Spatial distribution, environmental risk and sources of heavy metals and polycyclic aromatic hydrocarbons (PAHs) in surface sediments-northwest of Persian Gulf

Journal Pre-proof Spatial distribution, environmental risk and sources of heavy metals and polycyclic aromatic hydrocarbons (PAHs) in surface sediments-Northwest of Persian Gulf Meisam Rastegari Mehr, Behnam Keshavarzi, Farid Moore, Sahar Fooladivanda, Armin Sorooshian, Harald Biester PII:

S0278-4343(19)30419-4

DOI:

https://doi.org/10.1016/j.csr.2019.104036

Reference:

CSR 104036

To appear in:

Continental Shelf Research

Received Date: 22 February 2019 Revised Date:

30 November 2019

Accepted Date: 8 December 2019

Please cite this article as: Mehr, M.R., Keshavarzi, B., Moore, F., Fooladivanda, S., Sorooshian, A., Biester, H., Spatial distribution, environmental risk and sources of heavy metals and polycyclic aromatic hydrocarbons (PAHs) in surface sediments-Northwest of Persian Gulf, Continental Shelf Research, https://doi.org/10.1016/j.csr.2019.104036. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

Spatial distribution, environmental risk and sources of heavy metals and polycyclic aromatic hydrocarbons (PAHs) in surface sediments-Northwest of Persian Gulf

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Meisam Rastegari Mehra, Behnam Keshavarzib*, Farid Mooreb, Sahar Fooladivandab, Armin Sorooshianc, d, Harald Biestere a

Department of Applied Geology, Faculty of Earth Science, Kharazmi University, Tehran 15614, Iran Department of Earth Sciences, College of Sciences, Shiraz University, Shiraz 71454, Iran c Department of Chemical and Environmental Engineering, University of Arizona, Tucson, AZ 85721, USA d Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ 85721, USA b

e

Institut für Geökologie, AG Umweltgeochemie, Technische Universität Braunschweig, 38106 Braunschweig, Germany

14 15

*Corresponding author;

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Tel/fax: +98 71 32284572 E-mail address: [email protected]

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Abstract

20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

In the current study, environmental risk, potential sources and spatial distribution of heavy metals and polycyclic aromatic hydrocarbons (PAHs) were investigated in the sediments of Musa Estuary, the largest Estuary in the Persian Gulf. A total of 68 surface sediment samples were collected and analyzed for heavy metals and PAH concentration using inductively coupled plasma mass spectrometry (ICP-MS) and High-Performance Liquid Chromatography (HPLC). Enrichment factor (EF) and ecological risk (E) calculation revealed the highest contamination and risk for Hg, mostly due to the activity of a petrochemical complex in the area. Also, most samples showed a mean probable effect level (PEL) quotient of 0.51 to 1.50, and a probable 49% toxicity. Pearson’s correlation coefficient and principal component analysis (PCA) indicated the same (anthropogenic) origin for Cu, Hg, Pb and Zn. Five-six ring PAHs are dominant in sediments, and most studied compounds showed higher concentrations than their effect range low (ERL) and effect range median (ERM). Moreover, the highest toxic equivalent (TEQ) and ecological risk were observed in the main treatment lagoon. PAH diagnostic ratios and PCA revealed both petrogenic and pyrogenic sources for these compounds, and calculated mass inventory (I) values indicated a relatively high potential of the sediments (0.2-12.28 tons) for contaminating the marine environment. The results indicated that the wastewater treatment has good efficiency in reducing contaminant levels, as Mann-Whitney U test results showed a significant difference in Ni, Cr, Hg and ∑PAH concentration between treatment lagoons and estuarine sediments.

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Keywords: Sediment; Ecological risk; Treatment lagoon; Heavy metals; PAHs; Persian Gulf 1

41

1. Introduction

42

Most large cities around the world are located in coastal zones (Kim et al., 2016), where rapid

43

economic development, industrialization and urbanization, population growth and agricultural

44

practices have resulted in serious problems for the environment (Paramasivam et al., 2015). Due

45

to geomorphological and hydrodynamic features of estuarine systems, economic growth is

46

deeply rooted in these systems and they have numerous advantages for human settlement. A

47

large amount of pollutants including heavy metals and persistent organic pollutants are

48

discharged into the aquatic ecosystems through various pathways (Yin et al., 2011). In terms of

49

food resources and ecosystem services, coastal zones also have significant advantages for

50

humans. On the other hand, human activities pose severe negative impacts on the coastal and

51

estuarine ecosystems and the viability of the resources. Thus, management and pollution control

52

for conservation of aquatic organisms and marine environment is a necessity (Pejman et al.,

53

2015). It is a well-known fact that sediments are important reservoirs for many persistent and

54

toxic chemicals, and a route through which contaminants enter aquaculture and wildlife food

55

chains (Long et al., 1995; Praveena et al., 2007; Moore et al., 2015). Depositional zones can be

56

loaded with pollutants derived from anthropogenic sources and reflect the severity of

57

anthropogenic activities (Dudhagara et al., 2016). More recently, there has been increasing

58

interest in the study of heavy metals and PAHs within the scientific community owing to their

59

toxic effects (Zhang et al., 2016; Delshab et al., 2017).

60

In recent decades, a huge amount of contaminants originated mostly from rapid industrial and

61

agricultural development, and shipping traffic has entered the Persian Gulf and threatened its

62

aquatic ecosystem (Monikh et al., 2013; Pourkerman et al., 2017; Keshavarzi et al., 2018). The

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current study investigates two important categories of contaminants (heavy metals and PAHs) in

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surface sediments of Musa Estuary, as the most important industrial zone in Iran, famous for its

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petrochemical complexes and the Imam Khomeini Port. The main objectives of this study are (1)

66

examining selected heavy metals and PAHs in the sediments of the Musa Estuary, with a focus

67

on characterizing their spatial distribution and concentrations; (2) investigating the role of

68

previous activity of the chlor-alkali unit on Hg contamination; (3) identifying potential sources

69

of these contaminants, and assessing the efficiency of the current treatment system to remove the

2

70

pollutants; and (4) evaluating the degree of contamination and the resulting ecological risk

71

caused by the heavy metals and PAHs.

72 73 74

2. Materials and Methods

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2.1. Site description

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The Persian Gulf, with an average depth and surface area of 35 m and 240,000 km2, respectively,

77

forms the coastline of several countries including Iran, Saudi Arabia, the United Arab Emirates,

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Oman, Qatar, Bahrain, Iraq and Kuwait. This shallow sea, similar to Baltic and North Sea, has a

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warm and saline water (Agah et al., 2009). The Persian Gulf’s hydrological system is such that

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surface water moves toward the coasts, sinks, and subsequently flows out the Gulf by

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counterflow at lower levels with the highest salinities, in both bottom and surface coastal water

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(Sugden, 1963).

83

Musa Estuary is the largest Estuary in Iran, located northwest of the Persian Gulf in Mahshahr

84

County (30° 15′–30 ° 32′, 49°–49° 20′) (Fig 1). Irregular bed elevations observing in the main

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branches of the Estuary, resulted from bed erosions and tidal current complexities. Therefore, no

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regular stream is present in this multi branch Estuary. It has an arid climate with an annual

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average temperature of 25.5 °C and annual average precipitation of 213.4 mm (Keshavarzi et al.,

88

2018). Agricultural lands and industrial sectors including LPG plants, petrochemical complexes

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and oil transfer docks are built along the coast of the Estuary (Mortazavi and Sharifian, 2011;

90

Lahijanzadeh et al., 2019). Also, the two main urban population centers in this area include

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Mahshahr and Bandar-e-Eman Cities. Mahshahr is an important industrial hub in Iran because of

92

its petrochemical complexes, Imam Khomeini Port, and metal and petroleum industries. Most of

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the petrochemical complexes in the area transfer their wastewater to the Fajr Wastewater

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Treatment Plant, in which wastewater is treated, held in treatment lagoons located in the

95

petrochemical zone (north of the Estuary), and then discharged into the Musa Estuary. Also,

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some petrochemical units have their own wastewater treatment facilities or discharge their

97

untreated wastewater directly into the Musa Estuary.

3

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2.2. Sampling and sample analysis

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In this study, a sampling strategy was developed based on the information regarding the main

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contaminating industries, shipping routes and the main fisheries and docks (obtained from

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Khuzestan Environmental Protection Office). A total of 36 surface sediment samples (S1-S36)

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were collected using a stainless steel van Veen grab sampler in February 2015 in the Musa

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Estuary and its tributaries (24 samples; S1-S24), the northwest shore of the Persian Gulf (7

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samples; S25-S31) and treatment lagoons of the petrochemical zone (5 samples; S32-S36). A

106

hand-hold GPS was used to record sampling locations (Table 1 and Fig 1). Six subsamples were

107

taken and mixed thoroughly to obtain a bulk/representative sample for each sampling site. The

108

composite samples were transported to the laboratory (placed into polyethylene bags). A fraction

109

of each sediment sample was air dried, homogenized in a porcelain mortar and passed through a

110

63 µm sieve to achieve a fine-grained sediment sample. Concentration of seven heavy metals

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(Al, Cr, Cu, Hg, Ni, Pb and Zn) was measured following aqua regia digestion using inductively

112

coupled plasma mass spectrometry (ICP-MS) in Acme Analytical Laboratories, Ltd. Quality

113

assurance and control (QA/QC) included duplicate analyses, procedural blank, and use of

114

standard reference materials (STD DS10 and STD OREAS45EA). The relative standard

115

deviation (RSD) was less than 4% for each element, and the recovery percentages ranged from

116

96-103%, and the blank was below the detection limit.

117

The pH, electrical conductivity (EC) and cation exchange capacity (CEC) of sediment samples

118

were determined based on methods summarized by Ryan et al. (2007). In all sediment samples

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the organic matter (OM) content was analyzed using the LOI procedure (Schulte and Hopkins,

120

1996). In order to determine sediments texture, each sediment was sieved through a 2 mm mesh

121

sieve (Keshavarzi et al., 2015), and particle size distribution (sand, silt, and clay content) was

122

determined using the hydrometer method (Gee and Bauder, 1986).

123

To determine 16 PAH concentrations, 32 surface sediment samples (H1-H32) were collected at

124

the same locations as for heavy metals sampling (each sample comprising six subsamples) from

125

Musa Estuary and its tributaries (21 samples; H1-H21), northwest shore of the Persian Gulf (7

126

samples; H22-H28) and treatment lagoons of the petrochemical zone (4 samples; H29-H32)

127

(Table 1 and Fig 1). The samples were kept in a solvent-cleaned glass jar and stored in a cool 4

128

box at 4 °C and transported to the laboratory of Isfahan University of Technology to be prepared

129

and analyzed. In the laboratory, EPA 3550 B and EPA 3630 C methods were used for extraction

130

and clean-up procedures, respectively. The 16 PAH compounds were measured using a RIGOL

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L-3000 High-Performance Liquid Chromatography (HPLC) system, with a Hewlett-Packard

132

1046 A fluorescence detector and a RIGOL L-3500 UVvis detector. For the extraction of

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analytes, 100 mL dichloromethane (CH2Cl2) was added to 2 g of each homogenized sample, and

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mixed for 8 h. Then, a rotary vacuum evaporator was used to concentrate the extracts to 1 mL,

135

and 20 µL of this extract was injected for PAHs analysis. The mobile phase consisted of

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acetonitrile/water in gradient mode at a flow rate of 1.0 mL min−1 and the temperature set at 35

137

°C. Duplicates, method blanks and standard reference materials (Sigma-Aldrich Co. LLC EPA

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525 PAH Mix A and EPA 525 PAH Mix B) were used to assess quality assurance and quality

139

control, and RSD and the average recovery for the spikes were <9% and ranged between 89-

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98%, respectively. The detection limits varied between 0.01 and 1 µg/kg for individual PAHs

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(Table 5).

142 143

2.3. Data analysis

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2.3.1. Enrichment Factor (EF)

145

Enrichment factor (EF) is widely used to discriminate between natural and anthropogenic

146

sources and to reflect the status of environmental contamination. It is calculated as follows (Hu et

147

al., 2013; Pang et al., 2015):

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EF = (X/Al) sample / (X/Al) Background

(1)

150 151

where X refers to the concentration of a heavy metal of interest. In this study, Al was used to

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normalize the metal concentrations, and mean elements’ concentration in the Earth’s crust was

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considered as background sample, to calculate the enrichment factor. EF<1 indicates no

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enrichment, 1-3 minor enrichment, 3-5 moderate enrichment, 5-10 moderate to severe

5

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enrichment, 10-25 severe enrichment, 25-50 very severe enrichment, and >50 extremely severe

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enrichment (Chabukdhara and Nema, 2012).

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2.3.2. Potential ecological risk index (PER)

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The potential ecological risk index (PER) assesses the contamination degree of heavy metals in

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sediments, and is calculated as follows (Bastami et al., 2015):

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PER = ∑ E

(2)

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E = TC

(3)

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C = Ca/Cb

(4)

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where C is the single element pollution factor, Ca is the concentration of the element in samples,

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and Cb is the background reference value of the element (mean elements’ concentration in the

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Earth’s crust was used in this study). PER is a comprehensive potential ecological index, E is the

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ecological risk of individual metals or potential risk factor, T is toxic response factor which for

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the analyzed elements is taken as Zn = 1 < Cr = 2 < Cu = Ni = Pb = 5 < AS = 10 < Cd = 30 < Hg

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= 40. The potential risk factors are classified as: low (E < 40); moderate (40 ≤ E < 80);

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considerable (80 ≤ E < 160); high (160 ≤ E < 320); and very high (E ≥ 320). Consequently the

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potential ecological risk categories are as follows: low (PER < 150); moderate (150 ≤ PER <

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300); considerable (300 ≤ PER < 600); or very high (PER ≥ 600) (Hakanson, 1980; Rastegari

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Mehr et al., 2016).

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2.3.3. Mean PEL quotient

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Heavy metals always occur in sediments as complex mixtures, and they form combined toxicant

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groups in sediments. In order to evaluate the possible biological effects of the coupled toxicity of

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the studied metals the mean probable effect level (PEL) quotient method was used (Zhang and

181

Gao, 2015): 6

182 ∑

Mean PEL quotient =

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(5)

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where Cx is the sediment concentration of metal ‘‘x’’, PELx is the PEL for metal ‘‘x’’, and ‘‘n’’

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is the number of the studied metals.

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2.3.4. Toxic equivalents (TEQs) of PAHs

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To quantify the toxicity of PAH compounds relative to benzo[a]pyrene (i.e., an assumed

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reference chemical), toxic equivalency factors (TEFs) were calculated. Toxic equivalents (TEQs)

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of PAHs in each sampling station were calculated using the following equation (Nisbet and

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LaGoy, 1992):

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TEQ = ∑ Cn TEFn

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(6)

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where Cn is the concentration of PAHs, and TEFn is the toxic equivalency factor for PAHs

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(Table 5).

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2.3.5. Ecological risk of PAHs

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M-ERM-Q was used to assess the ecological risk of multiple toxic chemicals exceeding their

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effects range median (ERM) guidelines. The quantity is calculated according to the following

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equation (Li et al., 2015):

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M-RM-Q =

∑(

)

205 7

(7)

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where Ci is the concentration of compound i in a sediment sample, ERMi is the ERM for

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compound i, and n is the number of compounds.

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2.3.6. Mass Inventory

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The mass inventories of PAHs in the sampled sediments, as a potential source of pollution to

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oceanic environment, were calculated using the following equation (Li et al., 2015):

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I = CAdρ

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(8)

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where I is the inventory (in ton), C is concentration of total PAHs (µg/kg) in sediment, A is the

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water area of sampling region (km2), d is sediment thickness (7 cm in this study), and ρ is the

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sediment density (1.5 g/cm3).

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2.4. Statistical analysis and geographic information system

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Statistical analysis of the data was carried out using SPSS 19.0 for Windows. Multivariate

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statistical techniques such as correlation coefficients and principal component analysis (PCA)

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were performed for the dataset to reveal relationships between parameters and for source

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identification. Also, the Mann-Whitney U test, which is often considered the non-parametric

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alternative to the independent t-test, was used to compare heavy metal and PAH concentrations

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between the treatment lagoon and estuarine sediments. Also, ArcGIS (version 10) was applied to

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plot the location of sampling stations, and to determine the area of treatment lagoons and

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sampled region of the Estuary for calculation of mass inventories. For these purposes, satellite

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images obtained from SASPlanet (V.12.8.8) with appropriate magnification were georeferenced

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and used as base maps.

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3. Results and discussion

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3.1. Physicochemical parameters and total metals concentration

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Table 2 provides the descriptive statistics of heavy metal concentrations and physicochemical

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parameters in sediment samples, as well as their PEL and mean values in Earth’s crust. Based on

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the United States Department of Agriculture (USDA) ternary diagram, sediment texture could be

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classified as silty clay, clayey silt, clayey loam and silty sand showing the dominance of fine

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texture, particularly in the Estuary. The sediments collected from the interior of the Estuary have

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a reduced sand size fraction compared to the northwestern coast of the Persian Gulf. This coarse

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texture in S25-S31 samples is probably the result of local elevated hydrodynamics that interfere

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with fine particle deposition.

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The sediment pH ranges from 7.91 to 8.57 in treatment lagoons and 8.23 to 9.08 in the Estuary

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with mean values being 8.28 and 8.49, respectively. These high values reveal the alkaline nature

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of the sediments, and are comparable with the values measured in Tapti River Estuary in India

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(~8.29) (Shah et al., 2013). Seemingly, two main factors that affect pH values in the present

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study include the presence of a large amount of carbonate shells and discharge of several high

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pH petrochemical wastewaters (8 to 11) into the Estuary, reported by Moore and Keshavarzi,

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(2016). The mean sediment CEC value in the Estuary (34.99 meq/100g) is comparable with that

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of treatment lagoons (32.11 meq/100g). Sediment EC values are variable, ranging from 5.32 to

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34.72 and 2.97 to 32.62 mS/cm with an average of 16.99 and 13.36 mS/cm in the Estuary and

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treatment lagoons, respectively. Also, mean organic matter content values of sediments in the

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lagoons and the Estuary are 11.23 % and 10.52 %, respectively.

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Considering elemental concentrations, a decreasing trend is observed from the northern part of

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the Estuary to the outer Estuary (and northwestern coast of the Persian Gulf) and from the east of

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the petrochemical zone to the southwest. Two key source variables for this trend include

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petrochemical companies and urban activities, as well as the finer grain size of sediments in the

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North of the study area causing more pollutants adsorption. Aluminum is the most abundant

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metal in all sediment samples, and the mean concentration of Hg and Ni in both treatment

258

lagoons and Estuary exceeds their characteristic concentrations in the Earth’s crust. The highest

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Hg concentration (17952 µg/kg) is measured in sample S21 (east of the petrochemical zone).

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Also, mean Zn concentration in lagoon sediments exceeds its concentration in Earth’s crust. In

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comparison with probable effect level, Hg and Ni are the only elements with higher mean 9

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concentrations in surface sediment samples in the study area. Moreover, the mean Zn

263

concentration in treatment lagoons typically is enhanced relative to other samples. The difference

264

could also be seen for Cu and Pb, although to a lesser extent. This situation, clearly, shows the

265

role of treatment lagoons in reducing metal concentrations (particularly Zn) in wastewater. In

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fact, the present wastewater treatment system is relatively efficient in removing heavy metals.

267

However, if the produced enriched sediments is not disposed properly, a greater problem will

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emerge.

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3.2. Contamination level and risks of heavy metals

271

The enrichment factor (EF) of metals at each station was calculated to quantify the influence of

272

anthropogenic sources. Values of EF decreases in the following order: Hg > Ni > Zn > Cu > Pb >

273

Cr (Fig 2). The mean EF of Ni (27.60) and Hg (167.57) exceeds 25, indicating very severe and

274

extremely severe enriched levels, respectively. Cu and Zn are classified as moderately to

275

severely enrich with mean EF values of 5.32 and 7.78, respectively. Also, Pb (3.58) and Cr

276

(2.40) exhibit moderate and minor enrichments, respectively. It must be noted that, despite high

277

EF of Ni, it should not be considered as a highly anthropogenically influenced element in the

278

area. Negligible variations in Ni concentration (and EF) in different sampling stations and its low

279

skewness (which may show the normal distribution) confirms the high natural concentration of

280

Ni in the study area, consistent with past work (Moore and Keshavarzi, 2016). Moreover, in

281

contrast to higher EF values of Pb and Zn in treatment lagoons, the highest EFs for Hg are

282

observed in the eastern and southeastern parts of the petrochemical zone, probably due to the

283

activity of a chlor-alkali unit with a mercury-cell process in this location.

284

Chemical conditions of the sedimentary environment, pollutant inputs and physical

285

characteristics are important factors that affect heavy metal contamination in sediments (Sun et

286

al., 2015). In this study, higher heavy metal concentrations in samples collected from the eastern

287

part of the petrochemical zone, clearly illustrate the influence of anthropogenic metal input. The

288

role of the metal input is already reported in other parts of the Persian Gulf (Almasoud et al.,

289

2015; Alharbi et al., 2017; Bibak et al., 2018; Janadeleh et al., 2018). On the other hand,

290

generally, lower metal concentrations are measured in areas with intensive tidal washing (Mistch

291

et al., 2009). As mentioned earlier, the interior of the Estuary has a reduced hydrodynamics 10

292

where, greater contents of fine particles, as well as low washing mechanism of heavy metals, has

293

led to further accumulation of contaminants in the sediments. This is confirmed by the much

294

lower concentration of heavy metals in the northwestern coast of the Persian Gulf (samples S25-

295

S31) with coarse sediment texture.

296

The potential ecological risk index (PER) of heavy metals and ecological risk index (E) of

297

individual metals were calculated using the concentrations of studied elements (Cr, Cu, Hg, Ni,

298

Pb and Zn) in sediment samples. Figure 3 shows the calculated E values for each metal and PER

299

of the six studied elements in the surface sediments. Values of E for all investigated metals,

300

except Hg, in all sampling stations exhibit a low potential risk (below 40). Among the studied

301

metals, Hg shows the highest E value due to severe contamination, particularly east of the

302

Estuary (samples S19, S20, S21and S22), and its high toxic-response factor. Mercury

303

contamination also reaches 32.9 and 38.8 mg/kg in tissues of Cynoglossurs arel and Belangerii

304

of this area (Keshavarzi et al. 2018). Considering calculated PER values, low, moderate,

305

considerable and very high potential ecological risk are revealed for 10 (27.7 %), 8 (22.22 %), 13

306

(36.11 %) and 5 (13.88 %) of sediment samples, respectively. However, a decreasing trend in

307

ecological risk is observed from the inner Estuary to the outer Estuary and northwestern coast of

308

the Persian Gulf. It should be also noted that, because the wastewater of Bandar Imam

309

Petrochemical Company, as the main source of high Hg contamination, is discharged directly

310

and without treatment to the Estuary, PER is higher in the North of the Estuary than in sediments

311

of treatment lagoons.

312

To assess possible combined biological effects of heavy metals in sediments of the study area,

313

the mean PEL quotient of each sampling station was calculated (Fig 4). Based on previous

314

studies (Long et al., 2000; Zhang and Gao, 2015), mean PEL quotients of <0.1, 0.11–0.5, 0.51–

315

1.5 and >1.50, show 9%, 21%, 49% and 76% probable toxicity, respectively. The mean PEL

316

quotient of a large part of the Estuary ranges from 0.51 to 1.50, and consequently the probability

317

of such areas being toxic is 49%. Also, the highest probability for toxicity is measured for

318

sediments in the eastern part of the petrochemical zone with mean PEL quotient exceeding 1.5.

319 320

3.3. Metal Interrelationships and Source Identification

11

321

Pearson’s correlation coefficient analysis was performed to identify the relationship among

322

heavy metals, and between metals and physicochemical parameters in sediment samples (Table

323

3). The data normality was evaluated using Shapiro-Wilk normality test, and non-normal

324

parameters were normalized before the analysis. Pearson’s correlation analysis between the

325

concentrations of Cr, Ni and Al shows that these metals has a strong positive correlation (r =

326

0.89 to 0.97, p < 0.01). With regard to very slight variations in Ni, Cr and Al concentration in the

327

study area, their high correlation coefficients could be due to the same, natural, source. Also, the

328

relatively strong correlation between Al concentration and clay percent, and consequently its

329

correlation with CEC, stems from the role of Al in clay mineral structure (Brady and Buckman,

330

1960). Significant correlations are also observed between Cu, Pb and Zn with relatively severe

331

concentration variations in different sampling stations. Despite the similarity of Hg with Cu, Pb

332

and Zn in terms of concentration variations, there is no correlation between Hg and these metals,

333

mainly because of their different sources and distribution. The medium correlation between OM,

334

and Cu and Hg could arise from the affinity of these elements for organic matter, which is also

335

reported in previous studies (Yang et al., 2015; Keshavarzi et al., 2015; Amjadian et al., 2016).

336

The effect of sediments OM, silt and clay content on CEC is clearly obvious considering the

337

strong correlation between CEC and these parameters.

338

Principle component analysis (PCA) using factor extraction with an eigenvalue >1 after varimax

339

rotation was also applied to identify elements’ sources. Three principal components explaining

340

more than 78 % variance of the data were extracted (Table 4). The first factor is significantly

341

loaded with three metals (sand percentage, Al, Cr and Ni in descending order of loading values)

342

explaining 34.96 % of the total variance. As also evident from EF and correlation coefficients,

343

these metals are believed to be mostly accounted for by geogenic sources and to a minor extent

344

by anthropogenic sources. However, despite the fact that Ni is significantly enriched, it displays

345

a low variation coefficient, symmetric box plot and low range. Based on Paul et al., (2003) high

346

natural concentration of Ni in south Iran is the result of the proximity to the Zagros Mountain

347

belt, where, limestone, shale, sandstone and conglomerate are the main outcrops, while Ni is

348

enriched in some bauxite deposits. Association of these three metals with sand percentage in the

349

sediment samples confirms their geogenic source. The second component explained 22.87 % of

350

the total variance and loadings heavily on Pb, Zn and clay, and moderately on Cu, OM and silt.

351

The metals of this component seem to be affected by human activities, mainly wastewater of 12

352

petrochemical companies, Bandar-e-Emam port, pipe manufacturing units and commercial

353

dockyards. Placement of the metals with OM, clay and silt indicates their adsorption by mineral

354

and organic colloids. Also, the third component is loaded primarily by Hg, Cu and OM, and

355

moderately by clay accounting for 20.79 % of the total variance. Cu and Hg (particularly Hg),

356

are enriched in sediments of the study area and probably have anthropogenic sources, and their

357

correlation with clay and (particularly) organic matter reveals that these colloids have an

358

important role in accumulation of Hg and Cu in sediments. Overall, considering PCA, EF and

359

variation coefficients Hg, Cu, Zn and Pb are mostly affected by human activities.

360 361

3.4. Polycyclic aromatic hydrocarbons (PAHs) concentration and composition

362

Table 5 shows the descriptive statistics of PAH concentrations in sediments of the treatment

363

lagoons, and the Estuary. Total PAH concentrations (∑PAH), low-molecular-weight PAH (2–3

364

rings), and high-molecular-weight PAH (4–6 rings) range from 13 to 53449 µg/kg (averaging

365

11834.9 µg/kg), 1.95 to 29100 µg/kg (averaging 6500.11 µg/kg), and 11.05 to 24349 µg/kg

366

(averaging 5334.79 µg/kg) in lagoons, and 9.48 to 1514.3 µg/kg (averaging 148.76 µg/kg), 1.65

367

to 967 µg/kg (averaging 75.33 µg/kg) and 7.73 to 547.3 µg/kg (averaging 73.42 µg/kg) in the

368

Estuary, respectively. Sediment quality guidelines (the effect range low (ERL) and effect range

369

median (ERM) were used to assess the ecological risk of individual PAHs. Results show that

370

median concentrations of all PAHs are lower than both ERL and ERM, while in lagoon

371

sediments the mean concentrations of Acenaphthene (Ace), Fluorene (Fl), Fluoranthene (Flu),

372

Benzo[a]anthracene (BaA), and Chrysene (Chr) are higher than their ERL, and mean

373

concentrations of Phenanthrene (Phe) and Anthracene (Ant) exceed their ERM. Also,

374

Naphthalene (Np), Ace, Fl and Phe concentrations in sediments of the eastern part of the

375

petrochemical zone, affected by untreated wastewater, exceeded their ERL.

376

PAHs classification based on the number of aromatic rings in sediment samples is presented in

377

Figure 5. The results show that in the study area, the concentrations of 2-3 rings, 4 rings and 5-6

378

rings PAHs account for 4.44 to 31.92 %, 9.30 to 30.37 % and 50.94 to 78.89 % of total

379

concentration respectively. Therefore, 5-6 rings PAHs (BeP, BbF, BkF, BaP, DiBA, BgPer and

380

IndPy) are the dominant compounds in both lagoons and estuarine sediments.

13

381

To characterize the carcinogenic properties of PAH mixtures, TEQs were calculated (Fig 6). The

382

results indicate that total TEQ for sediment samples of treatment lagoons and the Estuary range

383

from 1.31 to 1981.51 µg/kg and 1.30 to 23.54 µg/kg, respectively. The highest TEQ values are

384

observed in the main treatment lagoon and eastern part of the petrochemical zone. Also, total

385

concentration of carcinogenic (BaA, Chr, BeP, BaP, BbF, BkF, IndPy, DiBA) PAHs in treatment

386

lagoons and the Estuary range from 12.30 to 19787 µg/kg, and 2.57 to 213.1 µg/kg, respectively.

387

To analyze the ecological risk of PAHs in sediment samples, the mean ERM quotient was

388

calculated. Based on ecological risk, the sampling sites are classified as low (<0.1), medium–low

389

(0.11-0.5), medium–high (0.51-1.50), and high-priority (>1.50), and are determined to coincide

390

with <11%, 25–30%, 46–53%, and >75% incidences of acute toxicity (Li et al. 2015). The

391

results show that except for the two main treatment lagoons, all sampling stations have m-ERM-

392

Q values below 0.1, indicating low priority, and the probability of being toxic was less than 11%.

393

The highest mean ERM quotient value (2.68) is observed in the main treatment lagoon to which

394

wastewater of different petrochemical companies are discharged. This indicates the importance

395

of these lagoons and, consequently, standard disposal of their bottom sediments. In fact, the

396

bottom sediments of the treatment lagoons in which PAHs are concentrated should be considered

397

as a potential source for contamination of the environment, and therefore, it needs to be disposed

398

safely after each periodic dredging of bottom sediments.

399 400

3.5. Identification of PAHs sources

401

The diagnostic ratios including Low Molecular Weight to High Molecular Weight

402

(LMW/HMW), Phe/Ant, Flu/Pyr, and Flu/Flu+Pyr were used to characterize the PAH potential

403

sources in sediments. The LMW/HMW ratio indicates that PAHs in 75% of the samples have

404

originated from pyrogenic sources (LMW/HMW< 1) (Readman et al., 2002). Also, the range of

405

Phe/Ant, Flu/Pyr, and Flu/Flu+Pyr are 2-55, 0.03-2.25, and 0.03-0.69, respectively. Based on

406

Qiao et al., (2006), Flu/Pyr values < 1 are indicative of petrogenic sources and values > 1 are

407

indicative of combustion origins; Flu/(Flu + Pyr) ratios below 0.5 and greater than 0.5 are typical

408

of petrogenic and pyrogenic origins respectively; and Phe/Ant values > 15 and < 10 are

14

409

indicators of petrogenic and pyrogenic sources, respectively. Therefore, apart from pyrogenic

410

input as a major source, there are also some petrogenic sources for PAHs in sediments.

411

Principal component analysis (PCA) was also conducted to reduce the number of variables (PAH

412

compounds) and to analyze relationships. Due to non-normal distribution of the data (based on

413

Shapiro-Wilk normality test) the data was log-transformed prior to performing PCA. Two

414

principal components (PCs) with accumulative variance of more than 85 % were extracted for

415

the sediment samples after Varimax rotation (Table 6). All PAH compounds have a positive

416

coordinate in PC1. This component, which explains 64.87 % of total variance, has strong

417

correlations with all compounds except DiBA, BgPer and IndPy. Regarding the strong

418

correlation of PAH compounds in PC1 with the total PAH concentration, it could be concluded

419

that the first component is a quantitative correlation component and corresponds to the total

420

PAHs concentration. PC2, contributing 20.24 % of the total variance, is dominated by 5-6 rings

421

PAHs (DiBA, BgPer and IndPy) related to pyrogenic sources, e.g., petroleum combustion and

422

refined petroleum products (Li et al. 2015). The highest concentrations of these three PAH

423

compounds are found in sediments close to Bandar Imam and Razi petrochemical complexes and

424

also in the main treatment lagoon. There are several fired flairs in the petrochemical zone,

425

emitting gaseous and particulate contaminants which may affect surrounding environments as

426

fallout. It seems that this atmospheric source, is the main pyrogenic origin of PAHs in this

427

region.

428 429

3.6. Mass inventory of total PAHs

430

Contaminated sediments in the study area could act as a source for the oceans. To assess this

431

potential, mass inventories of total PAHs were calculated. In this study, the mass inventories of

432

treatment lagoons and estuarine areas were calculated separately. Geographical information

433

system (GIS) was used to determine the water surface area of each sampling region and

434

accordingly, the area of lagoons and Estuary were 2.4 and 786 km2, respectively. Also, the

435

median and mean concentrations of the PAHs as lower and upper limits, respectively, were used

436

to deduce the conservative estimate of the concentrations. The calculated inventories range from

437

0.2 to 2.98 tons and 1.96 to 12.28 tons in the treatment lagoons and estuarine area, respectively.

438

Regarding the lower area of the lagoons compared with estuarine regions in this study, its

439

calculated mass inventories could be considered high, and urgent attention to lagoons’ sediments 15

440

is required. However, the mass inventory assessment may have some limitations and

441

uncertainties. For example, a homologous spread of PAHs in sediments, and a flat seabed with

442

no topographic variability was assumed in the calculations. This uncertainty for the Estuary may

443

be even more significant for the treatment lagoons. Nevertheless, the results obtained from this

444

approach can provide an initial view for the potential of a polluted area (as a secondary source)

445

to contaminate the marine environment. This approach could become more valuable if, for

446

instance, the mass inventory is also calculated for other coastal areas in the Persian Gulf. It

447

should be noted that, using mean concentrations of contaminants can reduce uncertainty to some

448

extent.

449 450

3.7. Mann-Whitney U test

451

The Mann-Whitney U test is used to compare differences between two independent groups when

452

the dependent variable is either ordinal or continuous, but not normally distributed. In the present

453

study, this test was performed to compare heavy metals and ∑PAH concentrations in sediment

454

samples collected at lagoons and estuarine areas due to their non-normal distributions (Table 7).

455

Results show that the concentrations of Cu, Pb, Zn and Al (p > 0.05) are not statistically different

456

between treatment lagoons and the Estuary. On the other hand, there are significant differences

457

in Ni, Cr, Hg and ∑PAH concentrations between the two sediment groups (p < 0.05).

458 459

3.8. Comparison with other Estuaries around the world

460

Heavy metal and PAH concentrations of sediment samples from Musa Estuary were compared

461

with data reported for some other locations around the world, particularly from other parts of the

462

Persian Gulf (Table 8). The concentrations of Pb, Cu, and Zn in the Musa Estuary are lower than

463

those at Jobos Bay (the main anthropogenic sources include fire event and Tire-tread materials),

464

Nemrut Bay (enriched mainly by smelting and refining, steel-producing industries and additives

465

in gasoline) and Djiboty (mainly effected by sewage discharges and the electrical power station),

466

but higher than the Red Sea (with oil shipping, marine paints and terrigenous sediments derived

467

from basement rocks as the main sources). Compared with data obtained from other locations in

468

Persian Gulf, Cu concentrations are higher in the Musa Estuary except for the Hormoz Strait

469

where commercial waste and domestic discharges are the main anthropogenic sources.

470

Moreover, Zn concentrations in the study area are much higher than those at other locations in

16

471

the Persian Gulf. Concentrations of Ni and Cr in this study are relatively higher when compared

472

with those at other coasts (except Nemrut Bay for Cr, which is mainly originated from steel-

473

producing industries).

474

Considering the mean concentration of Hg, sediments of the Musa Estuary are much more

475

contaminated than Assaluyeh and Qatar Coasts, but higher mean concentration is observed in

476

Nemrut Bay, where the main sources of Hg are smelting and mining activities. However,

477

maximum Hg concentration in sediments of the eastern part of the petrochemical zone in Musa

478

Estuary is severely higher (up to 17952 µg/kg). Mining activities caused higher Hg

479

concentrations in some other areas such as Idrija- Slovenia (up to 610 mg/kg), West Jawa-

480

Indonesia (up to 22.68 mg/kg), Karaburun- Turkey (Up to 100 mg/kg) and Steamboat Creek-

481

USA (Up to 21.43 mg/kg) (Gemici and Oyman, 2003; Stamencovic et al., 2004; Hidayati et al.,

482

2009; Bavec et al., 2014). Furthermore, the Musa Estuary is more contaminated with PAHs than

483

other parts of the Persian Gulf including Assaluyeh Coast and Khark Island. Like the Musa

484

Estuary, in these two areas, oil spill, and combustion of fossil fuels and natural gas in oil refinery

485

and petrochemical complexes have been reported as the main sources of PAH contamination, but

486

it seems that due to the coarse texture of sediments, less PAHs are concentrated in Assaluyeh

487

Coast and Khark Island. The Musa Estuary has also higher PAHs concentrations than the Gulf of

488

Aden (where the PAHs are mainly originated from urban activities), and lower than Jobos Bay

489

(with tire recycling center and thermoelectric plant that generates oil-based energy), Yellow Sea

490

(with municipal sewage and untreated industrial wastewater), Lenga Estuary (with combustion of

491

fossil fuels as the main source) and Daya Bay (with emissions and effluents from power plants

492

and nuclear power stations).

493

Many factors including industries, commercial ship traffic, urban effluents, ignorance about the

494

environmental principles and improper environmental management all play a role in the high

495

contamination in the study area. Some of these sources are mentioned also for other areas. For

496

instance, alkyl lead additives for gasoline are an important Pb source in Nemrut Bay, and also

497

petrochemical companies producing gasoline in the Musa Estuary may use this compound. Also,

498

the same urban and industrial sources for studied metals and PAHs were mentioned. Moreover,

499

water flow direction (from south towards the Iranian coasts) and the finer sediments texture (due

500

to hydrological conditions) could also have a role in the higher sediment contamination by the

17

501

Iranian coast of the Persian Gulf as compared to Arabian coasts (Reynolds, 1993; Agah et al.,

502

2009; Delshab et al., 2017).

503 504

4. Conclusions

505

In the present study, the pollution status, potential sources and ecological risk of selected heavy

506

metals and PAHs in the surface sediments of the Musa Estuary were investigated. The results

507

reveal the efficiency of treatment lagoons in preventing discharge of contaminants (particularly

508

PAHs) into the Estuary. However, direct discharge of untreated wastewater from petrochemical

509

units into the Estuary has resulted in high contamination of bottom sediments. These sources

510

together with local fallout from atmospheric sources, qualify Musa Estuary as one of the most

511

contaminated areas in the Persian Gulf. Regarding the high PAHs contamination of the lagoons’

512

sediments, standard disposal is a priority. Dredging of bottom sediments in the Bandar Imam

513

Port, which is periodically performed, will change the physicochemical conditions and move the

514

contaminants, particularly Hg and PAHs, from sediments to the soluble phase in the water. This

515

could make the chemicals bioavailable to aquatic organisms, especially fish, and consequently

516

pose a potential threat to consumers. This is a matter of great concern due to high pollutant loads

517

in Musa Estuary as compared to other coastal areas in the Persian Gulf. Considering the

518

importance of the Persian Gulf for sailing (since shipping traffic is an important source of

519

contamination) and fishing, it will be interesting to determine the share of each of the eight

520

Persian Gulf’s states for contamination risk by calculating mass inventories. By identifying the

521

types and major sources of contaminants, it will be possible to take preventive measures to

522

reduce pollution to this important aquatic environment.

523 524

Acknowledgements

525 526 527

This research was financially supported by Bandar Imam Petrochemical Company. The authors wish to express their gratitude to the Research Committee and Medical Geology Center of Shiraz University for logistic and technical assistance.

18

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Rastegari Mehr, M., Keshavarzi, B., Moore, F., Sacchi, E., Lahijanzadeh, A.R., Eydivand, S., Jaafarzadeh, N., Naserian, S., Setti, M. and Rostami, S., 2016. Contamination level and human health hazard assessment of heavy metals and polycyclic aromatic hydrocarbons (PAHs) in street dust deposited in Mahshahr, southwest of Iran. Human and Ecological Risk Assessment: An International Journal, 22(8), pp.1726-1748. Readman, J.W., Fillmann, G., Tolosa, I., Bartocci, J., Villeneuve, J.P., Catinni, C. and Mee, L.D., 2002. Petroleum and PAH contamination of the Black Sea. Marine Pollution Bulletin, 44(1), pp.48-62. Reynolds, R.M., 1993. Physical oceanography of the Gulf, Strait of Hormuz, and the Gulf of Oman—Results from the Mt Mitchell expedition. Marine Pollution Bulletin, 27, pp.35-59. Ryan, J., Estefan, G. and Rashid, A., 2007. Soil and plant analysis laboratory manual. ICARDA. Schulte, E.E. and Hopkins, B.G., 1996. Estimation of soil organic matter by weight loss-onignition. Soil organic matter: Analysis and interpretation, (soilorganicmatt), pp.21-31. Shah, B.A., Shah, A.V., Mistry, C.B. and Navik, A.J., 2013. Assessment of heavy metals in sediments near Hazira industrial zone at Tapti River estuary, Surat, India. Environmental earth sciences, 69(7), pp.2365-2376. Stamenkovic, J., Gustin, M.S., Marvin-DiPasquale, M.C., Thomas, B.A. and Agee, J.L., 2004. Distribution of total and methyl mercury in sediments along Steamboat Creek (Nevada, USA). Science of the total environment, 322(1-3), pp.167-177. Sugden, W., 1963. The hydrology of the Persian Gulf and its significance in respect to evaporite deposition. American Journal of Science, 261(8), pp.741-755. Sun, R.X., Lin, Q., Ke, C.L., Du, F.Y., Gu, Y.G., Cao, K., Luo, X.J. and Mai, B.X., 2016. Polycyclic aromatic hydrocarbons in surface sediments and marine organisms from the Daya Bay, South China. Marine pollution bulletin, 103(1-2), pp.325-332. Sun, Z., Mou, X., Tong, C., Wang, C., Xie, Z., Song, H., Sun, W. and Lv, Y., 2015. Spatial variations and bioaccumulation of heavy metals in intertidal zone of the Yellow River estuary, China. Catena, 126, pp.43-52. Yang, X., Yuan, X., Zhang, A., Mao, Y., Li, Q., Zong, H., Wang, L. and Li, X., 2015. Spatial distribution and sources of heavy metals and petroleum hydrocarbon in the sand flats of Shuangtaizi Estuary, Bohai Sea of China. Marine Pollution Bulletin, 95(1), pp.503-512. Yin, H., Deng, J., Shao, S., Gao, F., Gao, J. and Fan, C., 2011. Distribution characteristics and toxicity assessment of heavy metals in the sediments of Lake Chaohu, China. Environmental Monitoring and Assessment, 179(1-4), pp.431-442. Zhang, D., Liu, J., Jiang, X., Cao, K., Yin, P. and Zhang, X., 2016. Distribution, sources and ecological risk assessment of PAHs in surface sediments from the Luan River Estuary, China. Marine pollution bulletin, 102(1), pp.223-229. 23

Zhang, J. and Gao, X., 2015. Heavy metals in surface sediments of the intertidal Laizhou Bay, Bohai Sea, China: distributions, sources and contamination assessment. Marine pollution bulletin, 98(1-2), pp.320-327.

24

Table 1 UTM coordinates and texture of sediment sampling sites (Zone N38) HMs

PAHs

X

Y

Texture Clayey Loam Clayey Loam

S1

H1

295815

3357481

S2

-

301496

3356482

S3

H2

302928

3364916

Clayey Silt

S4

H3

311506

3364123

Clayey Silt

S5

-

317566

3363811

Clayey Loam

S6

H4

321955

3363651

Clayey Silt

S7

H5

323672

3366247

Clayey Silt

S8

H6

323357

3361766

Clayey Loam

S9

H7

324275

3370738

Clayey Silt

S10

-

328630

3370758

Silty Clay

S11

H8

313810

3376347

Silty Clay

S12

H9

314337

3373922

Silty Clay

S13

H10

314383

3371393

Silty Clay

S14

H11

311056

3370805

Silty Clay

S15

H12

308593

3373897

Silty Clay

S16

H13

313012

3368120

Clayey Silt

S17

H14

317026

3366416

Clayey Silt

S18

H15

327071

3373286

Silty Clay

Location Musa Estuary Musa Estuary Musa Estuary Musa Estuary Musa Estuary Musa Estuary Musa Estuary Musa Estuary Musa Estuary Musa Estuary Musa Estuary Musa Estuary Musa Estuary Musa Estuary Musa Estuary Musa Estuary Musa Estuary Musa Estuary

HMs

PAHs

X

Y

Texture

Location

S19

H16

317930

3371933

Silty Clay

Musa Estuary

S20

H17

318349

3370626

Silty Clay

Musa Estuary

S21

H18

319286

3369230

Silty Clay

Musa Estuary

S22

H19

318936

3367936

Silty Clay

Musa Estuary

S23

H20

321162

3374522

Clayey Silt

Musa Estuary

S24

H21

322093

3369855

Clayey Silt

Musa Estuary

S25

H22

415592

3330808

Clayey Silt

Persian Gulf

S26

H23

381276

3331237

Clayey Silt

Persian Gulf

S27

H24

281120

3321048

Silty Sand

Persian Gulf

S28

H25

276127

3307373

Clayey Loam

Persian Gulf

S29

H26

358244

3327376

Clayey Loam

Persian Gulf

S30

H27

297315

3322298

Silty Sand

Persian Gulf

S31

H28

331465

3335081

Silty Sand

Persian Gulf

S32

H29

317407

3370643

Silty Clay

Treatment Lagoon

S33

H30

317325

3372512

Silty Clay

Treatment Lagoon

S34

-

316398

3373590

Silty Clay

Treatment Lagoon

S35

H31

316015

3375330

Silty Clay

Treatment Lagoon

S36

H32

316114

3371665

Silty Clay

Treatment Lagoon

25

Table 2 Descriptive statistics of selected heavy metals and physicochemical parameters in surface sediment samples

Limit of Detection Musa Estuary and Persian Gulf Mean Lagoons Musa Estuary and Persian Gulf Median Lagoons Musa Estuary and Persian Gulf Std. Deviation Lagoons Musa Estuary and Persian Gulf Skewness Lagoons Minimum

Maximum

Variation coefficient

Musa Estuary and Persian Gulf Lagoons Musa Estuary and Persian Gulf Lagoons Musa Estuary and Persian Gulf Lagoons

Cu (mg/kg) 0.01

Pb (mg/kg) 0.01

Zn (mg/kg) 0.1

Ni (mg/kg) 0.1

Cr (mg/kg) 0.5

Al (%) 0.01

Hg (µg/kg) 5

CEC (meq/100g) -

OM (%) -

19.27

7.25

54.14

78.45

52.7

1.16

1629.68

34.99

20.6

8.53

181.98

68.66

46.52

1.11

104.4

18.41

6.43

48.5

79

52

1.09

17.73

8.38

73.8

69

47.2

3.84

3.45

16.49

9.11

7.9

2.60

227.14

0.72

5

2.07

-

EC (mS/cm) -

Sand (%) -

Clay (%) -

Silt (%) -

10.52

8.49

16.99

29.15

31.23

39.62

32.11

11.23

8.28

13.36

44.43

25.2

30.37

420

36.04

10.15

8.48

16.5

22.96

31.76

44

1.15

59

32.67

12.03

8.34

9.53

37.52

23.2

29.28

5.56

0.20

3820.1

3.69

3.27

0.16

7.17

22.36

11.19

12.79

8.6

6.12

0.09

116.95

2.94

2.66

0.28

11.91

13.77

11.40

7.11

1.91

-0.31

-0.19

0.06

3.42

-0.82

0.59

1.61

0.77

0.92

-0.35

-0.78

1.41

2.04

-0.02

0.38

-0.27

2

-0.77

-0.30

0.44

1.35

0.53

-0.4

0.79

12

5.04

34.6

56.7

39.4

0.75

99

23.61

5.7

8.23

5.32

0.8

3.2

4

15

6.11

36.4

59.2

40.3

1

25

27.63

7.67

7.91

2.97

30.8

9.2

22

29.76

25.19

111.4

95.8

64.1

1.57

17952

41.47

17.32

9.08

34.72

92.8

51.2

63.28

34.53

12.82

580.6

78.4

55

1.22

309

35.53

14.49

8.57

32.62

61.52

37.2

41.28

0.19

0.47

0.30

0.11

0.1

0.17

2.34

0.10

0.31

0.01

0.42

0.76

0.35

0.32

0.38

0.30

1.24

0.12

0.13

0.08

1.12

0.09

0.23

0.03

0.89

0.30

0.45

0.23

pH

Mean concentration in Earth crust*

26

15

66

20

155

8.2

55

-

-

-

-

-

-

-

PEL**

108.2

112.2

271

42.8

160.4

-

700

-

-

-

-

-

-

-

*

Kabata-Pendias and Mukherjee (2007)

**

Probable effect level

26

Table 3 Pearson’s correlation coefficients of metals and physicochemical parameters Ni Cr Al Cu Pb Zn Hg CEC OM pH EC sand clay silt

Ni

Cr

Al

Cu

Pb

Zn

Hg

CEC

OM

pH

EC

sand

clay

silt

1 0.97 0.92 0.44 0.22 0.18 0.28 0.12 0.37 -0.14 0.36 0.41 0.28 0.33

1 0.89 0.38 0.23 0.22 0.29 0.27 0.34 -0.12 0.34 -0.53 0.45 0.34

1 0.41 0.25 0.26 0.16 0.63 0.51 -0.25 0.44 -0.48 0.73 0.42

1 0.58 0.73 0.53 0.35 0.54 -0.52 0.33 -0.49 0.41 0.39

1 0.61 -0.02 -0.06 0.03 -0.24 0.02 -0.12 0.15 0.07

1 0.2 -0.02 0.32 -0.54 -0.01 -0.11 0.12 0.08

1 0.32 0.59 -0.09 0.43 -0.32 0.18 0.4

1 0.57 -0.29 0.53 -0.69 0.64 0.61

1 -0.54 0.53 -0.6 0.48 0.39

1 -0.32 0.35 -0.28 -0.36

1 -0.56 0.4 0.62

1 -0.91 -0.93

1 0.7

1

27

Table 4 Principal component analysis of the physicochemical parameters and heavy metals to reduce the dimensions and analyze relationships Component 1

2

3

Cu

0.35

0.48

0.67

Pb

0.10

0.73

0.14

Zn

-0.09

0.79

0.25

Ni

0.95

0.18

0.17

Cr

0.93

0.11

0.16

Al

0.89

0.24

0.09

Hg

-0.16

0.03

0.95

OM%

0.21

0.48

0.85

Sand

0.65

0.16

-0.14

Silt

0.33

0.42

0.17

Clay

0.28

0.59

0.49

28

Table 5 Descriptive statistics of PAHs in sediment samples (µg/kg)

Compound

Mean LOD

TEF

ERL

Median

Std. Deviation

Skewness

Minimum

Maximum

ERM Lagoon

Musa Estuary

Lagoon

Musa Estuary

Lagoon

Musa Estuary

Lagoon

Musa Estuary

Lagoon

Musa Estuary

Lagoon

Musa Estuary

Naphthalene (Np)

0.01

0.001

160

2100

23.04

28.53

1.4

4.8

43.28

88.09

2.17

4.87

ND

ND

100

460

Acenaphthene (Ace)

0.01

0.001

44

640

589.09

3.81

3.4

0.23

1184.54

10.47

2.20

3.33

ND

ND

2700

45

Fluorene (Fl)

0.01

0.001

19

540

499

4.7

2.7

0.23

1010

15.52

2.20

4.45

ND

ND

2300

78

Phenanthrene (Phe)

1.00

0.001

240

1500

4166

36

20

1.4

7827.24

97.77

2.11

3.15

0.5

0.2

18000

370

Anthracene (Ant)

0.10

0.01

853

1100

1222.96

2.21

2.8

0.2

2670.86

5.70

2.23

3.03

0.1

ND

6000

24

Fluoranthene (Flu)

1.00

0.001

600

5100

678.16

5.81

6.9

0.5

1167.58

11.65

1.92

2.37

0.3

0.3

2700

44

Pyrene (Pyr) Benzo[a]anthracene (BaA) Chrysene (Chr)

1.00

0.001

665

2600

443.82

29.62

8

5.6

603.17

62.03

0.67

2.86

3.8

2.7

1200

240

0.10

0.1

261

1600

2048.83

10.8

21

1.7

4445.79

22.47

2.23

2.97

ND

0.2

10000

99

1.00

0.01

384

2800

1548.14

8.25

16

1.2

3328.48

16.59

2.23

2.59

0.4

ND

7500

67

1.00

-

-

-

127.56

6.24

3.5

1.2

259.1

10.94

2.21

2.50

0.4

ND

590

47

0.1

320

1800

102.5

1.16

1.7

0.2

216.76

2.08

2.23

2.56

ND

ND

490

8.7

0.1

280

1620

228.31

3.83

2.7

0.6

487.52

7.35

2.23

2.52

ND

ND

1100

28

1

430

1600

114.68

0.56

0.2

0.2

254.53

1.33

2.24

4.90

ND

ND

570

7.1

1

63.4

260

17.57

2.93

1.9

2.1

36

2.10

2.23

1.53

ND

ND

82

8.5

0.01

430

1600

15.71

2.81

2.2

2.1

31.5

2.25

2.22

2.90

ND

ND

72

12

0.1

-

-

9.52

1.40

0.9

0.9

19.84

1.43

2.23

1.32

ND

ND

45

5.3

Benzo[e]pyrene (BeP) Benzo[b]fluoranthene (BbF) Benzo[k]fluoranthene (BkF) Benzo[a]pyrene (BaP) Dibenzo[ah]anthracene (DiBA) Benzo[ghi]perylene (BgPer) Indene[1,2,3-cd]pyrene (IndPy) ∑PAHs

0.10

-

-

-

-

11834.90

148.76

78.85

23.75

23385.83

326.60

2.18

3.35

13

9.48

53449

1514.3

LMW

-

-

-

-

6500.11

75.33

26.8

6.4

12716

201.77

2.17

3.84

1.95

1.65

29100

967

HMW

-

-

-

-

5334.79

73.42

59.05

17.30

10671.58

133.18

2.19

2.60

11

7.73

24349

547.3

1.00 0.10 0.50

0.10 0.01

LOD, Limit of Detection; ∑PAHs, total PAHs concentration; LMW, low molecular weight PAHs; HMW, high molecular weight PAHs; TEF, toxic equivalency factor for PAHs; ERL, effect range low; ERM, effect range medium.

29

Table 6 Principal component analysis of PAHs to reduce the dimensions and analyze relationships

Np Ace Fl Phe Ant Flu Pyr BaA Chr BeP BbF BkF BaP DiBA BgPer IndPy

Component 1 2 0.686 0.374 0.926 0.098 0.912 0.095 0.930 0.189 0.951 0.120 0.955 0.206 0.886 0.298 0.917 0.248 0.927 0.284 0.891 0.360 0.883 0.206 0.842 0.301 0.767 -0.035 0.132 0.968 0.048 0.971 0.401 0.783

30

Table 7 Mann-Whitney U test results for heavy metals and PAHs Mann-Whitney U Wilcoxon W Sig.

Cu

Pb

Zn

Ni

Cr

Al

Hg

∑PAHs

69 84 0.723

40 536 0.091

38 534 0.074

34 49 0.047

31.5 46.5 0.032

69.5 84.5 0.723

11 26 0.001

14 420 0.013

31

Table 8 Range (and mean) heavy metals and PAHs concentration in sediments from different locations Location

Cu (mg/kg)

Pb (mg/kg)

Zn (mg/kg)

Ni (mg/kg)

Cr (mg/kg)

Hg (µg/kg)

Musa Estuary (Persian Gulf) Assaluyeh Coast (Persian Gulf) Jobos Bay (Puerto Rico) Nemrut Bay (Turkey)

12.01-29.76 (19.27) 1.9-304.8 (15.43)

5.04-25.19 (7.25) 1-14.5 (3.39)

34.60-111.40 (54.14) 5.1-123.6 (21.08)

56.70-95.80 (78.45) 5.4-80 (19.04)

39.40-64.10 (52.70) 5.7-52.4 (16.13)

99-17952 (1629.68) 32-365 (116.38)

1.6-53 (29)

1.5-22 (11)

10.8-129 (64)

-

-

9.6-43.7 (29.5) 1-72.2 (36.6) 25-32.5 (27.5)

22.3-89.4 (60.4) 0.7-44.8 (22.65) 20-25 (22.50)

75-271 (182.9) 0.3-288.1 (144.2) 45-52.5 (48.8)

18.1-63.4 (50.4) 0.8-39.7 (20.25)

1.2-8.2 (4.4)

0.4-3.9 (2.4)

-

0.04-0.72 (0.38)

1.12-4.94 (2.56)

-

Djibouti-city Hormoz strait (Persian Gulf) Qatar Coast (Persian Gulf) Red Sea (Egypt) Yellow Sea (China) Lenga Estuary (Chile) Daya Bay (China) Gulf of Aden (Yemen) Assaluyeh Coast (Persian Gulf) Khark Island (Persian Gulf)

∑16 PAHs (µg/kg) 9.48-1514.3 (148.76)

Reference This study

-

Delshab et al., 2017


40.4-1912 (593.12)

Aldarondo-Torres et al., 2010

35.7-98.8 (69.6) 1.9-63 (32.45)

1700-9600 (5750)

-

Esen et al., 2010

-

-

Ahmed et al., 2017

-

-

-

-

Bastami et al., 2015

11.5-40.8 (27.1)

1.1-6.7 (1.7)

-

De-Mora et al., 2004

5.26-12.36 (7.66)

0.7-20.8 (11.2) 1.40-4.83 (3.16)

-

-

-

Nour et al., 2006

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

32

52.3-1870.6 (472) 290-6118 (2025) 340-710 (480) 2.2-604 (82.36) 12.8-81.25 (28.6) 2.95-253.30 (71.87)

Jiao et al., 2012 Pozo et al., 2011 Sun et al., 2016 Mostafa et al., 2009 Keshavarzifard et al., 2017 Akhbarizadeh et al., 2016

Fig. 1 Map of the study area showing location of the sampling sites.

Fig. 2 Box plot showing enrichment factor of heavy metals

Fig. 3 Ecological risk of individual metals and potential ecological risk in sediment samples

Fig. 4 Bar graph of calculated mean PEL quotient for heavy metals

Fig. 5 Ternary diagram of PAH compositions in sediments

Fig. 6 Bar graph of PAHs’ toxic equivalents for sediment samples in the study area

Highlights • • • • •

Persian Gulf is threatened by contaminants from industrial and shipping activities. Sediments of Musa estuary are highly contaminated by PAHs and HMs (especially Hg). Contaminants originating from petrochemical zone pose high ecological risk to aquatic life. The Estuary has a high potential for contaminating the marine environment. Treatment lagoons have a great role in reducing metal and PAH concentrations.

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: