Interim Annual Assessment Report for 2015 - Copernicus...Copernicus Atmosphere Monitoring Service Copernicus Atmosphere Monitoring Service CAMS D71.1.1. | Interim Annual Assessment

Interim Annual

Assessment Report for

2015

European air quality in 2015

Issued by: INERIS

Date: 28/07/2016

REF.:

CAMS71_2016SC1_D71.1.1.2_201609

Copernicus Atmosphere Monitoring Service


CAMS D71.1.1. | Interim Annual Assessment Report for 2015

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.




Interim Annual Assessment Report for

2015

European air quality in 2015

NILU (L. Tarrasón, P.Hamer, C. Guerreiro)

INERIS (F. Meleux, L. Rouïl)

Date: 29/09/2016

REF.: CAMS71_2016SC1_D71.1.1.2_201609




Contents:

Contents ........................................................................................................................ 1

Contents: .......................................................................................................... 3

Executive Summary ............................................................................................ 2

1. Introduction .............................................................................................. 4

1.1 Timeliness .............................................................................................. 4

1.2 Origin of episode events ........................................................................... 5

1.3 Extended use of CAMS data and information ............................................... 5

2. Pollution episodes in 2015 ........................................................................... 7

2.1 Rationale for episode identification ............................................................ 7

2.2 Identified pollution events in 2015 ............................................................. 7

2.3 Origin of pollution episodes ..................................................................... 10

2.3.1 1st – 5th July Ozone Episode ............................................................... 12

2.3.2 12th- 20th February PM10 Episode ........................................................ 14

2.3.3 17th- 20th March PM10 Episode ............................................................ 17

2.3.4 29th October to 7th November PM10 Episode ......................................... 20

3 Air Quality Indicators in 2015 ....................................................................... 25

3.1 Ozone in 2015 ...................................................................................... 25

3.1.1 Meteorological characterisation .......................................................... 25

3.1.2 Ozone Health Indicators.................................................................... 28

3.1.3 Ozone Ecosystem Indicator ............................................................... 30

3.2 Nitrogen Dioxide in 2015 ........................................................................ 31

3.2.1 Seasonal variations .......................................................................... 31

3.2.2 Nitrogen Dioxide Health Indicators ..................................................... 32

3.3 PM10 in 2015 ......................................................................................... 33

3.3.1 Meteorological characterisation .......................................................... 33

3.3.2 PM10 Health Indicators ..................................................................... 34

3.4 PM2.5 in 2015 ......................................................................................... 35

3.4.1 Meteorological characterisation and health indicators ........................... 35

4 Conclusions ................................................................................................ 37

5 References ................................................................................................. 39


CAMS D71.1.1. | Interim Assessment Report for 2015 2

Executive Summary

This is the Copernicus Atmosphere

Monitoring Service (CAMS) Interim Annual Assessment report (IAAR) for 2015. It provides timely reference

information for environmental authorities to support them when

reporting and assessing air quality in their countries under European

legislation. The report is elaborated on the basis of

non-validated up-to-date observations gathered by the European Environment

Agency (EEA) and selected modelled data from the CAMS services. Therefore, its timeliness is considerably advanced

with respect to other existing European-wide air quality assessments. Since the

CAMS Interim Annual Assessment is based on non-validated data, the report does not aim at presenting a

fullyquantitative estimate of the background European air quality

situation in 2015 regarding regulatory objectives, but rather a characterization of that year’s air quality status with

respect to previous years and an analysis of the origin of identified episodes.

The IAAR report is based on a number of products and data developed within the

CAMS services: the interim CAMS re-analyses of the regional model ensemble,

information from the CAMS regional green-scenario calculations, as well as the global aerosol production of dust

concentrations. It provides information on the origin of single episodes by

identifying areas where the episodes are susceptible to have a significant natural dust contribution as well as an indication

of what can be the main anthropogenic emission sectors responsible of specific

episodes.

The year 2015 has been characterised by the World Meteorological Organisation

(WMO) as a historically warm record year

globally. In Europe as a whole, 2015 was the second warmest in the last five

years. There were floods caused by heavy rain in February in parts of Albania, the former Yugoslav Republic of

Macedonia, Greece and Bulgaria and record high monthly precipitation records

for different months over Northern Europe and Scandinavia. Still, some areas remained particularly dry, which

gave rise to a series of forest fires that had consequences for recorded air

quality values. In terms of air quality, 2015 experienced

the highest maximum daily 8-hour mean ozone values over Central Europe over

the last five years and elevated annual levels of PM10 over the last years. A

series of large scale pollution events affected European air quality over the different seasons in 2015. There were

ozone episode events during the summer and significant PM10 pollution events in

winter, spring and autumn. In 2015, a significant PM10 pollution

event took place from 12th to 20th February, affecting most areas in Europe.

The origin of this winter pollution episode varies from country to country but it is a complex combination of different

anthropogenic and natural sources. Emissions from residential heating,

including wood and coal combustion, dominate the PM10 pollution levels of the winter episode, especially in Southern

and Eastern Europe, followed closely by the contribution of ammonia emissions

from agriculture. In Central Europe, however, agriculture emissions dominate as origin of this PM10 episode over other

anthropogenic sources. In the winter episode from 12th to 20th February, a

Saharan dust intrusion affected also PM10 pollution levels over Southern and Western Europe. The results from the

CAMS post-processed PM20 data can be used as indicator of the importance of the



contribution of Saharan dust in PM10.

The winter PM10 episode involved also contributions from forest fires. Although

these contributions have not yet been quantified, ongoing work will provide such quantification in future analysis.

There was another important PM10

episode in March 2015. It took place from 17th to 20th March and recorded the highest PM10 daily levels in 2015 over

areas in The Netherlands, Belgium, Luxembourg, France, Germany, and

Southern United Kingdom. In these areas, the episode included an important contribution from a Saharan dust

intrusion. It is interesting to note that while the winter PM10 episode was

primarily driven by a combination of residential heating emission and

emissions from agriculture, the March episode is clearly dominated by agriculture emissions in the areas of

highest PM10 levels. In Eastern Europe, however, the main anthropogenic

contribution is from residential sources, not agriculture. The natural contribution from Saharan dust plays also a

significant role in the elevated pollution levels in Eastern Europe on 18th-20th

March. The Saharan dust intrusion showed very high PM20 levels over Southern and Central Europe. Although

PM20 is only valid as an indicator to the actual Saharan dust contribution to PM10,

it is clear that in some areas over Italy, Spain and France, the Saharan dust contribution was much higher in this

episode than in any of the other identified episodes in 2015.

There were no marked summer episodes of PM10 in 2015. Instead, the series of

heatwaves affecting Europe in 2015 resulted in different ozone episodes. The

largest ozone episode occurred between 1st and 5th July 2015. Traffic and industrial emissions are the main

emission sectors contributing to this ozone episode event.

The PM10 episode of 29th October to 7th November was the largest autumn

episode and it was actually divided in two different episodes. The first one, from 29th October to 31st October occurred

over Central and Northern Europe. The second one, from 3rd November to 7th

November affected mostly Eastern and Southern Europe. The first part of the autumn episode was dominated by

agriculture emissions in Northern and Central Europe and, to a lesser degree,

by residential emissions. The influence of Saharan dust intrusions on this part of the episode were very limited. The

second part of the episode, in the beginning of November 2015, was

centred over Germany, Poland and most of Eastern Europe. It was dominated by

agriculture emissions, with significant contributions from residential and industrial emissions. In this second part,

the presence of a Saharan dust intrusion was identified reaching as far north as

Germany. Understanding the main emission

sources behind identified episodes is a requirement in the reporting obligations

of the Members States under the Air Quality Directive (EU, 2008). The episode evaluation in this report can be

extended to other type of situations and can be used as an example of how CAMS

products can support reporting the cause of specific pollution levels in different countries.



1. Introduction

The Copernicus Atmosphere Monitoring

Service (CAMS http://atmosphere.copernicus.eu/) delivers global and regional atmospheric

composition information. As part of the CAMS services, some products are

specifically designed to support policy users especially in the area of air quality.

The CAMS policy products aim at describing air quality in Europe and its evolution over the years, identifying air

pollution episodes that impact on health and the environment, as well as the main

drivers responsible for such pollution events. These drivers may differ significantly from region to region and

depend on the period. Good understanding of the origin of air

pollution is essential for policy users to define the most appropriate and efficient control strategies, both in the long-term

and in the short-term.

The information, data and assessments from the CAMS policy product services aim to support European environmental

authorities in reporting and assessing air quality under European legislation.

This report is the first CAMS Interim Annual Assessment Report. The CAMS

Interim Annual Assessment Reports (IAAR) are elaborated on the basis of so

called “interim re-analyses” of air quality that are provided by other CAMS regional services. Interim re-analyses of air

quality are issued from model runs corrected by up-to-date observation data

using state-of-the-art data assimilation techniques. They provide best estimated maps of air pollution patterns. Each

Member State of the European Union and associated countries has specific

obligations in terms of compliance and reporting air quality every year. The

objective of the CAMS IAAR reports is to provide concrete inputs to the national

experts who are in charge of air quality

reporting.

This Interim Annual Assessment Report (IAAR) documents the status of air quality in Europe for the year

2015. It provides timely reference information for policy makers about the

climatological characterization of 2015 and explains how this affects background air quality and the occurrence of large-

scale episodes.

The first part of the report provides a review of the main episodes that occurred over the past year (2015),

when monitoring stations registered exceedance of the limit or target values

over large European regions, and an analysis of the reasons and main drivers

behind these episode events. The second part of the report describes the background situation for the main

regulatory pollutants (ozone, NO2, PM10 and PM2.5) in terms of concentrations and

main environmental indicators as compared to previous years.

The main differences of this IAAR with respect to previous Assessment Reports

from the pre-operational MACC project are:

- The timeliness of the report; - The focus on characterising the

origin of identified episodes; - The extended use of data and

information from CAMS.

1.1 Timeliness

The timeliness of Interim Annual Assessment (IAAR) reports has been

established to respond to the demands from policy users. Earlier feedback from these users indicated the need to have

the reports produced shortly after the considered year. Consequently, the

interim annual assessments reports use



up-to-date non-validated observational

data and the elements highlighted in the IAAR are to be revised and confirmed in

the Annual Assessment Reports (AAR). The AARs are released one year later and are based on validated observation data.

Up-to-date (UTD) data are reported by

the Member States to the European Environment Agency (EEA) as soon as possible after their production, according

to the AQ e-reporting process. These data may be verified and validated some

time after they are reported first and the UTD data are updated in the EEA database. Interim air quality re-analyses

for a given day are produced within a twenty days’ delay. Therefore, the data

is not formally validated (in the regulatory perspective) but should be

reliable enough for assimilation in the CAMS models and interim re-analyses production. Such a process should allow

the production of the IAAR for the previous year by early June each year1.

.

1.2 Origin of episode

events

Since the CAMS Interim Annual Assessment is based on non-validated data, the report does not aim to present

a fully quantitative estimate of background European air quality

situation regarding regulatory objectives, but rather a characterization of the year’s status with respect to

previous years and an analysis of the origin of identified episodes. Such

information is often more reliable as it is primarily associated to the comparison of modelling products with acknowledged

systematic deviation that can be accounted for.

1 Except this year for the 2015 IAAR

because the service started only in April.

Episode events are identified in this

report in terms of up-to-date observations from the European

Environment Agency (EEA). Their origin, however, is established based on: a) modelling results from the CAMS interim

re-analyses of the regional model ensemble, b) results from the CAMS

regional green-scenario calculations, and c) results from the global aerosol production of dust concentrations. In this

way, the report provides information on areas where episodes are susceptible to

have a significant natural dust contribution as well as an indication of what can be the main anthropogenic

emission sectors responsible for specific episodes. Such information is subject to

fewer uncertainties, as it relies on documented modelling approaches.

1.3 Extended use of CAMS data and information

The IAAR focusses on the

characterization of the origin of European-wide pollution episodes. In

this report, the contribution of Saharan dust intrusions to European-wide air

pollution episodes is presented, as well as an identification of the relative importance of agricultural sources versus

industrial, traffic, and residential sources in such events. However, the

contribution of forest fires to pollution events is only superficially addressed. For the next editions, it is envisaged to

add to the analysis of the contribution from forest fires and sea salt. In this way,

a plausible characterization of natural versus anthropogenic contributions to different pollution events will be given

going forward.

For future editions, it is also envisaged to relate to the new source-receptor



calculations under the operational CAMS

policy product service development. CAMS will initiate the production of daily

forecasts of air quality for the main capitals in Europe showing the influence of local versus transboundary air

pollution. It is also planned for a series of on-demand country-to-country source–

receptor allocation runs to determine which countries are mainly responsible for specific episode events. Such data will

be incorporated in future IAAR analysis as it becomes available.

With this content, we believe that the report will prove useful to policy users in

supporting the process of reporting the cause of exceedances in their countries

and in the elaboration of their plans and programs related to air quality.



2. Pollution episodes in 2015

2.1 Rationale for episode identification

An air pollution episode is a combination

of emissions and meteorology that gives rise to elevated levels of air pollution over a large area, lasting for a period of

a few days up to 2-3 weeks.

In the context of CAMS, air pollution episodes are defined as situations with pollution levels over EU short-term

standards affecting a large number of stations reporting under EIONET, the

European Environment Information and Observation Network. The identification of elevated pollution levels draws on

observations and not model results to avoid systematic errors, while the origin

of the episodes is analysed in terms of modelling to help in their interpretation.

The observations used are up-to-date data compiled under EIONET by the

European Environment Agency (EEA). The CAMS policy products are relevant for assessing rural and urban

background concentrations and, consequently, the episode events

considered here correspond to elevated pollution levels in background rural and urban areas. Only rural, suburban and

urban background station data are considered for episode identification. The

products from CAMS are not intended for mapping or interpreting local episodes

and exceedances at hotspots, i.e. at street level or near industrial sites. The model resolution is too coarse to

reproduce correctly such situations and appropriate local models should be used

for such analysis. This implies that neither episodes of SO2, related to

2 I.e. Daily means for PM10 and maximum

daily 8-hour mean for ozone. There is no

daily limit value for PM2.5

industrial sites nor episodes of NO2,

related mostly to traffic sites, are included in the CAMS episode event

analysis. The CAMS episodes are identified for

PM10 and ozone on the basis of short-term indicators2 elevated above the EU

limit or target values, as they are representative of the short-term nature of air pollution episodes.

2.2 Identified pollution events in 2015

Up-to-date observations from the

EEA/EIONET were analysed to identify pollution episode events in 2015. For

ozone, this involved data from 99 rural, 102 suburban, and 144 urban background stations; while, for PM10, it

involved data from 53 rural, 61 suburban, and 142 urban background

stations. The number of stations considered here are largely the same as in previous Annual Assessments reports.

Ozone episodes occur under special

meteorological situations characterised by stagnant high-pressure areas. Since

the formation of ozone requires sunlight, ozone episodes tend to occur mainly during summer. PM10 episodes are

usually related to stable dry conditions and, due to the seasonal variations of

their main emissions, PM10 episodes tend to occur in winter, spring and autumn.

Figure 1 shows the basis for episode characterisation in 2015. The upper

panels show the number of elevated ozone incidences above the information threshold; while the lower panels show

the number of incidences of PM10 values above the EU legislation daily threshold.



For ozone, the European Union's Air Quality Directive (2008/50/EC) sets four standards to reduce ozone (O3) air

pollution and its impacts on health. Of these, daily values are regulated by a

long-term objective on the maximum daily 8-hour mean concentration of ozone that should not exceed 120μg/m3.

Still, in this report, and in order to keep consistency with previous assessment

reports under MACC-III (Rouïl et al., 2015) the ozone episodes have been identified by the number of incidences

exceeding the regulatory information threshold of 1-hour average ozone

concentration above 180μg/m3. In future IAARs, the long-term objective value will be used instead.

For PM10, the episode identification is done with respect to the number of incidences exceeding the daily average

concentration threshold of 50μg/m3; as established in the Air Quality Directive.

The left panels in Figure 1 show the number of incidences with observations

above threshold values in five different areas. These areas correspond to the

country selection in Figure 2 and include Western Europe (EUW), Central Europe (EUC), Southern Europe (EUS), Northern

Europe (EUN) and Eastern Europe (EUE).

Figure 1: Episode identification in 2015. Upper left panel shows the number of incidences of ozone observed values above the information threshold of 180 µg/m3 for hourly means in different European regions. Upper right panel shows the ability of the CAMS regional models and their ensemble to reproduce the observed number of incidences of ozone values above the information threshold. Lower left panel shows the number of incidences of PM10 values above the threshold daily mean value of 50µg/m3 in different European areas. Lower right panel shows the ability of the CAMS regional models and their ensemble to reproduce the observed number of incidences of PM10 values above the daily threshold.



Figure 2: European countries included in the classification of European regions used throughout this report.

The right panels in Figure 1 are included to qualify the results of episode characterisation when using the data

from CAMS modelling results in order to provide information about the origin of

the episodes. The figures provide the number of incidences as registered in observations versus the number of

incidences modelled by the regional CAMS models and their ensemble. As it

is indicated in these panels, the largest ozone episodes are generally well reproduced by the models in the CAMS

regional re-analysis production, but with a marked bias to underestimate the

incidence of the episodes. Still the episodes are mostly well reproduced by

the models in 2015 probably because these took place in Central and Western Europe, where the models usually

perform better. The capabilities of the models to reproduce the episodes of PM10

are more limited, as they missed the January, and October episodes. Still, for the three largest PM10 episodes, although

underestimating, the models managed to reproduced the episode incidences.

The above episode identification is summarised in Table 1 that shows how

the identified episodes occur mainly in Western and Central Europe. Of these,

the ones with the largest number of

incidences were considered for further

analysis.

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. Scarce number of stations in Eastern and Southern Europe imply the possibility that a number of episodes

would remain unregistered in these areas.

Region Ozone PM10

Western Europe

June 5th–6th, July 1st–5th, July 10th–17st

January 1st–9th, January 22nd–23rd; February 10th –16th;

March 12th–21st; April 8th-10th, April 23rd-24th; October 4th-14th; November 1th-7nd; December, 18th–

19th

Central Europe

June 5th–6th; July 1st–5th August 7th-9th, August 11th-16th

August30th to

September 3nd.

January 1st–9th; February 10th-20th; March 15th-25th, April 22nd-23rd;

October 28th-

31st; November 1st-9th, November 26th-29th; December 18th-

20th

Southern Europe

No significant episodes in data

No significant episodes in data

Northern Europe

No significant episodes in

data

July 13th-15th, July 24th-26th

Eastern Europe

No significant episodes in

data

February 2nd -3;

Table 1. Episodes identified by region according to the EEA/EIONET observation database.



For ozone, the largest episode occurred

between:

1st to 7th July when exceedances of the hourly information threshold were registered in Central and Western

Europe.

For PM10, the three largest episodes took place from:

12th to 20th February with daily concentrations over threshold in

Central and Western Europe 17th to 20th March when

concentrations over daily threshold were registered in Central and

Western Europe

29th October to 7th November when concentrations over daily threshold were registered in Central and

Western Europe.

2.3 Origin of pollution episodes

The origin and evolution of a pollution episode is intrinsically determined by a

combination of meteorological conditions and the contribution from different emission sources. A first evaluation of

the main emissions contribution to concentration levels during the identified

pollution events is presented below. The evaluation of the main emission

sources contributing to the episode events is based on three different CAMS

products. These are:

1. The interim regional re-analyses

products, carried out on the basis of up-to-date in-situ surface data

reported to EIONET/EEA in combination with the CAMS operational regional air quality

modelling system at

(http://atmosphere.copernicus.e

u/services/air-quality-atmospheric-composition).

These data have been used to visualise the extent of the episode event.

2. The dust aerosol forecast data

products from the global CAMS production chain (Morcrette et al., 2008) were post-processed

to calculate PM20 mass concentrations, to allow

comparison with the regional interim re-analysis PM10 concentration levels. The

resulting PM20 concentration provides a valuable upper

estimate to the relative contribution of natural dust when

present in a PM10 pollution episode. In the global CAMS system, observations of Aerosol

Optical Depth from the US instrument MODIS have been

assimilated.

3. The green scenario forecast data

from the CAMS policy products (http://atmosphere.copernicus.e

u/services/air-quality-atmospheric-composition) provides information on the

contribution of emissions from four main sectors to the

forecasted concentration levels. These data have been post-processed to provide valuable

estimates of the contribution from agriculture, industry, traffic

and residential sources to the concentration levels for each day of the episode.

The CAMS modelled results are

appropriate for the identification of sources and their relative contributions to pollution levels, because the relative

results are not affected by the systematic errors (biases, underestimations) that



limit the applicability of concentration

results to define exceedance areas.



2.3.1 1st – 5th July Ozone Episode

The summer of 2015 was characterised

by a series of heatwaves affecting Europe from May throughout September (WMO

statement, 2016), with monthly average records for July both in Austria and Spain. As a result, elevated ozone levels

were observed during the summer of 2015. The largest episode occurred

between 1st and 5th July, stopped on the 6th and continued some places until the 7th July.

Figure 3 presents the modelled ozone

averaged fields as provided by the CAMS interim re-analysis data for the first five days of the episode. It provides an

illustration of the evolution of the episode ad the areas affected by it. The ozone

averages are 8-hourly mean values from 11:00 to 19:00 GMT, considered

representative of the maximum values

during the day.

Traffic and industrial emissions are the main contributors to this ozone episode as shown in Figure 4. The figure presents

the contribution of the main four emission sectors (agriculture, industry,

traffic and residential combustion) to the ozone levels in the first three days of the episode.

The values in Figure 4 are provided as

concentration in µg/m3, but represent differences (and not absolute concentration like in Figure 3) between a

reference run with current emission levels and CAMS green scenarios

characterized by sectoral emission

Figure 3: Panel of CAMS regional ensemble modelled results for maximum daily 8-hour mean (11:00 to 19:00) for the 1st to 5thJuly summer episode. Each plot represents a different day: (a) July 1st, (b) July 2nd, (c) July 3rd, (d) July 4th, and (e) July 5th.Units: [µg/m3]



reduction by 30%. Still, the green

scenario results are directly comparable to each other, so that the different

contributions can be ranged in order of importance. In this way, results from the

green scenarios post-processed as daily

averages, are useful to rank the influence of the different emission source

contributions to the given episode in different areas across Europe.

Figure 4: Panel of daily mean differences in ozone between each CAMS green scenario simulation and the reference run from 3rd July to 5th July. Each row is a different green scenario; from top to bottom: agricultural, industrial, traffic, and residential. Each column is a different day from the three-day simulations: first column for 3rd July, second column for the 4th July and third column for the 5th July.



2.3.2 12th- 20th February PM10 Episode

The PM10 episode of 12th to 20th February was the largest winter episode extending over different European areas in 2015.

Figure 5 shows that this winter episode

extended over most of Europe, also Southern and Eastern Europe, reaching parts of Northern Europe, although it was

originally identified here on the basis of elevated measured values in Central and

Western Europe. The modelled PM10 daily averages from the CAMS ensemble interim re-analysis in Figure 5 can be

used as an indication of the most probable temporal and spatial evolution

of the episode. When drawing conclusions from Figure 5, it is important

to remember that the modelled PM10 concentrations are generally representative of background

concentrations, and we can therefore expect daily averages above the EU

threshold of 50 µg/m3 in locally in some European regions that are not represented in Figure 5.

The CAMS modelled results are

appropriate for the identification of sources and their relative contributions to PM10 levels, because the relative

results are not affected by the systematic errors (biases, underestimations) that

limit the applicability of concentration results to define exceedance areas.

To support source allocation, data from the CAMS global aerosol dust forecast

has been post-processed to be comparable to PM10 air concentrations. The dust products consist of three

different size bins with diameters of 0.03 - 0.55µm, 0.55 - 0.9µm, and 0.9 - 20µm

and are given in units of kgdust/kgair. The mass from all three bins has been added and converted to µg/m3. By doing so, the

dust aerosol data provides information about PM20 mass concentrations. PM20

includes PM10 and additional coarse

particles with diameters larger than 10µm.

Figure 6 shows that there was an intrusion of Saharan dust over Europe at

the beginning of the winter PM10 episode with significant effects from 13th to 15th

February. The figure is very valuable to show the temporal and spatial extent of the Saharan dust intrusion, in

conjunction with the PM10 evolution. The actual PM20 values are also valuable in

comparison with the PM10 calculations, as they represent an upper limit of the contribution of Saharan dust to PM10

concentrations.

For instance, over Southern UK, on 15th February, CAMS modelled PM10 levels are

calculated to about 30µg/m3, while the dust PM20 contribution is identified to be about 5µg/m3. This means that the

actual contribution of Saharan dust to the PM10 levels over the UK is likely to be

well below 16% this day. PM10 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. As in the case of the summer ozone episode, we

have compiled information from the CAMS green scenario calculations to facilitate such evaluation.



Figure 5: Panel of daily CAMS PM10 average ensemble model concentrations for the 12st to 20thFebruary winter episode. Each plot represents a different day. Units: [µg/m3]

Figure 6: Panel of daily averaged CAMS modelled dust concentrations as PM20 for the 12st to 20th February winter episode. Each plot represents a different day. Units: [µg/m3]



Figure 7 shows the available data from

the CAMS green scenarios, for the last 3 days of the episodes. Data for the

residential sector contribution on 18th February was also unavailable.

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 PM10 levels of the winter

episode is larger over Central Europe, where it even dominates as origin over

Figure 7: Panel of daily mean differences in PM10 between each CAMS green scenario simulation and the reference run from 18th to 20nd February. Each row is a different green scenario; from top to bottom: agricultural, industrial, traffic, and residential. Each column is a different day from the three-day simulations. Note that green scenario data are missing for residential heating emissions on 18thFebruary.



the residential emission sector. The

contribution from traffic and industrial emissions is smaller European-wide.

In addition to anthropogenic sources and Saharan dust intrusions, the winter PM10

episode also involved contributions from forest fires, especially those in

Kaliningrad. Unfortunately, such information is not easy to process nor visualise, yet.

2.3.3 17th- 20th March PM10 Episode

The PM10 episode of 17th to 20th March was the largest early spring episode and

with the highest recorded PM10 values in many places in 2015.

Figure 8 shows modelled PM10 daily averages from the CAMS ensemble

interim re-analyses. Very high PM10 values (above 70µg/m3) were modelled over Central and Western Europe. A

Saharan dust intrusion is also clearly depicted in the temporal and spatial

evolution of this March episode. Figure 9 shows the evolution of the

Saharan dust intrusion as PM20 (from the CAMS aerosol products) during this early

spring episode of 17th to 20th March. The intrusion shows very high PM20 levels over Southern and Central Europe.

Although PM20 is only valid as an upper limit of the actual Saharan dust

contribution to PM10, it is clear that in some areas over Italy, Spain and France, the Saharan dust contribution was much

higher in this episode than in any of the other identified episodes in 2015.

As for the other episodes, we have

compiled information from the CAMS green scenario calculations to evaluate the influence of anthropogenic emissions

and rank their contribution to the pollution episode. Figure 10 shows the

available data from the CAMS green scenarios for the March episode. Also in

this case, some of the data was missing.

Data was not available for the 18th March, and for the 17th March some data

for the industrial and residential sector contribution was not available either.

It is interesting to note that, while the winter PM10 episode was primarily driven

by a combination of residential heating emissions and emissions from agriculture, the March episode is clearly

dominated by agriculture emissions in the areas of higher PM10 levels. These

areas are The Netherlands, Belgium, Luxembourg, France, Germany and Southern United Kingdom. In these

areas, the Saharan dust intrusion has also a significant contribution to the

pollution event. In Eastern Europe, however, the main anthropogenic

contribution is from residential sources, not agriculture. The natural contribution from Saharan dust plays also a relevant

role in the elevated pollution levels in Eastern Europe on 18th-20th March.

However, the main emission sector contributions to the episode event vary

from place to place and needs to be considered by specific analysis of the

area in question.



Figure 8: Panel of daily CAMS PM10 average ensemble model concentrations for the 17th to 20th March early spring episode. Each plot represents a different day. Units: [µg/m3]

Figure 9: Panel of daily averaged CAMS modelled dust concentrations as PM20 for the 17st to 20th March early spring episode. Each plot represents a different day. Units: [µg/m3]



Figure 10: Panel of daily mean differences in PM10 between each CAMS green scenario simulation and the reference run for17th, 19th and 20th March. Each row is a different green scenario; from top to bottom: agricultural, industrial, traffic, and residential. Each column is a different day from the three-day simulations. Note that green scenario data are missing for the industrial and residential heating emissions on 17thMarch



2.3.4 29th October to 7th November PM10 Episode

The PM10 episode of 29th October to 7th November was the largest autumn episode in 2015 and it was actually

divided in two different episodes. The first one, from 29th October to 31st

October occurred over Central and Northern Europe. The second one, from 3rd November to 7th November affected

mostly Eastern and Southern Europe.

Figure 11 shows modelled PM10 daily averages from the CAMS ensemble interim re-analysis for the first part of the

episode. The influence of Saharan dust intrusions on the first part of the episode

is very limited as indicated in Figure 12 that shows the evolution of the Saharan

dust intrusion as PM20 from the CAMS aerosol products during this autumn episode of 29th to 31st October.

The first part of the autumn episode was

dominated by agriculture emissions in Northern and Central Europe and, to a lesser degree on residential emissions,

as depicted in Figure 13 that shows the available data from the CAMS green

scenarios for the October episode. The second part of the episode, in the

beginning of November 2015, was centred over Germany, Poland and most

of Eastern Europe. Figure 14 shows the temporal and spatial evolution of the second part of the episode and Figure 15

shows the presence of a Saharan dust intrusion associated to the November

episode reaching as far north as Germany. The PM20 levels in Figure 14 are to be considered as an upper limit to

the actual Saharan dust contribution to PM10 in November 2015.

The second part of the autumn episode in November 2015, was dominated by

agriculture emissions, with significant contributions from residential and

industrial emissions. This is depicted in

Figure 16 that compiles information from the CAMS green scenario calculations,

which evaluate the influence of anthropogenic emissions and rank their contribution to the pollution episode.



Figure 11: Panel of daily CAMS PM10 average ensemble model concentrations for the 29th to 31st October autumn episode over Germany and Northern Europe. Each plot represents a different day. Units: [µg/m3]

Figure 12: Panel of daily averaged CAMS modelled dust concentrations as PM20 for the 29th to 31st October autumn episode over Germany and Northern Europe. Each plot represents a different day. Units: [µg/m3]



Figure 13: Panel of daily mean differences in PM10 between each CAMS green scenario simulation and the reference run for29th, 30th and 31st October. Each row is a different green scenario; from top to bottom: agricultural, industrial, traffic, and residential. Each column is a different day from the three-day simulations.



Figure 14: Panel of daily CAMS PM10 average ensemble model concentrations for the 3rd to 7tht November autumn episode. Each plot represents a different day. Units: [µg/m3]

Figure 15: Panel of daily averaged CAMS modelled dust concentrations as PM20 for the 3rd to 7th November autumn episode. Each plot represents a different day. Units: [µg/m3]



Figure 16: Panel of daily mean differences in PM10 between each CAMS green scenario simulation and the reference run for the 3rd to the 6th November. Each row is a different green scenario; from top to bottom: agricultural, industrial, traffic, and residential. Each column is a different day from the four-day simulations. Note that green scenario data are missing for all emission sectors for 7th November.



3 Air Quality Indicators in 2015

The World Meteorological Organisation Statement on the Status of the Global

Climate in 2015 (WMO, 2016) has characterised 2015 as a record warm

year globally. In Europe as a whole 2015 was the second warmest in the last five years. Heatwaves affected Europe from

May throughout September, with monthly average records for July, both in

Austria and Spain, affecting ozone levels in the areas. There were floods caused by

heavy rain in February in parts of Albania, the former Yugoslav Republic of Macedonia, Greece and Bulgaria and

record high monthly precipitation records for different months over Northern

Europe and Scandinavia. Still, some areas remained particularly dry. Like in April, in Austria, which gave rise to a

series of forest fires that had consequences for recorded air quality

levels. The meteorological conditions of 2015

affect air quality levels in conjunction with emission data as reflected in the air

quality status presented here for ozone, nitrogen dioxide and particulate matter, both as PM10 and PM2.5. The air quality

indicators in this chapter are derived for 2015 meteorological conditions but are

based on non-validated air quality observations. Therefore, the indicators here do not aim at presenting a

quantification of the background European air quality situation in 2015

regarding regulatory objectives, but rather a characterization of that year’s air quality status with respect to previous

years.

3.1 Ozone in 2015

3.1.1 Meteorological characterisation

Background ozone concentrations are strongly linked to temperature through

key photochemical reactions responsible for the formation of ozone; higher temperatures typically lead to higher

ozone levels. Here we compare ozone average concentrations in winter, spring,

summer, and autumn 2015 and relate it to the analysis of differences between seasonal average temperatures in 2015

and the corresponding average over a decade (2000-2010).

The seasonal temperature anomalies

relative to the 2000-2010 meteorology are estimated by the Copernicus Climate Change Service (C3S). The temperature

anomalies presented in Figure 17 are calculated for Europe on the basis of the

C3S/ECMWF ERA interim reanalysis (Dee et al., 2011).

The ERA-Interim daily reanalysis is available freely (for member states) from

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

As indicated in Figure 17, the 2015 winter was warmer than the average winter

temperature in the period 2000-2010

over most of Europe by 0.2-4.0C. The warm anomaly was strongest in the

Eastern and Northern parts of Europe as shown in Figure 17. There were only few

exceptions to the prevailing warm conditions mostly only over the Iberian Peninsula. The effects of these

temperature conditions are visible upon the winter ozone mean reanalysis in

Figure 18. Mean ozone concentrations were higher, in general, over Eastern and Northern Europe compared to previous

years, but were slightly lower over Southern France and the Iberian

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

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



Peninsula in comparison with other

years.

Figure 17. 2015 winter mean temperature anomalies relative to a 2000-2010 baseline (source: C3S/ECMWF ERA-Interim).

Figure 18. Ozone winter average concentrations for 2015. Units: [µg/m3]

Figure 17 also showed that

spring 2015 was warmer over Southern France, the Western Mediterranean, and

the Iberian Peninsula by 0.2-1.0C, and

warmer over Northern Eastern Europe

by 0.2-4.0C. In other regions, the temperature was consistent with the 10-

year average temperature. The influence of the spring temperature anomalies is

reflected on the mean 2015 spring ozone concentration, leading to elevated levels of ozone over the Iberian Peninsula,

Italy, Southern France, Eastern Europe,

and Scandinavia. The spring ozone average is shown in Figure 19.

Figure 19. Ozone spring average concentrations for 2015. Units: [µg/m3]

Figure 17 showed that the 2015 summer

was warmer over Southern and Central Europe as well as France, Southern UK, the Benelux, and the Southern part of

Eastern Europe by 0.5-2.0C. In general, it was cooler over Northern UK,

Scandinavia, and Russia by up to -2.0C.

The hot conditions over Southern, Central and Eastern Europe led to high

ozone levels over most of Europe, including even those regions that experienced cooler than average

temperatures. It is likely that the cooler than average regions were affected by

long-range transport of ozone. Only Northern Russia and Scandinavia experienced more typical and lower

levels of ozone. Figure 20 shows the summer ozone average in 2015.



Figure 20. Ozone summer average concentrations for 2015 Units: [µg/m3]

The temperature anomalies in Figure 17

also showed that it was warmer during autumn 2015 over all of Europe by up to

2.0C except over UK and parts of western Europe. These generally warmer autumn conditions led to higher than

normal ozone concentrations over most of Europe, compared to previous years. Only the UK had lower ozone

concentrations than in the reanalyses of previous years, which may be explained

by the cooler temperature in autumn 2015, compared to the average 2000-2010 autumn temperature. The autumn

ozone average for 2015 is shown in Figure 21.

Figure 21. Ozone autumn average concentrations for 2015. Units: [µg/m3]

Overall, 2015 was a hotter than average

year over most of Europe compared to the 2000-2010. This led to higher mean

annual ozone concentrations throughout Europe compared to previous years, and these elevated ozone levels were

particularly pronounced during spring and summer.



3.1.2 Ozone Health Indicators

The European Union's Air Quality

Directive (EU, 2008) sets four standards to reduce air pollution by ozone and its impacts on health:

an information threshold: 1-hour average ozone concentration of

180μg/m3, an alert threshold: 1-hour average

ozone concentration of 240μg/m3,

a long-term objective: the maximum daily 8-hour mean

concentration of ozone should not exceed 120μg/m3,

and a target value: long-term

objective should not be exceeded on more than 25 days per year,

averaged over 3 years.

3 For the comparison of ozone interim

concentrations in 2015 with previous years,

we used the CAMS re-analyses of ozone

from 2007 to 2013 (http://macc-raq-

In addition, the World Health

Organisation (WHO) has defined the sum of maximum 8-hour ozone levels over

35ppb (70μg/m3) or SOMO35 as a measure for the quantification of health hazards from ozone. This indicator is

used as a health impact constraint in impact assessment modelling (WHO,

2008). Below follows a comparison of the ozone

information threshold indicator, the alert threshold and SOMO35 for 2015 with the

same indicators calculated for earlier years 3 (2007 to 2013). Note that the information for 2015 is based on the

CAMS interim re-analysis, while the information from previous years is based

on validated data. Still, the figures below provide a good characterisation of the

ozone values in 2015 and their

op.meteo.fr/index.php?category=eva_acces

s) Validated values for 2014 are still not

available.

Figure 22: Number of days when the 8-hour daily average of ozone exceeds the information threshold of 180μg/m3. The different figures in the panel show results for different meteorological years (from left to right: 2015, 2013, 2012, 2011, 2010, 2009, 2008 and 2007). Unit: [Number]

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





associated health impacts with respect to

previous years.

Figure 22 shows the number of days when the 8-hour daily average exceeded

the ozone information threshold of

180μg/m3 from 2007 to 2013 and for 2015. It shows that the generally

elevated values of ozone in 2015 with respect to previous year’s results also in

Figure 23: Number of days when the maximum 8-hour daily mean of ozone exceeds the long-term objective value of 120μg/m3. The different figures in the panel show results for different meteorological years (from left to right: 2015, 2013, 2012, 2011, 2010, 2009, 2008 and 2007). Unit: [Number]

Figure 24: WHOs health indicator SOMO35. This is the sum of maximum daily 8-hour running mean of ozone above 35ppb (70μg/m3). The different figures in the panel show results for different meteorological years (from left to right: 2015, 2013, 2012, 2011, 2010, 2009, 2008 and 2007). Unit: [μg/m3.day]



a higher number of days with

exceedances of the information threshold, especially in Central and

Western Europe. In Central and Western Europe, the situation in 2015 for this indicator is similar to the levels of the

extreme year 2010. Also for the long-term objective indicator, the number of

days when the maximum 8-hour daily mean of ozone exceeds 120μg/m3 is generally higher in 2015 than in the

previous 3-4 years, as shown in Figure 23. What seemed to be a general

decreasing trend for high ozone peak values as reported by EEA (EEA, 2014) was interrupted in 2015. By contrast, the

results shown in Figure 24, on the evolution of the SOMO35 indicator,

shows less differences between 2015 and the previous years. This is consistent

with the reported trends of an even increase in background ozone levels (EEA, 2014) of which SOMO35 is also a

good indicator.

3.1.3 Ozone Ecosystem 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 40ppb, 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

18.000 (μg/m3·hours), with a long term objective of 6.000 (μg/m3·hours). As

indicated in Figure 25, 2015 was characterized by elevated AOT40 levels,

especially in Southern and Central Europe, in some places even exceeding the levels of the extreme year 2010.

Figure 25: AOT40 indicator for protection of crops and vegetation. The different figures in the panel show results for different meteorological years (from left to right: 2015, 2013, 2012, 2011, 2010, 2009, 2008 and 2007). Unit: [μg/m3.hour]



3.2 Nitrogen Dioxide in 2015

3.2.1 Seasonal variations

High nitrogen dioxide concentrations are

generally measured in traffic or industrial stations. Nitrogen dioxide is generally

associated with hotspots situations that develop near busy roads or at industrial sites. The products from CAMS are not

intended for reproducing the air quality situation at hotspots because the model

resolution is too coarse and appropriate local models should be used instead. However, the CAMS products can provide

information on background nitrogen dioxide concentrations.

Figure 26 show the seasonal variation of

background nitrogen dioxide (NO2) in 2015. The concentrations of nitrogen dioxide in background air clearly relate to

their emission sources. The footprint of

main European city areas and maritime traffic emissions are the most significant

features of the spatial distribution of background NO2 for all seasons.

Winter and autumn are the seasons with the highest average values. This is

related to the higher frequency of stable meteorological conditions in winter and autumn, with meteorological inversions

that trap the NO2 to the ground and allow the build-up of the pollutant near its

sources. It is also in winter and autumn when the photochemical processes that reduce NO2 levels are less active, thus

contributing to further accumulation of NO2 levels.

Figure 26: Seasonal averages of background nitrogen dioxide in 2015. Upper left pane is winter; lower left panel is spring; upper right panel is summer and lower right panel is autumn. Unit: [μg/m3]



3.2.2 Nitrogen Dioxide Health Indicators

There are two health indicators for

nitrogen dioxide in the Air Quality Directive (EU, 2008). The first one imposes a limit value of 200 µg/m3 to the

hourly concentration of NO2 not to be exceeded more than 18 times per year.

This is an episode-related indicator that applies at hotspots and is not properly addressed without local scale modelling.

The second indicator refers to annual mean nitrogen dioxide concentrations

that should not exceed the limit value of 40 µg/m3 to be in compliance with the 2008 AQ Directive. This annual mean

indicator mapped throughout Europe is shown in Figure 27 for 2015. The figure

4 For the comparison of NO2 interim

concentrations in 2015 with previous years,

we used the CAMS re-analyses of NO2 from

2009 to 2013 (http://macc-raq-

also shows a comparison of the annual

mean of NO2 calculated for earlier years 4 (2009 to 2013). The cities

footprint is clearly marked with annual background concentrations ranging from 20 to 30µg/m3 in most of the places, and

reaching 40µg/m3 or being close to the limit value in few ones, especially in the

Pô Valley, Paris area and in Russia. There is little difference between 2015 and previous years with respect to the annual

mean concentrations of nitrogen dioxide, indicating the larger influence of

emission sources in the spatial distribution of this pollutant.


s) Validated values for 2014 are still not

available.

Figure 27: Annual mean value of nitrogen dioxide. The different figures in the panel show results for different meteorological years (from left to right: 2015, 2013, 2012, 2011, 2010, 2009). Unit [µg/m3]






3.3 PM10 in 2015

The Air Quality Directive (EU, 2008) sets

two standards to reduce air pollution by PM10 and its impacts on health:

an annual PM10 concentration

limit value of 40μg/m3,

a daily PM10 concentration limit value of 50μg/m3, not to be

exceeded more than 35 times per year

3.3.1 Meteorological characterisation

Background PM10 concentrations are linked to precipitation, temperature and

stability conditions as these meteorological parameters play

important roles in PM10 formation and losses mechanisms. Precipitation is a very important removal mechanism for

PM10 in the atmosphere. Drier conditions are therefore more frequently associated

with higher PM10 levels, while PM10

concentrations decrease with precipitation. This association between

precipitation and PM10 is stronger at lower temperatures, when evaporative PM10 mass loss plays less of a role in PM10

removal. Furthermore, low temperature is a major driver of emissions from

household combustion, in autumn, spring, and especially in winter.

Figure 28 shows PM10 average concentrations in winter, spring,

summer, and autumn 2015. As emissions are larger for PM10 in winter and autumn, the average concentrations

in air are also larger in these seasons. Spring, summer and autumn in 2015 was

generally drier than in previous years, as illustrated by the precipitation anomalies

presented in Figure 29. The analysis of differences between seasonal average precipitation in 2015 and the

corresponding averages over a decade

Figure 28: Seasonal averages of background PM10 in 2015. Upper left pane is winter; lower left panel is spring; upper right panel is summer and lower right panel is autumn. Unit: [μg/m3]



Figure 29. Seasonal precipitation rate anomalies for 2015 derived from C3S/ECMWF ERA interim re-analysis.

(2000-2010) is been calculated from the

ERA interim on-line data for 2015 (Dee et al., 2011).

3.3.2 PM10 Health Indicators

Figure 30 shows the number days with exceedance of the daily limit value of 50µg/m3. The indicator is quite similar in

2015 and previous years. Also for the annual PM10 indicator, the background

concentrations in 2015 are very similar to the results from previous years. This is illustrated in Figure 31 where the

annual PM10 concentrations in 2015 are compared with annual averages from

earlier years5 (2007 to 2013).

5 Using MACC/CAMS PM10 reanalysis

(http://macc-raq-

Figure 30. Number of days with PM10 above 50µg/m3 for 2015. Unit [Number]


s)






3.4 PM2.5 in 2015

The Air Quality Directive (EU, 2008) sets

a standard to reduce air pollution by PM2.5 and its impacts on health: an annual PM2.5 concentration limit value of

25μg/m3.

3.4.1 Meteorological characterisation and health indicators

Background PM2.5 concentrations are also linked to precipitation and temperature,

in much the same way as explained in section 3.3.1 for PM10. PM2.5 is more strongly affected by wet removal than

PM10, and therefore precipitation is a stronger predictor of PM2.5.

In this analysis, we have used the 2015 seasonal precipitation anomalies shown

in Figure 29 to support the interpretation

6 Using MACC/CAMS PM2.5 re-analyses

(http://macc-raq-

of PM2.5 average seasonal concentrations, compared with earlier

years6. The generally drier conditions in 2015 resulted in elevated levels of PM2.5

concentrations, with concentrations above the limit value in particular over

the PO valley. Figure 32 shows the 2015 average

seasonal PM2.5 concentrations over Europe. Again, as emissions are larger

for PM2.5 in winter and autumn, the average concentrations in air are also larger in these seasons.

Compared to previous years, PM2.5

levels were relatively high over the Iberian Peninsula, the Po Valley, and most of Central and Eastern Europe. This

may be explained by the drier conditions prevailing in 2015 and consequent

reduced PM2.5 wet removal. The differences with previous are well illustrated in Figure 33.


s)

Figure 31: Annual mean value of PM10. The different figures in the panel show results for different meteorological years (from left to right: 2015, 2013, 2012, 2011, 2010, 2009, 2008 and 2007). Unit [µg/m3]






Figure 32: Seasonal averages of background PM2.5 in 2015. Upper left pane is winter; lower left panel is spring; upper right panel is summer and lower right panel is autumn. Unit: [μg/m3]

Figure 33: Annual mean value of PM2.5 The different figures in the panel show results for different meteorological years (from left to right: 2015, 2013, 2012, 2011, 2010, 2009 and 2008). Unit [µg/m3]



4 Conclusions

The year 2015 has been characterised by

WMO as a record warm year globally (WMO, 2016). In Europe as a whole 2015

was the second warmest in the last five years as indicated by results from the ERA interim reanalysis (Dee et al. 20111)

from the Copernicus Climate Change Service (C3S). Heatwaves affected

Europe from May throughout September, with monthly average records for July both in Austria and Spain. There were

floods caused by heavy rain in February in parts of Albania, the former Yugoslav

Republic of Macedonia, Greece and Bulgaria and record high monthly

precipitation records for different months over Northern Europe and Scandinavia. Still, some areas remained particularly

dry, which gave rise to a series of forest fires that had consequences for recorded

air quality levels. The effect of the meteorological

conditions of 2015 on air pollution has been studied here in conjunction with

emission data. The generally warm conditions in 2015 result in elevated ozone peak levels with respect to

previous years. In Central and Western Europe, the situation in 2015 for ozone

over information threshold indicator is similar to the levels of the extreme year 2010. The generally drier conditions in

2015 resulted also in elevated PM2.5 annual levels, and the calculations show

the highest annual PM2.5 values over the past few years in the Po Valley, the Iberian Peninsula and most of central

and Eastern Europe.

A series of large-scale pollution events affected European air quality over the different seasons in 2015. There were

ozone episode events during the summer and significant PM10 pollution events in

winter, spring and autumn.

The four most significant air pollution

episodes, affecting an extended European area, for each of the four

seasons were identified. Their origin has been evaluated with the help of currently available CAMS products: a) the CAMS

regional ensemble interim re-analysis for 2015, b) the CAMS global aerosol dust

products and c) the CAMS green scenario calculations for anthropogenic emissions.

In 2015, a significant PM10 pollution event took place from 12th to 20th

February, affecting most areas in Europe. The origin of this winter episode varies from country to country and is a complex

combination of different anthropogenic and natural sources. Emissions from

residential heating dominate the PM10 pollution levels of the winter episode,

especially in Southern and Eastern Europe, followed closely by the contribution of ammonia emissions from

agriculture. In Central Europe, however, agriculture emissions dominate as origin

of this PM10 episode over other anthropogenic sources. Furthermore, a Saharan dust intrusion affected also PM10

pollution levels over Southern and Western Europe. In addition, the winter

PM10 episode involved also contributions from forest fires in a few locations.

Another important PM10 episode occurred in March 2015. It took place from 17th to

20th March and it is a typical early spring episode. While the winter PM10 episode was primarily driven by a combination of

residential, heating emissions and emissions from agriculture, the March

episode is clearly dominated by agriculture emissions in the areas of higher PM10 levels. These areas are in

Central and Western Europe, where also Saharan dust intrusions has a significant

contribution to the pollution event. In Eastern Europe, however, the main anthropogenic contribution is from

residential sources, not agriculture. The natural contribution from Saharan dust



plays also a relevant role in the elevated

pollution levels in Eastern Europe on 18th-20th March. The Saharan dust

intrusion lead to very high PM20 levels over Southern and Central Europe. Although PM20 is only valid as an upper

limit to the actual Saharan dust contribution to PM10, it is clear that in

some areas over Italy, Spain and France, the Saharan dust contribution was much higher in this episode than in any of the

other identified episodes in 2015.

There were no summer episodes of PM10 in 2015. Instead, the series of heatwaves affecting Europe in 2015 resulted in

different ozone episodes. The largest ozone episode occurred between 1st and

5th July 2015. Traffic and industrial emissions are the main contributors to

this ozone episode event. The PM10 episode of 29th October to 7th

November was the largest autumn episode and it was divided in two

different episodes. The first one, from 29th October to 31st October, occurred over Central and Northern Europe. The

second one, from 3rd November to 7th November affected mostly Eastern and

Southern Europe. The first part of the autumn episode was dominated by agriculture emissions in Northern and

Central Europe and, to a lesser degree, on residential emissions. The influence of

Saharan dust intrusions on this part of the episode were very limited. The second part of the episode, in the

beginning of November 2015, was centred over Germany, Poland and most

of Eastern Europe. It was dominated by agriculture emissions, with significant contributions from residential and

industrial emissions. In this second part, the presence of a Saharan dust intrusion

was identified reaching as far north as Germany. However, the main emission contributions to the episode event vary

from place to place.



5 References Berrisford, P., et al. "The ERA-Interim archive Version 2.0, ERA Report Series 1, ECMWF, Shinfield Park." Reading, UK 13177 (2011). Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N. and Vitart, F. (2011), The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q.J.R. Meteorol. Soc., 137: 553–597. doi:10.1002/qj.828 EU (2008) Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008 on ambient air quality and cleaner air for Europe (OJ L 152, 11.6.2008, p. 1–44). EEA (2014) Air pollution by ozone across Europe during summer 2013 – Overview of exceedances of EC ozone threshold values: April-September 2013. EEA Technical Report No. 3/2014. ISBN 978-92-9213-422-8 Morcrette, J.-J., A. Beljaars, A. Benedetti, L. Jones, and O. Boucher (2008), Sea-salt and dust aerosols in the ECMWF IFS model, Geophys. Res. Lett., 35, L24813, doi:10.1029/2008GL036041 Rouïl, L. et al. (2015) European Air Quality assessment report for 2013 - MACC-III report 54.7 Schulz, M. A. Valdebenito, M. Gauss, A. Mortier, H. Fagerli, A. Nyiri, P. Wind (2016) Methodology and system setup for the production of regional and city source receptor calculations, CAMS-D71.3.1 Schaap,M., R. Kranenburg, S. Jonkers, A. Segers, M. Schulz, S. Valiyaveetil, A. Valdebenito (2016) Methodology and system setup for the production of country source receptor calculations CAMS-D71.3.2 WHO (2008) Health risks of ozone from long-range transboundary air pollution-ISBN 978 92 890 42895 WMO (2016) WMO Statement of the Status of Global Climate in 2015, WMO No. 1167 ISBN 978-92-63-11167-8

http://dx.doi.org/10.1029/2008GL036041




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