Heavy metal contamination in surface water and sediment of the Meghna River, Bangladesh

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Heavy metal contamination in surface water and sediment of the Meghna River, Bangladesh

MARK

⁎

Md. Simul Bhuyana, , Muhammad Abu Bakarb, Aysha Akhtara, M. Belal Hossainc, Mir Mohammad Alid, Md. Shafiqul Islama a

Institute of Marine Sciences and Fisheries, University of Chittagong, Chittagong, Bangladesh Bangladesh Council of Scientific and Industrial Research, Chittagong, Bangladesh c Noakhali Science and Technology University, Noakhali, Bangladesh d WorldFish, Bangladesh b

A R T I C L E I N F O

A B S T R A C T

Keywords: Heavy metal Contamination Water Sediment Meghna River

Globally alarming ten heavy metal (Zn, Al, Cd, Pb, Cu, Ni, Fe, Mn, Cr, Co) concentrations were estimated in surface water and sediment of the Meghna River in Bangladesh from September 2015 to March 2016. Heavy metals were analyzed by Atomic Absorption Spectrophotometer (AAS). Results indicated that, all the metals in water were found below the safe limit of drinking water standard of WHO (1993) and EU (1998) with the exclusion of Fe, Ni and Al. In sediment, all the trace metals were recorded below the limit compared to other scientific results. For three seasons at two sampling points no significant difference in heavy metals was founded at the significance level (p > 0.05). Multivariate statistical analyses such as principal component analysis and correlation matrix disclosed prevalent anthropogenic interferences of Zn, Al, Cd, Pb, Cu, Ni, Fe, Mn, Cr, Co in water and sediment. The very strong positive correlation was recorded between Fe vs Al (0.992), Mn v’s Cu (0.948), Fe vs Mn (0.939), Zn vs Al (0.929), Fe vs Zn (0.920) in water. In sediment, very strong linear relationships were found in Cd vs Zn (0.999), Cd vs Cu (0.998), Zn vs Cu (0.996), Cd vs Ni (0.995), Ni vs Cu (0.994), Ni vs Zn (0.993) etc. at the 0.05 significance level which direct their common origin exclusively from industrial effluents, municipal wastes and agricultural inputs. Necessary steps should be taken to protect this River from pollution and also to reduce the environmental risk.

1. Introduction The huge amount of toxic heavy metals is discharged by anthropogenic activities (Gao et al., 2009; Nduka and Orisakwe, 2011; Kassim et al., 2011) as well as by natural actions that contribute metal contamination in aquatic environments (Tarra-Wahlberg et al., 2001; Akif et al., 2002; Jordao et al., 2002; Wilson and Pyatt 2007; Khan et al., 2008; Sekabira et al., 2010; Zhang et al., 2011; Bai et al., 2011; Grigoratos et al., 2014; Martin et al., 2015). The concentrations of heavy metals are extremely high in sediment than the water column (Sultan and Shazili, 2009) because of metals tend to amass in bottom deposits (Namminga and Wilhm, 1976; Nobi et al., 2010; He et al., 2009; Rezayi et al., 2011). Heavy metals are persistent, toxic and bioaccumulative and some of the metals are carcinogenic. Metals such as Cd, Pb can bioaccumulate and biomagnify in seafood (mussels, oysters, shrimps, fish) and can be transferred to humans via the food chain pathways (Camusso et al., 1995; Zhou, 1995; Sun et al., 2001; Papagiannis et al., 2004; Zhou et al., 2004; Sankar et al., 2006; Pekey, ⁎

Corresponding author.

E-mail address: simulbhuyan@gmail.com (Md. S. Bhuyan).

http://dx.doi.org/10.1016/j.enmm.2017.10.003 Received 1 August 2016; Received in revised form 17 April 2017; Accepted 2 October 2017 2215-1532/ © 2017 Elsevier B.V. All rights reserved.

2006; Sharma et al., 2007; He et al., 2009; Nobi et al., 2010; Yi et al., 2011; Vieira et al., 2011; Forti et al., 2011; Banerjee et al., 2011; Alhashemi et al., 2012; Pan and Wang, 2012; Rahman et al., 2013; Fang et al., 2014; Islam et al., 2015a; Ahmed et al., 2015a,b). In the present time bioaccumulation and toxicity of heavy metal pollution (Rainbow et al., 2000; Shuhaimi-Othman and Pascoe, 2007) is a matter of worldwide concern (Islam et al., 2014). It has negative health effects for humans, fish and invertebrates (Yi et al., 2011; Islam et al., 2014; Martin et al., 2015; Islam et al., 2015b; Islam et al., 2015d; Ahmed et al., 2015c).

Lately, least developed countries like Bangladesh is facing serious difficulties with heavy metal contaminations (Ali et al., 2016; Kibria et al., 2016a,b; Islam et al., 2015c) from different industries, domestic wastes and agrochemicals that deteriorating water superiority (Khadse et al., 2008; Venugopal et al., 2009; Islam et al., 2015a,c). During the last decade, various studies were conducted in rivers, estuary, marine water and lakes giving special preference to the aquatic environment (Ozmen et al., 2004; Begum et al., 2005; Fernandes et al., 2008; Ozturk

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et al., 2008; Pote et al., 2008; Praveena et al., 2008). But no sound research was reported in the Meghna River, close to Narsingdi district, an industrially developed area that is highly contaminated with industrial wastes and domestic sewages that contribute a gigantic amount of Zn, Al, Cd, Pb, Cu, Ni, Fe, Mn, Cr and Co (Bhuyan et al., 2016).

The present study was designed to evaluate the temporal variation of ten metals (Al, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Zn) in the surface water and sediments of the Meghna River, Bangladesh during rainy, winter and pre-monsoon seasons by generating some baseline information on metal pollution in these proposed areas.

Table 1 Spectral lines used in emission measurements and the instrumental detection limit for the elements measured by using AAS.

2. Materials and methods

Elements

Wavelength (nm)

Instrumental detection limit (mg/l)

Fe Pd Cr Co Cd Mn Ni Zn Cu Al

248.3 217.0 357.9 240.7 228.8 279.5 232.0 213.9 324.8 309.3

0.0043 0.013 0.0054 0.01 0.0028 0.0016 0.008 0.0033 0.0045 0.028

2.1. Sampling sites water and sediment samples were acidified with nitric acid (PH = 2) and transferred to the laboratory of Bangladesh Council of Scientific and Industrial Research (BCSIR), Chittagong immediately. Hardness, pH, temperature and dissolved oxygen were measured from studied sites during sampling of water and sediments.

The Meghna River near Narsingdi Sadar is used as a river Ghat (A riverside place used for crossing the river from one side to another by boat) to move in various directions from Narsingdi district by cluster of wood made engine boats awaiting for passengers. Boro bazaar is in the launch terminal, just steps away from this Ghat. Surrounded by the agricultural lands where various types of pesticides e.g. DDT, Algin, Organophosphates etc. are widely used (Site-1). Boiddamar Char is the place, having a lot of textile mills, dying industries and jute industries etc. (Site- 2).

2.3. Heavy metal determination Collected water and sediment samples were analyzed by the AAS (Model: is 3300, Thermo Scientific, Designed in UK, Made in China) using standard analytical procedure (Table 1).

Samples were carefully handled. Recommended clean powder free latex gloves and lab coats were used during the samples handling for avoiding contamination. Glassware was properly cleaned by chromic acid solution and distilled water. Analytical grade chemicals and reagents were used throughout the study. Blank determinations were used to get the correct instrument readings.

2.2. Sample collection and preservation Water and sediment samples were collected from two points: 1.

Effluent discharge area (Boro Bazar) and far from the discharge area (Boiddamar Char) of the Meghna River near Narsingdi District (Fig. 1).

Sampling was performed in three phases: firstly, September 2015 (Rainy season); secondly, January 2016 (Winter season) and thirdly, March 2016 (Pre-monsoon).

A total of 24 samples (12 water samples and sediment samples, respectively) was collected. About 2 l of surface water sample were collected and about 1 kg of sediment samples were collected from the river bed by grab method (Shanbehzadeh et al., 2014). After collection,

2.4. Sample preparation (Sediment) The samples were weighed accurately by a suitable quantity (10–20 g) in a tarred silica dish. After that the samples were dried at Fig. 1. Map showing sampling points of the Meghna River.

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the permissible limit set by WHO (1993) and EU (1998) with the exception of Fe, Ni and Al. In sediment all the trace metals were recorded below the limit compared to other scientific results. There were no significant differences of heavy metals found in water and sediment in terms of both spatially and temporally as the significance level (p > 0.05).

120 °C in a laboratory oven. These dishes were then placed in the muffle furnace at ambient temperature and slowly raised temperature to 450 °C at a rate of 50 °C/h. The samples were ignited in a Muffle furnace at 450 °C for at least 8 h. Precaution was to be taken to avoid losses by volatilization of elements. After cooling the dishes of the samples were removed from the furnace. Then samples were digested in desired amount of 50% nitric acid on a hot plate. After that the samples were filtrated into a 100 ml volumetric flask using Whatman No. 44 filter paper and washed the residue. All the preparation time of each sample solution was made up to the mark with distilled water.

3.1. Water sample The highest concentration of Fe (3.68 mg/kg) was recorded during pre-monsoon that exceeded the permissible limit (0.2 mg/kg) set by the EU (1998) for drinking water and the lowest concentration was found (0.18 mg/kg) that is below the admissible limit. Ozturk et al. (2009) found a similar amount of Fe in Avsar Dam Lake and Balkis et al. (2010) found the highest concentration in Gokova Bay in Turkey. The average amount of lead was recorded (0.01 mg/kg) below the detection limit of WHO (1993) and EU (1998). This result is far lower than the Ahmad et al. (2010), recorded in the Buriganga River but similar to the result of Ayas et al. (2007).

The concentration of Cr was found (0.02 mg/kg) far below the limit (0.05 mg/kg) of WHO (1993) and EU (1998) during all seasons except pre-monsoon. Begum et al. (2009) and Ahmad et al. (2010) recorded higher value of Cr. Moreover, Alam et al. (2003) reported that, higher concentration of Cr was found in rainy season (3–13 mg/kg) than the concentration of dry season (1.2–8 mg/kg). Khan et al. (1998) recorded the higher concentrations of Cr in the water of the GBM (GangesBrahmaputra-Meghna) estuary.

Average Co concentration was measured in all water samples (0.009 mg/kg) that was below the detection limit. Co is favorable to health, but excess levels of Co may pose lung and heart effects and dermatitis (ATSDR, 2004).

The mean value of Cd was recorded (0.018 mg/kg) below detection limit but above the drinking water standard of WHO (1993) and EU (1998). Similar results were recorded by Ayas et al. (2007) in Nallihan Bird Paradise, Turkey and Alam et al. (2003) in the Buriganga River.

Ahmed (1998) recorded 0.018 and 0.007 mg/kg of Cd in water of the Sundarban Forest Reserve. Similar results were found by Rao et al.

(1985), Peterson et al. (1972) and Rojahn (1972) and Khan et al.

(1998).

The highest concentration of Mn was recorded (0.5 mg/kg) in an industrial polluted zone during pre-monsoon that was below the permissible limit of EU (1998) and the lowest concentration was recorded 0.02 mg/kg in rainy season. Balkis et al. (2010) recorded 0.2 mg/kg from Gokova Bay, Turkey and Tankere et al. (2001) measured 0.066–1.593 mg/kg Mn in the Black Sea Water Column.

The maximum amount of Ni was found (0.3 mg/kg) in impacted site during the winter season that exceeded the allowable limit (0.02 mg/ kg) of WHO (1993) and EU (1998). The minimum value of Ni was found (0.01 mg/kg) during pre-monsoon. The result of Ahmad et al. (2010) exceeded the present study and Ayas et al. (2007) was below the detection limit.

Zn was found (0.04 mg/kg) below the permissible limit (3 mg/kg) of WHO (1993) for all seasons but Balkis et al. (2010) recorded the higher concentration (4.9 mg/kg) of Zn from Gokova Bay, Turkey.

The average concentration of Cu was found (0.027 mg/kg) that is far below the permissible limit (2 mg/kg) of WHO (1993) and EU (1998). This concentration was much lower than the Ahmad et al.

(2010), Ahmed (1998) and Rao et al. (1985). The concentrations of Al range between (0.48–1.5 mg/kg). The highest concentration was recorded 1.5 mg/kg from polluted sites during pre-monsoon that was exceeded the permissible limit (0.2 mg/kg) of WHO (1993) and EU (1998). The lowest amount of Al found 0.48 mg/kg during the rainy season from pristine area. But in the sea water, Balkis et al. (2010) found 2 mg/kg of Al. Mean concentrations (μg/kg) of heavy metals in water during three seasons shown in Fig. 2.

2.5. Sample preparation (Water) The collected water samples were put into the PVC bottle and about 100 ml water of each sample was taken in a beaker. Then the samples were digested with adding 5 ml conc. HNO3 on a hot plate. After that the samples were filtrated into a 100 ml volumetric flask using Whatman No. 44 filter paper and made up to the mark with distilled water.

2.6. Standard preparation The metal standard solution was prepared for calibration of the instrument for each element being determined on the same day as the analyses were performed due to possible deterioration of standard with time. All samples were prepared by the chemicals of analytical grade with distilled water. About 1gm of Cadmium, Copper, Lead, Nickel was dissolved in HNO3 solution; 1 g of Cobalt, Iron, Manganese, Zinc, Aluminum were dissolved in HCl solution; 2.8289 g K2Cr2O7 (=1 g Chromium) was dissolved in water and made up to 1 l in a volumetric flask with distilled water, thus stock solution of 1000 mg/l of Cd, Cu, Pb, Ni, Co, Fe, Mn, Zn, Al and Cr were prepared (Cantle, 1982). Then 100 ml of 0.1, 0.25, 0.5, 0.75, 1.0 and 2.0 mg/l of working standards of each metal except iron was prepared from these stocks using micropipettes in 5 ml of 2N nitric acid. 100 ml of 2.0, 2.5, 5.0, 10.0 and 20.0 mg/l of working standards of iron metal was prepared from iron stock solution. Reagent blank was also prepared to avoid reagent contamination.

2.7. Analysis of samples Atomic Absorption Spectrophotometer was setting up with flame condition and observance were optimized for the analyses. Then the blanks (deionized water), standards, sample blank and samples were aspirated into the flame in AAS (Model- iCE 3300, Thermo Scientific, Designed in UK, Made in China). The calibration curves obtained for concentration vs. absorbance. Data were statistically analyzed using the fitting of a straight line by the least square method. A blank reading was also taken and necessary corrections were made during the calculation of concentration of various elements.

2.8. Statistical analysis One Way Analysis of Variance (ANOVA) was done to show the variations in concentration of heavy metal in terms of seasons. The graph was used for graphical presentation of heavy metal against seasons (SPSS v. 22). According to Dreher (2003), Principal Component Analysis (PCA) was performed on the original data set (without any weighting or standardization). Pearson’s product moment correlation matrix was done to identify the relation among metals to make the result strong obtained from multivariate analysis (SPSS v.22). Additionally the site map was tailored by the ‘Arc GIS (v. 10.3)’ software.

3. Results and discussion The present study exposed all the metals in water were found below 275

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Fig. 2. Ggraph showing mean concentrations (Îźg/kg) of heavy metals in water during three seasons.

Fig. 3. Ggraph showing mean concentrations (mg/ kg) of heavy metals in sediment during three seasons.

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Table 2 Component matrix of three factors model with strong to moderate loadings in water and sediment.

Water Eigenvalues (1)

Fe Pb Cr Co Cd Mn Ni Zn Cu Al Eigen value % Total variance Cumulative%

Sediment Component

Eigenvalues (0.6)

PC 1

PC 2

PC 3

0.929 0.934 −0.130 0.687 0.068 0.969 −0.566 0.844 0.975 0.877 5.921 2.222 1.129

−0.338 0.189 0.011 0.666 0.945 −0.070 −0.594 −0.412 0.089 −0.447 59.214 22.220 11.289

0.123 −0.061 0.976 0.015 0.241 0.161 0.197 −0.018 −0.117 0.144 59.214 81.435 92.723

Component PC 1

Ni Cu Cd Zn Cr Mn Al Fe Pb Co Eigen value % Total variance Cumulative%

0.995 0.985 0.983 0.979 0.961 0.919 0.913 0.859 0.555 0.657 7.963 1.101 0.774

PC 2

0.175 0.129 −0.361 −0.480 0.830 79.633 11.011 7.736

PC 3

−0.151 −0.176 −0.200 −0.155 0.284 0.117 0.738 79.633 90.645 98.380

3.3.1. Correlation matrix of heavy metals in water In case of water, the very strong linear relationship was found in Fe vs Al (0.992), Mn v’s Cu (0.948), Fe vs Mn (0.939), Zn vs Al (0.929), Fe vs Zn (0.920) at the significance level 0.05. Strong relationships were observed in Cu vs Al (0.794), Mn vs Zn (.788), Cu vs Zn (0.732) at the alpha level 0.01. A very strong linear relationship was found between Fe and Cu (0.861) and strong between Mn and Al (0.895) at the significance level 0.01.

3.2. Sediment sample The concentration of Fe was ranged between 737 and 2385 mg/kg.

The maximum value was recorded 2385 mg/kg in impacted site during the winter season that is lower than the value of Balkis et al. (2010) from Gokova Bay, Turkey.

The highest concentration of Pb was recorded (6.98 mg/kg) in the industrial zone during the winter season that is similar to Begum et al.

(2009). Ahmad et al. (2010) reported that maximum value of Pb (77.13 mg/kg) was found from the Buriganga River during pre-monsoon, but a higher amount of Pb (52.9 μg/g) was recorded by Topcuoglu et al. (2004). While Ayas et al. (2007) reported the Pb value below detection limit from Nallihan Bird Paradise, Turkey; Khan et al.

(1998) recorded 2.355–26.086 mg/kg in sediment in Ganges Brahmaputra-Meghna Estuary.

Cr concentrations varied between (1.27–6.81 mg/kg), where highest value was found from industrial areas during the winter season.

This result was lower than the results of Ergul et al. (2008), Yucesoy and Ergin (1992) and Ahmad et al. (2010) but higher than the results of Begum et al. (2009).

The maximum value of Co was recorded (0.86 mg/kg) in impacted site during the winter season that is far below than the results of Topcuoglu et al. (2004) and Balkis et al. (2007) but the minimum value of Co was (0.20 mg/kg) found from pristine zone during rainy season.

The concentrations of Cd ranged between (BDL-0.53 mg/kg) are quite similar to the results of Ergul et al. (2008), Balkis et al. (2007), Ayas et al. (2007), Topcuoglu et al. (2004) and Yucesoy and Ergin (1992). But the higher amount of Cd was founded by Ahmad et al.

(2010) and Begum et al. (2009).

In the present study, the concentrations of Mn, Ni, Zn, Cu and Al are far below than the results reported by Balkis et al. (2010), Ergul et al.

(2008), Balkis et al. (2007), Ayas et al. (2007), Topcuoglu et al. (2004) and Yucesoy and Ergin (1992).

Mean concentrations (mg/kg) of heavy metals in sediment during three seasons shown in Fig. 3.

3.3.2. Correlation matrix of heavy metals in sediment In sediment, very strong linear relationships were found in Cd vs Zn (0.999), Cd vs Cu (0.998), Zn vs Cu (0.996), Cd vs Ni (0.995), Ni vs Cu (0.994), Ni vs Zn (0.993), Cr vs Ni (0.972), Cr vs Cu (0.971), Cr vs Zn (0.966), Cr vs Cd (0.965), Fe vs Al (0.928), Cd vs Al (0.925), Zn vs Al (0.921), Ni vs Al (0.918), Fe vs Cu (0.808), Cr vs Al (0.803) at the significance level 0.05. Strong relationships were observed in Co vs Mn (0.784), Fe vs Mn (0.779), Mn vs Al (0.763), Fe vs Cr (0.739), Pb vs Cr (0.677), Fe vs Co (0.617), Pb vs Mn (0.612) at the significance level 0.05. Moreover, moderate correlations were observed in Co vs Ni (0.583), Pb vs Cu (0.571), Co vs Cu (0.549), Pb vs Ni (0.540), Pb vs Cd (0.535), Co vs Al (0.533), Pb vs Zn (0.531), Cr vs Co (0.522), Co vs Cd (0.522) at the alpha level 0.05. Furthermore, very strong linear relationships were found between Cu and Al (0.909), Mn and Ni (0.882), Cr and Mn (0.860), Fe and Ni (0.853), Mn and Cu (0.845), Cd and Mn (0.840), Mn and Zn (0.833), Fe and Cd (0.826), Fe and Zn (0.823) at the alpha level 0.01.

3.4. Principal component analysis The extraction method was executed to find out the principal components (PC) in PCA analysis that was Eigen values. In water, the components were taken as principal components whose Eigen values were greater than 1 were taken into account. 3 PCs were extracted by using correlation matrix which reflects the processes influencing the heavy metal composition having 92.72% of total sample variance (Table 2). The total variance of the PCs was 2.22%, 22.22% and 81.44% for PC 1, PC 2 and PC 3 respectively. PC 1 is strongly correlated with Fe, Pb, Mn, Cu, Al and PC 2 with Cd. PC 3 is also strongly correlated with Cr. The source of PC 1, PC 2 and PC 3 can be deliberated as different source from both lithogenic and anthropogenic inputs.

In the sediment, the components were considered as principal components whose Eigen values were higher than 0.6. 3 PCs were extracted by using correlation matrix which reflects the processes influencing the heavy metal composition having 98.38% of total sample variance (Table 2). The total variance of the PCs was 1.1%, 11.01% and 90.65% for PC 1, PC 2 and PC 3 respectively. PC 1 is strongly correlated

3.3. Correlation matrix In the aquatic environment, the interrelationship among metals in water and sediment provided significant information of sources and pathways of variables (heavy metals). The result of correlations between heavy metals acquiesced with the results of PCA and CA that confirm some new relations between parameters. Very strong, strong and moderate correlation indicates that, their sources of origin are similar, especially from industrial effluents, municipal wastes and agricultural inputs.

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with Ni, Cu, Cd, Zn, Cr, Mn, Al, Fe, Co and PC 2 with Pd. PC 3 is also strongly correlated with Co. The source of PC 1, PC 2 and PC 3 can be considered as a mixed source of anthropogenic inputs, particularly from industrial effluents and agricultural activities in the study area.

4. Conclusion Meghna River near the Narsingdi district is one of the most important rivers in Bangladesh. The probable source of the pollutants is anthropogenic, arising from agricultural activities, electroplating materials and lubricants used near the lake. Based on the present study, it can be concluded that the degree of contamination and seasonal variation of heavy metals were high in water and sediment. Efforts should be needed to protect this River from pollution and also to reduce environmental risks. This study and the valuable data will pave the way for future research on this river.

Conflicts of interest None.

Acknowledgements The authors are grateful to the Bangladesh Council of Scientific and Industrial Research (BCSIR), Chittagong. We want to acknowledge the Biodiversity, Environment, Climate Change and Risk Assessment Research Laboratory, Institute of Marine Sciences and Fisheries, University of Chittagong which has the major contributions to conduct this research. Special thanks are extended to those people who were helped in different capacities of this research.

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