Introduction
Southeast Asia (SEA), with a large population and fast-growing
economy, is an important contributor to the emissions of air pollution
and greenhouse gases in Asia (Streets et al., 2003; Zhang et al.,
2009). The emissions of anthropogenic aerosol from Asia, and
specifically from SEA, are expected to rise in the near future due to
the increase in the energy demand and rapid industrialization
(Lawrence and Lelieveld, 2010; Ohara et al., 2007). High levels of
fine particulate matter (PM with diameter less than 2.5 µm or
PM2.5), the most detrimental air pollutant affecting health
(Janssen et al., 2011; WHO, 2012), are observed in many
developing Asian cities, with the annual average often exceeding the
WHO guideline of 10 µgm-3 by many times (Kim Oanh
et al., 2006; Hopke et al., 2008). Components of PM,
e.g., PM2.5, PM10 (PM with diameter less than
10 µm), black carbon (BC), and organic carbon (OC), have been
monitored in some Asian cities and the results, although fragmented,
showed considerably high levels (Kondo et al., 2009; Kim Oanh et al.,
2006; Hopke et al., 2008). The fine particles and their precursors are
also involved in long-range transport (LRT), hence causing
regional phenomena such as atmospheric brown clouds (ABC) (UNEP and
C4, 2002; Ramanathan et. al., 2001) and affecting the
climate (UNEP-WMO, 2011). Globally, measures aiming to reduce emissions
of BC (and co-emitting pollutants) were shown to reduce the number of
premature deaths and slow down the near-future temperature increase, in addition to other benefits to be gained in Asia, where current emissions are
high (UNEP-WMO, 2011; Shindell et al., 2012).
To comprehensively assess the co-benefits of emission reduction
measures on a regional scale, finer temporal and spatial resolutions of
the modeling results are required. Several studies have been conducted
for various Asian domains using a regional climate model with
chemistry (Nair et al., 2012) or chemical transport models (CTMs) with
an additional aerosol optical module. Most of the Asian regional
modeling studies mainly focused on the domains of East (Han et al.,
2011; Park et al., 2011; Chen et al., 2013; Zhang et al., 2016),
South (Goto et al., 2011), and continental East and Southeast Asia (Lin
et al., 2014). These studies also highlighted several challenges for
models to reproduce the ground-observed PM due to inaccurate emission
inventory (EI), simulated meteorological fields, and the extent of
model representations (e.g., secondary organic aerosol formation,
gas–particle partitioning, dry and wet deposition).
There are currently no detailed modeling studies conducted for the SEA
domain, especially maritime SEA, which includes Indonesia with its large
biomass open burning (OB) emissions. For such a modeling effort, first a
reasonably accurate regional EI database should be prepared to generate input
data. Several global and regional EI databases are available which also cover
the SEA domain. These datasets have been developed using the activity data
taken from several international data sources (Zhang et al., 2009;
EC-JRC/PBL, 2010) or based on the results of large-scale energy models
(Streets et al., 2003). Efforts therefore should be put forward to update the
SEA EI databases in order to generate
emission input data for SEA regional modeling studies.
Our research used integrated EI and modeling tools to provide the
spatial and seasonal distributions of aerosol species (PM10,
PM2.5, and BC) in SEA for 2007 and the co-benefits (for air
quality, health, and climate forcing) of selected emission reduction
measures for 2030. This paper presents the SEA emissions for the base
year of 2007 and the WRF–CHIMERE performance evaluation. CHIMERE
(Menut et al., 2013, and references therein) was used to simulate
three-dimensional (3-D) aerosol concentrations in the domain using the
meteorological fields generated by the Weather Research and
Forecasting (WRF) model (Michalakes et al., 2004). The model results
were evaluated using available ground-based measurements of
PM10, PM2.5, and BC in several SEA cities. The
extended aerosol optical depth (AOD) module (AODEM), detailed in Landi
and Curci (2011), was applied to calculate the total columnar AOD and
BC AOD. The modeled total AOD was evaluated using the observed AOD
from both ground-based Aerosol Robotic Network (AERONET) and the
Moderate Resolution Imaging Spectroradiometer (MODIS) satellite
product. The results are used in the follow-up study, which
investigated the potential co-benefits of various emission reduction
measures implemented in Indonesia and Thailand for air quality
improvement, reduction of premature deaths, and climate forcing
mitigation in 2030 (Permadi et al., 2017a).
Methodology
Emission inventory and emission input data
The emissions from major anthropogenic sources in Indonesia, Thailand,
and Cambodia were developed following the EI framework given in the
Atmospheric Brown Cloud Emission Inventory Manual (ABC EIM)
(Shrestha et al., 2013) using the activity data summarized in
Table 1. A detailed EI methodology for Indonesia was presented in Permadi
et al. (2017b).
Summary of activity data level from different emission sources in three countries.
Sectors
Types of activity data
Activity data
Indonesia
Thailand
Cambodia
Power generation
Fuel consumption (Mtyr-1)
Coal
23.4
20.5
–
Natural gas
3.2
29.8
–
Fuel oil
9.4
0.75
0.62
Biomass
6.3
–
–
Manufacturing industry
Fuel consumption (Mtyr-1)
Coal
5.4
12.3
–
Gasoline
0.34
0.013
–
Fuel oil
1.8
2.4
0.52
Biomass
–
20.7
–
On-road transport
Number of registered vehicles
48
26
1.9
(millionyr-1)
Air traffic
LTO (× 1000 yr-1)
344
555
39
Residential and commercial
Fuel consumption (Mtyr-1)
Coal
0.028
–
–
Wood
100.5
7.6
0.4
Kerosene
7.3
0.13
0.003
LPG
1
1.15
0.005
Charcoal
20.4
3.9
0.042
Other biomass
–
0.14
–
Fugitive emissions from fuel
Coke production (Ktyr-1)
182
–
–
Gas production (Tgyr-1)
8654
31.24
–
Oil production (Tgyr-1)
29
6.2
–
Gasoline distributed (Mtyr-1)
13.7
5.4
–
Agro-residue open burning
Total dry crop residue openly burned (Mtyr-1)
43.5
18.2
4.3
Forest fire
Total forest area burned, including peatland fire (hayr-1)
545 881
1 851 850
98 761
Solid waste open burning
Total dry solid waste burned (Mtyr-1)
1.26
0.28
0.175
Agriculture-related activities
Total number of livestock population (head, ×106)
1359
328
22.3
Fertilizer consumption (Mtyr-1)
6.8
3.6
–
Solvent and product use
Paint (Ktyr-1 of paint)
606
ne
ne
Degrease (tyr-1 of solvent consumed)
103
Chemicals (Ktyr-1 of products)
1269
Other products use (i.e., ink, domestic solvent, glue and adhesives) (Kt of products)
161
The biomass OB categories considered in this study included crop residue open
burning (CROB) and forest fires (aboveground forest fires and peatland
fires). The CROB emissions (aerosol and trace gases) for Thailand for 2007
were taken from Kanabkaew and Kim Oanh (2011), and both CROB and aboveground
forest fire emissions for Indonesia were from Permadi and Kim Oanh (2013),
also for 2007. For Cambodia, CROB emissions for 2007 were also included in
the inventory (Permadi, 2013) but forest fire emissions were taken from the
Global Fire Emission Database v3 (GFEDv3) database (van der Werf et al.,
2010). CROB emissions for Thailand, Indonesia, and Cambodia were estimated
from crop production statistics, residue to crop ratio, dry matter to crop
residue ratio, fraction of biomass burned in the field, combustion
efficiency, and emission factors. The emission results
were higher than other databases that used the MODIS product (e.g., GFEDv3)
because the satellite may not efficiently capture short-lived, small-sized,
sporadic CROB fires (Permadi and Kim Oanh, 2013). For other countries in the
domain, the emissions from the aboveground forest fires were from Song
et al. (2009) while those from CROB were from GFEDv3 for the base year of
2007. The 2007 emissions from peatland fires of all countries in the SEA
domain were also taken from GFEDv3 (van der Werf et al., 2010). The GFEDv3
database was developed using a combination of MODIS burned area and active
fire products, which is believed to better detect the peatland fires than the
MODIS burned area product MCD45A1 alone
for forest fire detection (Shi et al., 2014).
Biogenic emissions were calculated online by the CHIMERE model using
the methodology described in Simpson et al. (1999) that considers
seasons and vegetation cover types taken from the Global Land Cover
Facility (GLCF) (http://glcf.umd.edu) with a resolution of
1km×1km. CHIMERE incorporates the Model of
Emissions of Gases and Aerosol from Nature (MEGAN) module (Guenther
et al., 1995) for estimation of volatile organic compounds (VOCs) from natural vegetation and NO
emissions from soil. We did not include the emissions from
international shipping in this simulation, but “inland
waterway” sources were included in the inventories for the three
countries. Other sources of PM such as unpaved road and wind-blown
dust emissions were also not included in this study.
Spatial distributions were made based on source activity data or
relevant proxies collected for the administrative boundaries. For
example, for Indonesia, the emissions were disaggregated at the district
level (300 districts), while for Thailand and Cambodia emissions were
presented for the provincial level (76 and 24 provinces,
respectively). Further, the emissions were gridded into 0.25∘×0.25∘ using the Geographic Information System (GIS)
tool. For other countries in SEA, the emissions of SO2,
NOx, CO, VOC, PM10, PM2.5, BC, and OC
were taken from the available online gridded EI databases (grid size
of 0.5∘, i.e., ∼50 km) compiled by the Center for
Global and Regional Environmental Research (CGRER) (Zhang et al.,
2009). The gridded CH4 and NH3 emissions that were not
included in CGRER were taken from the global Emission Database for
Global Atmospheric Research (EDGAR) (EC-JRC/PBL, 2010), with a grid
resolution of 10km×10km. Emissions from all
considered sources were compiled for the base year of 2007 and were
gridded to 0.25∘×0.25∘ (∼30km×30km) for the modeling input.
Monthly based activity data were obtained whenever available to construct
monthly emissions, but when the data were not available relevant proxies were
used. Specific country hourly variations of emissions were extracted from the
available studies in SEA. For Indonesia, detailed methodology and data
sources to construct monthly and hourly emissions of major anthropogenic
sources are detailed in Permadi et al. (2017b). For aboveground forest
vegetation, crop residue, and municipal solid waste OB emissions, the monthly
and hourly profiles were adopted from Permadi and Kim Oanh (2013). For
Thailand, the hourly profiles for power plants and industry were obtained
from Pham et al. (2008) while those for other major anthropogenic sources
were taken from Vongmahadlek et al. (2009). The survey-based information on
hourly profiles reported in Kanabkaew and Kim Oanh (2011) was used for CROB
emissions. For other countries, relevant data from regional EI from Streets
et al. (2003) were utilized. The hourly emission input for the domain were prepared using a Fortran program
developed at the Asian Institute of Technology (AIT) for this purpose.
The lumping of non-methane volatile organic compounds (NMVOCs)
emissions into the model species was done according to the MELCHIOR
mechanism (Middleton et al., 1990). The aggregation produced the
emissions of 33 species, including trace gases and aerosol in units of
molcm-2s-1. Aerosol fluxes were also converted to the
“molecule-like” units in the emission input data using a fictive
molar mass equal to 100 gmol-1 (Bessagnet et al., 2004).
Modeling domain
The choice of domain size and resolution affects the balance between
the boundary and internal modeling forcing in the simulated
concentrations (Seth and Giorgi, 1998). For this study, it is
important that the defined domain allows the transport of air
pollutants by the monsoon circulation across SEA. Therefore, we set
the domain to cover as much as possible the major upwind emission
sources and to capture meteorological processes in the region of
interest.
The SEA domain horizontally covered nine countries of the
Association of Southeast Asian Nations (ASEAN) and three provinces of
Southern China (Fig. S1 in the Supplement). The WRF domain extended
from central Myanmar to northern Australia, covering
230×200 grids. The CHIMERE domain extended from Southern
China (24∘ N, 95∘ E) to eastern parts of Indonesia
(9∘ S, 137∘ E), consisting of 169×133 grids. The grid resolution of the WRF and CHIMERE was set to be
the same, 0.25∘×0.25∘
(∼30 km ×30km).
WRF and CHIMERE model configuration
WRF version 3.3 was used with lateral boundaries and initial meteorological
conditions taken from the National Centers for Environmental Prediction
(NCEP) final (FNL) global analyses that are available at 1∘×1∘ grid resolution every 6 h
(http://rda.ucar.edu/datasets/ds083.2/). The WRF Preprocessing System
(WPS) of geographical input data (i.e., land use, vegetation index, soil
type, and albedo) was also obtained from the NCEP database. In total, 28
vertical levels were simulated, with the lowest level having the physical
height of about 38 m. Analysis nudging was performed in the planetary
boundary layer (PBL) and other layers for wind components (u and v),
temperature (T), and relative humidity (RH). Nudging coefficients
were set for all parameters at 0.00005 s-1. The time interval
between analyses was set at 360 min, which is equivalent to the 6-hourly
boundary input data used in our study. This analysis nudging was performed
because it is suitable for coarse-resolution simulations (30km×30km) to drive regional air quality models since it can
improve the accuracy for the downscaled and/or nested fields (Dudhia,
2012; Bowden et al., 2012). Note
that, due to the insufficiency of spatially distributed meteorological
observations in the domain, the observation nudging was not performed.
In the WRF simulation, the following physics options were used: simple ice
microphysics, unified Noah Land Surface Model for the land-surface scheme, Rapid Radiative Transfer Model (RRTM)
and Dudhia schemes for long- and shortwave radiation, a PBL parameterization
scheme from Yonsei University, and a Kain–Fritsch scheme with deep and
shallow convection option for cumulus parameterization. These schemes were
selected as they are suitable for mesoscale grid size and have been used in
previous studies (Jankov et al., 2005; Osuri et al., 2012).
This study used CHIMERE version 2008c with the MELCHIOR 2 chemical
mechanism that was adapted from the original European Monitoring and
Evaluation Programme (EMEP) and consisted of around 120 reactions and
40 chemical species. The vertical profiles of updated reaction rates
in MELCHIOR 2 have been developed using tabulated clear-sky photolysis
rates taken from the Tropospheric Ultraviolet and Visible (TUV) model
(EC4MACS, 2012). This version of CHIMERE has an aerosol module which
consists of the total primary PM emissions (BC, OC, and other primary
particles) and secondary inorganic PM species, such as nitrate,
sulfate, ammonium, and secondary organic aerosol (Bessagnet
et al., 2004). CHIMERE applies the sectional approach to discretize
particle size distribution into a finite number of bins. The
considered particle size range was from 40 nm to
10 µm, which were distributed into eight bins (0.039, 0.078,
0.156, 0.312, 0.625, 1.25, 2.5, 5, 10 µm) (Pere et al.,
2011). Most of aerosol-related dynamic processes, such as
condensation, coagulation, wet and dry deposition, adsorption, and
scavenging, are incorporated in the model
(http://www.lmd.polytechnique.fr/chimere/). This version of
CHIMERE only allows tropospheric simulations below 200 hPa
(∼12 km).
We used eight vertical layers in this study, from sigma level 0.999 (∼20 m) to ∼0.5 (∼5500 m), equivalent to the
500 hPa pressure level. This upper limit was selected based on
a suggestion that in the modeling of anthropogenic pollution, extending the
vertical dimension beyond 500 hPa would not substantially change the
modeled aerosol concentrations for the ground level (Menut et al., 2013).
However, it is recognized that the top of the domain at 500 hPa may
not be able to include the free-tropospheric LRT of the pollution and it
brings in uncertainty to the total column AOD results. Note that in the
CHIMERE version used in this study, the photolysis rates are calculated under
clear-sky conditions as a function of height using the TUV model (Madronich
et al., 1998) and photolysis rates are estimated only up to 9000 m.
However, with the present formulation for cloud–radiation photolysis,
assuming that the model domain is below the cloud, cloud albedo was not taken
into account. Monthly mean boundary conditions of gases and aerosol were
taken from the simulation results for
a period of 1998–2002 by the Laboratoire de Météorologie Dynamique
(LMDZ) – Interaction avec la Chimie et les Aérosols (INCA) (Schulz
et al., 2006), which are available at the CHIMERE website. To assess the
effects of the somewhat aged boundary conditions compared to the model year,
a comparison between the monthly average concentrations at the boundaries
between 2007 and 1998–2002 was made which showed that the difference among
the datasets differed by 0.98–1.23 considering the ratios for aerosol and PM
precursor gases (i.e., BC, OC, NO2, CO, SO2,
C2H4, CH3CHO, and NH3) between the two
datasets. This implies that basically the two datasets were almost similar.
Thus, the impacts of the aged boundary
conditions on the simulation are expected but with a small magnitude. Initial
conditions of gases and aerosol concentrations in every grid were
interpolated from the outputs of the global CTM of the LMDZ-INCA simulation.
A 1-year simulation (1 January–31 December 2007) was performed by both WRF
and CHIMERE with a spinup period of 1 week prior to the main simulation
period.
Aerosol optical depth calculation
A standalone post-processing tool, known as AODEM, developed by Istituto di
Scienze dell'Atmosfera e del Clima – Consiglio Nazionale delle Ricerche
(ISAC-CNR) of Italy (Landi and Curci, 2011) was used to calculate optical
parameters of AOD (extinction coefficients and single scattering albedo)
using the 3-D aerosol species mass concentration fields output of
WRF–CHIMERE for different size bins. AODEM calculates 3-D particle number
concentrations from these mass concentrations and provides the extinction
coefficients for each grid cell, assuming the spherical shape of particles
(Landi, 2013). Three options of the aerosol mixing state were provided in
AODEM: external, internal homogeneous, and internal coated spheres. Aerosol
optical properties are simulated by AODEM following the Mie theory (Bohren
and Huffman, 1983) for the wavelength range from 340 to 1640 nm. We
selected the “aerosol internal mixing” option in the calculation because
existing field measurements confirmed that aerosol is typically found in the
internally mixed state (Lesins et al., 2002) largely due to coagulation and
growth of aerosol particles (Jacobson, 2000). Note that AOD was calculated
from the surface to the model's top layer of 500 hPa; hence it could
not represent the transport through the convective processes taking place
above the model top layer or the LRT in the free troposphere mentioned above.
For calculation of optical aerosol properties, AODEM provides the
particle number concentrations separately for five components: BC, OC,
sea salt, dust, and secondary inorganics (nitrate, sulfate, and
ammonium). The AOD scattering was simulated using “brute force” by
excluding BC in the simulation (Landi and Curci, 2011). BC AOD was
calculated by subtracting the AOD scattering from the total AOD.
Model evaluation
The evaluation of WRF outputs was done using observed data from
eight airport meteorological stations in five SEA countries that
captured major subclimate zones (upper, near-Equator, and lower
latitude) in the domain. Hourly observations from all these airport
stations in 2007 were obtained from
http://weather.uwyo.edu/surface/meteorogram/. The statistical
evaluation of WRF outputs was done using the criteria provided by
Emery et al. (2001), which include the mean bias error (MBE), mean
absolute gross error (MAGE), and root mean square error (RMSE). The
modeled surface pressure and upper wind fields (at 850 hPa)
were compared with the European Centre for Medium-Range Weather
Forecasts (ECMWF) Reanalysis (ERA) Interim data extracted from
http://apps.ecmwf.int/datasets/ for the same vertical
levels. Simulated monthly accumulated precipitation fields were
compared with the satellite-based observations provided by the
Tropical Rainfall Measuring Mission (TRMM-3B43)
(https://disc.sci.gsfc.nasa.gov/datasets/TRMM_3B43_V7/summary?keywords=TRMM).
Only limited air pollution data were available in SEA for the model
performance evaluation. This study collected the observed
concentrations of aerosol (BC, OC, PM2.5, and PM10)
and related gases from various sources. For example, daily
(24 h) concentrations of PM10, PM2.5, BC,
and OC in four SEA cities (i.e., Manila, Hanoi, Bandung, and Bangkok)
in 2007 were taken from the measurement data generated by the Improving Air Quality in Asian Developing
Countries (AIRPET)
project “Improving Air Quality in Asian Developing Countries” (Kim
Oanh et al., 2006, 2014). Hourly BC and OC
concentrations were taken from the measurement results of the Asian
Pacific Network (APN) project at the AIT located in Pathumthani
province of the Bangkok Metropolitan Region, Thailand (Kondo et al.,
2009). Hourly PM10 in Bangkok (Thailand), Kuala Lumpur
(Malaysia), and Surabaya (Indonesia) in 2007 were collected from the
respective national monitoring networks. The statistical evaluation of
simulated aerosol levels was done using mean fractional bias (MFB) and
mean fractional error (MFE) (Boylan and Russel, 2006). Definitions of
the statistical measures used in the model performance evaluation are
given in Table S1 in the Supplement.
The monthly AERONET data for 2007 were downloaded from the National
Aeronautics and Space Administration (NASA) website
(http://aeronet.gsfc.nasa.gov/) for the evaluation of the modeled AOD.
The AERONET data were level 2
quality controlled and recorded at 10 AERONET stations (using a sun
photometer) listed in Table S2 in the Supplement. This AERONET dataset has
already been pre- and post-field calibrated with cloud screening and quality
assurance. The selected 10 AERONET stations had more complete datasets in
2007 and they represent all subclimate zones in the domain. The sun
photometer measures AERONET AOD at six different wavelengths (1020, 870, 675,
500, 440, and 380 nm). Therefore, to compare with the modeled AOD at
550 nm, the AERONET AOD at 500 nm was converted to that
550 nm using a logarithmic interpolation (Chung et al., 2012).
For a qualitative evaluation of the spatial distributions we checked the
consistency between the modeled AOD spatial distribution over the SEA domain
and the monthly MODIS AOD (level 3 data measured at 550 nm wavelength
downloaded from
https://giovanni.sci.gsfc.nasa.gov/giovanni/#service=TmAvMp&starttime=&endtime=&variableFacets=dataFieldMeasurement%3AAerosol%20Optical%20Depth%3B).
Results and discussion
Base year emissions
The obtained total national emission estimates of Indonesia, Thailand,
and Cambodia for 2007 were compared with the existing regional EI
databases of EDGAR for 2007 and CGRER for 2006. Table 2 shows
a reasonable agreement in the ranges of the estimates between the
emission databases for three countries: Cambodia, Indonesia, and
Thailand. Detailed EI results for Indonesia were presented in Permadi
et al. (2017b). There are certain discrepancies between the databases
that may be explained by several factors, including the uncertainty in
activity data levels and emission factors used as well as the different coverage
of the emission sources by different EI works. Specifically, for the
emission sources of N2O, our EI for the three countries
did not cover the direct emissions from cultivated soil (fertilized
land) and the indirect N2O emissions from agriculture-related
activities (microbial nitrification and denitrification), hence
resulting in lower N2O emission estimates. Similar reasoning may
be used to explain our lower estimates for CH4 as compared to
EDGAR for all three countries.
EI results for base year in comparison with the existing regional
EI datasets (Ggyr-1).
Species
Indonesia
Thailand
Cambodia
Other SEA
Southern
Total
This studya
EDGARb
CGRERc
This studya
EDGARb
CGRERc
This studya
EDGARb
CGRERc
countriesd
part of China
domain
SO2
997
2433
1499
827
721
961
41
42
34
2695
6204
10 781
NOx
3282
2162
1896
701
882
1086
97
92
27
2623
4166
10 910
CO
24 169
32 246
26 703
9095
12 553
10 815
2877
2453
570
19 054
33 377
89 252
NMVOC
3840
4528
8225
1120
525
3052
331
18
113
5644
4441
15 613
NH3
1258
1617
1390
469
675
388
110
95
86
1543
2247
5645
CH4
3950
10 300
6443
1053
4541
3567
713
1969
708
13 833
14 640
34 218
PM10
2046
3454
1838
782
1196
474.9
115
268
68
1763
3644
8458
PM2.5
1644
2023
1609
607
781
388.5
65
201
61
1466
2653
6519
BC
226
173
229
47
73
72
7
13
7
159
362
821
OC
674
711
1246
240
234
364
40
73
32
604
643
2245
CO2
508 022
1 700 450
587 000
260 988
235 644
351 000
28 296
185 211
36 000
856 225
1 406 860
3 092 654
(514 882)
(229 500)
(174 300)
N2O
180
329
ne
84
71
ne
60
73
ne
271
346
941
Note: ne means not estimateda EI conducted for base year of 2007 using the ABC EIM framework
(Shrestha et al., 2013). Detailed methodology and results were presented in
Permadi et al. (2017a).b EDGAR for base year of 2007 (Olivier et al., 2001). Retrieved
from http://edgar.jrc.ec.europa.eu/overview.php?v=42FT2012. The
CO2 emissions, excluding forest fire postburn decay and decay of
drained peatland, are given in brackets for comparison with our estimates.c CGRER for base year of 2006 (Zhang et al., 2009). Retrieved
from https://cgrer.uiowa.edu/projects/emmison-data. For NH3,
CH4, and CO2,emissions were taken from CGRER inventory in
2000 (Streets et al., 2003). Peatland fire for SEA for 2007 was taken from
GFED v3.Retrieved from
https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1191.
d Other SEA countries include Brunei, Lao PDR, Malaysia,
Myanmar, Philippines, Singapore, and Vietnam.
For Indonesia, our emission estimates were between those of CGRER and EDGAR
for a number of species. The estimates for PM10, PM2.5,
and BC actually agreed well between these databases while OC of CGRER
appeared to be higher. However, the SO2 estimates differed a lot
between the databases and our value was lower than others (mainly for on-road
transportation and industry), which may be attributed to a more bottom-up
approach used in our EI that relied on actual sulfur content used in the
country and implementation of air pollution control devices (Permadi et al.,
2017b). For example, our SO2 estimate for the power plants of
Indonesia was 300 Ggyr-1, which was more comparable with the
CGRER estimate (409 Ggyr-1) but much lower than the EDGAR
estimate (1000 Ggyr-1). The most striking difference was for the
CO2 emissions, which showed a much higher value by EDGAR; this could
be clearly explained by the inclusion of two major sources in the EDGAR
dataset: (i) the forest fire post-burn decay (698 000 Ggyr-1)
and (ii) decay of drained peatland (504 000 Ggyr-1). If these
two emission sources are excluded from the EDGAR results, the CO2
estimates of all three databases are similar for Indonesia. The EI results
for Thailand and Cambodia also showed reasonable agreements between the
available datasets, except for CO2, which was estimated higher by
EDGAR for Cambodia. The BC and OC emissions for these two countries were
mostly comparable, i.e., differing by a factor less than 2.0 among the
datasets.
The emission shares by source category for the three countries are
presented in Fig. S2 in the Supplement. The emissions of aerosol
species (PM10, PM2.5, BC, and OC) were mainly from
the residential and commercial combustion in Indonesia (43–80 %)
and Cambodia (55–78 %) while for Thailand the biomass OB (forest
fire and crop residue) emissions were dominant, i.e.,
31–74 %. For SO2, the emission in Indonesia were mainly
contributed by the transport sector (36 %) and thermal power
plants (33 %) but the industry was the main contributor in both
Thailand (66 %) and Cambodia (33 %). For NOx, the
total emissions in Indonesia were dominated by the fugitive emissions
from oil and gas operation (44 %), in Thailand by power plants
(34 %), and in Cambodia by forest fires (60 %). The total
emission of NH3, an important precursor for PM2.5,
in all three countries were mainly from manure management and
fertilizer application, i.e., 63 % for Indonesia, 75 %
for Thailand, and 78 % for Cambodia.
The emissions from other SEA countries and from the non-SEA part
of the domain (southern part of China) used in our modeling study are
also included in Table 2. The emissions from Southern China had high shares
in the total emissions from the modeling domain. It is seen that Indonesia
and Thailand were collectively the largest emitters of all pollutants,
sharing of 25–66 % of 2007 SEA emissions and 17–44 % of the
modeling domain emissions. Thus, emission reduction measures implemented for
these two countries are expected to contribute remarkably to air quality
improvement in the region, which will be analyzed in the companion paper
(Permadi et al., 2017a). The spatial distributions of the annual average
emissions of BC and CO at 0.25∘×0.25∘ (∼30km×30km) resolution are
presented in Fig. 1, showing higher emission intensity over large urban areas
in the domain.
Gridded (0.25∘×0.25∘) annual
emissions for the selected pollutants over the SEA domain.
WRF model results and evaluation
Model statistical performance evaluation
The WRF hourly outputs, including surface T, RH, and wind speed (WS) for 2007, were compared with the
observed data at eight international meteorological stations in five SEA
countries (Table 3). The comparison was done for two seasons:
3 months, 1 January–31 March, to represent the dry season in the
continental SEA (but the wet season in Indonesia) and 3 months,
1 August–31 October, to represent the wet season in the continental
SEA (but the dry season in Indonesia). The time series of daily
average modeled vs. observed meteorological parameters, as shown in
Fig. S3a and b in the Supplement, showed that the model appeared to
reasonably reproduce all parameters for the considered stations. In
general, the model performance for T and WS simulations at
all the stations was better than for RH during both periods.
Statistical parameters for WRF model performance evaluation for two
periods (bold values are those meeting the evaluation criteria).
Station
Statistical parameters
MBE
MAGE
RMSE
N
RH
T
WS
RH
T
WS
RH
T
WS
(%)
(∘C)
(ms-1)
(%)
(∘C)
(ms-1)
(%)
(∘C)
(ms-1)
Jan–Mar 2007
Olongapo, Philippines
5.4
-0.9
1.3
15.8
3.1
0.18
19.3
3.9
2.7
1861
Davao, Philippines
-4
-1.6
2.7
10.4
2.8
2.9
12.6
3.6
3.4
1250
Don Muang, Thailand
-13
-0.4
1
16
2.5
1.6
18.5
3.8
2
2148
Trat, Thailand
5.7
-1.9
1.6
12.7
3.1
3.1
15.2
3.7
3.6
996
Pnom Penh, Cambodia
7.7
0.7
0.5
15.5
3.6
2.1
18.1
4.5
2.6
1513
Jakarta, Indonesia
-7.1
-0.8
0.7
14.5
3.2
2.1
19.7
4.2
2.6
2036
Kuala Lumpur, Malaysia
-2.5
-0.14
0.14
6.8
1.2
1.1
10.3
2.2
1.4
2143
Sarawak, Malaysia
-1.8
-0.13
-0.3
5.6
1.2
0.9
9.2
2.1
1.2
2148
Aug–Oct 2007
Olongapo, Philippines
1.5
2.3
0.5
8.3
2.5
1.3
17.6
3.5
3.1
1958
Davao, Philippines
-0.81
-0.13
0.2
6.4
2.2
0.7
12.8
4.8
1.3
1262
Don Muang, Thailand
2.6
-0.4
-0.1
11.1
2.1
1.4
15.4
3.5
2.2
2139
Trat, Thailand
0.86
-0.1
2.1
5.4
1.3
2.7
11.6
3.8
3.3
1017
Pnom Penh, Cambodia
-6.7
0.7
0.1
10.1
1.7
1.1
14.6
3.4
1.7
1602
Jakarta, Indonesia
0.8
2.3
0.47
5.6
2.4
2.3
11.2
3.3
3.1
1958
Kuala Lumpur, Malaysia
-5
0.3
-0.1
10.4
1.5
0.98
13.2
1.9
1.23
2159
Sarawak, Malaysia
-5.4
-0.1
-0.6
8.9
3.5
1.1
11.56
4.2
1.4
2159
Note: bolded values represent satisfactorily model output. Criteria for MBE:
WS ≤±0.5 ms-1, T≤±0.5 ∘C, RH ≤±10 %. Criteria for
MAGE: T≤ 2 ∘C, RH ≤ 2 %.
Criteria for RMSE: WS ≤ 2 ms-1. N is the number of
data points. Description of the statistics measures is presented in Table S1 in the Supplement.
The statistical performance evaluation for the hourly simulated values
against the MBE, MAGE, and RMSE criteria is given in Table 3. MBE for the
January–March period range was -1.9–+0.7 ∘C for T,
-0.3–+2.7 ms-1 for WS, and -7.1–+7.7 % for RH. The
corresponding range obtained for the August–October period was
-0.1–+2.3 ∘C, -0.6–+2.1 ms-1, and
-5.4–+2.6 %. Other statistical measures of MAGE and RMSE varied
between the stations and the deviations from the suggested criteria were
generally small. This suggested a relatively good model performance of WRF
for both dry and rainy seasons. Overall, for the stations located in the
northern latitudes (above the Equator line), the model performed better in
the wet season (August–October), while for those located near and lower than
the Equator line the model performance was equally good for both dry and wet
seasons. The discrepancy between model results and observations was perhaps
partly due to the fact that the domain covers some regions, such as the
Indonesian maritime continent, that are principally characterized by active
convection with a frequent presence of deep convection. These local
processes, e.g., deep convection, are difficult to simulate using the
mesoscale meteorological model of WRF with a rather coarse resolution
(0.25∘ ∼ 30 km)
used in this SEA modeling study. Therefore, finer resolutions are required to
capture the dynamical processes undergoing on smaller scales. Different
physics options may be required for subregion domains to capture the
processes and this should be done in future studies. In addition, a certain
discrepancy is always expected because the model provided a grid average
value, i.e., one value per grid, while the observation is point based at individual
stations.
Synoptic-scale model evaluation
Spatial distribution of surface pressure over the WRF domain is
presented together with the ERA-Interim dataset in Fig. S4 for 3
selected days (1 January 2013, 8 October 2007, and 7 November 2007;
00:00 UTC). Both modeled and ERA data showed similar spatial
distribution patterns of pressure but WRF appeared to produce slightly
lower surface pressure over central Papua of Indonesia for all three cases
presented. In fact, both datasets showed lower pressure zones over the
high mountain areas of the Himalayas, eastern parts of China, and central
Papua of Indonesia that indicated the effects of the topography.
The simulated wind fields at 850 hPa (∼1500 m)
are compared with the ERA-Interim upper wind fields in Fig. S5 that
also showed a consistency of the two datasets and more in the center
of the domain both for wind speeds and wind directions. A large
discrepancy was seen in the northwestern corner of the modeling domain and this
may be attributed to the boundary conditions (taken from NCEP FNL in
this study). The modeled monthly precipitation for 2 selected
months (August and October 2007) was compared with the TRMM-3B43
dataset in Fig. 2, which showed good agreement in the distribution
patterns, but the model somehow underestimated the domain maximum
monthly precipitation column that occurred, e.g., over Myanmar
in August 2007 and over the central part of Vietnam in October.
Comparison of modeled monthly precipitation and the TRMM-3B43
dataset in August and October 2017.
The domain maximum hourly values of simulated PBL in different months
of 2007 (Fig. S6) showed the PBL of 1800–3900 m. The maximum
value of 3900 m occurred in March, which was lower than the
model top level of 500 hPa (∼5500 m) mentioned
above, while the lowest PBL was in August.
CHIMERE model results and evaluation
Aerosol simulation always presents a big challenge due to the complex
multiphase chemistry and transport processes. Lack of ground
monitoring data of aerosol in the SEA region is an obstacle to
a comprehensive model performance evaluation. For model performance
evaluation, the CHIMERE results of PM10, PM2.5,
BC, and the ratios of PM2.5/PM10 and
BC/PM are discussed when comparing with available
observed data in the domain in 2007.
PM10
The daily (24 h) modeled PM10 concentrations were
estimated using the hourly data and the results were compared with the
data gathered from the governmental monitoring networks that are
available in three big cities of SEA (i.e., one station in Kuala Lumpur,
two stations in Bangkok, and one station in Surabaya). Note that the same
two periods, as for WRF evaluation above, were used to represent dry
and rainy seasons for both northern and southern parts of the
Equator. Overall, model results ranged from near 0 to
85 µgm-3 while the observations ranged from 5 to
90 µgm-3 in the three cities. The period average of
modeled PM10 in the three cities ranged from
21.7 to 29.2 µgm-3 while the corresponding observations
ranged from 25.9 to 45.2 µgm-3 (Fig. 3).
Comparison of modeled and observed 24 h
PM10 in Kuala Lumpur, Malaysia (one station), Surabaya,
Indonesia (one station), and Bangkok, Thailand (three stations). Note that
the stations included in the comparison are those located within the
cell.
Scatter plots of daily average observed and modeled values are
presented in Fig. 3 showed that the model appeared to reasonably
capture the range of 24 hPM10 in the cities but it showed
nonlinear correlation. The model underestimated the low observed
values at the Kuala Lumpur station (one station); i.e., the observed
levels were 30–60 µgm-3, while the modeled levels
fluctuated from near 0 to about 60 µgm-3.
A better agreement in the range of 24 hPM10 was shown for
Surabaya, i.e., both were from 5 µgm-3 to
85 µgm-3, but the linear correlation was still quite
low. For Bangkok, the modeled 24 h PM10 ranged from
10 to 60 µgm-3 while the upper limit of the observed
values was 90 µgm-3. It is noted that although the
ranges of the modeled 24 hPM10 were comparable with the
observed ranges, the correlations were not clear for all three
cities.
The reason for the discrepancy in the day-to-day variations between the
modeled and observed 24 hPM10 values could be
attributed to the lower accuracy
of the temporal variations of the emission input data and the coarse
resolution of the model, which, for example, may
not be able to represent the weather variables in a convection-dominated
climate. It is always challenging to compare the regional-scale modeling
results obtained for a coarse resolution (i.e., 30km×30km) with the point-based observations, especially in complex
mixed urban areas. A lack of systematic monitoring data for PM10 in
rural sites of the domain during the modeling periods prevented us from
making a more comprehensive model performance evaluation. The statistical
evaluation showed that in all three cities, the MFB and MFE values for
24 hPM10 (in total 179 data points for each city) were within
the suggested criteria (Table 4). The MFB values in Bangkok, Kuala Lumpur,
and Surabaya were -53, -56, and -9 %, respectively, i.e., meeting the criteria of ≤±60 %. The MFE values in Bangkok, Kuala Lumpur, and Surabaya were 55,
56, and 18 %, respectively, which were also well within the criteria of
≤+75 %. The simulated monthly averages of PM10 in Kuala
Lumpur and Bangkok were consistently lower than the observed values in all
months (Fig. 3), which should be expected in principle due to the grid
averaging of the model results. For Surabaya, however, the model-simulated
monthly PM10 values were higher than the observed during the period
of January–March 2007 but lower than the observed for the period of
August–October.
Statistical parameters for CHIMERE model performance (PM10 and
BC) evaluation.
Parameters and station name
Statistical measures
MBE
MFB
MFE
(µgm-3)
(%)
(%)
PM10a
1. BMR (average of 10 and 11 T)b
-17.5
-53.3
55.7
2. SUF1 (Surabaya)c
-2.6
-8.9
18
3. Jerantut, Kuala Lumpurd
-13.6
-56.3
66.5
4. Petaling Jaya, Kuala Lumpure
-10.3
-41.1
56.1
BCf
AIT site
-0.12
-3.3
20.8
Note: criteria from Boylan and Russel (2006). MFB: PM ≤±60 %. MFE: PM ≤+75 %; all the
parameters satisfy the
criteria of MFB and MFE. No criteria are available for MBE.a Period taken was from January to March and August to
October 2007 for
all stations (daily average concentrations).b Urban mixed site.c Urban mixed site.d Background concentration.e Urban mixed site.f Period taken was from March to December 2007 (daily average
concentrations).
Overall, the discrepancy between the modeled and observed
PM10 and other parameters may be caused by several factors
including the input fields of meteorology and emission data for the
simulation. The WRF model evaluation presented above showed an
acceptable performance (see Sect. 3.2) but still with discrepancies
with the observed data. More uncertainty, however, was expected from the
EI input data. In addition, the uncertainty may arise from the
monitoring data, especially with a large number of missing data points
such as in Surabaya, Indonesia. Overall, the simulation of urban
areas would require more refine emission input data to capture the
local emission sources, such as roads or industries, and this should
be addressed in future studies.
PM2.5
Only some fragmented PM2.5 measurement data were available
in the domain in 2007 for the model evaluation (Fig. 4). This study
used the 24 hPM2.5 data monitored in the SEA cities of
Bandung, Bangkok, Hanoi, and Manila, under the AIRPET project (Kim Oanh
et al., 2006, 2014). The observation data were only
available for some specific periods in 2007 at different sites and hence
the modeled results were extracted for the corresponding periods for
comparison. The observed sites were the mixed sites which were
influenced by typical emission sources in the respective cities. The
AIT site, located about 650 m away from a heavily traveled
road, represented a suburban site with the influences of emissions
from traffic and OB of rice straw (Kim Oanh et al., 2009). Thuong Dinh
(TD) of Hanoi was a mixed urban site influenced by traffic and
residential combustion among other sources (Hai and Kim Oanh,
2013). Both Tegalega (TG), located in Bandung, Indonesia, and Manila
observatory (MO) in Manila, Philippines, were mixed urban sites with
strong influence of traffic and other typical urban sources. The data
therefore represent different periods of the year and different urban
characteristic sites and are only for model performance evaluation, not to compare the levels between
the cities.
Scatter plots of modeled vs. observed 24 hPM2.5
at four AIRPET sites, 2007.
Overall, the available observed 24 PM2.5 data in four AIRPET
cities ranged from 4 to 120 µgm-3 while the modeled
values for the same data periods ranged from 5 to
64 µgm-3. The average levels of the observed
PM2.5 over all the data periods ranged from 35 to
43 µgm-3 as compared to the modeled, i.e., from 9.7 to
21 µ gm-3. Scatter plots of observed and modeled
24 hPM2.5 at four AIRPET stations (Fig. 4) clearly showed
that the model underestimated 24 hPM2.5 in all
stations. In the mixed polluted urban site in Bandung (TG), modeled
24 hPM2.5 were within the range of
11–33 µgm-3 while the observed were
27–69 µgm-3. In the TD urban site in Hanoi (close to
a busy road), the simulated 24 hPM2.5 were
5–64 µgm-3 as compared to the observed of
20–120 µgm-3. In the mixed urban site of MO in
Manila the simulated 24 hPM2.5 were
6–37 µgm-3 as compared to the observed range of
4–55 µgm-3. As discussed above, the four selected
AIRPET sites were located quite close to heavily traveled roads
(although they were not directly on the roadside) and hence the local traffic
emissions could directly affect the monitored pollution levels. This
may be an important reason for the discrepancy between the monitored
levels and the simulated grid average values. In addition, the
observed data points were quite limited for 2007 (≤30 at each
site) and were thus not sufficient for the statistical model performance
evaluation. The PM2.5 monitoring efforts should be enhanced
to characterize the pollution in SEA and also provide sufficient data
points for the model evaluation.
Time series comparison and scatter plot of modeled
vs. observed 24 h elemental carbon in AIT site, 2007.
Black carbon
For model evaluation purposes, we used available measurements in
the previous projects for SEA. The 24 h BC measured by the
optical method was available at several SEA sites under the AIRPET project
(Kim Oanh et al., 2014). The hourly-based EC (elemental carbon, measured by
a Sunset analyzer) measurements, available from the APN project (Kondo
et al., 2009) for the AIT site (suburban), were used to
calculate 24 h BC levels. EC was measured using thermal
optical method while BC was measured using light absorption method
(continuous soot monitoring system or COSMOS). The model performance
evaluation was done using 24 h BC data of both APN and AIRPET
projects.
The APN hourly EC dataset for the AIT site was available for both dry and wet
seasons, from March to December 2007. The hourly EC and hourly BC
measured simultaneously by the
APN project at AIT were found to have a strong linear correlation (Kim Oanh
et al., 2009). Therefore, we used the observed Sunset EC to compare with the
modeled output of BC. This is because for the PM mass closure, EC seems to be
better while BC is suitable for radiative transfer budget analysis
(Gelencsér, 2004). Figure 5 presents the time series of the modeled and
observed 24 h BC for the AIT site. The modeled 24 h BC was
from 1.0 to 10 µgm-3 that is comparable with the observed
range from 0.8 to 10 µgm-3. However, correlation between the
modeled and observed BC shown in the scatter plot was fairly low. The discrepancy between the
modeled and observed BC seen in the time series may principally be due to the
gridded average of the model output as compared to the point-based
measurement. Higher BC levels measured at the AIT site were contributed by
multiple local sources, such as nearby highway traffic activity and biomass
OB (of rice straw) that occurred more intensively during the dry season
(December). However, these sources, especially small-scale rice straw field
burning activity, may not be well represented spatially by the EI input data
made for a large resolution (30km×30km). Three
statistical measures of MBE, MFB, and MFE were considered for the model
performance evaluation in the BC simulation at the AIT site (Table 4). The
MFB and MFE values were -24 and 49 %, respectively, which meet the
suggested criteria (for PM). The MBE value was -0.12 µgm-3
for the AIT site, which showed that the model somewhat underestimated the
observed BC values, but there are no MBE criteria available for PM for
comparison.
Comparison of 24 h simulated and observed BC at four
AIRPET sites in SEA domain, 2007.
The 24 h BC (optically) measured on the 24 hPM2.5
sampled filters collected in the same locations of PM2.5
measurements in SEA under the AIRPET project (Kim Oanh et al., 2006, 2014) were compared with the 24 h modeled BC
extracted for the sites and dates of 2007. Figure 6 shows that the
modeled 24 h BC were lower than the observed at all the sites.
The ranges of observed values and modeled values were in somewhat
better agreement for the AIT site and MO site than the other two
sites. At AIT, the observed BC values were
1.3–3.4 µgm-3 (January, February, and May) were
higher but quite comparable to the modeled range of
0.5–1.8 µgm-3. At MO, the observed 24 h BC
of 7–13 µgm-3 (January and February) was quite
close to the modeled 24 h BC of
4.2–13 µgm-3. More discrepancies were found for the
Bandung site, with observed 24 h BC values ranging from 4.2
to 9.8 µgm-3 (July 2007) as compared to the modeled
values of 1.3–3.2 µgm-3. Similarly, the observed BC
values at the mixed site of TD, Hanoi, ranged from 12 to
23 µgm-3 (January 2007), much higher than the modeled
values of 1–7 µgm-3. The effects of local sources,
especially traffic emissions, at the quoted sites should be a main
cause of the discrepancies when compared to the grid average modeled
BC with the observed values. The limited measurement data available
prevented a more comprehensive model performance evaluation. Note
that due to the limited measurement data points, a statistical
performance evaluation was not conducted for the BC simulation.
Ratios between fine and coarse PM and between BC and
PM
In fact, PM2.5 mass is principally contributed by both local
combustion sources and secondary particles formation by chemical
reactions in the atmosphere. The gaseous precursors of NOx,
SOx, and VOCs for the PM2.5 formation may be of
both local and LRT origins. The coarse fraction (PM10-2.5)
would mainly consist of primary particles of the geological origin
(Chow et al., 1998), and these are mainly contributed by local sources
of soil, road dust, and construction activities (Hai and Kim Oanh,
2013). Due to its formation process as well as the ability to
participate in the regional transportation, the fine particles
(PM2.5) are more uniformly distributed in an urban area than
the coarser particles. The PM2.5/PM10 ratios
could provide some information of the dominance of local sources of
PM2.5. We compare the
PM2.5/PM10 ratios based on the modeled
24 hPM2.5 and 24 hPM10 (PM10=PM2.5+PM10-2.5) with those computed from the
observed PM data available at the four AIRPET monitoring sites
discussed above. Overall, the modeled PM2.5/PM10
ratios ranged from 0.47 to 0.59 while the observed values were higher,
0.6–0.83. More pronounced differences were for TD, i.e., 0.74
observed vs. 0.47 modeled, and for TG of Bandung, 0.83 observed
vs. 0.55 modeled. Better agreements were obtained for MO,
0.61 observed vs. 0.47 modeled, and the AIT site, 0.60 observed
vs. 0.59 modeled. The urban mixed sites of TD in Hanoi and TG in
Bandung were located in the traffic areas and thus higher contributions of the
primary PM2.5 emitted from traffic to the total measured PM10 may be seen compared to the MO
and AIT sites. However, to evaluate the variations in the
PM2.5/PM10 ratios, contributions of various
sources of the coarse particles, such as road dust and construction
dust, should be further analyzed. It is noted that the ratios used to
compare with the model-simulated values were all derived from the
observations made in large cities in SEA. Lack of observation data
in rural areas and remote sites presents an obstacle for more in-depth
analysis of the model performance. The remote sites, with less
influence of the local sources, could be valuable for the model
performance evaluation, both for the PM mass concentrations and their
ratios.
BC is emitted directly from the combustion sources with higher fractions in
PM emitted from the diesel exhaust (Kim Oanh et al., 2010) and lower
fractions from biomass OB (Kim Oanh et al., 2011). Hence the ratio of
BC/PM2.5, for example, can infer the contribution of the
primary particles from these combustion activities.
BC/PM2.5 and BC/PM10 ratios were
calculated using the observed 24 h data at four AIRPET sites. Modeled
BC/PM2.5 ratios ranged from 0.05 to 0.33 as compared to
the observed ratios of 0.05–0.28. For BC/PM10, the
modeled values ranged from 0.03 to 0.16 while the observed values ranged from
0.034 to 0.17. Observed BC/PM2.5 ratios were higher than
the modeled values at TG of Bandung (0.16 vs. 0.1) and AIT (0.055 vs. 0.05)
sites. In TD and MO, the observed ratios (0.22 and 0.23) were lower than the
modeled (0.28 and 0.33). As for BC/PM10, the observed
ratios at three AIRPET sites of TG, TD, and AIT (0.13, 0.17, and 0.034) were
higher than the modeled values (0.06, 0.13, and 0.03), while for MO the
opposite was shown with a lower observed (0.14) as compared to the modeled
(0.16) value. The simulated BC/PM ratio was the highest in
TD, 0.22 % for PM2.5 and 0.17 % for PM10, during the dry period of
January–February 2007, which confirmed the strong influence of traffic
emission at this site.
The lack of data for the areas outside the cities is a remaining issue. Generally, we expect that PM2.5 mass may be more
uniform in an urban area; for example, measurements conducted in
several mountain areas in Asia showed high PM2.5
concentrations which were mainly due to the regional transport (Hang
and Kim Oanh, 2014; Co et al., 2014) or local combustion sources
(e.g., residential cooking, biomass OB) such as found in China (Liu
et al., 2017). However, the BC fraction of PM may vary a lot with much
lower values in remote sites but a lack of data prevents
a more in-depth analysis.
As seen in the statistical model evaluation, a negative MBE was obtained for
PM10, -3 to -17, and BC, -0.12, at all sites (not enough data
for statistical evaluation of PM2.5), which showed an
underestimation of PM10 and BC concentrations by the model at
the sites. This may be explained by
the coarse resolution (30km×30km) of emission
input data which could not adequately
represent the spatial distributions of local sources on a smaller scale, such
as road traffic. These local sources, for example road traffic and
residential cooking, affect PM measured at all sites, hence affecting the
PM2.5/PM10 and BC/PM ratios. The road
and soil dust emissions contribute more to PM10-2.5, thus
lowering PM2.5/PM10 ratios in urban areas, but this
coarse fraction of PM emission was not included in our emission input file.
In addition, the LRT pollution above the model top layer (>500 hPa) may contribute to the pollution in the domain, more to
PM2.5 and BC than the coarse PM. The free-tropospheric LRT of
aerosol and high convective processes should be also considered by extending
the vertical model setup in future studies.
Spatial distribution of modeled monthly PM10,
PM2.5, and BC
Spatial distributions of the modeled monthly average PM10,
PM2.5, and BC are presented in Fig. 7 for January, August, and
November while those of the respective annual averages are presented
in Fig. S7 in the Supplement. The highest monthly average
concentrations of PM10 in January, August, and November 2007
simulated in the domain (one value for the whole domain) were 69, 58,
and 44 µgm-3 while corresponding values of
PM2.5 were 40, 37, and 27 µgm-3,
respectively. The simulated maximum monthly average BC concentration
in the domain was higher in January (8.2 µgm-3) as
compared to August (7.8 µgm-3) and November
(5.9 µgm-3).
Spatial distribution of monthly average PM10,
PM2.5, and BC in the selected months, 2007.
The simulated highest hourly PM10 values in the considered months
of January, August, and November 2007 were 325, 245, and
164 µgm-3, respectively, while the PM2.5
corresponding values were 188, 150, and
99 µgm-3. The highest values of simulated annual
average in the domain for PM10 and PM2.5 were 51
and 32 µgm-3, respectively. The maximum simulated
annual average in the domain for BC was
6 µgm-3. A summary of the simulated pollutant levels
in the domain is presented in Table S3 in the Supplement.
For all considered pollutants over the domain, higher concentrations
were observed over East Java, Indonesia, particularly over Surabaya, which shows the effects of emission from residential and traffic
in the city and surrounding satellite cities as well as the crop
residue OB (Permadi and Kim Oanh, 2013; Permadi et al., 2017b). High
concentrations were consistently observed in several places in
Indonesia including Java, West Sumatra (Padang), and West
Kalimantan (Pontianak) and over Bangkok, Thailand. Large hotspots but
with lower concentrations were also observed over Southern China and
over Hanoi and Ho Chi Minh (Vietnam), which can be largely explained
by the influence of the local sources (Fig. 7).
The monsoon circulation plays an important role in transporting PM from the
emission source regions to other parts of the domain. In the dry months,
higher emissions of biomass OB are expected, and higher concentrations of PM
should be seen in the region near and downwind of sources. Accordingly, in
the northern part of the domain, higher PM levels were found in
January–April, while in the southern part of the domain higher
concentrations were found during the period of April–August. In January in
the Northern Hemisphere, the Northeast Monsoon transports pollutants from the source regions to the
southwest, while in the Southern Hemisphere (Indonesia) the plume moved to
the northeast–east. The opposite is seen in August and November (Fig. 7). In
August in the Southern Hemisphere, the plumes of PM moved northwesterly and
turned northeasterly after reaching the Equator line. The plumes of
PM10 and PM2.5 converged in the South China Sea in
January and November when the Northeast Monsoon was prevalent that brought
PM pollution from the southern part of mainland China to the South China Sea
(Figs. S8a–d). WRF results showed no rainfall over the South China Sea
during the particular period, which may also contribute to the
high PM levels in the converged zone (Figs. S8e–f).
In August and November, the dry months in the southern domain, the
PM10 and PM2.5 plumes showing the effects of
biomass OB (crop residue and forest fire) emissions in Indonesia
that originated in Riau province (Sumatra) and western and southern
parts of Borneo were seen clearly moving northeastward. In January,
the dry season month in the northern domain, the plumes of
PM10 and PM2.5 intensified by biomass OB in the
central and northern parts of Thailand were shown moving
southwestward. BC plumes generally originated from big
cities in the domain, showing a significant influence of fossil
fuel combustion emissions, specifically from traffic and other urban
activities for all months of the year. During the dry period, BC
plumes from the areas that have intensive biomass OB emissions were
not as clearly seen as the PM plumes and this may be because biomass OB
contributed more to OC than BC emissions.
Effects of precipitation on the PM levels were also seen; e.g., higher
PM levels (Fig. 7) were simulated over Indochina in January, October,
and November as compared to August because the latter was a rainy
month in this part of the domain, i.e., less biomass OB and
more wet removal in principle. The opposite was actually seen in the
southern part of modeling domain, e.g., above Indonesia, where lower PM
levels were simulated in October (rainier month in this part) than
other months.
Aerosol optical depth
Both total AOD and BC AOD were considered for the model
evaluation. The monthly average of the total columnar AOD (scattering
and absorbing), at the wavelength of 550 nm, was produced
from the AODEM simulation for 2007. The simulated monthly AOD data were
compared with the monthly Terra MODIS AOD, also at 550 nm,
retrieved from the NASA website. Figure 8 showed that the modeled AOD
was lower than the MODIS observed; for example in January, the maximum
AOD simulated for the Southern China part of the domain was about 0.36
as compared to the MODIS AOD of 0.42–0.58. In the same month, the
modeled AOD values over Java, Indonesia, were 0.072–0.28
while the MODIS AOD values were 0.26–0.42. In April, the model results over
Southern China were 0.25–0.75 while the observed MODIS AOD was
0.42–0.90. Near the border between Myanmar and Bangladesh (northwest corner
of the domain), the modeled AOD and the observed MODIS AOD were similar,
0.74–0.75. However, the modeled AOD values over Java in April were higher,
i.e., 0.02–1.0, than the observed MODIS AOD of 0.26–0.42. The simulated
hourly maximum and monthly average PM10 and PM2.5
concentrations, and hence AOD, over Java were the
highest throughout the year. In particular in April, there was a hotspot of AOD simulated
over the location that may be due to the meteorological conditions. For example, the restricted dispersion conditions in April could be seen from the smaller dispersion plumes in this month as compared to other months in Fig. 8.
Spatial distribution of monthly modeled AOD as compared to
the MODIS Terra AOD for the selected months, 2007.
In October, a hotspot with the maximum AOD of 0.8 was observed by
MODIS over Riau, Sumatra, and Singapore that was well above the
model result for the grid of 0.4. The model was also not able to
capture AOD hotspots over mainland Southern China in this
month. The results for August and November both showed some
significant underestimation of AOD as compared to the MODIS-observed
values. There are several reasons for these discrepancies, including
the temporal and spatial inconsistency in the observed and modeled
values used for comparison. For example, the Terra MODIS satellite
daily passed a region for a particular time (i.e., 13:30), thus giving
only a snapshot of the value, while the model provided the hourly
average for 13:00–14:00. Thus there is certainly inconsistency in the
monthly averages derived from these two datasets. The discrepancy may
come from the fact that in the simulation AOD was covering up to
500 hPa and could not include aerosol in the upper layers as
mentioned above. Different spatial resolutions of modeled AOD
(30km×30km) and MODIS AOD (10km×10km) can be another reason. In addition, shipping
emissions and the natural sources of aerosol, such as wind-blown dust,
were not included in our emission input data so the model would
produce lower AOD (as well as PM10) values. Consistent
with the PM results, the effects of precipitation on AOD were
captured, i.e., higher in the dry months and lower in the wet months in
the respective parts of the domain. Overall, this qualitative analysis
of the modeled vs. MODIS AOD provided only some insight into the
regional distributions. Further efforts to conduct more comprehensive
model evaluation are still required.
Simulated monthly AOD values were also compared with the observed data
retrieved from 10 AERONET stations located in the domain, i.e., in
Vietnam, Singapore, Hong Kong, Taipei, Thailand, and Indonesia, which
also showed lower simulated AOD values than the AERONET-observed values
(Fig. 9). The model appeared to better capture seasonal variability at
Bac Lieu (Vietnam), Silpakorn University, and Songkhla (Thailand)
stations, while at other stations the model underestimated the AOD. The model
seemed to be able to simulate well the monthly average AOD at Hok Sui
station (Hong Kong) but only for the months of October, November, and
December 2007. The strong seasonal variation of aerosol in SEA,
largely caused by the biomass OB and meteorological conditions,
creates a huge challenge for models to reproduce. At Puspiptek Serpong
(Indonesia), where emissions of urban activities from the capital city
of Jakarta would dominate, the high AOD in October was reasonably
captured by the model. The seasonal variation in the emission input
file for anthropogenic sources would need to be further refined to
improve the situation. Better proxies should be used for transport,
industry, residential combustion, and thermal power generation, which
would reflect the actual variation in the monthly activity data of
each sector.
Monthly average of simulated vs. observed AOD at 10 AERONET
stations, 2007.
The BC AOD (absorbing) was calculated as the difference between the
total AOD (scattering + absorbing) and the scattering AOD
following the same method presented in Landi and Curci (2011). The
spatial distribution of monthly average BC AOD is presented in Fig. S9
in the Supplement. In January, the dispersion plumes of high BC AOD
spread over Southern China (maximum AOD of 0.027) and
eastern parts of Indonesia (maximum 0.018), which shared 7.5–10 %
of the total AOD of 0.36 and 0.18, respectively. In
April, the highest value of the modeled BC AOD was seen over Surabaya
(East Java province, Indonesia) with a range of 0.051–0.078,
followed by relatively high values over Hong Kong and Shenzhen of
0.06–0.069. The contributions of the BC AOD to the total AOD in
Surabaya, Hong Kong, and Bangladesh were 9 % (of 0.89), 11 %
(of 0.6), and 12 % (of 0.54), respectively.
In other months, the highest monthly average BC AOD was shown in
different parts of the domain ranging between 0.015 and 0.027 while the
total AOD was 0.18–0.36; hence the shares of BC AOD in the total AOD
were 7.5–8.6 %. Our BC AOD contributions to the total AOD were
higher than the reported global average value of 3 % (Reddy
et al., 2005), but in the same range of those reported for different
regions with intensive emission sources. The relative contribution of
BC to total AOD has been reported to depend on both wavelength,
i.e., increasing with decreasing wavelength, and the dominant
emission sources. For example, measurements showed typical
contributions of around 12 % under the influence of natural dust
(Chiapello et al., 1999) and around 5–12 % when biomass OB is
dominant (Eck et al., 1999; Dubovik et al., 2002). The modeled BC AOD
serves as input to estimate BC direct radiative forcing of
anthropogenic emissions for the SEA domain, which will be analyzed in
our companion paper (Permadi et al., 2017a).
Summary and conclusions
This study developed and evaluated the EI databases for Indonesia,
Thailand, and Cambodia for 2007. The results were compiled with the
existing CGRER and EDGAR emission datasets to generate the emission
input data of the entire SEA domain for regional
WRF–CHIMERE modeling. Our EI results for the three
countries were comparable to other existing databases and the
differences are explained mainly by the differences in the sources
covered by different EI works. The BC emissions were mainly from
residential and commercial combustion in Indonesia (71 %) and
Cambodia (70 %) but were dominated by biomass OB emissions in
Thailand (31 %).
The model performance for 2007 was evaluated using the hourly and
daily observed data in the SEA domain. The WRF model outputs were in
good agreement with the observed data at eight international airport
stations in Indonesia, Thailand, Vietnam, Cambodia, and
the Philippines. The WRF–CHIMERE model satisfactorily reproduced
the aerosol species of PM10, PM2.5, and BC in terms
of the spatial distributions and seasonal variations. The statistical
evaluation was conducted for 24 hPM10 and
24 h BC, which had sufficient observed data points for the
analyses. The modeled 24 hPM10 in three cities (Thailand,
Malaysia, and Indonesia) had MFB and MFE values that met the suggested
criteria. Similarly, the modeled 24 h BC values met the MFB
and MFE criteria for PM when compared the observed data at a suburban
site in Thailand (AIT).
The PM2.5/PM10 ratios calculated from the
modeled outputs were lower than those estimated from the observed data
at four AIRPET sites and this would imply a necessity of further
improvement of the PM speciation of the emission input data. The
modeled BC/PM2.5 ratios were in compatible range
(0.05–0.33) with the observed values (0.05–0.28) and were lower in
two sites (AIT and Bandung) but higher in the others (Hanoi and
Manila). The modeled BC/PM10 ratios ranged
between 0.03 and 0.16, which were comparable to the observed values
range (0.034–0.17). Lack of systematic observed BC data prevented
a more comprehensive model performance evaluation. Nevertheless,
further improvement of the EI for primary aerosol, especially the PM
speciation of major sources, as well as inclusion of unpaved road and
wind-blown dust emissions are required. Vertical model setup
should be extended beyond 500 hPa (∼5500 m)
in future studies to better incorporate the free-tropospheric LRT
of aerosol.
The spatial distributions of the total columnar AOD estimated for the
WRF–CHIMERE output PM concentrations using AODEM were comparable with the
observed (MODIS and AERONET) in 2007. In particular, exclusion of the unpaved
road and wind-blown dust emissions (coarse particles) from the emission input
in this study was a reason for the discrepancy in the modeled and observed
total AOD due to the possible underestimation of
the coarse PM concentrations. The lower values of aerosol species simulated
by the model were explained by the grid averaging effects: WRF–CHIMERE had
a larger grid of 30 km, as compared to MODIS AOD of 10 km,
while AERONET is actually point based. Thus, the spatial distribution of local sources of a smaller size
cannot be captured well by WRF–CHIMERE.
The spatial distribution patterns of the modeled aerosol species in the
domain may be explained by the intensive biomass OB emissions. The plumes of
PM10 and PM2.5 originated from Sumatra and Borneo of
Indonesia in August–November and from central and northern Thailand during
January–April, which coincided with the dry months in the respective areas
and subsequently more biomass OB. Spatial distributions of BC showed the
influence of the traffic emission and residential combustion in big SEA
cities. Based on the model results, the contribution of BC AOD to total AOD
in the domain was around 7.5–12 %, which is consistent with the
literature reported values for intensive emission areas. Effects of
precipitation were captured by the model that produced lower PM and AOD
levels in the months with higher precipitation simulated.
The EI data and WRF–CHIMERE performance for 2007 were
satisfactory in terms of reproduction of the key aerosol species in
the domain. In the companion paper (Permadi et al., 2017a), we
present the WRF–CHIMERE simulation results for PM and
BC for the SEA domain in the business as usual emission scenario
(BAU2030) and in the emission reduction scenario (RED2030) to quantify
potential co-benefits for air quality improvement, reduction in the number
of premature deaths, and radiative forcing mitigation in Southeast
Asia.