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Behavior of trace metals in sedimentary cores from marine and lacustrine environments in Algeria

  • Houria Athmani EMAIL logo
Published/Copyright: October 10, 2024
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Open Geosciences
From the journal Open Geosciences Volume 16 Issue 1

Abstract

This study highlights the potential risks of pollution due to the presence of trace metals in sediment samples from six sediment cores from four wetlands (Chott Melghir, Fontaine des Gazelles dam, Lake Mellah, and Algiers Bay) in Algeria, assessing the level of contamination of these trace metals, and understanding the behavior of metals as a function of the depth of the sediment cores and the nature of the different sampling media. A total of 130 sediment samples were collected and subjected to chemical analysis (loss on ignition, inductively coupled plasma mass spectrometry, and atomic absorption spectroscopy to determine M.O. and trace metal contents) and physical analysis (X-ray fluorescence and DRX to identify the chemical composition and mineralogy of the sediments). Calculation of the enrichment factor enabled us to differentiate between anthropogenic and natural sources of trace metals such as Zn, Pb, Cr, Ag, and Cd, showing contamination at various sites due to urban or agricultural inputs. The dominant mineralogical composition of the sediments at the four sites was quartz, carbonate, and sandstone. Principal component analysis revealed strong positive correlations in the chemical composition of sediment samples between chlorine and Na2O, K2O, TiO2, Al2O3, and Fe2O, the main components of clays. The presence of organic matter in the four study areas is directly linked to the introduction of organic matter from the wadis into the watershed. Sediments have a high redox activity, which facilitates the movement of trace metals.

Keywords: sediment; EF; chemical and mineralogique compositions; principal component analysis; trace metals

1 Introduction

On a worldwide scale, the presence of trace metals in surface waters is a major concern due to their toxicity, persistence, and bioaccumulation potential [1], which are devastating factors for aquatic ecosystems. These trace elements have an impact on soil quality, water pollution, the food chain [2], and the health of the human population; this type of pollutant can cause serious health problems [3] by causing cancer and damaging certain organs and the nervous system. It is therefore important to stress the existence of a significant correlation between exposure and/or consumption of trace metals and secondary effects on human health [4]. The extent of this type of pollution is linked to the dynamics of urbanization and industrialization [5], as well as agricultural activities in the world’s cities.

Algeria is home to various categories of wetlands, including freshwater marshes, marine marshes, wadis, dams, and reservoirs [6].

These wetlands are confronted with substantial risks arising from human activities, such as salt extraction and excessive water pumping, which result in degradation and increased susceptibility [7]. Analyses carried out in these wetlands in Algeria, particularly in the Gulf of Annaba, where polluted and contaminated wastewater from neighbouring urban areas and industrial zones is collected in the gulf [8], revealed high levels of Cu, Zn, Pb, Cd and Hg contamination in the sediments and muscles of Mugil Cephalus fish, posing a serious health risk to local residents [9], the Djebel Onk region showed contamination of surface water, spring water and groundwater by lead, uranium, iron, lithium, selenium and magnesium, highlighting the dangerous impact on human health, particularly children [4]. The Ain Dallia, Beni Haroun, and Koudiat Medouar dams face major trace metal pollution problems, with high levels of contamination by various metals such as Cu, Zn, Fe, Pb, Cr, and Cd [10,11,12]. Trace metal levels in the aquatic environment are linked to several factors, such as alkalinity, organic carbon, and sulfur content, which influence trace metal concentration, and the nature of the sediment, which plays a role in the release of these pollutants or their fixation in the sediment, known as heavy metal inversion [13]. The trace metal content of sediments is directly dependent on the parent rock, which represents the source and reserve [14].

This study aims to assess the degree of trace metal contamination in the sediments of six sediment cores from four wetlands in Algeria: Chott Melghir, Fontaine des Gazelles dam, Lake Mellah, and Algiers Bay. This study is essential to understand the extent of contamination and the potential risks associated with these ecosystems, as well as the behavior of these trace metals in relation to the nature of the sediment, the level of organic matter and carbonates, and the depth of the sediment cores.

2 Methods

2.1 Study area

Six sediment cores were collected from four different study sites (Figure 1 and Table 1). These cores were cut into layers of 0.5, 1, and 2 cm to better understand how metal concentration varies with depth. The cores were then lyophilized, ground, and sieved at 2 mm. The samples were subjected to a variety of analytical techniques, including X-ray diffraction to determine mineral composition [15] and portable X-ray fluorescence to analyze major elements [16]. The total carbonate (CaCO3) content was determined using Bernard calcimetry in accordance with NF X 31-105, and organic matter was measured using the loss on ignition method [17]. Trace metals were identified by digesting the sediment with highly concentrated acids and oxidants, and their concentrations were measured using inductively coupled plasma mass spectrometry (for cores C1, C2, C3, and C6) at CEREGE [18] and atomic absorption spectroscopy (for cores C4 and C5) at CRSTRA [19].

Figure 1 Geographical location of the four study areas: Bay of Algiers, Lake Mellah, Fontaine des Gazelles dam, and Chott Melghir (and position of the sample cores).
Figure 1

Geographical location of the four study areas: Bay of Algiers, Lake Mellah, Fontaine des Gazelles dam, and Chott Melghir (and position of the sample cores).

Table 1

Geographical location of the four study areas: Bay of Algiers, Lake Mellah, Fontaine des Gazelles dam, and Chott Melghir

Study area Algiers bay Lake Mellah Fontaine des Gazelles dam Chott Melghir
Cores C1 C2 C3 C4 C5 C6
Latitude (N) 36°46.715 36°48.160 36°46.115 36°54. 371 35°08. 115 34°22.756
Longitude (E) 3°07.910 3°08.420 3°08′.353 8°18′.579 5°35′.353 6°15.813
Number of samples 25 21 26 23 16 19
Depth 43 m 70 m 30.5 m 6 m 6 m 21 m

2.2 Special characteristics of each site

Algiers Bay, located in the Coastal Algiers watershed, is subjected to a variety of pollutants on a daily basis, including organic, suspended matter, detergents, and lubricants. Coastal currents, primarily caused by swell, play an important role in active sedimentation [20]. The warm, dry climate of the Mediterranean basin causes significant evaporation, which plays an important role in thermohaline water circulation [21]. Local topography disrupts currents, influencing circulation in the western Mediterranean [22]. The source of Oued El Harrach, located north of the Atlas Blidéen and flowing into the Bay of Algiers after approximately 67 km, is fed by 70 wastewater discharge points from densely populated and urbanized areas, 26 of which flow directly into the Port of Algiers [23]. The region’s heavy urbanization, particularly around the Bay of Algiers, and intensive agriculture on the Mitidja plain add to environmental stress, with irrigation using contaminated water from the Oued El Harrach.

Lake Mellah is Algeria’s only coastal lagoon and one of the country’s few paralic basins [24]. Lake Mellah, which covers an area of 8,250 ha, is part of the entire PNEK protection area in El Kala, northeast Algeria [25]. The region has a Mediterranean climate with abundant rainfall, making it one of Algeria’s wettest areas. El Kala has a milder climate, with annual rainfall ranging between 785 and 814.7 mm, and a rainy season that lasts from September to January [26]. The lake’s salinity is the most distinguishing hydrochemical feature, with a gradient from north to south across the lagoon [27]. The lake’s fish production is dependent on direct and secondary food chains, which are critical for many of its fish species. The lake is fed by the waters of the Er Rekibet, Mellah, and El Aroug wadis, which are connected to the sea via a narrow channel. All wastewater from El Tarf’s coastal communes is dumped into the wadis (El Tarf, Berrihane, Echatt, and Bouteldja) or directly into the sea. The communes discharge approximately 6,893 m3/day, which equates to an annual volume of 2,515,945 m3 [28]. Overexploitation of the water table is the result of recent population growth and intensified agriculture. The resulting water scarcity in the region has been identified as a significant threat to the National Park’s wetlands. The National Park’s wetlands are under threat due to the region’s excessive fertilizer use [29].

Fontaine des Gazelles Dam, 37 km from Biskra, is an irrigation reservoir. Built-in 2,000 on the Oued El Hai, it has a gross capacity of approximately 55,491,000 m3 and a regular volume of 14,000,000 m3 [30]. The reservoir irrigates 1,100 ha of farmland. Biskra has a hot and dry climate, with the coldest months being January and December and the hottest being July, August, and June. The average monthly rainfall from its exploitation until the year of core sedimentation has been the coldest, with July having the highest average at 1.5 mm and the lowest annual average at 184.0 mm. The prevailing winds come from the southeast [31]. The Oued El-Hai basin is a synclinal basin containing Upper Cenomanian limestone and Turonian marl-limestone formations [32]. The basin’s geology is defined by sedimentary formations dominated by carbonates [33]. The Oued El-Hai basin covers an area of 1,788 km² and spans 892.2 km. It receives approximately 4.92 tons of solid waste per day, including municipal and industrial waste, food and fuel, recycled batteries, plastic waste, and vehicle washings, which pose potential risks to surface and groundwater [34].

The Chott Melghir, located in the southern Sahara, is a large depression that extends from southern Tunisia to the Atlas Mountains in northern Algeria [6]. The Ramsar Convention has protected this wetland since February 2002. Chott Melghir is Algeria’s largest Salt Lake, measuring 551,500 ha and part of the Great Basin [35]. It is located in the Sahara bioclimatic zone, which has an average monthly humidity of 45% and a Saharan climate with little and irregular rainfall [36]. The region is distinguished by a continuous chain of dunes, with the Chott Melghir forming a shallow depression between the Chott and the Sebkha. The difference between the two types of wetlands is in the distribution of nutrients. The Chott Melghir covers 67,875 km² and includes 30 sub-basins, with 25,000 being part of the active endoreism domain [37]. It receives water from the main wadis, which include Oued El Arab, Oued El Abiod, Oued Biskra, and Oued Djeddi. Chott Melghir contains 14 endangered plant species, six of which have a restricted distribution in Algeria [35].

All sites are subjected to various human activities that contribute to pollution of the studied environment, particularly by metallic elements[38]. The semi-enclosed shape of Algiers Bay generates a slow, clockwise counter-current [39]. This helps to replenish the water by removing pollutants [40]. The Bay of Algiers watershed is the third largest in surface area among the sites studied. Due to the rate of urbanization and intensive industrial and agricultural activities, the Bay of Algiers is subject to more pressure and anthropogenic pollution than other sites, followed by the Fontaine des Gazelles dam, Chott Melghir, and Lake Mellah [38].

2.3 Statistical methods

2.3.1 Enrichment factor (EF)

The proposal is to define the intensity of pollution by standardizing and differentiating anthropogenic contributions from natural sources using the EF. The clay portion is typically represented by a conservative element, such as Sc or Al [41]. For this computation, the stationary reference element selected was scandium (Sc):

EF = ( [ M ] / [ SC ] ) s / ( [ M ] / [ SC ] ) RM ,

where EF is the enrichment factor, [M] is the concentration in the studied metal, [Sc] is the concentration in scandium, s is the sample, and RM is the reference materials.

There are five classifications in which EFs can be categorized [42]]: FE < 2: no or low enrichment; 2 < EF < 5: moderate enrichment; 5 < EF < 20: significant enrichment; 20 < EF < 40: extreme enrichment.

2.3.2 Principal component analysis (PCA)

PCA is a statistical technique used to establish correlations between complex quantitative data. It is used to explain relationships between the chemical composition of sediments and sites. Environmental data in marine aquatic environments are often assessed using principal component analysis (PCA), as it enables complex links between the elements analysed to be analysed [43].

3 Results and discussion

3.1 The mineralogical composition and level of organic matter in the sediment

Quartz is the most abundant mineral, followed by carbonates, which have similar and variable values and influence the geological structure of the watersheds studied. Sediments in the Bay of Algiers and the Fontaine des Gazelles dam contain a high concentration of calcite, which can be attributed to carbonate deposits of detrital origin such as fluvial inputs (Oued El Harrach and Oued El Hai) or biogenic origin [44]. The sediments of the Fontaine des Gazelles dam and Lake Mellah cores contain high levels of biodetritic aragonite (shell debris and microfaunal remains). Gypsum is a minor component of the suite because it dissolves more readily in saline environments (Bay of Algiers and Chott Melghir). According to Rouahna [45], the solubility of gypsum at room temperature is approximately 2 g/L. It rises to 7 g/L in media containing 120–130 g/L sodium and magnesium chloride and then falls for more concentrated solutions.

The results of DRX slides show that the clay fraction in the sediment of the Alger Bay and Chott Melghir cores is primarily composed of illite and interlayers (illite-vermiculite). The presence of quartz in the original substrate, combined with a sandy texture, suggests that this is sandstone, with the sands cemented by silica from the same quartz and carbonates (dolomites and calcite). These rocks have a structure very similar to micas (muscovite, biotite) and other silicates (feldspar, feldspathoid, orthoclase, and others), from which they derive through bisiallitization, the gradual transformation of source rock minerals into clays rich in silica and base cations. This mineralogical phase is commonly found in sediments due to the alteration of micaceous layers inherited from source rocks. These minerals may include muscovites, biotites, and chlorites. However, the decomposed feldspars in the source rock already contain 7 Å minerals. Thus, the neoformation of minerals in the kaolinite family is primarily caused by the latter’s hydrolysis products [46].

The presence of organic matter in the sediment cores from the four study sites could be attributed to biogenic input from river water or biomass produced (with high primary productivity) in the water body at each site. The amount of mineralized organic matter in sediment is proportional to particle size; in the case of low granularity (as in clays), the sediment is not very permeable, and pore waters are rarely renewed. More voluminous sediments, such as sands or bioclastic limestones, promote more efficient pore water circulation, replenishing the pore space with dissolved oxygen. Given the high variability of the aforementioned parameters, predicting typical depths at which oxygen depletion occurs is impractical. Lac Mellah has an anoxic environment and the highest organic matter levels among the sites studied (Table 2).

Table 2

Average organic matter content (%) and average CaCO3 content in sediment samples from study site cores

Cores C1 C2 C3 C4 C5 C6
MO% Average 9.06 11.19 5.84 16.17 3.64 9.01
SD 2.24 3.97 3.46 5.10 2.53 1.54
Max 16.04 21.22 13.27 24.64 7.29 12.31
Min 7.07 5.23 3.03 7.93 1.02 5.88
CaCO3% Average 23.35 30.09 28.40 4.02 62.84 6.06
SD 2.89 3.77 4.27 2.30 4.16 1.71
Max 27.08 44.00 39.62 9.17 70.42 9.57
Min 18.33 25.33 21.53 1.25 55.83 3.48

3.2 Chemical composition of sediments

PCA of the chemical composition of sediment samples from the six cores revealed that sediments from the Mellah Lake core show a strong correlation between chlorine and Na2O, as well as significant positive correlations between K2O, TiO2, Al2O3, and Fe2O3. These elements are the primary components of the clay mineral [47]. Ba, Rb, Zn, Y, Ni, As, and Ce (0.52 < r < 0.93) can be adsorbed on the surface of clay minerals, indicating limited mobility in sediment concentrations [48]. Sr has a positive relationship with CaO, implying that it may replace CaO in the composition of calcium carbonate [49]. The sandy fraction of the sediments is primarily composed of biogenic carbonates, particularly in the Fontaine des Gazelles core sediments, where they predominate over SiO3 and Al2O3.

The sediment samples from Lake Mellah have the strongest correlations (0.92 < r < 0.97) with the Br/V, Br/Ga, V/Ga, Pb/Ga, Cu/Ce, and Ni/Ce pairs of the other samples, indicating that they come from the same source. However, despite their presence in all samples analyzed from the four sites studied, Zr and Cr are unrelated to any chemical element, which could be explained by the sediments’ different origins (Table 3). The parameters studied (SiO2, Al2O3, Fe2O3, K2O, TiO2, SO3, MgO, P2O5, Na2O, MnO, CaO, Y, Cd, Cl, Pb, Nb, Br, V, Ga, Sr, Ba, Rb, Zn, Ni, As, Ce, Zr, Cr, Cu) are distributed along two main axes (F1 and F2), accounting for 70.09% of the total variance. The first contribution (F1) is attributed to elements such as Ni, Cu, Ce, As, Zn, Ba, Al2O3, K2O, TiO2, SO3, MgO, and P2 O5. The second component (F2) consists of MnO, Nb, Pb, Fe2O3, Rb, Y, Na2O, Cl, Br, V, Ga, Sr, CaO, and Cd.

Table 3

Correlation between principal components and variables obtained for all data in sediment samples from the six study site cores

Parameters F1 F2
SiO2 0.324 0.479
Al2O3 0.714 0.594
CaO 0.405 −0.603
Fe2O3 0.668 0.717
K2O 0.742 0.272
MgO −0.500 −0.476
TiO2 0.900 0.290
SO3 −0.842 0.164
P2O5 −0.549 −0.438
Cl −0.573 0.782
Na2O −0.577 0.786
MnO −0.016 0.694
Sr 0.134 −0.858
Ba 0.562 0.326
Zr 0.019 −0.008
Rb 0.481 0.783
Zn 0.719 0.341
Pb −0.318 0.903
Y 0.589 0.753
Nb 0.007 0.974
Cr 0.083 0.289
Ni 0.962 0.029
Cu 0.907 −0.236
As 0.771 0.065
Cd −0.230 −0.536
Ce 0.956 −0.237
Br −0.569 0.777
V −0.573 0.752
Ga −0.591 0.795

Bold values indicate a positive contribution of trace metals on both axes F1 and F2.

The observation diagram (Figure 2(b)) depicts the spatial and temporal divergence of the geochemistry. Three groups can be distinguished based on the first two components. The first group consists of metals like Cu, Ni, Cu, Ce, As, Zn, Ba, Al2O3, K2O, TiO2, and SiO2. These elements characterize the sediment samples from the Algiers Bay sampling sites, which range from sandy to muddy. Marine pollution in Algiers Bay, particularly trace metal contamination from sediments transported by rivers, is a threat to public health and marine ecology [50]. Metals (Cu, Ni, Cu, Ce, As, Zn, and Ba) are linked to their natural origin through the lithology of the study area [53]. The second group includes the chemical elements Na2O, Cl, Br, V, and Ga, which are associated with sedimentary samples from Lac Mellah.

Figure 2 (a) Correlation circle showing the distribution of chemical elements along the F1 and F2 axes. (b) Component analysis (PCA) diagram of chemical elements in sediment samples from the six study site cores).
Figure 2

(a) Correlation circle showing the distribution of chemical elements along the F1 and F2 axes. (b) Component analysis (PCA) diagram of chemical elements in sediment samples from the six study site cores).

According to Table 3, these chemical elements are only found in Lac Mellah samples, determining the lithology of the study area. Sedimentary samples from the two study areas, Barrage Fontaine des Gazelles and Chott Melghir, are classified into the third group, which includes the chemical compounds MgO, P2O5, Cd, Sr, and CaO. All sediment samples from the Fontaine des Gazelles and Chott Melghir dams have the highest levels of these chemical elements, with the exception of cadmium, which only affects the Chott Melghir study area (Table 4). Although the other chemical compounds (MnO, Fe2O3, SO3, Cr, Zr, Nb, Rb, and Y) are present in the sediments at all four study sites, they do not characterize them, as shown in Figure 2(b).

Table 4

Correlation between principal components and variables obtained for all data in sediment samples from study site cores

F1 F2
Mn 0.629 0.545
Zn 0.639 0.239
Sr −0.491 0.281
Ag 0.345 −0.548
Cs 0.942 −0.069
Tl 0.908 −0.111
Pb 0.744 0.500
Bi 0.355 0.185
U −0.095 0.206
Sc −0.825 0.361
Cr 0.519 −0.680
Fe 0.717 0.598
Co 0.741 −0.527
Ni 0.890 −0.242
Cu 0.773 0.540
As 0.937 −0.005
Cd 0.337 0.699
Sn 0.943 −0.001
Sb 0.841 −0.079
Ba 0.671 −0.016

Bold values indicate a positive contribution of trace metals on both axes F1 and F2.

3.3 Trace metal behavior

PCA was applied to the metal concentrations (Mn, Zn, Sr, Ag, Cs, Tl, Pb, Bi, U, Sc, V, Cr, Fe, Co, Ni, Cu, As, Cd, Sn, Sb, Ba) measured in all sediment samples (130 sediment samples) from the six cores collected at Algiers Bay, Lac Mellah, Barrage Fontaine des Gazelles, and Chott Melghir. The analyzed parameters (Mn, Zn, Sr, Ag, Cs, Tl, Pb, Bi, U, Sc, V, Cr, Fe, Co, Ni, Cu, As, Mo, Cd, Sn, Sb, Ba) are evenly distributed along the two main axes (F1 and F2), accounting for 64.19% of the total variance. The contributions are as follows: F1 (50.30%) and F2 (13.90%) (Figure 3a). Metals can be classified into three groups based on axes (Table 4):

  1. The first group (F1) on the axis consists of metals with positive contributions, including Zn, Cs, Tl, V, Ni, As, Ba, Sn, and Sb. Cs correlates positively with these trace metals (0.53 < r < 0.93). Only with Sr and Mo does scandium show a positive correlation (0.71; 0.68).

  2. The second group on the F2 axis consists of Ag (negative contribution) and Cd (positive contribution). Ag has positive correlations with Co and Cr and Cd with Cu, Mn, and Bi.

  3. The third group consists of metals that appear on both the F1 and F2 axes: Mn, Fe, Cu, and Pb play a positive role on both axes and are positively correlated with one another, whereas Cr and Co play a positive role on the F1 axis and a negative role on the F2 axis.

Figure 3 (a) Correlation circle showing the distribution of trace metals along the F1 and F2 axes. (b) Component analysis (PCA) diagram of sediment samples from study site cores according to trace metals.
Figure 3

(a) Correlation circle showing the distribution of trace metals along the F1 and F2 axes. (b) Component analysis (PCA) diagram of sediment samples from study site cores according to trace metals.

Figure 3(b) illustrates the spatial and temporal variation in the geochemistry of metals found in sediments from the six sediment cores. Three groups can be identified from the first two components. Sediment samples from the Chott Melghir sampling site show high Sr concentrations (679.3 μg/g) (Table 5), indicating the presence of saline soils in the area [51]. The first group includes elements such as Sc, Sr, and Mo. The second group includes sedimentary samples from Lac Mellah that contain metallic elements such as Ag, Co, and Cr. These metallic elements have high concentrations but low EFs (Figure 4 and Table 6).

Table 5

Average concentration of trace metals (μg/g) in sediment core samples from study sites (values are given as mean ± standard deviation (mean ± SD))

Mn Zn Cr Ag Fe Co Pb Cd Ni Cu Sc
C1 409 ± 22.2 104 ± 9.4 129 ± 16.1 1.23 ± 0.54 52,083 ± 3,571 13.3 ± 1.6 39.7 ± 9.5 0.67 ± 0.05 42 ± 4.2 33.4 ± 2.2 5 ± 1.3
C2 286 ± 18.7 125 ± 29.8 94 ± 20.7 0.51 ± 0.15 40,788 ± 1693.3 10.4 ± 0.7 43.5 ± 7.7 0.23 ± 0.05 30.4 ± 2.3 32.2 ± 5.7 8.3 ± 4.05
C3 414 ± 22.6 165.6 ± 68.2 139 ± 25.6 1.55 ± 0.7 55,422 ± 2415.5 14.3 ± 1.1 54.7 ± 18.5 0.8 ± 0.12 41.9 ± 1.7 45.3 ± 12.5 12.35 ± 3.8
C4 195 ± 107.9 88.5 ± 36.7 218.6 ± 69.5 2.6 ± 0.6 6536.9 ± 560.1 15.9 ± 1.2 6.6 ± 1.4 0.3 ± 0.1 36.2 ± 2.1 7.1 ± 0.1 11.19 ± 1.2
C5 301 ± 68 206.6 ± 104.5 90.8 ± 11.8 1.3 ± 0.4 34,994 ± 3,933 8.9 ± 1.2 31.3 ± 13.1 0.7 ± 0.06 23.4 ± 2.2 29.3 ± 5.8 7.1 ± 0.7
C6 230 ± 87 19.5 ± 6.8 32.6 ± 11.4 0.61 ± 0.06 14,690 ± 5,399 4.5 ± 1.6 5.9 ± 1.1 0.6 ± 0.04 11.2 ± 3.9 8.2 ± 2.3 3.44 ± 0.7
As Sb Ba U Tl V Cs Sn Sr Bi
C1 16.5 ± 1.8 1.3 ± 0.2 284 ± 22.6 3.7 ± 0.27 0.42 ± 0.04 167.9 ± 1.7 6.45 ± 0.87 4.7 ± 0.7 386 ± 27.6 7.3 ± 1.5
C2 10.7 ± 1.2 1.4 ± 0.5 302 ± 39.2 1.83 ± 0.5 0.41 ± 0.03 113.1 ± 8.8 5.2 ± 0.95 3.5 ± 0.4 343 ± 29.5 0.5 ± 0.2
C3 17.1 ± 2.4 1.6 ± 0.4 388.6 ± 56.4 2.53 ± 1.05 0.42 ± 0.1 148.1 ± 5.7 6.7 ± 1.2 5.1 ± 1.3 471.7 ± 36.3 5.3 ± 2.2
C4
C5 10.4 ± 1.5
C6 3.4 ± 1.01 0.24 ± 0.04 225 ± 34.3 2.3 ± 0.6 0.16 ± 0.03 41.9 ± 14.2 1.1 ± 0.5 0.9 ± 0.3 679.3 ± 261.8
Figure 4 Maximum–minimum EF trace metal EFs in sediment CORE samples from study sites.
Figure 4

Maximum–minimum EF trace metal EFs in sediment CORE samples from study sites.

Table 6

EFs for all metals in sediment core samples from study sites (values are given as range [minimum–maximum])

Cores F.E (Mn) F.E (Zn) F.E (Ag) F.E (Pb) F.E (Fe) F.E (Cr) F.E (Ni) F.E (Cu) F.E (Cd) F.E (Co)
C5 0.87–2.35 0.88–5.16 0.97–2.83 0.75–4.34 0.84–1.54 0.96–5.64 0.97–1.73 0.69–3.38 0.66–2.52 0.81–1.55
C4 0.66–4.79 0.84–5.39 0.72–1.98 0.86–2.34 0.77–1.33 0.66–2.51 0.92–1.34 0.53–1.59 0.65–3.94 0.76–1.24
C1 0.84–2.40 0.86–4.96 0.87–5.19 0.87–3.65 0.82–2.22 0.78–2.03 0.78–2.02 0.83–2.57 0.82–2.31 0.78–1.95
C2 0.88–3.43 0.91–5.66 0.36–4.21 0.85–5.69 0.90–3.33 0.08–3.43 0.92–3.16 0.93–3.41 0.81–6.87 0.91–3.22
C3 0.95–3.26 0.95–5.71 0.91–7.30 0.79–4.97 0.98–3.18 0.97–3.72 0.95–3.04 0.90–4.03 0.93–3.37 0.92–3.02
C6 1.45–5.98 0.47–1.81 0.70–1.41 3.13–6.82 0.42–1.68 0.46–1.63 0.44–1.69 0.56–1.53 0.74–1.38 0.42–1.69
Cores F.E (Cs) F.E. (Ba) F.E. (Tl) F.E. (Bi) F.E. (U) F.E. (V) F.E. (As) F.E. (Sr) F.E. (Sn) F.E. (Sb)
C5 – – – – – – – – – –
C4 – – – – – – – – – –
C1 0.78–2.10 0.85–2.48 0.82–1.92 0.82–3.80 0.86–2.04 0.79–2.02 0.79–2.82 0.90–2.47 0.82–2.51 0.78–1.89
C2 0.57–3.84 0.79–3.66 0.89–3.65 0.68–2.06 0.71–6.94 0.93–3.36 0.87–4.14 0.94–4.04 0.91–5.48 0.85–7.26
C3 0.81–3.88 0.92–3.78 0.94–3.96 0.58–5.04 0.86–8.63 0.97–3.14 0.96–4.26 0.88–2.95 0.82–3.64 0.86–5.28
C6 1.14–7.12 2.64–5.77 2.55–6.65 — 0.7–1.76 0.48–1.67 0.55–1.63 0.39–1.35 0.44–1.7 0.72–1.52

Bold values indicate a significant enrichment factor.

The second group consists of the metallic elements Ag, Co, and Cr discovered in Lac Mellah sediment samples. Lac Mellah samples contain high concentrations of these metallic elements and have low enrichment. As a result, these trace metals help to define the lithology of the study area. The third group consists of sediment samples from the two study sites, the Fountain of Gazelles Dam and Algiers Bay. These areas have high average zinc concentrations and EFs as a result of human activity. This group also includes the trace metals U, Bi, Zn, Ba, As, Sb, Cs, and Tl, which are abundant in Alger Bay sediment samples and have EFs ranging from moderate to significant, particularly for uranium. Positive correlations have also been found between these metals, with the exception of uranium, which correlates only with Bi, possibly due to a different source of contamination for this element. Despite their presence in the four study sites’ sediments, the other traced metals (Mn, Fe, Pb, Cu, and Cd) do not distinguish these sediments or their behavior from the other elements.

In summary, this study examined the extent of sediment contamination by inorganic trace metals and their behavior in sediments as a function of elemental composition and origin, highlighting potential pollution risks. Certain trace metals, such as Zn, Pb, Cr, Ag, and Cd, are present at various sites with significant EFs indicating contamination of these environments. These trace metals are considered to be the most toxic [52]. Their anthropogenic origin results from untreated domestic wastewater and agricultural wastes and industrial processes such as metal plating, fertilizer manufacture, petrochemicals and drinking water production, fertilizer manufacture, petrochemicals, paper mills, and mining [53]. The presence of organic matter in the four study areas is directly linked to the introduction of organic matter from the wadis in the watershed. This organic matter can come from domestic and/or industrial wastewater discharges and agricultural activities [54] along with wadis and biomass present in the aquatic environment. Sediments exhibit high redox activity due to the presence of organic matter and various oxides, which facilitates the migration of trace metals into marine environments [55,56]. Redox conditions in sediments play a crucial role in controlling the distribution and enrichment of redox-sensitive elements [57]. PCA of chemical elements shows a strong association between chlorine and sodium, as well as notable positive correlations between potassium, nitrate, aluminum, and iron. The aforementioned elements are the main constituents of clay minerals, which can be used in the purification of trace metals in contaminated water. The study by Aneke and Adu [53] shows that the use of clays with the nano-slag at a percentage of 50:50 can remove up to 98% Zn, 95.45% Cu, 93.3% Fe, 97% Ni, and 89% Hg, thus contributing to environmental protection and public health. Finally, it should be mentioned that inadequate infrastructure, rapid urbanization, and inadequate sanitation systems are the main causes of this pollution [58], and to reduce the level of contaminants in aquatic environments, communities must get involved in safeguarding these water sources.

4 Conclusions

A global concern is the presence of trace metals in surface waters, due to their toxicity to aquatic ecosystems, persistence, and bioaccumulation potential. Wetlands in Algeria are particularly at risk of contamination. This study assesses trace metal contamination in the sediments of Chott Melghir, Fontaine des Gazelles dam, Lake Mellah, and Algiers Bay, to understand its extent, potential risks, and the behavior of trace metals in these ecosystems.

The results of the sediment survey of the sites studied reveal significant mineralogical diversity, with high concentrations of calcite, aragonite, and illite. The presence of organic matter and tracer metals in the sediments varies according to the geological origin of the sites. PCA of sediment samples from the six study sites revealed significant correlations between various chemical elements. These results shed light on sediment origin, transport processes, and potential sources of contamination. The elements associated with each study site indicate distinct geochemical characteristics, offering valuable insights into lithology and relevant environmental factors. However, despite their presence in all the samples analyzed, zinc and chromium are not linked to a chemical element, which could be explained by different sediment origins. Metals such as copper, nickel, cerium, arsenic, zinc, and barium characterize sediment samples from the Bay of Algiers, while sodium, chlorine, bromine, vanadium, and gallium are associated with sediment samples from Lake Mellah. Sediment samples from the Fontaine des Gazelles and Chott Melghir dams fall into a third group, comprising chemical elements such as magnesium, phosphorus, cadmium, strontium, and calcium.

The EFs for the trace metals studied vary overall between natural variability [0.72 < EF(Ag) < 1.98, 0.76 < EF(Co) < 1.24 and 0.66 < EF(Cr) < 2.51], indicating that these trace metals characterize the lithology of this study area, to significant above 5, which is due to anthropogenic inputs, and their behavior in sediments provides crucial information on the impact of human activities and natural processes in the areas studied. In conclusion, the importance of continuing environmental monitoring and research to gather as much information as possible on trace metal contamination levels and behaviors, and to protect these aquatic ecosystems, must be emphasized. Also, add sequential extraction methods for trace metals to determine the chemical forms of these metal contaminants.

Acknowledgements

This study is dedicated to our colleagues at CRNA (Centre de Recherche Nucléaire d’Alger), CRSTRA Biskra, ENSSMAL, and CEREGE for their help, respectively, during the sample analysis processing phase.

  1. Author contribution: The author confirms the sole responsibility for the conception of the study, presented results and manuscript preparation.

  2. Conflict of interest: The authors declare no competing interests.

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Received: 2024-05-29
Revised: 2024-07-22
Accepted: 2024-08-13
Published Online: 2024-10-10

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  105. Deeply buried clastic rock diagenesis evolution mechanism of Dongdaohaizi sag in the center of Junggar fault basin, Northwest China
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  119. Modification of dolomitization on reservoir spaces in reef–shoal complex: A case study of Permian Changxing Formation, Sichuan Basin, SW China
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  129. Influence of pore structure characteristics on the Permian Shan-1 reservoir in Longdong, Southwest Ordos Basin, China
  130. Study on sedimentary model of Shanxi Formation – Lower Shihezi Formation in Da 17 well area of Daniudi gas field, Ordos Basin
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  137. Erratum
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  139. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part I
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  141. Exploring environmental awareness, knowledge, and safety: A comparative study among students in Montenegro and North Macedonia
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  143. Application of remote sensing in monitoring land degradation: A case study of Stanari municipality (Bosnia and Herzegovina)
  144. Optimizing agricultural land use: A GIS-based assessment of suitability in the Sana River Basin, Bosnia and Herzegovina
  145. Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index
  146. Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin
  147. GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)
  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
  149. Geosite assessment as the first step for the development of canyoning activities in North Montenegro
  150. Urban geoheritage and degradation risk assessment of the Sokograd fortress (Sokobanja, Eastern Serbia)
  151. Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia
  152. Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities
  153. Forest soil CO2 emission in Quercus robur level II monitoring site
  154. Characterization of glomalin proteins in soil: A potential indicator of erosion intensity
  155. Power of Terroir: Case study of Grašac at the Fruška Gora wine region (North Serbia)
  156. Special Issue: Geospatial and Environmental Dynamics - Part I
  157. Qualitative insights into cultural heritage protection in Serbia: Addressing legal and institutional gaps for disaster risk resilience
https://doi.org/10.1515/geo-2022-0689
Keywords for this article
sediment; EF; chemical and mineralogique compositions; principal component analysis; trace metals
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  110. Extreme hydroclimatic events and response of vegetation in the eastern QTP since 10 ka
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  122. Enhancing the total-field magnetic anomaly using the normalized source strength
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  128. Characteristics and control techniques of soft rock tunnel lining cracks in high geo-stress environments: Case study of Wushaoling tunnel group
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  130. Study on sedimentary model of Shanxi Formation – Lower Shihezi Formation in Da 17 well area of Daniudi gas field, Ordos Basin
  131. Multi-scenario territorial spatial simulation and dynamic changes: A case study of Jilin Province in China from 1985 to 2030
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  134. Prediction and assessment of meteorological drought in southwest China using long short-term memory model
  135. Communication
  136. Essential questions in earth and geosciences according to large language models
  137. Erratum
  138. Erratum to “Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan”
  139. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part I
  140. Spatial-temporal and trend analysis of traffic accidents in AP Vojvodina (North Serbia)
  141. Exploring environmental awareness, knowledge, and safety: A comparative study among students in Montenegro and North Macedonia
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  143. Application of remote sensing in monitoring land degradation: A case study of Stanari municipality (Bosnia and Herzegovina)
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  145. Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index
  146. Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin
  147. GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)
  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
  149. Geosite assessment as the first step for the development of canyoning activities in North Montenegro
  150. Urban geoheritage and degradation risk assessment of the Sokograd fortress (Sokobanja, Eastern Serbia)
  151. Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia
  152. Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities
  153. Forest soil CO2 emission in Quercus robur level II monitoring site
  154. Characterization of glomalin proteins in soil: A potential indicator of erosion intensity
  155. Power of Terroir: Case study of Grašac at the Fruška Gora wine region (North Serbia)
  156. Special Issue: Geospatial and Environmental Dynamics - Part I
  157. Qualitative insights into cultural heritage protection in Serbia: Addressing legal and institutional gaps for disaster risk resilience
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