© 2012 Macmillan Publishers Limited. All rights reserved.
LETTERS
PUBLISHED ONLINE: 12 AUGUST 2012 | DOI: 10.1038/NCLIMATE1658
El Niño and health risks from landscape fire
emissions in southeast Asia
Miriam E. Marlier
1
*
, Ruth S. DeFries
2
, Apostolos Voulgarakis
3†
, Patrick L. Kinney
4
,
James T. Randerson
5
, Drew T. Shindell
3
, Yang Chen
5
and Greg Faluvegi
3
Emissions from landscape fires affect both climate and air
quality
1
. Here, we combine satellite-derived fire estimates and
atmospheric modelling to quantify health effects from fire
emissions in southeast Asia from 1997 to 2006. This region has
large interannual variability in fire activity owing to coupling
between El Niño-induced droughts and anthropogenic land-use
change
2,3
. We show that during strong El Niño years, fires
contribute up to 200 µg m
−3
and 50 ppb in annual average fine
particulate matter (PM
2.5
) and ozone surface concentrations
near fire sources, respectively. This corresponds to a fire con-
tribution of 200 additional days per year that exceed the World
Health Organization 50 µg m
−3
24-hr PM
2.5
interim target
4
and
an estimated 10,800 (6,800–14,300)-person (∼2%) annual
increase in regional adult cardiovascular mortality. Our results
indicate that reducing regional deforestation and degradation
fires would improve public health along with widely established
benefits from reducing carbon emissions, preserving biodiver-
sity and maintaining ecosystem services.
Fires are pervasive instruments of land management in the
tropics for clearing debris in the process of deforestation and
agricultural management. These fires enable an economical and
effective method for expanding and maintaining agricultural
production, but release gases (including O
3
precursors) and
aerosols (mostly black and organic carbon) that interact with the
climate system
5
, degrade surface air quality
1
and jeopardize public
health
6
. Fires associated with deforestation emitted ∼1.0 Pg C yr
−1
from 2000 to 2010 (ref. 7), with considerable interannual variability
from droughts in tropical forests
8
. Fires also contribute to
PM
2.5
and O
3
increases, which are both detrimental to public
health
1,4
. Projections of greater fire activity in a warming climate
9
suggest increasing contributions to atmospheric concentrations and
population exposure.
Globally, most fires occur in Africa and South America
8
, but
recent studies have highlighted the importance of southeast Asia
because of high population densities near high fire activity
10
.
Regional emissions may differ by a factor of 50 between opposite
phases of the El Niño–Southern Oscillation. In the Global Fire
Emissions Database version 3 (GFED3), regional fire emissions
were 1,069 Tg C during the 1997 El Niño but only 21 Tg C
during the 2000 La Niña
8
, illustrating the nonlinearity between
fires and drought
11
. During the warm phase of El Niño–Southern
Oscillation and the Indian Ocean Dipole, cool sea surface
temperature anomalies near Indonesia decrease regional rainfall
2,12
.
Landowners ignite fires to clear land and manage agricultural
areas
3
and, although typically too wet to combust, deforestation
1
Department of Earth and Environmental Sciences, Lamont-Doherty Earth Observatory of Columbia University, Palisades, New York 10964, USA,
2
Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, New York 10027, USA,
3
NASA Goddard Institute for Space
Studies and Columbia University, New York, New York 10025, USA,
4
Mailman School of Public Health, Columbia University, New York, New York 10032,
USA,
5
Department of Earth System Science, University of California, Irvine, California, 92697, USA.
†
Present address: Department of Physics, Imperial
College London, London, SW7 2AZ, UK. *e-mail: marlier@ldeo.columbia.edu.
100° E 110° E 120° E
Longitude
130° E 140° E
0 10 50 75 100 500 750 1,000 5,000 7,500 10,000
10° N
0°
100° E 110° E 120° E 130° E 140° E
10° N
0°
500 75025010075502510510
Longitude
a
b
LatitudeLatitude
Figure 1 | Study area population and locations of fire activity. a, 2005
population density, in persons per km
2
, for countries belonging to the
ASEAN. Data from CIESIN GPWv3 (ref. 29) at 0.25
◦
resolution.
b, 1997–2006 mean fire emissions, in g C m
2
per month at 0.5
◦
resolution,
from the GFED3 (ref. 8).
and degradation have enhanced the susceptibility of peatland
forests (with carbon-rich peat deposits) to human-ignited fire
during droughts
13
.
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LETTERS
NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE1658
Annual concentration (µg m
¬3
)
Annual concentration (ppb)
Exceedances (days)
Exceedances (days)
100 110 120 130
100 110 120 130
10
0
¬10
10
0
¬10
100 110 120 130
100 110 120 130
10
0
¬10
100 110 120 130
100 110 120 130
10
0
¬10
10
0
¬10
100 110 120 130
100 110 120 130
10
0
¬10
10
0
¬10
10
0
¬10
0 0.2 0.5 1 2 5 10 20 50 100 200
0 1 2 5 10 15 20 50 150100 200
0 0.25 0.5 1 2.5 5 7.5 10 25 50 75
GISS G-C
0 1 2 5 10 15 20 50 150100 200
a
b
Figure 2 | Modelled annual mean surface concentrations and corresponding additional daily exceedances in 1997 owing to fires only. a,b, PM
2.5
(a) and
O
3
(b). Annual concentrations (in µm
−3
PM
2.5
and ppb O
3
, respectively) and daily exceedances over WHO interim targets (50 µg m
−3
daily PM
2.5
(IT-2)
and 80 ppb 8-hr maximum O3 (IT-1)). Annual concentrations are from 24-hr PM
2.5
and 8-hr maximum O
3
. GISS refers to GISS-E2-PUCCINI and G-C
refers to GEOS-Chem.
PM
2.5
and O
3
exposure increases hospital admissions and
mortality from respiratory and cardiovascular diseases, even at low
concentrations
4
. During the 1997–1998 fires in southeast Asia, daily
ground-level particulate-matter concentrations reached hazardous
levels
6
, with concomitant negative impacts on respiratory and
general health
14
. Increases in respiratory illnesses were also
reported in Singapore from transported emissions
15
. Although
these studies offer some information on the health effects of fires,
they have been confined to specific locations or time periods by
limited data availability.
We expand on these local studies by using satellite data and two
atmospheric models, the National Aeronautics and Space Adminis-
tration (NASA) GISS-E2-PUCCINI general circulation model and
Harvard University’s GEOS-Chem chemical transport model, to
estimate pollutant concentrations and corresponding regional mor-
tality from 1997 to 2006, applying existing concentration–response
functions from the epidemiological literature (see Methods).
Atmospheric models simulate the transport of fire emissions and
formation of pollutants, offer a continuous spatiotemporal data
set in a region with limited ground monitoring but large rural
populations and allow us to examine how climate and emissions
influence aerosol and trace-gas concentrations interannually.
Our study region is a 50
◦
× 30
◦
area (92.5
◦
E–142.5
◦
E,
20
◦
N–10
◦
S) encompassing the Association of Southeast Asian
Nations (ASEAN). In 2005, the population was approximately 540
million (Fig. 1a). Fire activity, predominately in the Indonesian
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LETTERS
a
c
1998 2000 2002
Year Year
Year Year
2004 2006
Fires-on 24-hr PM
Fires-off 24-hr PM
Population exposure (person-days)
1998 2000 2002 2004 2006
Fires-on annual PM
Fires-off annual PM
Fires-on 8-hr max O
3
Fires-off 8-hr max O
3
Population exposure (person-days)
10
6
10
7
10
8
10
9
10
10
10
11
¬1
0
1
2
3
4
5
6
10
7
10
8
10
9
10
10
10
11
1998 2000 2002 2004 2006
24-hr PM
Annual PM
8-hr max O
3
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1998 2000 2002 2004 2006
Population exposure (person-years × 10
7
)
Exposure fraction from fires
b
d
Figure 3 | Population exposure above WHO interim targets. a, Exposure over 50 µg m
−3
24-hr PM
2.5
interim target (IT-2). b, Exposure over 25 µg m
−3
annual PM
2.5
interim target (IT-2). c, Exposure over 80 ppb 8-hr maximum O
3
interim target (IT-1). d, Fraction of population exposure above each WHO
interim target that is attributable to fires. Each case is calculated with and without GFED3 fire emissions using GISS-E2-PUCCINI results, which was close
to the average concentration estimate. Refer to Supplementary Table S1 for estimated health effects. PM, particulate matter. Note the logarithmic scale for
a and c.
islands of Sumatra and Borneo (Fig. 1b), peaks during the
dry southern monsoon of September and October, along with
potential spring burning
2,6
.
The additional contribution of fires to annual surface PM
2.5
and O
3
concentrations in 1997, a strong El Niño year, greatly
increases the number of days that exceeded the World Health
Organization (WHO) interim targets of 50 µg m
−3
24-hr PM
2.5
(IT-2) and 80 ppb 8-hr maximum O
3
(IT-1), which are both twice
the WHO’s air-quality guidelines (Fig. 2 and Supplementary Table
S1). In 1997, both models show two distinct areas of fire-derived
PM
2.5
over Sumatra and Borneo with concentrations increased by
50–200 µg m
−3
and with increases of 50–150 days over the WHO
interim targets. O
3
, in contrast, had widely distributed increases
of 25–50 ppb and up to 150 exceedance days. Corresponding
results with all sources in 1997 are in Supplementary Fig. S1, this
simulation captured the general temporal evolution seen in ground
observations (Supplementary Figs. S2–S4 and Table S2).
We explored how modelled concentrations with and without
fire emissions affect population exposure to WHO interim targets
(Supplementary Table S1)
4
. Decadal exposure over these interim
targets, along with the fraction of exposure owing to fire, shows how
the major influence of fires was not confined to the 1997–1998 El
Niño (Fig. 3). Interannual variability in exposure for both short-
and long-term guidelines is dominated by the fire contribution of
PM
2.5
and O
3
; the WHO’s 25 µg m
−3
annual PM
2.5
interim target
(IT-2) is never exceeded without including fire emissions.
We also tested the sensitivity of regional health impacts,
including exceedances and cardiovascular disease mortality,
to using the original model or satellite-scaled model PM
2.5
estimates (Supplementary Fig. S5). The mortality estimates
combine modelled pollutant-concentration changes from fires with
published epidemiological relationships between exposure to O
3
or PM
2.5
total mass and cause-specific mortality (see Methods). In
Table 1, 1997 and 2000 highlight the considerable differences in
health effects between years with high and low fire contributions.
For example, PM
2.5
annual exposure in 2000 hardly exceeds
the WHO interim target and O
3
exposure is 100 times lower
than in 1997. During high fire years, fire emissions increase the
adult cardiovascular disease mortality burden by approximately
10,800 (6,800–14,300) annual deaths from PM
2.5
exposure and an
additional 4,100 (2,300–5,900) annual deaths from O
3
.
Modelled annual adult cardiovascular disease mortality shows
a strong correlation with the multivariate El Niño–Southern
Oscillation index
16
, which was averaged over the July–October
dry season (Fig. 4). We present the most conservative mortality
estimates, but this relationship holds with varying relative risk
relationships or durations of exposure (Supplementary Fig. S6).
Reduced convection during El Niño years probably increases
exposure by increasing emissions
5,11
and increasing aerosol lifetimes
by reducing wet deposition.
Uncertainties in our health-effect estimates come primarily
from: the fire emissions data set, atmospheric modelling and
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LETTERS
NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE1658
Table 1 | Fires-only concentration, exposure and mortality using different models for an El Niño (1997) and La Niña (2000).
PM
2.5
Concentration Exposure above IT-2 Mortality
(µg m
−3
) (×10
6
person-years) (×10
3
people)
1997 2000 1997 2000 1997 2000
GISS 7.8 0.3 55.6 0.0 9.9 (8.0–11.4) 1.0 (0.8–1.2)
G-C 3.7 0.2 25.8 0.0 8.7 (6.8–10.7) 1.5 (1.1–1.9)
GISS MISR 10.7 0.4 57.0 0.0 11.2 (9.6–13.5) 1.3 (1.0–1.6)
G-C MISR 7.4 0.5 59.1 4.7 10.1 (8.1–11.8) 1.7 (1.4–2.1)
GISS MODIS 12.0 0.4 66.6 0.0 12.5 (10.1–14.2) 1.5 (1.1–1.8)
G-C MODIS 8.3 0.5 50.3 0.0 12.1 (9.7–14.3) 2.3 (1.8–2.8)
Average 8.3 0.4 52.4 0.8 10.8 (6.8–14.3)* 1.6(0.8–2.8)*
O
3
Concentration Exposure above IT-1 Mortality
(ppb) (×10
7
person-days) (×10
3
people)
1997 2000 1997 2000 1997 2000
GISS 9.0 1.4 395 3.5 4.3 (2.6–5.9) 1.0 (0.6–1.4)
G-C 7.1 1.3 346 0.0 3.8 (2.3–5.2) 1.0 (0.6–1.4)
Average 8.0 1.4 371 1.8 4.1 (2.3–5.9)* 1.0 (0.6–1.4)*
Average ASEAN annual concentration owing to fires only (from 24-hr PM
2.5
and 8-hr maximum O
3
); additional exposure owing to fires above the annual 25 µg m
−3
PM
2.5
interim target (IT-2; ×10
6
person-years) and above the 80 ppb 8-hr maximum O
3
interim target (IT-1; ×10
7
person-days); cardiovascular mortality owing to fires only (×10
3
people), with the range from 95% confidence intervals
from epidemiological studies. GISS refers to GISS-E2-PUCCINI and G-C refers to GEOS-Chem, also with satellite scaling factors. *Maximum error range.
concentration–response equations. First, a previous study
8
estimated fire carbon emissions uncertainty at 20% globally,
though higher in equatorial Asia owing to peat carbon stock
uncertainties and in years before Moderate Resolution Imaging
Spectoradiometer (MODIS) data
8
. Second, although the lack
of ground stations precludes an in-depth evaluation, available
ground data and satellite aerosol optical depth (AOD) indicate
that both models are probably conservative (Supplementary Fig.
S3–S5). The range between the two model scenarios (PM
2.5
and O
3
) and two satellite AOD optimized results (PM
2.5
only)
provides some insight about uncertainty related to transport and
deposition processes. There is up to a factor of two difference
between models (less among satellite-optimized estimates), but
this range is expected given previous findings that model physics
and parameterizations drive more variation in aerosols than
emissions
17
. Differences in our PM
2.5
concentrations are primarily
driven by lower precipitation and wet deposition in the GISS model,
which increase aerosol lifetime relative to GEOS-Chem (data not
shown). However, for the purposes of health impacts the results are
much closer (Table 1). This is owing to the nonlinear relationship
between the relative risk and exposure, which reduces differences
between mortality estimates at high concentrations. Finally, we
address mortality equation uncertainties through 95% confidence
intervals around the concentration–response estimates (Table 1)
and various estimates of the relative risk and PM
2.5
-exposure
relationship (Supplementary Tables S3 and S4 and Fig. S6).
Additional epidemiological factors that we did not address are
extrapolation of relative risk equations to high concentrations and
applying equations developed in the USA to non-US populations.
Furthermore, evidence for potential differences in PM
2.5
toxicity
between urban pollution in US cities and southeast Asian fire
emissions is too limited to warrant using separate epidemiological
equations
18
, so we assume that total PM
2.5
mass is the most
appropriate metric.
These uncertainties and additional factors contribute to our
substantially lower regional PM
2.5
mortality estimates relative to
the global analysis of a previous study
10
. The two estimates are not
PM CVD GISS
PM CVD G–C
O
3
CVD GISS
O
3
CVD G–C
July¬Oct. MEI
Cardiovascular disease mortality
Multivariate El Niño index
97 98 99 00 01 02
Year
03 04 05 06
¬2
¬1
0
1
2
3
0
2,000
4,000
6,000
8,000
10,000
Figure 4 | Estimated additional annual cardiovascular disease mortality
from exposure to fire-contributed annual PM
2.5
and 24-hr O
3
, along with
the multivariate El Niño index
16
. Results for 1997–2006 are from the
baseline GISS-E2-PUCCINI and GEOS-Chem concentrations, with the
power-law relative risk relationship for cardiovascular disease mortality.
R
2
= 0.87–0.91 for PM
2.5
and R
2
= 0.82–0.89 for O
3
. AOD-scaled results
and sensitivity analysis are in Table 1, Supplementary Table S4 and Fig. S6.
CVD, cardiovascular disease; MEI, multivariate El Niño–Southern
Oscillation index.
directly comparable. Our conservative estimates (Supplementary
Table S5) are based on a tailored regional analysis for ASEAN
countries and use updated fire emissions, two atmospheric models,
epidemiological equations developed over a wide concentration
range and cause-specific disease estimates (Supplementary Table
S6). We did not include children as the epidemiological equations
were developed for adults over 30 years; this cuts out more than half
of the population and ignores risks to infants and children.
Although previous work in Borneo has emphasized the value
of avoided deforestation in terms of carbon emissions
19
, it is also
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NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE1658
LETTERS
important to account for health. By demonstrating the direct link
between climate variability and health impacts from fire emissions
throughout southeast Asia, we offer additional support for policies
that use regional climate forecasts to restrict burning during high-
fire-risk seasons. Fire emissions during 1997 to 2006 repeatedly
exposed 1–11% of the population in southeast Asia to PM
2.5
and
O
3
above WHO interim targets during El Niño years. Although the
regional influence of climate change is uncertain
20
, these observed
trends would be exacerbated by the potential for more frequent
droughts related to El Niño and increased baseline cardiovascular
disease caused by demographic shifts towards sedentary lifestyles
and increased animal-product consumption. Reducing fires from
deforestation and land management would benefit public health in
addition to global scale benefits for carbon storage and biodiversity.
Methods
Fire-emissions estimates are from GFED3, a global gridded monthly emissions data
set that combines surface reflectance and active fire-detection data from several
satellites to detect the spatiotemporal variability of burned area
21
. This drives a
biogeochemical model that estimates fuel loads, combustion completeness and
emissions
8
. GFED3 is available since 1997 at 0.5
◦
× 0.5
◦
resolution. We define
landscape fires to include all burning sources; in southeast Asia this includes peat,
forest, agricultural waste burning, deforestation and degradation.
We use two models: the NASA GISS-E2-PUCCINI general circulation model
from 1997 to 2007 and Harvard University’s GEOS-Chem chemical transport
model
22
from 1997 to 2006. See Supplementary Information for descriptions of the
models, spin-up and boundary conditions. Both were run at 2
◦
× 2.5
◦
resolution,
including a control run without fire emissions and a perturbed run with GFED3
emissions. We define years from 1 July to 30 June to avoid splitting a burning
season into two years. As meteorological fields for GEOS-Chem are available to
December 2006, we only have a complete 2006 fire year from GISS. For PM
2.5
, we
analysed 24-hr and annual average concentrations. For O
3
, we used 13:00–14:00
concentrations as a proxy for the 8-hr maximum (Supplementary Fig. S4) and
24-hr concentrations for mortality calculations.
AOD data from the Multi-angle Imaging Spectroradiometer (MISR) and
MODIS satellite instruments, available from 2001 to 2006, were used to scale
modelled AOD; these scaling factors were then applied to modelled PM
2.5
(Supplementary Fig. S5). Although AOD represents total column aerosol loading, it
is often closely related to surface abundance
23
and hence provides some measure of
large-scale biases in the models. Scaling factors were applied to surface PM
2.5
for all
grid boxes, maintaining the modelled spatial and temporal distribution of aerosols.
We evaluate health effects by estimating: exposure above WHO short-term
and annual air quality targets, and cause-specific adult mortality. Mortality
attributable to fires combines the relative risk from changes in pollutant exposure
with baseline observed mortality rates. We focus on cardiovascular disease because
it is a proximal outcome from exposure that will be experienced annually. However,
this underestimates total mortality owing to other long-term effects and short-term
exposure. The equations that we use were developed for adults (less than half of
the regional population
24
).
We applied a power-law relationship between relative risk (RR) and PM
2.5
.
Owing to the lack of data on differential health effects of biomass smoke particles
18
,
we use an equation developed for total PM
2.5
mass:
Cardiovascular RR = 1 + α(I×C)
β
(1)
which describes the relationship between PM
2.5
exposure and cardiovascular disease
mortality risk over a large concentration range
25
. A previous study
25
published
values for α and β by reanalysing previous estimates of RR and dose of PM
2.5
(in
mg) from ambient air pollution, second-hand smoke and cigarette smoke. For
cardiovascular disease, α = 0.2685 and β = 0.2730. Although ambient PM
2.5
concen-
trations from fires will not reach the cigarette smoke doses included in ref. 25, this
equation was essential owing to our high ambient concentrations above the range of
other studies. As 95% confidence intervals were given for each individual study but
not the overall relationship, we refit a power-law relationship to approximate the
uncertainty based on the individual studies’ upper or lower limits, respectively. The
annual average of 24-hr total mass PM
2.5
concentrations were used for (C), assuming
a constant average inhalation rate (I) of 18 m
3
d
−1
to convert to PM
2.5
dose (in mg;
ref. 25). We separately calculated the RR using concentrations with and without
fires owing to the equation’s nonlinearity. We then followed the approach of ref. 26
to calculate the attributable fraction (AF) and annual mortality (1M
annual
):
AF = (RR− 1)/RR (2)
1M
annual
= M
b
× P× (AF
fire
− AF
nofire
) (3)
where equation (3) combines the results for equations (1) and (2), along with
the average annual baseline mortality rate (M
b
), which was calculated from adult
deaths owing to cardiovascular disease, averaged over the countries in ASEAN
(ref. 27). Population (P) with ages greater than 30 years was from the United
Nations Population Division
24
and CIESIN’s Gridded Population of the World
version 3 and Future Estimates, aggregated to the model resolution
28,29
; both were
interpolated from five-yearly data to annual estimates.
For O
3
, the linear RR is given by:
Cardiovascular RR = exp[δ(C
fire
− C
nofire
)] (4)
where δ = 1.11 (0.68–1.53) is the per cent increase in cardiovascular disease
morality per 10 ppb increase in 24-hr O
3
concentrations, based on a meta-analysis
of US and non-US studies
30
. Daily mortality owing to fire pollution is then
estimated with equation (5):
1M
daily
= (M
b
/365)× P× (AF
fire
− AF
nofire
) (5)
using equations (4) and (2), along with the population characteristics described
above. We assume that mortality is evenly spread throughout the year (M
b
is
not year-specific so we divide consistently by 365) and sum by days per year
to obtain annual estimates. GEOS-Chem includes leap years, but GISS uses a
fixed 365-day calendar. A previous study
30
concluded that the O
3
mortality
burden was insensitive to particulate matter
30
, indicating that this is separate
from PM
2.5
mortality.
Received 13 March 2012; accepted 10 July 2012; published online
12 August 2012
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