Risks Assessment of Potentially Toxic Elements’ Contamination in the Egyptian Red Sea Surficial Sediments

Abstract

The potential impact of tourism, industrial, and urban activities on Egypt’s Red Sea coastline, which is well-known for its economic and environmental importance, was investigated at fifteen coastal sites. In the present study, the concentration of cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn) in marine sediments from these sites, was determined using Inductively Coupled Plasma Mass Spectrometers (ICP-MS). In detail, various pollution indices, statistical analyses, and spatial distribution patterns were used to assess the pollution status, impacts of human activities, ecological risks, and sources of potentially toxic elements (PTEs) in surface marine sediment. A detailed comparison with up-to-date data was conducted. These sediments were composed predominantly of fine and very fine sands. Mean grain size distribution typically depends on the source of the sediment from the following two prime sources: terrigenous (autochthonous) and biogenic (allochthonous). The detected PTE mean concentrations were as follows: Fe > Mn > Zn > Cr > Ni > Pb > Co > Cu > Cd. Multivariate statistical analysis results revealed their close distribution and association. Cd and Pb levels in the studied area have been slightly impacted by anthropogenic inputs. According to the calculated pollution indices, although a minimal or moderate contamination degree was detected in the study area, it was determined that there was a low to moderate ecological risk. The slightly high degree of contamination and risk centered in the middle of the study area around phosphate mining and related activities. More attention should be given to the concentrations and sources of Cd, Ni, and Pb as the main pollution factors.

1. Introduction

Potentially Toxic Elements (PTEs) are vital indicators for aquatic ecosystems since they originate from multiple sources and have bio-accumulative, persistent, and toxic characteristics [1,2]. PTEs are imported into aquatic ecosystems through both natural and anthropogenic processes. They are typically released through natural processes such as rock weathering and erosion, and they strike the water via various transport pathways. Anthropogenic activities, rapid industrial growth, and urbanization represent the critical factors contributing to enhanced PTE concentrations over recent decades [3,4,5,6]. For example, Christophoridis et al. [7] reported a hotspot of Cd, Cu, Ni, Pb, and Zn contamination in the northern part of Thessaloniki Bay, Greece, indicating the cumulative effect of the anthropogenic load from industrial and maritime activities on urban coastal zones. Looi et al. [8] linked PTE pollution in the Tanjung Dawai’s marine sediments, Malaysia, to fisheries, aquaculture, paddy cultivation, use of insecticides and arsenical herbicides, the leaching of antifouling paints, and the discharge of wastewater sludge. Hoai et al. [9] found that stations near river inflows, urban areas, and coal mining had greater As, Cu, and Pb concentrations than offshore stations in Vietnam’s Ha Long Bay. Radomirović et al. [10] ascribed elevated As, Cr, Cu, Ni, Pb, and Zn concentrations in Tivat bay (Montenegro) marine sediments to shipyard activities, grit excavation, port waste, and wastewater discharges. Apaydın et al. [11] noted that concentrations of Cu, Pb, and Zn were higher in marine sediments in the Middle and Eastern Black Sea region, Turkey, due to natural rock erosion as well as anthropogenic inputs (mining, agricultural activities, and traffic emissions). Oura et al. [12] attributed the elevated As, Cd, Cr, Sb, and Pb concentrations in marine sediments recorded along the Ivorian coast to maritime trade, offshore oil production, and petrochemical plants.

Currently, PTE contaminations in coastal environments are increasingly encountered and are considered one of the most pressing global issues [9,13,14,15,16,17]. PTEs are thought to be the main pollutants which directly affect the marine environment [18,19]. While PTEs such as Cu, Ni, and Zn are necessary for aquatic life, they can be harmful at certain concentrations, posing a threat to aquatic biological diversity. PTE-contaminated sediment and water have toxic effects on aquatic fauna and flora [8,20,21,22,23,24]. Because aquatic living organisms are part of the food chain, increasing PTE levels in the aquatic environment have toxic impacts not only on aquatic living organisms but also on humans. Humans’ exposure to PTEs occurs via the gastrointestinal tract (water drinking, food ingestion), inhalation, and dermal contact [25,26]. The main toxic impacts on human health associated with exposure to PTEs are cancers, dermatitis and muscular impairment, reproductive system damage, low infant birth weight, lactation problems, gastrointestinal and lung diseases, kidney pathology, immune system dysfunction, and neurological disorders [26]. Thus, the measurement of ecological and toxicological effects of some PTEs is meaningful for evaluating their severity degree [27,28]. Consequently, contamination by PTEs represents a considerable socioeconomic problem and has become a worldwide environmental concern with potential long-term effects on humans and the ecosystem [18,19,25].

Coastal sediment acts as an important sink of PTEs in the marine environment and plays a fundamental role in element transmission, complexation, and deposition [29]. In addition, coastal sediments are considered to be conveyors and potential secondary sources of PTEs in the marine environment [30]. PTE pollutants cannot be frequently settled in sediment; they may be released back into the water column because of possible changes in environmental conditions such as redox potential, temperature, pH, and microbial activity [31]. However, PTEs are expected to be more concentrated in the sediment in the aquatic system than in the water body [23,32]. Therefore, continual surveys of pollutant concentrations (e.g., PTEs) in the sediment of coastal areas are not only valuable for assessing pollution in the aquatic environment but moreover provide essential updated information on environmental health status and help to evaluate future risks [33].

The Red Sea has immense universal value due to its facilitation of important maritime transportation between six countries and as a connection between two continents (Africa and Asia). The Red Sea is characterized by a length of ≈1930 km with an average width of 280 km [34,35]. In the past two decades, the Red Sea has been regarded as one of the most important industrial, commercial, tourism and recreation centers in Egypt. The economic development activities on the Egyptian side of the Red Sea have been accompanied by ecological damage. Recently, the Red Sea coastal area has suffered from multiple environmental challenges such as coastal activities, shipping, maritime transport, spillage problems, industrial and sewage effluents, and natural oil seeps [29]. As a result, these continuous inputs have generated tremendous and diverse types of contaminants, including PTEs. Therefore, the present study attempted to provide the most recent picture of PTE levels along the Egyptian side of the Red Sea coastal area and to identify their potential sources. In addition, performing a multi-index risk assessment of PTEs in the studied marine sediment provides decision makers with data for a future control plan.

2. Materials and Methods

2.1. Sampling and Samples Preparation

Coastal Red Sea sediments are the product of a complex interplay of sedimentary material influx from aeolian, fluvial, and marine sources. The fluvial sediment is deposited by intermittently active wadies as a consequence of the weathering of Miocene and younger sedimentary succession and the Precambrian basement rocks of Red Sea mountains. In the current study, a total of fifteen coastal sites along the Egyptian side of the Red Sea coast were investigated (Figure 1 and

Table 1. Location and contributing sources of pollution of Red Sea sampling sites.

Site No.	Coordinates		Sample Sites	Depth (m)	Contributing Sources of Pollution
	Latitude	Longitude
1	27°38′15.11″	33°31′ 26.28″	Gemsha	8.0	Petroleum exploration, sulphur mining, fishing port
2	27°26′13.95″	33°39′51.05″	North Hurghada	6.5	Tourism
3	27°10′33.53″	33°50′34.32″	Hurghada	6.0	Urban, tourism, fishing port
4	27°06′50.21″	33°49′54.79″	South Hurghada	11.0	Tourism
5	26°45′26.39″	33°57′18.23″	North Safaga	9.0	Urban, commercial harbor
6	26°33′47.67″	34°02′24.15″	South Safaga	8.0	Urban, phosphate seaport
7	26°15′12.01″	34°12′04.66″	El-Hamrawein	9.0	Urban, phosphate seaport
8	26°10′19.84″	34°15′42.15″	Qusier	14.0	Urban, phosphate mining, fishing port
9	25°40′08.02″	34°34′10.28″	N. Marsa Umm El-Griufat	6.0	Tourism, mangrove ecosystem
10	25°36′01.69″	34°36′48.74″	N. Sharm El Bahria	5.0	Tourism, mangrove ecosystem
11	25°31′39.23″	34°38′40.02″	Abu Dabab	4.0	Tourism
12	25°28′23.90″	34°40′4821″	Sharm El Naaba	6.0	Tourism
13	25°03′45.50″	34°54′07.20″	Marsa Alam	10.0	Urban, tourism, fishing port
14	24°11′57.44″	35°25′56.30″	Wadi Lahmi	5.0	Tourism
15	23°08′13.99″	35°40′17.67″	Marsa Humira	8.0	Tourism

). The investigated coastal locations were extended to include different natural and anthropogenic probable sources. From every sampling site, up to 15 subsamples were collected during the field survey (July 2020), utilizing the Van Veen Grab Sampling tool (thickness of about 10 cm) to represent a perfect sample that reflects chemical, physical and biological exchange processes. The sample collection strictly followed the standard methods reported by [36]. The collected sediment samples were preserved in airtight polyethylene bags, labeled, and transported to the Geology Department, Faculty of Science, Suez University Labs, for preparation. The Collected samples were air-dried to a constant weight, carefully mixed, homogenized, and divided into subsamples for further analyses.

2.2. Sediment Granulometry

In order to determine grain-size distribution, the air-dried sediment samples were analyzed using the dry sieving technique [37]. A total of 100 g of each sample was separated using a Ro-Tap shaking machine for 20 m, with a set of standard mesh sieves (−1, 0, 1, 2, 3, and 4 Ø). For samples that contained mud (<64 µm) portions of >5%, the pipetting technique was used to separate silt and clay fractions [38]. Using data obtained from cumulative curves, the textural parameters including mean size (Mz), kurtosis (KG), skewness (SkI), and sorting (σI) were calculated according to [39]. Samples preparation and granulometry analyses were conducted at the Geology Department, Faculty of Science, Suez University Laboratories.

2.3. Geochemical Analyses

After multi-acid solution digestion, PTE total concentrations were measured in the well-powdered sediment samples. An acid mixture (HF + HCl + HNO₃; 1:4:5, in volume) was added to an accurate weight of 0.25 g of a sample in a pre-cleaned Teflon crucible. It was gently heated by using a heating plate. The digestion was continued until a small amount of the white residue was obtained. Then, the solution was heated until nearly dry and extracted with HNO₃ [40]. Cadmium, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn concentrations were determined with Inductively Coupled Plasma Mass Spectrometers (Varian 810/820-MS ICP, Varian Inc., Mulgrave, VIC, Australia). The geochemical analyses were carried out at the accredited Reference Laboratory for Drinking Water, Holding Company for Water and Wastewater, Shubra El-Khima Water Treatment Plant, Cairo, Egypt (ISO 17025).

The Teflon vessel and glassware were cleaned by soaking in a 10% (v/v) HNO₃ solution for 15 min and then rinsed with deionized water about three times and dried before use. All solutions were carefully prepared by using double distilled water. Reagent-grade chemicals (HF, HCl, and HNO₃; Merck, Darmstadt, Germany) were used for all purposes. The accuracy and precision of the analytical procedures were examined by analyzing a standard reference material (HISS-1, marine reference sediment, NRCC). The recovery of the measured elements fluctuated between 96.47% and 108.53%, with the uncertainty of the measured PTEs ranging from 1.9% to 5.0%. The detection limits of Cadmium, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn were 0.53, 1.00, 0.60, 0.65, 2.00, 0.20, 0.60, 2.00, and 3.00 (µg/L), respectively.

2.4. Pollution Assessment

Environmental indices are useful mathematical tools for both analyzing and simplifying complex environmental data, and they are a valuable technique in evaluating environmental risk and sediment pollution [41]. In addition, indices make it easier to evaluate PTE pollution results in terms of their origin, availability, and toxicity [42]. Furthermore, contamination indices can be used to determine whether the accumulation of PTEs is caused by natural or anthropogenic sources. In data interpretation, selecting background values is critical. Background values are typically used to distinguish between the natural quantities of elements and their elevated concentrations due to anthropogenic influences [43]. As of yet, a national Egyptian background concentration for PTEs in sediment has not been determined, as well as sediment quality guidelines. The average element levels in the upper continental crust were utilized in the absence of local and regional geochemical background levels [7,8,14,44].

Single pollution indices such as the geo-accumulation index (I_geo) [45], and the contamination factor (C_f) [46] were calculated to quantify the anthropogenic inputs of every element above its natural levels in Red Sea sediments. Furthermore, integrated pollution indices such as degree of contamination (C_deg) [46], the Ecological Risk Index (RI) [46,47], Contamination Security Index (CSI) [42], and mean ERM quotient (MERMQ) [48] were calculated to conduct an integrative assessment of sediment ecosystem quality at every sampling site and evaluate the potential ecological risks posed by the PTE contamination considering multi-element contamination. The categories of single and integrated pollution indices are summarized in Table S1 (in Supplementary Materials).

2.4.1. Geo-Accumulation Index (I_geo)

I_geo is widely used in studies concerned with the assessment of PTE pollution in coastal areas. According to [45], I_geo was calculated using Equation (1).

$${\mathrm{I}}_{geo}={\log }_2\left(\frac{C_s}{1.5 C_{ref}}\right)$$

(1)

where C_s is the concentration of PTE in the investigated sample and C_ref is the reference value.

2.4.2. Contamination Factor (C_f)

C_f is a direct and the most straightforward pollution indicator used to assess PTE contamination levels in coastal sediment [4,22,23,29]. C_f is calculated using Equation (2) [46].

$$C_f^i=C_s^i/C_b^i$$

(2)

where $C_s^i$ is the element concentration in the analyzed samples, and $C_b^i$ is the reference value of investigated PTEs.

2.4.3. Degree of Contamination (C_deg)

C_deg is considered a more comprehensive method for understanding the contamination level and was calculated for the Red Sea sediments according to Equation (3) [46].

$$C_{deg}={\displaystyle \sum }_{i=1}^n{C}_f$$

(3)

where C_deg is the degree of contamination of investigated sediment, C_f is the contamination factor, and n is the number of the examined PTEs.

2.4.4. Ecological Risk Index (RI)

Among the ecological risk indices, the individual ecological risk factor ($E_i^r$) and overall ecological risk index (RI) were investigated. ($E_i^r$) was suggested by Hakanson [46] for assessing individual ecological risk factors according to Equation (4).

$$\left(E_i^r\right)=C{f}_i\times T_i^r$$

(4)

The overall ecological risk caused by all toxic elements (RI) was determined as the sum of $E_i^r$ as shown in Equation (5).

$$\mathrm{RI}={\displaystyle \sum }_{i=1}^n{E}_i^r$$

(5)

where $C{f}_i$, $T_i^r$ and n represent the contamination factor, toxicological response factor for each toxic element proposed by Hakanson [46] (1, 2, 5, 5, 5, and 30 for Zn, Cr, Pb, Cu, Ni, and Cd, respectively), and the number of investigated metals, respectively.

2.4.5. Contamination Security Index (CSI)

Although CSI is a relatively new index, it is considered valuable. It is used to evaluate the toxicity limit and both biological and ecological impacts for most PTEs [49]. CSI was adopted by Pejman et al. [42] and can be calculated using Equation (6).

$$\mathrm{CSI}={\displaystyle \sum }_{i=1}^n\mathrm{W} [ \left(\frac{C_S}{\mathrm{ERM}}\right)²+\left(\frac{C_S}{\mathrm{ERL}}\right)½]$$

(6)

where C_S, n, W, ERM and ERL are the metal concentration, number of investigated toxic elements, computed weight of element (0.25, 0.134, 0.075, 0.251, 0.215 and 0.075), effects range median ERM (9.6, 370, 270, 218, 51.6 and 410), and effects range low (1.2, 81, 34, 46.7, 20.9 and 150) for Cd, Cr, Cu, Pb, Ni and Zn, respectively [48].

2.4.6. Mean ERM Quotient (MERMQ)

MERMQ was implemented to assess the ecological impacts of PTEs with an accurate scale. The mean effects range-medium quotient (MERMQ) was calculated according to Equation (7) [50].

$$\mathrm{MERMQ}=\frac{{\sum }_{i=1}^n\left(\frac{C_S}{\mathrm{ERM}}\right)}{n}$$

(7)

where C_S, ERM, and n were element concentration, effects range median, and number of investigated toxic elements, respectively [48].

2.5. Data Treatment

Sediment samples were classified using the ternary chart adopted by Folk [37]. Both cumulative frequency and cumulative weight-percentage curves were generated and studied to identify potential grain-size trends. PTEs concentration and the calculated integrated pollution indices were represented in a distribution pattern using the proportional/graduated symbols method (using Arc-Map 10.3, ESRI, Inc., Redlands, CA, USA). To determine if the PTE concentrations were normally distributed, the Kolmogorov–Smirnov (K-S) test was performed using OriginLab (version OriginPro 2021). Furthermore, statistical analyses including Pearson’s Correlation Coefficient (PCC), a Principal Component Analysis (PCA R-Mode), and Cluster Analysis (CA R-Mode and Q-Mode) were conducted using IBM SPSS (version 23, Armonk, NY, USA) and OriginLab (version OriginPro 2021) to investigate relations between the studied PTEs in terms of their geochemical affinity and their origins. CA was applied to show similarity relations between the studied metals, their association and to illustrate different geochemical groups in a dendrogram.

3. Results and Discussion

3.1. Textural Attributes

The results of the grain-size analysis of the collected samples are given in

Table 2. Grain size data, textural nomenclature, and Statistical grain size parameters of the studied sediments.

Sample No.	Gravel %	Sand %						Mud %		Nomenclature	Mean Size	Sorting	Skewness	Kurtosis
		VC Sand	C Sand	M Sand	F Sand	VF Sand	Total Sand	Silt	Clay
1	3.5	7.0	11.4	31.3	31.5	15.4	96.5	0.0	0.0	GS	F Sand	PS	NS	LK
2	18.7	6.2	6.0	13.0	50.2	5.9	81.3	0.0	0.0	GS	M Sand	PS	SCS	PK
3	3.7	1.5	2.7	46.7	37.1	8.4	96.3	0.0	0.0	GS	M Sand	MS	NS	MK
4	0.0	0.0	0.0	0.0	55.3	23.2	78.5	18.3	3.2	MS	VF Sand	PS	SFS	VLK
5	1.9	0.0	0.0	0.0	50.3	43.7	94.0	0.0	0.0	MGS	VF Sand	MWS	NS	Mk
6	7.6	1.3	1.2	26.0	59.1	4.8	92.4	0.0	0.0	GS	F Sand	MS	SCS	VLK
7	0.0	0.0	0.0	1.1	6.2	47.5	54.8	41.3	3.9	MS	VF Sand	PS	FS	LK
8	0.0	5.2	7.5	23.0	43.8	14.9	94.4	4.5	1.2	MS	F Sand	PS	NS	LK
9	11.3	5.7	9.1	22.0	29.5	17.9	84.1	0.0	0.0	MGS	M Sand	PS	CS	MK
10	9.82	6.2	5.8	18.5	51.5	8.2	90.2	0.0	0.0	GS	M Sand	PS	SCS	LK
11	1.91	0.0	0.0	5.6	28.3	63.6	97.4	0.0	0.0	GS	F Sand	WS	SCS	LK
12	7.1	0.0	0.0	16.5	47.8	25.1	89.4	0.0	0.0	MGS	F Sand	PS	CS	VLK
13	4.74	0.0	0.0	11.0	33.9	14.4	59.4	25.9	10.0	GMS	VF Sand	VPS	SFS	PK
14	8.32	0.0	0.0	41.4	41.4	4.8	87.5	0.0	0.0	MGS	F Sand	PS	CS	VLK
15	6.41	2.2	12.0	16.1	45.2	15.3	90.7	0.0	0.0	MGS	F Sand	PS	SCS	LK

VC = Very Coarse, C = Coarse, M = Medium, F = Fine, VF = Very Fine, GS = Gravelly Sand, MS = Muddy Sand, MGS = Muddy Gravelly Sand, GMS = Gravelly Muddy Sand, PS = Poorly sorted, MS = Moderately sorted, MWS = Medium well sorted, WS = Well sorted, VPS = Very poorly sorted, NS = Near symmetrical, SCS = Strongly coarse skewed, SFS = Strongly fine skewed, FS = Fine skewed, CS = Coarse skewed, LK = Lepto-kurtic, PK = Platy-kurtic, MK = Meso-kurtic, VLK = Very lepto-kurtic.

. The data reveal that Red Sea sediment is composed of gravelly sand (40%), muddy gravelly sand (33.33%), muddy sand (20%) and gravelly muddy sand (6.67%). Sand fractions were found to be dominant in all the studied sites (54.8–97.4%). On the other hand, gravel and mud fractions have no clear trend. It is evident that Red Sea sediments are mainly of terrigenous origins and are composed mainly of fine to very fine sands; they are also rich in terrigenous constituents that originate from surrounding areas. Gravel distribution depends on topographical features and benthic fauna shell fragments (biogenic fragments), leading to an increased gravel fraction in many sites. The limited distribution of fine fractions in the studied area may be due to a low fine sediment supply to coastal areas and/or the re-transportation of fine fractions to deeper areas as a result of the longshore current. Mud increased in areas of lagoons, where stagnant water makes it possible for mud (silt and clay fractions) to deposit [4,23,29,51].

The mean grain size varied between medium sand and very fine sand. Sorting fluctuated between well sorted and very poorly sorted. The skewness ranged from strongly coarse-skewed to strongly fine-skewed. Kurtosis varied from platy-kurtic to very leptokurtic (Table 2). The difference in the textural parameters implies that they are primarily influenced by dynamic processes. The variations in the particle size parameters in aquatic sediment depend on different factors (act on sediment together or separately), such as sedimentary processes, transportation of sediment, and the distance between source material and the deposition basin [52,53,54]. The studied sediments have two main sediment sources, namely terrigenous (autochthonous) and biogenic (allochthonous) sources, with different agents of transportation. Sorting indicates variations in kinetic energy, such as a decreasing velocity of the transporting agent [55]. The sediment sorting wide ranges point to turbulent conditions [53]. These sediments have negatively skewed values, indicating a high energy environment [56]. Kurtosis suggests the characteristics of deposition medium flow. Most of the studied sites’ sediment were lepto- and very leptokurtic in their nature, which suggests that the sediment is mainly sand with minor populations of other grain sizes.

3.2. PTE Distribution

Geochemical analyses were carried out with surface sediment samples of the Egyptian Red Sea coast. Their analytical values of PTEs and the background values of the upper continental crust [57] and average global sediment [58] are listed in

Table 3. Descriptive statistics of PTEs (ppm) in Red Sea sediments.

Sample No.	Area	Cd	Co	Cr	Cu	Fe	Mn	Ni	Pb	Zn
1	Gemsha	0.27	2.07	11.74	1.98	4266.82	78.04	4.33	1.00	21.74
2	North Hurghada	0.10	2.03	10.06	1.06	1011.01	62.84	6.24	3.00	18.92
3	Hurghada	0.02	2.65	9.89	1.34	2190.88	47.09	11.26	9.05	30.56
4	South Hurghada	0.06	1.67	9.70	1.87	1621.47	59.65	9.99	4.03	26.93
5	North Safaga	0.37	2.67	16.96	2.66	2387.05	155.00	12.11	7.43	42.13
6	South Safaga	0.17	6.56	21.73	1.62	6402.44	256.02	10.48	6.45	42.10
7	El-Hamrawein	0.42	7.98	56.47	2.97	4972.29	177.40	29.83	3.96	44.73
8	Qusier	0.18	3.64	35.83	0.95	3342.87	277.95	32.88	3.88	28.98
9	N. Marsa Umm El-Griufat	0.09	6.97	37.97	5.07	7050.76	354.10	51.05	5.98	31.94
10	N. Sharm El Bahria	0.14	4.93	28.94	3.74	652.93	188.47	22.94	3.48	12.74
11	Abu Dabab	0.20	1.02	12.86	0.84	501.98	108.24	9.93	2.29	19.74
12	Sharm El Naaba	0.07	18.08	30.97	8.97	6032.35	221.30	41.73	3.05	36.93
13	Marsa Alam	0.05	1.83	10.62	1.94	1673.93	84.81	6.82	2.23	28.83
14	Wadi Lahmi	0.11	3.05	19.42	1.82	1304.65	49.93	16.82	3.92	32.92
15	Marsa Humira	0.10	1.67	8.87	0.83	446.32	66.83	9.92	1.08	17.39
Minimum		0.02	1.02	8.87	0.83	446.32	47.09	4.33	1.00	12.74
Maximum		0.42	18.08	56.47	8.97	7050.76	354.10	51.05	9.05	44.73
Mean		0.16	4.45	21.47	2.51	2923.85	145.85	18.42	4.06	29.10
Standard Deviation		0.12	4.34	13.94	2.13	2275.57	96.66	14.20	2.28	9.72
Coefficient of Variation (CV) (%)		75.13	97.47	64.95	84.98	77.83	66.28	77.06	56.30	33.41
K-S test (p-value)		0.54	0.30	0.54	0.20	0.58	0.51	0.18	0.32	1.00
Upper continental crust [57]		0.102	11.60	35.00	14.30	30,890.00	527.00	18.60	17.00	52.00
Average global sediment [58]		-	14.00	72.00	33.00	41,000.00	770.00	52.00	19.00	95.00

. Their distribution was determined using the proportional/graduated symbols method and is shown in Figure 2. PTE distributions in the Red Sea sediments indicated a wide variation between not only the considered sites but also the metals themselves. The recorded maximum value of the investigated PTEs seemed to be 21, 18, 6, 11, 16, 8, 12, 9, and 4 orders higher than the corresponding minimum value of Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn, respectively, suggesting the heterogeneity of their distribution. The results indicate that mean concentrations were in the order of Fe: 2923.85 ppm > Mn: 145.85 ppm > Zn: 29.10 ppm > Cr: 21.47 ppm > Ni: 18.42 ppm > Co: 4.45 ppm > Pb: 4.06 ppm > Cu: 2.51 ppm > Cd: 0.16 ppm. Fe and Mn are regarded as major elements in sediments because of their natural abundance, and their concentration is often higher than that of other metals/metalloids.

The recorded values are comparable with the background values. In the Red Sea sediments, the mean concentrations of all PTEs were lower than those generally found in sediments globally [58]; however, the sites affected by intensive human activities showed concentrations of Cd, Co, Cr and Ni higher than those of the upper continental crust concentrations [57] (Table 3). This indicates that Red Sea sediments are dominated by a large amount of natural sediment with a low PTE content and are slightly affected by human activities.

Elements such as Fe and Mn possess a lithogenic origin. Fe and Mn mean concentrations in the Red Sea sediments are much lower than upper continental crust [57] and global sediments [58], most likely due to their leaching during weathering and the close association within the geochemical cycle. The lower abundances of Fe and Mn at all locations relative to the crustal average could be attributed to their increased attraction to the dissolved phases due to mineral ion exchange with salt water [59]. Cr, Cu, Ni, Pb, and Zn, which are naturally or anthropogenically mobilized elements, are associated with sedimentary phases such as carbonate, exchangeable Fe/Mn oxyhydroxides, and organic matter [60,61]. Accumulation effects in the deep seabed associated with mud particles, the initial PTEs contents of rock and parent materials, different weathering processes, and varying sources of anthropogenic input at specific sampling sites are some of the key factors impacting the relative abundance of PTEs in marine sediment [12,14,62]. Moreover, a significant source of PTEs is maritime transport that is linked to the density of traffic in the marine environment [7]. Cu and Pb are often used as active ingredients in antifouling paints used on ships, whereas Zn is commonly used as a binding agent in paint formulation [20,63].

Zn, Cu, Ni, Cr, and Co distributions have the same trend in all the studied sites (Figure 2). Thus, they increase in the middle areas and decrease both northwards and southwards, while Pb and Co distributions have no clear trends. Various affinities of PTEs for sediments and their mixing sources could explain the different distribution patterns found among the measured PTEs in the Red Sea sediments.

In this study, PTE concentrations in sediment samples from Egypt’s Red Sea Coast were compared to up-to-date data from other similar areas in Egypt and throughout the world (

Table 4. Comparison of PTEs concentration in marine sediments reported in the various regions of Egypt and worldwide.

Location	N	Cd	Co	Cr	Cu	Fe	Mn	Ni	Pb	Zn	Ref.
Egypt
Red Sea Coast	15	0.16	4.45	21.47	2.51	2923.85	145.85	18.42	4.06	29.10	Present study
Red Sea Coast	32	-	4.81	53.84	7.70	14,562.50	291.94	15.37	4.89	27.55	[4]
Red Sea Coast (Abu Zenima)	20	0.55	-	-	5.07	2384.00	-	2.87	17.30	22.40	[23]
Red Sea Coast (Ras Gharib, Quseir)	18	1.05	2.10	18.30	25.40	7094.4	136.30	11.50	8.55	24.00	[24]
Red Sea Coast (Shalateen)	18	0.39	2.59	19.17	4.17	-	-	10.19	3.76	25.17	[65]
Red Sea Coast (Hurghada)	30	0.14	1.66	-	1.26	355.44	51.95	1.74	42.38	7.77	[66]
Mediterranean Sea Coast (Kafr Elsheikh)	12	0.36	4.32	-	2.99	7284.58	132.60	8.36	4.84	13.52	[32]
Mediterranean Sea Coast (North Nile Delta)	99	-	11.70	160.00	-	25,465	636.20	24.80	-	42.5	[64]
Worldwide
Saudi Arabia (Jeddah)	80	-	-	9.56	9.18	-	36.52	3.68	77.34	18.02	[67]
Saudi Arabia (Ras Tanura)	15	32.74	0.53	3.27	-	-	-	1.95	1.79	0.60	[15]
Saudi Arabia (Gulf of Aqaba)	33	0.91	4.5	39	30	3374	184	14	6.6	24	[68]
Yemen (Red Sea)	37	0.76	-	-	17.34	-	-	8.98	6.47	36.81	[69]
China (Daya Bay)	19	0.09	-	108.7	24.1	-	-	26.8	35.3	108.9	[70]
China (Pearl Bay)	20	0.55	-	35.78	24.16	-	-	-	31.28	48.53	[17]
India (Puducherry coast)	72	0.4	2.8	8.1	2.5	2520	92	3	2.4	4.3	[71]
India (Diu coast)	72	1.4	13	24.2	27	15,290	502	25	4.2	25.3	[71]
Vietnam (Ha Long Bay)	48	0.08	-	-	14.53	-	-	-	30.39	50.87	[9]
Malaysia (West Coast)	48	0.11	4.06	27.58	16.96	-	-	14.21	21.34	54.65	[8]
Iran (Asaluyeh port)	48	-	2.20	16.13	15.43	4800	168.71	19.04	3.39	21.08	[59]
Nigeria (Abereke)	24	0.55	-	0.52	2.42	369.34	-	7.35	0.67	14.33	[14]
Nigeria (Awoye)	24	0.85	-	0.74	1.28	286.17	-	7.84	2.07	25.15	[14]
Nigeria (Ayetoro)	24	0.32	-	0.24	1.79	417.50	-	3.73	0.08	2.44	[14]
Spain (Asturias)	15	0.06	-	9.78	4.15	-	-	7.80	6.32	20.91	[72]
Ivorian coastal zone	72	3.08	-	4313	7.57	19,144	249.12	17.37	8.39	28.82	[12]
Turkey (Black Sea)	16	-	-	87.31	104.06	46,000	565.38	34.38	32.31	109.88	[11]
Morocco (Khnifiss Lagoon)	6	0.16	3.57	26.6	6.60	-	-	16.50	6.13	51.70	[16]

). The findings suggest that the PTE concentrations in the study area are comparable to those in the other regions listed. The mean values of Co, Cr, Ni, and Zn are exhibited to be higher than their corresponding values reported in earlier studies in Egypt, except for Co and Cr, which were lower than those reported by Badawy et al. [4] in the Red Sea sediments, and for Zn in the Mediterranean Sea Coast sediments reported by Badawy et al. [64]. On the other hand, Cd, Cu, Fe, Mn, and Pb mean values are lower than those reported in most areas on the Red Sea coast and Mediterranean Sea coast in Egypt.

Overall, the PTE concentrations evaluated in this study revealed significant variability compared to the recent literature regarding the coastal locations shown in Table 4. The mean concentration of Cd in the Red Sea sediments is lower than all other locations in the world except Daya Bay (China) [70], Ha Long Bay (Vietnam) [9], the West Coast of Malaysia [8] and Asturias (Spain) [72]. The Co mean concentration in this study is higher than that in other countries except for the Gulf of Aqaba (Saudi Arabia) [68] and Diu coast (India) [71]. The Cr mean concentration in the present study is higher than in some locations such as Jeddah and Ras Tanura (Saudi Arabia) [15,67], the Puducherry coast (India) [71], Asaluyeh port (Iran) [59], Nigeria [14], and Asturias (Spain) [72]. The Cu and Fe are lower than all other locations in the world except Nigeria [14] and the Puducherry coast (India) [71]. The mean concentration of Ni is higher than all other locations in the world except Daya Bay (China) [70], Diu coast (India) [71], Asaluyeh port (Iran) [59], and Black Sea (Turkey) [11]. The mean concentration of Pb in the Red Sea sediments is lower than all other locations in the world except Ras Tanura (Saudi Arabia) [15], Puducherry coast (India) [71], Asaluyeh port (Iran) [59], and Nigeria [14]. The Zn mean concentration in the present study is higher than some locations such as Jeddah, Ras Tanura, and Gulf of Aqaba (Saudi Arabia) [15,67,68], Puducherry and Diu coast (India) [71], Asaluyeh port (Iran) [59], Nigeria [14], Asturias (Spain) [72], and Ivorian coastal zone [12].

3.3. Statistical Analyses

The K-S test was used to evaluate the normality of the PTE distributions, and the distribution was considered normal (not showing a significant statistical difference) if the p-value was >0.05 [3,11,71]. As noted from the K–S test (p-value) (Table 3), the measured PTEs in the Red Sea marine sediment showed a normal distribution and did not show any significant statistical difference (p-value ranged from 0.20 to 1.00 > 0.05). The close relationship between the mean and standard deviation of Cd, Co, Cr, Cr, Ni, and Pb, as well as the high value of the coefficient of variation (CV), indicates the presence of temporal and spatial changes likely induced by pollution sources (Table 3). Natural resources are denominated in the inputs of metals with a low CV value, whereas human-induced activities are denominated in the inputs of metals with a considerable CV value [11]. In contrast, the CV values for Zn (33%) indicate that this element’s spatial distribution was more uniform among different stations.

Pearson’s correlation coefficients were calculated to interpret the relationship among the studied PTEs (

Table 5. Pearson’s correlation coefficient for all variables (n = 15).

	Sand %	Mud %	Cd	Co	Cr	Cu	Fe	Mn	Ni	Pb	Zn
Gravel %	0.055	−0.420	−0.403	0.108	−0.135	0.161	−0.011	0.049	0.062	−0.065	−0.320
(p-value)	0.847	0.119	0.136	0.702	0.630	0.566	0.968	0.861	0.825	0.818	0.245
Sand %		−0.921	−0.128	−0.089	−0.367	−0.074	−0.079	0.053	−0.090	0.177	−0.282
(p-value)		0.000001	0.650	0.752	0.179	0.794	0.781	0.851	0.749	0.528	0.309
Mud %			0.254	0.001	0.355	−0.051	0.047	−0.093	0.00009	−0.151	0.323
(p-value)			0.360	0.998	0.194	0.857	0.868	0.740	1.000	0.590	0.240
Cd				−0.009	0.457	−0.082	0.190	0.167	−0.003	0.005	0.399
(p-value)				0.975	0.087	0.771	0.498	0.552	0.992	0.987	0.141
Co					0.584	0.904	0.679	0.548	0.707	0.072	0.459
(p-value)					0.022	0.000003	0.005	0.035	0.003	0.800	0.086
Cr						0.459	0.593	0.710	0.792	0.122	0.479
(p-value)						0.085	0.020	0.003	0.0004	0.664	0.071
Cu							0.570	0.487	0.715	0.040	0.298
(p-value)							0.027	0.065	0.003	0.887	0.281
Fe								0.736	0.622	0.286	0.647
(p-value)								0.002	0.013	0.301	0.009
Mn									0.798	0.259	0.367
(p-value)									0.0003	0.352	0.179
Ni										0.165	0.312
(p-value)										0.557	0.257
Pb											0.555
(p-value)											0.032
Weak			Moderate			Strong			Very Strong

). Correlations values of (0.20–0.39), (0.40–0.59), (0.60–0.79), and (0.80–1.00) can be considered as weak, moderate, strong, and very strong correlations; respectively [35,73,74]. The results specify that Fe has a strong positive correlation with Co (r = 0.679; p-value = 0.005), Mn (r = 0.736; p-value = 0.002), Ni (r = 0.622; p-value = 0.013), and Zn (r = 0.647; p-value = 0.009), and a moderate positive correlation with Cr (r = 0.593; p-value = 0.02) and Cu (r = 0.570; p-value = 0.027). Likewise, Mn has a strong positive correlation with Cr (r = 0.710; p-value = 0.003) and Ni (r = 0.798; p-value = 0.0003), and a moderate positive correlation with Co (r = 0.548; p-value = 0.035) and Cu (r = 0.487; p-value = 0.065). Additionally, Zn has a moderate positive correlation with Co (r = 0.459; p-value = 0.086), Cr (r = 0.479; p-value = 0.071), and Pb (r = 0.555; p-value = 0.032). Moreover, Cu has a very strong positive correlation with Co (r = 0.904; p-value = 0.000003), a strong positive correlation with Ni (r = 0.715; p-value = 0.003), and a moderate positive correlation with Cr (r = 0.459; p-value = 0.085). Additionally, Ni was strongly positively correlated with Co (r = 0.707; p-value = 0.003) and Cr (r = 0.792; p-value = 0.0004), and Cr was moderately positively correlated with Cd (r = 0.457; p-value = 0.087) and Co (r = 0.584; p-value = 0.022).

These various positive correlations may suggest a shared source or mode of regulation by similar geochemical processes that properly control their evolution in the environment. Under certain physicochemical conditions, this significant positive correlation indicates that PTEs can share sources and comparable behaviors during transportation, transformation, and migration [12,51]. The direct linear correlation between Co, Cr, Cu, Ni, Fe and Mn indicates the considerable influence of the scavenging effect of Fe and Mn on the distribution of other elements [43,60,75]. Fe/Mn oxides and oxyhydroxides would lead to the coprecipitation of PTEs from the water column, resulting in elevated element concentrations in sediments [61]. Ni has a strong ability to bond to metals, particularly sulphides to Fe (pyrite). In addition, Mn and Ni combine to form a less-mobile species that settles to the bottom sediments [59,76]. Strong and moderate significant correlations among the studied elements indicate a close distribution and association, which is in line with the findings of Nour and El-Sorogy [23], El Nemr et al. [29], Palleyi et al. [52], Zhang et al. [77], and Oura et al. [12].

On the other hand, there was no observed association between Cd and Pb and the other elements, except that between Cd-Cr and Pb-Zn. As a result, Cd and Pb concentrations were unaffected by a sole factor. However, they could be influenced by various natural and anthropogenic activities and their potential existence may have been affected by many human activities. Furthermore, only weak positive correlations were noted between mud content and Cd, Cr, and Zn, indicating that granulometry is not the dominant factor that controls the distribution of PTEs into Egyptian Red Sea surficial sediments. Jones et al. [78] observed a lack of strong positive association between fine-sized sediments and PTEs in Kembla Harbour, New South Wales, Australia. They hypothesized that PTEs are also associated with the coarser components, such as fugitive metal-bearing ore particles, marine invertebrate faecal pellets, concentrated in Fe oxyhydroxide coatings on sand-sized particles or affiliated with coarse diagenetic pyrite.

In the present study, PCA (Figure 3) reduced data into three components, and clearly shows that the eigenvalues of the components were 82.03% in the extracted data. The first component (PC1), with an eigenvalue of 4.72, accounted for 52.53% of the total variance and was highly loaded with all of the studied PTEs. The second component (PC2) had an eigenvalue of 1.53, accounted for 17.02% of the total variance and was positively loaded with Cd, Cr, Fe, Pb, and Zn and negatively loaded with Co, Cu, Mn, and Ni. Finally, the third component (PC3) had an eigenvalue of 1.12, accounted for 12.48% of the total variance and was positively loaded with Co, Cu, Fe, Pb, and Zn and negatively loaded with Cd, Cr, Mn, and Ni.

CA was applied in the R-Mode (variables) and Q-Mode (observations) to produce a dendrogram. The R-Mode dendrogram confirms the deduced association between the measured element (Figure 4a). The Q-Mode dendrogram (Figure 4b) contains two main clusters, subdivided, in turn, into two sub-clusters (4 sub-clusters). The first main cluster includes (a) sites 2, 4, 10, 11, 13, 14, and 15 (represent the tourist activity), and (b) sites 3, 5, and 8 (represent commercial and tourist port activity), while the second cluster was subdivided into (c) sites 1 and 7 (represent phosphate and petroleum industrial activity), and (d) sites 6, 9 and 12 (represent different activities). Generally, the PCA and CA results were strongly matched with the results of Pearson’s correlation.

3.4. Pollution Assessment

The first group of indices was used to assess the contamination level for each measured PTE separately (including I_geo and C_f). The I_geo results (Table S2; Figure 5) of all studied PTEs were found between 0 and 1, which classified them into class (1) [45], suggesting uncontaminated to moderately contaminated sediments at all of the studied sites (Table S2; Figure 5). The C_f results indicated that most of the studied sites were within the low contamination degree category (unpolluted sediment with C_f < the unite) [46], which was in parallel with the trend in I_geo values. Some sites recorded C_f values for Cd, Co, Cr, and Ni of between 1 and 3 and were classified as moderately polluted sediment. However, only at sites 5 (Gemsha) and 7 (El-Hamrawein) were cadmium levels within the category of a considerable contamination factor (C_f values were 3.627 and 4.118, respectively) (Table S3; Figure 5). The highest I_geo and C_f values were recorded for Cd in the Gemsha, North Safaga, and El-Hamrawein sites, which are affected by petroleum and phosphate industries.

The present results of C_f-based $E_i^r$ indicate that most of the studied site’s sediments are within the low-risk category ($E_i^r$ < 40), as a consequence of the toxicity response factors of the toxic elements [46]. The calculated $E_i^r$ of Cd shows a moderate risk (Table S4; Figure 5). The results indicate that the measured PTEs were probably a result of some natural sources. These results agreed with those reported by Badawy et al. [4] and El Nemr et al. [29].

The calculated C_f-based C_deg values proposed that the studied sites had a low and moderate degree of contamination [46] (

Table 6. C_deg, RI, CSI, and MERMQ values.

Sample No.	Area	Cdeg		RI		CSI		MERMQ
1	Gemsha	4.29	Low	83.73	moderate risk	0.35	uncontaminated	0.03	low
2	North Hurghada	2.54	Low	34.43	low risk	0.34	uncontaminated	0.04	low
3	Hurghada	2.68	Low	14.84	low risk	0.40	uncontaminated	0.06	low
4	South Hurghada	2.59	Low	24.42	low risk	0.38	uncontaminated	0.05	low
5	North Safaga	6.79	Moderate	118.67	high risk	0.54	uncontaminated	0.08	low
6	South Safaga	5.40	Low	60.57	moderate risk	0.48	uncontaminated	0.07	low
7	El-Hamrawein	9.81	Moderate	142.59	high risk	0.73	very low	0.17	medium
8	Qusier	6.34	Moderate	68.93	moderate risk	0.66	very low	0.18	medium
9	N. Marsa Umm El-Griufat	7.51	Moderate	51.85	moderate risk	0.86	very low	0.35	medium
10	N. Sharm El Bahria	4.94	Low	54.73	moderate risk	0.55	very low	0.10	medium
11	Abu Dabab	3.74	Low	64.47	moderate risk	0.41	uncontaminated	0.05	low
12	Sharm El Naaba	7.49	Moderate	47.88	low risk	0.73	very low	0.33	medium
13	Marsa Alam	2.35	Low	20.13	low risk	0.33	uncontaminated	0.04	low
14	Wadi Lahmi	3.93	Low	42.48	low risk	0.48	uncontaminated	0.08	low
15	Marsa Humira	2.50	Low	34.70	low risk	0.35	uncontaminated	0.05	low

). To evaluate the degree of the severity, the overall ecological risk index (RI), contamination security index (CSl), and Mean EMR quotient (MERMQ) were calculated (Table 6). Results of RI show a wide range of low to high ecological risks. The CSI results indicated that the studied sites were within an uncontaminated to very low degree of severity (CSI < 0.5; 0.5 > CSI < 1, respectively) [42]. MERMQ of the middle area indicates a medium ecological risk level with a 21% probability of toxicity (0.1 > MERMQ < 0.5). The recorded values were 0.17, 0.18, 0.35, 0.10, and 0.33 for sites 7, 8, 9, 10 and 12, respectively. Meanwhile, the rest of the studied sites were within a low ecological risk level with a 9% probability of toxicity (MERMQ < 0.1) (Table 6). The distribution pattern maps of C_deg, RI, CSI, and MERMQ, in the Red Sea coast sediment samples are shown in Figure 6. Despite the degrees of assessment for these four indicators not being entirely analogous, their distribution pattern maps clearly show that the slightly high degree of contamination and risk is concentrated in the middle of the study area around phosphate mining and related activities. The results of the ecological risks indices of $E_i^r$, RI, CSI, and MERMQ were in line with those published by Badawy et al. [4] and El Nemr et al. [29] on the Egyptian Red Sea coast. However, the previously published results may vary slightly due to the variation in sampling sites and different analytical methods used. In addition, the results of indices in the present paper are in agreement with statistical analyses, thereby indicating that the studied PTEs are generally of natural origin, and their concentrations were enhanced in some sites by human activities.

The economic importance of the Red Sea region in Egypt’s strategic planning has increased dramatically in the last few years. The region has become an attractive area for international investments in several fields, including the development of seaports and transportation hubs, mining industries, and tourism. The possible expansion in these activities may be accompanied by a continuous increase in the amount of hazardous waste materials and the concomitant environmental hazards, necessitating the periodic assessment and monitoring of PTE concentrations.

4. Conclusions

In recent decades, PTE pollutants have become a serious environmental concern in the aquatic ecosystem. The granulometric analysis demonstrated a diversity of facies represented by gravelly sand, muddy gravelly sand, muddy sand, and gravelly muddy sand. The Red Sea coastal sediment is composed mainly of fine to very-fine sand. There were two main sediment source, namely biogenic (allochthonous) and terrigenous (autochthonous), with different agents of transportation. Wide ranges of sediment sorting values indicate turbulent conditions. The results showed that Fe and Mn were the most abundant elements in all investigated samples. The mean concentrations of all PTEs were lower than those usually found in sediments globally. Few sites showed concentrations of Cd, Co, Cr, and Ni of higher than those of the upper continental crust. A strong significant correlation among the studied elements indicates their close distribution and association. The direct linear correlation between Co, Cr, Cu, Ni, and Fe and Mn indicates the considerable influence of the scavenging effect of Fe and Mn on the distribution of other elements. Cd and Pb concentrations were unaffected by a sole factor and could be influenced by a variety of natural and anthropogenic activities. According to the computed pollution indices, Red Sea sediment shows minimal to moderate pollution levels. The highest I_geo and C_f values were recorded for Cd sites affected by petroleum and phosphate industries. The distribution pattern maps of C_deg, RI, CSI, and MERMQ clearly show that the slightly high degree of contamination and risk is concentrated in the middle of the study area around phosphate mining and related activities. The results, including correlations and pollution indices, suggest that the PTE contents in the Red Sea sediments are of natural origin and are slightly affected by anthropogenic sources, especially petroleum and phosphate mining and related activities. More attention should be paid to Cd, Ni, and Pb concentrations as the main pollution factors. Finally, the findings of this study will be relevant and informative for future research and economic development because they provide updated data on PTE contamination levels in Red Sea marine sediments. In addition, the present results may be considered as a good base for establishing local guidelines. PTE concentrations in the Red Sea coastline must be adequately monitored on a regular basis to avoid any environmental hazards.