Next Article in Journal
Evidence of Sexual Reproduction in Out-Planted Coral Colonies
Previous Article in Journal
Status of Coral Reef Communities on the Caribbean Coast of Costa Rica: Are We Talking about Corals or Macroalgae Reefs?
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Spatial Distribution and Contamination Level Assessment of Marine Sediment of the Safi Bay (Moroccan Atlantic Coast)

Laboratory LIRBEM, Department of Life and Earth Sciences, Higher Normal School, Cadi Ayyad University, Marrakech 40000, Morocco
Associated Unit URAC 45, Marine Geosciences and Sol Sciences Laboratory, Department of Geology, Faculty of Sciences, Chouaib Doukkali University, El Jadida 24000, Morocco
National Center for Energy, Sciences and Nuclear Techniques, Rabat 10000, Morocco
Polydisciplinary Faculty of Ouarzazate (FPO), Ibnou Zohr University, Ouarzazate 45000, Morocco
Physical Geography Laboratory, Department of Natural Sciences and Geography, Polydisciplinary Faculty, Cadi Ayyad University, Safi 46000, Morocco
Author to whom correspondence should be addressed.
Oceans 2023, 4(4), 331-349;
Submission received: 26 July 2023 / Revised: 21 September 2023 / Accepted: 18 October 2023 / Published: 23 October 2023


This study assesses the spatial distribution and contamination level of heavy metals in Safi Bay surface sediments. In this order, 28 surface sediment samples were retrieved from the study area and analyzed using the x-fluorescence method. To assess the contamination of the examined sediment, we used geo-ecological indices such as contamination factor (CF), degree of contamination (DC), geo accumulation index, and pollution load index (PLI). The results show that only Pb and Cd present moderate and considerable contamination in some sampling sites, while other elements (Cr, Cu, Zn, and Ni) indicate no contamination and low contamination by these elements. The inhomogeneous distribution of metal concentrations along the bay suggests different heavy metal sources. Given the ecological and socioeconomic importance of the study area, there is a need for a further analysis of both sediments and biological samples for a better understanding of the contamination levels and origin of metals, in addition to the sustainability of Safi Bay.

1. Introduction

Heavy metals are common environmental pollutants in marine and coastal ecosystems because of their toxicity, non-biodegradability in sediments, and their bio-accumulative nature [1,2,3,4,5,6,7,8]. Once these pollutants reach aquatic environments from both natural processes and/or anthropogenic activities, they may damage marine organisms or can be accumulated in sediments via chemical adsorption and or physical precipitation [9,10,11,12], causing biodiversity loss [13,14]. In aquatic ecosystems, heavy metals do not remain for long in the water column but are precipitated and deposited in sediments as a final receptor [15]. Sediments represent an essential ecological component and play an important role in pollutant storage and distribution, which reflects actual system quality [7,16,17,18,19]. Accordingly, marine sediments can be used as an indicator of pollutants [20]. The growth of human activities around coastal cities, mainly industrial activities in response to the development of socioeconomic sectors, has resulted in human overpopulation and pollution level increase, affecting water resources, sediment, and marine organisms [8,11,21,22]. Indeed, coastal sediments have been found to be polluted by wastewater effluent issued from domestic and industrial activities [17,22,23,24], where several studies indicated that coastal sediment is an important tool for analyzing pollution levels by heavy metals [11,17,22,25,26,27,28,29,30,31,32,33,34]. According to statistical analysis, about 85% of heavy metals are accumulated in superficial sediments [11,22,35,36]. In this order, the study of the pollution level in a coastal environment, which provides various ecosystem services of environmental, socioeconomic, cultural, and recreational value [37], imposes analyzing sediment as an essential ecological component of the coastal environment.
The Bay of Safi is located on the Moroccan Atlantic coast, where multiple human activities are concentrated, including industrial activities, agriculture, and fishing, among others. Thus, the Bay of Safi is subjected to pollutants released from the growth of industrial activities along this littoral zone. Indeed, Kouali et al. and Rafiq et al. [38,39] have identified the intertidal area in Safi as a hotspot of metal contamination. However, few studies have been carried out to analyze the pollution level in this coastal zone despite its socioeconomic and ecological interests. Thus, this ongoing study is the first investigation of the degree of heavy metal pollution in the marine sediment of Safi Bay.
The main aims of this study are to:
  • Analyze and quantify the pollution level by heavy metals in Safi Bay sediment using various environmental indices, including contamination factor (CF), degree of contamination (Cd), index of geo accumulation (Igeo), and pollution load index (PLI).
  • Calculate the potential ecological risk of metals detected in the Safi Bay sediment.
  • Establish the spatial distribution of heavy metals in Safi Bay.
  • Attempt to define the possible origin of the heavy metals in this coastal environment to allow for the adequate and efficient management of Safi Bay for sustainable socioeconomic and environmental development.

2. Materials and Methods

2.1. Study Area

The Safi Bay is located on the Moroccan Atlantic coast (32°18′ N–9°15′ W) along a coastal line that extends about 5 km from the tip of the cliff of Sidi Bouzid in the north to Jorf Ammouni in the south (Figure 1). The study region includes the city beach with an NW-SE orientation and the sharp cliff south of the port with an N-S direction [40]. Safi Bay is a triangular bay which has experienced strong population growth and economic development in the last decade. It is characterized by chemical (phosphates) and para-chemical industries (cement, brickworks, plateries) and the agro-industry, especially the fish canneries related to the fishing sector.
At the bottom of this bay, a port with industrial and mineral vocations has been established and is one of the main commercial ports in Morocco. This port has a general traffic of about 5 million tons, a major part of which is destined for mining exports. It is composed of three units (fishing port, commercial port, and ore carrier port). The commercial port has traffic of cereals, gypsum, barite, potash, manganese, fertilizers, and preserves, and the mineral port has traffic of phosphates, sulfur, and phosphoric acid; these industrial, urban, and port activities can constitute sources of heavy metals in this bay.
In Safi Bay, the slope of the continental shelf varies according to the depth, where it reaches 4% between the zero line and the isobath −10 m. The bottoms from −10 m to −25 m have only a slope between 2.5% and 1%. A vast plateau develops from −25 m to −30 m and gradually flares until it reaches a width of 2.5 km. The wave direction is dominant from NW to NNW [40], with the most frequent waves reaching 0.5 to 1.5 m [41]. The distribution of sediments at the bay is controlled by this swell, and an estimated transit of 250,000 m3/year is generated [42].

2.2. Sampling

The superficial sediments of the bay are composed of mostly bioclastic material, with significant biogenic sedimentation favored by the development of bedrock on which fixed organisms are abundant. In contrast, terrigenous detrital inputs are of little importance, while quartz grains and lutites are present only in sheltered coastal areas [43,44]. The main sedimentary inputs in the Bay of Safi come from the erosion of the north coast (cliffs) and are then transited by a north–south littoral drift [40,41,42]. This sediment distribution is controlled by natural (waves, currents) and anthropogenic (geomorphological modification by port developments) factors.
Twenty-eight sediment samples were retrieved in August 2019 on radials perpendicular to the coastline at different depths, with the water depth ranging from 3.5 m to 31.8 m (Figure 1 and Table 1). All samples were collected with a Van Veen grab, preserved in sealed polyethylene bags, and marked for particle size distribution determination, while only the fine fraction of the samples (sediment <63 µm) was considered for chemical composition analysis. These samples were dried at 50° in an oven and then sieved through 63 μm mesh size, crushed using the grinder, and placed in a new polyethylene bag.

2.3. Heavy Metals Determination

Samples were prepared and analyzed at the Marine Geosciences and Soil Science Laboratory (LGMSS) to determine sediment texture and carbonate content using Bernard calcimetry (Table 1). The grain distribution of each sample was measured using a dry setting on a series of AFNOR standard sieves, and the granulometric parameters were calculated after classification according to [45].
For chemical analysis, only the fine fraction of samples <63 µm was analyzed, where 1 g of each sample was weighed, placed in a bag, and sent for analysis to the ICP-AES laboratory of the Centre National de Recherche Scientifique et Technique de Rabat (CNRST).
The first step of metals analysis consists of removing all organic matter from sediment, so approximately 1 g of dried samples and 10 mL of concentrated HNO3 were placed together in a plastic digestion tube using a microwave digestion system. Then, a mixture of HNO3-HFHCl was added to the sediment sample and heated to a temperature of 100–110 °C to obtain a transparent solution [46]. A total of 50 mL of demineralized water was added to the resulting solution and stored at room temperature for the subsequent analysis of heavy metals. The elements Cu, Zn, Cr, Ni, Pb, and Cd were determined, in the Division of Technical Support Units for Scientific Research (UATRS) in the National Center for Scientific and Technical Research of Rabat (CNRST), using Jobin Yvon’s ICP AES model Ultima 2 unit and controlled with 7.4 ICP-Expert Sequential software.

2.4. Statistical Analyses

ArcGis 10.8 software (Esri Co., Ltd., Redlands, CA, USA) was used to draw distribution maps of metal concentrations in surface sediments. All statistical analyses of the data (mean, minimum, maximum, Pearson correlation matrix, and contamination indices) were determined using XLSTAT statistical software 2019. Metal sources were analyzed using primary component analysis (PCA) and Pearson correlation analysis. The PCA was employed to examine both natural and man-made sources of potential pollution [47].

2.5. Assessment of the Environmental Pollution Degree

The environmental indices employed in this study have been widely used in studies related to pollution level assessment and quantification in coastal sediments [18,34,48,49,50]. Some authors have adopted many calculation methods [51,52,53,54,55], whose proposed pollution impact ranges to convert the measured concentrations of heavy metals in soil and sediment into a comprehensive description of pollution levels based on the intensity increase. Thus, in the present study, we used heavy metals pollution indices such as CF, DC, PLI, Igeo, and the potential ecological risk index ( E r i and RI) to assess the pollution degree of Safi Bay sediments.

2.5.1. Contamination Factor (CF)

The contamination factor is an environmental indicator used to monitor and track the pollution level by heavy metals, where it allows for the assessment of the contamination degree by each metal in each sediment sample. CF values were obtained by dividing the concentration of each metal in the sediment by the baseline or background value (Equation (1)) [52].
CF = C sample C background
where Csample is the concentration of metal measured in the studied samples, and Cbackground designates the metal concentration of the reference material or the background value. The authors in [52] defined four categories for sediment quality based on the CF calculated value ranging from low contamination (CF < 1), moderate contamination (1 ≤ CF < 3), considerable contamination (3 ≤ CF < 6), to very high contamination (CF ≥ 6).

2.5.2. Degree of Contamination (DC)

The degree of contamination (DC) is an index that can be derived from CF values and defined as the sum of all individual metal contamination factors [52]. It is calculated as follows (Equation (2)):
DC = i = 1 9 C i f
As the utilized components for this index are not always possible to analyze, Abrahim et al. (2008) [56] proposed an alternative method. The modified equation proposed to calculate the degree of contamination is given below (Equation (3)):
mCd = i = 1 9 C i f
Regarding the classification and description of the modified contamination degree of sediments, the following terminology was adopted (Table 2):

2.5.3. Pollution Load Index (PLI)

The PLI is used as an indicator of contamination level by heavy metals for each sampling point [57,58]. The PLI is calculated through the following equation:
PLI = (CF1 × CF2 × CF3 ×…× CFn)1/n
where “n” is the number of determined metals, and CF designates the contamination factor. The contamination can be estimated as follows:
  • PLI < 1, no metal pollution;
  • PLI > 1, polluted condition.

2.5.4. Geo-Accumulation Index (Igeo)

The Igeo was used to assess the metal pollution in the sediment of Safi Bay. The following equation is used to calculate the Igeo for each metal in each sample [51]:
I g e o = l o g 2 C n 1.5 B n
where Cn is the measured concentration of the heavy metal (n) in the investigated sediments, and Bn is the background value; here, we used the average abundance value of the upper continental crust (UCC) of a given metal. Also, 1.5 is the background matrix correction factor due to lithogenic effects [59,60,61,62,63]. The classification of sediment pollution level depends on the value of the Igeo, where 7 classes have been defined in Table 3.

2.5.5. Potential Ecological Risk Index ( E r i and RI)

The potential ecological risk factor E r i and the potential ecological risk index (RI) were proposed by [52]. Those indices were widely used to evaluate the potential ecological risk associated with heavy metals in aquatic sediments [6,64,65,66,67,68].
E r i is calculated using the following equation [52]:
C f i = C i / C n i
E r i = T r i × C f i
R I = i = 1 n E r i
where E r i is the potential ecological risk factor of each heavy metal, and T r i is the toxic response factor for heavy metal i. The toxic response factor for Cu, Zn, Cr, Ni, Pb, Cd, and V is 5, 1, 2, 5,5, 30, and 2, respectively [52,69,70], C f i is the contamination factor of heavy metal i, and C i is the measured concentration of heavy metal i in the sediment. The E r i and RI values were deduced as follows [7,10,22,71,72]:

2.5.6. Sediment Quality Guidelines (SQGs)

Several authors [11,12,13,33,34] have assessed the impact of metals on the toxicity of aquatic organisms using sediment quality guidelines (SQGs). The threshold effect level (TEL) and probable effect level (PEL) were used in this study to assess the potential biotic influence of metals contained in sediment samples.

3. Results and Discussions

3.1. Spatial Distribution of Sediment Grain Size and CaCO3 Content

The spatial distribution of sediment grain size and CaCO3 content are shown in Figure 2. The collected samples are almost sandy except for S23, S7, and S3, which show a relatively important fine fraction of 32.42%. The sand fraction oscillates between 99% and 67.85%. The CaCO3 content ranged from 37 to 82.5%, while its spatial distribution shows a similar trend as sand fraction, reflecting a significant correlation between sand and CaCO3 content. The lowest values of CaCO3 were recorded in the stations with a significant portion of mud, mainly S29, S30, and S23.

3.2. Assessment of the Surface Sediment Contamination by Heavy Metals

Twenty-eight sediment samples retrieved from Safi Bay have been analyzed for Cu, Zn, Cr, Ni, Pb, and Cd concentration determination. The concentrations fluctuate from one sampling point to another, probably reflecting different origins. The metals concentrations varied over the following ranges: Cd (0.305–0.801 mg/kg); Cr (0.305–120.983 mg/kg); Cu (1.740–12.715 mg/kg); Ni (1.573–81.319 mg/kg); Pb (16.575–63.381 mg/kg); and Zn (14.266–127.719 mg/kg). The highest concentrations were recorded in the following stations: S15 (Ni), S17 (Zn), S23 (Cr et Pb), and S29 (Cd et Pb). Industrial and domestic discharges into the ocean are the possible origin of the highest values detected in these sediment samples as they are collected near the city. In addition, these domestic and industrial discharges flow directly into the ocean as the treatment plant is still an ongoing project. The lowest values were registered in the samples retrieved from the north part of the bay, distant from the city and related anthropogenic discharges. Noting also that natural factors such as the granulometric composition of the sediment, seawater dilution levels, and the hydrodynamic conditions that characterize the sampling sites can also affect the distribution of heavy metals in Safi Bay [73]. Noting that stations S23 and S29 indicate significant content fine fractions compared to other sites, explaining the highest recorded concentrations. It is well known that fine fractions accumulate pollutants more than coarser fractions because of their high specific surface area, whereas the presence of important pollutant concentrations in coarser fractions can be explained most likely by the coating phenomena. Organic matter coating on mineral particles may enhance the capacity of dissolved pollutants adsorption [74,75]. Moderate concentrations were found in the following stations, S5, S6, S7, S8, S9, and S10, located near the entrance of loading and unloading vessels for chemical and mining products in Safi port. Figure 3 displays the spatial distribution of heavy metals in Safi Bay, suggesting that the highest concentrations of heavy metals are associated mainly with human activities and port activities, which is in good accordance with the findings of [67], as the lowest concentrations were recorded in the north part far from the Safi port and anthropogenic discharges.
The average concentrations of heavy metals of all sampling stations for each metal Zn = 36.546 mg/kg, Pb = 33.972 mg/kg, Cr = 11.343 mg/kg, Ni = 10.395 mg/kg, Cu = 3.850 mg/kg, Cd = 0.395 mg/kg were compared to the average concentrations found in sediments from similar ecosystems in the world and to upper continental crust (UCC) values Table 4. The heavy metal concentrations of this study were almost less high than those reported in Dakhla Bay, except for Ni and Zn [76]. Cu, Zn, and Pb show low concentrations compared to almost all the coastal ecosystems listed in Table 5. The heavy metal concentrations were indeed compared to the sediment quality guideline values, including the threshold effect level (TEL) used to show the concentrations below, where no effect of metal content was detected in sediment on aquatic organisms and the toxic effect threshold (TET) utilized to indicate the heavy metals concentrations, above which substantial effects are likely to occur [77]. Heavy metal concentrations in Safi Bay sediments are less significant than the TELs values in Table 4; therefore, they do/did not present a toxicity risk to marine organisms living in these sediments.

3.3. Correlation among Metals

The Pearson correlation among the heavy metals results is shown in Table 5. The results indicate no significant correlation among heavy metals measured in Safi Bay sediment, suggesting a different origin of these metals in the bay in good accordance with their inhomogeneous distribution along the bay. Only Cu shows a highly significant correlation with Pb (p < 0.01), while Cu, Cr, and Zn indicate significant correlations (p < 0.05), which is in good accordance with Kouali et al.’s (2022) [38] findings. These significant correlations among Cu, Cr, and Zn may signify similar chemical properties, a similar degree of contamination, or derived from the same sources [85,86].
As shown in Table 6, a positive and significant correlation between Cr, Cu, and Pb with mud reveals the affinity of these metals to fine elements. The correlation matrix shows a negative and significant correlation observed for Cr, Cu, Pb, and Zn with CaCO3 and sand, except for Cr and CaCO3 (non-significant negative value), indicating that these metals are not associated with CaCO3.
The Dendrogamme (Figure 4) based on Ward’s method [87] shows three different clusters: Cluster 1 (sand and CaCO3), Cluster 2 (Pb and Zn), and Cluster 3 (Cr, Ni, Cd, Cu, and mud).

3.4. Assessment of Heavy Metal Contamination

The results of the calculated indices CF, DC, mCd, and PLI for the sediment collected from Safi Bay are shown in Table 6. Except for S17 and S29, which show considerable contamination by Cd, almost all the stations display low to moderate contamination by Cd CF > 3. The contamination factor for Pb indicates considerable contamination in stations S3, S7, S8, S18, S23, S29, S30, and S38, suggesting an anthropogenic origin, while moderate contamination was recorded in the other sampling sites. Other investigated heavy metals show low contamination, suggesting that Cd and Pb mainly contaminate the study area, which is in good accordance with [38], where the Cd exhibited moderate contamination of coastal sediment from Jorf Lihoudi in Safi. Both cadmium and lead indicated a strong positive correlation and considerable contamination, reflecting similar sources into the sediment bay. In addition, the inhomogeneous distribution of contamination levels along the bay reflects different origins of heavy metals, such as domestic, industrial, and ship effluent. Industrial- and port-activities-related waste are reported as the main activities having heavy impacts on the bay environment [88]. The management of this ecosystem is a major concern as 70% of the released waste is thrown into the bay without pre-treatment [88]. In addition, the navigation activities may have potential impacts on this ecosystem environment by causing physical modifications of seabed substrate and habitats [89]. Ships were first identified as a major source of metal pollution in the 1970’s, particularly copper [90].
A considerable degree of contamination was recorded in S23 and S29, while the other stations indicate low and moderate contamination in good accordance with the CF values.
The PLI values calculated for each station are shown in Table 7 and Figure 5. Almost all studied stations indicate no pollution by heavy metals, where the PLI values are inferior to 1. These results may be explained by the strong dilution and dispersion of heavy metal concentrations in seawater because of the strong agitation. The authors in [67] reported high PLI values for sediment retrieved inside the Safi harbor where the sediment grain size is finer, allowing for the accumulation of heavy metals. In contrast, almost all investigated samples were sandy with a high CaCO3 content, suggesting that those factors are not controlling the distribution and accumulation of the heavy metals in Safi Bay sediments. Thus, further investigation of sediments mainly from the harbor area is needed to provide a clearer picture of the pollution levels and sources.
The geo-accumulation index (Igeo)-calculated values for the investigated sediment are shown in Table 8. Almost all sampling sites show no pollution by Cr, Cu, Ni, and Zn. Only Cd and Pb show values that fluctuate between 0 and 2, indicating weak and moderate pollution, which is in good accordance with other calculated indices.
The ecological risk index E r i was used to assess the risk associated with heavy metal pollution in Safi Bay. The results of E r i calculation are presented in Table 9. The values of E r i ranged from 30.50 to 80.10 for Cd, with an average of 39.49; from 0.01 to 2.69 for Cr, with an average of 0.25; from 0.19 to 1.41 for Cu, with an average of 0.43; from 0.12 to 5.98 for Ni, with an average of 0.76; from 4.14 to 15.85 for Pb, with an average of 8.49; and from 0.18 to 1.34 for Zn, with an average of 0.38. The ecological risk index E r i of the investigated heavy metals was less than 40, reflecting a low ecological risk.

4. Conclusions

This study analyzed the spatial distribution of Cr, Zn, Cu, Ni, and Pb in Safi Bay sediments. The results showed an inhomogenous distribution of heavy metals along the bay, suggesting different sources of these metals. However, the concentrations of heavy metals were more elevated in the sampling sites located near the city and the Safi Port, while lower concentrations were recorded in the northern part of Safi City where human activities are less intense.
The use of environmental indices, including CF, DC, mCd, PLI, and E r i , allowed for the gathering of an understanding of the pollution levels of the bay sediments. The results revealed that the Safi Bay sediment is mainly contaminated by Cd and Pb according to the CF and DC factors. The PLI values were almost >1, suggesting that Safi Bay is an unpolluted area. However, the obtained results imply that there should be a further investigation of the bay sediments, mainly the sediment cores, which would allow for a better understanding of the pollution history in the study area to be obtained.
Factors such as the hydrodynamic conditions, currents, waves, and resuspension/deposition characterizing the study area need to be analyzed to obtain a better understanding of the pollutant spatial distribution and their concentration fluctuations sources.
This study is the first investigation of the contamination of Safi Bay sediments by heavy metals. Thus, the present work provides a database for future investigations of the bay’s environmental quality, mainly its ecological and socioeconomic interests, contributing towards developing conservation and protection projects for the sustainable use of its related ecosystem services.

Author Contributions

A.M. and N.M.: investigation and visualization. N.M: writing—original draft and writing—review and editing. N.M., K.E.K., M.B., A.F., M.C. and B.Z.: validation, conceptualization, supervision, and writing—review and editing. All authors have read and agreed to the published version of the manuscript.


The authors have no relevant financial or non-financial interests to disclose.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Varol, M. Assessment of heavy metal contamination in sediments of the Tigris River (Turkey) using pollution indices and multivariate statistical techniques. J. Hazard. Mater. 2011, 195, 355–364. [Google Scholar] [CrossRef] [PubMed]
  2. Haruna, A.; Uzairu, A.; Harrison, G. Chemical fractionation of trace metals in sewage water-irrigated soils. Int. J. Environ. Res. 2011, 5, 733–744. [Google Scholar]
  3. Tam, N.; Wong, Y. Spatial variation of heavy metals in surface sediments of Hong Kong Mangrove Swamps. Environ. Pollut. 2000, 110, 195–205. [Google Scholar] [CrossRef] [PubMed]
  4. Ghaderi, A.; Abduli, M.; Karbassi, A.; Nasrabadi, T.; Khajeh, M. Evaluating the effects of fertilizers on bioavailable metallic pollution of soils, case study of Sistan farms, Iran. Int. J. Environ. Res. 2012, 6, 565–570. [Google Scholar]
  5. Gao, C.; Lin, Q.; Bao, K.; Zhao, H.; Zhang, Z.; Xing, W.; Wang, G. Historical variation and recent ecological risk of heavy metals in wetland sediments along Wusuli River, Northeast China. Environ. Earth Sci. 2014, 72, 4345–4355. [Google Scholar] [CrossRef]
  6. Liu, J.; Yin, P.; Chen, B.; Gao, F.; Song, H.; Li, M. Distribution and contamination assessment of heavy metals in surface sediments of the Luanhe River Estuary, northwest of the Bohai Sea. Mar. Pollut. Bull. 2016, 109, 633–639. [Google Scholar] [CrossRef]
  7. Malvandi, H. Preliminary evaluation of heavy metal contamination in the Zarrin-Gol River sediments, Iran. Mar. Pollut. Bull. 2017, 117, 547–553. [Google Scholar] [CrossRef]
  8. Anbuselvan, N.; Senthil Nathan, D.; Sridharan, M. Heavy metal assessment in surface sediments off Coromandel Coast of India: Implication on marine pollution. Mar. Pollut. Bull. 2018, 131, 712–726. [Google Scholar] [CrossRef]
  9. Priya, A.K.; Gnanasekaran, L.; Dutta, K.; Rajendran, S.; Balakrishnan, D.; Soto-Moscoso, M. Biosorption of heavy metals by microorganisms: Evaluation of different underlying mechanisms. Chemosphere 2022, 307, 135957. [Google Scholar] [CrossRef]
  10. Maanan, M.M.; Saddik, M.; Chaibi, M.; Assobhei, O.; Zourarah, B. Environmental and ecological risk assessment of heavy metals in sediments of Nador lagoon, Morocco. Ecol. Indic. 2014, 48, 616–626. [Google Scholar] [CrossRef]
  11. Zhang, Z.; Li, J.; Mamat, Z.; Ye, Q. Sources identification and pollution evaluation of heavy metals in the surface sediments of Bortala River. Northwest China. Ecotoxicol. Environ. Saf. 2015, 126, 94–101. [Google Scholar] [CrossRef] [PubMed]
  12. Ali, M.M.; Ali, M.L.; Islam, M.S.; Rahman, M.Z. Preliminary assessment of heavy metals in water and sediment of Karnaphuli River, Bangladesh. Environ. Nanotechnol. Monit. Manag. 2016, 5, 27–35. [Google Scholar] [CrossRef]
  13. Mejjad, N.; Laissaoui, A.; Fekri, A.; Benmhammed, A.; El Hammoumi, O.; Cherif, E.K. Does human activities growth lead to biodiversity loss in the Moroccan coastal lagoons? A diagnostic comparison study. In Proceedings of the 4th Edition of International Conference on Geo-IT and Water Resources 2020, Geo-IT and Water Resources 2020, Al-Hoceima, Morocco, 11–12 March 2020. [Google Scholar]
  14. Yozukmaz, A.; Yabanlı, M. Heavy Metal Contamination and Potential Ecological Risk Assessment in Sediments of Lake Bafa (Turkey). Sustainability 2023, 15, 9969. [Google Scholar] [CrossRef]
  15. Manav, R.; Uğur Görgün, A.; Filizok, I. Radionuclides (210Po and 210Pb) and some heavy metals in fish and sediments in Lake Bafa, Turkey, and the contribution of 210Po to the radiation dose. Int. J. Environ. Res. Public Health 2016, 13, 1113. [Google Scholar] [CrossRef] [PubMed]
  16. Antizar-Ladislao, B.; Mondal, P.; Mitra, S.; Sarkar, S.K. Assessment of trace metal contamination level and toxicity in sediments from coastal regions of West Bengal, eastern part of India. Mar. Pollut. Bull. 2015, 101, 886–894. [Google Scholar] [CrossRef]
  17. Qutu, J.; Junyu, H.; Guanqiong, Y.; George, C. Heavy metal contamination assessment of surface sediments of the East Zhejiang coastal area during 2012–2015. Ecotoxicol. Environ. Saf. 2018, 163, 444–455. [Google Scholar]
  18. Mejjad, N.; Laissaoui, A.; El-Hammoumi, O.; Fekri, A.; Amsil, H.; El-Yahyaoui, A.; Benkdad, A. Geochemical, radiometric, and environmental approaches for the assessment of the intensity and chronology of metal contamination in the sediment cores from Oualidia lagoon (Morocco). Environ. Sci. Pollut. Res. 2018, 25, 22872–22888. [Google Scholar] [CrossRef]
  19. Lin, L.; Li, C.; Yang, W.; Zhao, L.; Liu, M.; Li, Q.; Crittenden, J.C. Spatial variations and periodic changes in heavy metals in surface water and sediments of the Three Gorges Reservoir, China. Chemosphere 2020, 240, 124837. [Google Scholar] [CrossRef]
  20. Hazem, T.A.; Talaat, A.H.; Ibrahim, M.S.; Khalid, M.E. Assessment of Heavy Metals Pollution in Marine Sediments along the Mediterranean Sea, Egypt. J. Geogr. Environ. Earth Sci. Int. 2016, 7, 1–11. [Google Scholar] [CrossRef]
  21. Vidović, J.; Dolenec, M.; Dolenec, T.; Rožič, P.Ž. Benthic Foraminifera Assemblages as Elemental Pollution Bioindicator in Marine Sediments Around Fish Farm (Vrgada Island, Central Adriatic, Croatia). Mar. Pollut. Bull. 2014, 83, 198–213. [Google Scholar] [CrossRef]
  22. Zhang, M.; He, P.; Qiao, G.; Huang, J.; Yuan, X.; Li, Q. Heavy metal contamination assessment of surface sediments of the Subei Shoal, China: Spatial distribution, source apportionment and ecological risk. Chemosphere 2019, 223, 211–222. [Google Scholar] [CrossRef] [PubMed]
  23. Wang, X.S.; Qin, Y.; Sang, S.X. Accumulation and sources of heavy metals in urban topsoils: A case study from the city of Xuzhou, China. Environ. Geol. 2005, 48, 101–107. [Google Scholar] [CrossRef]
  24. Bat, L.; Özkan, E.Y. Heavy metal levels in sediment of the Turkish Black Sea Coast. In Oceanography and Coastal Informatics: Breakthroughs in Research and Practice; Information Resources Management Association, Ed.; IGI Global: Hershey, PA, USA, 2019; pp. 86–107. [Google Scholar] [CrossRef]
  25. Jayaprakash, M.; Jonathan, M.P.; Srinivasalu, S.; Muthuraj, S.; Ram-Mohan, V.; Rajeshwara-Rao, N. Acid-leachable trace metals in sediments from an industrialized region (Ennore Creek) of Chennai city, SE coast of India: An approach towards regular monitoring. Estuary Coast. Shelf Sci. 2008, 76, 692–703. [Google Scholar] [CrossRef]
  26. Muthu Raj, S.; Jayaprakash, M. Distribution and enrichment of trace metals in marine sediments of Bay of Bengal, off Ennore. south-east coast of India. Environ. Geol. 2018, 56, 207–217. [Google Scholar] [CrossRef]
  27. Gao, X.; Li, P. Concentration and fractionation of trace metals in surface sediments of intertidal Bohai Bay, China. Mar. Pollut. Bull. 2012, 64, 1529–1536. [Google Scholar] [CrossRef]
  28. Pan, K.; Wang, W.X. Trace metal contamination in estuarine and coastal environments in China. Sci. Total Environ. 2012, 421, 3–16. [Google Scholar] [CrossRef]
  29. Scheibye, K.; Weisser, J.; Borggaard, O.K.; Larsen, M.M.; Holm, P.E.; Vammen, K.; Christensen, J.H. Sediment baseline study of levels and sources of polycyclic aromatic hydrocarbons and heavy metals in Lake Nicaragua. Chemosphere 2014, 95, 556–565. [Google Scholar] [CrossRef]
  30. Chen, H.; Chen, R.; Teng, Y.; Wu, J. Contamination characteristics, ecological risk and source identification of trace metals in sediments of the Le’an River (China). Ecotoxicol. Environ. Saf. 2016, 125, 85–92. [Google Scholar] [CrossRef]
  31. Lu, J.; Li, A.; Huang, P. Distribution, sources and contamination assessment of heavy metals in surface sediments of the south yellow sea and northern part of the east China sea. Mar. Pollut. Bull. 2017, 124, 470–479. [Google Scholar] [CrossRef]
  32. Liu, Q.; Wang, F.; Meng, F.; Jiang, L.; Li, G.; Zhou, R. Assessment of metal contamination in estuarine surface sediments from Dongying City, China: Use of a modified ecological risk index. Mar. Pollut. Bull. 2018, 126, 293–303. [Google Scholar] [CrossRef]
  33. Benmhammed, A.; Laissaoui, A.; Mejjad, N.; Ziad, N.; Chakir, E.; Benkdad, A.; Bouh, H.A.; El Yahyaoui, A. Recent pollution records in Sidi Moussa coastal lagoon (western Morocco) inferred from sediment radiometric dating. J. Environ. Radioact. 2021, 227, 106464. [Google Scholar] [CrossRef]
  34. Madadi, R.; Mejjad, N.; De-la-Torre, G.E. Geochemical speciation, ecological risk, and source identification of heavy metal(loid)s in sediments and waters from Musa Estuary, Persian Gulf. Mar. Pollut. Bull. 2023, 190, 114836. [Google Scholar] [CrossRef]
  35. Zahra, A.; Hashmi, M.Z.; Malik, R.N.; Ahmed, Z. Enrichment and geoaccumulation of heavy metals and risk assessment of sediments of Kurang Nallah-feeding tributary of the Rawal Lake reservoir, Pakistan. Sci. Total Environ. 2014, 470–471, 925–933. [Google Scholar] [CrossRef]
  36. Jiang, X.; Teng, A.; Xu, W.; Liu, X. Distribution and pollution assessment of heavy metals in surface sediments in the Yellow Sea. Mar. Pollut. Bull. 2014, 83, 366–375. [Google Scholar] [CrossRef]
  37. Mejjad, N.; Rovere, M. Understanding the impacts of blue economy growth on deep-sea ecosystem services. Sustainability 2021, 13, 12478. [Google Scholar] [CrossRef]
  38. Kouali, H.; Chaouti, A.; Achtak, H.; Elkalay, K.; Dahbi, A. Contamination and ecological risk assessment of trace metals in surface sediments from coastal areas (El Jadida, Safi and Essaouira) along the Atlantic coast of Morocco. J. Afr. Earth Sci. 2022, 186, 104417. [Google Scholar] [CrossRef]
  39. Rafiq, F.; Techetach, M.; Achtak, H.; Boundir, Y.; Kouali, H.; Sisouane, M.; Mandri, B.; Cherifib, O.; Dahbi, A. First assessment of domestic and industrial effluents impact on intertidal zone of Safi coastline (west of Morocco): Physicochemical characteristics and metallic trace contamination. Desalin. Water Treat. 2022, 245, 167–177. [Google Scholar] [CrossRef]
  40. Minoubi, A.; El Khalidi, K.; Chaibi, M.; Zourarah, B.; Ayt Ougougdal, M.; Poizot, E.; Mear, Y. Impact des ouvrages portuaires sur l’évolution du trait de côte de la baie de Safi (littoral atlantique-Maroc). Rev. Marocaine Géomorphol. 2018, 2018, 18–35. [Google Scholar]
  41. Zakarya, E. La Houle et Son Impact sur le Littoral Atlantique Marocain, Approche par Modélisation. Ph.D. Thesis, Université de Paris IV, Paris, France, 1994. [Google Scholar]
  42. Charrouf, L. Les Problèmes d’Ensablement des Ports Marocains sur la Façade Atlantique: Leur Impact Sédimentologique sur le Littoral. Ph.D. Thesis, Université de Paris-Sud, Centre Scientifique d’Orsay, Bures-sur-Yvette, France, 1989. [Google Scholar]
  43. Cirac, P.; Faugeres, J.C.; Gayet, J. Résultats préliminaire d’une reconnaissance sédimentaire du plateau atlantique marocain. Bull. l’Inst. Géol. 1979, 25, 69–81. [Google Scholar]
  44. Mathieu, R. Sédiments et Foraminifères Actuels de la Marge Continentale Atlantique du Maroc. Ph.D. Thesis, Université de Pierre et Marie Curie, Paris, France, 1986. [Google Scholar]
  45. Folk, R.L.; Ward, W.C. A Study in the Significance of Grain-Size Parameters. J. Sediment. Petrol. 1957, 27, 3–26. [Google Scholar] [CrossRef]
  46. Allen, S.E.; Grimshaw, H.M.; Rowland, A.P. Chemical analysis. In Methods in Plant Ecology; Moore, P.D., Chapman, S.B., Eds.; Blackwell Scientific Publication: Hoboken, NJ, USA, 1986; pp. 285–344. [Google Scholar]
  47. Li, G.; Hu, B.; Bi, J.; Leng, Q.; Xiao, C.; Yang, Z. Heavy metals distribution and contamination in surface sediments of the coastal Shandong Peninsula (Yellow Sea). Mar. Pollut. Bull. 2013, 76, 420–426. [Google Scholar] [CrossRef] [PubMed]
  48. Remeikaitė-Nikienė, N.; Garnaga-Budrė, G.; Lujanienė, G.; Jokšas, K.; Stankevičius, A.; Malejevas, V.; Barisevičiūtė, R. Distribution of metals and extent of contamination in sediments from the south-eastern Baltic Sea (Lithuanian zone). Oceanologia 2018, 60, 193–206. [Google Scholar] [CrossRef]
  49. Elsagh, A.; Jalilian, H.; Aslshabestari, M.G. Evaluation of heavy metal pollution in coastal sediments of Bandar Abbas, the Persian Gulf, Iran: Mercury pollution and environmental geochemical indices. Mar. Pollut. Bull. 2021, 167, 112314. [Google Scholar] [CrossRef] [PubMed]
  50. Ferreira, S.L.; da Silva Junior, J.B.; dos Santos, I.F.; de Oliveira, O.M.; Cerda, V.; Queiroz, A.F. Use of pollution indices and ecological risk in the assessment of contamination from chemical elements in soils and sediments–Practical aspects. Trends Environ. Anal. Chem. 2022, 35, e00169. [Google Scholar] [CrossRef]
  51. Muller, G. Index of geoaccumulation in the sediments of the Rhine River. GeoJournal 1969, 2, 108–118. [Google Scholar]
  52. Hokanson, L. Ecological risk index for aquatic pollution control, a sedimentological approach. Water Res. 1980, 14, 975–1001. [Google Scholar] [CrossRef]
  53. Salomons, W.; Forstner, U. Metals in the Hydrocycle; eBook; Springer: Berlin/Heidelberg, Germany; Tokyo, Japan, 1984. [Google Scholar] [CrossRef]
  54. Sakan, S.M.; Djordjevic, D.S.; Manojlovic, D.D.; Polic, P.S. Assessment of heavy metal pollutants accumulation in the Tisza river sediments. J. Environ. Manag. 2009, 90, 3382–3390. [Google Scholar] [CrossRef]
  55. Sekabira, K.; Oryem Origa, H.; Basamba, T.; Mutumba, G.; Kakudidi, E. Assessment of heavy metal pollution in the urban stream sediments and its tributaries. Int. J. Environ. Sci. Technol. 2010, 7, 435–446. [Google Scholar] [CrossRef]
  56. Abrahim, G.; Parker, R. Assessment of heavy metal enrichment factors and the degree of contamination in marine sediments from Tamaki estuary, Auckland, New Zealand. Environ. Monit. Assess. 2008, 136, 227–238. [Google Scholar] [CrossRef]
  57. Tomlinson, D.L.; Wilson, J.G.; Harris, C.R.; Jeffrey, D.W. Problems in the assessment of heavy-metal levels in estuaries and the formation of a pollution index. Helgol. Meeresunters. 1980, 33, 566–575. [Google Scholar] [CrossRef]
  58. Suresh, G.; Sutharsan, P.; Ramasamy, V.; Venkatachalapathy, R. Assessment of spatial distribution and potential ecological risk of the heavy metals in relation to granulometric contents of Veeranam lake sediments, India. Ecotoxicol. Environ. Saf. 2012, 84, 117–124. [Google Scholar] [CrossRef]
  59. Stoffers, P.; Glasby, G.P.; Wilson, C.J.; Davis, K.R.; Walter, P. Heavy metal pollution in Wellington Harbour. N. Z. J. Mar. Freshw. Res. 1986, 20, 495–512. [Google Scholar] [CrossRef]
  60. Förstner, U.; Ahlf, W.; Calmano, W.; Kersten, M. Sediment Criteria Development: Contributions from Environmental Geochemistry to Water Quality Management. In Sediments and Environmental Geochemistry; Springer: Berlin/Heidelberg, Germany, 1990; pp. 311–338. [Google Scholar]
  61. Loska, K.; Cebula, J.; Pelczar, J.; Wiechula, D.; Kwapulinski, J. Use of enrichment, and contamination factors together with geoaccumulation indexes to evaluate the content of Cd, Cu, and Ni in the Bybnik water reservoir in Poland. Water Air Soil Pollut. 1997, 93, 347–365. [Google Scholar] [CrossRef]
  62. Gonzáles-Macías, C.; Schifter, I.; Lluch-Cota, D.B.; Méndez-Rodríguez, L.; Hernández-Vázquez, S. Distribution, enrichment and accumulation of heavy metals in coastal sediments of Salina Cruz Bay, Mexico. Environ. Monit. Assess. 2006, 118, 211–230. [Google Scholar] [CrossRef]
  63. Chen, C.W.; Kao, C.M.; Chen, C.F.; Dong, C.D. Distribution and accumulation of heavy metals in the sediments of Kaohsiung Harbor, Taiwan. Chemosphere 2007, 66, 1431–1440. [Google Scholar] [CrossRef] [PubMed]
  64. Saraee, K.R.E.; Abdi, M.R.; Naghavi, K.; Saion, E.; Shafaei, M.A.; Soltani, N. Distribution of heavy metals in surface sediments from the South China Sea ecosystem, Malaysia. Environ. Monit. Assess. 2011, 183, 545–554. [Google Scholar] [CrossRef] [PubMed]
  65. Maanan, M.; El Barjy, M.; Hassou, N.; Zidane, H.; Zourarah, B.; Maanan, M. Origin and potential ecological risk assessment of trace elements in the watershed topsoil and coastal sediment of the Oualidia lagoon, Morocco. Hum. Ecol. Risk Assess. 2017, 24, 602–614. [Google Scholar] [CrossRef]
  66. El Barjy, M.; Maanan, M.; Maanan, M.; Salhi, F.; Tnoumi, A.; Zourarah, B. Contamination and environmental risk assessment of heavy metals in marine sediments from Tahaddart estuary (NW of Morocco). Hum. Ecol. Risk Assess. 2020, 26, 87–102. [Google Scholar] [CrossRef]
  67. Rafiq, F.; Techetach, M.; Boundir, Y.; Achtak, H.; Mandri, B.; Benhamdoun, A.; Cherifi, O.; Dahbi, A. First vertical exploration of organometallic contamination of sediment at the fishing port of Safi (West of Morocco). Moroc. J. Chem. 2022, 10, 115–126. [Google Scholar] [CrossRef]
  68. Mejjad, N.; Laissaoui, A.; Benmhammed, A.; Fekri, A.; El Hammoumi, O.; Benkdad, A.; Amsil, H.; Chakir, E.M. Potential ecological risk assessment of rare earth elements in sediments cores from the Oualidia lagoon, Morocco. Soil Sediment Contam. Int. J. 2022, 31, 941–958. [Google Scholar] [CrossRef]
  69. Xu, Z.Q.; Ni, S.J.; Tuo, X.G.; Zhang, C.J. Calculation of heavy metals’ toxicity coefficient in the evaluation of potential ecological risk index. Environ. Sci. Technol. 2008, 31, 112–115. [Google Scholar]
  70. Zhang, L.H.; Du, Y.F.; Wang, D.D.; Gao, S.; Gao, W.H. Distribution patterns and pollution assessments of heavy metals in the Spartina alterniflora salt-marsh wetland of Rudong, Jiangsu province. Environ. Sci. 2014, 35, 2401–2410. [Google Scholar] [CrossRef]
  71. Soliman, N.F.; Nasr, S.M.; Okbah, M.A. Potential ecological risk of heavy metals in sediments from the Mediterranean coast, Egypt. J. Environ. Health Sci. Eng. 2015, 13, 70. [Google Scholar] [CrossRef]
  72. Sun, G.; Chen, Y.; Bi, X.; Yang, W.; Chen, X.; Zhang, B.; Cui, Y. Geochemical assessment of agricultural soil: A case study in Songnen-Plain (Northeastern China). Catena 2013, 111, 56–63. [Google Scholar] [CrossRef]
  73. Mejjad, N.; Laissaoui, A.; Fekri, A.; Hassen, N.E.H.; Benmhammed, A.; El Hammoumi, O.; Benkdad, A.; Amsil, H. Tracking natural and human impact on sediment dynamics using radiometric approach in Oualidia lagoon (Morocco). Int. J. Environ. Anal. Chem. 2022, 102, 4300–4315. [Google Scholar] [CrossRef]
  74. Kim, Y.; Kim, K.; Kang, H.D.; Kim, W.; Doh, S.H.; Kim, D.S.; Kim, B.K. The accumulation of radiocesium in coarse marine sediment: Effects of mineralogy and organic matter. Mar. Pollut. Bull. 2007, 54, 1341–1350. [Google Scholar] [CrossRef]
  75. Mejjad, N.; Laissaoui, A.; El-Hammoumi, O.; Benmansour, M.; Benbrahim, S.; Bounouira, H.; Benkdad, A.; Bouthir, F.Z.; Fekri, A.; Bounakhla, M. Sediment geochronology and geochemical behavior of major and rare earth elements in the Oualidia Lagoon in the western Morocco. J. Radioanal. Nucl. Chem. 2016, 309, 1133–1143. [Google Scholar] [CrossRef]
  76. Zidane, H.; Maanan, M.; Mouradi, A. Environmental and ecological risk of heavy metals in the marine sediment from Dakhla Bay, Morocco. Environ. Sci. Pollut. Res. 2017, 24, 7970–7981. [Google Scholar]
  77. Wenning, R.J.; Ingersoll, C.G. Summary of the SETAC Pellston Workshop on Use of Sediment Quality Guidelines and Related Tools for the Assessment of Contaminated Sediments, Fairmont, MT, USA, 17–22 August 2002; Society of Environmental Toxicology and Chemistry (SETAC): Pensacola, FL, USA, 2002. [Google Scholar]
  78. Zhan, S.; Peng, S.; Liu, C.; Chang, Q.; Xu, J. Spatial and temporal variations of heavy metals in surface sediments in Bohai Bay, North China. Bull. Environ. Contam. Toxicol. 2010, 84, 482–487. [Google Scholar] [CrossRef]
  79. Gu, Y.G. Heavy metal fractionation and ecological risk implications in the intertidal surface sediments of Zhelin Bay, South China. Mar. Pollut. Bull. 2018, 129, 905–912. [Google Scholar] [CrossRef]
  80. Zourarah, B.; Maanan, M.; Robin, M. Sedimentary records of anthropogenic contribution to heavy metal content in Oum Er Bia estuary (Morocco). Environ. Chem. Lett. 2009, 7, 67–78. [Google Scholar] [CrossRef]
  81. Legorburu, I.; Rodríguez, J.G.; Borja, Á.; Menchaca, I.; Solaun, O.; Valencia, V.; Galparsoro, I.; Larreta, J. Source characterization and spatio–temporal evolution of the metal pollution in the sediments of the Basque estuaries (Bay of Biscay). Mar. Pollut. Bull. 2013, 66, 25–38. [Google Scholar] [CrossRef] [PubMed]
  82. Taylor, S.R.; McLennan, S.M. The Continental Crust: Its Composition and Evolution; Blackwell Scientific Publication: Carlton, Australia, 1985; p. 312. [Google Scholar]
  83. Viers, J.; Dupré, B.; Gaillardet, J. Chemical composition of suspended sediments in World Rivers: New insights from a new database. Sci. Total Environ. 2009, 407, 853–868. [Google Scholar] [CrossRef] [PubMed]
  84. Burton, G.A., Jr. Sediment quality criteria in use around the world. Limnology 2002, 3, 65–76. [Google Scholar] [CrossRef]
  85. Yi, Y.; Yang, Z.; Zhang, S. Ecological risk assessment of heavy metals in sediment and human health risk assessment of heavy metals in fishes in the middle and lower reaches of the Yangtze river basin. Environ. Pollut. 2011, 159, 2575–2585. [Google Scholar] [CrossRef]
  86. Liu, J.L.; Wu, H.; Feng, J.X.; Li, Z.J.; Lin, G.H. Heavy metal contamination and ecological risk assessments in the sediments and zoobenthos of selected mangrove ecosystems, South China. Catena 2014, 119, 136–142. [Google Scholar] [CrossRef]
  87. Ward, J.H. Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 1963, 58, 236–244. [Google Scholar] [CrossRef]
  88. Hajji, C.; Bendou, A.; Hassou, A. Management of Moroccan solid harbor waste: Comparative analysis and recommendations. J. Water Environ. Sci. 2017, 1, 59–76. [Google Scholar]
  89. Byrnes, T.A.; Dunn, R.J.K. Boating- and shipping-related environmental impacts and example management measures: A review. J. Mar. Sci. Eng. 2020, 8, 908. [Google Scholar] [CrossRef]
  90. Warnken, J.; Dunn, R.J.K.; Teasdale, P.R. Investigation of recreational boats as a source of copper at anchorage sites using time-integrated diffusive gradients in thin film and sediment measurements. Mar. Pollut. Bull. 2004, 49, 833–843. [Google Scholar] [CrossRef]
Figure 1. Study area and sampling locations, (S1, …, S38 are the sampling sites codes).
Figure 1. Study area and sampling locations, (S1, …, S38 are the sampling sites codes).
Oceans 04 00023 g001
Figure 2. Spatial distribution map of sand, mud, and CaCO3.
Figure 2. Spatial distribution map of sand, mud, and CaCO3.
Oceans 04 00023 g002
Figure 3. The spatial distribution map of Cd (mg/kg), Cr (mg/kg), Zn (mg/kg), Ni (mg/kg), Pb (mg/kg), and Cu (mg/kg) determined in the fine fractions of sediment samples.
Figure 3. The spatial distribution map of Cd (mg/kg), Cr (mg/kg), Zn (mg/kg), Ni (mg/kg), Pb (mg/kg), and Cu (mg/kg) determined in the fine fractions of sediment samples.
Oceans 04 00023 g003
Figure 4. Dendrogram produced with the classification ascendant hierarchy (CAH) representing all variables.
Figure 4. Dendrogram produced with the classification ascendant hierarchy (CAH) representing all variables.
Oceans 04 00023 g004
Figure 5. Spatial distribution map of CF, CD, and PLI.
Figure 5. Spatial distribution map of CF, CD, and PLI.
Oceans 04 00023 g005
Table 1. Sampling stations, depth (m), sand (%), mud (%), carbonate content, and textural class of each sample.
Table 1. Sampling stations, depth (m), sand (%), mud (%), carbonate content, and textural class of each sample.
Mud (%)%CaCO3Textural Class
S132°19′48.30″ N9°17′17.10″ W18.497.392.6172.34Sand
S232°19′21.05″ N9°16′7.33″ W6.598.791.2182.13Sand
S332°19′22.04″ N9°16′45.43″ W23.282.1617.8462.98Slightly muddy sand
S432°19′21.54″ N9°17′17.67″ W30.0897.162.8478.30Sand
S532°18′45.88″ N9°17′10.03″ W29.395.154.8572.77Sand
S632°18′49.35″ N9°16′37.21″ W25.592.027.9871.06Slightly muddy sand
S732°18′50.34″ N9°16′11.42″ W21.984.6415.3674.47Slightly muddy sand
S832°18′50.83″ N9°15′42.71″ W17.0596.863.1479.57Sand
S932°18′52.32″ N9°15′23.37″ W15.298.551.4582.13Sand
S1032°18′53.31″ N9°15′4.03″ W3.598.861.1482.55Sand
S1232°18′17.65″ N9°15′28.64″ W13.7698.411.5979.57Sand
S1332°18′18.64″ N9°16′6.73″ W23.595.884.1279.57Sand
S1532°17′43.48″ N9°16′12.58″ W26.594.255.7577.45Sand
S1632°17′42.49″ N9°15′41.53″ W25.895.414.5977.87Sand
S1732°17′42.99″ N9°15′14.00″ W21.496.983.0276.17Sand
S1832°17′43.97″ N9°14′51.73″ W12.596.943.0669.36Sand
S1932°17′43.97″ N9°16′40.12″ W28.296.463.5482.55Sand
S2032°17′42.48″ N9°17′2.39″ W29.7493.726.2865.53Slightly muddy sand
S2132°17′6.83″ N9°16′45.97″ W30.696.263.7479.57Sand
S2232°17′7.33″ N9°16′13.75″ W28.493.856.1573.62Slightly muddy sand
S2332°17′6.84″ N9°15′46.21″ W26.867.5732.4352.34Muddy sand
S2832°16′30.19″ N9°15′48.56″ W24.898.301.7077.45Sand
S2932°16′30.68″ N9°16′13.74″ W26.587.4112.5937.02Slightly muddy sand
S3032°16′31.67″ N9°16′43.62″ W29.290.799.2141.28Slightly muddy sand
S3232°15′49.57″ N9°16′31.31″ W26.397.352.6580.00Sand
S3332°15′48.59″ N9°16′2.03″ W22.599.020.9877.45Sand
S3432°15′18.87″ N9°15′59.69″ W11.2296.963.0472.34Sand
S3832°15′19.34″ N9°17′15.84″ W31.897.822.1877.02Sand
Table 2. Description of the modified contamination degree classification.
Table 2. Description of the modified contamination degree classification.
0 < mCd < 1.5Nil–very low degree of contamination
1.5 < mCd < 2Low degree of contamination
2 < mCd < 4Moderate degree of contamination
4 < mCd < 8High degree of contamination
8 < mCd < 16Very high degree of contamination
16 < mCd < 32Extremely high degree of contamination
mCd ≥ 32Ultra-high degree of contamination
Table 3. Geo-accumulation index classes and designation.
Table 3. Geo-accumulation index classes and designation.
Igeo ValueIgeo ClassDesignation of Sediment Quality
>56Extremely contaminated
4–55Strongly to extremely contaminated
3–44Strongly contaminated
2–33Moderately to strongly contaminated
1–22Moderately contaminated
0–11Uncontaminated to moderately contaminated
Table 4. The E r i and RI values classification.
Table 4. The E r i and RI values classification.
E r i ValueRisk IntensityRI ValueRisk Intensity
<40Low ecological risk<150Low ecological risk
40 < E r i ≤ 80Moderate ecological risk150 ≤ RI < 300Moderate ecological risk
80 < E r i ≤ 160Appreciable ecological risk300 ≤ RI < 600Considerable ecological risk
160 < E r i ≤ 320High ecological riskRI > 600Very high ecological risk
E r i > 320Serious ecological risk
Table 5. Comparison table of the heavy metal concentrations measured in Safi Bay surface sediment and other similar marine ecosystems. (ND: not determined).
Table 5. Comparison table of the heavy metal concentrations measured in Safi Bay surface sediment and other similar marine ecosystems. (ND: not determined).
LocationsMetal Concentrations (mg/kg)References
Safi Bay, Morocco0.39511.3433.85010.39533.97236.546Present study
Dakhla Bay, Morocco0.410817.35.871.320.8[76]
Bohai Bay, China0.04–0.84ND7.20–44.0ND5.90–97.056.3–309[78]
Bay of Bengal, India19.8109.4576.4527.98449.62978.76[8]
Zhelin Bay, China0.06323.077.957.535.6974.95[79]
Laizhou Bay, China0.1932.6910.9917.3813.3750.63[11]
Tahaddart Estuary, Morocco0.1691.8357.134.9225.5573.73[66]
Oualidia Lagoon, Morocco0.66102.417.715.7310.175.8[18]
Nador Lagoon, Morocco1.671.6150.845.2135554.9[10]
OumEr Bia estuary, Morocco0.369.519.6ND28138[80]
The fishing port of Safi, Morocco1.2–2.4ND45–90NDND276–552[67]
Bay of Biscay, Spain0.08–0.112.13–6.1611.8–21.764.17–15.126.88–26.2328.8–66.79[81]
Upper Continental Crust0.093525202071[82]
The suspended sediment of World Rivers1.5513375.974.561.1208[83]
Table 6. Pearson correlation matrix of studied heavy metals, sand, mud, and CaCO3 in Safi Bay.
Table 6. Pearson correlation matrix of studied heavy metals, sand, mud, and CaCO3 in Safi Bay.
VariablesCd (mg/kg)Cr (mg/kg)Cu (mg/kg)Ni (mg/kg)Pb (mg/kg)Zn (mg/kg)Sand (%)Mud (%)%CaCO3
Cd (mg/kg)1.00
Cr (mg/kg)−0.071.00
Cu (mg/kg)0.270.62 *1.00
Ni (mg/kg)0.05−
Pb (mg/kg)0.440.320.83 **0.081.00
Zn (mg/kg)0.340.450.68 **0.250.53 *1.00
Sand (%)−0.18−0.71 **−0.82 **−0.09−0.70 **−0.501.00
Mud (%)0.180.71 **0.82 **0.090.70 **0.50−1.001.00
%CaCO3−0.35−0.26−0.78 **−0.05−0.81 **−0.480.63 *−0.63 *1.00
Significant correlation marked (** p < 0.01) and (* p < 0.05).
Table 7. Metal contamination factors (CFs), degree of contamination (DC), modified degree of contamination (mCd), and pollution load indices (PLIs) for sediments of all sites studied in Safi Bay.
Table 7. Metal contamination factors (CFs), degree of contamination (DC), modified degree of contamination (mCd), and pollution load indices (PLIs) for sediments of all sites studied in Safi Bay.
SitesContamination Factor (Cf)Degree of
Contamination (DC)
Modified Degree of
Bold values represent significant contamination.
Table 8. Geo-accumulation index (Igeo) for metals in Safi Bay.
Table 8. Geo-accumulation index (Igeo) for metals in Safi Bay.
SitesGeo-Accumulation Index (Igeo)
Bold values represent significant contamination.
Table 9. Potential ecological risk factors ( E r i ) and potential ecological risk index (RI) for studied metals in Safi Bay.
Table 9. Potential ecological risk factors ( E r i ) and potential ecological risk index (RI) for studied metals in Safi Bay.
SitesEcological Risk Factor ( E r i )RIRI Grade
E r i gradeLowLowLowLowLowLowLow
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Minoubi, A.; Mejjad, N.; El Khalidi, K.; Bouchkara, M.; Fadili, A.; Chaibi, M.; Zourarah, B. Spatial Distribution and Contamination Level Assessment of Marine Sediment of the Safi Bay (Moroccan Atlantic Coast). Oceans 2023, 4, 331-349.

AMA Style

Minoubi A, Mejjad N, El Khalidi K, Bouchkara M, Fadili A, Chaibi M, Zourarah B. Spatial Distribution and Contamination Level Assessment of Marine Sediment of the Safi Bay (Moroccan Atlantic Coast). Oceans. 2023; 4(4):331-349.

Chicago/Turabian Style

Minoubi, Abdenaim, Nezha Mejjad, Khalid El Khalidi, Mohammed Bouchkara, Ahmed Fadili, Mohamed Chaibi, and Bendahhou Zourarah. 2023. "Spatial Distribution and Contamination Level Assessment of Marine Sediment of the Safi Bay (Moroccan Atlantic Coast)" Oceans 4, no. 4: 331-349.

Article Metrics

Back to TopTop