Distribution of Heavy Metals along the Mediterranean Shoreline from Baltim to El-Burullus (Egypt): Consequences for Possible Contamination

Abstract

This work is mainly concerned with the effect of anthropogenic activities, the presence of black sand spots, factory construction, and shipping, in addition to other activities like agriculture, on soil heavy metal pollution along the Mediterranean shores of Lake El-Burullus, Egypt, to assess the contamination levels and to identify possible sources and the distribution of these metals. This study focuses on the various heavy metal contamination levels in El-Burullus Lake coastal sediments. Sediment samples were collected and analyzed by the XRF technique for heavy metals, including Cr, Cu, Ni, Zn, Zr, Pb, Ba, Sr, Ga, Rb, V, and Nb. Statistical analyses, including correlation coefficient, factor analysis, and cluster analysis, were employed to understand the interactions and sources of these metals. The highest concentrations recorded were for Zr (84–1436 mg/kg) and Pb (1–1166 mg/kg), with average concentrations of 455.53 mg/kg and 79.27 mg/kg, respectively. Cr, Zr, Nb, and Pb showed average values higher than the average shale concentration, indicating potential pollution. Correlation analysis revealed strong associations between several metals, suggesting common sources of both natural and anthropogenic origin and similar distribution patterns. Factor analysis indicated four main factors accounting for 94.069% of the total variance, with the first factor heavily dominated by Cr, Ni, Zn, and Ba. The contamination factor (Cf) and degree (DC) analyses revealed varying contamination levels, with most metals exhibiting the greatest values in the western half of the area. The pollution load index (PLI) indicated high-quality sediment samples without significant pollution. Our findings highlight the importance of continued monitoring and management techniques to reduce possible environmental and health concerns associated with these pollutants.

1. Introduction

Heavy metal and trace element contamination is becoming more common on the world’s beaches. This is a severe environmental issue because of the potential harm to marine ecosystems and human health. Coastal regions, dynamic interfaces between land and sea, regularly collect contaminants from many sources, making them valuable places for environmental research. Heavy metals and trace elements can be sourced from natural processes (e.g., rock weathering) and human activities. These pollutants can persist in the environment, bio-accumulate in the food chain, and threaten aquatic life and humans [1,2,3,4,5].

Many authors have reported the heavy mineral richness of the El-Burullus area in Egypt [6,7], recording the presence of zircon, monazite, ilmenite, sphene, magnetite, cassiterite, garnet, and monazite minerals. The El-Burullus sediments appear to be slightly better delivery sites for thorium than uranium due to the concentration of thorium-bearing minerals such as monazite. According to [8], the overall economic mineral tonnage in the West El-Burullus area is around 850,500 tons, with magnetite accounting for approximately 238,050 tons, ilmenite accounting for approximately 477,900 tons, leucoxene accounting for approximately 30,150 tons, rutile accounting for approximately 1800 tons, zircon accounting for approximately 33,300 tons, monazite accounting for approximately 2700 tons, titanite accounting for approximately 2250 tons, and garnet accounting for approximately 48,150 tons. El-Burullus Lake samples generally contain more muck (silt and clay). Silica, alumina, and iron are the most abundant components in the lake sediments, accounting for 57.49%, 14.56%, and 8.79%, respectively, followed by CaO, MgO, TiO₂, Na₂O, and K₂O [9,10,11]. The organic matter content increases with increased Al content, ranging from 1.75% to 0.63%, with an average of 1.47%. The higher organic matter content in sediments with a significant clay fraction can be attributed to several factors.

Additionally, clay-rich environments often accumulate in areas with high primary productivity, providing abundant sources of organic matter input [12]. The fine pore spaces and protective effects of clay minerals can also create microenvironments that support diverse microbial communities, further contributing to organic matter accumulation [6,13]. These physical, chemical, and biological interactions between clay minerals and organic matter explain the typically higher organic matter content observed in sediments with a high clay fraction. The area faces various anthropogenic pressures, including urbanization, industrialization, and agricultural activities. These factors contribute to the influx of heavy metals and trace elements into the coastal environment, necessitating a thorough investigation.

Beach sediments play a crucial role in coastal ecosystems because they absorb various metals. This is because the physical and chemical processes associated with the adsorbed compounds’ characteristics and the sediment matrix’s composition allow the beach sediments to accumulate these metals [14]. The intricate interactions between the sediment particles, organic matter, and metal ions result in the beach sediments serving as a sink for a range of heavy metals and trace elements [15,16]. This makes the beach sediments an essential component in understanding the overall environmental health and contaminant dynamics of coastal systems. Other human activities, like mining, smelting, electroplating, burning of solid waste, untreated sewage, and nonpoint surface runoff, could lead to metal enrichment in beach sands [17]. When heavy metal concentrations reach dangerous levels under various settings, they can cause ecological damage [18,19,20,21]. Heavy metal accumulation is influenced by various parameters, including the sediment metal concentration, particle size, salinity, temperature, pH, and organic matter [22,23,24,25,26]. In higher concentrations, all metals are toxic [27]. Although Fe, Zn, Cu, and Mg are essential metals that play important roles in the biological system, they become hazardous in high concentrations [28]. Despite this, certain heavy metals with relatively high densities, such as As, Cd, and Pb, are highly harmful, even at low doses [29,30]. Heavy metals such as Fe, Co, Ni, Cr, Cd, Pb, Zn, Mn, and Cu are considered key polluting elements in aquatic ecosystems due to their environmental persistence, toxicity, and ability to integrate into food chains [31,32]. Because metals are poorly soluble in water, they accumulate and adsorb in bottom sediments [33,34]; as a result, the metal concentrations in sediment are often higher than those in water [35,36], and marine sediments act as a repository for trace metals, which are used to evaluate ecological conditions [37,38,39,40,41,42]. Cu, Co, and Zn are important elements for growth, and their deficiencies cause biological degeneration; in larger amounts, they can also be toxic [43]. They enter terrestrial habitats through natural and artificial means.

This region has grown in popularity due to the construction of numerous factories and is located close to Damietta Port (Figure 1).

The geology of the beach sands along the Mediterranean coast is intimately tied to the evolution of the Nile Delta and its predecessors. Heavy minerals were found mostly in the aeolian sand dune belts that stretched from Gamsa to El-Burullus, as well as in Idku’s sand dunes and along Sinai’s northern shore. The primary activities of the people in and around the lake are fishing and weed cutting, grazing, and agriculture.

In this paper, beach sand heavy metal concentrations in the coastal region between Baltim and El-Burullus (Figure 1a) were investigated to offer a geochemical framework for determining the most likely source and mechanism of metal input, as well as spatial distribution and enrichment. Pollution’s effects on the ecosystem and how it affects sediment quality standards were investigated to establish the geochemical signature of anthropogenic influences in this coastal environment.

2. Materials and Methods

2.1. Sediment Sampling and Analytical Methods

El-Burullus Lake, located in Kafr El-Sheikh Governorate (30°22′–31°35′ N; 30°33′–31°08′), Egypt, covers around 410 km² and is the second largest coastal lake in northern Egypt. It is located on the eastern bank of the Rosetta branch of the River Nile (Figure 1b). Fifteen samples were collected along the Mediterranean seashore between Baltim and El-Burullus (Figure 2) with the aid of a spiral bar, rotated to a depth of one meter. The coordinates of the sampling points are presented in

Table A1. Coordinates of the sampling points.

Sample No.	Latitude	Longitude
1	31°35′21.5″ N	31°0′10.9″ E
2	31°35′26.6″ N	31°0′36.8″ E
3	31°35′31.1″ N	31°0′58.4″ E
4	31°35′16.8″ N	31°0′1.8″ E
5	31°35′13.7″ N	30°59′44″ E
6	31°35′11.3″ N	31°0′5.7″ E
7	31°35′20.8″ N	31°0′23.4″ E
8	31°35′26.1″ N	31°1′4.5″ E
9	31°35′25.4″ N	31°1′18.5″ E
10	31°35′23.4″ N	31°1′39.7″ E
11	31°35′26.7″ N	31°1′42.9″ E
12	31°35′35.8″ N	31°1′32.4″ E
13	31°35′32.2″ N	31°2′4.9″ E
14	31°35′39.4″ N	31°2′23.2″ E
15	31°35′32.4″ N	31°2′51.5″ E

, Appendix A. The collected sediment samples were stored in plastic bags, kept in an ice box at (4 °C), and transferred to the laboratory, where they were kept in plastic bags for analysis after being dried in air at room temperature.

The X-ray Fluorescence (XRF) spectrometry method was employed at the laboratories of Egypt’s Nuclear Materials Authority [44] on fused beads to determine the trace elements (Cr, Cu, Ni, Zn, Zr, Ga, Sr, Y, Rb, V, Nb, Pb, and Ba) in 15 selected samples. The trace elements were analyzed with a Philips-PW 1480 X-ray spectrometer X-unique II (Philips, Amsterdam, The Netherlands) with automated sample changeover (PW 1510). The trace elements concentration was calculated from the program’s calibration curves, which were set up according to international reference materials (standards), such as SARM-46, SARM-50, and Stream-1.

Descriptive statistical analyses (mean, maximum, and minimum) were conducted based on the existing results to analyze the sample characteristics and explore any unknown correlations between the gathered data. MS Excel 2019 was used to handle the data and graphing, and SPSS was used to perform descriptive statistics. Pearson’s correlation coefficient analysis provided support for the multivariate study’s findings. It enabled the investigation of the relationships between the examined elements in terms of geochemical affinity and site of origin. The goal of factor analysis, a multivariate statistical technique, is to reduce many existing independent variables (variables) to a manageable number of new independent variables (factors), each of which contributes significantly to the variation in the original data set [45]. This method offers a means whereby trace metals may be re-expressed as a metal association or factor defined as a linear combination of the original data set. The variables measured in R-mode factor analysis may not all be directly comparable, so it would seem necessary to convert them into a standardized form. Standardization consists of transforming a data set so it has a mean of zero and a variance (or standard deviation) of one [46]. Principal components analysis (PCA) is widely used in conjunction with cluster analysis (CA). Using the dendrogram, it was feasible to determine the relationship between heavy metal levels and other geochemical categories and show intuitive similarities [47,48].

2.2. Geo-Accumulation Index (Igeo)

The geo-accumulation index (Igeo) is a numerical measure of the contamination of sediments with heavy metals. It is obtained by comparing the heavy metal concentrations (Cn) in the current study to pre-industrial levels as the geochemical background (Bn) [48]. The following formula is used to calculate Igeo:

Igeo = log₂ (Cn/(k × Bn))

(1)

The background matrix correction factor (k = 1.5) and the geochemical background of the element in typical shale are provided by [49]. Geochemical represents the measured metal concentrations in sediment. By multiplying the corresponding background content (Bn) by the fixed factor of 1.5, the following results are found: the upper limit of the lowest load class 0 is obtained, the impact of any background value fluctuations brought on by the lithologic variations in the sediments is minimized, and naturally occurring variations in the environment’s concentration of a given substance, as well as minor anthropogenic influences are examined [50,51,52,53,54,55,56].

2.3. Contamination Factor (Cf) and Contamination Degree (DC)

The contamination factor is the ratio of a certain site’s sediment metal concentration to its typical content level [47,48,57,58]. The contamination factor (Cf) and degree of contamination (DC) are employed in this study to describe the pollution grade of beach and sabkha sediments. The following equation was used to determine Cf and DC:

$$\mathrm{D}\mathrm{C}=\sum _{\mathrm{i}=1}^{\mathrm{n}}\mathrm{C}\mathrm{f}\left(\mathrm{i}\right)=\sum _{\mathrm{i}=1}^{\mathrm{n}}{\displaystyle \frac{{\mathrm{C}}_{\mathrm{i}}}{{\mathrm{C}}_{\mathrm{s}}}}$$

(2)

where Cf(i) is the contamination factor of element i, C_i is the mean content of a substance in sediments, and C_s is the standard level (background) for that metal. The values of the contamination indices are displayed according to the criteria specified in

Table 1. Terminologies used to describe the contamination factor (Cf) and degree (DC).

Cf	DC	Description
Cf < 1	DC < 6	Low degree of contamination
1 < Cf < 3	6 ≤ DC < 12	Moderate degree of contamination
3 < Cf < 6	12 ≤ DC < 24	Considerable degree of contamination
Cf > 6	DC ≥ 24	Very high degree of contamination

[41,59].

2.4. Pollution Load Index (PLI)

The pollution load index (PLI) was introduced by Tomlinson et al. (1980) [58] to determine each sample’s overall pollution level [47,48]. The PLI approach is used to assess the severity of the sediment pollution load at each sampling site. The PLI is stated in the equation below as the nth root of the product of the contamination factors (Cfs) of all the target elements, which provides a simple and comparative method for estimating the degree of heavy metal pollution:

$$\mathrm{P}\mathrm{L}\mathrm{I}=\sqrt[\mathrm{n}]{\mathrm{C}\mathrm{f}\left(1\right)\times \mathrm{C}\mathrm{f}\left(2\right)\times \dots \dots \times \mathrm{C}\mathrm{f}\left(\mathrm{n}\right)}$$

(3)

where n is the number of metals and Cf(i) is the contamination factor of element i. PLI values are divided into two groups: PLI ≤ 1, which is close to the background concentration and not contaminated, and PLI > 1, which indicates site pollution [47,48,60].

3. Results

3.1. Heavy Metals Contents

The results obtained for the heavy metal concentrations in studied sediments are given in

Table 2. Concentrations of heavy metals (mg/kg) in sediments of the study area.

Sample Number	Cr	Cu	Ni	Zn	Zr	Ga	Sr	Y	Rb	V	Nb	Pb	Ba
1	250	21	65	20	491	31	128	2	2	164	16	3	562
2	85	22	23	11	125	19	30	1	2	20	4	2	88
3	94	22	34	9	139	34	34	1	2	25	5	1	110
4	225	20	62	21	571	12	147	3	2	165	18	2	725
5	338	22	45	19	1436	37	351	1	2	205	47	1160	680
6	272	22	68	22	539	24	142	2	2	167	17	2	552
7	115	26	41	15	84	13	21	1	2	30	3	3	131
8	158	20	39	16	749	14	190	3	2	121	24	3	538
9	126	20	37	11	368	21	100	2	2	56	12	2	246
10	279	20	58	21	595	8	149	3	2	156	19	4	720
11	228	20	60	20	631	1	159	3	2	182	20	2	804
12	117	20	32	11	222	1	57	1	2	42	7	2	182
13	166	20	39	14	598	18	157	2	2	77	19	1	335
14	93	22	36	12	103	14	24	1	2	28	4	1	118
15	114	21	38	12	182	1	42	1	2	32	32	1	142
Minimum	85	20	23	9	84	1	21	1	2	20	3	1	88
Maximum	338	26	68	22	1436	37	351	3	2	205	47	1160	804
Average	177.33	21.20	45.13	15.60	455.53	16.53	115.40	1.80	2.00	98.00	16.47	79.27	395.53
Average shale [61]	90	45	68	95	160	19	300	26	140	130	11	20	580
WHO, FAO, and Ewers standards [62,63]	100	100	50	300	-	-	-	-	-	-	-	-	100

. The distribution patterns of heavy metals in sediment samples were as follows: Zr > Ba > Cr > Sr > V > Ni > Cu > Ga > Nb > Zn > Pb > Y > Rb. The Cr content ranged from 85 to 338 mg/kg, with an average of 177.33 mg/kg, which is higher than the average Cr concentration in shale (Table 2). Only six samples with moderately high Cr concentrations (>177 mg/kg) were found in both the western and central examined coastal zones.

The Ni concentrations ranged from 23 to 68 mg/kg, with an average of 45.13 mg/kg, which is lower than the average concentration in shale [61]. The copper concentrations ranged from 20 to 26 mg/kg, averaging 21.20 mg/kg. The presence of urban and farmed regions was connected to the high geographical distribution of Cr, Ni, and Cu (Figure 3). The heavy metal concentrations of Ni, Cu, and Cr in the area were relatively low at the Eastern part of the studied coast and increased westward (Figure 3). The decrease in the Ni, Cu, and Cr heavy metal concentration in the Eastern parts corresponds to the areas which were characterized by a very high erosion rate (Figure 4). However, the increase in the beach sediments at the western zone was related to the areas which were characterized by high to very high accretion rates.

The minimum and maximum concentrations (mg/kg) of Zn, Zr, and Pb were 9:22, 84:1436, and 1:1166, respectively, with means of 15.60, 455.53, and 79.27. Only one sediment sample (no. 5) in the western portion of the examined area had a significant concentration of Pb (1160 mg/kg), compared with the average in shale given by [61]. This high concentration of the sample was associated with urban activities and a high accretion area in the western half of the examined area, which could be attributed to the shipment of Rosetta port. The lake receives fresh water from numerous drains along the southern and eastern margins (see Figure 1a). El-Burullus Lake receives only agricultural runoff water not contaminated by industrial wastes [64]. In the research region, the spatial distribution of Zr and Zn was similar in terms of concentration and distribution (Figure 5). The highest levels of these metals were associated with urban activities (Figure 5).

The Ba values in the sediment samples ranged from 88 to 804 mg/kg, with a mean of 395.53 mg/kg. Ba contents above the average for shale have been found in seven samples of both types of sediments studied [32]. The western zone, which is connected to urban activities and a high accretion area (Figure 5), and the central zone, which is connected to agricultural activities and a high erosion area, both had the largest concentrations of Ba. With an average of 115.40 and 16.53 mg/kg, the Sr and Ga values varied from 21 to 351 mg/kg and 1 to 37 mg/kg, respectively. In shale, both element concentrations were below average. In beach sediment samples, Sr and Ga had essentially comparable regional distributions. In the research area’s shoreline, they were numerous (Figure 6). The same Rb value of 2 mg/kg was found in every sediment sample studied, much less than the average shale content reported by [61] (Table 2). The average V concentration was 98 mg/kg, with values ranging from 20 to 205 mg/kg. In the western and central zones of the study region, seven samples had significantly higher amounts (Figure 7). The average concentration of niobium was 16.47 mg/kg, greater than that in shale, with niobium values ranging from 3–47 mg/kg. Only a small number of tests showed amounts higher than the legal limits (9 mg/kg). In samples with high V concentrations, Nb concentrations are also high (Table 2). Urban areas and agricultural activities situated in the high-accretion zones along the El-Burullus Lake coast are associated with the spatial distribution of samples with high concentrations of heavy metals (Figure 4 and Figure 8).

Figure 9a illustrates the distribution of trace elements in the El-Burullus beach sediments, showcasing their concentration variations across different samples. Elements such as Cr, Zn, Zr, and Ba exhibited significant fluctuations in concentration, suggesting varying sources or geochemical behaviors across the samples. Figure 9b provides a box plot summarizing the concentration distribution of these elements. The box plot highlights that Zr and Ba had the highest concentrations and variability, indicating potential areas of interest for further investigation regarding their sources and environmental impact.

3.2. Sediment Quality Guidelines (SQGs)

The world average concentrations of Cr (90 mg/kg), Cu (45 mg/kg), Ni (68 mg/kg), Zn (95 mg/kg), Zr (160 mg/kg), Ga (19 mg/kg), Sr (300 mg/kg), Y (26 mg/kg), Rb (140 mg/kg), Nb (11 mg/kg), Pb (20 mg/kg), and Ba (580 mg/kg) reported for world shale were considered as the background values of sediments [61,62,63]. The concentrations of heavy metals, such as Cr, Cu, Ni, Zn, and Pb in sediment samples were compared with WHO, FAO, and Ewers criteria [47,48]. After the comparison, it was found that Cu and Zn were below the standard limit, and this result indicated that the study area was also unpolluted by Cu and Zn. The Cr contents were above the standard limit in the study area, except for only one sample (no. 2). The Zr contents in the sediment samples ranged from 1436 mg/kg to 84 mg/kg, with an average of 455.5 mg/kg; nearly eleven sediment samples were above the standard limit. The concentrations of Cu, Zn, Rb, and Y were below the standard limit and ranged between 20 mg/kg and 26 mg/kg, 9 mg/kg and 22 mg/kg, 2 mg/kg and 2 mg/kg, and 1 mg/kg to 3 mg/kg, respectively, indicating that the El-Burullus area was unpolluted by these elements. The Ni and Pb concentrations were within the safe limit, except for sample no. 6 for nickel, and sample no. 5 had a very high Pb concentration, reaching up to 58-fold the standard level. The Ba concentrations were below the standard limit, except for those in four samples from the study area, while Nb and V had similar spatial patterns, to some extent, with common samples having higher values than the standard limit. The samples’ concentrations were between the safe and unsafe limits. The Ga concentration in six samples was above the standard limit, and those in the rest of the samples were below it. The Sr concentrations in all sediment samples were above the standard limit. The normalization values of Pb, V, and Cr were 6.3, 1.01, and 1.9, respectively, indicating that these heavy metals were enriched in the study area. The concentrations of heavy metals in beach sediments were lower than the typical concentrations of these elements in the continental crust [65], except for Pb and Zr, indicating that the area under study was polluted and unsafe for living creatures.

Table 3. Comparison of average heavy metal concentrations along the Mediterranean Sea shoreline at Baltim–El–Burullus and other locations.

Area	Cr	Ni	Zn	Pb	Cu	Zr	Reference
Baltim–El-Burullus coast	177.33	45.13	15.60	79.27	21.20	455.53	Present study
Manzala Lake (n = 31)	1.49	-	70.39	4.77	15.07	-	[66]
Idfu Lake (n = 12)	22.07	14.11	-	30.92	-	-	[67]
Qarun Lake	15.31	27.36	-	18.28	-	-	[79]
Nasser Lake	30.79	27.56	35.38	10.91	21.78	-	[68]
Port Said–Damietta Coast	57.9	26.45	31.2	3.505	21.7	280	[80]
Abu-Qir Bay	-	30.02	28.19	2.57	115.81	-	[81]
The eastern coast of Alexandria	-	-	4.150	16.98	5.4–37.5	-	[70]
Eastern Mediterranean coast of Egypt	-	-	1.8–14.0	8.5–19.1	0.6–17.9	-	[71]
Western part of the Egyptian Mediterranean Sea	-	-	26.3–112.1	20.7–35.6	26.6–33.3	-	[82]
Arabian Gulf, Saudi Arabia	-	77.07	48.26	5.25	297.29	-	[72]
Mediterranean Sea, Libya	-	22.65	26.36	11.69	17.30	-	[73]
Azerbaijan, Caspian Sea	-	50.1	83.2	19.6	31.9	-	[74]
Indian Ocean, Salaam Coast, Tanzania	-	1.1	5.7	1.2	0.8	-	[75]
Gokcekaya Dam Lake, Turkey	-	125.7	265.8	74.4 7	108.99	-	[76]

-: not detected.

shows the average amounts of several analyzed heavy metals in beach sediments from the research area and similar deposits from local lakes and other parts of the world [66,67,68,69,70,71,72,73,74,75,76,77,78]. The abundance of heavy metals and trace elements of the studied sediments in comparison with local lakes sediments (Manzala, Idfu, Qarun, and Nasser Lakes) indicated that these sediments were highly enriched in Cr, Ni, Pb, and Zr compared with other lakes, resulting from the presence of ferromagnesian minerals, zircon, the contents of clay minerals and organic matter, in addition to probable anthropogenic reasons. The current research area contained less Ni, Cu, and Zn than the beach sediments from the Arabian Gulf and the Caspian Sea (Russia), but more Pb. The studied area exhibited higher Ni, Cu, Pb, and Zn concentrations than the Salaam Coast of the Indian Ocean (Tanzania). The beach sediments in the current study, on the other hand, had higher concentrations of Cr, Ni, Pb, Cu, Zn, and Pb than those of the Port Said–Damietta Coast, but lower concentrations than the beach sediments in Gokcekaya Dam Lake (Turkey), the Caspian Sea (Azerbaijan), and the Red Sea (Arabian Gulf).

3.3. Geo-Accumulation Index (Igeo)

The geo-accumulation index (Igeo) is a numerical indicator of heavy metal pollution in sediments. It was obtained by comparing the heavy metal concentrations in the current study to pre-industrial levels.

Table 4. Igeo and PLI for different metals in sediment samples of the study area.

Sample No.	Igeo													PLI
	Cr	Cu	Ni	Zn	Zr	Ga	Sr	Y	Rb	V	Nb	Pb	Ba
1	0.89	−1.68	−0.65	−2.83	1.03	0.12	−1.81	−4.29	−6.71	−0.25	−0.04	−3.32	−0.63	0.51
2	−0.67	−1.62	−2.15	−3.70	−0.94	−0.58	−3.91	−5.29	−7.71	−3.29	−2.04	−3.91	−3.31	0.19
3	−0.52	−1.62	−1.58	−3.98	−0.79	0.25	−3.73	−5.29	−7.71	−2.96	−1.72	−4.91	−2.98	0.20
4	0.74	−1.75	−0.72	−2.76	1.25	−1.25	−1.61	−3.70	−6.71	−0.24	0.13	−3.91	−0.26	0.49
5	1.32	−1.62	−1.18	−2.91	2.58	0.38	−0.36	−5.29	−7.71	0.07	1.51	5.27	−0.36	0.96
6	1.01	−1.62	−0.58	−2.70	1.17	−0.25	−1.66	−4.29	−6.71	−0.22	0.04	−3.91	−0.66	0.51
7	−0.23	−1.38	−1.31	−3.25	−1.51	−1.13	−4.42	−5.29	−7.71	−2.70	−2.46	−3.32	−2.73	0.20
8	0.23	−1.75	−1.39	−3.15	1.64	−1.03	−1.24	−3.70	−7.71	−0.69	0.54	−3.32	−0.69	0.46
9	−0.10	−1.75	−1.46	−3.70	0.62	−0.44	−2.17	−4.29	−7.71	−1.80	−0.46	−3.91	−1.82	0.32
10	1.05	−1.75	−0.81	−2.76	1.31	−1.83	−1.59	−3.70	−6.71	−0.32	0.20	−2.91	−0.27	0.51
11	0.76	−1.75	−0.77	−2.83	1.39	−4.83	−1.50	−3.70	−6.71	−0.10	0.28	−3.91	−0.11	0.42
12	−0.21	−1.75	−1.67	−3.70	−0.11	−4.83	−2.98	−5.29	−7.71	−2.22	−1.24	−3.91	−2.26	0.20
13	0.30	−1.75	−1.39	−3.35	1.32	−0.66	−1.52	−4.29	−7.71	−1.34	0.20	−4.91	−1.38	0.37
14	−0.54	−1.62	−1.50	−3.57	−1.22	−1.03	−4.23	−5.29	−7.71	−2.80	−2.04	−4.91	−2.88	0.18
15	−0.24	−1.68	−1.42	−3.57	−0.40	−4.83	−3.42	−5.29	−6.71	−2.61	0.96	−4.91	−2.62	0.21

displays the geo-accumulation index data for thirteen heavy metals. The results were compared to the Muller classification [48]. The Igeo values showed that the sediments were uncontaminated with Ba, Pb, Rb, Y, Sr, Zn, Ni, and Cu.

In contrast, the Igeo values for Cr and Nb varied between uncontaminated to moderately contaminated, Ga from uncontaminated to moderately contaminated, and Zr from uncontaminated to moderately heavily contaminated. The Igeo of Ga in samples nos. 1–5 was uncontaminated to mildly contaminated, whereas the remaining samples were almost uncontaminated. For Cr, Zr, and Nb, Igeo had comparable results for sediment samples, indicating that these elements virtually did not contaminate samples nos. 2, 3, 7, 12, and 15. The Igeo for Cr, Zr, and Nb ranged from uncontaminated to moderately contaminated sediment in samples 4, 8, 11, and 13. Sample no. 7 was moderately to heavily polluted by Cr and Nb and moderately to heavily contaminated by Zr.

3.4. Contamination Factor (Cf) and Degree of Contamination (DC)

The contamination factor (Cf) is the ratio of a certain site’s sediment metal concentration to its typical content level. The values of Cf for Cu, Zn, Y, and Rb of all samples were similar (

Table 5. Values of Cf and DC in sediment samples from the research region for various metals.

Sample No.	Cf													DC
	Cr	Cu	Ni	Zn	Zr	Ga	Sr	Y	Rb	V	Nb	Pb	Ba
1	2.78	0.47	0.96	0.21	3.07	1.63	0.43	0.08	0.0143	1.26	1.45	0.15	0.97	13.46
2	0.94	0.49	0.34	0.12	0.78	1.00	0.10	0.04	0.0071	0.15	0.36	0.1	0.15	4.58
3	1.04	0.49	0.50	0.09	0.87	1.79	0.11	0.04	0.0071	0.19	0.45	0.05	0.19	5.83
4	2.50	0.44	0.91	0.22	3.57	0.63	0.49	0.12	0.0143	1.27	1.64	0.1	1.25	13.15
5	3.76	0.49	0.66	0.20	8.98	1.95	1.17	0.04	0.0071	1.58	4.27	58	1.17	82.27
6	3.02	0.49	1.00	0.23	3.37	1.26	0.47	0.08	0.0143	1.28	1.55	0.1	0.95	13.82
7	1.28	0.58	0.60	0.16	0.53	0.68	0.07	0.04	0.0071	0.23	0.27	0.15	0.23	4.82
8	1.76	0.44	0.57	0.17	4.68	0.74	0.63	0.12	0.0071	0.93	2.18	0.15	0.93	13.31
9	1.40	0.44	0.54	0.12	2.30	1.11	0.33	0.08	0.0071	0.43	1.09	0.1	0.42	8.37
10	3.10	0.44	0.85	0.22	3.72	0.42	0.50	0.12	0.0143	1.20	1.73	0.2	1.24	13.75
11	2.53	0.44	0.88	0.21	3.94	0.05	0.53	0.12	0.0143	1.40	1.82	0.1	1.39	13.43
12	1.30	0.44	0.47	0.12	1.39	0.05	0.19	0.04	0.0071	0.32	0.64	0.1	0.31	5.38
13	1.84	0.44	0.57	0.15	3.74	0.95	0.52	0.08	0.0071	0.59	1.73	0.05	0.58	11.25
14	1.03	0.49	0.53	0.13	0.64	0.74	0.08	0.04	0.0071	0.22	0.36	0.05	0.20	4.52
15	1.27	0.47	0.56	0.13	1.14	0.05	0.14	0.04	0.0143	0.25	2.91	0.05	0.24	7.25

) and indicated a low degree of contamination. Except for Ni in sample no. 6 and Sr in sample no. 5, the Cf values for Ni, Sr, and Pb were in the low degree of contamination range, while Pb in sample 5 exhibited a very high degree of contamination. The Cf values of Ga, V, and Ba for more than half of the samples indicated a low degree of contamination, and the rest indicated a moderate degree. The Cf values of Cr indicated different degrees of contamination, from a low degree in sample number 2, moderate in 11 samples, and a very high degree of contamination in samples 5, 6, and 10. Cf values of Zr are in different degrees of contamination (four degrees); samples 5 and 8 have very high degrees of contamination.

The DC values for the sediment samples are lower than 6 in samples nos. 2, 3, 7, 12, and 14, which indicates a low degree of contamination. Three samples (9, 13, and 15) show moderate contamination. Samples nos. 1, 4, 6, 13, 10, and 11 exhibit a considerable degree of contamination. Sample no. 5 has a high degree of contamination, with a value of 82.27.

3.5. Pollution Load Index (PLI)

The pollution load index (PLI) was used to calculate the total pollution level of each sample [58,83]. The calculated PLI values presented in Table 4 indicate that the sediment samples in the research region were of high quality (PLI < 1).

3.6. Statistical Analysis

Various statistical methods were employed to measure the geochemical features of the collected sediments to establish the source and degree of metal contamination. A better understanding of the interelement interaction between heavy metals in collected sediments was possible using multivariate statistical methods, including correlation coefficients, component analysis, and cluster analysis. These approaches made the identification of potential sources impacting the collected sediments possible.

3.6.1. Correlation Coefficients

Table 6. Pearson correlation coefficient of elements in beach sediments at El-Burullus.

	Cr	Cu	Ni	Zn	Zr	Ga	Sr	Y	Rb	V	Nb	Pb	Ba
Cr	1
Cu	−0.204	1
Ni	0.783 **	−0.165	1
Zn	0.889 **	−0.126	0.917 **	1
Zr	0.833 **	−0.293	0.402	0.610 *	1
Ga	0.278	0.263	0.050	0.041	0.343	1
Sr	0.838 **	−0.312	0.422	0.622 *	0.999 **	0.344	1
Y	0.475	−0.586 *	0.633 *	0.657 **	0.380	−0.247	0.408	1
Rb	0.527 *	−0.280	0.818 **	0.699 **	0.110	−0.271	0.120	0.523 *	1
V	0.953 **	−0.320	0.825 **	0.918 **	0.823 **	0.191	0.833 **	0.644 **	0.568 *	1
Nb	0.668 **	−0.284	0.291	0.445	0.834 **	0.134	0.822 **	0.197	0.274	0.628 *	1
Pb	0.548 *	0.137	−0.001	0.210	0.768 **	0.491	0.743 **	−0.255	−0.217	0.430	0.708 **	1
Ba	0.886 **	−0.421	0.808 **	0.904 **	0.764 **	0.019	0.775 **	0.785 **	0.599 *	0.969 **	0.568 *	0.297	1

** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).

shows the interelement correlation analysis of the variables Cr, Cu, Ni, Zn, Zr, Ga, Sr, V, Nb, Pb, and Ba in the research area. The correlation matrix revealed a substantial relationship between numerous elements that could represent the mineral composition and the consequences of any pollution hazards. Cr had significant positive correlations with Ni (r = 0.78), Zn (r = 0.89), Zr (r = 0.88), Sr (r = 0.838), V (r = 0.95), Nb (r = 0.67), Ba (r = 0.89), Pb (r = 0.55), and Rb (r = 0.53). Ni was strongly correlated with Zn (r = 0.92), Rb (r = 0.82), V (r = 0.83), Ba (r = 0.81), and Y (r = 0.63), suggesting the effect of ferromagnesian minerals in the distribution of these elements. Zn was correlated strongly with V (r = 0.92), Ba (r = 0.90), Rb (r = 0.69), Sr (r = 0.62), Y (r = 0.66), and Zr (r = 0.62). Sr (r = 0.99), V (r = 0.83), Nb (r = 0.83), Pb (r = 0.77), and Ba (r = 0.76) all had positive correlations with Zr. Sr was correlated positively with V (r = 0.83), Nb (r = 0.82), Pb (r = 0.74), and Ba (r = 0.78), which could be related to the high contents of organic matter and clay minerals in these sediments. Rb (r = 0.52), V (r = 0.644), and Ba (r = 0.79) all had a positive connection with Y. Rb exhibited a favorable relationship with both V (r = 0.57) and Ba (r = 0.59). V had a positive relationship with Nb (r = 0.63) and Ba (r = 0.97). Nb displayed a positive connection with Pb (r = 0.71) and Ba (r = 0.57), indicating that these elements were closely associated with minerals originating from diverse sources (acidic and basic) in the examined stream sediments.

3.6.2. Expanded Discussion on Correlation Analysis

In the expanded discussion on correlation analysis, various elements were examined for their interrelationships, revealing significant patterns that suggest both natural and anthropogenic influences. Chromium (Cr) demonstrated strong positive correlations with elements such as nickel (Ni), zinc (Zn), zirconium (Zr), strontium (Sr), vanadium (V), niobium (Nb), barium (Ba), and lead (Pb). These strong correlations indicated a shared source or similar geochemical behavior, with the connections to Ni, Zn, and Ba potentially pointing to anthropogenic sources, possibly from industrial activities. In contrast, the correlations with Sr and Zr suggested a natural geological component, likely related to specific minerals within the sediments.

Nickel (Ni) was found to have a strong correlation with Zn, rubidium (Rb), V, and Ba, indicating a tendency for these elements to co-occur. Ferromagnesian minerals may influence this co-occurrence and suggest potential anthropogenic sources, particularly from urban runoff or industrial discharges. Zinc (Zn) exhibited strong correlations with V, Ba, Rb, Sr, yttrium (Y), and Zr, highlighting a complex interaction that both natural processes and human activities, such as industrial or agricultural practices, could influence.

Zirconium (Zr) showed high correlations with Sr, V, Nb, Pb, and Ba, which implied a significant influence from heavy minerals like zircon. This could point to natural geological processes or indicate pollution from mining activities. Gallium (Ga) and Y were also noted for their correlations with other elements, suggesting influences from various sedimentary processes and possible contributions from urban or industrial sources.

Based on these findings, several recommendations have been made to address the implications of these correlations. Given their strong correlations and potential links to anthropogenic sources, it is essential to monitor and manage the levels of Cr, Ni, Zn, and Pb. Regular water and sediment quality assessments are advised to track any changes over time. Further investigations into specific industrial or urban activities are recommended to better understand the sources of these elevated metal levels. Techniques such as isotopic studies or detailed geochemical fingerprinting could help to pinpoint the origins of these elements.

Mitigation strategies should be implemented to control runoff and discharge from urban and industrial areas, and best management practices should be promoted in agriculture to reduce contaminant introduction into the lake. Additionally, further research is needed to explore the mineralogical composition of sediments to differentiate between natural and anthropogenic contributions and investigate the bioavailability and potential ecological impacts of these metals on the lake’s ecosystem.

3.6.3. Factor Analysis

Factor analysis of all measured variables of sediment samples in the research region was performed to identify the various factors that govern the distribution of elements in the sediment samples and to learn about the content, nature, and sources of the sediments. The four factors accounted for about 94.069% of the data variability (

Table 7. R-mode varimax rotated factor matrix of elements in beach sediments at El-Burullus.

Element	Component
	1	2	3	4
Cr	0.737	0.615	0.092	0.180
Cu	−0.051	−0.153	−0.890	0.222
Ni	0.974	0.079	0.086	0.040
Zn	0.926	0.304	0.095	0.051
Zr	0.316	0.871	0.261	0.251
Ga	−0.007	0.245	−0.222	0.876
Sr	0.332	0.851	0.288	0.263
Y	0.618	−0.018	0.728	−0.035
Rb	0.839	−0.040	0.058	−0.412
V	0.775	0.538	0.262	0.159
Nb	0.210	0.912	0.090	−0.168
Pb	−0.060	0.909	−0.251	0.265
Ba	0.772	0.452	0.406	0.053
Eigenvalue	4.894	4.197	1.855	1.283
Variance %	37.645	32.286	14.271	9.867

). The commonalities demonstrated that this model accounted for all elements. With a high eigenvalue of 4.894, the first factor (Factor 1) explained 37.645% of the total variance. It was the most common of the obtained factors. It had a strong relationship with Cr, Ni, ZN, Y, Rb, V, and Ba. With an eigenvalue of 4.197, the second factor (Factor 2) accounted for 32.286% of the data variability. It had a high correlation with Cr, Zr, Sr, Nb, and Pb and may have indicated the presence of heavy minerals, such as zircon, columbite, and galena. With an eigenvalue of 1.855, the third factor (Factor 3) accounted for 14.271% of the data variability in this model. It had a significant correlation with Y, which could be due to the existence of xenotime. With an eigenvalue of 1.283, the fourth factor (Factor 4) explained an additional 9.867% of the variation. It was highly correlated with Ga and may have been related to clay minerals.

3.6.4. Cluster Analysis

The correlation between the examined chemical components was assessed using R-mode cluster analysis. The applied analysis relies on the Euclidean average linkage approach (within groups), which is adequate because its clusters are different and essential in revealing the kind of sediment in the study area. R-mode cluster analysis can be used to verify the outcomes, even though it is like factor analysis [84]. The R-mode cluster analysis dendrogram (Figure 10) revealed three unique groupings of element connection. The first set of elements consisted of Zr, Sr, Nb, V, Ba, Ni, Zn, Rb, Cr, Cu, and Y, the second of V and Ga, and the third of Rb and Pb.

Principal component analysis was the extraction method. Varimax rotation with Kaiser normalization; after six repetitions, the rotation converged.

4. Discussion

The world average shale values are widely recognized and used in geochemical studies as a standard reference. These values represent the average concentration of elements in shale worldwide and provide a baseline for comparing local sediment concentrations. Given the geological and mineralogical composition of the study area, the shale average offers a relevant comparison. The sedimentary nature of the El-Burullus region, with its significant shale content, justifies using these values as a background. These background levels provide a benchmark against which we can assess the extent of anthropogenic influence and natural variability in metal concentrations. Our approach aligns with commonly used guidelines in environmental studies, ensuring that our assessment of pollution levels is grounded in a widely accepted framework. By using these criteria, we aimed to provide a robust and scientifically sound basis for evaluating the pollution levels in the El-Burullus Lake shoreline sediments. This approach helps in accurately determining the extent of metal contamination and its potential natural and anthropogenic sources. Strong Positive Correlations: Certain elements, like Cr, Ni, Zn, and Ba, showed strong positive correlations. This could indicate a common source or similar geochemical behaviors. For instance, Cr’s strong correlation with Ni, Zn, Zr, and Ba could suggest their co-occurrence in specific mineral phases, possibly influenced by anthropogenic activities. The strong correlation between Ni and elements like Zn, Rb, and Ba suggests their association with ferromagnesian minerals. This is indicative of both natural geological processes and potential anthropogenic sources.

The correlation of Sr with elements like Zr, V, Nb, and Ba could be attributed to the high content of organic matter and clay minerals in sediments, highlighting the role of the natural sediment composition in heavy metal distribution. Factor analysis helps in identifying groups of elements that behave similarly or have familiar sources. The R-mode cluster analysis further supports the factor analysis findings by identifying clusters of elements with similar behaviors or sources. For example, the Zr, Sr, Nb, V, Ba, Ni, Zn, Rb, Cr, Cu, and Y clusters suggest a shared geological or environmental source. Identification of Unique Sources: Separating certain elements into distinct clusters could indicate unique sources or pathways of contamination not shared with other elements.

The expanded discussion of the correlation analysis highlights the complex interactions between various heavy metals in the El-Burullus Lake sediments. This study suggests that natural geological processes and anthropogenic activities influence these interactions. By understanding these interactions, we can make more informed decisions about monitoring, source identification, and mitigation strategies to protect the lake’s ecosystem and surrounding environment. Soil pollution by heavy metals leads to changes in its properties, like difficult degradation, long duration, and concealment [85,86,87]. Heavy metal may infiltrate the soil and water through surface runoff, leaching, and seepage [88,89]. Increments in heavy metal contents may damage root tissues in plants and gradually enter animals and human bodies through the food chain, resulting in various degrees of damage to the nervous, reproductive, and digestive systems [87,90]. Anthropogenic activities, the presence of black sand spots, factory construction, mining, shipping, and other activities like agriculture on soil in the study area may cause the accumulation, migration, and diffusion of heavy metals [91,92]. The study area’s contamination profile is likely a result of anthropogenic and natural sources. While industrial, agricultural, and urban activities significantly contribute to the metal load, the underlying geological formations and hydrological dynamics also play a crucial role in the distribution and concentration of these elements. Understanding these sources is critical for developing targeted strategies to mitigate contamination and protect the lake’s ecosystem. Future research could focus on more detailed source apportionment using advanced techniques like isotopic analysis, which can distinguish between natural and anthropogenic sources of metals.

5. Conclusions

This study discovered significant quantities of heavy metals, like chromium (Cr), zinc (Zn), lead (Pb), and niobium (Nb), in the sediments of the El-Burullus Lake shoreline, which exceeded usual shale concentrations. The spatial distribution patterns showed a higher accumulation degree in areas with urban and agricultural activity. Notably, the Pb and Cr levels were elevated in certain samples, indicating local pollution. Understanding the distribution of heavy metals is critical for identifying potential public health risks, especially in communities that rely on the lake for fishing and agriculture. This study emphasizes the need for ongoing monitoring and research into the causes and impacts of heavy metal pollution in aquatic ecosystems. Finally, our findings highlight the complex interplay between natural processes and human actions in determining the pollution profile of coastal habitats. This area has significant heavy metal contamination, necessitating regular soil pollution monitoring to ensure a safe environment. The findings have major implications for environmental management, policymaking, and public health in the El-Burullus Lake region and similar coastal ecosystems worldwide.