network. Figure 39 shows the evolution of this dust episode as calculated both by the CAMS global and the regional service. The figure shows well how the regional CAMS interim re-analysis represents better the evolution of particulate matter over the Eastern Iberian Peninsula in association with the dust event and corrects for the underestimation by the global service. Interestingly, another feature takes place over the eastern Mediterranean border of the CAMS regional domain, indicating a possible secondary intrusion of Saharan dust also over this area. Figure 40 shows the evolution of the August dust event on the basis of the ACT tool calculations with the CHIMERE model. This is one of the models in the CAMS regional ensemble and although its values can be different from the CAMS reanalysis there are intrinsic similarities between them. The information on the different contributions to the dust event from the ACT tool are very valuable, especially the option that illustrates the effect of natural sources and boundary conditions. The upper panels in Figure 40 show the evolution of background PM10, all sources included, between 1st and 4th August over Europe. The lower panels in Figure 40 show the evolution of only European anthropogenic sources of background PM10. There is a large difference between the two series of panels, indicating that the high PM10 levels during the episode are primarily of non-anthropogenic in origin. The contributions removed from the lower panels are those originating from natural sources and from boundary conditions. The natural sources are primarily dust, sea-salt and biogenic VOCs contributing to the formation of secondary organic aerosols. In Figure 40, the main contribution of the plume over the Iberian Peninsula is most probably due to Saharan dust as indicated by the results from the CAMS global service in Figure 39. Elevated natural PM10 levels over the Eastern Mediterranean can also be of dust origin, while the plume over central and Northern Europe can be related to biogenic VOC. To better understand the composition of the PM10 natural contribution, it is useful to check the chemical speciation results from the CAMS SR calculations.



a) Global

b) Regional

Figure 39. Evolution of the dust episode from 1st to 4th August. Comparison of a) daily mean PM10 dust concentrations from the CAMS global system (upper panels) and b) daily mean PM10 concentrations from the CAMS regional system(lower panels). Each plot represents a different day. Units: [μg.m-3].



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Figure 40. Simulated daily mean PM10 from the Air Control Toolbox showing the sum of natural and anthropogenic PM10 component concentrations on the upper panels, and only the anthropogenic PM10 components on the lower panels. These maps show PM10 concentrations during the dust event occurring between 1st and 4th August. The difference indicates the extent of the natural contribution from dust and sea-salt as well as boundary conditions. Units: [μg.m-3].

Figure 41 shows the chemical speciation of the background PM10 levels over Madrid, in Central Spain as calculated by the EMEP model for the CAMS SR service. The upper graph shows the evolution of the dust episode arriving at Madrid by 3rd August. Although the model calculations may be lower than observations, the chemical speciation results show clearly how the dust event reaches Madrid and contributes with over 50% to the background PM10 values over the city as shown in the daily average pie charts in the lower panels. It is interesting to note that in the course of this episode, the concentrations of backgrounds PM10 over Madrid had also an important contribution from Secondary Organic Aerosols (SOA). This contribution was stable during the period and not associated to the dust event. Further analysis of the results from the CAMS SR service shows that the SOA component is primarily of biogenic origin. The situation over the eastern part of the Mediterranean is considerably different. Figure 42 shows the chemical speciation of the background PM10 levels over Athens for the same period. The secondary dust episode affects the background PM10 concentrations over Athens from the 2nd August, but with a lesser contribution ranging from 26% on 2nd August, to 13% on the 3rd and 5% on the 4th August. There is a lesser contribution of sea-salt during the episode in the Eastern Mediterranean area and much lower contribution from biogenic sources. In fact, the predominant component in background PM10 over Athens during the time of the episode seems to be anthropogenic sulphate, mainly from Bulgarian sources.



Figure 41. Chemical speciation of the PM10 levels over Madrid calculated by the EMEP model for the dust episode from 1st-4th August. Upper panel is a time series of concentration levels for different species. Units: [μg.m-3]. Lower panel are daily averaged pie charts with the different contributions from the species given in the legend. Note that the dust episode reaches Madrid most strongly on 3rd August.

Figure 42. . Chemical speciation of the PM10 levels over Athens calculated by the EMEP model for the dust episode from 1st-4th August. Upper panel is a time series of concentration levels for different species. Units: [μg.m-3]. Lower panel are daily averaged pie charts with the different contributions from the species given in the legend. Note that the eastern dust episode reaches Athens most strongly on 3rd August but in less strong than the western dust event.



Dust episode from 16th to 20th October The episode of PM during the second half of October 2018 that we analysed in section 3.3.2., had a significant dust component over the eastern part of the Mediterranean. It was in fact related to two dust outbreaks over central and eastern Mediterranean Basin that MODIS detected. The first outbreak was observed on 16th October with origin in Libya, affecting the central Mediterranean by the 18th. The second dust event came in from Syria, moving towards Turkey and continued beyond the 16th October and continued to the 20th October.

a) Global

b) Regional

Figure 43. Comparison of a) daily mean PM10 dust concentrations from the CAMS global system and b) daily mean PM10 concentrations from the CAMS regional system. Results the dust episode from October 16th to 20th. Each plot represents a different day. Units: [μg.m-3]



Figure 43 shows the evolution of the dust event from the global and the regional services. The CAMS global reproduced the spatial distribution of the two dust plumes despite the model tendency to underestimate the observed maximum values, in particular for the event which originated in Libya, while the regional CAMS re-analysis corrects for that underestimation. Figure 44 shows how the dust event from Libya reaches Nicosia in the central Mediterranean basin on the 18th October and contributes to elevated background PM10 levels above the daily limit value. It also shows how the eastern part of the episode reached Europe through the Turkey and affects Valetta already from the 16th October.

a) Nicosia

b) Valetta

Figure 44. . Chemical speciation of the PM10 levels over a) Nicosia and b) Valetta calculated by the EMEP model for the dust episode from 16th-20th October. Upper panels are a time series of concentration levels for different species. Units: [μg.m-3]. Lower panels are daily averaged pie charts with the different contributions from the species given in the legend Unit: [%].



3.3.4 Forest Fires in July 2018 The meteorological conditions of 2018, with high temperatures and dry conditions increased the risk for vegetation damage and forest fires. A series of wildfires in Greece, during the 2018 European heat wave, began in the coastal areas of Attica in July 2018 with as many as 102 people confirmed dead. The fires were the second-deadliest wildfire event in the 21st century, after the 2009 Black Saturday bushfires in Australia that killed 180. Wildfires reached also an unprecedented extent in Sweden in mid-July, with over 25 000 hectares burned, in what was considered to be the most serious wildfire event in the country’s modern history. Figure 45 shows the CAMS regional re-analyses surface PM2.5 data during the firestorm. The wildfires contributed to elevated values of PM2.5 well above the WHOs guidance values in the July 23rd and 24th daily mean. The figure also provides an indication of the spatial spreading of the effects of firestorms in air pollution covering most of Greece. Figure 46 shows how the CAMS regional re-analysis captures the Swedish fire event in terms of its effects on PM2.5 elevated daily concentrations.

Figure 45. Daily mean PM2.5 concentrations from the CAMS ensemble re-analyses for the July 23rd to 24th wildfire event in Attica, Greece. Each plot represents a different day. Units: [μg.m-3].

Figure 46. Daily mean PM2.5 concentrations from the CAMS ensemble re-analyses for the July 18th to 19th wildfire event in Sweden. Each plot represents a different day. Units: [μg.m-3].



4. Conclusions The meteorological conditions in 2018 were very favorable to ozone formation and have therefore lead to higher concentrations than in previous years, especially in Northern Europe. Globally, 2018 was the fourth warmest year on record. In fact, the past four years (2015, 2016, 2017 and 2018) are the four warmest years in the WMO series. In Europe, 2018 was the third warmest year in record. The year began with weak La Niña conditions which are typically associated with lower temperatures, but, after March, Europe experienced the warmest late spring, summer and autumn temperatures on record. Consequently, Europe experienced high ozone values in 2018 with ozone health indicator values comparable to those of 2015. For SOMO35, the elevated ozone levels over all seasons consequently lead to higher values of the SOMO35 indicator than in any of the previous years, especially in Northern Europe. Europe experienced also high AOT40 values in 2018, well over the target value of 18 000 μg.m-3.hours. The high ozone values in Central Europe caused crop damages and severely affected husbandry even in southern Scandinavia. The year 2018 was a dry year over northern Europe and wetter than average year in southern Europe. The Northern Mediterranean region experienced many heavy rainfall events in 2018. Southwest Europe experienced one of the two wettest springs since 1950 while southeast Europe had one of the six wettest summers of the last 70 years. Still, large parts of Europe (in particular northern and central Europe) experienced also exceptional heat and drought conditions through the late spring and summer of 2018. There was an extensive period of drought in northern Europe, with 80% less than normal precipitation during the main growing and harvesting periods. These high temperatures and dry conditions increased the risk for vegetation damage and forest fires. A series of wildfires in Greece, during the 2018 European heat wave, began in the coastal areas of Attica in July 2018 with as many as 102 people confirmed dead. The fires were the second-deadliest wildfire event in the 21st century. Wildfires reached also an unprecedented extent in Sweden, with over 25 000 hectares burned, in what was considered to be the most serious wildfire event in the country’s modern history. The annual mean concentrations of background PM10 and PM2.5 over most regions in Europe were similar to those of 2016, 2015 and 2014. The background values for nitrogen dioxide in 2018 were not significantly different from those of previous years since meteorology plays a smaller role in the inter-annual variability of this pollutant than year-to-year NOx emission variations. In a showcase of available CAMS products, we have studied the evolution and characterised the origin of nine (9) different air pollution events in 2018, all related to a high number of exceedances of EU limit values for ozone and PM10 and of WHO guidance value for PM2.5. This year, we have focused on explaining the capabilities of two recently added policy products, namely the CAMS Air Control Tool and the chemical speciation in the CAMS Source-Receptor (SR) calculations. These are particularly appropriate to characterise the origin of pollution and identify the long-range transport components. The nine (9) air pollution events include one for ozone, three for particulate matter, three for dust and two wildfire events.



OZONE: The ozone episode that took place from 30th July to 9th August is the one that registered the highest number of exceedances, due to its persistence over 12 days and its broad spatial extent, covering large areas over Europe. Traffic and industrial emissions were as expected the main anthropogenic contributors to this ozone episode while the effect of non-methane volatile organic compounds (NMVOC) from biogenic sources and boundary conditions are also taken into consideration. The extent of the biogenic contribution and existence of titration effects from local sources are well illustrated in the results from the CAMS SR calculations for the cities of Frankfurt and Milan. We also can trace the titration effects when analyzing how the emission reductions planned under the NEC2030 scenario would affect the ozone episode. While in central and southern Europe reductions reach up to 8-10 μg.m-3, representing about a 10-20% reduction, over the English Channel there can be increases of the ozone levels by the same order of magnitude. PARTICULTE MATTER: The three selected PM episodes are very different in nature. The first selected PM10 episode from 7th to 10th February began in central Europe, with the highest values over Poland and developed over time to include also large areas in Germany, Denmark and the Baltic region. At the same time, a Saharan dust intrusion took place over the eastern Mediterranean region reaching Turkey and the Black Sea. In this episode, central Europe experienced also a very large number of exceedances of the WHO guidance value of 25 μg.m-3 for PM2.5. This episode had a significant long-range component, but it had also significant local contributions, as indicated by the fact that emissions from residential heating, including wood and coal combustion dominated the PM levels, especially in southern and eastern Europe. Examples for Warsaw and Ljubljana from the CAMS SR service shows the balance between the long-range and local contributions to background air and helps identify the origin of the long-range transport contribution from different countries. While the PM background air over Warsaw had mostly contributions from Warsaw itself and the rest of Poland, the background air over Ljubljana was affected by emissions in the rest of Slovenia, Austria and Germany. The second PM episode from 21th to 28th February was studied as part of an activation program for CAMS Source-Receptor calculations. During the last week of February 2018 there was a high pressure system located over Scandinavia and a low pressure system over south-eastern Europe, resulting in very strong eastern winds with very low temperatures in north-western Europe. The CAMS ensemble did not manage to reproduce the observed PM values and consistently underestimated the observations. An important reason for this underestimation seems to be the use of a default profile for domestic heating emissions that implies missing local emissions, especially during very cold episodes. These missing local emissions can explain the general underestimation by the background PM results and should also be taken into consideration when evaluating the different contributions to the pollution episode. For instance, when the CAMS SR service indicates that the background PM air over Amsterdam originates mainly from long-range transport sources, caution should be used in interpreting the results, because the local component is probably underestimated in the CAMS SR calculations. The PM episode that took place from 21st to 26th October 2018 was characterized by a combination of a sea-salt episode over the Atlantic Coast and a Saharan dust intrusion over the Mediterranean area and southeastern Europe. For this type of PM episode, the products available from CAMS are



particularly useful because they are well suited to identify the long-range natural component of the aerosol, both as sea-salt and as dust. DUST: The combination of data from the CAMS services on dust provides useful information to determine the timing, extent and magnitude of dust episodes. While the dust product from the CAMS global service serves to identify the timing of the dust events, the combination of regional CAMS products, with the ACT tool and the chemical speciation products allow a better quantification of the magnitude of the dust events. In 2018, we identified three major dust storm events affecting the European continent. They took place from 22nd to 27th April affecting the Iberian Peninsula and the western Mediterranean basin, from 1st to 4th August, also over the Iberian Peninsula and from 16th to 20th October in an event affecting the central and eastern Mediterranean basin. FIRES: The forest fire events of 23rd and 24th July in Greece and the wildfires from 17th to 18th July in Sweden contributed to elevated PM2.5 concentrations in their surrounding areas. The CAMS interim re-analysis captured the elevated PM values but at present it is still difficult to provide an estimate of the contribution of these fires to PM concentrations. We hope that this report can support national experts in the Member States reporting the origin of specific background pollution levels under the Air Quality Directive (EU, 2008) with detailed information on the status of air pollution in 2018, the occurrence of episodes and the given insights about the main contributing sources.



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