ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-363-2015Importance of transboundary transport of biomass burning emissions to regional air quality in Southeast Asia during a high fire eventAouizeratsB.benjamin.aouizerats@vu.nlvan der WerfG. R.BalasubramanianR.BethaR.Faculty of Earth and Life Sciences, VU University Amsterdam, Amsterdam, the NetherlandsDepartment of Civil and Environmental Engineering, National University of Singapore, SingaporeSingapore-MIT Alliance for Research and Technology (SMART), Centre for Environmental Sensing and Modeling (CENSAM), SingaporeB. Aouizerats (benjamin.aouizerats@vu.nl)13January201515136337313March20147May201420November201422November2014This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://www.atmos-chem-phys.net/15/363/2015/acp-15-363-2015.htmlThe full text article is available as a PDF file from https://www.atmos-chem-phys.net/15/363/2015/acp-15-363-2015.pdf
Smoke from biomass and peat burning has a notable impact on ambient air
quality and climate in the Southeast Asia (SEA) region. We modeled a large
fire-induced haze episode in 2006 stemming mostly from Indonesia using the
Weather Research and Forecasting model coupled with chemistry (WRF-Chem). We
focused on the evolution of the fire plume composition and its interaction
with the urbanized area of the city state of Singapore, and on comparisons of
modeled and measured aerosol and carbon monoxide (CO) concentrations. Two simulations were run
with WRF-Chem using the complex volatility basis set (VBS) scheme to
reproduce primary and secondary aerosol evolution and concentration. The
first simulation referred to as WRF-FIRE included anthropogenic, biogenic
and biomass burning emissions from the Global Fire Emissions Database (GFED3)
while the second simulation referred to as WRF-NOFIRE was run without
emissions from biomass burning. To test model performance, we used three
independent data sets for comparison including airborne measurements of
particulate matter (PM) with a diameter of 10 µm or less (PM10) in
Singapore, CO measurements in Sumatra, and aerosol optical depth (AOD) column
observations from four satellite-based sensors. We found reasonable agreement
between the model runs and both ground-based measurements of CO and
PM10. The comparison with AOD was less favorable and indicated the model
underestimated AOD, although the degree of mismatch varied between different
satellite data sets. During our study period, forest and peat fires in
Sumatra were the main cause of enhanced aerosol concentrations from regional
transport over Singapore. Analysis of the biomass burning plume showed high
concentrations of primary organic aerosols (POA) with values up to
600 µgm-3 over the fire locations. The concentration of POA
remained quite stable within the plume between the main burning region and
Singapore while the secondary organic aerosol (SOA) concentration slightly
increased. However, the absolute concentrations of SOA (up to 20 µgm-3) were much lower than those from POA, indicating a minor role of
SOA in these biomass burning plumes. Our results show that about 21 % of the
total mass loading of ambient PM10 during the July–October study period
in Singapore was due to biomass and peat burning in Sumatra, but this
contribution increased during high burning periods. In total, our model
results indicated that during 35 days aerosol concentrations in Singapore
were above the threshold of 50 µgm-3day-1 indicating
poor air quality. During 17 days this was due to fires, based on the
difference between the simulations with and without fires. Local pollution in
combination with recirculation of air masses was probably the main cause of
poor air quality during the other 18 days, although fires from Sumatra and
probably also from Kalimantan (Indonesian part of the island of Borneo) added to
the enhanced PM10 concentrations. The model versus measurement
comparisons highlighted that for our study period and region the GFED3
biomass burning aerosol emissions were more in line with observations than
found in other studies. This indicates that care should be taken when using
AOD to constrain emissions or estimate ground-level air quality. This study
also shows the need for relatively high resolution modeling to accurately
reproduce the advection of air masses necessary to quantify the impacts and
feedbacks on regional air quality.
Introduction
Biomass burning plays an important role in atmospheric composition and
chemistry . Fires occurring close to
populated areas severely impact air quality affecting millions of inhabitants
. Governments and international
organizations such as the World Health Organization (WHO) have produced
pollution guidelines in the last decade , but the
contribution of biomass burning emissions to local air quality is neither
well understood nor quantified.
Southeast Asia (SEA), especially Indonesia, has high biomass burning fuel consumption (up to
20 kg C per m-2 burned) due to fires burning in the peatlands
. This, in combination with frequent fire activity
ensures that the region has the highest density of fire emissions globally.
Fire activity is highly modulated by the El Niño–Southern Oscillation
(ENSO) and the Indian Ocean Dipole (IOD)
. Densely populated areas such as Java
and the city of Singapore are located relatively close to large fires mainly
in Sumatra and Kalimantan and regularly show high particulate pollution
levels which are often related to emissions from forest, agriculture and
peat fires . Models
that accurately simulate biomass burning plumes and their air quality impacts
in this complex orographic and meteorological region are necessary to better
understand the transport and evolution of smoke plumes.
Air pollution caused by aerosol particles is of concern because of reduction
in visibility and adverse environmental and health impacts
. Depending on their size and chemical composition,
aerosol particles can penetrate into the respiratory system and increase
throat and lung infections . In
addition, aerosols also increase the risk of developing lung cancers
. Fires emit high concentrations of particles of small sizes
as well as volatile and semi-volatile organic compounds which may act as
precursors in the formation of secondary aerosols
. In this study, we
focus on transboundary particulate pollution levels affecting the Republic of
Singapore (population of over 5 million) due to the release of aerosol
particles from biomass burning in Indonesia. We used WRF-Chem to (1) advect
the aerosol and gaseous precursor concentrations emitted by biomass burning,
(2) represent the evolution of the aerosol plume dynamics and chemistry and
(3) evaluate the interactions between this transported and aged air mass from
fires with freshly emitted urban pollution in Singapore.
WRF-Chem setup and evaluationModel setup
We used the online-coupled regional Weather Research and Forecasting model
with chemistry (WRF-Chem) v3.4 to simulate meteorology and
atmospheric composition at a regional scale. The fully coupled model WRF-Chem
computes at each time step the dynamic processes including advection as well
as the microphysics and the atmospheric chemistry and aerosol processes.
The simulation was done for a domain with 100 × 100 grid points, each with a
15 km × 15 km horizontal resolution. The domain included Sumatra (Indonesia),
the Republic of Singapore and the southern part of the Malaysian peninsula
(see Fig. 1). The model had 30 vertical levels from ground level up to 23 km
height with a stretching resolution from 60 m to 1.6 km for the bottom and
top level, respectively. The simulation was run from 1 July to 31 October
2006 (a 4-month period) including a high fire episode in Sumatra occurring in
October. The temporal resolution of the simulation was 90 s.
The domain was initialized by the National Centers for Environmental
Prediction FiNaL reanalysis (NCEP-FNL) data for the meteorological variables
and by the MOZART4-NCEP model output for the chemical gases
and primary aerosols initialization . The boundaries of
the domain were also forced by the NCEP-FNL and MOZART4-NCEP re-analyses
model outputs which were called for input every 6 h. The WRF-Chem
configuration used the volatility basis set (VBS) scheme for aerosol chemistry ,
the MADE (modal aerosol dynamics model for europe) module for the aerosol
dynamic processes and the RACM (regional atmospheric chemistry modeling)
reaction scheme for the gaseous chemistry reactions.
The aerosol particle population was described by three modes (Aitken,
accumulation and coarse), each of them following a lognormal distribution.
Each aerosol mode was composed of primary particles (primary organic carbon,
black carbon, dust and sea salt) and secondary particles (sulfate, nitrate,
ammonium, 4 classes of anthropogenic secondary organic aerosol, 4 classes of
biogenic secondary aerosols and resulting water). Dust, sea salt and biogenic
particles showed concentration values lower than 1 % of the total aerosol
concentrations and are therefore not discussed in the rest of this study, but
were included in the model runs. The simulation included anthropogenic,
biogenic and biomass burning emissions prepared by the PREP-CHEM-SRC software tool
.
Monthly averaged emissions in µgm-2s-1 of
primary organic carbon from anthropogenic (top) and biomass burning (bottom)
sources for October 2006. SG is short for Singapore and BKT indicates the CO measurement station Bukit Kototabang.
The anthropogenic emissions were derived from the EDGARv4
and RETRO inventories. The biogenic emissions were computed
by the MEGAN model v2.1 . The daily biomass burning
emissions were taken from Global Fire Emissions Database (GFED3) and the emission
factors for the volatile organic compounds (VOCs) as well as for the primary
aerosol particles are deduced from . As mentioned by
, the methodology used in GFED3 based on burned area may
fail to detect relatively small fires such as those burning in agricultural
areas . On the other hand, GFED3 burned area in
deforestation regions received a boost based on fire persistence partly to
account for missing these fires .
Preliminary GFED4 emissions estimates, which are corrected for small fires
and subsequently do not receive the boost anymore, indate that these two factors were of similar magnitude.
Table 1 shows the emission factors of aerosol species as used in the GFED3 database and
deduced from the newer emission factor compilation from as
used in our simulations. Table 1 shows that for the 4 months of interest, the
aerosol particle emissions from biomass burning used for the simulation are
27.7 % higher than in the GFED3 database. Emissions of primary organic
carbon from anthropogenic sources and biomass burning sources are shown in
Fig. 1.
A total of 24 h averaged aerosol mass concentration observed (in black
crosses) and modeled (in blue line) over Singapore for our study period. The
50 µgm-3 indicates the WHO definition of polluted air.
Comparison of aerosols emission factors (in gram per kilogram of dry matter)
used in GFED3 and from Akagi et al. (2011) as used in the simulations. The
relative differences in percentage are given in parenthesis.
OCpBCPM10GFED4.490.555.04Akagi et al. (2011)6.23 (+38.6 %)0.20 (-165 %)6.43 (+27.7 %)Comparison with observations
We compared the model outputs with observations to gain confidence in our
model setup. The observations used include ground-based measurements
(PM10 and carbon monoxide, CO) as well as a set of various satellite sensors measuring
AOD. The PM10 observations were the averaged values of five urban ambient
air quality stations located in different parts of Singapore and monitored by
Singapore's National Environment Agency. The CO observations were located on
the Bukit Kototabang site on the island of Sumatra; see Fig. 1
. The AOD observations at 550 nm used in this study
are 2-week averaged observations of a 1∘×1∘ area
centered over Singapore from the Moderate Resolution Imaging
Spectroradiometer (MODIS), Multi-angle Imaging SpectroRadiometer (MISR),
Ozone Monitoring Instrument (OMI) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) sensors.
The model results indicated that there were three distinct time periods with regard to aerosol
concentrations in Singapore (Fig. 2).
The first period lasted from July to the end of September, and the 24 h
averaged aerosol concentrations in Singapore were relatively low and almost
never exceeded the value of 50 µgm-3 for PM10, indicated
by the World Health Organization as the threshold for
classifying the ambient air quality as polluted. The averaged value for this
period was 35 µgm-3 representing urban background
concentrations in Singapore. During this time period, only small fires
occurred in Sumatra and the wind regime did not advect the resulting plumes
in the direction of Singapore. During the second period, from the end of
September until the middle of October, the aerosol concentrations (PM10)
were high (values reaching 160 µgm-3) and were coupled with
relatively steady southeasterly winds with a surface mean velocity values of
7 m s-1. The third period ran from the middle of October until the end
of October, and the aerosol concentrations remained high (values reaching 160 µgm-3). The wind regime over Singapore showed relatively low
velocities (4 m s-1) and directions varied between day and night,
indicating that the main wind component in Singapore during this period was
the thermal wind regime between land and sea. Fires also occurred in Sumatra
during this latter period, but the wind regime did not advect the resulting
plumes to Singapore according to our model.
WRF-Chem modeled aerosol optical depth (AOD) and AOD observed by
MODIS, MISR, OMI and SeaWiFS. AOD values are averaged over a 1∘×1∘ area centered over Singapore.
In Fig. 2, the 24 h modeled average values of aerosol mass concentrations in
Singapore at ground level and the 50 µgm-3 threshold as used
by the WHO to define polluted air are shown. The modeled results agreed
reasonably well with surface observations. Figure 2 shows that the WRF-Chem
model managed to reproduce the evolution of the aerosol mass concentration in
Singapore both for background aerosol concentrations and during the
haze period, characterized by elevated aerosol concentrations occurring in
October. The correlation coefficient (R) between field observations and
model results for the whole period was 0.62.
Besides the local aerosol concentration at ground level, we also compared our
modeled results to aerosol optical depth (AOD) as measured by various
satellite instruments. Figure 3 shows 2-weekly average AOD modeled at the
wavelength of 550 nm as observed by different satellite sensors. The data
are shown as 2-week moving averages, this was done to present a consistent
comparison of the various sensor measurements and minimize the error and
noise due to the different overpass times from the various satellites and as a
result of cloud contaminated pixels. For the first period (July–September)
with low values, the different observations and model results were in
relatively good agreement with AOD. However, Fig. 3 also shows that the
modeled AOD was low during the month of October compared to observations from
MODIS, MISR and SeaWiFS. The quantitative disagreement varies between the
different sensors but is largest when compared to MODIS observations with up
to a factor of 2.5 in the middle of October. In addition,
showed that AOD measurements in this region are often underestimated by up to
50 % which would deteriorate the comparison even further. There is,
however, agreement on the temporal trend in aerosol concentrations with most
of the observations. The discrepancy with measured AOD can probably be
explained by an elevated aerosol layer observed over Singapore as described
by and . This pollution layer appears to come from
outside the domain and is represented in the boundary conditions entering the
domain from the east. After entering the domain, the model located this
advected pollution layer south of Singapore and therefore it is not represented
in the simulated AOD over Singapore. Due to the height of the transported
pollution layer (2500 m) it does not affect our results which focus on the
lower atmosphere. Another explanation of the model AOD underestimation may be
contamination of the observed AOD due to tropical cirrus and opaque clouds
as described by several studies .
CO concentration observed (in black crosses) and modeled with WRF-Chem
either with fires (red) or without (blue) over Bukit Kototabang for our study period.
In addition to these comparisons with aerosol observations, we compared our
results with one station in Sumatra with continuous carbon monoxide (CO)
observations . The CO is measured by a TEI48C TL
instrument installed in 2001 and the data set can be accessed through the
Global Atmosphere Watch network (http://gaw.empa.ch/). Figure 4 shows
the evolution of the CO concentrations during our 4-month study period at the
Bukit Kototabang station (BKT, see Fig. 1). The model results are drawn in
blue and red lines and indicate the simulations excluding biomass burning
emissions (referred to as WRF-NOFIRE later on this document) and including
biomass burning emissions (referred to as WRF-FIRE), respectively. The model
managed to correctly represent the background concentrations as well as the
high level of CO concentrations (up to 1300 ppb) in October due to biomass
burning, inducing that both model transport and CO emissions from the GFED3
database are correctly represented in this study. One can also note, however,
that several smaller fire episodes were not well captured by either WRF or
GFED3, especially in August.
Aerosol plume analyses: composition and distribution
The comparison of model outputs with observations shows that the WRF-Chem
model setup is capable of representing quite accurately the evolution of the
total aerosol mass concentrations for the 4 months of simulation. While the
PM10 comparison indicated the model was able to reproduce the
measurements, we cannot conclusively state that the model managed to
reproduce the aerosol chemical composition because no measurement information
on the exact aerosol composition was available. However, given our efforts to
accurately take into consideration the partitioning of emissions (including
various VOCs) as well as the use of one of the most
accurate aerosol–chemistry reaction schemes available at the present time (VBS
scheme), the good match between the total aerosol mass concentrations modeled
and observed yields some confidence in these results. We now turn our focus
on the composition of aerosol particles at the biomass burning emission
location, along the plume, and in Singapore. Figure 5 shows the horizontal
cross section of primary aerosol mass concentration on the left and SOA mass
concentration on the right, at the surface level on 3 October 2006 at
12:00 LT
(local time). Although being a snapshot, it is a representative one involving
the interaction between remotely emitted biomass burning aerosols and freshly
emitted urban aerosols in Singapore.
Figure 5 illustrates that primary aerosols were highly concentrated over
emission sources and reached values of 350 µgm-3 at the main
biomass burning location (marked as point A) and 180 µgm-3 in
Singapore (marked as point B). Those high concentrations of primary aerosols
thus rapidly decreased away from the emission sources. On the other hand, SOA
reached high concentrations a few kilometers away from the emissions sources.
While the amplitude and the variability were much lower than for the primary
aerosols (from 1 to 10 µgm-3 compared with 20 to
600 µgm-3), Fig. 5b shows that SOA were formed remotely from
the biomass burning emissions along the plume and were mixed with freshly
formed secondary organic aerosols from fast chemical reactions in Singapore.
Primary aerosol (secondary organic aerosol) mass concentrations on
the top (bottom) with values higher than 10 (1) µgm-3 at the
surface level on 3 October 2006 at 12:00 LT. The wind speed vectors are
overlaid in black arrows.
Aerosol mass composition evolution from point A to point B in Fig. 5
with (a) showing the primary organic carbon versus non-primary
organic carbon speciation, and (b) details the non-primary organic
aerosol composition. Each bar is spaced by 15 km along the transect drawn in
Fig. 5. The numbers in the bars indicate the relative contribution.
Not only does the aerosol concentration change rapidly along the plume, its
chemical composition also shows substantial fluctuations, as seen in Fig. 6.
The figure shows a transect from the source in Sumatra marked by A
in Fig. 5 to the city of Singapore marked with a B. The main
message from Fig. 6 is that the total aerosol population, in the A to B
transect, was largely dominated by primary organic aerosols (POA) representing 83 to 95 % of the total
aerosol mass concentration. The contribution of POA varied along the plume
with the highest values at the biomass burning location. It sharply decreased
about 75 km away from the biomass burning location, but then slightly
increased again along the plume. This initial decrease was due to deposition
of bigger particles close to the fire location while the smallest ones kept
being advected toward the Malaysian peninsula. The contribution of POA to
total aerosol concentration was relatively stable along the plume around
92 %, but dropped to 83 % close to the city of Singapore largely due to
increased non-POA concentrations. While the percentage of POA may appear high
compared to other recent studies , it remains
consistent for this intense fire episode with the emission ratios reported in
. Moreover, the results in this study show a significantly
lower SOA / POA ratio in the plume than the ratio reported by several studies
mainly focused over North America . This difference may be related to
the high density of fire emissions leading to very large emissions of both
primary particles and precursory gases responsible for the formation of
secondary organic aerosols. However, the formation of secondary organic
aerosols is a strongly non-linear process which depends on numerous and
complex processes (such are the VOC concentrations, ozone concentrations,
NOx concentrations, water vapor, aerosol internal mixing rate,
etc.) . Therefore, its formation can quickly
reach its saturation mixing ratio or a threshold due to a limiting factor,
while the primary particles are linearly emitted as a function of the burned
fuel. In our case we believe that the partitioning between the vapor and
aerosol phase has quickly reached a saturation point due to the
NOx and ozone conditions. So even though the VOC concentrations
needed for the formation of SOA were still high other factors became limiting,
suggesting that the SOA / POA ratio varies between different fire types
and intensities. It should be mentioned though that due to the complexity
involved in the chemical reactions, almost every numerical model tends to
underestimate the secondary aerosol formation .
Aerosol mass concentrations from the simulations WRF-FIRE (red line)
and WRF-NOFIRE (blue line) in Singapore for our study period in 2006. The fire
emissions of primary organic carbon aerosols in Sumatra are drawn as a dashed black line.
The non-POA aerosol concentration (represented in Fig. 6b) shows first
relatively high values at the biomass burning location dominated for 63 %
by black carbon (BC). The non-POA fraction sharply decreased away from the
source to about half its original value. The absolute concentration of
non-POA aerosol mass concentration increased slightly along the plume due to
an increase of the SOA formation. Finally in Singapore,
the local anthropogenic emissions of BC dominated the non-POA
aerosol concentrations while the SOA concentration remained stable. The
differences in the contribution of primary aerosols between BC and POA in the
biomass burning location and in Singapore were due to the difference in the
emission factors for peat fires and combustion .
Comparison of speciated averaged aerosol mass concentrations in
µgm-3 over Singapore for the FIRE simulations (columns 2 and
4) and the NOFIRE simulations (columns 3 and 5) for the 4-month period
(columns 2–3) and for the high fire period (columns 4–5). The relative
differences between the two runs are given in columns 3 and 5 in parentheses.
4-month period (Jul–Oct) 28 Sep–13 Oct WRF-FIREWRF-NOFIREWRF-FIREWRF-NOFIRETotal aerosol53.342.1 (-21 %)97.450.5 (-48 %)Black carbon10.710.1 (-6 %)14.112.3 (-13 %)Organic carbon40.730.0 (-26 %)81.036.4 (-54 %)Secondary organic carbon1.51.4 (-7 %)3.32.0 (-39 %)Inorganic aerosols0.40.4 (0 %)0.60.4 (-67 %)Relative and absolute contribution of aerosols from biomass burning to pollution level in Singapore
In order to identify and quantify the impact of biomass burning on aerosol
pollution levels in Singapore, we ran two different simulations to isolate
the impact of fires on the region. The first one included the biomass burning
emissions and is referred to as WRF-FIRE. The second one only included
anthropogenic and biogenic emissions and is referred to as WRF-NOFIRE.
The results for both simulations with regard to aerosol mass concentration in
Singapore are shown in Fig. 7. From July to the end of September the two
simulations varied marginally. From early October until the middle of October
large fires in Sumatra induced big differences between the two simulations in
Singapore. The maximum difference was found on 10 October with values of 40
and 140 µgm-3 for the WRF-NOFIRE and WRF-FIRE, respectively.
Somewhat surprisingly, the second half of the month of October shows high
values of aerosol concentrations but no major differences between the two
simulations. During this period, 12.7 % of the aerosol concentration was
coming from outside the domain and was probably due to advected fire plumes
emitted in southern Kalimantan as shown by and
, and 14.4 % was due to fires occurring in Sumatra. Thus,
the model indicated that during the second half of October, 73 % of the
aerosol concentration was due to anthropogenic emissions within the domain.
The high aerosol concentration during this period can be explained by the
fact that from 13 October to the end of the simulation the wind regime showed
quite low intensities and a recirculation of the wind pattern, resulting in
an accumulation of anthropogenic pollution over Singapore. Although the
results from this modeling study showed a relatively good match with
observations and indicate that the high aerosol concentrations for the second
half of October 2006 are dominated by local pollution, it should be noted
that other studies attribute this high pollution levels to biomass burning
occurring in southern Kalimantan . While it
appears that transport from Kalimantan to Singapore occurred, our
model indicated that the emitted particles were not in the lower layers of
the atmosphere when reaching Singapore but were mostly concentrated at higher
altitude (between 3 and 5 km above ground level). HYbrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) simulation (see
Supplement) shows the ground concentrations (0–100 m a.g.l.) evolution from
an idealized emitted mass at the location of the fires in Kalimantan. This
simulation supports the notion that the emitted particles from fires in
Kalimantan did not reach the surface in Singapore.
To characterize the aerosol pollution levels in Singapore, we compared the
aerosol composition for the two simulations and calculated the number of days
for which the 24 h averaged aerosol mass concentration was above the
threshold of 50 µgm-3, also for both simulations.
Table 2 shows the average mass concentrations for total aerosol, POA, black
carbon, SOA and inorganic particles for the two simulations. Those values are
presented both for the total 4-month study period and for the 2-week period when
Singapore was affected most by biomass burning. The relative difference (as a
percentage) between the WRF-FIRE and WRF-NOFIRE simulations is also reported
for each aerosol component. For the 4 months of simulation, 21 % of the
total aerosol particles in Singapore were due to fires in Sumatra. This
increase of particles from biomass burning is largely dominated by primary
organic carbon. On the other hand black carbon, inorganics and SOA
concentrations in Singapore showed less than 7 % increase due to fires in
Sumatra.
Focusing on the 28 September–13 October period during which fires in Sumatra
had the highest impact on Singapore, Table 2 shows that almost half of the
total aerosol particles in Singapore were due to fires. Again, this pollution
was highly dominated by primary organic carbon particles (54 %). SOA showed
low absolute concentrations but the relative increase due to fires was
substantial (39 % increase).
Finally, the number of days when 24 h averages of aerosol mass concentration
in Singapore was above the threshold of 50 µgm-3 shows that
while observations indicated 37 days with such values, WRF-FIRE and
WRF-NOFIRE showed 35 and 17 days, respectively. These results indicate once
more the importance of biomass burning in affecting local and regional air
quality. However, they also highlight the importance of properly accounting
for regional meteorology.
In the past, GFED estimates have been found too low to properly model AOD
e.g.,. Our results initially support
this notion; while we boosted aerosol emissions by 28 % by applying new
emission factors we still underestimated AOD. However, we were able to
reproduce ground-level concentrations of PM10 and CO. It is important to
note here that our increase of 28 % is substantially lower than
who showed an underestimation up to 300 % of biomass
burning aerosol emissions in Indonesia, or in who
increased the aerosol emissions from fires with 226 %. Clearly, if we had
boosted our emissions that much we had overestimated the ground observations
to a large degree. In our study region, coarse scale inverse model setups
constrained by AOD would probably boost fire emissions to account for lower
than observed AOD, while in reality the discrepancy in AOD may also be
related to other factors including grid cell size and the use of simplified
aerosol chemistry modules in models which may have difficulty calculating all
optical properties correctly. Although just a case study, our results
highlight the complexity of the various processes involved in the evolution
of the regional and long-range transported aerosol particles and indicate
that more work is needed to reconcile the differences in emissions strength
required to match AOD versus ground observations.
Conclusions
We used the atmospheric model WRF-Chem with VBS configuration to simulate the
aerosol evolution during 4 months over Sumatra and Singapore. The main
objectives were to estimate, simulate and analyze the aerosol particle
emission and evolution due to biomass burning in Sumatra. We focused on the
year 2006, the highest fire year in the last decade in the region. The
comparison with observations of PM10 and CO showed that the WRF-Chem
model managed to reproduce quite accurately the surface concentrations.
However, we underestimated AOD possibly related to regionally transported
elevated particle layers misrepresented in the simulation, or tropical cirrus
clouds affecting the AOD measurements. This mismatch is of concern and was
also found in other studies. However, here we focused on air quality for
which matching surface observations is more relevant than matching column
concentrations For this simulation, we used new emission factors which were
28 % above those used in GFED3. This increase is much smaller than
suggested by several other studies, yet it resulted in a good match with surface
observations.
The analysis of the biomass burning plume composition mixing with the freshly
emitted urban aerosol population in Singapore highlighted the very high
concentrations of primary organic carbon with maximum values of
600 µgm-3 at the fire source. SOA were formed within the
plume but with much lower values of up to 20 µgm-3. Black
carbon concentrations were highest in Singapore where combustion processes
from anthropogenic sources such as traffic with high black carbon emission
factors are dominating. The analysis of the differences between two
simulations, including and omitting fire emissions, allowed us to isolate and
quantify the impact of biomass burning on aerosol pollution levels in
Singapore. We showed that 21 % of the total aerosol concentration was due
to biomass burning occurring in Sumatra during the 4-month period of the
simulation, and 48 % when focusing on a 2-week period in October when smoke
reaching Singapore was most intense. This contribution of fires resulted in
18 days when the 50 µgm-3 threshold was exceeded, in addition
to 17 days due to a mixture of mainly local anthropogenic pollution and
smaller contributions from fires in Sumatra and probably Kalimantan.
Accurate quantification of the contribution from biomass burning to
particulate pollution levels in highly populated cities such as Singapore,
Kuala Lumpur and Jakarta may help to develop strategies to either control
the amount and timing of biomass and peat burning depending on the
meteorology and the urban pollution levels, or apply more effective urban air
pollution reduction plans when fire plumes significantly impact the air
pollution levels in populated areas.
Acknowledgements
The authors are grateful to the WRF-Chem developers and community. The
SeaWiFS, MODIS, OMI and MISR satellite products used in this study were
acquired using the GES-DISC Interactive Online Visualization and Analysis
Infrastructure (Giovanni). B. Aouizerats and G. R. van der Werf are supported
by the European Research Council (ERC), grant number 280061. The authors
would like to thank Singapore's National Environment Agency for collecting
and archiving the surface air quality data. The authors acknowledge the
HYSPLIT model team. Edited by: M. Shao
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