In this work we couple the HTAP_v2.2 global air pollutant
emission inventory with the global source receptor model TM5-FASST to
evaluate the relative contributions of the major anthropogenic emission
sources (power generation, industry, ground transport, residential,
agriculture and international shipping) to air quality and human health in
2010. We focus on particulate matter (PM) concentrations because of the
relative importance of PM2.5 emissions in populated areas and the
well-documented cumulative negative effects on human health. We estimate
that in 2010, depending on the region, annual averaged anthropogenic
PM2.5 concentrations varied between ca. 1 and 40 µg m-3,
with the highest concentrations observed in China and India, and lower
concentrations in Europe and North America. The relative contribution of
anthropogenic emission sources to PM2.5 concentrations varies between
the regions. European PM pollution is mainly influenced by the agricultural
and residential sectors, while the major contributing sectors to PM
pollution in Asia and the emerging economies are the power generation,
industrial and residential sectors. We also evaluate the emission sectors
and emission regions in which pollution reduction measures would lead to the
largest improvement on the overall air quality. We show that air quality
improvements would require regional policies, in addition to local- and urban-scale measures, due to the transboundary features of PM pollution. We
investigate emission inventory uncertainties and their propagation to
PM2.5 concentrations, in order to identify the most effective
strategies to be implemented at sector and regional level to improve
emission inventories, knowledge and air quality modelling. We show that the
uncertainty of PM concentrations depends not only on the uncertainty of
local emission inventories, but also on that of the surrounding regions.
Countries with high emission uncertainties are often impacted by the
uncertainty of pollution coming from surrounding regions, highlighting the
need for effective efforts in improving emissions not only within a region but
also from extra-regional sources. Finally, we propagate emission inventory uncertainty to PM concentrations and health impacts. We estimate 2.1 million
premature deaths per year with an uncertainty of more than 1 million premature
deaths per year due to the uncertainty associated only with the emissions.
Introduction
Ambient particulate matter pollution ranks among the top five risk factors
globally for loss of healthy life years and is the largest environmental risk
factor (Lim et al., 2013; Anderson et al., 2012; Anenberg et al., 2009; Cohen
et al., 2017). The World Health Organization (WHO, 2016) reported about
3 million premature deaths worldwide attributable to ambient air pollution in
2012. Health impacts of air pollution can be attributed to different
anthropogenic emission sectors (power generation, industry, residential,
transport, agriculture, etc.) and sector-specific policies could effectively
reduce health impacts of air pollution. These policies are usually
implemented under national legislation (Henneman et al., 2017; Morgan, 2012),
while in Europe transboundary air pollution is also addressed by the regional
protocol under the UNECE Convention on Long-Range Transboundary of Air
Pollution (CLRTAP). At city and local level, several studies have been
developed to assess the contribution of sector-specific emissions to
PM2.5 (particulate matter with a diameter less than 2.5 µm)
concentrations with the aim of designing air quality plans at local and
regional level (Karagulian et al., 2015; Thunis et al., 2016). Indeed,
particulate matter can travel thousands of kilometres, crossing national
borders, oceans and even continents (HTAP, part A, Dentener et al., 2010).
Local, regional and international coordination is therefore needed to define
air pollution policies to improve global air quality and possibly human
health. The CLRTAP's Task Force on Hemispheric Transport of Air Pollution
looks at the long-range transport of air pollutants in the Northern
Hemisphere, to identify promising mitigation measures to reduce background
pollution levels and their contribution to pollution in rural as well as
urban regions. Although primary PM2.5 and intermediately lived
(days-to-weeks) precursor gases can travel over long distances, the
transboundary components of anthropogenic PM are mainly associated with
secondary aerosols which are formed in the atmosphere through complex
chemical reactions and gas-to-aerosol transformation, transport and removal
processes of gaseous precursors transported out of source regions (Maas and
Grennfelt, 2016). However, the most extreme episodes of exposure often occur
under extended periods of low wind speeds and atmospheric stability,
favouring the formation of secondary aerosols close to the source regions.
Secondary aerosol from anthropogenic sources consists of both inorganic –
mainly ammonium nitrate and ammonium sulfate and ammonium bisulfate and
associated water, formed from emissions of sulfur dioxide (SO2),
nitrogen oxides (NOx) and ammonia (NH3) – and
organic compounds involving thousands of compounds and often poorly known
reactions (Hallquist et al., 2009). Exposure to and impact from aerosols on
humans can be estimated by a variety of approaches, ranging from
epidemiological studies to pure modelling approaches. The Burnett et
al. (2014) risk–response methodology is often used in models to estimate
premature deaths (PDs) due to air pollution exposure, e.g. in Lelieveld et
al. (2015) and Silva et al. (2016), who report a global mortality in 2010 due
to air quality issues induced by anthropogenic emissions of 2.5 and
2.2 million people, respectively. A higher global mortality is found in a
more recent work by Cohen et al. (2017), who account for 3.9 million
premature deaths per year due to different model assumptions. In Europe,
Brant et al. (2013) estimate 680 000 premature deaths, which is twice as
high as the numbers reported for the CAFE (Clean Air for Europe) study
(Watkiss et al., 2005). Recently, using the same emission database as in this
study, Im et al. (2018) report a multi-model mean estimate of PD of 414 000
(range 230–570 000) for Europe and 160 000 PDs for the USA. At the global
scale, models, in some cases using satellite information (Brauer et al.,
2015; Van Donkelaar et al., 2016), are the most practical source of
information of exposure to air pollution. However, model calculations are
subject to a range of uncertainties related to incomplete understanding of
transport, chemical transformation, removal processes and, not least,
emission information.
This work is developed in the context of the TF HTAP Phase 2 (Galmarini et
al., 2017), where a number of models are deployed to assess long-range
sensitivities to extra-regional emissions, using the same HTAP_v2.2
anthropogenic emission inventory (Janssens-Maenhout et al., 2015).
Differences in model results illustrate uncertainties in model formulations
of transport, chemistry and removal processes and are addressed in separate
studies (Liang et al., 2018),
but not of uncertainties in emission inventories. The objectives and
novelties of this study are the evaluation of (i) the relative contribution
of anthropogenic emission sources to PM2.5 concentrations at global
scale, (ii) the emission sectors and emission regions in which pollution
reduction measures would lead to the largest improvement on the overall air
quality, and (iii) the relevance of uncertainties in regional sectorial
emission inventories (power generation, industry, ground transport,
residential, agriculture and international shipping), and their propagation
in modelled PM2.5 concentrations and associated impacts on health. This
work applies the global source–receptor model TM5-FASST (TM5-FAst Scenario
Screening Tool), which is extensively described and evaluated in this special
issue (Van Dingenen et al., 2018), and couples it to the HTAP_v2.2 global
emission inventory for the year 2010 to estimate global air quality and
associated health impacts in terms of PM2.5 concentrations. The regional
and global scale and the focus on annual PM2.5 and associated health
metrics warrant the use of the TM5-FASST model. However, the most extreme
episodes of pollution may occur at more local-to-regional scales, justifying
the need for local measures. For instance, a recent study performed over hundreds of
cities in Europe (Thunis et al., 2017) shows that in order to comply with the standards prescribed by the
Air Quality Directives and the health guidelines by WHO, local actions at the
city scale are needed.
Specifically, we show that the impact of emission inventory uncertainty on
mortality estimates is comparable with the range of uncertainty induced by
air quality models and population exposure functions. We also investigate the
uncertainties in PM2.5 from within the region to extra-regional
contributions. Based on our analysis of the importance of emission
uncertainties at sector and regional level on PM2.5, we aim at informing
local, regional and hemispheric air quality policy makers on the potential
impacts of sectors with larger uncertainties (e.g. residential and
agriculture) or regions (e.g. developing and emerging countries).
MethodologyTM5-FASST model and emission perturbations
This work is an application of the TM5-FASST model, which is extensively
documented in a companion publication in this special issue. Van Dingenen et
al. (2018) provide an extensive evaluation of the model, model assumptions
and performance with regard to the linearity and additivity of concentration response
to different size of emission perturbations and future emission scenarios.
Below we summarise the most important features of relevance for this work
and refer for more detail to Van Dingenen et al. (2018).
In order to calculate PM2.5 concentrations corresponding to the
HTAP_v2.2 emissions, we use the native 1∘× 1∘
resolution source–receptor grid maps obtained for TM5-FASST_v0 (Van
Dingenen et al., 2018). The TM5-FASST source–receptor model is based on a
set of emission perturbation experiments (-20 %) of SO2,
NOx, CO, NH3, and volatile organic compounds
(VOCs) and CH4 using the
global 1∘× 1∘ resolution TM5 model, the
meteorological year 2001 (which was also used for the HTAP Phase 1
experiments) and the community emission dataset prepared for the IPCC AR5
report (RCP, Representative Concentration Pathway)
for the year 2000
(Lamarque et al., 2010). TM5-FASST uses aggregated regional emissions (i.e.
one annual emission value per pollutant or precursor for each of the 56
regions + shipping), with an implicit underlying
1∘× 1∘ resolution emission spatial distribution
from RCP year 2000 which was partly based EDGAR methodology and grid maps.
The concentration of PM2.5 contributing from and to each of 56 receptor
regions is estimated as a linear function of the emissions of the source
regions, including the aerosol components BC, primary organic matter (POM),
SO4, NO3, and NH4. While secondary organic
aerosol (SOA) from natural sources is included in the model calculations
using the parameterisation described in Dentener et al. (2006), no explicit
treatment of anthropogenic SOA is considered, since no reliable emission
inventories of SOA precursor gases was available, and formation processes
were not included in the parent TM5 model. A recent study by Farina et
al. (2010) indicates a global source of 1.6 Tg, or ca. 5.5 % of the
overall SOA formation due to anthropogenic SOA. The relative importance of
anthropogenic SOA varies widely by region, and is deemed higher in regions with fewer VOC emission
controls. Inclusion of SOA would possibly lead to a somewhat larger role of
the transboundary pollution transport (Farina et al., 2010; Peng et al.,
2016; Shiraiwa et al., 2017), mainly for regions and sectors with large PM
and VOC emissions (e.g. residential, and to some extent transport and
industry).
Under the assumption that the individual sector contributions add up linearly
to total PM2.5 – this assumption is evaluated in Van Dingenen et
al. (2018) and holds in most regions within 15 % error – the comparison of
PM2.5 concentrations calculated for the reference and scenario case
yields an estimation of the contribution of each sector to total PM2.5
concentrations.
Specifically, the reduced-form model TM5-FASST is computing the concentration
resulting from an arbitrary precursor emission strength Ei using a first-order perturbation approach, i.e. for each PM component j, the change in
concentration dPMj
resulting from a change in emission strength Eix of
precursor i in source region x, relative to a reference emission
Ei,refx, is approximated by the first linear
term of a Taylor expansion of PM as a function of emissions:
dPMjy≅Aijx,yEix-Ei,refx,
where
Aijx,y=ΔCjyΔEixwithΔEix=0.2Ei,refx.Aijx,y is a set of independently computed source–receptor
matrices, expressing the linearised emission–concentration response between
each relevant precursor (i) emission and PM component j concentration,
for each pair of source (x) and receptor (y) regions (Van Dingenen et
al., 2018).
In Sect. S1.2 we explain in detail how Eq. (1) can also be applied for
evaluating the attribution by sector as well as by source region, based on
the work by Van Dingenen et al. (2018). Thus to calculate total PM2.5
concentration in each receptor region, the 56 source regions' individual
contributions must be summed. Using this approach, it is possible to evaluate
the PM2.5 concentrations from “within-region” and “extra-regional”
PM2.5 emissions. The extra-regional contribution represents the RERER
metric (Response to Extra-Regional Emission Reduction) for a specific region
used across the whole HTAP experiment (Galmarini et al., 2017), in particular
focusing on the PM2.5 concentration reduction due to the contribution of
the emissions of each anthropogenic sector (Eq. 3):
RERER=∑R(foreignregions)∑R(allregions),
where R represents the concentration response to each sector emission
decrease.
As depicted in Fig. S1 in the Supplement, the 56 TM5-FASST regions cover the
entire globe, but their areal extent differs in terms of size, population,
emission magnitude and presence of neighbouring countries (e.g. Europe
comprises 18 TM5-FASST regions). In order to make the evaluation of external
impacts on smaller regions (e.g. European countries) comparable to those of
larger regions (like the USA, China and India), in this work an aggregation
procedure to 10 world regions (refer to Table S2 in the Supplement) has been
applied (China+, India+, SE Asia, North America, Europe, Oceania, Latin
America, Africa, Russia and the Middle East). In this work we focus on
particulate matter due to its negative effects on human health (WHO, 2013;
Pope and Dockery, 2006; Worldbank, 2016). The TM5-FASST model includes an
assessment of the premature mortality due to ambient PM2.5
concentrations on an exposed population following the methodology developed by
Burnett et al. (2014), as discussed in Sect. 4. Health impacts due to indoor
air pollution or ozone are not evaluated in this work.
In the following, we will address the uncertainty of sector-specific
emissions from this inventory in a quantitative way as well as the
differences we observe from one region to the other, based on the uncertainty
of activity data and emission factors. As discussed in the next section, the
reason to use HTAP_v2.2, and not for example the RCP2000 as the basis for
our assessment of emission propagation is that the TF HTAP aims at bringing
policy-relevant information, and to this end, it has compiled a
policy-relevant emission inventory (HTAP_v2.2) for the most recently
available year. While the RCP2000 was at the basis of the FASST calculations,
and presented the best community emissions effort at the time, the
HTAP_v2.2 inventory is now much more accurate, in particular given the
focus on regional emission analysis of our work.
HTAP_v2.2 emissions
The global anthropogenic emission inventory HTAP_v2.2 for the year 2010
(Janssens-Maenhout et al., 2015) is input to the global source–receptor model
TM5-FASST to evaluate PM2.5 concentrations for each world region/country
with the corresponding health effects. The HTAP_v2.2 inventory includes
for most countries official and semi-official annual anthropogenic emissions
of SO2, NOx, CO (carbon monoxide), NMVOC
(non-methane volatile organic compounds), PM10 (particulate matter with
a diameter less than 10 µm) PM2.5, BC (black carbon) and OC
(organic carbon) by country and sector (Janssens-Maenhout et al., 2015). Here
we focus on the six major anthropogenic emission sectors contributing to global
PM2.5 concentrations, namely the power generation (“power”), non-power
industry, industrial processes and product use (“industry”), ground
transportation (“transport”), residential combustion and waste disposal
(“residential”), agriculture (“agriculture”), and international shipping
(“ship”). International and domestic aviation emissions are not considered
in this study due to the lower contribution to air pollution compared to
other anthropogenic sectors. It should be noted that agricultural emissions
do not include agricultural waste burning and forest and savannah fires.
Details on the emissions included in each aggregated sector can be found in
Janssens-Maenhout et al. (2015). In addition to the reference HTAP_v2.2
emissions for the year 2010, a set of emission perturbation scenarios has
been created by subtracting from the reference dataset the emissions of each
sector.
Emission inventory uncertainties
In order to investigate how computed PM2.5 concentrations are affected
by the uncertainty of emission inventories, we perform a sensitivity analysis
testing the upper and lower range of HTAP_v2.2 emissions including their
uncertainties. Aggregated emissions of a certain pollutant p, from a sector i
and country c, are calculated as the product of activity data (AD) and
emission factors (EFs); therefore the corresponding uncertainty
(σi,c,p) is calculated as following:
σEMIi,c,p=σADi,c2+σEFi,p,c2,
where σAD and σEF are the uncertainties
(%) of the activity data and emission factors for a certain sector (i),
country (c) and pollutant (p). Uncertainty values of the activity data by
sector and country are obtained from Table 2 of Janssens-Maenhout et
al. (2017) and Olivier et
al. (2016). Using this approach, the uncertainty in the global total
anthropogenic CO2 emissions is estimated to range from -9 %
to +9 % (95 % confidence interval), with larger uncertainties of
about ±15 % for non-Annex I countries, and uncertainties of less
than ±5 % are obtained for the 1990 OECD countries
OECD
countries in 1990: Australia, Austria, Belgium, Canada, Denmark, Finland,
France, Germany, Greece, Iceland, Ireland, Italy, Japan, Luxembourg,
Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland,
Turkey, United Kingdom, United States.
for the time series from 1990
(Olivier et al., 2016) reported to UNFCCC. Uncertainty values for the
emission factors of gaseous pollutants are retrieved from the EMEP/EEA
Guidebook (2013) and Bond et al. (2004) for particulate matter. In this work
we assume that reported countries' emissions are based on independent
estimations of activity data and EFs, and hence no cross-country
correlation structure is assumed. This is in contrast to bottom-up gridded
emission inventories like EDGAR, where the use of global activity datasets
may lead to correlated errors between countries.
Therefore, we can calculate the overall uncertainty σEMIp,c with the following equation (EMEP/EEA, 2013).
σEMIp,c=∑iσEMIi,c,p⋅EMIi,c,pEMItot,c,p2,
where EMIi,c,p (in kt)
represents the emission of a certain pollutant (p) in a certain country (c)
from a specific sector (i) and EMItot,c,p (in kt) the
corresponding emissions from all sectors for that country and pollutant.
Table S3 reports the overall uncertainty calculated for each pollutant and
for each TM5-FASST region. Using an additional constraint that EFs and
activities cannot be negative, a lognormal distribution of the calculated
uncertainties is assumed (Bond et al., 2004). Therefore we can calculate the
upper and lower range of emission estimates, multiplying and dividing the
reference emissions by (1+σp,c), respectively. We do not
account for the uncertainties of the atmospheric transport model and the
uncertainties due to aggregation, which are larger over smaller TM5-FASST
regions. Based on the upper and lower emission range per region, new
TM5-FASST model runs have been performed per source region to retrieve the
corresponding range of concentrations in receptor regions (therefore the
total number of computations is 56⋅2 for the uncertainty analysis).
TM5-FASST modelling results
In this section, we first provide “central” estimates of regional
(Sect. 3.1), sectorial (Sect. 3.2) and gridded (Sect. 3.3) contributions,
whereas the corresponding uncertainty estimates are discussed from Sect. 3.4
onward.
Within-region vs. imported extra-regional anthropogenic
population-weighted PM2.5 concentrations (%) for aggregated world
regions based on “central” estimates. Annual average population-weighted
anthropogenic concentrations (in µg m-3) are reported on top
of each bar together with the RERER metric (%). Shipping emissions
are not included.
Regional contributions to PM2.5 concentrations
Figure 1 provides a global perspective on the fraction of within-region and
extra-regional PM2.5 concentrations for 10 aggregated world receptor
regions using emissions of the year 2010, with the extra-regional fraction
(using the RERER metric) broken down into source region contributions. Annual
average population-weighted anthropogenic PM2.5 concentrations (refer to
Van Dingenen et al., 2018, for the calculation of this metric)
range from a few
µg m-3 (e.g. in Oceania or Latin America), around
7–8 µg m-3 for North America and Europe, up to
33–39 µg m-3 in China+ (including also Mongolia) and
India+ (including also the rest of south Asia). Anthropogenic PM2.5
pollution in China+ and India+ is mainly affected by large emission
sources within the country (98 % and 96 %, respectively; RERER
2 %–4 %), although 4 % of the Indian anthropogenic PM2.5
pollution is mainly transported from the Gulf region and the Middle East, as
was also observed by Venkataraman et al. (2018). North America (98 %) and
Oceania (98 %) are mainly influenced by within-regional pollution due to
their geographical isolation from other regions. TM5-FASST computations
attribute 11 % of the
PM2.5 in Europe to extra-regional sources; for the Middle East and Gulf
region extra-regional contributions amount to 18 % (mainly from Europe
and Russia), for Africa 25 % (mainly from Europe and the Middle East),
and Russia 28 % (mainly from Europe, the Middle East and the Gulf region,
and China). Shipping emissions are not considered in this figure due to their
international origin, while inland waterway emissions are still included in
the ground transport sector. Transboundary air pollution is known to be an
important issue in the rest of Asia, in particular for pollution transported
from China to North and South Korea and Japan (Park et
al., 2014) and we estimate that the contribution of transported PM is up to
40 % in south-eastern Asia (mainly from China and India). Within-region
and extra-regional PM2.5 concentrations for all the TM5-FASST regions
are reported in Table S2.
Focusing on Europe, Fig. 2 shows within-region (in black) vs. extra-regional
absolute population-weighted PM2.5 concentrations (in
µg m-3) for 16 EU countries plus Norway and Switzerland,
defined in TM5-FASST, as well as the source regions contributing to this
pollution. Regional annual averages of population-weighted PM2.5
concentrations in Europe vary between 2 and 4 µg m-3 in
northern European countries (like Finland, Norway and Sweden) up to
10–12 µg m-3 for continental Europe. Although most of the
computed annual average PM2.5 concentrations for Europe are below the
World Health Organization Air Quality Guideline of 10 µg m-3
PM2.5 (as annual average), these values represent only regional averages
while several exceedances in urban areas are often observed in Europe. As
further discussed in Sect. 3.2, an additional contribution to PM2.5
concentrations comes from the shipping sector, mainly influencing
Mediterranean countries (like Italy, Spain and France) and countries facing
the North Sea, Baltic Sea and Atlantic Ocean (e.g. Benelux, Sweden, Great
Britain). Transboundary air pollution from external regions contributes by
27 % to 75 % and on average by 51 % to PM2.5 pollution in
European countries. Countries surrounded by oceans are mainly influenced by
within-region pollution due to their geographical isolation from other source
regions (e.g. Italy, Spain, Great Britain and Norway); therefore, the
fraction of extra-regional pollution ranges from 27 % to 35 %. The
largest extra-regional contributions are calculated for Hungary (75 %,
mainly from Austria, the Czech Republic, the rest of central EU, Poland and
Germany), the Czech Republic (67 %, mainly from Poland, Germany and
Austria), Austria and Slovenia (66 %, mainly from the Czech Republic,
Germany and Italy), Sweden + Denmark (65 %, mainly from Germany,
Norway and Poland), Bulgaria (63 %, mainly from Romania), and Greece
(61 %).
Anthropogenic PM2.5
concentrations in 18 countries and sub-regions in Europe separated in
within-region and extra-regional contributions. The RERER metric (%) is
reported on top of each bar.
The remaining EU countries are affected by both within-region and
extra-regional pollution (the latter ranging from 40 % to 59 %),
highlighting the importance of transboundary transport of PM2.5
concentrations. For example Switzerland is influenced by the pollution coming
from France, Italy and Germany; the rest of the central EU by Poland and Germany;
Germany by France and Benelux; and Poland by the Czech Republic and Germany.
Interestingly, Romania, Bulgaria, Greece and Hungary are also significantly
affected by the pollution transported from Ukraine and Turkey, which is
included in the “rest of the world” contribution of Fig. 2. Our results are
consistent with the findings of the latest UNECE Scientific Assessment Report
(Maas and Grennfelt, 2016), which highlights the importance of transboundary
transport of organic and inorganic PM. As discussed in Sect. 3.4, insights
into the uncertainty of within-region and extra-regional contributions to
PM2.5 concentrations are provided in Fig. 5 for each TM5-FASST region.
Fraction of within-region and extra-regional (shaded areas)
anthropogenic PM2.5 concentrations separated by sector for receptor
region within the macro-regions: Asia and Africa (a),
Europe (b), North America, Latin America, the Middle East, Russia, and
Oceania (c). Annual averaged PM2.5 anthropogenic concentrations
(in µg m-3) are reported on top of each bar. The RERER metric
(%) for the 56 TM5-FASST regions is also reported in Table S2.
Sectorial contributions to PM2.5 concentrations
Figure 3 shows the relative sectorial contributions to anthropogenic
PM2.5 concentrations for the 56 TM5-FASST receptor regions, separating
the fraction of extra-regional (RERER) (shaded colours) and within-region
pollution, while Table 1 shows regional average values of sector-specific
relative contributions. In most African regions (except Egypt) anthropogenic
PM2.5 concentrations are mainly produced by emissions in the residential
sector. Agriculture is an important sector for Egypt, while northern Africa
is strongly influenced by shipping emissions in the Mediterranean (30 %).
PM2.5 concentrations in emerging economies in
Asia, Latin America and the Middle East are dominated by the residential
sector, power generation and industry. Asian countries such as China, India,
Indonesia and the Philippines are mainly influenced by within-region
pollution, with the largest contributions coming from power, industry and
residential sectors. PM2.5 pollution in Japan is characterised by the
contribution of local sources like transport and agriculture, but it is also
affected by transported pollution from China, especially from the industrial
sector. Anthropogenic PM2.5 in the remaining Asian countries is
influenced by more than 50 % by the pollution coming from China (e.g.
Vietnam, Malaysia, Thailand, Mongolia, South Korea, Taiwan) or India (e.g.
the rest of south Asia and south-eastern Asia) from the power, industry and
residential activities. A different picture is seen for Europe where,
according to our calculations, annual PM concentrations stem mainly from the
agricultural and residential sectors, with a somewhat lower contribution from
the transport sector. In eastern European countries noticeable contributions
are also found from the power and industrial sectors due to the relatively
extensive use of polluting fuels like coal. PM2.5 concentrations in USA
and Canada are mostly from the power, industry and agricultural sectors. In
Oceania industry and agriculture are the most important sectors. PM2.5
from ship emissions mainly affects coastal areas of northern Africa, SE Asia
(e.g. in Japan, Taiwan, Malaysia, Indonesia and the Philippines),
Mediterranean countries (Spain 11 %, Italy 5 %, France 7 % of
their corresponding country totals), northern EU regions (Great Britain
10 %, Norway 6 %, Sweden and Denmark 10 % of their corresponding
country totals) and Oceania (22 % of the regional total). Over the
international areas of sea and air no distinction between within-region and
extra-regional concentrations is reported. Further details on within-region
and extra-regional concentrations can be found in Sect. S2 of the Supplement.
Sector-specific contribution (%) to annual anthropogenic
PM2.5 concentrations for aggregated world regions based on the
“central” estimates which do not consider uncertainty. The largest
contributing sectors (above a threshold of 15 %) are shaded in bold font.
Total anthropogenic PM2.5 concentrations
(µg m-3) and sectorial contributions using 2010 emissions.
Gridded PM2.5 concentrations
Figure 4 shows the global 1∘× 1∘ grid maps of
anthropogenic PM2.5 concentrations in 2010 for the reference case as
well as the computed contributions from each of the major anthropogenic
emission sectors. Anthropogenic PM2.5 is ubiquitous globally and covers
a range from a few µg m-3 or less over the oceans and seas to more
than 50 µg m-3 over Asia. As shown in Fig. 3, the most
polluted countries in Asia are China, India and the rest of south Asia (which
includes Afghanistan, Bangladesh, Bhutan, Nepal and Pakistan), with annual
average anthropogenic PM2.5 concentrations ranging from 29 to
40 µg m-3; Mongolia and North Korea, Vietnam, South Korea,
the rest of south-eastern Asia (including Cambodia, Lao People's Democratic
Republic and Myanmar), Thailand, Japan, and Taiwan are rather polluted areas,
with PM2.5 concentration in the range of 6 to 14 µg m-3.
The highest annual PM2.5 concentrations in Africa are computed in Egypt
(11 µg m-3 as annual average), the Republic of South Africa
(6.1 µg m-3 as annual average) and western Africa
(4.0 µg m-3 as annual average). The highest pollution in
Europe is observed in the Benelux region, Italy and in some of the eastern
countries (e.g. Romania, Bulgaria and the Czech Republic), while in Latin America
the most polluted areas are Chile (13.7 µg m-3 as annual
average) and Mexico (4.2 µg m-3 as annual average). The Middle
East, the Gulf region, Turkey, Ukraine and the former USSR are also characterised
by PM2.5 concentrations ranging between 7.5 µg m-3 and
9.2 µg m-3 as annual averages. Table 2 reports annual average
PM2.5 concentrations and the corresponding uncertainty range for each
TM5-FASST region as discussed in Sect. 3.4.
Annual average PM2.5 concentrations (µg m-3)
with upper and lower ranges in brackets due to emission inventory uncertainty (1 standard deviation, σ). The upper and lower range of
PM2.5 concentrations are calculated as the reference concentrations
multiplied and divided by (1+σ) respectively. The third column
reflects the fractional uncertainty due to the contribution of primary
PM2.5 emissions.
The TM5-FASST model (Van Dingenen et al., 2018) is also validated against
concentration estimates derived from the WHO database and satellite-based
measurements (van Donkelaar et al., 2010, 2014). The TM5-FASST modelled
PM2.5 concentrations are
compared to satellite products which are based on aerosol optical depth
measurements together with chemical transport model information to retrieve
from the total column the information of PM concentrations in the lowest
layer of the atmosphere (Boys et al., 2014; van Donkelaar et al., 2010,
2014). The regional comparison of annul mean population-weighted
concentrations shows consistent results with the satellite-based retrievals
(e.g. rather good agreement for the globe as a whole, EU and USA within less
than 15 % deviation, with lower agreement for developing and emerging
countries). Section S4 in the Supplement of the paper by van Dingenen et
al. (2018) also reports the comparison between the PM2.5 concentrations
modelled by TM5-FASST and the measured ones reported in the WHO database,
showing rather good agreement for Europe, North America and partly for China
due to the higher accuracy of the measurements. The comparison for
Latin America and Africa is much less robust and the scatter possibly
highlights non-optimal modelling of specific sources relevant for these
regions by the TM5-FASST model (e.g. large-scale biomass burning).
In our work, modelled PM2.5 concentrations are in the range of the
measurements and satellite-based estimates provided in several literature
studies (Brauer et al., 2012, 2015; Boys et al., 2014; Evans et al., 2013;
Van Donkelaar et al., 2016), reporting annual averaged
PM2.5 concentrations for all of Europe in the range between 11 and
17 µg m-3, for Asia from 16 to 58 µg m-3,
Latin America 7–12 µg m-3, Africa and the Middle East
8–26 µg m-3, Oceania 6 µg m-3, and North
America 13 µg m-3 (note that measurements and satellite
estimates would not separate anthropogenic and natural sources of PM, e.g.
dust, large-scale biomass burning, while the concentrations in this study
consider anthropogenic emissions alone).
In order to understand the origin of global PM2.5 concentrations, we
look at sector-specific maps (Fig. 4). The power and industrial sectors are
mainly contributing to PM concentrations in countries with emerging economies
and fast development (e.g. the Middle East, China and India), while the
ground transport sector is a more important source of PM concentrations in
industrialised countries (e.g. North America and Europe) and in developing
Asian countries. The residential sector is an important source of PM all over
the world, also affecting indoor air quality (Ezzati, 2008; Lim et al., 2013;
Chafe et al., 2014). PM concentrations in Africa and Asia are strongly
influenced by this sector due to the incomplete combustion of rather dirty
fuels and solid biomass deployed for domestic heating and cooking purposes.
Interestingly, the agricultural sector is strongly affecting pollution in
Asia as well as in Europe (Backes et al., 2016; Erisman et al., 2004) and
North America, confirming the findings of the UNECE Scientific Assessment
Report and several other scientific publications (Maas and Grennfelt, 2016;
Pozzer et al., 2017; Tsimpidi et al., 2007; Zhang et al., 2008). The
residential and agriculture sectors are less spatially confined, and it is
more difficult to regulate emissions effectively than point source emissions
of the industrial and power sectors (e.g. in Europe the Large Combustion
Plant Directive, the National Emission Ceilings Directive, the Industrial
Emissions Directive, the European emission standards for road transport).
Finally, shipping is mainly contributing to the pollution in countries and
regions with substantial coastal areas, and with ship tracks on the
Mediterranean Sea and the Atlantic, Pacific and Indian Oceans, as depicted in
Fig. 4.
Within-region and extra-regional anthropogenic PM2.5
concentrations and emission-related uncertainties for Asia (a);
Europe (b); North America, Latin America, Oceania and
Russia (c); and Africa, the Gulf region and the Middle East (d). The
error bars are calculated by multiplying and dividing the reference emissions by
(1+σ) as discussed in Sect. 2.3.
Uncertainty from emissionsPropagation of emission uncertainties to anthropogenic
PM2.5 concentrations
Table 2, as well as Fig. 5, report the annual average anthropogenic
PM2.5 concentrations (µg m-3) estimated by TM5-FASST with
the uncertainty bars representing the upper and lower range of concentrations
due to emission inventory uncertainty. The extra-regional contribution to
uncertainty is also addressed as well as the contribution of the uncertainty
of primary particulate matter emissions to the upper range of PM2.5
concentrations (Table 2). Primary PM emissions represent the dominant source
of uncertainties, contributing from 45 % to 97 % to the total
uncertainty in anthropogenic PM2.5 concentrations for each
country/region.
Figure 5 depicts the results of the propagation of the lowest and highest
range of emissions including their uncertainty to PM2.5 concentrations
in Asia (Fig. 5a) and – in more detail – Europe (Fig. 5b), highlighting the
contribution of within-region and extra-regional PM2.5 concentrations
and the corresponding uncertainties (error bars). Due to their large sizes,
Indian and Chinese PM2.5 concentrations and uncertainties are mainly
affected by uncertainties from the residential, transport and agricultural
sectors within these countries. Interestingly, in south-eastern and eastern
Asia uncertainties in PM2.5 are strongly influenced by the Indian
residential emissions. On the other hand, PM2.5 in Thailand, Japan,
Taiwan, South Korea, Mongolia and Vietnam are strongly affected by the
uncertainty in the Chinese residential and industrial emissions. Consequently
reducing the uncertainties in the Chinese and Indian emission inventories
will help in improving the understanding of the long-range contribution of
PM2.5 pollution in most Asian countries.
In Europe, the highest uncertainties in PM2.5 concentrations are
associated with the emissions from the residential, agriculture and transport
sectors. In most of the central and eastern European countries modelled
PM2.5 is strongly affected by the uncertainty of transported
extra-regional pollution, produced from the residential, agricultural and
transport sectors. Conversely, uncertainties in Norway are dominated by
national emissions, mainly from the residential and transport sectors, and in
Italy from the residential and agriculture sectors. The remaining European
countries are affected both by within-country and imported uncertainties.
Figure 5c represents the results of the propagation of the emissions range
including their uncertainty to PM2.5 concentrations for North America,
Latin America, Oceania and Russia, while Fig. 5d displays emission
uncertainties for Africa, the Middle East and the Gulf region. The uncertainty in
the USA agricultural and residential emissions affect more than 50 % of
modelled Canadian PM2.5 concentrations and the uncertainty in Mexico and
Argentina is influenced by similar magnitudes (30 %–50 %) from
neighbouring countries. The uncertainty in within-region emissions,
especially from the residential sector, dominates the overall levels of
PM2.5 uncertainties in Latin America. However, in addition, Chile's own
agriculture and power sectors contribute significantly to the overall
uncertainty levels. PM2.5 levels in most of the African regions are
strongly affected by the uncertainty in their own residential emissions,
while in Egypt they are mostly influenced by the agricultural sector
uncertainties (refer to Fig. 5d). Interestingly, anthropogenic PM2.5 in
northern Africa is influenced by uncertainties in Italian emissions
as well as those from shipping emissions. Conversely, the Middle
East and Turkey regions are influenced by a range of extra-regional emission
uncertainties (e.g. the Middle East is affected by the uncertainty of Turkey,
Egypt and the Gulf region, while Turkey is affected by Bulgaria, the Gulf region and the rest of
the central EU).
Ranking the sector-specific contribution to emission
uncertainties
Figure 6 shows the average sector-relative contribution to total
emission-inventory-related uncertainty for the main PM2.5 concentration
precursors and world regions. These contributions can be interpreted as a
ranking of the most effective improvements to be taken regionally to better
constrain their inventories and reduce the final formation of PM2.5
concentrations. The complete overview of all TM5-FASST regions contributions
is provided in Fig. S2, where the share of each term of the sum of Eq. (5)
σEMIi,c,p⋅EMIi,c,pEMItot,c,p2 represents the sector contribution to the uncertainty of each
pollutant in each region. SO2 uncertainties mainly derive from the
power generation sector, especially in countries with dominant coal use;
however, substantial contributions are also computed for the industrial
sector in South Africa, Asia, Norway, some Latin American countries, Canada
and Russia. Interestingly, for SO2 some contributions are also
observed from the residential sector in Africa and from the transport sector
in some Asian countries (e.g. North and South Korea, Vietnam, Indonesia,
south-eastern Asia). Smith et al. (2011) report a range of regional
uncertainty for SO2 emissions up to 30 %, while our estimates
are slightly higher (up to 50 %). NOx emission
uncertainty mainly stems from the transport sector, although some
contributions are also seen from power generation in Russia, countries
strongly relying on gas (e.g. Russia), the Middle East and the residential
sector in Africa. Depending on the region, CO uncertainty (not shown) is
dominated by either the transport or residential (particularly in Africa and
Asia) sectors and for some regions by a similar contribution of these two
sectors. NMVOC emission uncertainties mainly derive from poorly characterised
industrial, transport and residential activities due to the complex mixture
and reactivity of such pollutants. As expected, NH3 emission
uncertainty is dominated by the agricultural sector which appears to be less
relevant for all other pollutants. Among all air pollutants, primary
PM2.5 represents one of the most uncertain pollutant due to very
different combustion conditions, different fuel qualities and lack of control
measures (Klimont et al., 2017).
Contribution of anthropogenic sectors to the emission uncertainty of
various pollutants for different world regions.
Primary particulate matter emissions should be mainly improved for the
residential, transport and in particular industrial sectors. Black carbon
emission inventories should be better characterised in Europe, Japan, North and South Korea,
Malaysia etc. for the transport sector, where the higher share of diesel
used as fuel for vehicles leads to higher BC emissions; in addition, BC
emissions from the residential sector require further effort to better
define EFs for the different type of fuels used under different combustion
conditions. To constrain and improve particulate organic matter emissions,
efforts should be made to improve residential emissions estimates.
Therefore, in the following section, we try to assess one of the major
sources of uncertainty in the residential emissions in Europe, which is the
use of solid biofuel.
Assessing the uncertainty in household biofuel consumption with
an independent inventory in Europe
The combustion of solid biomass (i.e. biofuel) for household heating and
cooking purposes is one of the major sources of particulate matter emissions
in the world. Wood products and residues are widely used in the residential
sector, but national reporting often underestimates the emissions from this
sector, due to the fact that often informal economic wood sales are not
accurately reflected in the official statistics of wood consumption (AD)
(Denier Van Der Gon et al., 2015). An additional uncertainty is related to
the lack of information in the inventory regarding the EF variability, which
depends on the combustion efficiency and type of wood (Weimer et al., 2008;
Chen et al., 2012). In our work we estimate the uncertainty attributable to
wood combustion in the residential sector (σAD,RES_bio)
by comparing it to the recent TNO RWC (Netherlands Organization for Applied
Scientific Research, Residential Wood Combustion) inventory of Denier van der
Gon et al. (2015), which includes a revised biomass fuel consumption with the
corresponding EDGARv4.3.2 activity data (Janssens-Maenhout et al., 2017), as
shown in Table S4. In the TNO RWC inventory, wood use for each country has
been updated comparing the officially reported per capita wood consumption
data (from GAINS, Greenhouse Gas and Air Pollution Interactions and
Synergies, and IEA, International Environmental Agency) with the expected
specific wood use for a country, including the wood availability information
(Visschedijk et al., 2009; Denier Van Der Gon et al., 2015). We can therefore
assume that the TNO RWC inventory represents an independent estimate of wood
consumption in the residential sector, allowing a more precise uncertainty
estimation of the AD for this sector. Assuming that emissions are calculated
as the product of AD and EF, the corresponding uncertainty can be calculated
with Eq. (4), where σAD ranges from 5 % to 10 % for
European countries and Russia as reported for international statistics
(Olivier et al., 2016). We can therefore calculate the residential emission
factors uncertainty of each individual pollutant (σEFp) from
Eq. (4). In addition, based on the comparison of the recent estimates of wood
consumption provided by TNO RWC AD, which should match better with
observations, and the EDGARv4.3.2 data, we can evaluate the mean normalised
absolute error (MNAE) considering all N countries, following Eq. (6) (Yu et
al., 2006), which represents our estimate of
σAD,RES_bio.
MNAE=1N⋅∑jNTNORWCj-EDGARv4.3.2jTNORWCj
We estimate a value of σAD,RES_bio of 38.9 %, which
is much larger compared to the 5 %–10 % uncertainty reported for the
fuel consumption of the international statistics (σAD). The
issue of biofuel uncertainty mainly affects rural areas where wood is often
used instead of fossil fuel. Then, using Eq. (4) and the calculated σAD,RES_bio and σEFp, we can evaluate a new
σEMIp,RES_bio for the residential sector including the
uncertainty of the AD due to the use of wood as fuel for this sector, as
reported in Table S5. Comparing the results shown in Table S5 with the
factor-of-2 uncertainty values expected for PM emissions from the residential
sector (Janssens-Maenhout et al., 2015), we derive that the uncertainty
associated with the emission factors for biomass combustion in the
residential sector is the dominant source of uncertainty compared to the
uncertainty in wood burning activity data. Large increases in reported
biomass usage for domestic use has been noted in IEA energy statistics for
some European countries (IEA, 2016) and further increases are expected as
countries are shifting their methodologies to estimate biofuel activity data
away from fuel sales statistics to a modelling approach based on energy
demand. In addition, several EU countries are increasing the use of biomass
in order to accomplish the targets set in the context of the renewable energy
directive (2009/28/EC) as reported in their national renewable energy action
plans (http://ec.europa.eu/energy/node/71, last access: April 2019).
When comparing the UNFCCC and the TNO RWC data, a higher value of
σAD,RES_bio is obtained (59.5 % instead of
38.9 %), although its effect on the final residential emission
uncertainty is less strong, as shown in Table S6. Table 3 shows the impact of
biofuel combustion uncertainty on PM2.5 concentrations in the
residential sector. Upper-end uncertainties indicate that PM2.5
concentrations could be between 2.6 and 3.7 times larger than those derived
from the HTAP_v2.2 inventory.
PM2.5 concentrations due to the residential sector emissions
in Europe, European part of Russia, Ukraine and Turkey and the uncertainty range
including the uncertainty in the biomass consumption for the same sector.
Annual population-weighted PM2.5 concentrations represent the most
robust and widely used metric to analyse the long-term impacts of particulate
matter air pollution on human mortality (Pope and Dockery, 2006; Dockery,
2009). As described in Sects. 2.5 and S5 of the paper by Van Dingenen et
al. (2018), the mortality estimation in TM5-FASST is based on the integrated
exposure-response functions defined by Burnett et al. (2014). The increased
risk from exposure to air pollution is estimated using exposure-response
functions for five relevant causes of death, namely ischaemic heart disease
(IHD), cerebrovascular disease (CD, stroke), chronic obstructive pulmonary
disease (COPD), lung cancer (LC) and acute lower respiratory infections (ALRIs).
The relative risk (RR) represents the proportional increase in the assessed
health outcome due to a given increase in PM2.5 concentrations (Burnett
et al., 2014).
In this section, we investigate the impact of total and sector-specific
anthropogenic population-weighted PM2.5 concentrations on health and we
show comparisons with mortality estimates provided by WHO and recent
scientific publications (Silva et al., 2016). Figure 7 represents the
PD distribution due to air pollution, using population-weighted PM2.5 concentrations and representative for anthropogenic
emissions in the year 2010. The most affected areas are China and India, but
also some countries of western Africa and urban areas in Europe (in
particular in the Benelux region and eastern Europe). Our computations
indicate that annual global outdoor premature mortality due to anthropogenic
PM2.5 amounts to 2.1 million premature deaths, with an uncertainty range
related to emission uncertainty of 1–3.3 million deaths per year. In our work we
only evaluate how the uncertainty of emission inventories influences the
health impact estimates focusing on the interregional aspects (i.e. we do not
evaluate effects of misallocation of sources within regions) and not all the
other sources of uncertainties, such as the uncertainty of
concentration-response estimates, of air quality models used to estimate
particulate matter concentrations, etc. An overview of the propagation of the
uncertainty associated with an ensemble of air quality models to health and
crop impacts is provided by Solazzo et al. (2018). Solazzo et al. (2018) find
in their analysis over the European countries a mean number of PDs due to
exposure to PM2.5 and ozone of approximately 370 000
(inter-quantile range between 260 000 and 415 000). Moreover, they estimate
that a reduction in the uncertainty of the modelled ozone concentration by
61 %–80 % (depending on the aggregation metric used) and by 46 %
for PM2.5 produces a reduction in the uncertainty in premature
mortality and crop loss of more than 60 %. However, we show here that the
often neglected emission inventories' uncertainty provides a range of
premature deaths of ±1.1 million at the global scale, which is of the
same order of magnitude of the uncertainty of air quality models and
concentration-response functions (Cohen et al., 2017). In 2010, using our
central estimate, 82 % of the PDs occur in fast-growing economies and
developing countries, especially in China with 670 000 and India with an
almost equal amount of 610 000 PDs per year. Table 4 summarises our estimates
of premature mortality for aggregated world regions, with Europe accounting
for 210 000 PDs per year and North America 100 000 PDs per year.
Absolute and population size normalised number of premature
deaths per year due to anthropogenic PM2.5 air pollution in world regions
and corresponding uncertainty range.
Global distribution of premature deaths in 2010 caused by
anthropogenic particulate matter pollution estimated using the methodology
described in Burnett et al. (2014). A threshold value of
5.8 µg m-3 is assumed and no urban increment adjustments are
considered. The relative risk functions of Burnett et al. (2014) are used for
the premature death dose-response estimates.
Number of premature deaths for each receptor region including the
within-region and extra-regional attribution based on PM2.5 “central”
estimates, which do not consider uncertainty. For the RERER metric refer also
to Table S2.
World regionsTM5-FASST region namePDs in receptor regionWithin-region PDsExtra-regional PDs(deaths per year)(deaths per year)(deaths per year)AfricaEastern Africa16 70582188487AfricaEgypt17 28211 3805902AfricaNorthern Africa542434271997AfricaRep. of South Africa90658797268AfricaSouthern Africa34532223AfricaWestern Africa25 08119 7855296AsiaChina655 870643 12912 741AsiaIndonesia17 78014 8032977AsiaIndia474 660412 29862 362AsiaJapan25 63615 18110 455AsiaSouth Korea25 295751017 784AsiaMongolia + North Korea12 65740768581AsiaMalaysia20141058957AsiaPhilippines1219427AsiaRest of south Asia134 28067 17067 110AsiaRest of south-eastern Asia23 316381419 502AsiaThailand21 23110 49510 736AsiaTaiwan344310282415AsiaVietnam30 75020 28610 464EuropeAustria + Slovenia607318064267EuropeBulgaria473917093030EuropeBenelux909042014889EuropeSwitzerland320015681632EuropeCzech Republic793626965240EuropeGermany36 25618 59517 661EuropeSpain + Portugal11 29184872804EuropeFinland000EuropeFrance22 04613 3208727EuropeGreat Britain + Ireland13 94994594490EuropeGreece + Cyprus311711331984EuropeHungary14 211382010391EuropeItaly + Malta24 41716 3128105EuropeNorway674516158EuropePoland + Baltic28 68615 87712 809EuropeRest of central EU676434183346EuropeRomania14 15569797176EuropeSweden + Denmark265010211629Latin AmericaArgentina + Uruguay1337558Latin AmericaBrazil42613968293Latin AmericaChile3332328349Latin AmericaMexico10 47884472031Latin AmericaRest of Central America34132772640Latin AmericaRest of South America44894164325Middle EastGulf region15 17611 2253951Middle EastMiddle East678428043980Middle EastTurkey34 15124 1919960North AmericaCanada326214911771North AmericaUSA97 87790 1767701OceaniaAustralia28253OceaniaNew Zealand24159OceaniaPacific Islands312RussiaKazakhstan338911002290RussiaFormer USSR Asia10 75764204337RussiaRussia (Asia)1348601746RussiaRussia (EU)25 14912 70412 445RussiaUkraine71 72444 60427 120
Our results are comparable with Lelieveld et al. (2015) and Silva et
al. (2016) who, using the same Burnett et al. (2014) methodology, estimate a
global premature mortality of 2.5 and 2.2 million people, respectively, due
to air quality in 2010 for the same anthropogenic sectors. However, a recent
work published by Cohen et al. (2017) estimates a higher value of global
mortality (3.9 million PDs per year) mainly due to a lower minimum risk
exposure level set in the exposure response function, the inclusion of the
urban increment calculation and the contribution of natural sources. When
comparing mortality estimates we need to take into account that several
elements affect the results, like the resolution of the model, the urban
increment subgrid adjustment (including information on urban and rural
population; refer to Van Dingenen et al., 2018), the inclusion or not of
natural components, the impact threshold value used, and RR functions. In
this study, we use the population-weighted PM2.5 concentration
(excluding natural components) at 1×1∘ resolution as the
metric for estimating health effects due to air pollution, with a threshold
value of 5.8 µg m-3, no urban increment adjustment and
relative risk functions accordingly with Burnett et al. (2014). We also
estimate that 7 % of the global non-accidental mortalities from the
Global Burden of Disease (IHME, 2015,
http://vizhub.healthdata.org/gbd-compare, last access: April 2019;
Forouzanfar et al., 2015) are attributable to air pollution in 2010;
8.6 % of total mortality in Europe is due to air pollution, ranging from
less than 1 % up to 17 % depending on the country; similarly, Asian
premature mortality due to air quality is equal to 8.7 % of total Asian
mortality, with 10.6 % contribution in China and 8.5 % in India.
Lower values are found for African countries and Latin America where other
causes of mortalities are still dominant compared to developed countries.
Table 5 shows the number of premature deaths for each receptor region,
highlighting the premature mortality induced within the country itself and
outside the receptor region. The PD induced by Chinese and Indian emissions
are mainly found within these two countries; however, the annual PDs caused
by China and India in external regions contribute an additional 700 000
and ca. 500 000 PDs per year, respectively, representing more than 50 %
of the global mortality. Clearly, reducing emissions and emission
uncertainties in these two regions will therefore have the largest overall
benefit on global air quality improvement as well as on global human health.
As explained in Sect. 3.1, PDs attributed to internal and external emissions are
directly linked (proportional) to the internal and external PM2.5
contributions. For most of the TM5-FASST regions, PDs due to anthropogenic
emissions within the source region are higher than the extra-regional
contributions. However, there are marked exceptions, such as Hungary, the Czech
Republic, Mongolia, etc., where the extra-regional and within-region
contributions to mortality are at least comparable. For instance, Hungary and
the Czech Republic are strongly influenced by polluted regions in Poland
(mainly); likewise, Mongolia is affected by the vicinity of sources in China.
The Gulf region produces a lot of its own pollution but is also influenced
by transport from Africa and Eurasia as reported by Lelieveld et al. (2009).
Detailed information on the premature deaths for each TM5-FASST region and
the contributing anthropogenic emission sectors is shown in Fig. 8a and b.
Health effects induced by air quality in industrialised countries are mainly
related to agriculture (32.4 % of total mortality or 68 000 PDs per
year), residential combustion (17.8 % or 37 000 PDs per year) and road
transport (18.7 % or 39 000 PDs per year) for Europe and with power
generation (26.4 % or 26 000 PDs per year), industry (19 % or
19 000 PDs per year), residential (17 % or 17 000 PDs per year) and
agriculture (24.0 % or 24 000 PDs per year) for North America. The
health impacts observed in most western EU countries is due to both
within-region and extra-regional pollution, while in several eastern EU
countries the impact of neighbouring countries is even larger compared to
within-region pollution. The premature deaths induced by international
shipping emissions represent 5.5 % of total EU PD, which is in the range
of the results of Brandt et al. (2013) (ca. 50 000 PDs).
PM-related mortality in developing countries and fast-growing economies is
mostly affected by industrial (up to 42 % in China or 279 000 PDs per
year) and residential activities (ranging from 27 % in China and 76 %
in western Africa), and also by power generation (up to 24 % in India or
113 000 PDs per year). Chinese emissions have a strong impact on China,
Japan, Vietnam, Mongolia + North Korea, and Thailand while the
Indian emissions impact the rest of south and south-eastern Asia. Reducing
Chinese and Indian emissions will reduce the PM-related mortality in almost
all countries in Asia. Our results are in agreement with the study of Oh et
al. (2015) where they highlight the role of transported pollution from China
in affecting the PM2.5 concentrations and health standards of North and
South Korea and other south-eastern Asian countries, as well as the need for
international measures to improve air quality.
Anthropogenic emission sector contributions to premature mortality
(deaths per year) due to PM2.5 population-weighted concentrations in the
TM5-FASST receptor regions of (a) Asia (left) and Europe (right) and
(b) North America, Latin America, Russia, the Middle East and
Oceania (left hand side) and Africa (right hand side). Note that mortality
estimates for Argentina + Uruguay, Australia, New Zealand and Pacific
Islands are not reported being several orders of magnitude lower than other
countries estimates. Sector and region contributions pertain to the
“central” emission estimates.
Conclusions
In this work we couple the global anthropogenic
emission estimates provided by the HTAP_v2.2 inventory for 2010 (merging
national and regional inventories) with the global source receptor model TM5-FASST, to study PM2.5
concentrations and the corresponding health impacts, including an evaluation
of the impacts of uncertainties in national emission inventories. Annual and
regionally averaged anthropogenic PM2.5 concentrations, corresponding to
the 2010 emissions, vary between ca. 1 and 40 µg m-3, with
the highest annual concentrations computed in China
(40 µg m-3, range: 22.4–76.6 µg m-3), India
(35 µg m-3, range: 16.6–73.4 µg m-3), North
America (8 µg m-3, range: 4.4–14.4 µg m-3)
and Europe (on average ca. 8 µg m-3, range:
5–18 µg m-3). Anthropogenic PM2.5 concentrations are
mainly due to emissions within the source region, but extra-regional
transported air pollution can contribute by up to 40 % (e.g. from China
to SE Asia, from EU to Russia). Moreover, due to the transport of PM between
European countries, EU-wide directives can help improve the air quality
across Europe.
For our analysis we aggregate our results derived from 56 TM5-FASST source
regions into 10 global regions to facilitate the comparison of results in
regions of more equal size. The relative contribution of anthropogenic
sectors to PM2.5 concentrations varies in different regions. In Europe
in 2010, the agriculture and residential combustion sectors contribute
the most PM2.5 concentrations and these sectors are also associated
with relatively large emission uncertainties. PM2.5 concentrations in
China and other emerging economies are predominantly associated with the
power generation, industry and residential activities.
Using the HTAP_v2.2 emission inventory and TM5-FASST, we also evaluate how
the uncertainty in sectors and regions propagates into PM2.5
concentrations. The aim of our analysis is to provide insights into where
improvement of country emission inventories would give the largest benefits,
because of their highest uncertainty and highest contribution to the
formation of PM2.5 concentrations. The uncertainty of PM concentrations
depends in variable proportions to the uncertainties of the emissions within
receptor regions, and surrounding regions. We show that reducing the
uncertainties in the Chinese and Indian emission inventories (e.g. from
industry and residential sectors) will be highly relevant for more accurate
quantification of the contribution of the long-range sources to PM2.5
pollution in most Asian countries. Here we demonstrate how analysis of
uncertainties in national and regional sectorial emission inventories can
further inform coordinated transboundary and sector-specific policies to
significantly improve global air quality. Among all anthropogenic emission
sectors, the combustion of biomass for household purposes represents one of
the major sources of uncertainties in emission inventories both in terms of
wood consumption and emission factor estimates. Further effort is therefore
required at national level to better characterise this source.
Finally, we analyse the air quality effects on health. Global health effects
due to PM2.5 concentrations calculated with TM5-FASST and anthropogenic
emissions in 2010 are estimated to be ca. 2.1 million premature deaths per year,
but the uncertainty associated with emission ranges between 1 and 3.4 million
deaths per year, with the largest fraction of PD (82 %) in developing
countries.
Data availability
Data used in this research are publicly accessible through
the EDGAR website (http://edgar.jrc.ec.europa.eu/htap_v2/index.php,
last access: April 2019; 10.2904/JRC_DATASET_EDGAR; Janssens-Maenhout
et al., 2013) and the TM5-FASST online tool
(http://tm5-fasst.jrc.ec.europa.eu/, last access: April 2019).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-5165-2019-supplement.
Author contributions
MC developed the scenarios, performed the TM5-FASST
analysis and drafted the manuscript. GJM developed the concept of the work
and supervised it. DG compiled the HTAP_v2.2 emission inventory. RVD and
FD developed the TM5-FASST model and the concept of this
work.
Competing interests
The authors declare that they have no conflict of
interest.
Special issue statement
This article is part of the special issue “Global and regional
assessment of intercontinental transport of air pollution: results from HTAP,
AQMEII and MICS”. It is not associated with a conference.
Acknowledgements
The authors acknowledge financial support by the Administrative Arrangement
AMITO2 with DG ENV. This analysis was inspired by HTAP2 joint studies on
regional contributions to global air pollution. This publication is an
application of the companion paper “TM5-FASST: a global atmospheric
source–receptor model for rapid impact analysis of emission changes on air
quality and short-lived climate pollutants” by Van Dingenen et al. (2018).
Review statement
This paper was edited by Kathy Law and reviewed by two
anonymous referees.
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