ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-10497-2018HTAP2 multi-model estimates of premature human mortality due to intercontinental transport of air pollution and emission sectorsHTAP2 mortality from intercontinental air pollution and sectorsLiangCiao-KaiWestJ. Jasonjjwest@email.unc.eduhttps://orcid.org/0000-0001-5652-4987SilvaRaquel A.BianHuishengChinMianDavilaYankohttps://orcid.org/0000-0001-5872-8211DentenerFrank J.https://orcid.org/0000-0001-7556-3076EmmonsLouisahttps://orcid.org/0000-0003-2325-6212FlemmingJohanneshttps://orcid.org/0000-0003-4880-5329FolberthGerdhttps://orcid.org/0000-0002-1075-440XHenzeDavenImUlashttps://orcid.org/0000-0001-5177-5306JonsonJan EiofKeatingTerry J.https://orcid.org/0000-0003-3470-0104KucseraTomLenzenAllenLinMeiyunhttps://orcid.org/0000-0003-3852-3491LundMarianne Tronstadhttps://orcid.org/0000-0001-9911-4160PanXiaohuahttps://orcid.org/0000-0003-1211-709XParkRokjin J.https://orcid.org/0000-0001-8922-0234PierceR. BradleySekiyaTakashiSudoKengohttps://orcid.org/0000-0002-5013-4168TakemuraToshihikohttps://orcid.org/0000-0002-2859-6067Department of Environmental Sciences and Engineering, University of
North Carolina at Chapel Hill, Chapel Hill, North Carolina, USAOak Ridge Institute for Science and Education at U.S. Environmental
Protection Agency, Research Triangle Park, NC, USAGoddard Earth Sciences and Technology Center, University of Maryland,
Baltimore, MD, USAEarth Sciences Division, NASA Goddard Space Flight Center, Greenbelt,
MD, USADepartment of Mechanical Engineering, University of Colorado, Boulder,
CO, USAEuropean Commission, Joint Research Center, Ispra, ItalyAtmospheric Chemistry Observations and Modeling Laboratory, National
Center for Atmospheric Research (NCAR), Boulder, CO, USAEuropean Center for Medium-Range Weather Forecasts, Reading, UKUK Met Office Hadley Centre, Exeter, UKAarhus University, Department of Environmental Science,
Frederiksborgvej, 399, Roskilde, DenmarkNorwegian Meteorological Institute, Oslo, NorwayUS Environmental Protection Agency, Research Triangle Park, NC, USAUniversities Space Research Association, NASA GESTAR, Columbia, MD, USASpace Science & Engineering Center, University of Wisconsin-Madison, WI, USAAtmospheric and Oceanic Sciences, Princeton University, Princeton,
NJ, USACICERO Center for International Climate Research, Oslo, NorwayEarth System Science Interdisciplinary Center, University of
Maryland, College Park, MD, USASeoul National University, Seoul, KoreaNOAA National Environmental Satellite, Data, and Information Service,
Madison, WI, USANagoya University, Furocho, Chigusa-ku, Nagoya, JapanResearch Institute for Applied Mechanics, Kyushu University, Fukuoka,
JapanJ. Jason West (jjwest@email.unc.edu)23July20181814104971052022December201711January201816May201821June2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/10497/2018/acp-18-10497-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/10497/2018/acp-18-10497-2018.pdf
Ambient air pollution from ozone and fine particulate matter is associated
with premature mortality. As emissions from one continent influence air
quality over others, changes in emissions can also influence human health on
other continents. We estimate global air-pollution-related premature
mortality from exposure to PM2.5 and ozone and the avoided deaths due to
20 % anthropogenic emission reductions from six source regions, North
America (NAM), Europe (EUR), South Asia (SAS), East Asia (EAS),
Russia–Belarus–Ukraine (RBU), and the Middle East (MDE), three global emission
sectors, power and industry (PIN), ground transportation (TRN), and
residential (RES), and one global domain (GLO), using an ensemble of global
chemical transport model simulations coordinated by the second phase of the
Task Force on Hemispheric Transport of Air Pollutants (TF HTAP2), and
epidemiologically derived concentration response functions. We build on
results from previous studies of TF HTAP by using improved atmospheric
models driven by new estimates of 2010 anthropogenic emissions (excluding
methane), with more source and receptor regions, new consideration of source
sector impacts, and new epidemiological mortality functions. We estimate
290 000 (95 % confidence interval (CI): 30 000, 600 000) premature O3-related
deaths and 2.8 million (0.5 million, 4.6 million) PM2.5-related
premature deaths globally for the baseline year 2010. While 20 % emission
reductions from one region generally lead to more avoided deaths within the
source region than outside, reducing emissions from MDE and RBU can avoid
more O3-related deaths outside of these regions than within, and
reducing MDE emissions also avoids more PM2.5-related deaths outside of
MDE than within. Our findings that most avoided O3-related deaths
from emission reductions in NAM and EUR occur outside of those regions
contrast with those of previous studies, while estimates of
PM2.5-related deaths from NAM, EUR, SAS, and EAS emission reductions
agree well. In addition, EUR, MDE, and RBU have more avoided
O3-related deaths from reducing foreign emissions than from
domestic reductions. For six regional emission reductions, the total avoided
extra-regional mortality is estimated as 6000 (-3400, 15 500)
deaths per year and 25 100 (8200, 35 800) deaths per year through changes in
O3 and PM2.5, respectively. Interregional transport of air
pollutants leads to more deaths through changes in PM2.5 than in
O3, even though O3 is transported more on interregional
scales, since PM2.5 has a stronger influence on mortality. For NAM and
EUR, our estimates of avoided mortality from regional and extra-regional
emission reductions are comparable to those estimated by regional models for
these same experiments. In sectoral emission reductions, TRN emissions
account for the greatest fraction (26–53 % of global emission reduction)
of O3-related premature deaths in most regions, in agreement with
previous studies, except for EAS (58 %) and RBU (38 %) where PIN
emissions dominate. In contrast, PIN emission reductions have the greatest
fraction (38–78 % of global emission reduction) of PM2.5-related
deaths in most regions, except for SAS (45 %) where RES emission
dominates, which differs with previous studies in which RES emissions
dominate global health impacts. The spread of air pollutant concentration
changes across models contributes most to the overall uncertainty in
estimated avoided deaths, highlighting the uncertainty in results based on a
single model. Despite uncertainties, the health benefits of reduced
intercontinental air pollution transport suggest that international
cooperation may be desirable to mitigate pollution transported over long
distances.
Introduction
Ozone (O3) and fine particulate matter with an aerodynamic diameter of less
than 2.5 µm (PM2.5) are two common air pollutants with known
adverse health effects. Epidemiological studies have shown that both
short-term and long-term exposures to O3 and PM2.5 are
associated with elevated rates of premature mortality. Short-term exposure to
O3 is associated with respiratory morbidity and mortality (Bell et
al., 2005, 2014; Gryparis et al., 2004; Ito et al., 2005; Levy
et al., 2005; Stieb et al., 2009) while long-term exposure to O3
has been associated with premature respiratory mortality (Jerrett et al.,
2009; Turner et al., 2016). Short-term exposure to PM2.5 has been
associated with increases in daily mortality rates from all natural causes,
and specifically from respiratory and cardiovascular causes (Bell et al.,
2014; Du et al., 2016; Powell et al., 2015; Pope et al., 2011), while
long-term exposure to PM2.5 can have detrimental chronic health effects,
including premature mortality due to cardiopulmonary diseases and lung cancer
(Brook et al., 2010; Burnett et al., 2014; Hamra et al., 2014; Krewski et
al., 2009; Lepeule et al., 2012; Lim et al., 2012). The Global Burden of
Disease Study 2015 (GBD 2015) estimated 254 000 deaths per year associated with
ambient O3 and 4.2 million associated with ambient PM2.5
(Cohen et al., 2017). A comparable study using output from an ensemble of
global chemistry–climate models estimated 470 000 deaths per year associated
with O3 and 2.1 million premature deaths per year associated with
anthropogenic PM2.5 (Silva et al., 2013). These differences in GBD
estimates result mainly from differences in concentration response functions
and estimates of pollutant concentrations.
Numerous observational and modeling studies have shown that anthropogenic
emissions can affect O3 and PM2.5 concentrations across
continents (Dentener et al., 2010; Heald et al., 2006; Leibensperger et al.,
2011; Lin et al., 2012, 2017; Liu et al., 2009a; West et al.,
2009a; Wild and Akimoto, 2001; Yu et al., 2008). As changes in emissions
from one continent influence air quality over others, several studies have
estimated the premature mortality from intercontinental transport (Anenberg
et al., 2009, 2014; Bhalla et al., 2014; Duncan et al.,
2008; Im et al., 2018; Liu et al., 2009b; West et al., 2009b; Zhang et al.,
2017). In 2005, the Task Force on Hemispheric Transport of Air Pollutants
(TF HTAP) was launched under the United Nations Economic Commission for
Europe (UNECE) Convention on Long-Range Transboundary Air Pollution (LRTAP).
One of its tasks is to investigate the impacts of emission reductions on the
intercontinental transport of air pollution, air quality, health, ecosystem,
and climate effects using a multi-model ensemble to quantify uncertainties
due to differences among models (Anenberg et al., 2009, 2014; Fiore et al., 2009; Fry et al., 2012; Huang et al., 2017; Stjern et
al., 2016; Yu et al., 2013).
In TF HTAP Phase 1 (TF HTAP1), human premature mortality due to 20 %
anthropogenic emission reductions in four large source regions was
investigated by Anenberg et al. (2009, 2014). They found that 20 %
foreign O3 precursor emission reductions contribute approximately
30 to > 50 % of the deaths avoided by reducing precursor
emissions in all four regions together (Anenberg et al., 2009). Similarly,
reducing emissions in North America (NAM) and Europe
(EUR) was found to avoid more O3-related premature deaths
outside the source region than within (Anenberg et al., 2009), which agrees
with other studies that together show for the first time that emission
reductions in NAM and EUR have
greater impacts on mortality outside the source region than within (Duncan et
al., 2008; West et al., 2009b). In contrast, Anenberg et al. (2014) estimate
that 93–97 % of PM2.5-related avoided deaths from reducing
emissions in all four regions occur within the source region while 3–7 %
occur outside the source region from transport among continents. Despite the
longer atmospheric lifetime of O3 and its relatively larger scale
of influence, PM2.5 was found to cause more deaths from intercontinental
transport (Anenberg et al., 2009, 2014). These prior studies have
consistently concluded that most avoided O3-related deaths from
emission reductions in NAM and EUR occur outside of those regions, while most
avoided PM2.5-related deaths occur within the regions. Similarly, an
ensemble of regional models in the third phase of the Air Quality Model
Evaluation International Initiative (AQMEII3) found that a 20 % decrease
in emissions within the source region avoids 54 000 and 27 500 premature
deaths in Europe and the US (from both O3 and PM2.5), while
the reduction of foreign emissions alone avoids ∼ 1000 and 2000
premature deaths in Europe and the US (Im et al., 2018). Crippa et al. (2017)
used the TM5-FASST reduced-form model with HTAP2 emissions to estimate a
global sensitivity to 20 % emission reductions of PM2.5-related
premature deaths of 401 000 globally, and 42 000 and 20 000 for Europe and
the US, respectively.
In addition, several studies have evaluated the relative importance of
individual emission sectors (Barrett et al., 2010; Bhalla et al., 2014; Chafe
et al., 2014; Chambliss et al., 2014; Corbett et al., 2007) or multiple
sectors (Lelieveld et al., 2015; Silva et al., 2016a) to ambient
air-pollution-related premature mortality. Lelieveld et al. (2015) estimated
that residential energy use such as for heating and cooking has the largest
mortality impact globally (for PM2.5 and O3 mortality
combined), particularly in South Asia (SAS) and East Asia (EAS). Silva et
al. (2016) likewise found that residential and commercial emissions are most
important for ambient PM2.5-related mortality, but they also found that
land transportation had the greatest impact on O3-related
mortality, particularly in NAM, South America, EUR, former Soviet Union (FSU), and the Middle East
(MDE). Understanding the impact of different sectors on the global burden and
the relative importance of each sector among regions can help stimulate
international efforts and region-specific air pollution control strategies.
Nevertheless, those studies were limited by using a single atmospheric model,
reflecting a need to understand whether results differ among models and
apportionment approaches.
In this study, we estimate the impacts of interregional transport and of
source sector emissions on human premature mortality from O3 and
PM2.5, using an ensemble of global chemical transport models coordinated
by the Task Force on Hemispheric Transport of Air Pollutants Phase 2
(TF HTAP2) (Galmarini et al., 2017; Huang et al., 2017; Janssens-Maenhout et
al., 2015; Stjern et al., 2016). Anthropogenic emissions were reduced by
20 % in six source regions, North America (NAM), Europe (EUR), South Asia
(SAS), East Asia (EAS), Russia–Belarus–Ukraine (RBU), and the Middle East
(MDE), three emission sectors, power and industry (PIN), ground
transportation (TRN), and residential (RES), and one worldwide region (GLO).
Human premature mortality due to these reductions is calculated using a
health impact function based on a log-linear model for O3 (Jerrett
et al., 2009) and an integrated exposure–response (IER) model for PM2.5 (Burnett et al., 2014), within the six source regions and elsewhere in the
world. We conduct a Monte Carlo simulation to estimate the overall
uncertainty due to uncertainties in relative risk, air pollutant
concentrations (given by the spread of results among different models), and
baseline mortality rates.
MethodsModeled O3 and PM2.5 surface concentration
Global numerical modeling experiments initiated by TF HTAP2, the regional
experiments by the Air Quality Model Evaluation International Initiative
(AQMEII) over EUR and NAM, and the Model Intercomparison Study-Asia
(MICS-Asia) were coordinated to perform consistent emission perturbation
modeling experiments across the global, hemispheric, and continental/regional
scales (Galmarini et al., 2017). Simulation periods, meteorology, emission
inventories, boundary conditions, and model output are also consistent. The
Joint Research Centre's (JRC) EDGAR (Emission Data Base for Global Research)
team in collaboration with regional emission experts from the U.S.
Environmental Protection Agency (US EPA), European Monitoring and Evaluation
Programme (EMEP), Centre on Emission Inventories and Projections (CEIP),
Netherlands Organization for Applied Scientific Research (TNO), and the
MICS-Asia Scientific Community and Regional Emission Activity Asia (REAS)
provide a global emission inventory at 0.1∘× 0.1∘
resolution for TF HTAP2 modeling experiments (Janssens-Maenhout et al.,
2015). The emission dataset was constructed for SO2,
NOx, CO, non-methane volatile organic compounds,
NH3, PM10, PM2.5, black carbon (BC), and organic
carbon (OC) and seven emission
sectors (shipping, aircraft, land transportation, agriculture, residential,
industry, and energy) for the year 2010 (Fig. S1 in the Supplement).
This study uses outputs from 14 global models/model versions (Table S1) participating in TF HTAP2. Overall, TF HTAP2 model
resolutions are finer than in TF HTAP1. In TF HTAP2, each model performed a
baseline simulation and sensitivity simulations in which the anthropogenic
emissions in a defined source region or sector were perturbed (reduced by
20 % in most cases). Based on the number of models that simulated
different experiments, we choose to focus on emission reductions from six
source regions, three emission sectors, and one global domain. More
specifically, all anthropogenic emissions are reduced by 20 % in the
NAM, EUR, SAS, EAS,
RBU, and MDE continental regions,
in the PIN, TRN, and RES emission sectors globally, and in GLO (Fig. S2).
Unlike TF HTAP1 (Dentener et al., 2010), which defined rectangular regions
that included ocean or some sparsely inhabited regions, TF HTAP2 regions are
defined by geopolitical boundaries.
We selected output from the models that provided temporally resolved volume
mixing ratios of O3 and mass mixing ratios of PM2.5
(“mmrpm2p5”) for the baseline and at least one regional or sectoral
emission reduction scenario. Among the 14 models, 11 models reported
O3 and eight reported PM2.5 for regional emission perturbation
scenarios. Four models reported O3 and four reported PM2.5 for
sectoral emission perturbation scenarios, and 10 models reported O3
and eight reported PM2.5for the global emission perturbation. All models
used prescribed meteorology for the year 2010, although this meteorology was
derived from different (re)analysis products and was not uniform across models.
Modeled concentrations are processed by calculating metrics consistent with
the underlying epidemiological studies to estimate premature mortality. For
O3, we calculate the average of daily 1 h maximum O3
concentration for the 6 consecutive months with the highest concentrations in
each grid cell (Jerrett et al., 2009), for the baseline and each 20 %
emission reduction scenario. While some models reported hourly O3
metrics, others only reported daily or monthly O3. We include these
models by first calculating the ratio of the 6-month average of daily 1 h
maximum O3 to the annual average of O3 in individual grid
cells, for models reporting hourly O3, and then applying that ratio
to the annual average of ozone for those models that only report daily or
monthly O3, following Silva et al. (2013, 2016b). For PM2.5,
we calculate the annual average PM2.5 concentration in each cell using
the monthly total PM2.5 concentrations reported by each model
(“mmrpm2p5”). Model results for these two metrics are then regridded from
each model's native grid resolution (varying from 0.5∘× 0.5∘
to 2.8∘× 2.8∘) to a consistent 0.5∘× 0.5∘
resolution used in mortality estimation. We estimate regional and sectoral
multi-model averages for each 20 % emission reduction scenario in the
year 2010, but for each perturbation case, we only include models that report
both the baseline and perturbation cases.
Model evaluation
Measurements from multiple observation networks are employed in this study to
evaluate the model performance around the world. We evaluate model
performance for the 2010 baseline simulation for 11 TF HTAP2 models for
O3 and eight models for PM2.5 (Table S1). For O3, we use ground
level measurements from 2010 at 4655 sites globally, collected by the
Tropospheric Ozone Assessment Report (TOAR) (Schultz et al., 2017; Young et
al., 2018). The TOAR dataset identifies stations as urban, rural, and
unclassified sites (Schultz et al., 2017). Model performance is evaluated for
the average of daily 1 h maximum O3 concentrations for the 3
consecutive months (3m1hmaxO3) with the highest concentrations in each
grid cell, including models that only report daily or monthly O3 as
described above. This metric for O3 differs slightly from the
6-month average of daily 1 h maximum metric used for health impact
assessment and is chosen because TOAR reports the 3-month metric but not the
6-month metric. For PM2.5, we compare the annual average PM2.5,
using PM2.5 observations from 2010 at 3157 sites globally selected for
analysis by the Global Burden of Disease 2013 (GBD2013) (Forouzanfar et al.,
2016; Brauer et al., 2016). Statistical parameters including the normalized mean bias (NMB),
normalized mean error (NME), and correlation coefficient (R) are selected to
evaluate model performance.
Tables S2 and S3 present statistical parameters of model evaluation for
O3 and PM2.5, and Figs. S3–S10 show the spatial O3 and
PM2.5 evaluation as NMB around the world and in NAM, EUR,
and EAS. For 3m1hmaxO3, the model ensemble mean shows good
agreement with measurements globally with a NMB of 7.3 % and NME of
13.2 % but moderate correlation with R of 0.53 (Table S2). For
individual models, eight models (CAM-Chem, CHASER_T42,
CHASER_T106, EMEPrv48, GEOS-Chem Adjoint, GEOS-Chem,
GFDL_AM3, and HadGEM2-ES) overestimate 3m1hmaxO3 with
NMB of 9.2 to 23 % while three models (C-IFS, OsloCTM3.v2, and RAQMS)
underestimate 3m1hmaxO3 by -10.8 to -19.4 % globally (Fig. S3). In the six
perturbation regions, the model ensemble mean is also in good agreement with
the measurements, with -11.2 to 25.3 % for NMB, 9.8 to
25.3 % for NME, and -0.09 to 0.98 for R. The ranges of NMB for individual
models are -18.1 to 32.3 %, -24.1 to 21.3 %, -24.5
to 45.0 %, -26.4 to 24.5 %, -30.5 to 20.3 %, and
-35.3 to 5.4 % in NAM, EUR, SAS, EAS, MDE, and RBU, respectively
(Figs. S4–S6). Note that some regions (SAS, MDE, and RBU) have very few
observations for model evaluation, making the comparison less robust. The
underestimated O3 in the western US and overestimated O3
in the eastern US in most models are very close to the model performance results of
Huang et al. (2017), who compare eight TF HTAP2 models with CASTNET observations
(Fig. S4) , as well as earlier studies under HTAP1 (Fiore et al., 2009).
Similarly, Dong et al. (2018) find that O3 is overestimated in EUR
and EAS by six TF HTAP2 models, consistent with our ensemble mean result in
these two regions (Figs. S5–S6).
For PM2.5, the model ensemble mean agrees well with measurements
globally, with a NMB of -23.1 %, NME of 35.4 %, and R of 0.77
(Table S3). For individual models, only one model (GEOS-Chem Adjoint)
overpredicts PM2.5 by 20.3 %, while the other seven models underpredict
PM2.5 by -60.9 to -7.4 % around the world (Fig. S7). In six
perturbation regions, the model ensemble mean is also in good agreement with
measurements, with ranges of NMB of -49.7 to 19.4 %, 21.2 to
49.7 % for NME, and 0.50 to 1.00 for R. The ranges of NMB for individual
models are -46.6 to 13.9 %, -76.0 to 31.9 %, -35.0
to 49.7 %, -50.4 to 29.5 %, -52.6 to 31.5 %, and
-74.1 to -19.8 % in NAM, EUR, SAS, EAS, MDE, and RBU, respectively
(Figs. S8–S10). Dong et al. (2018) show that PM2.5 is underestimated
in EUR and EAS by six TF HTAP2 models, consistent with our ensemble mean result
in these two regions (Figs. S9–S10). Note that many observations used are
located in urban areas, and models with coarse resolution may not be expected
to have good model performance. Several models also neglect some PM2.5
species, which may explain the tendency of models to underestimate.
Health impact assessment
We use output from the TF THAP2 model ensemble to estimate annual global
O3- and PM2.5-related cause-specific premature
mortality and avoided mortality from the 20 % regional and sectoral
emission reductions, following the same methods used by Silva et al. (2016a, b). The annual O3- and PM2.5-related premature mortality
is calculated using a health impact function based on epidemiological
relationships between ambient air pollution concentration and mortality in
each grid cell: ΔM=y0×AF×Pop, where ΔM is
premature mortality, y0 is the baseline mortality rate (for the exposed
population), AF =1-1/RR is the attributable fraction,
where RR is relative risk of death attributable to the change in air
pollutant concentration (RR = 1 when there is no increased
risk of death associated with a change in pollutant concentration), and Pop
is the exposed population (adults aged 25 and older).
For O3 mortality, we use a log-linear model for chronic respiratory
mortality (RESP) from an American Cancer Society (ACS) study (Jerrett et al. 2009), following recent studies including the GBD (Cohen et al., 2017), but
Turner et al. (2016) recently published new results for chronic ozone
mortality, and adoption of these results would lead to more ozone-related
deaths overall (Malley et al., 2017). RR is calculated as
RR=eβΔx,
where β is the concentration response factor, and Δx
corresponds to the change in pollutant concentrations among simulations
with perturbed emissions and the baseline simulation. For O3, RR = 1.040 (95 % CI: 1.013–1.067 ) for a 10 ppb
increase in O3 concentrations (Jerrett et al., 2009), which from
Eq. (1) gives values for β of 0.00392 (0.00129–0.00649). We estimate
O3-related premature deaths due to respiratory disease (RESP) based
on decreases or increases in O3 concentration (i.e., Δx) due
to 20 % regional and sectoral emission reduction scenarios relative to
the baseline. For regional and sectoral reductions, we do not assume a
low-concentration threshold below which changes in O3 have no
mortality effects as there is no clear evidence for such a threshold,
following Anenberg et al. (2009, 2010) and Silva et al. (2013, 2016a, b).
However, we evaluate global O3 premature mortality for the baseline
2010 simulation, relative to a counterfactual concentration of 37.6 ppb (Lim
et al. 2012), for consistency with GBD estimates (Cohen et al., 2017).
For PM2.5 mortality, we apply the IER
model, which is intended to better represent the risk of exposure to
PM2.5 at locations with high ambient concentrations (Burnett et al.,
2014). RR is calculated as
Forz<zcf,RRIERz=1,Forzz≧zcf,RRIERz=1+α{1-exp[-γz-zcfδ]},
where z is the PM2.5 concentration in micrograms per cubic meter and zcf is
the counterfactual concentration below which no additional risk is assumed,
and the parameters α, γ, and δ are used to fit the
function for cause-specific RR (Burnett et al., 2014). The overall
PM2.5-related cause-specific premature deaths related to ischemic heart
disease (IHD), cerebrovascular disease (STROKE), chronic obstructive
pulmonary disease (COPD), and lung cancer (LC) are estimated using RRs per age
group for IHD and STROKE and RRs for all ages for COPD and LC. A uniform
distribution from 5.8 to 8.8 µg m-3 is used for
zcf as suggested by Burnett et al. (2014), which does not vary in space
nor time. For uncertainty analysis, we use results from 1000 Monte Carlo
simulations of Burnett et al. (2014) to calculate RR in each grid cell using
Eq. (2) or (3). We estimate avoided premature mortality in 20 % emission
perturbation experiments by taking the difference in premature mortality
estimates with the 2010 baseline. However, in the IER model, the
concentration response function flattens off at higher PM2.5 concentrations, yielding different estimates of avoided premature mortality
for identical changes in air pollutant concentrations from less-polluted vs.
highly polluted regions. That is, one unit reduction of air pollution may
have a stronger effect on avoided mortality in regions where pollution levels
are lower (e.g., EUR, NAM) compared with highly polluted regions
(e.g., EAS, India), which would not be the case for a log-linear
function (Jerrett et al., 2009; Krewski et al., 2009). Therefore, using the
IER model in this study may result in smaller changes in avoided mortality in
highly polluted areas than using the linear model.
For the exposed population, we use the Oak Ridge National Laboratory's
LandScan 2011 Global Population Database at approximately 1 km resolution
(30′′× 30′′) (Bright et al., 2012). For the population of adults aged 25 and
older, we use ArcGIS 10.2 geoprocessing tools to estimate the population per
5-year age group in each cell by multiplying the country level percentage in
each age group by the population in each cell. We obtained cause-specific
baseline mortality rates for 187 countries from the GBD 2010 mortality
dataset (IHME, 2013). The population and baseline mortality per age group
were regridded to the 0.5∘× 0.5∘ grid (Table S4 and Fig. S11).
Cause-specific baseline mortality rates vary geographically, e.g., RESP and
COPD are relatively more dominant in SAS, IHD in EUR, STROKE in
Russia, and LC in NAM.
Finally, we conduct 1000 Monte Carlo simulations to propagate uncertainty
from baseline mortality rates, modeled air pollutant concentrations, and the
RRs in health impact functions. We use the reported 95 % CIs for
cause-specific baseline mortality rates, assuming lognormal distributions.
For modeled O3 and PM2.5 concentrations we use the absolute
value of the coefficient of variation among models in each grid cell, for
each 20 % emission perturbation case minus the baseline, assuming a
normal distribution. For O3 RRs, we use the reported 95 %
confidence intervals (CIs), assuming a normal distribution. For PM2.5
RRs, we use the parameter values (i.e., α, γ, δ, and
zcf) of Burnett et al. (2014) for 1000 simulations. One should acknowledge
that the range of modeled air pollution concentrations in an ensemble is not a true reflection
of the uncertainty in emissions or concentrations. The mean health outcome of the 1000 Monte Carlo simulations (the
empirical mean) may differ from the mean when using the mean RR.
We also quantify the uncertainties in mortality due to the spread of air
pollutant concentrations across models, RRs, and baseline mortality rates,
as contributors to the overall uncertainty, expressed as a coefficient, of
variation and compare the result with the Monte Carlo analysis estimate. To
do so, we hold two variables at their mean values and change the variable of
interest within its uncertainty range; for example, using mean RRs and
baseline mortality rates, we analyze the spread of the model ensemble to
calculate the coefficient of variation caused by model uncertainty. Given
that our 0.5∘× 0.5∘ grid cell resolution can capture most
of the population well in a given region, uncertainty associated with
population was assumed to be negligible. We estimate the impacts of
extra-regional emission reductions on mortality by using the response to
extra-regional emission reduction (RERER) metric defined by TF HTAP
(Galmarini et al., 2017):
RERERi=Rglobal-Rregion,iRglobal,
where for a given region i, Rglobal is the change in mortality in the
GLO 20 % reduction simulation relative to the base simulation,
and Rregion,i is the change in mortality in response to the 20 %
emission reduction from that same region i. A RERER value near 1 indicates
a strong relative influence of foreign emissions on mortality within a
region, while a value near 0 indicates a weak foreign influence. We also
estimate the total avoided extra-regional mortality from a source perspective
as the sum of avoided deaths outside of each of the six source regions, and
from a receptor perspective by summing Rglobal-Rregion,i for all
six
regions.
ResultsResponse of O3 and PM2.5 concentrations to 20 % regional and
sectoral emission reductions
Previous TF HTAP studies reported area-averaged concentrations to quantify
source–receptor relationships, averaging concentrations over a region (Doherty
et al., 2013; Fiore et al., 2009; Fry et al., 2012; Huang et al., 2017;
Stjern et al., 2016; Yu et al., 2013). Here, we present the
population-weighted concentration over a region, which is more relevant for
health. Among six receptor regions, the population-weighted multi-model mean
O3 concentrations range from 48.38 ± 8.05 ppb in EUR to
65.72 ± 10.08 ppb in SAS with a global average of 53.74 ± 8.03 ppb,
while the annual population-weighted multi-model mean PM2.5
concentrations range from 9.36 ± 2.62 µg m-3 in NAM to
39.27 ± 13.50 µg m-3 in EAS with a global average of
25.98 ± 5.05 µg m-3 (Tables 1 and S5–S6 and Figs. S12–S13).
Population-weighted multi-model mean O3 (ppb) and
PM2.5 concentration (µg m-3) for the 2010 baseline, for the
6-month O3 season average of 1 h daily maximum O3 and
annual average PM2.5, shown with the standard deviation among models.
For 20 % perturbation scenarios, in general the impact on the multi-model
mean change in surface O3 and PM2.5 concentration is greater
within the source region (i.e., domestic region) than outside of it (i.e.,
foreign region) (Figs. 1–2). This is also true for individual model results
(Figs. S14–S16). Among six source regions, the emission reduction from SAS
has the greatest impact on global population-weighted O3
concentration (Tables 2 and S5), while that from EAS has the greatest impact on
PM2.5 (Tables 3 and S6). The source–receptor pairs with the greatest
changes in O3 and PM2.5 concentration reflect the geographical
proximity among regions and the magnitude of emissions (Tables 2–3) –
e.g., EUR → MDE (0.34 ± 0.08 ppb), EUR → RBU
(0.34 ppb ± 0.09), EAS → NAM (0.29 ± 0.14 ppb),
EAS → RBU (0.27 ± 0.12 ppb), and NAM → EUR
(0.26 ± 0.55 ppb) for O3, and EUR → RBU
(0.26 ± 0.19 µg m-3), EUR → MDE
(0.18 ± 0.08 µg m-3), MDE → SAS
(0.12 ± 0.06 µg m-3), SAS → EAS
(0.08 ± 0.08 µg m-3), and EAS → SAS
(0.08 ± 0.07 µg m-3) for PM2.5. Our ensemble shows
ozone responses in the western US to emission reductions from EAS
(Fig. 1c) similar to those modeled by Lin et al. (2012, 2017), who show that a
model can capture the measured western US ozone increases due to rising Asian
emissions.
Global difference in multi-model mean O3 concentrations
(ppb) in 20 % emission reduction scenarios relative to the baseline for
the year 2010 in (a) North America (NAM), (b) Europe (EUR), (c) East Asia (EAS),
(d) South Asia (SAS), (e) the Middle East (MDE), and (f) Russia–Belarus–Ukraine (RBU),
the (g) power and industry (PIN), (h) transportation (TRN), and (i) residential
(RES) sectors,
and (j) globally (GLO), shown for the 6-month O3 season average of 1 h
daily maximum health-relevant metric.
Global difference in multi-model annual mean PM2.5
concentrations (µg m-3) in 20 % emission reduction
scenarios relative to the baseline for the year 2010 in (a) North
America (NAM), (b) Europe (EUR), (c) East Asia (EAS),
(d) South Asia (SAS), (e) the Middle East (MDE), and
(f) Russia–Belarus–Ukraine (RBU), the (g) power and industry
(PIN), (h) transportation (TRN), and (i) residential (RES) sectors, and
(j) globally
(GLO).
Population-weighted multi-model mean change in O3 (ppb) in
receptor regions due to 20 % regional (NAM, EUR, SAS, MDE, and RBU),
sectoral (PIN, TRN, and RES), and global (GLO) anthropogenic emission
reductions, for the 6-month O3 season average of 1 h daily
maximum. The diagonal, showing the effect of each region on itself, is
shown in italics. All numbers are rounded to the nearest hundredth, and are shown
with standard deviations among models.
Population-weighted multi-model annual average change in PM2.5
concentrations (µg m-3) in receptor regions due to 20 % regional
(NAM, EUR, SAS, MDE, and RBU), sectoral (PIN, TRN, and RES), and global (GLO)
anthropogenic emission reductions. The diagonal, showing the effect of each
region on itself, is shown in italics. All numbers are rounded to the nearest
hundredth, and are shown with standard deviations among models.
For each receptor region, reducing foreign anthropogenic emissions by
20 % (estimated by global minus within-region reductions) can decrease
population-weighted O3 concentrations by 29–74 % of the change
in O3 concentration and 8–41 % of the change in PM2.5
concentration (Tables 2–3). In some cases, regional emission reductions cause
small O3 concentration increases within the source region or in
foreign receptors, reflecting O3 nonlinear responses (Fig. S14).
For instance, C-IFS_v2 predicts O3 concentration
increases in EUR by 0.04 ppb from domestic emission reductions, which is in
agreement with results from TF HTAP1 (Anenberg et al., 2009). Similarly,
CAM-Chem shows more local O3 increases, particularly in SAS, than
other models (Fig. S14). The change in O3 concentration in foreign
receptors is broader than for PM2.5, reflecting that O3 has a
longer atmospheric lifetime than PM2.5.
For sectors, TRN emission reductions cause the greatest decrease in global
population-weighted O3 by 1.13 ± 0.19 ppb, while PIN emission
reductions cause the greatest decrease in surface PM2.5 by 1.46 ± 0.56 µg m-3 globally (Tables 2–3 and Figs. 1–2). The 20 %
emission reductions from individual sectors also have different effects in
different regions. Of the three sectors, emission reductions from TRN have
the greatest effect on population-weighted O3 in NAM, EUR, SAS,
MDE,
and MDE (40–50 % of the global emission reduction) while PIN emission
reductions dominate in EAS (57 %). Emission reductions from PIN have the
greatest effect on population-weighted PM2.5 in NAM, EUR, EAS, MDE, and
MDE (41–84 %) while RES emission reductions dominate in SAS (43 %).
The response of PM2.5 concentration to sectoral emission reductions
differs significantly across models, which reflects in part the PM2.5
species simulated by each model (Table S1 and Figs. S15–S17). For instance,
we found that models that simulate PM2.5 nitrate (i.e.,
CHASER_t42 and GEOS-Chem Adjoint) predict a greater impact on
PM2.5 concentration from TRN emission reduction than those without
nitrate (i.e., GOCARTv5 and SPRINTARS) (Fig. S17).
Global mortality burden associated with anthropogenic air
pollution
Table 4 shows the annual multi-model mean O3- and
PM2.5-related premature deaths in six regions and globally for the year 2010
baseline with 95 % CIs based on Monte Carlo
sampling. Tables S7–S8 show estimates of premature deaths due to
anthropogenic O3 and PM2.5 from individual models. For the
ensemble model mean, we estimate 290 000 (30 000, 600 000) premature
O3-related deaths globally using a 37.6 ppb counterfactual
concentration, and 2.8 million (0.5 million, 4.6 million) PM2.5-related
premature deaths using a uniform distribution of counterfactual concentration
from 5.8 to 8.8 µg m-3. Highly populated areas of
India and EAS have the highest number of O3- and PM2.5-related
deaths, and those regions together account for 82 and 66 % of the
global total O3- and PM2.5-related deaths. Compared with the
GBD 2015 (Cohen et al., 2017), our global burden estimates are greater than the
254 000 (97 000, 422 000) premature deaths per year for O3 from GBD,
while there are less than 4.2 million (3.7 million, 4.8 million) premature deaths for
PM2.5. Lelieveld et al. (2015) estimate 142 000 (CI: 90 000, 208 000)
O3-related deaths and 3.2 million (1.5 million, 4.6 million)
PM2.5-related premature deaths for 2015. These differences can be
explained mainly by exposure estimates. Here we used a multi-model ensemble,
whereas Lelieveld et al. (2015) used a single model, and Cohen et al. (2017)
used a single model for O3 and a single model combined with surface
and satellite observations for PM2.5. In addition, Cohen et al. (2017)
use RRs for particulate matter for IHD and stroke mortality that are modified
from those used by Burnett et al. (2014) and applied age modification to the
RRs, fitting the IER model for each age group separately. The updated IER
with estimated higher relative risks, together with greater global pollution
and baseline mortality rates in the low-income and middle-income countries in
EAS and SAS, leads to the higher absolute numbers of attributable
deaths and disability-adjusted life-years (DALYs) in GBD 2015 than estimated in GBD
2013 (Forouzanfar et al., 2016). Also, GBD 2015 includes lower child
respiratory infection estimates whereas we do not. Our wider range of
uncertainty for the global mortality reflects the uncertainty in baseline
rates, RRs, and spread of air pollutant concentration across models whereas
Cohen et al. (2017) consider national-level population-weighted mean
concentrations and uncertainty of IER function predictions at each
concentration, and Lelieveld et al. (2015) only account for the statistical
uncertainty of the parameters used in the IER functions.
Annual multi-model empirical mean O3- and
PM2.5-related premature deaths with 95 % CI from Monte Carlo
simulations in parentheses (including uncertainty in baseline mortality
rates, RRs, and air pollutant concentration across models) in the year 2010
baseline. All numbers are rounded to three significant figures or the nearest
100 deaths. Empirical mean is the mean of 1000 Monte Carlo simulations.
Reducing global anthropogenic emissions of air pollutant by 20 % avoids
47 400 (11 300, 99 000) O3-related deaths and 290 000 (67 100,
405 000) PM2.5-related premature deaths (Tables 5–6 and S9–S10). Most
avoided air-pollution-related deaths were found within or close to the source
region (Figs. 3–6). Reducing anthropogenic emissions by 20 % from NAM,
EUR, SAS, EAS, MDE, and RBU can avoid 54, 54, 95, 85,
21, and 22 % of the global change in O3-related deaths
within the source region (the number of avoided deaths within the source region
is divided by the number of avoided deaths globally), and 93, 81,
93, 94, 32, and 82 % of the global change in
PM2.5-related deaths, respectively (Tables 5–6). Whereas the most
O3-related premature deaths can be avoided by reducing SAS
emissions (20 000 (3600, 42 200) deaths per year), reducing EAS emissions avoids
more O3-related premature deaths (1700 (-1300, 5400)) outside of
the source region than for any other region (500 (180, 870) deaths per year to
1300 (-1200, 4400) deaths per year (Table 5). Similarly, while reducing EAS
emissions avoids the most PM2.5-related premature deaths (96 600 (3500,
136 000) deaths per year), reducing EUR emissions avoids more PM2.5-related
premature deaths (7400 (930, 9500) deaths per year) outside of the source
region than for any other region (1400 (-320, 2300) deaths per year to 5500
(3 000, 7800) deaths per year) (Table 6). While emission reductions from one
region generally lead to more avoided deaths within the source region than
outside of it, 20 % anthropogenic emission reductions from MDE (i.e., 79
and 68 % of global avoided deaths outside the source region for
O3 and PM2.5, respectively) and RBU (78 % for O3) can
avoid more premature deaths outside of the source region than within
(Tables 5–6). This result for RBU is in agreement with West et al. (2009b).
However, the results for NAM and EUR do not agree with previous studies that
found that emission reductions in these regions cause more
O3-related avoided premature deaths outside of the source region
than within (Anenberg et al., 2009; Duncan et al., 2008; West et al.,
2009b). For PM2.5, our results are comparable with Anenberg et al. (2014)
and Crippa et al. (2017), who found that for most regions, PM2.5-related
avoided premature deaths are higher within the source region than outside.
The above difference in results with TF HTAP1 may be in part because of the
definition of regions. Whereas the TF HTAP2 regions are defined by
geopolitical boundaries, the TF HTAP1 regions are defined by square domains
which are larger and include more ocean areas (Anenberg et al., 2009). In
addition, updated atmospheric models and emission inputs, as well as
different atmospheric dynamics in the single years chosen in TF HTAP1 vs.
TF HTAP2 may contribute to the differences.
Annual avoided O3-related premature deaths in 2010 per
1000 km2 due to 20 % emission reduction scenarios relative to the
base case in (a) North America (NAM), (b) Europe (EUR),
(c) East Asia (EAS), (d) South Asia (SAS), (e) the Middle East (MDE), and (f) Russia–Belarus–Ukraine (RBU),
the (g) power and industry (PIN), (h) transportation (TRN), and (i) residential
(RES) sectors, and (j) globally (GLO).
Annual avoided multi-model empirical mean O3-related
premature respiratory deaths with 95 % CI from Monte Carlo simulations in
parentheses due to 20 % regional (NAM, EUR, SAS, MDE, and RBU), sectoral
(PIN, TRN, and RES), and global (GLO) anthropogenic emission reductions in each
region and worldwide. The diagonal, showing the effect of each region on
itself, is shown in italics. For regional reductions, we also use the RERER (Eq. 4) as
the percent of total avoided deaths in each receptor region that result from
foreign emission reductions, as well as the percent of global avoided deaths
from emission reductions in each source region. All numbers are rounded to
three significant figures or the nearest 10 deaths.
Annual avoided multi-model empirical mean PM2.5-related
premature deaths (IHD + STROKE + COPD + LC) with 95 % CI from Monte Carlo
simulations in parentheses due to 20 % regional (NAM, EUR, SAS, MDE, and
RBU), sectoral (PIN, TRN, and RES), and global (GLO) anthropogenic emission
reductions in each region and worldwide. The diagonal, showing the effect of
each region on itself, is shown in italics. For regional reductions, we also use the
RERER (Eq. 4) as the percent of total avoided deaths in each receptor region
that result from foreign emission reductions, as well as the percent of
global avoided deaths from emission reductions in each source region. All
numbers are rounded to three significant figures or the nearest 10 deaths.
Using individual models, different conclusions may result for the relative
importance of interregional transport. For example, for O3, eight
models predict that NAM emission reductions cause more O3-related
premature deaths within NAM (i.e CAM-Chem, CHASER_T42, CHASER_T106, C-IFS,
GEOS-Chem Adjoint, GEOS-Chem, GFDL_AM3, and HadGEM2-ES), whereas two models
predict more deaths outside NAM (i.e., EMEPrv48 and OsloCTM3.v2). Five models
suggest that EUR emission reductions cause more O3-related
premature deaths within EUR (i.e., CAM-Chem, CHASER_T42, CHASER_T106,
GFDL_AM3, and HadGEM2-ES), whereas four show more deaths outside (i.e.,
C-IFS, GEOS-Chem Adjoint, EMEPrv48, and OsloCTM3.v2). Each individual model
shows that emission reductions from SAS and EAS avoid more
O3-related premature deaths within than outside, and that those
from MDE and RBU avoid more O3-related premature deaths outside
than within (Fig. S18). For PM2.5, each individual model shows that
emission reductions from NAM, EUR, SAS, EAS, and RBU avoid more
PM2.5-related premature deaths within than outside, while for emission
reductions from MDE, three models (EMEPrv48, GEOS-Chem Adjoint, and
SPRINTARS) show more
PM2.5-related premature deaths within, while three (CHASER_T42, GEOS5,
and GOCART) show more PM2.5-related premature deaths outside (Fig. S19).
The variation in health effect reflects the differences in processing of
natural emissions, atmospheric physical and chemical mechanisms, numerics,
etc., across models.
For each receptor region, reducing domestic anthropogenic emissions by
20 % contributes about 66, 39, 84, 72, 45, and 25 % of the total O3-related avoided premature mortality
(from the global reduction) and 90, 78, 87, 87, 58, and 66 % of the total PM2.5-related avoided premature
mortality (from the global reduction) in NAM, EUR, SAS, EAS, MDE, and RBU,
respectively (Tables 5–6). Therefore, reducing emissions from foreign regions
avoids more O3 premature deaths in EUR (foreign emissions account
for 61 % of total avoided deaths from the global reduction), MDE
(55 %), and RBU (75 %) than reducing domestic emissions (Tables 5–6),
in agreement with the results for EUR from Anenberg et al. (2009). Whereas
EAS has the greatest number of avoided O3-related premature deaths
due to foreign emission reduction (3800 (3600, 3900) deaths per year), RBU has
the greatest fraction of O3 mortality from foreign emission
reductions (75 %) (Table 5). Similarly, for PM2.5, while EAS has the
greatest number of avoided PM2.5-related premature deaths due to foreign
emission reductions (13 600 (3500, 18 800) deaths per year), MDE has the
greatest fraction of PM2.5 mortality from foreign emission reduction
(42 %) (Table 6).
Overall, adding results from all six regional reductions, interregional
transport of air pollution from extra-regional contributions is estimated to
lead to more avoided deaths through changes in PM2.5 (25 100 (8200,
35 800) deaths per year) than in O3 (6000 (-3400, 15 500) deaths per year),
consistent with Anenberg et al. (2009, 2014). This result is due to the
greater influence of PM2.5 on mortality, despite the shorter atmospheric
lifetime of PM2.5 relative to O3.
The contributions of different factors to the overall uncertainties in
mortality are shown in Tables S11–S12, considering uncertainties due to the
spread of air pollutant concentrations across models, RRs, and baseline
mortality rates, expressed as coefficients of variation. For both
O3 and PM2.5 mortality, the spread of model results generally
contributes most to the overall uncertainty, followed by uncertainty in RRs
and in baseline mortality rates, for most source–receptor pairs. The spread
of model results is generally wider for PM2.5 (14 to 3974 %
among source–receptor pairs) than for O3 (13 to 1065 %).
The uncertainty in RRs for O3 mortality has a constant value
(33 to 34 %) due to the fixed uncertainty range of RRs from Jerrett
et al. (2009), whereas PM2.5 mortality leads to a wider range of
uncertainty (1 to 247 %) in RRs because the uncertainty differs at
different PM2.5 concentrations (Burnett et al., 2014). Low uncertainty
in baseline mortality rate was found for most source–receptor pairs
(< 20 %) except for the response of PM2.5 mortality in SAS
to 20 % reduction from RBU (66 %).
Effect of sectoral reductions on mortality
Reducing global anthropogenic emissions by 20 % in three sectors (i.e., PIN,
TRN, and RES) together avoids 48 500 (7100, 108 000) O3-related
premature deaths and 243 000 (66 800, 357 000) PM2.5-related premature
deaths globally (Tables 5–6), with the greatest number of avoided air-pollution-related
premature deaths in highly populated areas (e.g., NAM,
EUR, India, China) (Figs. 3–6). For instance, reducing anthropogenic
emissions by 20 % in three sectors together avoids the highest number of
O3-related deaths in SAS (24 000 (6000, 49 600) deaths per year) and
PM2.5-related deaths in EAS (83 400 (29 400, 135 000) deaths per year). We
compare our estimates of O3- and PM2.5-related premature deaths
attributable to PIN, TRN, and RES emissions with previous studies by
multiplying our results for 20 % emission reductions by 5 and by
combining their sectors to nearly match each of the three sectors in this
study (Table 7). Compared with Silva et al. (2016a), our estimate of
O3- and PM2.5-related premature deaths attributable to PIN and
TRN are very comparable, but that attributable to RES is lower here. In comparison with
Lelieveld et al. (2015), we estimate greater O3- and
PM2.5-related premature deaths attributable to PIN and TRN, but fewer for
RES.
Annual avoided O3-related premature deaths in 2010 per
million people due to 20 % emission reduction scenarios relative to the
base case in (a) North America (NAM), (b) Europe (EUR), (c) East Asia (EAS),
(d) South Asia (SAS), (e) the Middle East (MDE), and (f) Russia–Belarus–Ukraine (RBU),
the (g) power and industry (PIN), (h) transportation (TRN), and (i) residential (RES) sectors, and
(j) globally (GLO).
Annual avoided PM2.5-related premature deaths in 2010 per
1000 km2 due to 20 % emission reduction scenarios relative to the
base case in (a) North America (NAM), (b) Europe (EUR), (c) East Asia (EAS),
(d) South Asia (SAS), (e) the Middle East (MDE), and (f) Russia–Belarus–Ukraine (RBU),
the (g) power and industry (PIN), (h) transportation (TRN), and (i) residential (RES) sectors, and
(j) globally (GLO).
Annual avoided PM2.5-related premature deaths in 2010 per
million people due to 20 % emission reduction scenarios relative to the
base case in (a) North America (NAM), (b) Europe (EUR), (c) East Asia (EAS),
(d) South Asia (SAS), (e) the Middle East (MDE), and (f) Russia–Belarus–Ukraine (RBU),
the (g) power and industry (PIN), (h) transportation (TRN), and (i) residential (RES) sectors, and
(j) globally (GLO).
Comparison of O3- and PM2.5-related premature deaths
attributable to PIN, TRN, and RES emissions with previous studies. Results
from this study (for 20 % reductions) are multiplied by 5. For Silva et
al. (2016), we combine results for “energy” and “industry” to represent
PIN, and use “land transportation” to represent TRN and “residential &
commercial” to represent RES. For Lelieveld et al. (2015), we combine the
“power generation” and “industry” sectors to represent PIN, and use
“land traffic” to represent TRN and “residential energy” to represent
RES.
Like Silva et al. (2016a) and Lelieveld et al. (2015), different locations
show relatively different mortality responses to changes in sectoral
emissions. Whereas PIN emission reductions cause the greatest number of
avoided O3-related premature deaths globally (19 300 (1400,
45 000) deaths per year), TRN emission reductions cause the greatest fraction of
avoided deaths in most of the six regions (26–53 % of the global emission
reduction), except for EAS (58 %) and RBU (38 %), where the effect of
reducing PIN emissions dominates. In comparison with other studies (Table 7),
our conclusion that PIN emissions cause the most O3-related deaths
and TRN emissions cause the greatest fraction of avoided deaths in most
regions agrees well with Silva et al. (2016a). For PM2.5, reducing PIN
emissions avoids the most PM2.5-related premature deaths globally
(128 000 (41 600, 179 000) deaths per year) and in most regions (38–78 % of
the global emission reduction), except for SAS (45 %), where the RES
emissions dominate. Although these findings differ from those of Lelieveld et
al. (2015) and Silva et al. (2016), who find that RES emissions have
the greatest impact on PM2.5 mortality globally and in most regions,
all studies agree that PIN emissions have the greatest impact in NAM. Our
result is also comparable with Crippa et al. (2017), who find that PIN
emissions have the greatest health impact in most countries. Although
comparable emission inventories are used (i.e., Lelieveld et al. (2015) and
this study use EDGAR emissions while Silva et al. (2016) use RCP8.5
emissions), our lower mortality estimate for RES emissions may be explained
by our 20 % reductions relative to the zero-out method and the different
years simulated.
Considering results from individual models, we found that mortality from TRN
emission reductions shows greater relative uncertainty than from PIN or RES
(Tables 5–6 and S9–S10), reflecting a greater spread of results across
models. Regional impacts from individual models also differ from the ensemble
mean result – e.g., for O3, GEOS-Chem Adjoint and OsloCTM3.v2 show
that reducing PIN emissions causes the greatest fraction of avoided
O3-related deaths in EUR, while GEOS-Chem Adjoint, HadGM2-ES, and
OsloCTM3.v2 show that TRN emissions have the greatest fraction of avoided
O3-related deaths in RBU (Fig. S20). For PM2.5,
CHASER_t42 and GEOS-Chem Adjoint show that reducing PIN
emissions causes the greatest fraction of avoided PM2.5-related deaths
in SAS (Fig. S21).
Discussion
We aggregate the avoided deaths attributable to 20 % reductions from four
corresponding source regions (i.e., NAM, EUR, SAS, and EAS) and compare with
the findings from TF HTAP1. We estimate that these regional emission
reductions are associated with 36 000 (-1500, 90 300) avoided deaths
globally through the change in O3 and 207 000 (41 500, 304 000)
avoided deaths through the change in PM2.5, more than those estimated by
Anenberg et al. (2009, 2014) – 21 800 (10 600, 33 400) deaths for
O3 and 192 000 (146 000, 230 000) deaths for PM2.5. This
discrepancy might be attributed to different health impact functions,
emission datasets, region definitions, updated population, or baseline
mortality rates. In particular, for O3 respiratory mortality, we
use a log-linear model for chronic mortality (Jerrett et al., 2009), instead of
the short-term O3 mortality estimate based on a daily time series
study (Bell et al., 2004) used by Anenberg et al. (2009). For PM2.5
mortality, Anenberg et al. (2014) only included the simulated changes in
BC, particulate organic matter (POM = primary organic
aerosol + secondary organic aerosol), and sulfate for PM2.5
concentration, while we use the total model-reported PM2.5
concentration,
which includes more species for some models. We also apply the IER model (Burnett et al., 2014) for PM2.5, as
opposed to the log-linear model of Krewski et al. (2009) used by Anenberg et
al. (2014).
For regional reductions, our multi-model average results suggest that NAM and
EUR emissions cause more deaths inside of those regions than outside, which
disagrees with previous studies (Anenberg et al., 2009; Duncan et al., 2008;
West et al., 2009b), whereas similar regional impacts are found for EAS and
SAS. Also, total avoided deaths through interregional air pollution transport
are estimated as 6000 (-3400, 15 500) deaths per year for O3 and
25 100 (8200, 35 800) deaths per year for PM2.5 in this study, in contrast
with 7300 (3600, 11 200) deaths per year for O3 and 11 500 (8800,
14 200) deaths per year for PM2.5 in Anenberg et al. (2009, 2014). These
differences likely result from different concentration response functions and
the use of six regions here vs. four by Anenberg et al. (2009, 2014). In addition,
updated atmospheric models and emission inputs, as well as different
atmospheric dynamics in the single years chosen in TF HTAP1 vs. TF HTAP2 may
contribute to the differences. In addition, updated atmospheric models and
emission inputs, as well as different atmospheric dynamics in the single
years chosen in HTAP vs. HTAP2 may contribute to the differences. Overall,
whereas O3 accounts for a higher percentage of the total deaths in
foreign regions than PM2.5, PM2.5 leads to more deaths in general,
which agrees well with the results of Anenberg et al. (2009, 2014).
Using regional models in AQMEII3, driven by a single global model
(C-IFS_v2), Im et al. (2018) estimated that 20 % domestic
emission reductions would avoid 54 000 and 27 500 premature deaths (for
O3 and PM2.5 combined) in Europe and the US, respectively,
as opposed to ∼ 1000 and 2000 premature deaths due to foreign
emission reductions. These results are comparable to our estimates that
32 900 and 19 500 premature deaths result from 20 % domestic emission
reductions in Europe and the US, while 670 and 570 premature deaths result
from foreign emission reductions. Although our defined US region is
slightly bigger than Im et al. (2018), the majority of US emission sources
and population are located within the region defined by Im et al. (2018).
This comparison shows that regional and global models show similar impacts on
mortality from air pollution transport.
Differences in our estimates of premature mortality attributable to air
pollution from three emission sectors (multiplied by 5) may be explained by
methodological differences relative to previous studies (Silva et al., 2016;
Lelieveld et al., 2015), including our use of 20 % emission reductions
versus the zero-out method in those studies, different emission inventories,
a multi-model ensemble versus single models, and differences in baseline
mortality rates, population, and concentration response functions. Our
finding that TRN emissions contribute the most avoided deaths for
O3 in most regions agrees well with the result by Silva et al. (2016a), but differs for PM2.5 mortality for which we find that PIN
emissions cause the most deaths, while both Silva et al. (2016a) and Lelieveld
et al. (2015) find that RES emissions are responsible for the most deaths.
This discrepancy may be explained by different PM2.5 species included in
individual models, as we showed that changes in PM2.5 concentration to
TRN emission differ across models.
By using an ensemble of multi-model results here, we highlight the relative
importance of different source–receptor pairs for mortality in a way that is
more robust than using a single model, particularly since some individual
models yielded different conclusions than the ensemble mean. The air
pollutant concentration changes reported by the HTAP2 models may be different
among models; it may result from a variety of processes, e.g., atmospheric
physical and chemical mechanisms, processing of natural emissions, and
transport time step (Table S1), but not anthropogenic emissions since
those were nearly identical among models. In addition, the coarse model
resolution used by global models may underestimate health effects by
misaligning peak concentration and population, particularly in urban areas
and for PM2.5 (Punger and West, 2013), but it is not known how model
resolution would affect the relative contributions of extra-regional and
intraregional health benefits. Future research should explore the possible
bias from using coarse global models for extra-regional and intraregional
mortality estimates in metropolitan regions by comparing with
finer-resolution chemical transport models.
Another uncertainty in this paper (and other global studies) lies in
applying the same RRs worldwide because of lack of long-term records of the
chronic influences of ambient air pollution on mortality outside of NAM and EUR. We consider only the population of adults ≥ 25 years
old, ignoring possible mortality effects on the younger population, and
consequently we may underestimate premature mortality overall. Likewise, the
effects of air pollution on several morbidity end points are omitted. We
assume that all PM2.5 is equally toxic, for lack of clear evidence for
greater toxicity of some species. Interregional transport may also change
the toxicity of PM2.5 by changing the size distribution or chemical
composition, where transport likely causes particles to become more oxidized
(West et al., 2016). Future research on PM2.5-related mortality should
include estimating health effects for different PM2.5 chemical
components.
Conclusions
We estimate O3- and PM2.5-related premature mortality from
simulations with 14 global chemistry transport models participating in the
TF HTAP2 multi-model exercise for the year 2010. An estimate of 290 000
(30 000, 600 000) global premature O3-related deaths and
2.8 million (0.5 million, 4.6 million) global PM2.5-related premature
deaths is obtained from the ensemble for the year 2010 in the baseline case.
We focus on model experiments simulating 20 % regional air pollutant
emission reductions (excluding methane) in six regions, three sectors, and
one global domain. For regional scenarios, six source emission reductions
altogether can cause 84 % of the global avoided O3-related
premature deaths within the source region, ranging from 21 to 95 % among
six regions, and 16 % (5 to 79 %) outside of the source region. For
PM2.5, 89 % of global avoided PM2.5-related premature deaths
are within the source region, ranging from 32 to 94 % among six regions,
and 11 % (6 to 68 %) outside of the source region. While most avoided
mortality generally occurs within the source region, we find that emission
reductions from RBU (only for O3) and MDE (for both O3
and PM2.5) can avoid more premature deaths outside of these regions than
within. Considering the effects of foreign emissions on receptor regions,
20 % foreign emission reductions lead to more avoided
O3-related premature deaths in EUR, MDE, and RBU than domestic
reductions. Reductions from all six regions in the transport of air pollution
among regions are estimated to lead to more avoided deaths through changes in
PM2.5 (25 100 (8200, 35 800) deaths per year) than for O3
(6000 (-3400, 15 500) deaths per year). For NAM and EUR, our estimates of
avoided mortality from regional and extra-regional emission reductions are
comparable to those estimated by regional models in AQMEII3 (Im et al., 2018)
for these same emission reduction experiments. Overall, the spread of modeled
air pollutant concentrations contributes most to the uncertainty in mortality
estimates, highlighting that using a single model may lead to erroneous
conclusions and may underestimate uncertainty in mortality estimates.
For sectoral emission reductions, reducing anthropogenic emissions by
20 % in three sectors together avoids 48 500 (7100, 108 000)
O3-related premature deaths and 243 000 (66 800, 357 000)
PM2.5-related premature deaths globally. Of the three sectors, TRN had the
greatest fraction (26–53 %) of O3-related premature deaths
globally and in most regions, except for EAS (58 %) and RBU (38 %)
where PIN emissions dominate. For PM2.5 mortality, PIN emissions cause
the most deaths in most regions (38–78 %), except for SAS (45 %)
where RES emissions dominate.
In this study, we have gone beyond previous TF HTAP1 studies that quantified
premature mortality from interregional air pollution transport by using
more source regions, analyzing source emission sectors, and using updated
atmospheric models and health impact functions. The estimate of air
transport premature mortality could vary due to differences in exposure
estimate (single model vs. ensemble model), health impact function, regional
definitions, and grid resolutions. These discrepancies highlight uncertainty
estimated by different methods in previous studies. Despite uncertainties,
our results suggest that reducing pollution transported over a long distance
would be beneficial for health, with impacts from all foreign emission
reductions combined that may be comparable to or even exceed the impacts of
emission reductions within a region. Additionally, actions to reduce
emissions should target specific sectors within world regions, as different
sectors dominate the health effects in different regions. This work
highlights the importance of long-range air pollution transport and
suggests that estimates of the health benefits of emission reductions on
local, national, or continental scales may underestimate the overall health
benefits globally, when interregional transport is accounted for.
International cooperation to reduce air pollution transported over long
distances may therefore be desirable.
Output from the TF-HTAP2 model experiments are available
through the AeroCom servers
(http://aerocom.met.no/data.html, Labonne et al., 2017).
Ozone ground level measurements can be accessed from the Tropospheric Ozone
Assessment Report (TOAR) database at
https://doi.pangaea.de/10.1594/PANGAEA.876108 (Schultz et al., 2018).
A full listing of PM2.5 ground measurement data sources can be accessed
at
https://pubs.acs.org/doi/suppl/10.1021/acs.est.5b03709/suppl_file/es5b03709_si_001.pdf (Brauer et al., 2016, accessible upon request).
Baseline mortality data are available from the Institute for Health Metrics
and Evaluation (IHME) Global Burden of Disease Study 2010: (GBD 2010) – Ambient Air Pollution Risk Model 1990–2010
at
http://ghdx.healthdata.org/record/global-burden-disease-study-2010-gbd-2010-ambient-air-pollution-risk-model-1990-2010 (Global Burden of Disease Study, 2010).
Population data are available from the Oak Ridge National Laboratory (ONRL)
LandScan 2011 Global Population Dataset at
https://landscan.ornl.gov/download (Oak Ridge National Laboratory (ONRL), 2011).
Information about the Supplement
A detailed description of the models
participating in the ensemble, a map of six priority regions used in this
analysis, and additional results can be found in the Supplement.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-18-10497-2018-supplement.
CKL collected all model output used in this study,
performed health impact calculations, and wrote the paper. JJW conceived of
the study and supervised CKL. RAS developed the health assessment code and
preprocessed the required health input data. CAM-Chem model experiments were
prepared by LE. CHASER_T42 and CHASER_T106 model experiments were prepared by
KS and TS. C-IFS model experiments were prepared by JF. EMEPrv48 model
experiments were prepared by JEJ. GEOS5 model experiments were prepared by
HB, MC, and XP. GEOSCHEMADJOINT model experiments were prepared by DH and YD.
GEOS-Chem model experiments were prepared by RJP. GFDL_AM3 model
experiments were prepared by ML. GOCART model experiments were prepared by
TK. HadGEM2-ES model experiments were prepared by GF. OsloCTM3.v2 model
experiments were prepared by MTL. RAQMS model experiments were prepared by
RBP and AL. SPRINTARS model experiments were prepared by TT. UI provided
AQMEII3 regional ensemble model output for comparison. FJD and TJK
coordinated HTAP2. All authors commented on drafts of the
paper.
The authors declare that they have no conflict of
interest.
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
We sincerely acknowledge the contribution of modeling groups from the second
phase of Task Force on Hemispheric Transport of Air Pollutants (TF HTAP2).
This work was supported by a scholarship from the Taiwan Ministry of
Education, grants from NIEHS (1 R21 ES022600-01) and NASA (NNX16AQ30G and
NNX16AQ26G), funding from BEIS under the Hadley Centre Climate Programme
contract (GA01101) and from the European Union's Horizon 2020 research and
innovation program under grant agreement no. 641816 (CRESCENDO). The
National Center for Atmospheric Research is sponsored by the National Science
Foundation. We thank all scientists who made the ground-level observations available in the Tropospheric
Ozone Assessment Report (TOAR) database and the ground-level PM2.5 observation dataset for GBD2013, particularly
Owen Cooper and Michael Brauer, who gave us access to these data. Edited
by: Gregory Carmichael Reviewed by: two anonymous referees
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