Interim Annual Assessment Report - Copernicus · Copernicus Atmosphere Monitoring Service CAMS71_2016SC2 – 2016IAAR Page 5 of 54 Executive Summary This is the Copernicus Atmosphere

ECMWF COPERNICUS REPORT

Copernicus Atmosphere Monitoring Service

Interim Annual Assessment Report

European air quality in 2016

Issued by: INERIS

Date: 15/07/2017

Ref: CAMS-71_2016SC2_D71.1.1.6_201707_IAAR2016_v4

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_2016SC2- 2016IAAR Page 3 of 54

Contributors

NILU P. Hamer

L. Tarrasón

INERIS F. Meleux

L. Rouïl


CAMS71_2016SC2- 2016IAAR Page 4 of 54

Table of Contents

1. Introduction 8

2. Pollution episodes in 2016 10

2.1 Rationale for episode identification 10

2.2 Identified pollution events in 2016 10

2.3 Origin of Pollution Episodes 14

2.3.1 August 25th -28th Ozone Episode 15

2.3.2 January 1st – 9th PM10 Episode 18

2.3.3 March 9th – 20th PM2.5 Episode 20

2.3.4 October 24th – 28th PM2.5 Episode 24

2.3.5 December 4th – 9th PM2.5 Episode 27

2.3.6 December 17th – 24th PM10 Episode 30

2.3.7 Additional Episodes 32

3. Air Quality Indicators in 2016 34

3.1 Ozone in 2016 34

3.1.1 Meteorological characterization 34

3.1.2 Ozone Health Indicators 38

3.1.4 Ozone Ecosystem Indicators 40

3.2 Nitrogen Dioxide in 2016 41

3.2.1 Seasonal variations 41

3.2.2 Nitrogen Dioxide Health Indicators 42

3.3 PM10 in 2016 44

3.3.1 Climatological Characterization of 2016 for PM10 44

3.3.2 PM10 regulatory Indicators for health protection 45

3.4 PM2.5 in 2016 48

4. Conclusions 50

5. References 52


CAMS71_2016SC2 – 2016IAAR Page 5 of 54

Executive Summary

This is the Copernicus Atmosphere Monitoring Service (CAMS) Interim Annual Assessment report

(IAAR) for 2016. This report provides an analysis of air pollution episodes and background air

pollution levels estimated over the year 2016.

The report is based upon non-validated up-to-date observations gathered by the European

Environment Agency (EEA); the up-to-date observations are evaluated directly and they are also

assimilated within models to derive the CAMS interim reanalysis. The timeliness of the IAAR is

therefore considerably advanced with respect to other existing European-wide air quality

assessments that rely upon the validated observations from the EEA. This reliance of the IAAR upon

non-validated data means that it does not present a fully quantitative estimate of the background

European air quality situation in 2016 vis-à-vis regulatory objectives. Instead, the IAAR presents a

characterization of that year’s air quality status with respect to previous years (2007-2015

corresponding to the MACC/CAMS period) and also an identification and evaluation of the origins of

pollution episodes. Such information can be timely and valuable for environmental authorities to

support them when reporting and assessing air quality in their countries under European legislation.

An important new addition in this year’s IAAR with respect to the previous report is the inclusion of

an assessment of PM2.5 pollution episodes. In order to achieve this, we have used the World Health

Organisation (WHO) guideline (WHO, 2006) daily mean value of 25 µg m-3 to define elevated levels

of PM2.5 concentration that in turn form the basis for episode identification. Another new addition is

in relation to ozone episode identification; we now use the EU directive (EU, 2008) long term

objective maximum daily 8-hour mean concentration threshold of 120 µg m-3 to identify pollution

episodes as well as the ozone indicators used in previous reports. A final addition is the use of the

CAMS global near-real-time forecast for sea salt aerosols, which we use to help evaluate the origin

of PM2.5 and PM10 episodes.

In addition to using up-to-date EEA observations, 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; the CAMS global near-

real-time forecast of dust and sea salt aerosol concentrations; ECMWF’s ERA-interim reanalyses for

temperature and precipitation; and the CAMS global fire data assimilation products for characterising

fire emissions. The report provides information on the origin of single episodes by identifying areas

where episodes have a significant natural contribution from dust and sea salt as well as an indication

of the main anthropogenic emission sectors responsible of specific episodes.

The World Meteorological Organisation (WMO) has characterised 2016 as the warmest record year

globally (WHO, 2017). In Europe, 2016 was the third warmest in the last five years and in the overall

record.

Averaged as a whole throughout the entire year, precipitation during 2016 was fairly average in

Western and Central Europe (WMO, 2017). However, the first half of the year was very wet in these

regions, and the latter half of the year was very dry. May to June was particularly wet in France and

Germany, which led to flooding in these countries and generally low PM2.5 and PM10 levels. Following

the wet start to the year, July through September was very dry in Western and Central Europe where

France experienced its driest July and August on record. Finally, the year ended with a particularly

dry December over France and Switzerland.



In terms of air quality, 2016 showed relatively average or only slightly above average levels of daily

8-hour mean ozone values compared to the 2007-2015 MACC/CAMS period. For both PM10 and

PM2.5, the annual levels in Western and Central Europe were lower than previous years due to the

wet spring conditions. A series of large scale pollution events affected European air quality over the

different seasons in 2016. There were ozone episode events during the summer and significant PM2.5

and PM10 pollution events in winter, spring and autumn.

In 2016, a significant PM10 pollution event took place from January 1st to 9th affecting mainly Central

and, to a lesser extent, Northern Europe. The episode included a single day with the largest number

of exceedances above the PM10 daily mean limit value (50 µg m-3) for any event occurring during

2016. No significant contributions to PM10 concentrations from sea salt or dust were detected to the

episode over Central Europe; although an intrusion of sea salt aerosol did occur over western France.

We lack the green scenario forecasts for this episode, so we cannot make a robust determination of

the episode origins, but, based upon the location, timing, i.e., winter, and green scenario results from

other winter episodes in Central Europe, a significant contribution from residential heating (wood

and coal burning) seems plausible.

From March 9th to 20th a very large PM2.5 pollution episode occurred over many areas of Europe. In

the subset of EEA observations we looked at, this episode had the second highest number of

exceedances above the WHO guideline daily mean limit for any episode in 2016. In addition, it was

the largest spring 2016 episode in our evaluation. Our evaluation of the contributions to the episode

from anthropogenic emission sources showed that agricultural emissions of ammonia made the

single largest contribution to the PM2.5 pollution levels and that the contribution was particularly

large over Western, Central Europe, and Eastern. Following agricultural emissions, residential heating

(over Central Europe) and industrial emissions (over Western Europe) contributed in a more minor

way. This episode was also accompanied by a dust intrusion over Eastern and Central Europe that

likely had some influence on the pollution levels on its eastern extent.

Between October 24th and 28th a PM2.5 episode developed in Central, Western, and Northern Europe.

This episode appears to have had two distinct components: elevated levels of PM2.5 over the UK,

France and Central Europe associated primarily with ammonia emissions from agriculture (only minor

contributions from other sectors), and a separate, yet coincident in time, intrusion of Saharan desert

dust over Spain and Portugal.

From December 4th to 9th a very large PM2.5 episode occurred over many areas of Europe. This

episode included a day with the highest number of incidents above the WHO guideline daily mean

value limit for any day during 2016. Our evaluation of the contributions from the anthropogenic

pollution emission sources indicates are rather complex picture. It seems that agricultural emissions

of ammonia made important contributions over different areas of Western and Central Europe,

traffic emissions seems to have made moderate contributions over most of continental Europe, and

that residential heating emissions made very large contributions over Central and Southern Europe.

A PM10 pollution episode between December 17th and 24th affected Central, Western, and Northern

Europe. Our evaluation of the anthropogenic emission sources shows large contributions to the PM10

concentrations over Western and Central Europe. In addition to this, residential heating emissions

from wood and coal burning seem to have made very large contributions to PM10 levels over Central

and Southern Europe.

In summer 2016, various ozone episodes were identified exceeding the EU long term objective

(maximum daily 8-hour mean limit of 120 µg m-3). The largest of these ozone episodes occurred



between August 25th and 28th 2016 over Western, Central, and Northern Europe. Traffic and

industrial emissions are the main emission sectors contributing to this ozone episode event.

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 presented in this report is intended as an example of how CAMS products can support the

reporting of causes of specific pollution levels in different countries.



1. Introduction

The Copernicus Atmosphere Monitoring Service (CAMS- http://atmosphere.copernicus.eu/)

provides a range of operational services describing both global and regional atmospheric

composition information. A specific aim of the wider Copernicus programme is to provide relevant

information to policymakers and public authorities. Therefore, some of the CAMS services and

products are specifically aimed to provide support to policy users in the areas of air quality. The CAMS

policy products aim to describe air quality in Europe and its evolution over the years, to identify air

pollution episodes that represent a hazard to human health and the environment, and to identify the

origins of such pollution events. The origin of these pollution episodes will vary according to the

region in question and according to the season and specific meteorology. High quality information

on the origin of air pollution is an essential prerequisite for policy users to be able to develop the

most effective and appropriate 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 second CAMS Interim Annual Assessment Report (IAAR). The CAMS IAARs are based

on the interim re-analyses of regional air quality that are produced by the CAMS regional services.

The interim re-analyses of air quality consist of the mean of an ensemble of model runs that are

corrected by non-validated up-to-date observation data (from the European Environment Agency –

EEA) using state-of-the-art data assimilation techniques. The interim reanalyses provide best

estimates of air quality in the form of maps of air pollution over the European CAMS domain. The use

of non-validated up-to-date observations to make the interim reanalysis distinguishes the IAAR from

the Annual Assessment Reports (AAR). The AAR is based upon the reanalysis, which is derived from

the validated up-to-date observations.

The non-validated data are 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 allows the production of the IAAR for the previous year by early summer each year. Each

Member State of the European Union and associated countries has specific obligations in terms of

compliance and reporting air quality every year with respect to specific EU directives for individual

air pollutants. The CAMS IAAR reports 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 during

2016. It thereby presents timely reference information for 2016 regarding the climatological

characterization of 2016 and links this to background air quality over Europe, and also gives details

on the occurrence and origins of large-scale episodes.

Section 2 of the IAAR reviews the main episodes that occurred throughout 2016. Pollution episodes

are identified using up-to-date non-validated observations from monitoring stations that indicate

when exceedances of the limit, target, or guideline values (i.e., the WHO guideline (WHO, 2006) for

daily mean PM2.5 concentrations – see section 1.1 for more details) occur over large areas of Europe.

In addition to episode identification this section includes a characterisation of the origins and main

drivers behind these episode events. Section 3 of the report describes the climatologies for the main



regulatory pollutants (ozone, NO2, PM10 and PM2.5) in terms of seasonal and annual mean

concentrations and environmental indicators as compared to previous years.

The IAARs focus on identifying pollution episodes uses non-validated observations from surface

stations, and explores their origins with the help of various CAMS model data. In this way, the report

provides information on areas where episodes have a significant natural dust, sea salt, or wildfire

contribution as well as an indication of what can be the main anthropogenic emission sectors

responsible for specific episodes.

A final advantage is in the extensive use of CAMS data to make the evaluation of the pollution episode

origins. The data are produced to a high level of quality and allow the exploration of pollution origins

from a diverse range of sources. We also hope that the use of these CAMS data in the IAAR could

serve as a showcase for their benefits that might encourage third parties to carry out their own

evaluations.

We have made various improvements and additions to the 2016 IAAR compared to the 2015 IAAR.

There are three main changes related to the identification of episodes:

• We now include an identification of PM2.5 pollution episodes based on the WHO

guideline (WHO, 2006) daily mean concentration of 25 µg m-3. We also carry out

an evaluation of the origins of PM2.5 pollution episodes.

• In addition to using the ozone information threshold limit value (180 µgm-3) to

identify ozone pollution episodes, we also now use the long-term objective

(maximum daily 8-hour mean of 120 µgm-3) for episode identification purposes.

This is a useful addition because the long-term objective gives us a better measure

of the temporal persistence of ozone episodes compared to the information

threshold.

• In the 2015 IAAR, pollution episodes were identified using observations from the

EEA stations that were used as a validation dataset for the interim reanalysis and

reanalysis. However, in addition to the validation stations, we now use the data

from the stations used in the assimilation that creates the interim reanalysis. This

represents an increase in the number of stations we use for episode identification,

which will improve the spatial coverage of this evaluation.

We have also included two important changes to the way in which we evaluate the origins of

pollution episodes:

• In the 2015 IAAR we evaluated dust contributions to PM10 pollution episodes using the PM20

dust product from the CAMS global near-real-time forecasts. In the 2016 IAAR we follow

advice from ECMWF in order to post-process the CAMS global near-real-time forecast PM20

dust product to estimate the contributions to PM2.5 and PM10 from dust. We therefore now

include these PM2.5 and PM10 dust estimates in the 2016 IAAR.

We now include estimates of sea salt contributions to PM2.5 and PM10 pollution episodes using the

CAMS global near-real-time forecast. We follow the advice from ECMWF to post process the global

sea salt PM20 product in order to estimate sea salt contributions to PM2.5 and PM10 .



2. Pollution episodes in 2016

2.1 Rationale for episode identification

We define an episode as elevated levels of pollution that cover a large area during a period lasting

from two day to 2-3 weeks, i.e., a pollution event that is persistent in space and time. Pollution

episodes arise due to the interaction between pollutant emissions and meteorology that either

favour the production or 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 pollutants.

We define elevated pollution levels for the purposes of episode identification using exceedances of

the short-term pollution thresholds for the various pollutants. We derive the occurrence of

exceedances using the up-to-date observations from the large number of the European Environment

Agency’s (EEA’s) European Environment Information and Observation Network (EIONET) stations.

These are the same observations that are used within the production of the CAMS ensemble

reanalysis for both assimilation and validation. Thus, the episodes are identified using observations

and not the model products, which prevents systematic model errors from affecting episode

identification. However, we do use the model products to provide relevant evaluation to characterise

the origin of pollution episodes and to interpret their causes.

The spatial resolution of the CAMS products means they are useful for interpreting and

understanding pollutant concentrations at the rural and suburban scales and are not relevant for

examining pollution hotpots near to traffic or industrial sources. For this reason, only rural, suburban,

and urban stations from EOINET are used for episode identification. As a result, neither episodes of

NO2 related to traffic stations, nor SO2 episodes related to pollution at industrial sites are considered

in the evaluation.

We identify exceedances for PM2.5 using the WHO guideline (WHO, 2006) daily mean concentration

25 µgm-3. As mentioned in section 1.1, the inclusion of PM2.5 episode identification based on the 25

µgm-3 limit is new to the 2016 IAAR and represents an evolution of the CAMS policy user support.

PM10 exceedances are identified using the EU directive (EU, 2008) daily mean limit value of 50 µg/m3.

The use of these daily mean indicators for PM2.5 and PM10 allows us capture and characterise the

temporal persistence of pollution episodes, which is consistent with our definition of an episode

mentioned earlier, i.e., persistence in space and time. In the case of ozone, we use the EU directive

(EU, 2008) 8-hour daily maximum mean limit of 120 µgm-3 to define exceedances. Again, the

relatively long duration of this ozone pollution indicator allows us to establish the temporal

persistence of the ozone pollution episodes.

2.2 Identified pollution events in 2016

To characterise episodes we used non-validated data from the EOINET stations. In this IAAR report

we have used all station data available from EEA, which is an improvement with respect to the

previous IAAR for 2015 (Tarrason et al, 2016). We have used a total of rural 290, suburban 224, and

444 urban stations to identify ozone exceedances. For PM10, we used 166 rural, 169 suburban, and



367 urban stations in the evaluation. We used 72 rural, 64 suburban, and 182 urban stations for the

PM2.5 exceedance identification.

Figure 1 shows the basis for episode identification in 2016. It shows a series of time series with the

number of incidences of observed exceedances for the different pollutants.

The top left panel of Figure 1 shows the number of exceedances of the EU directive (EU, 2008) long-

term objective for ozone ( 120 µgm-3 daily maximum mean concentration); the top right panel shows

exceedances of the EU directive (EU, 2008) ozone information threshold for hourly means (180 µgm-

3); the bottom left panel shows the number of exceedances of the EU legislation (EU, 2008) daily

threshold for PM10 (50 µgm-3); and the bottom right panel shows the number of exceedances of the

WHO guideline (WHO, 2006) daily mean concentration of 25 µg m-3.

Figure 2 shows the spatial extent of the five different regions included in the evaluation: Western

Europe, Central Europe, Eastern Europe, Southern Europe, and Northern Europe.

This gives an explanation to the different regions identified in Figure 1, and we will refer to these

different regions throughout the report.

Figure 1. Episode identification in 2016. 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/m3 in different regions in Europe. The upper right panel shows the number of

incidences of observed ozone concentrations above the information threshold of

180 μg/m3 for 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 50μg/m3 in different European

areas. The lower right panel shows the number of incidences of PM2.5 values above the daily mean threshold

value of 25μg/m3 in different European areas



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

used in the reanalysis production both for assimilation and validation.

Region Ozone 120µgm-3 PM10 PM2.5

Western Europe May 2nd-13th;

May 19th-23rd;

June 22nd-25th;

July 5th-12th;

July 16th-August 4th;

August 23rd-28th;

August 31st-

September 8th-15th

January 15th-26th;

March 7th-14th;

October 22nd-28th;

December 4th-December 9th;

December 17th-24th;

December 29th-31st

January 1st-10th;

January 15th-29th;

March 3rd-6th;

March 9th-20th;

October 24th -October 28th;

December 4th-9th;


December 27th-31st

Central Europe May 2nd-13th;

May 19th-23rd;

June 22nd-25th;

July 5th-12th;

July 16th-August 4th;

August 23rd-28th;

August 31st-

September 8th-15th

January 1st-12th;

January 15th-26th;

March 7th-14th;

March 24th-27th;

April 4th-6th;

October 22nd-28th;


December 17th-24th;

December 29th-31st

January 1st-10th;

January 15th-29th;

March 3rd-6th;

March 9th-20th;


December 4th-9th;


December 28th-31st

Southern Europe May 19th-23rd; No significant episodes in data No significant episodes in data

Northern Europe May 2nd-13th;

May 19th-23rd;

August 23rd-28th;

January 1st-12th;

January 15th-26th;

March 7th-14th;

March 16th-21st;

April 4th-6th;

April 12th-13th;

October 22nd-28th;

January 1st-10th;

January 15th-29th;

March 3rd-6th;

March 9th-20th;


December 4th-9th;


Eastern Europe No significant

episodes in data

January 15th-26th;

March 24th-27th;

No significant episodes in data



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

Figure 1 shows the difference in the overall number of exceedances for both PM2.5 and PM10. Despite

the WHO guideline daily mean concentration for PM2.5 being 25 µg m-3, half the EU directive daily

mean concentration for PM10, there are more than double the number of PM2.5 incidents over the

WHO guideline. The higher relative number of PM2.5 incidents occurs because the PM coarse fraction

typically makes a smaller contribution to PM10 than the fine fraction except in the case of strong

influences on PM10 from mineral dust, sea salt, and some forest fires.

We have identified several of the largest episodes (as seen in Figure 1) for each pollutant, and for

each region in Europe (consult Figure 2), and have summarised this information in in Table 1. We

have not included every episode visible in Figure 1 within Table 1 in an effort to highlight the more

severe cases. The largest number of episodes occurs in Central and Western Europe. The spatial

distribution of the EIONET monitoring network is biased over Western and Central Europe. Therefore,

the scarce number of stations in Eastern and Southern Europe imply that some episodes may have

occurred but that they have remained undetected due to the lack of observations.

We have made a selection of the largest pollution episodes for each pollutant using the information

in Figure 1 and Table 1. We will subsequently assess these episodes in more detail and explore their

origins.

We focus on the largest episode for ozone:

� August 25th to 28th when ozone concentrations exceeded the information threshold across

large areas of Western, Central and Northern Europe.

We identify two major PM10 episodes for further study:

� January 1st to 9th. An episode that includes the highest number of exceedances of the EU

directive daily mean limit value over Central, Western, and Northern Europe.

� December 17th to 24th. An episode of moderate intensity that affected Central, Western, and

Northern Europe.



For PM2.5, we focus on three major events throughout the year:

� March 9th to 20th. One of the largest PM2.5 episodes in 2016 which was part of a series of

episodes persisting for much of March over Central, Western and Northern Europe.

� October 24th to 28th. A more minor episode that occurred concurrently with a desert dust

intrusion in Spain and Portugal. Overall this episode affected Central, Western, and Northern

Europe.

� December 4th to 9th. A large PM2.5 episode that resulted in the highest number of incidents

above the WHO guideline across different EIONET stations. This episode affected Central,

Western, and Northern Europe.

In addition to the largest pollution episodes for each pollutant, we also discuss few days when limit

or target values were exceeded (not included in Table 1). These events occurred on July 19th-20th,

August 26th-28th, and on September 6th-7th. In each case, these were PM episodes that had the

unusual characteristic over occurring during summer or very early autumn.

2.3 Origin of Pollution Episodes

The evolution of a pollution episode is linked to the meteorological conditions and its origin to the

pollution sources. We therefore provide a characterisation of the evolution in time and space, and

an assessment of contributions to the pollution episodes from different sources. For this purpose,

we use various CAMS products. The CAMS modelling 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.

We provide an in-depth list below of the different products we use and explain where the data can

be obtained. Our episode characterisation can serve as an example of evaluation one could perform

using these data, and we hope that policy users become aware of these products and potentially use

them to identify the origin of pollution episodes.

We use the CAMS regional interim reanalyses to visualise the spatial extent and temporal evolution

of the pollution episodes. The interim reanalysis product is derived by combining the EIONET/EEA

station data with the CAMS regional modelling system through data assimilation. The interim

reanalysis data were downloaded from http://atmosphere.copernicus.eu/services/air-quality-

atmospheric-composition.

The green scenario forecasts from the CAMS policy products

(http://policy.atmosphere.copernicus.eu/) can be used to provide information on the contributions

of emissions from four sectors (traffic, industrial, agricultural, and residential heating) to the different

pollutants. After post-processing the data we have derived daily mean estimates of the contributions

from each sector for each day of the episode. The data themselves are available on-demand from

Meteo France.



The CAMS global near-real-time dust forecast products1 http://apps.ecmwf.int/datasets/data/cams-

nrealtime/levtype=ml/ (Morcrette et al., 2008) were post-processed to derive the modelled dust

contributions to PM2.5 and PM10. The PM2.5 and PM10 global model dust results give us an indication

of when dust makes a contribution to the PM2.5 and PM10 episodes represented by the regional

interim reanalysis.

The CAMS global near-real-time sea salt aerosol forecast1 (Morcrette et al., 2008) were post-

processed in a similar way as for dust to derive the global modelled sea salt contributions to PM2.5

and PM10. These sea salt PM2.5 and PM10 products in turn give us an indication of whether sea salt

made a contribution to the PM2.5 and PM10 pollution episodes.

The CAMS global fire assimilation system provides estimates of fire emissions2 based on satellite

observations of fires. The fire emission data are used in the CAMS regional modelling system, and

the emission data can provide an indication of whether wildfires have made a contribution to

particular pollution events.

Note that both the CAMS global near-real-time sea salt and dust products also benefit from the data

assimilation of aerosol optical depth data from the MODIS instruments.

The analyses of the pollution episodes are presently sequentially in the following sub sections.

2.3.1 August 25th -28th Ozone Episode

Ozone pollution episodes occur under warm, sunlit conditions favoured by stagnant high pressure

conditions. These conditions tend to occur in a period from May to September and are particularly

frequent and intense during the summer months.

A large number of exceedances of the maximum daily 8-hour mean ozone concentration began on

August 24th, but the episode we focus on here built up in intensity to produce a large number of

exceedances of the information threshold from August 25th to 28th (see Figure 2). This event

represents the worst ozone pollution episode during 2016 and ended up covering large areas of

Western, Central, and also Northern Europe.

Figure 3 shows the maximum daily 8-hour mean ozone concentration (long-term objective) from the

interim reanalysis for the August 25th to 28th period. These plots allow us to see the evolution of the

ozone pollution in time and space, and showing which areas were affected. It is clear that the episode

reaches its peak on August 26th and 27th, and that the episode stretches into Northern Europe on

August 26th due to the long-range transport of ozone and its precursors from Central Europe.

Figure 4 shows the results from our evaluation of the green scenarios for the period August 25th to

27th (unfortunately data from August 28th are unavailable). The results depict the differences in

concentration (in units of µgm-3) between a reference simulation using the emission inventory for

the current years and a sequence of CAMS green scenario simulations run with sectoral emission

reductions of 30%. The results include three sectors: agricultural, industrial, and residential heating.

However, it is important to note that for episodes after October 2016 the residential heating green

scenario was modified, a fact that will have consequences for further use of green scenarios in IIAR

reports and would need further discussion in next year’s reports.

1 http://apps.ecmwf.int/datasets/data/cams-nrealtime/levtype=ml/ 2 http://apps.ecmwf.int/datasets/data/cams-gfas/



The standard relative reduction in sectoral emissions allows us to observe the relative importance of

the different emission sectors in contributing to the pollution episode and to rank each sector in

terms of its relative importance. The results shown in Figure 4 are of the daily mean differences

between the reference simulation and the green scenario simulations. In this way, the daily means

provide good consistency with the daily plots shown in Figure 3. We can therefore see in Figure 4

that that industrial emissions make an important contribution to the ozone pollution episode, but

the absence of the traffic green scenario (due to an unfortunate error in the operational production)

prevents us from seeing the traffic sector contribution. Based on the evaluations presented in

Tarrasón et al. (2016), it is likely that traffic also made an important contribution to this ozone

pollution episode.

Figure 3. Panel of CAMS regional ensemble interim reanalysis results for the maximum daily 8-hour mean for

the August 25th to 28th ozone pollution episode. Each plot represents a different day: (a) August 25th, (b)

August 26th, (c) August 27th, and (d) August 28th. Units: [μg/m3].



Figure 4. Panel of daily mean differences in ozone between each CAMS green scenario simulation and the

reference run from August 25th to August 27th. Each row is a different green scenario; from top to bottom:

agricultural, industrial, and residential. Please note the absence of the traffic sector contribution for the

evaluation of this episode. Each column is a different day from the three-day simulations: first column for

the 25th August, second column for the 26th August and third column for the 27th August.



2.3.2 January 1st – 9th PM10 Episode

The accumulation of particulate matter in the atmosphere is favoured by stagnant dry conditions.

Combined with this, and as a result of seasonal variations in emissions, PM10 and PM2.5 episodes tend

to occur during winter, spring, and autumn. As an example, residential biomass heating emissions

peak during the colder months.

The PM10 lasting from January 1st to 9th represents the largest PM10 episode of 2016 that mainly

affected Central Europe with some minor impacts on Western and Northern Europe.

Figure 5 shows the daily mean PM10 concentrations from the interim reanalysis for the period of the

episode. We can see that the episode location is represented over Central Europe, and that a plume

of elevated PM10 concentrations moves North West from northern Germany that occasionally

influences Western Europe and Denmark (located in the Northern European region). The intensity of

the PM10 episode peaks in the interim reanalysis from January 5th to 7th, which closely corresponds

to the peak in the incidence of observed PM10 exceedances according to the EU directive daily mean

limit value. It is important to remember that the results in the interim reanalysis are representative

of background concentrations and it is therefore possible that some incidences of PM10

concentrations above the daily mean limit value of 50 µgm-3 occurring at local scale are not

represented by the reanalysis in Figure 5.

Unfortunately a failure of the green scenario forecast operational service means that we have no

green scenario data to provide an assessment of the contributions to the episode from the different

anthropogenic emission sectors for this episode. Given the location of the PM10 pollution episode,

however, and its occurrence in the middle of winter, it is very possible that residential heating

emissions played a major role in its development.

Using an evaluation of the CAMS global dust products to provide model estimates of PM10 (results

not shown here), we can rule out an influence of desert dust on this PM10 pollution episode.

To further assess the contributions of natural sources to the PM10 episode, we use an evaluation of

the CAMS global near-real-time sea salt aerosol forecast.

The results of this evaluation for sea salt for this PM10 episode are shown in Figure 6. We can see that

a PM10 sea salt intrusion does occur over France and then later over northern Germany as well.

However, a careful comparison between Figure 5 and Figure 6 indicates that the sea salt intrusion

occurs to the West of the PM10 episode, and that when sea salt reaches northern Germany, its arrival

is actually associated with lower levels of PM10 as represented in the interim reanalysis. We can

therefore conclude that while sea salt contributes up to 25-35 µgm-3 of PM10 in coastal regions of

France, it does not contribute to the PM10 episode in Central Europe.



Figure 5. Panel of daily mean PM10 concentrations from the ensemble reanalysis for the January 1st to 9th

PM10 episode. Each plot represents a different day. Units: [μg/m3].



Figure 6. Panel of daily mean PM10 sea salt concentrations from the C-IFS global model for the January 1st to

9th PM10 episode. Each plot represents a different day. Units: [μg/m3].

2.3.3 March 9th – 20th PM2.5 Episode



Figure 7. Panel of daily mean PM2.5 concentrations from the ensemble reanalysis for the March 9th to 20th

PM2.5 episode. Each plot represents a different day. Units: [μg/m3].

The March 9th to 20th PM2.5 episode was the largest PM2.5 event during spring and had the second

highest number of incidents throughout the year above the WHO daily mean guideline limit of 25

µgm-3 (see Figure 1). According to the observed exceedances it occurred over Central, Western, and

Northern Europe.

Figure 7 displays the daily mean PM2.5 concentrations from the interim reanalysis during this March

episode. During this PM2.5 episode there seem to have been two particularly intense periods with

one lasting from March 11th to 13th and the other lasting from March 17th to 20th.



Figure 8. Panel of daily mean differences in PM2.5 between each CAMS green scenario simulation

and the reference run from March 18th to March 20th. Each row is a different green scenario; from

top to bottom: agricultural, industrial, traffic, and residential. Each column is a different day during

the pollution episode.

The episode seems to have covered all of the different regions of Europe at one time or other

throughout its duration. However, this large spatial extent was not fully captured in the observations

perhaps due to a lack of coverage of stations in Southern and Eastern Europe.

Similarly to PM10, local PM2.5 pollution events will not be represented in the interim reanalysis

because it simulates background concentrations only.

Figure 8 shows the results of the green scenario evaluation for this episode. Please note though that



the coverage of this evaluation was limited by the failure of the operational production of the green

scenario forecasts in the early part of this episode.

Figure 9. Panel of daily mean PM2.5 dust concentrations from the C-IFS global model for the March 9th to 16th

PM2.5 episode. Each plot represents a different day. Units: [μg/m3].

The green scenario results indicate a significant contribution to the PM2.5 episode from agriculture

and only minor contributions from the other emission sectors.

As an additional support for source allocation for this episode we analyse the CAMS global near-real-

time forecast dust products to assess the impact of dust on the PM2.5 episode. The dust products

consist of three size bins (0.03-0.55 µm, 0.55-0.9 µm, and 0.9-20.0 µm) and are in units of kgdustkgair-

1. We post-processed them according to ECMWF’s recommendation3 in order to derive the dust

contributions to PM2.5 and PM10 in µgm-3. Figure 9 shows the result of the PM2.5 dust evaluation for

the March 9th to 20th pollution episode, and the results indicate an influence of dust on the PM2.5

concentrations during the episode in Eastern and Southern Europe. The dust contribution seems to

be strongest from March 9th to 13th and then becomes more minor after that point.

3

https://software.ecmwf.int/wiki/display/CKB/How+are+PM10+and+PM25+in+CAMS+global+products+calcul

ated



2.3.4 October 24th – 28th PM2.5 Episode

Figure 10. Panel of daily mean PM2.5 concentrations from the ensemble reanalysis for the October 24th to

28th PM2.5 episode. Each plot represents a different day. Units: [μg/m3].

The October 24th to 28th PM2.5 pollution episode was of moderate intensity and covered Central,

Western and Northern Europe. Figure 10 shows the daily mean PM2.5 concentrations from the

interim reanalysis, which allows us to see the distribution and evolution of the PM2.5 episode as it

developed. The reanalysis indicates that October 27th was the most intense day of the episode, and

this too is confirmed in the observed number of exceedances (see Figure 1).

One of the most interesting aspects of this episode was the occurrence of a large dust intrusion over

the Iberian Peninsula (see Figure 11) that arrived over land on October 25th and lasted until October

28th.



Figure 11. Panel of daily mean PM2.5 dust concentrations from the C-IFS global model for the October 24th

to 28th PM2.5 episode. Each plot represents a different day. Units: [μg/m3].

Please note the consistent colour scales in both Figure 10 and Figure 11. A comparison of the

concentrations in both figures shows that the PM2.5 dust estimates from the global forecast are much

higher than those in the regional interim reanalysis. It obviously isn’t possible to have more PM2.5

dust present than the total PM2.5 from all sources concentration. In order to draw valid conclusions

about the dust contribution to PM2.5 for this episode we used the autumn 2016 CAMS 84 global

model validation report (Eskes et al., 2017), which examined this particular dust intrusion using

station observations as it made landfall over Corsica and Majorca. Figure 3.5.15 of Eskes et al. (2017)

shows that the global model overestimates the PM2.5 concentrations compared to the station

observations at both the sites in Corsica (Venaco) and Majorca (Hosptial Jean March). Further, the

observed range of PM2.5 concentrations at the stations in both Corsica (25-30 µgm-3) and Majorca

(30-45 µgm-3) shows that the regional interim reanalysis (see Figure 10) actually agrees more closely



with the observations. We can conclude from this that the dust contribution is overestimated in the

global model. However, the global model dust estimates are still useful as a means of identifying a

dust influence on PM2.5 concentrations and therefore its contribution to the PM2.5 episode over Spain

and Portugal.

Figure 12. Panel of daily mean differences in PM2.5 between each CAMS green scenario simulation and the

reference run from October 24th to October 28th . Each row is a different green scenario; from top to

bottom: agricultural, industrial, traffic, and residential. Each column is a different day during the pollution

episode.



An evaluation of anthropogenic source contributions from the green scenarios shows that ammonia

emissions from the agricultural sector made the largest contribution to PM2.5 during the episode.

Following that, the remaining sectors only made rather minor contributions equivalent to one

another.

2.3.5 December 4th – 9th PM2.5 Episode

Figure 13. Panel of daily mean PM2.5 concentrations from the ensemble reanalysis for the December 4th to

9th PM2.5 episode. Each plot represents a different day. Units: [μg/m3].



Incidences of PM2.5 concentrations above the WHO guideline daily mean of 25 µgm-3 occurred

continuously between late November and the end of December 2016. Embedded within this

relatively polluted period were two large PM2.5 episodes that occurred during December. The earlier

of the two episodes included a day that had the highest number of incidents (over 300) exceeding

the WHO guideline for all of 2016 (see Figure 1). For this reason we focus on the earlier of the two

PM2.5 pollution episodes that lasted from December 4th to 9th, which covered Central, Western and

Northern Europe.

Figure 14. Panel of daily mean differences in PM2.5 between each CAMS green scenario simulation and the

reference run from December 5th to December 8th. Each row is a different green scenario; from top to

bottom: agricultural, industrial, traffic, and residential. Each column is a different day during the pollution

episode.



Figure 13 shows the daily mean PM2.5 concentrations from the interim reanalysis during the episode.

Consistent with the observed exceedances (Figure 1), December 5th to 7th showed the highest

number of incidents above the WHO daily mean guideline. Contrary to EIONET station observations,

the reanalysis shows that concentrations exceeded the guideline in all regions of Europe during at

least one day. This discrepancy is probably due to the scarcity of stations in Southern and Eastern

Europe.

No contributions from sea salt or dust were detected in the CAMS global near-real-time forecast

products.

The green scenario results are presented in Figure 14 and indicate the sectoral contributions to the

PM2.5 episode that vary in space and time. Please note that the residential heating green scenario

changed after October to take into account a shift in residential heating technology away from old

wood burners to a new low emission variant. The impacts of the changes in emissions change by

county according to the relative abundances of the older stoves that pollute more. Previously the

residential heating scenario considered a 30% reduction in emissions like the other green scenarios.

The results in Figure 14 should be considered with caution when assessing the relative contributions

of the different sectors. A change in residential heating to the cleaner stove technology appears to

reduce PM2.5 concentrations the most over Southern and Eastern Europe, and these reductions rank

the highest amongst the different sectors. The traffic sector shows the next largest contribution to

the PM2.5 episode, followed by agricultural that has a strong impact over northern Central Europe.

The industrial sector makes the smallest contribution to the pollution during the episode.

The new formulation of the green scenarios brings forward the question of comparability of the

results and the possibility of using these to rank emission impacts from different anthropogenic

sectors. In this context, an approach considering the effect of mass emission reductions to

concentrations is probably worth being further investigated.



2.3.6 December 17th – 24th PM10 Episode

Figure 15. Panel of daily mean PM10 concentrations from the ensemble reanalysis for the December 17th to

24th PM10 episode. Each plot represents a different day. Units: [μg/m3].



Exceedances of the EU directive daily mean concentration limit value of 50 µgm-3 occurred

throughout much of December (see Figure 1) and two major PM10 episodes occurred during this

period. Having focused on the former of these two events in the context of PM2.5, we now focus on

the second event from the point of view of PM10, which occurred from December 17th to 24th. The

observed exceedances of the PM10 daily mean limit value occurred in Western and Central Europe,

which is mostly consistent with the daily mean concentrations of PM10 from the interim reanalysis

(Figure 15). The interim reanalysis appears to show some exceedances in Eastern Europe as well, and

these were perhaps missed in the observations due to a lack of station coverage.

The contributions to the PM10 pollution episode from different sectors are examined in Figure 16.

Please note again that the residential heating green scenario has now changed to consider the impact

of changing all of the older wood burners to the newer cleaner technology. The green scenario

analyses seem to show that agricultural emissions make the largest contributions to the PM10

pollution in the more northern parts of Western and Central Europe. On the other hand, a switch to

cleaner burning wood stoves in the residential heating scenario seems show a large contribution to

PM10 in the southern part of Central Europe and in Southern Europe. The results show that the two

remaining sectors make more minor contributions to the PM10 pollution episode.



Figure 16. Panel of daily mean differences in PM10 between each CAMS green scenario simulation and the

reference run from the 18th December to 20th December. Each row is a different green scenario; from top

to bottom: agricultural, industrial, traffic, and residential. Each column is a different day during the pollution

episode.

2.3.7 Additional Episodes

In addition to the pollution episodes that we focus on in the previous sections, we also examine some

of the other interesting pollution episodes in lesser detail. We do not include figures to support our

evaluation, but we reference results from the different CAMS products in the text to support the

discussion. All of the selected episodes are PM2.5 or PM10 episodes that occurred either during

summer or early autumn at times when particulate pollution is usually at a minimum. The occurrence

of such events at this time of year is an indication that they might have an unusual cause.

The first episode was a PM10 event that occurred on July 19th-20th in the middle of summer in Western

Europe over the Iberian Peninsula. PM10 episodes from anthropogenic sources do not usually occur

during summer time due to the minima in emissions. Examination of the CAMS global near-real-time

dust forecast shows that a desert dust intrusion occurred over the Iberian Peninsula during this day



that likely had a strong influence on PM10 concentrations in this region. We can therefore attribute

the cause of this event to the dust intrusion.

The second episode was a PM2.5 episode August 25th-27th (peaking on August 26th) that occurred

mostly over Western Europe in France and the low-countries. The unusual aspect of this episode,

besides its occurrence in summer, was that it happened at the same time and in similar locations as

the ozone episode discussed above in section 2.3.1. Due to the concurrent time and location, it seems

likely that this PM2.5 episode developed as a result of the stagnant meteorological conditions that

also favoured ozone formation. We examined the CAMS global near-real-time forecast for both dust

and sea salt and eliminated these natural sources as contributors to the PM2.5 episode. Focusing

instead on anthropogenic causes, we used the green scenarios to identify emissions from the

agricultural and industrial sectors as the primary causes of this PM2.5 event.

The third episode we examine here was a PM10 episode that occurred over Western Europe mostly

over Portugal that also affected Spain on September 6th and 7th. An examination of the CAMS global

near-real-time dust and sea salt forecast products shows that desert dust makes a contribution to

the elevated PM10 over Spain. In addition to this, we also performed an evaluation of the CAMS global

fire assimilation products, which shows that a series of wildfires occurred on September 6th and 7th

in northern Portugal in the region where PM10 concentrations were elevated. We lack a way to

precisely estimate the contributions these fires made to the PM10 episode, but it seems very likely

these fires made an important contribution to the development of this episode. We can therefore

attribute this PM10 episode to influences from wildfires and a desert dust intrusion.



3. Air Quality Indicators in 2016

Following from 2015, which was previously the warmest year globally, the World Meteorological

Organisation (WMO, 2017) and ECMWF4 have both now classified 2016 as the record warmest year.

Overall, 2016 was the third warmest year in Europe being cooler only than 2014 (warmest) and 2015

(second warmest). Temperatures were 1-2 °C warmer over the whole continent compared to the

1961-1990 baseline.

Precipitation during 2016 was fairly average over the entire year in Western and Central Europe

(WMO, 2017). However, while the first half of the year was very wet in these regions, the latter half

of the year was very dry. The western most areas of Europe experienced a particularly wet January

to February. May to June was particularly wet in France and Germany leading to flooding in these

countries and the fifth highest recorded flood level of the Seine River in Paris. Following this, the July

to September was very dry in Western and Central Europe with France experiencing its driest July

and August on record. Finally, the year ended with a particularly dry December over France and

Switzerland.

The 2016 air quality indicators presented in this chapter for ozone, nitrogen dioxide, PM10, and PM2.5

are derived from modelling that considers both the 2016 meteorology and emissions.

In addition to the modelling component, these indicators benefit from the added value provided by

the data assimilation of surface air quality observations. However, it should be noted that the interim

reanalysis assimilates non-validated surface station observations. Therefore, the indicators

presented here do not provide an absolute quantification of the background 2016 European air

quality situation with respect to regulatory objectives, but instead offer a timely characterization of

2016’s air quality status with respect to previous years.

3.1 Ozone in 2016

3.1.1 Meteorological characterization

Atmospheric temperature has a strong influence on background ozone concentrations due to the

effect temperature has on the rates of key photochemical reactions indirectly responsible for the

formation of ozone; higher atmospheric temperatures typically lead to enhanced ozone formation

and higher background ozone levels.

We therefore present maps of the ozone average concentrations over Europe in winter, spring,

summer, and autumn 2016 and compare them to the 2016 seasonal temperature anomalies (the

anomalies are calculated relative to the seasonal means for the 2007-2015 period). The 2016

seasonal temperature anomalies are presented in Figure 17. We estimated the seasonal temperature

anomalies relative to the 2007-2015 period because this represents the period during which

reanalyses have been produced by the MACC, MACC-II, MACC-III, and CAMS projects. This allows us

to compare the 2016 ozone to the previous years’ ozone reanalyses.

4 https://www.ecmwf.int/en/about/media-centre/news/2017/2016-was-warmest-year-yet-ecmwf-data-

show



We estimated the 2016 seasonal temperature anomalies relative to the 2007-2015 climatology using

the ECMWF ERA-interim reanalysis (Dee et al., 2011). The ERA-Interim daily reanalysis is available

freely (for member states) from http://www.ecmwf.int/en/research/climate-reanalysis/era-interim.

2016 winter temperatures were warmer over the majority of Europe relative to the 2007-2015

average by between 0.2-3.0°C (see Figure 17). This warmth was strongest over continental Europe

with the southern and eastern areas of the continent typically being the warmest. Only two regions

ran counter to this prevailing warmth showing temperatures colder than the 2007-2015 climatology:

an area covering the Gulf of Bothnia, Sweden and Norway; and over the Eastern Mediterranean. The

impact of these temperatures is visible in the winter seasonal ozone mean compared to the previous

years of reanalysis (2007-2015). Fitting with the warmer temperatures, ozone levels are higher than

for the majority of years in the MACC/CAMS record over the Iberian Peninsula, the UK, the Alps, and

the region stretching from Poland to the Black Sea. Conversely, ozone concentrations are lower over

Finland, Crete, Cyprus, and Eastern Turkey compared to most of the previous years (note that prior

to 2011, Eastern Turkey was not included in the regional model domain), which is consistent with the

cooler temperatures in these regions during winter 2016.

Figure 17. 2016 seasonal mean temperature anomalies over the CAMS regional domain relative to a 2007-

2015 baseline (source: ECMWF ERA-Interim).

Figure 17 shows that the spring 2016 temperature anomalies can be divided broadly into two distinct

regions. Firstly, temperatures of between -0.5°C and -2.0°C colder than the 2007-2015 average over

a region covering the middle band of Europe running East from the Iberian Peninsula and parts of the

UK, France, Central Europe, and an area of Russia North of the Black Sea. Secondly, warmer

temperatures of up to 3.0°C to the north and south of the cooler central area.



Figure 18. Ozone winter average concentrations for 2016. Units: [μg/m3].

The cold temperature anomalies over the western and eastern portions of Europe are reflected in

the mean spring ozone concentrations (Figure 19) where ozone levels are lower there compared to

most years in the MACC/CAMS record. These effects are most visible over France and the western

portion of the Iberian Peninsula. Consistent with the warmer temperatures elsewhere, spring ozone

is generally elevated over Southern and Northern Europe compared to most years of the

CAMS/MACC reanalyses.

Figure 19. Ozone spring average concentrations for 2016. Units: [μg/m3].

Following the warmth of summer 2015 over continental Europe, the summer of 2016 continued that

trend of warm temperatures. There were some notable differences, however, that have important

implications for how ozone pollution developed during the season. The most notable difference

between the summers of 2015 and 2016 was in the location of the hottest regions. The warmest

temperature anomaly (+2-3°C) during the 2016 summer was over the Iberian Peninsula. On the other

hand, the warmest region (by 2-3°C) during the summer of 2015 was over the middle of Central

Europe.



More generally, 2016 summer temperatures over the eastern part of Central Europe, Southern

Europe, and parts of Eastern Europe were either only slightly above the climatological mean for the

2007-2015 period, or in a few cases, slightly below the mean. Elsewhere, Turkey, the Middle East and

the bulk of the European domain north of Germany showed temperatures that were both warmer

than the summers during the 2007-2015 period and the summer of 2015 on its own.

Despite some general similarities in the level of warmth of the 2015 and 2016 summers, average

summer ozone was lower during summer 2016 than during 2015. The differing locations for the

center of the warm anomalies in 2015 and 2016, i.e., Central Europe and the Iberian Peninsula,

respectively, seem to have been key to this. Warmer temperatures over Central Europe in 2015 seem

to have triggered a high number of ozone episodes in that region.

A comparison of the 2016 summer ozone averages to those from the 2007-2015 period shows that

ozone levels were relatively high over the Iberian Peninsula, Central Europe, and Turkey consistent

with the warmer average temperatures in those regions.

Figure 20. Ozone summer average concentrations for 2016 Units: [μg/m3].

Figure 17 indicates that a cold temperature anomaly of between -0.5°C and -4.0°C persisted over a

large part of Europe during autumn 2016. However, the 2016 autumn was warmer by 0.5-3.0°C than

the 2007-2015 average over the Iberian Peninsula, North Africa, Southern France, Italy, the northern

half of the UK, and Norway.



Figure 21. Ozone autumn average concentrations for 2016. Units: [μg/m3].

The 2016 autumn ozone averages (see Figure 21) follow these patterns of warmer and colder

temperatures over Europe. This is most clearly visible in the higher than average ozone levels over

the Eastern Iberian Peninsula, the South of France, and Italy. Background ozone levels in Eastern

Europe were amongst the lower autumn averages when compared to the previous years’ reanalyses,

i.e., the 2007-2015 period.

3.1.2 Ozone Health Indicators

The European Union's Air Quality Directive (EU, 2008) sets four standards to reduce ozone air

pollution and its associated health impacts:

• the information threshold: a 1-hour average ozone concentration of 180μg/m3,

• the alert threshold: a 1-hour average ozone concentration of 240μg/m3,

• the long-term objective: the maximum daily 8-hour mean concentration of ozone should not exceed

120μg/m3,

• and the target value: the long-term objective should not be exceeded on more than 25 days per year,

averaged over 3 years.



Figure 22. Number of hours when the 1-hour average of ozone exceeds the information threshold of

180μg/m3. The different figures show results for different meteorological years (from left to right

descending: 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, and 2007). Note that the 2015 and 2016

figures show interim reanalysis and the 2007 to 2014 figures show reanalysis. Unit: [Number].

The World Health Organisation 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 (70µg m-3). SOMO35

is used to help quantify health hazards from ozone and is used in health impact assessment modelling

(WHO, 2008).

We now present plots showing the ozone information threshold exceedance, the long-term objective

indicator for ozone, and SOMO35 indicators for 2016 and, to provide additional context, we compare

them with the same indicators for the 2007-2015 period. Please note, however, that the 2016 interim

reanalysis is produced using an assimilation of non-validated station data whereas the 2007-2015

reanalyses are produced using validated data. This comparison does still provide a useful preview for

how 2016 fits into the context of previous years’ ozone health impacts.

Figure 22 shows the number of hours in 2016 that interim reanalysis ozone concentrations were

above the information threshold, and it also shows the same information for the years 2007-2015.

We can see that after the hot summer of 2015 that produced a relatively high number of information

threshold exceedances, there were fewer such exceedances in 2016. Although the 2016 summer was

relatively hot compared to the 2007-2015 period, it was warmest over the Iberian Peninsula. On the

other hand, the 2015 summer was warmest over Central Europe in a sensitive region that has a

relatively high number of ozone pollution events each year (see in Figure 23). The regular occurrence

of ozone pollution events there indicates an abundance of ozone precursor emissions and the

warmer temperatures of the 2015 summer seem to have helped trigger an abnormal number of

events above the information threshold. Please refer to section 3.1.1 for more details regarding the

meteorology.

In fact, 2016 was a relatively average year for the information threshold exceedance indicator

compared to the 2007-2015 reanalyses, and exceedances appeared in Western, Central, and

Southern Europe in the interim reanalysis.



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 descending: 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, and 2007). Note that the

2015 and 2016 figures show interim reanalysis and the 2007 to 2014 figures show reanalysis. Unit: [Number]

The differing prevailing summer conditions during 2016 compared to 2015 (see section 3.1.1 and

discussion above) resulted in an overall fewer number of days of ozone concentrations being above

the long-term objective (i.e., that the maximum daily 8-hour mean concentration of ozone should not

exceed 120μg/m3) across the continent (see Figure 23). As a whole across Europe, 2016 had a fairly

average number of days above the long-term objective compared to the 2007-2015 period. There

were, however, two general exceptions to these fairly typical levels: a higher than average number

of days above the long-term objective over the Iberian Peninsula, and a lower than average number

of days below the objective in the Eastern half of Central Europe.

The large contrast that is clearly visible between 2016 and 2015 in Figure 22 and Figure 23 (i.e., a

lower number of exceedances in 2016 compared to 2015), is less visible in the SOMO35 indicator in

Figure 24. This is because SOMO35 is a better measure of background ozone levels than of the more

elevated ozone associated with pollution events. SOMO35 in 2016 seems to be relatively elevated

over most of Europe compared to many of the earlier years in the 2007-2015 record. This could be

consistent with the reported trends (EEA, 2014) of steadily increasing background ozone in Europe

and is consistent with 2016 having been the third warmest year on record in Europe.



Figure 24. The 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 descending: 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008 and 2007). Note

that the 2015 and 2016 figures show interim reanalyses and the 2007 to 2014 figures show reanalyses. Unit:

[μg/m3.day]

3.1.4 Ozone Ecosystem Indicators

According to the Air Quality Directive (EU, 2008) the impact of ozone on vegetation should be

assessed in regulatory reporting using the AOT40 indicator, which is the accumulated dose of ozone

over a threshold of 40 ppb. AOT40 is calculated by summing the differences between the hourly

ozone concentration in ppb and the 40 ppb threshold for each hour when the concentration exceeds

this threshold value during daylight hours (8:00-20:00 UTC). The Air Quality Directive sets a target

value of 18,000 μg/m3·hours (calculated between May to July) and a long-term objective of 6,000

μg/m3·hours. Figure 25 shows the 2016 AOT40 calculated from the interim reanalysis over Europe,

and it also shows AOT40 calculated for 2007-2015 from the reanalysis. Note again that the 2007-2015

reanalysis uses validated data but the 2016 data from the interim reanalysis uses non-validated data.

Ozone pollution in Europe was less severe in 2016 compared to 2015 (see section 3.1.1 and 3.1.2),

and this is also reflected in the lower AOT40 levels in 2016 compared to 2015. Overall 2016 shows

slightly above average levels of AOT40 with respect to the 2007-2015 period. Regionally, there are

some notable differences between 2016 and the 2007-2015 period: AOT40 in 2016 was relatively high

over the Iberian Peninsula, the Middle East, and Eastern Europe; and relatively low over France.



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 descending: 2016, 2015, 2014, 2013, 2012, 2011,

2010, 2009, 2008 and 2007). Note that the 2015 and 2016 figures show interim reanalysis and the 2007 to

2014 figures show reanalysis. Unit: [μg/m3.hour]

3.2 Nitrogen Dioxide in 2016

3.2.1 Seasonal variations

The highest levels of NO2 pollution either occurs in urban areas close to traffic or near industrial

emission sources. These very high levels of NO2 are distributed over relatively fine spatial scales (i.e.,

from tens meters to a few kilometers) compared to the spatial resolution of the CAMS reanalysis (i.e.,

20 km). As a result, the spatial scale of the reanalysis limits it from capturing these high concentrations

of NO2 occurring at small spatial scales close to traffic, etc. Further, monitoring stations for NO2 are

situated at relatively short distances from the pollution sources and, thus, they routinely report much

higher levels of NO2 than the reanalysis. Again, the coarser spatial resolution of the reanalysis

prevents it from being able to capture the details of NO2 pollution near to its sources. The reanalysis

can provide information about background NO2 at the regional scale. A model with finer spatial

resolution would be required to simulate NO2 concentrations near to traffic and industrial emission

sources.

Figure 26 presents the seasonal mean concentrations of NO2 throughout 2016. The background

concentrations of NO2 during every season align with the locations of its emission sources, i.e., from

urbanised areas, and major road networks.

Both winter and autumn show the highest surface concentrations of NO2 throughout the year.

Background NO2 concentrations peak at the surface during these months because of the increased

prevalence of temperature inversions in the lower layers of the atmosphere that promote the

accumulation of NO2. In addition, the lower levels of sunlight during these seasons reduces 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.



Figure 26. Seasonal averages of background nitrogen dioxide in 2016. Upper left pane is winter; upper right

panel is spring; lower left panel is summer and lower right panel is autumn. Unit: [μg/m3].

3.2.2 Nitrogen Dioxide Health Indicators

The EU Air Quality Directive (EU, 2008) sets two standards for the control of NO2: 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.

The 200 µg m-3 limit value is aimed at controlling NO2 concentrations close to emission sources (see

discussion in section 3.2.1) where concentrations reach and exceed these levels. The models in the

ensemble reanalysis are not able to capture such hotspots due to their limited spatial resolution and

so we do not present an indicator relevant to this limit value.

We do present a map over Europe of the annual mean of NO2 indicator in Figure 27 which is relevant

for the 40 µg m-3 annual mean limit value. We should still caution readers in the interpretation of this

indicator, however, as the coarse resolution of the reanalysis prevents it from capturing the higher

annual average concentrations that will occur at pollution hotspots. The annual mean of the

reanalysis does provide useful information on background concentrations and whether the annual

mean limit value is exceeded at the regional scale.



Figure 27 shows the 2016 annual average NO2 concentration as well as the annual mean

concentrations from the 2007-2015 period. Similarly to Figure 26, the effects of emissions from urban

areas, major road networks, and shipping lanes are clearly visible (for years prior to 2016) with

background concentrations averaging between 15-25 µg m-3 in these areas. Background

concentrations approach the 40 µg m-3 limit value in three areas: Paris, Moscow, and the Pô Valley.

The inter-annual variability for NO2 is very limited.

Figure 27. Annual mean value of nitrogen dioxide. The different figures in the panel show results for different

meteorological years (from left to right descending: 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008,

2007). Note that the 2015 and 2016 figures show interim reanalyses and the 2007 to 2014 figures show

reanalyses. Unit [μg/m3].



3.3 PM10 in 2016

3.3.1 Climatological Characterization of 2016 for PM10

Meteorological conditions have a strong influence upon PM10 concentrations at the regional scale

due to their effects on the formation, loss, emissions, 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 components of PM10

leading to lower concentrations. As a result, 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 and resulting combustion emissions of particulates.

Figure 28. Seasonal averages of background PM10 in 2016. Upper left pane is winter; lower left panel is

summer; upper right panel is spring and lower right panel is autumn. Unit: [μg/m3]



Figure 29. Seasonal mean daily accumulated precipitation anomalies for 2016 over the CAMS regional

domain relative to a 2007-2015 baseline (source: ECMWF ERA-Interim).

Figure 28 shows the seasonal average PM10 concentrations for 2016. We can see that 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 favours the accumulation of PM10 also contributes to the higher

concentrations during this season compared to autumn.

The seasonal precipitation anomalies for 2016 are shown in Figure 29, and they depict a rather mixed

pattern of both wet and dry anomalies covering different geographical regions throughout the

seasons. We will therefore focus the discussion on a number of the clearest wettest precipitation

anomalies during each of the four seasons of 2016 and briefly discuss their impact on PM10

concentrations.

PM10 concentrations were relatively low over Portugal and other parts of the Iberian Peninsula,

France, the Middle East, Eastern Europe and Eastern Turkey during winter 2016 (see Figure 28)

compared to the 2007-2015 period as a result of the wetter conditions over those regions

(see Figure 29). The very wet spring over Portugal and France resulted in very low spring PM10

concentrations over both countries. The summer was relatively wet over Italy, Yugoslavia, Northern

Scandinavia, and Russia, and it is possible to see that PM10 concentrations were relatively low

compared to the 2007-2015 period during summer over these regions. Finally, a relatively confined

wet anomaly over Romania, Ukraine and Belorussia and another over the Iberian Peninsula during

autumn seem to have caused relatively low PM10 concentrations over these regions.

3.3.2 PM10 regulatory Indicators for health protection



The EU’s Air Quality Directive (EU, 2008) sets two standards to reduce particulate air pollution and its

impacts on health through the regulation of PM10:

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

• A daily PM10 concentration limit value of 50 μg m-3 that is not to be exceeded more than 35

times per year.

Figure 30. Number of days with PM10 above 50 μg m-3 for 2015. Unit [Number].

Figure 30 shows the number of days where PM10 concentrations exceed the 50 μg m-3 daily limit value

over Europe in the ensemble interim reanalysis. In relative contrast to many years in the 2007-2015

period, there were just a few locations with 20 or more days with PM10 daily limit value exceedances

in the interim reanalysis (excluding North Africa and the Middle East). These locations were limited

to cities known for their heavy industry rather there being a high numbers of exceedances with a

regional extent.

The comparison of the 2016 annual PM10 averages to the averages from the 2007-2015 period are

shown in Figure 31. The annual averages again demonstrate that PM10 levels were relatively low in

2016 compared to the other years in the record. A region covering the Iberian Peninsula and France,

and another region that includes Belarus, western Ukraine and the Baltic States show particularly

reduced PM10 levels. Both regions experienced prolonged wet precipitation anomalies during 2016

compared to the 2007-2015 period.

Finally, it is interesting to note on Figure 30 and Figure 31 that since 2014 influence of natural desert

dusts is taken into account in the PM re-analyses. It is very clear considering PM10 levels in North-

Africa which exceed the EU limit values set for health protection. Actually, the European thresholds

(daily and annual) refer to PM concentrations induced by anthropogenic sources and member states

can subtract from the levels they report the natural contributions. In the present figures, total

concentrations are proposed and the red colour in the Southern boundary only means high PM10

which are not necessary exceedances of the limit values.



Figure 31. Annual mean value of PM10 for 2016 (top left). The different figures in the panel show results for

different meteorological years (from left to right descending: 2016, 2015, 2014, 2013, 2012, 2011, 2010,

2009, 2008 and 2007). Note that the 2015 and 2016 figures show interim reanalysis and the 2007 to 2014

figures show reanalysis. Unit [μg/m3].



3.4 PM2.5 in 2016

In order to reduce the impacts of PM2.5 pollution on health, the EU Air Quality Directive sets an annual

PM2.5 concentration limit of 25μg m-3.

Figure 32. Seasonal averages of background PM2.5 in 2016. Upper left pane is winter; upper right panel is

spring; lower left panel is summer and lower right panel is autumn. Unit: [μg/m3].

Similarly to PM10 (see description in section 3.3.1), PM2.5 removal processes are also controlled by

precipitation and temperature.

We first look at seasonal changes during 2016 in PM2.5 in Figure 32. We can see the same pattern of

variability for PM2.5 as we did for PM10, i.e., maxima during autumn and winter, and a minimum during

summer. These maxima are mainly due to the peaks in PM2.5 emissions that occur during the autumn

and winter months. For the comparison of the 2016 seasons with the previous years, and in the case

of Figure 33, the inter-comparison of the annual averages, one should note an important caveat.

Comparison of 2016 with years earlier in the record (particularly prior to 2010) is complicated by the

fact that many of the models in the ensemble lacked certain components of PM2.5 during the early

years of the MACC projects, and over time models have begun to represent more of these

components, which has led to higher background levels of PM2.5 in the later years of the record.

Comparing the 2016 seasonal PM2.5 averages (see Figure 32) to the preceding years in the 2007-2015

period, we can see that the maxima and minima follow some of the patterns that we see for PM10.

We will now highlight some of those most notable aspects of this comparison.

During winter 2016 we see relatively low average PM2.5 concentrations over France and the northern

Iberian Peninsula consistent with the wetter than average conditions over these regions



(see Figure 29). The very wet conditions over northern France that resulted in flooding in spring 2016

also caused relatively low average PM2.5 concentrations over France compared to the 2007-2015

period. A wet spring over the Iberian Peninsula also caused relatively low PM2.5 concentrations there

too.

The effects of the dry conditions that persisted over western Europe in the latter half of 2016 are

visible in the relatively low average autumn PM2.5 concentrations over northern France in particular.

Meanwhile, the wetter autumn conditions over Ukraine, Belarus, and the Baltic States resulted in

relatively low PM2.5 concentrations over these countries compared to the 2007-2015 period.

Figure 33. Annual mean value of PM2.5 for 2016 (top left). The different figures in the panel show results for

different meteorological years (from left to right descending: 2016, 2015, 2014, 2013, 2012, 2011, 2010,

2009, 2008 and 2007). Note that the 2015 and 2016 figures show interim reanalysis and the 2009 to 2014

figures show reanalysis. While the values for 2007 and 2008 are for model calculations only, without re-

analysis or use of observations. Unit [μg/m3]

Figure 33 shows the annual concentration averages for PM2.5 along with the annual averages from

the 2007-2015 CAMS/MACC period. Caution should be used when interpreting the time-series of

PM2.5 concentrations, especially concerning 2007 and 2008, as during these two years the results in

Figure 33 shown here are not re-analysis data but only for modelled results, without use of

observations. Comparing 2016 to the 2014-2015 period, it is clear that PM2.5 concentrations have

been relatively low over a region covering the Iberian Peninsula and France as well a larger area over

Eastern Europe. Both minima can be attributed to the wet precipitation anomalies that persisted over

both of these areas during much of 2016 (see Figure 29). With the caveat mentioned above, it is still

possible to see that PM2.5 levels in 2016 were lower over the Iberian Peninsula and France in, 2010,

and 2012.



4. Conclusions

The World Meteorological Organisation (WMO) has characterised 2016 as the warmest record year

globally (WHO, 2017). In Europe, 2016 was the third warmest in the last five years and in the overall

record.

Averaged as a whole throughout the entire year, precipitation during 2016 was fairly average in

Western and Central Europe (WMO, 2017). However, the first half of the year was very wet in these

regions, and the latter half of the year was very dry. May to June was particularly wet in France and

Germany, which lead to flooding in these countries and generally low PM2.5 and PM10 levels. Following

the wet start to the year, July through September was very dry in Western and Central Europe where

France experienced its driest July and August on record. Finally, the year ended with a particularly dry

December over France and Switzerland.

In terms of air quality, 2016 showed relatively average or only slightly above average levels of daily

8-hour mean ozone values compared to the 2007-2015 MACC/CAMS period. For both PM10 and PM2.5,

the annual levels in Western and Central Europe were lower than previous years due to the wet spring

conditions. A series of large scale pollution events affected European air quality over the different

seasons in 2016. There were ozone episode events during the summer and significant PM2.5 and PM10

pollution events in winter, spring and autumn.

In 2016, a significant PM10 pollution event took place from January 1st to 9th affecting mainly Central

and, to a lesser extent, Northern Europe. The episode included a single day with the largest number

of exceedances above the PM10 daily mean limit value (50 µg m-3) for any event occurring during

2016. No significant contributions to PM10 concentrations from sea salt or dust were detected to the

episode over Central Europe; although an intrusion of sea salt aerosol did occur over western France.

We lack the green scenario forecasts for this episode, so we cannot make a robust determination of

the episode origins, but, based upon the location, timing, i.e., winter, and green scenario results from

other winter episodes in Central Europe, a significant contribution from residential heating (wood

and coal burning) seems plausible.

From March 9th to 20th a very large PM2.5 pollution episode occurred over many areas of Europe. In

the subset of EEA observations we looked at, this episode had the second highest number of

exceedances above the WHO guideline daily mean limit for any episode in 2016. In addition, it was

the largest spring 2016 episode in our evaluation. Our evaluation of the contributions to the episode

from anthropogenic emission sources showed that agricultural emissions of ammonia made the

single largest contribution to the PM2.5 pollution levels and that the contribution was particularly large

over Western, Central Europe, and Eastern (although). Following, agricultural emissions, residential

heating (over Central Europe) and industrial emissions (over Western Europe) contribute in a more

minor way. This episode was also accompanied by dust intrusion over Eastern and Central Europe

that likely had some influence on the pollution levels on its eastern extent.

Between October 24th and 28th a PM2.5 episode developed in Central, Western, and Northern

Europe. This episode appears to have had two distinct components: elevated levels of PM2.5 over the

UK, France and Central Europe associated mainly with ammonia emissions from agriculture, and a

separate, yet coincident in time, intrusion of Saharan desert dust over Spain and Portugal.

From December 4th to 9th a very large PM2.5 episode occurred over many areas of Europe. This episode



included a day with the highest number of incidents above the WHO guideline daily mean value limit

for any day during 2016. Our evaluation of the contributions from the anthropogenic pollution

emission sources indicates are rather complex picture. It seems that agricultural emissions of

ammonia made important contributions over different areas of Western and Central Europe, traffic

emissions seems to have made moderate contributions over most of continental Europe, and that

residential heating emissions made very large contributions over Central and Southern Europe.

A PM10 pollution episode between December 17th and 24th affected Central, Western, and Northern

Europe. Our evaluation of the anthropogenic emission sources shows large contributions to the PM10

concentrations over Western and Central Europe. In addition to this, residential heating emissions

from wood and coal burning seem to have made very large contributions to PM10 levels over Central

and Southern Europe.

There were no marked summer episodes of PM2.5 or PM10 in 2016. Instead, there were various

different ozone episodes exceeding the EU directive long term directive (maximum daily 8-hour mean

limit of 120 µg m-3). The largest of these ozone episodes occurred between August 25th and 28th 2016

over Western, Central, and Northern Europe. Traffic and industrial emissions are the main emission

sectors contributing to this ozone episode event.



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