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Atmospheric Environment 43 (2009) 1760–1770

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Atmospheric Environment

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Source apportionment of particulate matter at urban mixed site inIndonesia using PMF

Puji Lestari*, Yandhinur Dwi MauliadiDepartment of Environmental Engineering, Bandung Institute of Technology (ITB), JL. Ganesha No. 10, Bandung 40132, Indonesia

a r t i c l e i n f o

Article history:Received 7 April 2008Received in revised form18 August 2008Accepted 16 December 2008

Keywords:Receptor modelPMFSource profilesFine particlesCoarse particles

* Corresponding author. Fax: þ62 22 2502647.E-mail address: pujilest@indo.net.id (P. Lestari).

1352-2310/$ – see front matter � 2008 Elsevier Ltd.doi:10.1016/j.atmosenv.2008.12.044

a b s t r a c t

A receptor model of positive matrix factorization (PMF) was used to identify the emission sources of fineand coarse particulates in Bandung, a city located at about 150 km south-east of Jakarta. Total of 367samples were collected at urban mixed site, Tegalega area, in Bandung City during wet and dry season inthe period of 2001–2007. The samples of fine and coarse particulate matter were collected simultaneouslyusing dichotomous samplers and mini-volume samplers. The Samples from dichotomous Samplers wereanalyzed for black carbon and elements while samples from mini-volume samplers were analyzed forions. The species analyzed in this study were Na, Mg, Al, Si, K, Ca, Ti, Cr, Mn, Fe, Ni, Cu, Pb, Cl�, NO3

�, SO42�,

and NH4þ. The data were then analyzed using PMF to determine the source factors. Different

numbers of source factors were found during dry and wet season. During dry season, the main sourcefactors for fine particles were secondary aerosol (NH4)2SO4, electroplating industry, vehicle emission, andbiomass burning, while for coarse particles, the dominant source factors were electroplating industry,followed by aged sea salt, volcanic dust, soil dust, and lime dust. During the wet season, the main sourcefactors for fine particulate matter were vehicle emission and secondary aerosol. Other sources detectedwere biomass burning, lime dust, soil and volcanic dust. While for coarse particulate matter, the mainsource factors were sulphate-rich industry, followed by lime dust, soil dust, industrial emission andconstruction dust.

� 2008 Elsevier Ltd. All rights reserved.

1. Introduction

Bandung (latitude 6�550S, longitude 107�360E) is the capital cityof West Java Province of Indonesia. Located at about 150 km south-east of Jakarta, it has a current estimated population of more than2.5 millions. Fig. 1 shows the map of West Java Province and map ofIndonesia. Geographically, Bandung is surrounded by hills andvolcanic mountains which some of them are still active. The city iscongested with a large number of motor vehicles, including bothpublic and private transportations. Moreover, construction ofbuildings takes place continuously throughout the city. Manysmall-scale factories are also located in and around the city.Commercial activities are mostly concentrated in the center of thecity, while industrial activities are concentrated in the west andeast side of Bandung. The residential areas are distributed alongthe city.

In general, Indonesia has two seasons, which are dry season(occurs during April–October) and wet season (occurs during

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October–April). In Bandung, in the dry season, major wind blowsfrom east direction while during wet season major wind blowsfrom west direction. Bandung has a tropical weather with averagetemperature of 23 �C and rainfall intensity of 120 mm h�1. Naturalsources of pollution that have significant effects on the city aresecondary aerosol, soil dust, road dust, lime dust and volcanic dust.Anthropogenic sources consist of industry, biomass burning, andvehicular emissions.

The source identification process is an important step in airquality management. Receptor modeling offers a method tocomplete the process by measurements of the pollutant concen-trations at a sampling site (Hopke, 1991). One type of receptormodel is the multivariate model. Recently, this model is improvedsignificantly. Positive matrix factorization (PMF) developed byPaatero (Paatero and Tapper, 1993, 1994; Paatero, 1997) providesa flexible modeling approach that can effectively use the informa-tion content in the data. Using a least squares approach, PMF solvesthe problem arising in factor analysis by integrating non-negativityconstraints into the optimization process and utilizing the errorestimates for each data value as point-by-point weights (Begumet al., 2004). PMF has been used world wide in the analysis ofreceptor modeling, and successfully applied.

Fig. 1. Map of West Java Province.

P. Lestari, Y.D. Mauliadi / Atmospheric Environment 43 (2009) 1760–1770 1761

The main objective of this study is to apportion the sources ofthe atmospheric aerosol in Bandung. The work focused on PM withaerodynamic diameter less than 2.5 mm (fine particulate matter –FPM) and between 2.5 and 10 mm (coarse particulate matter – CPM).To obtain the source apportionment, the data sets were analyzedusing PMF model. Conditional probability function (CPF) was alsoused to analyze the results from PMF.

2. Methods

2.1. Sampling equipment

Sampling was conducted using two types of samplers,dichotomous samplers (capable of collecting particulate matter inthe coarse fraction (PM2.5–10) and in the fine fraction (PM2.5)) and

Table 1The mean and standard deviation of FPM and CPM.

Dry season

Fine Coarse

Mean � SD Range Mean � SD Range

Mass 48 � 11 29–78 19 � 6 8–37BC 11.26 � 7.30 0.68–32.07 – –Cl� 0.96 � 0.60 0.09–3.28 0.99 � 1.17 0.06–7.74NO3� 1.18 � 1.84 0.00–9.77 1.25 � 1.93 0.00–11.40

SO42� 1.69 � 1.58 0.00–9.55 1.76 � 1.09 0.09–4.51

NH4þ 0.30 � 0.56 0.00–2.51 0.32 � 0.45 0.00–1.57

Na 0.41 � 0.25 0.16–1.63 0.49 � 0.37 0.03–2.15Mg 0.11 � 0.08 0.00–0.32 0.15 � 0.12 0.01–0.51Al 0.43 � 0.57 0.00–3.76 0.59 � 0.49 0.00–1.82Si 0.33 � 0.84 0.00–3.88 0.50 � 0.87 0.00–3.49K 0.14 � 0.11 0.00–0.47 0.08 � 0.10 0.00–0.50Ca 0.95 � 1.04 0.00–4.99 1.07 � 1.59 0.10–9.60Ti 0.03 � 0.03 0.00–0.11 0.03 � 0.03 0.00–0.13Cr 0.06 � 0.08 0.00–0.66 0.04 � 0.03 0.00–0.21Mn 0.01 � 0.01 0.00–0.04 0.02 � 0.01 0.00–0.08Fe 0.33 � 0.51 0.00–3.30 0.31 � 0.40 0.00–1.98Ni 0.01 � 0.02 0.00–0.09 0.01 � 0.01 0.00–0.06Cu 0.02 � 0.01 0.00–0.07 0.02 � 0.04 0.00–0.27Zn 0.46 � 0.78 0.00–3.80 0.40 � 0.63 0.00–3.86Pb 0.03 � 0.03 0.00–0.14 0.01 � 0.01 0.00–0.03

mini-volume samplers (capable of collecting particulate matter inthe PM10 and PM2.5 size fractions). Dichotomous sampler wasused to collect samples which were then analyzed for blackcarbon and elements. While mini-volume samplers were used tocollect samples which were analyzed for ions. The two equip-ments have been previously evaluated by conducting co-locatedsampling for PM2.5 and PM10–2.5. The coarse particle concentra-tions from mini-volume samplers were obtained by subtractingPM10 value with PM2.5 value. The results indicated that the twoequipments were well compared (Lestari, 2005). The type offilters used in this research was a 37 mm Teflon filter with 0.8 mmpore size for dichotomous sampler and a 47 mm quartz fiber filterwith 0.3 mm pore size for mini-volume sampler. Filters from mini-volume samplers were extracted immediately and placed ina refrigerator.

Wet season

Fine Coarse

Mean � SD Range Mean � SD Range

39 � 10 19–64 16 � 4 9–289.80 � 4.57 2.29–20.05 – –1.46 � 1.96 0.04–10.16 0.70 � 0.88 0.08–3.791.44 � 1.94 0.00–6.76 2.39 � 3.59 0.00–13.081.91 � 2.14 0.19–10.09 1.01 � 1.08 0.00–4.420.42 � 0.51 0.00–2.05 0.49 � 0.59 0.00–2.360.70 � 0.48 0.22–2.48 0.62 � 0.40 0.186–2.270.15 � 0.22 0.00–0.98 0.18 � 0.24 0.00–1.270.46 � 0.76 0.00–3.43 0.38 � 0.38 0.00–1.440.43 � 0.47 0.00–2.03 0.48 � 0.40 0.00–1.650.22 � 0.23 0.00–1.18 0.19 � 0.34 0.00–1.940.52 � 0.36 0.00–1.33 0.57 � 0.43 0.01–2.220.06 � 0.15 0.00–0.74 0.04 � 0.03 0.00–0.130.02 � 0.01 0.00–0.06 0.02 � 0.02 0.00–0.100.02 � 0.03 0.00–0.17 0.02 � 0.01 0.00–0.050.51 � 0.62 0.00–2.70 0.50 � 0.31 0.00–1.140.03 � 0.04 0.00–0.13 0.05 � 0.13 0.00–0.750.01 � 0.02 0.00–0.07 0.01 � 0.01 0.00–0.020.36 � 0.63 0.00–3.92 0.30 � 0.34 0.00–1.810.03 � 0.04 0.00–0.21 0.01 � 0.01 0.00–0.04

Fig. 2. Wind rose of 2001–2007.

P. Lestari, Y.D. Mauliadi / Atmospheric Environment 43 (2009) 1760–17701762

2.2. Sampling location

The sampling site is located in the center of the city. Samplerswere placed on the flat roof of Governmental building at Tegalegaarea in Bandung City. Tegalega site represents an urban mixed site(commercial, industrial and traffic areas). The roof height is 3 m andthe samplers were placed on a platform with 1.5 m high.

2.3. Sampling time

Samples were collected over 24 h period during wet and dryseason during the year 2001–2007. Meteorological conditions suchas wind speed, wind direction, temperature, and relative humiditywere also measured simultaneously at sampling site.

2.4. Samples’ analysis

After sampling, filters were placed in protected Petri dishes andbrought back to the laboratory for stabilizing and weighing.Particulate mass was measured using gravimetric method byweighing samples before and after sampling and the measurementwas blank corrected.

After total mass of particulate matter was determined, filtersfrom dichotomous sampler were re-stabilized for 24 h prior toblack carbon analysis. The concentration of black carbon (BC) in thefine fraction of the samples was determined by reflectancemeasurement using an EEL type Smoke Stain Reflectometer. The BCvalues were measured from coarse fraction filters and then con-verted to fine fraction values. Secondary standards of known blackcarbon concentrations are used to calibrate the reflectometer.

Element concentrations were measured using Atomic Absorp-tion Spectrophotometer with flame ionization and graphite furnacedetectors. Prior to AAS analysis, the samples were extracted usingMicrowave Digestion. Fourteen elements which include Na, Mg, Al,Si, K, Ca, Ti, Cr, Mn, Fe, Ni, Cu, Zn, and Pb were measured.

Samples from mini-volume sampler were extracted usingUltrasonic Bath and were analyzed for ion concentrations using IonChromatograph. Four ions which include Cl�, NO3

�, SO42�, and NH4

þ

were determined. The ion concentrations were blank corrected.Concentrations of ions in the coarse fraction were obtained bysubtracting the ion concentration of PM10 with the ion concentra-tion of PM2.5. No negative values were found from thesesubtractions.

2.5. Data analysis by PMF

Before applying the PMF model, the data screening was appliedto remove outliers, unusual events, and extremely high values.After data screening, all concentrations and uncertainty valueswere treated for optimal data weighing essential for receptormodeling according to the recommendations by Polissar et al.(1998). Concentrations below the detection limit were replaced byhalf of the detection limit value, while the uncertainty value wasthe sum of half the mean detection limits and a third of theirdetection limit ((5/6) � detection limits). Missing values werereplaced by available values of geometric mean while the uncer-tainty value was four times that of geometric mean value.

Positive matrix factorization is a method developed by Paateroand Tapper (1993, 1994). The principles of the PMF model were tofind solutions for the problems arisen by the PCA method (Paateroet al., 2005) and to apply this method when source profiles aremostly unknown (Hopke, 2003).

In PMF model any matrix X of data of dimension n rows and mcolumns, where n and m are the number of samples and thenumber of species, can be factorized into two matrixes, namelyG(n � p) and F(p � m), and the residual matrix, E, where p is thenumber of factors or sources extracted.

X ¼ GF þ E (1)

The product of G and F can explain the systematic variations in X.The residuals, eij can be explained as,

Eij ¼ Xij � bX ¼ Xij �Xp

h¼1

Gih$Fhj (2)

The object function Q that has to be minimized is defined as

QðEÞ ¼Xmi¼1

Xn

j¼1

"Eij

sij

#2

(3)

where sij is the uncertainty in xij. The next step of the process is tominimize Q as a function of G and F with the constrains of allelements of G and F to be nonnegative. The solution of Eq. (3) isobtained using the program of PMF2 (Paatero, 1997).

The PMF model needs a few adjustments to its features. Theimportant feature for the analysis was FPEAK value that needs to beset to control the rotations and the number of factors expected. By

Fig. 3. Source factors for FPM during dry season.

P. Lestari, Y.D. Mauliadi / Atmospheric Environment 43 (2009) 1760–1770 1763

setting specific values of FPEAK, the model is forced to subtract theF factors from each other and resulting more physically realisticsolutions (Paatero et al., 2002). The FPEAK value used in this studyvaries from�0.4 toþ0.8. After the adjustments of FPEAK value andnumber of factors were set, the PMF model will iterate the FPEAKvalue and the number of factors inputted until the results showrealistic value of source profile and source contribution.

Conditional probability function (CPF) was also used to analyzethe results from PMF. The CPF predicts the probability that a givensource contribution from a given wind direction will exceed a pre-determined threshold criterion (Kim and Hopke, 2004). The samedaily contribution was assigned to each hour of a given day tomatch to the hourly wind data. The CPF is defined with thefollowing equation:

CPFDq ¼mDq

nDq(4)

where mDq is the number of occurrence from wind sector Dq thatexceeded the threshold criterion, and nDq is the total number ofdata from the same wind sector. In this study, 24 sectors were used(Dq ¼ 15�). Calm wind (<1 m s�1) periods were excluded from thisanalysis due to the isotropic behavior of wind vane under calmwinds.

3. Results and discussion

Total of 367 samples were collected during wet and dry seasonin urban mixed site of Tegalega area, Bandung city. Table 1 shows

Fig. 5. Source profile for CPM during dry season.

Fig. 4. CPF for FPM during dry season.

P. Lestari, Y.D. Mauliadi / Atmospheric Environment 43 (2009) 1760–17701764

Fig. 6. CPF for CPM during dry season.

P. Lestari, Y.D. Mauliadi / Atmospheric Environment 43 (2009) 1760–1770 1765

the average mass, element and ion concentrations with standarddeviations for both fine and coarse particulate matters.

The results of PMF model were divided into two differentseasons, dry and wet. The meteorological data were also collectedfrom local meteorological station and nearby airport. The typicalwind rose for Bandung city during 2001–2007 is presented in Fig. 2.The wind rose in Fig. 2 shows that during dry season, wind blowndominantly from easterly direction with average wind speed of2 m s�1. While during wet season, wind blown dominantly fromwesterly direction with average wind speed of 3 m s�1.

3.1. Dry season

PMF model applied to the dry season data produced sevensource factors for fine particulate matter and five factors for coarseparticulate matter. The results for fine particulate matter (FPM) areshown in Fig. 3.

3.1.1. Fine particulate matterThe first factor was characterized by high black carbon and

sulphate. Those elements were clearly the fingerprint of dieselvehicle emissions. Local source of diesel emission comes fromnearby bus station, located at about 2 km west of the sampling site.Second factor was identified as biomass burning emission. The highvalue of black carbon and potassium was the indicators. Gasolinevehicle emission was identified in the third factor. Lead, which isstill used as fuel additive in the form of tetraethyl lead, is the mainfingerprint of gasoline vehicle emission. The emission source wasprobably contributed by the busy road near the sampling site, thepublic transport station and also the Padaleunyi Highway which islocated at the south and east part of Bandung city.

The fourth factor has high value of sodium and sulphate. Thisfactor was identified as aged sea salt emission. This factor is origi-nated from sea salt aerosol which has undergone the chloride lossreaction in the atmosphere,

2SO2DO2D2H2O/2H2SO4ðagÞ

H2SO4ðagÞD2NaClðpÞ/Na2SO4ðpÞD2HClðgÞ

The fifth factor was identified as industrial emission. High values ofAl and Cr were predicted to be the source of small electroplatingindustries, which were spread along the east side of the city.

Motorcycle emission was identified as the source of the sixthfactor. The marker elements for the source were BC, Cl, Mg and Ca.Recent local study on motorcycle emission source profile (Lestari,2006) shows that marker elements for motorcycle emission wereBC, Cl�, SO4

2�, NO3�, NH4

þ, Fe, Ca, Mg, Al, K, and Na. The significantamount of Mg and Ca may come from the engine additive oil. Theseventh factor was characterized by high value of NO3

�, SO42�, and

NH4þ. This factor was identified as secondary aerosol. Low value of

NO3� indicated that this factor represents (NH4)2SO4. The complete

results of CPF for FPM were presented in the Fig. 4.

3.1.2. Coarse particulate matterThe results of source factors for coarse particulate matter (CPM)

and CPF results are shown in Figs. 5 and 6.The first factor was identified as aged sea salt aerosol. The high

value of Na, Mg and sulphate clearly shows the source of emission.During the dry season, the sources were influenced by easterly andnorth-easterly wind direction. Even though the dominant windblown was from easterly direction, the south-west wind alsooccurred during the dry seasons. Therefore, the source of aged seasalt aerosol was then predicted to come from the Java Sea, in theIndramayu District, located about 130 km at the north-east side ofBandung as well as from Indian Ocean which is in the south west ofBandung. This is also indicated from the CPF results (Fig. 6).

The second factor was characterized by Al, sulphate, Cr and Cl.This factor was identified as electroplating industry emission. Thethird factor was characterized by Cl, sulphate, Na, Mg, Ca and Fe.The high value of sulphate and the presence of crustal elements

Fig. 7. Source profile for FPM during wet season.

P. Lestari, Y.D. Mauliadi / Atmospheric Environment 43 (2009) 1760–17701766

show that this factor has probably come from volcanic emission.However, the CPF results show that this factor mostly affected bywind from south-easterly direction and therefore, the possiblesource of this emission was predicted to come from sulphate-richindustry which spread on the south and east part of Bandung.

The fourth factor was characterized by Al, Ca, Ti and Fe. Thisfactor shows abundance of crustal elements. Although Si was notincluded in the PMF analysis of coarse particulate matter due tohigh missing value and S/N ratio, the crustal elements identified inthis factor show that the source of emission was from soil dust.

Fig. 8. CPF for FPM during wet season.

P. Lestari, Y.D. Mauliadi / Atmospheric Environment 43 (2009) 1760–1770 1767

The fifth factor was characterized by Ca, Mg, and K. The presenceof Ca indicated that this factor is from lime industry, which islocated at the west side of Bandung City. The dry season-wind roseshows a small but significant amount of wind blowing from thewest. This condition supports the transportation of lime dust fromthe lime industry to Bandung.

3.2. Wet season

During wet season, PMF model produces nine factors of fineparticulate matter and six factors of coarse particulate matter. Theresults of source factors for FPM and results of CPF are shown in theFigs. 7 and 8.

3.2.1. Fine particulate matterLime dust was identified as the first factor of fine particulate

matter. This factor was characterized by Ca and other crustalelement of Si, K and Fe.

The second factor was characterized by black carbon and Pb,which shows the emission of gasoline vehicles. The use of leadedgasoline is still common in Bandung. The presence of Zn could also

be noted as the fingerprint of this type of emission source (Huanget al., 1994). CPF result shows that the source of traffic emission waspredicted to come from the Padaleunyi highway located at the eastside of Bandung.

The third factor was characterized by black carbon and potas-sium. Those elements were the fingerprints of biomass burningemission. In Bandung, especially in the rural area, still has a largearea of rice fields which is the main source of biomass burning. Theother potential source of biomass burning is from solid wasteburning activity especially from residential areas. Industrial emis-sion was identified as the source of the fourth factor.

Local study on source profile from diesel and motorcycle emis-sions (Lestari, 2006) shows that diesel emission was characterizedby BC, SO4

2�, Fe and Zn while motorcycle emission was character-ized by BC, Cl�, SO4

2�, NO3�, NH4

þ, Fe, Pb, Ca, Mg, Al, K, Na, and Si.According to this study, the fifth factor can be identified asmotorcycle emission while the eighth factor can be identified asdiesel vehicle emission.

The sixth factor was clearly from secondary aerosol. This factor wascharacterized by the high value of NH4

þ and SO42�, and indicated as

(NH4)2SO4. The seventh factor was characterized by NO3�, SO4

2�, and

Fig. 9. Source profile for CPM during wet season.

P. Lestari, Y.D. Mauliadi / Atmospheric Environment 43 (2009) 1760–17701768

NH4þ. This factor was also contributed by secondary aerosol. The pres-

ence of nitrate in this factor shows that the type of secondary aerosolcontributed to the factor was NH4NO3. The ninth factor was charac-terized by crustal elements and indicated as the soil dust emission.

3.2.2. Coarse particulate matterThe results of source factors for coarse particulate matter (CPM)

during wet season and the results of CPF are presented in Figs. 9and 10.

The first factor was characterized by NH4þ, Si, Ti, Mn and Fe. The

presence of crustal element of Si, Ti and Fe indicated the source ofsoil dust emission. The second factor was characterized by K, Ti, Feand Zn. The presence of Zn and Fe may be the result from industrialactivities, particularly from steel industries.

Sulphate-rich industry was identified in the third factor. Thepresence of SO4

2� and Cu indicated as the fingerprint of this source.As indicated in the CPF results, the potential wind was from south-east where many industries are located.

The fourth factor was characterized by crustal elements andsulphate. This factor was identified as the volcanic dust, originatedfrom the Tangkuban Parahu Volcano. The fifth factor was charac-terized by Ca. The dominant west wind direction helps in identifyingthis factor as a lime dust, resulted from the lime industry in Pada-larang district, located at about 25 km north-west side of Bandung.

The sixth factor was characterized by SO4, Al, Ca, Ti and Ni. Thisfactor was identified as construction dust. Throughout the city,construction of building and housing takes place continually.

3.3. Source contribution

The source contribution was calculated by multiple regression ofthe G matrix against the measured mass. Fig. 11 shows the contri-bution of each source factor.

Secondary aerosols have almost equal contribution to fineparticles during both seasons. Vehicle emissions contributed 35%during wet season and 22% during dry season. The Bandung–

Fig. 10. CPF for CPM during wet season.

Fig. 11. Source contribution.

P. Lestari, Y.D. Mauliadi / Atmospheric Environment 43 (2009) 1760–1770 1769

P. Lestari, Y.D. Mauliadi / Atmospheric Environment 43 (2009) 1760–17701770

Jakarta highway at the west side of Bandung city was predicted tobe the main source of traffic emission during wet season which wasdominantly influenced by westerly wind direction. While duringdry season, other potential source of traffic emission was thePadaleunyi highway, located at the east side of Bandung.

Industrial source contributed 24% to the fine particles during dryseason, while during wet season, industrial emission contributed6% to fine particles. The Cisaranten industrial area at the east side ofBandung city was then predicted to be the source of industrialemission during dry season.

Sea salt aerosols (in the form of aged sea salt) contributedsignificantly to fine and coarse particles during dry season. Thesource of sea salt aerosols during dry season was expected to comefrom Java Sea and Indian Ocean. Electroplating industry contrib-uted dominantly to fine and coarse particles during dry season. Thesource of this emission was predicted to come from local electro-plating industry in the east side of Bandung city.

During wet season, lime dust contribution to coarse particleswas higher compared to the contribution in the dry season. Thiswas caused by the location of lime industries which are at thenorth-west side of Bandung city and during the wet season, thewind dominantly blown from west direction.

Volcanic dust contribution was detected during wet season. Thismay come from the Tangkuban Perahu volcano which is located atthe north side of Bandung. As seen in the CPF result, the north windoccurred more frequently during wet season and it could transportmore emission from volcanic dust.

4. Conclusions

The PMF model could identify the source of particulate matterin Bandung city using the chemical species obtained from thisstudy. The results show that during dry season, seven sourcefactors were produced for FPM and five source factors for CPM.During wet season, nine source factors were produced for FPM andsix source factors for CPM. The results show that during wetseason, fine particles were contributed dominantly by vehicle’semission (35%) and by secondary aerosol (26%). Other sourcesdetected were biomass burning, lime dust, and soil dust. The maincontributors for coarse particles were sulphate-rich industry (26%),followed by lime dust (20%) and soil dust (20%), while industrialemission and construction dust contributed 15% each. During dryseason, fine particles were contributed dominantly by secondary

aerosol (NH4)2SO4 (25%), electroplating industry (24%), vehicleemission (22%) and biomass burning (16%). For the coarse fraction,the main contributors were electroplating industry (30%), followedby aged sea salt (23%), volcanic dust (18%), soil dust (19%), and limedust (10%).

Acknowledgement

This research project was funded by Swedish InternationalDevelopment Cooperation Agency (SIDA) through Asian RegionalResearch Program in Environmental Technology (ARRPET) coordi-nated by Asian Institute of Technology (AIT).

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