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Fires, Smoke Exposure, and Public Health: An Integrative Framework to Maximize Health Benefits From Peatland Restoration


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Abstract Emissions of particulate matter from fires associated with land management practices in Indonesia contribute to regional air pollution and mortality. We assess the public health benefits in Indonesia, Malaysia, and Singapore from policies to reduce fires by integrating information on fire emissions, atmospheric transport patterns, and population exposure to fine particulate matter (PM2.5). We use adjoint sensitivities to relate fire emissions to PM2.5 for a range of meteorological conditions and find that a Business‐As‐Usual scenario of land use change leads, on average, to 36,000 excess deaths per year into the foreseeable future (the next several decades) across the region. These deaths are largely preventable with fire reduction strategies, such as blocking fires in peatlands, industrial concessions, or protected areas, which reduce the health burden by 66, 45, and 14%, respectively. The effectiveness of these different strategies in mitigating human health impacts depends on the location of fires relative to the population distribution. For example, protecting peatlands through eliminating all fires on such lands would prevent on average 24,000 excess deaths per year into the foreseeable future across the region because, in addition to storing large amounts of fuel, many peatlands are located directly upwind of densely populated areas. We also demonstrate how this framework can be used to prioritize restoration locations for the Indonesian Peatland Restoration Agency based on their ability to reduce pollution exposure and health burden. This scientific framework is publicly available through an online decision support tool that allows stakeholders to readily determine the public health benefits of different land management strategies.
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Fires, Smoke Exposure, and Public Health: An Integrative
Framework to Maximize Health Benets
From Peatland Restoration
Miriam E. Marlier
, Tianjia Liu
, Karen Yu
, Jonathan J. Buonocore
Shannon N. Koplitz
, Ruth S. DeFries
, Loretta J. Mickley
, Daniel J. Jacob
, Joel Schwartz
Budi S. Wardhana
, and Samuel S. Myers
The RAND Corporation, Santa Monica, CA, USA,
Department of Ecology, Evolution, and Environmental Biology,
Columbia University, New York, NY, USA,
Department of Earth and Planetary Sciences, Harvard University, Cambridge,
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA,
Center for Climate,
Health, and the Global Environment, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA,
Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA,
Badan Restorasi Gambut, Jakarta,
Harvard University Center for the Environment, Harvard University, Cambridge, MA, USA
Abstract Emissions of particulate matter from res associated with land management practices in
Indonesia contribute to regional air pollution and mortality. We assess the public health benets in
Indonesia, Malaysia, and Singapore from policies to reduce res by integrating information on re
emissions, atmospheric transport patterns, and population exposure to ne particulate matter (PM
). We
use adjoint sensitivities to relate re emissions to PM
for a range of meteorological conditions and nd that
a BusinessAsUsual scenario of land use change leads, on average, to 36,000 excess deaths per year into
the foreseeable future (the next several decades) across the region. These deaths are largely preventable with
re reduction strategies, such as blocking res in peatlands, industrial concessions, or protected areas,
which reduce the health burden by 66, 45, and 14%, respectively. The effectiveness of these different
strategies in mitigating human health impacts depends on the location of res relative to the population
distribution. For example, protecting peatlands through eliminating all res on such lands would prevent on
average 24,000 excess deaths per year into the foreseeable future across the region because, in addition to
storing large amounts of fuel, many peatlands are located directly upwind of densely populated areas. We
also demonstrate how this framework can be used to prioritize restoration locations for the Indonesian
Peatland Restoration Agency based on their ability to reduce pollution exposure and health burden. This
scientic framework is publicly available through an online decision support tool that allows stakeholders to
readily determine the public health benets of different land management strategies.
Plain Language Summary Regularly occurring res in Indonesia are associated with drought
conditions and agricultural practices. These res contribute to dangerous levels of particulate matter
pollution that are harmful to public health throughout the region. We develop an interdisciplinary scientic
framework to connect land use decisions with re activity and public health outcomes. Our estimates nd
that if current trends continue, exposure to air pollution from Indonesian res would cause, on average,
36,000 excess deaths per year across Indonesia, Malaysia, and Singapore. These deaths can be prevented
through land management strategies, which would reduce the number of deaths attributable to air pollution
from res. Current plans to restore peatlands would reduce regional re emissionsrelated mortality by
approximately 66% if fully implemented.
1. Introduction
Over the past several decades, biomass burning in Indonesia has become a substantial contributor to glo-
bal re emissions (van der Werf et al., 2017) and health impacts due to regional air pollution (Johnston
et al., 2012; Koplitz et al., 2016; Marlier et al., 2013). Most recently, the severe haze that blanketed equa-
torial Asia during 2015 was a stark reminder of the climate change and public health consequences asso-
ciated with land management activities in Indonesia. The res, which peaked during September and
October of 2015, released CO
emissions comparable to Japan or India's annual fossil fuel emissions
(Field et al., 2016), exposed more than 69 million people to unhealthy air (Crippa et al., 2016), and
©2019. The Authors.
This is an open access article under the
terms of the Creative Commons
Attribution License, which permits use,
distribution and reproduction in any
medium, provided the original work is
properly cited.
Key Points:
Current trends in Indonesia's re
activity would lead to 37,000 excess
deaths per year across the region
from air pollution exposure
Restoring peatlands could reduce
rerelated mortality by nearly 70%
Our integrative scientic tool can be
used to support land management
strategies most benecial for public
Supporting Information:
Supporting Information S1
Correspondence to:
S. S. Myers,
Marlier, M. E., Liu, T., Yu, K.,
Buonocore, J. J., Koplitz, S. N., DeFries,
R. S., et al. (2019). Fires, smoke
exposure, and public health: An
integrative framework to maximize
health benets from peatland
restoration. GeoHealth,3,178189.
Received 13 FEB 2019
Accepted 4 JUN 2019
Accepted article online 24 JUL 2019
Published online 24 JUL 2019
The copyright line for this article was
changed on 7 AUG 2019 after original
online publication.
cost more than $16 billion USD, without considering longterm health or ecosystem impacts (World Bank
Group, 2016).
In this study, we present a novel approach that integrates information on the drivers of re emissions in
Indonesia (Marlier, DeFries, Kim, Gaveau, et al., 2015; Marlier, DeFries, Kim, Koplitz, et al., 2015;
Marlier, DeFries, Pennington, Nelson, et al., 2015), the transport of smoke to downwind regional popula-
tion centers (Kim et al., 2015), and the resulting population exposure to air pollution (Koplitz et al., 2016)
in order to quantify the health impacts of different land management scenarios. We apply this frame-
work to prioritize locations for peatland restoration sites to reduce population exposure to re emissions
in Indonesia, Malaysia, and Singapore and to develop an online decision support tool to evaluate the ef-
cacy of other potential policy scenarios. This integrative framework is applicable to reprone regions
around the world to develop strategies that reduce negative health outcomes associated with
biomass burning.
Indonesia's re activity depends on complex interactions between land use and land cover (LULC) change,
the extent of burning on carbonrich peatlands, and meteorological variability. Indonesia's LULC has rapidly
changed over the past few decades (Figure 1). Satellite observations reveal substantial forest loss and degra-
dation across the islands of Sumatra and Kalimantan (Indonesian Borneo) in particular (Margono et al.,
2014). Although industrialscale plantations account for nearly half of Indonesia's deforestation (Abood
et al., 2015), monitoring forest degradation, such as logging, is also important because roughly 80% of
degraded forests are eventually cleared (Miettinen et al., 2012). In addition, the magnitude of re emissions
depends on burning in fuelrich peatlands. Peatlands are naturally protected from re by a high water table
Figure 1. (a) Distribution of 2005 land use and land cover across Indonesia (Hansen et al., 2013; Margono et al., 2014), including intact primary forest, degraded
primary forest, nonforested areas, and combined tree plantations and secondary forest. (b) Areas of change from 2005 to 2010 (light shades) and stable areas that did
not change (dark shades) in peatland and nonpeatland areas.
but become highly ammable following drainage and degradation (Wösten et al., 2008). Nearly 80% of tro-
pical peatlands are located in Indonesia (Page et al., 2011).
Fire activity also varies with meteorology. Higher re activity typically occurs during droughts associated
with El Niño (van der Werf et al., 2008), but recent re events in nonEl Niño years indicate that localized,
intense res are associated with other meteorological mechanisms (Gaveau et al., 2014; Koplitz et al., 2018).
There is a nonlinear sensitivity of res to drought conditions, particularly below 4mm/day precipitation
(Field et al., 2016), and anthropogenic inuences have increased the susceptibility of the landscape to res
during droughts (Field et al., 2009). Although undisturbed forests rarely burn, large inadvertent re events
(escaped res) associated with combined anthropogenic and drought inuences have been observed since
the 1960s in Sumatra and 1980s in Kalimantan (Field et al., 2009).
Fires emit organic and black carbon (OC and BC) aerosols (in addition to trace gases) that are the primary
components of to smokerelated surface particulate matter concentrations (Koplitz et al., 2016). Although
there are a limited number of epidemiological observational studies focused on health outcomes from re
pollution in equatorial Asia (Ramakreshnan et al., 2018), at a global scale, exposure to ne particulate matter
(particles with diameter <2.5 μm, PM
) from res is associated with allcause mortality, respiratory symp-
toms, and cardiovascular outcomes (Reid et al., 2016), including serious health impacts for children (Rees,
2016). Public health impacts of res depend on the magnitude of emissions, atmospheric transport patterns,
spatial proximity to population centers, and underlying population characteristics. Fire activity and conse-
quent health impacts vary with meteorological conditions, with a roughly sevenfold increase in estimated
mortality observed between opposite phases of the El NiñoSouthern Oscillation (Johnston et al., 2012;
Marlier et al., 2013). Atmospheric modeling studies suggest that exceedances above World Health
Organization air quality guidelines across the region are largely due to the contribution of Indonesian res
(Marlier et al., 2013). Singapore, for example, is repeatedly affected by re emissions from peatlands in
Sumatra and, to a lesser extent, in Kalimantan (Kim et al., 2015; Marlier, DeFries, Kim, Gaveau, et al.,
2015; Reddington et al., 2014).
Potential policy interventions to reduce res include the Indonesian government's moratorium on granting
new industrial plantation concessions in peatlands or primary forests and programs such as Reducing
Emissions from Deforestation and Degradation (REDD+). The Peatland Restoration Agency (Badan
Restorasi Gambut, BRG) was recently established following the 2015 haze event. BRG's mission is to restore
the hydrology of at least 2 million hectares of damaged peatlands in Sumatra, Kalimantan, and Papua over a
5year period ( Other policy interventions include supply chain certication schemes, such
as the Roundtable on Sustainable Palm Oil. Finally, policies focused on crossborder health impacts include
Singapore's Transboundary Haze Pollution Act, which holds plantation companies liable for proven contri-
butions to transboundary haze (Lee et al., 2016; Tan, 2015).
There is growing consensus on the need for government support of refree land management alternatives
such as mechanical clearing or wet peat soil cultivation in order to avoid the public health impacts of res
(Carmenta et al., 2017). In this study, we prioritize locations for reducing future res in order to reduce
cumulative downwind exposure to smoke PM
from 2020 to 2030, based on future scenarios of LULC
and land management. Our interdisciplinary scientic framework to evaluate the public health implica-
tions of potential land use decisions in Indonesia is publicly available through an online decision
support tool.
2. Materials and Methods
2.1. Overview
Our modeling framework consisted of the following steps (Figure S1 in the supporting information): (1)
quantify trends in past LULC, (2) estimate relationships between observed re emissions and past LULC,
(3) develop a spatially explicit BAU scenario of future trends in LULC and associated re emissions (from
Step 2), (4) calculate the sensitivities of different receptor locations to the location of re emissions, (5) quan-
tify future exposure to air pollution from res, and (6) estimate populationlevel future health outcomes. We
use the BAU future scenario as a baseline case against which to compare multiple spatially explicit re
reduction strategies.
2.2. Land Use and Land Cover
The LULC classication was based on the Margono et al. (2014) data set for 2005 and 2010, which mapped
primary intact forests, primary degraded forests (subject to forest utilization, such as logging), and nonfor-
ested areas. We aggregated the original 30m resolution to 1km resolution. We also made several modica-
tions. First, within the large nonforested areas (67% and 44% of total area in Sumatra and Kalimantan in
2005), we incorporated the Hansen et al. (2013) Global Forest Change (v1.4) data set to further allocate these
areas into areas with tree cover (tree plantations or regrowth) and without tree cover (shrubland, agriculture,
or cleared areas). Hansen et al. (2013) mapped annual forest change but did not distinguish between primary
(intact or degraded) forests, secondary forests, or tree plantations, so by combining with Margono et al.
(2014), we could separate an aggregated tree plantation and secondary forest class from other nonforested
areas. Second, we separated peatlands, which have a high re emissions potential, from nonpeatland areas
for all LULC types (Ministry of Agriculture, 2015; Figure 1).
We used Dinamica EGO Version 3.0.17 (SoaresFilho et al., 2009) to simulate future LULC. Dinamica EGO is
a spatially explicit model that uses a Bayesian Weights of Evidence approach to calculate the effect of differ-
ent spatial variables on a given LULC transition and estimate the spatial probabilities of a given transition.
Input data sets were static variables for landform type (Margono et al., 2014), soil type (FAO, IIASA, ISRIC,
ISSACAS, JRC, 2012), elevation (Jarvis et al., 2008), slope, protected areas (World Resources Institute,
2017a), oil palm, logging, and wood ber industrial concessions (World Resources Institute, 2017b, 2017c,
2017e), population density (CIESIN, 2016), distance to roads (CIESIN, 2013), distance to rivers (https://, and distance to oil palm mills (World Resources Institute, 2017d), as well as
dynamic variables that were updated at each model time step and represented the distance to each indivi-
dual LULC class. We checked for multicollinearity of all input data sets for a variance ination factor <5.
Spatial patterns of change were calibrated with observed behavior from 2005 to 2010.
Using the 2005 and 2010 maps as a training period (Figure S2), we simulated a BAU scenario in 5year incre-
ments from 2010 to 2030 (Figure S3). The BAU scenario assumes that observed 20052010 trends continue in
the future and is used as our baseline estimate from which we explore the impact of multiple re reduction
strategies. In 2005, 40% of peatlands and 31% of nonpeatlands in Sumatra were covered by intact or degraded
primary forests; by 2010 this had declined to 33% and 30%, respectively (Figures 1 and S3). In Kalimantan,
intact and degraded forest coverage declined from 49% to 45% in peatlands and 57% to 56% in nonpeatlands
from 2005 to 2010; much of the remaining forest is located in mountainous areas that are difcult to access.
2.3. Fire Emissions
We downscaled 0.25° × 0.25° emissions estimates from the Global Fire Emissions Database version 4s
(GFED4s; van der Werf et al., 2017) with 1km
MODerate resolution Imaging Spectroradiometer
(MODIS) Collection 6 re radiative power (FRP) observations from the Aqua and Terra satellites (Giglio
et al., 2016). The Collection 6 algorithm retrieves FRP with a radiancebased approach, which decreases
FRP in all but the most intense res and helps to capture large res potentially hidden by smoke and clouds
(Giglio et al., 2016). As the original 0.25° × 0.25° re emissions are too coarse, the downscaled 1km
sions are used to estimate the contribution of individual LULC transitions to monthly emissions while also
retaining meteorological variability from 2005 to 2009.
To produce future re emissions estimates for the BAU scenario over 20102029, we rst calculated scaling
factors, or emissions rates, based on observed monthly re emissions for each LULC transition type over
20052009 for each 0.25° grid cell. Our results are likely somewhat conservative as re emissions in this per-
iod were slightly lower than the 20year average (1.3 Tg OC + BC for 20052009 versus 1.6 Tg OC + BC for
19972016 in Indonesia, for example; van der Werf et al., 2017). In addition, the 2006 El Niño was not as
severe as other El Niño events such as 1997 or 2015 (Koplitz et al., 2016; Marlier et al., 2013).
We also accounted for grid cells with nonzero GFEDv4s emissions and no FRP observations by distributing
emissions according to the ratio of area for each LULC transition type. We then multiplied individual LULC
transition types by the associated monthly emissions per unit area. Fires may occur repeatedly at the same
location, for example, burning associated with agricultural maintenance activities or in degraded areas that
are susceptible to res during drought conditions. In these cases, we apply the average associated emissions.
We also matched the different re types from our 1km
estimates according to GFED4 categories and
applied associated emissions factors for OC and BC (Akagi et al., 2011; van der Werf et al., 2017).
2.4. GEOSChem Adjoint Model
Following Kim et al. (2015) and Koplitz et al. (2016), we used the adjoint of the GEOSChem chemical trans-
port model v80201 (; Bey et al., 2001; Henze et al., 2007) to calculate the sensitivities of
populationweighted PM
concentrations in three countries (Singapore, Malaysia, and Indonesia) to regio-
nal re emissions and their spatiotemporal distributions (hereafter referred to as adjoint sensitivities).
GEOSChem is driven by GEOS5 assimilated meteorology from the NASA Global Modeling and
Assimilation Ofce. Our simulations were conducted at 0.5° × 0.67° horizontal resolution over the nested
East Asia domain (70°150°E, 11°S55°N; Chen et al., 2009). We focused on OC and BC, the primary con-
stituents of smoke aerosol; the GEOSChem OC/BC simulation is described in detail in Wang et al.
(2011). A forward simulation for the high re period of JulyNovember 2006 using modied GFED3 emis-
sions reproduced PM
observations in the Malaysian Peninsula and Borneo with a spatial correlation coef-
cient r= 0.84 (50 sites) and a mean bias of 27% (Kim et al., 2015). Multiplication of the adjoint sensitivities
by the emissions enables us to immediately infer the smoke exposure in the receptor regions for any re
emissions scenario, since the relationship between emissions at the source and smoke exposure at the recep-
tor is assumed to be linear.
For this work, we calculated the adjoint sensitivities given meteorological conditions for the years 2005
2009, yielding a set of monthly mean sensitivity maps spanning these ve years. While meteorology is linked
to droughts and the incidence of res, this approach also allowed us to capture some of the interannual var-
iation in the meteorological processes, such as winds and precipitation, which affect smoke transport to the
receptors. This ensemble of years included the 2006 El Niño, characterized by strong drought conditions in
equatorial Asia. By assuming that the future interannual variability in transport is similar to that in
20052009, we can then multiply the sequence of 20052009 sensitivities by the future BAU emissions
scenario, repeating the sequence every 5 years. In this manner, we derive future exposure to surface PM
at the receptors from re emissions. Validation of modeled PM
concentrations is described in the
supporting information.
2.5. Health Impact Modeling
Population data for 2005 was from the UNadjusted GPW data set (CIESIN, 2016). Countrylevel popula-
tion age structure was backcalculated from the Global Burden of Disease (GBD) project to subset the
gridded population data set to adults 25 years of age and older (Global Burden of Disease Collaborative
Network, 2017). We then used background mortality rates for these countries from the GBD project
(Global Burden of Disease Collaborative Network, 2017) to produce a gridded data set with population
25 years of age and older and background mortality estimates. Our population data do not reect changes
over time as this information was not available at the needed spatial resolution. This likely makes our
estimates somewhat conservative due to expected population increases and demographic shifts, but hold-
ing other factors constant over time, the error is directly proportionate to the change in population within
the age group at risk.
To calculate the excess mortality due to exposure to air pollution from res occurring mainly in the year fol-
lowing exposure, we used the modelcalculated contribution of re emissions to PM
. To represent the rela-
tionship between PM
exposure and mortality risk in adults, we used a function with a slope of 1.03%
increase in annual allcause mortality per 1μg/m
increase in annual average PM
concentrations (95%
CI: 0.971.11%), from Vodonos et al. (2018), a 53study metaanalysis of mortality risk and longterm
exposures. This study used a multivariate linear random effects model and t a nonlinear parametric
function to estimate the slope of the relationship between PM
exposure and mortality risk. While our
model framework does require the use of a linear function, this function represents a linear approximation
of an underlying nonlinear concentration response function that best ts the modeled exposures in this
region. For each scenario, the populationweighted PM
concentrations were matched to this curve to nd
the percentage increase in mortality risk, and then this percentage increase in mortality risk was multiplied
by background mortality rate and the total adult population for each receptor country. This method yielded
the PM
attributable mortality with and without res, while incorporating nonlinearity at high concentra-
tions. To nd the mortality attributable to the res, we calculated the difference between the two
To calculate the health burden in children, we used a method similar that used for adults. We used total
population data from GPW (CIESIN, 2016) and backcalculated the proportion of children that were under
5 years of age from the GBD (Global Burden of Disease Collaborative Network, 2017). To calculate excess
deaths in children under 5, we applied results of a metaanalysis showing an association between PM
exposure and acute lower respiratory infection (ALRI) in children under 5 (Mehta et al., 2011). This analysis
indicated a 12% increase in risk of ALRI (95% CI: 330%) per 10μg/m
increase in annual average ambient
exposure. We produced a concentrationresponse function with a linear 1.2% increase in mortality per
increase in annual average ambient PM
between 0 and 50 μg/m
, and loglinear at exposure levels
above 50 μg/m
, consistent with the concentration response function (CRF) from the GBD at annual ambi-
ent air pollution levels 50 μg/m
2.6. DecisionSupport Tool
The steps above were integrated into an online software tool to support land management decisionmaking.
The tool quanties the impact of blocking re activity in targeted areassuch as peatlands, industrial con-
cessions, protected areas, and planned restoration activities by BRGon air quality and public health
impacts. The online tool also incorporates the exibility for the user to choose from past re emissions years
(2005 to present) and future re emissions (until 2029), variable meteorology (2005 to 2009), and receptor
regions (Indonesia, Malaysia, and Singapore). Users can also design custom scenarios by exploring re
reduction strategies in individual provinces.
3. Results
3.1. Development of Future BusinessAsUsual Scenario
We rst estimated monthly observed emissions for 20052010, matching the temporal (5 years) and spatial
) resolution of the LULC maps (Figure 2). When aggregated to the island or country scale, the largest
proportion of emissions contributions was from stable (nontransitioning) LULC classes, especially the com-
bined tree plantations and secondary forest LULC class, followed by nonforested areas, rather than from
direct deforestation (Figure S4). Fires in peatlands contributed more to total emissions despite having rela-
tively small total areas, for both aggregated (Figure 2) and individual LULC classes (Figure S4). Finally, most
emissions occurred during the months of July to October, and the highest re years were during 2006 and
2009 El Niño conditions, although Sumatra exhibits a small spring burning season as well (Figure S5).
Our BAU scenario of 20102030 LULC projected declines in intact forests and an expansion of the tree plan-
tation and secondary forest class, as well as nonforest (Figure S3). In Sumatra, total forest cover declines
from 31% to 24% from 2010 to 2030 and forest cover within peatlands declines from 33% to 8%. BAU trends
in Kalimantan predict a decline in intact forest cover from 54% to 49% and 45% to 28% in peatland intact for-
ests. Much of this forest clearance on peatlands is due to expansion of both nonforested areas (13 to 29%) and
plantations and secondary forests (54 to 62%) in Sumatra, and nonforested areas in Kalimantan (11 to 27%).
For the analysis of re emissions and health outcomes associated with the BAU LULC scenario, we focused
on the upcoming decade from January 2020 to December 2029. The cumulative emissions over this 10year
period associated with BAU are 12.7Tg OC + BC (Table 1). Across the three receptor regions, this produces
July to October PM
average populationweighted exposures of 6.6 μg/m
in Indonesia, 5.5 μg/m
Malaysia, and 6.0 μg/m
in Singapore and an average of 36,000 excess adult allcause deaths every year
for the 20202029 period. Of these deaths, 92% occur in Indonesia, 7% in Malaysia, and 1% in Singapore.
Total regional mortality varies largely with meteorological conditions, ranging from <100 to 80,000 annual
deaths depending on the year. This exposure is also associated with 1,100 deaths per year for children under
the age of 5 due to ALRI, with 99% of cases in Indonesia (Table S1). While we have projected for the decade
from 2020 to 2030, sociodemographic trends suggest that these numbers are likely to be conservative esti-
mates of health effects for the ensuing several decades.
3.2. Fire Reduction Strategies
Different land management strategies can alter the spatial location and magnitude of re emissions. The
adjoint modeling framework applied here examines where re activity has a disproportionate impact on
air quality on selected receptors downwind (Figure 3). Blocking res in all peatland areas would reduce
July to October OC + BC emissions by 65% relative to BAU and average smoke PM
exposure by 61% in
Indonesia, 71% in Malaysia, and 68% in Singapore (Figure 4). The reduction in average annual adult all
cause mortality is broadly similar, reducing deaths by 65% in Indonesia, 73% in Malaysia, and 70% in
Singapore, relative to BAU (Table 1).
We explored the impact of blocking res in all industrial oil palm, wood pulp, and logging concessions,
which are examples of the options available in the online decision support tool. Blocking such res reduced
July to October OC + BC emissions by 39% and average smoke PM
exposures by 38% in Indonesia, 60% in
Malaysia, and 47% in Singapore. We also blocked res in existing conservation areas, which reduced emis-
sions by 24%. Unlike the other scenarios, this scenario reduced PM
exposure more in Indonesia (17%) than
in Malaysia or Singapore (9% and 15%, respectively), with a similar distribution for associated mortality.
While the scenarios above are focused on average smoke exposures and health responses over the course of a
decade, there is also substantial interannual variability. This is largely due to the interplay between meteor-
ological variability and re activity, such as higher re activity occurring during a drought year. For our
baseline BAU case, our individual annual exposure estimates were calculated by repeating 2005 to 2009
meteorology sequentially in two 5year periods for 20202029. We nd wide variation in smoke exposure
Figure 2. (a) Total emissions estimates (Tg DM) and (b) area (km
) for Sumatra, Kalimantan, and all of Indonesia, over
20052009. Emissions and area are proportioned into peatlands and nonpeatlands that were stable over the time period or
transitioned to a new land use or land cover category. Percentages above the stacked bars represent the contribution of
peatlands (stable and transitions) to total emissions or area and indicate the disproportionate inuence of peatlands on
total emissions.
Table 1
Cumulative JulyOctober Indonesian re emissions (Tg OC + BC), average JulyOctober smoke exposure (μg/m
), and estimated annual average future mor-
tality for Indonesia, Malaysia, and Singapore, from January 2020 to December 2029
JulOct total
(Tg OC + BC)
JulOct mean smoke
exposure (μg/m
Annual adult
allcause mortality
Indonesia Malaysia Singapore Indonesia Malaysia Singapore
BAU 12.7 6.6 5.5 6 33,000 (31,00036,000) 2,400 (2,2002,600) 360 (340380)
Fires from:
Peatlands 4.4 2.6 1.6 1.9 12,000 (11,00013,000) 630 (590680) 110 (100110)
Concessions 7.7 4.1 2.2 3.2 19,000 (18,00020,000) 900 (850980) 180 (170200)
Conservation areas 9.6 5.5 5 5.1 29,000 (27,00031,000) 2,100 (2,0002,300) 300 (290330)
BRG sites 7.7 4.1 4.2 3.2 22,000 (21,00024,000) 1,800 (1,7002,000) 190 (180210)
Note. First row provides estimates for BusinessAsUsual (BAU) scenario, and the remaining rows give reductions in emissions, exposure, and health impacts
associated with blocking re emissions in peatlands, industrial concessions, conservation areas, and BRG sites. Ranges in mortality reect uncertainties in
the concentration response function.
in different years. For example, Singapore experiences 20.1 μg/m
in a dry year (such as the 2006 El Niño)
but only 0.35 μg/m
in a lowre year.
3.3. Priority Conservation Areas
Indonesia's BRG has proposed priority peatland restoration locations in eastern Sumatra and southern
Kalimantan. These restoration activities aim to restore peatland hydrology to a more natural state. Since
the benet that each individual site could have on reduced re incidence is highly site specic and variable
(Konecny et al., 2016), we explored how our modeling framework could be used to determine which sites
would have the most benecial impact on public health for downwind populations considering a suite of
restoration efforts across the a 0.25° grid cell (Table 1). The model prioritizes different restoration efforts
for reducing PM
concentrations, selecting sites in eastern Sumatra for all receptors, but with a more south-
ern Sumatra focus for the Indonesia receptor. An example of the online tool output is shown in Figure S6. On
average, blocking res in BRG sites would reduce adult mortality by 34% in Indonesia, 22% in Malaysia, and
46% in Singapore. The tool also calculates the top ve priority locations of all BRG sites that are most ben-
ecial to public health in terms of reducing PM
concentrations for the receptor of interest.
Figure 3. GEOSChem adjoint sensitivities [(μg/m
) / (g/m
/s)] of the three populationweighted receptor regions (a
Singapore, b Indonesia, and c Malaysia) to the contribution of particulate matter emissions in each grid cell. These
examples are for July to October of the 2006 meteorological year. We note that there is an error in the units presented in
similar plots of adjoint sensitivities in Kim et al. (2015); Figure 5) and Koplitz et al. (2016); Figure S3).
4. Discussion
We present a novel approach, based on integration of land use science, atmospheric modeling, and public
health, for evaluating the implications of different land management scenarios in Indonesia on regional pub-
lic health outcomes over the coming decade. This endtoend framework is also relevant to other reprone
regions around the world. We found that the effectiveness of different strategies for improving public health
outcomes depends on the spatial relationship between the location of res and population centers and the
atmospheric transport patterns that carry emissions from regions experiencing re to population centers.
Sciencebased decisions about allocation of scarce restoration and re reduction strategies require informa-
tion that is not necessarily intuitive. The online software tool allows decisionmakers to readily assess the
public health outcomes associated with land use decisions.
We used a BAU case to explore how alternative land management scenarios could reduce re emissions, pol-
lution exposure, and public health outcomes in adults and children. Protecting peatlands from re would
reduce July to October total re emissions by 65% and average smoke exposure by 61% in Indonesia, 71%
in Malaysia, and 68% in Singapore. Overall, we calculate that peatland protection could prevent on average
22,000, 1,700, and 250 average excess deaths per year in Indonesia, Malaysia, and Singapore, respectively,
into the foreseeable future. If all BRG sites in peatlands are restored, the estimated benet would be reduc-
tions of 11,000, 520, and 160 average deaths per year, respectively.
There are several limitations to this work, but we are primarily focused on the relative changes between a
BAU scenario and other re reduction strategies, an approach which likely helps to reduce the impact of
Figure 4. Contribution of PM
) from res in individual grid cells to three populationweighted receptor regions:
Indonesia, Malaysia, and Singapore. Example shows how the contribution shifts under the (a, c, and e) BusinessAsUsual
scenario, compared with (c, d, and f) protecting peatlands from res. These examples show the 2006 meteorological
year and 2020 emissions. Mean JulyOctober smoke PM
exposure is shown inset.
these uncertainties. First, we used LULC maps from 2005 and 2010, as these were based on a consistent
methodology to analyze changes over time; newer LULC maps are not always compatible because of chan-
ging data sets and methodology (Miettinen et al., 2016). Second, the GFED4s re emissions inventory and
MODIS re detections may miss res during peak burning times, which would make our estimates conser-
vative. GFED4s currently uses MODIS C5.1 MCD64A1 burned area (van der Werf et al., 2017), which under-
estimates the contribution from small res relative to C6 (Giglio, 2016) and therefore implies lowerthan
expected re emissions. There are also improvements in previously unclassied grid cells and omission
error. Third, emissions factors of OC and BC from tropical peatland burning are uncertain. Information from
new eld campaigns suggests that our results may be conservative (Roulston et al., 2018; Wooster et al.,
2018). Fourth, there is uncertainty in the health impacts, with our estimates based on studies in the U.S.
and Europe. As these estimates are based on epidemiology done on developed countries, they reect emis-
sions sources, particle constituents, and size distributions, and health care infrastructure of developed coun-
tries. Further, these health impacts do not consider the likelihood of increased baseline risk of
cardiovascular disease caused by future demographic shifts. Our approach is standard for air pollution
health impact assessments, since doing primary epidemiology would involve recruiting a cohort, collecting
data on air pollution exposures and relevant confounders, and following them over long periods of time.
Unless the entire population is being followed individually and all appropriate confounders are controlled
for, an epidemiological approach will not provide a full accounting of impact. Populationlevel data are
not sufcient since it cannot account for confounders and other populationlevel changes. Incorporating
newly available data on health outcomes (as well as other input data sets) is a future extension of this work.
Finally, although climate changes could alter relationships between re and atmospheric transport, we do
not explore these relationships in the online tool as we are focused on nearterm changes. The inuence
of climate is an area of future work.
The online tool is publicly available at
tool. While we focused on the maximum benets of blocking res in peatlands, industrial concessions,
and protected areas, users can also dene their own re reduction strategies in individual provinces and
evaluate the benets for air quality and public health outcomes. For example, we used the tool to rank pro-
posed peatland conservation sites from BRG.
5. Conclusion
In this study, we present a novel approach that integrates information on the drivers of re emissions in
Indonesia, the transport of smoke to downwind regional population centers, and the resulting population
exposure to air pollution. As a result, this approach quanties health impacts of different land management
scenarios today and into the future. Using this framework, we evaluate the health impact of future res
under different land management scenarios and nd that a BAU scenario is likely to lead to an average of
36,000 excess deaths annually into the foreseeable future. However, approximately 66% of this excess mor-
tality could be averted through aggressive peatland restoration efforts. We anticipate that similar integrated
approaches might be effective in developing optimal policy approaches to reducing health impacts from bio-
mass burning in other parts of the world.
Conict of Interest
The authors declare no conicts of interest relevant to this study.
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This work was funded by the
Rockefeller Foundation and the
Gordon and Betty Moore Foundation
through the Health & Ecosystems:
Analysis of Linkages (HEAL) program.
We also acknowledge the Winslow
Foundation for their support.
Development of the adjoint tool was
funded by the Harvard Dean's
Competitive Program for Promising
Scholarship. We thank the Nick Clinton
and the Google Earth Engine team for
their assistance. We thank Montira
Pongsiri for helpful discussions. All
data sets are publicly available at
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... The EPI approach is used to infer a causal association and allows quantification of the exposure-response relationship. The HBE approach is used to quantify the attributable health burden (using the exposure-response function derived from EPI studies) over an exposure at an average concentration of pollutant [19] or preventable mortality considering different scenarios [40,41]. The HRA approach is the process to estimate the nature and probability of adverse health effects in humans who may be exposed to chemicals in contaminated environmental media, now or in the future [42]. ...
... Among the 70 studies, 42, 11, and 15 were EPI, HBE and HRA studies, respectively; two were both EPI and HBE. Forty-nine studies were conducted in the maritime area (Indonesia [41,[44][45][46][47][48][49][50][51][52][53][54][55][56][57], Malaysia [29,[58][59][60][61][62][63][64][65][66][67][68], Singapore [69][70][71][72][73][74][75][76][77][78][79][80][81][82], Brunei [83,84], multiple countries in maritime area [40,[85][86][87][88][89]), 17 in the mainland area (Thailand [32,[90][91][92][93][94][95][96][97][98][99][100][101][102][103][104], multiple countries in mainland area [105]); and 4 in multiple countries in the entire Southeast Asia [19,[106][107][108] (Fig 2). studies [29,48,59,60,68,[71][72][73][74][91][92][93][94][95][96] assessed potential cancer and non-cancer risks, which could not be clearly distinguished as mortality or morbidity. ...
... Among the 12 studies that did not use specific exposure indicators, seven described haze-related diseases [44,49,50,56,57,76,85], lung function [100], and symptoms with perceived PSI level [75], and three made a temporal comparison of health outcomes between haze and non-haze periods [61,83,98]. All HBE studies [19,40,41,47,[86][87][88][89]104] and all EPI-and HBE-combined studies [107,108] used PM2.5 as the exposure indicator; whereas, two HBE studies [105,106] used both PM2.5 and ozone as indicators. HRA studies used specific PM constituents such as PAHs [60,68,73,91,92,[94][95][96], trace metal elements [29,48,59,71,72,74], and black carbon [93] as exposure indicators. ...
Full-text available
Smoke haze due to vegetation and peatland fires in Southeast Asia is a serious public health concern. Several approaches have been applied in previous studies; however, the concepts and interpretations of these approaches are poorly understood. In this scoping review, we addressed issues related to the application of epidemiology (EPI), health burden estimation (HBE), and health risk assessment (HRA) approaches, and discussed the interpretation of findings, and current research gaps. Most studies reported an air quality index exceeding the ‘unhealthy’ level, especially during smoke haze periods. Although smoke haze is a regional issue in Southeast Asia, studies on its related health effects have only been reported from several countries in the region. Each approach revealed increased health effects in a distinct manner: EPI studies reported excess mortality and morbidity during smoke haze compared to non-smoke haze periods; HBE studies estimated approximately 100,000 deaths attributable to smoke haze in the entire Southeast Asia considering all-cause mortality and all age groups, which ranged from 1,064–260,000 for specified mortality cause, age group, study area, and study period; HRA studies quantified potential lifetime cancer and non-cancer risks due to exposure to smoke-related chemicals. Currently, there is a lack of interconnection between these three approaches. The EPI approach requires extensive effort to investigate lifetime health effects, whereas the HRA approach needs to clarify the assumptions in exposure assessments to estimate lifetime health risks. The HBE approach allows the presentation of health impact in different scenarios, however, the risk functions used are derived from EPI studies from other regions. Two recent studies applied a combination of the EPI and HBE approaches to address uncertainty issues due to the selection of risk functions. In conclusion, all approaches revealed potential health risks due to smoke haze. Nonetheless, future studies should consider comparable exposure assessments to allow the integration of the three approaches.
... By 2015, more than 70% of peatlands in Sumatra and Kalimantan had been damaged (Miettinen et al 2016b) and the ecosystem services and benefits they provide in their intact state severely compromised. In addition, degraded peatlands are prone to burning and constitute the primary source of transboundary haze (fire-related air pollution) that periodically blights the region, which has become a major source of geopolitical tension (Marlier et al 2019). Haze presents a major health concern, and contribute to excess morbidity and premature mortality in the region (Koplitz et al 2016). ...
... Spatial optimization has been used in Indonesia to determine the area for restoration needed to offset the impacts of harmful land uses (Budiharta et al 2016(Budiharta et al , 2018 and to target specific peatland restoration interventions, such as the blocking of drainage canals and the prevention of fire (Santika et al 2020, Urzainki et al 2020. Kalimantan has featured prominently in these studies (Budiharta et al 2018, Santika et al 2020, which have tended to focus on a singular objective (Marlier et al 2019, Santika et al 2020, paying limited attention to the economic costs of restoration. At the national level, identification of priority sites for restoration was based on past histories of fires, topography, canal infrastructures, and the functional zoning of peatlands (BRG 2016). ...
... This study builds upon an optimization of tropical forest restoration for carbon sequestration and biodiversity conservation (Budiharta et al 2014(Budiharta et al , 2018 by expanding the geographical scope of interest to the rest of Indonesia and including an additional fire risk reduction objective. Additionally, multiple objectives for restoration are considered rather than single objectives such as fire mitigation (Marlier et al 2019, Santika et al 2020 and canal block allocation (Urzainki et al 2020) in isolation, while potential trade-offs and synergies in restoration outcomes are identified. The study determines the benefits and financial costs associated with restoring degraded tropical peatlands in Indonesia, and where restoration sites may be allocated to maximize multiple co-benefits while accounting for trade-offs and cost-effectiveness. ...
Full-text available
Ecosystem restoration is increasingly employed as a nature-based solution to a range of crises. Decisions over restoration must balance limited resources, land constraints, and competing demands. Peatlands in Southeast Asia have been heavily impacted by agricultural expansion over the past three decades, with Indonesia now accounting for a substantial proportion of degraded tropical peatlands globally. Using spatial linear programming, we focus on prioritizing peatland restoration sites in Indonesia for fire risk reduction, climate change mitigation, species conservation, and cost-effectiveness. The study finds that restoring peatlands at 1 km ² planning units can generate multiple co-benefits such as reduced fire risks by 6–37%, attenuated extinction risks of peatland specialist bird species and mitigated climate change potential of 0.002–0.36 Pg CO 2 e yr ⁻¹ . These benefits were reduced but still of comparable magnitude when larger areas of planning (defined by village and catchment boundaries) were used. The results, although indicative, support tropical peatland restoration as a cost-efficient strategy for mitigating climate change, reducing fire, conserving biodiversity, and supporting sustainable development that can be offset by carbon prices of USD 2–37/Mg CO 2 e.
... Relatively few studies, however, have examined the role of meteorology to modulate smoke exposure on the populations downwind or the efficacy of prescribed burns as a policy intervention. We adopt an existing framework connecting fire emissions with transport to calculate population-weighted smoke exposure for an array of target regions Koplitz et al., 2016;Marlier et al., 2019). To date, this framework has been limited to case studies in Southeast Asia. ...
... The adjoint considers the advection, convection, and deposition processes in smoke plumes as they traverse the region. Following the approach of previous studies Koplitz et al., 2016;Marlier et al., 2019), we use the adjoint of the GEOS-Chem v8-02-01 (Bey et al., 2001;Henze et al., 2007) to quantify these sourcereceptor relationships. GEOS-Chem is driven by GEOS-FP assimilated meteorology from the NASA Global Modeling and Assimilation Office. ...
Smoke from wildfires presents one of the greatest threats to air quality, public health, and ecosystems in the United States, especially in the West. Here we quantify the efficacy of prescribed burning as an intervention for mitigating smoke exposure downwind of wildfires across the West during the 2018 and 2020 fire seasons. Using the adjoint of the GEOS-Chem chemical transport model, we calculate the sensitivities of population‐weighted smoke concentrations in receptor regions, including states and rural environmental justice communities, to fire emissions upwind of the receptors. We find that the population-weighted smoke exposure across the West during the September 2020 fires was 44 ug/m3 but would have been 20-30% greater had these wildfires occurred in October or November. We further simulate a set of prescribed burn scenarios and find that controlled burning interventions in northern California and the Pacific Northwest could have reduced the population-weighted smoke exposure across the western United States by 21 ug/m3 in September 2020, while doing so in all other states would have reduced smoke exposure by only 1.5 ug/m3. Satellite records of large, prescribed burns (>1000 acres, or 4 km2) reveal that northern California and western Oregon conducted only seven such prescribed fires over a 6-year period (2015-2020), even though these regions have a disproportionate impact on smoke exposure for rural environmental justice communities and population centers across the West. Our analysis suggests that land managers should prioritize northern California and the Pacific Northwest for prescribed burns to mitigate future smoke exposure.
... One study estimated that more than 300,000 premature deaths are attributable to exposure to PM emitted from vegetation fires, with the highest number of deaths occurring in sub-Saharan Africa and Southeast Asia 26 . In Southeast Asia, some studies assessed the health burden of vegetation fire smoke in the Maritime region [27][28][29][30][31][32] . ...
... Only a few studies have estimated the health burden of exposure to air pollution from vegetation fire events, particularly in terms of morbidity. Previously studies mainly addressed mortality on a global scale or in the equatorial Southeast Asian region [26][27][28][29][31][32][33][34][35] . Some studies used morbidity as a health outcome, such as a study in Australia which examined hospitalization for cardiovascular disease and asthma 36 , and another that targeted respiratory diseases in the United States 37 . ...
Full-text available
The air quality in Upper Northern Thailand (UNT) deteriorates during seasonal vegetation fire events, causing adverse effects especially on respiratory health outcomes. This study aimed to quantitatively estimate respiratory morbidity from vegetation fire smoke exposure, and to assess the impact of a burning ban enforced in 2016 on morbidity burden in UNT. We computed daily population exposure to fire-originated PM10 and estimated its health burden during a 5-year period from 2014 to 2018 using daily fire-originated PM10 concentration and the concentration–response function for short-term exposure to PM10 from vegetation fire smoke and respiratory morbidity. In subgroups classified as children and older adults, the health burden of respiratory morbidity was estimated using specific effect coefficients from previous studies conducted in UNT. Finally, we compared the health burden of respiratory morbidity before and after burning ban enforcement. Approximately 130,000 hospital visits for respiratory diseases were estimated to be attributable to fire-originated PM10 in UNT from 2014 to 2018. This estimation accounted for 1.3% of total hospital visits for respiratory diseases during the 5-year period, and 20% of those during burning events. Age-specific estimates revealed a larger impact of PM10 in the older adult group. The number of hospital visits for respiratory diseases attributable to fire-originated PM10 decreased from 1.8% to 0.5% after the burning ban policy was implemented in the area. Our findings suggest that PM10 released from vegetation fires is a health burden in UNT. The prohibition of the burning using regulatory measure had a positive impact on respiratory morbidity in this area.
... The World Bank stated that the resulting economic losses reached USD 5 billion in 2019 [5]. Not only having an impact on economic losses, forest and land fires also affect the quality of public health due to air pollution [6] and the balance of flora and fauna ecosystems [7]. Therefore, there needs to be a quick effort made by the stakeholders to control forest and land fires. ...
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Forest and land fires are disasters that greatly impact various sectors. Burned area identification is needed to control forest and land fires. Remote sensing is used as common technology for rapid burned area identification. However, there are not many studies related to the combination of optical and synthetic aperture radar (SAR) remote sensing data for burned area detection. In addition, SAR remote sensing data has the advantage of being a technology that can be used in various weather conditions. This research aims to evaluate the burned area model using a hybrid of convolutional neural network (CNN) as a feature extractor and random forest (CNN-RF) as classifiers on Sentinel-1 and Sentinel-2 data. The experiment uses five test schemes: (1) using optical remote sensing data; (2) using SAR remote sensing data; (3) a combination of optical and SAR data with VH polarization only; (4) a combination of optical and SAR data with VV polarization only; and (5) a combination of optical and SAR data with dual VH and VV polarization. The research was also carried out on the CNN, RF, and neural network (NN) classifiers. On the basis of the overall accuracy on the part of the region of Pulang Pisau Regency and Kapuas Regency, Central Kalimantan, Indonesia, the CNN-RF method provided the best results in the tested schemes, with the highest overall accuracy reaching 97% using Satellite pour l’Observation de la Terre (SPOT) images as reference data. This shows the potential of the CNN-RF method to identify burned areas, mainly in increasing precision value. The estimated result of the burned area at the research site using a hybrid CNN-RF method is 48,824.59 hectares, and the accuracy is 90% compared with MCD64A1 burned area product data.
... The culmination of this degradation has resulted in only 6% of remaining SEA tropical peatland area being considered to be "pristine peat-swamp forest" as of 2015 (Miettinen et al. 2016). The impacts of this degradation include carbon storage loss (Page et al. 2004, carbon emissions Hirano et al. 2007;Hooijer et al. 2006), modifications in hydrology and increased drainage (Ritzema et al. 2014;Rieley et al.,19961996) and subsequent forest fires (Page and Hooijer 2016;Osaki et al. 2016), biodiversity losses (Posa et al.,2011;Yule 2010) and public health risks (Marlier et al. 2019). To address these impacts, SEA governments alongside non-governmental organisations (NGOs) have developed restoration initiatives such as rewetting, revegetation and revitalisation to raise the groundwater level, replant native species and develop more peat-friendly local economies in degraded peatlands to limit peat decomposition and subsidence, as well as decrease fire risks (Giesen and Nirmala 2018; Dohong et al. 2018;Ritzema et al. 2014). ...
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Tropical peatlands in Southeast Asia (SEA) have undergone large-scale degradation in recent times due to extensive land use changes and drainage associated with their conversion for economic gains, and resulting fires during dry periods. This has had detrimental impacts on key peatland ecosystem processes and services such as hydrology, peat formation, carbon storage, fire prevention and biodiversity. Palaeoecological and geochemical proxies have been increasingly used in tropical peatland studies to extend contemporary instrumental records of peat conditions. Despite not yet being used to actively inform tropical peatland degradation and restoration interventions, these proxies are able to provide long-term trends in responses, resilience (threshold) and feedback processes of vegetation dynamics, groundwater level, peat pH, peat decomposition and accumulation rates, and degradation history. In this review, through the assessment of relevant tropical peatland studies in SEA, the palaeoecological and geochemical proxies were evaluated for their potential to reconstruct long-term peatland responses to climatically and anthropogenically-driven degradation. This information can potentially be utilised to provide better understanding of the extent of degradation and assist with the development of restoration management plans in SEA through its application in peat-hydrology restoration models.
... Based on research, about 65% of residents exposed to forest and forest fire smoke in 2015 were around 65% who experienced obstruction or narrowing of the airways. However, continuous exposure to smoke can make Chronic Obstructive Pulmonary Disease (COPD) worse or exacerbate and make it difficult for sufferers to breathe because airflow from the lungs is blocked by swelling and mucus or phlegm [12]. ...
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Forest and land fires generally carried out by concession holders in recent years are very detrimental to the community, nation, and state. The purpose of this paper is to analyze and elaborate application of the principle of piercing the corporate veil for companies on forest and land burning and who should be responsible for the loss of society. Because it corresponds to the concept of the rule of law as a system of laws and regulations, the approach adopted is normative law. Primary, secondary, and tertiary legal materials are used in the research. The results of this study are that in the case of a forest and land burning company, based on Article 3 number 2 in particular letters b and c of Law 40/2007, the principle of limited liability for shareholders does not apply if the shareholder concerned either directly or indirectly in bad faith uses The Company for personal interest and or the shareholders concerned are involved in unlawful acts committedby the Company.
... Over the next few decades, air pollution from fires could cause 36,000 excess deaths each year on average in Indonesia, Malaysia and Singapore. Comprehensive land management strategies, including peatland restoration, could cut this mortality by about 66 per cent (Marlier et al. 2019). ...
Technical Report
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We can no longer deny that we are a part of our environment, which we are degrading at an alarming rate. In order to embark on a more sustainable pathway, we need both to conserve and restore ecosystems. This report makes the case for why restoration, in particular, is so important and outlines how the UN Decade can catalyse a movement to restore the world’s ecosystems. Healthy, stable and biodiverse ecosystems are the foundation of our health and well-being, as well as that of our fellow species. They help to regulate our climate and control extreme events, pests and diseases, as well as to provide us with water, food, raw materials and spaces for recreation. They absorb our wastes, sustain economic sectors and the livelihoods of millions of people, and they nurture our health, culture and spiritual fulfilment (IPBES 2019). However, we have been overexploiting and degrading the world’s ecosystems and wild species, causing the erosion of the very services we depend on (UNEP 2021). Driving this degradation are the ways we produce food (Benton et al. 2021) and alter our landscapes and oceans, along with climate change, pollution and invasive species (IPBES 2019). The global economy has seen incredible growth over recent decades – growth that has been fuelled by the erosion of the world’s natural assets. Thus, our massive gains in income and poverty reduction come at the expense of a significant deterioration of the health of the biosphere. We are using the equivalent of 1.6 Earths to maintain our current lifestyle (Global Footprint Network 2021) and are putting the future of our economies at extreme risk (Dasgupta 2021). This report represents a synthesis of recent research. All selected ecosystems – farmlands; forests; freshwater; grasslands, shrublands and savannahs; mountains; oceans and coasts; peatlands; and urban areas – are being degraded, often at an accelerating rate. We are fast approaching a tipping point for the climate (IPCC 2018) and are close to overshooting some of our other ‘planetary boundaries’. The demands humanity places on the biosphere – our ecological footprint – are simply too much (Dasgupta 2021). Because ecosystem degradation does not affect everyone equally, its worst impacts mainly affect people living in poverty, women and girls, members of indigenous and traditional communities, older persons, persons with disabilities, ethnic, racial or other minorities and displaced persons (Stoeckl et al. 2013; OHCHR 2018; UN HLCP 2021). These are the same groups of people who are suffering the worst effects of the COVID-19 pandemic, as it is exacerbating pre-existing inequalities (UNEP and FAO 2020). Chapter 2 provides a snapshot of the current state of the world’s ecosystems. The need to restore damaged ecosystems has never been greater. Degradation is undermining hard-won development gains and threatening the well-being of today‘s youth and future generations, while making national commitments increasingly more difficult and costly to reach. None of the agreed global goals for the protection of life on Earth and for halting the degradation of land and oceans have been fully met (UNEP 2021), and only 6 of the 20 Aichi Biodiversity Targets have been partially achieved (CBD 2020a). We need to re-create a balanced relationship with nature, not only by conserving ecosystems that are still healthy, but also by urgently and sustainably restoring degraded ones. Ecosystem restoration alone cannot solve the crises we face, but it is key to averting the worst of them. Chapter 3 details the myriad ways that nature-based solutions like restoration can benefit the climate, food systems, health and the economy. Much has been done already, and we can build on the lessons learned from existing restoration approaches and initiatives. Commitments by 115 governments to restore a total of nearly 1 billion hectares of land as a contribution to achieving the objectives of the CBD, UNCCD, UNFCCC and the Bonn Challenge (Sewell et al. 2020) are a good start. However, achieving restoration goals will require a fundamental shift in the way we value ecosystems, their biodiversity and the vital services we depend on (Dasgupta 2021). Chapter 4 provides an overview of different approaches to restoration, guiding principles and helpful technical and scientific innovations, as well as the broader conditions needed to address the drivers of degradation and enable the transition to a more sustainable way of life. The UN Decade on Ecosystem Restoration aims to prevent, halt and reverse the degradation of all kinds of ecosystems, contributing to reductions in global poverty and ensuring that no one is left behind. Running from 2021 until 2030, the UN Decade launches a global movement to restore ecosystems worldwide. This will help to achieve multiple global goals, including the Post-2020 Global Biodiversity Framework under the CBD, the Paris Agreement under the UNFCCC, the Sustainable Development Goals (SDGs) under 2030 Agenda and the Land Degradation Neutrality targets under the UNCCD. There are also clear complementarities with the efforts being developed in both the UN Decade of Ocean Science for Sustainable Development (2021–2030) and the UN Decade of Family Farming (2019–2028). The UN Food Systems Summit 2021 provides an opportunity to promote scaled up action on restoring farmlands and other food-producing systems. Chapter 5 presents the overall strategy for the UN Decade and the way forward. The impacts of the COVID-19 pandemic will be felt for generations. Yet this crisis has also demonstrated the power of international cooperation and provided us with an opportunity to steer away from our current destructive trajectory (UNEP 2021). To put countries on a path that is green, sustainable and fair, national governments must include ecosystem restoration in their pandemic recovery plans. This Decade can serve as a launchpad to accelerate the transformative changes we need to combat the climate crisis, prevent mass extinctions and build social and economic resilience.
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Wildfire activity in the western U.S. has increased in frequency and severity in recent decades. Wildfire smoke emissions contribute to elevated fine particulate matter (PM 2.5 ) concentrations that are dangerous to public health. Due to the outdoor and physically demanding nature of their work, agricultural workers are particularly vulnerable to wildfire smoke pollution. In this study, we quantify the potential exposure of agricultural workers in California to past (2004–2009) and future (2046–2051) smoke PM 2.5 . We find that while absolute increases in smoke PM 2.5 exposure are largest in northern California, agricultural regions in the Central Valley and Central Coast may be highly vulnerable to future increases in smoke PM 2.5 concentrations. We find an increase from 6 to 8 million worker smoke exposure days (+35%) of ‘smokewave’ exposure for agricultural workers across the state under future climate conditions, with the largest increases in Tulare, Monterey, and Fresno counties. Under future climate conditions, we find 1.9 million worker smoke exposure days of agricultural worker exposure to levels of total PM 2.5 pollution deemed ‘Unhealthy for Sensitive Groups.’ This is a 190% increase over past climate conditions. Wildfire smoke PM 2.5 contributes, on average, to more than 90% of these daily PM 2.5 exceedances compared with non-fire sources of air pollution. Using the recent extreme wildfire season of 2020 as a case study, we show that existing monitoring networks do not provide adequate sampling of PM 2.5 in many future at-risk wildfire regions with large numbers of agricultural workers. Policies will need to consider the changing patterns of smoke PM 2.5 exposure under future climate conditions to better protect outdoor agricultural workers.
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Southeast Asia experiences frequent fires in fuel-rich tropical peatlands, leading to extreme episodes of regional haze with high concentrations of fine particulate matter (PM2.5) impacting human health. In a study published recently, the first field measurements of PM2.5 emission factors for tropical peat fires showed larger emissions than from other fuel types. Here we report even higher PM2.5 emission factors, measured at newly ignited peat fires in Malaysia, suggesting that current estimates of fine particulate emissions from peat fires may be underestimated by a factor of 3 or more. In addition, we use both field and laboratory measurements of burning peat to provide the first mechanistic explanation for the high variability in PM2.5 emission factors, demonstrating that buildup of a surface ash layer causes the emissions of PM2.5 to decrease as the peat fire progresses. This finding implies that peat fires are more hazardous (in terms of aerosol emissions) when first ignited than when still burning many days later. Varying emission factors for PM2.5 also have implications for our ability to correctly model the climate and air quality impacts downwind of the peat fires. For modelers able to implement a time-varying emission factor, we recommend an emission factor for PM2.5 from newly ignited tropical peat fires of 58 g of PM2.5 per kilogram of dry fuel consumed (g/kg), reducing exponentially at a rate of 9%/day. If the age of the fire is unknown or only a single value may be used, we recommend an average value of 24 g/kg.
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Deforestation and draining of the peatlands in equatorial SE Asia has greatly increased their flammability, and in September–October 2015 a strong El Niño-related drought led to further drying and to widespread burning across parts of Indonesia, primarily on Kalimantan and Sumatra. These fires resulted in some of the worst sustained outdoor air pollution ever recorded, with atmospheric particulate matter (PM) concentrations exceeding those considered “extremely hazardous to health” by up to an order of magnitude. Here we report unique in situ air quality data and tropical peatland fire emissions factors (EFs) for key carbonaceous trace gases (CO2, CH4 and CO) and PM2.5 and black carbon (BC) particulates, based on measurements conducted on Kalimantan at the height of the 2015 fires, both at locations of “pure” sub-surface peat burning and spreading vegetation fires atop burning peat. PM2.5 are the most significant smoke constituent in terms of human health impacts, and we find in situ PM2.5 emissions factors for pure peat burning to be 17.8 to 22.3 g·kg−1, and for spreading vegetation fires atop burning peat 44 to 61 g·kg−1, both far higher than past laboratory burning of tropical peat has suggested. The latter are some of the highest PM2.5 emissions factors measured worldwide. Using our peatland CO2, CH4 and CO emissions factors (1779 ± 55 g·kg−1, 238 ± 36 g·kg−1, and 7.8 ± 2.3 g·kg−1 respectively) alongside in situ measured peat carbon content (610 ± 47 g-C·kg−1) we provide a new 358 Tg (± 30%) fuel consumption estimate for the 2015 Indonesian fires, which is less than that provided by the GFEDv4.1s and GFASv1.2 global fire emissions inventories by 23% and 34% respectively, and which due to our lower EFCH4 produces far less (~3×) methane. However, our mean in situ derived EFPM2.5 for these extreme tropical peatland fires (28 ± 6 g·kg−1) is far higher than current emissions inventories assume, resulting in our total PM2.5 emissions estimate (9.1 ± 3.5 Tg) being many times higher than GFEDv4.1s, GFASv1.2 and FINNv2, despite our lower fuel consumption. We find that two thirds of the emitted PM2.5 come from Kalimantan, one third from Sumatra, and 95% from burning peatlands. Using new geostationary fire radiative power (FRP) data we map the fire emissions’ spatio-temporal variations in far greater detail than ever before (hourly, 0.05°), identifying a tropical peatland fire diurnal cycle twice as wide as in neighboring non-peat areas and peaking much later in the day. Our data show that a combination of greatly elevated PM2.5 emissions factors, large areas of simultaneous, long-duration burning, and very high peat fuel consumption per unit area made these Sept to Oct tropical peatland fires the greatest wildfire source of particulate matter globally in 2015, furthering evidence for a regional atmospheric pollution impact whose particulate matter component in particular led to millions of citizens being exposed to extremely poor levels of air quality for substantial periods.
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Seasonal haze episodes and the associated inimical health impacts have become a regular crisis among the ASEAN countries. Even though many emerging experimental and epidemiological studies have documented the plausible health effects of the predominating toxic pollutants of haze, the consistency among the reported findings by these studies is poorly understood. By addressing such gap, this review aimed to critically highlight the evidence of physical and psychological health impacts of haze from the available literature in ASEAN countries. Systematic literature survey from six electronic databases across the environmental and medical disciplines was performed, and 20 peer-reviewed studies out of 384 retrieved articles were selected. The evidence pertaining to the health impacts of haze based on field survey, laboratory tests, modelling and time-series analysis were extracted for expert judgement. In specific, no generalization can be made on the reported physical symptoms as no specific symptoms recorded in all the reviewed studies except for throat discomfort. Consistent evidence was found for the increase in respiratory morbidity, especially for asthma, whilst the children and the elderly are deemed to be the vulnerable groups of the haze-induced respiratory ailments. A consensual conclusion on the association between the cardiovascular morbidity and haze is unfeasible as the available studies are scanty and geographically limited albeit of some reported increased cases. A number of modelling and simulation studies demonstrated elevating respiratory mortality rates due to seasonal haze exposures over the years. Besides, evidence on cancer risk is inconsistent where industrial and vehicular emissions are also expected to play more notable roles than mere haze exposure. There are insufficient regional studies to examine the association between the mental health and haze. Limited toxicological studies in ASEAN countries often impede a comprehensive understanding of the biological mechanism of haze-induced toxic pollutants on human physiology. Therefore, the lack of consistent evidence among the reported haze-induced health effects as highlighted in this review calls for more intensive longitudinal and toxicological studies with greater statistical power to disseminate more reliable and congruent findings to empower the institutional health planning among the ASEAN countries.
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Across leading environmental challenges—fire management, climate change, deforestation – there is growing awareness of the need to better account for diverse stakeholder perceptions across complex, multi-level governance arrangements. Perceptions often condition behavior, compliance and engagement in ways that impact environmental outcomes. We illustrate the importance of, and approaches to, examining perceptions across scales of governance (e.g. international, national, local) and sectors (e.g. civil society, government, corporate) through the example of Indonesian peatland fires. Peatlands are crucial global carbon stocks threatened by land use change and fire and subject to a range of policy interventions that affect many different stakeholder groups. Peatland drainage and conversion to plantation agriculture has been associated with severe, uncontrolled peat fires that present significant climate, public health and economic risks. Peatland fire management has become a domestic and international priority, spurring intensely contentious debates, policies and legal proceedings. Previous fire management interventions (FMI) are numerous yet have suffered widespread implementation failures. Against this backdrop, our manuscript provides a thematically and methodologically novel analysis of how diverse stakeholders, from local farmers to international policy makers, perceive peatland fires in terms of, i) how they prioritize the associated benefits and burdens, and ii) the perceived effectiveness of FMI. We adopt an innovative application of Q method to provide needed insights that serve to quantify the areas of contention and consensus that exist among the stakeholders and their multi-dimensional perspectives. We show that many of the contemporary FMI were perceived as among the most effective interventions overall, but were also the most controversial between groups. Clear consensus areas were related to the shared concerns for the local health impacts and the potential of government support for fire-free alternatives as a solution pathway. Improved understanding of stakeholder perceptions has potential to: give voice to marginalized communities; enable transparent mediation of diverse priorities; inform public education campaigns, and shape future policy and governance arrangements.
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Climate, land use, and other anthropogenic and natural drivers have the potential to influence fire dynamics in many regions. To develop a mechanistic understanding of the changing role of these drivers and their impact on atmospheric composition, long-term fire records are needed that fuse information from different satellite and in situ data streams. Here we describe the fourth version of the Global Fire Emissions Database (GFED) and quantify global fire emissions patterns during 1997–2016. The modeling system, based on the Carnegie–Ames–Stanford Approach (CASA) biogeochemical model, has several modifications from the previous version and uses higher quality input datasets. Significant upgrades include (1) new burned area estimates with contributions from small fires, (2) a revised fuel consumption parameterization optimized using field observations, (3) modifications that improve the representation of fuel consumption in frequently burning landscapes, and (4) fire severity estimates that better represent continental differences in burning processes across boreal regions of North America and Eurasia. The new version has a higher spatial resolution (0.25◦) and uses a different set of emission factors that separately resolves trace gas and aerosol emissions from temperate and boreal forest ecosystems. Global mean carbon emissions using the burned area dataset with small fires (GFED4s) were 2.2 × 1015 grams of carbon per year (Pg C yr−1) during 1997–2016, with a maximum in 1997 (3.0 Pg C yr−1) and minimum in 2013 (1.8 Pg C yr−1). These estimates were 11 % higher than our previous estimates (GFED3) during 1997–2011, when the two datasets overlapped. This net increase was the result of a substantial increase in burned area (37 %), mostly due to the inclusion of small fires, and a modest decrease in mean fuel consumption (−19 %) to better match estimates from field studies, primarily in savannas and grasslands. For trace gas and aerosol emissions, differences between GFED4s and GFED3 were often larger due to the use of revised emission factors. If small fire burned area was excluded (GFED4 without the “s” for small fires), average emissions were 1.5 Pg C yr−1. The addition of small fires had the largest impact on emissions in temperate North America, Central America, Europe, and temperate Asia. This small fire layer carries substantial uncertainties; improving these estimates will require use of new burned area products derived from high-resolution satellite imagery. Our revised dataset provides an internally consistent set of burned area and emissions that may contribute to a better understanding of multi-decadal changes in fire dynamics and their impact on the Earth system. GFED data are available from
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Vegetation and peatland fires cause poor air quality and thousands of premature deaths across densely populated regions in Equatorial Asia. Strong El-Niño and positive Indian Ocean Dipole conditions are associated with an increase in the frequency and intensity of wildfires in Indonesia and Borneo, enhancing population exposure to hazardous concentrations of smoke and air pollutants. Here we investigate the impact on air quality and population exposure of wildfires in Equatorial Asia during Fall 2015, which were the largest over the past two decades. We performed high-resolution simulations using the Weather Research and Forecasting model with Chemistry based on a new fire emission product. The model captures the spatio-temporal variability of extreme pollution episodes relative to space- and ground-based observations and allows for identification of pollution sources and transport over Equatorial Asia. We calculate that high particulate matter concentrations from fires during Fall 2015 were responsible for persistent exposure of 69 million people to unhealthy air quality conditions. Short-term exposure to this pollution may have caused 11,880 (6,153–17,270) excess mortalities. Results from this research provide decision-relevant information to policy makers regarding the impact of land use changes and human driven deforestation on fire frequency and population exposure to degraded air quality.
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In September–October 2015, El Niño and positive Indian Ocean Dipole conditions set the stage for massive fires in Sumatra and Kalimantan (Indonesian Borneo), leading to persistently hazardous levels of smoke pollution across much of Equatorial Asia. Here we quantify the emission sources and health impacts of this haze episode and compare the sources and impacts to an event of similar magnitude occurring under similar meteorological conditions in September–October 2006. Using the adjoint of the GEOS-Chem chemical transport model, we first calculate the influence of potential fire emissions across the domain on smoke concentrations in three receptor areas downwind—Indonesia, Malaysia, and Singapore—during the 2006 event. This step maps the sensitivity of each receptor to fire emissions in each grid cell upwind. We then combine these sensitivities with 2006 and 2015 fire emission inventories from the Global Fire Assimilation System (GFAS) to estimate the resulting population-weighted smoke exposure. This method, which assumes similar smoke transport pathways in 2006 and 2015, allows near real-time assessment of smoke pollution exposure, and therefore the consequent morbidity and premature mortality, due to severe haze. Our approach also provides rapid assessment of the relative contribution of fire emissions generated in a specific province to smoke-related health impacts in the receptor areas. We estimate that haze in 2015 resulted in 100 300 excess deaths across Indonesia, Malaysia and Singapore, more than double those of the 2006 event, with much of the increase due to fires in Indonesia's South Sumatra Province. The model framework we introduce in this study can rapidly identify those areas where land use management to reduce and/or avoid fires would yield the greatest benefit to human health, both nationally and regionally.
In June 2013, the Malay Peninsula experienced severe smoke pollution, with daily surface particulate matter (PM) concentrations in Singapore greater than 350 μg/m³, over 2 times the air quality standard for daily mean PM10 set by the U.S. Environmental Protection Agency. Unlike most haze episodes in the Malay Peninsula in recent decades (e.g., the September 2015 event), the June 2013 haze occurred in the absence of an El Niño, during negative Indian Ocean Dipole conditions, with smoke carried eastward to the Peninsula from fires in the Riau province of central Sumatra. We show that June 2013 was not an exceptional event; inspection of visibility data during 2005–2015 reveals two other severe haze events in the Malay Peninsula (August 2005 and October 2010) occurring under similar conditions. Common to all three events was a combination of anomalously strong westerly winds over Riau province concurrent with late phases of the Real-Time Multivariate Madden-Julian Oscillation Index, during negative phases of the Indian Ocean Dipole. Our work suggests that identifying the meteorological mechanism driving these westerly wind anomalies could help stakeholders prepare for future non-El Niño haze events in Singapore and the Malay Peninsula.
On 25 September 2014, Singapore’s new Transboundary Haze Pollution Act came into operation. The Act is a dramatic piece of legislation that creates extra-territorial liability for entities engaging in setting fires abroad that cause transboundary smoke or "haze" pollution in Singapore. The impetus for the Act’s enactment can be traced to the serious haze pollution that hit Singapore in June 2013.