1 Introduction

Costal zones are particularly important, because they constitute a small portion of physical space where all four spheres interact, namely, the lithosphere, hydrosphere, atmosphere and biosphere. Hundreds of tons of pollutants are dumped into the atmosphere every day. Among these pollutants, heavy metals are considered the most serious, especially in aquatic environments because of they tend to bioaccumulate inside aquatic organisms (Sundararajan et al. 2017; Noa Tang et al. 2021; Chougong et al. 2021).

Heavy metals in living environments most often originate from natural processes (e.g., volcanic eruptions, rock weathering, erosion and deposition of dust) and anthropogenic activities (e.g. urban development, industry, agriculture and change in rivers and lacustrine environments) (Jha et al. 2019; Ayala-Pérez et al. 2021). Compared to other contaminants, heavy metals are widespread persistent contaminants, which are potentially toxic, non-biodegradable, and accumulate in living organisms through the food chain (Fan et al. 2002; Buccolieri et al. 2006; Mapel-Hernández et al. 2021). In most cases, these heavy metals cause toxic effects in many organisms (Bryan and Langston 1992) after reaching certain concentrations. They can thus cause a serious threat to ecological safety and human health. Some heavy metals such as Zn, Cu, Mn and Fe are essential for the growth and well-being of living organisms, including humans. Other elements, such as Pb, Hg, and Cd, are not required for metabolic functions and have hazardous effects (Biney et al. 1994). Sediments are one of the principal storages of heavy metals, as they are used as an efficient mean to appraise the level of contamination of marine environments (Ke et al. 2017; Mandeng et al. 2019).

Several factors must be taken into consideration when identifying the source of heavy metals in terrestrial and coastal environments, including grain size, mineral composition and sedimentary environment. On the other hand, metals that have reached sediments from anthropogenic sources are more often found in relatively more bioavailable forms (Gleyzes et al. 2002; Ekoa Bessa et al. 2018a, b; Tehna et al. 2019; Noa Tang et al. 2021). They are deposited within beach sediments by adsorption, hydrolysis and co-precipitation, whereas a smaller portion of free metal ions remains in the water column (Bartoli et al. 2012).

The pollution of aquatic and coastal environments by organic and inorganic micropollutants, such as persistent organic pollutants, pharmaceutical drugs and agricultural uses, is of concern (Thevenon et al. 2011; Mubedi et al. 2013). With such a broad coastal environment, very few works based on environmental studies have been done in Cameroon. Whereby too many pollutants are found along the coasts. The aim of this work is to (i) investigate the distribution of heavy metal contents along the central part of the littoral zone of Cameroon, (ii) evaluate the level of contamination based on indices of contamination, and (iii) identify the possible source of pollutants.

2 Materials and Methods

2.1 Study Area

The study area is located in the central part of the littoral zone of Cameroon, more precisely, the outlet of the Sanaga River found on the coast of the Atlantic Ocean, ranging from 3°37' to 3°41' N and 9°37' to 9°40' E, in the south west (SW) of Cameroon (Fig. 1). The dry season occurs between December and February, whereas the rainy season occurs between March and November, with a peak in August. The main activities carried out in this sector are artisanal fishing and agriculture. Geologically, the central part of the coast is dominated by sedimentary formations linked mainly to the Douala Basin. These formations were put in place by marine transgressions from the Upper Cretaceous to the Tertiary (Ngueutchoua et al. 2019; Mbesse et al. 2020).

Fig. 1
figure 1

Investigated area and sampling location: a) location of the study area in Cameroon; b) Location of sampling area and station along the Central part of the Littoral of Cameroon

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The Cameroonian coastline lies unconformably on the Precambrian basement and begins with a marine series made up of marls and fossiliferous limestones of inferior Turonian–Campanian age and continues with a continental series of superior Campanian–Paleocene age to present (Ntamak-Nida et al. 2010). Also called the Dizangué series, it is made up of small conglomerate at the base, and fine and coarse sandstone at the top. It also presents intercalations of clay, kaolinite and ferruginous sandstone with intersecting stratification (Ngueutchoua et al. 2019; Ekoa Bessa et al. 2021).

Another continental sedimentary series of the middle Cretaceous is observed in the upstream basin of the Sanaga, made up of a succession of fine sandstones, black and beige, and arkosic conglomerate sandstone (Ekoa Bessa et al. 2021). The study area includes activities, such as fishing, leisure and popular touristic areas. This part of the coastline is also affected by activities, such as agriculture and industries. Studies of the pollution of sediments by heavy metals along the littoral of Cameroonian coast are still not well known (Ekoa Bessa et al. 2020).

2.2 Sampling and Analysis

Sediment samples have been collected from three different stations, making a total of 24 sampling points, chosen based on environment, sea level, slope, water-sampling and lithological nature along the central part of littoral. The three stations include the Yoyo, Mouanko and Mbiako beaches (Table 1). The samples were collected in December 2019, from the designated sites using a trowel. A trowel is used to remove the top (5–10 cm) layer of the sediments to avoid contamination. Polythene bags were used to package and transport the samples to the laboratory, approximately 2 kg of each sample was collected. The samples were dried in the open air, crushed and then sieved. 0.2 g of sample powder was dissolved with 1.5 g of LiBO2. The intensity of metal concentration of Fe, Mn, Cr, Ni, Co, Cs and V was resolved into air/acetylene fire using the Inductively Coupled Plasma Mass Spectrometry (ICP–MS) pulp at ALS (Australian Laboratory Services), Vancouver (Canada). Analytical precision varies from 0.1 to 0.5% for trace metals. The detection limits range from 0.05 × 10–6 to 4.0 × 10–6 mg/g depending on the element. Total organic carbon (TOC) was determined using a Primacs SLC analyzer in the laboratories of the International Institute for Tropical Agriculture (IITA; Yaoundé, Cameroon). Total Carbon (TC) was first determined by combustion at 105 °C, then the inorganic carbon (IC) at 105 °C. Prior to the analysis, the sample is put in a test tube in which oxygen is purged to remove CO2. To decompose the inorganic bounded carbon to gaseous carbon dioxide, orthophosphoric acid is added to the sample. The flow oxygen purges the carbon dioxide from the liquid into the IR detector to be measured again. The concentration of TOC is determined using the formula: TOC = TC–IC. The amount of organic matter (OM) was obtained by the calculation of Walkley and Black (1934) formula where OM (%) = TOC (%).

Table 1 Latitude and longitude of sampling tested sites along the Central part of the Littoral of Cameroon
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Descriptive statistics of heavy metals, TOC and OM were calculated using PAST program (multivariate statistical package, version 1.72). The box and whisker plots of soil quantification (soil indices) have also been drawn for the three stations to show the variations of studied HMs (Heavy metals) value using the PAST program. In addition, the HMs data of the three stations was subjected to an agglomerative hierarchical cluster (AHC) and principal component analysis (PCA) to construct a matrix of correlation, and to identify whether a significant difference exists between different treatments. PCA was performed using XLSTAT statistical computer software package, version 14.

2.3 Data Processing

2.3.1 Enrichment Factor (EF)

The enrichment factor (EF) is a tool generally used to distinguish between natural and anthropogenic sources of heavy metals in sediments (Salati and Moore 2010):

$${\text{EF }} = \, \left[ {\text{M}} \right]{\text{Sample/}}\left[ {{\text{Fe}}} \right]{\text{sample/}}\left[ {\text{M}} \right]{\text{Background/}}\left[ {{\text{Fe}}} \right]{\text{background,}}$$
(1)

where [M]Sample∕[Fe]sample is the sample concentration of metals in the analyzed sediment samples and [M]Background∕[Fe]background refers to the background value of the metal. Iron (Fe) is the normalizing metal for the following reasons: Fe is associated with fine to medium solid elements; its geochemistry is comparable to that of many heavy metals; and its natural concentration in the study area tends to be uniform (Bhuiyan et al. 2010). Enrichment factor (EF) values have been defined in conjunction with Acevedo-Figueroa et al. (2006), EF ˂ 1 denotes no enrichment; 1 − 3 denotes minor; 3 − 5 denotes moderate; 5 − 10 denotes fairly bad; 10 − 25 denotes bad; 25 − 50 denotes quite severe; and ˃50 denotes extremely serious enrichment.

2.3.2 Geo-accumulation Index (Igeo)

The geoaccumulation index (I-geo) is a quantitative measurement of the level of metal pollution in aquatic sediments. Muller’s geoaccumulation index was originally described as

$${\text{I}} - {\text{geo }} = {\text{ Log}}_{{2}} \left[ {{\text{Cn Sample}}/ \, \left( {{1}.{5} \times {\text{ Cn Background}}} \right)} \right],$$
(2)

where Cn sample is the obtained concentration of metal n, and Cn background is the standard value for the metal n. Factor 1.5 has been used in the environment due to potential fluctuations in background values for a given metal, as well as very slight anthropogenic impacts. While I-geo was designed to work with worldwide standard shale values as background metals rates, Rubio et al. (2000) found that regional background values yielded more acceptable results.

2.3.3 Contamination Factor (CF), Degree of Contamination (DC) and Pollution Load Index (PLI)

The contamination factor (CF) is the ratio calculated from the equation of Tomlinson et al. (1980a, b)

$${\text{CF }} = \, \left( {{\text{C}}_{{{\text{metal}}}} /{\text{C}}_{{{\text{background}}}} } \right),$$
(3)

where Cmetal represents the pollutant concentration in the sediments and Cbackground refers to metal background values. According to Hakanson (1980), contamination factor is classified into four classes. When CF values are ≤ 0.1 this indicates low contamination; 0.1 ≤ CF < 0.3 indicates moderate contamination; 0.3 ≤ CF < 0.6 shows considerable contamination and CF ≥ 0.6 indicates very high contamination.

The degree of contamination (DC) is the sum of all contamination factors for a given site (Hakanson 1980). The DC can be calculated by the following equation:

$${\text{DC}} = \, \sum_{i = 1}^{n} {\text{CFi,}}$$
(4)

where CF is the single contamination factor and n is the amount of the element present. DC < n, would indicate low degree of contamination; n ≤ DC < 2n, moderate degree of contamination; 2n ≤ DC < 4n, considerable degree of contamination and DC > 4n, very high degree of contamination. For this study, the number of elements selected is 7 (n = 7).

The pollution load index (PLI) is derived as factors of concentration (CF). The PLI commonly was defined by Tomlinson et al. (1980a, b), as follows:

$${\text{PLI}} = \sqrt[n]{{({\text{CF}}1 \times {\text{CF}}2 \times {\text{CF}}3 \times \ldots \ldots \times {\text{CF}}n}},$$
(5)

where CF is the contamination factor and n is the number of metals. The pollution load index (PLI) value > 1 is polluted, while < 1 indicates no contamination (Harikumar et al. 2009). amination and when CF ≥ 0.6 indicate very high contamination by the different metals.

3 Results

3.1 Sediment Characteristics

The beach sediments were predominantly sandy in nature. The mean grain size distribution (Φ) ranges from 1 to 1.75 Φ, classified as medium-grained sand. However, in most of the observations, the sand was found to be medium to fine-grained in size. This can be attributed to a dynamic deposition linked to an agitated environment or to the higher wave energy regime, resulting from the local topography which would prevent the sedimentation of fine-grained particles. The TOC and organic matter (OM) contents vary from 0.42 to 5.80% and 0.72 to 9.98%, respectively (Table 2; Fig. 2), with the highest OM values observed at Yoyo beach. Spatial distribution of heavy metals observed in the beach sediments along the Central part of the Littoral Cameroon reveals that mean concentrations (mg/kg) were found in the following decreasing order: Fe (13,638.30–24,339.12) ˃ Mn (38.73–387.25) ˃ Cr (20.53–54.74) ˃ V (16.00–40.00) ˃ Ni (10.00–35.00) ˃ Co (3.40–5.60) ˃ Cs (0.50–0.80) (Table 2; Fig. 2).

Table 2 Descriptive statistical of metals concentration (mg/kg), TOC (%) and OM (%) of surface sediment samples along the Central part of the Littoral of Cameroon
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Fig. 2
figure 2

Distribution patterns of total metal concentration (mg/kg) and OM (%) in the beach sediments

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3.2 Enrichment Factor and Geo-accumulation Index

The level of metal enrichment is evaluated based on the obtained results, listed in Supplementary Appendix 1 (Fig. 3). The results obtained are presented according to each site and in the order of abundance of each metal. The decreasing order of the EF metals of Yoyo beach is Cr > Ni > Mn > Fe > V > Co > Cs; the decreasing order of the EF metals of Mouanko is Cr > Mn > Fe > Ni > V > Co > Cs and the decreasing order of the EF metals of Mbiako is Mn > Cr > Fe > Co > Ni > V > Cs. The order of average metal EF of the three sites is Cr > Mn > Ni > Fe > V > Co > Cs. The Yoyo beach presents more enrichment in Fe, Mn, Cr and Ni compared to Mouanko and Mbiako beaches (Fig. 4).

Fig. 3
figure 3

Variations of enrichment factor (EF) of different metals in the Yoyo, Mouanko and Mbiako beach sediments

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Fig. 4
figure 4

Calculation of geoaccumulation indices of metal concentrations in sediments of the Central part of the Littoral of Cameroon

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The I-geo values of all metals in the sediments of the study area are < 0, unpolluted at all sites.

3.3 Contamination Factor, Degree of Contamination and Pollution Load Index

In this study, contamination factor is classified into two classes (Fig. 5; Supplementary Appendix 2). In the Yoyo and Mouanko beaches CF values for Co, Cs, Ni and V range from 0.1 to 0.3, suggesting that the sediments are moderately contaminated; Fe, Mn, Cr and Ni ranged from 0.3 to 0.6 which show that the sediments are considerably contaminated. On the other hand, in Mbiako beach, values for Mn, Cr, Ni, Co, Cs and V ranged from 0.1 to 0.3, showing that the sediments are moderately contaminated, while Fe and Mn ranged from 0.3 to 0.6, showing the considerable contamination of the sediments. The DC values range between 1.29 and 2.36. DC values belongs to a low degree of contamination. The results for each site are presented in Fig. 6.

Fig. 5
figure 5

Contamination factors of different metals in the sediment of Central part of Littoral of Cameroon

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Fig. 6
figure 6

Degree of contamination for trace metals in the sediments

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However, the values of PLI recorded for all the stations ranged from 0.11 to 0.22 (Supplementary Appendix 3). The PLI values in all sampling sites are less than 1 (Fig. 7), which suggests no contamination.

Fig. 7
figure 7

Pollution load index (PLI) of trace metals for different locations in the Central part of the Littoral zone of Cameroon

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3.4 Statistical Analysis

A multivariate statistical analysis (Pearson correlation cluster and PCA) was used to identify links among variables. The multivariate analysis was conducted using estimated metal concentrations, derived indexes, and surface sediment properties (Figs. 8 and 9).

Fig. 8
figure 8

Statistical analysis of sampling sites according to trace metals in sediment samples along the beaches a) Principal component analysis (PCA) b) Similarity dendrogram

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Fig. 9
figure 9

Scree plot, eigenvalue and variance of principal components

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A correlation matrix analysis was realized using COSTAT 6.3 software. The correlation coefficient values are listed in Table 3. Correlation matrix indicates that the Fe concentration was significantly positive (P ˂ 0.01) with Cr (0.89), V (0.81) and Co (0.72). However, several elements such as V and Co had significant positive correlations (P < 0.01) with Cr (0.87), Mn (0.52) and Cr (0. 72), V (0.62), respectively. The elements such as Ni vs TOC (0.46), Ni vs OM (0.46), TOC vs V (0.45), OM vs V (0.45) and Ni vs V (0.41) reveal a significant positive correlation (P < 0.05).

Table 3 Pearson correlation matrix for selected parameters analyzed in the sediment samples
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Principal component analysis (PCA) was conducted on the sediment properties and heavy metals to better understand the relationship between these variables and identify their origin. As shown in Supplementary Appendix 4 and Fig. 9, the first two principal components with eigenvalues greater than 1 are accounted for 67.6% of the total variance. Therefore, these two components play an important role in explaining contamination with heavy metals and their source in the studied area. The results demonstrated that the first principal components (PC1) accounted for 51.9% of total variance, with high loadings of Cr, V, Fe and Co, these results could indicate that these elements have a natural origin by the presence of Fe in the same component. The second PC (PC2) accounted for 15.67% of the total variance with load of Cs, which could indicate that these elements have an artificial origin by the presence of high value of Cs. Therefore, the high values of Fe in the component PC1 indicates that its presence in the sediments will be mainly from natural sources, unlike PC2 which will have an artificial origin due to the chemicals released by the industries. Figure 8a shows that the sediment samples from the Yoyo station are clustered in the negative side of PC1, and mainly consist of the variables Ni and Mn, which show the maximum concentrations in these sediment samples. However, sediment samples from the Mouanko beach grouped in the positive side of PC2, and mainly consist of the most variable (Co, Fe, Cr and V) which show the maximum concentrations in these samples. Sediment samples from the Mbiako station grouped in the negative side of PC2 (Fig. 8a).

Cluster analysis (CA) was further used to assess the relationships among sediments. The aim of the hierarchical agglomerative grouping is to measure the greatest similarity between each of the variables, then to group the sites close to each other (Šulc and Řezanková 2019). In the present study, the cluster analysis by metals of the sediment sites maintained quite similar trends, presenting four distinct clusters (A–D) (Fig. 8b), each composed of similar studied sites distributed among these three beach stations. The cluster model indicates that the similarity index of cluster sampling sites is as follows: A to D. Group A simultaneously represents the Mouanko and Yoyo stations, while group B is mainly made up of Mbiako station. Group C is made up of Yoyo and Mbiako stations, but mostly those of Yoyo. Group D represents most of the Mouanko station. A similarity index is observed among all the sites and stations following the same trend. In all groups, all sites have 99% similarities.

4 Discussion

The most important factors controlling the spatial variations of metals in the littoral regions include grain size, organic matter, sediment sources, and anthropogenic pollution (Lin et al. 2002). The sediment samples of the littoral Cameroon are classified as medium to fine-grained. This characteristic of beach sediments can be attributed to the higher wave energy regime due to the topography, which would present the sedimentation of fine-grained particles, where metal traces are accumulated preferentially (Ellis and Revitt 1982; Lin et al. 2002; Armstrong-Altrin et al. 2019, 2021).

The OM in these sediments is of great importance, because it allows metals to be concentrated either by ion exchange and/or complexation (Leenheer et al. 1982). Relatively high OM can be due to influx of organic waste through surface runoff as well as delocalization of solid waste dumped into the riverbed from the continent and the waves from Mediterranean Sea (El-Said and Youssef 2013; Armstrong-Altrin 2020). The sediments from the studied sites can be moderately polluted by organic matter (OM) due to their high content in the selected samples.

The enriched levels of Fe may mostly be attributed to natural processes such as leaching of source rocks, transported from the continent and directly from the Ocean by watercourses and wave movements (Ekoa Bessa et al. 2020). Some researchers have reported that Mn is a naturally occurring trace element found in crude oils, used to enhance fuel oil combustion (Long et al. 1998); according to Srichandan et al. (2016), Mn is also an essential transition metal at low concentration but becomes lethal for human at very high concentrations (> 5 mg/m3). In this study, the average concentrations obtained would be due to the degree of natural erosion (Szefer et al. 1995) and the presence of mangrove vegetation (Kasilingam et al. 2016). The sources of Cr are river drainage, dredging sludge and household waste (Noa Tang et al. 2021). The presence of Cr would be due to the incineration of plastic waste, anthropogenic waste dumping and dredging sludge through tourism and agriculture. The considerable values of V would be caused by the combustion of fossil substances, and more by natural weathering of rocks (Ramos-Vázquez and Armstrong-Altrin 2021) and certain anthropogenic sources, such as garages and markets (Li et al. 2015). Nickel is a very important metal that helps in algal growth (Price and Morel 1991). The moderate concentration of Ni could be attributed to fishing, touristic activities and leaching of rocks. Cesium and Co enrichments are not significant in this study.

The EF values of selected metals ranged between 1 and 3, reflecting minor enrichment; this value shows that a significant portion of metals, different from crustal materials or natural weathering processes may contaminate the sediments. In the present study, EF values are > 1 for all the metals, except Co, Cs and V, which showed no enrichment. These low levels of enrichment factors have been largely attributed to both natural mechanisms, such as mineral spoilage and anthropogenic activities according to Abrahim and Parker (2008). In addition, these low values suggest a primary natural source. This result is the same as that made by Li et al. (2013) in the Shandong Peninsula and in the northern part of the Cameroonian coast (Ekoa Bessa et al. 2020). The geo-accumulation indices (I-geo) values of all metals of the study area, characterize the sediments as unpolluted, this should be explained by the fact that none of the anthropogenic activities in the area contribute to sediment contamination. In the same order of ideas, the values obtained for I-geo in this study indicate that there is no metal pollution in the whole studied area, although a certain difference is observed depending on each metal and the location of sampling points. This result correlates with those obtained on coastal sediments in the Shandong Peninsula (Li et al. 2013), but different from those obtained in the northern part of the same littoral, where oil extraction and many other human activities are present (Ekoa Bessa et al. 2020). In the Yoyo and Mouanko stations, we observe considerable contamination for selected metals, such as: Fe, Ni, Mn and Cr. In addition, the Mbiako station is moderately contaminated with all metals. In the same way, this observation is also made with the Yoyo and Mouanko stations, but only V, Co, Cs and some samples for Ni. In general, in this study, the sediments have moderate and considerable contamination for these metals due to the influence of main natural sources, such as leaching of nearby rocks and alteration of sand grains. It can also be the result of agricultural runoff and other anthropogenic activities, such as dumping of household waste, tourism and fishing activities. This result is slightly similar to that obtained by Ekoa Bessa et al. (2021) in the sediments of Limbé coastal areas in the northern part of the littoral.

The results obtained on the CF lead to the conclusion that the sediments of all beach stations have low DC; this could be due to natural sources (weathering and alteration of sands) and little anthropogenic inputs. The values of PLI in this study are very low and the sediments are classified as unpolluted. The PLI in the present study are very low compared to other coastal environments reported by Sankarappan et al. (2021), Ekoa Bessa et al. (2021) and Wang et al. (2020).

Statistical analyses were applied to confirm the similarity between metals and the influence of natural sources on the sediment’s quality. Therefore, these metals were probably not influenced by anthropogenic factors, but rather by natural sources, such as weathering and erosion of nearby rocks. In addition, the results showed that the three metals (Mn, Ni and V) were mainly distributed in sediments by nearby urbanized areas and urban constructions. It is likely considered that urban pollution contributed to the accumulation of Mn and V in the beach sediments (Saiful Islam et al. 2015). The results of the statistical analysis indicated that the concentrations were grouped into stations with a probability level of 0.05 for all selected stations grouped by hierarchical cluster analysis. Contaminants from point sources could be responsible for the highly polluted stations, whereas those from non-point sources could be responsible for the less polluted stations.

5 Conclusions

Sediment samples along the central part of the littoral of Cameroon have been investigated to infer the distribution and source of metal contamination in sediments. Results of the present study revealed that most of the beach sediment grain size was medium to fine. This study showed that the coastal zone is weakly concentrated in heavy metals, except Fe and Mn, which showed higher contents exceeding the average values. The I-geo values of all metals of the study area, characterize the sediments as unpolluted, and this would be attributed to the non-use of fertilizers in agricultural activities. In the same order of ideas, the values obtained for I-geo in this study indicate that there is no pollution of the studied metals in the whole studied area, although a certain difference is observed depending on each metal and the location of sampling points. The results of pollution indices (EF, I-geo, CF, DC and PLI) revealed that all the sites in the central part of the coast were not polluted by the studied metals. Statistical analyses were applied to confirm the similarity between metals and the influence of anthropogenic activities. This low level of pollution by various metals in this study implies low anthropogenic activities near the beaches of the central coast but show diagenetic processes with high leaching and erosion which contributes to the contaminated level of pollution in these beaches. In general, this study can be considered as a baseline study presenting the current contamination risk and will serve as a reference for future environmental studies.