Organic Geochemistry 41 (2010) 355–362

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Organic Geochemistry

journal homepage: www.elsevier .com/locate /orggeochem

Polycyclic aromatic hydrocarbons in the surface soil of Shanghai, China:Concentrations, distribution and sources

Ying Liu a, Ling Chen a,*, Jianfu Zhao a,b, Yanping Wei a, Zhaoyu Pan a, Xiang-Zhou Meng a, Qinghui Huang b,Weiying Li a

a State Key Laboratory of Pollution Control and Resources Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, Chinab Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China

a r t i c l e i n f o

Article history:Received 3 June 2009Received in revised form 7 November 2009Accepted 23 December 2009Available online 4 January 2010

0146-6380/$ - see front matter � 2009 Elsevier Ltd. Adoi:10.1016/j.orggeochem.2009.12.009

* Corresponding author. Tel./fax: +86 21 65984261E-mail addresses: liu_ying@tongji.edu.cn (Y.

(L. Chen).

a b s t r a c t

We quantified 18 polycyclic aromatic hydrocarbons (PAHs) in 54 surface soil samples covering an area of6400 km2 in Shanghai. An isopleth map of total concentrations of the 18 PAHs, which was constructedusing an ordinary Kriging approach with log transformed data, clarified the regional variability and iden-tified regional hot spots in the urban and industrial areas of Shanghai. These hot spots all suffer from highPAH pollution, suggesting that local human activities (e.g., vehicular traffic, petrochemical industry andcoal combustion) may be the main contributors. Coal or oil fired power plants and their locations seem tobe a significant factor controlling the PAH concentrations in surface soil. The higher molecular weightPAHs are often accumulated near pollution sources and are more heterogeneous in Shanghai soil, becausethey are less easily transported and biodegraded than 2 ring PAHs. The total concentrations are not cor-related with soil total organic carbon. We successfully applied hierarchical cluster analysis (HCA) andprincipal components analysis (PCA) based on a centered log ratio procedure to explore the characteris-tics and possible sources of soil PAHs. The high PAH contamination in the Shanghai surface soil is mainlyattributed to the contribution of pyrogenic sources (vehicular traffic pollution and combustion of coal andbiomass). Furthermore, we applied PAH percentages by ring number, isopleth maps of total concentra-tions of 18 PAHs and source diagnostic ratios of PAHs to help assign the pyrogenic sources in Shanghaisoils. Such map based approaches have only rarely been applied in investigations published in OrganicGeochemistry.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

Polycyclic aromatic hydrocarbons (PAHs) are common contam-inants that are difficult to biodegrade in the environment. They arederived from natural processes (e.g., forest fires) and/or anthropo-genic activities (e.g., combustion of coal, waste incineration, vehic-ular traffic, spillage of petroleum). Some PAHs have beenrecommended as priority pollutants by the United States Environ-mental Protection Agency (US EPA) and as persistent toxic sub-stances (PTS) by the United Nations Environment Programme(UNEP) due to their persistent, toxic, mutagenic and carcinogeniccharacteristics (Zedeck, 1980; NRC, 1983; UNEP, 2002). In recentyears, there has been an increasing focus on regional soil contam-ination by PAHs, especially in China (Cai et al., 2008). PAHs are eas-ily accumulated in soils over many years due to their biochemicalpersistence and hydrophobicity (Johnsen et al., 2005). As a result,soils become an important reservoir of PAHs. In addition, PAHs in

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.Liu), chenling@tongji.edu.cn

soils may further accumulate in vegetables and other biota via foodchains (Kipopoulou et al., 1999; Li et al., 2008), leading to direct orindirect exposure in humans. Therefore, studies on soil contamina-tion by PAHs are necessary to minimize the risk of human expo-sure and environmental pollution.

Shanghai is one of the most comprehensively industrial andcommercial cities in China, ranking first in population and popula-tion density. Recently, many studies have been conducted inShanghai on the levels and sources of PAHs in the atmosphere(Feng et al., 2006; Cheng et al., 2007), sediments (rivers and estu-ary) (Bouloubassi et al., 2001; Liu et al., 2008), road dust (Ren et al.,2006; Liu et al., 2007a) and urban soils (Jiang et al., 2009). How-ever, few data have been reported on the spatial distribution andsources of PAHs in Shanghai soils, especially on the large scale. Re-gional soil PAH contamination mainly originates from dry and wetatmospheric deposition. PAH concentrations in road dust vary from3100–32,000 ng/g in the urban area of Shanghai, which have beenattributed to emissions from vehicles (Ren et al., 2006) and a mix-ture of traffic and coal combustion (Liu et al., 2007a). Concentra-tions of atmospheric PAHs range from 0.07–270 ng/m3, withmajor sources being fossil fuel combustion, coal burning, industrial

Fig. 1. Sampling locations (a) and isopleth map of total concentrations of 18 PAHs (b) in surface soils of the Shanghai area.

356 Y. Liu et al. / Organic Geochemistry 41 (2010) 355–362

furnaces and engine exhaust emissions (Feng et al., 2006; Chenget al., 2007). PAH concentrations in urban soils have been foundto range from 442–19,700 ng/g, with PAHs attributed primarilyto combustion (Jiang et al., 2009). Further studies are needed tounderstand and explore combustion sources of these PAHs.

The soil is a sink and source for atmospheric PAHs and a sourcefor sedimentary PAHs. Our previous work has shown that a riversegment near Site 606 (Fig. 1a) is heavily polluted by PAHs and coalburning is one of the contributors of pyrogenic PAHs (Liu et al.,2008). Therefore, studies on the concentrations and spatial distri-bution of soil PAHs are useful to further understand the distribu-tion of sedimentary PAHs in Shanghai. The objectives of thepresent study are to determine the concentrations and spatial dis-tributions of PAHs in Shanghai soils, and to explore their character-istics and possible combustion sources. Such map basedapproaches have only rarely been applied in investigations pub-lished in Organic Geochemistry. The results of this study will inputnew data to the global PAH database and provide valuable infor-mation for regulatory actions to improve the environmental qual-ity of the Yangtze River Delta.

2. Materials and methods

2.1. Study area and sample collection

Shanghai is located in the east of the Yangtze River Delta, asshown in Fig. 1a. Bordered by Jiangsu and Zhejiang provinces inthe west, Shanghai is bounded by the East China Sea to the eastand Hangzhou Bay to the south. North of the city, the Yangtze Riverflows into the East China Sea. Shanghai has a total area of6340 km2, including a land area of 6218 km2 and a water area of122 km2. As is typical of subtropical areas, Shanghai has a marinemonsoon climate with a clear division of four seasons, enough sun-shine and ample rainfall. The average annual temperature andrainfall are about 15.8 �C and 1149 mm, respectively.

We collected 54 surface soil samples from Shanghai in April2007 using the systematic grid method. Each 5–10 cm deep samplewas obtained using a Luoyang shovel and consisted of five* sub-samples from the surrounding area of each sampling site (within10 m2). All samples were air dried in the dark and sieved to<0.076 mm (200 mesh) after removing stones and residual roots,then stored at �4 �C until the PAHs were analyzed.

2.2. Sample extraction and cleanup

The five sub-samples were mixed together to obtain a represen-tative sample for each site and each mixed sample was analyzed induplicate. Sample extraction and cleanup followed Method 3541(automated Soxhlet extraction), Method 3630C (silica gel cleanup)and Method 3660B (sulfur cleanup) recommended by the US EPA(1996). Five grams of soil sample were blended with anhydrous so-dium sulfate (1:1, w:w) and extracted by a FOSS Soxtec Avanti2050 automatic system using 70 ml of a mixture of acetone andhexane (1:1, v:v), a hot plate temperature of 160 �C and an extrac-tion time of 60 min for boiling and 60 min for rinsing. The extractwas concentrated by a rotary evaporator, and the extract solventwas exchanged to cyclohexane. The detailed cleanup procedure isreported elsewhere (Liu et al., 2007b, 2008). Briefly, the extractswere further cleaned using a chromatographic column packed with5 g activated silica gel and the eluate was finally concentrated to1 ml.

2.3. Sample analysis and quality control

Sixteen PAHs characterized by the US EPA as priority pollutantswere analyzed, including naphthalene (Nap), acenaphthylene(AcNy), fluorene (Fl), acenaphthene (AcNe), phenanthrene (PhA),anthracene (An), fluoranthene (FlA), pyrene (Py), benz[a]anthra-cene (BaA), chrysene (Chy), benzo[b]fluoranthene (BbF),benzo[k]fluoranthene (BkF), benzo[a]pyrene (BaP), indeno[1,2,3-cd]pyrene (IP), benzo[ghi]perylene (BghiP) and dibenz[a,h]anthra-cene (DBahA). In addition, benzo[e]pyrene (BeP), perylene (Pery),1-methylnaphthalene and 2-methylnapthalene were measuredqualitatively. Concentrations of the two methylnaphthalene iso-mers were combined and reported as total methylnaphthalenes(MNap).

PAH analysis was carried out by high performance liquid chro-matography (HPLC) with a photodiode array detector. Identifica-tion of PAH compounds was based on chromatographic retentiontime and the ultraviolet spectra of PAH standards. The quantifica-tion was performed by the external standard method. The ultravi-olet measurement wavelengths included 218 nm (NaP), 223 nm(MNaP), 226 nm (AcNe and AcNy), 249 nm (IP), 254 nm (Fl, PhAand An), 266 nm (Chy), 286 nm (FlA and BaA), 300 nm (BbF, BkF,BaP, DBahA and BghiP), 330 nm (BeP), 334 nm (Py) and 433 nm

Y. Liu et al. / Organic Geochemistry 41 (2010) 355–362 357

(Pery). Detailed measurement procedures are described elsewhere(Liu et al., 2007b, 2008).

All analyses were calculated on a dry weight (dw) basis. Methoddetection limits (MDLs) for all targets ranged from 1–19 ng/g dw.The recoveries for spiked PAHs varied from 87%–113%. Each mixedsample was analyzed in duplicate and the relative standard devia-tion was <20%.

Total organic carbon (TOC) analysis was performed with theShimadzu TOC-Vcpn analyzer with the solid sample module(SSM-5000A). The overall precision of measurements was less than3% (n = 3).

2.4. Data analysis

Before statistical analysis, analytical replicates were averagedand undetectable values were assigned the value of half of theMDLs. AcNy was eliminated from the dataset as it was undetect-able in all samples. Statistical analyses, including the Kolmogo-rov–Smirnov (K–S) test, descriptive statistics, hierarchical clusteranalysis (HCA) and principal components analysis (PCA), were per-formed using SPSS 13.0 for Windows.

Before HCA and PCA, PAH data were normalized using a cen-tered log ratio procedure (Bonn, 1998; Yunker and Macdonald,2003; Pollard et al., 2006). Given a sample S ¼ ðx1; x2; . . . ; xNÞ,where the xi are the percentages of N species, the transformed sam-ple is expressed as Str ¼ ðln½x1=g�; ln½x2=g�; . . . ; ln½xN=g�Þ, where g isthe geometric mean of sample S, which is calculated using theequation gs ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffix1 � x2 � � � � � xN

Np

. Autoscaling (scale to variablemean and standard deviation) was then applied.

To explore the structure between variables (PAH), the normal-ized dataset was hierarchically clustered using the weighted aver-age linkage between the groups and the Pearson correlation for thecluster intervals (Kavouras et al., 2001; Zhang et al., 2006). In addi-tion, to further understand the relationship between variables(PAH) and identify major sources of PAHs, we applied PCA to ex-tract the principal components (PCs) with a varimax rotation.The rotation maximizes or minimizes the loading of each variableon each PC while preserving its trend (Kavouras et al., 2001; Yun-

Table 1Descriptive statistics of PAHs in surface soils of Shanghai.

No. PAHs Abbrev. No. of rings Distribution

1 Naphthalene Nap 2 Normal2 Methylnaphthalene MNap 2 Normal3 Acenaphthene AcNe 3 Skewed4 Fluorene Fl 3 Skewed5 Phenanthrene PhA 3 Lognormal6 Anthracene An 3 Lognormal7 Fluoranthene FlA 4 Lognormal8 Pyrene Py 4 Lognormal9 Chrysene Chy 4 Lognormal10 Benz[a]anthracene BaA 4 Lognormal11 Benzo[b]fluoranthene BbF 5 Lognormal12 Benzo[k]fluoranthene BkF 5 Lognormal13 Benzo[a]pyrene BaP 5 Lognormal14 Benzo[e]pyrene BeP 5 Skewed15 Perylene Pery 5 Skewed16 Dibenz[a,h]anthracene DBahA 5 Skewed17 Indeno[1,2,3-cd]pyrene IP 6 Lognormal18 Benzo[ghi]perylene BghiP 6 LognormalP

18 PAHs LognormalP

16 PAHs (US EPA) Lognormal

a MDL, method detection limit.b Mean, arithmetic mean.c Geo., geometric mean.d DF, detectable frequency.e ND, not detected.

ker and Macdonald, 2003). Finally, the ordinary Kriging approachwith log transformation was applied to data interpolation for thetotal concentrations of 18 PAHs, and inverse distance weightedinterpolation was used for source diagnostic ratios of PAHs (afteromitting the data with undetectable values of PAH), using the Geo-statistical Analyst tool of ESRI ArcMap 9.2 software to draw iso-pleth maps of Shanghai.

3. Results and discussion

3.1. Concentration and probability distribution of PAHs

The concentrations of individual PAHs (P3 rings) show largevariations (Table 1). The highest concentration of a single PAH isof FlA, which ranges from undetected to 5200 ng/g dw, and themaximum concentrations for the P4 rings PAHs and PhA weremore than 1000 ng/g dw. The minimum concentrations for allPAHs analyzed were less than the method detection limits, exceptfor PhA. However, the 2 ring PAHs Nap and MNap have a smallerrange between the highest and lowest concentrations and similartrends were found between the average and median concentra-tions as compared with other PAHs. Hence, the residual levels of2 ring PAHs are more homogeneous in the surface soils than thoseof other PAHs.

Zhang and Selinus (1998) suggested that the probability distri-butions of environmental geochemical variables for major ele-ments (i.e., O and Si) generally follow normal distributions, buttrace elements tend to follow log normal distributions. In thisstudy, the concentrations of trace PAHs in soils follow similar prob-ability distributions. The K–S test indicated that the concentrationsof Nap and MNap were normally distributed (p < 0.05), as shown inTable 1, while the other PAHs followed log normal or positivelyskewed distributions. By comparison, Nap and MNap have lowboiling points (relatively easy to volatilize) and good water solubil-ity (tend to transport with water). As a result, they can be biode-graded and transported easily. Zhang et al. (2008) also believedthat vertical transport of PAHs in soils were controlled by the nat-ure of PAHs (i.e. log Koc, molecular weight). Other PAHs, despite

MDLa Min. Max. Meanb Median Geo.c DF (%)d

(ng/g dw)

4.0 NDe 110 28.6 24.7 21.6 943.6 ND 90.3 30.5 25.4 23.7 941.9 ND 188 10.0 ND 2.40 3518.3 ND 243 19.9 ND 12.8 241.1 9.5 2300 130 34.3 43.8 1001.8 ND 807 28.3 2.8 3.9 655.1 ND 5190 259 40.5 44.4 917.1 ND 4290 220 30.7 41.2 912.2 ND 2230 127 22.5 25.1 934.9 ND 2440 124 16.9 20.4 801.9 ND 3590 177 29.5 25.8 813.1 ND 1380 72.8 12.0 12.7 806.5 ND 3130 181 30.6 33.6 8019.1 ND 2790 129 ND 24.4 5017.1 ND 2620 139 26.9 32.7 616.4 ND 1190 59.5 6.5 10.4 526.4 ND 2260 118 15.0 19.8 726.1 ND 2680 144 20.9 25.0 80– 94 37,400 2000 390 526 100– 62.4 31,900 1700 314 405 100

358 Y. Liu et al. / Organic Geochemistry 41 (2010) 355–362

low concentrations in the environment, are usually enriched inheavy traffic or industrial areas, which may lead to a positivelyskewed distribution (Table 1). However, the concentrations ofAcNe, Fl, BeP, Pery and DBahA do not follow log normal distribu-tions, because the detectable frequencies of those compounds arelow (<55%) and the undetectable values were assigned values ofhalf of the MDLs (Table 1). In general, the arithmetic mean is usedfor normally distributed parameters, such as Nap and MNap, whilethe geometric mean is used for parameters that follow a log normalor positively skewed distribution, such as Fl, PhA and BaP.

The total concentrations of 18 PAHs (P

18 PAHs) range from94–37,300 ng/g dw with a geometric mean of 526 ng/g dw. In�80% of samples,

P18 PAHs is <1000 ng/g dw (Fig. S1). The K–S

test also indicated thatP

18 PAHs followed a log normaldistribution.

3.2. The relationship between total PAHs and total organic carbon(TOC) in soil

TOC is an important controlling factor of sorption of PAHs in soiland may pre-determine the level of soil contamination with PAHs(Tang et al., 2005). In this study, soil TOC varies greatly from 4.25–46.1 mg/g dw. Pearson correlation analysis shows that the totalconcentrations of 18 PAHs in soils are not significantly correlatedwith soil TOC (r = 0.062 and p = 0.65, see Fig. S2). Jiang et al.(2009) also reported a similar result when they researched therelationship between PAHs and TOC in urban soil of Shanghaiand believed the result to be due to non-equilibrium adsorptionbetween TOC and PAHs in soils. In our work, the inputs of PAHsto each site vary and not all adsorption of PAHs on TOC reachesequilibrium. Therefore, the effect of soil TOC on the amount ofPAH accumulated in soil is ignored in the following spatial distri-bution of PAHs.

3.3. Spatial distribution of total PAHs

Many interpolation methods can be used to analyze the spatialdistribution of PAH levels in the environment. A surface tensionspline function was used for dispersive interpolation and spatialdistribution contouring of PAHs (Ye et al., 2006). Geostatisticscan provide an optimal interpolation approach known as Kriging.This theory has been developed and applied widely by geologiststo estimate mineral reserves. Geochemists have also tried to usethe Kriging approach for the spatial distribution analysis of heavymetals (Zhang and Selinus, 1997) and PAHs (Wang et al., 2003).Kriging methods work best when the data are approximately nor-mally distributed. In addition, the log transformation is often usedwhere the data has a positively skewed distribution and there aresome very large values in order to avoid a proportional effect(Zhang and Selinus, 1997; Johnston et al., 2004). In the presentstudy, the total PAH concentration follows a lognormal distribu-tion. We therefore applied the ordinary Kriging method with logtransformation to predict the PAH values in the non-sampled areain Shanghai. Fig. 1b shows the spatial distribution contours of totalPAH concentrations in the surface soils, residential and industrialareas and main power plants sites.

Isopleth mapping has proved to be a valuable approach foridentification of regional hot spots for PAH pollution (Lehndorffand Schwark, 2009). In this study, three regions with high PAHconcentration were delimited based on the region function, heredesignated as regions A, B and C. Regions A and B are located in ur-ban areas, and Region C is situated in a petrochemical industrialarea. These regions are severely influenced by human activity. Re-gion A is located in the central part of Shanghai with heavy traffic.As a result, the surrounding soil is contaminated by road dust,which is an agglomeration of various traffic related PAH sources,

e.g., automobile exhaust, lubricating oil, gasoline, diesel fuel,weathered materials of street surfaces and tire particles (Muraka-mi et al., 2005). Liu et al. (2007a) reported that 16 PAH concentra-tions in road dust from the central part of Shanghai (Region A)were very high, with a range from 6875–32,573 ng/g dw, whichsuggests heavy contamination in the surface soils of Region A.The high PAH concentrations in Region B may be associated withregional industrial pollution. Region B is situated in an old indus-trial district with many manufacturing facilities, e.g., chemicaland coke oven plants. Nowadays, some plants with heavy pollutionhave been encouraged to move elsewhere, and a new residentialand financial district is gradually forming. However, traffic pollu-tion is replacing industrial pollution and continues to contaminatethe surrounding soils. We note that coal fired power plants in Re-gion B contribute to PAH contamination in soil. Region C adjoinsthe sea and Shanghai’s principle petrochemical station and its oilrefinery are located in this region. The high PAH contents in the soilmay be due to contamination from the petrochemical industry.

To understand the effect of power plants on the spatial distribu-tion of PAHs, we show the main large scale coal or oil fired powerplants in Shanghai in Fig. 1b. Clearly, the power plants are situatedin the higher PAH pollution areas. This indicates that power plantsseem to be an important factor controlling the PAH contents of sur-face soils in Shanghai.

3.4. PAH composition comparison using multivariate statistic analysis

The relationship of PAH compositions and sources in surfacesoils of Shanghai were examined using two multivariate statisticalanalysis methods. AcNy, AcNe and Fl were excluded in the analysisdue to their low detectable frequencies (Table 1).

The normality of the data is an important factor when interpret-ing analytical results, since many inferences are based on theassumption of normality. Multivariate statistical applications arestill almost exclusively based on correlation or covariance matricesdetermined directly from raw data. However, normalizing data canimprove the explanatory power of a multivariate analysis (Kuceraand Malmgren, 1998). In a concentration based multivariate model,the samples with the highest concentrations usually project sepa-rately as single samples or small groups of samples (generally withpositive projections), while the remaining (lower concentrations)samples project in a group near the origin and make little or no con-tribution to the model. If normalization to the total PAH concentra-tion has been used, PAH concentration data are transformed intocompositional data with the constant sum constraint (CSC). TheCSC introduces spurious negative correlations between major vari-ables and positive correlations between minor ones (Johanssonet al., 1984). The log ratio transformation can effectively reducethe CSC from compositional data (Kucera and Malmgren, 1998),and the centered log ratio transformed values have been used as in-puts into PCA (Bonn, 1998; Yunker and Macdonald, 2003; Pollardet al., 2006). In this paper, we applied a pattern based approach tomultivariate statistical analysis using the centered log ratio proce-dure to produce a normalized dataset as input into HCA and PCA.

3.4.1. Hierarchical cluster analysisHierarchical cluster analysis (HCA) was used to explore struc-

ture of the normalized dataset. No significant difference was foundwhen different linkage methods were used based on the results ofthe HCA. Fig. 2 shows the results presented in the form of a dendro-gram. Sixteen variables (PAHs) were classified into three majorgroups. The first group is composed of low molecular weight PAHswith 2–3 rings and alkyl substituted PAHs, including Nap, PhA andMNap. The second group includes DBahA, BeP, An and Pery. Detect-able frequencies of the four compositions were less than 70% (Ta-ble 1). In this group, PAH compositions with similar detectable

Fig. 2. Hierarchical dendrogram for PAHs using average linkage between groupsand Pearson correlation as a measure interval.

Y. Liu et al. / Organic Geochemistry 41 (2010) 355–362 359

frequencies were clustered together, e.g., DBahA and BeP. The thirdgroup, including most 4–6 rings PAHs, was subdivided into twosubgroups, namely 4 ring PAHs (FlA, Py, Chy and BaA) and 5–6 ringPAHs (BbF, BkF, BaP, BghiP and IP). These PAHs are usually detectedin pyrogenic sources, e.g., combustion of coal, wood, vehicle fueland waste tire (Levendis et al., 1998; Wang et al., 2007).

3.4.2. Principal components analysisPrincipal components analysis (PCA) was performed to further

understand the relationship between PAH compositions and possi-ble chemical sources for each factor. Factor loadings of the normal-ized dataset are listed in Table S1 and plotted in Fig. 3a. Most of thevariance (76.2%) of the normalized dataset was explained by thefirst four factors.

Factor 1 explains 34.0% of the total variance. High negativeloadings of MNap, Nap and PhA and high positive loadings ofBbF, BkF, BaP, IP and BghiP were observed in this factor(Table S1). These PAH compositions are clustered on the left andright sides of the x-axis in Fig. 3a, respectively. MNap, Nap andPhA belongs to the low molecular weight PAHs with 2–3 rings oralkyl substituted PAHs, which are abundant in petrogenic sourcemainly caused by petroleum spills (Marr et al., 1999; Dobbins

Fig. 3. Loading plot (a) of the first two principal components and score plot (b) of eachcolumn graphs show percentages accounted for by 2–6 ring PAHs in the samples.

et al., 2006). Samples with high negative score have a dominanceof 2–3 rings PAHs, which originated mainly from petrogenicsource. BghiP is identified as a tracer of auto emissions because itwas found to be enriched in a traffic tunnel along with BaP (Harri-son et al., 1996; Larsen and Baker, 2003; Boonyatumanond et al.,2007). IP is found in petroleum as a tracer of diesel combustion(Kavouras et al., 2001). The high level of BkF relative to other PAHsis suggested to indicate diesel vehicles (Larsen and Baker, 2003).BkF and its isomers (e.g., BbF and BaP) are dominant compoundsin particulate samples of roadside air (Boonyatumanond et al.,2007). Samples with high positive score have a dominance of 5–6rings PAHs, and vehicular traffic pollution is a major contributorof PAHs contamination in the samples. To sum up, the loadingsfor Factor 1 are primarily due to the separation between petrogenicsource and vehicular traffic pollution.

Factor 2, accounting for 16.5% of total variance, is negativelydominated by MNap and Nap and positively by FlA, Py, Chy andBaA (Table S1). In Fig. 3a, MNap and Nap are projected in the lowerleft quadrant, and they are related to petrogenic source. FlA, Py,Chy and BaA are projected in the upper right quadrant. The fourcompositions are high molecular weight PAHs with 4 rings asmarkers of coal combustion (Duval and Friedlander, 1981). The fur-nace effluents from combustion of pulverized coals contained FlAand Py (Levendis et al., 1998). Likewise, the semi-volatile PAHs ofFlA and Py are attributed to unburned fossil fuels (Kavouraset al., 2001). Contents of the four PAHs in soils mainly reflect thecontribution of combustion of coal and biomass. Therefore, theloadings for Factor 2 are primarily due to the separation betweenpetrogenic source and combustion of coal and biomass.

Factors 3 and 4 are responsible for 14.5% and 11.2% of the var-iance, respectively (Table S1). Factor 3 is heavily weighted by BePand DBahA, while Factor 4 is dominated by An and Pery. However,no chemical sources with such a PAH profile have been reporteduntil now. They might be likely to represent ‘‘noise” due to theirlow detectable ratios (50–65%).

It is interesting that the PCA result in this paper is comparedwith the PCA result of sedimentary PAHs in our previous work,and PAH compounds with high positive loadings in extracted fac-tors are parallel between in soil and in sediment (Liu et al.,2009). This indicates that soil PAHs and sedimentary PAHs inShanghai have common sources.

Fig. 3b shows a plot of the scores of samples for the first twoprincipal components and stack column graphs of the percentagesthat 2–6 ring PAHs account for in the samples. In Fig. 3b, the urbaninfluenced samples (I) project in the upper right quadrant, indicat-

sample. The variance accounted for by each PC is shown after the axis label. Stack

360 Y. Liu et al. / Organic Geochemistry 41 (2010) 355–362

ing that these samples suffered greatly from contamination byvehicular traffic and combustion of coal and biomass. Likewise,PAHs from vehicular traffic and combustion of coal and biomassbelong to pyrogenic PAHs and should have higher proportions of4–6 rings PAHs, which agrees with their stack column graphs (I).Furthermore, these samples (I) were collected from heavy PAHcontaminated regions in Fig. 1b. The regions are close to powerplants and located in urban or industrial areas where vehiculartraffic pollution is obvious. We note that Site 901 is situated in apetrochemical industrial region, where vehicular traffic pollutionis not prominent in theory, but there is a similar stack column asother sites, e.g., Sites 604 and 405. According to photos picturednear Site 901 (Fig. S3), exhaust from petrochemical processing usu-ally includes combustion products from hydrocarbons gases. Thetreatment of the exhaust by direct combustion produces highmolecular weight PAHs and contaminates the surrounding soils,as does combustion of gasoline or diesel fuel in vehicles. Therefore,it is reasonable that the high PAH contamination in the Shanghaisoil is due to the contribution of vehicular traffic and combustionof coal and petroleum.

Fig. 4. Isopleth maps of source diagnostic ratios o

In Fig. 3b, samples from Chongmingdao Island (II) are projectedin the upper left quadrant, illustrating a weak contribution ofvehicular traffic and a strong contribution of combustion of coaland biomass. Chongmingdao Island is an isolated region currentlywithout linkage of a bridge or tunnel to the mainland of Shanghai.The isolation limits development of vehicular traffic, leading topollution from vehicular traffic on the island being less than thaton the mainland. Nowadays, Chongmingdao Island supports largescale agriculture, especially green agriculture and industrial devel-opment polluting the environment is limited. Here, the strong con-tribution of combustion of coal and biomass is attributed to strawcombustion. In local agricultural activity, rice straw is burnedin situ to release nutrients for the next growing season. The con-centrations of atmospheric PAHs with lighter molecular weights,e.g., Py, FlA, PhA, Fl and Nap (2–4 ring PAHs), are obviously higheron rice straw burning days than on non-burning days, but no con-centration difference is observed for higher molecular weightPAHs, e.g., 5–6 ring PAHs (Yang et al., 2006). The stack columngraphs of Site A02 and Site A04 (Fig. 3bII) also reflect the domi-nance of 2–4 ring PAHs. In the samples from III (Sites 201, 301

f PAHs in surface soils of the Shanghai area.

Y. Liu et al. / Organic Geochemistry 41 (2010) 355–362 361

and 302), the PAH concentration is low, even less than the MDLsand the total concentrations of PAHs are <150 ng/g dw.

Overall, multivariate statistical analysis based on the centeredlog ratio procedure unveils the relationships of PAH compositionsand identifies vehicular traffic and combustion of coal and biomassas dominant contributors of PAH contamination in soils. The spa-tial distribution contour of total PAH concentrations and stack col-umn graphs of 2–6 ring PAH percentages further confirm theresults of the multivariate statistic analysis.

3.5. Source diagnostic ratios of PAHs

The relative abundances or diagnostic ratios are valid indicatorsof PAH sources because isomer pairs are diluted to a similar extentupon mixing with natural particulate matter and are distributedsimilarly to other phases as they have comparable thermodynamicpartitioning and kinetic mass transfer coefficients (Dickhut et al.,2000). Diagnostic ratios of PAHs, such as ratios of An/(PhA + An),FlA/(FlA + Py), BaA/(BaA + Py) and IP/(IP + BghiP), can be applied toidentify the possible emission sources, as noted in Fig. 4 (Yunkeret al., 2002). Isopleth maps of source diagnostic ratios of PAHs areshown Fig. 4. The ratio of An/(An + PhA) ranges from 0.01–0.26,FlA/(FlA + Py) from 0.11–0.77, BaA/(BaA + Chy) from 0.30–0.60 andIP/(IP + BghiP) from 0.30–0.56. The results of these ratios indicatethat the combustion of biomass, coal and petroleum are the majorsources of PAHs in Shanghai. The region with higher PAH contamina-tion is companied with a higher ratio of An/(An + PhA) (Fig. 4a). Theratios of FlA/(FlA + Py) and BaA/(BaA + Chy) show that combustion ofcoal and biomass are the predominant sources (Fig. 4b and c), butthat of IP/(IP + BghiP) shows petroleum combustion is the majorsource (Fig. 4d). This may be because FlA, Py, BaA and Chy originatemainly from the contribution of combustion of coal and biomass,while IP and BghiP source chiefly from vehicular traffic pollution(e.g. combustion of fuel oils), according to PCA results. Therefore,the results from source diagnostic ratios of PAHs can reflect basicallythe contribution of combustion sources of PAHs.

4. Conclusions

Although the total PAH concentrations ranged from 94–37,300 ng/g dw, most surface soils in Shanghai have low PAH con-tamination. The concentrations of total PAH and most individualPAHs follow log normal distributions except for the Nap and MNapcomponents. Isopleth maps are a valid approach to identify localhot spots in PAHs contamination and three regions with highPAH contamination were delimited using the ordinary Krigingmethod with log transformation. The surface soils with high PAHsconcentrations are found in the urban area and petrochemicalindustrial area and anthropological influence is the major contrib-utor of PAHs contamination in Shanghai surface soil. The ecologicalrisk from PAH contamination should be considered for the personsliving in the three regions.

A centered log ratio transformation is an effective normalizationprocedure for PAH concentration data. Two multivariate statisticanalysis methods based on this data normalization procedure havebeen successfully applied to explore the characteristics and toidentify PAH sources. Vehicular traffic and combustion of coaland biomass are the main sources of PAHs in Shanghai surface soil.The results from multivariate statistical analysis are combinedwith PAH percentages by ring number and isopleth maps of totalconcentrations of 18 PAHs and source diagnostic ratios of PAHs,which further confirm the sources of PAHs. Such map based ap-proaches are one of valid path in identification of PAH sources.

Acknowledgements

This work was supported by the National Natural Science Foun-dation of China (No. 20907034), Program for Young Excellent Tal-ents in Tongji University (No. 2008KJ022), the Foundation of theState Key Laboratory of Pollution Control and Resource Reuse(No. PCRRK09001), and the National Key Technology R&D Program(No. 2008BAC46B02). We also thank Mark Bernard Yunker, LorenzSchwark, Xue-Tong Wang and an anonymous reviewer for helpfulcomments and suggestions.

Appendix A. Supplementary material

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.orggeochem.2009.12.009.

Associate Editor—Mark Yunker

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