Levels, distribution, and risk assessment of organochlorines in surficial sediments of the Red Sea coast, Egypt

Levels, distribution, and risk assessment of organochlorinesin surficial sediments of the Red Sea coast, Egypt

Ahmed El Nemr & Abeer A. Moneer &

Azza Khaled & Amany El-Sikaily

Received: 19 February 2012 /Accepted: 24 September 2012 /Published online: 7 October 2012# Springer Science+Business Media Dordrecht 2012

Abstract The analyses of environmentally persistentpollutants like polychlorinated biphenyls (PCBs), hex-achlorocyclohexane (HCH) isomers, and dichlorodi-phenyltrichloroethane (DDT) and its metabolites insurficial sediment samples collected from 17 locationsalong with the coast of the Red Sea in Egypt werecarried out using gas chromatography–mass spectrom-etry. Several potential organic contaminants from ag-ricultural (e.g., DDT and its breakdown products,lindane, endrin, dieldrin, and endosulfan) and indus-trial (PCBs) sources were measured. The levels of 20organochlorine pesticides (OCPs) and ten PCB con-geners in sediment collected from 17 stations along~1,200 km were investigated. Concentrations ofPCBs, HCHs, DDTs, and cyclodienes ranged from0.40 to 6.17, 0.01 to 0.09, n.d. to 0.46, and 0.08 to0.90 ppb dry weight. Two statistical programs wereapplied on the data (principal component analysis,PCA, and cluster analysis, CA), and it was concludedthat it is impossible to predict the distribution patternsof the OCPs in a contaminated area. Risk assessment

of the organochlorines contaminated in the sedimentsof the studied area was investigated.

Keywords Pesticides . HCHs . DDTs . Cyclodienes .

PCBs . Red Sea . Sediment

Introduction

Organochlorine pesticides (OCPs) and substances likepolychlorinated biphenyls (PCBs) are man-made com-pounds which have become widespread environmen-tal pollutants and food contaminants on a global scale(Wania and Mackay 1996). Many of the active ingre-dients of OCP formulations and their metabolites, aswell as congeners of chlorobiphenyl and chloronaph-thalene, are highly persistent chemicals in marineenvironments and are toxic, bioaccumulate, and bio-magnify in marine food webs (Falandysz et al. 2001;Zhao et al. 2010a). Many OCPs were used on globalscales from the 1950s to the mid1980s, most of whichare stable and persistent in the environment. Usage ofOCPs has been prohibited in most countries (Barra etal. 2001; Turgut 2003).

Where the OCPs pose great risks to ecosystems andhuman health, with low water solubility and highhydrophobicity, so OCPs are readily sorbed onto sus-pended particulate matter and subsequently depositedinto river and marine sediments (Yang et al. 2005; Quet al. 2011; El Nemr et al. 2012a). Sediments are oneof the important repositories of OCPs, where they can

Environ Monit Assess (2013) 185:4835–4853DOI 10.1007/s10661-012-2907-3

A. El Nemr (*) :A. A. Moneer :A. Khaled :A. El-SikailyEnvironmental Division, National Institute ofOceanography and Fisheries,Kayet Bey, El-Anfoushy,Alexandria, Egypte-mail: [email protected]

A. El Nemre-mail: [email protected]

be resuspended and release bound OCPs from par-ticles into water under favorable conditions, whichresult in second contamination of water (Chau 2005).Through interaction between sediment and water,transfer of OCPs from sediments to organisms isnow regarded as a major route of exposure for manyspecies (Zoumis et al. 2001). Consequently, the resi-dues of OCPs in sediments can serve as a useful indexof pollution and potential environmental risks (Sun etal. 2010; Qu et al. 2011). Coastal sediments are actingas temporary or long-term sinks for many classes ofanthropogenic contaminants and, consequently, act asthe source of these substances to the ocean and biota(Kilemade et al. 2004; El Nemr et al. 2012b). OCPsenter the marine environment from agricultural culti-vations, drainage, runoff, and atmospheric deposition(Gil and Vale 1999), so estuaries are one of the mainreservoirs of pesticides. Sediments are one of the bestmedia for monitoring organic compounds as they pro-vide a valuable record of contamination in aquaticenvironments (Chang and Doong 2006). Distributionof various chlorinated hydrocarbons in marine andestuarine environment depends on residues of highlypersistent OCP physicochemical properties of the eco-system as well as partition coefficients of individualchlorinated hydrocarbons. The particulate matter aswell as various species of organisms which generallyinteract with persistent chlorinated hydrocarbons ulti-mately sinks, thereby enhancing the concentration ofthe pollutants in the bottom sediment.

As the OCPs have a great affinity for particulatematter, so they can be deposited in the aquatic systemsduring sedimentation and can remain in sediments forvery long due to their long half-life times. Importantfactors controlling sorption of OCPs to sediments aremainly surface area and organic matter contents, apartfrom other physicochemical properties like pH, cationexchange capacity, or ionic strength (Delle Site 2001;Yang et al. 2010).

OCPs have been used substantially in Egypt forcontrol of agricultural pests. Although the usage ofPCBs in Egypt is unknown, past usage of these sub-stances in transformers, electrical equipment, shippainting, and other industries has been common. Theaim of the present work is to investigate the distribu-tion and the risk assessments of different OCPs andPCBs in the surface sediments collected from 17 loca-tions along the Red Sea coast and their correlationswith the total organic carbon (TOC) of the sediments

and to study the possible sources of these pollutants.The importance of this study is its evaluation of thepollution status of the Egyptian Red Sea coast, whichis covered by the selected 17 locations that representthe expected most polluted area due to area develop-ment and human activities near these locations. Therisk assessment done in this study is the evaluation ofthe impact of pollutants contaminating the sedimentdue to human stay near these locations or the touristlocations located within the studied area.

Materials and methods

Sampling

The 17 sampling stations were located along approx-imately 1,200 km of the Egyptian Red Sea coast, fromShalatin to Taba (Fig. 1). The surface sediment sam-ples were collected during May 2009 with a van Veengrab (0–5 cm) and layers were carefully taken to avoiddisturbance. The upper 5-cm layer was selected be-cause it is more biologically and chemically activethan deeper layers, and exchanges of substances be-tween sediment and water occur in this layer. Imme-diately after collection, samples were placed inaluminum bags, refrigerated, and transported to thelaboratory. Samples were dried in an oven at 105 °Cto constant weight and sieved to separate stones andshells, lightly ground in an agate mortar for homoge-nization, and prepared for analysis.

Grain size analysis, total carbonates, and TOC

The samples were collected from 17 different regions.The sediment samples were spread over a glass sheetand left to dry in air. After drying, the samples werethen disaggregated with fingers and split into portionsby the cone and quarter technique. Part of the sedimentwas washed and dried at 105 °C for mechanical anal-ysis. Another part of the unwashed samples was driedand grounded to pass through 63-μmSieve for chemicalanalysis.

Grain size analysis was determined according toFolk (1974). Total carbonates were estimated as de-scribed by Molnia (1974). TOC was determinedaccording to the method described by Walkely andBlack (1934). Total organic matter was calculated bythe following equation:

4836 Environ Monit Assess (2013) 185:4835–4853

TOM% ¼ TOC%� 1:8:

Statistical analysis

Principal component analysis (PCA) is widely used toreduce data (Loska and Wiechula 2003; El Nemr et al.2006; 2007; 2012c; Khaled et al. 2010) and to extracta small number of latent factors for analyzing relation-ships among the observed variables. PCA was, there-fore, applied to the correlation matrix and withvarimax-normalized rotation. Cluster analysis (CA)was performed to further classify elements of differentsources on the basis of the similarities of their chem-ical properties. As the variables have large differencesin scaling, standardization was performed before com-puting proximities, which can be done automaticallyby hierarchical CA procedure. A dendogram was

constructed to assess the cohesiveness of the clustersformed, where correlations among elements can read-ily be seen. In the present study, SPSS for Windows,version 19, was utilized for the multivariate analysisand for correlation analysis.

Pollutant extraction

Dry sediment was homogenized and 20 g was analyzedfor organochlorine pollutants followed by well-established techniques (UNEP/IOC/IAEA, 1989; 1991).Sediment (20 g of dry weight) was then transferred to aprecleaned extraction thimble and extracted with n-hex-ane/dichloromethane [(1:1), 300 ml] for 8 h in a Soxhletapparatus cycling five to six times per hour. Thimble wasextracted in the same fashion as the sample and used asthe blank and its value was subtracted from the results.The extracted solvents were concentrated with a rotaryevaporator down to about 15 ml (maximum temperature:40 °C) and then concentrated to 1 ml under a gentlestream of pure nitrogen gas. The remaining extract wastransferred to the top of a glass column (50 ml) packedwith 20 g Florisil followed by elution with 70 ml ofhexane for PCB congener fraction (F1). Then, the columnwas eluted with 60 ml of mixture containing 70 % ofhexane and 30 % of dichloromethane for the pesticidefraction (F2). Activation of Florisil was achieved byheating at 130 °C for 12 h, followed by partially deacti-vating with 0.5 %water by weight and storing in a tightlysealed glass jar with ground glass stopper and the mixturewas allowed to equilibrate for 1 day before use.

Each fraction was concentrated and injected into aCLASS-GC10 gas chromatograph (Shimadzu, Japan)equipped with a 63Ni electron capture detector. A fused-silica capillary column (30 m×0.32 mm×0.52 μm) coat-ed with DB-1 (5 % diphenyl and 95 % dimethyl poly-siloxane) was used for quantification. The oventemperature was programmed from an initial temperatureof 70 (2-min hold) to 280 °C at a rate of 5 °Cmin−1, andwas then maintained at 280 °C for 20 min. Injector anddetector temperatures were maintained at 270 °C and300 °C, respectively. Helium was used as the carrier(1.5 mlmin−1), and nitrogen as the makeup (60 mlmin−1) gas. Concentrations of individually resolved peakswere summed to obtain the total PCB concentration.

Compound identification was confirmed by GCcoupled to mass spectrometry in the chemical ioniza-tion mode and negative ion recording (Trace DSQ IIMs with capillary column: Thermo TR-35 MS Mass

32° 33° 34° 35° 36°

23°

24°

25°

26°

27°

28°

29°

30°

Egypt

Red Sea

Sinai

SaudiArabia

Taba

Nuweiba

Dahab

Suez

Ras Suder

Ain Sukhna

Ras GharibEl Tour

Na'ama bay

SharmRas

Mo

ham

ed

Hurghada

Safaga

Quseir

Marsa Alam

Bir ShalatinShalatinRahaba

Fig. 1 Location map

Environ Monit Assess (2013) 185:4835–4853 4837

Selective Detector). Ion repeller was 1.5 V. Data wasscanned fromm/z 50 to 450 at 1 s/decade. Data were alsoacquired in a selected ion monitoring mode with dwelltime and span of 0.06 s and 0.10 amu, respectively.

To control analytical reliability and assure recoveryefficiency and accuracy of the results, four analyses wereconducted on organochlorine compounds in IAEA—383reference materials provided by EIMP-IAEA. The labo-ratory results showed that recovery efficiency rangedfrom 91.1 % to108.6 % (Table 1). The limit of detections(LODs) in the present study was estimated to be 0.2 ng/gfor PCBs and 0.3 ng/g for pesticides based on the mini-mum quantity of sample required for a discernible peak toappear on the chromatogram.

Risk measurements

Incidental ingestion of sediment

Exposure data Incidental ingestion of sediment mayoccur during recreational activities such as swimming orwading. The intake equation includes different intakescenarios for children and adults to account for the like-lihood that children ingest more sediment than adults.

Estimated dose Chronic daily intake of incidental in-gestion of sediment is estimated as follows (USEPARegion 10 Guidance, 1991):

intakemg

kg� day

� �

¼ CsxCf 11RcxEfxEDc

Bwc þ 1RaxEFxEDaBWa

AT� Cf 2

( )

where CS 0 contaminant concentration in sediment(milligram per kilogram); CF1 0 conversion factor(0.000001 kg/mg); CF2 0 conversion factor (365 days/year); IRc 0 intake rate, child (200 mg/day); IRA 0

intake rate, adult (100 mg/day); EF 0 exposure frequen-cy (22 days/year); EDc 0 exposure duration, child(6 years); EDA 0 exposure duration, adult (24 years);BWc 0 body weight, child (15 kg); BWA 0 bodyweight, adult (70 kg); AT 0 averaging time (30 years×365 days/year010,950 days). All exposure parametervalues are standard default values from USEPA region10 guidance, with the exception of exposure frequencytaken from the statewide recreation survey.

Results and discussions

Sediment fractionations, carbonates, TOC, and TOM

Generally, sediments covering the area of study have anature from fine sand to coarse sand (Table 2). Theoccurrence of fine sand here may be due to the domi-nance of terrigenous fine grain size sediments. The sedi-ments in the different study areas are predominantlysands, with one exception at Quseir location, wherethe sediments have 15.66 % silt which may be attributedto the low contribution with terrestrial deposits. Sortingranged frommoderately to very poorly sorted indicating

Table 1 The results obtained (ng/g) for reference material IAEA383

Pollutant Required Range Found

PCBs

PCB 18 0.38 0.11–0.93 0.40

PCB 28 1.00 0.77–1.40 0.93

PCB 52 2.50 1.1–2.8 2.48

PCB 44 1.10 0.92–1.20 1.09

PCB 101 2.90 1.3–4.2 2.76

PCB 149 3.20 2.3–3.7 3.11

PCB 153 4.30 2.3–5.4 4.26

PCB 138 4.40 2.6–6.1 4.43

PCB 180 2.50 1.9–3.4 2.39

PCB 194 0.54 0.31–0.73 0.49

Pesticides

α-HCH 0.29 0.13–3.7 0.32

β-HCH 0.57 0.26–9.7 0.54

γ-HCH 0.46 0.16–1.1 0.43

Heptachlor 1.00 0.51–5.9 0.96

Aldrin 1.40 0.84–5.9 1.36

Heptachlor epoxide 1.50 0.42–5.9 1.55

γ-Chlordane 1.40 0.8–0.73 1.31

Dieldrin 0.27 0.1–0.57 0.26

p,p′-DDE 1.20 0.75–1.8 1.14

α-Chlordane 0.47 0.06–0.73 0.46

Endrin 1.10 0.4–1.8 1.00

Endosulfan I 0.31 0.15–0.57 0.29

Endosulfan II 1.70 9.2–7.1 1.69

p,p′-DDD 1.80 0.8–3.6 1.72

p,p′-DDT 2.40 0.86–6.1 2.29

Endosulfan sulfate 1.70 0.92–7.1 1.68


turbulent conditions. The coarse sand showed poor sort-ing, whereas the fine sand grain is moderately sorted.

Total organic matter ranges between 0.002 % at RasMohamed and 3.15 % at Ras Gharib (Table 2). The TOMis low at Na'ama Bay, Ras Mohamed, and Hurghadawhich may be an indication of some prevailing hydrody-namic factors that provoked winnowing any material lessdense than shells. Also, the low value is attributed to theposition of the sampling from the terrestrial dischargewhich is regarded as the main contributor of the organicdetritus; thus, it may indicate the dominance of oxidizingconditions, which may be due to permanent sedimentreworking and a low sedimentation rate.

In general, the carbonate contents in the studied loca-tion showed fluctuation between high and low levels(Table 2). The high carbonate content records at somelocations (Dahab, El Tour, Ras Suder, Ain Sukhna, RasGharib, Hurghada, Bir Shalatin, and Shalatin Rahaba)may have resulted from wave directions, displaces, andconcentrate sand comprising whole shells and fragments.

Levels of organochlorine pollutants in sediment

Tables 3, 4, and 5 showed the concentrations of organ-ochlorine pollutants [ten PCBs (PCBs 18, 28, 44, 52, 101,

118, 138, 153, 180, and 194)], α-, β-, γ-, δ-hexachlorocyclohexanes (HCHs), p,p′-DDE, p,p′-DDD,p,p′-dichlorodiphenyltrichloroethane (DDT), aldrin, diel-drin, endrin, endrin aldehyde, endrin ketone, heptachlor,heptachlor epoxide, γ-chlordane, α-chlordane, methoxy-chlor, endosulfan I, endosulfan II, and endosulfan sulfatein the surface sediment samples from the 17 locationsalong the Red Sea coast. The results represented that thePCB concentrations ranged from 0.40 to 6.17 ng/g, α-HCH (n.d. to 0.002 ng/g), β-HCH (n.d.to 0.007 ng/g), γ-HCH (n.d. to 0.002 ng/g), δ-HCH (0.009 to 0.082 ng/g),p,p′-DDE (n.d. to 0.119 ng/g), p,p′-DDD (n.d. to0.214 ng/g), p,p′-DDT (n.d. to 0.202 ng/g), aldrin (n.d.to 0.008 ng/g), dieldrin (n.d. to −0.004 ng/g), endrin (n.d.to 0.123 ng/g), endrin aldehyde (n.d. to 0.163 ng/g),endrin ketone (n.d. to 0.292 ng/g), heptachlor (n.d. to0.130 ng/g), heptachlor epoxide (n.d. to 0.117 ng/g), γ-chlordane (n.d. to 0.438 ng/g), α-chlordane (n.d. to0.04 ng/g), methoxychlor (n.d. to 0.117 ng/g), endosulfanI (n.d. to 0.072 ng/g), endosulfan II (n.d. to 0.076 ng/g),and endosulfan sulfate (n.d. to 0.075 ng/g). Throughoutthe studied area, it can be observed that the mean con-centrations were 0.40–6.17 ng/g for total PCBs, 0.009–0.086 ng/g for total HCHs, 0.003–0.457 ng/g for totalDDTs, and from 0.076 to 0.903 ng/g for total cyclodienes.

Table 2 Grain size, water content (WC%), CO3%, TOC%, and TOM% in collected sediment samples from the Red Sea

Site no. Location Position (°) Depth(m)

WC% CO3% TOC% TOM% Sand%

Silt%

Mud% Sorting Sedimenttype

1 Taba 34.88°E; 29.46°N 5 15.06 2.1 0.36 0.60 94.83 5.1 0.07 0.79 Med. sand

2 Nuweiba 34.69°E; 29.02°N 5 8.41 6.2 0.21 0.35 99.61 0.34 0.05 1.4 Coarse sand

3 Dahab 34.53°E; 28.50°N 5 8.18 22.1 0.24 0.46 99.26 0.70 0.04 1.27 Coarse sand

4 Na'ama Bay 34.28°E; 27.80°N 4 21.2 14.2 0.01 0.02 97.77 2.19 0.05 1.29 Coarse sand

5 Sharm 34.27°E; 27.72°N 5 26.6 18.1 0.21 0.36 93.1 6.9 0.00 1.69 Coarse sand

6 Ras Mohamed 34.19°E; 27.76°N 6 8.81 10.2 0.00 0.00 93.33 6.67 0.00 1.77 Med. sand

7 El Tour 33.56°E; 28.24°N 5 31.6 22.3 0.21 0.36 96.01 3.05 0.94 0.82 Med. sand

8 Ras Suder 32.67°E; 29.13°N 5 19.5 56.3 0.31 0.54 98.20 1.60 0.20 0.61 Med. sand

9 Suez 32.67°E; 29.63°N 3 21.3 2.1 0.25 0.45 99.96 0.02 0.02 1.25 Coarse sand

10 Ain Sukhna 32.49°E; 29.92°N 3 17.6 54.2 0.11 0.21 95.70 4.30 0.00 0.63 Fine sand

11 Ras Gharib 33.13°E; 28.35°N 4 23.4 28.3 1.70 3.13 99.78 0.22 0.00 0.00 Coarse sand

12 Hurghada 33.85°E; 27.26°N 3 24.2 24.3 0.05 0.10 99.70 0.22 0.08 0.54 Med. sand

13 Safaga 34.06°E; 26.58°N 3 24.7 6.1 0.18 0.32 97.66 2.34 0.00 0.61 Med. sand

14 Quseir 34.26°E; 26.17°N 3 19.1 2.1 0.14 0.28 84.34 15.66 0.00 1.35 Fine sand

15 Marsa Alam 34.92°E; 25.06°N 3 20.1 2.1 0.31 0.55 99.64 0.26 0.10 1.30 Coarse sand

16 Bir Shalatin 35.66°E; 23.12°N 3 20.8 44.6 0.35 0.63 99.18 0.82 0.00 0.77 Fine sand

17 Shalatin-Rahaba 35.64°E; 23.15°N 4 22.4 43.4 0.35 0.63 98.30 1.66 0.04 0.64 Fine sand


The total PCB is the predominance organochlorine pollu-tants at all studied locations.

Distribution of PCBs in sediments

The concentrations of PCBs in sediment are summarizedin Table 3. The concentration of total PCBs was arrangeddescending in the following order Ras Suder (6.17 ng/g)> Safaga (4.78 ng/g) > Taba (3.60 ng/g) > Hurghada,Marsa Alam (2.54 ng/g) > Sharm (2.33 ng/g) > RasMohamed (2.30 ng/g) > Quseir (2.15 ng/g) > Bir Shalatin(1.76 ng/g) > Na'ama Bay (1.69 ng/g) > Nuweiba(1.17 ng/g) > Ain Sukhna (1.08 ng/g) > Ras Gharib(1.00 ng/g) > Suez (0.91 ng/g) > El Tour (0.74 ng/g) >Dahab (0.42 ng/g) > Shalatin Rahaba (0.40 ng/g).

Among the ten identified PCBs congeners, PCB 18,28, 44, 52, 118, and 180 are found to be dominant, andthis can be attributed to some industrial activities. Thepresence of small amounts of the higher chlorinatedcongeners 101, 138, and 153 and very low concentra-tions of 194 suggest that there are no significant localsources of PCBs. According to Tolosa et al. (1995), asignificant depletion of the higher chlorinated congenersis found in samples from remote areas because these less

volatile congeners are more easily removed from theatmosphere and they cannot be transported to thoseregions. The lower chlorinated congeners (below PCB101) represented 13.55–19.94 % of total PCB concen-tration in the sediments. The presence of tetrachlorobi-phenyl (44 and 52), pentachlorobiphenyl (101 and 118),and hexachlorobiphenyl (138 and 153) in most studiedsamples suggest a contribution from the commercialmixtures, which have been widely used in transformers,electrical equipment, and other industries in severalcountries (Barkat et al. 2002). Generally, the presenceof PCBs is due to their low rate of degradation, vapor-ization, low water solubility, and partitioning to particlesand organic carbon (Kennish 1992).

Distribution of DDTs in sediments

DDT was widely used in Egypt on a variety of agri-cultural crops and for the control of disease vectors.The largest agricultural use of DDT has been on cot-ton, which accounted for more than 80 % of the usebefore its ban (Barakat et al. 2002). Although its usagewas banned in 1988, its detection, along with detec-tion of its breakdown products (i.e., DDEs + DDDs),

Table 3 PCBs concentration (ng/g of dry weight) in sediment samples collected from the Egyptian Red Sea coast

Siteno.

Location PCB18 PCB28 PCB44 PCB52 PCB101 PCB118 PCB138 PCB153 PCB180 PCB194 Total Mean±SD

1 Taba 0.62 0.89 0.07 1.15 0.48 0.19 0.13 0.07 n.d. n.d. 3.60 0.33±0.42

2 Nuweiba 0.02 0.01 0.43 0.09 0.07 0.13 n.d. 0.12 0.27 0.03 1.17 0.13±0.14

3 Dahab n.d. n.d. 0.07 n.d. n.d. 0.35 n.d. n.d. n.d. n.d. 0.42 0.04±0.11

4 Na'amaBay

0.27 0.12 0.57 0.31 0.16 0.11 0.1 0.03 n.d. 0.02 1.69 0.15±0.18

5 Sharm 0.35 0.37 0.36 0.73 0.12 0.27 0.05 0.04 n.d. 0.05 2.33 0.22±0.24

6 RasMohamed

0.55 1.13 0.13 0.08 0.08 0.11 n.d. 0.21 n.d. 0.01 2.30 0.19±0.35

7 El Tour 0.02 0.01 0.22 0.01 0.02 0.06 0.14 0.09 0.15 0.03 0.74 0.08±0.07

8 Ras Suder 0.97 0.93 0.80 0.96 n.d. 0.79 n.d. 0.09 1.32 0.33 6.17 0.57±0.48

9 Suez 0.18 0.14 0.23 0.23 0.06 0.07 n.d. 0.01 n.d. n.d. 0.91 0.08±0.95

10 Ain Sukhna 0.06 0.04 0.69 0.12 0.07 0.06 n.d. 0.04 n.d. n.d. 1.08 0.11±0.22

11 Ras Gharib 0.1 0.06 0.18 0.23 0.06 0.26 n.d. 0.11 n.d. n.d. 1.00 0.10±0.10

12 Hurghada 0.58 0.45 0.08 0.93 0.16 0.25 n.d. 0.07 n.d. 0.02 2.54 0.20±0.30

13 Safaga 0.37 0.34 0.46 0.96 0.26 0.72 0.45 0.14 1.05 0.03 4.78 0.48±0.35

14 Quseir 0.17 0.07 0.99 0.39 0.04 0.18 0.21 0.07 n.d. 0.03 2.15 0.22±0.31

15 MarsaAlam

0.47 0.36 0.31 0.78 0.16 0.37 n.d. 0.03 n.d. 0.07 2.54 0.23±0.25

16 Bir Shalatin 0.06 0.06 0.53 0.11 0.04 0.07 n.d. 0.01 0.88 n.d. 1.76 0.19±0.31

17 Shalatin-Rahaba

0.02 n.d. 0.06 0.01 0.01 0.04 0.06 0.08 n.d. 0.12 0.40 0.04±0.04


in sediments is expected because the reported environ-mental half-life of DDTs is estimated to be 10–20 years(Woodwell et al. 1971).

The contents of DDTs in the 17 sites along the Red Seacoast were presented in Table 4. The residues of DDTswere detected in all samples. In the present study,∑DDTs(equivalent sum of p,p′-DDE + p,p′-DDD + p,p′-DDT)ranged from 0.003 to 0.457 ng/g. DDTs were detected inall sediment samples, but the contribution of individualmetabolites showed differences. The concentration oftotal DDTs reached maximum value at El Tour(0.457 ng/g) followed by Safaga (0.321 ng/g) and RasGharib (0.146 ng/g). The minimum values of total DDTswere recorded at two stations; Qusir and Na'ama Bay(0.003 ng/g), whereas the other stations followed almostan equal trend of DDT distribution ranging from 0.007 to0.103 ng/g dry weight.

DDTs undergo degradation to DDDs and DDEs innatural environment by chemical and biological pro-cesses (Baxtor 1990). Over 94 % of the total DDTs insediments from all stations except Abu Quir and El

Jamil (in which DDT% ranges from 55 to 75 % of thetotal DDTs) as they were presented as p,p′-DDT. Thedominance of DDT in the sediment indicates slowdegradation of DDT or recent inputs of fresh DDT atthese locations (Tavares et al. 1999).

According to Stranberg et al. (1998), the ratio of p,p′-DDT/p,p′-DDE provides a useful index to know wheth-er the DDTs at a given site is fresh or aged input. Furthera value <0.33 generally indicates an aged input. In thepresent study, the value of >0.33 was found in all sitesexcept for Nuweiba, Hurghada, and Quseir, indicatingfresh inputs of DDT to those locations (Table 4). Thisclearly shows the possibility of long range transport ofDDT to open ocean environment and/or poor degrada-tion of DDT in offshore sediments.

The relative concentration of the parent DDT com-pared to its biological metabolites, DDD and DDE, canbe used as indicative indices for assessing the possiblepollution sources. Since the degradation pathway ofDDT in sediments is redox potential-dependent, theDDD/DDE balances may indicate the prevalent

Table 4 DDT and HCH concentration (ng/g of dry weight) in sediment samples collected from the Egyptian Red Sea coast

Station p,p′-DDE p,p′-DDD p,p′-DDT ∑DDTs p,p′-DDT/p,p′-DDE

α-HCH β-HCH γ-HCH δ-HCH ∑HCHs α/γ-HCH

Taba 0.046 0.017 0.040 0.103 0.87 0.002 0.001 0.001 0.018 0.021 2.00

Nuweiba 0.007 n.d. n.d. 0.007 n.d. n.d. n.d. n.d 0.009 0.009 n.d.

Dahab 0.015 n.d. 0.022 0.037 1.46 0.001 0.001 n.d. 0.011 0.013 n.d.

Na'ama Bay 0.002 n.d. 0.001 0.003 0.50 0.001 0.008 0.002 0.032 0.042 0.50

Sharm 0.002 0.078 0.010 0.089 5.00 0.002 0.006 0.001 0.045 0.054 2.00

Ras Mohamed 0.003 n.d. 0.008 0.010 2.66 0.001 0.005 0.001 0.018 0.025 1.00

El Tour 0.119 0.136 0.202 0.457 1.70 0.002 0.001 0.001 0.082 0.086 2.00

Ras Suder 0.035 n.d. 0.012 0.047 0.34 0.001 n.d. 0.001 0.022 0.024 1.00

Suez 0.003 0.023 0.013 0.039 4.30 0.001 0.002 0.001 0.028 0.032 1.00

Ain Sukhna 0.061 n.d. 0.006 0.067 0.10 0.002 0.001 0.002 0.023 0.027 1.00

Ras Gharib 0.044 0.044 0.058 0.146 1.32 0.001 0.001 n.d. 0.011 0.013 n.d.

Hurghada n.d. n.d. 0.018 0.018 0.00 0.002 0.003 0.002 0.024 0.031 1.00

Safaga 0.037 0.214 0.070 0.321 1.89 0.001 0.007 0.002 0.010 0.020 0.50

Quseir n.d. n.d. 0.003 0.003 0.00 0.001 0.002 0.001 0.044 0.047 1.00

Marsa Alam 0.002 0.021 0.007 0.031 3.50 0.001 0.001 0.001 0.039 0.042 1.00

Bir Shalatin n.d. n.d. 0.044 0.044 0.00 n.d. 0.001 0.001 0.037 0.039 n.d.

Shalatin-Rahaba 0.002 0.009 0.005 0.016 2.50 n.d. n.d. 0.001 0.013 0.015 n.d.

CSQG 6.75 8.51 4.77

ER-L 3.0

ER-M 350.0

CSQG Canadian sediment quality guideline, ER-L effects range low, ER-M effects range median, ∑DDT sum of DDT, DDE, and DDD


conditions in the area. DDE is the main metabolite ofDDT in oxic conditions, whereas the main metabolite inanoxic conditions is DDD (Tolosa et al. 1995). The ratioof (DDE + DDD)/∑DDTs>0.5 can be thought of asbeing subjected to long-term weathering (Hitch andDay 1992; Zhang et al. 1999). In our study, this ratioranges from 0.0 to 1.0 (Fig. 2) and it was more than 0.5in all sites, except for Dahab, Ras Mohamed, Hurghada,Quseir, and Bir Shalatin, showing that DDT in thesediment from these sites mainly came from the weath-ered agriculture soils.

When the ratio of DDD/DDE is less than unity, thisreflects that biodegradation of DDTs was predominantunder aerobic conditions, while when it is more thanunity, it means that the biodegradation was underanaerobic conditions (Hitch and Day 1992). In allstudied sediment samples, the degradation was carriedout under aerobic conditions, except for Sharm, Suez,Ras Gharib, El Tour, Safaga, Marsa Alam, and Shala-tin Rahaba stations at which the DDT degradationtook place under anaerobic conditions (Fig. 3).

The distribution of HCHs in sediments

Concentrations of total HCHs were in the range of 0.009to 0.086 ng/g, which were recorded in Nuweiba and ElTour, respectively. A sediment sample collected formSharm has the next higher value of total HCHs(0.054 ng/g). All the remaining stations showed relativelylow values ranging from 0.013 to 0.047 ng/g dry weight.

Composition differences of HCH isomers in the envi-ronment could indicate different contamination sources(Doong et al. 2002). Technical HCH has been utilized as abroad spectrum pesticide for agricultural purpose. Thetypical technical HCH generally contains 55–80 % of α-HCH, 5–14 % of β-HCH, 8–15 % of γ-HCH, and 2–16 % of δ-HCH (Lee et al. 2001). The physicochemicalproperties of these HCH isomers are different. β-HCHhas the lowest water solubility and vapor pressure and isthe most stable and relatively resistant to microbial deg-radation (Ramesh et al. 1991). Also it should be noted thatα-HCH can be converted to β-HCH in the environment(Wu et al. 1997; Walker et al. 1999). Therefore, thepredominance of α-HCH isomer in some environmentalsamples reflects the recent use of technical HCH (Kannanet al. 1995). Many studies have reported that β-HCH wasdominant in sediments from the river or estuary environ-ment after long-termmigration and transformation (Wu etal. 1999; Lee et al. 2001; Doong et al. 2002). Investigation

of the composition ofHCH isomersmeasured in thisworkshowed that the average compositions of HCH isomersmeasured in the samples are α-HCH (3.3 %), β-HCH(7.2 %), γ-HCH (3.2 %), and δ-HCH (86.46 %) whichreflects the transformation of most α-HCH to δ-HCH tobe the predominant isomer in the sediments collectedfrom the Red Sea coast. The ratios of α- to γ-isomer (α/γ ratios) range from 0 to 2 which indicated that there areno fresh inputs of HCHs to the environment (Table 4).

The distribution of cyclodienes in sediments

Cyclodiene chemical compounds such as aldrin, diel-drin, chlordane, and endrin are used as insecticides androdenticides. Production of chlordane was suspended in1976, and aldrin and dieldrin were banned in mostdeveloping countries. Cyclodienes are readily bioaccu-mulated and picked up in marine food webs becausethey are adsorbed on sediment and other organic matter.Total cyclodienes (aldrin, dieldrin, endrin, endrin alde-hyde, endrin ketone, heptachlor, heptachlor epoxide, γ-chlorodane, α-chlordane, methoxychlor, endosulfan I,endosulfan II, and endosulfan sulfate) ranged from0.075 ng/g at Shalatin Rahaba to 0.902 ng/g at Quseir.γ-Chlordane was the majority in all studied samples,which is expected, since γ-chlordane is one of the maincomponents of technical-grade chlordane (Dearth andHites 1991). The next higher quantity was endrin ketone(0.292 ng/g) at Quseir and then methoxychlor at MarsaAlam (0.117 ng/g) (Table 5). Aldrin was not recorded inmost of the sites except at Ras Mohamed, Hurghada,and Shalatin Rahaba, with values of 0.008, 0.005, and0.001 ng/g, respectively. Dieldrin was not detected in allof the sites, except for Safaga and Marsa Alam withvalues of 0.002 and 0.004 ng/g, respectively. Also,endrin aldehyde was not detected at all sites except Tabaand El Tour with values of 0.002 and 0.163 ng/g. Hep-tachlor epoxide residues were lower than its parentheptachlor because heptachlor epoxide dissolves moreeasily in water although it can remain in the sediment formany years (Khan et al. 2010).

Endosulfan was widely used as an insecticide inagriculture. Nevertheless, plant protection products con-taining endosulfan cannot be applied any longer since2006, as the European Decision EC/864/2005 estab-lished the withdrawal of products containing endosulfan(Gómez et al. 2011). Technical endosulfan contains twoisomers, endosulfan I and endosulfan II in an approxi-mately 7:3 ratio along with some impurities and


degradation products (Rice et al. 1997). In the presentstudy, endosulfan II is predominant over endosulfan I,which may be due to the fact that isomerization betweenthe parent isomers can occur in aqueous systems. Endo-sulfan sulfate is the predominant residue of technical-grade endosulfan, which finds its way into aerobic andanaerobic aquatic environments (Gómez et al. 2011). Itis less volatile and more persistent than the parent com-pounds, which explains why its quantities are almostequal to that of endosulfan II and more than that ofendosulfan I in most of the stations.

The average composition of cyclodiene compo-nents are 21.5 % for γ-chlordane, 15.3 % for endrinketone, 11.2 % for methoxychlor, 10.5 % for endosul-fan sulfate, 8.3 % for endrin, 7.5 % for heptachlor,6.5 % for endosulfan II, 6.1 % for endosulfan I, 6 %for α-chlordane, 4.7 %, heptachlor epoxide, 2.7 % forendrin aldehyde, 0.2 % for aldrin, and 0.1 % fordieldrin.

Comparison with data from other regions

Marine sediments are considered as one of the mostimportant sinks for OCPs, which concentrations insediments have been used in monitoring programs toindicate environmental quality. Concentrations of arange of organochlorines (OCls) have been measuredworldwide, and the results are summarized in Table 6,compared with the results obtained from this study. Itis clear that the levels of all kinds of OCls in theEgyptian Red Sea coast are lower than that of all thementioned regions all over the world.

Correlation of OCPs and characteristics of sediments

The concentration of contaminant in sedimentdepends largely on the retaining capacity of thesediments as it is evident from the fact that sedi-ments with large amounts of clay minerals canretain larger amounts of pesticide residues thanthe sandy clay or sandy silt sediments (Sarkar1994). From Table 7, it is clear that all the sam-ples are sandy sediments which explain the lowlevels of OCPs in most of the samples. Moreover,the diffusion of organochlorine contaminantsthrough the pores of the sedimentary layers quiteefficiently influences the variation in concentrationof various OCPs in sediments of different charac-teristics (Sarkar 1994).T

able

5Cyclodienes

concentration(ng/gof

dryweigh

t)in

sedimentsamples

collected

from

theEgy

ptianRed

Sea

coast

Cyclodienes

12

34

56

78

910

1112

1314

1516

17

Aldrin

n.d.

n.d.

n.d.

n.d.

n.d.

0.00

8n.d.

n.d.

n.d.

n.d.

n.d.

0.00

5n.d.

n.d.

n.d.

n.d.

0.00

1

Dieldrin

n.d.

n.d.

n.d.

n.d.

n.d.

n.d.

n.d.

n.d.

n.d.

n.d.

n.d.

n.d.

0.00

2n.d.

0.00

4n.d.

n.d.

End

rin

0.02

0.08

0.08

0.03

40.02

n.d.

0.12

3n.d.

n.d.

0.01

50.03

0.03

7n.d.

n.d.

n.d.

0.05

40.00

9

End

rinAldehyd

e0.00

2n.d.

n.d.

n.d.

n.d.

n.d.

0.16

3n.d.

n.d.

n.d.

n.d.

n.d.

n.d.

n.d.

n.d.

n.d.

n.d.

End

rinKeton

e0.03

0.05

0.11

n.d.

0.02

n.d.

0.05

0n.d.

0.09

0.03

10.03

0.00

9n.d.

0.29

20.10

40.10

20.00

9

Heptachlor

n.d.

n.d.

n.d.

n.d.

0.03

n.d.

n.d.

0.07

0.13

n.d.

n.d.

0.12

5n.d.

n.d.

n.d.

0.08

70.00

3

Heptachlorepox

ide

0.01

80.01

0.01

0.00

50.01

0.03

80.00

60.00

30.01

0.117

0.02

0.01

20.02

10.00

30.01

0n.d.

0.00

4

γ-Chlordane

0.14

8n.d.

n.d.

0.01

40.06

0.00

90.10

30.05

0.05

0.08

10.12

0.03

60.06

20.43

80.02

90.08

30.01

7

α-Chlordane

0.04

0n.d.

n.d.

0.02

80.04

0.03

40.04

00.02

6n.d.

0.02

70.09

n.d.

0.03

5n.d.

n.d.

n.d.

0.00

3

Metho

xychlor

0.05

20.07

0.09

n.d.

0.03

n.d.

n.d.

0.00

80.03

n.d.

0.01

0.03

0n.d.

0.10

60.117

0.10

90.00

5

End

osulfanI

0.01

30.02

n.d.

0.07

20.03

n.d.

0.05

80.00

60.03

0.00

60.02

0.00

40.03

5n.d.

0.00

40.05

50.01

8

End

osulfanII

0.03

2n.d.

0.00

3n.d.

0.08

n.d.

0.07

60.02

40.01

0.02

20.00

2n.d.

0.05

00.03

5n.d.

n.d.

0.00

4

End

osulfansulfate

0.07

50.05

0.02

4n.d.

0.05

n.d.

0.03

00.02

30.02

0.01

90.07

50.06

40.01

70.02

90.10

00.04

20.00

3

∑cyclod

ienes

0.43

0.28

0.31

70.15

30.37

0.08

90.64

90.21

0.37

0.31

80.39

70.32

20.22

20.90

30.36

80.53

20.07

6


Table 7 shows the Pearson's correlations betweensediment characteristics, TOC%, TOM%, CO3%, totalPCBs, total pesticides, and total cyclodines. TotalPCBs has no correlation with any of the above varia-bles, while total pesticides represented only low cor-relations with Mud%. Total cyclodines representedlow correlations with sand% and silt%, while totalDDTs and total HCHs showed high correlation withMud%. No correlations were observed betweenTOC% or TOM% and the above-studied variables.

PCA

PCA is a multivariate technique used to reduce thenumber of variables to a smaller set of orthogonal

factors for easier interpretation by displaying the cor-relations existing among the original variables. Datasubmitted for the analysis were arranged in a matrix,where each column corresponds to one organochlorinecomponent and each row represents sediment location.The number of factors extracted from the variableswas determined according to Kaiser's rules. This cri-terion retains only factors with eigenvalues that exceedone. Three sets of variable data were subjected to theabove factor analysis. The first step in the multivariatestatistical analysis was the application of PCAwith theaim of grouping the 17 locations by the loading plotsfor 30 OCls contaminants. Since the raw data haveprovided negative loadings, we applied the varimaxrotation for the correlation greater than 0.30 concen-trations of 17 locations and 30 OCls were also

0.000

0.200

0.400

0.600

0.800

1.000

1.200

Con

cent

rati

ons

(ppb

dry

wei

ght)

Stations

DDE+DDD/ DDTFig. 2 Ratio of (DDE +DDD)/DDT of sedimentsamples collected from theEgyptian Red Sea coast

0.000

5.000

10.000

15.000

20.000

25.000

30.000

35.000

40.000

45.000

conc

entr

atio

ns (

ppb

dry

wei

ght)

Stations

DDD/DDE

Fig. 3 Ratio of DDD/DDEof sediment samples col-lected from the EgyptianRed Sea coast


selected. The majority of the variance (91.69 %) of thescaled data was explained by two eigenvectors–prin-cipal components. The first principal component fac-tor (PCF-1) explained 59.45 % and the secondprincipal component factors (PCF-2) explained32.25 % of the total variances (Table 8). PCF-1 hada strong significant correlation with Taba (0.919),Nuweiba (0.753), Na'ama Bay (0.876), Ras Mohamed

(0.906), Sharm (0.885), Ras Suder (0.918), Suez(0.758), Hurghada (0.919), Safaga (0.878), MarsaAlam (0.898), and Shalatin Rahaba (0.740). PCF-2had a strong significant correlation with Dahab(0.836) and Ras Gharib (0.719), while Ain Sukhna,Quseir, and Bir Shalatin locations are distributed be-tween PCF-1 and PCF-2 in almost equal loading fac-tors. This factor analysis shows that most of the

Table 6 Concentrations (ng/g dry weight) of PCBs and organochlorine pesticide residues in sediments collected worldwide

Location Pollutant Range Survey year Reference

Hugli estuary, West Bengal, northeast India ∑HCHs 0.11–0.40 2003 Guzzella et al. (2005)∑DDTs 0.18–1.93

∑PCBs 0.18–2.33

Salton Sea, California, USA ∑DDTs 10–40 2000–2001 Sapozhnikova et al. (2004)∑PCBs 116–304

Mid-Black Sea Turkey Aldrin 25–151 1998–2000 Bakan and Ariman (2004)Lindane 5–37

Dieldrin 7–64

p,p′-DDT 18–31

p,p′-DDD 24–71

p,p′-DDE ND–7

α-HCH ND–5

δ-HCH 5–36

Heptachlor epoxide ND-21

Masan Bay, Korea ∑PCBs 1.24–41.4 2003 Hong et al. (2003)∑DDTs 0.28–89.2

∑HCHs nd–1.03

Dar es Salaam City, Tanzania ∑DDTs 12–48.4 2002 Mwevura et al. (2002)Dieldrin <1.2–38.1

γ-HCH <0.8

The sewer system in Hanoi, Vietnam ∑DDTs 4.4 to 1,100 2006 Hoai et al. (2010)∑PCBs 1.3 to 384

∑HCHs <0.2 to 36

Vietnam Hong et al. (2008)

Ha Long Bay ∑HCHs n.d.–0.85 2003–2004Hai Phong Bay 0.15–1.0

Ba Lat estuary 0.03–0.26

Ha Long Bay ∑PCBs 0.11–10.1

Hai Phong Bay 0.45–18.7

Ba Lat estuary 0.04–0.26

Gulf of Aden, Yemen ∑PCBs 0.40–4.97 2007 Mostafa et al. (2007)∑DDT ND–0.74

∑HCHs ND–0.07

Red Sea, Egypt ∑PCBs 0.40–6.17 2009 Present study∑DDTs 0.003–0.457

∑HCHs 0.009–0.086

Cyclodienes 0.075–0.902


studied locations have similarity in the distribution ofOC pollutants. This was proved by the CA as representedin Fig. 4, which shows similar groups of distribution.

The second step in the multivariate statistical analysiswas the application of PCAwith the aim of grouping theten PCB contaminants as variables by the loading plotsfor 17 locations. The majority of the variance (86.12 %)of the scaled data was explained by four eigenvectors–principal components distributed as PCF-1 (31.66 %),PCF-2 (25.80 %), PCF-3 (15.39 %), and PCF-4(13.28 %) (Table 9). PCF-1 had a strong significantcorrelation for PCBs 18, 28, 52, and 101, while PCF-2has a strong significant correlation for PCB 44, 180, and194. PCF-3 and PCF-4 represents only one PCB (PCB138 and PCB 153, respectively), which explains thedifferent patterns for these two PCBs. This factor anal-ysis was proved by Pearson's correlations obtained forindividual PCB (Table 10) as shown by significantcorrelations between PCB 18, 28, 52, and 101. Also,significant correlations were reported between PCB 44,180, and 194, while PCB 138 and PCB 153 showed nosignificant correlations with any of the remaining PCBs.

The third step in the multivariate statistical analysiswas the application of PCAwith the aim of grouping 20pesticide contaminants as variables by the loading plotsfor 17 locations. The majority of the variance (84.31 %)of the scaled data was explained by seven eigenvectors–principal components distributed as PCF-1 (22.33 %),PCF-2 (12.72 %), PCF-3 (11.74 %), PCF-4 (11.59 %),PCB-5 (9.51 %), PCF-6 (9.07 %), and PCF-7 (7.35 %)(Table 11). The distribution of seven factors represents

the low correlations between the OCPs variables and thedifferent natures of their input to the environment, which

Table 7 Pearson's correlations between sediment characteristics and total PCBs, total pesticides, total cyclodienes

Variable ∑PCBs ∑pest ∑Cyclod. CO3% TOC% TOM% Sand% Silt% Mud% ∑DDTs

Total PCBs 1

Total pest −0.069 1

Total cyclodiene −0.249 0.633 1

CO3% 0.027 −0.180 −0.212 1

TOC% −0.150 0.121 0.106 0.144 1

TOM% −0.153 0.120 0.106 0.146 1.000 1

Sand% −0.103 −0.413 −0.575 0.240 0.255 0.256 1

Silt% 0.108 0.379 0.554 −0.240 −0.247 −0.248 −0.998 1

Mud% −0.071 0.582 0.325 0.112 −0.085 −0.087 0.034 −0.090 1

Total DDTs 0.069 0.639 0.254 −0.047 0.139 0.139 0.021 −0.059 0.709 1

Total HCHs −0.265 0.521 0.542 −0.101 −0.296 −0.297 −0.322 0.287 0.704 0.457

Correlation is significant at p<0.05 (low) (italic); correlation is significant at P<0.005 (medium) (bold); correlation is significant atP<0.001 (high) (bold)

Table 8 Factor loadings (varimax normalized with Kaiser nor-malized: marked loadings are >0.70) for two principal compo-nent factors (PCFs) for noncontaminated and for fairlycontaminated locations

Sampling location Component

PCF-1 PCF-2

Taba 0.919 0.330

Nuweiba 0.753 0.595

Dahab 0.325 0.836

Na'ama Bay 0.876 0.434

Ras Mohamed 0.906 0.228

Sharm 0.885 0.443

El Tour 0.167 0.932

Ras Suder 0.918 0.345

Suez 0.758 0.611

Ain Sukhna 0.663 0.644

Ras Gharib 0.662 0.719

Hurghada 0.919 0.331

Safaga 0.878 0.419

Quseir 0.677 0.683

Marsa Alam 0.898 0.412

Shalatin Rahaba 0.740 0.556

Bir Shalatin 0.682 0.600

% of variance 59.45 32.25

Cumulative % 59.45 91.69

Bold is the marked loading


are confirmed by the lack of correlations between indi-vidual OCPs (Table 12).

The property of individual organochlorine compo-nents which causes their dominancy in each factorcannot be clearly indicated. Therefore, it is impossibleto predict the distribution patterns of individual organ-ochlorine components in contaminated areas. Thestrong adsorption of organochlorines by sedimentscaused by long-range atmospheric transport processesand regional fallout deposition in combination withtheir transformation, behavior in sediment–water sys-tem, and mobility imply random distribution.

Hierarchical CA (Ward's method applying Pear-son's correction) of organochlorine components usedaverage linkage between groups, and squared Euclid-ean distance and standard deviation <1 showed goodefficiency for sediment samples collected from theRed Sea coast, which presents different sources depo-sition (Fig. 5). Two big clusters with subgroups andthree small clusters could be distinguished which coin-cides with the previously defined factor analysis. Thelinkage distance between classes is high (15.0–20.0),implying a significant distance between them. A suf-ficient number of data can statistically explain the highvalues obtained for linkage distance. The dendrogram

of Fig. 5 shows that the main clusters contain domi-nantly PCBs, α-chlordane, dieldrin, β-HCH, endosul-fan I, p,p′-DDD, endosulfan II, endrin aldehyde, p,p′-

Fig. 4 Hierarchical dendo-gram for 17 sampling loca-tions represented by Ward'shierarchical clusteringmethod

Table 9 Factor loadings (varimax normalized with Kaiser nor-malized: marked loadings are >0.70) for two principal compo-nent factors (PCFs) for noncontaminated and for fairlycontaminated with PCBs

PCBs Component

PCF-1 PCF-2 PCF-3 PCF-4

PCB 18 0.899 0.293 −0.155 0.203

PCB 28 0.802 0.048 −0.174 0.477

PCB 52 0.892 0.216 0.296 −0.129PCB 44 −0.169 0.719 0.151 −0.193PCB 101 0.701 −0.356 0.549 −0.061PCB 118 0.508 0.694 0.177 0.104

PCB 138 0.048 0.242 0.914 0.166

PCB 153 0.099 0.032 0.148 0.962

PCB 180 0.168 0.831 0.133 0.149

PCB 194 0.317 0.755 −0.407 0.120

% of variance 31.66 25.80 15.39 13.28

Cumulative % 31.66 57.46 72.85 86.12



Table 10 Pearson correlations obtained for PCB data

PCB 18 PCB 28 PCB 52 PCB 44 PCB 101 PCB 118 PCB 138 PCB 153 PCB 180 PCB 194

PCB 18 1

PCB 28 0.888 1

PCB 52 0.793 0.572 1

PCB 44 0.079 −0.106 0.076 1

PCB 101 0.412 0.430 0.694 −0.234 1

PCB 118 0.611 0.390 0.649 0.242 0.141 1

PCB 138 0.017 −0.025 0.339 0.225 0.402 0.366 1

PCB 153 0.275 0.490 0.045 −0.092 0.093 0.185 0.278 1

PCB 180 0.365 0.230 0.310 0.429 −0.065 0.687 0.303 0.163 1

PCB 194 0.570 0.364 0.322 0.331 −0.262 0.605 −0.092 0.112 0.585 1

∑PCBs 0.861 0.704 0.820 0.352 0.410 0.817 0.373 0.298 0.699 0.601

Correlation is significant at p<0.05 (low) (italic); correlation is significant at P<0.005 (medium) (bold); correlation is significant atP<0.001 (high) (bold)

Table 11 Factor loadings (varimax normalized with Kaiser normalized: marked loadings are >0.70) for two principal componentfactors (PCFs) for noncontaminated and for fairly contaminated with pesticides

Pesticides Component

PCF-1 PCF-2 PCF-3 PCF-4 PCF-5 PCF-6 PCF-7

α-HCH 0.355 0.245 0.186 0.449 0.515 0.190 0.394

β-HCH −0.152 0.830 −0.093 0.168 −0.149 −0.094 0.017

γ-HCH 0.010 0.798 −0.073 −0.106 0.391 −0.020 0.196

δ-HCH 0.791 0.125 0.383 −0.179 −0.080 0.061 0.145

Heptachlor −0.069 0.040 −0.083 −0.185 −0.189 −0.023 0.817

Aldrin −0.215 0.072 −0.298 −0.037 0.320 −0.189 0.445

Heptachlor epoxide −0.072 0.055 −0.102 0.098 0.875 −0.101 −0.195γ-Chlordane 0.017 −0.018 0.930 0.118 0.056 −0.062 −0.076Dieldrin −0.076 0.252 −0.093 −0.229 0.039 0.851 −0.298p,p′-DDE 0.771 −0.083 −0.037 0.413 0.335 −0.019 −0.231α-Chlordane 0.116 0.014 −0.125 0.898 0.072 −0.044 −0.210Endrin 0.761 −0.360 −0.170 −0.105 −0.221 −0.145 −0.050Endosulfan I 0.479 0.406 −0.213 0.017 −0.506 −0.223 −0.154Endrin aldehyde 0.962 −0.002 0.010 0.084 0.021 −0.061 −0.032Endosulfan II 0.525 0.387 0.366 0.464 0.005 0.082 −0.033p,p′-DDD 0.441 0.523 −0.064 0.403 −0.0148 0.266 −0.284p,p′-DDT 0.896 0.031 −0.035 0.279 −0.080 0.050 −0.097Endrin ketone 0.000 −0.192 0.852 −0.400 −0.110 0.077 −0.094Endosulfan sulfate 0.002 −0.401 0.104 0.200 −0.079 0.803 0.179

Methoxychlor −0.120 −0.382 0.454 −0.532 −0.278 0.428 −0.015% of variance 22.33 12.72 11.74 11.59 9.51 9.07 7.35

Cumulative % 22.33 35.05 46.79 58.38 67.89 76.96 84.31



Tab

le12

Pearson

'scorrelations

obtained

betweenpesticidedata

AB

CD

EF

GH

IJ

KL

MN

α-H

CH

1

β-H

CH

0.21

91

γ-H

CH

0.39

30.51

0

δ-HCH

0.37

3−0

.001

1

Heptachlor

0.02

5−0

.091

−0.006

Aldrin

0.09

50.23

7−0

.200

Heptachlorepox

ide

0.37

4−0

.025

−0.219

γ-Chlordane

0.15

0−0

.133

0.25

61

Dieldrin

−0.060

0.08

30.00

8−0

.124

p,p′-D

DE

0.48

9−0

.233

0.39

80.09

51

α-Chlordane

0.36

70.19

7−0

.091

0.04

50.53

31

End

rin

0.02

1−0

.226

0.49

4−0

.166

0.47

30.00

01

End

osulfanI

−0.109

0.36

60.35

2−0

.150

0.23

60.19

40.41

71

End

rinaldehy

de0.33

1−0

.146

0.75

60.07

00.78

10.20

50.67

70.42

71

End

osulfanII

0.55

80.23

40.53

50.30

10.52

40.32

70.13

70.22

40.53

01

p,p′-D

DD

0.23

90.38

50.20

30.00

00.49

50.39

10.116

0.37

60.45

20.67

81

p,p′-D

DT

0.30

5−0

.084

0.59

10.08

50.82

70.39

50.62

20.47

60.90

60.53

60.65

61

End

rinketone

−0.163

−0.260

0.36

90.76

6−0

.199

−0.424

−0.009

−0.221

−0.016

−0.003

−0.217

−0.086

1

End

osulfansulfate

0.21

8−0

.335

0.02

80.09

80.00

70.117

0.07

1−0

.224

−0.056

−0.009

−0.034

0.04

50.13

21

Metho

xychlor

−0.321

−0.341

0.18

00.30

7−0

.412

−0.56

70.10

1−0

.238

−0.231

−0.262

−0.350

−0.238

0.74

20.48

7

∑pesticides

0.34

8−0

.153

0.70

60.65

30.58

50.16

80.411

0.25

30.67

70.61

40.46

30.73

20.52

60.24

1

Correlatio

nissign

ificantat

p<0.05

(low

)(italic);correlationissign

ificantat

P<0.00

5(m

edium)(bold);correlationissign

ificantat

P<0.00

1(high)

(bold)

Aα-H

CH,B

β-H

CH,C

δ-HCH,D

γ-chlordane,E

p,p′-D

DE,F

α-chlordane,G

endrin,H

endo

sulfan

I,Iendrin

aldehy

de,J

endo

sulfan

II,K

p,p′-D

DD,L

p,p′-D

DT,

Mendrin

ketone,

Nendo

sulfan

sulfate


DDT, and endrin as well as the other small clustersincluding the remaining organochlorines.

Risk assessment

Contaminated sediments comprise organic and in-organic materials that accumulate on the bottom ofwater bodies which contain substances that mayadversely affect human health, the environment,or environmental values. Contamination of sedi-ments may result from both point sources (e.g.,drains and spills) and/or diffuse sources (e.g., ae-rial deposition, soil erosion, and groundwater dis-charges). It is important to consider both sedimentparticles and sediment pore waters as contaminantsources when assessing sediments. The importance

of these sources and associated exposure pathwaysvaries depending on the nature of the sediment-dwelling organisms and assemblages present. Thedual role of sediments as both a source and sinkof dissolved contaminants is widely recognized. Inaddition to their influence on surface water quality,sediments represent a source of bioavailable con-taminants to benthic biota and, hence, bioaccumu-lation in the food chain. Contaminated sedimentscan, therefore, affect aquatic ecosystem health andmay indirectly impact human health, e.g., throughconsumption of contaminated shellfish. Table 13demonstrates the chronic daily intake values ofpesticides along the Red Sea coast of Egypt andwhen we compare the result of the Red Sea valueswith the sediment assessment levels (Interim

Fig. 5 Hierarchical dendo-gram for 30 objects repre-sented by Ward'shierarchical clusteringmethod


Sediment Quality Guidelines or ISQGs). Theguidelines contain two concentrations, the ISQG-Low concentration (or trigger value) and theISQG-High concentration. The trigger value is athreshold concentration and below this concentra-tion, the frequency of adverse effects is expectedto be very low. The ISQG-High concentration isintended to represent a concentration above whichadverse biological effects are expected to occurmore frequently. From our results, we found thatPCB's intake from sediment ranged from 2.38 to36.35 μg/kg/day and when compared with ISQGvalues, we can concluded that all stations withinthe trigger value, except at Ras Suder and Safaga,recorded an elevated level indicating that the haz-ard may have occurred from ingestion in these twostations. In contrast, ∑DDT intake was less thantrigger values of ISQGs.

Conclusions

This study documents the analysis of PCBs and OCPresidues in sediments along the Egyptian Red Seacoast. OCP residues detected were DDTs, HCHs,

aldrin, dieldrin, endrin, endrin aldehyde, endrin ke-tone, heptachlor, heptachlor epoxide, γ-chlordane, α-chlordane, methoxychlor, endosulfan I, endosulfan II,and endosulfan sulfate. It was concluded that thePCBs are predominant over the rest of the pesticidesin all stations. For HCHs, δ-HCH was the most abun-dant among the studied isomers of HCHs along thecoast. The ratio of DDE + DDD/∑DDT indicated thatDDT in the sediment from these sites mainly camefrom the weathered agriculture soils. The ratio ofDDD/DDE showed that DDT degradation took placeunder anaerobic conditions. PCA was applied on thedata and it was found that the property of individualorganochlorine components which causes their domi-nancy in each principal component factor (PCF) can-not be clearly indicated, and their clustering was notobserved. Therefore, it is impossible to predict thedistribution patterns of individual organochlorinecomponents in the contaminated area. Through appli-cation of CA, it was concluded that the main clus-ters contain dominantly PCBs, α-chlordane,dieldrin, β-HCH, endosulfan I, p,p′-DDD, endosul-fan II, endrin aldehyde, p,p′-DDT, and endrin aswell as the other small clusters including theremaining organochlorines.

Table 13 Chronic daily intakeof organochlorine compoundsfrom sediment's of the Red Sea

Places PCB's intake Pest intake ∑DDT intake ∑HCH intake ∑cyclo

Taba 21.20 3.27 0.61 0.13 25.39

Nuweibaa 6.92 1.76 0.04 0.06 16.67

Dahab 2.45 2.28 0.22 0.24 18.21

Na'amaa 9.96 1.17 0.02 0.25 8.99

Ras Mohamed 13.53 0.73 0.06 0.15 5.24

Sharm 13.73 2.93 0.53 0.32 20.89

El-Tour 4.37 7.03 2.69 0.51 38.26

Ras Suder 36.35 1.66 0.28 0.14 12.48

Suez 5.34 1.06 0.23 0.19 20.81

Ain Sukhna 6.35 2.44 0.39 0.16 18.82

Ras Ghareb 5.88 3.31 0.86 0.08 23.68

NIOF (Hur) 14.99 2.18 0.11 0.18 18.92

Safaga 28.14 3.32 1.89 0.12 13.14

Quseir 12.66 5.61 0.02 0.28 53.14

Marsa Alam 14.99 2.60 0.18 0.25 21.70

Shalatin Rahba 2.38 0.63 0.26 0.23 31.40

Bir Shalatin 10.37 3.63 0.09 0.09 4.43


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Documents

Levels, distribution, and risk assessment of organochlorines in surficial sediments of the Red Sea coast, Egypt