ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-1539-2015Modelling street level PM10 concentrations across Europe: source apportionment and possible futuresKiesewetterG.kiesewet@iiasa.ac.atBorken-KleefeldJ.SchöppW.HeyesC.https://orcid.org/0000-0001-5254-493XThunisP.BessagnetB.TerrenoireE.FagerliH.NyiriA.AmannM.International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, AustriaJoint Research Centre, Institute for Environment and Sustainability (JRC-IES), Ispra, ItalyNational Institute for Environment and Risks (INERIS), Paris, FranceNorwegian Meteorological Institute, Oslo, NorwayG. Kiesewetter (kiesewet@iiasa.ac.at)13February20151531539155325April201410July201418December20147January2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://www.atmos-chem-phys.net/15/1539/2015/acp-15-1539-2015.htmlThe full text article is available as a PDF file from https://www.atmos-chem-phys.net/15/1539/2015/acp-15-1539-2015.pdf
Despite increasing emission controls, particulate matter (PM) has
remained a critical issue for European air quality in recent
years. The various sources of PM, both from primary particulate
emissions as well as secondary formation from precursor gases, make
this a complex problem to tackle. In order to allow for credible
predictions of future concentrations under policy assumptions,
a modelling approach is needed that considers all chemical processes
and spatial dimensions involved, from long-range transport of
pollution to local emissions in street canyons. Here we describe
a modelling scheme which has been implemented in the GAINS integrated
assessment model to assess compliance with PM10 (PM with
aerodynamic diameter <10µm) limit values at individual
air quality monitoring stations reporting to the AirBase database. The
modelling approach relies on a combination of bottom up modelling of
emissions, simplified atmospheric chemistry and dispersion
calculations, and a traffic increment calculation wherever
applicable. At each monitoring station fulfilling a few data coverage
criteria, measured concentrations in the base year 2009 are explained
to the extent possible and then modelled for the past and future. More
than 1850 monitoring stations are covered, including more than 300
traffic stations and 80 % of the stations which exceeded the EU
air quality limit values in 2009. As a validation, we compare modelled
trends in the period 2000–2008 to observations, which are well
reproduced. The modelling scheme is applied here to quantify
explicitly source contributions to ambient concentrations at several
critical monitoring stations, displaying the differences in spatial
origin and chemical composition of urban roadside PM10
across Europe. Furthermore, we analyse the predicted evolution of
PM10 concentrations in the European Union until 2030 under
different policy scenarios. Significant improvements in ambient
PM10 concentrations are expected assuming successful
implementation of already agreed legislation; however, these will not
be large enough to ensure attainment of PM10 limit values in
hot spot locations such as Southern Poland and major European
cities. Remaining issues are largely eliminated in a scenario applying
the best available emission control technologies to the maximal
technically feasible extent.
Introduction
Particulate matter (PM) has become a major concern for public health
in recent years . Especially particles with an
aerodynamic diameter below 2.5 µm (PM2.5) have
been associated with increased mortality mainly due to cardiovascular
diseases. The most important sources of primary PM (PPM) emissions include
domestic combustion in household heating, road traffic, and industrial
combustion. In addition to the emissions of primary particulate
matter, particles are also formed in ambient air by chemical and
physical processes from precursor gases.
Current European legislation states legally binding
limit values on ambient concentrations of PM below 10 µm
diameter (PM10): daily average PM10 concentrations
must not exceed 50 µgm-3 for more than 35
days per calendar year, and the annual mean concentration must not
exceed 40 µgm-3. Additional targets exist for PM2.5 (for individual
monitoring stations as well as for average population exposure), which will enter into force in 2015.
Despite tightening of emission control legislation, EU Member States
have been facing severe difficulties to attain these limit
values . Compliance problems have been widespread and
continuous at many locations. As the EU is currently revising its air
quality legislation and planning new national emission reduction commitments for
2030, the question arises how compliance will evolve under different
policy scenarios.
Assessing compliance with air quality limit values poses a significant new
challenge to the modelling framework underpinning policy advice in the EU.
Earlier emission control legislation such as the National Emissions Ceilings
Directive and the Gothenburg Protocol had a
focus on acidification, eutrophication and tropospheric ozone abatement. When
assessing compliance of control scenarios with the objectives, e.g. excess of
critical loads for acidification, a simulation of pollution concentrations at
a small scale, like a street canyon, was not necessary. In the meantime, the
focus of interest has shifted to pollutants like NO2 and PM, which
are mostly characterized by urban sources like road traffic, and whose
highest concentrations are usually observed in urban areas, particularly
along busy roads . Therefore, compliance
with the policy objectives, i.e. with the limit values for NO2,
PM10 and PM2.5, needs to be checked also at roadside
spots.
Consequently, modelling down to urban background scale with a resolution of a
few km2, as it has been done for the Clean Air for Europe program mid
of the last decade, is not sufficient. Modelling tools used for analysing
emission control scenarios to tackle PM and NO2 problems need to
resolve also the street canyon scale, to assess to what extent Europe-wide
emission control scenarios lead to compliance with the legally binding limit
values for ambient PM and NO2.
Modelling capacities of atmospheric PM have improved strongly in
recent years. An overview of the state of the art modelling approaches
is given by .
The GAINS integrated assessment model is employed
in the revision of the EU Thematic Strategy on Air Pollution (TSAP) as
a policy tool to test the impacts of different pollution control
options and calculate least cost solutions for achieving given policy
targets . GAINS calculates particulate matter as the
sum of PPM and secondary aerosols caused by
anthropogenic emissions of NH3, SO2,
NOx, and non-methane volatile organic compounds.
We have recently introduced a downscaling scheme in GAINS to model
NO2 concentrations at different kinds of monitoring stations in the
EU, including roadside stations . Here
a similar scheme is developed which is now in use to assess future
attainment of PM10 limit values in GAINS. In line with the
methodology applied for NO2, we model annual mean concentrations
based on past monitoring data. At each air quality monitoring station,
measured concentrations in the base year 2009 are disaggregated into
contributions from regional background, urban increment, and roadside
increment if appropriate. Individual contributions are then subject to
the changes in the responsible emissions to calculate concentrations
for scenario years.
This paper presents an introduction to the methodology used,
a validation of trends against observations, and applications of the
model in the context of the revision of the EU air quality
legislation. We quantify for several stations with high ambient
concentrations the source contributions, pointing out large
differences in the composition, and present an estimate of the
evolution of PM10 concentrations in Europe until 2030 under
different policy assumptions.
The remainder of this article is organised as follows: the modelling
scheme is detailed in Sect. . A validation of
modelled trends against independent observations for the years
2000–2008 is presented in Sect. . Uncertainties
and shortcomings of the methodology are discussed in
Sect. . Section presents
results: source contributions to different stations are analysed, and
the evolution of compliance with limit values in the EU is assessed
under different assumptions for the evolution of anthropogenic
emissions. Summary and conclusions are given in
Sect. .
Methodology
European legislation states two different limit values for
PM10 concentrations : annual mean
concentrations must not exceed a value of
40 µgm-3, and daily average concentrations must
not exceed 50 µgm-3 for more than
35 days in a calendar year. Out of these two limit values,
the limit on daily average concentrations has proven more challenging
to attain: e.g. while in 2009 more than 640 monitoring stations did
not attain the daily limit value, only about 240 stations reported
annual mean concentrations >40µgm-3 (numbers
refer to stations in the EU with more than 80 % data
coverage). All of the latter did not attain the daily limit
either. Hence, an assessment of future compliance with PM10
standards must focus on the daily limit value.
Relation between annual mean concentrations and the
36th highest daily average concentration in AirBase
observations (data: all AirBase stations in 2009 with >80 % daily data
coverage). The limit on daily exceedances of 50 µg m-3 is
well represented by an annual mean limit of
30 µg m-3.
All calculations in GAINS are done on an annual mean basis and hence
cannot address daily exceedances directly. However, a compact linear
relation exists between the annual mean and the 36th highest daily
average which is decisive for attainment of the daily limit value (see
Fig. , showing observations
from the AirBase
AirBase, the European air quality
database. http://acm.eionet.europa.eu/databases/airbase/
database in 2009): a 36th highest daily average of
50 µgm-3 corresponds to an annual mean
concentration of 29.6 µgm-3. In a similar
approach, used a quadratic relationship between
the number of days with PM10 concentrations greater than
50 µgm-3 and the annual mean to derive an
equivalent annual mean concentration of
31.5 µgm-3. Hence we assess compliance with
respect to an equivalent annual mean limit value of
30 µgm-3.
As seen in Fig. , within a certain
range of annual mean concentrations both compliance and non-compliance with
the daily limit value are possible. All stations below
25 µgm-3 annual mean PM10 comply with the daily
mean limit value, above which value the complying fraction decreases steadily. Less than 10 % of stations with
annual mean around 35 µgm-3 are in compliance with the limit
on daily average. This range of ±5µgm-3 is later used as an uncertainty range around the
limit value within which no definite statement on attainment of the daily
mean limit value can be made.
The modelling approach is similar to the one laid out
by for NOx and NO2. A schematic overview of the modelling approach is shown in
Fig. . The modelling scheme
combines past monitoring data with bottom up emission modelling and
a simplified atmospheric chemistry and dispersion calculation. The
starting point of all calculations is monitoring data reported to
AirBase in 2009. To ensure quality of the data, we consider only
stations with more than 80 % temporal coverage of the daily mean
data. For any roadside monitoring station that fulfils this
requirement, we first identify contributions from the ambient
background and local road traffic emissions, and then model each of
these contributions individually. The background itself is modelled as
the sum of regional background contributions (primary and secondary)
from Europe-wide emissions, an urban increment related to PPM
emissions from low-level sources, natural dust, and – if appropriate
– a residual regarded as contribution from unknown sources. As
a pessimistic assumption, this residual may be left constant in
scenario calculations, as done with NO2
residuals ; a more realistic treatment
attempts an allocation of this residual to natural contributions,
regional and local emissions, as detailed below. Differences are only
relevant in limited parts of Europe where the bottom up calculated
concentrations significantly underestimate observations in 2009.
The following sections provide a description of the methodology for
modelling the different contributions to the background
(Sect. ), and the roadside increment
(Sect. ). The synthesis of the
different steps is described in
Sect. .
Schematic overview of the PM10 modelling scheme for roadside
stations.
Modelling background concentrations
Bottom up calculation of background concentrations is done in two
steps, a coarse resolution transfer calculation and a fine scale
increment relying on local emissions. All steps described here are
done for PM10 and PM2.5 independently; however, as
the focus of this article is on PM10 we do not mention
PM2.5 explicitly here. Regional background concentrations
are calculated from linear transfer coefficients at a resolution of
0.5∘(long) × 0.25∘(lat) or roughly 28km×28 km, based on sensitivity calculations with the EMEP chemistry
transport model (CTM) . The EMEP model contains
secondary inorganic as well as organic aerosol formation and
calculates PM10 concentrations from the source pollutants
primary PM (PPM10), NH3, NOx,
SO2, and non-methane volatile organic compounds. In
order to match the expected situation best, expected emissions for the
year 2020 under current legislation were used as base case for the
EMEP model calculations. In each of the sensitivity runs, country
total emissions of one pollutant p from one source region r were
reduced by 15 % to calculate linear transfer coefficients
π(i,p,r) from r to each grid cell i,
π(i,p,r)=[PM10]base(i)-[PM10]red(i)0.15Ebase(p,r)
with E(p,r) denoting country total emissions of pollutant p in
region r and the subscripts base and red
referring to the model run with full 2020 emissions and that with
reduced emissions, respectively. Fifty-seven source regions are
included, covering Europe and the surrounding sea regions, as
described by .
PM10 concentrations for each EMEP grid cell i are
then calculated as the sums of contributions from all source regions
r and pollutants p,
PM10(i)=δPM10+∑r=157∑p∈{P,A,N,S,V}π(i,p,r)⋅E(p,r)
with P, A, N, S, V denoting the source pollutants
PPM10 (“P”), NH3 (“A”), NOx
(“N”), SO2 (“S”), VOC (“V”). δPM10
denotes the residual resulting from non-linearities in the system and
boundary conditions; it is calculated as the difference between the sum of
linear contributions from base case emissions and the base case
concentrations modelled with the full EMEP CTM. This model-intrinsic
residual is slightly negative in the Po valley, and between 0.5 and
2 µgm-3 in the rest of Europe.
The linear approach does not take into account the cross-dependencies
between different precursors for secondary inorganic aerosol
formation; in particular, it does not explicitly calculate an
equilibrium state between ammonium sulphate and ammonium nitrate
formation but assumes that the modelled effects of reducing one
pollutant by 15 % can be extrapolated linearly. It is clear
that this approach has its limitations, in particular if emission
changes are unbalanced between different precursors. Modelled
concentrations are credible as long as changes in the three precursor
gases are similar.
The 0.5∘×0.25∘ resolution of the linear
transfer coefficients is not sufficient to calculate realistic urban
background PM concentrations. used a full
year simulation performed with the CHIMERE chemistry transport
model with a grid resolution of 0.125∘ (long) × 0.0625∘ (lat) or roughly 7km×7 km to
calculate for NOx a sub-grid increment to the urban
background level. Here we use the same simulation to derive
an urban concentration increment for PM10. As the formation of
secondary PM takes place on timescales of hours, the urban increment
is calculated as a function of PPM emissions
The assumption that secondary PM formation can be
neglected at the local scale is a simplification. E.g., reported
that local nitrate formation accounts for about 4 % of total PM10 in Berlin,
and this fraction is missed or misattributed to PPM in the model.
. For the CHIMERE
model runs used here, showed that most of the
concentration increment from the 28km×28 km to the 7km×7 km resolution is explained by emissions of PPM. This
approach is used here to calculate a regression coefficient ξ
relating increments in the PM10 concentration to
emissions of primary PM10 in the lowest atmospheric layer, so that in a sub-grid cell m of
the 28km×28 km grid cell i the PM10
concentration is calculated as
[PM10](m)=[PM10](i(m))+ξ(i(m))×eL(m)-eL(i(m))
with eL(m) the low level (traffic and household) emissions in m
and eL(i(m)) the same averaged over the
corresponding EMEP grid cell i. The parameter ξ relates
the pattern of concentration increments to the pattern of
PPM emissions.
ξ depends largely on the meteorological characteristics of
the area in question. Although calculated only for 2009, ξ
introduces a parameterization of the urban increment with low level
emissions that can easily be transferred to different scenario
years. Since this resolution-dependent concentration increment is
relevant mostly in urban areas, we refer to it also as urban
increment, although it is calculated for every EMEP grid cell
regardless of its location and may also be negative in sub-urban grid
cells. EMEP grid cells containing parts of the same urban area are
combined in the regression analysis, enhancing the statistical
significance of the calculation. Each major city is thus assigned
a single characteristic value of ξ.
The regression coefficient ξ relating additional primary PM
emissions within each EMEP grid cell to PM concentration
increments.
A map of ξ for the whole domain of the CHIMERE model is shown in
Fig. . Large differences are visible between
different regions owing to the different orography and local meteorological conditions
that influence boundary layer mixing. Particularly, the effect of low
wind speed and frequent inversion layers is visible in Alpine regions
and the Po valley, whereas the higher wind speeds lead to
correspondingly lower ξ values close to the Atlantic or North Sea
shorelines.
R2 values for the regression used in ξ calculation are high
especially in major urban areas with significant PM emissions. Major
European cities like Paris, London, Berlin, Madrid show values around
0.9 or higher.
While the urban background in large urban areas is represented well by
the 7km×7 km concentrations, concentrations in smaller
cities are underestimated as the CHIMERE grid cells are not small
enough to capture inner city concentrations. Adopting the methodology
described by , we use population density on
a 0.01∘×0.01∘ grid (∼0.75 km (long) × 1.1 km (lat) resolution) to redistribute domestic and
light duty vehicle emissions and apply Eq. () to inner
urban emission densities for 376 European cities with more than
100 000 inhabitants.
Roadside increments of NOx and PM10 at
Marylebone Road monitoring site, London: daily mean AirBase observations in
2009.
Modelling the traffic increment
Roadside concentrations of PM are typically a few
µgm-3 higher than concentrations in ambient urban
background air (around 5 µgm-3 on the European
average, see Fig. , but with
a large spread); the difference originates from traffic related
emissions of particles in the street canyon itself. We define the
PM10 roadside increment as
Δ[PM10]=[PM10]road-[PM10]B
with [PM10]road and
[PM10]B the roadside and urban background
concentrations of PM10 (equivalently for fractions of
PM10 or other tracers).
On timescales relevant for the mixing of air within street canyons,
secondary particle formation can be neglected. Traffic related PM
originates not only from combustion processes, but contains also
a significant fraction of non-exhaust emissions from brake and tyre
wear, road surface abrasion, and resuspension of road
dust .
The coarse fraction of PM (PMcoarse=PM2.5-10=PM10-PM2.5) has
been found to consist almost entirely of non-exhaust
particles , and at the same time is more affected
by resuspension as it may accumulate on the road surface. Between
different regions, large differences exist in the size partitioning
and thus exhaust or non-exhaust origin of the PM10 roadside
increment: in London, determined a roughly even
split of the roadside increment in PM2.5 and
PMcoarse, while in Nordic countries the coarse fraction
dominates, caused by the widespread use of studded tires and
application of traction sanding in winter .
As both the sources and the dispersion behaviour of fine and coarse
traffic related PM are different, fine and coarse fractions are
treated individually in the traffic increment calculation. Only few
monitoring sites in Europe enable a distinction of fine and coarse
roadside increment from observations. Thus, in our model the
components are estimated via a correlation with the NOx
roadside increment, of which measurements are widely available.
The approach followed here distinguishes and idealises the fine and
coarse fractions of PM. We assume that primary PM2.5 is
dispersed like NOx, which is chemically inert at the
timescales involved, while PMcoarse is subject to
accumulation and resuspension. The activity that causes the
concentration increments in NOx and PM2.5 is the
same (namely vehicular emission in the street canyon in question),
hence we can write
Δ[PM2.5]=Δ[NOx]⋅EPM2.5ENOx
with EPM2.5 and ENOx the national total
emissions of each pollutant from road traffic. Due to the lack of
station specific data we assume that the fleet composition at any
station is well represented by the national average for urban
conditions. A similar concept has been used by
for estimating the resuspension contribution to the roadside PM
increment. Figure shows this relation
for Marylebone Road traffic station in London, using AirBase daily
observations for the year 2009. Some roadside stations also show good
correlation between Δ[NOx] and Δ[PM10]; however, we do not use this relation but
focus on the fine fraction here. To avoid unrealistically large
PM2.5 roadside increments in case of observational errors,
the fine fraction is limited to 90 % of the total
PM10 increment in the base year.
Bottom up calculated vs. observed PM2.5 and PM10
concentrations at urban and rural background monitoring stations in 2009.
Panel (a) distinguishes into stations located in cities >100 000 inhabitants
(dots) and those not (circles). For better viewing, only urban stations in
cities >100 000 inhabitants are shown for PM10(c).
The coarse fraction of the traffic increment is then estimated as the
residual
Δ[PMcoarse]=[PM10]roadobs-[PM10]Bobs-Δ[PM2.5]
with [PM10]Bobs and
[PM10]roadobs the observed background and
roadside concentrations, respectively.
Once the fine and coarse fractions of the roadside increment are
estimated for the base year, each of them is scaled individually with
the appropriate trend in urban PM2.5 or PMcoarse
road traffic emissions (exhaust + non-exhaust). The trend in
PMcoarse traffic emissions is essentially proportional to
the trend in traffic volume as these non-exhaust emissions are not
controlled on a large scale so far. As the PMcoarse roadside
increment contains a significant fraction of re-suspended dust, the
assumption that concentrations scale proportional to emissions may be
too pessimistic, as the additional contribution of a single vehicle to
dust resuspension decreases with total traffic
volume .
Wherever possible, the same background stations are used for
PM10 and NOx in the roadside increment
calculation. Provided that sufficient temporal overlap exists
(>75% of all days in 2009),
Δ[PM10] and Δ[NOx] are
calculated as annual averages over all days when NOx and
PM10 roadside and background stations provide data. If
station pairs are not available, NOx and PM10
background are calculated independently; if for a station pair
sufficient overlap period is not available,
Δ[PM10] and Δ[NOx] are
calculated without temporal synchronisation.
Combination of the different modelling steps
The different modelling steps are combined as indicated in Fig. .
Model calculations are possible for every station in the AirBase
database which fulfils a few data coverage criteria: for background
stations, all stations with more than 80 % coverage of daily
mean PM10 concentration data are included. For roadside
stations, in addition NOx data are required for the same
station, and at least one suitable PM10 and one NOx
background station, ideally identical, are needed. All of these
stations must fulfil the 80 % temporal coverage
criterion. With these criteria, a total of around 1 870 PM10
stations are covered by the model, of which 316 are traffic stations
and 492 did not attain the equivalent limit value as defined in
Sect. in 2009 (315 if contributions from
natural dust and sea salt are subtracted).
Calculations involve two steps: first, the calculation is done for the
base year 2009. For a traffic station, the observed background PM10 is determined
as the mean of the observations from all background stations within the same city (according to AirBase metainformation) or within 20 km if the former is not available,
Modelled background PM10 is calculated as described in
Sect. as the sum of 28×28 km2 background
(light green in Fig. ) and the urban increment from
low level PPM emissions within the 28 km grid cell (dark blue), and calculated concentrations of PM10 from natural origin (dark green). GAINS transfer coefficients pertain
only to anthropogenic emissions. Suspension and dispersion of natural
dust and sea salt are calculated in the EMEP CTM for the year
2009. These natural fields are subtracted from observations before
determining the residual between total modelled and observed concentrations. This residual
is then attributed to the likely sectors of origin (see below). For a traffic station, the
fine and coarse
fractions of the observed roadside increment are calculated as
described in Sect. .
As a second step, calculations are done for any scenario year by
replacing base year emissions with emissions for the scenario year in
question. GAINS calculates emissions bottom up from projections of
anthropogenic activity, estimated shares of emission control
technologies and appropriate emission factors for each
technology . GAINS provides emissions typically in
five-year intervals extending from 2000 to 2030; for other years
emissions are interpolated linearly between these points.
In case of a positive residual in base year background concentrations
(negative bias, model under-explaining observations), the residual may be related to
natural dust, re-suspension of dust, missing emissions or a missing
representation of boundary layer inversions in the EMEP or CHIMERE
model simulations. While the unexplained residual is kept constant in
the NO2 scheme , this treatment seems too
pessimistic for PM10 in some European regions: particularly
in Southern Poland, extreme measured concentrations are at some
stations not matched by the model. However, both temporal profile as
well as geographical distribution of the offsets suggest a clear
relation to domestic combustion in winter, indicating that domestic
emissions are underestimated in emission inventories, or boundary
layer mixing is overestimated in the CTM simulations. Consequently,
a simple “best estimate” disaggregation of the residual
concentration is undertaken. First, the residual is disaggregated into
a regional and a local unexplained component; the regional component
is determined as the linear interpolation of unexplained residuals at
nearby rural background monitoring stations, while the remainder is by
definition caused by local emissions. Within the regional component,
natural dust is increased up to a reasonable maximum (the
PM10 dust fields used in the CHIMERE simulation, which are
considerably higher than the EMEP dust fields), and the rest is assumed to be composed like the modelled
28km×28km concentrations at this location. The local
residual component, on the other hand, is assumed to be related to an
underestimation of local emissions or their enhancement through
inversion situations, and are attributed proportionally to the gridded
PPM emissions within a radius of 20 km. While this
methodology can only provide a rough estimate and takes into account
only “known unknowns”, it still seems more realistic than keeping
the residual constant.
If the residual is negative (positive bias, model over-explaining observed
background), the ratio of observed to calculated background
PM10 in the base year is used to scale calculated
concentrations in scenario years.
Validation
Validating a model which calculates PM concentrations for roughly 1 870
air quality monitoring stations is challenging. Here we show
a comparison of bottom up calculated background PM concentrations for
various background stations in Europe, and a validation of trends at
background and roadside monitoring stations. Since the model is
constrained by observations in the base year, validating absolute
modelled concentrations at roadside monitoring stations is not
possible.
Time series of modelled and observed PM10 averaged across
different categories of monitoring stations in the
EU.
Figure compares PM2.5 and
PM10 background concentrations from bottom up modelling to
observed concentrations at background monitoring stations, for urban
and rural background stations separately. This provides a validation
of the background calculation methodology from linear transfer
coefficients plus downscaling to the urban background level. Each dot
in the figure represents the annual mean at one monitoring
station. The offset to the 1:1 line is compensated in scenario
calculations as described in
Sect. . We here use a subset of the
model performance indicators proposed by : absolute
bias, normalised mean bias, and correlation
coefficient. PM2.5 concentrations are generally well
modelled with a residual of -2.5µgm-3
(normalised mean bias -15%) remaining on the European average,
94 % of stations between a factor of two margins from the
observations. The mean bias decreases to
-0.9µgm-3 (-5%) at urban background
stations located in cities >100 000 inhabitants, where urban
polygons were defined as described by (black
dots in Fig. (a)). Urban background stations in
smaller cities for which urban polygons are not defined (open circles
in Fig. (a)) have a considerably higher offset
of -6.6µgm-3 or a normalised mean bias of
-36%. This points to the added value of the last downscaling step
beyond the 7 km CHIMERE grid resolution wherever possible, and
at the same time supports the re-allocation of local residuals to
nearby PPM emissions as described in
Sect. . At rural background
stations (Fig. (b)) the model has a mean bias
of -1.9µgm-3 (-15% normalised mean
bias).
The performance of the model is less encouraging for the coarse PM
fraction. The spatial variability between stations is underestimated,
leading to an average bias of -6.5µgm-3 or
26% of observed PM10 in the base year (for urban
background stations, -3.2µgm-3 or -12% at
stations within urban polygons, compared to
-10.8µgm-3 or -37% at stations without urban
polygons). Correlation coefficients between model and observations are
0.76 and 0.83 for urban background and rural background
PM2.5, respectively, and around 0.6 for PM10.
Aside from uncertainties in direct anthropogenic emissions of PM or
its precursors, offsets partly arise from uncertainties in the natural
emissions and effects of re-suspended dust.
For the full PM10 model, since offsets in the base year are
compensated, only trends can be validated. Modelled trends in the
decade 2000–2009 are compared to observations in
Fig. . Here, model predictions
at different categories of monitoring stations are compared to the
annually averaged observations (only stations with at least five years
of data are included here).
Modelled composition of PM10 at seven monitoring stations with
different characteristics in the year 2009: spatial source contributions.
“nat”: natural, “trbd”: transboundary.
Different observational methods are applied in different
locations. Particularly the use of the tapered element oscillating
microbalance (TEOM) causes difficulties in comparing results to the
standard gravimetric method as some semi-volatile compounds are lost
in the measurement process due to the necessary heating of the
sample e.g..
Similar difficulties are associated with monitors based on beta ray attenuation.
Scaling factors are usually applied
to correct for these offsets to the reference method; however, there
is no uniform methodology as to how these are calculated across the EU. TEOM
measurement data from France exhibit a step increase when a new
methodology (adjustments based on TEOM Filter Dynamics Measurement
System measurements) was introduced in 2007 to include the
semi-volatile components. To establish a consistent time series and
foster comparison with other monitoring sites, raw data from French
TEOM measurement sites before 2007 were scaled by average correction
factors as reported by : +20% for
roadside stations and +30% for background stations.
Trends are well captured by the model: slight declines of around
-0.36µg m-3yr-1 (urban background),
-0.45µg m-3yr-1 (traffic), and
-0.48µg m-3yr-1 (rural background) are
seen in the decade 2000–2009. The decline in observed roadside
PM10 concentrations is stronger than modelled (-0.71±0.20µg m-3yr-1), which is due to
a stronger decline in the roadside increment in observations. This
possibly points to successful local measures that have been
implemented during this decade in order to reduce exhaust emissions or dust suspension from
road traffic at hot spot sites (e.g. local traffic management / low emission zones, dust binding
measures in Scandinavian countries, changes in winter road
maintenance) and that are not represented in the Europe-wide emission
calculation scheme. The conclusion from
Fig. is that rural and urban
background concentrations are on average modelled well, while the
model may be slightly pessimistic for future roadside concentrations.
Modelled composition of PM10 at seven monitoring stations with
different characteristics in the year 2009: chemical
composition.
Uncertainties and caveats
The simplifications needed in a Europe-wide modelling of PM down to
individual street canyons lead to considerable
uncertainty.
A general limitation of this modelling approach is that it only provides
concentration projections for monitoring stations for which AirBase data are
available for 2009 and indeed only for a subset of stations for which the
mentioned data criteria have been met. However, these locations are used for
assessing compliance with the EU Air Quality Directive, and the model covers
80 % of
the stations exceeding the limit value in 2009.
provided a thorough discussion
of the uncertainties associated with the roadside NO2 calculation
scheme which follows a very similar approach. Hence, we only
provide here a short discussion of the uncertainties specific to the PM
scheme and refer the reader to the cited reference for a more general
treatment.
Limitations induced by the linearised approach taken here have been
mentioned in Sect. , and are
discussed by .
Bottom up calculated emissions of PM and its precursor gases in the
EU-28 under current legislation (lines) and the maximum technically feasible
reductions in 2030 (circles).
Considerable uncertainties stem from the emission inventory used for
the base year. The emission inventory itself is described
by . Emissions from domestic combustion are
uncertain in critical regions such as Southern Poland or Bulgaria,
where this sector is believed to be of key importance. Test runs with
the CHIMERE CTM revealed that domestic heating emissions in Southern
Poland are considerably underestimated in official reports and
previous versions of GAINS. Consultations with national experts led to
the conclusion that this discrepancy is likely caused by the more
widespread use of low quality coal for household heating in coal
mining and adjacent areas than previously assumed. As a preliminary
solution, domestic combustion emissions from provinces with active
coal mines were multiplied by a factor of 8, while those in neighbouring
provinces were adjusted by a factor of 4. These adjusted emissions
lead to a distinctively better match of modelled with measured
PM10 concentrations in Poland.
While such a flat correction factor adjusts the average well, at some
monitoring stations a significant unexplained share remains
(particularly in small cities, while concentrations in large cities
are a bit overestimated).
As a worst case scenario this residual may be left constant, as it is not explained by the emission inventory (including adjustments). However, in this case several regions would have little chance of attaining air quality limit values, which seems unrealistic in case of targeted action such as assumed in the policy scenarios. Therefore, residuals were site-specifically attributed to their likely sources as described in Sect. ; however, the air quality benefits achieved under control scenarios in these regions are subject to considerable uncertainty.
Cumulative distribution of PM10 concentrations modelled at all
stations covered in GAINS, for the base year 2009 and scenario year 2030,
assuming either current legislation (CLE) or maximum technically feasible
emission reductions (MTFR). The equivalent annual mean limit value of
30 µgm-3 is indicated as grey line. Natural contributions are
not included.
While unit emissions of particles and aerosol precursors from combustion processes are well quantified, non-exhaust emissions are more uncertain, and suspension of natural or road dust is not well quantified at all. Road dust resuspension is only considered in the roadside increment in our scheme, where it is included in the residual from calculated PM2.5 increment to the full PM10 increment. However, this simple scheme does not take account of the many factors usually considered in detailed road dust resuspension models such as Nortrip . Detailed input data as required in these models are not readily available for hundreds of roadside monitoring stations in Europe. The estimation of fine and coarse roadside increment from the proportionality to the NOx increment creates a strong dependency on the quality of observations, particularly on inter-comparability of PM and NOx observations.
PM concentrations are subject to strong inter-annual variability (see Fig. ) due to changeable meteorological conditions and dust episodes. Due to practical limitations in computing time, the urban increment calculation with 7km×7km resolution could only be performed for one year, which was selected as the most recent year with AirBase observations and meteorological fields available at the starting time of this work. Judging from the historical trend shown in Fig. , 2009 does not seem to show unusually high or low concentrations in relation to other years on the European average; however, we do acknowledge that the reliance on one year introduces systematic station related uncertainty in modelled concentrations for the future.
Given the uncertainties and approximations, it is clear that this
modelling scheme is not able to, nor is it supposed to, substitute
detailed local scale modelling. A Europe-wide integrated model must
make compromises, and there is definitely space for refinements in the
methodology in the future. Results for individual stations need to be
used with care, results are best analysed as an ensemble. Still, as
a more detailed look at individual stations shows, the model is able
to give a reasonable representation of different stations with
different characteristics
(Sect. ). Hence, it offers the unique
possibility of studying – with all uncertainties and caveats
mentioned – the effects of Europe-wide air quality policy choices on
ambient concentrations at the whole variety of monitoring stations
available in Europe, and to estimate the remaining compliance gap left by EU wide legislation, which is supposed to be closed by additional measures on national level and local level.
Results and discussion
This section applies the modelling scheme introduced in this article
to quantify source contributions to PM10 concentrations for
a set of critical stations (Sect. ),
and to provide an outlook on the evolution of Europe-wide
PM10 concentrations and the possible attainment of limit
values under future emissions (Sect. ).
Source allocation of PM10: examples of critical stations
Thanks to the structure of the model, the source composition of
modelled PM10 in terms of component and origin can be traced
for every single station. This section attempts to give some examples
for source attributions of PM10 at urban monitoring stations
in the base year.
Figure shows the spatial allocation of
origin for seven monitoring stations in the base year. The set is
rather arbitrary but stations were selected as examples for critical
stations with different characteristics. PM10 concentrations
are disaggregated into contributions from natural dust and sea salt,
transboundary, national, urban, and street canyon increments, similar
to the categories used e.g. by ; all of the
anthropogenic contributions are further split into fine and coarse PM
fractions. To arrive at the disaggregation shown here, regional
background levels have been determined from the interpolation of
nearby rural background stations, and unexplained residuals are
allocated to missing emissions as described in
Sect. . Before the re-allocation,
residuals at these stations were between -20 % and 20 %.
Modelled annual mean PM10 concentrations at AirBase
stations for the year 2030 under the CLE
scenario.
Stations selected here are located in Paris (FR04058, A1 Saint Denis), Krakow (PL0038 A
AirBase station name: MpKrakowWIOSPrad6115
), Turin (IT0469A, Consolata),
Stockholm (SE0003A, Hornsgatan), Essen (DENW134, Gladbecker Str.), London (GB0682A,
Marylebone Road), and Vienna (AT9RINN, Rinnböckstraße). While all of these
stations exceeded the 30 µgm-3 equivalent limit value in 2009, source
allocations show large differences in the reasons for the exceedances. Five of the six
stations shown are traffic stations, with Krakow – the station with the second highest
2009 annual mean among them – being the only exception as an urban background station.
With urban background concentrations at this level, the situation at curbside locations
may be expected to be even worse. All spatial source categories shown have their part,
although contributions of each fraction vary strongly between stations: while Turin is
shielded by the Alps and consequently transboundary transport contributes only little
to ambient PM, Vienna or Essen are significantly influenced by transboundary transport
of pollution due to their geographical locations. Conversely, a high regional background
related to Italian emissions is found in Turin, whereas in Stockholm the influence of
Swedish emissions outside the city itself is almost negligible. The regional background,
composed of natural, transboundary and national contributions, is around 20 µgm-3
in most of the cities included here; lower levels are found in London and Stockholm.
Such regional background levels leave only little room for urban and roadside
increments if a limit of 30 µgm-3 is to be matched, pointing
to the multi-scale nature of the problem.
Focussing more on the local contributions, extreme differences are seen in
both the urban and roadside increments, relating to local emission densities
in the domestic and transport sectors as well as to atmospheric mixing
conditions in the boundary layer (for the urban increment) or the layout of
the street canyon. Note the strong differences regarding the split of the
roadside increment into fine and coarse PM fractions as estimated using the
observed NOx increment. While the fine fraction, caused mostly by
exhaust emissions, slightly dominates at most stations, a dominating coarse
component is found in regions with intensive use of traction sanding in
winter or even studded tires such as in Stockholm. Both extreme examples,
London Marylebone Road (large fine increment) and Hornsgatan (large coarse
increment), offer PM2.5 observations in AirBase which confirm the
split of the roadside increment; in Turin and Vienna the PMcoarse
fraction of the roadside increment seems rather high and may be a bit
over-estimated.
The highest roadside increment is seen in the Paris station, which is
understandable as it is located at a suburban highway. Measurements at a
station in Paris, comparable to the station chosen here, report for the year
2010 a fine fraction of the roadside increment of
62 %, which is a bit higher than the share
estimated in our model using the NOx increment (54 % fine).
This may be due to the different emission characteristics (fleet and speed)
at a highway as compared to urban driving conditions which are assumed here;
if national average driving conditions are assumed, the estimated fine share
increases to 58 %.
A large roadside increment can be viewed as an opportunity – if the
main cause of the problem is a local one, local action has a chance to
alleviate the problem. If, on the other hand, only Europe-wide policy
measures are adopted, which address only the fine, combustion
generated particulates, cities with strong resuspension of road dust
will face severe difficulties in reducing ambient concentrations.
Figure shows the chemical composition
of PM at the same set of monitoring stations as before. Chemical
constituents are split up into natural, primary anthropogenic PM
(PPM), secondary inorganic aerosol (SIA), and secondary organic
aerosol (SOA), for both fine and coarse fractions. The primary coarse
component includes non-exhaust emissions and resuspended dust, which
is not distinguished explicitly in the model.
Comparing the chemical composition to observations is challenging for
two reasons. Firstly, measured composition data are usually only
available on a short term basis, often for episodes of high pollution;
however, during such episodes the contributions can deviate
significantly from the annual mean. Secondly, measured source
categories are not easily translated into composition as modelled in
GAINS. Hence, while a complete validation of the chemical composition
is beyond the scope of this article, the purpose of this section is to
point out a few characteristics.
The fine fraction constitutes about two-thirds (59–73 %) of total
PM10 at six out of the seven stations, with Stockholm being
the only exception (only 27 %) for the reasons discussed above. As
for the spatial origin of PM, large differences are also encountered
in terms of chemical composition. Dust and sea salt contribute
1–5 µgm-3 to PM10, mostly in the coarse
fraction. The largest contribution to PM10 comes from
primary particles (49–85 %); however, in the fine fraction,
secondary aerosol concentrations are slightly higher than primary ones
in Vienna and Essen.
Secondary inorganic aerosol concentrations are straightforward to be
compared to observations. report SIA
concentrations of 6.5 µgm-3 at Paris roadside
locations, which is matched well by GAINS
(6.3 µgm-3). For Vienna, give
annual average SIA concentrations of 11 µgm-3, close
to the values shown in Fig.
(10.5 µgm-3); however, measurements were made in
2004. In Stockholm, SIA formation is considerably lower, with the
3.6 µgm-3 modelled in the range of observations
reported by (3–5 µgm-3).
Among the stations included here, the highest SIA contribution in
absolute terms is modelled in Krakow (12.7 µgm-3) due
to high SO2 emissions and subsequent sulfate formation in
this region. Overall, SIA contributes 10 % (Stockholm) to 34 %
(Vienna) to PM10. 80–95 % of the SIA is in the fine
fraction of PM, with only minor contributions in the coarse fraction
(essentially NaNO3). Secondary organic aerosol formation is
modelled but not of significant importance
(0.3–2.1 µgm-3 or 1–6 % of PM2.5), with
the highest values found in Turin.
Due to the simplifications of the model construction, the source
attribution presented here can only give a rough estimate. It is meant
to show the differences between individual stations and regions rather
than provide exact results for which urban scale modelling based on
local emission inventories is needed.
An outlook on the attainment of air quality standards
The modelling scheme described in this article has been applied in the
ongoing revision of the EU air quality legislation to derive estimates
of compliance with limit values under various emission scenarios. Here
we show results for two specific scenarios, assuming either
a political stagnation at currently approved emission control
legislation levels (“CLE” is the current legislation scenario), or a very
ambitious policy scenario applying the most efficient control
technologies available (“MTFR” is the maximum technically feasible
reductions scenario).
Figure shows the trends of PM and precursor gas
emissions under the scenarios used. The CLE scenario was used as the
baseline case for the revision of the EU Thematic Strategy on Air
Pollution (TSAP); it has been described in detail
by , with recent updates described
by .
Considerable decreases in PM2.5 and SO2, NOx and
volatile organic compound emissions are expected under current legislation from ongoing
implementation of exhaust cleaning technologies. No further reductions
are expected for PMcoarse, and hardly any for NH3
emissions.
Analysis conducted for the TSAP revision has highlighted the potential
for emission reductions beyond the baseline case. The MTFR scenario
assumes that (within certain limitations of feasibility) all pollution
sources are equipped with the best available emission control
technology. Emissions under the MTFR scenario for 2030 are shown as
circles in Fig. . Considerable reductions beyond
the baseline are possible for all pollutants, however, this may come
at relatively high costs. Realistic strategies are usually based on
a partial closure of the gap between baseline and full application of
the best available technologies. The strength of the GAINS model is
then to find cost-optimal solutions for given health or air quality
targets. However hypothetical for practical implementation, the MTFR
scenario provides a quantification of what is possible in terms of
emission reductions without changing the levels of anthropogenic activities, i.e. no behavioural changes and no switches
to other fuel classes or renewable energy generation other than
assumed in the baseline case which relies on the latest PRIMES-2013
scenario for energy consumption.
Figure shows distributions of modelled
PM10 concentrations at all stations covered in the modelling
scheme, for the base year as well as the scenario year 2030, comparing
the modelled evolution under CLE and MTFR scenarios. Since EU
legislation allows for natural contributions to be subtracted from
measured concentrations, dust and sea salt fields as used in the EMEP
model are subtracted here from total modelled concentrations
Technically, also
contributions from traction sanding in winter may be neglected when determining compliance,
which is particularly relevant for Nordic countries; however, as our model does not quantify
this fraction explicitly, we do not subtract it here.
. While
about 320 (17 %) of the stations exceed the equivalent limit
value of 30 µgm-3 in 2009 (dashed), increasing
controls on emissions are expected to result in decreasing
concentrations and consequently a higher fraction of attainment of the
limit value across the EU already in the baseline case. However, after
2020 concentration decreases are slow, and about
80 (4.2 %) of
the stations operative in 2009 are expected to remain above the
equivalent limit value in 2030. A large amount of stations remains
close to the equivalent limit value, so that definite statements about
compliance are difficult.
Considering that the equivalent limit value is defined on
a statistical base, with some stations exceeding the daily limit value
even at annual mean concentrations below 30 µgm-3
(Fig. ), and also taking into
account inter-annual meteorological variability, only stations below
25 µgm-3 should be considered to be in safe
compliance. This 5 µgm-3 margin corresponds to the
mean range of inter-annual Europe-wide PM10 variations as
seen in Fig. , and is also an uncertainty
range for compliance with the daily mean limit value for a given annual mean concentration. More than
10 %
of the stations are not expected to meet this
25 µgm-3 limit in 2030 under CLE assumptions.
Full propagation of the maximum technically feasible emission
reduction technologies would improve the compliance situation
drastically, eliminating close to all stations above
30 µgm-3
(0.3 %),
and bringing
99 %
of the
stations below 25 µgm-3. Several stations remain at
annual mean concentrations close to the limit value, so that
attainment of the limit value is not certain, particularly in years
with unfavourable meteorological conditions. Additional local efforts
may be warranted to ensure compliance in these cases.
Critical areas are identified easily in
Fig. showing a map of air quality
monitoring stations colour coded by their modelled PM10
concentrations under the CLE scenario in 2030. From the discussion
above, only the “green” stations below 25 µgm-3 can
be assumed to be in relatively safe compliance.
Difficulties are expected to remain in several European cities,
Southern Poland and bordering areas in the Czech and Slovak Republics,
Northern Italy, and Bulgaria. Different causes are responsible for the
remaining difficulties: large cities are mainly under pressure from
increasing traffic, with the unregulated non-exhaust emissions (and
dust resuspension) eventually becoming dominant, while typically
relatively clean fuels are used for household heating. If traffic
volumes within large cities increase further, and if no additional
measures on non-exhaust emissions are taken, several cities may move
out of the compliance zone again.
Additional local measures targeting road traffic may be required to
ensure safe attainment of the limit values, which may include the
reduction of traffic volumes through local traffic management such as low emission zones
or incentives for public transport use, the reduction of road dust abrasion through
restrictions on studded tyre use in Scandinavian countries ,
use of enhanced road surface material or advanced road cleaning/dust binding
practices .
Eastern European
countries, on the other hand, suffer from the widespread use of solid
fuels such as low-grade coal or inefficient wood burning. Efficient
emission cleaning technologies can improve the situation dramatically,
as shown in Fig. ; however, a hypothetical
switch to cleaner fuels would provide for even better results.
Conclusions
This paper presents an introduction to the station based modelling methodology that has
been introduced in the GAINS integrated assessment model to calculate concentrations of
PM10 and estimate compliance with limit values. Results are calculated for a total
of about 1 870 monitoring stations reporting to AirBase. The modelling approach is based on
explaining observed concentrations for the base year 2009 to the extent possible with a chain
of simplified atmospheric chemistry and transport calculations with models of different scales.
Concentrations for other years are then calculated by substituting emissions from the GAINS
bottom up emission calculation scheme.
Due to the complexity of the system involving different spatial
scales, simplifications are necessary. The modelling scheme is not
intended to replace detailed small scale dispersion modelling. The
focus here is to provide an estimate of the effects of Europe-wide air
quality policies on the attainment of limit values. Although results
are calculated for each station individually, they are best evaluated
on an ensemble base, as individual emission trends are not calculated
for each station. On the contrary, GAINS quantifies for each station
the effects of Europe-wide policy measures.
Different locations face different challenges for attaining safe PM
levels. Both the geographical origin as well as the chemical
composition vary considerably. While parts of the PM problem –
particularly secondary aerosol formation – are related to
transboundary transport in many EU Member States, calling for
synchronised EU wide action, cities also suffer from the local
increment generated mainly by household heating and road traffic.
Historical trends in observed concentrations are well reproduced by
the model, a prerequisite for trustworthy conclusions on the future
evolution. For the future, under the assumption of successful
implementation of current legislation, reductions in ambient
PM10 concentrations are expected and consequently a higher
attainment of the PM10 limit value. However, current
legislation is not expected to lead to Europe-wide attainment of the
PM10 limit value. Challenges are foreseen particularly in
Eastern Europe, where widespread use of coal and inefficient wood
burning in domestic heating hampers significant improvement, and in
several major urban areas which suffer from increasing road traffic
and stagnating household emissions. Considering that many of the
remaining exceeding stations are located in densely populated areas,
a significant proportion of the European population can be expected to
remain exposed to PM concentrations violating EU air quality standards
unless further political action is taken.
A range of technical emission control measures is readily available to
decrease PM and precursor emissions beyond the baseline, as discussed
by . Exploiting the full range of emission controls
available, concentrations could be decreased significantly further, and most
cases of severe non-compliance persisting in 2030 could be eliminated.
However, even in this scenario, safe attainment of the limit value is not
achieved at all stations given uncertain meteorological conditions and
possible single events. A solution could lie in the switch to cleaner fuels
in domestic heating such as natural gas in Eastern European Member States.
Another challenge to safe attainment of limit values specific to urban
areas is the possibly increasing burden of road and tire abrasion, and road dust resuspension. Although the linear relation
between PMcoarse emissions and their contributions to ambient concentrations that is used in this approach
is
pessimistic, it seems logical that more traffic generates more
dust. A simple solution to this problem is yet to be found; targeted
measures such as
local traffic management (e.g. low emission zones),
improved road surface material use,
dust binding or enhanced road cleaning may be helpful
to ensure that reductions in exhaust emissions are not compensated by
increases in
non-exhaust emissions and
resuspended dust.
Acknowledgements
This work was partially supported by the EC4MACS (European Consortium
for the Modelling of Air pollution and Climate Strategies) project
with the contribution of the LIFE financial instrument of the European
Community (contract no. LIFE06 ENV/PREP/A/000006), as well as the
Service Contract on Monitoring and Assessment of Sectorial
Implementation Actions (contract no. 07.0307/2011/599257/SER/C3) of
DG-Environment of the European Commission. Monitoring data used in this study were obtained from
AirBase (version 5).
Edited by: F. Dentener
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