1 Introduction
Pollution of water by inorganic pollutants such as heavy metals is considered as a global problem. Due to the no degradability of heavy metals, their discharge into water resources creates severe environmental hazards (Zeraatkar et al., 2016). The natural cycling rates of many metals are being results from anthropogenic activities, especially that released from industrial, domestic and urban effluents (Chary et al., 2008). Heavy metal evaluation in water can be carried out by using signal particular conditions such as algae, (Al-
Homaidan et al., 2011; Topcuoğlu et al., 2003;
Khaled et al., 2014) and molluscs (El-Said 2013) in which pollutants accumulate proportionally to their environmental concentration. Among those heavy metals, Cd and Pb are potentially toxic even at very low levels. Whereas, Cu has irreplaceable vital properties with different threshold levels in different types of plants, organisms and human. In this context, the appraisal of heavy metal concentrations in marine organisms like algae comprises an important area of interest.
(Singh and Kumar 2017)
Evaluation and potential health effect of heavy metals in surface water and seaweeds in the Egyptian Mediterranean Sea coast of Alexandria
Dalia M. S. A. Salem 1, Manal M. El Sadaawy2,, Amany El-Sikaily2
1 Marine Chemistry Laboratory, Marine Environment Division, National Institute of Oceanography and Fisheries, Kayet Bay, El-Anfoushy, Alexandria, Egypt
2 Marine Pollution Laboratory, Marine Environment Division, National Institute of Oceanography and Fisheries, Kayet Bay, El-Anfoushy, Alexandria, Egypt
Abstract. this study aimed to assess the concentrations and the bioaccumulation of heavy metals (cadmium, chromium, copper, manganese, nickel, lead, zinc, iron and cobalt) by surface water and seaweeds from the Egyptian Mediterranean Sea coast of Alexandria and their risk to human health.
Bioaccumulation factor calculations indicated that Cd, Mn, and Cr had high seaweed uptake. Human health risk assessment of studied heavy metals for seawater and seaweeds was conducted using hazard index (HI) and hazard quotient analyses (HQ). The results were below 1, accordingly, these seaweed species were of high quality and might be used in the field of nutrition.
Keywords: Metals; surface seawater; seaweeds; bioaccumulation factor; multivariate analysis; human health risk assessment.
Macroalgae are capable of accumulating trace metals
thousands of times higher than their corresponding concentrations in seawater.
Algae converge only free metal ions, depending on the nature of suspended particulate, by forming both organic and inorganic complexes (Bastami et al., 2015).
Moreover, algae satisfy all of the basic requirements of bioindicators: they are sedentary, easy to identify and collect, widely distributed, and able to accumulate metals to a satisfactory degree, in addition to their suitable dimensions (Karthick et al., 2012; Tamayo et al., 2014)
Due to their ability to produce many secondary metabolites having a large spectrum of interesting biological activities, including antifungal, antioxidant, antiviral as well as antibacterial properties, marine seaweeds have been considered as potential sources of bioactive compounds (El-Said 2013). Several methods have been performed for the assessment of the potential human health risks from the exposure to hazardous substances. To identify the exposure and tendency of heavy metals in the surface water with reference to human body, risk assessment method has been successfully applied (Liang et al., 2017).
Therefore, this study will be carried out to assess the suitability of various aquatic seaweeds, particularly those associated with some hot spots along Alexandria coast, as biomonitors of trace metals. The study will be focused on multivariate analysis and bioconcentration factor calculations. Furthermore, the adverse effects on human health of hazardous contaminants in seaweeds and surface water samples from the Egyptian Mediterranean Sea coast will be assessed.
Area of study
Water and algal species samples were collected from six locations (Abu Qir, Sheraton, Stanly, El-Shatby, Eastern Harbour and Agamy) along the Egyptian Mediterranean Sea coast of Alexandria, during May 2016 (Fig. 1). These locations were selected to cover three polluted hot spots of Alexandria (Abu Qir, Sheraton, and Eastern Harbour), which are characterized by industrial and/or human activities. Whereas, the other three locations, Stanlly, El-Shatby and Agamy were selected as unpolluted regions.
Figure 1: Sampling stations along the Alexandria Mediterranean sea coast.
Abu Qir Bay is semicircular basin, lies approximately 25 km east of Alexandria, along the Egyptian Mediterranean coast. It is bordered from the west by Abu Qir headland and Rosetta headlands in its eastern side.
Many seaweed species are found along this bay due to the small and fine holes existed in the rocks of this bay, which afford excellent domains for seaweed attachment. Huge amounts of industrial, sewage, agricultural wastes reach the bay from El Tapia pumping station. Furthermore, this bay is affected by brackish water coming through El Maadya which connects to Abu Qir Bay and Lake Edku. On the other hand, Eastern Harbor is
one of the main fishing and hatching harbors of Alexandria. It is a semicircular basin with an area of 2.53 km2 and an average depth of 6 m. It is mainly influenced by several kinds of human activities including fishing, yacht sport, land-based effluents, boat building workshops, recreation and sailing boats anchoring inside the Harbor. The Harbor is suffering from human activities including industrial, domestic and agricultural waste discharges.
Materials and methods Samples collection
Surface water samples were collected by Niskin bottles from the coastal area of Abu- Qir Bay to Agamy (Fig. 1). The surface water temperature was measured at the field by using a pocket thermometer. The pH of water samples was measured using a digital portable Jenway 3505 pH meter. Water salinity was measured by an induction salinometer (Beckman model RS-10). Immediately after collection of water samples, total alkalinity (TA) was determined by volumetric titration versus standard 0.01 N hydrochloric acid using methyl orange as indicator (APHA, 1999).
Oxidizable organic matter (OOM) was determined according to the method described by FAO (1976). Dissolved oxygen (DO) was measured using Winkler's method with standard iodimetric titration (Strickland and Parsons 1972). Ammonium ion concentrations were determined spectrophotometrically using the indophenol blue technique (IOC, 1983).
Chlorophyll-a (Chl-a) was measured in 3L seawater samples, which were filtered by using 0.45 µm membrane filters, then extracted using 90% acetone and finally measured spectrophotometrically according to Strickland and Parsons (1972).
Seaweed species of different classes including, (Ulva linza and Ulva fasciata;
Division Chlorophyceae), (Colpomenia sinuosa and Sargassum vulgare; Division Phaeophyceae) and (Amphiroa rigida, Corollina officinalis, Pterocladia capillacea
and Jania rubens; Division Rhodophyceae) were collected along Alexandria coast by handpicked from the subtidal zone. Samples were carefully selected in such a way to ensure that all species were at a similar stage of development. Before transferring to the laboratory, samples were washed with seawater at the sampling site and then packed in pre-cleaned polyethylene bags under refrigerated conditions. Upon arrival at the laboratory, samples were thoroughly cleaned under tap water for a few seconds and any sediment was carefully removed with nylon brushes and then samples were identified by species (Riedel, 1970; Aleem, 1993). To minimize any possible metal loss during the procedure,algal species were rapidly rinsed in deionized water (Milli-Q, Millipore Corp).
The cleaned algal species were air dried then kept in oven overnight at 30 °C and grounded to pass a 20 mesh-screen. Finally, the grounded materials were stored in plastic bags.
Chemical analyses Water analysis
Heavy metals in water
Determination of heavy metals (Zn, Cu, Fe, Cd, Ni, Co, Cr, Pb, Mn) in water samples were carried out after the pre-concentration from seawater by using chelex-100 cation- exchange resins according to Riley and Taylor (1968). Atomic absorption spectrophotometer (AAS)/flame mode (Shimadzu AA-6800) was used for measuring the metal content.
Heavy metals in algae
The studied heavy metals were quantified through the digestion of 0.1 g of dried sample in a solution containing a mixture of HNO3, HClO4 and HF acids (3:2:1) for 3 h. The digested samples were heated until most of the acids had evaporated to a small volume. Then diluted to 25 ml with deionized water into a polytetrafluoroethylene flask and finally, filtered into an acid-clean PVC bottle. After this procedure, the concentration of each metal (Zn, Cu, Fe, Cd, Ni, Co, Cr, Pb and Mn) was
measured by using atomic absorption spectrophotometer (AAS)/flame mode, Shimadzu AA-6800). To achieve the accuracy and precision of the analytical method, the certificated reference material NMIJ CRM 7405-a: trace elements in seaweed (Hijiki) produced by the National Metrology Institute of Japan (NMIJ) were carried out in each batch. For almost all of the investigated metals, the obtained results of analyzing the reference materials fell within the range of the certificated value. A good agreement with the reference and analytical values of the reference materials has been obtained with recovery rates of around 95.3–106.9%.
Calculations
To compare the total heavy metals content in different classes of seaweeds in the study area, the metal pollution index (MPI) was used (Usero et al., 2005).
(1) Where Cf n is the concentration of the metal n in the seaweed
Bioaccumulation Factor (BAF)
The BAF of the heavy metals in the seaweed samples were obtained by using the following formula (Saeed and Moustafa.
2013):
(2)
Where BAF refers to the bioaccumulation factor, Corg and CW are the concentration of a heavy metal in the seaweed and water, respectively.
Human health risk assessment:
For Seawater
This method was applied to identify the exposure and tendency of heavy metals seawater with reference to human body. There are two central ways of metals exposure and pathways in human body, either through water consumption (ingestion) or through skin (dermal absorption). The value of ADD Ingestion
and ADDDermal were calculated using the following equation (USEPA, 1989)
(3) (4) where ADDing and ADDderm are the average daily dose (µg/kg day) by ingestion and dermal absorption, respectively. Cw is the mean metals concentration in seawater (µg/L), IR is the rate of ingestion (1.8 and 2.2 L/day for children and adults, respectively), EF is the frequency of the exposure (350 days/year), ED is the duration of the exposure (6 and 70 years for children and adults, respectively), BW is the average body weight which is 15 and 70 kg for children and adults, respectively. AT is the averaging time (2190 days for children and 25550 days for adults), SA is the exposed skin area (cm2, 18000 for adults and 6600 for children), ET is the exposure time (hours/day, 0.58 for adults and 1 for children), CF is the unit conversion factor (L/cm3, 0.001), and KP
is the dermal permeability coefficient (cm/h) (USEPA, 1989; USEPA, 2004; Giri and Singh 2014).
Risk evaluation related to the noncarcinogenic risks was quantified by computing hazard quotient (HQ). Significant noncarcinogenic risk is associated with HQ >
1. The HQ is a ratio of average daily dose of heavy metals from exposure ways (ingestion/
dermal) to the related reference dose (RfD) which was calculated using the following equation:
(5) Hazard index (HI) was computed to determine the total possible noncarcinogenic risks posed by multipath ways. The HI was computed by adding the HQs from all probable pathways as below:
(6) Where HI ing/derm is the hazard index via ingestion or dermal contact (unitless). HI > 1
indicated a potential for an adverse effect on human health or the necessity for further study (USEPA, 2004)
For Seaweeds
Ingestion of seaweeds is calculated by applying the following equation (Albering et al., 1999):
(7) Where CF represents the heavy metal concentration in seaweed (mg/Kg fresh weight (fw); IRF is the rate of the ingestion (kg fw / day (EPA, 1998)), [0.029 and 0.010 kg fw /day for adult and child, respectively]; FI represents the fraction heavy metal (unitless) [0.5 for both child and adult]; AF is the absorption factor (unitless) [1 for both adult and child];
and BW is the body weight (in kg) [70 and 7 kg for an adult and a child, respectively].
Estimated daily intake (EDI) for noncancer health effects
The estimated daily intake for the studied heavy metals (Co, Mn, Zn, Cd, Cu, Cr, Ni, Fe, and Pb) in their noncancerous health contents was calculated using the following equation (Health Consultation, Land Crab Evaluation, National Oceanographic Atmospheric Administration Data 2006; Herbicide Risk Assessment for the Aquatic Plant Management Final Supplemental Environmental Impact Statement 2001):
(8) where C is the average concentration of the heavy metal (mg/kg) ; IR represents the rate of ingestion [0.1135 kg/ day (4-oz meal) for child and 0.227 kg/day (8-oz meal) for adult]; EF is the frequency of the exposure, or number of exposure events per year (365 days/year); ED is the duration of the exposure, or the duration over which exposure occurs [6 years for child and 70 years for adult]; BW is the body weight (7 for child/toddler and 70 kg for adult); AT represents the averaging time, or the period over which cumulative exposures are averaged
(noncancer/lifetime = ED×365 days/year) (USEPA (1989).
Hazard quotient (HQ)
The comparison between the estimated daily intake of the heavy metal and its RfD, yielding an hazard quotient (HQ), will be used for assessing the potential for adverse effects resulting from the exposure to noncarcinogens as follows (Port Angeles Harbor Sediment Characterization Study Port Angeles 2008):
(9)
Where, HQ is the hazard quotient (unitless);
EDI represents the estimated daily intake (mg/kg/day); RfD refers to the reference dose (mg/kg/day). However, a HQ of any heavy metal is utilized as a reference point, and can be used to assess the acceptable exposure.
When HQ is less than or equal to 1.0, the potential exposure is considered to be acceptable or “safe” (Canada North Environmental Services Limited Partnership, 2007). On the other hand, the exposure exceeds the acceptable exposure limit when the HQ is higher than 1.0.
Total hazard quotient (THQ)
(10) The higher the value of THQ, the greater the level of concern. When THQ is higher than 1, further level of investigation is suggested to be undertaken due to the possibility of a potential for adverse human health effects (Giri and Singh 2015; Singh and Kumar 2017).
Statistical analyses
Spearman’s correlation coefficient, multiple stepwise linear regression, and cluster analyses were used to test the relationships among seawater samples and the different seaweed contaminants in the three classes of seaweeds (red, brown and green). Log- transformed data was used to analyze all of the multivariate (Leung et al., 2005; Hill and Lewicki 2006). Spearman’s correlation coefficient (r) with a significance level of P ≤
.05 was used to determine the existence of a multivariate relationship. A linear regression model with a multiple stepwise and smaller mean square of residuals in the analysis of variance output of the multiple regression analysis was chosen. To build Eq (11), heavy metals of seaweeds classes and seawater were used (significance of P ≤ .05 and multiple regression coefficients [R]):
B + C + ….. (11) Where y is the heavy metals concentrations in seaweeds or seawater samples; βo is constant; β1, β2, β3 are regression coefficients; and A, B, and C are selected contaminants, with multiple regression coefficient (R). STATISTICA 99 edition was used to analyze the Spearman’s correlation and regression analysis (Hill and Lewicki 2006).
Results and discussion Seawater characteristics
Physic-chemical parameters of seawater of the selected area are shown in (Table 1).
The average pH values along the investigated area were found to be between 8.03 and 8.48.
Seawater salinity showed approximately similar values (average salinity = 36.205 ± 1.17 psu) along the studied areas. Seawater temperature varied from 22.0 to 26.5°C with an average of 24.5 ± 1.84°C. The distribution pattern of DO at the studied area fluctuated between 7.874 and 3.405 mg/L, at Abu-Qir and Eastern Harbor, respectively.
Heavy metals are the most important pollutants that can cause serious problems to human health by entering the food chains (Abdel-Satar et al; 2017). The heavy metals concentrations in seawater samples from the six stations of the coastal area are summarized in Table 1. Metals concentrations in water were found to be in the following order: Fe >
Cu > Pb > Zn > Ni > Mn > Cr ≈ Co > Cd. Iron is the most abundant metal found in the samples with an average value of 342.59 ±
180.23 µg/L. High concentration of Pb (6.39
± 4.85 µg/L), which was observed in seawater, may be attributed to the deposition of the air born particulate matter as well as the spill from fishing boat used for transportation (Olusola and Festus 2015). Compared to the guideline values recommended by (FME 2001; USEPA 2002). The mean concentrations of Cu, Zn Ni, Cr, Cd, Pb were found to be higher than the permissible recommended limits for both agencies (Table 1).
Table 1: Quality of seawater of the selected stations during May 2016
Quality of seaweeds
Table 2 illustrates the average concentrations of determined elements in red, green and brown seaweed species. Spatial variations in the metal contents of seaweeds and variations in their accumulation are related to change in growth and metabolic rates, which are influenced by the interactions among metal ions resulting in competition for binding sites in seaweed, growth rates, morphology and life span of the species, and local and seasonal environmental conditions (Ryan et al., 2012; Chakraborty and Owens 2014). As can be seen from the obtained results, Fe, Zn, Mn and Pb were the dominant metals in all species. The maximum amount of Fe was detected in brown seaweeds (1428.671 µg/g), while the lowest value was found in green seaweeds (266.529 µg/g). The consistently high levels of Fe encountered in seaweed can be attributed to several factors including; the contamination levels from industrial and other operations (Eisler 1981), the ability of most algal species to bioaccumulate Fe from the surrounding environment, and the established need of Fe for normal growth of marine plants (Goldberg 1952). Some species are able to absorb elements directly from sediment through thallus and rhizoids, in which metal concentrations are much higher than in the water column (Zbikowski et al., 2006).
Meanwhile, Zinc is an essential heavy metal;
however, it affects numerous metabolic or developmental processes in all living organisms (Přibyl et al., 2008; El-Said 2013).
Zinc concentrations in algae were found to be decreased in the order of Rhodophyceae (35.352 µg/g) > Phaeophyceae (34.312 µg/g)
> Chlorophyceae (30.481 µg/g). Copper contents of the tested seaweeds were found to be in the range from 10.91 ± 10.76 µg/g to 3.89 ± 2.16 µg/g. The maximum Cu value was
recorded in the Phaeophyceae (Sargassum vulgare) at Abu-Qir station, while the lowest level was detected in Phaeophyceae (Colpomenia sinuosa ) at Agamy station. The presence of Cu in high concentrations causes great danger to all marine organisms, including fish crustacean, phyto- and zeo- plankton, algae and filter feeders organisms.
Industrials effluents from the extensive oil production, high traffic shipping could act as source of Cu pollution. On the other hand, effluents from industrial and agricultural wastes may be entering the seawater and may become a source of Cu contamination. Also, fish boats painted with Cu antifouling coatings could be another source of Cu pollution in areas of study (Dadolahi-Sohrab et al., 2011).
According to Giusti (2001)and Caliceti et al., (2002), copper contamination is associated with algal levels above 20.0 μg/g dw.
Similarly, Sawidis et al., (2001) recorded that a range of 20 to 70 μg/g dw in green macroalgae as a characteristic of contaminated sites. So, the obtained results for these selected areas indicated that the concentrations of copper are below the contamination level.
Table 2. Distribution of the different studied heavy metals in the seaweed species.
Nickel levels in seaweed species followed the following pattern: Red algae > Brown algae > Green algae. In this study, the seaweeds samples collected were attached to sand and rocks distributed along the coast. The maximum value of lead (Pb) in the tested seaweeds was recorded in the green algae (37.09 ± 6.64 µg/g) while the minimum was found in the red algae (30.94± 12.69 µg/g).
The relatively high levels of some heavy metals in the algae reflect their high concentrations in the seawater of studied areas as well as the capacity of the algae to uptake them (Khairy and El-Sheikh 2015) . On the same manner, Chromium content in the studied seaweeds varied from 4.3 µg/g in green algae to 30.64 µg/g in brown algae. On the other hand, the highest cadmium concentration (2.57 µg/g) corresponded to brown algae, while the lowest (1.46 µg/g) was recorded in green algae. Among all the studied species of seaweeds, the red seaweeds recorded the highest content of Mn (69.26 µg/g), while the green one recorded the lowest content (29.68 µg/g). The red seaweeds recorded the highest content of Cobalt (15.42 µg/g) compared to green seaweeds (9.61 µg/g) (Table 2). Generally, the content of heavy metals in seaweeds depends on several environmental parameters such as;
concentrations of elements in water (Andrade et al., 2004), interactions between elements, pH, salinity, light intensity in addition to the dilution of element contents due to seaweed growth (Zbikowski et al. 2006; Akcali and Kucuksezgin 2011; El-Said and El-Sikaily, 2013)
Bioaccumulation factor (BAF)
BAF is a useful parameter to evaluate the potential of seaweeds for accumulating metals.
Among all the studied metals, Cd and Mn showed the highest BAF values in red, brown and green seaweeds (Fig. 2). This indicates
that these seaweeds have the bioavailability to accumulate metals from the surrounding medium and can be used as a good bioindicator for the presence of essential and highly toxic metals. Accordingly, these bioaccumulation values suggested that seaweed should be a practical indicator of risk of contaminants’ exposures.
Figure 2: Average BAF values of the studied heavy metals in different classes of seaweeds.
Metal pollution index (MPI)
Among the numerous studied classes of seaweeds, the MPI were found to be highest for Rhodophyceae and lowest for Chlorophyta, (Fig. 3). Besada et al., 2009, concluded that the relationship between heavy metals and algae is based on their color (Chlorophyceae, green algae; Phaeophyceae, brown algae and Rhodophyceae, red algae) and their ability to uptake of heavy metals depends on the cell wall polysaccharides (Karthick et al., 2012;
Abbas et al., 2014).
Figure 3: Metal pollution indexes in different classes of seaweed.
Human Health Risk Assessment For Seawater
The HI for both adult and child were evaluated for the studied heavy metals (Fig. 4).
The HI values for both adult and child were less than unity. Therefore, no adverse health effects would be expected from both ingestion and dermal of surface water. Generally, the hazard index value of all determined elements for child (5.1E-02) are higher than those calculated for adult (1.3E-02). Among all the studied heavy metals, lead showed the highest hazard index for both adult and child (5.1E-02 and 1.9E-01, respectively).
Figure 4: Hazard index for : a) Zn; b) Cu;
c) Fe; d) Ni; e) Cr; f) Pb; g) Co; h) Mn; and i) Cd in water (mg/kg/day).
For Seaweeds
The ingestion of seaweeds, estimated daily intake, hazard quotient (HQ) and ƩHQs are listed in Table 3. Human health risk assessment of all the studied heavy metals in seaweed samples showed that ƩHQs are less than unity. So, no adverse health effects would be expected from the ingestion of these seaweeds. Among all the studied seaweeds, brown species showed the highest ƩHQs
values for both child and adult (2.04E-01 and 8.63E-02 mg/kg/day, respectively)
Table 3: The calculated ingestion, estimated daily intake, hazard quotients and ƩHQ values of heavy metals in the studied seaweeds for child and adult.
Seaweeds Metals Ingestion of seaweeds Estimated daily intake HQ
Child Adult Child Adult Child Adult
Green
Seaweeds Zn 2.18E-02 6.31E-03 2.16E-01 9.88E-02 7.21E-04 3.29E-04
Cu 2.78E-03 8.05E-04 2.76E-02 1.26E-02 6.89E-04 3.15E-04
Fe 1.90E-01 5.52E-02 1.89E+00 8.64E-01 6.30E-03 2.88E-03
Cd 7.74E-03 1.99E-03 1.03E-02 4.72E-03 1.03E-02 4.72E-03
Ni 1.34E-02 3.89E-03 1.33E-01 6.10E-02 6.67E-03 3.05E-03
Co 7.74E-03 1.99E-03 6.82E-02 3.12E-02 6.82E-03 3.12E-03
Cr 3.07E-03 8.91E-04 3.05E-02 1.40E-02 1.02E-02 4.65E-03
Pb 2.65E-02 7.68E-03 2.63E-01 1.20E-01 6.58E-02 3.01E-02
Mn 2.12E-02 6.15E-03 2.11E-01 9.63E-02 6.38E-03 2.92E-03
ƩHQ 1.14E-01 5.21E-02
Brown seaweeds
Zn 2.45E-02 7.11E-03 2.43E-01 1.11E-01 8.11E-04 3.71E-04
Cu 7.79E-03 2.26E-03 7.74E-02 3.54E-02 1.94E-03 8.85E-04
Fe 1.02E+00 2.96E-01 1.01E+01 4.63E+00 1.54E-02 5.15E-05
Cd 1.84E-03 5.33E-04 1.83E-02 8.35E-03 1.83E-02 8.35E-03
Ni 1.84E-02 5.35E-03 1.83E-01 8.37E-02 9.16E-03 4.19E-03
Co 1.02E-02 2.96E-03 1.01E-01 4.63E-02 1.01E-02 4.63E-03
Cr 2.19E-02 6.35E-03 2.17E-01 9.94E-02 7.24E-02 3.31E-02
Pb 2.57E-02 7.47E-03 2.56E-01 1.17E-01 6.39E-02 2.92E-02
Mn 4.01E-02 1.16E-02 3.98E-01 1.82E-01 1.21E-02 5.52E-03
ƩHQ 2.04E-01 8.63E-02
Red seaweeds
Zn 2.53E-02 7.32E-03 2.51E-01 1.15E-01 8.36E-04 3.82E-04
Cu 5.09E-03 1.48E-03 5.06E-02 2.31E-02 1.26E-03 5.78E-04
Fe 4.78E-01 1.39E-01 4.75E+00 2.17E+00 1.58E-02 7.23E-03
Cd 1.40E-03 4.05E-04 1.39E-02 6.34E-03 1.39E-02 6.34E-03
Ni 1.87E-02 5.41E-03 1.85E-01 8.47E-02 9.26E-03 4.23E-03
Co 1.10E-02 3.19E-03 1.09E-01 5.00E-02 1.09E-02 5.00E-03
Cr 1.67E-02 4.86E-03 1.66E-01 7.60E-02 5.54E-02 2.53E-02
Pb 2.21E-02 6.41E-03 2.19E-01 1.00E-01 5.49E-02 2.51E-02
Mn 4.95E-02 1.43E-02 4.91E-01 2.25E-01 1.49E-02 6.81E-03
ƩHQ 1.77E-01 8.10E-02
Multivariate analysis
The Pearson correlation matrix and multiple regression equations were used to test the relationships among the different studied heavy metals in the three classes of seaweeds (red, brown and green) and surface water samples. The presence of positive or negative relationships between some metals in Chlorophyta, Phaeophyta and Rhodophyta species reflected the synergistic or antagonistic interactions of ions in binding with the anionic sites offered by the plants (Karez et al., 1994;
Singh et al., 2015). Fe and Ni showed strong positive correlations (r = 0.929). Similarly, Cu and Cr (r = 0.907), Fe and Co (r = 0.979), Fe and Cr (r = 0.928), Ni and Co (r = 0.939), Co and Mn (r = 0.908), Cr and Mn (r = 0.904), and finally between Co and Cr (r = 0.954).
Whereas, moderate positive correlation was observed between Cu and Co (r = 0.888), Cu and Mn (0.891), Fe and Mn (r = 0.864), Ni and Mn (r = 0.819), as well as between Cu and Ni (r = 0.797). However, the strong significant negative correlation found between some studied metals is clearly noted, evidencing the antagonistic effect, where in some instances the accumulation of one metal did not favor the accumulation of another. Co showed
negative correlation with Pb (r = -0.725), Fe and Pb (r = -0.795) and Pb and Mn (r = - 0.799). The complexity of the processes can be indicated by the observed interactions between trace metals within plants, being at times both synergistic and antagonistic in nature, and occasional involvement in the metabolism of more than two elements. The antagonistic metals are mostly linked to the enzymatic pathway and to the processes of absorption by plants (Kitagishi and Obata 1981).
The multiple stepwise linear regressions of all the studied heavy metals in both seawater and seaweeds show strong relations among these contaminants of high R values (0.99).
There is a strong relation between Fe and Zn;
however, they are essential metals for many
physiological processes (Stewart 1974). Also, from Table 4 there is a positive correlation between (Zn, Pb, Cr, Co and Cd) in surface water, which may be due to having similar sources, anthropogenic activities, industrial effluents and domestic sewage.
Table 4: Multiple regression analyses of heavy metals for both surface seawater and seaweeds (A for seaweeds and W for seawater) and multiple regression coefficient (R)
Conclusion
The Egyptian Mediterranean sea coast of Alexandria is exposed to intensive pollution as it is subjected to industrial, agricultural, sewage and drainage waste. It is therefore advisable to study the bio-accumulation of pollutants along the studied coast and evaluate their effects on human health by using marine organisms as bioindicators. Red, brown and green seaweeds have the highest potency to be used as bioindicator for metal pollution in the marine environment due to their fundamental prerequisites properties. The bioaccumulation factor (BAF) calculations for seaweeds indicated that Cd and Mn have higher BAF for all the studied seaweeds. Human health risk assessment for both surface water and seaweeds was identified using hazard quotient, the hazard index of all the calculated heavy metals was below 1 for the seaweeds, indicating that the studied area did not pose any adverse effects on human health.
Generally, this information is crucial for the development of policies relating to the use and disposal of hazardous substances in aquatic environments. A sufficient treatment for wastewater is highly recommended to be carried out before fluxing into the coastal areas. Furthermore, seasonal and long-term studies are required in the future to monitor and update the status of different chemical contaminants.
Acknowledgment
The authors are grateful to Dr. Mona M.
Ismail (Taxonomy and biodiversity of aquatic biota Lab., National Institute of Oceanography and Fisheries, Alexandria branch) for her efforts in systematic identification of the collected seaweed species.
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ل يج ل لا كا لاو تا لا قت تا لفل
لا ها لل ةل ق لا ة لا با عﻷاو ة
نا نﻹا ة ص ىلع اه ا مو ةر سﻹا لا س لا لا لحاس م
2
يل ق لا ينامأو
2وا ع لا لا م ،
1لاس ا لاد
،كا سﻷا يا مو تا لا م لعل ي لا هع لا
،ة لا ة لا ق ،ة لا ءا لا ل عم
1. م ةر سﻹا
،كا سﻷا يا مو تا لا م لعل ي لا هع لا ،ة لا ة لا ق ، لا ث ل لا ل عم
2. م ،ةر سﻹا
ل م ل يج ل لا كا لاو تا لا قت ىلإ ةسار لا ه ه ف ه .
تا لفل ا لاو مو لاو م مدا لا) ةل ق لا س
لا لحاس م ة لا با عﻷاو ة لا ها لل ( لا لاو ي لاو ن لاو صاص لاو ل لاو غ لاو ا مو ةر سﻹا لا س لا ىلإ لا كا لا لماع تا ا ح تراشأ .ة لا .نا نﻹا ىلع اه
ة نا نﻹا ة لا ا لا قت ءا جإ ت .ة لا با عﻷا ة سا ب مو لاو غ لاو م مدا لل يلاع صا ما ا لا ش م ما سا ة لا با عﻷاو لا ها ل ةسور لا ةل ق لا تا لفلل (HI)
تﻼ ل تو ا لا لصاح
(HQ).
م لقأ جئا لا ناك 1
و ة لاع ةد ج تاذ ة لا با عﻷا م عا نﻷا ه ه نا يلا لا و ،
ة غ لا لا م يف اهما سا .
