Air pollution is one of the main causes of damages to
human health in Europe, with an estimate of about 380 000 premature deaths
per year in the EU28, as the result of exposure to fine particulate matter
(PM2.5) only. In this work, we focus on one specific region in Europe,
the Po basin, a region where chemical regimes are the most complex, showing
important non-linear processes, especially those related to interactions
between NOx and NH3. We analyse the sensitivity of PM2.5
concentration to NOx and NH3 emissions by means of a set of EMEP
model simulations performed with different levels of emission reductions,
from 25 % up to a total switch-off of those emissions. Both single and
combined precursor reduction scenarios are applied to determine the most
efficient emission reduction strategies and quantify the interactions
between NOx and NH3 emission reductions. The results confirmed the
peculiarity of secondary PM2.5 formation in the Po basin, characterised
by contrasting chemical regimes within distances of a few (hundred)
kilometres, as well as non-linear responses to emission reductions during
wintertime. One of the striking results is the slight increase in the
PM2.5 concentration levels when NOx emission reductions are
applied in NOx-rich areas, such as the surroundings of Bergamo. The
increased oxidative capacity of the atmosphere is the cause of the increase
in PM2.5 induced by a reduction in NOx emission. This process could
have contributed to the absence of a significant PM2.5 concentration
decrease during the COVID-19 lockdowns in many European cities. It is
important to account for this process when designing air quality plans,
since it could well lead to transitionary increases in PM2.5 at some
locations in winter as NOx emission reduction measures are gradually
implemented. While PM2.5 chemical regimes, determined by the relative
importance of the NOx vs. NH3 responses to emission reductions,
show large variations seasonally and spatially, they are not very sensitive
to moderate (up to 50 %–60 %) emission reductions. Beyond 25 % emission
reduction strength, responses of PM2.5 concentrations to NOx emission
reductions become non-linear in certain areas of the Po basin mainly during
wintertime.
Introduction
Air pollution is one of the main causes of damages to human health in Europe,
with an estimate of about 380 000 premature deaths per year in the EU28, as
the result of exposure to fine particulate matter (PM2.5) only (EEA,
2020). Many of the exceedances to the EU limit values occur in urban areas
where most of the population is exposed.
PM2.5 is partly emitted directly (primary particles) and partly formed
through photo-chemical reactions that involve gaseous precursors like
SOx, NOx, NH3 and non-methane volatile organic compounds
(NMVOC) to form secondary inorganic and organic aerosol (SIA and SOA). The
secondary fraction is often dominating the total concentration of
particulate matter in urban areas as shown by e.g. Beekmann et al. (2015)
for the Greater Paris region and by De Meij et al. (2006, 2009) or Larsen et al. (2012) in northern Italy, hence the importance of understanding the complex
chemical processes that lead to its formation. In particular, it is key to
identify the precursors involved in these reactions in order to target the
right sectors of activity in air quality plans to effectively reduce
pollution levels. According to the EDGAR estimates for 2015 for Italy, about
90 % of the NH3 is directly emitted in the atmosphere by the
agriculture sector, while SOx precursors are predominantly released by
the energy production and use (industrial) sectors (EDGAR, 2020). For
NOx, emissions are spread among various sectors, with transport
(50 %), industry (40 %) and agriculture (4 %) being the main ones. The gaseous precursors of secondary organic aerosols (SOA) include a vast range
of NMVOCs among which are biogenic terpenes and anthropogenic aromatics. The
main sources of aromatics in Italy were in 2012 transport (58 %), use of
fuels and solvents (32 %), and domestic heating (15 %) (EDGAR, 2020).
Regarding SIA, early works used box models with thermodynamic schemes to
address the sensitivity of ammonium nitrate and sulfate concentrations to
gaseous NH3, NOx and SO2 emissions (Watson et al., 1994;
Blanchard and Hidy, 2003; Pozzer et al., 2017; Guo et al., 2018; Nenes et al.,
2020). These models were later on integrated into chemical transport models
(CTMs), in particular to address the benefit of additional NH3 emission
reductions in addition to already ongoing SO2 and NOx emission
reductions. For North America, Makar et al. (2009) simulated with a regional
CTM that a 30 % reduction of ammonia emissions would lead to about
1 µg m-3 reduction in PM2.5. For Europe, Bessagnet et al. (2014) simulated the effects of a 30 % NH3 emission reduction in
addition to those foreseen by the Gothenburg Protocol for 2030 and found
that the G ratio defined as the ratio between free ammonia and total nitrate
(Ansari and Pandis, 1998) was a good predictor for the efficiency of
NH3 reductions on SIA concentrations. These sensitivities to emission
reductions are often governed by complex chemical mechanisms. A well-known
phenomenon is the release of free ammonia as a result of decreased SO2
emissions and sulfate formation, which allows for the formation of
additional particulate nitrate, as described for example by Blanchard and
Hidy (2003) and Shah et al. (2018) for wintertime PM2.5 over the
eastern United States. For eastern China, Fu et al. (2017) and Lachatre et al. (2019)
showed both from modelling and satellite observations that such processes
lead to strongly increased ammonia tropospheric columns. Finally, several
works compared CTM simulations to specific observations. For instance, Pay
et al. (2012) showed that the G ratio was generally underestimated over
Europe, inducing that the Caliope model they used could probably
overestimate the efficiency of NH3 emission reductions. Petetin et al. (2016) came to a similar conclusion comparing CHIMERE CTM simulations to
observations in the Paris region. They ascribed this underestimation to
missing NH3 emissions especially during warmer periods.
The formation of SOA results from even more complex reactions involving
photo-chemical oxidation (as for SIA), nitration, fragmentation and oligomerisation of gaseous precursors or secondary products (Kroll and
Seinfeld, 2008; Shrivastava et al., 2017). Models generally use simplified
parameterisations to calculate the SOA formation yield from various classes
of parent VOCs (Tsigaridis et al., 2014), and comparison with measurements
often shows that SOA sources are still missing in models (Huang et al., 2020;
Tsimpidi, 2016).
In this work, we focus on one specific region in Europe, the Po basin. In a
companion paper (Clappier et al., 2021) that analyses PM secondary formation
chemical regimes across Europe, the Po basin is clearly identified as a
peculiar area where the chemical regime distributions are the most complex,
showing non-linear processes (Thunis et al., 2013, 2015; Carnevale, 2020;
Bessagnet, 2014), especially those related to interactions between NOx
and NH3. The Po basin is also one of the pollution hot spots in Europe
where the number of days above the limit values prescribed by the European
Ambient Air Quality Directives (AAQD) for PM10 is yet largely exceeded
(EEA, 2020). This situation results from the high emission density in this
region and also from the geographical setting of the area, in the border of the
Alps and Apennines mountain ranges that lead to very weak winds in the area,
favouring the accumulation of atmospheric pollutants.
We focus the present analysis on the NH3–NOx chemical processes
and describe their spatial and seasonal variability, which could help to
design more effective mitigation strategies. We start by describing the
modelling set-up and detail the series of simulations required to perform
our analysis. Section 3 provides a brief overview of the modelled base-case
concentrations. In Sect. 4, we analyse the sensitivity of PM2.5
concentrations to NH3 and NOx emissions. Section 5 provides an
analysis of the non-linearity in PM2.5 response to these emissions,
while in Sect. 6 we discuss the implications of these results in terms of
mitigation measures and design of air quality plans. Conclusions are finally
proposed.
MethodologyModelling set-up
The modelling study is performed with the EMEP air quality model,
version rv4_17 (Simpson et al., 2012). The emission input
consists of gridded annual national emissions (SO2, NO, NO2,
NH3, NMVOC, CO and primary PM2.5) at 0.1×0.1∘
resolution, based on data reported every year by parties to the Convention
on Long-range Transboundary Air Pollution (CLRTAP). These emissions are
provided for 10 anthropogenic source sectors classified by SNAP (Selected
Nomenclature for Air Pollution) codes (EMEP, 2003). Meteorological input
data are based on forecasts from the Integrated Forecasting System (IFS), a
global operational forecasting model from the European Centre for
Medium-Range Weather Forecasts (ECMWF). Meteorological fields are retrieved
at a 0.1×0.1∘ longitude–latitude resolution and are
interpolated to the 50×50 km2 polar stereographic grid
projection (EMEP, 2011).
The gas-phase chemistry is based on the evolution of the so-called “EMEP
scheme” as described in Simpson et al. (2012) and references therein. The
chemical scheme couples the sulfur and nitrogen chemistry to the
photochemistry using about 140 reactions between 70 species
(Andersson-Sköld and Simpson, 1999; Simpson et al., 2012). In the EMEP
Status Report 1/2004 (Fagerli et al., 2004) the reactions that
cover acidification, eutrophication and ammonium chemistry are described. The aqueous-phase chemistry describes the formation of sulfate in clouds via SO2
oxidation by ozone and H2O2 and catalysed by metal ions. An
important pathway of particulate nitrate formation is through the hydrolysis
of N2O5 on wet aerosol surfaces that converts NOx into
HNO3. More information on the chemical equations is given in Simpson et
al. (2012), Sect. 7.
The EMEP model has two size fractions for aerosols, fine aerosol
(PM2.5) and coarse aerosol (PM10-2.5). The aerosol components presently accounted for are sulfate (SO42-), nitrate
(NO3-), ammonium (NH4+), anthropogenic primary PM and sea salt.
For inorganic aerosols, EMEP uses the MARS equilibrium module to calculate
the partitioning between gas and fine-mode aerosol phase in the system of
SO42-, HNO3, NO3-, NH3 and NH4+
(Binkowski and Shankar, 1995). Aerosol water is calculated to account for
particle water within the PM2.5 mass, which depends on the mass of
soluble PM fraction and on the type of salt mixture in particles. Sea salt
(sodium chloride) and dust components are not accounted for by MARS, which
might lead to PM underestimations close to coastal sites and where the dust
contribution is important. More information on the gas and aerosol
partitioning is given in Simpson et al. (2012), Sect. 7.6.
Regarding secondary organic aerosols (SOA), the EmChem09soa scheme is used,
which is a simplified version of the so-called volatility basis set (VBS)
approach (Robinson et al., 2007; Donahue et al., 2009). The VBS mechanism is
discussed in detail in Bergström et al. (2012). The main difference
between the VBS schemes and EmChem09soa is that all primary organic aerosol
(POA) emissions are treated as non-volatile in EmChem09soa. This is done to
keep the emission totals of both PM and VOC components the same as in the
official emission inventories. The semi-volatile biogenic and anthropogenic
SOA species are assumed to further oxidise (also known as ageing process) in
the atmosphere by OH reactions. This will lead to a reduction in volatility
for the SOA and thereby increased partitioning to the particle phase. More
information on SOA is given in Simpson et al. (2012), Sect. 7.7.
The modelling domain covers the entire Po basin
(Fig. 1) with a 0.1 by 0.1∘ resolution
(polar stereographic projection centred at 60∘ N) and includes 20
vertical levels. The initial and background concentrations for ozone are
based on Logan (1998) climatology, as described in Simpson et al. (2003).
For the other species, background/initial conditions are set within the
model using functions based on observations (Simpson et al., 2003; Fagerli
et al., 2004). The simulations cover the entire meteorological year 2015. We
will not discuss the validation of the base-case simulation, as this is
available in other publications (e.g. Simpson et al., 2012) and regular
status reports by EMEP (https://emep.int/mscw/mscw_publications.html, last access: 1 June 2021).
Seasonal variations of the base-case
PM2.5 (in µg m-3, a, b, c) and SIA (in percent of the
PM2.5 concentration, d, e, f). The abbreviations Be, Ma
and Bo represent the locations selected for more detailed analysis: Bergamo
(Be), Mantua (Ma) and Bologna (Bo). Other cities are indicated by their first letter for convenience: Venice, Milan, Turin and Genoa.
In this work, we simulated a series of 24 scenarios in which NOx and
NH3 emissions were reduced independently or simultaneously by 25 %, 50 %,
75 % and 100 % from the base-case reference levels. Emission reductions were
applied over the entire Po basin domain for a complete meteorological year
(2015).
Spatial and temporal focus
Results are generally presented in terms of maps, but three locations within
the domain were selected for a more detailed analysis. The locations are
Bergamo (Be) in the northern part of the domain, Mantua (Ma; also known as Mantova) in the central
eastern part of the Po basin and Bologna (Bo) in its southern part. As described in the following sections, we will see that these locations show
very different behaviours in terms of response to emission changes.
We also aggregate results into two seasons: winter and summer, which cover the
period from November to February and from April to September, respectively.
These two seasons are characteristic of different chemical regimes as
illustrated in the following sections. The process to define the temporal
bounds of these two seasons is discussed in Appendix A. The two remaining
months (March and October) represent transition periods and are not
considered in our analysis.
As we only analyse processes involving inorganic gas-phase precursors, our
focus is on secondary inorganic PM2.5, although most of the results are
expressed in terms of total PM2.5 concentrations. The impact of
NOx emission reductions on SOA concentration is only briefly discussed
in Sect. 5.
Indicators
To describe the interactions between NH3 and NOx emissions, we use
the relationship proposed by Stein and Alpert (1993) and Thunis and Clappier
(2014). This relation expresses the change in concentration resulting from a
reduction of both precursors NOx and NH3 simultaneously, as the
sum of two single concentration changes and an interaction term, as follows:
ΔCNOxNH3α=ΔCNOxα+ΔCNH3α+C^NOxNH3α,
where ΔC stands for the PM2.5 concentration change (reference
minus scenario) for a percentage α emission reduction (thus the term ΔC/α is defined as positive for a concentration reduction,
consecutive to an emission reduction) and C^ for the interaction
term. We then scale each of these terms by the emission reduction (α) to generate potential impacts (P) (Thunis and Clappier, 2014).
This division by the factor α is a means to extrapolate virtually the
impact resulting from any percentage emission reduction to 100 %.
Potential impacts facilitate the comparison of concentration changes
obtained for different emission reduction levels. Indeed, equal potential
impacts imply a linear relationship between emission reductions and
concentration changes. For example, Pα=Pβ⇒ΔCα=αβΔCβ for α
and β, two emission reduction levels.
The overall potential impact is therefore the sum of two single potential
impacts and one interaction term.
A relation between the potential impacts of combined emission reductions at
two levels of intensity is obtained by writing Eq. (2) for two reduction levels
α and β and subtracting the two equations. This leads to the
following relation:
While the two first terms on the right-hand side of Eq. (4) represent the
single potential impacts, the remaining right-hand side terms quantify the
magnitude of non-linearities. P^NOxNH3α
quantifies the NOx–NH3 interaction at level α;
P^NOxβ-α and P^NH3β-α are
the single non-linearities associated with NOx and NH3 emissions,
respectively, between levels α and β; and
P^NOxNH3β-α represents the incremental change in
the NOx–NH3 interaction between levels α and β.
Information about non-linearity is important to design air quality plans as
it informs on the robustness of a given response, i.e. whether or not this
response remains valid over a certain range and type of emission reductions.
Because air quality models often provide responses for a limited set of
scenarios that are then used as a basis to interpolate/extrapolate the
responses to other emission reduction levels, robustness shall always be
carefully assessed.
In the next section, we present the baseline results in terms of spatial and
temporal variations.
Baseline concentrations of PM2.5 and gaseous
inorganic precursors
Before analysing the impact of emission changes on concentrations, it is
worth having a look at the baseline concentration fields. In
Fig. 1, the yearly averaged PM2.5
concentration fields show a widespread pollution plume covering most of the
area, with peak values extending in its central part. The maximum modelled
yearly values reach 29 µgm-3, which represents an average between
maximum winter values (maximum of 59 µgm-3) and minimum summer
values (17 µgm-3).
The seasonal fields of PM2.5 clearly show that high yearly average
values mostly result from the winter season contributions when more stable
atmospheric conditions lead to stagnant conditions, favouring the
accumulation of particulate matter in the area (Pernigotti et al., 2014;
Raffaelli et al., 2020). The increased emissions from the residential sector
(heating, especially wood burning) foster this process (Ricciardelli et al.,
2017; Hakimzadeh et al., 2020). Wintertime low temperatures also favour the
partitioning of semi-volatile components (e.g. ammonium nitrate) towards the
particulate phase. Overall, the relative contribution of secondary inorganic
particles (SIA) ranges between 40 % and 50 %, regardless of the season, and
is quite homogeneously distributed spatially over the entire area.
Strategies targeting SIA have therefore the potential to abate about half of
the total PM2.5 concentration.
As mentioned earlier, the secondary inorganic fraction of PM2.5 results
from complex atmospheric processes that involve gaseous precursors (mainly
SO2, NOx and NH3), which can be summarised by the two
following chemical pathways:
where g means gas phase, (aq) means aqueous phase,
and p means particulate matter, and the
character →→ symbolises a chemical pathway that summarises a set
of underlying reactions.
The second of these pathways (Reaction R2) is generally slower than the first one (Reaction R1), with the
NO2 oxidation specific time constant being typically some hours to a
day and that of SO2 being 1 to several days (Seinfeld and Pandis, 2006).
The spatial fields for the seasonal average concentrations of these
precursors (Fig. 2) reflect their emission
spatial patterns, resulting in a NO2-rich area that comprises Milan plus
its northern districts (and to a lesser extent, Turin), while NH3 is
more abundant in the central part of the Po basin, east of Milan, where
intensive agriculture practices take place. Finally, high SO2
concentrations are collocated with the NO2-rich areas nearby Milan but
with an additional zone around the harbour city of Genoa (also known as Genova; along the south
coast), reflecting the more important SO2 emissions from the shipping
sector there. However, SO2 concentrations are about 1 order of
magnitude below those of NO2. Seasonal variations are well marked for
NO2 and SO2 concentrations – not so much in terms of minimum and
maximum values but rather in terms of spread with an extended high-concentration zone during wintertime. In contrast, NH3 concentrations
remain very similar in summer and winter, both in terms of values and
spatial distribution.
Summer (a, b, c) and winter (d, e, f) concentration base-case
fields for SO2(a, d),
NO2(b, e) and NH3(c, f) expressed in µg m-3.
The abbreviations Be, Ma and Bo represent the locations selected for more detailed analysis: Bergamo (Be), Mantua (Ma) and Bologna (Bo). Other cities
are indicated by their first letter for convenience: Venice, Milan, Turin
and Genoa.
In next sections, we simulate a series of emission reduction scenarios to
analyse the response of PM2.5 concentration to single and combined
reductions of NH3 and NOx.
Analysis of the SIA formation chemical regimes Seasonal trends
From a strategic point of view, it is important to know whether NOx
(mostly emitted by the transport, industry and residential sectors) or NH3 (mostly emitted by agriculture) needs to be reduced in priority in
order to reach effective results on particulate pollution mitigation. In
Clappier et al. (2021), we have observed a great heterogeneity in SIA
formation chemical regimes across the Po basin, with different regimes being
present in limited geographical areas. Here we intend to look at these
regimes in more detail.
To analyse in detail the chemical regimes in the Po basin, we compare the
two single potential impacts PNOx25%,PNH325%
obtained for moderate emission reductions of 25 %.
Figure 3 provides a spatial overview of the
difference between these two potential impacts (PNOx25%-PNH325%). This indicator tells whether reductions of NOx
or NH3 will lead to the greatest PM2.5 concentration abatement,
i.e. if the regime is rather NOx- or NH3-sensitive, with positive
and negative values, respectively.
Winter (b) and summer (a) chemical regimes obtained
from an emission reduction level of α=25%.
The maps represent the
PNOx25%-PNH325% (unit:
µg m-3) indicator that shows the
NOx- and NH3-sensitive areas
in yellow/red and blue, respectively. The abbreviations Be, Ma and Bo indicate the
location of Bergamo, Mantua and Bologna, respectively. Other cities are indicated by their first letter for convenience: Venice, Milan, Turin and
Genoa.
During summertime (Fig. 3 – left), the entire
area is under weak NOx-sensitive conditions with a maximum intensity in
its central part, between Bergamo and Mantua.
During wintertime (Fig. 3 – right), the situation
is contrasted with a wide and intense NH3-sensitive area that appears
around and south-eastwards of Bergamo. This area includes big cities like
Milan. Other (not as marked) NH3-sensitive regime zones appear near
coastal areas. Most strongly NOx-sensitive areas are located in the
eastern parts of the domain, north of Bologna and Venice. NH3-sensitive
regimes are generally collocated with the NO2- and SO2-rich areas
(Fig. 2), whereas NOx-sensitive regimes
coincide with NH3-rich areas. The cases of the three selected cities
(Bergamo, Mantua and Bologna) representative of the NH3-sensitive,
NOx-sensitive and neutral regimes, respectively, are further analysed below.
The chemical regimes deducted from the results of emission reduction
scenarios can be compared with the maps of the G ratio
(Fig. 4), defined by Ansari and Pandis (1998) as
the ratio between free ammonia (NH3 and NH4+) and total
nitrate (HNO3+ NO3-) after neutralisation of
H2SO4. Values of the G ratio below 1 indicate a NH3-limited
chemical regime, while values above 1 characterise a HNO3-limited
chemical regime.
G ratio for winter (b) and summer (a) times. The
abbreviations Be, Ma and Bo indicate the location of Bergamo, Mantua and Bologna,
respectively. Other cities are indicated by their first letter for
convenience: Venice, Milan, Turin and Genoa.
G=NH3g+NH4+p-2SO42-(p)HNO3g+NO3-p
During summer, the G-ratio values well above unity indicate a HNO3
limited chemical regime across the Po basin. This corresponds to a NOx-sensitive chemical regime in this region (Fig. 3).
Moreover, the location of the G-ratio maximum between Bergamo and Mantua
spatially coincides with the most pronounced NOx-sensitive regime and
to a maximum of NH3 concentrations of about 20 µg m-3
(Fig. 2). Indeed, NOx emission reductions lead to HNO3
concentration reductions, which is the limiting factor in NH4NO3
formation according to the G ratio. During winter, the G ratio still shows
large values in the region south-east of Bergamo, but, contrary to summer,
the chemical regime is clearly NH3-sensitive
(Fig. 3). More generally, G-ratio values remain
above unity over the whole Po basin, while both NOx- and NH3-sensitive chemical regimes prevail in different areas. Therefore, the
G ratio, related to the abundance of total free ammonia and total nitrate,
provides information which differs from that obtained by determining the
distribution of the NH3- and NOx-emission-sensitive chemical
regimes. These differences illustrate the impossibility to directly use the
G ratio for air quality management, an interesting result in itself. We will
further discuss this interesting behaviour later when addressing
non-linearity in Sect. 4.3.
Impact of the emission reduction strength
In this section, we repeat the analysis of Sect. 4.1 for yearly average concentrations but looking at
the step changes of regimes as we progressively reduce emissions from the
base-case situation. (Fig. 5). Chemical regimes
are well in place for a 25 % level reduction (top left) and are only
slightly perturbed from 25 % to 50 % with a reinforcement of the NOx
limited regime (top right). Despite this slight change in intensity, the
regimes therefore keep the same spatial patterns. From 50 % onward,
chemical regimes tend to attenuate and reverse themselves from 75 % to
100 % (bottom right).
In other words, locations that are NH3-sensitive for the first steps
emission reductions will become NOx-sensitive for the last steps
emission reductions, and vice versa.
Yearly averaged chemical regimes obtained from a 25 %
emission reduction starting at different levels of emissions corresponding
to α=0, 25, 50 and 75. The maps represent the
(PNOx-PNH3) between the
starting and ending levels (unit: µg m-3) showing the NOx- and
NH3-sensitive areas in yellow/red and blue,
respectively. The abbreviations Be, Ma and Bo indicate the location of Bergamo, Mantua and Bologna, respectively. Other cities are indicated by their first letter for convenience: Venice, Milan, Turin and Genoa.
A summarised overview: the PM2.5 isopleths
Like isopleth plots that show the variations in the O3 concentrations
as a function of NOx and VOC concentrations (Dodge, 1977), similar
plots can be created for PM2.5 concentrations as a function of NOx
and NH3 emissions. Simulation results have indeed often been presented
as 2D isopleths of PM2.5 or nitrate as a function of precursor
emissions, which allows showing in a comprehensive manner their sensitivity
and also in a qualitative manner non-linear effects (for example Watson et
al., 1994, over the USA; and Xing et al., 2018, over the Beijing–Tianjin–Hebei
region in China). Figure 6 shows the PM2.5
isopleths obtained through an interpolation among the 25 simulation
concentration values (these 25 simulations correspond to the white square
symbols in each isopleth diagram) at the three locations Be, Ma and Bo previously defined. The x and y axes represent the strengths of the
NH3 and NOx emission sources, respectively. With this type of
graphical representation, it is possible to visualise the response of
PM2.5 to a NOx emission change by moving vertically, the reaction
to a NH3 emission change by moving horizontally or the reaction to a
combined NOx–NH3 emission change by moving diagonally. The larger
the number of isopleths we cross on the path (high gradient), the larger the
expected impact from an emission reduction will be. A simple theoretical
model to generate and interpret these isopleths is proposed in Appendix B.
PM2.5 isopleths during winter (a, b, c) and summer (d, e, f) at the three locations of interest (see maps).
PM2.5 concentrations are expressed in µg m-3 as a function of the intensity of
the NOx (y axis) and NH3
(x axis) emissions, respectively. The overlaid dashed oblique lines on each
diagram connect similar PM2.5 concentration values
for single NOx and NH3
reductions. The more vertical these lines are, the larger the
NH3 abatement impact compared to the
NOx abatement impact is; the more horizontal they are,
the larger the NOx abatement impact compared to
the NH3 abatement impact is. The dashed line delineates
the ridge between the NH3- and
NOx-sensitive areas.
From the analysis of the isopleths, we note the following points.
In general, the isopleths show a regular pattern with a progressive decrease
in PM2.5 concentration when either the NOx or NH3 emissions
are reduced, with the only exception being Bergamo during wintertime, where
NOx reductions up to 70 % lead to a small increase in PM2.5
(Fig. 6 – top left), whatever the reduction in
NH3 emission is. We discuss this particular feature later in this
section.
The diagram areas can be divided into two zones separated by a ridge (dashed
line in Fig. 6). Above the ridge line, PM2.5
is more sensitive to NH3, while below the ridge line, it is more
sensitive to NOx emission reductions. The orientation of the ridge
(tending to vertical or horizontal) informs on the type of chemical regime
(NOx- or NH3-sensitive, respectively).
The efficiency of a NOx vs. NH3 emission reduction varies across
locations. We can compare the efficiency of a given reduction by looking at
the horizontal (for NOx) and vertical (for NH3) gradients. To
support this comparison, we included in each diagram dashed oblique lines
that connect similar PM2.5 concentration values for single NOx and
NH3 emission reductions. The more vertical these lines are, the larger
the NH3 abatement impact compared to the NOx abatement impact is.
Conversely, the more horizontal they are, the larger the NOx
abatement impact compared to the NH3 abatement impact is. For moderate
emission reductions (up to 50 %, top-right corner), different behaviours
are observed: while in Bergamo PM2.5 is more sensitive to NH3
reductions, it is more sensitive to NOx reductions in Mantua, and it is
equally sensitive to both precursors in Bologna. This corresponds to the
spatial patterns of NOx- and NH3-sensitive regimes depicted in
Fig. 3.
Winter- and summertime isopleths show completely different patterns in
Bergamo, whereas at the two other locations, they remain similar.
At Mantua where moderate NOx reductions (e.g. 50 %) are the most
efficient among the three sites, NH3 emission reductions are more efficient
than NOx emission reductions for larger additional reductions (going
for example from 75 % to 100 %). At Bergamo, NH3 reductions are the
most effective for moderate reductions, whereas NOx reductions become
more effective for larger reductions, as seen by the isopleths spacing. This
confirms the finding of reversed chemical regimes for larger additional
emission reductions detailed in the previous section and illustrated in
Fig. 5.
The special pattern of Bergamo's PM2.5 isopleths during wintertime
needs some additional discussion. The increase in the inorganic fraction of
PM2.5 as a response to NOx reductions during wintertime has
already been noted by several authors (e.g. Le et al., 2020; Sheng et al.,
2018). It has been related to an increase in the oxidising capacity of the
atmosphere and in particular to increased ozone levels. This is due to the
prevailing titration of O3 by NO in wintertime high-NOx conditions
and in the absence of photochemical ozone production due to reduced solar
radiation (Kleinman et al., 1991).
NOg+O3g→NO2g+O2g
The impact of NOx emission reductions on the concentration of various
pollutants in Bergamo during wintertime is illustrated in
Fig. 7. As expected, a 50 % reduction in
NOx emissions leads to a decrease in NO2 concentration (from 47 to
28 µg m-3, i.e. a factor of 1.7). In contrast, O3
concentration increases from 8 to 16 µg m-3, roughly a factor of
2. These compensating changes result in a small increase in NO3
radical production (Reaction R4), which is the initial step of the major pathway of
wintertime HNO3 and particulate nitrate formation (Kenagy et al.,
2018).
Wintertime isopleths in Bergamo for the species: dry
PM2.5, O3,
NO2, sulfate (SO4), nitrate
(NO3), ammonium (NH4), organic
matter (OM), and anthropogenic and biogenic secondary aerosols (ASOA and BSOA,
respectively). The two numbers on the vertical axis indicate concentration
values for the base case and at the 50 % NOx emission level.
NO2g+O3g→NO3g+O2g
In this pathway, the NO3 radical formation is followed by combination
with NO2 to form N2O5, a reversible process, and
heterogeneous HNO3 formation on wet particle surfaces.
R5NO3g+NO2g↔N2O5gR6N2O5g+H2Oaerosolsurface→2HNO3g
The NO3 radical has three major rapid sinks: reaction with NO,
photolysis, and reaction with NMVOCs, especially terpenes.
Reactions (R3) to (R10) induce additional dependence of HNO3 formation on
NOx species on top of Reaction (R1), but which partly cancel out, as they are
both involved in formation and sink processes.
SOA is formed through a series of chemical reactions of gaseous precursors
– mainly volatile, intermediate volatility or semi-volatile organic compounds
(VOCs) with the oxidants O3, OH and nitrate radical (NO3) (Li et
al., 2011).
(S,I)VOCg+oxidants→(S,I)OVOC(g)↔SOA(s)
Putting all the arguments together, it follows that wintertime ammonium
nitrate formation over Bergamo is most probably controlled by NO3
radical formation (Reaction R4). The fact that this behaviour is observed in Bergamo
and not in Mantua or Bologna is due to the much larger NO2 levels in
the Bergamo–Milan area (above 50 µg m-3 during winter,
Fig. 2). Such large NO2 levels are also simulated locally over the Turin
area and also lead to a slightly NH3-sensitive regime there despite a
G ratio well above unity. Beyond 50 % NOx reduction, NH4NO3
formation decreases because NO2 decreases more rapidly than ozone
increases up to its maximum (at 75 % NOx emission reduction; see
Fig. 7).
The negative response of NH4NO3 to NOx emission reductions
in Be during wintertime explains the apparent discrepancies with the
analysis of G ratio, which indicates that NH4NO3 is strongly
HNO3 limited. Simply, the HNO3-limited chemical regime cannot be
extrapolated to sensitivity to NOx emissions in the case of the above shown
negative response. Total nitrate is less abundant than free ammonia (defined
as NH3g+NH4+p-2SO42-(p)),
but NOx emission reductions up to about 50 % do not reduce its
concentration, and NH3 emission reductions are thus more efficient. In
this respect, the G ratio cannot provide information about negative
responses.
At Bergamo during winter, the increase in PM2.5 (+1.8 µg m-3) arising from a 50 % reduction in NOx emission also results
from an increase in sulfate (+0.3 µg m-3) and in SOA (+0.6 µgm-3) concentrations. Both sulfate and SOA concentrations are
closely related to O3 concentrations (Fig. 7). The sulfate increase
is comparable in magnitude to the nitrate increase, even if sulfate levels
are much smaller than nitrate ones. Figure 7
actually shows a strikingly similar response of sulfate, SOA and ozone to
NOx emission reductions (given that SO2 and NMVOC emissions are
held constant). Indeed the prevailing wintertime aqueous production of
H2SO4 requires oxidants and in particular ozone (Le et al., 2020;
Sheng et al., 2018). In addition, the formation of SOA in both the gas and
particulate phases also requires oxidants (Vahedpour et al., 2011; Huang et
al., 2020; Feng et al., 2016, 2019; Li et al., 2011; Tsimpidi et al., 2010).
Pinder et al. (2008) also note an oxidant limitation for SIA formation over
the eastern United states for the 2000 to 2020 period, but in their simulations,
it mainly affects sulfate that increases as a result of NOx emission
reductions while nitrate decreases. This is due to a more important sulfate-to-nitrate ratio in the eastern United States than over the Po basin. Fu et al. (2020)
derive from combined measurements and modelling that wintertime nitrate
during haze events in the North China Plain (NCP) is nearly insensitive to
30 % NOx emission reductions, because increased ozone levels increase
the NOx to HNO3 conversion efficiency. Following these authors,
this conversion also involves the homogeneous HNO3 formation via the
NO2+OH. This reaction also could play a role in the Po basin, in
addition to the heterogeneous pathway. Also Leung et al. (2020) simulate
that wintertime nitrate abatement in the NCP is buffered with respect to
emission reductions by increased oxidant build-up but also by sulfate to
nitrate conversion by liberating free NH3 through sulfate concentration
reduction, which can then enhance nitrate formation. Womack et al. (2019)
find an oxidant limitation of nitrate formation over wintertime Utah (USA)
and show that nitrate concentration diminishes when reducing VOC emissions.
Analysis of non-linearities
Clappier et al. (2021) highlighted the specificities of the Po basin area
within Europe. They showed that non-linearities are present in this region.
One peculiarity of the Po basin is a marked difference between the chemical
regimes encountered within a confined area, which have implications on the
linearity of PM2.5 responses to emission changes. In this section, we
analyse in more details these non-linearities.
Information on the NOx–NH3 interaction term at the reduction level
α=25 %, P^NOxNH325%[=PNOxNH325%-(PNOx25%+PNH325%)] (first non-linear term in Eq. 4), is provided in
Fig. 8. At α= 25 %, the interaction
term is negative (or null) across the entire modelling domain (most data
points are below the 1:1 line) regardless of the chemical regimes and
averages to approximately -10 %, as indicated by the linear fit slope (=0.9). In relative terms, this interaction term is also almost constant
regardless of the season (not shown).
Yearly mean non-linear interaction term for a 25 %
reduction in NOx and NH3. The
scatter plot compares the sum of the potential impacts of the two single
reductions (x axis) with the potential impact of the combined emission
reduction (unit: µg m-3) (y axis).
Departure from the 1:1 line quantifies the overall non-linearity. Each data
point represents the yearly average values for a grid cell in the modelling
domain.
This negativity can be explained by the fact that a reduction of only
NOx implies a reduction of both NO3- and NH4+, and
the same happens when reducing only NH3; therefore a simultaneous
reduction of both precursors is lower than the sum of the two. Single
impacts would therefore lead to an overestimation (of about 10 %) in
PM2.5 reduction if added up to extrapolate linearly the impact of
combined 25 % NOx and NH3 emission reductions on yearly averaged
PM2.5 concentrations. This result is expected for what concerns
particulate NH4NO3, as a consequence of the gas–particle
equilibrium described in Reaction (R1), although non-linear relationships between
NOx emissions and HNO3 concentrations also play a role.
Qualitatively, this negative interaction is also highlighted by the
hyperbolic shapes of the PM2.5 isopleths determined for three different
sites of the domain (Fig. 6). As discussed in Appendix B, linearity would
result in isopleths parallel to the descending diagonal lines.
When emission reductions increase from 25 % to 50 %, three additional
non-linear terms are generated (three last terms in Eq. 4).
Figures 9 and 10 provide an overview of these
non-linear terms during wintertime and summertime, respectively. The top-left panel of each figure represents their sum, i.e. the total non-linearity
generated between 25 % and 50 % emission reduction. The right column shows
the non-linearities associated with NOx and NH3, while the bottom-left panel reports the non-linear interaction between the two precursors.
Wintertime maps of the overall non-linearity term (a) and of its components expressed as potential impacts between 25 % and
50 %: the single NOx non-linearity term (a, b), the single NH3 non-linearity term (d) and the NOx–NH3
interaction term (c). The three locations of interest, Bergamo,
Mantua and Bologna, are indicated by their first two letters, while other
cities are indicated with their first letter for convenience (Venice, Milan,
Turin and Genoa). The hand-drawn contours roughly indicate the central
position of the NH3 (blue) and
NOx (red) sensitive regime areas (see
Fig. 3 – right).
Same as Fig. 9 but for summertime.
Overall, non-linearities are more important during wintertime than during
summertime. This is true both in absolute and relative (not shown) terms.
Non-linearities tend to be the largest in between areas that are
characterised by well-marked NH3- or NOx-sensitive regimes
(indicated by the blue and red drawn contours). This can be explained by the
fact that when one of the two components (NOx or NH3) is in large
excess (compared to the other one), reductions of this compound have then
generally only little impact, implying that both single and combined
reductions only involve one compound and are therefore similar.
During wintertime, the overall non-linearity (Fig. 9 top left) is largely dominated by the single NOx-related
non-linearity (P^NOx50 %–25 %,
Fig. 9 top right) – a singularity in Europe as the
Po basin is the only area where this occurs to this extent (Clappier et al.,
2021). In the region of Bergamo, the NOx non-linearity remains weak
despite the peculiar PM2.5 responses to NOx emission reductions
(i.e. an increase in PM2.5 concentrations for NOx emission
reduction up to 50 %, Fig. 7). In this NH3-sensitive region, this behaviour can be explained by the strong oxidant
limitation of HNO3 formation outlined above (Fig. 7). It is worth
mentioning that, although atypical, this behaviour is quasi-linear with
PM2.5 responses that remain proportional to the emission reduction
strength (up to 50 %) but with a negative slope. Similarly to NOx,
NH3 single non-linearities (Fig. 9, bottom
right) are positive but weaker, indicating slightly larger potential impacts
for emission reductions in the range 25 %–50 % than in the 0 %–25 % range.
Finally, the NOx–NH3 non-linear interaction terms
(Fig. 9, bottom left) are mostly negative,
indicating that the interaction term for 50 % emission reductions is more
negative than the corresponding term for 25 %, pointing out to a
strengthening of the NOx–NH3 non-linearity when more intense
emission reductions are considered.
In relative terms, the overall wintertime non-linearity terms increase by
about 30 % when emission reductions increase from 25 % and 50 %
(Fig. 11 top, blue points). Note that these
non-linearity terms reach larger values in some places of the modelling
domain as highlighted by the data point dispersion. In a previous work,
Thunis et al. (2015) quantified the non-linearity of model responses to
emission reductions in three areas in Europe, among which is the Po Basin. One
of their conclusions was that non-linearities remain relatively low for
yearly averaged responses. Although the results presented here show
important non-linearities, these occur mainly during wintertime and are
limited to specific areas. It is also worth noting that these non-linear
responses (for moderate emission reductions up to 50 %) only occur in the
Po Basin (Clappier et al., 2021).
Changes in the overall non-linearity terms from 25 % to
50 % (a), from 50 % to 75 % (b) and from 75 % to 100 % (c) in
NOx and NH3. The overall
non-linearity is visualised as the distance from the 1:1 diagonal, i.e. the
difference between the overall potential impact at two levels of emission
reduction, x and y axis. Each point represents one land grid cell within
the domain for wintertime (blue) and summertime (red). The “fit”
parameter indicates the slope of the regression line, while R2 and RMS
provide information on the coefficient of determination and the root mean
square error, respectively.
During summertime, the magnitude of non-linear terms is smaller than during
wintertime. The overall non-linearity term (top left) is dominated by the
NH3–NOx non-linear interaction term
(P^NOxNH350 %–25 %, Fig. 10, bottom left). In
summer, the PM2.5 concentration is NOx-sensitive in almost all the
domain (Fig. 3), and emission reductions do not lead to shifts in the
chemical regimes. In this case, the interaction terms become more important.
The non-linear interaction terms are negative everywhere in the Po basin,
implying again that the interaction terms at 50 % are more negative than at
25 % emission reduction (reinforcement of the non-linearities when more
intense emission reductions are considered). Most negative values appear in
the western and northern part of domain.
Figure 11 shows the increase in the overall
non-linear terms for emission reduction steps starting from different
points, from 25 % to 50 %, from 50 % to 75 % and finally from 75 % to
100 %. Regardless of the emission reduction step, summertime
non-linearities remain small all over the domain, with the regression slope close
to 0.9 and very limited data point dispersions (i.e. low RMSE). Wintertime
non-linearities further increase significantly from 50 % to 75 %
reduction levels (regression fit parameter close to 1.21) but tend to
stabilise between 75 % and 100 % reduction levels (regression fit parameter
close to 1.05). It is interesting to note that potential impacts in winter
increase for all segments (all winter points are above the 1:1 line),
indicating that the same percentage reduction (25 %) gains progressively
more impact when more intense reductions are considered.
Discussion
When designing air quality plans, it is important to identify the key
precursors on which to act in priority to hit a specific air quality target
but also to understand the consequences of these choices for various seasons
(temporal variations), locations (spatial variability), emission reduction
levels (strength) and strategies (combined or single emission reductions).
As the information to make this decision is generally incomplete, assessing
the robustness of the available model responses is essential. From the
results presented here, a few key points appear.
The seasonal and spatial variabilities in the response of PM2.5 to the
reduction of NOx and NH3 emissions are extremely large, with
different and sometimes opposite responses to emission changes. Yearly
averages do not represent the appropriate time window to evaluate the impact
of such emission reductions, and a focus on wintertime (November to February)
seems to be the right option, especially because concentrations are larger
during this period of the year.
The responses of PM2.5 to emission reduction plans that cover the whole
area (i.e. uniform emission reductions are applied everywhere in the domain)
vary from location to location: opposite responses occur within a few
hundred kilometres for some reduction levels. In the region of Bergamo,
the PM2.5 response to NOx emission reductions can be negative, meaning
an increase in PM2.5 when reducing the NOx emissions. It is
important to combine NOx and NH3 emission reductions in winter or
to go for stronger emission reductions to make sure these unwanted effects
are limited.
Despite quite important non-linearities, PM2.5 responses to emission
reductions are not chaotic. Indeed, regardless of the emission reduction
level, the non-linear terms related to NH3 emission reduction and to
NOx–NH3 interactions are quite uniform spatially. This is not the
case of NOx emission reduction, for which care must be taken to ensure
that the detailed response of PM2.5 is captured.
Although they are location specific, PM2.5 isopleths represent an
interesting tool to assess the impact of different NOx and NH3
emission reductions on PM2.5 concentration. They indeed allow
visualising in one single diagram the impact of any type of reductions on
concentrations in a single grid cell (or set of grid cells). We must however
remember that these isopleths derive from uniform emission reduction over
the whole domain. Comparing sites where PM2.5 responses to the same
“domain-wide” policy are different, it appears challenging to define a
single domain-wide policy efficiently reducing PM at all locations.
Conclusions
In this work, we analysed the sensitivity of PM2.5 to NOx and
NH3 emissions by means of a set of EMEP simulations performed with
different levels of emission reductions, from 25 % up to a total
switch-off of those emissions. Both single and combined precursor reduction
scenarios were applied to determine the most efficient emission reduction
strategies and quantify the interactions between NOx and NH3
emission reductions. Our results confirm the peculiarity of secondary
inorganic PM2.5 formation in the Po basin suggested by Clappier et al. (2021), characterised by contrasting chemical regimes within distances of
a few (hundred) kilometres, as well as non-linear responses to emission
reductions during wintertime. One of the striking results is the increase in
the PM2.5 concentration levels when NOx emission reductions are
applied in NOx-rich areas, such as the surroundings of Bergamo. The
isopleths in the graphs showing PM2.5, nitrate, sulfate, SOA and
O3 concentrations as a function of NH3 and NOx emissions
(Fig. 7) indicate that the increased oxidative capacity of the atmosphere
is the cause of the increase in PM2.5 induced by a reduction in
NOx emission of up to 50 %. This process can have contributed to the
absence of significant PM2.5 concentration decrease during the COVID-19
lockdowns in many European cities (EEA, 2020; Putaud et al., 2021). It is
important to account for this process when designing air quality plans,
since it could well lead to transitionary increases in PM2.5 at some
locations in winter as NOx emission reduction measures are gradually
implemented. At this type of location, mitigation measures that would be
optimal in the long term might not be efficient in the short term.
Joint analyses of PM sensitivity to emissions and the G ratio can give a
clue if a NH3-sensitive chemical regime is due to either a lack of
NH3 or to a non-linear and negative response of HNO3 concentration
to NOx emissions. In this latter case, the chemical regime is NH3-sensitive in terms of NH3 and NOx emission reductions, but it can
be HNO3 limited in terms of the G ratio, as observed for the Bergamo–Milan region. Inversely, a G ratio greater than 1, indicating HNO3
limitation to particulate nitrate formation, does not necessarily indicate a
larger sensitivity to NOx than to NH3 emissions. Thus, our results
show the impossibility to directly use the G ratio for air quality
management, an interesting result in itself. While PM2.5 chemical
regimes (determined by the relative importance of the NOx vs. NH3
responses to emission reductions) show large variations seasonally and
spatially, they are not very sensitive to moderate (up to 50 %) emission
reductions. Beyond 25 % emission reduction strength, responses of
PM2.5 concentrations to NOx emission reductions become non-linear
in certain areas of the Po basin mainly during wintertime.
Since sulfate concentrations are low in the Po basin, the impact of SO2
emission reductions was not evaluated here. However, the simulations
performed in Clappier et al. (2021) indicate that air quality improvement
plans addressing SO2 emissions may still lead to additional PM2.5
decreases. Further works should also test if NMVOC emission would further
affect the concentration of oxidants and subsequently of nitrate (and
sulfate) during winter. This depends on the fraction of ozone formed
photochemically in the Po basin, compared to the one transported from
outside by advection or entrainment.
Finally, it would be important to compare the results obtained in this work
from the EMEPrv4_17 model with similar results obtained from
other models. With its complex setting, the Po basin represents a good test
case for such inter-comparisons.
Selection of seasons
Chemical regimes show a great variability with time. To select the extension
of the seasons, we analyse the monthly behaviour of the NH3 and
NOx responses as well as the interaction terms
(Fig. A1). The analysis is performed for two
locations: Bergamo, which lies in a NH3-sensitive zone; and Mantua, which
lies within a NOx-sensitive zone. On these plots, we identify two major
seasons with a consistent behaviour: winter from November to February and
summer from April to September. These two seasons are similar at both
locations. The two remaining months, March and October, are transition months
and are not considered in the analysis. It is interesting to note that these
two transition periods correspond more or less to the switching time between
the NOx and NH3 concentration time profiles
(Fig. 12 – right panel). On the latter figures,
the SO2 and NO2 temporal evolutions are almost identical, in
contrast to NH3.
Monthly averaged responses to NH3
(blue), NOx (red) reductions (25 %) and interaction
terms (grey) at two locations: Bergamo (a, b) and Mantua (c, d). Panels (b) and (d) shows the monthly evolution of the concentrations of
NO2, NH3 and
SO2 at those two locations. Note that concentrations
are normalised by their average values.
A theoretical example for the isopleths
To facilitate the interpretation of the isopleths diagrams, we use a simple
theoretical example that mimics the complex reactions process schematised by
Reactions (R1) and (R2) above. Our simplified process is described by the
following relation: Ccx,y=minEAx,EBy, where Cc
is the concentration of a compound “c” that is given by the minimum
between two emitted species A and B. The concentration depends on the
strengths of these emissions, specified by the parameters x and y. For each
emission strength (x or y), the two emission species are proportional to
their full-scale value (100 %): EAx=xEA100 and EBy=yEB100, respectively. If we choose
EA(100)≫EB(100), we create a B-sensitive
environment (Fig. B1 – left column) and inversely
(Fig. 13 – right column). If we select mixed
situations, representing for example an average of days, during which we
alternate between A- and B-sensitive regimes, we obtain the two bottom
isopleth diagrams that represent cases where a larger number of A-sensitive
(right) or B-sensitive (left) events are recorded. Although extremely
simple, these diagrams illustrate properties that are observed on the real
test cases.
Let us take the example of an A-sensitive regime. Similar observations can be
made in the case of a B-sensitive regime. We note the following points.
The diagram area can roughly be divided into two zones separated by a ridge:
a top-left triangle where the sensitivity to emission reductions of species
“B” dominates and a bottom-right triangle where the sensitivity to A
dominates. The slope of the ridge (larger or less than 1) informs on the
type of regime.
In the case of a single A-sensitive day (top right) with
EB(100)=2EA(100), the concentration of compound “c” remains
unchanged for emission reductions of B up to 50 %, while its concentration
react in a linear way to emission changes in A from 0 % to 100 %. Between
the base case and a reduction level of 50 %, the A gradient is therefore
larger than the B gradient. This implies that the B gradient is larger than
the A gradient between the 50 % and 100 % reduction levels because we know
that for a 100 % reduction of A or B, the concentration must be zero. In
our simple example, the gradient of B is zero from 0 % to 50 % but is twice
as large as A between 50 % and 100 %.
While the combination of several events (e.g. days) characterised by
different regimes leads to smoother isopleths (bottom), the same
characteristics can be noted. In particular, the inclination (tending to the
horizontal or vertical) provides information on the type of chemical regime.
Isopleths for a simple theoretical system consisting of
two emission precursors (A and B) competing through non-linear reactions to
the concentration of a pollutant. See details in the text.
Code availability
The Open Source EMEP model and all necessary input data to run the model are available through this link: https://github.com/metno/emep-ctm (Norwegian Meteorological Institute, 2021).
Data availability
Data are in the process of being transferred to a public repository. In the meantime they are available upon request to the authors.
Author contributions
PT and AC designed the methodology, analysed the results and drafted a first version of the paper. MB and JPP contributed to the analysis and interpretation of the results. ADM and JM performed the simulations. CC contributed to the data treatment and visualization aspects. All co-authors critically reviewed the paper.
Competing interests
The authors declare that they have no conflict of interest.
Review statement
This paper was edited by Astrid Kiendler-Scharr and reviewed by two anonymous referees.
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