ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-6823-2016Continental anthropogenic primary particle number emissionsPaasonenPaulipauli.paasonen@helsinki.fihttps://orcid.org/0000-0002-4625-9590KupiainenKaarleKlimontZbigniewVisschedijkAntoonDenier van der GonHugo A. C.https://orcid.org/0000-0001-9552-3688AmannMarkusDepartment of Physics, University of Helsinki, Helsinki, FinlandInternational Institute for Applied Systems Analysis (IIASA), Laxenburg, AustriaFinnish Environment Institute (SYKE), Helsinki, FinlandTNO, the Netherlands Organisation for Applied Scientific Research, Utrecht, the NetherlandsPauli Paasonen (pauli.paasonen@helsinki.fi)6June201616116823684016December201519January201625April201625April2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/6823/2016/acp-16-6823-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/6823/2016/acp-16-6823-2016.pdf
Atmospheric aerosol particle number concentrations impact our climate and
health in ways different from those of aerosol mass concentrations. However,
the global, current and future anthropogenic particle number emissions and
their size distributions are so far poorly known. In this article, we present
the implementation of particle number emission factors and the related size
distributions in the GAINS (Greenhouse Gas–Air Pollution Interactions and
Synergies) model. This implementation allows for global estimates of particle
number emissions under different future scenarios, consistent with emissions
of other pollutants and greenhouse gases. In addition to determining the
general particulate number emissions, we also describe a method to estimate
the number size distributions of the emitted black carbon particles. The first results
show that the sources dominating the particle number emissions are different
to those dominating the mass emissions. The major global number source is
road traffic, followed by residential combustion of biofuels and coal
(especially in China, India and Africa), coke production (Russia and China),
and industrial combustion and processes. The size distributions of emitted
particles differ across the world, depending on the main sources: in regions
dominated by traffic and industry, the number size distribution of emissions
peaks in diameters range from 20 to 50 nm, whereas in regions with intensive
biofuel combustion and/or agricultural waste burning, the emissions of
particles with diameters around 100 nm are dominant. In the baseline (current
legislation) scenario, the particle number emissions in Europe, Northern and
Southern Americas, Australia, and China decrease until 2030, whereas
especially for India, a strong increase is estimated. The results of this
study provide input for modelling of the future changes in aerosol–cloud
interactions as well as particle number related adverse health effects, e.g.
in response to tightening emission regulations. However, there are
significant uncertainties in these current emission estimates and the key
actions for decreasing the uncertainties are pointed out.
Introduction
Aerosol particles affect both our health and the climate in many ways. These
effects depend partly on the composition of the particles and partly on their
sizes and concentrations (WHO, 2013; Stocker et al., 2013). Furthermore,
different effects are linked to different metrics of concentration – mass and
number. Because of the cubic relation between particle mass and diameter,
dp, it is common that these metrics of concentration are dominated
by particles with very different sizes. Aerosol number concentrations are
typically dominated by particles in ultrafine particle (UFP) size range, with
dp< 0.1 µm, or the smaller end, roughly
< 0.3 µm, of fine particles (FP, here 0.1–2.5 µm). On
the contrary, the mass concentration depends mostly on the larger and
heavier, but typically fewer FP, with dp> 0.3 µm
(see Fig. 1 for schematic representation). Because the particles in different
size ranges originate from different sources and atmospheric processes impact
them differently, the particle number (PN) concentrations and particle mass
concentrations (PM, e.g. PM2.5 describing mass concentration of
particles with dp< 2.5 µm) are often poorly
correlated even if considering only stationary measurements (e.g. Rodríguez
et al., 2007; Rodríguez and Cuevas, 2007).
Number size distribution of a fictional and simplified particle
population within the planetary boundary layer with four lognormal particle
size modes (upper panel) and the same population represented with mass size
distribution (lower panel). Note that in the literature it is common to use the term
“fine particles” (FP) when referring to all particles with diameters below
2.5 µm, including ultrafine particle (UFP) size range. However, in
this article we exclude UFP size range from FP.
According to WHO (2013), there is increasing epidemiological evidence on the
association between short-term exposures to ultrafine particles and
cardiorespiratory health, as well as the health of the central nervous
system. Clinical and toxicological studies indicate that the health effects of ultrafine particles are (in part) caused by different mechanisms than those of larger particles, such as PM2.5 or PM10 (WHO, 2013). Also
the climate effects of aerosol particles depend on their size (Stocker et
al., 2013). All particles can, depending on their chemical composition,
either absorb solar radiation (mainly black carbon aerosol) or scatter it
partly back to space. In addition to these so-called aerosol–radiation
interactions, the particles with diameter close to or over 0.1 µm
can act as cloud condensation nuclei (CCN), i.e. they can form cloud droplets
when the air mass moves upwards and cools down. Since the clouds efficiently
reflect solar radiation back to space, these aerosol–cloud interactions have
a significant cooling effect on our climate. One of the problems in assessing
the total radiative forcing of aerosols is the non-linear relationships of
these different interactions, e.g. depending on the initial sizes and
atmospheric growth of black carbon particles, their warming effect due to
light absorption can be turned over, either partly or entirely, by their
ability to act as CCN and thus form cooling cloud droplets (e.g. Chen et al.,
2010). The future reductions in anthropogenic emissions of aerosol and their
precursors have been estimated to accelerate global warming (e.g. Wigley,
1989; Arneth et al., 2009; Makkonen et al., 2012; Westervelt et al., 2015).
However, the changes in aerosol–cloud interactions have been so far (if not
ignored) assessed by assuming similar relative changes in particle mass and
number emissions, which leads to incorrect results if the actual size
distributions of emitted particles change.
The ultrafine and fine particles originate from a number of sources and
atmospheric processes. New particle formation (i.e. nucleation) produces
particles with diameters below 2 nm (0.002 µm) from vapours such
as sulphuric acid, organic vapours and nitrogen-containing bases. This can
happen both during regional scale atmospheric new particle formation events
and at a smaller scale, for example in combustion plumes, when vapours
suddenly cool immediately upon their introduction to ambient air. In this
work, the latter, particles formed during the initial cooling and rapid
dilution after the vapours are emitted to atmosphere, are also considered
primary particles in addition to those emitted directly in particle phase.
Somewhat larger UFP particles, still in nucleation mode size range, are
formed, e.g. in new particle formation processes occurring already before the combustion plume is emitted to the atmosphere and thus producing cores for cooling vapours to
condense on (e.g. Rönkkö et al., 2007; Lähde et al., 2010). Black
carbon, i.e. soot particles, formed in flames by agglomeration of cyclic
carbon molecules and emitted often with a coating of condensed organic or
inorganic vapours, are also partly in UFP size range (< 0.1 µm),
but their size distribution extends to FP size range. FP are emitted also
from other thermal sources, as well as from mechanical sources like dust
resuspension, wear, fragmentation and suspension of biological matter. Fine
particles are also formed from ultrafine particles by growth via atmospheric
condensation of anthropogenic and biogenic organic compounds, sulphuric acid
and nitrates on the particle's surface. Biogenic condensation growth of UFP
is a significant contributor to fine particle number concentrations. It has
been estimated that out of the total number of fine particles over the
European continent, roughly 50 % have been formed through growth of UFP
by condensed biogenic organic vapours (Paasonen et al., 2013a).
The legislation on aerosol emissions and concentrations is based on particle
mass, mainly due to the well-established knowledge on the correlation of
PM2.5 and adverse health effects (Pope et al., 2002, 2009). However,
the increasing evidence of the adverse health impacts of UFP, as well as the
unresolved significant uncertainties on the aerosol–climate effects due to
aerosol–cloud interactions, require more attention to the anthropogenic
particle number emissions. The mass emissions cannot be directly converted
to number emissions, because the ratio of mass and number emissions depends
greatly on the size distribution of emitted particles. Additionally, because
the main removal mechanism of the smallest of UFP in the atmosphere is their
coagulation to larger particles (e.g. Kerminen et al., 2001), a decrease
in PM2.5 emissions might even increase PN concentrations (Pirjola et
al., 2015).
In global climate modelling work, the number emissions are typically
extracted from mass emissions applying constant factors and size
distributions for different highly aggregated source sectors (e.g., traffic,
biomass burning, power generation, etc.). This approach can be used for
producing future scenarios also for number emissions and their size
distributions. An example of such an approach is the widely applied emission
database, the AeroCom project (Dentener et al., 2006), in which the size
distributions are fixed and averaged over a wide variety of different sources
under the main sectors. Thus, the changes in technology and fuels are
reflected in number emissions through a linear dependence between mass and
number emissions, since the size distribution is assumed to remain the same.
On the other hand, the aerosol number emissions and their size distributions
with information on different emission abatement techniques have been studied
lately resulting in a size-resolved European particle number emission
inventory (Denier van der Gon et al., 2009, 2010, 2013, 2014; Kulmala et al., 2011)
which has been tested in several UFP modelling exercises (e.g. Fountoukis et
al., 2012; Kukkonen et al., 2016). Emission inventories are not directly
applicable for estimating the future trends in emissions as they are based on
available statistics, which generally lag several years behind the present day.
However, in combination with projections of activity data and assumptions
about penetration of control technologies a present-day inventory can form a
starting point for projected future emissions.
Here we describe and present the first results of the implementation of
aerosol number emission factors and their size distribution to the global
emission scenario model GAINS (Greenhouse Gas – Air Pollution Interactions
and Synergies; Cofala et al., 2007; Amann et al., 2011) developed at IIASA
(International Institute for Applied Systems Analysis, Austria). The
implementation of these factors in the GAINS-Europe model, describing only
European emissions, was published in an IIASA report (Paasonen et al., 2013b).
We also estimate emissions and size distribution of the black carbon
containing particles and the black carbon cores in them.
The GAINS model has
a more detailed technological structure than many available inventories and
thus we are able to estimate the implications of future abatement technology
changes on number emissions and size distributions. GAINS has been
previously applied to analyse the effect of emission abatement policies and
other factors affecting the emissions in terms of traditional air
pollutants, including particle mass and greenhouse gases. The GAINS model
has supported the Commission in the review of the Thematic Strategy on
Air Pollution (TSAP; European Commission, 2005) and its related legal
instruments on ambient air quality and national emission ceilings through
modelling of emission baselines and scenarios for different policy options
and their related impacts (Amann et al., 2013). With the implementation of
aerosol number emission factors to GAINS, the future particle number
emissions can be estimated in a consistent manner with other air pollutants
and greenhouse gases. This information can be used for estimating the
effects of emission regulations and technological improvements on the health
effects of ultrafine particles and on aerosol–climate effects in future
decades, as well as for planning particle number emission measurements for
the sources that are so far not well enough reported.
MethodsThe GAINS model
The GAINS model
(Amann et al., 2011) is an integrated assessment model that brings together
information on the sources and impacts of air pollutant and greenhouse gas
emissions and their interactions. GAINS combines data on economic
development, the structure, control potential and costs of emission sources,
the formation and dispersion of pollutants in the atmosphere and an
assessment of environmental impacts of pollution.
GAINS assesses all the main air pollutants and greenhouse gases (SO2,
NOx, PM, NMVOC, NH3, CO2, CH4, N2O, F-gases) with
more than 1000 measures to control the emissions to the atmosphere for each
of its nearly 170 regions. Applying built in source–receptor relationships
(developed in collaboration with atmospheric groups running chemical
transport models for a given domain), GAINS identifies the least-cost balance
of emission control measures across pollutants, economic sectors and
countries that meet user-specified air quality and climate targets.
In GAINS, emissions from different sources are calculated with three basic
input parameters (Klimont et al., 2002):
Annual activity levels (A) in a given sector, corresponding to certain
fuels (e.g., fuel wood used (burned) per year in domestic single house
boilers),
Shares (X) of abatement technologies applied to fuel consumption of the
activity (e.g., improved boilers with accumulation tank, pellet boilers,
boilers with electrostatic precipitator, etc.) such that ∑X=1,
Emission factors (EF) for each sector-fuel-technology –combination
(emissions per unit of activity).
Activity levels A in GAINS are based on the information from international
statistics available from International Energy Agency (IEA), Organisation for
Economic Co-operation and Development (OECD), United Nations (UN) and Food
and Agriculture Organization of the United Nations (FAO), Eurostat, and
national statistics. The shares of control technologies X are derived from
published information on national and international legislation, for example
for transport sector from diesel.net, discussions with the national experts,
and scientific publications where similar assessment has been performed. The
emission factors EF are determined from the scientific publications and
measurement databases.
The yearly emissions E in region i are calculated as
Ei=∑ijkmEijkm=∑ijkmAijkmXijkmEFijkm,
where the indices j refer to source sector, k to fuel and m to
abatement technology.
Within GAINS, future emissions are estimated for different scenarios of
anthropogenic activities (e.g., energy use), for which shares X of
different technology levels for all emission sources are assumed. Here we
present results based on the Current Legislation (CLE) baseline scenario
created in the ECLIPSE project, specifically version 5 of this scenario
(ETP_CLE_v5, Klimont et al., 2016a, b; Stohl et al., 2015).
The diameter ranges of particles in the size classes applied for PN
emissions. Diameters are electrical mobility diameters, except for *
aerodynamic diameter (see text).
Size class, n123456789Minimum dp (nm)31020305070100200400Maximum dp (nm)10203050701002004001000*Particle number emission factors and size distributions
The determination of emission factors (EFPN) for particle number
(PN) emissions and particle size distributions (PSD) is based on the European
particle number emission inventory developed by TNO (Netherlands Organisation
for Applied Scientific Research; Denier van der Gon et al., 2009, 2010)
during the EUCAARI project (Kulmala et al., 2011). The emission factors and
emissions described both in TNO work and in this study include both the
particles emitted to atmosphere directly in particle phase, as well as those
formed from vapours immediately after the emission during the rapid cooling
and dilution of the exhausts. We consider here particles of both these types
as primary particles. The uncertainties related to the emission factors in
terms of particles formed immediately after the emissions are discussed in
Sect. 4.1.
Particle size distributions present the size segregation of the number
emissions into size classes, i.e. the proportions Pn of the total
number of emitted particles in each size sector n. Thus, the emission factor
for a single size class n is written as
EFPN,n=PnEFPN,
and the ΣPn=1. Values for the proportions Pn are calculated
from modal presentations of PSDs, consisting of one to three lognormal modes.
The diameter ranges of the size classes applied in the GAINS emissions are
shown in Table 1.
EFPNs were determined through two alternative ways. For some source
sectors, including traffic and domestic combustion, both
EFPNs and PSDs were determined from the literature directly
(these are called hereafter as direct emission factors). For other source
sectors, EFPNs were determined based on PM1 mass
emission factors (EFPM1) from an earlier version of the GAINS model
(Kupiainen and Klimont, 2007). However, deriving an EFPN directly
from the EFPM1 would make the EFPN very sensitive to the
estimated number of close to 1 µm particles, since their mass is
significantly larger in comparison to the mass of those with diameter below
or around 100 nm. Thus, emission factors for PM in the size range
10–300 nm (EFPM0.3) were first derived from EFPM1 based
on literature on emission mass size distributions and particle densities
(Denier van der Gon et al., 2009, 2010; Kulmala et al., 2011). Then, by applying
the particle number size distributions from the literature, the
EFPNs consistent with EFPM0.3 were resolved. The
latter type of emission factors is called PM-based emission factors,
hereafter.
In our analysis, we employ for many source sectors the emission factors and
size distributions provided in the TNO studies (Denier van der Gon et al.,
2009, 2010). However, for sources that are most important for particle
numbers, such as road transport and wood combustion in the domestic sector,
we developed new emission factors and size distributions in order to better
fit in the GAINS model, especially in terms of the emission abatement
technologies within it. The modifications to the TNO study are described
below.
We extended the PSDs in GAINS to cover sizes from electrical mobility
diameter (dM) of 3 nm up to aerodynamic diameter (dA) of
1 µm (see Table 1), whereas the particle size range in the TNO
study was from dM=10 nm to dA=300 nm. The size
range was extended to larger sizes in order to allow for comparison between
the emission factors for particle number and PM1 mass, the latter being
determined as the total mass of particles with dA≤1µm. Additionally, even though the number share of particles
larger than 300 nm in all emitted particles is negligible, large particles
are important in some source sectors. The extension towards smaller diameters
was made to provide the whole particle size range for climate model
calculations, but it should be noted that no modes with diameters below
10 nm were introduced. These extensions of the particle size ranges required
recalculation of the EFPNs for source sectors that were
originally based on PM0.3 emission factors, with the formula
EFPN=1ρ∑nPnπ6dn3RPM0.3/PM1EFPM1,
where ρ is the estimated density of the emitted particles, Pn is
the proportion of particles in size class n out of the total number of
emitted particles, dn is the geometric mean diameter of the particles in
size class n, and R(PM0.3/PM1) describes the ratio of
PM0.3 and PM1-masses. The values for ρ,
R(PM0.3/PM1) and PSDs were taken from the TNO analysis,
with the exception of the PSDs mentioned below.
New PSDs were introduced for road transport sources with the highest
activities (diesel heavy-duty trucks and busses, both diesel and gasoline
light-duty trucks and passenger cars), based on the EU FP7 project TRANSPHORM
database (Vouitsis et al., 2013). Additionally, the emission factors for
diesel-fuelled road transport were made dependent on the fuel sulphur content
(FSC), based on vehicle-specific FSC dependent emission factors provided by
the Laboratory of Applied Thermodynamics at the Aristotle University of
Thessaloniki, which is responsible also for the TRANSPHORM database. Also
EFPNs and PSDs for domestic wood combustion (including pellet
burning and medium size district heating boilers) and for shipping emissions
(fuel sulphur content-dependent EFPNs and PSDs) were updated (domestic sector:
Gaegauf et al., 2001; Hedberg et al., 2002; Johansson et al., 2004;
Johansson et al., 2008; Kinsey et al., 2009; Lack et al., 2009; Lamberg et al., 2011; Bäfver
et al., 2011; Boman et al., 2011; Pettersson et al., 2011; Chandrasekaran et
al., 2011; shipping: Hobbs et al., 2000; Sinha et al., 2003; Petzold et al.,
2008; Murphy et al., 2009; Moldanova et al., 2011; Diesch et al., 2013), as
well as for two-stroke vehicles in road transport (Ntziachristos et al.,
2005; Etissa et al., 2008). A new PSD was introduced also for flaring in gas
and oil industry (Canteenwalla et al., 2006). The EFPN for tyre
wear, previously based on EFPM0.3, was replaced with a direct PN
emission factor (Dahl et al., 2006).
We note that many of the measured EFPNs and PSDs are not
representing the particles which either have diameters below 10 nm or are
volatile in temperatures above typical atmospheric temperatures. Thus, it is
likely that in the current set of emission factors the nucleation mode
particles (dp< 20 nm), which are formed from vapour molecules
during their initial cooling when introduced to the atmosphere, is largely
overlooked.
Black carbon size distribution estimates
In addition to determining the emission factors and size distributions for
total particle number emissions, we also made estimates for black carbon
emission size distributions. Two different size distributions were
determined, one for the whole particles in black carbon mode
(BCmode), which considers both the black carbon cores and the
condensed material on them, and one for the black carbon cores of these
particles (BCcore).
The division of emitted particles to black carbon containing particles and
other particles was made depending on the source of particles and the
geometric mean diameters of the number size modes of the emitted particles.
Naturally, only the combustion-related sources were considered to produce
black carbon. Of the combustion sources, only the modes with geometric mean
diameters (GMD) equal to or above 50 nm were taken as black carbon modes.
This rough estimate for a minimum GMD was chosen, because the agglomeration
in BC formation produces a roughly lognormal mode and we assumed that it would
not form particles in the smallest size ranges of the modes with GMD below
50 nm (Sorensen and Feke, 1996; Kholghy et al., 2013). This assumption seems
reasonable for diesel-fuelled vehicles, but might not be valid for
gasoline-fuelled vehicles (Liggio et al., 2012). However, as the global
emissions from diesel-fuelled vehicles are found to dominate the transport
emissions, we will leave the further improvements on defining the black
carbon modes to future studies.
Upper panel: shares of different source sectors in number
emissions of all (PNtot), ultrafine (PNUFP) and fine (PNFP)
particles and in aerosol mass emissions of particles with diameters below
1 µm (PM1) for 2010. Lower panel: shares of UFP and FP in PN emissions
for each SNAP-sector.
The size distribution of the black carbon cores in the black carbon
containing particles was calculated with two combinations of assumptions. In
both it was assumed that all the BC mode particles (defined as above) have a
black carbon core and that both the core and the particle are spherical. The
difference was that in one calculation we assumed that there is only organic
carbon (OC) condensed on the BC core, and in the other calculation that all
PM1 additional to BC is condensed onto this core. The shares of BC, OC
and other PM1 were defined with mass emission factors for BC, OC and
PM1 in GAINS. A further, simplified assumption was made that the shares
of BC and OC (or BC and other PM1, when the latter is considered as
condensed matter) were the same in all BC containing particles regardless of
their size. This might slightly overestimate the share of condensed matter in
BC mode for the sources in which there is significant non-BC mode (with
GMD < 50 nm). The geometric mean diameters of the BC-cores were derived
simply from these assumptions based on the mass emission factors and BC-mode
geometric mean diameter GMDBCmode. For the case of only OC
condensing on the particles the geometric mean diameter of the core was
GMDBCcore1=GMDBCmode×EFBCEFBC+EFOC1/3
and, for the case of all PM1, except for BC, assumed to be formed
through condensation
GMDBCcore2=GMDBCmode×EFBCEFPM11/3.
Uncertainties
In the results presented in Sect. 3 we have not depicted error bars or shown
other illustration of uncertainties. The major sources of uncertainties are
mentioned in text within Sect. 3, and discussed in more detail in Sect. 4.
Evolution of continental anthropogenic particle number emissions
from 2010 to 2030 according to the current legislation scenario in different
parts of the world and the whole world.
Results
The calculated aerosol number emissions in 2010 were dominated by ultrafine
particles, which contributed to total PN emissions by about 80 %.
However, emissions from different sources varied in terms of particle size,
which is presented in the lower panel of Fig. 2 as the division of number
emissions to UFP and FP size ranges in each source sector. The upper panel of
Fig. 2 shows the shares of different sources in the global anthropogenic
continental total particle number emissions, number emissions of ultrafine
particles (UFP, dp< 0.1 µm) and FP
(dp> 0.1 µm), as well as mass emissions of particles
with dp< 1 µm (PM1), all for year 2010. The
main source of UFP was road transport, corresponding to 40 % of UFP
emissions and thus being the largest contributor to total aerosol particle
number emissions. Power production also contributed to the UFP emissions with
20 % share, mainly due to emissions from coke production, and residential
combustion with 17 % share. In FP size range, the shares of residential
combustion and road transport were quite similar, roughly 30 % each,
whereas the mass emissions of particles with diameters below 1 µm
(PM1) were dominated by residential combustion (> 50 %). These
differences indicate the need for assessing the size-segregated number
emissions of aerosols in addition to mass emissions, in order to better
understand their role in atmospheric processes as well as their climate and
health effects. It is also important to notice that there is most probably
more difference between number emissions and PM2.5 mass emissions (which
is often the regulated and monitored quantity) than between number emissions
and PM1 emissions.
Overall emissions in different parts of the world
Total annual aerosol number emissions and their current trend for different
continents, with Eurasia divided to major countries and the rest of Europe
and Asia, are depicted in Fig. 3. The future trend is based on the current
legislation baseline scenario (ETP_CLE_v5, Klimont et al., 2016a, b;
Stohl et al., 2015). In 2010, China had by far the major PN emissions with
40 % estimated share of the global emissions, followed by Asia (excl.
China, India and Russia) and Europe (excl. Russia). However, the actions
determined in the current legislation scenario resulted in a decrease in
emissions in China, as well as in Europe, North and South America. On the
contrary, especially in India, but also in Russia, Asia and Africa, the
increase in activities seems to offset the benefits of more stringent
legislation. The global sum of continental anthropogenic emissions is
expected to decrease from 2010 to 2020 by roughly 15 % (from
1.5 × 1028 to
1.3 × 1028 year-1), but remain quite constant from 2020 to 2030.
Contributions of different source sectors to particle number
emissions in different parts of the world, from 2010 to 2030. Note the
different y axis scales.
Main aerosol number sources in 2010 and expected changes until
2030
The aerosol number emissions were dominated by road transport in Europe,
North and South America, Asia and Australia in 2010 (blue bars in
Fig. 4). In Africa and India the emissions from residential combustion were
the main sources together with road transport, whereas in Russia, the
emissions from industrial processes, road transport and non-road transport
were on a similar level. In China, the major source sector for particle
number emissions was power production, followed by residential and industrial
combustion emissions. In general, it should be noted that with the current
set of emission factors the uncertainties are lesser in North America and
Europe, where most of the applied emission factor measurements are made (more
in Sect. 4).
In the following subsections (3.2.1–3.2.5), we discuss separately the major
sources of aerosol number emissions and their predicted changes from 2010 to
2030. In these subsections, the percentages given for the shares of different
sources refer to emissions in 2010, if not stated otherwise.
Power production emissions
The dominance of the power production emissions in China was caused by the
emissions from coke production, which accounted for 95 % of Chinese power
production emissions in 2010. The significant contributions of power
production to emissions in Russia and India were caused by coke production
(88 and 79 %, respectively).
The coke production emissions in China were estimated to decrease over
50 % from 2010 to 2020, whereas in India and Russia coke production
emissions were predicted to increase by 200 and 70 %, respectively. The
decrease in Chinese emissions resulted mainly from large-scale replacement
and closure of small inefficient coke ovens with modern installations, often
equipped also with measures to capture and remove dust emissions, which
offsets the 20 % increase in activity level. For India and Russia,
changes in abatement technology shares did not take place in the applied
CLE scenario, and thus the changes were due to increased activity levels
only.
However, the coke production emissions are subject to significant
uncertainties. Additionally, the emission factors applied for (coal-fired)
power plants are not dependent on the sulphur removal technologies or sulphur
contents of the fuels, but only on particle removal technologies. The applied
power plant emission factors are designed for power plants in Europe, where
sulphur removal technologies are in place. This may cause significant
underestimation in the emission estimates for power plants using high sulphur
fuels (for other power production sources than coke production) in many parts
of the world, where a significant fraction of the power plants are not
equipped with such technologies. Thus, the presented results on power
production emissions have to be considered as preliminary estimates. It seems
obvious that coke production causes at least a significant part of the
aerosol number emissions in question, but the future trends especially in
China are very uncertain, depending on the rate of activity level increase
and overall emission factor decrease due to improving technology. These
uncertainties, also influenced by the general uncertainties related to the
representativeness of the PN emission factors for nucleation mode
sulphate/sulphuric acid particles, are discussed in more detail in Sect. 4.
Residential combustion
Residential combustion was a significant source of particles, especially in
China, India and Africa. All these emissions originated mainly from cooking
stoves, but used fuels varied. In India, firewood, agricultural residues and
coal contributed each by a share of 25 % or more to the residential
combustion emissions, and also dung combustion had a share of over 10 %.
In China 64 % of the emissions originated from coal combustion, roughly
24 % from combustion of agricultural residues and only 7 % from
firewood combustion, whereas in Africa 85 % of emissions came from
firewood combustion (activity levels for dung combustion are available only
for India). The uncertainties related to residential combustion emissions are
discussed in Sect. 4.
In India and Africa the residential combustion emissions were expected to
increase slightly due to the increase in the activity levels. On the other
hand, the emissions from residential combustion in cooking stoves in China
were estimated to decrease by 25–30 % per decade due to the reduced coal
use in residential sector which results in an overall decrease in residential
combustion emissions in China.
Industrial combustion and processes
Industrial combustion was estimated to contribute significantly to the total
aerosol number emissions in China and India, and the emissions from
industrial processes were notable in Russia and India. In China, the
industrial combustion emissions were dominated by cast iron production
(75 % of industrial combustion emissions in 2010) and cement production
(10 %), whereas in India the cement production contributed to the
industrial combustion emissions by 50 % and cast iron production by less
than 10 %. It is notable that in India 20 % of industrial combustion
emissions were related to biomass fuel combustion.
Of industrial processes, the main source of particle number emissions was
estimated to be basic oxygen furnaces, producing over 80 % of Indian and
50 % of Russian emissions. In Russia the other main sources were primary
aluminium production (17 %), open hearth furnaces (16 %) and electric
arc furnaces (13 %), the latter contributing by 13 % also to Indian
industrial processes emissions.
For all industrial emissions, PM-based emission factors were applied. Thus,
the differences in PN emission factors for different emission abatement
technologies are not expected to be fully consistent (see Sect. 4).
Traffic emissions
The emissions from traffic were the major source of aerosol particles in most
parts of the world in 2010. This was the case especially in Western countries
and Asia excluding China, India and Russia. Interestingly, even though the
total consumption of fuels in road traffic was highest in Northern America
(42 000 PJ year-1 compared to
31 000 PJ year-1 in
Asia and 27 000 PJ year-1 in Europe) the calculated emissions were
the highest in Asia and the lowest in North America. The low emissions in
North America were due to much smaller percentage of diesel vehicles than
in Europe, whereas the high emissions in Asia were due to (i) the significant
share of (diesel) fuel having higher sulphur content than in Europe and
North America, and (ii) the smaller proportion of vehicles with new emission
abatement technologies.
Based on the measurements collected by Vouitsis et al. (2013), applied for PN
emission factors in the GAINS model, the tightening regulation on particle
mass emissions decreased drastically the number emissions, as well. This
leads
to a major decrease in European, North American and Australian emissions from
2010 to 2030, as can be seen in Figs. 3 and 4. Additionally, traffic
emissions are the only source of particulate matter, for which also number
emissions have been regulated. The new diesel vehicles under EURO
VI-technology are limited not to have higher number emissions than
6 × 1011 km-1 for
passenger cars (the same limit should be applied also for gasoline vehicles
after 2017) and 6–8 × 1011 kWh-1 for heavy-duty vehicles. However, these limits are set only for solid
particles larger than 23 nm. In practice, this means that only particles
with black carbon core are taken into account, since the secondary particles
are not considered as solid (they evaporate when the sample is heated) and
the nucleation mode particles with a non-volatile core (Rönkkö et
al., 2007; Lähde et al., 2010) have diameters well below 23 nm after
evaporation of condensed matter. Thus, the particle number emission limits
mentioned above are in principle reached already when older diesel vehicles
are equipped with a diesel particle filter (DPF) (Samaras et al., 2005).
In addition to the emission abatement technologies and fuel type (here in
principle gasoline vs. diesel, since the global shares of gas- or ethanol-fuelled vehicles are very small), the particle number emissions from traffic
were highly sensitive to fuel sulphur content (FSC). This effect is
demonstrated in Table 2, where we present the relative change in road
transport PN emissions arising from the assumption of replacing all the
diesel fuel with ultra low FSC diesel, such as that demanded by legislation, e.g.
in the EU and US. Table 2 shows how much the emissions would decrease, in
comparison to the actual CLE scenario, if all the consumed diesel fuel was
replaced with ultra-low FSC diesel. In Europe, there are some non-EU
countries for which, in the CLE scenario, the share of higher FSC diesel
remains constant until 2030. Since the total European road traffic emissions
are decreasing significantly due to the improving emission abatement
technologies, the relative share of emissions from higher FSC diesel
increases with time. The table also reveals that the expected decrease in
road transport emissions in Australia, Africa, South America and Russia
from 2010 to 2020 (see Fig. 4) was caused by decreasing the FSC in diesel,
whereas (according to CLE scenario) in China, India and Asia the share of
ultra-low FSC diesel is either not increasing or the effect of its increase
is (partly) invalidated by the increasing volume of road transport.
The relative change in annual road transport PN emissions in
comparison to the CLE scenario, if (in addition to the technological
advancements described in the CLE scenario) all the diesel fuel (consumed in
road transport) is assumed to be replaced with ultra-low sulphur
content–fuel (FSC = 10 ppm). The lowest row shows the change in total emissions
from all sources. Note that, e.g. in Europe, the impact increases with time,
because in the CLE scenario the emissions decrease drastically in most
countries, but a small share of high FSC fuel remains present in some
(non-EU) countries. Thus, the proportion of the high FSC contribution to
total emissions in the CLE scenario increases with time.
Agriculture has a significant share on particle number emissions in Russia,
India and Africa and these emissions were entirely (> 99 %) caused by
agricultural waste burning (in which slash and burn of forests or other
vegetation and forest fires were not included).
In Russia, Europe and Northern America the non-road transport emissions
formed a considerable part of the emissions. However, this large non-road
transport share was partly due to including the gas pipeline compressor
emissions in this sector. These were dominant in Russian non-road transport
emissions (95 %) and constituted a major source also in Northern America
(35 %). In Europe the non-road transport emissions came mainly from
maritime vessels and the inland waterway transport was also a significant
contributor to North American emissions.
One PN source, which might have a notable share in regional emissions but was
not included in this study due to lack of data on particle number emission factors,
are brick kilns. Brick kilns are a significant source of PM especially in
India and other Southeast Asia (Bhat et al., 2014).
Spatial distribution of emissions
Aerosol particles are short-lived climate forcers with lifetimes roughly up
to a week and the aerosol number size distributions evolve rapidly especially
under high concentrations close to the sources. Thus, the regional particle
concentrations leading to health and climate effects cannot be defined with
emissions described in country or region level, but it is essential to assess
the emissions with higher spatial resolution. The gridding of emissions down
to 0.5∘× 0.5∘ resolution, as applied in the GAINS
emission model allows for estimating the regional concentrations when
combined with air quality or climate models. The gridded particle number
emissions presented here can be downloaded from GAINS model website
(http://www.iiasa.ac.at/web/home/research/researchPrograms/air/PN.html)
with a distribution to different size bins as presented in Sect. 3.4.
In the upper panel of Fig. 5 the gridded global emissions are presented for
the year 2010. The gridded emissions ranged in a span of various orders of
magnitude (note the logarithmic colour axis in Fig. 5, where the values below
1016 km-2 year-1 are shown as having the value of 1016). The highest emissions
were seen in North-Eastern China, but all the continents had various grid
cells with emissions higher than
1021 km-2 year-1.
Spatial distribution (in 0.5∘× 0.5∘ grid)
of global continental anthropogenic particle number emissions in units
km-2 year-1 (upper panel) and predicted relative change in particle number
emission from 2010 to 2030 (lower panel). The gridded emissions are
available from http://www.iiasa.ac.at/web/home/research/researchPrograms/air/PN.html.
Particle number size distributions of the major global aerosol
emission sources (upper panel) and normalized number size distributions for
each region (lower panel). The left side figures are for 2010 and the right
side ones for 2030.
Estimated global number size distributions of the black carbon
mode particles (left panel) and of their black carbon cores, assuming only
OC is condensing on the BC cores (middle panel). Number size distribution of
global total PN emissions and emissions of particles with BC core (right
panel). The source categories in the left and middle panels are the same as in
Fig. 6.
In the lower panel of Fig. 5, we have depicted the estimated change in total
aerosol particle number emissions from 2010 to 2030 based on the current
legislation scenario. The main areas of significant decrease in emissions
were Western Europe, Eastern United States, Brazil, Australia, Japan and
China, whereas the emissions in Africa, India and the European part of Russia
were predicted to increase notably.
Emission number size distributions
The number size distributions of the major source sectors are presented for
years 2010 and 2030 in Fig. 6 (upper panels), respectively. Here we divided
the emissions to different sectors (e.g. according to the used fuel) than in
previous figures in order to present the differences in size distributions
and total emissions related to the different fuels. Especially the domestic
combustion of coal and biomass resulted in notably different size
distribution with peak values in 20–40 nm and ∼ 100 nm,
respectively. The most significant single particle number sources mentioned
in Sect. 3.2 (road transport with diesel fuel and coke production) had peak
values in sizes from 30 to 50 nm in diameter. The difference in size
distributions from different sources was visible also when assessing the
regional emissions (Fig. 6, bottom panels). In 2010, the emissions in Africa
and India were dominated with biofuel combustion and agricultural waste
burning peaking at diameters close to 100 nm, whereas the other regions
showed highest emissions around 40 nm diameter. However, the estimated
increases in Indian power production, industrial and road traffic emissions
towards 2030 moved the size distribution to smaller diameters. On the
contrary, the notable decrease in Australian road traffic emissions shifted
the size distribution to larger sizes, because one of the main sources in
2030 was estimated to be agricultural waste burning.
Black carbon emission size distribution
The size distributions of black carbon containing particles as well as the
size distribution of the black carbon cores for year 2010, calculated with
Eq. (4), are presented in Fig. 7. The global black carbon mode particle
emissions were dominated with diesel fuel road transportation, but the
contributions of domestic biomass combustion and agricultural waste burning
were much higher than for the total particle numbers (compare to Fig. 6,
upper left panel). The black carbon mode count median diameter varied from 70
to 100 nm. This variation seems to be at least partly due to the amount of
vapours condensed on the black carbon cores: the black carbon core size
distributions shown in the middle panel of Fig. 7 show more similar
count median diameters of roughly 60 nm for all other sources than
industrial combustion and domestic coal combustion. The difference between
the assumptions of the composition of the coating of BC cores, i.e. the
choice between coating including only OC (Fig. 7, middle panel) and coating
including all PM1 except BC (figure not shown), was significant only in
industrial combustion emissions, for which the BC core mode shifted to much
smaller sizes (from ∼ 100 to 30–40 nm) when assuming all PM1 is
condensed on BC cores. This is because in industrial PM1 combustion
emissions the shares of OC and BC are relatively small. It is to be noted
that the method of defining the source-specific BC modes was approximate, as
discussed in Sect. 2.3, and some of the sub-50 nm particles here defined as
non-BC particles might in reality have a BC core. Even though this possible
underestimation of smaller BC particles is unlikely to concern the diesel
emissions (Liggio et al., 2012), which is the main source for BC number
emissions, the black carbon size distributions from other sources should be
assessed in more detail in future.
Future trends of emissions in different PN and PM metrics
The projected future trends of PN emissions (UFP and FP separately) and, for
comparison, the mass emissions PM1, PM2.5 and PMBC are
depicted in Fig. 8 with indicated global contributions of different source
sectors. The significant contribution of road traffic to PN emissions caused
a decrease from 2010 to 2020 in PN emissions in both UFP and FP size range
and the decrease in UFP emissions was enhanced by the decrease in coke
production emissions. The decrease in PN emissions was predicted to stop
after 2020 due to increase in industrial emissions. This was estimated to
cause a slight increase in UFP emissions from 2020 to 2030, but the global FP
number emissions seemed to remain constant. Comparison to PM mass emissions
revealed that the trends of particle numbers and mass can be very different.
The major source in all the depicted mass emissions, PM1, PM2.5 and
PMBC, was residential combustion, but PM1 and BC emissions
from residential combustion emissions were estimated to decrease more than
PM2.5. As the PM2.5 emissions showed the steepest increase in
industrial emissions, whereas the BC emissions are affected very little by
industrial process emissions, the total PM2.5 emissions showed increase,
PM1 remained rather constant and BC emissions showed clear decrease.
Shares of different source sectors to the future global trends in
particle number and mass emissions under current legislation scenario: PN
emissions in ultrafine and fine size ranges and particle mass emissions
PM1, PM2.5 and black carbon.
Continental future trends of particle number and mass emissions
under current legislation scenario. Emissions are normalized to unity in
2010. Note the different y axis scale in the subplot for the whole world.
In most parts of the world, the future changes in UFP and FP emissions are
predicted to be rather similar (Fig. 9), but the relative change in UFP
emissions is typically a bit more pronounced than that of FP particles.
However, especially in India the UFP emissions are estimated to increase much
more than FP emissions. This is because the emissions from residential
combustion and agricultural waste burning, which emit both FP and UFP, are
not increasing in India, but the industrial, traffic and coke production
emissions, all emitting mainly in UFP size range, are predicted to increase
significantly (see Fig. 4). Also in Russia, which is the other area where the
number emissions are clearly increasing, the relative increase of UFP
emissions is larger than that of FP emissions. In Russia the road traffic
emissions are predicted to decrease and the increase in UFP emissions is
mainly caused by increases in emissions from industrial processes, coke
production and gas pipeline compressors. The mass emissions are depicted also
in Fig. 9 for reference, but the reasons for different regional trends are
not discussed here.
Uncertainties related to the particle number emission factors
This article has it main focus on describing the implementation of particle
number emission factors in the global GAINS emission scenario model. We
present the initial results and demonstrate the future needs for improving
the emission factor database. The uncertainties in the particle number
emission factors are large and often based on gap-filling. Based on the
presented results, further research can be planned and we see these
estimates, albeit uncertain, as progress and part of the results.
The uncertainties in the emission factors are due to the following main
reasons, (i) the lack of reliably reported measurements for the particle
number emission factors and the related size distributions, (ii) geographic
unrepresentativeness of the applied emission factors, (iii) application of
number emissions factors based on PM mass emission factors (instead of
applying a direct number emission factor), and (iv) a lack of representative
measurements for fuels with high and/or varying sulphur contents. High
sulphur contents give rise to high emission of particles of a very small size
(< 10 nm), these numbers can be expected to dominate total PN emissions
in many sources.
The above-listed causes for uncertainties are in many cases linked; e.g. the
reason for applying PM-based emission factors for determining number
emission factors is due to the lack of available direct number emission
factors. They also make the geographic variation of uncertainties very
prominent. In Europe and Northern America, the overall uncertainties, even
though significant in absolute values, are smaller in comparison to the
other continents, both in terms of current and future emissions. This is
because most of the emission factor measurements have been conducted in
these continents and in both the dominant sources of emissions are road
traffic and residential wood combustion, both with well-established direct
number emission factor database for different emission abatement
technologies. On the contrary, the emission factors for the dominant
particle number sources in Asia (including China, India and Russia) are in
most cases based on only few (often European or American) studies, and the
effect of emission abatement technologies is typically based on PM1
emission factors. Also the pronounced wood combustion emissions from cooking
in Africa are based on emission factors from (traditional western) heating
stoves and are thus rather uncertain.
In the following we discuss shortly the most important individual causes for
uncertainties in the results presented in Sect. 3.
General causes of uncertainties in PN emission factorsApplying PM-based emission factors in general
The emission abatement technologies have typically different removal
efficiencies for particles with different diameters. However, when the
emission factors for different technologies are determined by simply scaling
the emission factor with the corresponding change in PM emission factor, the
PSD remains unchanged. This may result in erroneous estimates of
EFPN, e.g. if a source with high emissions of fine particles and
condensable vapours is controlled with a removal technology for the fine
particles, the formation of ultrafine particles from the vapours may increase
due to drastic decrease in the condensation sink for the vapours and
coagulation sink for the freshly formed particles.
Effect of sulphur on PSDs and emission factors
It is well known that sulphuric acid, formed from SO2 after oxidation to
SO3, is a key player in atmospheric new particle formation. It has been
also shown in many studies that, by increasing the fuel sulphur content, the
primary emissions of ultrafine particles are increased (e.g. Rönkkö
et al., 2013). However, the nucleation mode particles formed from sulphur
(and other condensable vapours) are often not well represented, in some cases not at
all, in the PN emission factors and PSDs in the literature. Some
instruments applied for the measurements are not able to measure
concentrations of particles with diameters below 10 nm, and in some cases
the nucleation mode particles are evaporated before they are detected. It can
be expected that by making new experiments on the PN emission factors and
PSDs with instruments suitable for detection of nucleation mode particles,
the overall figure of UFP emissions will alter significantly. It might be
also possible to derive semi-empirical estimates of the nucleation mode
particle emissions by taking into account the SO2 emissions.
Additional uncertainties related to sulphur emissions arise from the lack
of emission factors for different fuel sulphur contents in sources other
than road traffic. Especially in coal combustion the emissions can be
expected to depend heavily on the coal sulphur content. Also for the road
traffic emissions, the uncertainties are considerably higher for higher FSC
diesel than for ultra-low FSC diesel or gasoline.
Effects of ambient conditions on emissions
The numbers and size distributions of emitted particles depend also on the
ambient conditions in which they are emitted, e.g. on temperature. The
volatility of vapours is strongly dependent on temperature, which naturally
causes evaporation when fuel is heated. Some of the vapours that do not
effectively condense onto particles and/or form new nucleation mode
particles in room temperatures may still be condensable when temperature is
lower. This would affect the emissions most probably in the colder parts of
the world and especially in winter. Also the particle concentrations prior
to emission can be presumed to affect the PN number emissions (at least when
the immediate formation of secondary particles are considered as PN
emission), due to the competition of (emitted) vapour uptake between new
particle formation and condensation to pre-existing particles. These kinds
of effects are, however, issues for future research and their impact cannot
be implemented directly to the GAINS model.
Sector-specific causes of uncertainties in PN emission
factorsCoke production
Emission factor for coke production is based on PM1 emission factors and
the conversion from mass to number factor and the particle number size
distribution are derived from a publication by Weitkamp et al. (2005), in
which the authors study the emissions from a large coke production facility
near Pittsburgh, US. Other studies for comparing the number size distribution
related to coke production, especially in Asia, are needed for verifying the
drastic impacts of coke production to regional aerosol emissions.
Furthermore, the effects of emission abatement technologies – such as
cyclone, 1- and 2-field electrostatic precipitators and high-efficiency
dedusters – on the particle size distribution and number emission factor
need to be studied.
Residential coal combustion
Residential coal combustion number emission factors are PM-based and were
produced with particle size distributions taken from Bond et al. (2002).
Further studies for different coal types, including varying sulphur
contents, and stove technologies are needed to better estimate the share of
residential coal combustion on the particle number emissions especially in
China.
Residential wood combustion in traditional cooking stoves
The emission factors for the cooking stoves, e.g. in African and Asian
countries have been adapted from no-control emission factors for heating
stoves, which are mostly based on Northern-European and North American studies.
Obtaining emission factors for traditional cooking stoves down to a three
stone fire, would give a better picture on the residential combustion emissions
especially in Africa. Furthermore, estimating the dung combustion activity
levels in countries other than India could alter the overall figure to some
extent.
Power plant and industry emissions
The emission factors for power plants and industry are all PM-based, which
causes uncertainties especially when assessing the future emissions with
improved technologies. Also the fuel sulphur contents are not taken into
account, which increases the uncertainty levels.
On the effects of anthropogenic emissions on particle number
concentrations
In this paper we have presented the first results of global anthropogenic
primary particle number emissions from the GAINS model. It is important to
note that the future trends presented here should not be interpreted as
trends for future particle number concentrations, because the relation
between particle number emissions and number concentrations are far from
linear. Typically, particle number concentrations vary much less than the
emissions, because (i) in the areas of low anthropogenic primary emissions
the natural emissions and atmospheric aerosol formation (both in terms of
vapours condensing on pre-existing particles and formation of new particles
from vapours) play a relatively more important role (Paasonen et al., 2013a)
and (ii) the most efficient sink for the smallest of aerosol particles in
nucleation mode is their coagulation with larger particles (e.g. Kerminen et
al., 2001). Because this coagulation sink of particles correlates in many
cases with the number emissions (e.g. in the street canyons both the number
concentrations and sink are high, and in general both increase when
approaching the emission source), the implementation of the GAINS number
emissions to air quality or climate models even with the higher spatial
resolution (0.5∘× 0.5∘) may lead to overestimating
the concentrations. In order to better approach the health effects of
particle number concentration within cities, it is possible to downscale the
GAINS emissions to a street canyon scale with the methods presented by
Kiesewetter et al. (2014).
Comparison of the global emission trends of different aerosol concentration
metrics (Figs. 8–9) reveals their different predicted trends. The global
mass emissions of black carbon aerosol, the main aerosol component causing
global warming, are predicted to decrease in the future, whereas the global
emissions of cooling aerosols, i.e. mass emissions non-BC aerosol (cooling
due to scattering of solar radiation) and the number emissions of FP (acting
as cloud condensation nuclei, CCN) are predicted to increase or decrease less
than BC mass emissions. The predicted changes in BC mass emissions and PN
emissions suggest that, even though the BC particles can act as CCN after
atmospheric aging (Chen et al., 2010), the overall global decrease in BC mass
emissions does not lead to a similar decrease in number emission of FP.
However, it should be noted that the climate effects do not follow directly
the emissions, especially in the case of cloud droplet formation. There are
several processes, which can either overrule or dampen the formation of cloud
droplets from emitted FP. Firstly, the UFP from both anthropogenic emissions
and atmospheric new particle formation grow to CCN sizes, and this growth often produces much more CCN than primary FP emissions, and secondly, the
boundary layer height and dilution also affect the concentration levels
resulting from the emissions (Paasonen et al., 2013a). Thirdly, the cloud
droplet concentration (at least partly) saturates when CCN concentrations
increase, which lessens the cloud forming effect of FP emitted in moderately
or more polluted areas (e.g. Gultepe and Isaac, 1999).
Conclusions
The aerosol particle number (PN) emission factors and the related size
distributions have been implemented in the global GAINS model. The regional
PN emissions are dominated by different sources than the particle mass
emissions. In most parts of the world the emissions from diesel-fuelled road
vehicles were the major source in 2010. Other significant sources for
particle numbers were residential combustion of biofuels and coal
(especially in China, India and Africa), coke production (Russia and China),
industrial combustion and processes (Russia, China and India) and gas
pipeline compressors in Russia. However, the PN emission factors for
residential coal combustion, coke production and gas pipeline compressors
have high uncertainties, which can be reduced only with further new
experimental studies on the emission factors.
According to the current legislation scenario, the PN emissions are expected
to decrease significantly by 2030 in Europe, North and South America
and Australia (64, 49, 26 and 76 %, respectively), mainly because of
introduction of diesel particulate filters (DPF) in order to comply with new
diesel vehicle legislation; the DPFs cut efficiently both particle mass and
number emissions. In Southern America and Australia the decrease in road
traffic emissions is also partly due to intended switch to ultra-low sulphur
content fuels, which is already the only fuel type in use in
North America and most of the European countries. Also in China the total
PN emissions are estimated to decrease by 23 % from 2010 to 2030, mainly
due to the decreases in coke production and residential coal combustion
emissions. However, in India the emissions are increasing by over 80 %
from 2010 to 2030, in Russia by 37 % and in the rest of Asia by 19 %,
whereas in Africa the emissions are estimated to increase only by 7 %.
The number size distributions of particles differ significantly depending on
the source. In terms of the major number sources, traffic, coke production
and residential coal combustion show highest emissions in ultrafine particle
(UFP) size range, with diameters between 30 and 50 nm, whereas the
residential biofuel combustion and agricultural waste burning, as well as
industrial combustion, show peaks with diameters around 100 nm. These
differences, naturally, cause variation in the total number size
distributions of emitted particles in different parts of the world.
The sizes of emitted particles are important when assessing the impacts of
the emitted particles. The globally significant climate impact of particle
number concentrations arises from the aerosol–cloud interactions, i.e. the
activation of particles with diameters close to or over 100 nm as cloud
droplets. On the other hand, the adverse health effects related to particle
number concentration are coupled with UFP concentrations. This, together with
the dominance of traffic emissions in this size range and the fact that road
traffic is a pollution source very close to our everyday life, justifies the need
for better assessment of size-segregated PN emissions also in the population
health perspective. Thus, this work provides input for both climate and air
quality modelling and makes the evaluation between the effects of the future
changes in aerosol number emissions and aerosol mass emissions possible.
However, the work described in this paper is the first implementation of the
particle number emissions to an emission scenario model such as GAINS. In
order to improve the estimates of current and future PN emissions, more
experiments on the PN emission factors and size distributions of the sources
indicated in Sect. 4 are crucial, as well as a thorough reassessment of the
effects of fuel sulphur content and ambient conditions on the emission.
Acknowledgements
This work was funded by the Academy of Finland through Centre of Excellence
(grants no. 1118615 and 272041) and WHITE project (grant no. 286699),
European Commission 7th Framework projects ECLIPSE (Project no. 282688),
PEGASOS (265148), TRANSPHORM (243406) and “Assessment of hemispheric air
pollution on EU air policy” (contract no. 07.0307/2011/605671/SER/C3), the
Nordic Top-level Research Initiative (TRI) Cryosphere-Atmosphere Interactions
in a Changing Arctic Climate (CRAICC) and the Otto A. Malm foundation. We
thank Leonidas Ntziachristos and Ilias Vouitsis at Aristotle University of
Thessaloniki (Greece) for help and assistance in applying the emission
factors for road transport sector, Qiang Zhang from Tsinghua University
(Beijing, China) for the spatial distribution of Chinese power plants for
2000, 2005, and 2010, and the personnel at the Air Quality and Greenhouse
Gases program at IIASA for their help, especially Imrich Bertok and Binh Nguyen for making
the offline work possible and Chris Heyes for gridding the emission
data. Edited by: R. Krejci
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