New estimate of particulate emissions from Indonesian peat fires in 2015

Kiely, Laura; Spracklen, Dominick V.; Wiedinmyer, Christine; Conibear, Luke; Reddington, Carly L.; Archer‐Nicholls, Scott; Lowe, Douglas; Arnold, S. R.; Knote, Christoph; Khan, Firoz; Latif, Mohd Talib; Kuwata, Mikinori; Budisulistiorini, Sri Hapsari; Syaufina, Lailan

doi:10.5194/acp-19-11105-2019

Cited by 82 publications

(94 citation statements)

References 83 publications

(128 reference statements)

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“…The six BB emission datasets examined in this study have different definitions of major biome types, for example, there are six major biome types applied in GFED4.1s (Table 1 in van der Werf et al, 2017), but eight in GFAS1.2 (Table 2 in Kaiser et al, 2012), and only four in QFED2.4 ( Table 2 in Darmenov and da Silva, 2015). In particular, peat combustion is an important emission source in some regions, such as equatorial Asia (e.g., Kiely et al, 2019). However, not all the emission datasets include peat biome or consider its unique characteristics.…”

Section: Pan Et Al: Six Global Biomass Burning Emission Datasetsmentioning

confidence: 99%

“…Although much progress has been made over the last couple of decades in improving the quality of BB emission datasets, for example, by incorporating more recent satellite measurements with better calibration and spatial resolution (e.g., van der Werf et al, , 2017, biomass burning aerosol emissions still have large uncertainty and thus are still poorly constrained in models at global and regional levels (e.g., Liousse et al, 2010;Kaiser et al, 2012;Petrenko et al, 2012Petrenko et al, , 2017Bond et al, 2013;Zhang et al, 2014;Pan et al, 2015;Ichoku et al, 2016a;Reddington et al, 2016;Pereira et al, 2016). Specifically, large uncertainty exists in the description of the magnitude, patterns, and drivers of wildfires and types of biomass burning (e.g., Hyer et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Six global biomass burning emission datasets: intercomparison and application in one global aerosol model

et al. 2020

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Abstract. Aerosols from biomass burning (BB) emissions are poorly constrained in global and regional models, resulting in a high level of uncertainty in understanding their impacts. In this study, we compared six BB aerosol emission datasets for 2008 globally as well as in 14 regions. The six BB emission datasets are (1) GFED3.1 (Global Fire Emissions Database version 3.1), (2) GFED4s (GFED version 4 with small fires), (3) FINN1.5 (FIre INventory from NCAR version 1.5), (4) GFAS1.2 (Global Fire Assimilation System version 1.2), (5) FEER1.0 (Fire Energetics and Emissions Research version 1.0), and (6) QFED2.4 (Quick Fire Emissions Dataset version 2.4). The global total emission amounts from these six BB emission datasets differed by a factor of 3.8, ranging from 13.76 to 51.93 Tg for organic carbon and from 1.65 to 5.54 Tg for black carbon. In most of the regions, QFED2.4 and FEER1.0, which are based on satellite observations of fire radiative power (FRP) and constrained by aerosol optical depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS), yielded higher BB aerosol emissions than the rest by a factor of 2–4. By comparison, the BB aerosol emissions estimated from GFED4s and GFED3.1, which are based on satellite burned-area data, without AOD constraints, were at the low end of the range. In order to examine the sensitivity of model-simulated AOD to the different BB emission datasets, we ingested these six BB emission datasets separately into the same global model, the NASA Goddard Earth Observing System (GEOS) model, and compared the simulated AOD with observed AOD from the AErosol RObotic NETwork (AERONET) and the Multiangle Imaging SpectroRadiometer (MISR) in the 14 regions during 2008. In Southern Hemisphere Africa (SHAF) and South America (SHSA), where aerosols tend to be clearly dominated by smoke in September, the simulated AOD values were underestimated in almost all experiments compared to MISR, except for the QFED2.4 run in SHSA. The model-simulated AOD values based on FEER1.0 and QFED2.4 were the closest to the corresponding AERONET data, being, respectively, about 73 % and 100 % of the AERONET observed AOD at Alta Floresta in SHSA and about 49 % and 46 % at Mongu in SHAF. The simulated AOD based on the other four BB emission datasets accounted for only ∼50 % of the AERONET AOD at Alta Floresta and ∼20 % at Mongu. Overall, during the biomass burning peak seasons, at most of the selected AERONET sites in each region, the AOD values simulated with QFED2.4 were the highest and closest to AERONET and MISR observations, followed closely by FEER1.0. However, the QFED2.4 run tends to overestimate AOD in the region of SHSA, and the QFED2.4 BB emission dataset is tuned with the GEOS model. In contrast, the FEER1.0 BB emission dataset is derived in a more model-independent fashion and is more physically based since its emission coefficients are independently derived at each grid box. Therefore, we recommend the FEER1.0 BB emission dataset for aerosol-focused hindcast experiments in the two biomass-burning-dominated regions in the Southern Hemisphere, SHAF, and SHSA (as well as in other regions but with lower confidence). The differences between these six BB emission datasets are attributable to the approaches and input data used to derive BB emissions, such as whether AOD from satellite observations is used as a constraint, whether the approaches to parameterize the fire activities are based on burned area, FRP, or active fire count, and which set of emission factors is chosen.

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Section: Pan Et Al: Six Global Biomass Burning Emission Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Six global biomass burning emission datasets: intercomparison and application in one global aerosol model

et al. 2020

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“…Fires emit substantial amounts of CO 2 and contribute to climate change. In 2015, fires were estimated to have emitted around 700-800 Tg CO 2 [1,2]. Trace gas and particulate emissions from fire cause regional air pollution [3].…”

Section: Introductionmentioning

confidence: 99%

“…When fires burn on peat, they can burn deep into organic soils resulting in substantial emissions [6]. During the 2015 fires in Indonesia, peat burning contributed 55% of CO 2 emissions and 70% of primary fine particulate matter emissions from fires [1].…”

Section: Introductionmentioning

confidence: 99%

Forest and Land Fires Are Mainly Associated with Deforestation in Riau Province, Indonesia

et al. 2019

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Indonesia has experienced extensive land-cover change and frequent vegetation and land fires in the past few decades. We combined a new land-cover dataset with satellite data on the timing and location of fires to make the first detailed assessment of the association of fire with specific land-cover transitions in Riau, Sumatra. During 1990 to 2017, secondary peat swamp forest declined in area from 40,000 to 10,000 km 2 and plantations (including oil palm) increased from around 10,000 to 40,000 km 2 . The dominant land use transitions were secondary peat swamp forest converting directly to plantation, or first to shrub and then to plantation. During 2001-2017, we find that the frequency of fire is greatest in regions that change land-cover, with the greatest frequency in regions that transition from secondary peat swamp forest to shrub or plantation (0.15 km −2 yr −1 ). Areas that did not change land cover exhibit lower fire frequency, with shrub (0.06 km −2 yr −1 ) exhibiting a frequency of fire >60 times the frequency of fire in primary forest. Our analysis demonstrates that in Riau, fire is closely connected to land-cover change, and that the majority of fire is associated with the transition of secondary forest to shrub and plantation. Reducing the frequency of fire in Riau will require enhanced protection of secondary forests and restoration of shrub to natural forest.Remote Sens. 2020, 12, 3 2 of 12 to protected areas [15,16]. Fire is used as part of the land-conversion process, to clear vegetation in preparation for agriculture and plantations [17]. In Riau, Indonesia, fires are six times more frequent in regions experiencing recent tree cover loss compared to regions with no loss [16].Understanding the links between land-cover change and fire is necessary to inform land and fire management and fire suppression efforts. However, there is still poor understanding of the fraction of fire that is associated with specific land-cover changes. Satellite datasets provide some information on land-cover change (i.e., canopy cover loss), but there is rarely detailed information on the specific land-cover transitions that occur. Here we combine a new land-cover dataset with information on the location and timing of fires from satellite, to make the first assessment of the association between fire and specific land-cover transitions in Indonesia. We focus on Riau province, one of the most active areas of fire in Indonesia. Materials and MethodsOur study area consists of the province of Riau, Sumatra, covering 89,691 km 2 and consisting of 43% peatland [16]. We used the land-cover map provided by the Indonesian Ministry of Environment and Forestry the land-cover classification was conducted as a part of National Forest Inventory (NFI) project which predominantly relied on analysis of Landsat imagery. During 2000During -2009 Landsat images were combined with 1000 m SPOT Vegetation and 250 m MODIS images, but the classification still depended on visual image interpretation. Finally, since 2009 only Landsat images have b...

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“…Emissions from vegetation and peat fires in Indonesia contribute to climate change and cause severe regional air quality issues [2,3]. The large fires across Indonesia in September-October 2015, emitted 700-800 Tg CO 2 [9,10], and exposed 69 million people to poor air quality [3]. Exposure to particulate pollution is estimated to have caused 11,880 mortalities in the short term [3] with as many as 100,300 premature mortalities over the longer term [11].…”

Section: Introductionmentioning

confidence: 99%

Relationship Between Fire and Forest Cover Loss in Riau Province, Indonesia Between 2001 and 2012

Adrianto

Spracklen

Arnold

2019

Forests

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Forest and peatland fires occur regularly across Indonesia, resulting in large greenhouse gas emissions and causing major air quality issues. Over the last few decades, Indonesia has also experienced extensive forest loss and conversion of natural forest to oil palm and timber plantations. Here we used data on fire hotspots and tree-cover loss, as well as information on the extent of peat land, protected areas, and concessions to explore spatial and temporal relationships among forest, forest loss, and fire frequency. We focus on the Riau Province in Central Sumatra, one of the most active regions of fire in Indonesia. We find strong relationships between forest loss and fire at the local scale. Regions with forest loss experienced six times as many fire hotspots compared to regions with no forest loss. Forest loss and maximum fire frequency occurred within the same year, or one year apart, in 70% of the 1 km2 cells experiencing both forest loss and fire. Frequency of fire was lower both before and after forest loss, suggesting that most fire is associated with the forest loss process. On peat soils, fire frequency was a factor 10 to 100 lower in protected areas and natural forest logging concessions compared to oil palm and wood fiber (timber) concessions. Efforts to reduce fire need to address the underlying role of land-use and land-cover change in the occurrence of fire. Increased support for protected areas and natural forest logging concessions and restoration of degraded peatlands may reduce future fire risk. During times of high fire risk, fire suppression resources should be targeted to regions that are experiencing recent forest loss, as these regions are most likely to experience fire.

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New estimate of particulate emissions from Indonesian peat fires in 2015

Cited by 82 publications

References 83 publications

Six global biomass burning emission datasets: intercomparison and application in one global aerosol model

Six global biomass burning emission datasets: intercomparison and application in one global aerosol model

Forest and Land Fires Are Mainly Associated with Deforestation in Riau Province, Indonesia

Relationship Between Fire and Forest Cover Loss in Riau Province, Indonesia Between 2001 and 2012

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