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Atmos. Chem. Phys., 24, 397–425, 2024
https://doi.org/10.5194/acp-24-397-2024
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Research article
Impact of urbanization on fine particulate matter
concentrations over central Europe
Peter Huszar, Alvaro Patricio Prieto Perez, Lukáš Bartík, Jan Karlický, and Anahi Villalba-Pradas
Department of Atmospheric Physics, Faculty of Mathematics and Physics, Charles University, Prague,
V Holešoviˇ
ckách 2, 18000, Prague 8, Czech Republic
Correspondence: Peter Huszar (peter.huszar@matfyz.cuni.cz)
Received: 17 May 2023 – Discussion started: 28 August 2023
Revised: 17 October 2023 – Accepted: 24 November 2023 – Published: 11 January 2024
Abstract. Rural-to-urban transformation (RUT) is the process of turning a rural or natural land surface into an
urban one, which brings about important modifications in the surface, causing well-known effects like the urban
heat island (UHI), reduced wind speeds, and increased boundary layer heights. Moreover, with concentrated
human activities, RUT introduces new emission sources which greatly perturb local and regional air pollution.
Particulate matter (PM) is one of the key pollutants responsible for the deterioration of urban air quality and is
still a major issue in European cities, with frequent exceedances of limit values. Here we introduce a regional
chemistry–climate model (regional climate model RegCM coupled offline to chemistry transport model CAMx)
study which quantifies how the process of RUT modified the PM concentrations over central Europe including
the underlying controlling mechanisms that contribute to the final PM pollution. Apart from the two most stud-
ied ones, (i) urban emissions and (ii) urban canopy meteorological forcing (UCMF; i.e. the impact of modified
meteorological conditions on air quality), we also analyse two less studied contributors to RUT’s impact on air
quality: (iii) the impact of modified dry-deposition velocities (DVs) due to urbanized land use and (iv) the impact
of modified biogenic emissions due to urbanization-induced vegetation modifications and changes in meteoro-
logical conditions which affect these emissions. To calculate the magnitude of each of these RUT contributors,
we perform a cascade of simulations, whereby each contributor is added one by one to the reference state, while
focus is given on PM2.5(particulate matter with diameter less then 2.5µm). Its primary and secondary com-
ponents, namely primary elemental carbon (PEC), sulfates (PSO4), nitrates (PNO3), ammonium (PNH4), and
secondary organic aerosol (SOA), are analysed too.
The validation using surface measurements showed a systematic negative bias for the total PM2.5, which is
probably caused by underestimated organic aerosol and partly by the negative bias in sulfates and elemental
carbon. For ammonium and nitrates, the underestimation is limited to the warm season, while for winter, the
model tends to overestimate their concentrations. However, in each case, the annual cycle is reasonably captured.
We evaluated the RUT impact on PM2.5over a sample of 19 central European cities and found that the
total impact of urbanization is about 2–3 and 1–1.5 µgm−3in winter and summer, respectively. This is mainly
driven by the impact of emissions alone causing a slightly higher impact (1.5–3.5 and 1.2–2µg m−3in winter
and summer), while the effect of UCMF was a decrease at about 0.2–0.5µg m−3(in both seasons), which was
mainly controlled by enhanced vertical eddy diffusion, while increases were modelled over rural areas. The
transformation of rural land use into an urban one caused an increase in dry-deposition velocities by around
30 %–50%, which alone resulted in a decrease in PM2.5by 0.1–0.25 µg m−3in both seasons. Finally, the impact
of biogenic emission modifications due to modified land use and meteorological conditions caused a decrease
in summer PM2.5of about 0.1 µgm−3, while the winter effects were negligible. The total impact of urbanization
on aerosol components is modelled to be (values indicate winter and summer averages) 0.4 and 0.3µg m−3for
PEC, 0.05 and 0.02 µgm−3for PSO4, 0.1 and 0.08 µgm−3for PNO3, 0.04 and 0.03 µgm−3for PNH4, and 0 and
0.05 µg m−3for SOA. The main contributor of each of these components was the impact of emissions, which was
usually larger than the total impact due to the fact that UCMF was counteracted with a decrease. For each aerosol
Published by Copernicus Publications on behalf of the European Geosciences Union.
398 P. Huszar et al.: Urbanization impact on PM concentration
component, the impact of modified DV was a clear decrease in concentration, and finally, the modifications of
biogenic emissions impacted SOA predominantly, causing a summer decrease, while a very small secondary
effect of secondary inorganic aerosol was modelled too (they increased).
In summary, we showed that when analysing the impact of urbanization on PM pollution, apart from the
impact of emissions and the urban canopy meteorological forcing, one also has to consider the effect of modified
land use and its impact on dry deposition. These were shown to be important in both seasons. For the effect
of modified biogenic emissions, our calculations showed that they act on PM2.5predominantly through SOA
modifications, which only turned out to be important during summer.
1 Introduction
In the upcoming years, more the 60 % of Earth’s population
will live in cities (United Nations, 2018), while urban areas
in general represent only a tiny fraction of the habitable land.
Moreover, the process of urbanization is predicted to con-
tinue by the end-of-the 21st century under all SSPs (Shared
Socioeconomic Pathways) (Gao et al., 2020). It is thus a great
desire to quantify the environmental footprints’ urbanization
or, more precisely, the causes of rural-to-urban transforma-
tion (RUT).
RUT acts via two primary pathways: (a) with the hu-
man activities concentrated in urban areas, a great amount of
emissions are introduced (both green-house-gas and short-
lived pollutants), which not only affect local but also re-
gional and global air pollution (Im and Kanakidou, 2012;
Markakis et al., 2015; Timothy and Lawrence, 2009; Butler
and Lawrence, 2009; Stock et al., 2013; Huszar et al., 2021),
and (b) urban land surfaces differ greatly from rural ones
through the introduction of artificial objects and surfaces in
specific geometries (e.g. street canyons and buildings), which
affect the surface-air fluxes of energy, momentum, and mate-
rial, with strong consequences on meteorological conditions
(Oke, 1982; Oke et al., 2017; Karlický et al., 2020), and at
the same time, in the long term, they modify the regional
climate (Huszar et al., 2014; Karlický et al., 2018). In other
words, cities have a strong impact on the whole atmospheric
environment (Folberth et al., 2015).
Regarding the first pathway, it is clear that urban emissions
alone substantially deteriorate the local air pollution (Thunis
et al., 2021). They are composed of a mixture of different
gases like oxides of nitrogen (NOx) originating mainly from
road transportation (Huszar et al., 2016a, 2021), along with
volatile organic compounds (VOCs), carbon monoxide (CO),
and sulfur dioxide (SO2) or ammonia (NH3). Further, urban
emissions also contain primary aerosol in the form of ele-
mental carbon (PEC), primary organic aerosol (POA), and
other primary material like metals (Freney et al., 2014; Al-
lan et al., 2010; Rivellini et al., 2020; Yang et al., 2023).
By introducing urban emissions, large quantities of these pri-
mary pollutants are added to their background levels. More-
over, they are potentially responsible – as precursors – for
the formation of secondary pollutants too. NOx, together
with VOCs (partly supported by CO), leads to the forma-
tion of ozone (O3), while the NOx-to-VOC ratio determines
the amount of O3formed or destroyed (Beekmann and Vau-
tard, 2010; Xue et al., 2014). Emissions of gaseous pollutants
further lead to formation of secondary aerosol. NOx, SO2,
and NH3are absorbed by water droplets, leading to the for-
mation of secondary inorganic aerosol (SIA). This includes
sulfates (PSO4), nitrates (PNO3), and ammonium (PNH4).
The main precursor for PSO4is SO2, and despite it exhibit-
ing decreasing global emissions (Zhong et al., 2020), many
urban areas are still marked with a significant perturbation
of aerosol burden due to this pollutant (Guttikunda et al.,
2003; Yang et al., 2011). It has to be further noted that sul-
fates can be emitted directly too and thus contribute to total
particulate matter (PM) pollution (Li et al., 2018). Nitrogen
oxides are the main precursors for nitrate aerosol, via form-
ing nitric acid (HNO3), which is easily absorbed by water
(Seinfeld and Pandis, 1998), and it is well known that ur-
ban NOxcan significantly contribute to total PM pollution
via the formation of PNO3(e.g. Lin et al., 2010). Ammo-
nia (NH3), while it constitutes a relatively small fraction of
urban emissions (although there is an indication that trans-
port emits much more ammonia than previously thought (e.g.
Walters et al., 2022)), efficiently helps the formation of sul-
fate and nitrate aerosol by reacting to ammonium sulfates and
ammonium nitrates and is found to be very important in con-
nection with urban emissions (e.g. Behera and Sharma, 2010,
and references therein). The thermodynamical equilibrium of
the ammonium–sulfate–nitrate–water solution is, in general,
rather complicated and highly dependent on the ratio of emis-
sions of SO2–NOx–NH3as well as on the prevailing mete-
orological conditions (Martin et al., 2004). This presents the
potential of high variability of the contribution of different
cities to total aerosol burden.
There is a large number of studies that investigate the
perturbation of the atmospheric composition due to the ur-
ban emissions: Lawrence et al. (2007), Butler and Lawrence
(2009), and Stock et al. (2013) investigated the global impact
of emissions from large urban agglomerations. On a regional
scale, Im et al. (2011a, b), Im and Kanakidou (2012), Finardi
et al. (2014), Skyllakou et al. (2014), Markakis et al. (2015),
Hodneborg et al. (2011), Huszar et al. (2016a), and Hood et
al. (2018) looked at European cities (e.g. Paris, London, Is-
Atmos. Chem. Phys., 24, 397–425, 2024 https://doi.org/10.5194/acp-24-397-2024
P. Huszar et al.: Urbanization impact on PM concentration 399
tanbul, Athens), but the regional fingerprint of Asian megac-
ity emissions has also been of great interest (Guttikunda
et al., 2003, 2005; Tie et al., 2013). They all showed that the
concentrations of primary pollutants (both gaseous and pri-
mary aerosol) are substantially increased locally but also on
regional scales. On the other hand, secondary pollutants like
ozone can respond differently: for example, for these cities,
decreases in urban cores are often modelled due to emis-
sions caused by high NOx-to-VOC ratios (e.g. Huszar et al.,
2016a, 2021). Further, it was found that air pollution in cities
is mainly determined by local sources; however a consider-
able part of the total concentration is associated with rural
ones (Panagi et al., 2020; Thunis et al., 2021; Huszar et al.,
2021).
Besides the direct impact of urban emissions, urbanization
also influences air chemistry via so-called “urban canopy me-
teorological forcing” (UCMF), as introduced by Huszar et
al. (2020a). The urban land surface brings about higher tem-
peratures (urban heat island or UHI; Oke, 1982; Karlický
et al., 2020; Sokhi et al., 2022), drag-induced wind-speed
reductions (Huszar et al., 2018b; Zha et al., 2019), and en-
hanced vertical turbulent diffusion, along with elevated plan-
etary boundary layer height (Ren et al., 2019; Wang et al.,
2021). Further, it has a clear impact on the hydrological cy-
cle by removing the precipitated water via drainage and thus
decreasing the humidity over cities (Richard, 2004; Huszar et
al., 2018b). UCMF then propagates to modifications in trans-
port, deposition, and chemical transformation of the emitted
pollutants, leading to modifications of their concentrations
and linking the urban meteorological conditions to urban pol-
lution very tightly (Ulpiani, 2021). The impact of UCMF on
air quality in and around cities (or also rural areas) was a
focus of many modelling studies that found that the most im-
portant components of UCMF are temperature, wind speed,
and turbulence (Struzewska and Kaminski, 2012; Liao et
al., 2014; Kim et al., 2015; Jacobson et al., 2015; Zhu et
al., 2017; Zhong et al., 2018; Y. Li et al., 2019; Huszar et
al., 2018a, 2020a, b; Wei et al., 2018). Due to UCMF, pri-
mary gas-phase pollutants and PM are decreased over cities
(driven mainly by urban-land-surface-induced vertical eddy
diffusion increase). In the case of secondary pollutants, the
situation is more difficult as the total impact of UCMF is a
combination of the direct impact on the secondary pollutant
and the impact on its precursors; for example, for ozone the
resulting effect is an increase over the surface (Janssen et al.,
2017; Yim et al., 2019; Y. Li et al., 2019; Kim et al., 2021;
Kang et al., 2022; Huszar et al., 2022).
Apart from the impact of urban emissions and the im-
pact of UCMF, RUT influences the final air pollution via
two other less studied pathways too. The first is the impact
of urbanization-induced land-surface change on the dry de-
position of pollutants, and the second is the modification
of biogenic emissions due to change (decrease) of vegeta-
tion distribution due to urbanization. The land-surface type
determines the resistances of that surface (and the canopy
layer), which in turn determines the dry-deposition veloci-
ties (DVs) (Zhang et al., 2003; Cherin et al., 2015; Hardacre
et al., 2021). It has been shown by many that by urbaniza-
tion and the consequent reduction of vegetation in urban ar-
eas, the deposition velocities are greatly reduced for some
gaseous pollutants (e.g. NO2, O3), leading to their increased
concentrations (Nowak and Dwyer, 2007; Mcdonald-Buller
et al., 2001; Song et al., 2008; Tao et al., 2015); for others
due to higher reactivity on solid surfaces (compared to veg-
etated ones), the DVs are increased, leading to concentration
increase (Zhang et al., 2003). For aerosol, DVs are mainly de-
termined by the sedimentation of the particles and by aerody-
namic and boundary resistances (Zhang et al., 2001). While
sedimentation is determined by particle size and shape, sur-
face resistances are a function of the roughness length and
friction velocities (Wesely, 1989), which are enhanced over
urbanized land surfaces compared to rural ones. This alone
would lead to a decrease in PM concentration, bearing larger
DVs. However, this is also modulated by the modifications
of precursor concentrations as a result of the land-surface
changes associated with RUT. For example, Huszar et al.
(2022) modelled decreases in NO2and SO2concentration
due to land-surface change (and hence dry-deposition mod-
ifications) alone, which would imply an amplification of
the land-surface-induced decrease in nitrates and sulfates.
Further, it can be assumed that ammonia (NH3) concentra-
tions are also modified by modified dry-deposition velocities,
which in turn has impacts on the amount of ammonium salts
formed.
Regarding the influence of the modified biogenic emis-
sions (BVOCs, biogenic volatile organic compounds) as a
result of urbanization, one has to realize that (i) the urban-
ization reduces the amount of vegetation (e.g. turning crop-
land into urban built-up areas), which alone reduces the emis-
sion of biogenic substances (Song et al., 2008), and (ii) it
was detailed above that urban areas exhibit higher tempera-
tures, and moreover it seems that cloudiness is somewhat re-
duced above cities too (with respect to rural regions), mean-
ing higher solar incident radiation at the surface (Karlický et
al., 2020) – both promoting the vegetation metabolism, re-
sulting in higher fluxes of BVOCs (Guenther et al., 2006).
These two effects (i and ii) counteract each other, but the
dominant one is probably the vegetation effect (Y. Li et al.,
2019; Huszar et al., 2022); i.e. due to urbanization, BVOC
emissions are reduced. As for the effect of such reduction,
it is expected that near-surface ozone concentration will be
decreased as urban areas are usually VOC-limited (Song et
al., 2008). In the case of PM, this will act via modification
of the formation of secondary organic aerosol (SOA). It has
been shown by many that BVOCs are important precursors of
SOA and responsible for the formation of biogenic secondary
organic aerosol (BSOA; Gao et al., 2022). Couvidat et al.
(2013) showed that almost one-third of the organic material
in the Paris region originates from biogenic VOCs. The great
importance of BVOCs in urban SOA formation was also con-
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400 P. Huszar et al.: Urbanization impact on PM concentration
firmed by Sartelet et al. (2012), Hu et al. (2017), Nagori et
al. (2019), and Ma et al. (2023), but of course, anthropogenic
precursors also remain very important (Zhang et al., 2015;
Guo et a., 2022). While it is clear that most of the BVOC
emissions originate from rural and natural land surfaces (i.e.
“non-urban” areas Lin et al., 2016), it is expected that any
change in urban BVOC emissions as a result of urban expan-
sion will have an immediate affect on SOA concentrations
too and, hence, the total PM.
To summarize, urbanization (RUT) greatly modifies the air
composition over both the cities themselves but also over sur-
rounding rural areas, while this modification is the result of
four impacts or contributors that add up to the background
air pollution level (i.e. that without urbanization): namely,
(i) the effect of urban emissions (“DEMIS”); (ii) the effect
of urban canopy meteorological forcing (UCMF) on trans-
port and chemical transformations (“DMET”); (iii) the effect
of modified dry-deposition; velocities as a result of modified
(urbanized) land surface (“DLU_D”) and, finally, (iv) the ef-
fect of modified emissions of BVOCs due to modified veg-
etation cover and meteorology (“DBVOC”). Together, they
constitute the total RUT impact (“DTOT”).
These four impacts were also formulated in our previous
paper (Huszar et al., 2022) in which we looked, using re-
gional chemistry-transport models coupled to regional cli-
mate models, at their effect on gas-phase chemistry; however
most of the other studies mentioned above focused either on
the total impact of the urbanization or on some of the indi-
vidual impacts without a detailed analysis of the contribution
of each of them. The above-mentioned Huszar et al. (2022)
study indeed aimed at the quantification of each individual
impact as well as the total impact as one of the first studies
of its kind analysing the central European domain at a mod-
erate horizontal resolution. Here, as a follow-up study, we
extend our analysis conducted there to particulate matter and
will investigate how the total PM (as PM2.5, particles of di-
ameter less than 2.5 µm) as well as its primary and secondary
components, responds to these impacts. To fulfil this goal,
a background or reference state has to be defined to which
these impacts will be gradually added: for our purpose, that
reference state will be the non-urbanized land surface with-
out any urban emissions (only rural ones) and without the ef-
fect of UCMF. The analysis will focus on present-day condi-
tions, which includes present day driving climate, emissions
and land use. The four listed impacts will then be gradually
added to this reference (i.e. non-urbanized) state in a cas-
cading manner. To consider the uncertainty arising from a
different background climate, size, and emissions from dif-
ferent cities, we conduct our analysis on a larger selection of
19 cities from central Europe.
As mentioned, our analysis will focus on PM2.5and its
components. Despite notable improvements in European PM
pollution, EEA (2022) reports that in 2020, 96 % of the ur-
ban population in the European Union was exposed to high
levels of PM2.5. This makes the investigation of the compo-
nents and contributors to urban PM pollution very important.
It also has to be noted that urban air quality is influenced
not only by the local effects. Im and Kanakidou (2012) and
Huszar et al. (2016a), for example, showed that emissions
from other areas (rural or other, even distant cities) repre-
sent a major fraction of urban pollution burdens. Also the
urban canopy meteorological forcing can act not only locally
over cities but over regional scales too, and UCMF triggered
by one city can have impact on other ones too, as shown by
Huszar et al. (2014). Here we however will be interested in
the local effects only, without looking at the inter-urban in-
fluences (i.e. the effect different cities pose on each other mu-
tually).
The study is structured in the following way: the Intro-
duction is followed by the presentation of experimental tools
(models), their configuration, and the data used. Next, the
experiments performed are described and the results summa-
rized in the Result section. Finally, these are discussed and
conclusions are drawn.
2 Methodology
2.1 Models used
The study uses the same models, model settings, and input
data as Huszar et al. (2022). Here we will therefore only sum-
marize the most relevant information about the model setup
stressing the eventual differences.
The chemistry transport model (CTM) simulations were
carried out by the CAMx version 7.10 model (Ramboll,
2020a) using the Carbon Bond 6 revision 5 (CB6r5) scheme
(Cao et al., 2021). Aerosol physics and chemistry were
treated with a static two-mode approach, and the ISOR-
ROPIA thermodynamic equilibrium model (Nenes and Pan-
dis, 1998) was applied for the secondary inorganic aerosol
formation. For secondary organic aerosol (SOA), the SOAP
equilibrium scheme (Strader et al., 1999) was used. For wet-
and dry-deposition treatment, the Seinfeld and Pandis (1998)
and Zhang et al. (2001, 2003) methods were used.
CAMx was driven by the regional climate model (RCM)
RegCM version 4.7 (Giorgi et al., 2012) using non-
hydrostatic dynamics. Planetary boundary layer (PBL)
physics, cloud and rain microphysics, convection, and ra-
diation are treated following Holtslag et al. (1990), Hong
et al. (2004), and Tiedtke et al. (1989). The atmosphere–
biosphere–surface coupling was treated with the Commu-
nity Land Model (CLM) version 4.5 (Oleson et al., 2013)
land-surface scheme, and to account for the urban scale pro-
cesses, the CLMU module within CLM4.5 was used (Ole-
son et al., 2008, 2010). CLMU adopts the classical canyon
geometry approach; i.e. cities are represented as networks
of street canyons with specified geometry and surface pa-
rameters (Oke et al., 2017), while anthropogenic heat (AH)
from heating and waste heat from air conditioning are ac-
counted for. AH from traffic is not calculated, which might
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P. Huszar et al.: Urbanization impact on PM concentration 401
result in some underestimation of temperature due to this
missing heat source. RegCM was coupled offline to CAMx
using the RegCM2CAMx interface developed by Huszar et
al. (2012). The vertical eddy diffusion coefficients (Kv) are
diagnosed using the CMAQ approach (Byun, 1999). Be-
cause of the offline character of the coupling, no feedback
between RegCM’s radiation scheme and chemistry is con-
sidered. Based on 10-year-long simulations, Huszar et al.
(2016b) concluded that the radiative effects of urban pol-
lutant emissions and secondarily formed pollutants are mi-
nor, which justified the offline coupling (which is not true for
short-term effects that can be considerable; for example, Pri-
eto Perez et al., 2022). On the other hand, all urban effects on
meteorology, i.e. urban heat island or moisture island (and
others, like increased vertical turbulence, lower over wind
speeds over cities), are included in the meteorological data
that are fed offline into CAMx, thus having a direct impact
on chemistry in our simulations.
2.2 Model setup and data
Model simulations with RegCM and CAMx were conducted
over a 9km ×9 km resolution domain covering “larger” cen-
tral Europe (from France to Ukraine and from northern Italy
to Denmark) with 189 ×165 grid boxes, centred over the
Czech capital, Prague (50.075◦N, 14.44◦E; Lambert conic
conformal projection). In the vertical, the model grid spans
40 layers in RegCM up to 5 hPa, while CAMx uses the low-
ermost 18 layers up to about 12 km. The simulated years
include December 2014 to December 2016, with the first
month serving as the spin-up. Fenech et al. (2018) showed
that the difference between the coarse- and fine-resolution
PM2.5concentrations is rather small, and our resolution is
comparable to the resolution of the emissions (see below).
The requirement of Tie et al. (2010) that the size of the city
to resolution should be 6 :1 means that preferably, a reso-
lution of 6 km or finer should be used; however we rely on
the fact that land use is represented as fractional land use,
so even the smallest cities are resolved within the surface
model in our RCM. Markakis et al. (2015) showed that the
modelled PM2.5concentrations for Paris are more sensitive
to the emissions inventory’s resolution than to the resolution
at which the meteorology is resolved in the driving RCM.
RegCM simulations are forced with the ERA-Interim re-
analysis (Simmons et al., 2010). Chemical boundary con-
ditions are taken from the CAM-chem global climate-
chemistry model (Buchholz et al., 2019; Emmons et al.,
2020). Land use for RegCM and for the CAMx dry-
deposition calculations is based on the 100 m resolu-
tion CORINE Land Cover (CLC 2012) data (https://land.
copernicus.eu/pan-european/corine-land-cover, last access:
16 May 2023) except a small area over Belarus, where
CORINE is not available, so the United States Geological
Survey (USGS) data are used.
Anthropogenic emissions are taken from the European
CAMS (Copernicus Atmosphere Monitoring Service) ver-
sion CAMS-REG-APv1.1 inventory (Regional Atmospheric
Pollutants; Granier et al., 2019) for the year 2015 combined
with the Czech national emission data, the Register of Emis-
sions and Air Pollution Sources (REZZO) dataset issued by
the Czech Hydrometeorological Institute (https://www.chmi.
cz/, last access: 16 May 2023) and the ATEM Traffic Emis-
sions dataset provided by ATEM (Ateliér ekologických mod-
el˚u – Studio of ecological models; https://www.atem.cz/, last
access: 16 May 2023). These annual sector-based emission
totals are decomposed to hourly speciated emissions fluxes
using the Flexible Universal Processor for Modeling Emis-
sions (FUME) emission model (http://fume-ep.org/, last ac-
cess: 9 January 2024; Benešová et al., 2018) using the speci-
ation and time-disaggregation factors of Passant (2002) and
van der Gon et al. (2011). To account for the SOA forma-
tion from intermediate VOCs (IVOCs) which are normally
not included in emission inventories, we proceeded following
Ciarelli et al. (2017); Giani et al. (2019) to calculate IVOCs
based on the known non-methane VOC and POA (primary
organic aerosol) emissions from gasoline and diesel vehi-
cles and emissions from biomass burning. This emission ad-
justment was not implemented in Huszar et al. (2022), but
there we looked at gas-phase chemistry impacts only. It has
been shown that major contributors to urban SOA precursors
in the urban environment are asphalt-based vehicular emis-
sions (Khare et al., 2020). These are strongly dependent on
solar radiation and temperature, and the currently used an-
thropogenic emission model does not include them. Thus our
estimates of the impact of urban emissions to SOA might be
somehow underestimated, especially during hot sunny days
(by not including these emissions, the winter error will, how-
ever, probably be much smaller). On the other hand, road dust
re-suspension (Rienda and Alves, 2021) is partly included
in our study, as the Czech ATEM traffic emissions consider
these types of emissions. They are however not included in
the European CAMS emissions; thus their effect is only in-
cluded for Prague, implying further potential underestima-
tion of the urbanization impact on total fine PM loads.
Biogenic emissions are computed offline using
MEGANv2.1 (Model of Emissions of Gases and Aerosols
from Nature version 2.1) with the algorithm described by
Guenther et al. (2012) driven by RegCM meteorological
fields (short-wave radiation, temperature, soil moisture,
humidity). The necessary inputs for MEGAN were not part
of the CORINE land-use information and were compiled
based on Lawrence and Chase (2007) and Sindelarova et
al. (2014, 2022). These include leaf area index (LAI) data
(weekly data), plant functional types (PFT), and emis-
sion potentials of different plant types. Besides BVOCs,
MEGAN also calculates the fluxes of soil-biogenic NO
(nitrogen monoxide) emissions from bacterial activity
(Yienger and Levy, 1995). As these emissions are a function
of LAI and meteorological conditions, a fraction of the
https://doi.org/10.5194/acp-24-397-2024 Atmos. Chem. Phys., 24, 397–425, 2024
402 P. Huszar et al.: Urbanization impact on PM concentration
“DBVOC” impact will be composed of soil-NOxemissions
modifications. Not presented here, in our experiments the
soil-NOxemissions were about 2 orders of magnitude
smaller compared to the BVOC emissions, and their effect
is expected to be much smaller, including the effect of their
urbanization-induced modulations. BVOC emission fluxes
are strongly temperature dependent; i.e. higher temperatures
result in enhanced emissions. In this regard, it is expected
that urbanization-induced temperature increase will lead
to higher BVOC fluxes. It has to be noted that PFT data
are fractional, meaning that even the smallest fraction of a
particular plant type is represented in the BVOC emissions,
which allows as to account for the very small but not zero
amount of urban vegetation. On the other hand, the PFT
input is relatively old (year 2007), and some inaccuracies
might be present in the vegetation fraction given the urban
development that has taken place for the chosen cities
since 2007.
For the purpose of the study, we had to isolate the emis-
sions originating from selected urban areas from rural emis-
sions. To achieve this, we used the masking capability of the
emissions model used (FUME), while we used the adminis-
trative boundaries of the chosen cities based on the GADM
public database (https://gadm.org, last access: 16 May 2023)
which provide geographic shape files of these boundaries.
Cities selected for the analysis are Berlin, Brussels, Bu-
dapest, Cluj-Napoca, Cologne, Frankfurt, Hamburg, Kraków,
Lodz, Lyon, Milan, Munich, Prague, Turin, Vienna, Warsaw,
Wroclaw, Zagreb, and Zurich. The choice considered the
same criteria as in Huszar et al. (2021, 2022): the diameter
of the city larger than 9 km (the grid cell size in our model);
minimal orographic variability to reduce orographic effects
(see, for example, Ganbat et al., 2015); sufficiently large dis-
tance between cities eliminating mutual influences; and, fi-
nally, no coastal cities to eliminate the effect of asymmetric
land-use effects, like the sea-breeze effect (e.g. Ribeiro et al.,
2018). Despite strict emission control policies, these cities
are still often burdened with high air pollution due to PM
(Khomenko et al., 2021; Sokhi et al., 2022; Balamurugan et
al., 2022; Putaud et al., 2023).
The mutual interaction between the regional climate and
chemistry transport model, as well as the emissions models
(for biogenic and anthropogenic emissions), is depicted in
Fig. 1. It shows as detailed above that meteorological condi-
tions generated by the regional climate model (RegCM) drive
both the biogenic emissions model (MEGAN) and the chem-
istry transport model (CAMx). The emissions fluxes calcu-
lated as the sum of anthropogenic and biogenic emissions
are then fed into CAMx.
2.3 Model simulations
In a similar fashion to that in Huszar et al. (2022), we de-
composed the total impact of urbanization (RUT) into the
individual impacts or contributors (i.e. “DEMIS”, “DMET”,
Figure 1. The models used in the study including their mutual in-
teraction (the flow of the meteorological and emission data).
“DLU_D”, “DBOC”) listed in the Introduction. This re-
quired us to carry out a series of model simulations with each
contributor added gradually one by one to the reference sim-
ulation to achieve the full urbanization case.
First, we carried out a pair of model simulations with
RegCM, “Urban” and “Nourban”, with the former account-
ing for urban land surface treated with RegCM’s urban
canopy module and the latter accounting for land use being
replaced by “crops” as the most common rural land-use type
in the region analysed. We performed five simulations with
CAMx that differ in the in-/exclusion of urbanized land sur-
face, UCMF (acting on both atmospheric chemistry in gen-
eral and on biogenic emissions), and urban emissions. These
simulations are summarized in Table 1. The first simula-
tion called “ENNNN” represents the hypothetical reference
(background) state without urban emissions and with the ur-
ban land surface replaced by rural land surface in RegCM
and CAMx, as well as in the BVOC calculations (with
MEGAN). In the second experiment, “ENYNN”, the urban
emissions are turned on. In the third experiment, “ENYUN”,
the urban land use was “switched on” for the dry-deposition
scheme in CAMx. In the fourth experiment, “ENYUU”, ur-
ban land use and UCMF (i.e. “Urban” meteorology) are ac-
counted for in the biogenic emissions model, and finally, in
the fifth experiment, “EUYUU”, all the urbanization-related
effects are considered, representing the most realistic full
case.
In the first experiment where urban emissions are disre-
garded, we only removed urban emissions for the 19 cities
selected. For the effect of rural-to-urban land-use transforma-
tion on meteorological conditions, dry deposition, and bio-
genic emissions, we replaced the urban land by rural land
over the entire domain (i.e. not only for the cities selected).
It is clear that this has an effect on the background level of air
pollutants and not only on local urban levels, but the effect
is probably much smaller than local effects as (1) emissions
from these areas were still considered, and (2) the urban me-
teorological effects from these (minor) urban areas have a
rather small influence on air pollutants as UCMF over them
is also small (see, for example, Huszar et al., 2014).
Atmos. Chem. Phys., 24, 397–425, 2024 https://doi.org/10.5194/acp-24-397-2024
P. Huszar et al.: Urbanization impact on PM concentration 403
Table 1. The list of CAMx simulations performed with the information of the effects considered. “Urban” and “Nourban” denote the urban
land surface treated with RegCM’s urban canopy module and the land use being replaced by “crops” as the most common rural land-use
type, respectively.
Regional chemistry transport model (CAMx) simulations
Experiment Driving meteorology Urban emissions Land use (deposition) BVOC emissions
1 ENNNN (reference) Nourban No Nourban Nourban
2 ENYNN Nourban Yes Nourban Nourban
3 ENYUN Nourban Yes Urban Nourban
4 ENYUU Nourban Yes Urban Urban
5 EUYUU Urban Yes Urban Urban
Similar to Huszar et al. (2022), we can mathematically ex-
press the concentrations ciof a pollutant iin a selected city
with respect to RUT in the following way:
ci=ci,rural +1ci,RUT,(1)
where ci,rural is the reference (background) concentration be-
fore RUT, and 1ci,RUT is the total impact of urbanization.
In this study, we are concerned about the contributors to
1ci,RUT (regardless of their sign), i.e.
1ci,RUT =1ci,EMIS +1ci,MET +1ci,LUD+1ci,BVOC,(2)
where 1ci,EMIS,1ci,MET ,1ci,LUD, and 1ci,BVOC are the
impacts of urban emissions, the impact of the urban canopy
meteorological forcing, the impact of modified land use
on dry deposition, and the impact of modifications of
BVOC emissions, denoted above as “DEMIS”, “DMET”,
“DLU_D”, and “DBVOC”.
These impacts will be calculated from the experiments
listed in Table 1 as indicated below (the experiment number
is shown in parentheses):
1ci,RUT =EUYUU(5) −ENNNN(1)
1ci,EMIS =ENYNN(2) −ENNNN(1)
1ci,MET =EUYUU(5) −ENYUU(4)
1ci,LUD=ENYUN(3) −ENYNN(2)
1ci,BVOC =ENYUU(4) −ENYUN(3).(3)
It has to be realized that, in fact, the contributors above
act simultaneously, and feedbacks are present between them,
so their impacts are not additive. The way that we calcu-
lated them however allows us to consider them to be addi-
tive, meaning that their sum is the total impact of urbaniza-
tion. This is also a consequence of Eq. (3). Also, the impact
of the order of application of the four contributors has to be
discussed. The choice of starting with the “DEMIS” was mo-
tivated by “filling” the atmosphere with pollutants before any
other contributor is “turned on”. We assumed with this choice
that the other contributors act on a more realistic base state
(see the “Discussion and conclusions” section for more de-
tails).
Our analysis will focus on near-surface PM2.5concentra-
tions as well as their secondary components, i.e. secondary
inorganic aerosol (SIA), composed of sulfates (PSO4), ni-
trates (PNO3), and ammonium (PNH3), and secondary or-
ganic aerosol. Moreover, we will also focus on primary ele-
mental carbon (PEC), which is an important fraction of urban
emission loads. As the emissions of primary organic aerosol
have very similar magnitude in our emission data compared
to PEC, and it has the same deposition velocity (which is
determined only by size), we will not explicitly analyse POA
concentrations as we assume that the impacts of urbanization
on POA will be very similar to the impact on PEC.
3 Results
This section presents the results, and only a brief, rather gen-
eral discussion is provided. A more detailed interpretation of
the results is given in the “Discussion and conclusions” sec-
tion, including a comparison with existing studies.
3.1 Validation
Here we compare the modelled concentrations of PM2.5
and their components (PSO4, PNO3, PNH4, SOA, and
PEC) to observations. The measured data for PM2.5are
taken from the AirBase European air quality data (http://
www.eea.europa.eu/data-and-maps/data/aqereporting-1, last
access: 16 May 2023), while for PM components, data are
taken from the EBAS database (https://ebas-data.nilu.no/,
last access: 16 May 2023) from EMEP background sites. Air-
Base data are taken from all available rural and urban back-
ground stations in order to distinguish between model per-
formances above both type of stations.
Figure 2 shows the average annual cycle of monthly means
for urban and rural stations including the corresponding
model values. Over urban stations, CAMx exhibits a strong
underestimation around 5–10 µgm−3, and the underestima-
tion is stronger in winter. CAMx performs slightly better over
rural stations, with a smaller negative bias. In both cases, the
annual cycle is reasonably captured. We also compared the
analysed components (sulfates, nitrates, ammonium, and el-
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404 P. Huszar et al.: Urbanization impact on PM concentration
Figure 2. Comparison of modelled (red) and observed (blue) PM2.5monthly concentrations over urban (a) and rural (b) AirBase stations
shown as a 2015–2016 mean. Shaded areas represent the standard deviation across all the stations. Units are µgm−3.
emental carbon) with measurements. In this case the EMEP
background station data acquired from EBAS (https://ebas.
nilu.no/data-access/, last access: 5 May 2023) were used.
Figure 3 shows that the model underestimates PSO4by about
0.5–1 µg m−3, especially during summer when the model
predicts minimum values, while in measurements, the val-
ues show a more or less uniform distribution during the year.
PNO3is overestimated during winter by about 2µg m−3, and
the model matches the summer values well. In the case of
PNH4, an 0.5 µg m−3overestimation of winter values and a
similar underestimation of summer values are encountered.
Thus, for both PNO3and PNH4, the amplitude of the annual
cycle is overestimated. For PEC, the match is very satisfac-
tory, with a uniform model bias of −0.25 µgm−3through-
out the year, meaning that the annual cycle is very well cap-
tured by the model. The presented underestimation of fine
PM is caused most probably by underestimation of the or-
ganic aerosol fraction, which is an important component of
the urban PM. The strong underestimation of sulfates may be
connected to overestimation of other inorganic aerosol com-
ponents; for example, overestimated nitrates can consume
the available ammonia and suppress the formation of PSO4.
See the “Discussion and conclusions” section for more de-
tails.
3.2 The impact of individual contributors to RUT
Here we present the total impact of RUT as well as its in-
dividual contributors on PM2.5concentrations as 2015–2016
DJF and JJA averages, averaged across the selected cities.
Values from model grid boxes that cover the city centres are
selected. The results are shown as box plots in Fig. 4; they
show the first and third quartiles and the median values, along
with the minimum and maximum values across all the cities.
As expected, the highest impact is attributed to the effect
of emission only causing an increase in urban concentra-
tions by around 1.5 to 3.4 µgm−3in DJF and by about 1.2
to 2 µg m−3in JJA. The effect of UCMF on concentration
is usually a decrease up to −1 and −0.4 µg m−3in winter
and summer, respectively. The impact of minor contributors
associated with modified land use and deposition velocities
and modified BVOC emissions is a decrease in “DLU_C”
for both seasons by −0.2 to −0.3 µg m−3in DJF and −0.08
to −0.15 µg m−3in JJA, while the effect of BVOC modifi-
cations is very small, around −0.05 µg m−3in summer and
almost 0 in DJF. The total impact of urbanization on PM2.5
is a 1.2 to 3 µgm−3increase in DJF and a smaller increase
from 1 to 1.6 µgm−3in JJA, while, of course, the impact of
emissions dominates.
To examine the contribution of the most important aerosol
components to these changes, we plotted a similar figure as
above but individually for PSO4, PNO3, PNH4, PEC and
SOA presented in Fig. 5. Sulfates respond to urban emissions
alone by an increase of about 0.05–0.15 µgm−3and up to
0.05 µg m−3in DJF and JJA, respectively. The urban canopy
meteorological forcing results in a decrease in sulfates in
DJF by up to −0.05 µg m−3, while increases are modelled
for JJA up to 0.025µg m−3. The impact of dry-deposition
change on PSO4is a decrease: up to −0.025 µg m−3in DJF
and −0.01 µg m−3in JJA. The BVOC effect on PSO4is a
slight increase in JJA and virtually 0 in DJF. The total impact
of RUT on PSO4is an increase up to 0.15 and 0.05µg m−3
in DJF and JJA, respectively.
In the case of nitrates, the urban emissions alone increase
urban concentrations by about 0.1 to 0.2 µg m−3in both sea-
sons (somewhat more in DJF). The effect of UCMF is an in-
crease in DJF by about 0.05 µgm−3, while in JJA, decreases
dominate up to −0.15 µg m−3. The impact of modified dry
deposition is a decrease by about −0.08 to −0.11 µg m−3
in DJF and −0.03 to −0.06 µg m−3in JJA. The impact of
modified BVOC emissions is negligible, and the total im-
pact of RUT on PNO3is an increase up to about 0.16 and
0.12 µg m−3in DJF and JJA, respectively.
For ammonium, urban emissions cause an increase by 0.04
to 0.09 µg m−3and by 0.04 to 0.05 µg m−3in DJF and JJA,
respectively. The sign of the UCMF impact can be positive
and negative in both seasons, with values between −0.01 and
0.025 µg m−3in DJF and between −0.03 and 0.02 µg m−3in
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P. Huszar et al.: Urbanization impact on PM concentration 405
Figure 3. Comparison of modelled (red) and observed (blue) PSO4, PNO3, PNH4, and PEC monthly concentrations over available EMEP
stations as 2015–2016 mean. Shaded areas represent the standard deviation across all the stations. Units are µgm−3.
Figure 4. The 2015–2016 DJF and JJA averaged total impact of
urbanization as well as of each contributor to the urban concentra-
tions of PM2.5averaged over all chosen city. The box plots show
the 25 % to 75 % quantiles including the minimum and maximum
value across all cities. The red line shows the median value. Val-
ues are taken from model grid cell that covers the city centre.
Panel (a) shows the two main contributors including the total im-
pact (“DEMIS”, “DMET” and “DTOT”), and panel (b) shows the
minor contributors (“DLU_D” and “DBVOC”). Units are µgm−3.
JJA. The impact of dry-deposition modifications is negative
in both seasons, with −0.03 to −0.04 µg m−3and −0.01 to
−0.02 µg m−3decreases in winter and summer, respectively.
The impact of modified BVOC emissions is again negligible.
Finally, the total impact of RUT on PNH4is an increase up
to about 0.04 and 0.03 µgm−3in DJF and JJA, respectively.
The impact on elemental carbon which is a primary com-
ponent chemically inert in CAMx (with no chemical decay
or reactions) is as follows: urban emissions cause an increase
in PEC by around 0.2 to 0.5 µgm−3in DJF and around 0.2
to 0.4 µg m−3in JJA. The UCMF causes a slight decrease in
PEC by around −0.05 to −0.1 µg m−3in both seasons. The
increased deposition velocities caused decreased concentra-
tions of PEC by about −0.01 to −0.015 µg m−3in DJF and
around −0.005 µg m−3in JJA. Being an inert PM compo-
nent in CAMx, no impacts of BVOC modifications on PEC
are modelled. The total impact of RUT on PEC is again dom-
inated by urban emissions and reached 0.6 and 0.3 µgm−3in
DJF and JJA, respectively.
Finally, the impact secondary organic aerosol only has
considerable values during JJA, when the oxidation of pri-
mary VOCs to semivolatile precursors of SOA dominantly
takes place. The impact of urban emissions is an increase
in SOA by up to 0.05 to 0.1µg m−3, while urban meteoro-
logical changes cause SOA modifications usually between
0 and 0.05 µg m−3. Due to land-use modifications and as-
sociated deposition velocity increases, SOA responds with
a decrease up to −0.02 µg m−3, while due to urbanization-
induced BVOC modifications, SOA decreases by around
−0.04 to −0.06 µg m−3. The total impact of RUT on SOA
is an increase by 0.07 µgm−3in JJA and a very tiny increase
by up to 0.01 µgm−3in DJF.
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406 P. Huszar et al.: Urbanization impact on PM concentration
Figure 5. Same as Fig. 4 but for PM2.5components: PSO4, PNO3, and PNH4(upper row) and PEC and SOA (lower row).
These results confirm that urban PM2.5levels are a re-
sult of mainly the local input represented by urban emissions
but get smaller if the urban meteorological characteristics are
taken into account (in the form of UCMF). It is also seen that
the urban land surface exhibits a stronger depositional sink
to PM, causing the land-use impact to be negative, and fi-
nally, the urbanization-induced decrease in BVOC emissions
leads, as expected, to suppressed SOA formation, negatively
contributing to the overall RUT impact.
3.3 The spatial distribution of the impacts
To obtain a spatially resolved information about the impact of
individual contributors to RUT, here we plot their 2-D distri-
bution as DJF and JJA averages. We start with presenting the
distribution of absolute modelled concentrations for compar-
ison of the changes with absolute values in order to resolve
the relative magnitude of these contributors.
Figure 6 shows the absolute modelled near-surface con-
centrations of DJF and JJA PM2.5and its analysed compo-
nents from the experiment with all urban effects considered
(“EUYUU”). The PM2.5concentrations reach 30–40 µg m−3
in winter, while rural areas are often over 8µgm−3. In
summer, concentrations are, as expected, smaller, reaching
10 µg m−3being above 4µgm−3over rural areas. In DJF, the
highest contribution is made by nitrates, reaching 20 µg m−3
over northern Italy and being about 4–6 µg m−3over cen-
tral Europe. The concentrations of sulfates are large, espe-
cially over Poland, reaching 2–3µg m−3, while ammonium
is largest over northern Italy, reaching 4–6 µg m−3, while
other areas exhibit concentrations around 1–2 µg m−3. Ele-
mental carbon contributes to total PM2.5by values around 2–
4 µg m−3over northern Italy, while the contribution is clearly
limited to urban areas over other regions within the do-
main (e.g. 1–2 µgm−3over urban areas in eastern Europe).
The SOA concentrations in JJA are usually between 0.2–
1 µg m−3, reaching maxima again over Italy (2–3 µg m−3).
In summer, the secondary inorganic aerosol concentrations
are somewhat smaller, especially for ammonium (less than
0.6 µg m−3), while nitrates are largest over western Europe,
reaching 2–3 µg m−3, and sulfates are largest over southern
Europe, also reaching around 2–3 µgm−3. The PEC concen-
trations in JJA are small, usually around 0.1 to 0.4µg m−3.
SOA is larger in summer than in winter, reaching con-
centrations up to 2 µgm−3and usually being around 0.4–
1.5 µg m−3.
3.3.1 The impact of urban emissions (DEMIS)
In Fig. 7 the DJF and JJA average spatial impact of urban
emissions (“DEMIS”) on the near-surface concentrations of
PM2.5and its five analysed components is presented. Ur-
ban emission impact is in general higher in winter expect
for SOA. In DJF, PM2.5is increased over urban areas by up
to 4 µg m−3, and the contribution to rural concentrations can
also reach 0.5 µg m−3. In JJA, urban emissions contribute to
total PM2.5by about 1–3 µgm−3over cities, while the ru-
ral contribution is small, reaching 0.02 µg m−3. The impact
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P. Huszar et al.: Urbanization impact on PM concentration 407
Figure 6. The absolute DJF (left panel) and JJA (right panel) concentrations of modelled PM2.5and its components (PEC, PSO4, PNO3,
PNH4, and SOA) from the “EUYUU” experiment (all urban effects considered) averaged over 2015–2016 in µgm−3.
of urban emissions on PEC reaches 1 and 0.5 µgm−3in city
centres in DJF and JJA, respectively, while the impact over
rural areas is less than 0.02 µgm−3. For sulfates, urban emis-
sions impact urban concentrations up to 0.5 µgm−3in win-
ter, while the summer impact is smaller, reaching 0.1 µg m−3.
The impact over rural areas is again less than 0.02µg m−3in
both seasons. A much larger impact is modelled for nitrates,
reaching 1 µg m−3over Italy and exceeding 0.2 over most of
central Europe in winter. In summer, the impact on PNO3
is smaller, reaching 0.5µg m−3over urban and 0.2 µg m−3
over rural areas. The impact of urban emissions on ammo-
nium reaches 0.5 µgm−3and is usually around 0.05 µg m−3
in DJF, while JJA concentrations are smaller, reaching 0.05
but usually less than 0.02 µgm−3. Finally, the impact on SOA
is negligible in winter reaching 0.02 µgm−3over city centres,
while in summer, it can reach 0.2µg m−3, and the contribu-
tions over rural areas can exceed 0.02µg m−3. These result
clearly show that emissions mainly act locally, but a large
fraction of urban pollution is also caused by rural emissions
or emissions from nearby cities. Further, it is clear that the
impact on primary pollutants is more localized than the one
on secondary components which are formed during ageing
of the urban aerosol plume, impacting distant areas.
3.3.2 The impact of modified meteorological conditions
(DMET)
In Fig. 8 the DJF and JJA average spatial impact of the urban
canopy meteorological forcing (“DMET”) is presented. For
PM2.5, it is characterized by decreases located above urban
areas, reaching −2 µg m−3in both seasons. Elsewhere, i.e.
above rural areas, PM2.5increases by up to 1–1.5µg m−3.
In the case of PEC, the decrease over urban areas is evi-
dent and reaches −0.2 µg m−3, especially during DJF, while
some minor increases are modelled over rural land, reaching
0.05 µg m−3. The impact on secondary aerosol components
is more complicated as apart from the direct impact, UCMF
also impacts their precursors. Sulfates decrease above urban
areas by about 0.2 to 0.5 µgm−3in DJF and by 0.1 µg m−3
in JJA. Large rural regions show, on the other hand, in-
creases of PSO4by up to 0.1–0.2 µgm−3, mainly in winter.
In the case of nitrates, some urban areas exhibit decreases in
DJF (e.g. Berlin, the Ruhr area), but large increases are also
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408 P. Huszar et al.: Urbanization impact on PM concentration
Figure 7. The spatial distribution of the 2015–2016 DJF (left panel) and JJA (right panel) average urban emission impact “DEMIS” on
PM2.5and its components. Units are µgm−3. Note that PM2.5has a separate colour bar.
modelled over rural areas and even urban ones, especially
over northern Italy along the Po River, reaching 0.5 µg m−3.
In summer, the decrease over urban areas is seen for most
of the cities; however, over other areas, a strong increase
inPNO3 is modelled reaching 0.5 µgm−3. The UCMF’s im-
pact on PNH4is somewhat smaller and is, again, character-
ized by decreases above cities up to −0.2 µg m−3in both
seasons, while increases are modelled over rural areas and
also some urban ones, reaching 0.3 µg m−3in winter and
0.2 µg m−3in summer. In the case of SOA, some urban ar-
eas over western and southern Europe exhibit decreases in
JJA up to −0.05 µgm−3, but increases dominate, reaching
0.2 µg m−3. In DJF, the impact is very small, with a minor
increase over rural areas up to 0.05 µg m−3. The simulated
impact of UCMF on fine PM is most probably the result of
enhanced vertical eddy diffusion caused by the urban canopy.
This leads to decreases in PM concentrations by transporting
material into higher levels. However, this can lead to elevated
concentrations further from the sources (cities) when this ma-
terial is deposited back to lower model levels.
3.3.3 The impact of dry-deposition modifications
(DLUC_D)
Figure 9 depicts the DJF and JJA average spatial impact
of the modified dry-deposition velocities due to urban land
use. In both seasons, a clear decrease in concentrations
is modelled, indicating that dry-deposition velocities in-
creased, as was expected. The total PM2.5concentrations de-
creased by up to −1.5 µg m−3in winter over cities, while
large rural areas exhibit a decrease up to −0.5 µg m−3. In
summer, the decreases have a smaller magnitude, reach-
ing −0.5 µg m−3over urban areas, while over rural ones
they reach −0.2 µg m−3. For PEC, decreases are limited
mostly to urban areas, reaching −0.05 µg m−3in DJF and
−0.01 µg m−3in JJA. Larger impacts are modelled for sec-
ondary aerosol, probably due to the fact that their pre-
cursors are also impacted. Sulfates decreased in winter by
about 0.05 µg m−3and by about 0.02µg m−3in summer,
mainly over urban areas. Among SIA, the largest impacts
are modelled for nitrates, exceeding −0.1 µg m−3decrease
in DJF over northern Italy but being large over rural areas
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P. Huszar et al.: Urbanization impact on PM concentration 409
Figure 8. The spatial distribution of the 2015–2016 DJF (left panel) and JJA (right panel) average UCMF impact “DMET” on PM2.5and its
components. Units are µgm−3. Note that PM2.5has a separate colour bar.
too (about −0.05 µg m−3). In summer, PNO3decreases by
around 0.02 to 0.05 µgm−3, mainly over cities. In the case of
PNH4the decreases are, again, largest above cities, reaching
−0.05 µg m−3in both seasons (slightly stronger decrease in
DJF). Over rural areas, the decrease is about −0.02 µg m−3
and −0.01 µg m−3in DJF and JJA, respectively. SOA de-
creased due to modified dry-deposition velocities by around
0.02 µg m−3above cities in both seasons, while over rural ar-
eas, it reaches about −0.01 µg m−3, slightly higher in JJA.
The decreases above are the result of increased deposition
velocities, which are depicted in Fig. 10 for PEC for both
winter and summer. As the DVs in the model used (CAMx)
are only a function of the size, all aerosols within the 0–2.5 µ
size range (where all the secondary aerosols belong) have the
same DV values, and here we only present the modification
of DV for this component (for others, the figure would be
the same). DVs increased clearly above urban areas, while
the increase reaches 0.1 cms−1in DJF for some cities. For
JJA, the increases are slightly smaller, usually between 0.02
and 0.1 cm s−1. Is has to be noted here that the deposition
model used considered spatially uniform surface parameters
relevant for deposition; however, cities are covered by a very
high variety of different materials and the deposition veloci-
ties can vary from place to place with a great magnitude, so
the results are rather a rough estimate.
3.3.4 The impact of biogenic emissions (DBVOC)
Figure 11 presents the impact of modified biogenic emissions
due to RUT on PM concentrations. It is clear that BVOC
emissions are mainly important during the warm season, and
that is why the impacts during DJF are much smaller than
during JJA. Moreover, during summer, BVOCs can more
readily oxidize to semi-volatile hydrocarbons forming SOA,
so the impact on PM2.5acts predominantly via impacting
secondary organics concentrations. However due to feed-
back on the overall gas-phase chemistry and thus SIA pre-
cursors, SIAs are also slightly modified. In winter the impact
on PM2.5is only considerable above northern Italy reach-
ing −0.05 µg m−3, while SOA and nitrates mainly contribute
to these PM modifications. In the case of PNO3, they de-
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410 P. Huszar et al.: Urbanization impact on PM concentration
Figure 9. The spatial distribution of the 2015–2016 DJF (left panel) and JJA (right panel) average impact of modified dry-deposition
velocities due to land-use change “DLU_D” on PM2.5and its components. Units are µgm−3. Note that PM2.5has a separate colour bar.
Figure 10. The spatial distribution of the 2015–2016 DJF (a) and
JJA (b) average impact of urban land use on dry-deposition veloci-
ties for PEC. Units are cms−1.
crease above the same region by around 0.01–0.02µg m−3,
while SOA decreased by a similar magnitude (and slightly
increased over other areas). Sulfates responded to BVOC
changes by a slight decrease up to −0.01 µg m−3. In JJA,
the impacts are in general much larger (as expected) and are
mainly determined by the decreased SOA but also modu-
lated by increases in SIA. The PM2.5JJA decrease reaches
−0.1 µg m−3(again mainly over northern Italy) but is be-
tween −0.02 to −0.05 µg m−3over large areas all over the
domain. Regarding SIAs, all of them increased: by up to
0.02 µg m−3for PNO3and up to 0.01µg m−3in the case
of PSO4and PNH4. For SOA there is a clear decrease
during JJA up to −0.1 µgm−3over northern Italy and be-
tween −0.02 and −0.05 µg m−3over large regions across
the domain. As PEC is not affected by either gas-phase or
aerosol chemistry, no modifications due to biogenic emission
changes are modelled.
3.4 The diurnal variation of the impacts
Human activities change during the day, causing a typical di-
urnal cycle of urban emissions. Moreover, the urban canopy
meteorological forcing also has a distinct diurnal pattern; for
example, the modification of temperature is strongest during
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P. Huszar et al.: Urbanization impact on PM concentration 411
Figure 11. The spatial distribution of the 2015–2016 DJF (left panel) and JJA (right panel) average impact of modified biogenic emissions
“DBVOC” on PM2.5and its components. Units are µgm−3.
the night, and the impacts on wind and turbulence are the
strongest during daytime (Huszar et al., 2018a). It is thus ex-
pected that the individual contributors to the total impact of
RUT analysed here will have also a distinct diurnal cycle.
In Figs. 12 and 13 we present the diurnal cycles for the
four contributors’ impact on PM2.5and its components dur-
ing winter and summer averaged over all urban centres (we
took the model grid box covering the city centre, in a sim-
ilar way to that shown in Fig 4). In the case of PM2.5in
winter, “DEMIS” causes a typical diurnal variation, resem-
bling the diurnal cycle of urban emissions (varying between
1.5 and 4 µg m−3); this is also seen for PEC, when max-
ima occur during morning and evening rush hours. A sim-
ilar diurnal pattern is also seen for sulfates varying between
0.1 and 0.2 µgm−3. For other secondary aerosol compo-
nents the diurnal cycles are characterized by only one maxi-
mum: for nitrates, the maximum occurs around noon; reach-
ing 0.25 µg m−3, while for ammonium, the maximum emis-
sion impact is reached during morning reaching 0.1 µgm−3.
SOAs are increased due to emission at most during early
afternoon by up to 0.015 µgm−3. In the case of the im-
pact of UCMF (“DMET”), it is usually negative for PM2.5
being lowest during the afternoon when it reaches around
−1 µg m−3. For PEC and PSO4, the maximum decrease is
about −0.2 and −0.1 µg m−3, respectively. For PNO3and
PNH4and SOA, it is again negative during afternoon hours
reaching −0.2, −0.07 and −0.02 µg m−3, respectively. The
impact of increased deposition velocities is negative in all
cases and throughout the whole day in winter. However, the
diurnal patterns indicate that the maximum decrease is mod-
elled for early afternoon hours, reaching −0.3 µg m−3for
PM2.5. For PEC, PSO4, PNO3, and PNH4, it reaches −0.02,
−0.02, −0.01, and −0.04 µg m−3, respectively. For SOA, the
maximum decrease reaches −0.016 µg m−3. As expected, the
impact of modified BVOC emissions is almost negligible
with weak maximum decrease during afternoon and evening
hours for SOA (around −0.002 µgm−3).
The JJA diurnal cycles of the impacts are similar to DJF
in the case of “DEMIS”, with two maxima for PM2.5, PEC,
and PSO4, while a single maximum due to emissions is
modelled for the other components. For the “DMET”, again
an early evening decrease is modelled, reaching −0.5 and
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412 P. Huszar et al.: Urbanization impact on PM concentration
Figure 12. Diurnal cycles of the impact of individual contributors to RUT averaged over 2015–2016 DJF for PM2.5, PEC, PSO4, PNO3,
PNH4, and SOA. Colours are as follows: brown – DEMIS, blue – DMET, red – DLU_D, and green – DBVOC. The left yaxis is for the
two major contributors, DEMIS and DMET, while the right yaxis belongs to the two smaller contributors, DLU_D and DBVOC. Units are
µgm−3.
−0.1 µg m−3for PM2.5and PEC. The impact on PSO4is
very small, reaching −0.01 µg m−3, while for PNO3, PNH4,
and SOA, the maximum decrease is about −0.15, −0.04, and
−0.05 µg m−3, respectively. The summer “DLU_D” impact
on PM2.5and its components has a distinct cycle compared
to DJF, usually with a morning maximum decrease. This
reaches −0.16 µg m−3for PM2.5. It is also very small for
PEC. For PSO4, PNO3, PNH4, and SOA, it reaches −0.02,
−0.15, −0.03, and −0.025 µg m−3, respectively. In contrast
to DJF, the impact of biogenic emissions changes due to ur-
banization shows a clear diurnal cycle for all PM components
except PEC, which does not interact with gas-phase species.
For PM2.5, concentrations decrease, and this decrease is at
its maximum during evening hours, reaching −0.08 µgm−3.
For PSO4, increases are modelled, reaching their maximum
around noon (0.005 µgm−3), while for PNO3and PNH4, an
increase is modelled too, but during the afternoon there is a
slight decrease in nitrate concentrations. In the case of SOA,
a strong decrease is modelled during evening hours reaching
−0.075 µg m−3, which clearly determines the overall cycle
for total PM2.5. For other hours, the decrease in SOA in JJA
is around −0.05 µg m−3.
The presented diurnal variations are in close relation to di-
urnal pattern of emissions, both anthropogenic (in the case
of “DEMIS”) and biogenic (in the case of “DBVOC”), and
also to the diurnal cycle of UCMF (in the case of “DMET”).
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P. Huszar et al.: Urbanization impact on PM concentration 413
Figure 13. Same as Fig. 12 but for JJA.
While for emissions, these are more or less well known (max-
imum during daytime for BVOCs and two daytime maxima
in the case of anthropogenic emissions), for UCMF the un-
derlying causes are hidden in the diurnal pattern of individual
components in UCMF (temperature, wind, planetary bound-
ary layer height, turbulence, etc.), and these have been mod-
elled and well described in our previous studies (Huszar et
al., 2018a, b, 2020a). However, the diurnal pattern of deposi-
tion velocities for PM and their urbanization-induced mod-
ifications have not yet been evaluated. Therefore, here, to
accompany the diurnal variation of concentrations, we plot
the diurnal variation of the changes of DV due to the urban
land surface as well as the absolute values (corresponding to
a non-urbanized land surface). We chose PEC as a represen-
tative PM component (note that DVs are only a function of
PM size, and we only consider fine aerosol in this study). The
results are depicted in Fig. 14. The absolute DVs range be-
tween 0.8 and 1.2 mms−1and 0.8 and 1.5mms−1in DJF and
JJA, respectively. The maximum values are reached around
noon. The changes due to the introduction of an urban land
surface follow a very similar pattern, with the highest impact
around noon, reaching 0.8 and 0.5 mms−1in DJF and JJA,
respectively, and being about 0.5–0.6 and 0.3 mms−1during
night-time for both seasons.
4 Discussion and conclusions
In this study, an analysis of the different contributors to the
overall impact of urbanization (called rural-to-urban trans-
formation, RUT) on fine particulate matter concentrations
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414 P. Huszar et al.: Urbanization impact on PM concentration
over central Europe was presented. It focused on the four
most important contributors to RUT: the impact of urban
emissions only (“DEMIS”), the impact of UCMF (urban
canopy meteorological forcing) on PM transport and chem-
istry (“DMET”), the impact of modified dry-deposition ve-
locities due to urbanized land cover (“DLU_D”), and the im-
pact of modified biogenic emissions due to modified land
cover (and associated vegetation change) and modified mete-
orological conditions (“DBVOC”). They were quantified by
performing a set of model simulations whereby each of the
contributors was added one by one, starting with the refer-
ence state, corresponding to non-urbanized land surface with
no urban emissions.
The model biases identified for PM2.5show that some of
the PM2.5components are strongly underestimated in CAMx
(Fig. 2). Very similar underestimation was encountered pre-
viously by Huszar et al. (2021) for selected European cities,
using the same resolution and same emission data (they used,
however, an older version of the models). ˇ
Doubalová et al.
(2020), also using CAMx, reported a comparable negative
bias. From the analysed aerosol components, sulfates were
underestimated, but this, along with the model performance
for nitrates and ammonia (Fig. 3), does not explain the strong
negative bias for PM2.5. Most probably, the organic aerosol
fraction was strongly underestimated in our model, which is
a general problem in CTMs and has been encountered pre-
viously by many authors (Jiang et al., 2019; Ciarelli et al.,
2017), while, at the same time, it is often considered the
largest fraction of fine particulates in urban environments
(Allan et al., 2010; Lanz et al., 2010). It is also probable that
the bias is partly caused by the fact that the local wind-blown
dust sources are not considered in our emissions model,
while this dust source can significantly contribute to overall
PM2.5over central Europe, as shown recently by Liaskoni et
al. (2023). The modelled negative bias for sulfates is similar
to Bartik el al. (2021), who applied CAMx over a similar Eu-
ropean domain using slightly newer emission data. Sulfates
were moreover underestimated over Europe by the majority
of the models used in the second phase of the Air Quality
Model Evaluation International Initiative (AQMEII; Im et
al., 2015) and can be connected to overestimation of other
secondary inorganic components; i.e. ammonium preferably
neutralizes nitrates instead of sulfates, leading to less ammo-
nium sulfate formation (Im et al., 2015).
Due to the overall underestimated PM2.5concentrations,
it is expected that the impacts presented here for PM2.5are
underestimated too. This is true, especially for the impact of
emissions, but as the effect of UCMF and deposition velocity
change is also proportional to the concentrations they act on,
the impacts presented below are underestimated most proba-
bly in these cases too.
The total impact of urbanization on PM2.5over central Eu-
ropean cities was calculated to be 1–1.5 µgm−3on average in
summer, while in winter, urbanization increased air pollution
even more, by around 2–3µg m−3(Fig. 4). When comparing
these results to other similar studies, one has to remember
that this includes not only the impact of urban emissions but
also the counteracting effect of UCMF and increased dry de-
position and, in a minor way, also the impact of biogenic
emissions changes. Indeed, urban emissions alone increased
PM2.5concentration by 1.2–2 µgm−3and 1.5–3.5 µg m−3in
winter and summer. These numbers are close to what was
modelled by Huszar et al. (2016a), who only looked at the
effect of emissions. The reason we got slightly smaller num-
bers is that they used 2005 emissions, which were higher
compared to our 2015 emissions used in this study. Our
PM2.5increases due to urban emissions only are also much
lower than in Im and Kanakidou (2012), but they modelled
Istanbul and Athens, which are large megacities, much larger
than the average of our central European selection. Previ-
ously, Skyllakou et al. (2014) showed for Paris that the con-
tribution of local sources to PM is smaller, around 1µg m−3;
however they used much coarser resolution and thus could
not capture the city’s core contributions. Indeed, the contri-
butions of urban emissions to urban air pollution over the
urban core are much larger if higher resolutions are applied,
as seen in Huszar et al. (2021), as the largest contributions
occur over city centres (Thunis et al., 2021). Finardi et al.
(2014) made estimates on the impact of Po valley, a highly
urbanized region in northern Italy, on PM2.5concentration,
and they found that local emissions contribute to local con-
centration by up to 10–20 µgm−3, which is a much larger
contribution than our 4 µg m−3simulated for Milan (a city
belonging to this region); however, they simulated the con-
tribution from a much larger, regional emission source and
not only one city.
As already seen, the total urban impact is lower if other
contributors besides the urban emissions are considered.
From these, the impact of UCMF showed a clear decrease
in PM2.5concentrations by around 0.5 µgm−3, counteracting
the increase due to urban emissions. Indeed, many showed
that the most important component of UCMF is the enhanced
vertical eddy transport, which removes pollutants from the
surface layer (where they are emitted), causing decreased
concentrations (Kim et al., 2015; Huszar et al., 2018b, 2020a;
Zhu et al., 2017; Wei et al., 2018). Moreover, our spatial re-
sults showed that PM2.5increases due to UCMF over rural
areas, which was also seen in Huszar et al. (2018b) and is
probably the result of the fact that the PM removed by in-
creased turbulence is deposited to lower model levels over
other regions further from the sources (cities). However, as
seen in Huszar et al. (2018a), the strong reduction of wind
speed over and around urban areas can be sometimes very
strong, resulting in the turbulence decrease being counter-
acted. This in turn causes an increase in urban PM2.5con-
centrations. This probably also contributed to the modelled
increases in PM2.5(e.g. over the Benelux states in JJA, simi-
lar to Huszar et al. (2018b)).
Regarding the impact of urbanized land surface on PM
deposition, the results are in line with the expectations
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P. Huszar et al.: Urbanization impact on PM concentration 415
Figure 14. Diurnal cycle of the DJF (a) and JJA (b) DV of PEC: solid lines denote absolute values, and dashed lines mean the change caused
by the urban land surface. Units are mms−1.
that increased DVs over cities (by 30%–50 %) result in
decreased concentrations of PM2.5(by around −0.12 and
−0.23 µg m−3in summer vs. winter). Although this is a mi-
nor decrease, it is seen over the whole domain, with maxima,
as expected, over cities. Moreover, the decrease in DV is the
same across all the PM2.5components as in the used CTM,
DV is a function of aerosol size only. Here we have to note,
however, that the dry-deposition parameterization used here
(Zhang et al., 2001) considers urban areas to be flat surfaces
with prescribed roughness length and other parameters rele-
vant for the dry deposition. This certainly differs from real-
ity, where the urban canopy is formed of individual objects
with different surface materials and also a vegetation frac-
tion, while in each of these cases the parameters controlling
the dry deposition are different. For example, Cherin et al.
(2015) showed that “dry-deposition velocities can vary by a
factor of 24 between two surface types in urban areas”. Our
results on the impact on urban land surface on PM2.5dry de-
position are therefore a very rough estimate.
Regarding the impact of modified biogenic emissions on
PM2.5, these are of course acting predominantly via modi-
fying SOA concentrations. As BVOCs are a main precursor
of biogenic SOA, a decrease in biogenic emissions results in
a decrease in SOA formation. This is more pronounced dur-
ing summer, of course, when BVOC emissions are at their
peak. We also modelled some secondary effects on PNO3
(and to a smaller extent on sulfates and ammonia), related
to the influence on OH radicals, which in turn influences the
oxidation of nitrates and also ammonium, causing them to
decrease (Aksoyoglu et al., 2017).
We further discuss the modelled RUT-induced modifica-
tions of the analysed PM2.5components. Looking at elemen-
tal carbon, it is an inert aerosol in CAMx without chemi-
cal decay, so it is influenced via direct pathways along with
changes of emissions, meteorological conditions, and depo-
sition. Indeed, urbanization increased PEC by about 0.2–
0.6 µg m−3in winter and by about 0.2–0.3µg m−3in summer
(Fig. 5), which is mainly influenced by the urban emissions
alone, causing slightly higher increases, and the increase is
predominately limited to urban areas. Indeed, for example,
Skyllakou et al. (2014) showed for Paris that almost 60% of
PEC originates from local sources, although they calculated
somehow stronger contributions of urban emissions to the
total PEC (around 0.3–0.4 µgm−3). However, Paris is at the
high end of the size distribution of cities we selected, so its
urban emissions will be also very large compared to the av-
erage in our selection. The total urbanization impact on PEC
is again smaller than that of emission only, which is caused
partly by the effect of UCMF on PEC. Similarly to the to-
tal PM2.5, PEC also responds to higher vertical eddy diffu-
sion above cities by decreases. A similar decrease in PEC
due to UCMF to that in our study was modelled by Huszar
et al. (2018b), who showed that this decrease is a counterac-
tion of a large decrease due to turbulence enhancement and
a smaller increase due to reduced wind speeds. Our results
further showed that the decrease is largest during afternoon
hours, which is in line with Huszar et al. (2018b) and Huszar
et al. (2020a). They showed that the impact of turbulence
enhancement is largest during these hours of the day when
the strongest mixing in and above urban canopy occurs. The
impact of enhanced deposition velocities is again expected
and exhibits a clear decrease in PEC concentrations above
urban areas. The impact is larger in winter in line with the
larger winter absolute PEC concentration compared to sum-
mer ones. Further we showed (and this was seen for PM2.5
too) that the strongest decrease in PEC due to increased DV
occurs during daytime, which can be clearly explained by
the peaking DV values during the day and thus the strongest
influence of urban land surface (as shown in Fig. 14). This
is a known behaviour of particle deposition velocities which
are modelled to peak during early afternoon hours (e.g. Nho-
Kim et al., 2004). Finally, as PEC is chemically inert, it does
not respond to modification of biogenic emissions.
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416 P. Huszar et al.: Urbanization impact on PM concentration
As a result of urbanization, sulfates increased by about
0.05 µg m−3in winter and 0.03–0.04µg m−3in summer,
while urban emissions alone caused a slightly larger increase
in winter (around 0.1 µgm−3) and a similar one in summer
(Fig. 5). These are almost 10 times smaller values compared
to PEC and in general show the low sulfur fraction of urban
emissions in Europe. Indeed, sulfates are emitted mainly as a
result of combustion; however strong reduction policies were
implemented during the 80s and 90s which substantially re-
duced the sulfur content of combustion products (Vestreng
et al., 2007). Sulfur emissions, and thus sulfate formation,
are larger in eastern Europe (Fig. 7), especially in Poland,
were coal combustion is still a significant energy produc-
ing method. This is also reflected in the emission data we
used, and consequently, the largest urban contributions to re-
gional sulfate levels are above eastern Europe where often
coal combustion facilities are located even within the cities
outskirts, or coal is used for domestic combustion. For this
reason, winter sulfate increases due to urban emission are
much larger than during summer. Previously, Skyllakou et al.
(2014) showed a very small contribution of local sources to
PSO4concentrations (on the case of Paris), reaching less than
0.1 µg m−3during summer, which is in line with the largest
contributions in our case (e.g. over Hamburg due to ship-
ping SO2emissions or over Polish cities). Due to the urban
canopy meteorological forcing, sulfates decreased over cities
and increased over rural areas (Fig. 8). This is expected (sim-
ilarly to PM2.5) as the main component acting within UCMF
is the enhanced vertical eddy diffusion which removes ma-
terial from the surface model layer and is deposited further
from the sources, causing increase elsewhere. This behaviour
was seen also by Huszar et al. (2018b), who have seen a de-
crease in sulfates of about up to −0.5 µg m−3, similar to our
results. Moreover, sulfates decreased also due to decreases in
their precursors, SO2and NH3, driven by the same mecha-
nism. Indeed, we showed in Huszar et al. (2022) too that SO2
usually decreases above cities by about 0.5ppbv. NH3also
decreases, as seen in Fig. 15, by about up to 1 ppbv in both
seasons, limiting the formation of ammonium sulfate. Also,
Kang et al. (2022) showed that near the surface, sulfates de-
crease due to the enhanced urban mixing (an increase in the
free troposphere above). It has to be noted, however, that
the UCMF-induced modifications are a trade-off between the
wind-speed decreases which increase the urban concentra-
tions and the turbulence-induced decreases. In some studies,
for example, Huszar et al. (2018b), the wind speed decrease
is more important and can cause higher urban concentrations
due to UCMF. As expected, sulfates decreased as a result
of increased dry-deposition velocities (Fig. 9), and this is
also amplified by the increased dry deposition of SO2due
to urbanization, as shown in Huszar et al. (2022). Finally, the
impact of reduced urban BVOC emissions on PSO4is neg-
ligible; only some very small increases during summer are
modelled. This may be explained by the fewer OH radicals
reacting to oxidize biogenic hydrocarbons, which thus can
oxidize more SO2(Aksoyoglu et al., 2017).
Urbanization increased nitrates and ammonium by about
0.1 and 0.25 µg m−3, respectively, with higher numbers in
winter (Fig. 5), which is clear as the absolute concentrations
of these secondary aerosols are also higher in winter. The im-
pact of emissions alone is also clearly higher in winter, which
is caused by stronger NOxemissions (mainly combustion
and transport) and usually by higher ammonia emissions too
during the colder parts of the year (although these can have
a smaller late summer peak too; for example, Drugé et al.,
2019) (see Fig. 7). Again, the emission impact is higher than
the one from total urbanization, and this is caused (similarly
to PM2.5, PEC or sulfates) by the effect of UCMF, which
is dominated by increased vertical eddy diffusion. This re-
duces the near-surface concentrations of both aerosols and
their precursors (NOxand NH3). Previously, Huszar et al.
(2018b) showed decreases in PNO3and PNH4over cen-
tral European cities due to UCMF by about 0.02–0.04 and
0.02 µg m−3in summer (they did not look at winter), which
is slightly smaller for nitrates than our numbers and is com-
parable to ammonium modification presented in this work.
The decrease in PNO3and PNH4is also caused by the de-
creases of nitrogen oxides and ammonia, as seen in Fig. 15
or previously in Huszar et al. (2018b, 2022). Recently, Kang
et al. (2022) also reported higher nitrate and ammonium val-
ues above a large Chinese agglomeration if the urban land
surface and the associated UCMF were not considered. Sim-
ilar to sulfates, PNO3and PNH4responded to urbanization-
induced land-surface changes by increased dry-deposition
velocities, resulting in their decrease. In winter, this was also
partly caused by the reduced NH3due to increased dry de-
position; however for JJA, we modelled increased ammonia
concentration due to dry-deposition changes due to urban-
ization (see Fig.15). Indeed, the dry-deposition model used
(Zhang et al., 2003) predicts smaller dry-deposition veloci-
ties for urban areas compared to rural ones (e,g, crops) for
NH3in the case of dry canopies (i.e. summer conditions). Fi-
nally, as a result of decreased urban BVOC emissions, some
increases in nitrates and ammonium are modelled, which can
be connected to more OH available to oxidize NOx(Aksoyo-
glu et al., 2017), but also, less NOxis reacting with organic
molecules to create organic nitrates (Fischer et al., 2014). For
example, Jiang et al. (2019) showed that smaller biogenic
emissions fluxes result in increased PNO3and PNH4con-
centrations over central Europe, while the impact on PNO3
is larger, which is in line with our results.
The impact of urbanization on secondary organic aerosol
concentration is only notable during summer (Fig. 5), ow-
ing to the suppressed oxidation of VOCs in winter and small
BVOC emissions responsible for biogenic SOA formation
during the cold parts of the year (Gao et al., 2022; Zhai et
al., 2023). The total summer impact of urbanization is about
0.03–0.05 µg m−3(reaching 0.1 µg m−3), while the emission
impact alone is again larger, often exceeding 0.1 µg m−3. A
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P. Huszar et al.: Urbanization impact on PM concentration 417
Figure 15. The DJF (a, b, c) and JJA (d, e, f) impact of “DEMIS”, “DMET”, and “DL_U” on near-surface NH3concentrations in parts per
billion by volume (ppbv).
very similar summer contribution of urban emissions from
Paris was modelled earlier by Skyllakou et al. (2014), who
pointed out that the contribution of SOA to the total ur-
ban impact is about 5 %–10 %. Freney et al. (2014) arrived
at similar results and argued that the contribution of SOA
gets larger as the urban plume ages. Regarding the impact
of UCMF on SOA in summer (Fig. 8), it is characterized by
both increases and decreases, depending on the city, with an
average slightly below 0 (around −0.01 µgm−3). The rea-
son for this is probably the interplay between the reduction
caused by the increased vertical eddy diffusion and the in-
crease caused by the decreased urban wind speeds, while
increased urban temperatures shift the partitioning between
the gas phase and particulate phase towards the gas phase
(Huszar et al., 2018b; Wang et al., 2009). This means the
impact over a particular city depends on the relative magni-
tude of these three components of UCMF and how they act
on SOA. The UCMF also caused a relatively large impact
over rural areas, which can also be explained by the fact that
SOA is formed more readily in an aged urban plume (Ortega
et al., 2016), so, probably, the SOA precursors removed by
the increased vertical eddy diffusion were transported over
rural areas, while they were oxidized and condensed into
SOA. Regarding the impact of increased PM deposition ve-
locities due to urbanization, their impact follows that of sec-
ondary inorganic aerosol or PEC; i.e. the urban concentra-
tions decreased (by around −0.02 µg m−3). Here, again, one
must realize that the modified DVs also impacted the pre-
cursor species which oxidize to semi-volatile compounds.
In CAMx in the Zhang deposition model for gases (Zhang
et al., 2003), the deposition velocities for SOA precursors
(and most of VOCs) are smaller for urban canopies, which
means their concentrations are larger for urban areas. Hence,
more SOA formation is favoured because of higher precur-
sor abundances. This consequently means that the final im-
pact on SOA is a counteraction between the decrease due to
the direct impact of urbanization-induced land-use change on
SOA deposition velocities and the increase due to higher pre-
cursor concentrations. In our simulation, the former was the
dominating effect as SOA decreased above all urban areas.
Finally, the urbanization-induced decrease in BVOC emis-
sions resulted in a reduction of SOA concentrations (by about
0.04–0.06 µg m−3), and this decrease is modelled not only
over urban areas but also over large rural regions, while the
largest decrease is above Milan, which also has the warmest
climate among the selected cities (Figs. 5 and 11). Indeed,
SOA of biogenic origin is a an important contributor to urban
SOA levels, so the modelled decreases were expected (Cou-
vidat et al., 2013; Hu et al., 2017). Ghirardo et al. (2016)
also calculated a strong influence of local BVOC emissions
from urban trees in a Chinese megacity (although the anthro-
pogenic influences were much larger). Again, the impact also
affected rural areas, which can be explained by the repeated
fact that in aged urban plumes, SOA forms more effectively.
In summary, we evaluated over a sample of 19 central Eu-
ropean cities the impact of rural-to-urban transformation and
https://doi.org/10.5194/acp-24-397-2024 Atmos. Chem. Phys., 24, 397–425, 2024
418 P. Huszar et al.: Urbanization impact on PM concentration
its four contributors on PM urban (and rural) concentrations,
including the impact on its primary and secondary compo-
nents. We found that the two main controlling drivers are the
impact of urban emissions themselves (increases the concen-
trations for PM2.5and all of its analysed components) and
the urban canopy meteorological forcing (usually decreases
over urban areas, increases over rural ones). We showed how-
ever that two additional controlling mechanisms can play an
important role within the process of urbanization although
smaller by an order of magnitude than the effect of emissions
and UCMF: the impact of dry-deposition velocity changes
due to urbanization of land surface and the reduction of bio-
genic emissions by turning rural/natural land surfaces into
urban built-up areas. The former results in decreases in con-
centrations due to increased deposition velocities, and the lat-
ter acts predominantly via modification of secondary organic
aerosols and results in a decrease in PM2.5concentrations
(by reducing SOA). In summary, when the impact of urban-
ization on PM air pollution is analysed, all four contributors
have to be accounted for.
It also has to be discussed what impact has the order of
application of the contributors on their magnitude. The ex-
periment design started with addition of the urban emission
(DEMIS) to the reference (non-urbanized) state as this im-
pact was assumed (and proven) to be the dominant. With this
choice, the urban atmosphere was already filled with pol-
lutants serving as the base state for the other impacts. The
impact of the order of addition of the two further contrib-
utors, DLU and DBVOC, is probably small as their effect
is also small. However, if they were applied before DEMIS,
their magnitude would probably be even smaller: the depo-
sition flux depends on the absolute concentrations (Zhang et
al., 2003), while adding BVOCs to a high-NOxair can have
mixed effects, leading to both increases and decreases in sec-
ondary organic aerosol (K. Li et al., 2019; Pullinen et al.,
2020). Further, in our previous paper, Huszar et al. (2022),
which had the same goal and modelling design as this pa-
per but looked at the gas-phase chemistry, we analysed the
effect of the order of the two sub-contributors of DBVOC
in detail, which are the impact of reduced vegetation (DB-
VOC_L; see Huszar et al., 2022) and the impact of changed
meteorological conditions that drive the MEGAN model, im-
pacting BVOC fluxes (DBVOC_M). We found that (1) with
regard to DBVOC, the changes of vegetation cover play a
much more important role than the changed meteorological
conditions, and (2) the partial impact of changed meteoro-
logical conditions is smaller if applied after DBVOC_L. As
for the DMET impact, this is in general the second-strongest
contributor, and one gets different magnitudes of the impacts
if DMET is applied before DEMIS. Let us suppose that first
the DMET contributor has been applied. This means that the
meteorological conditions that drive the impact of emissions
consider the urban canopy meteorological forcing (UCMF).
Huszar et al. (2021) however showed that the impact of urban
emissions is considerably (almost by 50 %) smaller if UCMF
is accounted for. On the other hand, DMET applied as the
first contributor would be very small as UCMF would act
on much less polluted air (with missing urban emissions). So
in conclusion, the DLU, DBVOC, and DMET impacts would
be smaller if applied before DEMIS and somehow decoupled
from reality ,motivating us to start the addition of the differ-
ent components of urbanization by the emissions themselves.
The choice of the order of the other three contributors rather
has a much smaller effect.
We must also stress that the cities selected in this study are
from a relatively small region, meaning that they do not ex-
hibit a substantially different background climate. Moreover
the typical “rural” vegetation was assumed to be crop, which
might not be the case if cities in other parts of the world were
considered (e.g. tropical areas), meaning that the impact of
modified biogenic emissions could be much stronger. Fur-
ther, some secondary effects of PM concentration changes
can play a role too via direct and indirect radiative effects.
For example, photolysis rates and temperatures are altered
via the direct effect of aerosol, which in turn influences air
chemistry (Han et al., 2020; Wang et al., 2022), or the ver-
tical structure of urban boundary layer can be altered by the
aerosol emitted that modifies the overall stability and convec-
tion (Miao et al., 2020; Slater et al., 2022; Fan et al., 2020; Yu
et al., 2020; López-Romero et al., 2021), which in turn can
modify the vertical mixing and precipitation with feedbacks
on species concentration. Consequently, to obtain a more ac-
curate quantification of the impact of rural-to-urban transfor-
mation on PM, these effects have to be included in modelling
studies.
Code and data availability. The RegCM4.7 model is freely
available for public use at https://github.com/ICTP/RegCM (ICTP,
2021) (https://doi.org/10.5281/zenodo.7548172, Giorgi et al.,
2023). CAMx version 7.10 is available at https://www.camx.com/
download/ (Ramboll, 2020b, a). The RegCM2CAMx meteorologi-
cal preprocessor used to convert RegCM outputs to CAMx inputs
and the MEGAN v2.10 code as used by the authors are available
upon request from the first author. The complete model configura-
tion and all the simulated data (3-dimensional hourly data) used for
the analysis are stored at the Dept. of Atmospheric Physics of the
Charles University data storage facilities (about 5TB) and are avail-
able upon request from the first author.
Author contributions. PH created the concept and designed the
experiments; PH and JK performed the model simulations; APPP,
LB, and AVP contributed to input data preparation, model configu-
ration, and analysis of the outputs; and all authors contributed to the
manuscript text.
Competing interests. The authors declare that they have no con-
flict of interest.
Atmos. Chem. Phys., 24, 397–425, 2024 https://doi.org/10.5194/acp-24-397-2024
P. Huszar et al.: Urbanization impact on PM concentration 419
Disclaimer. Publisher’s note: Copernicus Publications remains
neutral with regard to jurisdictional claims made in the text, pub-
lished maps, institutional affiliations, or any other geographical rep-
resentation in this paper. While Copernicus Publications makes ev-
ery effort to include appropriate place names, the final responsibility
lies with the authors.
Acknowledgements. We acknowledge the CAMS-REG-APv1.1
emissions dataset provided by the Copernicus Atmosphere Moni-
toring Service, the Air Pollution Sources Register (REZZO) dataset
provided by the Czech Hydrometeorological Institute, and the
ATEM Traffic Emissions dataset provided by ATEM (Studio of eco-
logical models). We also acknowledge the providers of AirBase Eu-
ropean Air Quality data (http://www.eea.europa.eu/data-and-maps/
data/aqereporting-1, last access: 9 January 2024).
Financial support. This research has been supported by the Tech-
nology Agency of the Czech Republic (grant no. SS02030031), the
Grantová Agentura ˇ
Ceské Republiky (grant no. 19-10747Y), and
the Univerzita Karlova v Praze (grant no. SVV 260709).
Review statement. This paper was edited by Manish Shrivastava
and reviewed by two anonymous referees.
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