Particulate Pollution Levels and Source Apportionment in Six Asian Cities:
Preliminary Findings of AIRPET
a
Kim Oanh N.T., aNabin U., bZhuang Y-H., bHao Z., cMurthy D.V.S., cSwaminanthan T.
,dLestari P., eVillarin J.T., eChengchua K., fCo H.X., fDung N.T., gLindgren E.S.
a
Environmental Engineering and Management Program, Asian Institute of Technology,
Thailand; bResearch Center for Eco-Environmental Sciences, Graduate School, University of
Science and Technology of China, Beijing; cDepartment of Chemical Engineering, Indian
Institute of Technology, Madras; dDepartment of Environmental Engineering, Institute of
Technology of Bandung, Indonesia; eManila Observatory, Quezon City, Philippines; fFaculty of
Environmental Sciences, Hanoi University of Science, Hanoi; gHogskolan I Boras, Sweden
Abstract
AIRPET, an Asian regional air pollution research network, which is coordinated by the Asian
Institute of Technology, involves six cities: Bandung, Bangkok, Beijing, Chennai, Manila, and
Hanoi. One of the objectives of the network is to provide a comprehensive assessment of PM air
quality though monitoring and receptor modeling tools jointly by 6 research teams in these cities.
This paper presents the results within the scope of this monitoring and modeling objective in
phase 1 (2001-2003). In total, the network collected over 2500 PM2.5 and PM10 samples from
characteristic urban sites. The samples were analyzed for ionic, element and BC or/and OC/EC
composition. QA/QC for sampling and analysis was followed to produce reliable and
comparable data produced by the research teams. In all these cities the levels of PM10 and
PM2.5 were found high especially during the dry season which the frequently exceeded the US
EPA standard for PM10 and PM2.5, especially at the traffic sites. Source apportionment by
receptor models (CMB and PMF) shows high contribution of traffic, secondary aerosol and
biomass burning to PM2.5 in most of the cities. For the PM10 or coarse fraction the substantial
contributions are from soil dust, construction and sea salt. Industrial contribution varies from city
to city.
Keywords: Fine PM, coarse PM, composition, source apportionment, comparative analysis, six
Asian Cities
1
1. Introduction
There has been growing concern on monitoring and characterization of size-segregated
particulate matter (PM) in the recent years. More efforts and resources are being spent to
understand the fate and develop mechanism that would help control this harmful substance in the
ambient air. In many developing countries, though TSP is still a regulated pollutant, there is a
clear tendency toward monitoring of fine particles. The atmospheric particles below 10 m
(PM10) have long been implicated to have adverse health impacts such as respiratory disease and
increased mortality (Dockery et al., 1994). Very small particles, PM2.5, and their associated
“high risk” have been discussed (Schwartz and Neas, 2000; Donaldson et al., 1998). Aerosols
interact directly and/or indirectly with solar radiation and change the global climate (Preining,
1991; Liu and Peter, 2002). Fine particles impair visibility by absorbing and/or scattering solar
radiation (Kim et al., 2001).
This paper presents some preliminary findings on PM levels and source apportionment of an air
pollution research network “Improving Air Quality in Asian Developing Countries (AIRPET)”
during phase 1, 2001-2004. AIRPET (http://www.serd.ait.ac.th/airpet), now in phase 2, is a
regional coordinated effort with one of the research objectives being to provide comprehensive
assessment of PM pollution in selected cities in Asia with the focus on PM2.5 and PM10/PM102.5. The network is coordinated by the Asian Institute of Technology (AIT) and funded by the
Swedish International Development Cooperation Agency (Sida). The network covers six cities,
namely Bangkok Metropolitan Region (Thailand), Bandung (Indonesia), Beijing (China),
Chennai (India), Manila (Philippines), and Hanoi (Vietnam).
2. Methodology
2.1 Monitoring design in the regional network
Six institutions are involved in this network, including AIT; Institute of Technology of Bandung;
Research Centre for Eco-Environmental Sciences, Beijing; Manila Observatory; Hanoi
University of Science; and Indian Institute of Technology, Madras. Each institution conducts
monitoring for one city in the country.
2
The selected cities are different in terms of geographical locations, topography, energy use,
industry, vehicular mixes and density. The climate of the region is dominated by monsoon with
two distinct seasons, dry and wet. Though each dry and wet season may cover different months
of the year in different countries, it has been decided to have only these two broad categories of
seasons for the consistency in inter-country comparison of the monitoring results.
The summary of PM monitoring schemes in the six cities is given in Table 1. Sampling sites
across the six cities are characterized into seven categories based on the activity and influence of
the emission sources, and their surroundings. Each city has 4-6 sites out of total 7 site types. The
common selected sites in all cities are upwind, mixed, traffic, residential and industrial sites. It
was intended to have the sampling intake located at the height of 3-5 m for all cities. However,
due to siting difficulty a few sites were located higher than 5 m, especially at BNU (Beijing) the
site is at 40 m above the ground, which may cause some inconsistency in the result comparison.
All cities conducted PM2.5 and PM10 monitoring using low volume samplers, dichot, minivol or
other equivalent samplers depending upon the samplers available. For the consistency in
comparison, we present and discuss the 24h PM2.5 and PM10 monitoring results. Fine (PM2.5)
and coarse (PM10-2.5) fraction particles by dichot were summed up to get PM10 in those cases
where these fractions of PM have been collected. Filter media were selected to suit the selected
analytical methods that follow. All the samples were conditioned for about 24h (temperature
20±5oC, relative humidity 40±10%) before and after sampling for the gravimetric mass
determination. The quartz filters were fired at 550oC for about 6 hours before conditioning and
sampling. The treatment of quartz filters at this temperature is necessary to remove any
carbonaceous/organic pollutants and is recommended for samples to be analyzed for organic
components. Detail on QA/QC is presented latter.
2.2 Sample analysis
The particle samples after gravimetric mass measurements were subjected to compositional
analyses such as ions by IC, elements by ICP-MS, PIXE, XRF, and NAA, organic and elemental
carbons (OC/EC) by thermal method, and/or black carbon (BC) by optical reflectometer. Usually
3
sections of quartz filters were analyzed for ions and OC/EC while samples on mixed cellulose or
Teflon filters were analyzed for elements. Numbers of parameters analyzed are slightly different
for different cities due to the availability of the equipment. The network aims at getting as
complete datasets as possible on PM composition for receptor modeling purpose which primarily
include mass, water soluble ions, elements and EC/BC.
2.3 Quality assurance and quality control
Data quality is of the foremost concern. Common methodology for sampling and analyses were
aimed at to maintain the regional data inter-comparison. The quality assurance and quality
control (QA/QC) of the monitoring scheme were followed at all possible levels from monitoring,
siting, sampling method selection, sampling execution, sampler calibration co-located sampling,
selection of filter media, filter preparation, sample weighing to the analysis. The analytical
methods for elements used by different institutions within the network were checked by a
standard reference material (SRM). The coarse flow of dichotomous samplers was corrected for
the fine fraction carried over. One filter for about every 20 filters was used as blank. All the
analyzed data were blank corrected. Most of the cities collected samples over a 24h period. Some
samples in Beijing were taken for 4-8 h, which were subsequently normalized to 24h and the
datasets for all cities are 24h averaged concentrations.
Results of co-located sampling between samplers used in the research teams are presented in
Table 2. Dichot is used as the reference sampler in the network hence all other samplers are
compared with the dichot. At AIT dichots were only samplers used hence a dichot was compared
to the US EPA FRM sampler and a Minivol sampler. The results showed that dichot agrees well
with the FRM and most of the low-vol used in different institutions with the R2 commonly 0.980.99 except for Bandung and Hanoi (PM10) cases which need to be further examined. The
medium samplers used by India (APM550) and China show a rather good agreement with the
dichot samplers. However, the APM550 seems to collect about 25% higher PM2.5 and 13%
higher PM10-2.5 than the dichot.
4
2.4 Source-receptor analysis
The seasonal averages of PM2.5 and PM10 from the six cities were subject to CMB analyses for
the source apportionment purpose. Each city used the source profiles independently. However, a
few sources are common to all while some sources are city specific such as fugitive dust and coal
burning in Beijing; solid waste disposal in Bandung, or industries and sea salt in Chennai.
For the receptor modeling purpose, missing data, data below detection limit (BDL), and the data
uncertainties were mostly treated according to Polissar (1998). Thus, missing values were
substituted with the mean of measured values; BDL with the half of the detection limit of a
parameter. Accordingly, assigned uncertainties for data points in each sample differ from one
parameter to another.
3. Results and discussion
3.1 PM levels
Totally in phase 1 the AIRPET research teams have collected over 2550 samples in the 6 cities
of which 1500 are PM2.5 samples. More samples were collected in dry season than in the wet
season, with higher numbers of samples collected for 2-3 intensive sites in each city. All samples
were analyzed for mass and most of samples were analyzed for chemical composition.
Seasonal averages of 24h PM2.5 and PM10 as well as major compositions over all monitoring
sites in each city are shown in Table 3. Significant difference in concentrations of PM2.5 and
PM10 has been observed over the two seasons. The results show that Beijing has the highest
average PM2.5 and PM10 during the dry season, followed by Hanoi and Chennai (PM10). In the
wet season the PM2.5 level in Beijing is still the highest. PM10 in the wet season is the highest
in Beijing and second highest in Chennai. Averages for PM2.5 and PM10 in Beijing exceed the
24h PM US EPA standards in both seasons, in Hanoi the standards exceedance of both PM2.5
and PM10 are observed in dry season while in Chennai PM10 exceeds the standard in the dry
season. High fluctuations in PM levels were observed in most sites in Beijing.
5
Sample size is an important factor for assessing the reliability of the average data. As seen from
Table 1, the sample size varies from one site to another and from one city to another. Chennai
has the smallest number of samples taken for averaging; hence the averages produced may not be
statistically significant. High PM levels in Beijing may be attributed mainly to coal burning as
major energy source, dust storms, and traffic re-entrained road side dust (Sun et al., 2004).
PM2.5/PM10 ratios at different sites in a city vary with seasons. The average for the dry season
ranged 0.58-0.72 in Bangkok, 0.51-0.66 in Beijing, 0.31-0.52 in Chennai, 0.52-0.73 in Bandung,
0.62-0.86 in Manila, and 0.58-0.84 in Hanoi. For Bandung and Manila the PM2.5/PM10 ratios
are almost the same in both seasons, while for Chennai, Beijing, Hanoi and Bangkok the ratios in
dry season were higher than in wet season by 6, 9, 12, and 17%, respectively.
3.2 Major chemical components
Analyzed chemical concentrations are divided into some major groups according to their nature
and source of origin (Table 3). We present here the compositions of samples as crustal, organic
matter (OM), EC/BC, NH4+, SO4=, NO3-, K+, sea salt and ‘others’ (the rest trace elements). Such
major groups of chemical compositions have been used in the mass reconstruction in various
studies, for example, Ho et al. (2003) and Chan et al. (1997). It is noted that, Chennai samples
were digested in acids (HCl, H2SO4) and analyzed by UV-Spectrometer. Hence the total ions
were probably obtained rather than the water soluble ions as in other cities. There were also no
PM10 composition data for Manila presented.
A significant of fractions of NO3- and SO4= were also found in coarse fraction (PM10-2.5) of
particles which suggest that not all NO3- and SO4= in the samples are in the form of ammonium
nitrate and ammonium sulfate. Crustal fraction includes Al, Ca, Si, Ti, Fe, and K and their
oxides. Organic carbon multiplied by the factor 1.4 is assumed to represent all organic
compounds. Sea salt is calculated as 2.54Na assuming all Na comes from the marine source. A
reference ratio Na/Al=0.3 for crustal composition was applied to check the marine origin of Na
in all average sample data. It was seen that most samples average Na/Al ratios were moderately
6
to significantly higher than crustal ratio 0.3, indicating the marine contribution. Trace elements
in the ‘others’ group include all elements except crustal elements, Na, and S.
OM, crustal, NO3-, SO4= and EC/BC have been found as major components of PM2.5 and PM10.
OC was not analyzed in most sites, except for BMR and Beijing, hence the comparison is not
possible. Crustal content of PM2.5 is low in most of the cities except for Beijing, Chennai and
Hanoi (wet season only). For PM10, the crustal content is also highest in Beijing in both season,
second highest in Chennai and Hanoi. Trace elements/compounds were also the important
components of Beijing particles, ranging from 3.4-14.5 μg/m3 in PM2.5 and 5.5-16.0 μg/m3 in
PM10. In all six cities, the highest contribution of crustal components to PM10 was found for
traffic sites, followed by mixed sites and industrial sites. Higher concentrations of crustal
elements are thought to be associated with the road side dust, and some local
construction/demolition activities.
NH4+ concentration was highest in Beijing PM2.5 ranging from 6.1-39.0 μg/m3. Chennai,
Bandung and Hanoi were other cities followed by Beijing with high ammonium: NH4+ ranged
1.3-17.1 μg/m3 in Chennai; 1.9-11.5 μg/m3 in Bandung, and 0.8-8.0 μg/m3 in Hanoi. Most NH4+,
NO3- and SO4= are present in PM2.5 in all cities, except higher NO3- in PM10-2.5 in Bangkok,
Chennai and Bandung; and higher NH4+ in Chennai, which need to be further investigated.
The detail results of element analysis show that S, Se, and Br are highest in PM2.5 than in coarse
fraction. Pb, Zn, and Cu are found in both fine and coarse fractions of PM10. Very high and
nearly the same range of S were found in Beijing and Hanoi. Bangkok had relatively low average
S in PM10 in both seasons. S in PM2.5 in Manila ranged 0.1-2.1 μg/m3 in dry and 0.2-1.0 μg/m3
the wet seasons. Higher S in the dry season at all sites could be attributed to the increased coal
use, and biomass burning during this season as compared to the wet season.
3.3 Source apportionment results
The results of chemical mass balance receptor modeling on city wise average PM2.5, PM10-2.5
and PM10 are presented in Table 4. The ambient data compositions were not complete in many
7
cases. Here we present the attempt to compile the results obtained by different cities using the
source profiles generated based on literature. It is important that the receptor model with some
important signatures missing from either the source profiles or the ambient data could have
misleading results. Furthermore, lack of in-depth knowledge on the contributing sources in a
specific area and their subsequent exclusion from the model run do not help to produce good
quantitative source apportionment. The results presented here are meant to give some overviews
on the major contributing PM sources in the six cities. Traffic has the highest PM2.5 contribution
in most cities with the exceptional high share (>70%) in Manila. Biomass burning appears to
contribute substantially to PM2.5 in BMR, Beijing (both dry and wet season), Hanoi and
Bandung (dry season). PM2.5 from the secondary formation (in the atmosphere) also has high
shares in BMR, Bandung and Hanoi. Industry has a high contribution in Chennai and coal
combustion has a substantial contribution in Beijing. For coarse fraction, it is shown that
construction, soil and biomass have substantial contributions in all 3 cities (BMR, Bandung and
Chennai) where the source apportionment was made for PM10-2.5. Industry and traffic also have
high contributions in Chennai while in Bandung has high contribution from other sources
including solid waste and lime. High NaNO3 (which is thought to be secondary particles formed
in the reaction between seasalt and secondary nitric acid) contribution to coarse fraction was
found for BMR. For the 2 cities that source apportionment was made for PM10 the high
contributions were found from biomass, secondary formation and soil for both cities. Traffic and
coal combustion have high contribution in Beijing while NaNO3 was found high in Hanoi.
4. Conclusions
The levels of PM2.5 and PM10 in the cities are higher in dry than in wet season. Highest levels
were found in Beijing in both seasons followed by Hanoi and Chennai. The PM2.5 constitutes a
larger fraction of PM10 in most of the cities and the fraction is higher in dry season than in the
wet season. The major chemical components in PM2.5 across six cities are BC/EC fractions
followed by NH4+, NO3- and SO4=. Similarly, PM10 comprised of mainly crustal elements (Al,
Si, Ca, Fe), and BC. Preliminary receptor modeling results indicated that traffic, secondary
aerosols, and biomass burning were the major sources of PM2.5 while soil dust, construction
activities, biomass burning, NaNO3, and traffic are among the major sources of PM10.
8
Acknowledgement
We would like to thank the Swedish International Development Agency for the generous
financial support without which the study would not be possible. The California Air Resource
Board (CARB) is acknowledged for donating 7 dichot samplers to AIT for the use in the
network. Tom Parsons, Tom Cackette, Dick Baldwin, Bill Loscutoff, Douglas Fox, and many
other colleagues who helped in the process of acquisition, preparation and shipment of the
samplers from CARB to AIT are cordially thanked.
References
Dockery, D.W., and Pope III, C.A., 1994. Acute respiratory effects of particulate air pollution.
Annual Revision Public Health 15, 107-132.
Donaldson, K., Li, X.Y., MacNee, W., 1998. Ultrafine (nanometer) particle mediated lung
injury. Journal of aerosol science 20, 1453-1456.
Kim, Y.J., Kim, K.W., and Oh, S.J., 2001. Seasonal characteristics of haze observed by
continuous visibility monitoring in the urban atmosphere of Kwangju, Korea. Environmental
Monitoring and Assessment 70: 35-46.
Liu, Y., and Peter, H.D., 2002. Anthropogenic aerosols: Indirect warming effect from dispersion
forcing; Nature 419, 580 – 581.
Polissar, A.V., Hopke, P., Paatero, P., Malm, W.C., and Sisler, J.F., 1998. Atmospheric aerosol
over Alaska - 2. Elemental composition and sources. Journal of Geophysical Research, vol.
103(D15): p. 19045-19057.
Schwartz, J., Neas, L.M., 2000. Fine particles are more strongly associated than coarse particles
with acute respiratory health effects in schoolchildren. Epidemiology 11, 6-10.
Sun, Y., Zhuang, G., Wang, Y., Han, L., Guo, J., Dan, M., Zhang, W., Wang, Z., and Hao, Z.,
2004. The air-borne particulate pollution in Beijing—concentration, composition, distribution
and sources. Atmospheric Environment 38, 5991–6004.
9
Table 1. Number of PM samples at different characteristic sites of six cities in dry and wet seasons.
a
Site
b
City
Site types
symbol n in dry season
n in wet season
Samplers
Filter
PM2.5
PM10
PM2.5 PM10
BMR
upwind
AIT
91
91
32
32
Dichot
Q, M
mixed site
BN
46
46
10
10
Dichot
Q, M
traffic site
DD
14
14
27
27
Dichot
Q, M
residential/centre
BS
10
10
27
27
Dichot
Q, M
Total
161
161
96
96
Beijing
upwind
MY
43
65
PM2.5-II
Q
traffic mixed
BNU
42
47
27
40
PM2.5-II
Q
residential/centre
YH
10
10
20
21
PM2.5-II
Q
industrial
CS
10
10
21
21
PM2.5-II
Q
commercial
PG
6
7
PM2.5-II
Q
Total
105
132
74
89
Chennai
upwind
IIT
17
15
3
3
Dichot/APM550
Q, M
traffic mixed
TNG
19
20
3
3
Dichot/APM550
Q, M
residential/centre
KT
Dichot/APM550
Q, M
industrial
MAMB 10
10
4
4
Dichot/APM550
Q, M
Total
46
45
10
10
Bandung upwind
DP
42
42
11
11
Dichot
Q, M
mixed site
TG
46
46
9
9
Dichot
Q, M
traffic site
LP
6
6
6
6
Dichot
Q, M
residential/centre
BT
6
6
6
6
Dichot
Q, M
industrial
CRT
6
6
6
6
Dichot
Q, M
Total
106
106
38
38
Manila
upwind
GS
55
10
44
6
MinVol
TF
mixed site
MO
188
32
165
36
MinVol/Dichot
NP/TF
traffic site
NPO
55
29
63
26
MinVol
TF
industrial
PAS
47
46
MinVol
TF
commercial
PGH
61
19
58
30
MinVol
TF
Total
406
90
376
98
Hanoi
upwind
GL
58
11
LowVol
Q, M
mixed site
TD
58
58
11
Dichot
Q, M
traffic site
CD
10
5
LowVol
Q, M
residential/centre
BK
10
10
5
5
LowVol
Q, M
commercial
DX
7
7
5
5
LowVol
Q, M
Total
75
143
10
37
Total
899
677
604
368
aSite description—BMR: BN, Bang Na; DD, Dindang; BS, Ban Somdej; Beijing: MY, MiYun; BNU, Beijing Normal
University; YH, YiHai; CS, Capital Steel; PG, Ping Gu; Chennai: IIT, Indian Institute of Technology; TNG, T. Nagar;
KT, Kottur; AMB, Ambatpur; Bandung: DP, Dagopakar; TG, Tegalega; LP, Lapan Pasteur; BT, Batununggal; CRT,
Cisaranten; Manila: GS, Good Shepherd Retreat House; MO, Manila Observatory; NPO, National Printing Office;
PGH, Philippine General Hosptal; Hanoi: GL, Gia Lam; TD, Thuong Dinh; CD, Chuong Duong; BK, Bach Khoa, DX,
Dong Xuan.
bFilters: Q, quartz fiber; M, mixed cellulose ester; TF, ringed Teflon; NP, nucleopore filter
10
Table 2. Colocated sampling results
y
Dichot
MinVol
MedVol
MedVol
MedVol2
MedVol2
APM550
APM550
x
FRM
Dichot
Dichot
Dichot
MedVol1
MedVol1
Dichot
Dichot
n
41
47
6
6
5
5
13
13
PM
PM2.5
PM2.5
PM2.5
PM10-2.5
PM2.5
PM10-2.5
PM2.5
PM10-2.5
R2
0.98
0.44
0.99
0.99
0.97
0.99
0.97
0.99
equation
y=1.04+1.70
y=0.85+14.52
y=1.02x-1.68
y=1.05x-0.62
y=1.06x-4.41
y=1.05x-0.54
y=1.13x-5.12
y=1.25x-3.95
Indonesia
LowVol
LowVol
Dichot
Dichot
20
26
PM2.5
PM10
0.60
0.61
y=1.08x-3.08
y=0.74x+21.87
Philippines
Vietnam
MinVol
LowVol
Dichot
Dichot
29
50
PM2.5
PM10
0.86
0.3
y=0.93x+4.77
y=0.69x+65.55
NRIs
AIT
China
India
11
Table 3. City wise average mass and major components of PM2.5 and PM10 (μg/m3) particles during the dry and wet seasons.
a
Mass Crustal
Cities
Dry season PM2.5
BMR
50
0.7
Beijing
190
22.4
Chennai
50
15.8
Bandung
50
2.7
Manila
44
2.9
Hanoi
177
6.8
Dry season PM10
BMR
74
7.61
Beijing
262
93.3
Chennai
141
29.2
Bandung
83
7.3
Manila
54
Hanoi
186
21.2
Wet season PM2.5
BMR
19
0.7
Beijing
108
9.0
Chennai
38
3.4
Bandung
38
1.7
Manila
43
2.7
Hanoi
59
11.1
Wet season PM10
BMR
33
5.9
Beijing
180
53.3
Chennai
145
7.6
Bandung
62
5.4
Manila
54
Hanoi
74
21.2
a
average of the mass of all samples; 
OM
EC(BC) Sea salt NH4+ NO3- SO4=
K+
Trace metals
% mass explained
0.8
2.3
2.0
0.5
0.25
10.1
6.7
1.6
1.0
7.4
47.36
86.7
91.3
65.1
64.4
13.3
17.34
53.0
 na



5.51
18.7
2.8
9.8
18.6

1.44
2.7
5.8
0.7
0.7
1.1
0.68
17.1
1.3
3.3


1.06
14.2
6.1
5.8
6.8

4.46
20.8
7.1
8.8
15.5

32.41
9.34


1.0
9.8
3.68
2.4
12.0
4.5
0.97
13.4
12.7
3.8
2.86
19.0
16.8
8.7
6.83
29.3
13.7
11.4
1.2
2.7
3.4
0.8
0.54
12.7
16.2
2.9
63.24
64.5
70.0
59.2

1.4
2.6
6.6
12.2
28.3
1.5
13.6
41.3

15.8




4.71
5.3
0.7
5.1
19.6
0.8
1.11
0.5
1.3
0.4
0.8
0.0
0.38
10.4
3.5
5.2


0.27
12.0
0.5
4.0
18.6

1.90
17.9
0.7
4.2
12.1

0.6
1.3
3.0
0.2
0.27
4.8
2.7
3.2
1.1
1.2
94.26
81.1
31.0
62.7
74.6
44.9
7.03
3.67
1.1
3.6
1.0
0.60
12.2
17.1
6.6
1.05
19.1
1.1
5.4
3.19
25.1
3.8
5.4
0.65
6.9
8.2
6.2
90.90
66.9
34.5
57.2
0.00
0.69
2.40
6.57
0.8
1.7
6.5
0.5

0.4
6.04
41.77


na
2.0
5.6
1.21

not analyzed
12
na



Table 4. City wise average source apportionment results for PM2.5, PM10-2.5 and PM10 by chemical mass balance receptor modeling.
_Beijing________
__Beijing____
__Chennai_ ___
_Bandung__ __
_Manila_____
_Hanoi________
D
W
A
D
W
A
D
W
A
D
W
A
D
W
A
D
W
A
PM2.5
Vehicle
35
21
28
22
34
22
28
22
7
15
72
78
75
18
18

Biomass
31
29
30
30
13
18
16
27
8
18
9
9
9
32
32

Secondary
29
36
32
15
29
39
34
11
6
9
33
33

NaNO3
10
10















Coal comb
18
















Fuel oil
3
3
4
11
7
7
4
6
1
1




Industries
3
9
6
3
35
35
35
5
9
7
10
10






Soil
2
2
2
10
14
20
17
4
11
7







Minor sources 2
2
2
2
4
5
5
6
6
6
14
14






PM10-2.5
Const
Soil
Biomass
Industry
Fuel oil
NaNO3
Vehicle
Minor sources
PM10
Vehicle
Biomass
Secondary
NaNO3
Coal comb
Fuel oil
Industries
Soil
Minor sources
39
16
19
0
0
15
4
6
27
29
10
2
2
20
3
8
33
23
14
2
2
17
4
7
20
14
24


38
4


















13
12
36


30
9
16
13
30


34
7
22
13
20

15
2
2
24
7
17
15
8
16
22
5
12
8
6
14
4
0
3
51
12
10
11
10
22
4
31
16
23
17

10
1
13
17
8
4
27
22
15
19
24
21


5

1
1
14
13
11
14
Note: D, dry season; W, wet season; A, annual; Beijing and Hanoi present results of PM2.5, and PM10; other cites present results of PM2.5 and PM10-2.5.
13