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Source apportionment of polycyclic aromatic hydrocarbons(PAHs) in surface sediments of the Huangpu River,Shanghai, China

Ying Liua, Ling Chena,⁎, Qing-hui Huangb, Wei-ying Lib, Yin-jian Tanga, Jian-fu Zhaoa

aState Key Laboratory of Pollution Control and Resources Reuse, College of Environmental Science and Engineering,Tongji University, Shanghai, 200092 ChinabKey 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 D A T A

⁎ Corresponding author. Tel./fax: +86 21 6598E-mail address: chenling@tongji.edu.cn (L.

0048-9697/$ – see front matter © 2008 Elsevidoi:10.1016/j.scitotenv.2008.12.046

A B S T R A C T

Article history:Received 17 April 2008Received in revised form30 November 2008Accepted 14 December 2008Available online 5 February 2009

Weapplied cluster analysis and principal component analysis (PCA) withmultivariate linearregression (MLR) to apportion sources of polycyclic aromatic hydrocarbons (PAHs) in surfacesediments of the Huangpu River in Shanghai, China, based on the measured PAHconcentrations of 32 samples collected at eight sites in four seasons in 2006. The resultsindicate that petrogenic and pyrogenic sources are the important sources of PAHs. Furtheranalysis shows that the contributions of coal combustion, traffic-related pollution and spillsof oil products (petrogenic) are 40%, 36% and 24% using PCA/MLR, respectively. Pyrogenicsources (coal combustion and traffic related pollution) contribute 76% of anthropogenicPAHs to sediments, which indicates that energy consumption is a predominant factor ofPAH pollution in Shanghai. Rainfall, the monsoon and temperature play important roles inthe distinct seasonal variation of PAH pollution, such that the contamination level of PAHsin spring is significantly higher than in the other seasons.Brief: We apportion PAHs in surface sediments of the Huangpu River and show that coalcombustion, traffic-related pollution, and petroleum spillage are the major sources.

© 2008 Elsevier B.V. All rights reserved.

Keywords:PAHsSurface sedimentsSource apportionmentPCA/MLRCluster analysis

1. Introduction

Polycyclic aromatic hydrocarbons (PAHs) containing two ormore fused benzene rings form one of the most importantclasses of environmental pollutants. Due to the persistent,toxic, mutagenic and carcinogenic characteristics of PAHs(Zedeck, 1980; NRC, 1983), some of them are on the US EPA listof priority pollutants. Pyrogenic and petrogenic sources aretwo major origins of anthropogenic PAHs in the environment.Pyrogenic PAHs are formed as trace contaminants by theincomplete combustion of organicmatter, such aswood, fossilfuels, asphalt, and industrial waste. Crude and refinedpetroleum contain petrogenic PAHs, and are also important

4261.Chen).

er B.V. All rights reserved

sources of PAHs. Once produced, PAHs can be widelydispersed into the environment by atmospheric transport orthrough stream pathways, and eventually accumulate in soilsand aquatic sediments.

The Huangpu River, the most important shipping artery ofShanghai, arises in the lake district of the Shanghai Munici-pality (Dianshan Lake) and flows northeast past Shanghai intothe Yangtze River. Although Shanghai is one of the mostcomprehensively industrial and commercial cities in China,ranking first in population and population density, a fewstudies have reported on the source apportionment ofsedimentary PAHs in Shanghai. Ren et al. (2006) reported thedistribution and sources of PAHs from dust collected in

.

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Shanghai, and affirmed that vehicle exhaust was the mainsource. Liu et al. (2007a) also characterized the PAH sources,identifying road dust PAHs in central Shanghai areas, andsuggested that road dust PAHs mainly came from the mixingof traffic and coal combustion and that PAH levels in mostsamples in the winter were almost always higher than thosein the summer. Feng et al. (2006) investigated the character-istics of organicmatter in PM2.5 in the atmosphere of Shanghaiand found a strong presence of combustion engine exhaustemissions.

Knowledge regarding the sources and pathways of pollu-tants in aquatic sediments is important for effective pollutionabatement. While diagnostic ratios of PAHs have been widelyapplied to identify sources in various environments (Socloet al., 2000; Yunker et al., 2002; Rocher et al., 2004; Zhang et al.,2004; Wang et al., 2006; Li et al., 2006a), their use is limited dueto a lack of reliability. More sophisticated statisticalapproaches have been demonstrated, including cluster ana-lysis, principal components analysis (PCA), and chemicalmass balance (CMB). However, there are limitations inrequiring an input of source emission profiles to calculatesource contributions when a CMB model has been used toidentify and quantify sources of pollutants (Duval andFriedlander, 1981; Li et al., 2001, 2003). PCA, which can provideinformation on source contributions, in conjunction withmultivariate linear regression (MLR), has been performed toidentity and apportion PAH sources in the air, soil, andsediment in many cities (Harrison et al., 1996; Larsen andBaker, 2003; Li et al., 2006b; Zuo et al., 2007).

In our previous work, we reported the concentrations,spatial distribution and sources of PAHs in surface sedimentsof the Yangtze estuary, Huangpu River and Suzhou River inShanghai, China, and identified pyrogenic sources as impor-tant contributors of sedimentary PAHs in the Huangpu River(Liu et al., 2008). The purpose of this work is to further identifythe major sources of sedimentary PAHs in the Huangpu Riverby cluster analysis and principal component analysis, and tocarry out quantitative sources apportionment and to discussseasonal variations of PAH pollution based on the PCA/MLR.Methylnaphthalenes and 18 PAHs, including 16 PAHs identi-fied by the US EPA as priority pollutants, were monitored insurface sediments. A total organic carbon (TOC) analysis wasalso carried out to normalize the sedimentary PAH concentra-tions of Huangpu River to reduce the effect of sedimentproperty on PAH concentration. The results of this study willprovide valuable information for regulatory actions toimprove the environmental quality of Huangpu River,Shanghai.

Fig. 1 – Sediment sampling locations in the Huangpu River.

2. Experimental methods

2.1. Sample collection and analysis

Eight sampling stations were selected along the HuangpuRiver in Fig. 1; details of the sampling stations are listedelsewhere (Liu et al., 2008). Surface sediment samples werecollected at 8 sampling stations using a grab dredge in April(spring), August (summer), October (autumn) and December(winter) of 2006. A total of 32 samples were used in this work.

Surface sediment samples were air-dried in the dark, sieved to<0.076 mm (200 mesh) after removing stones and residualroots, and stored at −4 °C until analysis.

16 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]anthracene (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), anddibenz[a,h]anthracene (DBahA). In addition to the 16 priorityPAHs, benzo[e]pyrene (BeP), perylene (Pery), 1-methyl-naphthalene and 2-methylnapthalene were also analyzed.Two isomers of methylnaphthalenes were pooled as totalmethylnaphthalenes (MNap).

Sample extraction and cleanup were carried out accordingto Method 3540C and Method 3630C published by US EPA(USEPA, 1996). After extraction and cleanup, samples wereconcentrated and adjusted to 1 mL volume for analysis. PAHanalysis was carried out by high performance liquid chroma-tography (HPLC) with a photodiode array detector. Identifica-tion of PAHs was based on retention time and the ultravioletspectra of PAH standards. The quantification was performedby the external standard method. The ultraviolet measuringwavelengths include 218 nm (NaP), 223 nm (MNaP), 226 nm(AcNe and AcNy), 249 nm (IP), 254 nm (Fl, PhA and An), 266 nm(Chy), 286 nm (FlA and BaA), 300 nm (BbF, BkF, BaP, DBahA andBghiP), 330 nm (BeP), 334 nm (Py) and 433 nm (Pery). Detailedprocedures for sample preparation, extraction, cleanup,measurement and quality control are described elsewhere(Liu et al., 2007b, 2008). Method detection limits were 1–19 ng/g-dw, and spiked recoveries of PAHs were 87–113%. All of thesamples taken were analyzed in triplicate, and the relativestandard deviation was less than 20%.

Total organic carbon (TOC) analysis was performed withthe Shimadzu TOC-Vcpn analyzer with the solid samplemodule (SSM-5000A). The overall precision of measurementswas less than 3% (n=3).

Fig. 2 – Hierarchical dendogram for 18 PAHs in the HuangpuRiver sediments using average linkage between groups andPearson correlation as measure interval.

Table 1 – The range of diagnostic ratios for PAHs sources

Diagnostic ratio Petrogenic Pyrogenic References

LMW/HMW >1 <1 Soclo et al. (2000),Rocher et al. (2004),Wang et al. (2006)

An/(PhA+An) <0.1 >0.1 Yunker et al. (2002),Zhang et al. (2004),Li et al. (2006a)

FlA/(FlA+Py) <0.4 >0.4 Yunker et al. (2002),Zhang et al. (2004),Li et al. (2006a)

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2.2. Statistical analyses

Before statistical analysis of data, we replaced undetectablevalues by a random number between zero and the limit ofdetection, and eliminated AcNy as it was undetectable inmostof the samples. Different hydraulic conditions at the differentsampling locations lead to different deposition rates and todifferent sediment properties, e.g., TOC content and particlesize distribution. Many researchers have indicated thatorganic carbon is an important controlling factor of thesorption of PAHs on sediments (Karickhoff et al., 1979; Wanget al., 2001; Zakaria et al., 2002). In order to reduce this effect,we sieved sediment samples to <0.076 mm (200 mesh) beforeanalysis, and normalized PAH concentrations to TOC con-tents. At the same time, normalization to TOC produced anormalized dataset for the following statistical analysis.

Statistical analyses, including the Kolmogorov–Smirnov(K–S) test, ANOVA, cluster analysis, principal componentsanalysis, and multivariate linear regression, were performedusing SPSS 13.0 for Windows. The K–S test was carried out totest the frequency distribution of PAH data, and all of thevariables after normalization to TOC achieved a normaldistribution with P>0.05. A repeated measures one-wayANOVA procedure was performed to test the significantdifferences of the PAH dataset. The contents of the individualPAHs were hierarchically clustered using weighted averagelinkage between the groups and the Pearson correlation forthe cluster intervals (Zhang et al., 2006). PCA, as a multivariateanalytical tool, was used to reduce the set of original variables(measured PAH contents in sediment samples) and to extracta small number of latent factors (principal components) toanalyze the relationships among the observed variables. Indetail, all factors with eigenvalues over 1 were extractedaccording to KMO and Bartlett's test of sphericity, and wererotated using the Varimax method. MLR was conducted using

PCA factor scores and the standardized normal deviation oftotal PAH concentrations (normalization first to organiccarbon and then scaled to mean and standard deviation) asindependent and dependent variables, respectively (Larsenand Baker, 2003). The regression was run using a forwardstepwise method. The standardized regression coefficientswere used to represent the relative contributions from varioussources (Larsen and Baker, 2003; Zuo et al., 2007).

3. Results and discussion

3.1. Source estimates from cluster analysis

Cluster analysis was performed to identify the homogeneousgroups of individual PAHs in the Huangpu River sediments.The result of the cluster analysis is shown in the hierarchicaldendogram (Fig. 2), which distinguishes the 18 individualPAHs into three major groups. The first group, which includesMNap, Nap, Fl and AcNe, belongs to the low molecular weightPAHs with 2–3 rings or alkyl-substituted PAHs, which areabundant in petrogenic sources mainly caused by petroleumspills, e.g., fresh or used crankcase oil, crude and fuel oil (Marret al., 1999; Utvik et al., 1999; Dobbins et al., 2006; Gonzalezet al., 2006; Ye et al., 2006). The second major group issubdivided into two subgroups. The first subgroup containsBbF, BkF, BaP, BeP, DBahA and BghiP, which are the highmolecular weight PAHs with 5–6 rings. The second subgroupconsists of PhA, FlA, Py, BaA, Chy and IP, most of which are 4ring PAHs. Both of these subgroups are usually detected inpyrogenic source, e.g., combustion of coal, wood, vehicle fueland waste tire (Levendis et al., 1998; Zakaria et al., 2002; Wanget al., 2007). The third major group contains only twocomponents of An and Pery, and currently has an unknownsource, which is further discussed in the PCA.

3.2. Diagnostic ratios of PAHs

The relative abundances or diagnostic ratios are usefulindicators of PAH sources because isomer pairs are diluted toa similar extent upon mixing with natural particulate matter,and are distributed similarly to other phases as they havecomparable thermodynamic partitioning and kinetic masstransfer coefficients (Dickhut et al., 2000). Diagnostic ratios ofPAHs, such as the ratio of LMW (2–3 ring PAHs) to HMW (4–6ring PAHs), An/(PhA+An) and FlA/(FlA+Py), can be used toidentify the possible emission sources, as summarized in

Table 2 – Rotated component matrix of 18 PAHs from theHuangpu River sediment a

No. PAH Principal components

1 2 3

1 MNap 0.03 −0.08 0.95b

2 Nap 0.03 −0.10 0.943 Fl 0.30 0.46 0.744 AcNe 0.32 0.35 0.825 PhA 0.69 0.10 0.496 An 0.20 0.87 0.087 FlA 0.90 −0.26 0.188 Py 0.91 0.23 0.189 Chy 0.88 −0.12 0.2210 BaA 0.93 −0.04 0.1511 BbF 0.82 0.34 0.1012 BkF 0.72 0.36 −0.2013 BaP 0.74 0.58 −0.1214 DBahA 0.68 0.59 −0.1715 IP 0.79 0.18 0.1816 BghiP 0.70 0.64 0.1517 BeP 0.76 0.45 0.2118 Pery −0.17 0.89 0.14

Estimated source Pyrogenic Unknown PetrogenicVariance (%) 43.9 20.1 19.9

a Rotation method: Varimax with Kaiser normalization.b Bold loadings>0.70.

Fig. 3 – PAH cross plots for the ratios of An/(An+PhA) vs. FlA/(FlA+Py).

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Table 1. The ratios of LMW/HMW in the Huangpu Riversediments range from 0.12 to 0.59 with a mean of 0.33. Theratio of LMW/HMW is <1, indicating a predominance ofcombustion source (Soclo et al., 2000; Rocher et al., 2004;Wang et al., 2006). In Fig. 3, the ratios of An/(An+PhA) rangettfrom 0.12 to 0.39 with a mean of 0.27, and the ratios of FlA/(FlA+Py) range from 0.39 to 0.57 with a mean of 0.49. Theseare similar to measures for combustion, especially coalcombustion of power plants and liquid fossil fuel (vehicleand crude oil) combustion (Yunker et al., 2002; Zhang et al.,2004; Li et al., 2006a). Therefore, pyrogenic sources are themajor sources of PAHs in the Huangpu River sediments.

3.3. Source estimates from principal components analysis

The purpose of PCA is to represent the total variability of theoriginal PAH data with a minimum number of factors. Bycritically evaluating the factor loadings, an estimate of thechemical source responsible for each factor can be made(Larsen and Baker, 2003). The rotated factors of 18 normalizedPAHs (by TOC) from the Huangpu River sediments arepresented in Table 2. The three factors account for 83.9% ofthe variability in the data. Factor 1, which explains 43.9% oftotal variance, is dominated by PhA, FlA, Py, Chy, BaA, BbF,BkF, BaP, DBahA, IP, BghiP and BeP. Factor 2, contributing20.1% of total variance, is highly weighted by An and Pery.Factor 3, which explains 19.9% of total variance, is dominatedby MNap, Nap, Fl and AcNe. The result of PCA is similar to thatof the cluster analysis above. Factor 3, corresponding to thefirst group, represents a petrogenic source; Factor 1, corre-sponding to the second group, represents pyrogenic sourcePAHs; and Factor 2, corresponding to the third group,represents an unknown source. In Table 2, the loadings ofAn and Pery are 0.87 and 0.89, respectively. It is unusual tohave An and Pery covary in environmental samples. Pery is anatural compound formed from biogenic precursors (e.g.,perylenequinone pigments) during early diagenesis, whiletrace concentrations of perylene are generated throughcombustion of fossil fuel (Hites et al., 1980; Venkatesan,1988; Boonyatumanond et al., 2006; Ye et al., 2006). The

biogenic production of An is negligible in comparison to Pery,because An is susceptible to biogradation (Santos et al., 2008),hence Pery might be primarily combustion generated. There-fore, Factor 3, namely the third group, is believed to be anunknown combustion source, although we cannot explainwhy An was classified in this group.

According to the cluster analysis, the pyrogenic source canbe subdivided into two subgroups, which represent two kindsof different pyrogenic sources. However, the results of PCAcannot differentiate the two subsets of pyrogenic sources,even if the number of principal components is set as 4. Sincepyrogenic sources of PAHs are the main objectives investi-gated in order to control PAH pollution in the Huangpu Riversediments, the unknown source can be ignored in thisinvestigation. We therefore removed data about An and Peryfrom the data matrix and performed the PCA again in order tofurther investigate the pyrogenic sources of PAHs.

The rotated factors of the 16 PAHs without Pery and An areshown in Table 3. There are again three factors, accounting for84.8% of the variability in the data.

The first factor is responsible for 35.0% of the total variance.This factor is heavily weighted in BkF, BaP, DBahA, BghiP andBeP, alongwithmoderate loadings for Py, BbF and IP. These PAHcomponents, the high molecular weight PAHs with 5–6 rings,basically belong to the first subgroup of Group 2 of the clusteranalysis. The source this factor represents appears to be roaddust collected from theShanghai urbanarea (Renet al., 2006; Liuet al., 2007a) and is vehicular (gasoline and diesel) in nature(Harrison et al., 1996; Larsen and Baker, 2003; Ye et al., 2006; Zuoet al., 2007). BghiP has been identified as a tracer of autoemissions because itwas found to be enriched in a traffic tunnelalong with BaP (Harrison et al., 1996; Larsen and Baker, 2003;

Table 3 – Rotated component matrix of 16 PAHs from theHuangpu River sediment a

No. PAH Principal components

1 2 3

1 MNap −0.17 0.13 0.93b

2 Nap −0.19 0.15 0.913 Fl 0.41 0.11 0.804 AcNe 0.34 0.17 0.875 PhA 0.36 0.59 0.496 FlA 0.23 0.92 0.127 Py 0.65 0.68 0.218 Chy 0.31 0.86 0.189 BaA 0.43 0.85 0.1310 BbF 0.69 0.55 0.1511 BkF 0.77 0.32 −0.1112 BaP 0.91 0.30 −0.0113 DBahA 0.88 0.24 −0.0614 IP 0.56 0.58 0.2115 BghiP 0.88 0.28 0.2616 BeP 0.78 0.39 0.29

Estimated source Traffic Coal PetrogenicVariance (%) 35.0 27.0 22.8

a Rotation method: Varimax with Kaiser normalization.b Bold loadings>0.70.

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Boonyatumanondet al., 2007). The higher level of BkF relative toother PAHs is suggested to indicate diesel vehicles (Venkatara-man et al., 1994; Larsen andBaker, 2003). On the other hand, thisfactor is moderately weighted in IP, which has also been foundin both diesel and gas engine emissions (May and Wise, 1984;Larsen and Baker, 2003) and gasoline vehicle soot (Boonyatu-manond et al., 2007). Therefore, this factor is selected torepresent the traffic-related source of PAHs.

The second factor is responsible for 27.0% of the totalvariance. This factor is predominately composed of FlA, Chyand BaA (4-ring PAHs) with moderate loadings of PhA, Py, BbFand IP. There are similar PAH components between this factorand the second subgroup of Group 2 in the cluster analysis. Theliterature reports that 4-ring PAHs are abundant in the roaddustin Bangkok city (Boonyatumanond et al., 2007) and KualaLumpur city (Zakaria et al., 2002), but the abundance of 4-ringPAHs is lower in the road dust in Shanghai city (Ren et al., 2006;Liu et al., 2007a) comparedwith these. The ratio of PAHs (4 rings)to PAHs (5–6 rings), abbreviated PAHs(4)/PAHs(5+6), is greaterthan 1 in Bangkok and Kuala Lumpur, according to the PAHprofilesof roaddust (Zakaria et al., 2002; Boonyatumanondetal.,2007). However, PAHs(4)/PAHs(5+6) is 0.3–0.8 in the centralShanghai area (Liu et al., 2007a) and 0.5–1.1 in the Shanghaiurbanarea (Ren et al., 2006). In this paper,we show that PAHs(4)/PAHs(5+6) in the sediment, 0.8–1.4 with a mean of 1.1, is morethan that in the road dust from Shanghai. This indicates thatthere should be an additional PAH source (not traffic-related)leading to the higher ratio of PAHs(4)/PAHs(5+6). Duval andFriedlander (1981) considered PhA, FlA, Py, BaA and Chy asmarkers of coal combustion. According to PAH data from thecombustion of pulverized coal and tire crumbs at 1000 °C (nearthe temperature of coal combustion in the coal-burning powerplant), PhA, FlA and Py are the dominant PAHs with lowerconcentrations of 5–6 ring PAHs detected in furnace effluents

(Levendis et al., 1998). Some researchershave also reportedPhA,FlA, Py as predominant in coal combustion profiles (Harrisonet al., 1996; Zuo et al., 2007). In Shanghai, coal is the mostimportant energy source and is used widely for industrial anddomestic purposes, especially in the steel and power industry.TheShanghaimunicipal electric power supply ismainlyderivedfrom coal-burning power plants. It is reasonable to assign thisfactor to coal combustion.

The third factor is responsible for 22.8% of the totalvariance, and is heavily weighted in MNap, Nap, Fl andAcNe, the same as the result of the former PCA. This factoris suggested to be indicative of volatilization or spill ofpetroleum-related products (Marr et al., 1999; Utvik et al.,1999; Zakaria et al., 2002; Luca et al., 2004; Dobbins et al., 2006;Gonzalez et al., 2006; Wang et al., 2006; Ye et al., 2006), e.g.,from the waterway transportation industry. This factor isbelieved to be the petrogenic source of PAHs.

In general, the first PCA of PAH data shows that there arethree PAH sources in the Huangpu River sediments, namelypyrogenic, petrogenic and an unknown combustion source.The second PCA of data without Pery and An (the unknownsource) divides the pyrogenic source of PAHs into two subsets,one traffic-related and the other due to coal combustion.

3.4. Contribution of PAH sources

The ultimate goal of source apportionment is to determine thepercent contribution of different PAH sources for a givensamples. We unveiled the major sources of sedimentary PAHsin the Huangpu River using diagnostic ratios, cluster analysisand PCA. We then calculated the percent contributions of themajor sources using multivariate linear regression (MLR) fromthe PCA factor scores and the standardized normal deviationof total PAH concentrations as our independent and depen-dent variables, respectively. Several authors (Harrison et al.,1996; Larsen and Baker, 2003; Zuo et al., 2007) have reportedapplying PCA/MLR to apportion sources of PAHs in the urbanatmosphere and surface soils.

Themean percent contribution of source i is the ratio of theregression coefficient for factor i to the sum of all theregression coefficients, according to the description in theliterature (Larsen and Baker, 2003). The factor scores are fromthe result of PCAwithout Pery and An. The R squared value forMLR is 0.983 and the P values for the regression coefficientsare less than 0.05. Thus, the mean contribution percents are40% for the vehicular source, 36% for the coal combustionsource, and 24% for the petrogenic source.

The traffic-related source (40%) is the first contributor tothe PAHs. There are two origins of traffic-related PAHs in theHuangpu River. For one thing, traffic-related PAHs in road dustcan enter the sediments through urban runoff (Zakaria et al.,2002; Murakami et al., 2005; Boonyatumanond et al., 2006,2007). In recent decades, a rapid increase in motor vehicles inShanghai has aggravated PAH pollution in the Huangpu Riversediments. For example, the number of taxis increased from11,298 in 1990 to 48,022 in 2006 (Yin, 2007). Exhaust from cargovessels and passenger ferries in the Huangpu River also playsan important role as a PAH contributor, and should not beignored in the discussion of sedimentary PAHs. In fact, cargovessels are responsible for most coal transportation to coal-

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burning power plants located along the Huangpu River.Passenger ferries also still serve the public, despite the rapiddevelopment of traffic facilities across the Huangpu River.Furthermore, compared with automobiles, more PAHs areemitted from vessels and ferries becausemost of them are notequipped with catalytic converters (Rogge et al., 1993).

Coal combustion (36%) is the second contributor to PAHs. Inrecent years, most coal in Shanghai is used to generate electricpower due to a control on the emission of SO2, NOx andsuspended particles. However, coal consumption in the powerindustry in Shanghai is continually increasing, from3.0milliontons in 1980 to 10.6 million tons in 1990 to 25.0 million tons in2005 (SLCO, 2008a), causing a high percent contribution of coalcombustion. Several large-scale coal-burning power plants arelocated along the Huangpu River, leading to an easy input ofcoal combustion PAHs into the Huangpu River.

Petrogenic sources (24%) form the third contributor to PAHs.Petrogenic sources include crude oil and refined products (e.g.,crude and fuel oil). Zakaria et al. (2002) thought that there weretwo major input routes to aquatic environments, namely(1) spillage and dumping of waste crankcase oil and (2) leakageof crankcase oils from vehicles onto road surfaces, withsubsequentwashout by street runoff. InChina,waste crankcaseoil has been included in the national list of hazardous wastes,and its dumping is illegal and punished by law. In general,mostused crankcase oil is recycled by garages in vehicle main-tenance. However, if crankcase oil is replaced in private, it maybe improperly stored or deposited, allowing for indiscriminatespillage to the ground and streets or even for oil to be poureddirectly into drains or the water environment, as there iscurrentlynoefficient recyclingprogramforusedcrankcaseoil inShanghai. In addition, vessel maintenance and fuel supplymaylead to input of petrogenic PAHs into the sediment.

3.5. Seasonal variation

Contamination of the bottom sediment is often a gradualprocess. However, the repeated measures one-way ANOVAresults indicate a distinct seasonal variation, that is, theconcentrations of total PAHs are significantly higher in thespring than in the other seasons (at the significant level of0.05). The mean concentrations of total PAHs in differentseasons are listed in Table 4. In order to further investigate thecauses, the contributions of three important sources in

Table 4 – Seasonal differentiation of PAHs sources

Seasona Temperature(°C) b

Electric power(BkW h) c

Total P(ng/mg-

Spring 8.4–19 16.4 137Summer 23–27 19.9 102Autumn 12–23 16.9 105Winter 3.6–6.2 18.4 103

a Spring in Shanghai is March–May, summer is June–August, autumn isb Data were official statistics from 1951 to 1990 (SLCO, 2008b).c Seasonal loads of Shanghai electric power grid, and unit is billion kilowShanghai Municipal Electric Power Company (SMEPC, 2008).d Mean of total PAHs concentrations of 8 sampling locations; total PAHse Mean source contributions of 8 sampling locations.

different seasons are calculated in Table 4, according to thefollowing formula of Larsen and Baker (2003).

Contribution of source i ng=mg � TOCð Þ =meanX

16PAHs

� Bi=X

Bi

� �+ BidPAHsFSi

where Bi/ΣBi is the ratio of the regression coefficient for factor ito the sum of all of the regression coefficients, FSi is the factorscore for factor i, and δPAHs is the standard deviation of totalPAH concentrations after normalization to organic carbon.

A higher concentration of total PAHs in the spring resultsfrom a higher contribution of coal combustion and petrogenicsources. The repeated measures one-way ANOVA resultssuggest that the contributions of coal combustion in spring aresignificantly higher than those in the summer and autumn, andthat those of the petrogenic source in spring are higher than inthe other seasons (at the significant level of 0.05). As for coalcombustion, 71.3 BkWhof electric powerwere generated during2006,most fromcoal-burningpower plants inShanghai (SMEPC,2008). This indicates that the consumption of electric power canreflect the consumption of coal in Shanghai. Statistical data inTable 4 show that the consumption of electric power in thesummer and winter (19.9 and 18.4 BkW h) is more than that inthe spring and autumn (16.4 and 16.9 BkW h). This results inhigher PAH pollution from coal combustion in the summer andwinter, which disagrees with the result of PAH source appor-tionment (higher PAH pollution in the spring). There are tworeasons leading to this disagreement. For one thing, winter inShanghai is a dry season with 102 mm of average seasonalrainfall (~9% of average annual rainfall), and a rainy periodfollows fromApril 15th toMay15thwith~14%of averageannualrainfall (157 mm) (SLCO, 2008b), leading to PAHs in the air andsoil generated in the winter being brought into the HuangpuRiver in the spring through rainfall and surface runoff. Inaddition, as a coastal city, Shanghai is affected by summermonsoons, which bring in clean oceanic winds in the summerthat dilute local air pollutants (Feng et al., 2006); PAHs from coalcombustion are typical air pollutants. As for petrogenic PAHs,the lower temperature in the winter and spring is an importantfactor, because low temperature decreases the evaporation ofpetrogenic PAHs (Feng et al., 2006). Meanwhile, rainfall in thespring brings particles with petrogenic and coal combustionPAHs into the Huangpu River. For the traffic-related source, thecontribution in the spring is significantly lower than that in

AHsTOC) d

Pollution source of PAHs (ng/mg-TOC) e

Traffic Coal combustion Petrogenic

.2 40.2 54.1 35.3

.5 33.7 34.5 28.7

.1 57.2 33.1 21.1

.4 48.8 40.1 21.6

September–November, and winter is December–next February.

att-hours. Data from Match 2006 to February 2007 were provided by

is sum of observed concentrations of 16 PAHs listed in Table 3.

Fig. 4 – Score plots of principal components analysis.

2937S C I E N C E O F T H E T O T A L E N V I R O N M E N T 4 0 7 ( 2 0 0 9 ) 2 9 3 1 – 2 9 3 8

the autumn (at the significant level of 0.05). Li et al. (2006b)suggested that when the Chinese traditional Spring Festivaltakes place in the spring with few vehicles and vessels on themove (people taking vacation out of the city), the traffic-relatedpollution is low.

Factor score plots of principal components analysis alsoshow a seasonal variation of sedimentary PAHs in the HuangpuRiver. Fig. 4a and b shows score plots of PC1 vs. PC2 and PC1 vs.PC3, respectively. Fig. 4 illustrates that samples collected in thespring and autumn are clustered in respective areas of thediagram. Samples collected in the autumn have higher trafficfactor scores (PC1) and lower factor scores of coal combustion(PC2) and petrogenic sources (PC3). On the other hand, scores inthe spring are higher in the coal combustion (PC2) andpetrogenic factors (PC3), and lower in the traffic factor (PC1).This indicates that there is a distinct variation between springand autumn for the contributions of PAH sources.

4. Conclusion

The combination of cluster analysis and principal componentanalysis is effective for identifying PAHs sources. Both multi-

variate analysis methods show that the contributions of coalcombustion, traffic-related pollution and spill of oil productsare dominant in the Huangpu River sediments. The results ofdiagnostic ratios show that pyrogenic sources are the majorsource of PAHs. PCA/MLR further apportions the sources'contributions, and the results show that the contributions ofcoal combustion, traffic-related pollution and spill of oilproduct are 40%, 36% and 24%, respectively. The pyrogenicsources (coal combustion and traffic-related pollution) con-tribute 76% of anthropogenic PAHs to the Shanghai sediments.Energy consumption is a predominant reason for PAH pollu-tion in Shanghai.

Sedimentary PAH pollution is significantly higher in thespring than in the other seasons. The higher concentrations inthe spring are attributed to the higher contribution of coalcombustion and petrogenic sources. Rainfall, monsoon andtemperature play important roles in producing the distinctseasonal variation of sedimentary PAHs.

Acknowledgements

This work was supported by the National Natural ScienceFoundation of China (Nos. 20477030 and 40601095) and theShanghai Science and Technology Commission (Nos.05JC14059 and 04JC14072).

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