Chemometric techniques in distribution, characterisation and source apportionment of polycyclic aromatic hydrocarbons (PAHS) in aquaculture sediments in Malaysia Ananthy Retnam a,b , Mohamad Pauzi Zakaria b,⇑ , Hafizan Juahir b,c , Ahmad Zaharin Aris b,c , Munirah Abdul Zali a,c , Mohd Fadhil Kasim d a Environmental Health Division, Department of Chemistry Malaysia, Jalan Sultan, 46661 Petaling Jaya, Selangor, Malaysia b Environmental Forensics Research Centre, Faculty of Environmental Studies, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia c Department of Environmental Sciences, Faculty of Environmental Studies, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia d Water Quality and Environmental Research Division, National Hydraulic Research Institute of Malaysia (NAHRIM), Ministry of Natural Resources and Environment, 5377, Jalan Putra Permai, 43300 Seri Kembangan, Selangor, Malaysia article info Keywords: PAHs Chemometric Aquaculture Source apportionment Surface sediment abstract This study investigated polycyclic aromatic hydrocarbons (PAHs) pollution in surface sediments within aquaculture areas in Peninsular Malaysia using chemometric techniques, forensics and univariate meth- ods. The samples were analysed using soxhlet extraction, silica gel column clean-up and gas chromatog- raphy mass spectrometry. The total PAH concentrations ranged from 20 to 1841 ng/g with a mean of 363 ng/g dw. The application of chemometric techniques enabled clustering and discrimination of the aquaculture sediments into four groups according to the contamination levels. A combination of chemo- metric and molecular indices was used to identify the sources of PAHs, which could be attributed to vehi- cle emissions, oil combustion and biomass combustion. Source apportionment using absolute principle component scores–multiple linear regression showed that the main sources of PAHs are vehicle emis- sions 54%, oil 37% and biomass combustion 9%. Land-based pollution from vehicle emissions is the pre- dominant contributor of PAHs in the aquaculture sediments of Peninsular Malaysia. Ó 2013 Elsevier Ltd. All rights reserved. 1. Introduction Since the 1960s, aquaculture production has increased dramatically and has become a major industry that provides approximately 43% of the seafood to consumers worldwide (FAO, 2006). In Malaysia, aquaculture has been identified as an important initiative to meet the increased demand for marine fish and shell fish, and the increased demand can be observed in the increase of aquaculture production (FAO, 2006). The Malaysian aquaculture sector has increased greatly, particularly over the last 30 years, and has contributed to the nation’s economic growth (Fig. 1)(FAO, 2012). The Malaysian aquaculture production was approximately 581,043 tonnes in 2010 compared to 73,262 tonnes in 1981, repre- senting an 87% increase (DOF, 2012). The aquaculture industries are exposed to many chemical, bio- logical and other pollutants. The use of antibiotics and feeds for- mulated with agrochemicals has resulted in the presence of many chemical and biological pollutants. Anthropogenic inputs, such as polycyclic aromatic hydrocarbons (PAHs), heavy metals, polychlorinated biphenyls (PCBs) and pesticides, have also contrib- uted to the presence of pollutants in aquaculture environments. The levels of contaminants in aquaculture products and poor man- agement of the industry are of particular concern because of the potential risk to the humans who consume these products. In June 2008, the European Union (EU) placed a ban on Malaysian seafood products. The ban was implemented due to health concerns result- ing from the failure of the seafood industries in Malaysia to meet the standards set by the agriculture audit authorities of the EU (BERNAMA, 2008). The seafood industry suffered losses of almost RM600 million (BERNAMA, 2008). Malaysia’s export of fish and fishery products to the EU had been valued at $190 million in 2007 (VIR, 2010). PAHs have been of scientific interest for several decades due to their carcinogenic, mutagenic and endocrine disruptor properties. PAHs originate from pyrogenic, petrogenic and natural sources, the former two are the major anthropogenic sources of PAHs in the environment. Pyrogenic sources coming from incomplete com- bustion of organic matters such as fossil fuels and wood while petrogenic sources originated from crude oil and petroleum products (Liu et al., 2009). Pyrogenic PAHs are released into the atmosphere as gasses and soot particles (Zakaria et al., 2002). They 0025-326X/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.marpolbul.2013.01.009 ⇑ Corresponding author. Tel.: +60 3 8946 6764; fax: +60 3 8943 6766. E-mail address: [email protected](M.P. Zakaria). Marine Pollution Bulletin 69 (2013) 55–66 Contents lists available at SciVerse ScienceDirect Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul
Transcript
Marine Pollution Bulletin 69 (2013) 55–66
Contents lists available at SciVerse ScienceDirect
Chemometric techniques in distribution, characterisation and sourceapportionment of polycyclic aromatic hydrocarbons (PAHS) in aquaculturesediments in Malaysia
Ananthy Retnam a,b, Mohamad Pauzi Zakaria b,⇑, Hafizan Juahir b,c, Ahmad Zaharin Aris b,c,Munirah Abdul Zali a,c, Mohd Fadhil Kasim d
a Environmental Health Division, Department of Chemistry Malaysia, Jalan Sultan, 46661 Petaling Jaya, Selangor, Malaysiab Environmental Forensics Research Centre, Faculty of Environmental Studies, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysiac Department of Environmental Sciences, Faculty of Environmental Studies, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysiad Water Quality and Environmental Research Division, National Hydraulic Research Institute of Malaysia (NAHRIM), Ministry of Natural Resources and Environment, 5377, Jalan PutraPermai, 43300 Seri Kembangan, Selangor, Malaysia
This study investigated polycyclic aromatic hydrocarbons (PAHs) pollution in surface sediments withinaquaculture areas in Peninsular Malaysia using chemometric techniques, forensics and univariate meth-ods. The samples were analysed using soxhlet extraction, silica gel column clean-up and gas chromatog-raphy mass spectrometry. The total PAH concentrations ranged from 20 to 1841 ng/g with a mean of363 ng/g dw. The application of chemometric techniques enabled clustering and discrimination of theaquaculture sediments into four groups according to the contamination levels. A combination of chemo-metric and molecular indices was used to identify the sources of PAHs, which could be attributed to vehi-cle emissions, oil combustion and biomass combustion. Source apportionment using absolute principlecomponent scores–multiple linear regression showed that the main sources of PAHs are vehicle emis-sions 54%, oil 37% and biomass combustion 9%. Land-based pollution from vehicle emissions is the pre-dominant contributor of PAHs in the aquaculture sediments of Peninsular Malaysia.
� 2013 Elsevier Ltd. All rights reserved.
1. Introduction
Since the 1960s, aquaculture production has increaseddramatically and has become a major industry that providesapproximately 43% of the seafood to consumers worldwide (FAO,2006). In Malaysia, aquaculture has been identified as an importantinitiative to meet the increased demand for marine fish and shellfish, and the increased demand can be observed in the increaseof aquaculture production (FAO, 2006). The Malaysian aquaculturesector has increased greatly, particularly over the last 30 years, andhas contributed to the nation’s economic growth (Fig. 1) (FAO,2012). The Malaysian aquaculture production was approximately581,043 tonnes in 2010 compared to 73,262 tonnes in 1981, repre-senting an 87% increase (DOF, 2012).
The aquaculture industries are exposed to many chemical, bio-logical and other pollutants. The use of antibiotics and feeds for-mulated with agrochemicals has resulted in the presence ofmany chemical and biological pollutants. Anthropogenic inputs,such as polycyclic aromatic hydrocarbons (PAHs), heavy metals,
ll rights reserved.
: +60 3 8943 6766.karia).
polychlorinated biphenyls (PCBs) and pesticides, have also contrib-uted to the presence of pollutants in aquaculture environments.The levels of contaminants in aquaculture products and poor man-agement of the industry are of particular concern because of thepotential risk to the humans who consume these products. In June2008, the European Union (EU) placed a ban on Malaysian seafoodproducts. The ban was implemented due to health concerns result-ing from the failure of the seafood industries in Malaysia to meetthe standards set by the agriculture audit authorities of the EU(BERNAMA, 2008). The seafood industry suffered losses of almostRM600 million (BERNAMA, 2008). Malaysia’s export of fish andfishery products to the EU had been valued at $190 million in2007 (VIR, 2010).
PAHs have been of scientific interest for several decades due totheir carcinogenic, mutagenic and endocrine disruptor properties.PAHs originate from pyrogenic, petrogenic and natural sources,the former two are the major anthropogenic sources of PAHs inthe environment. Pyrogenic sources coming from incomplete com-bustion of organic matters such as fossil fuels and wood whilepetrogenic sources originated from crude oil and petroleumproducts (Liu et al., 2009). Pyrogenic PAHs are released into theatmosphere as gasses and soot particles (Zakaria et al., 2002). They
Fig. 1. Malaysian aquaculture production and value trend (FAO, 2012).
56 A. Retnam et al. / Marine Pollution Bulletin 69 (2013) 55–66
are transported onto land through wet and dry deposition. Petro-genic PAHs are released through oil spills, leaking of engine andfuel oil, dumping of used crankcase oil and unburned fuel absorbedinto street dust (Zakaria et al., 2002). PAHs transported into theaquatic environment by storm runoff could contaminate aquacul-ture sediments over a long period, subsequently entering the foodchain where they will accumulate in seafood and eventually reachhumans.
Persistent organic pollutants (POPs), such as PAHs, are stablecompounds with long residence times in the environment thatcan be adsorbed onto suspended particulate matter and the sedi-ment, which then becomes the sink for these pollutants. Any dis-turbance of the contaminated sediments may lead to aredistribution and reintroduction of the pollutants back into theaquatic food chain, where they will be deposited in the fatty tissuesof aquatic animals, leading to health hazards for marine life andhuman beings (Gaspare et al., 2009). In order to understand the ex-act distribution of PAHs in aquaculture ecosystem, levels of sedi-mentary PAHs and levels of PAHs in aquaculture organisms hasto be done separately. This strategy can give ideas on the gap be-tween sedimentary PAHs and those of seafood animals. However,as a start this study will give some insight on which pollutionsource of PAHs to the aquaculture. Moreover, information concern-ing PAHs levels in aquaculture sediments in Malaysia is scarce.
Malaysia sedimentary PAHs impacted by petrogenic sources(Zakaria et al., 2001, 2002; Sakari et al., 2008) is unique comparedto other industrial countries reported pyrogenic as the main sourceof sedimentary PAHs (Harrison et al., 1996; Larsen and Baker,2003; Liu et al., 2009). Petrogenic sources with lower molecularweight PAHs (2–3 rings) are more water soluble and readily bio-available for aquatic animals. Higher level of petrogenic PAHs werefound to be accumulated in various organisms (Baumard et al.,1998a). Therefore the bioaccumulation of petrogenic PAHs fromsediments is greater for seafood animals. Generally aquaculturesites are located too near to potential sources of PAHs such astowns and oil tankers route especially along the Straits of Malacca.With cities, towns and industrial sites located in close vicinities toriver mouth and coastal water, there is potentially high risk ofPAHs pollution into the aquaculture sites. Furthermore the aqua-culture sites are located at protected areas with less water circula-tions and less dilution enhancing bioaccumulation of PAHs inseafood animals. The high human activities at the aquacultureareas such as boating, seafood harvesting and cage cleaning resus-pend and remobilize the sedimentary PAHs into water columnwith large amount of suspended particles. Therefore the PAHs pol-lution source for seafood animals can be from these suspended
particles. The PAHs distribution in particles and sediments are par-tially reduced in the lower molecular weight PAHs enhancingexposure to high molecular weight compounds (Baumard et al.,1998a). Furthermore, in Malaysia the concentration of sedimentaryPAHs is not regulated. Regulation is important in term of pollutioncontrol and to minimise the risk to human exposure.
Identification and apportionment of sources of PAHs becomesparticularly important for regulating the input of PAHs into theenvironment. PAHs occurs in a mixture of hundreds of compoundswith concentrations varies according to sampling sites and envi-ronmental conditions such as coastal, rural and urban areas. Fur-thermore these data normally contains internal relationshipamong variables often in partially hidden structures and requiresa very careful evaluation and interpretations. Chemometric tech-niques found to be significant in analysing large and complex PAHsdata in different geographical and environmental compartments(Kavouras et al., 2001; Larsen and Baker, 2003; Zuo et al., 2007;Gaspare et al., 2009; Liu et al., 2009). Application of different che-mometric techniques viz. cluster analysis (CA), discriminant analy-sis (DA) and principle component analysis (PCA) and sourceapportionment by multiple linear regressions on absolute principlecomponent scores (APCS/MLR) offers better understanding andinterpretation of complicated sedimentary PAHs. CA is an unsuper-vised pattern recognition method commonly used to group vari-ables and observations. The cluster results help in interpretingdata and indicating patterns of PAHs (Savinov et al., 2000; Dahleet al., 2003). CA is commonly used to group sampling sites havingsimilar PAHs fingerprints into clusters to explain the variations be-tween sites (Savinov et al., 2000; Dahle et al., 2003) and to identifysources of PAHs by grouping individual PAHs having similar char-acteristics (Kavouras et al., 2001; Liu et al., 2009). Contrary toexploratory features of CA, DA offer statistical classification of sam-ples with prior knowledge of membership of objects to particularcluster (such as spatial or temporal grouping of a sample is knownfrom its sampling sites or time). It is used to confirm the groupsfound by means of CA. Further, DA helps in grouping the samplessharing the common properties (Singh et al., 2005). Althoughapplication of DA in water quality and other data sets are wellestablished (Singh et al., 2005; Kannel et al., 2007; Al-Odainiet al., 2011; Osman et al., 2011) it is rarely applied in PAHs dataanalysis (Osman et al., 2011). The most frequently used chemo-metric techniques in PAHs data analysis is PCA. It is a non-para-metric method of classification without prior assumption aboutthe underlying statistical data distribution (Kannel et al., 2007).The main applications of PCA are to reduce the number of variablesand to detect structure in the relationship among the variables.PCA employed to detect relationship among variables for possiblesource identification of PAHs in air, sediment, biota and soil (Har-rison et al., 1996; Larsen and Baker, 2003; Luo et al., 2006, 2008;Pies et al., 2008; Gaspare et al., 2009). Source identification of PAHsalso deduced through molecular indices, but it is a qualitativemethod lacking in precision and reliability and challenging forcomplex environmental matrices (Baumard et al., 1998b; Pieset al., 2008; Liu et al., 2009). Lately researchers incline in usingPCA together with molecular indices for decisive identification ofsources of PAHs in complex environmental samples (Luo et al.,2006, 2008; Zuo et al., 2007; Pies et al., 2008; Liu et al., 2009). Fac-tor scores from PCA coupled with MLR (APCS/MLR) is a populartechnique for source apportionment of PAHs in environmentalmatrices (Harrison et al., 1996; Kavouras et al., 2001; Larsen andBaker, 2003; Wang et al., 2010). The advantage of APCS/MLR is thatit does not require prior knowledge on input of source emission tocalculate source contributions (Larsen and Baker, 2003; Liu et al.,2009). Previous studies on PAHs pollution in Malaysia have onlyfocused on source identification using molecular indices (Zakariaet al., 2002; Tahir et al., 2006; Elias et al., 2007; Sakari et al.,
A. Retnam et al. / Marine Pollution Bulletin 69 (2013) 55–66 57
2008; Bakhtiari et al., 2009). A comprehensive study using chemo-metric techniques for identification of possible sources of PAHs andin apportionment of the sources in aquaculture sediments offersvaluable tool for developing appropriate strategies for effectivemanagement of aquatic resources and prompt solution for pollu-tion problems.
The assessment of the PAHs pollution status and bioavailabilityof PAHs in the sediments of aquaculture environments in the coast-al areas of Peninsular Malaysia is important for the sustainability ofthe economy and for improvement of public health in the long term.Like other developing nations, Malaysia faces ever increasing pollu-tion problems, including environmental contamination with PAHs.The wide use of fossil fuel as an energy source for automobiles andnumerous industries, Malaysia’s strategic location as the world’sbusiest route for oil tankers and the unregulated and deliberate dis-posal of used oils have contributed to the serious problem of con-tamination of Malaysian rivers, coastal areas and oceans by PAHs(Zakaria et al., 2002; Tahir et al., 2006; Elias et al., 2007; Sakariet al., 2008; Bakhtiari et al., 2009). The fish and shellfish in thecoastal areas have accumulated high levels of PAHs and pose risksto humans who consume them (Mirsadeghi et al., 2011). Aquacul-ture products meant for direct human consumption are particularlyhazardous (Mirsadeghi et al., 2011). Therefore, the goals of thisstudy are (1) to measure and identify the major possible sourcesof sedimentary PAHs in aquaculture using chemometric tech-niques, (2) to carry out quantitative source apportionment usingACPS/MLR and (3) to compare the levels derived from the currentstudy with those obtained for other aquaculture zones worldwideand with the available sediment guidelines. To the best of ourknowledge, a quantitative source apportionment of PAHs in theaquatic environment has not yet been performed in Malaysia.
2. Materials and methods
2.1. Sample collection
Sediment samples were collected at aquaculture plots. Adescription of the sampling sites is provided in Table 1 andFig. 2. The experimental design required the collection of threesamples from each sampling site. Samples were collected from Jan-uary to December 2010. The sediment samples were collected fromthe aquaculture sites using an Ekman dredge to collect the top4 cm of the sediment, representing the surface sediments. Afterremoving debris, organisms and shells, the samples were sealedin aluminium containers with Teflon caps and stored in a coolerwith dry ice prior to being transported to the laboratory. Once inthe laboratory, the samples were frozen at �30 �C until furtheranalysis. For the analysis of PAHs, only the fine particles from thetop sediment layer were used where the samples were sievedusing 63 lm mesh. Sediment has been classified as grains withdiameter <63 lm and it is often associated with the contaminationlevels (Savinov et al., 2000; Dahle et al., 2003).
Table 1Description of sampling sites.
No. Sampling site ABV
1 Batu Lintang, Kedah BL2 Pulau Betong, Pulau Pinang PB3 Jelutong, Pulau Pinang JT4 Pasir Panjang, N. Sembilan PP5 Muar, Johor SM6 Merchang, Terengganu MT7 Kuala Setiu, Terengganu KS8 Pulau Kukup, Johor KU9 Gelang Patah, Johor GP
10 Teluk Jawa, Johor TJ
2.2. Sample analysis
Ten grams of freeze-dried sediment samples were spiked with100 ll of 10 lg/ml deuterated surrogates (SIS) (naphthalene-d8,acenaphtene-d10, phenantheren-d10, chrysene-d12 and pery-lene-d12). The samples were then subjected by soxhlet extractionwith 250 ml of dichloromethane (DCM) for 9 h. The extracted sam-ples were purified using 5% deactivated silica gel column chroma-tography to remove the polar compounds. This procedure wasfollowed by fully activated silica gel column chromatography tofractionate the PAHs compounds using 16 ml of 3:1 v/v hex-ane:DCM. The eluents were concentrated using nitrogen blowdown and reconstituted in a volume of 100 ll, and p-terphyl-d14as an internal standard (IIS) was added prior to injection into theGCMS. The concentrations and compositions of 25 PAHs weredetermined by GC–MSD using an HP6890 Series gas chromato-graph interfaced with an HP5973 MSD split/splitless injector(Hewlet Packard, USA). A non-polar phenyl polysiloxane columnwas used for the analyses (HP5, 30 m � 25 mm id and 0.25 lm filmthickness; Agilent Technologies, USA). The carrier gas was helium(99.9999 purity), which was controlled using the constant flowmode at 1.2 ml/min. The injections were performed at 70 �C, andthe oven temperature was held constant for 2 min. Thereafter,the temperature was increased at a rate of 30 �C per minute upto 150 �C. This step was followed by a slower ramp of 5 �C per min-ute up to a final temperature of 280 �C, and the final temperaturewas maintained for 10 min. The injector and detector temperatureswere maintained at 300 �C and 280 �C, respectively. An auto-injec-tor sampler (HP 7673) was used to inject 1 ll of sample in thesplitless injection mode. Data acquisition was performed usingChemStation software. The MSD was set for selective ion monitor-ing (SIM). The details of the method were described previously(Zakaria et al., 2002).
2.3. Quality control and quality assurance (AQ/QC)
To generate good quality data, the following quality assuranceand quality control measures were taken. One method blank wasprocessed together for each batch of samples. Identification ofthe compounds was performed based on the retention time of ref-erence standards with a purity of 99.99% (AccuStandard, USA). Afive-point calibration was established using 0.05, 0.1, 0.25, 0.5and 1.0 ppm with a correlation coefficient (R2) of greater than0.993. The recovery of the SIS ranged from 60% to 120%. The resultswere blank-subtracted, and the concentrations were corrected forthe recoveries of surrogate standards (Hellou et al., 2005; Cortazaret al., 2008). The limit of detection (LOD) of the method rangedfrom 0.06 to 7.0 ng/g dw for acenaphthene and retene. The quanti-fication of target analytes was calculated using an internal stan-dard quantification method by comparing the area of thequantification ion to that of the corresponding deuterated quanti-fication standard. The recoveries of the spiked standards of known
Latitude Longitude Farming activity
5� 3705200N 100� 240 3200E Oyster5� 180 5000N 100� 110 600E Oyster5� 220 5100N 100� 200 1600E Fish mix2� 250 1100N 101� 550 3300E Oyster and mussel2� 30 900N 102� 340 400E Oyster5� 30 600N 103� 170 2400E Oyster and fish mix5� 400 4200N 102� 420 3900E Oyster and fish mix1� 190 2700N 103� 260 2300E Fish Kerapu1� 220 2800N 103� 380 2200E Fish mix1� 280 2100N 103� 500 4000E Fish Kerapu
Fig. 2. Map of the sampling locations for the aquaculture sediments in PeninsularMalaysia, 1 = Batu Lintang (BL), 2 = Pulau Betong (PB), 3 = Jelutong (JT), 4 = PasirPanjang (PP), 5 = Sungai Muar (SM), 6 = Merchang (MT), 7 = Kuala Setiu (KS),8 = Pulau Kukup (KU), 9 = Gelang Patah (GP), 10 = Teluk Jawa (TJ).
58 A. Retnam et al. / Marine Pollution Bulletin 69 (2013) 55–66
amounts (0.05 ppm) averaged at 82%, (45–121%, n = 5) for naph-thalene and benzo(e)pyrene. The performance of the GCMS wasmaintained and monitored by regular column flushing, tuning ofthe MSD and performing an instrument blank run between thesample sequences.
2.4. Data pretreatment and chemometric techniques
Chemometric techniques are sensitive to non-normal geochem-ical data distributions and outliers. Thus, appropriate data pre-treatments, such as checking for a normal distribution and datatransformation, are important criteria. The pretreatment was per-formed through data assemblage and transformation. The datawere arranged according to the measured value and the samplesafter the data conversion into single matrix formed by the concen-tration values of the 24 PAHs analysed (variables) and the datapoints (cases), forming a [24 � 98] data matrix. The non-detectedvalues were replaced by half of the method detection values (Al-Odaini et al., 2011; Osman et al., 2011).
The standardised skewness and standardised kurtosis of the datawere checked to determine whether the samples fell into normaldistributions. Any values outside a range of�2 to +2 indicated a sig-nificant deviation from a normal distribution (Kannel et al., 2007).The analysis showed that the standardised skewness and standard-ised kurtosis were within the range of 0.91–4.09 and 0.54–21.0,respectively, indicating that most of the data were not normally dis-tributed and were skewed. The normality of the data was alsochecked using the Jarque–Bera test. The data transformation was
performed for data that were not normally distributed using theBox–Cox method with an optimal transformation for Y using the fol-lowing equation:
Z ¼ b0 þ b1X þ e ðKannel et al:; 2007Þ ð1Þ
where Z is the transformed variable, X is the regression variables, b0
and b1 are the unknown parameters and e is the error. The depen-dent variable Z is related to Y according to the following equation:
Z ¼ 1þ ðY � k2Þk1 � 1k1Kk1�1 for k – 0 ðKannel et al:; 2007Þ ð2Þ
Z ¼ K lnðY þ k2Þ for ðKannel et al:; 2007Þ ð3Þ
where k1 and k2 are the transformation parameters and K is thegeometric mean of Y + k2. The optimal transformation is the onethat minimises the mean squared error for Z.
2.4.1. Cluster analysis (CA)CA performed to disclose the natural groupings within the real
data both in term of similarity of sampling sites and individualPAHs of the study areas without prior assumptions. The CA classi-fies objects (observation/variables) into classes (clusters) on thebasis that each object is similar to the others within its class butdifferent from those in the other classes (Kannel et al., 2007). Hier-archical agglomerative cluster analysis (HACA) is the most com-mon approach and explains the similarity relationship betweenany one sample and the entire data set in a dendrogram (tree dia-gram). In this study, HACA was employed to group the samplingsites by content of PAHs compounds in aquaculture sedimentsand for identification of possible sources of PAHs. To achieve theseobjectives HACA was performed on normalised data with columnsrepresenting 24 PAHs compounds and rows representing samplingsites using Ward’s method with a squared Euclidean distance asthe linkage distance as a measure of similarity of the PAHs. Togroup the similar sampling sites (spatial variability) cluster rowwas performed while similarity among individual PAHs com-pounds for source identification was determined by cluster col-umn. The linkage distance is reported as Dlink/Dmax whichrepresent the quotient between the linkage distance for a particu-lar case divided by the maximal distance multiplied by 100 as away to standardise the linkage distance represented on y-axis(Singh et al., 2005).
2.4.2. Discriminant analysis (DA)For this study DA was applied to validate the results of CA anal-
ysis. DA determines the variables that discriminate between two ormore naturally occurring groups/clusters. It constructs discrimi-nant factors (DFs) for each cluster using the following equation:
f ðGiÞ ¼ ki þXn
j¼1
wijPij ðKannel et al:; 2007Þ ð4Þ
where i is the number of groups (G), ki the constant inherent toeach group, n the number of parameters used to classify a set ofdata into a given group and wj is the weight coefficient assignedby DF analysis to a given parameter Pj.
DA was used to construct DFs to evaluate the spatial variationsin the aquaculture sediments based on three different modes: stan-dard, forward stepwise and backward stepwise. The standard DAconstructs DFs using all variables. In the forward stepwise mode,the variables are added one by one, starting with the most signifi-cant, until no significant changes are attained. In the backwardstepwise mode, the variables are removed one by one, startingwith the least significant, until no significant changes are attained(Kannel et al., 2007; Al-Odaini et al., 2011; Osman et al., 2011). Inthis study, DA was applied to evaluate whether the clusters
A. Retnam et al. / Marine Pollution Bulletin 69 (2013) 55–66 59
differed with regard to the mean of a variable (PAHs) and to predictgroup membership using that variable. DA was performed on rawdata matrix with the columns containing all the measured PAHscompounds as independent variable and rows containing thegroups from CA as dependent variable.
2.4.3. Source apportionment using principle component analysis (PCA)and multiple linear regression (PCA–MLR)
PCA and MLR are the most frequently used receptor models inapportionment of PAHs in environmental samples. Firstly PCAwas applied to reveal the nature of hypothetical factors that ex-plain most of the data variance. PCA is the most powerful unsuper-vised pattern recognition technique that attempts to explain thevariance of a large set of inter-correlated variables and transformthem into a smaller set of independent (uncorrelated) variablescalled principle components (PCs). PCA also provides informationon the most significant parameters that describe the entire dataset by excluding the less significant parameters with a minimumloss of original information (Singh et al., 2005; Kannel et al.,2007). The PCs can be expressed as:
kij¼ ai1x1jþai2x2jþai3x3jþ���þaimxmj ðKannel et al:; 2007Þ ð5Þ
where k is the component score, a is the component loading, x isthe measures value of the variable, i is the component number, j isthe sample number and m is the total number of variables.
In this study, PCA was applied to the aquaculture sediment dataset to exclude insignificant data. This analysis is based on eigen-value criteria whereby a value >1 is considered significant, and anew group of variables was created based on the resemblance ofthe entire data set (Osman et al., 2011). The PCs generated byPCA are sometimes not readily interpreted; thus, it is advisableto rotate the PCs by varimax rotation to obtain new groups of vari-ables called varimax factors (VFs). The correlation between the VFsand the original variables is given by the factor loadings, while theindividual transformed observations are called factor scores (Vegaet al., 1998). The VF coefficients having a correlation >0.75 are con-sidered ‘strong’, correlations in the range of 0.74–0.50 are consid-ered ‘moderate’ and those in the range of 0.49–0.30 areconsidered ‘weak’ significant factor loadings (Liu et al., 2003). Forthis purpose, factor loading >0.75 both positive and negative wereconsidered.
Source apportionment was performed on the whole set of data.PCA performed after varimax rotation of the normalised data con-taining 24 PAHs compounds in columns and sampling sites in rowsto obtain VFs. The profile of factor loadings and specific indicativePAHs species were used to deduce the factors obtained and to iden-tify the possible sources of PAHs (Zuo et al., 2007).
Quantitative contribution of the various identified sources wasdetermined based on MLR of the PCA factor scores (APCS). APCS–MLR model is based on the assumption that the total concentrationof each contaminant is made up of the linear sum of the elementalcontribution from each pollution component collected. Source con-tributions was calculated after grouping the 24 PAHs compoundsin this study into number of factors and identify the possiblesources by PCA. The contribution from each factor was estimatedusing MLR using APCS values as independent variables and mea-sured total PAHs as dependent variables. The quantitative contri-butions of each source of individual contaminants werecompared with their measured values. The basic equation of thismodel is:
Y ¼ b0 þ b1x1 þ b2x2 þ � � � þ bp�1xp�1 þ e ðJuahir et al:; 2011Þ ð6Þ
where Y is the response variable and there are p � 1 explanatoryvariables x1, x2, . . .,xp�1 with p parameters (regression coefficients)b0, b1, b2, . . .,bp�1 and e as the random error.
The performance of the MLR model was assessed using correla-tion coefficient R2, adjusted correlation coefficient R2, Akaike’sInformation Criteria (AIC) and Schwarz Bayesian Criteria (SBC).The best model performance occurs when the R2, adjusted R2, AICand SBC values are close to unity. The R2 value explains the vari-ance in Y that is accounted for by the regression model, whilethe adjusted R2 value indicates the loss of predictive power orshrinkage. Meanwhile, the AIC and SBC estimate the loss of accu-racy. The small difference in AIC and SBC signify the MLR modelfit for the prediction of total PAHs (Aertsen et al., 2010; Juahiret al., 2011).
All statistical analyses were performed using XLSTAT2012 forWindows.
3. Results and discussion
3.1. Spatial distribution of PAHs in aquaculture sediments
The concentrations of individual and total PAHs are presentedin Table 2. The total concentration of PAHs in the surface sedimentsfrom aquaculture environments ranged from 20.4 to 1841 ng/g dwwith a mean of 363 ng/g dw. Fig. 3 shows the spatial distribution ofPAHs in the aquaculture sediments considered in this study. Thehighest concentration of PAHs was found in Teluk Jawa locatedat Masai, an urbanised and industrialised area in the State of Jo-hore. This area is located in between Johor Bahru and Pasir Gudang,both of which are urbanised and highly industrial areas in JohorState, with Pasir Gudang representing an important industrial zonein Malaysia. Furthermore, the land-link of the causeway betweenMalaysia and Singapore acts as a physical barrier for seawater ex-change across the causeway, causing a notably high concentrationat this site. The lowest concentration (20.4 ng/g dw) was observedin sediments from Kuala Setiu. This sampling site is located at Kua-la Setiu of Terengganu on the east coast, which is a relatively lesspolluted environment. An independent t-test showed that thePAHs levels in fish farms are significantly different from those inoyster farms (p < 0.05). The significant difference could be duethe additional input of PAHs at the fish farm associated with fishfeed and excretion. High level of PAHs detected in fish feed col-lected from fish farms ranging from 150 to 366 with average264 ng/g dw. A strong positive correlation (R2 = 0.717) was ob-served between PAHs in aquaculture sediment and PAHs in fishfeed suggesting fish feed as an additional sources of PAHs intothe aquaculture sediments.
The level of PAHs in the aquaculture sediment in Malaysia wascompared to that in other places around the world. The total PAHsconcentration in this study (20–1841 ng/g dw) was higher than theconcentrations measured at a salmon fish farm in Canada (<1–45 ng/g dw) (Hellou et al., 2005), at freshwater fish ponds in China(62–196 ng/g dw) (Kong et al., 2005) and at a main marine aqua-culture area of Guangdong China (42–158 ng/g dw) (Yan et al.,2009) and comparable to a marine fish farm in China (123–947 ng/g dw) (Wang et al., 2010) and a salmon fish farm in Canada(511–2736 ng/g dw) (Sather et al., 2006). Overall, the total PAHs inaquaculture sediments in Peninsular Malaysia ranged from low tomoderately high.
The total level of PAHs in all of the sediment samples was lowerthan the Dutch target value of 1000 ng/g dw set by the NetherlandsMinistry of Housing (Netherland Ministry of Housing, 2000). How-ever, the Teluk Jawa sediment samples nearly exceeded this value,with a total of 10 PAHs (NAP, PHE, ANT, FTH, BaA, CHR, BkF, BaP,IcdP and bghiP) at a concentration of 994 ng/g dw. In all surfacesediments, the HMW PAHs (4- to 6-ring PAHs) were found to bethe most abundant, contributing approximately 15–83% of thetotal PAHs. The high concentrations of 4- and 5-rings PAHs in
Table 2The concentration of PAHs (ng/g dw) in aquaculture surface sediments.
PAHsa Total sample Oyster farm sediment Fish culture sediment
c 16USEPA = sum of the concentration of Naphthalene + Acenaphthylene + Acenaphthene + Fluorene + Phenanthrene + Anthracene + Fluoranthene + Pyrene + Ben-za(a)anthracene + Chrysene + Benzo(bk)fluoranthene + Benzo(a)pyrene + Indeno(123-cd)pyrene + Dibenzo(ah)anthracene + Benzo(ghi)perylene.
d cPAHs = sum of the concentration of Benza(a)anthracene + Chrysene + Benzo(bk)fluoranthene + Benzo(a)pyrene + Indeno(123-cd)pyrene + Dibenzo(ah)anthracene.e 2–3 Rings = sum of the concentration of Naphthalene + 1 methylnaphthalene + Acenaphthylene + 1,2 dimethylnaphthalene + Acenaphthene + Fluorene + Dibenzothio-
phene + Phenanthrene + Anthracene + 2 methylphenanthrene + 2 methylanthracene + 3,6 dimethylphenanthrene.f 4 Ring = sum of the concentration of Fluoranthene + Pyrene + Retene + Benza(a)anthracene + Chrysene.g 5–6 Rings = sum of the concentration of Benzo(bk)fluoranthene + Benzo(a)pyrene + Indeno(123-cd)pyrene + Dibenzo(ah)anthracene + Benzo(ghi)perylene.
BL GP JT KS KU MT PB PP SM TJ0
500
1000
1500
2000
2500
Tot
al P
AH
s (n
g/g
dw)
sampling sites
Fig. 3. Spatial distribution of PAHs in aquaculture sediments.
SM BL PP MT
PB KS TJ
JT GP
KU
0
50
100
150
200
250
300
350
400
Dlin
k/D
max
*100
Clu
ster
1 :
M
Clu
ster
2 :
L
Cls
uter
4 :
H
Clu
ster
3 :
MH
Fig. 4. Dendrogram showing the different clusters of the sampling sites of surfacesediments at aquaculture sites (clustering significance level = 0.25Dmax). M = mod-erate pollution, L = low pollution, H = high pollution, MH = moderately highpollution.
60 A. Retnam et al. / Marine Pollution Bulletin 69 (2013) 55–66
A. Retnam et al. / Marine Pollution Bulletin 69 (2013) 55–66 61
aquaculture sites may be due to pyrolytic activity (Zhang et al.,2004). Furthermore, the high-ringed PAHs are less biodegradable,making them highly abundant in the sediments. The USEPAcarcinogenic PAHs (cPAHs – BaA, CHR, BbkF, BaP, BahA and IcdP)concentrations ranged from not detected (ND) to 645 ng/g dw,with a mean of 109 ng/g dw. BaP, which is considered the mostcarcinogenic of the cPAHs (USEPA, 2003), ranged from ND to107 ng/g dw. The highest and lowest concentrations werefound in sediments collected from Teluk Jawa and Merchang,respectively.
3.2. Spatial similarities of the sampling sites
To evaluate the similarity of sampling sites HACA was em-ployed. The output from HACA revealed the sampling sites havingsimilar PAHs contribution were clustered together. This resulted ingrouping of sampling sites into four clusters/groups (Fig. 4).
The levels of pollution by PAHs can be classified according tothe range of detected concentrations. A concentration <100 ng/gis classified as low pollution (L), 101–1000 ng/g is classified asmoderate pollution (M), 1001–5000 ng/g is classified as high pollu-tion (H) and >5001 ng/g is classified as very high pollution (VH)(Baumard et al., 1998b). Moderately high pollution (MH) is catego-rised by a concentration >500 ng/g (Wang et al., 2010). Based onclusters obtained from CA, the sampling sites in this study can begrouped according to these pollution levels. Cluster 1 representsmoderate PAHs pollution at Sungai Muar in Johor. Cluster 2 repre-sents low PAHs pollution at Batu Lintang Kedah, Pasir Panjang atNegeri Sembilan, Pulau Betong at Pulau Pinang and Merchangand Kuala Setiu at Terengganu. Cluster 3 represents moderatelyhigh PAHs pollution at Jelutong of Pulau Pinang, Gelang Patahand Kukup in Johor, and Cluster 4 represents high PAHs pollutionof at Teluk Jawa in Johor. The clusters generated were highly con-vincing, as the sites in the groups have similar characteristics andsampling backgrounds. The low pollution sites are generally lo-cated in remote areas, while the moderately polluted area of SMis located in the urban and industrial zone of Muar Town, withthe Tanjung Agas industrial area located adjacent to the aquacul-ture area. Meanwhile, the moderately high pollution sites of Jelu-tong are located in the highly urbanised and industrial zone of
Fig. 5. Plot of discriminant functions for PAHs based on pollution loadings. M = modera
Penang Island, Gelang Patah is located near the busy second linkhighway close to the Straits of Johor and Kukup is located at thebusy waterway of the Straits of Malacca and Tanjong Pelepas Port.The Teluk Jawa site meeting the criteria for high pollution is lo-cated between the urbanised and highly industrial zone of JohoreBaharu and Pasir Gudang and opposite of Sembawang Port inSingapore.
To further assess the spatial variations of PAHs among thedifferent sampling sites, DA was applied to the data set aftergrouping the sites into the four clusters obtained by cluster analy-sis. The clusters were treated as dependent variables, and the mea-sured PAHs compounds were treated as the independent variables.The DA results showed a clear separation of pollution loading(Fig. 5).
To explore the discriminating variables, the data were subjectedto standard, forward and backward stepwise DA (Table 3). Thestandard mode DA was able to discriminate the sampling sites to95.9% accuracy with 24 discriminant variables, while the forwardand backward stepwise modes had an accuracy of 92.9% with eightdiscriminant variables and 94.9% with 12 discriminant variables,respectively. Forward stepwise DA showed that NAP, ACY, ACE,36DMPHE, RET, BbkF, PER and IcdP were the significant variables,while the backward stepwise mode showed NAP, ACY, ACE, FLU,2 MANT, 36DMPHE, RET, CHR, BbkF, PER, IcdP and DahA werethe significant variables (p < 0.001). Thus, the DA results suggestthat forward stepwise DA with eight PAHs compounds (NAP,ACY, ACE, 36DMPHE, RET, BbkF, PER and IcdP) was able to discrim-inate the spatial variation of PAHs concentrations in the aquacul-ture sediments with more than 90% accuracy. This findingindicates that these variables have a high degree of variation interms of their spatial distribution; thus, DA rendered a consider-able data reduction. Box and whisker plots of the discriminatingvariables identified by forward stepwise DA were plotted to evalu-ate the different patterns associated with the spatial variation inthe aquaculture sites (Fig. 6). The increasing trend for NAP, ACY,ACE, 36DMPHE, RET, BbkF and IcdP from low to high pollution sitessuggests increasing input of anthropogenic sources into aquacul-ture sediments. On the other hand, PER showed smaller variationin median between sampling sites suggesting digenesis as the mostpossible sources of PAHs into the aquaculture sediments.
te pollution, L = low pollution, H = high pollution, MH = moderately high pollution.
Table 3Classification matrix for the discriminant analysis of the spatial variation of the PAHsin the aquaculture surface sediments.
Sampling site classification % Correct Sampling sites assigned by DA
62 A. Retnam et al. / Marine Pollution Bulletin 69 (2013) 55–66
3.3. Source identification of PAHs
In this study, the PAHs source identification was performedusing molecular indices, cluster analysis and principle componentanalysis.
3.3.1. Molecular indicesMolecular indices, such as ANT/ANT + PHE, FTH/FTH + PYR, BaA/
BaA + CHR, and IcdP/IcdP + BghiP, are widely used in PAHs sourceidentification. In this study, two molecular ratios, ANT/ANT + PHEand IcdP/IcdP + BghiP, were chosen to distinguish the possiblePAHs sources in aquaculture sediments mainly due to definitive ra-tio interpretations (Yunker et al., 2002). In this study, the site-spe-cific isomer ratios values for ANT/ANT + PHE and Icd/IcdP + BghiPshown in Table 4. Mean ratio of Ant/ANT + PHE ranging from0.06 to 0.25 in all sampling sites. From the molecular indices, aqua-culture sites Pasir Panjang, Sungai Muar, Kuala Setiu, Merchangand Gelang Patah are contaminated by petroleum, and Batu Lin-tang, Pulau Betong, Jelutong, Teluk Jawa and Kukup are contami-nated by combustion products. For more a specific identificationof the contamination sources, the isomer ratio IcdP/IcdP + BghiPwas used to distinguish among petroleum, liquid fossil fuel com-bustion (vehicle and crude oil) and biomass burning (wood, coaland grass). The mean ratio of IcdP/(Icdp + BghiP ranging from0.29 to 0.52 in all sampling sites. The Teluk Jawa and Gelang Patahsites had values >0.5, which might be impacted by biomass com-bustion. The other sites had values between 0.3 and 0.5, indicatingpetroleum combustion as the possible source of the PAHs. The vari-ations in the PAHs sources indicated by these indices could be dueto the different sampling sites with different hydrological and spa-tial variations (Chen et al., 2012). These findings suggest that thesources of PAHs in the aquaculture surface sediments could arisefrom mixed sources, with petroleum-related contamination asthe dominant source.
3.3.2. Cluster analysis (CA)In this study, HACA was performed to identify the homoge-
neous groups of individual PAHs. Fig. 7 displays the results of thecluster analysis in a hierarchical dendrogram displaying three dis-tinct clusters. Individual PAHs having similar chemical characteris-tics are clustered into the same group. For example cluster one wascomposed of NAP, alkylated NAP, FLU, ACY and ACE consisting all 2
ring PAHs, representing unburned fossil fuels that may have origi-nated from petroleum (Luo et al., 2008). Cluster 2 having 3–4 ringPAHs could be subdivided into two sub-clusters. The first sub-clus-ter consisted of 2 MANT, RET and CHR, while the second sub-clus-ter contained PHE, alkylated PHE, ANT and DBT. Retene is thepredominant PAHs produced by coniferous wood combustion(Ramdahl, 1983; Abas and Simoneit, 1996; Fang et al., 1999; Sim-cik et al., 1999; Zuo et al., 2007). As such the high abundance ofsedimentary RET in tropical country like Malaysia is very contro-versial. The source of RET in the aquaculture sediment is discussedin detail in Section 3.3.3. The third cluster consisted of 4–6 ringPAHs FTH, PYR, DahA, BaA, IcdP, BEP, BaP, BbkF, BghiP and PER,which are mostly high-molecular-weight 5- and 6-ring PAHs. ThisHMW PAHs indicates combustion as the main source mainly fromvehicle emission except for PER. In tropical environment, PER insediments coming from natural source due to transportation ofterrestrial soil materials into the aquatic environment (Boonyatu-manond et al., 2006). These soils contains PER precursors, perylen-equinone undergo early diagenesis to form PER (Boonyatumanondet al., 2006). A small percentage of petroleum and combustion ori-gin also found to contain PER (Fang et al., 2003; Luo et al., 2008).Based on the finding of this study, aquaculture sediments couldbe impacted by both natural and combustion sources which clusterPER with other HMW PAHs. PER has been detected in marine sed-iment, estuarine and riverine sediments with wide range of con-centration ranging from 100 to 4000 ng/g (Fang et al., 2003). Thepercentage of PER over
PPAHs in the range of 1–4% has suggested
of combustion sources and percentage of PER overP
5-ring PAHsfor concentration of >10% indicate probable natural source (Fanget al., 2003). Table 4 shows the percentage of PER over
PPAHs
and percentage of PER overP
5-ring PAHs. The percentage of PERover
PPAHs at all sites indicating natural origin except for BP, TJ
and JT. The higher percentage of PER overP
5-ring PAHs in all sam-ples except JT further indicating natural source of PER. The low per-centage of PER over
P5-ring PAHs at JT might be due to
combustion source or absent of natural precursor of PER at this site(Luo et al., 2006).
3.3.3. Source apportionment by principle component analysis (PCA)and multiple linear regressions (PCA/MLR)
To further explore the possible sources of PAHs, PCA was ap-plied to the aquaculture surface sediments. PCA revealed three fac-tors responsible for 89% of the total variation of PAHs in theaquaculture surface sediments of Peninsular Malaysia (Table 5).Varimax factor 1 (VF1) is responsible for 41.1% of the total varia-tion. VF1 is heavily loaded with 4- to 6-ring PAHs, including FTH,PYR, BaA, BbkF, BEP, BaP, IcdP, DahA and BghiP, similar to the clus-ter analysis of group 3. This composition is a typical marker forvehicular combustion (Larsen and Baker, 2003). According to Kha-lili et al., gasoline combustion was shown to predominantly pro-duce NAP, FLU, BEP, ACY, PYR (Khalili et al., 1995). Meanwhile,BghiP, BkF and Coronene have been identified as tracers for gaso-line engines, with IcdP and PYR found in both diesel- and gaso-line-powered engines (Larsen and Baker, 2003). Because dieseland gasoline emissions could not be differentiated through PCAin this study, VF1 was classified as a vehicular emission. Vehicularemissions are the most dominant source of PAHs among Malaysianatmospheric aerosols because almost 74% of the air pollutioncomes from vehicle exhaust (Omar et al., 2002, 2006). The PAHssources from vehicle emissions were estimated to range from65% to 75% in the air (Okuda et al., 2002).
VF2 is responsible for 24.4% of the total variance and is heavilyloaded with the 2-ring PAHs of NAP and alkylated NAP, corre-sponding to Cluster 1 of the HACA. Uncombusted petroleum isknown to be high in alkyl PAHs compared to parental PAHs (Simciket al., 1999). Other 2- and 3-ring PAHs with moderate loading
Fig. 6. Box and whisker plots of some of the PAH compounds separated by spatial DA in aquaculture sediments. Refer to Table 2 for the abbreviation of PAHs. H = highpollution, L = low pollution, M = moderate pollution, MH = moderately high pollution.
A. Retnam et al. / Marine Pollution Bulletin 69 (2013) 55–66 63
included ACY, ACE, FLU, PEH and ANT, suggesting a combustionsource. The emissions from oil-burning power generationplants are characterised by 2- and 3-ring PAHs, specifically
methylnaphthalenes and phenanthrenes (Larsen and Baker,2003). Based on these references, VF2 is classified as a petroleumoil product. The major source of petroleum oil pollution in the
Table 4Site-specific molecular indices of PAHs and their possible sources.
a Source: Yunker et al. (2002), Liu et al. (2009), Yan et al. (2009) and Chen et al. (2012).b Source: Fang et al. (2003);
P5-ring PAHs (BbkF + BEP + BaP + PER + DahA).
PERFTHPYR
DahABEPBaP
BbkFBghiP
BaAIcdPANTDBTPHE
2MPHE36DMPHE
RET2MANT
CHRNAP
1MNAP12DMNAP
ACEACYFLU
0 20 40 60 80 100 120 140
Dlink/Dmax*100
cluster 2
cluster 1
cluster 3
Fig. 7. Hierarchical dendrogram for the individual PAHs in aquaculture sedimentsbased on Ward’s method of linkage and the squared Euclidean distance as themeasure intervals (clustering significant level = 0.58Dmax).
Table 5Loadings of PAHs after varimax rotation for aquaculture sediments (high loadings>0.75 shown in bold) and relationship between
64 A. Retnam et al. / Marine Pollution Bulletin 69 (2013) 55–66
Malaysian environment is derived from the dumping of usedcrankcase oil and the washout of oil due to strong storms in a trop-ical countries (Zakaria et al., 2002; Sakari et al., 2008). However,the same pattern was not observed in Thailand, which has a similarclimate. Thailand has advanced environmental regulations to con-trol the disposal of used crankcase oil, which prevents it from beinga significant source of PAHs (Boonyatumanond et al., 2006).
In group three, VF3 contributed to 23.5% of the total variance.This group is dominated by alkylated PHE and RET with moderateloading on PHE, FLU and DBT, similar to Cluster 2 obtained in theHACA. The presence of alkylated PHE and RET is associated withcoal combustion (Simoneit, 2002), while RET is mostly linked towood combustion. RET is derived from diterpenoids resin acidand abietic acid, composition of conifers resin (Ramdahl, 1983;Fang et al., 1999; He et al., 2010). Sedimentary RET found in lakesand oceans mainly due to natural degradation of abietic acid (Ram-dahl, 1983) and pulp and paper mill effluents (Mandalakis et al.,2004). In this study RET detected as one of the major sedimentaryPAHs comparable to some parental PAHs such as FTH and PYR. InMalaysia PAHs originating from biomass combustion fromdomestic and garden wastes through uncontrolled combustion
and smoke from regional forest fires (Omar et al., 2002, 2006).RET or it’s precursors not produced during combustion of tropicalwoods and as such would not contribute to VF3 (Abas et al.,1995; Fang et al., 1999), The large scale biomass burning in Indone-sia caused haze episode in Malaysia with high levels of particulateorganic matters containing aliphatic hydrocarbons and PAHs. Dur-ing the earlier haze period in 1991, biomarker dehydroabietic acidwas reported and the source was indicated from some importedwood combustion (Abas and Simoneit, 1996). On contrary during
A. Retnam et al. / Marine Pollution Bulletin 69 (2013) 55–66 65
1997 haze episode Fang et al. (1999) reported neither RET nordehydroabietic acid were detected in the air samples contrary toAbas et al. (2004) who detected dehydroabietic acid during nighttime. Interestingly in neighbouring country Singapore, higher levelof PAHs detected during October 2006 compared to other monthswith 2–3 times higher at night time (He et al., 2010). During themonth of October hot spots were observed through satellite imagesin Sumatra Island indicating forest fire. RET was detected at signif-icantly higher level in October suggesting atmospheric RET in Sin-gapore is influenced by long-distance biomass combustion sourcesin Indonesia (He et al., 2010). The same influence can be suggestedfor Malaysia since Singapore is located relatively close by and thedetection of RET precursor in Malaysia air samples may supportthe source of RET in aquaculture sediments could be coming frombiomass burning in Indonesia. As such PAHs sources for VF3 can bededuced as long-distance biomass burning. Application of CA andPCA successfully apportion PAHs in aquaculture sediments intothree major source i.e. vehicle emission, petroleum oil and biomasscombustion for very first time.
Quantitative assessment of PAHs widely achieved throughreceptor model PCA/MLR. The main aim of PCA–MLR is to deter-mine the percent contribution of different PAHs sources in a givenrepository. In this study, the factor scores from PCA for VF1–VF3representing vehicles emissions, oil combustion and wood com-bustion as independent variables were regressed against the totalsum of 24 PAHs, total PAHs, as the dependent variable. The equa-tion obtained was:X
PAHs ¼ 320þ 327vehicleþ 223oilþ 54wood ð7Þ
The model performance is presented as the R2 and adjusted R2.The R2 value obtained was 0.826 (p < 0.001), which means that 82%of the variability of the PAHs could be explained by the three fac-tors and that the remainder of the variability was due to effect ofother explanatory variables that were not included in this analysis.The small difference between the R2 and adjusted R2 values (0.826and 0.821, respectively) and the Akaike’s Information Criteria (AIC)and Schwarz Bayesian Criteria (SBC) values of 1029 and 1040,respectively, indicated the good fit of the model. The standardisedcoefficients (b) of the model presented in Table 5 indicate the rel-ative influence of the VFs. Vehicle emissions have the greatestinfluence on the
PPAHs (b = 0.744), followed by oil (b = 0.508)
and biomass (b = 0.123), and this model can be written as Eq. (7).The b values describe the relative relationship between
PPAHs
and each VF. Positive b values indicate positive relationship be-tween VFs and
PPAHs.
The major contribution of the PAHs sources in surface sedimentof aquaculture sites came from vehicular emissions (54%), followedby oil (37%) and biomass combustion (9%). Overall vehicle emis-sions (VF1) and oil (VF2) contribute over 90% of the total PAHs con-centration in the aquaculture sediments, and both of these sourcesare petroleum related.
4. Conclusion
The total PAHs concentrations in aquaculture sediments in Pen-insular Malaysia ranged from 20 to 1841 ng/g dw, with an averageof 363 ng/g dw. The sediment is dominated by HMW PAHs, indicat-ing pyrogenic sources. This result is important for food productionin these aquatic systems due to the carcinogenic nature of thesePAHs. The combination of CA and DA was able to group and dis-criminate the aquaculture sites according to the contaminationlevels. The combination of CA and PCA was effective for identifyingthe PAHs sources. Both chemometric methods showed vehicle, oiland wood combustion as the main sources in aquaculture surfacesediments. Application of ACPS/MLR further confirmed that the
contributions of PAHs from vehicle, oil and wood combustion were54%, 37% and 9%, respectively. The levels of PAHs in this study iscomparable to other places around the world and it is lower thanthe Dutch guideline values for sediment.
Land-based pollution from vehicle emissions is the predomi-nant reason for PAHs pollution in aquaculture sediments in Penin-sular Malaysia. Vehicle emissions contain some of the most toxicPAHs, such as BaP. Therefore, there should be some control to min-imise the entry of these materials into aquaculture areas in thefuture.
Acknowledgements
The author would like to thank MOSTI for providing the schol-arship and FRGS No. 5524013 for providing financial support forthis research. The authors would also like to thank Mr. AdamuMusthapa for his constructive comments on the manuscript.
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