ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-9847-2016The effect of future ambient air pollution on human premature mortality to
2100 using output from the ACCMIP model ensembleSilvaRaquel A.WestJ. Jasonjjwest@email.unc.eduhttps://orcid.org/0000-0001-5652-4987LamarqueJean-Françoishttps://orcid.org/0000-0002-4225-5074ShindellDrew T.https://orcid.org/0000-0003-1552-4715CollinsWilliam J.https://orcid.org/0000-0002-7419-0850DalsorenStigFaluvegiGregFolberthGerdhttps://orcid.org/0000-0002-1075-440XHorowitzLarry W.NagashimaTatsuyaNaikVaishaliRumboldSteven T.SudoKengohttps://orcid.org/0000-0002-5013-4168TakemuraToshihikohttps://orcid.org/0000-0002-2859-6067BergmannDanielCameron-SmithPhiliphttps://orcid.org/0000-0002-8802-8627CionniIreneDohertyRuth M.https://orcid.org/0000-0001-7601-2209EyringVeronikahttps://orcid.org/0000-0002-6887-4885JosseBeatriceMacKenzieIan A.PlummerDavidhttps://orcid.org/0000-0001-8087-3976RighiMattiahttps://orcid.org/0000-0003-3827-5950StevensonDavid S.StrodeSarahhttps://orcid.org/0000-0002-8103-1663SzopaSophiehttps://orcid.org/0000-0002-8641-1737ZengastGuanghttps://orcid.org/0000-0002-9356-5021Environmental Sciences and Engineering, University of North Carolina,
Chapel Hill, North Carolina, USANCAR Earth System Laboratory, National Center for Atmospheric
Research, Boulder, Colorado, USANicholas School of the Environment, Duke University, Durham, North
Carolina, USADepartment of Meteorology, University of Reading, Reading, UKCICERO, Center for International Climate and Environmental
Research – Oslo, Oslo, NorwayNASA Goddard Institute for Space Studies and Columbia Earth Institute,
New York, New York, USAMet Office Hadley Centre, Exeter, UKNOAA Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USANational Institute for Environmental Studies, Tsukuba, JapanEarth and Environmental Science, Graduate School of Environmental
Studies, Nagoya University, Nagoya, JapanResearch Institute for Applied Mechanics, Kyushu University, Fukuoka,
JapanLawrence Livermore National Laboratory, Livermore, California, USAAgenzia Nazionale per le Nuove Tecnologie, l'Energia e lo Sviluppo
Economico Sostenibile (ENEA), Bologna, ItalySchool of GeoSciences, University of Edinburgh, Edinburgh, UKDeutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für
Physik der Atmosphäre, Oberpfaffenhofen, GermanyGAME/CNRM, Meteo-France, CNRS – Centre National de Recherches
Meteorologiques, Toulouse, FranceCanadian Centre for Climate Modeling and Analysis, Environment
Canada, Victoria, British Columbia, CanadaNASA Goddard Space Flight Center, Greenbelt, Maryland, USAUniversities Space Research Association, Columbia, Maryland, USALaboratoire des Sciences du Climat et de l'Environnement,
LSCE-CEA-CNRS-UVSQ, Gif-sur-Yvette, FranceNational Institute of Water and Atmospheric Research, Lauder, New
Zealandnow at: National Centre for Atmospheric Science (NCAS), University of
Reading, Reading, UKnow at: NIWA, Wellington, New ZealandJ. Jason West (jjwest@email.unc.edu)5August201616159847986210December20154February201626May20167July2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/9847/2016/acp-16-9847-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/9847/2016/acp-16-9847-2016.pdf
Ambient air pollution from ground-level ozone and fine
particulate matter (PM2.5) is associated with premature mortality.
Future concentrations of these air pollutants will be driven by natural and
anthropogenic emissions and by climate change. Using anthropogenic and
biomass burning emissions projected in the four Representative Concentration
Pathway scenarios (RCPs), the ACCMIP ensemble of chemistry–climate models
simulated future concentrations of ozone and PM2.5 at selected decades
between 2000 and 2100. We use output from the ACCMIP ensemble, together with
projections of future population and baseline mortality rates, to quantify
the human premature mortality impacts of future ambient air pollution.
Future air-pollution-related premature mortality in 2030, 2050 and 2100 is
estimated for each scenario and for each model using a health impact
function based on changes in concentrations of ozone and PM2.5 relative to 2000 and projected future population and baseline mortality
rates. Additionally, the global mortality burden of ozone and PM2.5 in
2000 and each future period is estimated relative to 1850 concentrations,
using present-day and future population and baseline mortality rates. The
change in future ozone concentrations relative to 2000 is associated with
excess global premature mortality in some scenarios/periods, particularly in
RCP8.5 in 2100 (316 thousand deaths year-1), likely driven by the large
increase in methane emissions and by the net effect of climate change
projected in this scenario, but it leads to considerable avoided premature
mortality for the three other RCPs. However, the global mortality burden of
ozone markedly increases from 382 000 (121 000 to 728 000) deaths year-1 in
2000 to between 1.09 and 2.36 million deaths year-1 in 2100, across RCPs,
mostly due to the effect of increases in population and baseline mortality
rates. PM2.5 concentrations decrease relative to 2000 in all
scenarios, due to projected reductions in emissions, and are associated with
avoided premature mortality, particularly in 2100: between
-2.39 and -1.31 million deaths year-1 for the four RCPs. The global mortality
burden of PM2.5 is estimated to decrease from 1.70 (1.30 to 2.10) million deaths year-1 in 2000 to between 0.95 and 1.55 million deaths year-1 in
2100 for the four RCPs due to the combined effect of decreases in
PM2.5 concentrations and changes in population and baseline mortality
rates. Trends in future air-pollution-related mortality vary regionally
across scenarios, reflecting assumptions for economic growth and air
pollution control specific to each RCP and region. Mortality estimates
differ among chemistry–climate models due to differences in simulated
pollutant concentrations, which is the greatest contributor to overall
mortality uncertainty for most cases assessed here, supporting the use of
model ensembles to characterize uncertainty. Increases in exposed population
and baseline mortality rates of respiratory diseases magnify the impact on
premature mortality of changes in future air pollutant concentrations and
explain why the future global mortality burden of air pollution can exceed
the current burden, even where air pollutant concentrations decrease.
Introduction
Ambient air pollution has adverse effects on human health, including
premature mortality. Exposure to ground-level ozone is associated with
respiratory mortality (e.g., Bell et al., 2005; Gryparis et al., 2004;
Jerrett et al., 2009; Levy et al., 2005). Exposure to fine particulate
matter with aerodynamic diameter less than 2.5 µm (PM2.5) is associated with mortality due to cardiopulmonary diseases and lung
cancer (e.g., Brook et al., 2010; Burnett et al., 2014; Hamra et al., 2014;
Krewski et al., 2009; Lepeule et al., 2012). Previous studies have estimated
the present-day global burden of disease due to exposure to ambient ozone
and/or PM2.5 (e.g., Apte et al., 2015; Evans et al., 2013; Forouzanfar
et al., 2015), with several studies estimating this burden using only output
of global atmospheric models (Anenberg et al., 2010; Fang et al., 2013a;
Lelieveld et al., 2013; Rao et al., 2012; Silva et al., 2013). However, few
studies have evaluated how the global burden might change in future
scenarios (Lelieveld et al., 2015; Likhvar et al., 2015; West et al., 2007).
Other global studies have estimated future air-pollution-related mortality
as a by-product of analyses of other future changes, such as the effects of
climate change or of climate change mitigation (e.g., Fang et al., 2013b;
Selin et al., 2009; West et al., 2013), but do not focus on the range of
plausible future mortality as their main purpose. Similarly, studies at
local and regional scales have evaluated the mortality impact of changes in
air quality due to future climate change (Bell et al., 2007; Chang et al.,
2010; Fann et al., 2015; Heal et al., 2012; Jackson et al., 2010; Knowlton
et al., 2004, 2008; Orru et al., 2013; Post et al., 2012; Sheffield et al.,
2011; Tagaris et al., 2009) but few such studies have evaluated changes
beyond 2050.
Future ambient air quality will be influenced by changes in emissions of air
pollutants and by climate change. Changes in anthropogenic emissions will
likely dominate in the near-term (Kirtman et al., 2013, and references
therein) and depend on several socioeconomic factors including economic
growth, energy demand, technological choices and developments, demographic
trends and land use change, as well as air quality and climate policies.
Climate change will affect the ventilation, dilution and removal of air
pollutants, the frequency of stagnation, photochemical reaction rates,
stratosphere–troposphere exchange of ozone and natural emissions (Fiore
et al., 2012, 2015; Jacob and Winner, 2009; von Schneidemesser et al., 2015;
Weaver et al., 2009). Climate change is likely to increase ozone in polluted
regions during the warm season, particularly in urban areas and during
pollution episodes. In remote regions, however, ozone is likely to decrease
due to greater water vapor concentrations, which increase the loss of ozone
by photolysis and subsequent formation of hydroxyl radicals (Doherty et al,
2013). The effects of climate change on PM2.5 concentrations are
generally uncertain as changes in temperature affect both reaction rates and
gas to particle partitioning, as well as wildfires and biogenic emissions,
and vary regionally primarily due to differing projections of changes in
precipitation (Fiore et al., 2012, 2015; Fuzzi et al., 2015; Jacob and
Winner, 2009; von Schneidemesser et al., 2015; Weaver et al., 2009).
The Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP)
simulated preindustrial (1850), present-day (2000) and future (2030, 2050
and 2100) concentrations of ozone and PM2.5 with an ensemble of 14
state-of-the-art chemistry climate models (Table S1 in the Supplement) (Lamarque et al.,
2013;
Stevenson et al., 2013) to support the IPCC's Fifth Assessment Report. Using
modeled 1850 and 2000 concentrations from this ensemble, we showed
previously that exposure to present-day anthropogenic ambient air pollution
is associated with 470 (95 % confidence interval (CI): 140, 900) thousand deaths year-1 from ozone-related respiratory diseases and 2.1 (1.3, 3.0)
million deaths year-1 from PM2.5-related cardiopulmonary diseases and
lung cancer (Silva et al., 2013). These results were obtained for a wider
range of cardiopulmonary diseases and using a different exposure–response
model for PM2.5 mortality than the present study, as discussed later.
The ACCMIP models simulated future air quality for specific periods through
2100, for four global greenhouse gas (GHG) and air pollutant emission
scenarios projected in the Representative Concentration Pathways (RCPs) (Van
Vuuren et al., 2011a, and references therein). The four RCPs were developed by
different research groups with different assumptions regarding the pathways
of population growth, economic and technological development, and air
quality and climate policies. Anthropogenic radiative forcing in 2100 ranges
from a very low level in the mitigation scenario RCP2.6 (Van Vuuren et al.,
2011b), to medium levels in the two stabilization scenarios, RCP4.5 (Thomson
et al., 2011) and RCP 6.0 (Masui et al., 2011), to a high level in the very
high baseline emissions scenario RCP8.5 (Riahi et al., 2011). All RCPs
assume increasingly stringent air pollution controls as countries develop
economically, leading to decreases in air pollutant emissions that reflect
the different methods of the different RCP groups (e.g., Smith et al.,
2011). However, as assumptions are similar among the RCPs, the four scenarios do
not span the range of possible futures published in the literature for
short-term species. For example, other studies have simulated scenarios in
which air pollution controls are kept at current levels while underlying
trends (e.g., energy use) increase overall emissions (Lelieveld et al.,
2015; Likhvar et al., 2015). While most air pollutants are projected to
decrease, ammonia increases in all RCPs due to the projected increase in
population and food demand, and methane increases in RCP8.5 because of its
projected rise in livestock and rice production. However, these scenarios
follow different pathways in different regions. In some regions, emissions
increase to mid-century before decreasing, while in other regions emissions
are already decreasing at present and continue decreasing to 2100. Models in
the ACCMIP ensemble incorporate chemistry–climate interactions, including
mechanisms by which climate change affects ozone and PM2.5, although
models do not all include the same mechanisms of interactions and do not
always agree on the net effect of these interactions (von Schneidemesser et
al., 2015).
Using modeled ozone and PM2.5 concentrations from the ACCMIP ensemble,
we estimate the future premature human mortality associated with exposure to
ambient air pollution. Our premature mortality estimates are obtained using
a health impact function, combining the relative risk of exposure to changes
in air pollution with future exposed population and cause-specific baseline
mortality rates. We estimate overall future premature mortality considering
the difference in air pollution associated with 2030, 2050 and 2100
emissions and climate relative to that resulting from 2000 emissions and
climate. Mortality estimates are obtained at a sufficiently fine horizontal
resolution (0.5∘× 0.5∘) to capture both global and
regional effects and inform regional and national air quality and climate
change policy but are not expected to capture local scale (e.g., urban) air
pollution effects.
MethodsAmbient ozone and PM2.5 concentrations
Concentrations of ozone and PM2.5 in surface air are calculated for the
present day (2000) and for the 2030, 2050 and 2100 decades for the four RCPs
using the output of simulations by the ACCMIP ensemble of chemistry–climate
models. As described by Lamarque et al. (2013), not all models are truly
coupled chemistry–climate models. OsloCTM2 and MOCAGE are chemical transport
models driven by offline meteorological fields, and UM-CAM and STOC-HadAM3
do not model the feedback of chemistry on climate.
All ACCMIP models used nearly identical anthropogenic and biomass burning
emissions for the present day and future, but they used different natural
emissions (e.g., biogenic volatile organic compounds, ocean emissions, soil
and lightning NOx), which mostly impacted emissions of ozone precursors
(Lamarque et al., 2013; Young et al., 2013) and natural aerosols (i.e., dust
and sea salt). Model output shows good agreement with recent observations,
both for ozone (Young et al., 2013) and for PM2.5 (Shindell et al.,
2013), although models tend to overestimate ozone in the Northern Hemisphere
and underestimate it in the Southern Hemisphere and to underestimate
PM2.5, particularly in East Asia. Future surface concentrations of air
pollutants vary across scenarios and models, but ozone is projected to
decrease except in RCP8.5, mostly associated with the large increase in
methane concentrations specific to this scenario and the effect of climate
change in remote regions (von Schneidemesser et al., 2015; Young et al.,
2013).
We obtained hourly and monthly output from the ACCMIP ensemble simulations
for a base year (2000) and for future projections under the four RCPs (2030,
2050 and 2100), with each time period corresponding to simulations of up to
10 years, depending on the model. Only two models reported results for all
four RCP scenarios and the three future time periods – GFDL-AM3 and
GISS-E2-R. PM2.5 is calculated as a sum of aerosol species reported by
six models (see Supplement), and four of these models also
reported their own estimate of total PM2.5 (Table S1 in the Supplement). Our PM2.5
formula includes nitrate; since this species was reported by three models
only, we calculate the average nitrate concentrations in each cell reported
by these models and add this average to PM2.5 for the other models,
following Silva et al. (2013). We use our PM2.5 estimates to obtain all
mortality results and perform a sensitivity analysis using the PM2.5
concentrations reported by four models using their own PM2.5 formulas, which differed among models, as done by Silva et al. (2013). The
native grid resolutions of the 14 models varied from 1.9∘× 1.2∘ to 5∘× 5∘; we regrid ozone and
PM2.5 species surface concentrations from each model to a common
0.5∘× 0.5∘ horizontal grid to take maximum advantage of
how the grids of different models overlap, following Anenberg et al. (2009,
2014) and Silva et al. (2013).
Ozone and PM2.5 concentrations are calculated in each grid cell for
each model separately. For both pollutants, we use identical metrics to
those reported in the epidemiological studies we considered for the health
impact assessment (next section):
seasonal average of daily 1 h maximum ozone concentration, for the 6
consecutive months with highest concentrations in each grid cell;
annual average PM2.5 concentration.
Among the 14 models, 5 models reported only monthly ozone concentrations,
while the remaining models reported both hourly and monthly values. We
calculate the ratio of the seasonal average of daily 1 h maximum to the
annual average of monthly concentrations, for each scenario/year, for those
that reported both hourly and monthly concentrations. Then we apply that
ratio to the annual average of monthly ozone concentrations for the former
five models, as previously done by Silva et al. (2013). The differences in
ozone and PM2.5 concentrations between future year (2030, 2050 and
2100) and 2000 are shown in Tables S2 and S3, for each model. For 10 world
regions (Fig. S1 in the Supplement), we also estimate regional multi-model averages for each
scenario/year (Figs. S2 and S3).
Health impact assessment
We estimate future air-pollution-related cause-specific premature mortality
using generally the same methods as those used by Silva et al. (2013) to
obtain present-day estimates, but with two important differences: (1) we use
the recently published Integrated Exposure–Response (IER) model for
PM2.5 (Burnett et al., 2014) instead of a log-linear model (Krewski et
al., 2009), and (2) we use projections of population and baseline mortality
rates from the International Futures (IFs) integrated modeling system
(Hughes et al., 2011).
We apply a health impact function to estimate premature mortality associated
with exposure to ozone and PM2.5 ambient air pollution (ΔMort) in
each grid cell: ΔMort =y0×AF×Pop, where y0 is the baseline mortality rate (for the
exposed population), AF =1-1/RR is the attributable fraction, RR is the relative risk
of death attributable to a change in pollutant concentrations (RR = 1 if
there is no increased risk of death associated with a change in pollutant
concentrations) and Pop is the exposed population (adults aged 25 and older).
We calculate changes in premature mortality by applying the change in
pollutant concentrations in each future year (2030, 2050 and 2100) relative
to year 2000 concentrations – the present-day state of air pollution – to
the future population. To estimate ozone mortality, we apply the
exposure–response function to the difference in ozone concentrations, while
for PM2.5 mortality we apply the exposure–response function to
concentrations in each year (future years and 2000) and then subtract the
mortality estimates. We therefore estimate “avoided”/“excess” premature
mortality due to decreases/increases in air pollutant concentrations in
the future years relative to 2000 concentrations. This approach differs from
a calculation of the global burden of air-pollution-related mortality since
we use 2000 rather than 1850 concentrations as baseline. We estimate
mortality changes due to future concentration changes, relative to the
present, to avoid applying the health impact function at very low
concentrations where there is less confidence in the exposure–response
relationship. For example, the simulated 1850 air pollutant concentrations
are often below the lowest measured value of the American Cancer Society
study (Jerrett et al., 2009; Krewski et al., 2009). For illustration, we
also estimate mortality relative to 1850 concentrations, which could be
regarded as global burden of disease calculations, following Silva et al. (2013).
For each model, we estimate ozone-related mortality due to chronic
respiratory diseases (RESP), using RR from Jerrett et al. (2009). We also
estimate PM2.5-related mortality due to ischemic heart disease (IHD),
cerebrovascular disease (STROKE), chronic obstructive pulmonary disease
(COPD) and lung cancer (LC), using RRs from the IER model (Burnett et al.,
2014). We use RR per age group for IHD and STROKE and RR for all ages for
COPD and LC. We apply the IER model instead of RRs from Krewski et al. (2009), used by Silva et al. (2013), as the newer model should better
represent the risk of exposure to PM2.5, particularly at locations with
high ambient concentrations. In the IER model, the concentration–response
function flattens off at higher PM2.5 concentrations yielding different
estimates of excess mortality for identical changes in air pollutant
concentrations in less-polluted vs. highly polluted locations. Specifically,
a one unit reduction of air pollution may have a stronger effect on avoided
mortality per million people in regions where pollution levels are lower
(e.g., Europe, North America) compared with highly polluted areas
(e.g.,
East Asia, 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 may result in smaller changes in avoided mortality in
highly polluted areas than using the log-linear model.
Each RCP includes its own projection of total population but not population
health characteristics. For all scenarios, we choose to use a common
projection of population and baseline mortality rates per age group from the
IFs (Figs. S6 and S7). IFs project population and mortality based on UN
and WHO projections from 2010 through 2100, per age group and country,
mostly based on three drivers – income, education and technology (Hughes
et al., 2011). Population projections from IFs differ from those underlying
each RCP but lie within the range of the RCPs (Fig. S6). In 2030, global
total population in IFs is within 0.08 % of that reported for RCP2.6,
RCP4.5 and RCP6.0 and 5 % lower than for RCP8.5; however, in 2100 IFs
project larger global populations than RCP2.6 (+7 %), RCP4.5
(+13 %) and RCP6.0 (+2 %) and considerably lower than RCP8.5
(-27 %). IFs project rising baseline mortality rates for cardiovascular
diseases (CVD) and RESP, globally and in most regions (particularly in East
Asia and India), reflecting an aging population. By using projections from
IFs, we have a single source of population and baseline mortality rates,
assuring their consistency and enabling us to isolate the effect of changes
in air pollutant concentrations across the RCPs. Had we used the population
projections from each scenario, the magnitude of the changes (increases or
decreases in premature mortality relative to 2000) would likely increase in
RCP8.5 but decrease in RCP2.6, RCP4.5 and RCP6.0. With the exception of
Europe, Former Soviet Union (FSU) and East Asia, where population is
projected to decrease in 2100 relative to 2000, had we used present-day
population and baseline mortality we would have obtained lower estimates for
excess or avoided mortality in each scenario/year, as projected increases in
population and baseline mortality magnify the impact of changes in air
pollutant concentrations. Therefore, we estimate the overall effect of
future air pollution (due to changes in emissions and climate change)
considering the population that will potentially be exposed to those
effects. We also obtain different estimates of changes in future mortality
than if we had calculated the global burden in each year, using air
pollutant concentrations, population and baseline mortality rates in that
year and subtracted the present-day burden. Our results do not reflect the
potential synergistic effect of a warmer climate on air-pollution-related
mortality, i.e., we do not account for potential changes in the
exposure–response relationships at higher temperatures (Pattenden et al.,
2010; Wilson et al., 2014, and references therein).
Country-level population projections for 2030, 2050 and 2100 are gridded to
0.5∘× 0.5∘ using ArcGIS 10.2 geoprocessing tools,
assuming that the spatial distribution of total population within each
country is unchanged from the 2011 LandScan Global Population Dataset at
approximately 1 km resolution (Bright et al., 2012) and that the exposed
population is distributed in the same way as the total population within
each country. IFs projections of mortality rates for CVD are used to
estimate baseline mortality rates for IHD and STROKE considering their
present-day proportion in CVD (using GBD 2010 baseline mortality rates), as
are RESP projections for COPD and malignant neoplasms for LC. IFs
projections for 2010 are comparable to GBD 2010 (Lozano et al., 2012)
estimates for CVD (+0.04 %), RESP (+2.5 %) and neoplasms (-12 %).
We estimate the number of deaths per 5-year age group per country using the
country level population. The resulting population and baseline mortality
per age group at 30 s × 30 s are regridded to the same
0.5∘× 0.5∘ grid as the concentrations of air pollutants.
Uncertainty from the RRs is propagated separately for each
model/scenario/year to mortality estimates in each grid cell, through 1000
Monte Carlo (MC) simulations; i.e., we repeat the calculations in each grid
cell 1000 times using random sampling of the RR variable. For ozone, we use
the reported 95 % CIs for RR (Jerrett et al., 2009)
and assume a normal distribution, while for PM2.5 we use the values for
the parameters alpha, gamma, delta and zcf (counterfactual) reported by
Burnett et al. (2014) for 1000 MC simulations (GHDx, 2013). Then for each of
the 1000 simulations, we add mortality over many grid cells to obtain
regional and global mortality and estimate the empirical mean and 95 % CI
of the regional and global mortality results. We assume no correlation
between the RRs for the four causes of death; thus we may underestimate the
overall uncertainty for PM2.5 mortality estimates. Uncertainty in air
pollutant concentrations is based on the spread of model results by
calculating the average and 95 % CI for the pooled results of the 1000 MC
simulations for each model. This estimate of uncertainty in concentrations
does not account for uncertainty in emissions inventories (as the ensemble
used identical emissions) or for potential bias in modeled air pollutant
concentrations. We also estimate the contribution of uncertainties in RR and
in air pollutant concentrations to the overall uncertainty in mortality
estimates using a tornado analysis; we obtained global mortality estimates
treating each variable as uncertain individually (year 2000 concentrations,
future year concentrations, RR for ozone and the four parameters in the IER
model for PM2.5) and used central estimates for all other variables,
and then we calculated the contribution of each variable to the overall
uncertainty (when all variables are treated as uncertain simultaneously).
Uncertainties associated with population and baseline mortality rates are
not reported by IFs and are not considered in the uncertainty analysis.
Results
Estimates of future ozone respiratory mortality for all RCP
scenarios in 2030, 2050 and 2100, showing global mortality for 13 models and
the multi-model average (million deaths year-1), for future air pollutant
concentrations relative to 2000 concentrations. Uncertainty for the
multi-model average shown for RCP8.5 is the 95 % CI, including uncertainty
in RR and across models. Only models with results for the 3 years have
lines connecting the markers.
First, we present our estimates of ozone and PM2.5-related
excess / avoided premature mortality in 2030, 2050 and 2100 for changes in
pollutant concentrations between 2000 and each future period, for the four
RCPs (Sects. 3.1 and 3.2, Figs. 1 to 7). Figures 1 and 4 show global
mortality for the different ACCMIP models. The multi-model average mortality
results are shown for individual grid cells (Figs. 2 and 5) and for
regional totals (Figs. 3 and 6). Finally, we include our estimates of the
global mortality burden of both air pollutants for future concentrations
relative to 1850 concentrations (Sect. 3.3, Figs. 8 and 9). In some
cases, the changes in future mortality due to changes in future
concentrations relative to 2000 show a different trend than the global
mortality burden; this difference reflects the combined effects of future
changes in concentrations relative to 1850, exposed population and baseline
mortality rates.
Future ozone respiratory mortality for all RCP scenarios in 2030,
2050 and 2100, showing the multi-model average in each grid cell, for future
air pollutant concentrations relative to 2000 concentrations.
Ozone-related future premature mortality
We find that future changes in ozone concentrations are associated with
excess global premature mortality due to respiratory diseases in 2030 but
avoided mortality by 2100 for all scenarios but RCP8.5 (Fig. 1, Table S5).
In 2030, all RCPs show excess multi-model average ozone mortality, ranging
from 11 900 (RCP2.6) to 264 000 (RCP8.5) deaths year-1. For each RCP, however,
some models yield avoided mortality in 2030. In 2050, multi-model averages
are obtained from only three or four models, depending on the scenario, which makes
it difficult to compare with the other two periods. In 2100, we estimate
excess ozone mortality in RCP8.5 (316 000 deaths year-1) but avoided ozone
mortality for the other three RCPs from -1.02 million (RCP2.6) to -718 000
(RCP6.0) deaths year-1 with all models agreeing in sign of the change.
Excess ozone-related future premature mortality (Figs. 2 and 3, Table S6)
is noticeable in some regions in 2030 for all RCPs, particularly in India
and East Asia for RCP8.5 (over 95 % of global excess mortality), but all
scenarios except RCP8.5 show avoided global ozone-related mortality in 2100.
Under this scenario in 2100, there are increases in ozone concentrations in
all regions except North America, East Asia and Southeast Asia (Fig. S2),
likely driven by the projected large increase in methane emissions as well
as by climate change. Avoided mortality in those three regions is outweighed
by excess mortality in India, Africa and the Middle East. Also, some regions
show different trends in future mortality relative to 2000 depending on the
RCP, reflecting the effects of distinct assumptions in each RCP about
economic growth and air pollution control with different trends in regional
ozone precursor emissions. For example, North America and Europe show
decreases in mortality through 2100 in all scenarios, except a slight
increase in Europe for RCP8.5 in 2100. In East Asia, mortality peaks in 2050
for RCP6.0, driven by peak precursor emissions in 2050 in this scenario, but
peaks in 2030 for the other three RCPs. India shows peaks in mortality in
2050 followed by decreases for all RCPs but RCP8.5, in which mortality
increases through 2100. Africa shows increases in mortality through 2100 for
RCP2.6 and RCP8.5, while it peaks in 2050 for RCP4.5 and decreases through
2100 for RCP6.0. Also, the effect of changes in population and baseline
mortality rates is noticeable in some regions when comparing the trends in
total ozone-related mortality and mortality per million people in each
region (Fig. S10). For example, decreases in population projected for 2100
in Europe, FSU and East Asia are reflected in greater changes in mortality
per million people than in total mortality, while the 3-fold increase in
population in Africa amplifies the changes in total mortality.
Future ozone respiratory mortality for all RCP scenarios in 2030,
2050 and 2100, showing the multi-model regional average (deaths year-1) in
10
world regions (Fig. S1) and globally, for future air pollutant
concentrations relative to 2000 concentrations.
For RCP8.5, we propagate input uncertainty to the mortality estimates
(Fig. 1, Table S5). Global future premature mortality changes from 264 000
(-39 300 to 648 000) deaths in 2030 to 316 000 (-187 000 to 1.38 million)
deaths in 2100. Uncertainty in RR leads to coefficients of variation (CV)
ranging from 31 to 37 % (2030), 31 to 40 % (2050) and 16 to 47 %
(2100) for the different models. Considering the spread of model results,
overall CV for the multi-model average mortality increases to 66 % (2030),
78 % (2050) and 125 % (2100). While uncertainty in RR and in modeled
ozone concentrations have similar contributions to overall uncertainty in
mortality results in 2050 (51 and 49 %, respectively), in 2030 modeled
ozone concentrations are the greatest contributor (81 %), and in 2100
uncertainty in RR contributes the most to overall uncertainty (88 %). For
2030, HadGEM2 differs in sign from the other 13 models with (avoided) global
mortality totalling -33 900 deaths year-1. For 2050, LMDzORINCA differs
substantially from the other three models with -38 900 deaths year-1. For 2100,
HadGEM2 is a noticeable outlier with 1.2 million excess deaths year-1 and
MOCAGE differs in sign from the other 12 models with -159 000 deaths year-1.
PM2.5-related future premature mortality
Estimates of future premature mortality
(IHD+STROKE+COPD+LC) for PM2.5 calculated as a sum of species,
for all RCP scenarios in 2030, 2050 and 2100, showing global mortality for
six models and the multi-model average (million deaths year-1), for future air
pollutant concentrations relative to 2000 concentrations. Uncertainty shown
for the RCP8.5 multi-model average is the 95 % CI, including uncertainty in
RR and across models.
Global PM2.5-related premature mortality, considering the difference in
future concentrations and 2000 concentrations, decreases substantially in
most scenarios, particularly in 2100 (Fig. 4, Table S7). In 2030, the
multi-model average varies from -289 000 (RCP4.5) to 17 200 (RCP8.5) deaths year-1, although one model (CICERO-OsloCTM2) shows excess mortality for
RCP2.6 and RCP8.5. In 2050, substantial avoided mortality is estimated for
all scenarios except RCP6.0, which shows a small increase in mortality
(16 700 deaths year-1), but this is the average of only three models that do
not agree on the sign of the change. In 2100, all scenarios show
considerable avoided mortality, ranging from -1.31 million (RCP8.5) to -2.39 million (RCP4.5) deaths year-1, reflecting the substantial decrease in
emissions of primary PM2.5 and precursors.
Future premature mortality (IHD+STROKE+COPD+LC) for
PM2.5 calculated as a sum of species, for all RCP scenarios in 2030,
2050 and 2100, showing the multi-model average in each grid cell, for future
air pollutant concentrations relative to 2000 concentrations.
Future premature mortality (IHD+STROKE+COPD+LC) for
PM2.5 calculated as a sum of species, for all RCP scenarios in 2030,
2050 and 2100, showing the multi-model regional average (deaths per year) in
10
world regions (Fig. S1) and globally, for future air pollutant
concentrations relative to 2000 concentrations.
In several regions (North America, South America, Europe, FSU and
Australia), PM2.5 future premature mortality decreases through
2100 for all RCPs (Figs. 5 and 6, Table S7). However, in East Asia,
Southeast Asia, India, Africa and the Middle East, for some scenarios,
PM2.5 mortality increases through 2030 or 2050 before decreasing. The
changes in future mortality reflect changes in future PM2.5
concentrations relative to 2000 (Fig. S3) and a substantial increase in
exposed population through the 21st century, particularly in Africa,
India and the Middle East (Fig. S6). That is, any reduction / increase in
mortality due to the decrease / increase in pollutant concentrations was
amplified by the increases in exposed population. The decreases in
population in Europe, FSU and East Asia have similar effects as those
mentioned above for ozone-related mortality. For example, while total
avoided mortality in 2100 in East Asia decreases compared to 2050, for
RCP2.6, RCP4.5 and RCP8.5, total avoided mortality per million people
increases in the same scenarios (Fig. S11). East and South Asia are the
regions with the greatest projected mortality burdens, and the variability
in PM2.5 among models is typically less in these regions than in
several other regions globally, depending upon the scenario and year (Fig. S9).
Future PM2.5-related mortality estimates are influenced by the
nonlinearity of the IER function. For example, in RCP8.5 in 2030, all models
project an increase in global population-weighted concentration (Table S3),
but all models except one show decreases in global PM2.5-related
mortality (Fig. 4). This outcome results in part because PM2.5
increases are projected in regions with high concentrations (particularly
East Asia) that are on the flatter part of the IER curve, whereas PM2.5
decreases in regions with low concentrations (North America and Europe) have
a steeper slope and therefore a greater influence on global mortality.
Considering the results of the MC simulations for RCP8.5, premature
mortality changes from -17 200 (-386 000 to 661 000) deaths in 2030 to -1.31
(-2.04 to -0.17) million deaths in 2100 (Fig. 4, Table S7). Uncertainty in
RR leads to a CV of 11 to 191 % for the different models in the 3
future years. The spread of model results increases overall CV to 1644 %
(2030), 20 % (2050) and 41 % (2100). Uncertainty in modeled PM2.5
concentrations in 2000 is the greatest contributor to overall uncertainty
(59 % in 2030, 45 % in 2050 and 49 % in 2100), followed by
uncertainty in modeled PM2.5 in future years (40 % in 2030, 26 % in
2050 and 32 % in 2100). Uncertainty in RR has a negligible contribution to
overall uncertainty in 2030 (< 1 %), as the multi-model mean
mortality change happens to be near 0 (one model projects a large
increase while the other five models project decreases) but contributes
29 % in 2050 and 20 % in 2100.
We compared mortality results using our estimates of PM2.5 from the sum
of reported species with results using PM2.5 reported by four models
applying their own formula to estimate PM2.5 (Fig. 7). The
multi-model average future avoided mortality for the four models which
reported PM2.5 is comparable although lower than the average for our
PM2.5 estimates for the same models. Individual models do not show the
same differences in mortality using their own vs. our PM2.5 estimates.
Also, for two models (GFDL-AM3 and MIROC-CHEM) the two sources of PM2.5
estimates yield mortality changes of different sign in 2030. These results
reflect the different aerosol species included by each model to estimate
PM2.5 (e.g., nitrate is not included by all models).
Estimates of global future premature mortality
(IHD+STROKE+COPD+LC) for RCP8.5 in 2030 and 2100, for PM2.5
reported by four models and PM2.5 estimated as a sum of species for
six models, showing global mortality for each model and the multi-model
average (million deaths year-1), for future air pollutant concentrations
relative to 2000 concentrations. Models signaled with * reported their own
estimate of PM2.5. Uncertainty shown for six models for sum of species
is the 95 % CI including uncertainty in RR and across models.
Global burden on mortality of ozone concentrations relative to
1850, in the present day for 2000 concentrations, showing multi-model
average and 95 % CI including uncertainty in RR and across models
(deaths per year), and in 2030, 2050 and 2100 for all RCPs, showing multi-model
averages (deaths per year) given by the deterministic values. Also shown are
future burdens using (Case A) 2000 concentrations relative to 1850 and
present-day population but future baseline mortality rates and (Case B) 2000
concentrations relative to 1850 but future population and baseline mortality
rates.
Global burden on mortality of PM2.5 concentrations relative
to 1850, in the present day for 2000 concentrations, showing multi-model
average and 95 % CI including uncertainty in RR and across models
(deaths per year), and in 2030, 2050 and 2100 for all RCPs, showing multi-model
averages (deaths per year) given by the deterministic values. Also shown are
future burdens using (Case A) 2000 concentrations relative to 1850 and
present-day population but future baseline mortality rates and (Case B) 2000
concentrations relative to 1850 but future population and baseline mortality
rates.
Global burden on mortality of ozone and PM2.5
Here we present estimates of the global burden on mortality of ozone and
PM2.5 concentrations in the future, considering the four RCPs relative
to preindustrial concentrations (1850) and future exposed population and
baseline mortality rates (Figs. 8 and 9, Tables S8 and S9). For context,
we estimate the present-day global burden, using 2000 concentrations,
population from LandScan 2011 Population Dataset and baseline mortality
rates from GBD2010, to be 382 000 (121 000 to 728,000) ozone deaths year-1
and 1.70 (1.30 to 2.10) million PM2.5 deaths year-1. These estimates are
18.7 % lower for ozone-related mortality and 19.1 % lower for
PM2.5-related mortality than those obtained in our previous study
(Silva et al., 2013), reflecting (a) more restrictive mortality outcomes
(chronic respiratory diseases rather than all respiratory diseases, and
IHD+STROKE+COPD rather than all cardiopulmonary diseases), (b) updated
population and baseline mortality rates and (c) the use of the recent IER model
(Burnett et al., 2014) for PM2.5 (instead of Krewski et al., 2009).
Compared with the GBD 2013 (Forouzanfar et al., 2015), our estimates are
76 % higher for ozone-related mortality and 42 % lower for
PM2.5-related mortality, likely due to the fact that we estimate the
global mortality burden using 1850 concentrations as baseline, while
Forouzanfar et al. (2015) consider counterfactual concentrations
(theoretical minimum-risk exposure) that are mostly higher for ozone
(uniform distribution between 33.3 and 41.9 ppb) and lower for PM2.5
(uniform distribution between 5.9 and 8.7 µg m-3) than 1850
concentrations. In addition, we consider ozone mortality from all chronic
respiratory diseases while Forouzanfar et al. (2015) only account for COPD,
and we restrict our mortality estimates to adult population while
Forouzanfar et al. (2015) include PM2.5 mortality from lower
respiratory tract infections in children under 5 years old. As a sensitivity
analysis, when we apply a counterfactual of 33.3 ppb (instead of using 1850
concentrations), our ozone-related mortality estimates are 23 % higher for
the multi-model mean, varying between +10 and +52 % among models.
Similarly, using the IER model counterfactual, our PM2.5-related
mortality estimates are 22 % lower for the multi-model mean, varying
between -8 and -44 % among models.
For ozone, the global mortality burden increases in all RCPs through 2050 to
between 1.84 and 2.60 million deaths year-1, and then it decreases slightly
for RCP8.5 and substantially for the other RCPs, ranging between 1.09 and
2.36 million deaths year-1 in 2100. The increase can be explained by the rise
in the baseline mortality rates for chronic respiratory diseases magnified
by the increase in exposed population, while the decline is likely mostly
related to the decrease in concentrations, slightly countered by further
population growth (Fig. 8). The global burden of mortality from PM2.5
shows a declining trend for all RCPs from 2030 to 2100, peaking between 2.4
and 2.6 million deaths year-1 in 2030 then declining to between 0.56 and 1.55 million deaths year-1 in 2100, except for RCP6.0 which peaks in 2050 (3.50 million deaths year-1)
before declining considerably. For PM2.5, the
increase in exposed population and the decline in concentrations have a much
greater effect than changes in baseline mortality rates (Fig. 9). These
results are similar to those of Apte et al. (2015) who report a stronger
effect of projected demographic trends in India and China in 2030 than of
changes in baseline mortality rates. Our estimates for the global burden of
PM2.5 mortality in 2050 (between 1.82 and 3.50 million deaths year-1 for
the four RCPs) are considerably lower than those of Lelieveld et al. (2015)
(5.87 million deaths year-1 for IHD+STROKE+COPD+LC), likely due to the
assumption in the RCP scenarios of further regulations on air pollutants,
while the Business-As-Usual scenario of Lelieveld et al. (2015) does not
assume regulations beyond those currently defined.
To help explain differences between the trends in future global burden
(Figs. 8 and 9) and in future mortality relative to 2000 (Figs. 1 and
4), we estimate the future global burden for two cases: Case A, using 2000
concentrations relative to 1850 and present-day population but future
baseline mortality rates; and Case B, using 2000 concentrations relative
to 1850 but future population and baseline mortality rates. Case A reflects
the effect of future baseline mortality rates on the global burden, if
concentrations in future years were maintained at 2000 levels, while Case B
reflects the combined effect of population and baseline mortality rates,
i.e., it is identical to Case A except that population changes. The
difference between the global burden for each RCP and Case B reflects the
effects of changes in future air pollutant concentrations and nearly equals
future mortality relative to 2000 concentrations in Figs. 1 and 4.
However, cases A and B are calculated for all 14 models for ozone and 6
models for PM2.5 (since all models reported air pollutant
concentrations in 2000), while future mortality relative to 2000 is
calculated for the models that report each scenario/year.
Discussion
In all RCP scenarios but RCP8.5, stringent air pollution controls lead to
substantial decreases in ozone concentrations through the 21st century,
relative to 2000. For RCP8.5, the higher baseline GHG (including methane)
and air pollutant emissions lead to increases in future ozone
concentrations. In contrast, global PM2.5 concentrations show a
decreasing trend across all RCP scenarios. These changes in air pollutant
concentrations, combined with projected increases in baseline mortality
rates for chronic respiratory diseases, drive ozone mortality to become more
important relative to PM2.5 mortality over the next century.
The importance of conducting health impact assessments with air pollutant
concentrations from model ensembles, instead of from single models, is
highlighted by the differences in sign of the change in mortality among
models and by the marked impact of the spread of model results on overall
uncertainty in our mortality estimates. In most cases assessed here (ozone
mortality in 2030 relative to 2000, PM2.5 mortality in 2030, 2050 and
2100 relative to 2000), uncertainty in modeled air pollutant concentrations
is the greatest contributor to uncertainty in mortality estimates. The
differences in air pollutant concentrations reported by the ACCMIP models
reflect different treatment of atmospheric dynamics and chemistry,
chemistry–climate interactions and natural emissions in each model (Young
et al., 2013). Although there is likely a bias in estimating health effects
using air pollutant concentrations from coarse-resolution models (Li et al.,
2016; Punger and West, 2013), particularly for PM2.5, we do not expect
resolution to be an important factor for the differences in simulated
concentrations across coarse-resolution models.
There are several uncertainties and assumptions that affect our results. We
applied the same RR worldwide and into the future, despite differences in
vulnerability of the exposed population, in composition of PM2.5 and
in other factors that may support the use of different risk estimates or
different concentration–response relationships. These uncertainties can be
addressed through additional long-term epidemiological studies, particularly
for large cohorts in developing countries, to improve RR estimates globally.
These studies should be more representative of wider ranges of exposure and air
pollutant mixtures than existing studies in the USA and Europe, and they
should control for confounding factors such as other environmental
exposures, use of air conditioning, socioeconomic factors, etc. Also, we
estimate mortality for adults aged 25 and older and do not quantify air
pollutant effects on morbidity, so we underestimate the overall impact of
changes in pollutant concentrations on human health. Uncertainty is
evaluated for a single future population projection, not accounting for the
wide range of projections in the literature, and does not reflect
uncertainty in baseline mortality rates, as these are not reported;
uncertainties in both population and baseline mortality rates would be
expected to increase with time into the future. The spread of model results
does not account for uncertainty in emissions inventories, as all ACCMIP
models used the same projections of anthropogenic emissions. Moreover,
climate and air quality interactions and feedbacks are not sufficiently
understood to be fully reflected in modeled air pollutant concentrations,
and global models simplify atmospheric physics and chemical processes. This
is particularly important when modeling air quality given scenarios of
future emissions and climate change. For example, most global models do not
fully address climate sensitivity to biogenic emissions (e.g., isoprene, soil
NOx and methane) and stratosphere–troposphere interactions (e.g.,
stratospheric influx of ozone). A better understanding of aerosol–cloud
interactions, of the impact of climate change on wildfires and of the
impact of land use changes on regional climate and air pollution is also
crucial.
Our results are limited by the range of air pollutant emissions projected by
the RCPs, which assume that economic growth strengthens efforts to reduce
air pollution emissions. All RCPs project reductions in anthropogenic
precursor emissions associated with more extensive air quality legislation
as incomes rise, except for methane in RCP8.5 and for ammonia in all
scenarios. These scenarios together do not encompass the range of plausible
air pollution futures for the 21st century, as the RCPs were not
designed for this purpose (van Vuuren et al., 2011a). Other plausible
scenarios have been considered, such as the Current Legislation Emissions
and Maximum Feasible Reductions scenarios used by Likhvar et al. (2015) and
the Business-As-Usual scenario of Lelieveld et al. (2015). As noted above,
our global burden estimates for 2050 are considerably lower than the
Business-As-Usual scenario of Lelieveld et al. (2015). If economic growth
does not lead to stricter air pollution control, emissions and health
effects may rise considerably, particularly for scenarios of high population
growth in developing countries (Amman et al., 2013).
Conclusions
Under the RCP scenarios, future PM2.5 concentrations lead to decreased
global premature mortality vs. what would occur with fixed year-2000
concentrations, but ozone-related mortality increases in some
scenarios/periods. In 2100, excess ozone-related premature mortality for
RCP8.5 is estimated to be 316 thousand (-187 thousand to 1.38 million) deaths year-1 (likely due to an increase in methane emissions and to the net
effect of climate change), while for the three other RCPs avoided ozone
mortality is between -718 thousand and -1.02 million deaths year-1. For
PM2.5, avoided future premature mortality is estimated to be between
-1.33 and -2.39 million deaths year-1 in 2100. These reductions in ambient air-pollution-related mortality reflect the decline in pollutant emissions
projected in the RCPs, but the large range of results from the four RCPs
highlights the importance of future air pollutant emissions for ambient air
quality and global health. Mortality estimates differ among models and we
find that, for most cases, the contribution to overall uncertainty from
uncertainty associated with modeled air pollutant concentrations exceeds
that from the RRs. Increases in exposed population and in baseline mortality
rates of respiratory diseases magnify the impact on mortality of the changes
in air pollutant concentrations.
Estimating future mortality relative to 2000 concentrations allows us to
emphasize the effects of changes in air pollution in these results. However,
increases in exposed population and in baseline mortality rates may drive an
increase in the future burden of air pollution on mortality. Even in the
most optimistic scenarios, the global mortality burden of ozone (relative to
1850 concentrations) is estimated to be over 1 million deaths year-1 in 2100,
compared to less than 0.4 million in 2000. For PM2.5, the global
burdens in 2030 and 2050 for the four RCPs are greater than the global
burden in 2000 but decrease to between 0.56 and 1.55 million deaths year-1 in
2100, compared to 1.7 million deaths year-1 in 2000. A strong decline in
PM2.5 concentrations for all RCPs together with demographic trends in
the 21st century (with a projected substantial increase in exposed
population) lead to a rising importance of ozone relative to PM2.5 for
the global burden of ambient air-pollution-related mortality.
The RCPs are based on the premise that economic development drives better
air pollution control, leading to improved air quality. This trend is
apparent in some developing countries now (Klimont et al., 2013), but it is
yet to be determined how aggressive many developing nations will be in
addressing air pollution. The assumed link between economic development and
air pollution control in the RCPs requires new and stronger regulations
around the world, as well as new control technologies, for the air pollution
decreases in the RCPs to be realized. The projected reductions in mortality
estimated here will be compromised if more stringent policies are delayed
(e.g., Lelieveld et al., 2015).
Data availability
Air pollutant concentrations are available from Atmospheric Chemistry & Climate Model Intercomparison Project (ACCMIP) datasets at http://catalogue.ceda.ac.uk/uuid/b46c58786d3e5a3f985043166aeb862d.
Data were retrieved from 08/2012 to 12/2013.
Present-day population data are available from the Oak Ridge National Laboratory (ONRL) LandScan 2011 Global Population Dataset at http://spruce.lib.unc.edu.libproxy.lib.unc.edu/content/gis/LandScan/.
Data were retrieved on 12/05/2012.
Present-day baseline mortality data are available from the Institute for Health Metrics and Evaluation (IHME) Global Burden of Disease Study 2010 (GBD 2010): Results by Cause 1990–2010 – Country Level, Seattle, United States,
2013, at
https://cloud.ihme.washington.edu/index.php/s/d559026958b38c3f4d12029b36d783da?path=/2010. Data were retrieved from 12/2013 to 03/2014.
Future population and baseline mortality data are available from the web-based International Futures (IFs) modeling system, version 6.54, at www.ifs.du.edu. Data were retrieved on 07/2012.
IER model data are available from the Institute for Health Metrics and Evaluation (IHME) Global Burden of Disease Study 2010: Global Burden of Disease Study 2010 (GBD 2010) – Ambient Air Pollution Risk Model
1990–2010, Seattle, United States, 2013, at http://ghdx.healthdata.org/record/global-burden-disease-study-2010-gbd-2010-ambient-air-pollution-risk-model-1990-2010.
Data were retrieved on 11/08/2013.
The Supplement related to this article is available online at doi:10.5194/acp-16-9847-2016-supplement.
Acknowledgements
The research here described was funded by a fellowship from the Portuguese
Foundation for Science and Technology, by a Dissertation Completion
Fellowship from The Graduate School (UNC – Chapel Hill) and by NIEHS grant
no. 1 R21 ES022600-01. We thank Karin Yeatts (Department of Epidemiology,
UNC – Chapel Hill) for her help in researching projections of future
population and baseline mortality rates, Colin Mathers (WHO) for advising us
on the IFs, Peter Speyer (IHME, University of Washington) for providing us
access to GBD2010 cause-specific mortality data at the country-level, and
Amanda Henley (Davis Library Research & Instructional Services, UNC –
Chapel Hill) for facilitating our access to LandScan 2011 Global Population
Dataset. The work of Daniel Bergmann and Philip Cameron-Smith was funded by the US Dept. of Energy (BER),
performed under the auspices of LLNL under Contract DE-AC52-07NA27344 and
used the supercomputing resources of NERSC under contract no.
DE-AC02-05CH11231. Ruth Doherty, Ian MacKenzie and David Stevenson
acknowledge ARCHER supercomputing resources and funding under the UK Natural
Environment Research Council grant NE/I008063/1. Guang Zeng acknowledges the NZ
eScience Infrastructure, which is funded jointly by NeSI's collaborator
institutions and through the MBIE's Research Infrastructure
programme.Edited by: C. H. Song
Reviewed by: three anonymous referees
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