A large fraction of the urban population in Europe is exposed to particulate
matter levels above the WHO guideline value. To make more effective mitigation
strategies, it is important to understand the influence on particulate
matter (PM) from pollutants emitted in different European nations. In this
study, we evaluate a country source contribution forecasting system aimed at assessing the domestic and transboundary contributions to PM in major European
cities for an episode in December 2016. The system is composed of two models (EMEP/MSC-W rv4.15 and LOTOS-EUROS v2.0), which allows the consideration of
differences in the source attribution.
We also compared the PM10 concentrations, and both models present
satisfactory agreement in the 4 d forecasts of the surface concentrations,
since the hourly concentrations can be highly correlated with in situ
observations. The correlation coefficients reach values of up to 0.58 for
LOTOS-EUROS and 0.50 for EMEP for the urban stations; the values are 0.58 for
LOTOS-EUROS and 0.72 for EMEP for the rural stations. However, the models
underpredict the highest hourly concentrations measured by the urban
stations (mean underestimation of 36 %), which is to be expected given the
relatively coarse model resolution used (0.25∘ longitude
× 0.125∘ latitude).
For the source attribution calculations, LOTOS-EUROS uses a labelling
technique, while the EMEP/MSC-W model uses a scenario having reduced
anthropogenic emissions, and then it is compared to a reference run where no
changes are applied. Different percentages (5 %, 15 %, and 50 %) for the
reduced emissions in the EMEP/MSC-W model were used to test the robustness
of the methodology. The impact of the different ways to define the urban
area for the studied cities was also investigated (i.e. one model grid cell, nine
grid cells, and grid cells covering the definition given by the Global
Administrative Areas – GADM). We found that the combination of a 15 %
emission reduction and a larger domain (nine grid cells or GADM) helps to
preserve the linearity between emission and concentrations changes. The
nonlinearity, related to the emission reduction scenario used, is suggested
by the nature of the mismatch between the total concentration and the sum of
the concentrations from different calculated sources. Even limited, this
nonlinearity is observed in the NO3-, NH4+, and H2O
concentrations, which is related to gas–aerosol partitioning of the species.
The use of a 15 % emission reduction and of a larger city domain also
causes better agreement on the determination of the main country
contributors between both country source calculations.
Over the 34 European cities investigated, PM10 was dominated by
domestic emissions for the studied episode (1–9 December 2016). The
two models generally agree on the dominant external country contributor
(68 % on an hourly basis) to PM10 concentrations. Overall, 75 % of the
hourly predicted PM10 concentrations of both models have the same top
five main country contributors. Better agreement on the dominant
country contributor for primary (emitted) species (70 % is found for primary organic matter (POM) and 80 %
for elemental carbon – EC) than for the inorganic secondary component of the aerosol (50 %),
which is predictable due to the conceptual differences in the source
attribution used by both models. The country contribution calculated by the
scenario approach depends on the chemical regime, which largely impacts the
secondary components, unlike the calculation using the labelling approach.
Introduction
The adverse health impacts from air pollution and especially from
particulate matter (PM) are a well-documented problem (e.g. Keuken et al.,
2011; REVIHAAP, 2013; Mukherjee and Agrawal, 2017; Segersson et al., 2017).
Furthermore, it affects crop yields (e.g. Crippa et al., 2016), visibility
(e.g. Founda et al., 2016) and even the economy (e.g. Meyer and Pagel,
2017). The mass of particulate matter with an aerodynamic diameter lower
than 10 µm (PM10) is an air quality metric linked to premature
mortality at high exposure (e.g. Dockery and Pope, 1994). The World Health
Organization (WHO) has established a short-term exposure PM10 guideline
value of 50 µg m-3 daily mean that should not be exceeded in
order to ensure healthy conditions (the long-term exposure guideline is 20 µg m-3 for annual mean PM10) (WHO, 2005). Although policies
have been proposed and implemented at the international (e.g. Amann et al.,
2011) and national (e.g. D'Elia et al., 2009) levels, European cities still
suffer from poor air quality (EEA report, 2017), especially due to high
PM10 concentrations. In short, to further decrease the adverse health
impacts of PM in Europe, its concentrations need to be reduced further.
PM10 concentrations in the atmosphere are highly variable in space and
time. Due to the relative short atmospheric life time (from some hours to
days), the variability is impacted by local sources, meteorological
conditions affecting dispersion, and long-range transport as well as chemical
regimes controlling the efficiency of secondary formation. PM10 consists of both primary and secondary components. Primary PM10
components include organic matter (OM), elemental carbon (EC), dust, sea
salt (SS), and other compounds. Secondary PM10 is comprised of compounds
formed by chemical reactions in the atmosphere from gas-phase precursors.
This includes various compounds such as nitrate (NO3-) from nitrogen
oxide (NOx) emissions, ammonium (NH4+) from ammonia
(NH3) emissions, sulfate (SO42-) from sulfur dioxide
(SO2) emissions, and a large range of secondary organic aerosol (SOA)
compounds from both anthropogenic and biogenic volatile organic compounds
(VOCs). The sources for PM and its precursors are numerous, but the main
anthropogenic sources are the transport, industries, energy production, and
agriculture. The main natural sources are composed of forest fires, mineral
dust, and sea salt. The main sink is the wet deposition. The dry deposition
can also be important and depends on the type of land surface such as grass,
tree leaves, and others and on meteorological conditions. With these
components being derived from various sources, we understand the importance of
reflecting properly the source contributions while using modelling for policy
support.
Many studies have already focused on source–receptor relationships to
calculate the transport of atmospheric pollutants, with country-to-country
relationships (e.g. EMEP Status Report, 2018) but also over cities (e.g.
Thunis et al., 2016, 2018). However, these studies focus on annual means,
whereas information is also required on exposure from episodes which cause
short-term limit value exceedances throughout Europe. Source apportionment
provides valuable information on the attribution of different sources to
PM10 concentrations. A country source calculation allows us to tackle
the emissions from the countries responsible for the air pollution episode.
Two distinct methodologies have been compared in this study. Indeed, the
country source contribution presented hereafter is performed by two regional
models, the EMEP/MSC-W model (Simpson et al., 2012) and LOTOS-EUROS (Manders
et al., 2017).
The EMEP calculations use a reduced anthropogenic emission scenario and
compare it to a reference run where no changes are applied. It is also known as
the scenario approach. With such a simulation comparison, the simulation
with reduced emissions over a source region (e.g. a country) allows us to
highlight the impact of this source on the concentrations over a receptor,
hereafter a city. Hence, the scenario approach is useful for analysing the
concentration changes due to emission reductions. On the other hand, one
simulation per source is needed to calculate the impact of each source, as is
done on annual means for each country in each EMEP report (e.g. EMEP Status
Report, 2018). The scenario approach may also lead to a nonlinearity in
the calculated concentrations, i.e. a slight difference between the
concentrations over a receptor and the sum of the estimated concentrations
from different sources over this same receptor, as shown by Clappier et al. (2017a). Thus, the scenario approach is more appropriate for the calculation
of the source contribution of the primary PM components than for nonlinear
species such as the secondary components (e.g. Burr and Zhang, 2011; Thunis
et al., 2019). LOTOS-EUROS traces the origin of air pollutants throughout a
simulation using a labelling approach. The advantage of the labelling
technique is the reduction in the computational time, in comparison with the
scenario approach. It also quantifies the contribution of an emission source
to the concentration of one pollutant at one given location. However, it is
not designed to study the impact of emission abatement policies on
pollutants concentrations (Grewe et al., 2010; Clappier et al., 2017b), and
only traceable atoms can be used in labelling approach, i.e. only conserved
atoms (C, N, and S), directly related to emission sources, in their different
oxidation states. Thus, for example, the origin of ozone (O3) cannot be
studied, which can be done with the scenario approach. Even if both
methodologies mainly aim to answer two different questions, i.e. the
emission control scenarios with the scenario approach and the attribution of
concentrations from a source by the labelling technique, it is still useful
to estimate the reliability of both methodologies in the estimation of the
source contribution to PM10 concentrations. For example, it is
important to ensure that the nonlinearity, related to the perturbation used
in the scenario approach, has a limited impact on the calculated
contributions and to show that both methodologies may present similar
results in the country source attribution.
Both models are part of the operational country source contribution (SC)
prediction system for the European cities within the Copernicus Atmosphere
Monitoring Service (CAMS). This system aims at attributing country
contribution to surface PM10 in European cities for 4 d forecasts.
The objective of this study is to evaluate the robustness of a new system
that provides forecasts of source-region-resolved PM for European cities.
The evaluation of the system is focused on an event occurring between
1 and 9 December 2016, which corresponds to the first event
listed from the beginning of the development of our system. To do so, the
predicted PM10 concentrations are compared with observations. The
simulations from both models, for the concentrations and the SC calculations,
are also intercompared.
Section 2 describes the country SC system composed of the two models and the
experiment. Section 3 describes the studied episode, and it presents the
evaluation of both predictions in terms of PM10 concentrations. The
methodology used for the SC calculations by both models is explained in
Sect. 4. Then Sect. 5 gives an overview of the composition and the
origin of PM10 over the cities predicted by both models and the issue
regarding the nonlinearity in the chemistry related to the EMEP SC
calculation. Section 6 is a comparison between the two country SC
calculations. Finally, the conclusions are provided in Sect. 7.
Description of the country source apportionment systemOverview of the system
Within CAMS, a country SC product has been developed. This is a new
forecasting and near-real-time source allocation system for surface
PM10 concentrations and its different components over all European
capitals. The predictions are available online at
https://policy.atmosphere.copernicus.eu/SourceContribution.php (last access:
24 January 2020). The concentrations are calculated over the 28 EU capitals
plus Bern, Oslo, and Reykjavik. Forecasts for Barcelona, Rotterdam, and Zurich
are also provided. In addition to providing information about the air
quality over the selected cities by focusing on PM10, this product aims
at quantifying the contributions of emissions from different countries in
each city (Fig. 1).
Hourly PM10 concentrations, in micrograms per cubic metre, over Paris
predicted by the EMEP model from 2 to 5 December 2016. The black curve highlights the total concentration. The eight main
country contributors are plotted in addition to the natural sources and
“Others”. Others contains hereafter other European countries,
boundary conditions, ship traffic, biogenic sources, aircraft
emissions, and lightning.
The system is composed of predictions from two regional models (the
EMEP/MSC-W model and LOTOS-EUROS), using two distinct source contribution
calculation methodologies. The EMEP/MSC-W chemistry transport model (Simpson
et al., 2012) has been used for decades to calculate source–receptor
relationships between European countries (and Russia) (e.g. EMEP
Status Report, 2018), and the LOTOS-EUROS chemistry transport model (Manders
et al., 2017) has also been used in several source apportionment studies
over Europe, especially for PM (Hendriks et al., 2013, 2016; Schaap et al.,
2013). Both models are involved in the operational air quality analysis and
forecasting for Europe in the CAMS regional ensemble system (Marécal et
al., 2015) and for China (Brasseur et al., 2019). For the simplicity of the
reading, the EMEP/MSC-W model is hereafter referred to as the EMEP model.
Both models are Eulerian models, but there are differences between these two
models such as the calculation of the planetary boundary layer (PBL) and of
the advection, the vertical resolution. There are also differences, which include the following: the presence of the secondary organic aerosol (included in the EMEP model and
not in LOTOS-EUROS), the PM10 diagnosing particle water explicitly in the
EMEP model and not in LOTOS-EUROS, the calculation of the biogenic
emissions, the description of the gas-phase chemistry, and the treatment of
dust (from agriculture and traffic are included in LOTOS-EUROS and not in
the EMEP model).
The main details about the models and the experiment are provided in the
Table 1, and a more complete description is provided in the following
sections.
Technical description of both models used in the SC
calculation system.
ModelEMEP/MSC-WLOTOS-EUROSModel versionrv4.15 (open-source version September 2017)V2.0 (open-source version 2016)Horizontal resolution0.25∘×0.125∘ long–lat0.25∘×0.125∘ long–latRegional domain30–76∘ N 30∘ W–45∘ E31–68.875∘ N 24∘ W–43.75∘ EPBLCalculation based on turbulent diffusion coefficients (Kz) (EMEP Status Report 1/2003)From ECMWFVertical resolution20 sigma layers up to 100 hPa, with about 10in the planetary boundary layerMixing layer approach with a 25 m surfacelayer; model top at 5 km.Gas phase chemistryEvolution of the “EMEP scheme” (Andersson-Sköld and Simpson, 1999; Simpson et al., 2012)TNO-CBM-IV (Schaap et al., 2009)Nitrate formationOxidation of NO2 by O3 on aerosols(night and winter) N2O5 hydrolysis on aerosol(Simpson et al., 2012)N2O5 hydrolysis on aerosol(Schaap et al., 2004)Sulfate productionSO2 oxidation by O3 and H2O2SO2 oxidation by O3 and H2O2Inorganic aerosolsMARS (Binkowski and Shankar, 1995)ISORROPIA II (Fountoukis and Nenes, 2007)Secondary organic aerosolsEmChem09soa (Bergström et al, 2012)Not included in this model versionWaterPM10 particle water at 50 % relative humidityNot diagnosedAdvectionScheme of Bott (1989)Monotonic advection scheme(Walceck and Aleksic, 1998)Dry deposition/sedimentationResistance approach for gases and for aerosol, including nonstomatal deposition of NH3(EMEP Status Report 1/2003)Resistance approach for gases and for aerosol,including compensation point for NH3(van Zanten et al., 2011; Wichnik Kuit et al., 2012; Zhang et al., 2001)Wet depositionWashout ratiopH-dependent washout ratio accounting forsaturationDustBoundary conditions + windblown dustBoundary conditions + soil, traffic, and agriculture (Schaap et al., 2009)Sea saltMårtensson et al. (2003), Monahan et al. (1986) production accounting for whitecap area fractions (Callaghan et al., 2008)Mårtensson et al. (2003), Monahan et al. (1986)Boundary valuesGlobal C-IFS 00:00 UTCGlobal C-IFS 00:00 UTC, except for sea saltInitial values24 h forecast from the day before24 h forecast from the day beforeAnthropogenic emissionsTNO-MACC-III for 2011TNO-MACC-III for 2011Fire emissionsCAMS product: GFASCAMS product: GFASBiogenic emissionsEmission factors as a function of temperatureand solar radiation (Simpson et al., 2012)Emission factors as a function of temperatureand solar radiation (Schaap et al., 2009)Meteorological driver12:00 UTC operational IFS forecast (previous day's)12:00 UTC operational IFS forecast (previous day's)Description of the EMEP model
The EMEP model is a 3D Eulerian chemistry-transport model described in
detail in Simpson et al. (2012). Initially, the model has been aimed at
European simulations, but the model has also been used over other regions
and at global scale for many years (e.g. Jonson et al., 2010). The EMEP
model version rv4.15 has been used here in the forecast mode. The version
rv4.15 has been described in Simpson et al. (2017) and references cited
therein. The main updates since Simpson et al. (2012), used in this work,
concern a new calculation of aerosol surface area (now based upon the
semi-empirical scheme of Gerber, 1985), revised parameterizations of
N2O5 hydrolysis on aerosols, additional gas–aerosol loss processes
for O3, HNO3, and HO2, a new scheme for ship NOx
emissions, a new calculated natural marine emissions of dimethyl sulfide
(DMS), and the use of a new land cover (used to calculate biogenic VOC
emissions and the dry deposition) (Simpson et al., 2017). This version is
the official EMEP Open Source version that was released in September 2017
(Table 1).
Vertically, the model uses 20 levels defined as sigma coordinates (Simpson
et al., 2012). The PBL is located within approximately the 10 lowest model
levels (∼ five levels below 500 m), and the top of the model
domain is at 100 hPa. The PBL height is calculated based on the turbulent
diffusivity coefficient as described in the EMEP Status Report (2003). The
numerical solution of the advection terms is based upon the scheme of Bott (1989).
The chemical scheme couples the sulfur and nitrogen chemistry with the
photochemistry using about 140 reactions between 70 species
(Andersson-Sköld and Simpson, 1999; Simpson et al., 2012). The chemical
mechanism is based on the “EMEP scheme” described in Simpson et al. (2012)
and references therein.
The biogenic emissions of isoprene and monoterpene are calculated in the
model by emission factors as a function of temperature and solar radiation
(Simpson et al., 2012). The soil-NO emissions of seminatural ecosystems are
specified as a function of the N deposition and temperature (Simpson et al.,
2012). The biogenic DMS emissions are calculated dynamically during the
model calculation and vary with the meteorological conditions (Simpson et
al., 2016).
PM emissions are split into EC, OM (here assumed inert), and the rest of
primary PM defined as the remainder, for both fine and coarse PM. The OM
emissions are further divided into fossil-fuel and wood-burning compounds
for each source sector. As in Bergström et al. (2012), the OM / OC ratios
of emissions by mass are assumed to be 1.3 for fossil-fuel sources and 1.7
for wood-burning sources. The model also calculates windblown dust emissions
from soil erosion. Secondary aerosol consists of inorganic sulfate, nitrate
and ammonium, and SOA; the last of these is generated from both anthropogenic and
biogenic emissions, using the “VBS” scheme detailed in Bergström et al. (2012) and Simpson et al. (2012).
The main loss process for particles is wet-deposition, and the model
calculates in-cloud and subcloud scavenging of gases and particles as
detailed in Simpson et al. (2012). Wet scavenging is treated with simple
scavenging ratios, taking into account in-cloud and subcloud processes.
In the EMEP model, the 3D precipitation is needed. An estimation of this 3D
precipitation can be calculated by EMEP if this parameter is missing in the
meteorological fields as in the data used in this work (see Sect. 2.4).
This estimate is derived from large-scale precipitation and convective
precipitation. The height of the precipitation is derived from the cloud
water. Then, it is defined as the highest altitude above the lowest level,
at which the cloud water is larger than a threshold taken as 1.0×10-7 kg water per kg air. Precipitations are only defined in areas
where surface precipitations occur. The intensity of the precipitation is
assumed constant over all heights where they are nonzero
Gas and particle species are also removed from the atmosphere by dry
deposition. This dry deposition parameterization follows standard
resistance formulations, accounting for diffusion, impaction, interception,
and sedimentation.
Description of LOTOS-EUROS
The LOTOS-EUROS model is an offline Eulerian chemistry-transport model
which simulates air pollution concentrations in the lower troposphere,
solving the advection-diffusion equation on a regular
latitude–longitude grid with variable resolution over Europe (Manders et
al., 2017) (Table 1).
The vertical grid is based on terrain following vertical coordinates and
extends to 5 km above sea level. The model uses a dynamic mixing layer
approach to determine the vertical structure, meaning that the vertical
layers vary in space and time. The layer on top of a 25 m surface layer
follows the mixing layer height, which is obtained from the European Centre
for Medium-Range Weather Forecasts (ECMWF) meteorological input data that are
used to force the model. The horizontal advection of pollutants is
calculated by applying a monotonic advection scheme developed by Walcek and
Aleksic (1998).
Gas-phase chemistry is simulated using the TNO CBM-IV scheme, which is a
condensed version of the original scheme (Whitten et al., 1980). Hydrolysis
of N2O5 is explicitly described following Schaap et al. (2004).
LOTOS-EUROS explicitly accounts for cloud chemistry by computing sulfate
formation as a function of cloud liquid water content and cloud droplet pH
as described in Banzhaf et al. (2012). For aerosol chemistry the
thermodynamic equilibrium module ISORROPIA II is used (Fountoukis and Nenes,
2007).
The biogenic emission routine is based on detailed information on tree
species over Europe (Schaap et al., 2009). The emission algorithm is
described in Schaap et al. (2009) and is very similar to the simultaneously
developed routine by Steinbrecher et al. (2009). Dust emissions from soil
erosion, agricultural activities, and resuspension of particles from traffic
are included following Schaap et al. (2009).
As in the EMEP model, the 3D precipitation is needed, and cloud liquid water
profiles are used to diagnose cloud base height and where below and in-cloud
scavenging takes place. The wet deposition module accounts for droplet
saturation following Banzhaf et al. (2012). Dry deposition fluxes are
calculated using the resistance approach as implemented in the DEPAC
(DEPosition of Acidifying Compounds) module (van Zanten et al., 2011).
Furthermore, a compensation point approach for NH3 is included in the
dry deposition module (Wichink Kruit et al., 2012).
Description of the experiment
The study focuses on the period from 1 to 9 December 2016.
In our system, the forecasts provided by the EMEP model cover a slightly
different regional domain than LOTOS-EUROS (Table 1). To perform properly the
analysis between both models, we have harmonized the use of different
parameters such as the horizontal resolution, the anthropogenic emissions
used, the definition of the city area, and meteorological data used (Table 1).
This harmonization has been revealed as being important for such a comparison and
increases the consistency of the model results. The impact of such choices
is illustrated by the city definitions, for which subjective choices can be
made, causing inconsistencies.
An initial spin-up of 10 d was conducted. Both models provide 4 d
air quality forecasts, and the simulations have been defined as
“forecast-cycling experiments”; i.e. the predicted fields have been used
to initialize successive 4 d forecasts (e.g Morcrette et al., 2009).
The pollution transport in both models is based on forecasted meteorological
fields at 12:00 UTC from the previous day, with a 3 h resolution, calculated by the Integrated Forecasting System (IFS) of ECMWF. These forecasted
meteorological fields correspond to the fields which were used in the online
SC production for these dates. The ECMWF operational system does not archive
3D precipitation forecasts, which is needed by the EMEP model and
LOTOS-EUROS as mentioned in Sect. 2.2 and 2.3. Therefore, a 3D
precipitation estimate is derived from IFS surface variables (large-scale
and convective precipitations) in the EMEP model, and the 3D field is based
on the cloud liquid water profile in LOTOS-EUROS.
The boundary conditions (BCs) at 00:00 UTC of the current day from the
atmospheric composition module (C-IFS) have been used. These BCs are
specified for ozone (O3), carbon monoxide (CO), nitrogen oxides (NO and
NO2), methane (CH4), nitric acid (HNO3), peroxy-acetyl
nitrate (PAN), SO2, ISOP, ethane (C2H6), some VOCs, sea salt,
Saharan dust, and SO4. In LOTOS-EUROS, sea salt BCs have not been used
as these are shown to be overestimated in comparison with the model. In the
EMEP model, the sea salt parameter has been used. This may cause a
difference between both models in the estimation of the contribution from
sea salt especially for the coastal cities.
Both models use the TNO-MACC emission data set for 2011 on 0.25∘×0.125∘ (longitude–latitude) resolution (Kuenen et al.,
2014; see
https://atmosphere.copernicus.eu/sites/default/files/repository/MACCIII_FinalReport.pdf, last access: 30 March 2020)
and the forest fire emissions are from GFASv1.2 inventory (Kaiser et al.,
2012).
Since the study aims to quantify the contributions of long-range transport
in each city to the urban background PM10, the effect of the choice of
the receptor, i.e. the city domain, has been tested. The city receptor has
been defined by three definitions: one grid cell (i.e. 0.25∘ long × 0.125∘ lat, corresponding to the emissions data set
resolution), nine grid cells, and all of the grid cells covering the
administrative area provided by the database of Global Administrative Areas
(GADM; https://gadm.org/data.html, last access: 27 March 2020). The last definition is the most precise
definition in terms of build-up area; however it may represent a large
region for a definition of a city as shown in Fig. S1 (e.g. London, Nicosia,
Riga, and Sofia). It is important to explain that this study does not aim to
quantify the contribution to PM10 at a street scale as done in
Kiesewetter et al. (2015) but over the full area defining the cities. The
relatively coarse definition of the cities is comparable to the definition
used in previous studies as in Thunis et al. (2016), which used an area of 35km×35km or in Skyllakou et al. (2014), which used a radius of 50 km from the city centre.
For the contribution, we also have harmonized the definition of the natural
contributions. The natural contributions are defined in this study as the
sum of the contributions from sea salt, dust, and forest fires, except for
the BCs. In LOTOS-EUROS, the natural sources (e.g. dust) coming from the
boundaries are classified as BCs and not natural.
Evaluation of the predicted surface concentrations during the episode
During December 2016, a PM episode of medium intensity (no more than 3
consecutive days beyond the WHO PM10 threshold) developed across
north-western Europe. As a consequence of a high pressure system over
central Europe pollutant concentrations were built up over western Europe
(see
http://policy.atmosphere.copernicus.eu/reports/CAMSReportDec2016-episode.pdf,
last access: 24 January 2020).
From 1 to 2 December, high concentrations were measured and
predicted over Paris (Figs. 1 and 2). In Fig. 2, we can also see from
3 to 8 December that levels of PM10 were
elevated in western Europe. Especially on 6 and 7 December,
concentrations at some measurement stations in France, Belgium, the
Netherlands, Germany, and Poland exceeded the daily limit value of 50 µg m-3 (e.g Fig. S2 – see Sect. 3.2 for more details about the observations).
Daily surface PM10 concentration, in micrograms per cubic metre, over Europe predicted by the EMEP model from 1 to 9 December 2016. The coloured dots correspond to the daily mean of AirBase stations (rural and urban stations).
During the following days relatively stable conditions with slow southerly
winds characterized the episode until fronts moved in western Europe on
9 December. Large concentrations (> 60 µg m-3) were also
predicted between 6 and 9 December over the Po Valley and over
UK on 6 December (Figs. 2 and S2).
Statistical metrics used
To properly estimate the quality of these forecasts, five statistical
parameters have been used, including the Pearson correlation (r), the mean
bias (MB), the normalized mean bias (NMB), the root-mean-square error (RMSE),
and the fractional gross error (FGE). The ideal score of these parameters is
0, except for the correlation, which is 1.
The MB provides information about the absolute bias of the model, with
negative values indicating underestimation and positive values indicating
overestimation by the model. The NMB represents the model bias relative to
the reference. The RMSE considers error compensation due to opposite sign
differences and encapsulates the average error produced by the model. The
FGE is a measure of model error, ranging between 0 and 2, and behaves
symmetrically with respect to under- and overestimation, without over
emphasizing outliers.
We have used M and R as notation to refer, respectively, to model and the
reference data (e.g. observations), and N is the size of the reference
data set (e.g. number of observations).
Thus, MB is calculated by Eq. (1) and expressed in micrograms per cubic metre as follows:
MB=∑i=1N(Mi-Ri)N.
NMB is calculated by Eq. (2):
NMB=∑i=1N(Mi-Ri)∑i=1NRi×100%.
RMSE is calculated by Eq. (3) and expressed in micrograms per cubic metre as follows:
RMSE=∑i=1NMi-Ri2N,
and FGE is calculated by Eq. (4) and is dimensionless,
FGE=2N∑i=1N|Mi-Ri||Mi+Ri|.
Comparison with observationsMethodology
In order to evaluate the reliability of the predictions over each city, the
modelled hourly PM10 concentrations have been compared with the AirBase
data (see https://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-8#tab-data-by-country, last access: 27 March 2020). The traffic
stations were not included in the comparison since a regional model with a
somewhat-coarse resolution will not be able to calculate very large
concentrations (e.g. hourly concentration higher than 200 µg m-3),
which may be measured by these stations. Indeed, the concentrations
calculated by a regional model over cities are mostly representative of the
urban background. By knowing this point, we can state that a comparison
with the observations presenting for example a correlation coefficient equal
to 0.5 or NMB lower than 15 % is a reasonable result (r≥0.7 and NMB ≤ 10 % are good results). The observations have also been categorized
into two sets of data by differentiating between the rural stations and the urban
stations (as shown in Fig. S2). This follows the procedure done in the
yearly evaluation of the EMEP model over Europe (e.g. EMEP Status Report, 2018). Due to the relatively coarse definition of a city, it appears that
stations classified as rural may be present in our city domain.
Scatterplots between the hourly PM10 concentrations, in micrograms per cubic metre, over all of the studied cities using the nine grid cells definition, predicted by the EMEP model on 6 December 2016 and the observations of the urban sites (blue dot) and rural sites (red square). For this case, there are 19 cities which have urban stations in their domain and five cities which have rural stations in their domain. The observations are collocated in time with the EMEP predictions and then averaged within the city edge to match the studied grid. The four panels correspond to the different predictions from 3 d before the 6 December to the actual day, i.e. 6 December. The correlation coefficient (r), the mean bias (MB), the normalized mean bias (NMB), the root-mean-square error (RMSE), and the fractional gross error (FGE) are provided on each panel. The blue and the red lines represent the linear fits.
As Fig. 3 for LOTOS-EUROS.
This was noticed for the smaller definition of the city edges, i.e. one grid cell there were no rural stations within the city domain. Obviously,
by increasing the size of the city domain, to nine grid cells or by using the
GADM definition, the number of rural stations present within the city domain
increases. Indeed, all of the hourly measurements are averaged within the city
boundary, by separating the urban and the rural stations. A comparison with
these two types of stations can highlight a difference between the urban
background and the urban concentrations. For such a comparison, the model
concentrations are also averaged over the city domain.
Results
Figures 3 and 4 show the comparison between the hourly averaged observations
within the city edges defined by the nine grid cells definition and the
predictions from EMEP and from LOTOS-EUROS, respectively.
Figures 3 and 4 show that for the urban stations, the different predictions
from the same model, for the same date, are consistent since the values for
the statistical parameters are relatively constant. It is noticed, however,
that the bias is slightly reduced when the starting date of the forecast is
closer to the target date. The available observations and thus the stations
may also differ from day to day (e.g. Fig. S2a). Figures 3 and 4 also show
that despite many differences, the models have very similar performances in
comparison with the urban stations.
In Fig. 3, it is also clear that the EMEP model has difficulties
reproducing the highest concentrations measured by the urban stations, which
are probably smoothed by the model over the large grid cells, as are the ones
defining the cities. The underestimation of the largest urban concentrations
is highlighted by the comparison with the rural stations. This also shows
that over the area defining the cities there is a large variability in the
measured PM10 concentrations and that few stations are not necessarily
representative of the model grids. It also shows with such a resolution, the
model represents urban background concentrations.
Only five cities have measurements defined as rural stations by using the nine
grids definition (i.e. Amsterdam, Berlin, Luxembourg, Rotterdam, and Vienna)
while there are up to 19 cities with urban stations. By comparing only the five
cities having urban and rural stations, the agreement between EMEP and the
urban stations is largely improved as shown in Fig. S3. We also notice
that the difference in concentrations predicted by the EMEP model between
both types of stations is also reduced. This shows that for these five
cities, the predicted PM10 concentrations on 6 December are
higher than over the other cities.
LOTOS-EUROS is less correlated with the concentrations measured by the rural
stations than EMEP (Fig. 4). However, like EMEP, LOTOS-EUROS also presents a
lower bias for these rural stations in comparison with the urban stations.
This is predictable since with such a resolution, the model calculates mainly
the urban background concentrations. By comparing the five cities having urban
and rural stations, as done with EMEP, only the bias and the FGE between the
predictions and the urban measurements are improved (Fig. S4). It is also
worth noting that the concentrations predicted by LOTOS-EUROS over these five
cities are lower than the ones calculated by the EMEP model (in Fig. S3).
By using the GADM definition, the number of cities having rural stations
decreases to two, while the number of cities with the urban stations remains
identical.
In general, both models present similar performances relative to the observations
especially for the NMB, RMSE, and FGE as presented in Figs. S5 and S6.
These figures show an overview of the statistical parameters for all 4 d
forecasts, i.e. the dates from 1 to 12 December 2016 with a
starting date from 1 to 9 December, for all of the cities
defined by nine grid cells, in comparison with the concentrations measured at
the urban and the rural stations, respectively.
As already shown by Figs. 3 and 4, LOTOS-EUROS shows slightly better
correlation coefficients for the urban stations than EMEP (Fig. S5; on
average RLOTOS-EUROS=0.31 and REMEP=0.25, with a maximum of 0.58 for LOTOS-EUROS and 0.5 for EMEP) and EMEP presents better correlations with the few rural stations (Fig. S6; on average RLOTOS-EUROS=0.23 and
REMEP=0.35, with a maximum of 0.58 for LOTOS-EUROS and 0.72 for
EMEP). However, the limited number of cities having rural stations explain
the larger variability in the correlations compared to the correlations
found with the urban stations. Similar results are found by using the GADM
definition (not shown), while by using only one grid to define the city edges,
the correlation coefficients with the urban stations are larger (up to 0.8),
with an increase in the bias and a decrease in the RMSE (Fig. S7).
On average, both models have a FGE equal to 0.5 over the cities defined by nine grid cells with the urban stations and 0.4 with the rural stations. For the
RMSE, it is 33 µg m-3 with the urban stations and 11 µg m-3 with the rural stations. While both models underestimate the
PM10 concentrations by 36 % on average by using the urban sites, EMEP
overestimates by 6 % with the rural stations, and LOTOS-EUROS
underestimates this by 6 %.
Performances of both models are improved with daily means, especially with
better correlation coefficients (not shown). For example, with the cities
defined by nine grid cells, the correlation coefficients reach 0.8 with the
urban stations for EMEP and LOTOS-EUROS and 0.98 with the rural stations for
EMEP. However, a lot of negative correlation coefficients between
LOTOS-EUROS and the rural stations are noticed. The correlation coefficient
with the rural stations remains difficult to interpret, related to the
limited number of stations available. Thus, EMEP presents a mean correlation
coefficient equal to 0.4 for the urban and rural stations, and LOTOS-EUROS
has a mean correlation of 0.5 with the urban stations and only 0.06 with the
rural stations. Better scores with the FGE and the RMSE are also noticed in
comparison to the hourly evaluation (not shown). Both models present, with
the nine grid cells definition, a mean FGE of 0.5 with the urban stations and 0.3
for the rural stations and a mean RMSE of 21 µg m-3 with the
urban stations and 10 µg m-3 with the rural stations.
Intercomparison in the concentrations predicted by both models
The second analysis has been focused on the agreement between both models.
During the episode, all 4 d forecasts present a high correlation between the
PM10 predicted by the EMEP model and LOTOS-EUROS as shown in Fig. 5a.
These correlations vary from day to day and city by city but remain large
for the different simulated periods (median = 0.7).
There is no clear geographical pattern in terms of performance between the
two models, even if the central European cities (e.g. Budapest, Vienna, and
Warsaw) presented the larger differences (Fig. 5b). These differences may be
explained by not only by slightly lower secondary inorganic aerosols (SIA =NO3-+NH4++SO42-) in LOTOS-EUROS for
these cities but also the lack of water in LOTOS-EUROS (which is not
diagnosed as mentioned in Sect. 2). Moreover, it confirms the larger
PM10 concentrations predicted by EMEP than by LOTOS-EUROS for the five
cities plotted in Figs. S3 and S4. It is also worth noting that LOTOS-EUROS
predicts more sea salt and dust for almost all of the cities during the studied
period (Fig. S8), which is representative of the overall feature over the
regional domain (not shown). Actually, it was noticed that for the predicted
PM10 with the larger positive NMB (EMEP predicting larger PM10
concentrations), EMEP has more SIA and ”other” than LOTOS-EUROS (Fig. S9a), while the PM10 from LOTOS-EUROS is dominated by natural
components when a larger negative NMB is predicted (Fig. S9b).
(a) Correlation coefficient and (b) bias in the predicted
PM10 concentrations between the EMEP model and LOTOS-EUROS over all of the
studied cities using the nine grid cells definition for each 4 d forecast
(1–4, 2–5, 3–6, 4–7, 5–8, 6–9, 7–10, 8–11, and 9–12 December 2016).
Methodology of the source contribution calculationThe EMEP modelEmission reductions
The SC calculation follows the methodology used in each EMEP annual report
to quantify the annual country-to-country source–receptor relationships
(e.g. EMEP Status Report, 2018). The experiment is based on a reference
run, where all of the anthropogenic emissions are included. The other runs are
the perturbation runs. These runs correspond to the simulations where the
emissions from every considered country are reduced by 15 %. As explained
in Wind et al. (2004), a reduction of 15 % is sufficient to give a clear
signal in the pollution changes. It also causes a negligible effect from
nonlinearity in the chemistry even if in this work it has been estimated.
The perturbation runs are done for anthropogenic emissions of CO, SOx,
NOx, NH3, non-methane volatile organic compounds (NMVOCs), and PPM (primary particulate matter). For
computational efficiency, in the perturbation calculations, all
anthropogenic emissions in the perturbation runs have been reduced here
simultaneously. This simultaneous reduction differs from the methodology
used in each EMEP annual report where the emissions are reduced
individually.
There are in total 31 runs for each date with reduced anthropogenic
emissions. Each run corresponds to the perturbations for one of the 28 countries related to the 28 EU capitals, plus Iceland, Norway and
Switzerland, giving the contribution for each country.
To calculate the concentration of the pollutant integrated over the studied
area, i.e. a selected city, coming from a source, we follow the Eq. (5):
Csource=Creference-Cperturbationx,
where x is the reduction in percent (i.e. 0.15), Creference is the
concentration of the pollutant integrated over the studied area from the
reference run, and Cperturbation is the concentration of the pollutant
integrated over the studied area from the perturbation run. Thus, by
differentiating over the studied area, the concentration from the
perturbed run with the concentration provided by the reference run, we
have an estimation of the influence of the source (i.e. country). By scaling
with the reduction used (parameter x), it gives the estimated concentration
related to the source.
Issue concerning the chemical nonlinearity
The reason why emissions should not be perturbed by 100 % in the model
simulations is to stay within the linear regime of involved chemistry. Even
limited, such a methodology may still introduce a nonlinearity in the
chemistry. The total PM10 over the receptor should be identical
theoretically to the sum of the PM10 originated from the different
sources. This is not always the case, and the difference between the total
PM10 and the sum from the various sources may lead to negative or
positive concentrations. This is a result of the perturbation used, which is
assumed to be linear for a 100 % perturbation.
The 15 % emission reduction has been used for many years for the annual country-to-country source–receptor relationships calculations (e.g. EMEP
Status Report, 2018). Clappier et al. (2017a) have already shown the
robustness of the methodology at the country scale on yearly averages and
for the highest daily concentrations. However, this emission reduction was
not used for smaller areas. Thus this 15 % emission reduction for the
study over a city and on hourly basis has been tested, in order to assess
the robustness of the calculations. The values 5 % and 50 % were the other selected
emission reductions. In total, 847 4 d runs have been performed in this
work (nine reference runs and nine dates × 31 countries × 3
perturbations runs).
Furthermore, by reducing the emissions simultaneously or separately may lead
to a different result in the concentrations, but as mentioned previously,
this effect is not addressed in this work for computational reasons.
LOTOS-EUROS
A labelling technique has been developed within each LOTOS-EUROS simulation
(Kranenburg et al., 2013). An important advantage of the labelling technique
is the reduction in computation costs and analysis work associated with the
calculations. The source apportionment technique has been previously used to
investigate the origin of PM (Hendriks et al., 2013, 2016), NO2 (Schaap
et al., 2013), and nitrogen deposition (Schaap et al., 2018).
Besides the concentrations of all species, the contributions of a number of
sources to all components are calculated. The labelling routine is only
implemented for primary, inert aerosol tracers and chemically active tracers
containing a C, N (reduced and oxidized), or S atom, as these are conserved
and traceable. This technique is therefore not suitable to investigate the
origin of e.g. O3 and H2O2, as they do not contain a
traceable atom. The source apportionment module for LOTOS-EUROS provides a
source attribution valid for current atmospheric conditions as all chemical
conversions occur under the same oxidant levels. For details and validation
of this source apportionment module we refer to Kranenburg et al. (2013).
To avoid violating the memory size and to avoid excessive computation times it
was chosen to trace the 28 EU countries, supplemented by Norway and
Switzerland. For convenience, a number of small countries were combined with
a neighbouring country. For example, Switzerland and Liechtenstein and
Luxembourg and Belgium were combined. In addition, all sea areas were
combined into one source area. To be mass consistent, all non-specified
regions, natural emissions, and the combined impact of initial
conditions and boundary conditions were given labels as well.
Mean composition of (a) Domestic, (b) 30 European countries, and (c) Others PM10, split into a negative concentration (left
panel) and a positive concentration (right panel), calculated by the EMEP
country SC over the 34 European cities and for each 4 d forecast. The
PM10 composition is highlighted with the colour code. The results for
the three city definitions (one grid, nine grids, and GADM) and for the percentage of
reduction used in the perturbation EMEP runs (5 %, 15 %, and 50 %) are
shown. The Domestic contribution corresponds to the contribution from
the domestic country to the city (e.g. from France to Paris). The label 30 European
countries corresponds to the other 30 European countries used in the
study. Others contains natural sources, the other countries included in
the regional domain, boundary conditions, ship traffic, biogenic
sources, aircraft emissions, and lightning. The red dot represents the
mean PM10 concentration.
Information provided by the source contribution calculationsIn the EMEP calculations
As presented in Fig. 1, the country contributions to the predicted PM10
concentrations in the cities is provided in our products.
Figure 6 presents the mean composition for the “Domestic”, “30 European”
countries, and “Others” PM10 contributions for all cities, for all
4 d predictions, and split into negative and positive concentrations. This
figure is a result of the perturbation runs by separating the positive and
the negative concentrations obtained in the calculations. The concentrations
have also been gathered by their calculated origin. The Domestic
contribution corresponds to the contribution from the domestic country to
the city (for example from France to Paris). The 30 European countries
corresponds to the other 30 European countries used in the study. Others
contains mainly natural sources, the other European countries included in the
regional domain (and not included in our SC calculations, e.g. Turkey), and
the boundary conditions. This figure gives a graphical illustration of the
composition of the different contributions and presents the effect of the
nonlinearity. Indeed, the positive concentrations show the overall
composition for each contribution, while the chemical reason of the
nonlinearity is highlighted by the negative contribution to the predicted
PM10 concentrations.
The main contributors to the Domestic PM10 are POM (∼ 20 %) and rest PPM (∼ 30 %) (which corresponds to the
remainder of coarse and fine PPM), as noticed for the positive
concentrations (Fig. 6a). Actually, the variation in the mean concentrations
is mainly influenced by the variation in these primary components.
NO3- is also an important component of the Domestic
PM10. The value of the mean concentration depends on the city
definition ,and so on the average of the concentrations over different size
of city. The mean PM10 concentration over a smaller area is larger,
showing that with a smaller grid, the PM10 is less diffused over the
integrated area. The 30 European countries PM10 is mainly
influenced by NO3- (by 38 %) (Fig. 6b).
Overall, 45 % of the contributions to the PM10 calculated over the
selected cities for this episode are Domestic and essentially due to
primary components. 35 % are from the 30 European countries,
essentially NO3-, and 25 % are from Others, mainly composed of
natural sources (representing 50 % of Others). Obviously, this feature
is an overview of all selected cities for all of the studied dates and it can
vary from city to city and from date to date.
By comparing the PM10 concentrations calculated over the same city
edges but by using different percentages in the perturbation runs, we have
calculated the impact of the nonlinearity for each contribution and
presented this in Fig. 7. This nonlinearity has been calculated for each
hourly concentration as the standard deviation of the hourly contribution
(which can be positive or negative) obtained by the three reduced emissions
scenarios and weighted by the hourly total concentration by following the
Eq. (6):
NONLINContrib=∑i=1nCcontribi-Ccontrib‾2nCtot×100%,
where n corresponds to the number of perturbations used (n=3), Ccontrib is the
hourly PM10 concentration for a specific contribution (Domestic,
30 European countries, or Others), and Ctot is the hourly PM10
concentration. This mean nonlinearity due to the Domestic contribution
represents a maximum of 0.9 % of the total PM10. This nonlinearity
from the 30 European countries contribution counts for 0.7 % of the
total PM10 and 1.5 % from Others. Actually, the nonlinearity
from the Others depends on the nonlinearity from the two other
contributions. The mean nonlinearity is not homogenously distributed over
all cities as shown in Fig. S10 and may vary from date to date (not
shown). It has remained limited even if some hourly contributions show
higher nonlinearity. At the maximum, 3 % of the calculated hourly
contributions for all 4 d forecasts over the selected cities have a
nonlinearity higher than 5 % (not shown). This shows that due to the
methodology used in the EMEP model, based on a reduced emission scenario,
the nonlinearity in the chemistry has a limited impact on the SC
calculation. This nonlinearity is slightly reduced by using the larger
domains to define the cities (e.g. nine grids) (Fig. 7). This also shows that
the responses to perturbation runs are robust, even if only the
nonlinearity in the chemistry related to the perturbation used and not the
one related to the reduction in each emission precursor has been estimated
in this study as mentioned in Sect. 4.1.
The black horizontal bars show the mean nonlinearity calculated
for each contribution presented in Fig. 6 and for the three city
definitions. The nonlinearity is calculated for each hourly concentration
as the standard deviation of the hourly contribution weighted by the hourly
total concentration.
Negligible negative contributions have been calculated for the Domestic
and 30 European countries contributions (Fig. 6a and b), and small
negative contributions are predicted in Others (Fig. 6c). These negative
PM10 are a result of negative values in NO3-, NH4+,
and H2O, which are a consequence of gas–aerosol partitioning of the
species. Indeed, NH3 reacts with nitric acid (HNO3) to form
ammonium nitrate (NH4NO3). This is an equilibrium reaction and
thus the transition from solid to gaseous phase depends on relative humidity
(e.g. Fagerli and Ass, 2008; Pakkanen, 1996). This shows that, for example,
a reduction in NOx over a country, which impacts the selected city, does
not necessarily only impact the NO3- over this city but may also
have an effect on NH3 chemistry over a second region. This second
region may also have itself an impact on the selected city. This combination
of NOx and NH3 chemistry from different regions may lead at the end to these negative concentrations.
The impacts of the percentage used in the perturbation runs and the size of
the city edges have no significant impact on the amount of negative
Others PM10 concentrations. The impact of both parameters is more
visible on the Domestic and 30 European countries concentrations but it
remains very small.
Averaging out over the larger grids reduces globally the nonlinearity. The
15 % emission reduction also reduces the negative nonlinearity in the
Domestic concentrations (e.g. H2O for the nine-grid and GADM runs).
Mean composition of (a) Domestic, 30 (b) European countries, and (c) Others PM10 calculated by the LOTOS-EUROS (L-E) country SC over the 34 European cities and for each 4 d forecast. The result from the EMEP country SC, by using a 15 % perturbation run, has also been added for comparison. The PM10 composition is highlighted with the colour code. Rest corresponds to the difference between the PM10 and the sum of the components listed on the plot. The results for the three city definitions (one grid, nine grids, and GADM) are shown. The Domestic contribution corresponds to the contribution from the domestic country to the city (e.g. from France to Paris). The label 30 European countries corresponds to the other 30 European countries used in the study. Others in the LOTOS-EUROS country SC is slightly different from the EMEP Others. Others in the LOTOS-EUROS country SC contains natural sources, other countries included in the regional domain, boundary conditions, dust emitted by road traffic and agriculture, ship traffic, the aircraft emissions, and lightning.
In the LOTOS-EUROS calculations
As presented with the EMEP predictions, Figure 8 presents the mean
composition for the Domestic, 30 European countries, and Others
PM10 contributions for all cities, for all 4 d predictions provided by
LOTOS-EUROS. The definition of Others is slightly different from the
EMEP one since, for example, the dust from agriculture and traffic is included (see
Sect. 2). For an easier comparison, the result for the EMEP model using the
15 % emission reduction has also been plotted with thinner charts, even
if, as just mentioned, the definition of Others slightly differs between
both models.
First of all, during the episode, LOTOS-EUROS confirms the general trend
calculated by the EMEP model, i.e. the dominant contribution to the surface
PM10 is Domestic, ranging between 40 % and 48 % of the
predicted PM10 over all selected cities and for all of the studied dates.
However, LOTOS-EUROS always presents more Domestic PM10 than the
EMEP model. LOTOS-EUROS also predicted slightly more influence from
Others than the 30 European countries, with ratios close to
25 %–30 % each. As a reminder, the EMEP model predicted a slightly larger
influence from the 30 European countries (35 %) than from Others
(25 %).
As with the EMEP model, the mean PM10 concentration over the smaller
city definition is larger, and the Domestic PM10 is largely driven
by POM. In the list of LOTOS-EUROS PM10 components there is one named
“Rest”. Rest corresponds to the difference between the total PM10
and the sum of all of the components, and Fig. 8 shows that it is also a large
component of this Domestic PM10. POM and Rest each represent
between 25 % and 30 % of the Domestic PM10.
The large influence of NO3- (48 %) on the 30 European
countries PM10 is also calculated by LOTOS-EUROS, as well as the
large contribution of the natural components (60 %) in Others. It is
noteworthy to see that, even being small, the dust emitted by the road traffic and
the agriculture is not negligible in Others PM10
(∼ 10 %).
Agreement on the determination of the dominant country contributor
for PM10, SO4, NO3, NH4, EC, and POM in percent,
determined over all of the studied cities using the nine grid cells definition and
for all 4 d forecasts. The line that divides the box into two parts
represents the median of the data. The ends of the box show the upper and
lower quartiles. The extreme lines show the highest and lowest values
excluding outliers, which are represented by grey diamonds. The red dots
correspond to the mean of each data set.
Comparison between both country source contribution calculations
Section 3 has highlighted the similar performance of both models in the
prediction of the PM10 concentrations over the European cities with
observations. It has also been shown in Sect. 3 that both models are
representative for a large area, and the predictions can underestimate the
concentrations and the contributions for the larger concentrations measured
by a specific station. Section 5 has shown similar results in terms of
composition of these PM10. It is also noteworthy to see in Fig. 9
that both SC calculations present a high rate of agreement over the selected
period with the common simulated components and the PM10 calculated by
both models. This rate corresponds to the number of occurrences in the
dominant contributor calculated for each hourly concentration in the 4 d
forecast over each city. So, a value of 100 % over a city shows that both
models predict the same dominant country contributor during a 4 d
forecast. In Fig. 9, both models show that, by using the nine grid cells
definition, on average 68 % of the hourly predicted PM10
concentrations have the same dominant country contributor. On average,
50 % of the secondary inorganic aerosols predicted by both models over all of the cities and all 4 d forecasts have the same main contributor. This
value goes up to 70 % for POM and 80 % for EC. For the two primary
components (POM and EC) the median is larger, with values of 77 % and
93 %, respectively, showing that the mean value in the agreement for both
compounds is reduced by a few low values (Fig. 9). On a daily basis, the
mean agreement is slightly improved, e.g. 70 % agreement for the
PM10 (Fig. S11). The main improvement is calculated for EC, with a
median equal to 100 % (Fig. S11).
The lower agreement for the SIA is predictable due to the various origins
(chemistry and primary emissions) for these particulates and the different
aerosols treatment (gas–aerosol partitioning) in both models. It is also
related to the differences in both methodologies (e.g. Clappier et al.,
2017b). Indeed, an emission reduction and a labelling technique will not
necessarily provide the same results for the secondary PM. An emission
reduction depends on the atmospheric composition already present. For
example, an amount of NOx emitted over a source can result in a certain NH4NO3 concentration in the receptor. If this NOx is emitted in excess (NH3-limited regime), a NOx emission reduction will have a small effect at the receptor point. On the other hand, in the NOx-limited regime, the same NOx reduction will have a large impact. The labelling method will give the same result in both cases, while
the scenario approach will give different results.
This agreement varies from city to city (Fig. 10), but it has been shown, in
addition to the example of PM10 (Fig. 5), that central European
cities often present a limited agreement due to their central location and
the influence of various countries. This limited agreement is also sometimes
observable for the cities close to the edge of the regional domain (Fig. 10), which could be explained by the influence of the boundary conditions such as
the dust transported from other regions (e.g. Valetta influenced by dust
from Sahara).
Agreement on the determination of the dominant country
contributor for PM10 in percent, and for each 4 d forecast (1–4, 2–5, 3–6, 4–7, 5–8, 6–9, 7–10, 8–11, and 9–12 December 2016) over all of the
cities using the nine grid cells definition.
The mean agreement increases to up to 75 % for the determination of the top five main country contributors to PM10 (Fig. 11). In that case, the rate
is calculated for the five main country contributors. A score of 100 %
means both models predict the same five main country contributors for each
hourly concentration but not necessarily in the same order. This rate is
around 70 % for SO42-, EC, and POM, close to 60 % for
NO3-, and equal to 65 % for NH4+ (Fig. 11). As for the
dominant country contributor, the agreement is slightly improved by using
daily means; e.g. we found 76 % agreement with the PM10 (not
shown).
It is also important to notice that these overall agreements are
significantly influenced by neither the definition of the area of the cities nor the
perturbation percentage tested for the EMEP SC calculations (Fig. S12). The
agreement becomes slightly better by using the smaller area (1 grid) in the
determination of the dominant country contributor and by
using a large domain (nine grids or GADM) in the determination of the two and five
main contributors.
Overall, a perturbation run using a reduction of 15 % and the use of a
larger city area (e.g. GADM or nine grids) allow a better determination of the
country contributors, with a better agreement with LOTOS-EUROS, and limit the
impact of the nonlinearity in the chemistry.
Agreement on the determination of the five main country
contributors for PM10, SO4, NO3, NH4, EC, and POM in
percent, determined over all of the studied cities using the nine grid cells
definition and for all 4 d forecasts. The line that divides the box into
two parts represents the median of the data. The ends of the box show the
upper and lower quartiles. The extreme lines show the highest and lowest
values excluding outliers, which are represented by grey diamonds. The red
dots correspond to the mean of each data set.
Conclusions
By focusing on a specific event, occurring from 1 to 9 December 2016 over Europe, this work is the first attempt to evaluate the
source contribution calculations provided by two regional models (EMEP and
LOTOS-EUROS) in a forecast mode. Together, the models compose the
operational source contribution prediction system for the European cities
within the Copernicus Atmosphere Monitoring Service (CAMS) and aim to
estimate the impact of the long-range transport to urban PM10. These
models also use two distinct source apportionment methodologies, a labelling
technique for LOTOS-EUROS and the use of perturbation runs for EMEP.
The methodology used for the EMEP model was tested by using three different
percentages (5 %, 15 %, and 50 %) in the perturbation runs. The
importance of the choice of the domain-defining the edges of the studied
cities was also investigated in terms of predicted concentrations and
calculated contributors. It was concluded that the 15 % emission reduction
and the use of large city areas (nine grids or GADM) were the more efficient.
It reduces the impact of nonlinearity, which especially impacts the
NO3-, NH4+, and H2O concentrations, and it presents
a better agreement on the determination of the main country contributors. The
mean nonlinearity always represents less than 2 % of the total modelled
PM10 for each contribution calculated by the EMEP SC and is
caused by the perturbation used, which is assumed to be linear for a 100 %
perturbation. Even if this nonlinearity is not identical for all cities and
for the different dates, the larger nonlinearities (> 5 %)
impact only 3 % of all of the calculated hourly contributions. However, the
nonlinearity related to the reduction in each emission precursor has not
been calculated in the study for computational reasons.
The predicted PM10 concentrations were compared with AirBase
observations, showing fair agreement even if the models remain perfectible
since they have difficulties reproducing the highest hourly concentrations
measured by the urban stations (mean underestimation of 36 %). It may
suggest that both models, which calculate the country contributions over the
cities, defined by a large area, may underestimate the contribution measured
by a specific station for the higher concentrations. It was also noticed that the
bias is slightly reduced when the forecast is closer to the studied date. An
intercomparison between both models was also performed showing satisfactory
results with few discrepancies in the predictions of the PM10
concentrations, mainly explained by an underestimation of sea salt and dust
by the EMEP model (compared to LOTOS-EUROS) and differences in SIA, caused
by different chemical aerosols treatment in both models.
During the episode, both models have shown that 45 % of the predicted
PM10 over the selected cities were from Domestic sources and
essentially composed of primary components. The rest of the contribution was
roughly equitably split into an influence from the other 30 European countries used
in the regional domain, essentially composed of NO3-, and an influence from
Others, mainly composed of natural sources.
We have shown that results from both source apportionment methodologies
agree on average by 68 % in the determination of the dominant country
contributor to the hourly PM10 concentrations and by 75 % for the top five
of these country contributors. Calculating the country attribution on a
daily-mean basis has similar agreement. Where there are differences, these
are mainly found in the country attribution of the secondary inorganic
component of the aerosol. These differences derive from a combination of the
different treatment of these secondary components and the different method used
to attribute country contributions between the models being compared.
A full year of evaluation will be necessary to confirm our satisfactory
results. Moreover, the bias of the predicted PM10 concentrations for
the urban observations probably suggests an underestimation of the local
background contribution (from the city), which is also predicted by the EMEP
model. This is investigated in a companion paper (Pommier et al., 2020), also
focusing on the same event.
Code and data availability
The EMEP model is an open-source model available on
10.5281/zenodo.3355041 (EMEP MSC-W, 2017). The base code of LOTOS-EUROS is
available under the license on https://lotos-euros.tno.nl/ (last access: 27 March 2020), but the code
used for this study, including the source apportionment, is only available in
cooperation with TNO. The data processing and analysis scripts are available
upon request.
The supplement related to this article is available online at: https://doi.org/10.5194/gmd-13-1787-2020-supplement.
Author contributions
MP, HF, and MiS designed the research. MP performed the experiment. MP
developed the analysing codes and analysed the data. AV developed the EMEP
part of the forecasting system. RK and MaS performed and provided the
LOTOS-EUROS results. MP wrote the paper with the inputs from all coauthors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work is partly funded by the EU Copernicus project CAMS 71 to provide
policy support. This work has also received support from the Research
Council of Norway (Programme for Supercomputing) through the EMEP project
(NN2890K) for CPU and the NorStore project “European Monitoring and
Evaluation Programme” (NS9005K) for storage. The EMEP project itself is
supported by the Convention on Long-range Transboundary Air
Pollution (LRTAP), under UNECE. The authors thank A. Mortier (Norwegian
Meteorological Institute) for the development and the design of the website
(https://policy.atmosphere.copernicus.eu/SourceContribution.php, last access: 27 March 2020).
Financial support
This research has been supported by the Research Council of Norway (grant no. NN2890K) and the NorStore project “European Monitoring and Evaluation Programme” (grant no. NS9005K).
Review statement
This paper was edited by Graham Mann and reviewed by Richard Pope and one anonymous referee.
References
Amann, M., Bertok, I., Borken-Kleefeld, J., Cofala, J., Heyes, C.,
Höglund-Isaksson, L., Klimont, Z., Nguyen, B., Posch, M., Rafaj, P.,
Sandler, R., Schöpp, W., Wagner, F., and Winiwarter, W.: Cost-effective
Control of Air Quality and Greenhouse Gases in Europe: Modeling and Policy
Applications, Environ. Model. Softw., 26 ,1489–1501, 2011.Andersson-Sköld, Y. and Simpson, D.: Comparison of the chemical schemes
of the EMEP MSC-W and the IVL photochemical trajectory models, Atmos. Environ., 33, 1111–1129, 10.1016/S1352-2310(98)00296-9, 1999.Banzhaf, S., Schaap, M., Kerschbaumer, A., Reimer, E., Stern, R., van der
Swaluw, E., and Builtjes, P.: Implementation and evaluation of pH-dependent
cloud chemistry and wet deposition in the chemical transport model
REM-Calgrid. Atmos. Environ., 49, 378–390, 10.1016/j.atmosenv.2011.10.069,
2012.Bergström, R., Denier van der Gon, H. A. C., Prévôt, A. S. H., Yttri, K. E., and Simpson, D.: Modelling of organic aerosols over Europe (2002–2007) using a volatility basis set (VBS) framework: application of different assumptions regarding the formation of secondary organic aerosol, Atmos. Chem. Phys., 12, 8499–8527, 10.5194/acp-12-8499-2012, 2012.Binkowski, F. S. and Shankar, U.: The Regional Particulate Matter Model 1.
Model description and preliminary results, J. Geophys.Res., 100,
26191–26209, 10.1029/95JD02093, 1995.Bott, A.: A positive definite advection scheme obtained by nonlinear
renormalization of the advective fluxes, Mon. Weather Rev., 117,
1006–1016, 10.1175/1520-0493(1989)117(1006:APDASO)2.0.CO;2, 1989.Brasseur, G. P., Xie, Y., Petersen, A. K., Bouarar, I., Flemming, J., Gauss, M., Jiang, F., Kouznetsov, R., Kranenburg, R., Mijling, B., Peuch, V.-H., Pommier, M., Segers, A., Sofiev, M., Timmermans, R., van der A, R., Walters, S., Xu, J., and Zhou, G.: Ensemble forecasts of air quality in eastern China – Part 1: Model description and implementation of the MarcoPolo–Panda prediction system, version 1, Geosci. Model Dev., 12, 33–67, 10.5194/gmd-12-33-2019, 2019.
Burr, M. J. and Zhang, Y.: Source apportionment of fine particulate matter
over the Eastern U.S. – Part II: source sensitivity simulations using
CAMX/PSAT and comparisons with CMAQ source sensitivity simulations,
Atmosp. Pollut. Res., 2, 318–336, 2011.Callaghan, A., de Leeuw, G., Cohen, L., and O'Dowd, C. D.: Relationship of
oceanic whitecap coverage to wind speed and wind history, Geophys. Res.
Lett., 35, L23609, 10.1029/2008GL036165, 2008.Clappier, A., Fagerli, H., and Thunis, P.: Screening of the EMEP source receptor
relationships: application to five European countries, Air Qual. Atmos.
Health, 10, 497–507, 10.1007/s11869-016-0443-y, 2017a.Clappier, A., Belis, C. A., Pernigotti, D., and Thunis, P.: Source apportionment and sensitivity analysis: two methodologies with two different purposes, Geosci. Model Dev., 10, 4245–4256, 10.5194/gmd-10-4245-2017, 2017b.Crippa, M., Janssens-Maenhout, G., Dentener, F., Guizzardi, D., Sindelarova, K., Muntean, M., Van Dingenen, R., and Granier, C.: Forty years of improvements in European air quality: regional policy-industry interactions with global impacts, Atmos. Chem. Phys., 16, 3825–3841, 10.5194/acp-16-3825-2016, 2016.Dockery, D. W. and Pope III, C. A.: Acute respiratory effects of
particulate air pollution, Ann. Rev. Public Health, 15, 107–132, 10.1146/annurev.pu.15.050194.000543, 1994.D'Elia, I., Bencardino, M., Ciancarella, L., Contaldi, M., and Vialetto,
G.: Technical and Non-Technical Measures for air pollution emission
reduction: The integrated assessment of the regional Air Quality Management
Plans through the Italian national model, Atmos. Environ., 43, 6182–6189,
10.1016/j.atmosenv.2009.09.003, 2009.EEA: Air quality in Europe 2017, EEA Report No 13/2017, available at:
https://www.eea.europa.eu/publications/air-quality-in-europe-2017 (last access: 27 March 2020), 2017.
EMEP: Transboundary acidification and eutrophication
and ground level ozone in Europe: Unified EMEP model description, EMEP Status Report 1/2003, The
Norwegian Meteorological Institute, Oslo, Norway, ISSN 0806-4520, 2003.
EMEP: Transboundary particulate matter,
photo-oxidants, acidifying and eutrophying components, EMEP Status Report 1/2018:, Joint MSC-W & CCC
& CEIP Report, ISSN 1504-6109, 2018.EMEP MSC-W: metno/emep-ctm: OpenSource rv4.15 (201709) (Version rv4_15), Zenodo, 10.5281/zenodo.3355041, 2017.Fagerli, H. and Aas, W.: Trends of nitrogen in air and precipitation: Model
results and observations at EMEP sites in Europe, 1980–2003, Environ. Poll.,
154, 448–461, 10.1016/j.envpol.2008.01.024, 2008.Founda, D., Kazadzis, S., Mihalopoulos, N., Gerasopoulos, E., Lianou, M., and Raptis, P. I.: Long-term visibility variation in Athens (1931–2013): a proxy for local and regional atmospheric aerosol loads, Atmos. Chem. Phys., 16, 11219–11236, 10.5194/acp-16-11219-2016, 2016.Fountoukis, C. and Nenes, A.: ISORROPIA II: a computationally efficient thermodynamic equilibrium model for K+-Ca2+-Mg2+-NH4+-Na+-SO42−-NO3--Cl−-H2O aerosols, Atmos. Chem. Phys., 7, 4639–4659, 10.5194/acp-7-4639-2007, 2007.
Gerber, H. E.: Relative-Humidity Parameterization of the Navy Aerosol Model
(NAM), Naval Research Laboratory, NRL report 8956, 1985.Grewe, V., Tsati, E., and Hoor, P.: On the attribution of contributions of atmospheric trace gases to emissions in atmospheric model applications, Geosci. Model Dev., 3, 487–499, 10.5194/gmd-3-487-2010, 2010.Hendriks, C., Kranenburg, R., Kuenen, J., van Gijlswijk, R., Wichink Kruit,
R., Segers, A., Denier van der Gon, H., and Schaap, M.: The origin of ambient
particulate matter concentrations in the Netherlands, Atmos. Environ., 69,
289–303, 10.1016/j.atmosenv.2012.12.017, 2013.Hendriks, C., Kranenburg, R., Kuenen, J.J.P., Van den Bril, B., Verguts, V., and Schaap, M.: Ammonia emission time profiles based on manure transport data
improve ammonia modelling across north western Europe, Atmos. Environ., 131,
83–96, 10.1016/j.atmosenv.2016.01.043, 2016.Jonson, J. E., Stohl, A., Fiore, A. M., Hess, P., Szopa, S., Wild, O., Zeng, G., Dentener, F. J., Lupu, A., Schultz, M. G., Duncan, B. N., Sudo, K., Wind, P., Schulz, M., Marmer, E., Cuvelier, C., Keating, T., Zuber, A., Valdebenito, A., Dorokhov, V., De Backer, H., Davies, J., Chen, G. H., Johnson, B., Tarasick, D. W., Stübi, R., Newchurch, M. J., von der Gathen, P., Steinbrecht, W., and Claude, H.: A multi-model analysis of vertical ozone profiles, Atmos. Chem. Phys., 10, 5759–5783, 10.5194/acp-10-5759-2010, 2010.Kaiser, J. W., Heil, A., Andreae, M. O., Benedetti, A., Chubarova, N., Jones, L., Morcrette, J.-J., Razinger, M., Schultz, M. G., Suttie, M., and van der Werf, G. R.: Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power, Biogeosciences, 9, 527–554, 10.5194/bg-9-527-2012, 2012.Keuken, M, Zandveld, P., van den Elshout, S., Janssen, N. A. H., and Hoek, G.:
Air quality and health impact of PM10 and EC in the city of Rotterdam,
the Netherlands in 1985–2008, Atmos Environ., 45, 5294–5301,
10.1016/j.atmosenv.2011.06.058, 2011.Kiesewetter, G., Borken-Kleefeld, J., Schöpp, W., Heyes, C., Thunis, P., Bessagnet, B., Terrenoire, E., Fagerli, H., Nyiri, A., and Amann, M.: Modelling street level PM10 concentrations across Europe: source apportionment and possible futures, Atmos. Chem. Phys., 15, 1539–1553, 10.5194/acp-15-1539-2015, 2015.Kranenburg, R., Segers, A. J., Hendriks, C., and Schaap, M.: Source apportionment using LOTOS-EUROS: module description and evaluation, Geosci. Model Dev., 6, 721–733, 10.5194/gmd-6-721-2013, 2013.Kuenen, J. J. P., Visschedijk, A. J. H., Jozwicka, M., and Denier van der Gon, H. A. C.: TNO-MACC_II emission inventory; a multi-year (2003–2009) consistent high-resolution European emission inventory for air quality modelling, Atmos. Chem. Phys., 14, 10963–10976, 10.5194/acp-14-10963-2014, 2014.Manders, A. M. M., Builtjes, P. J. H., Curier, L., Denier van der Gon, H. A. C., Hendriks, C., Jonkers, S., Kranenburg, R., Kuenen, J. J. P., Segers, A. J., Timmermans, R. M. A., Visschedijk, A. J. H., Wichink Kruit, R. J., van Pul, W. A. J., Sauter, F. J., van der Swaluw, E., Swart, D. P. J., Douros, J., Eskes, H., van Meijgaard, E., van Ulft, B., van Velthoven, P., Banzhaf, S., Mues, A. C., Stern, R., Fu, G., Lu, S., Heemink, A., van Velzen, N., and Schaap, M.: Curriculum vitae of the LOTOS–EUROS (v2.0) chemistry transport model, Geosci. Model Dev., 10, 4145–4173, 10.5194/gmd-10-4145-2017, 2017.Marécal, V., Peuch, V.-H., Andersson, C., Andersson, S., Arteta, J., Beekmann, M., Benedictow, A., Bergström, R., Bessagnet, B., Cansado, A., Chéroux, F., Colette, A., Coman, A., Curier, R. L., Denier van der Gon, H. A. C., Drouin, A., Elbern, H., Emili, E., Engelen, R. J., Eskes, H. J., Foret, G., Friese, E., Gauss, M., Giannaros, C., Guth, J., Joly, M., Jaumouillé, E., Josse, B., Kadygrov, N., Kaiser, J. W., Krajsek, K., Kuenen, J., Kumar, U., Liora, N., Lopez, E., Malherbe, L., Martinez, I., Melas, D., Meleux, F., Menut, L., Moinat, P., Morales, T., Parmentier, J., Piacentini, A., Plu, M., Poupkou, A., Queguiner, S., Robertson, L., Rouïl, L., Schaap, M., Segers, A., Sofiev, M., Tarasson, L., Thomas, M., Timmermans, R., Valdebenito, Á., van Velthoven, P., van Versendaal, R., Vira, J., and Ung, A.: A regional air quality forecasting system over Europe: the MACC-II daily ensemble production, Geosci. Model Dev., 8, 2777–2813, 10.5194/gmd-8-2777-2015, 2015.Mårtensson, E. M., Nilsson, E. D., de Leeuw, G., Cohen, L. H., and Hansson, H.C.: Laboratory simulations and parameterization of the primary marine aerosol production, J. Geophys. Res.-Atmos., 108, 4297,
10.1029/2002JD002263, 2003.Meyer, S. and Pagel, M.: Fresh Air Eases Work – The Effect of Air Quality on
Individual Investor Activity, NBER Working Paper No. 24048, 10.3386/w24048, 2017.
Monahan, E., Spiel, D., and Davidson, K.: A model of marine aerosol
generation via white caps and wave disruption, in:
Oceanic whitecaps, edited by: Monahan, E. and MacNiochaill, G.,
Dordrecht: Reidel, the Netherlands, 167–193, 1986.Morcrette, J.-J, Boucher, O., Jones, L., Salmond, D., Bechtold, P.,
Beljaars, A., Benedetti, A., Bonet, A., Kaiser, J. W., Razinger, M., Schulz,
M., Serrar, S., Simmons, A. J., Sofiev, M., Suttie, M., Tompkins, A. M., and
Untch, A.: Aerosol analysis and forecast in the ECMWF Integrated Forecast
System: Forward modeling, J. Geophys. Res., 114, D06206,
10.1029/2008JD011235, 2009.Mukherjee, A., and Agrawal, M.: World air particulate matter: sources,
distribution and health effects, Environmental Chemistry Letters, 15,
2,283-309, 10.1007/s10311-017-0611-9, 2017.Pakkanen, T. A.: Study of formation of coarse particle nitrate aerosol,
Atmos. Environ., 30, 2475–2482, 10.1016/1352-2310(95)00492-0, 1996.
Pommier, M., Fagerli, H., Schulz, M., and Valdebenito, A.: Prediction of source contributions to surface PM10 concentrations in European cities: a case study for an episode in December 2016 using EMEP/MSC-W rv4.15 – Part 2: The local urban background contribution, in preparation, 2020.REVIHAAP: Review of Evidence on Health Aspects of Air Pollution – REVIHAAP
Project Technical Report, World Health Organization (WHO) Regional Office
for Europe, Bonn, http://www.euro.who.int/__data/assets/pdf_file/0004/193108/REVIHAAP-Final-technical-report.pdf (last access: 27 March 2020), 2013.Schaap, M., van Loon, M., ten Brink, H. M., Dentener, F. J., and Builtjes, P. J. H.: Secondary inorganic aerosol simulations for Europe with special attention to nitrate, Atmos. Chem. Phys., 4, 857–874, 10.5194/acp-4-857-2004, 2004.
Schaap, M., Manders, A. M. M., Hendriks, E. C. J., Cnossen, J. M., Segers,
A. J. S., Denier van der Gon, H. A. C., Jozwicka, M., Sauter, F. J.,
Velders, G. J. M., Matthijsen J., and Builtjes, P. J. H.: Regional modelling
of particulate matter for the Netherlands, PBL-rapport 500099008, Den
Haag/Bilthoven: PBL, 2009.Schaap, M., Kranenburg, R., Curier, L., Jozwicka, M., Dammers, E., and
Timmermans, R.: Assessing the Sensitivity of the OMI-NO2 Product to
Emission Changes across Europe, Remote Sens., 5, 4187–4208,
10.3390/rs5094187, 2013.Schaap, M., Hendriks, C., Kranenburg, R., Kuenen, J., Segers, A., Schlutow,
A., Nagel, H.-D., Ritter, A., and Banzhaf, S.: PINETI-III: Modellierung und
Kartierung atmosphäri-scher Stoffeinträge von 2000 bis 2015 zur
Bewer-tung der ökosystem-spezifischen Gefährdung von
Biodiversität in Deutschland. UBA-Texte, available at:
https://www.umweltbundesamt.de/publikationen/pineti-3-modellierung-atmosphaerischer (last access: 30 March 2020), 2018.Segersson, D., Eneroth, K., Gidhagen, L., Johansson, C., Omstedt, G.,
Engström Nylén, A., and Forsberg, B.: Health Impact of PM10,
PM2.5 and Black Carbon Exposure Due to Different Source Sectors in
Stockholm, Gothenburg and Umea, Sweden, Int. J. Environ. Res. Public Health,
14, 742, 10.3390/ijerph14070742, 2017.Simpson, D., Benedictow, A., Berge, H., Bergström, R., Emberson, L. D., Fagerli, H., Flechard, C. R., Hayman, G. D., Gauss, M., Jonson, J. E., Jenkin, M. E., Nyíri, A., Richter, C., Semeena, V. S., Tsyro, S., Tuovinen, J.-P., Valdebenito, Á., and Wind, P.: The EMEP MSC-W chemical transport model – technical description, Atmos. Chem. Phys., 12, 7825–7865, 10.5194/acp-12-7825-2012, 2012.
Simpson, D., Ágnes Nyíri, A., Tsyro, S., Valdebenito, Á, and
Wind, P.: Updates to the EMEP/MSC-W model, 2015–2016 Transboundary
particulate matter, photo-oxidants, acidifying and eutrophying components.
EMEP Status Report 1/2016, The Norwegian Meteorological Institute, Oslo,
Norway, 15–36, ISSN 1504-6109, 2016.
Simpson, D., Bergström, R., Imhof, H., and Wind, P.: Updates to the
EMEP/MSC-W model, 2016–2017 Transboundary particulate matter,
photo-oxidants, acidifying and eutrophying components. EMEP Status Report
1/2017, The Norwegian Meteorological Institute, Oslo, Norway, 15–36, ISSN
1504-6109, 2017.Skyllakou, K., Murphy, B. N., Megaritis, A. G., Fountoukis, C., and Pandis, S. N.: Contributions of local and regional sources to fine PM in the megacity of Paris, Atmos. Chem. Phys., 14, 2343–2352, 10.5194/acp-14-2343-2014, 2014.Steinbrecher, R., Smiatek, G., Köble, R., Seufert, G., Theloke, J.,
Hauff, K., Ciccioli, P., Vautard, R., and Curci, G.: Intra- and
inter-annual variability of VOC emissions from natural and semi-natural
vegetation in Europe and neighbouring countries. Atmos. Environ., 43,
1380–1391, 10.1016/j.atmosenv.2008.09.072, 2009.Thunis, P., Degraeuwe, B., Pisoni, E., Ferrari, F., and Clappier, A.: On the
design and assessment of regional air quality plans: The SHERPA approach, J.
Environ. Manage., 183, 952–958,
10.1016/j.jenvman.2016.09.049, 2016.Thunis, P., Degraeuwe, B., Pisoni, E., Trombetti, M., Peduzzi, E., Belis, C.
A., Wilson, J., Clappier, A., and Vignati, E.: PM2.5 source allocation in
European cities: A SHERPA modelling study, Atmos. Environ., 187, 93–106,
10.1016/j.atmosenv.2018.05.062, 2018.Thunis, P., Clappier, A., Tarrason, L., Cuvelier, C., Monteiro, A., Pisoni,
E., Wesseling, J., Belis, C. A., Pirovano, G., Janssen, S., Guerreiro, C.,
and Peduzzi, E.: Source apportionment to support air quality planning:
Strengths and weaknesses of existing approaches, Environ. Int., 130, 104825,
10.1016/j.envint.2019.05.019, 2019.
Van Zanten, M. C., Sauter, F. J., Wichink Kruit, R. J., Van Jaarsveld, J.
A., and Van Pul, W. A. J.: Description of the DEPAC module: Dry deposition
modelling with DEPAC GCN2010, RIVM report 680180001/2010, Bilthoven, the
Netherlands, 74 pp., 2010.Walcek, C. J. and Aleksic, N. M.: A simple but accurate mass conservative
peak-preserving, mixing ratio bounded advection algorithm with fortran code,
Atmos. Environ., 32, 3863–3880,
10.1016/S1352-2310(98)00099-5, 1998.Whitten, G., Hogo, H., and Killus, J.: The carbon bond mechanism for
photochemical smog, Environ. Sci. Technol., 14, 690–700, 10.1021/es60166a008, 1980.WHO: Air quality guidelines for particulate matter, ozone, nitrogen dioxide
and sulfur dioxide – Global update 2005 – Summary of risk assessment, available at:
https://apps.who.int/iris/bitstream/handle/10665/69477/WHO_SDE_PHE_OEH_06.02_eng.pdf?sequence=1 (last access: 27 March 2020), 2005.
Wichink Kruit, R. J., Schaap, M., Sauter, F. J., van Zanten, M. C., and van Pul, W. A. J.: Modeling the distribution of ammonia across Europe including bi-directional surface–atmosphere exchange, Biogeosciences, 9, 5261–5277, 10.5194/bg-9-5261-2012, 2012.
Wind, P., Simpson, D., and Tarrasón, L.: Transboundary acidification,
eutrophication and ground level ozone in Europe, chap. 4, in: Source-receptor
calculations, EMEP Status Report 1/2004, Joint MSC-W & CCC &
CIAM & ICP-M&M & CCE Report, ISSN 0806-4520, 2004.Zhang, L., Gong, S., Padro, J., and Barrie, L.: A size-segregated particle
dry deposition scheme for an atmospheric aerosol module, Atmos. Environ., 35,
549–560, 10.1016/S1352-2310(00)00326-5, 2001.