Introduction

The coastal zones are significant natural ecosystems as transitional between terrestrial and aquatic areas (Crossland et al. 2005). The Red Sea coastal zones have an important role in the blue economy varying from mining, shipping, fisheries, biodiversity-related tourism, etc. (Cziesielski et al. 2021; Kabil et al. 2022). The mangrove habitats are common and dispersed along the Egyptian Red Sea. Mangroves play a vital ecological role as one of the most productive marine habitats (Friess et al. 2020). The mangrove trees provide shelters and feeding nurseries for both land and marine dwellers. The stands of mangroves offer natural protection from erosion and storms. The mangrove forests have an action in the cycle and are considered one of the most carbon-rich ecosystems (Alongi 2020; Almahasheer et al. 2017).

There are two mangrove species in the Egyptian Red Sea the Avicennia marina (gray mangrove) mainly dispreading along the coast from the north and Rhizophora mucronata (red mangrove) located only at the southern borders (Afefe et al. 2019). The mangrove zones are facing different threats, e.g., oil pollution, overcutting, overgrazing, and denudation. These impacts are causing damage and loss in the areas of these appreciated plants (Alongi 2020; Nunoo and Agyekumhene 2022). The mangrove habitat management needs assessment and incessant monitoring through gathering data from different disciplines such as soil contamination assessment and heavy metals concentrations (Schmitt and Duke 2015; De Alban et al. 2020; Afefe 2021).

The unmanaged development activities particularly industries with interference at the coastal zones have increased impacts on the marine environment. The Red Sea province has an intensive tourism development coastline and an increasing population growth and urban development that pressure on coastal environments (Abdel-Latif et al. 2012). In addition to the mining activity in the Red Sea mountains near the coast, there is one uncompleted industry zone which considers anthropogenic impacts (Mohamed 2005; Analuddin et al. 2023). On the other hand, the marine sediments work as reservoirs that concentrate the contaminants especially heavy metals of different discharges from terrestrial and marine (Tripathi et al. 2014; Tang et al. 2019. Heavy metals are mostly toxic contaminants in the environment resulting from non-biodegradable and bioaccumulation processes (El-Said and Youssef 2013; Tam and Wong 2000; Kumar et al. 2020; Elnaggar et al. 2022). The high levels of heavy metal contamination in mangrove forests have been studied comprehensively (Marchand et al. 2006; Praveena et al. 2008; Al-Mutairi and Yap 2021) reported that heavy metals are discharged into coastal mangrove areas due to mining practices, industrial and port activities. The concentration of metals in the environment is recognizing its toxicity to biota from normal to lethal or sub-lethal (Li and Davis 2008; Ama et al. 2014).

The remote sensing techniques were valuable in mapping environmental conditions and clarifying the resources of impacts especially, for the remote areas of mangroves. Mapping of Wadis routes with ancillary data of geologic components brings images of natural metals distributed on the Red Sea coast. Integration of remote sensing with in-situ data on contamination of heavy metals, where sediment quality assessment tools are respected in identifying sediment quality conditions recognized with the biological effects; geo-accumulation index, enrichment factor, contamination factor, degree of contamination, and pollution load index (Shafe et al. 2013; Kumar and Fulekar 2019).

This research aims to evaluate the environmental condition of sediment contamination with heavy metals affecting mangrove areas and to assess the natural and anthropogenic activities related to heavy metal concentrations in mangrove sites along the Egyptian Red Sea coast.

Material and methods

Study area

The study area extends for about one thousand kilometers along the Egyptian Red Sea coast from north Hurghada city to south Shalateen city (Fig. 1). The location of 13 mangrove areas was identified, mapped, and validated from field visits. The study area mangrove stands scattered along the shoreline north from the Abu-Shaara site at Hurghada, south to Suez Gulf to the south of the Shalateen site at Halayeb province in the south border of Egypt with Sudan country. Most mangrove sites are covered by small patches or aggregations of stunted Avicennia marina, except in Shalateen site has two species of mangroves Avicennia marina and R. mucronate.

Fig. 1
figure 1

The location of the study area

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The area of study is located in an eastern desert condition with sparse rainfall and short showers with semi-periodic flooding on the Red Sea mountains drains towards the sea be loading by various metals (Azab 2009) to mangrove areas on the coast border.

Sampling

Thirteen sites were visited and, in some sites, collected more than one sample according to the area of stand and, took two samples one towards the land and the other towards the sea. On the other hand, in some mangrove stands was difficult to collect two samples especially the sample towards the sea. So, the total number of samples was twenty-two (Table 1). The samples collected were from the surface sediment for inspecting the heavy elements. All samples were taken from 0.0 to 10 cm depth using a suitable grab sampler. The collected samples were put directly in air-sealed polyethylene bags and kept at 4 ℃ until analyses. Coordinates of sampling points were identified using a GPS instrument. The samples were air-dried (at room temperature) and the extraneous materials were removed.

Table 1 The name of the twenty-two site locations along the Red Sea coast
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Chemical analysis for the heavy metals in the sediment

The sediment samples about (500 g) were desiccated in a hot air oven at 110 °C for 24 h, ground in a mortar, and then passed through a 2 mm plastic sieve. Well-mixed 2 g sediment samples were treated with 10 ml of freshly prepared aqua regia (HNO3 + 3HCl) on a sand bath for 2 h. After the samples were completely dried, the samples were dissolved in 10 ml of 2% HNO3, filtered via Whatman filter paper No 541, and then diluted to 50 ml with milli-Q water (Chen and Ma 2001). The acid-digested sediment samples were transferred into acid-washed plastic bottles and analyzed for to determine the concentration of Seven heavy elements (Mn, Ag, Cd, Cu, Pb, Ni, and Fe) using the ICP-MS analytical instrument in ppm.

Indices of sediment contamination

Seven indices were utilized in this study to assess the contamination of the sediment as mentioned below:

Enrichment factor (EF)

Enrichment factor (EF) is used to differentiate metal origin from anthropogenic or natural sources. In the beginning, normalize the sample metal concentrations to reference elements, using iron in this study to determine whether a sediment sample is enriched with metals evaluated by the sample’s background environments. Equation (1) used to determine EF values, selected iron (Fe) as a normalizing element according to its major sorbent phase for trace metals and a quasi-conservative tracer of the natural metal-bearing phases in fluvial and coastal sediments (Schiff and Weisberg 1999; Turner and Millward 2000), expressed as

$${\text{EF}}=\frac{({\text{Cx}}/{\text{Fe}}){\text{sample}}}{({\text{Cx}}/{\text{Fe}}){\text{background}}}$$
(1)

where the Cx Sample and Cx background represent the concentration of selected metals. (Cx /Fe) background is the ratio of the background values of Fe. EF value of near unity means the elements that are naturally derived, while EF values of several orders indicate elements of anthropogenic origin. The classification of EF value according to Taylor (1964) to determine the degree of metal contamination is (i) EF < 2 = minimal, (ii) 2 ≤ EF < 5 = moderate, (iii) 5 ≤ EF < 20 = significant, (iv) 20 ≤ EF < 40 = very high, and (vii) EF ≥ 40 = extremely high.

Contamination factor (CF)

Contamination factor (CF) to assess the status of the surface sediment according to Hakanson (1980) based on the following equation:

$$\text{CF}=C\;\mathrm{metal}\;\mathrm{in}\;\mathrm{sediment}\;/\;C\;\mathrm{metal}\;\mathrm{background}$$
(2)

The CF values according to the four classes are depicted as follows:

(i) CF < 1 = low, (ii) 1 < CF < 3 = moderate, (iii) 3 < CF < 6 = considerable, and (iv) CF > 6 = very high.

Degree of contamination (Cd)

The degree of contamination (Cd) represents the sum of all the CF values for all the sampling sites. It was previously proposed by Hakanson (1980) as shown below:

$${\text{Cd}}={\sum }_{i=1}^{n}{\text{CF}}$$
(3)

The degree of contamination; (i) Cd < 6 = low, (ii) 6 < Cd < 12 = moderate, (iii) 12 < Cd < 24 = considerably high, and (iv) Cd > 24 = high.

Modified contamination degree (mCd)

Modified contamination degree (mCd) is the sum of all contamination factors for the element samples to the number of elements analyzed. This measure was proposed by Abrahim and Parker (2008) to investigate an unlimited number of heavy metals and is represented as

$${\text{mCd}}={\sum }_{i=1}^{n}{\text{CF}}/n$$
(4)

where n is the number of analyzed elements (i) is the element (or pollutant) examined and the contamination factor (CF).

Geo-accumulation index (Igeo)

Geo-accumulation index (Igeo) is used to analyze the level of pollution of trace elements and the contamination degree in marine sediments. It was initially described by Muller (1969) as Eq. (5):

$$\text{Igeo}=\text{log}2(\frac{\mathrm{Cn}}{\left(1.5\times\left(10\right)\mathrm{Bn}\right)})$$
(5)

where Cn is the trace metals calculated (measured concentrations of the sediment samples, respectively) and Bn is the background value (average value of crustal abundance) of a particular element.

Where; to decrease the possibility of variation in the background values for a specific trace element in the environment and minor anthropogenic influences, the concentration of each geochemical background value is multiplied by the factor of 1.5 Muller (1979). The sediment classification is based on the Igeo value as follows:

  1. (1)

    Igeo > 5 = extreme contamination,

  2. (2)

    4–5 = strong to extreme contamination,

  3. (3)

    3–4 = strong contamination,

  4. (4)

    2–3 = moderate to strong contamination,

  5. (5)

    1–2 = moderate contamination,

  6. (6)

    0–1 = uncontaminated to moderate contamination,

  7. (7)

     < 0 = uncontaminated.

Pollution load index (PLI)

Pollution load index (PLI) is a parameter to evaluate metal pollution in the marine environment, and it can be calculated from the following equation given by Tomlinson et al. (1980).

$$\mathrm{PLI}\;\mathrm{for}\;\mathrm a\;\mathrm{station}=\sqrt[n]{\text{CF}1\times\mathrm{CF}2\times\mathrm{CF}3..........\text{CFn}}$$
(6)

where CF is the contamination factor and n is the number of metals investigated.

A PLI value above one (> 1) indicates that an area is polluted, whereas values < 1 indicate no pollution or only background levels of pollutants are present (Chakravarty and Patgiri 2009; Mashiatullah, et al. 2013). While an estimation of PLI can be used to identify whether a site is collectively polluted or non-polluted by metals.

Potential ecological risk factor (E r) and risk index (RI)

The potential ecological risk factor is a method used to explore levels of contamination caused by metals and the risk to the aquatic environment. It was first introduced by Hakanson (1980). The formula is as follows:

$$Er=Ti\times Cf$$
(7)

where Ti is the toxic response factor and CF is the contamination factor.

The potential ecological risk index evaluates the environmental behavior and characteristics of heavy metal contaminants in the sediments. This method was previously proposed by Hakanson (1980) and its primary objective is to specify the agents that cause contamination. The RI is the summation of all risk factors for the detection of heavy metal contaminants in the sediment. The RI is calculated based on the following equation:

$${\text{RI}}={\sum }_{i=1}^{n}{\text{Er}}$$

Hakanson (1980) proposed a standardized toxic response factor of 1, 5, 5, 5, and 30 for Mn, Ni, Cu, Pb, and Cd.

Multivariate statistical analysis

Descriptive statistics, correlations, and principal component analysis (PCA) are the most common multivariate statistical methods used in environmental studies (Ganugapenta et al. 2018; Islam et al. 2018; Saher and Siddiqui 2019). These methods were applied to verify significant relationships among the heavy metal’s sediments and to identify contamination sources (natural and/or anthropogenic). Principal component analysis has been applied to the data set of 22 sediment samples and seven variables (Cd, Cu, Pb, Ni, Ag, pb, and Fe). Pearson correlations analysis was applied (Berman 2016) to confirm the relations between different variables. R-mode factor analysis with VARIMAX rotation with the Kaiser–Meyer–Olkin (KMO) test with a > 0.5 KMO (0.5) (Hutcheson and Sofroniou 1999), as well as the Eigenvalues > 1, was applied to the measure’s metals in the sediment samples.

Remote sensing and GIS analysis

Wadi basins extracting

The drainage system influencing the 13 mangrove sites was extracted using digital image processing of ASTER satellite data with a spatial resolution of 30 m utilizing the ArcGIS software environment by the “hydrology spatial analyst tool”; to extract basins in the study area. The Wadi’s have been selected according to every mangrove site, where there are sites that have received flooding from more than one Wadi. In addition to seawater current that transfers the drains load from north or south Wadi to the mangrove site. The satellite data was downloaded from the Copernicus Open Access Hub website https://scihub.copernicus.eu/.

The ancillary data of mineralization sites were obtained from the “Metallic and non-metallic deposits” maps with a scale of 1:1000000 that was issued by the Scientific Research Academy in 1998.

Results and discussion

Heavy metals in the sediments

All sample results for seven heavy metals, and the fundamental statistical (minimum, maximum, mean, and standard deviation) are shown in Table 2 to compare that with the concentration’s limits of the elements in the Earth’s crust after Turekian and Wedepohl (1961). The concentrations of Ag and Pb exceed the standard limits in all sites according to Turekian and Wedepohl (1961) metals limits. In contrast, Ni concentrations were high in stations; 1, 19, and 22 Abou-Shaar, Wadi Lahmy towards land and Marsa Sha’ab, respectively. The Cd was high at two stations 9 and 20 Wadi Mastourah and Wadi Lahmy, respectively.

Table 2 Heavy metal concentrations of sediment samples and limit values. Metal concentration (mg/kg dry weight)/(ppm) and their descriptive statistics
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Indices of sediment contamination

Enrichment factor (EF)

Enrichment factor analysis results for all samples are listed in Table 3. The values of EF related to the heavy metals noted Cu as minimal enrichment, whereas, Mn, Ni, Cd, and Pb showed significant enrichment. However, Ag showed as extremely high enrichment ranged from 202.37 to 1470.15 since it’s over 40 [ppm].

Table 3 Enrichment factor (EF)
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Contamination factor (CF)

The contamination factor analysis is shown in Table 4. An average CF value of Pb was moderately contaminated whereas Ag with an average of (33.63) indicates very high contamination sediment. The other metals Fe, Mn, Ni, Cu, and Cd are arranged as low contamination. The average CF results were ranked as the following: Fe < Cd < Mn < Cu < Ni < Pb < Ag.

Table 4 Contamination factor
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Degree of contamination (Cd)

The degree of contamination was agreed from considerably high to very high in all sites as shown in Table 5 where the highest sample was 11 from EL-Qulaan mangrove sediments.

Table 5 The degree of contamination (Cd)
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Modified degree of contamination (mCd)

Modified degree of contamination values for all sites as illustrated in Table 6 are categorized as moderate, high, and very high with a value of (13.10) at sample 11 from EL Qulaan mangrove sediments.

Table 6 Modified degree of contamination (m Cd)
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Geo-accumulation index

Geo-accumulation Index values of the heavy metals Table 7 noted that all sites were strong to extremely contaminated with silver while other sites were practically uncontaminated.

Table 7 Geo accumulation index (Igeo)
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Pollution load index (PLI)

Pollution load index results clarify two pure sites (samples 5 and 12) Sharm El-Qebly and EL-Qulaan seaward sample. In contrast, finding 4 values with the level of deterioration according to PLI were 11, 15, 19, and 20 samples, from sites of EL-Qulaan and Hamata landward, whereas at the Lahmy site, the samples were from landward and seaward mangrove sediments. The other samples were ideal in the level of baseline represented in Table 8.

Table 8 Pollution load index (PLI)
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Potential ecological risk factor

Potential ecological risk factors (Eri and RI) for sediments heavy metals are shown in Tables 9 and 10. All sites are represented low Ecological risk levels (Eri). As a result, the RI which is the summation of (Eri) confirmed a Low grade for all heavy metals excluding Cd with the value of (255.4) indicating a moderate potential ecological risk (Eri).

Table 9 Heavy metal contamination categories based on potential ecological risk
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Table 10 Classified levels of Eri and RI
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Multivariate statistical analyses

Applying the Pearson correlations for the seven parameters revealed the high correlations between Fe with Mn, Cu, and Pb 0.9, 0.82, and 0.85, respectively. There is a significant correlation type moderate between Fe and Ag 0.6. On the other hand, a high positive correlation appeared between Cu, Mn 0.74, and a moderate correlation between Cu and Cd 0.65 (Table 11).

Table 11 Correlation matrix for the estimated parameters n = 7
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Multivariate analysis (Principal component analysis, PCA) has been applied to the data set for 22 sediment samples and seven variables. Factor analysis is used to reduce the amount of data from 7 to 2 focusing on the most important variables that affect the study. This helps in interpreting the results and could build the appropriate model that represents the problem, in an inexpensive and objective description. The results give 4 factors. Eigenvalues above 1 are used to detect the most factors’ strength. Accordingly, two factors were extracted after the rotation for the data process.

Factor analysis can be summarized in (Table 12). The rotated component matrix of the factors was performed 0.7 for all 22 sites according to KMO. This also means the selected factor 1 and factor 2 comprise 70% of the total variance.

Table 12 Factor scores after Varimax rotation
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The first factor contributed 59.93% of the total Variance exhibiting high positive loadings on Fe and Mn. On the other hand, the second factor reflected 18.88% of the total variance and was composed of the following parameters Ag, Cd, Ni, Pb, and Cu, respectively.

Remote sensing and GIS

Remote sensing and GIS are utilized in this work to interpret the source of the trace element. The stream networks of the main Wadi were extracted and displayed the main mine sites of different elements. Thirteen mangrove sites along the coastline stand the termination of different Wadis flows from the Red Sea Mountains of the Eastern Desert are listed in Fig. 2 and Table 13. There are mangrove sites affected by more than one Wadi flow related to the coastal geomorphology and sea current flow where Wadis discharge the trace elements north or south. The high number of Wadis was in the south area from Wadi Qulaan to Wadi Lahmy reflected in the long coast with mangroves presence.

Fig. 2
figure 2

Drainage system of Wadis drains on mangrove sites

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Table 13 The area of Wadis basins drained into mangrove sites
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According to the metallic and non-metallic deposit maps detecting the sites of 22 main mines at the Wadi’s basins of the study area as shown in Fig. 3 and Table 14. These mines are rich in different trace elements of copper, lead, zinc, iron, manganese, and gold. In addition, silver is detected with gold on the eastern desert rocks.

Fig. 3
figure 3

Drainage system and mining activity in the study area

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Table 14 The main mining locations in the study area
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Discussion

The present study clearly showed spatial trends of heavy metal concentrations as the mean concentration of heavy metals was shown to decrease in the following order; Ag > Pb > Ni > Cu > Cd > Mn > Fe. The degree of pollution and health status of the sediment from seven geo-accumulation contamination factors analysis showed that the EF, CF, Cd, Igeo, Eri, RI, and PLI have fluctuated between the significant increases in the metal levels to moderate levels of ecological risk.

These results were in the same output for assessment of heavy metals contamination on mangrove sediments on the other side of the Red Sea, Suai Arabia that did not exceed the significant range from previous studies reported by Alzahrani et al. (2018); Alharbi et al. (2019); Aljahdali and Alhassan (2020); and Al-Hasawi (2022). Among the selected heavy metals, silver (Ag) concentrations were the highest according to the Igeo index which gives hints about the geographic sources. In addition to the presence of silver with gold in the most of region’s rocks (Zoheir and Lehmann 2011). The higher concentration of Ag in the sediments usually reaches the coastal mangroves drained from the Red Sea Mountains.

Principle component analysis has found the strength in the presence of Fe and Mn together is attributed to the environmental impacts and naturally occurred on mafic rock and granite respectively (Papachristou et al. 2014). Whereas, lead and Cadmium according to CF are moderately contaminated related to natural sources from Red Sea hills. Hanna (1992) noted that the total Cd content of the Red Sea sediment from 1934 and 1984 expeditions varied between 0.1 and 2 ppm, and he recommended that its origin is related to marine sediments lithogenous. Correspondingly, Mahdy et al. (2009) noted that lead–zinc mineralization is distributed between El Qusier and Ras Banas. In addition, cadmium is incorporated into the crystal structure of the zinc-lead minerals as a natural source. In the reverse Nour et al. (2022), that analysis heavy metals at Egyptian beaches on the Red Sea and the Gulf of Aqaba in Sinai and demonstrated that Cu, Pb, Cd, and Hg originated from anthropogenic sources. Moreover, Hanna (1992) pointed to the increasing concentrations of Ni, Pb, Cu, and Cd during the period (1934–1984) on the Red Sea coast. Where described the reasons from different sources of natural contamination as hot brine pools, and oil and minerals mining. In addition to anthropogenic impact the discharge of domestic, industrial waste, and marine transportation.

On the other hand, the PLI results suggest that the sediments are highly contaminated at Wadi EL-Qulaan, Hamata, and Wadi Lahmy as these are massive Wadis loading with elements discharge. Discussing the whole environment sediment metals sources that mangroves in the study area are situated in the coastal zone of the Eastern Desert of Egypt, which is dissected by the presence of rich alteration zones (El-Shafei 2011; Salem 2013; Amer et al. 2016; Ghoneim et al. 2018). These alteration zones are the perfect environment for several mineralization types including rare and heavy minerals. The mangrove sites are located on the end of Wadi’s that pass through the alteration zones. These Wadis act as the pathways/carriers of the washed minerals from the upstream rocks to the downstream outlet where the mangrove is present; thus, the carried minerals are objected to and deposited by the act of flooding the mangrove areas (Embabi 2018). The mining sites with several mineralization types fall within the drainage system and move with flooding to mangrove sites and are retained in sediment. This mineralization includes (copper, lead, zinc, gold, iron, and manganese) verified by Nour et al. (2019), Baioumy (2021), Abdelkareem and El-Shazly (2022), and Hegab et al. (2022). The existence of these mineralization sites within the stream network that ends up in the mangrove sites suggests the concentration of several heavy minerals being driven through the drainage network naturally.

The Red Sea mangrove in Egypt’s sediment is supposed to naturally originate from local minerals rather than contamination. In addition, it is located in protected areas with a conservation strategy (Okbah et al. 2005; El Daba and Abd El Wahab 2018) as the study results indicate weak enrichment. In contrast, other mangrove studies around the world showed that the increasing heavy metals contamination in mangrove sediment is due to pollution (Bodin et al. 2013; Rahman et al. 2014; Chai et al. 2019; Bakshi et al. 2019). In the case of the Mangrove, most of it is located in protected areas with conservation strategies.

Conclusion

The heavy metals (Mn, Ni, Cu, Fe, Cd, Ag, and Pb) in the mangrove sediment are shown to be from natural origin, rather than anthropogenic activities. The remote sensing and GIS techniques successfully contributed to interpreting the pattern of the origin of heavy metals along the Red Sea coast. In addition, it represents a synoptic outline view of the study area with its relevant environmental condition and origin. Finally, the investigation discovered a high content of silver metal concentrated in mangrove sediments, which necessitates rigorous further research to understand how to apply sustainable exploitation of this metal’s economics to ignore the negative impact on marine ecosystems.