hien et al., 1999 temporal variations of source impacts at the receptor, as derived from air pm...

Atmospheric Environment 33 (1999) 3133}3142

Temporal variations of source impacts at the receptor,as derived from air particulate monitoring data

in Ho Chi Minh City, Vietnam

P.D. Hien*,!, N.T. Binh", Y. Truong", N.T. Ngo"!National Atomic Energy Agency, 59 Ly thuong Kiet, Hanoi, Vietnam

"Dalat Nuclear Research Institute, Dalat, Lam dong, Vietnam

Received 18 November 1997; accepted 18 August 1998

Abstract

Varimax rotation factor analysis was applied to monthly concentrations of elements in total suspended air particulate(TSP) matter in Ho Chi Minh City collected from December 1992 to November 1996, covering four dry/rainy seasons.Six pollution source types were revealed. Resuspended soil/road dust accounts for 74% of the TSP mass loading. Motorvehicles and a source which emits particulates containing arsenic account for 10% and 9%, respectively. There are threeminor sources, namely, cement dust from the nearby construction site, road dust of local tra$c origin and burningemissions. The contributions from these source were estimated with high uncertainties. The interpretation of sources wascorroborated by studying source pro"les and temporal variations of source contributions. The monthly variations ofsource contributions at the receptor were modelled by using source apportionment techniques. From the variationpatterns, emission scenarios for burning, construction and motor vehicle sources were reproduced. Source contributionsalso exhibit seasonal variability induced by changes of meteorological conditions. No seasonal change was found for theAs-containing particulates, suggesting a speculation on their origin as coal #y ash emitting from any local coal burningsource. ( 1999 Published by Elsevier Science Ltd. All rights reserved.

Keywords: Total suspended air particulates; Ho Chi Minh City; Monthly concentration data; Source apportionment;Annual and seasonal variations

1. Introduction

Multivariate receptor modelling techniques based onthe variability of ambient elemental/chemical concentra-tions of suspended particulate matter have been success-fully developed and applied over the last two decades forthe purpose of characterizing air pollution sources inurban areas (Hopke et al., 1976; Thurston and Spengler,1985; Keiding et al., 1986). Most works published so far

*Corresponding author.

have dealt with urban air pollution in industrializedcountries. Meanwhile, the particulate levels in many ci-ties of developing countries exceed the WHOs healthprotection guideline and urban air pollution has beenamong severe environmental problems of the developingworld (WHO, 1987; Mage et al., 1996).

With some 5 million inhabitants, i.e. about 7% ofVietnams population, Ho Chi Minh City (HCMC) ac-counts for more than one-third of the countrys industrialoutput with an annual GDP growth rate at around 15%in the last few years. Although large industrial pollutionsources do not exist, the air quality has been a!ected by

1352-2310/99/$ - see front matter ( 1999 Published by Elsevier Science Ltd. All rights reserved.PII: S 1 3 5 2 - 2 3 1 0 ( 9 8 ) 0 0 3 3 7 - 9

the fast growing fuel consumption, vehicular tra$c,booming construction works, etc.

In the framework of a co-ordinated air pollutionmonitoring programme, concentrations of chemical ele-ments and radionuclides in TSP were measured inmonthly samples collected during the period from De-cember 1992 to November 1996 covering four dry/rainyseasons. Since July 1996, two sampling units have beenset up for daily monitoring of air particulates with dia-meters less than 10 lm (PM-10) and 2.5 lm (PM-2.5).For characterizing air particulate sources, major "ndingsare expected to be derived from PM-10 and PM-2.5 data.However, statistical analyses of TSPs monthly data haveyielded substantial understandings on air particulatesources. In particular, from the modelling of monthlyvariations of source contributions to the TSP mass at thereceptor, it was possible to reveal variabilities induced bychanges in the source strengths as well as seasonal e!ectsof atmospheric conditions. The capability of receptormodelling techniques in characterizing emission sourcesbased on TSP data has recently been demonstrated byHarrison et al. (1997) for other Asian city, i.e. Lahore,Pakistan. A clear cut distinction in the source structurewas obtained even for an urban area where soil dust ishighly abundant (607 lgm~3) and predominant (62%) inTSP mass.

2. Experimental

2.1. Sampling

The sampling site is located on the roof of an eight-storey building in a residential area near downtownHCMC. The siting was intended to collect representativedata for the area with minimum in#uences of road dustthat is abundant in the ground-level air. The industrialzone of the city is some 10 km north and northeast of thesampling site. However, there are numerous small facto-ries and handicraft units scattered throughout residentialareas in the city, especially, in the nearby Cho lon Chinatown.

A Russian-made high volume air sampler with a #owrate of &700 m3h~1 was used for collecting total sus-pended air particulates on chlorinated vinyl polychloridePetranow "lter FPP-15. The use of such a high #ow rateair sampler was necessary for a combined monitoring ofairborne chemical elements and radionuclides as re-quired by the environmental protection authority. Wealso have found some advantages of such a combinedmonitoring (Binh et al., 1996). Due to logistic constraints,sampling was only done in day time, from 8 a.m. to4 p.m., on week days (including Saturday). Elementalcompositions of TSP and airborne radionuclides weremeasured every month. This work included 48 monthly

samples collected from December 1992 to November1996.

2.2. Meteorological conditions

Meteorological conditions of HCMC is typical ofSoutheast Asia monsoon regime with two distinct sea-sons. From May to November, SW wind and cyclonicconditions bring about 90% of the total annual rainfall.The dry season begins in late November when HCMCcomes under the in#uence of the Asiatic high-pressurecentre (AHPC) with N or NE winds prevalent. Anticyc-lonic conditions are usually observed from late Decem-ber to early February, when the AHPC is in its mostactive phase, pushing large-scale cold air masses downfar to the Southeast Asia equatorial zone. From Febru-ary to May, SE wind prevails.

2.3. Analysis

Dust loaded "lters were cut into sections for elementaland radionuclide analyses. Elemental compositions ofTSP were determined by instrumental neutron activationanalysis (INAA) at the 500 kW Dalat nuclear researchreactor and by polarography method (for lead) usingOMEGA METROHM 647 polarograph. Experimentalconditions for INAA are described elsewhere (see e.g.Hien et al., 1992). Reactor irradiations were carriedout separately for short, medium and long lived nuclidesin 5 min., 20 min. and 10 h, respectively, by using apneumatic transfer system and by manual loading ofsamples into the rotary specimen rack at the graphitere#ector.

The INAA method provided concentrations for about30 elements with accuracies around 5}10%. The analyti-cal procedure for lead was described in our previouspaper (Hien et al., 1997). The accuracy for lead was about5%.

2.4. Analytical results

Table 1 shows relevant statistics and physical charac-teristics of concentrations of 24 elements, which wereused for source characterization by factor analysis. Byconverting concentration values from elements to chem-ical components, e.g. to oxides for metals, the overallcontribution of measured components in the averageTSP mass (77.7 lgm~3) can be estimated as about 30%.Silicon, the most abundant crustal element, was not ap-propriately measured by INAA. But the mass contribu-tion of silicon oxide in TSP can be estimated as high as25% assuming the Si-to-Al abundance ratio in TSP issimilar to that in the crust (Mason, 1966). The remainingTSP mass should be made up mainly of sulphate, nitrate,organics and black or elemental carbon which also werenot measured in the experiments.

3134 P.D. Hien et al. / Atmospheric Environment 33 (1999) 3133}3142

Table 1Statistical and physical characteristics of ambient concentrations

Mean (ng m~3) Standard Geometric mean Geometric DWR EFdeviation (ngm~3) Standard (for the mean)(ngm~3) deviation

Na 802 317 743 1.49 1.81 1.1Mg 640 242 567 1.75 1.65 1.1Al 2760 1461 2424 1.69 1.65 1.3Cl 1204 500 1108 1.53 1.69 356K 915 425 838 1.52 1.71 1.3Ca 3360 2838 2193 2.76 1.88 3.3Sc 0.58 0.21 0.55 1.42 1.65 1Ti 266 106 247 1.46 1.51 2.3V 7.6 4.5 6.6 1.70 1.89 2.1Cr 9.4 3.3 8.9 1.38 1.46 3.6Mn 37.8 11.6 36.1 1.36 1.5 1.6Fe 3078 1054 2919 1.38 1.56 2.3Co 1.23 0.46 1.15 1.42 1.54 1.9Cu 1.39 1.21 1.12 1.85 1.64 1Zn 203 100 181 1.64 1.07 117As 1.47 0.60 1.51 1.47 1.16 32Br 9.7 6.0 8.4 1.70 1.23 153Rb 4.1 1.4 3.9 1.44 1.67 1.7Sb 3.8 5.3 2.3 2.44 3.2 698Cs 0.53 0.46 0.41 1.99 1.6 6.7Ba 33 11 31 1.42 1.47 2.9Ce 3.3 1.0 3.2 1.35 1.54 2.1La 1.48 0.61 1.36 1.53 1.81 1.9Sm 0.22 0.09 0.20 1.57 1.77 1.3Eu 0.056 0.027 0.051 1.58 1.7 1.8Lu 0.017 0.005 0.016 1.42 1.43 1.4Pb 163 113 139 1.77 1.31 505Th 0.56 0.19 0.47 1.44 1.7 2.6U 0.18 0.08 0.17 1.52 1.51 3.9TSP 77700 24200 74300 1.35 1.52

Air particulate species have a common seasonalvariability feature, i.e. the concentrations are higher inthe dry season. The dry-to-wet seasonal mean ratios(DWR) were calculated in Table 1. Apart from sucha common trend, each species exhibits its own temporalvariability (Fig. 1). For example, the concentrationof Ca was a!ected by the nearby construction siteduring the period from mid-1993 to mid-1995. Theburning of "recrackers, that was a source of airbornelead, potassium, antimony, etc. (Hien et al., 1997), has"nally been banned by the government since mid 1994.The two eminent peaks of Sb in Fig. 1 were identi"ed asassociated with the "recracker burning during lunar NewYear festivities, that fell in January 1993 and February1994.

Concentration data follow quite well lognormal fre-quency distributions. At 5% signi"cance level of thegoodness-of-"t chi-square test, the lognormal distribu-

tion is rejected only for Ca, Sb and Zn. For all species, thegeometric means of ambient concentrations are less thanthe arithmetic means. The geometric standard deviations(GSD) of TSP and most of crustal elements are approx-imately constant, varying from 1.35 to 1.6, almost thesame range as was found by Kao and Fridelander (1995)for the aerosols in the South Coast Air Basin, USA.Meanwhile, the GSD for Ca, Cu, and Sb are higher, from1.8 to 2.8. Bimodal frequency distributions are found forthese species.

The enrichment factors (EF) are given in column 7 ofTable 1. The EF for a given element is de"ned as thedouble ratio of the concentration of this element (X) tothat of Sc in TSP and in the earth crust according toMason (1966) as following:

EF" (X/Sc)TSP(X/Sc)

M!40/

.

P.D. Hien et al. / Atmospheric Environment 33 (1999) 3133}3142 3135

Fig. 1. Monthly variations of elemental concentrations Fe, Al, Ca (lg m~3); Br, Sb, As (ng m~3).

Anthropogenic elements, such as Br, Cl, Pb, Sb, etc.,are clearly enriched in TSP, with EF100.

3. Source characterization

3.1. Factor analysis

Varimax rotation factor analysis was applied to ambi-ent concentration data to identify SPM sources a!ectingthe sampling site. Twenty four elements mentioned inTable 1 were used as variables as shown in Tables 2}4.These include all species having high enrichment factors,i.e. those of anthropogenic origin. TSP mass was alsoused as a variable to allow the determination of sourcepro"les and source contributions at the receptor follow-ing the procedure proposed by Keiding et al. [4].

Six factors accounting for 86% of the total variance inthe data set and 94% of the common variance wereretained. The varimax rotated factor matrix is shown inTable 2. The factor standard deviations were calculated

according to Heidam (1982). In Table 2, factor loadingsnumerically less than twice the factor standard deviationsare left out for clarity. The computed elemental pro"lesfor six source components at the receptor site, S1}S6, aregiven in Table 3.

Factor F1 with high loadings of crustal elements canbe attributed to resuspended soil/road dust. TSP mass isstrongly coupled with this factor. The enrichment factorscomputed from the source pro"le of S1 (Table 4) arearound 1}3 for both major (Al, Fe, Mn, Na, Ti) andminor (Cr, Co, Th, REEs) crustal elements. Meanwhile,Cl and As are found enriched in S1.

Source S5 is also dominated by crustal elements. Alu-minum and titanium are more abundant in S5 than inS1, while chlorine is enriched in both sources. On theother hand, vanadium, a marker of residual oil, iscoupled with factor F5 resulting in a high enrichment ofV in S5 (EF"15). Copper, which can be emitted fromdiesel engines, is also found enriched in S5. These suggestthat S5 can be assigned to road dust derived by localtra$c activities. This interpretation will be further cor-roborated by studying the source contribution to the


Table 2Varimax rotation factor loadings (F), standard deviations of the loadings (SD) and communalities (h2). For clarity, factor loadings lessthan two SDs are omitted

F1 F2 F3 F4 F5 F6 SD h2

Al 0.47 0.40 0.35 0.61 0.17 0.06 0.91As 0.26 0.35 0.18 0.66 0.12 0.68Br 0.21 0.87 0.08 0.84Ca 0.42 0.69 0.20 0.10 0.75Ce 0.93 0.14 0.17 0.15 0.04 0.95Cl 0.55 0.62 0.20 0.29 0.15 0.07 0.89Co 0.91 0.22 0.15 0.06 0.92Cr 0.78 0.46 0.18 0.07 0.89Cu 0.77 0.26 0.21 0.10 0.78Fe 0.93 0.11 0.17 0.18 0.11 0.04 0.96K 0.58 0.56 0.20 0.41 0.21 0.06 0.91La 0.78 0.20 0.24 0.31 0.08 0.83Lu 0.83 0.10 0.76Mn 0.68 0.37 0.26 0.16 0.39 0.16 0.07 0.87Na 0.84 0.23 0.23 0.20 0.07 0.87Pb 0.82 0.19 0.26 0.09 0.79Sb 0.12 0.84 0.31 0.09 0.82Sc 0.93 0.19 0.19 0.14 0.09 0.03 0.97Sm 0.88 0.22 0.17 0.24 0.13 0.05 0.93Th 0.93 0.12 0.11 0.19 0.10 0.05 0.95Ti 0.55 0.15 0.43 0.30 0.52 0.13 0.07 0.89U 0.65 0.40 0.36 0.11 0.72V 0.45 0.54 0.20 0.60 0.07 0.88Zn 0.80 0.11 0.69TSP 0.90 0.20 0.20 0.19 0.15 0.14 0.03 0.97Source Soil-1 Burning Auto Construc. Soil-2 As-related

TSP mass loading. In the following, S1 and S5 will benamed by soil-1 and soil-2, respectively.

Factor F2 with high loadings of Cl, Cu, K, Pb and Sblikely represents a mixture of burning emissions, includ-ing "recracker burning, that caused high levels of Sb, Pband K in January 1993 and February 1994. Burning ofdomestic "rewood and garbage may be other compo-nents of this source type.

The markers of motor vehicle emissions, Br and Zn areshown up in factors F3. Bromine is derived from thecombustion of leaded gasoline, while Zn is known to befound in tyre wear particles. The low loading of lead inthis factor (0.19) indicates the role of other sources ofairborne lead. However, the Pb/Br ratio calculated fromthe source pro"le S3 (Table 3) is 3.7$2.3, that is inreasonable agreement with literature values for autoex-haust (Harrison and Sturges, 1983).

Factor F4 has a high loading of calcium. As will beseen later, source S4 can be attributed to fugitive cementdust from the nearby construction site.

Factor F6 has a high loading of arsenic, a usual markerof coal #y ash. The elemental pro"le of S6 was notmuch di!erent from literature data for coal #y ash

(IAEA-TECDOC-854, 1995), except for the very highabundance of Pb. Uranium is found moderately corre-lated with this factor. However, there is no coal-"redpower plant in the area, and coal shares only a smallportion of fuel consumption in the city, about tentimes as low as that of oil. Selenium, another markerof coal, could not be included in factor analysis due toquite a few missing values in the data set. As argumentsare not su$cient for attributing factor F6 to coal #y ash,the term &As-related sourcewill be used for assigning thissource.

3.2. Source apportionment procedures

The modelling of source contributions and theirmonthly variations will give further insights into thenature of emission sources. The apportionment of TSP tosources has been done by multiple linear regression ofambient elemental concentrations upon source pro"les(Keiding et al., 1986). The regression coe$cient f

ijrepres-

ent the contribution of emission source i to the TSPmass loading for the sample j. In this procedure aweight (diagonal) matrix was used in order to lessen the


Table 3Elemental pro"les of source types (mgg~1). The relative errors in percent are given in brackets

Element Soil-1 Burning Auto Construction Soil-2 As-related

Al 32(13) 123(15) 111(17) 250(10) 70(36)As 0.008(45) 0.048(33) 0.13(18)Br 0.26(40) 1.07(10)Ca 55(24) 428(15) 150(50)Ce 0.043(5) 0.03(31) 0.035(26) 0.044(29)Cl 12(12) 65(11) 21(33) 32(23) 20(46)Co 0.019(6) 0.022(25) 0.019(38)Cr 0.12(9) 0.07(46)Cu 0.2(12) 0.064(37) 0.33(15) 0.07(46) 0.17(39)Fe 45(4) 25(37) 39(24) 52(23) 32(40)K 11(10) 50(11) 17(31) 38(15) 25(30)La 0.022(11) 0.026(42) 0.03(35) 0.041(28)Lu 0.0002(12)Mn 0.36(11) 0.9(20) 0.62(28) 0.4(45) 1.3(18) 0.5(45)Na 12.2(9) 15(32) 15(32) 17(37)Pb 19.4(11) 4(50) 8.5(36)Sb 1.1(10) 0.42(28)Sc 0.0089(4) 0.008(18) 0.008(18) 0.008(250) 0.005(38)Sm 0.0035(6) 0.004(25) 0.0031(32) 0.005(23) 0.003(41)Th 0.0081(5) 0.0047(39) 0.0044(44) 0.01(26) 0.005(48)Ti 2.6(12) 3.4(44) 9.2(16) 7(22) 15(13) 4(51)U 0.0024(17) 0.007(27) 0.008(30)V 0.093(16) 0.51(13) 0.18(35) 0.75(12)

16(14)

in#uence of elements that were poorly modelled, i.e. withlow communalities in factor analysis.

A satisfactory agreement was obtained between re-gressed and observed ambient concentrations. The mean($standard deviation) of the adjusted-R2s is 0.991($0.008). However, 65 out of 288 regression coe$cientswere negative, of which 31, 19 and 15 cases occurred withthe construction (S4), soil-2 (S5) and burning (S2) sources,respectively. Actually, only in nine cases, the negativeregression coe$cients are statistically signi"cant, i.e.fijO0 with a(0.05. Thus, the source contributions in

cases of negative regression coe$cients can be regardedas negligible, i.e., f

ij"0. The modelled source contribu-

tions are presented in Fig. 2a}g. &&Negative source con-tributions in cases of S4, S5 and S2 are set equal to zero(Fig. 2b, d, e). Some summary statistics of source contri-butions are given in Table 5.

In order to check to what extent the negative sourcecontributions are speci"c of the statistical procedure usedfor source apportionment, another procedure was tried,namely, the absolute principal component score (APCS)method proposed by Thurston and Spengler, (1985). Inthis method, an extra (49th) &&hypothetical zero-concen-tration sample was included in the data set for factoranalysis, and the corresponding factor scores were thenused for computing the renormalised factor scores, i.e. the

APCS. The source contributions were computed by re-gressing the TSP mass loadings against the APCSs. Theresults obtained from the two source apportionment pro-cedures are almost similar, except for minor discrepan-cies in the numerical values, that appears to be due to thealteration of the factor loading matrix by the inclusion ofan extra zero-concentration sample in the data set.

3.3. Results of source apportionment

Soil-1 is a predominant source, it accounts for 74% ofthe TSP mass. The corresponding regression coe$cientswere obtained with relative standard errors of about 5%.The remaining 26% of the TSP mass is distributed over"ve sources. Their contributions were, therefore, esti-mated with much larger relative errors, i.e. about 20% forS3 and S6 and even more larger for minor sources S2, S4and S5.

The contribution of soil-2 to TSP is 27 times as low asthat of soil-1. Such a low impact of soil-2 is understand-able, if suppose this source type contains mainly coarseparticles, derived into the air by local tra$c, and most ofthem settle quickly, not reaching the sampling site at25 m above ground. It is such a short residence time oflarge dust particles making soil-2 separated from (uncor-related with) soil-1, although these sources share a


Table 4Enrichment factors for soil-1 and soil-2 (not calculated forelements with factor loadings less than twice the factor standarddeviations)

Element EF(soil-1) EF(soil-2)

Al 1 4As 11 *Br * *Ca 4 *Ce 2Cl 240 1340Co 2 2Cr 3 *Cu * 10Fe 2 1K 1 *La 2 *Lu 1 *Mn 1 3Na 1 2Pb * *Sb * *Sc 1 1Sm 1 *Th 3 3Ti 1 2U 3 *V 2 10Zn * *

common origin, i.e. road dust. Apart from road dust,soil-1 contains wind-blown soil dust from constructionsites, building roofs, etc.

In Table 6, the ambient elemental concentrations cal-culated from the source contributions are compared withthe observed values in terms of variances (correlationcoe$cients) and mean values. Despite large errorsinvolved in calculating the contributions from minorsources, an overall satisfactory agreement has been ob-tained. The agreement is better for crustal elements thanfor anthropogenic elements (As, Cu, Pb, Sb and Zn),which generally have lower communalities and largerfactor standard deviations.

Table 7 shows some results of apportionment of ambi-ent concentrations to di!erent sources. Airborne lead ismainly derived from the As-related source (53%) andburning emissions (30%), the contribution from motorvehicles is only 17%. Bromine, on the contrary, is mainlyderived from motor vehicles (61%). The values obtainedfor Pb and Br explains again the low Br/Pb concen-tration ratio in TSP and weak correlation between thetwo elements, as observed experimentally. Airbornevanadium is found mainly in resuspended soil/roaddust (56%), the contribution from tra$c (S3 and S5) isonly 29%. Concerning As, 53% is derived from the

As-related source and 27% is coming from resuspendedsoil/road dust. Crustal elements Al, Ca, even thoughhaving high correlation with factors F5 and F4, respec-tively, are mainly derived from resuspended soil/roaddust.

4. Temporal variations of source contributions

4.1. Variations in source strengths. Annual means

Based on the modelled monthly variations of sourcecontributions (Fig. 2a}g), the annual means and theDWRs of source contributions were calculated andshown in Table 5. The annual mean was calculated forthe period starting from December.

The annual means of soil-1 and soil-2 contributionswere practically constant during the monitoring period.However, this is not the case for other sources, indicatingthe variations in the source emission strengths. Forexample, the burning emissions have sharply declinedsince mid 1993 (Fig. 2b). This can be identi"ed as theimpact of the restriction, followed by a complete ban of"recrackers in 1994. The source contribution declinedfrom 8% in 1993 to about 2% in the following years, thatcan be ascribed to the contribution from other burningsource components. The fugitive cement dust had prac-tically a!ected the sampling site only in 1994 (Fig. 2e). Infact, the construction of a "ve-star hotel &&Saigon NewWorld was going on about 200}300 m southeast of thesampling site and was accomplished in mid-1995. Thus,the source composition had changed during the monitor-ing period with the number of source types reduced fromsix to "ve since 1995. Furthermore, "recracker burningcomponent had been eliminated in 1994. Soil-1 remainsa predominant source which accounts for 72% of theTSP mass.

The motor vehicle emission (S3) was also considerablygoing down in 1994. This could happen as the impact ofmeasures introduced to mitigate tra$c congestion and torestrict the movement of fuel-ine$cient cars within thecity. A guideline was also promulgated in 1994 stipula-ting the lead level in imported gasoline not in excess of0.4 g l~1.

4.2. Seasonal variations

Soil-1 exhibits a regular seasonal variation, witha maximum in January and a minimum in September(Fig. 2a) and is in signi"cant anticorrelation with themonthly rainfall (r"!0.89). The DWR for soil-1 is 1.7.For soil-2, DWR"1.6 and r"!0.8. Signi"cant an-ticorrelation of soil-1 and soil-2 with rainfall can beexplained by the wet soil and less dusty roads in the rainyseason.


Fig. 2. Modelled monthly variations of source contributions to the TSP mass (lg m~3).

Table 5Summary statistics of source contributions

Source Source contribution Percent of TSP DWR Annual mean ($ std. err.) of source contribution, lgm~3mean ($ std. err. of mass 1993 1994 1995 1996the mean), lgm~3

Soil-1 56.8$3.0 74 1.7 52$5 66$5 61$7 48$6Burning 2.4$0.7 3 2.9! 6$2 1.4$0.7 0.8$0.3 2$0.5Auto 7.3$0.7 9.6 1.8! 11$2 5$2 7$1 6$1Construction 1.6$0.5 2 " 0.4$0.2 5$2 0.8$0.5 0Soil-2 2.0$0.4 2.7 1.6 3$1 2$1 2$1 2$1As-related 6.8$0.7 8.8 0.9 5.0$1 8$1.4 6$1 8$1

!1993s values excluded."Not calculated.


Table 7Apportionment of elemental concentrations to di!erent sources (%)

Element Soil-1 Burning Auto Construction Soil-2 As-related

Al 53 9 * 5 15 14As 27 7 * * * 53Ca 54 * * 12 5 14Fe 83 2 * 2 3 7Br * 6 61 * * *Pb * 30 17 * * 43V 56 13 13 * 16 *

Table 6Comparison of observed and estimated concentration values

Element r Observed mean Estimated mean Est./Ob.(ngm~3) (ngm~3) (%)

Al 0.98 2761 2762 1As 0.78 1.47 1.51 1.03Br 0.76 9.73 10 1.03Ca 0.93 3360 3270 0.97Ce 0.96 3.33 3.13 0.94Cl 0.97 1204 1120 0.93Co 0.94 1.23 1.16 0.94Cr 0.86 9.38 9.1 0.96Cu 0.66 1.39 1.79 1.28Fe 1 3078 3030 0.98K 0.98 915 960 1.05La 0.87 1.48 1.56 1.05Lu 0.82 0.017 0.015 0.87Mn 0.91 37.8 32.5 0.86Na 0.94 802 947 1.18Pb 0.72 163 174 1.07Sb 0.77 3.8 6.4 1.7Sc 0.97 0.58 0.66 1.13Sm 0.89 2.5 2.5 1Th 0.96 0.56 0.56 0.99Ti 0.92 266 247 0.93U 0.74 0.18 0.21 1.12V 0.81 7.6 10.5 1.38Zn 0.77 203 169 0.83TSP 0.96 77700 77000 0.99

For burning and motor vehicle emissions, the DWRscalculated excluding 1993 values are 2.9 and 1.8, respec-tively. If suppose the source strengths are roughly con-stant as can be seen from the annual means in Table 5,the seasonal changes of meteorological conditions willfully account for the DWRs. Unlike soil-1 and soil-2, themain factors facilitating the removal of air particles fromthese sources in the rainy season are the ascending airmotion in cyclonic conditions, the rain out e!ect and thestronger wind (Hien et al., 1997).

For As-containing particulates, no seasonal changewas found. The calculated DWR is 0.9, indicating thatfresh particulates from any local source was probably

collected on the air "lters. As lead is found highly abun-dant in the source pro"le of S6, it can be speculated thatAs is derived from any small local coal burning leadsmelter. With such a speculation, the relatively high con-tribution of this source to TSP would be understandable,even though coal has a low share in fuel consumption inHCMC.

5. Conclusions

Varimax rotation factor analysis applied to monthlydata on elemental compositions of TSP in HCMC hasrevealed six source types. Resuspended soil/road dust isa primary source which accounts for 74% of the TSPmass. Motor vehicle emissions is the major anthropo-genic sources, which account for nearly 10% of the TSPmass loading. The modelling of monthly variations ofsource contributions at the receptor allowed to revealemission scenarios and the e!ects of meteorological con-ditions for the various source types. However, the pre-dominance of resuspended soil/road dust in TSP hasgreatly obscured details of anthropogenic and minorsources. The source resolution and interpretation willhopefully be improved based on PM-10 and PM-2.5 datathat are being currently collected and analysed.

Acknowledgements

The authors express their appreciation and gratitudeto the HCMC Department of Research, Technology andEnvironment for "nancial support of this work.

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