Air quality and health impacts of vegetation and peat fires in Equatorial Asia during 2004–2015

Particulate matter (PM) emissions from vegetation and peat fires in Equatorial Asia cause poor regional air quality. Burning is greatest during drought years, resulting in strong inter-annual variability in emissions. We make the first consistent estimate of the emissions, air quality and public health impacts of Equatorial Asian fires during 2004–2015. The largest dry season (August—October) emissions occurred in 2015, with PM emissions estimated as 9.4 Tg, more than triple the average dry season emission (2.7 Tg). Fires in Sumatra and Kalimantan caused 94% of PM emissions from fires in Equatorial Asia. Peat combustion in Indonesian peatlands contributed 45% of PM emissions, with a greater contribution of 68% in 2015. We used the WRF-chem model to simulate dry season PM for the 6 biggest fire years during this period (2004, 2006, 2009, 2012, 2014, 2015). The model reproduces PM concentrations from a measurement network across Malaysia and Indonesia, suggesting our PM emissions are realistic. We estimate long-term exposure to PM resulted in 44 040 excess deaths in 2015, with more than 15 000 excess deaths annually in 2004, 2006, and 2009. Exposure to PM from dry season fires resulted in an estimated 131 700 excess deaths during 2004–2015. Our work highlights that Indonesian vegetation and peat fires frequently cause adverse impacts to public health across the region.


Introduction
Vegetation and peat fires in Equatorial Asia contribute to climate change (Page et al 2002, Tosca et al 2013 and poor regional air quality (Field et al 2009, Reddington et al 2014, Lee et al 2017. Fires are influenced by climate, land-use and land management (van der Werf et al 2008, Page and Hooijer 2016), and air quality degradation is greatest in dry years when the most extensive fires occur , Koplitz et al 2016, Crippa et al 2016. Largescale deforestation, forest degradation and agricultural development have increased the occurrence of fire (Sloan et al 2017) and extensive fires are no longer restricted to drought years (Gaveau et al 2014). However, the air quality impact of fires outside of drought years has not been studied. Here we develop a new fire emissions estimate for Equatorial Asia and make a consistent estimate of the impacts of fire on air quality and health during 2004-2015. Tropical peatlands store large amounts of organic carbon in peat soils (Page et al 2002(Page et al , 2011. Fires on peatland can burn into the peat and combust substantial amounts of biomass (Hu et al 2018, Roulston et al 2018. The majority of peatland fires occur on deforested land (Cattau et al 2016, Miettinen et al 2017, Adrianto et al 2019 or during deforestation (Adrianto et al 2020). Drainage canals established during plantation development lower the water table, increasing the chances of the peat burning (Wösten et al 2008). Peat fires also have higher emission factors for many atmospheric pollutants than vegetation fires (Hu et al 2018, Kiely et al 2019. Together these factors result in peat fires contributing 71%-86% of fire emissions in Equatorial Asia (Heil et al 2007, Kiely et al 2019. Fire emission inventories combine uncertainties in area burned, fuel loads, biomass consumption and pollutant-specific emission factors, resulting in substantial overall uncertainty (Reddington et al 2016). Emissions estimates from Indonesian fires are particularly uncertain (Liu et al 2020), due to difficulties in diagnosing peat burn depth and uncertainties around emission factors from peat combustion (Page et al 2002, Van Der Werf et al 2010, Kiely et al 2019. Many previous studies scaled particulate matter (PM) emissions to improve simulated atmospheric concentrations in comparison to observations (Reddington et al 2016).
In Equatorial Asia, fires occur predominantly in the dry season (August to October) and particularly during periods of drought, often associated with El Niño events, such as those in 1982-1983, 1997-1998(Ballhorn et al 2009, Wooster et al 2012, Field et al 2016. Recent work has also highlighted the role played by the Indian Ocean Dipole (Pan et al 2018). In 2015, an estimated 6-9.1 Tg PM was emitted from Indonesian fires (Wooster et al 2018, Jayarathne et al 2018, Kiely et al 2019. Climate change may lead to increased frequency of extreme El Niño events (Cai et al 2014) and increased future fire activity (Yin et al 2016).
PM less than 2.5 µm in aerodynamic diameter (PM 2.5 ) has been associated with adverse health impacts and premature mortality (Emmanuel 2000, Cohen et al 2017. The World Health Organisation recommends that 24-hour mean PM 2.5 concentrations exceeding 25 µg m −3 could be detrimental to health; regions of Indonesia, Malaysia and Singapore frequently experience concentrations greater than this limit due to smoke from fires , Crippa et al 2016, Lee et al 2017. Previous studies that have estimated the premature mortality attributable to exposure to PM 2.5 from fires across Equatorial Asia, have focused on El Niño years, when fire emissions are greatest (Johnston et al 2012, Sahani et al 2014, Crippa et al 2016, Koplitz et al 2016. Marlier et al (2012) estimated that fires in 1997 resulted in 10 800 excess premature deaths from cardiovascular mortality. For the 2015 haze event, Crippa et al (2016) found that long term exposure resulted in 75 600 excess premature mortalities (from respiratory, pulmonary and heart diseases, lung cancer and stroke). Koplitz et al (2016) estimated premature mortality from all causes with 100 300 excess deaths in 2015 and 37 600 premature deaths in 2006.
Different methods of calculating PM emissions, concentrations and health effects, complicate comparisons across years. Here we use a consistent methodology to provide a multi-year comparison of fire emissions, population exposure to PM and excess premature mortality for Equatorial Asia between 2004 and 2015. Through studying a wide range of years we provide new information on the interannual variability and long-term impacts of fire on air quality and human health in Equatorial Asia.

Methods
In this study, we calculate emissions from Equatorial Asian fires for 2004-2015. We then use a regional air quality model to simulate PM concentrations for the 6 biggest dry-season fire episodes during this period. We evaluate simulated PM against observations across Indonesia and Malaysia. Finally, we use the simulated PM 2.5 to estimate the public health impacts of exposure to the particulate pollution.

Fire emissions
Fire emissions are from FINNpeatSM, described in detail in Kiely et al (2019) and summarised briefly here. FINNpeatSM includes vegetation fire emissions from FINNv1.5 (Wiedinmyer et al 2011). When MODIS fire hotspots are detected on peatland (World Resources Institute 2017) we assume that fires burn into the peat. Emissions from peat fires are estimated from the burn area, peat burn depth, peat density and emission factors (EF). We assume 100 ha of surface burned area for each fire hotspot (as in FINNv1.5), but only 40 ha of peat burn to account for the fact that not all surface fires on peatland will burn into the peat. We estimate the burn depth of the peat based on daily soil moisture from the European Space Agency (ESA CCI SMv04.4) averaged to 2 • degree resolution (Liu et al 2012, Gruber et al 2017. We assume peat burn depth scales linearly with soil moisture between a maximum burn depth of 37 cm (averaged from Page et al 2002, Usup et al 2004, Ballhorn et al 2009

WRF-chem
WRF-chemv3.7.1 was used to simulate PM concentrations across Equatorial Asia (figure 1). The model has been run at 30 km resolution with 33 vertical levels, between the surface and 50 hPa. We used the model to simulate the 6 dry-seasons (August-October) with the greatest fire emissions over the 2004 to 2015 period: 2004, 2006, 2009, 2012, 2014 and 2015. Our domain excludes West Papua, where fires occurred in 2015 (Lohberger et al 2017). All simulations included a 14 day spin up for chemistry at the start of the time period, and with a 24 hour spin up for meteorology every 15-16 day using National Centre Environmental Prediction Global Forecast System (NCEP 2007). In between the meteorology was free running, to allow the model to simulate impacts of fire smoke on meteorology. The MOZART (Model for Ozone and Related Chemical Tracers, version 4; Emmons et al 2010) chemistry scheme was used to calculate gas-phase reactions, with MOSAIC (Model for Simulating Aerosol Interactions and Chemistry; Zaveri et al 2008, Hodzic andKnote 2014) used to represent aerosol processes, separated into 4 bins; 0.039-0.156 µm, 0.156-0.625 µm, 0.625-2.5 µm and 2.5-10 µm. SOA formation from fires in the model is calculated as 4% of the fire emitted CO based on Spracklen et al (2011). A more complete model description can be found in the supplement (table S1 (available online at stacks.iop.org/ERL/15/094054/mmedia)).
Anthropogenic emissions are from EDGAR-HTAP2 ( For each year, model simulations were completed with and without fire emissions. The contribution of fires to PM concentrations is calculated as the difference between the simulations with and without fire.

Observations
Hourly measurements of PM 10 (mass concentration of particulate matter < 10 µm aerodynamic diameter) are available from a network of 53 surface sites across Malaysia (Mead et al 2018) for all the periods of this study (figure 1). Hourly PM 10 is also available from Pekanbaru in Indonesia for 2013 and 2015, and from Bukit Kototabang in Indonesia for 2004, 2006 and 2009. Weekly averaged PM 10 measurements are available from six sites in Indonesia for 2014 and 2015. Hourly measurements of PM 2.5 from 5 locations in Singapore are available for 2014 and 2015, and are averaged to give mean concentrations for Singapore.
Measurements of PM are mainly from urban locations away from the locations of fires. To estimate the PM concentrations from fire at each measurement location we subtract the background PM concentration during months with little fire (months when PM 2.5 fire emissions are <0.1 Tg month −1 across Indonesia).
We averaged hourly data to give daily means, and calculated the fractional bias (FB), Pearson correlation (r), the normalized mean bias factor (NMBF) and normalized mean absolute error factor (NMAEF) (Yu et al 2006) to evaluate the model (supplementary methods).

Population weighted PM 2.5
Population weighted PM 2.5 (PW), a metric of population exposure to PM 2.5 concentrations, was calculated as, where C i is the PM 2.5 concentration and P i is the population of grid cell i, and P tot is the total population of the domain. The population data is from the Gridded Population of the World, Version 4 (GPWv4) (Center for International Earth Science Information Network and NASA Socioeconomic Data and Applications Center 2016). The total population within the domain is 477 million, with 255 million in the Indonesian part of the domain (total Indonesian population is 263 million).

Mortality
The long term premature mortality was calculated using the simulated annual mean PM 2.5 , with and without fire emissions. PM 2.5 from August from the simulation with no fires was used to represent January to July and November to December. Anthropogenic emissions in the tropics have little seasonal variation, and this method has been used previously to estimate population exposure to fires (Crippa et al 2016, Koplitz et al 2016.
Premature mortality per year, M, from disease j in grid cell i was calculated as, where P i is the population in i, I j is the baseline mortality rate (deaths year −1 ) for j, and RR jc is the relative risk for j at PM 2.5 concentration, c (µg m −3 ). The baseline mortality rates and the population age composition are from the GBD2017 (Institute for Health Metrics and Evaluation 2019), and the relative risks are taken from the Global Exposure Mortality Model (GEMM) (Burnett et al 2018) for non-accidental mortality (non-communicable disease and lower respiratory infections). The GEMM exposure function was calculated using the relationship between longterm exposure to outdoor PM 2.5 concentrations and mortality, from studies across many countries. The GEMM exposure function was chosen as it incorporates data from a study in China where PM concentrations are regularly high, as is the case in Equatorial Asia. Mean, upper and lower uncertainty intervals from the GEMM have been used to produce mortality estimates with a 95% uncertainty interval. Population count, population age, and baseline mortality rates were kept constant for 2004-2015 to estimate the variation due to changes in exposure only. To explore differences with previous studies, we also estimate mortality following the method used in Koplitz et al (2016), where the baseline mortality for all causes increases by 1% for every 1 µg m −3 increase in annual mean PM 2.5 concentrations below 50 µg m −3 .

Emissions
The greatest fire emissions occur between August and October each year, with a secondary peak in January to April (figure 2). The largest dry season emissions occurred in 2015, followed by 2006, 2009 and 2004. All of these years experienced monthly total fire emissions that were greater than 1 standard deviation above the long-term monthly mean. Other years with total dry season emissions above the median were 2012 and 2014. Table 1 compares dry season (August to October) burned area, biomass consumption and emissions for FINNpeatSM and GFED4s inventories (van der Werf et al 2017). Averaged across 2004-2015, FIN-NpeatSM has a greater burned area compared to GFED4s (fractional bias, FB = 1.01). Dry matter fuel consumption is more comparable (FB = 0.15) due to greater average dry matter consumption per unit area burned in GFED4s (15 189 g m −2 ) compared to FINNpeatSM (6476 g m −2 ), as a result of greater average peat burn depth in GFED4s. Peat makes up half of the average dry matter consumption in GFED, compared to a quarter of the dry matter consumption in FINNpeatSM. The average emissions of CO and CO 2 are similar (FB = −0.04 and FB = 0.07) for the two inventories, while FINNpeatSM has greater dry season PM 2.5 emissions (FB = 0.48) (table 1), due to higher PM 2.5 EF for peat combustion applied in FINNpeatSM (22.3 g kg −1 ) compared to GFED4s (9.1 g kg −1 ). The total emissions from fires depends on the percentage of peat burned, as well as the overall dry matter consumption (see supplementary results).
GFED4s uses MODIS burned area (Giglio et al 2013), whereas FINNpeatSM applies a 1 km 2 burned area to detected hotspots. Previous studies have also found that this method results in FINN having a larger burned area than other emissions inventories in Asia (Vongruang et al 2017), while Liu et al (2020) suggest thick haze in Indonesia in 2015 prevented detection of fires and that MODIS burned area may be underestimated by 93%. In FINNpeatSM, average burn depth is 7.3 ± 3.7 cm, compared to 10.8 ± 4.8 cm in GFED4s. These estimates are lower than many burn depths recorded in the field (Ballhorn et al 2009, Stockwell et al 2016, however field measurements are likely to be taken at large fires where burn depths may be deeper than average (Stockwell et al 2016).
There is a strong correlation between the dry season emissions simulated by FINNpeatSM and GFED4s (r = 0.87-0.98 for different pollutants, supplementary results), although GFED4s emissions have greater interannual variability (figure S1), due to greater variability in peat burn depth ( figure S2). Emissions are a product of burned area, burn depth and emissions factors. Compensating differences amongst these variables mean that two emission datasets can predict similar emissions for different reasons. Measurements of burned area, burn depth, and emission factors are needed to help further constrain the emission models. Figure 3 compares the spatial pattern of average dry season PM 2.5 emissions in FINNpeatSM and GFED4s. In both datasets South Sumatra and Kalimantan are responsible for the majority of fire emissions, with Sumatra accounting for 33%-42% of PM 2.5 emissions and Kalimantan accounting for 52-63%, in agreement with previous studies , Wooster et al 2018.

Model evaluation
Without fire emissions, the model greatly underestimates PM concentrations across Malaysia and Indonesia (NMBF = −3.72) and the temporal variability across the sites with daily data is poorly simulated (r = 0.27). When fire emissions are included, the model still underestimates observed PM (NMBF = −0.47), although the temporal variability is better simulated (r = 0.51) (figure S3). Most measurements are in urban locations and issues resolving urban-scale pollution are likely to contribute to model underestimation. To overcome this we estimated fire-derived PM from the observations by subtracting measured PM concentrations during periods without fire (see Methods), and compared with the simulated PM concentration from fires (the difference between simulations with and without fires). Figure 4 shows the comparison of simulated and observed fire-derived PM at each site. Across all years, the simulation of fire-derived PM is unbiased (NMBF = 0.14) and the model has reasonable skill in simulating the temporal variability at each site (r = 0.43), although there is year to year and site to site variability (see supplementary results). The NMAEF and FB for the comparison of fires derived PM are also low for each year (NMAEF = 1.07, FB = −0.02; figure S4). Our model skill in comparison against PM 10 observations at 52 sites is similar to a previous comparison by Crippa et al (2016) who reported a  Table 2 gives the average PM 2.5 concentration across the domain and the population-weighted PM 2.5 exposure for Equatorial Asia due to emissions from fires. PM 2.5 and population-weighted PM 2.5 concentrations are greatest in 2015. In 2004 and 2012 there is greater average population weighted PM 2.5 from fires than for 2009, despite 2004 and 2012 having lower total PM 2.5 fire emissions. This is due to there being more fires in Sumatra in 2012 than in 2009, close to populated areas. Despite having lower emissions than Kalimantan, fires in Sumatra can expose a greater population to poor air quality (Reddington et al 2014, Koplitz et al 2016. We estimated a population-weighted smoke exposure over July to October of 8.8 µg m −3 in 2006 (compared to 8 µg m −3 simulated by Koplitz et al (2016)) and 25.6 µg m −3 in 2015 (compared to 19 µg m −3 by Koplitz et al (2016)).

PM 2.5 exposure
Fires increase exposure to PM 2.5 concentrations above the WHO recommended limit of 25 µg m −3  Table 2. The average simulated PM2.5 concentration over Indonesia and population weighted PM2.5 concentration from fires over August to October; the number of people exposed to PM2.5 > 25 µg m −3 for at least half the days in August to October due to fires; the mortality, years of life lost (YLL) and disability adjusted life years (DALY) resulting from exposure to PM2.5 from fires in each year (calculated using GEMM). Descriptions of the calculation of YLL and DALY are in the supplement. The upper and lower estimates are shown in brackets.  5). In 2015 fires resulted in an average of 20 million people being exposed to a daily PM 2.5 concentration > 150 µg m −3 ( figure 5(a)), and 66.5 million people being exposed to daily PM 2.5 concentrations > 25 µg m −3 for at least one in two days during August-October ( figure 5(b)).

Figure 5.
Population exposure to poor air quality. (a) The average population per day exposed to 24-hr PM2.5 concentrations above levels shown on x axis, for simulation with fires (solid lines) and without fires (dashed lines). (b) The number of people exposed to 24-hr PM2.5 concentrations over 25 µg m −3 for at least half the days in August-October. Figure 6. Excess premature mortality due to exposure to PM2.5 from fires. The upper and lower 95% uncertainty interval for the total domain is shown as black lines. Triangles show comparison against previous studies as well as an estimate using our PM exposure combined with the health function used by Koplitz et al (2016). Crippa et al (2016) found that 69 million people in Equatorial Asia were exposed to unhealthy air quality for one day in two in 2015, and Mead et al (2018) found that 26 million people in Malaysia were exposed to PM 10 levels above the WHO recommended limit of 50 µg m −3 . For other years we estimate 22.2-51.7 million people were exposed to PM 2.5 concentrations above 25 µg m −3 for one day in two ( figure 5(b)). The majority of people exposed to poor air quality from fires live in Indonesia (51%-80% of people exposed) and Malaysia (15-30%). Table 2 shows the estimated excess premature mortality, years of life lost, and disability affected life years across the domain resulting from exposure to PM 2.5 from fires. For each year studied, exposure to PM 2.5 from fires resulted in over 13 000 excess premature deaths, 300 000 years of life lost and 500 000 disability affected life years. The greatest number of excess deaths resulting from fires was in 2015. We estimate exposure to PM 2.5 from fires caused 44 000 excess deaths in 2015, less than 75 600 excess deaths estimated by Crippa et al (2016) or the 100 300 excess deaths estimated by Koplitz et al (2016). This difference is due to different methods of estimating the health impacts of exposure to PM 2.5 . Koplitz et al (2016) applied a 1% increase in baseline mortality for all causes of nonaccidental death, for every 1 µg m −3 increase in annual mean PM 2.5 concentration. When we apply the same function with our simulated PM concentrations we estimate 106 000 premature mortalities in 2015, similar to that estimated by Koplitz et al (2016). In 2006, we estimate exposure to smoke from fires results in 22 100 premature mortalities, greater that the 6 000 excess deaths from cardiovascular mortality estimated by Marlier et al (2012) but less than the 37 600 deaths estimated by Koplitz et al (2016). Using the same relative risk as Koplitz et al (2016), we estimate 42 520 excess premature deaths from the 2006 fires, similar to their estimate. This comparison suggests that the largest uncertainty in health impacts is due to uncertainty in exposure response function (i.e. the sensitivity of health to PM exposure) rather than uncertainty in emissions or PM concentrations. Kushta et al (2018) also found that the majority of uncertainty in long term mortality estimates for Europe is related to the relative risk function. For China, Giani et al (2020) found that the uncertainty in the PM concentrations and the relative risk function contributed similarly to overall uncertainty, increasing the range of estimated mortality when both uncertainties were considered. Similar to this study, they found using a different relative risk function led to a much greater difference in estimated mortality, outside the 95% confidence interval. There may also be mortalities from exposure to fire related air pollution which have not been considered in our study. Jayachandran (2013) suggests that the pollution from the 1997 fires in Indonesia may result in early-life mortality, while we have only calculated health impacts for adults. Figure 6 shows the regional distribution of excess mortality due to PM 2.5 exposure from fires. The largest mortality occurs in Sumatra, with 38% of the total mortalities due to PM 2.5 exposure from fire. This is due to a large population with close proximity to the fires. Kalimantan, which has a higher proportion of the PM 2.5 emissions than Sumatra (table 1), has an average of 23% of the total mortalities. Averaged across the years, Malaysia accounts for 18% of the mortalities and Singapore accounts for 4%. Figure 7 shows the annual mean populationweighted PM 2.5 and the annual mortality resulting from exposure to PM 2.5 from fires as a function of particulate emission (primary PM 2.5 emissions and SOA formation; see Methods) from fires. For the years we have studied there is a linear relationship between particulate emission and population-weighted PM 2.5 (r = 0.99) and between emission and estimated premature mortality (r = 0.99). For each Tg of particulates emitted from fires, population weighted PM 2.5 increases by 2.1 µg m −3 , and excess annual premature mortality increases by 2940.

Public health impacts
A linear relationship between emission and exposure may not be expected; exposure to PM 2.5 and resulting impacts on health depend on the location and magnitude of the emissions, as well as the atmospheric transport of pollution. However, in Equatorial Asia, the location of fires and the direction of pollution transport varies little year to year. Each year, dry season fires occur in similar regions of Equatorial Asia (figure 3), consistent south-easterly winds over South Kalimantan and Sumatra result in similar atmospheric transport patterns (Chang and Wang 2005, Heil et al 2007, Wang et al 2013, Lee et al 2017, and the same areas are exposed to poor air quality ( figure S6). This leads to the strong linearity between PM 2.5 emissions, PM exposure, and mortality. The sample size used here is small (n = 6), however, our results indicate that it may be possible to make a simple estimate of PM exposure and health impacts from emissions alone. We used the relationship between PM emission and mortality, to estimate the health impacts from fires across 2004-2015. Total August-October PM 2.5 emissions from 2004 to 2015 were 44.8 Tg, resulting in an estimated 131 700 excess premature mortalities in this period. We note the 6 years studied in detail resulted in a combined total of 113 600 excess premature deaths. We also used this relationship combined with the particulate emission per unit area burned (table 1)

Conclusion
We combined a new method of calculating emissions from peat fires (FINNpeatSM), a regional air quality model and a concentration-response function to make the first consistent estimate of the impacts of smoke from Equatorial Asian fires on human health over the period 2004 to 2015. Over this period, FIN-NpeatSM has a larger burned area but shallower peat burn depth compared to GFED4s, leading to similar biomass consumption, CO and CO 2 emissions for both inventories. We estimate average August-October PM 2.5 emissions were 2.7 Tg yr −1 , 59% greater than in the GFED4s dataset, largely due to greater PM 2.5 emission factors for peat combustion in our estimates. We estimate that the largest fire emissions occurred in 2015, due to the greater area burned and deeper peat burn depth compared to other years. Deeper peat burn depth is a result of low soil moisture in 2015, confirming that soil moisture plays an important role in controlling emissions from peat fires. We estimate that 94% of PM 2.5 emissions from fire across Equatorial Asia are from Indonesian fires, with 60%-82% due to fires in Kalimantan. Improving emission estimates requires better estimates of both area burned and peat burn depth, including how this varies with soil moisture. A detailed evaluation against multiple in-situ and remote sensed data is needed to constrain emissions and better understand interannual variability.
We used the WRF-chem model to simulate PM concentrations for the six years during 2004-2015 with the largest fire emissions. Simulated PM concentrations resulting from these fire emissions reproduced measured concentrations across Indonesia and Malaysia, supporting our new emissions estimates. In contrast, previous studies have resorted to scaling PM emissions to better match surface concentrations (Koplitz et al 2016;Marlier et al 2012). In 2015, we estimate fires exposed 66.5 million people to daily mean PM 2.5 concentrations exceeding the WHO limit of 25 µg m −3 , for at least half of the August to October period. Measurements of PM 2.5 concentrations in regions impacted by fires are needed to evaluate these exposure estimates.
We used simulated PM 2.5 to estimate the health impact of fires across the different years. We estimate that exposure to PM 2.5 from fires resulted in 44 000 excess deaths in 2015, less than in previous studies due to the less sensitive relative risk function we used. New analysis is needed to help constrain the public health impacts of exposure to PM from fires. In other years (2004, 2006, 2009 and 2012) we estimate exposure to PM resulted in 14 000-22 000 premature deaths annually, with a total of 131 700 premature mortalities resulting from August-October fires during 2004-2015. Our work confirms that smoke from Indonesian fires regularly cause substantial impacts on human health across the region. Unless further action is taken to reduce fires, air pollution from fires will continue to cause substantial health burden across Equatorial Asia over the next decade (Marlier et al 2019). of Strengthening Research and Development Ministry of Research, Technology and Higher Education (No. 4097/IT3.1.1/PN/2019). We acknowledge the use of the WRFotron scripts developed by Christoph Knote to automatize WRFchem runs with reinitialized meteorology. We would like to acknowledge Syahrial Sumin, who provided us with PM 10 data from Pekanbaru, and Asep Firman Ilahi from The Indonesia Agency for Meteorology Climatology and Geophysics (BMKG) who provided PM 10 data from the Bukit Kototabang GAW station. Also PTSP-BMKG for providing the weekly PM 10 data from Indonesian stations for 2014 and 2015.

Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.