4.1. Peatland EF’s, Gaseous Emissions Totals and Dry Matter Fuel Consumption
In situ gaseous EFs for a subset of the locations included in
Table 1 were already reported by [
12], upon which the first published total carbonaceous gas emissions calculations for the 2015 Indonesian fire event were based (see [
12] for details). The gaseous EFs we report in
Table 4 are not only more extensive however, but also greater because they are based on our higher measured peat fractional carbon content (
Table 2) rather than the lower C-fraction assumed by [
12] whilst measured C-contents were unavailable. Use of our new
Table 2 trace gas emissions factors for burning of both pure peat (Locations 1, 2 and 5) and vegetation atop peat (Locations 3 and 4) enables us to calculate “peatland landscape” EFs, based on a 73% weighting of pure peat burning and 27% vegetation burning atop peat following [
12,
54,
72]. These peatland EFs are 1779 ± 55 g·kg
−1 for CO
2, 238 ± 36 g·kg
−1 for CO, and 7.8 ± 2.3 g·kg
−1 for CH
4, and since they are within 10% of those calculated by [
12] using the same weighting factors they provide little evidence for a major change to the total emissions of these three dominant carbonaceous gases reported therein. In fact, to derive total emissions of CO, CO
2 and CH
4, [
12] used a top-down optimization approach based on comparisons between spaceborne MOPITT atmospheric CO concentrations and
a priori Global Fire Assimilation System [GFAS] CO emissions from [
33] placed within the C-IFS atmospheric chemistry transport model (operated as part of the Copernicus Atmosphere Monitoring Service). The resulting CO emissions estimate reported by [
12] is therefore insensitive to changes in EF
CO. Furthermore, since the CO
2 and CH
4 emissions estimates of [
12] are based on this top-down, “optimized” CO emissions estimate, along with the ratios of the CO
2 and CH
4 emissions factors relative to those of CO (rather than the absolute EF values, as would be the case in “bottom-up” calculations such as employed by GFAS [
33] and the Global Fire Emissions Database (GFED) [
34]), the total CO
2 and CH
4 emissions estimates provided by [
12] are also unaltered by our use of a higher peat carbon content in Equation (2).
Table 5 presents the gaseous carbonaceous emissions totals derived by [
12] and confirmed herein, and compares these to those reported by GFASv1.2 [
33] and GFEDv4.1s [
34]. The primary difference is the much larger methane emission total reported by these latter databases, primarily related to their sensitivity to assumed CH
4 emissions factors and their assumption of tropical peat and tropical peatland EF
CH4 values of 20.8 g·kg
−1 and 11.8 g·kg
−1 respectively, based primarily on laboratory peat burning and EF summary databases [
16,
54]. These are far higher than the 7.9 and 7.8 g·kg
−1 EF
CH4 means we respectively report for ‘pure peat’ and ‘peatland’ fires on the basis of our in situ smoke sampling (
Table 4).
To provide the dry matter (DM) fuel consumption estimates for the 2015 Indonesian fires, [
12] use their total CO
2 emissions estimate (reported in
Table 5) and their CO
2 emissions factor (use of CO
2 is preferred because it is the species whose EF is typically the most consistent between periods of flaming and smoldering and between combustion of different fuel types, as well as it being the species emitted in greatest quantity). Unlike the gaseous emissions estimates, the DM fuel consumption estimate reported by [
12] is sensitive to changes in EF
CO2, and based on our in situ emissions factor update for these tropical peat and vegetation fires we calculate a new total DM fuel consumption of 358 Tg for Kalimantan and Sumatra during September–October 2015, distributed as shown in
Table 6 (we retain the GFASv1.2 standard tropical forest EF
CO2 for non-peatland regions of these islands, as did [
12]). Our DM fuel consumption total is overall slightly lower than that of [
12], reflecting our higher peatland fire CO
2 emissions factor (1779 ± 55 g·kg
−1) and the fact that the clear majority of the fuel consumption occurred in peatland landscapes. Our DM total is more significantly lower than those of GFEDv4.1s and GFASv1.2 (
Table 6), though in fact our Kalimantan total is quite similar to the basic GFAS inventory in September and October, but for Sumatra we calculate greatly reduced values compared to GFAS (primarily because optimization against MOPITT CO significantly lowers the Sumatra emissions compared to the
a priori GFAS, as detailed in [
12]). GFED’s total DM fuel consumption estimate (461 Tg) is almost exactly midway between that of GFASv1.2 (541 Tg) and the 358 Tg provided by our calculations, but its distribution between islands and between the two months of extreme burning is very different, possibly reflecting major differences between its purely bottom-up methodology and the MODIS burned area product that drives it, and that of the MODIS fire radiative power (FRP)-based GFAS (though the FRP-to-DM conversion factors used by GFAS are based on previous GFAS comparisons to GFED [
33]). Overall, our use of MOPITT CO observations, in situ gaseous EFs, and the deployment of these in adjusting the basic GFAS emissions (as detailed in [
12] and updated herein) has significantly reduced the DM fuel consumption estimates of the September–October Indonesian fires compared to the most widely used global fire emissions inventories.
Both GFAS and GFED use a PM
2.5 emissions factor of 9.1 g·kg
−1 for both tropical peatlands and forests, largely based on the EF data included in [
54]. This is far lower than every PM
2.5 emissions factor derived herein based on in situ measurements of peatland fire smoke (see
Table 4), apart from a few plumes encountered at location 5. The most recent GFASv1.2 (0.1° resolution) and GFEDv4.1s (0.25° resolution) show similar September–October 2015 combined Kalimantan and Sumatra fine particulate matter (PM
2.5) emissions totals, 3.99 Tg and 4.2 Tg respectively (see
Figures S2 and S3), but they also show significant differences in the partitioning between the two months and between the two islands of Kalimantan and Sumatra. This in part reflects the DM fuel consumption differences between these inventories discussed in
Section 4.1. Our far higher in situ derived EF
PM2.5 values (
Section 3.4), along with our updated DM fuel consumptions (
Table 6), enables us to provide new PM
2.5 emissions totals, and in addition we can utilize the very high temporal resolution (10-min) fire radiative power (FRP) data recently available from the Himawari geostationary satellite [
40] to fully resolve the diurnal cycle of these smoke emissions, which can be important when linking them to atmospheric chemistry transport models (CTMs) [
71]. Using the data of
Table 4, we derive a mean peatland EF
PM2.5 of 28 ± 6 g·kg
−1, calculated as with the carbonaceous gas EFs from a 73% weighting of pure peat burning and 27% vegetation burning atop peat following [
12,
54,
72]. For non-peatlands we continue use of the 9.1 ± 3.5 g·kg
−1 assumed by GFAS and GFED, following [
54]. Assuming these emissions factors, our final PM
2.5 emissions total for the September–October 2015 Indonesian fires is 9.1 ± 3.2 Tg, two thirds from Kalimantan (
Figure 12a,b) and 95% from burning peatlands. We note that some non-peatland fires were in cleared (non-forest) areas, and since [
54] assume EF
PM2.5 of 15 ± 7 g·kg
−1 for “land maintenance fires” these may have a higher EF
PM2.5 than pure tropical forest burns. However, substitution of this EF in place of 9.1 ± 3.5 g·kg
−1 would elevate our total PM emissions insignificantly, since our calculations indicate that 95% of the PM
2.5 emissions come from burning peatlands.
Himawari FRP data [
40] enable the spatio-temporal mapping of the particulate emissions at far higher detail than hitherto possible, and whereas their broad spatial pattern (
Figure 12a,b) is similar to that reported by GFASv1.2 (
Figure A1) and GFEDv4.1s (
Figure A2), our 9.1 ± 3.2 Tg PM
2.5 emission total is more than double (~×2.2) of those inventories. Their lower totals are primarily driven by their assumed EF
PM2.5 of 9.1 g·kg
−1, irrespective of whether a fire is burning atop peat, which more than counteracts the fact that both GFASv1.2 and GFEDv4.1s estimate DM fuel consumptions for the 2015 Indonesian fire event to be significantly higher than the 358 Tg upon which our 9.1 ± 3.2 Tg PM
2.5 emissions estimate is based (see
Table 6). Retaining instead the GFASv1.2 and GFEDv4.1s DM totals from
Table 6, but using our updated PM
2.5 emissions factors, would increase the magnification of our fine particulate matter emissions estimate over that of the GFED and GFAS values to around ×3. A further global inventory, the Fire Inventory for NCAR (FINN) v2 [
73], reports even lower PM emissions than GFEDv4.1s or GFASv1.2, around 5× lower than our 9.1 Tg. Since FINNv2 was recently used as the input for a CTM-based study estimating the extent and severity of short-term health impacts of PM
2.5 exposure across Aoutheast Asia (with 6153–17,270 excess mortalities estimated [
31]) our significantly higher PM
2.5 emissions total suggests a re-appraisal and potential uplift of these impacts maybe necessary. Using a 50% upscaling of the GFASv1.2 PM
2.5 emissions, which brings them closer (albeit still well below) those of the current work, [
26] previously estimated a much higher excess death total of around 100,000.
4.2. High Temporal Resolution Emissions
Exploring our very high temporal resolution PM
2.5 emissions timeseries (
Figure 12c), we see Kalimantans’ smoke particulate emissions rate peaking at more than of 15 tonnes-PM
2.5·s
−1 in mid-October 2015, shortly before PM
10 measurements in Palangkaraya showed their >3000 µg·m
−3 concentration maxima (
Figure 12d). This maximum is around ten times the threshold considered extremely hazardous for health (see
Section 1), and while the daily atmospheric PM concentration cycle recorded at Palangkaraya (
Figure 12d) mirrors that seen in the emissions (
Figure 12c), they have differently timed peaks because the atmospheric PM concentrations are driven by meteorology, plume processing, and aerosol deposition processes, as well as by the PM emissions rates themselves.
Further investigations using the geostationary FRP-based methodology of [
40] indicates that these extreme SE Asian fires show a diurnal cycle peaking generally later in the day than fires dominating most other tropical forest regions, for example those in parts of South America and tropical Africa [
74,
75,
76]. Specifically, the Indonesian fires show a diurnal cycle peaking on average between 16:00 and 18:00 h local solar time (
Figure 13), which appears most similar to the peak timing of Brazilian deforestation fires [
74] (where fuel is sometimes piled before burning) and fires in the swamp forests of southern Africa [
76] (which include areas of tropical peat). The 2015 Indonesian fires also seem to peak significantly later in the day than those seen during more “normal” (non-drought) years in the same region [
11,
77]. Much of this anomalous fire timing seems likely to be driven by fires in the degraded tropical peatlands accessing the dried-out peat substrate even more significantly during extreme drought than during more “normal” meteorological periods, agreeing with the very significant amplification of Indonesian fire activity often seen during drought events [
11,
36,
42]. Separating out our Himawari-derived 2015 fire diurnal cycles by landcover indicates specifically that Indonesia’s extreme peatland fires (
Figure 13a) peak on average ~2 h later in the day than those in non-peat areas burning under the same general climatological conditions (
Figure 13b), and the former also show a daily (full width, half-height) fire duration almost twice as long. This may reflect the fact that sub-surface peat combustion, which was seen occurring across very large regions of peatland during our field campaign (e.g.,
Figure 2b) and which is described in detail in [
9], is likely to be less influenced by the daily meteorological cycles of wind, relative humidity and air temperature, factors which (along with ignition timing) typically drive the diurnal variability of surface vegetation fires occurring in non-peat areas [
78].