Multivariate Statistical Analysis of Metal Contamination in Surface Water Around Dhaka Export Processing Industrial Zone, Bangladesh

Accepted Manuscript

Title: Multivariate statistical analysis of metal contamination in surface water around Dhaka export processing industrial zone, Bangladesh

Authors: Narottam Saha, M. Saﬁur Rahman

PII: S2215-1532(17)30252-0 DOI: https://doi.org/10.1016/j.enmm.2018.07.007 Reference: ENMM 167

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Received date: 25-10-2017 Revised date: 7-6-2018 Accepted date: 16-7-2018

Please cite this article as: Saha N, Rahman MS, Multivariate statistical analysis of metal contamination in surface water around Dhaka export processing industrial zone, Bangladesh, Environmental Nanotechnology, Monitoring and Management (2018), https://doi.org/10.1016/j.enmm.2018.07.007

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Multivariate statistical analysis of metal contamination in surface water around Dhaka export processing industrial zone, Bangladesh

Narottam Saha1*, M Safiur Rahman2*

1 School of Earth and Environmental Sciences, The University of Queensland, St Lucia, QLD 4072, Australia 2 Atmospheric and Environmental Chemistry Laboratory, Chemistry Division, Atomic Energy Center, Dhaka, 1000, Bangladesh

* Corresponding authors: [email protected] (N. saha); [email protected] (M S Rahman)

Highlights

 Metal distributions were not seasonally influenced.  Spatial gradients of metal concentrations were recorded.  Metals sourced from anthropogenic factors.  Effective measures should be taken to protect surface water.

Abstract Deterioration of water quality by anthropogenic heavy metal pollution is a critical issue, especially in the least developed countries. This study, therefore, utilizes multivariate statistical approaches to report on the sources of heavy metals contamination in water bodies close to Dhaka Export Processing Zone industrial area in Bangladesh. Correlation matrix showed a number of significant associations (p < 0.01 and p < 0.05) among the metals, with no major seasonal influence on metal associations. Spatial variability of metal concentrations, however, was observed with lowering of concentration as distance increased from the pollutionACCEPTED sources. Principal component and cluster MANUSCRIPT analysis identified three major sources of metal pollution including untreated industrial effluents, municipal wastes, and atmospheric deposition of metals from burning of fossil fuels. These three sources were responsible for the data structure explaining 79.97% of total variance. Hierarchical cluster analysis demonstrated

three clusters of nine sampling stations depending on the similarity of the data. Overall, the levels of metal concentrations determined in this study clearly illustrated the anthropogenic disturbances on water quality.

Keywords: Heavy metals; surface water; industrial pollution; environmental monitoring; multivariate statistics.

1. Introduction

Water resources play a crucial rule in the natural environment, and the knowledge of heavy metal concentrations and their distributions in water bodies is of fundamental importance for environmental and ecological studies. The pollution of surface water by toxic heavy metals has emerged as an urgent worldwide environmental problem in the past few decades (Ali et al., 2016; Belabed et al., 2017; Benabdelkader et al., 2018; Bhuyan et al., 2017; Costa-

Böddeker et al., 2017; Islam et al., 2000; Islam et al., 2016; Ismail et al., 2016; Khound and

Bhattacharyya, 2017; Rakotondrabe et al., 2018; Saha et al., 2016; Wu et al., 2015; Yin et al.,

2015; Zhang et al., 2018). Release of untreated industrial wastes enriched with different heavy metals into the riverine environment is considered to be one of the critical factors responsible for decline in surface water quality (Adamu et al., 2015; Akbulut and Tuncer, 2011;

Benabdelkader et al., 2018; Odukoya et al., 2017; Phiri et al., 2005). Once discharged into the rivers, the behavior and fate of polluting substances is determined by the combined effects of different variables such as the contaminants’ physicochemical properties, river hydrology and hydrochemistryACCEPTED (Kucuksezgin et al., 2008). It MANUSCRIPTis therefore necessary to conduct a comprehensive monitoring program on river water quality to evaluate the risks associated with man-made pollution of surface water bodies, and to protect public health and valuable fresh water resources (Kannel et al., 2007).

The study area, Dhalaibeel and Bangshi River, belongs to Dhamsona Union of Savar

Upazila (Sub-district) near Dhaka, capital of Bangladesh (Fig. 1). Savar is the fastest growing industrial city in the country, and it is located about 30-km north of Dhaka at latitude 23°5 north and longitude 90°15 east. This industrial area includes two export processing zones

(EPZ) with a number of industries, for example dyeing, textiles, leather goods, metal products, chemicals, fertilizers and so on (BEPZ, 2000). The effluent produced by several industries is being dumped into Dhalaibeel and finally ends up in Bangshi River, which may cause potential damage of the aquatic environments. The topography of this area covers irregular elevated land blocks on which people live and surrounded low-laying areas that are mostly cultivable lands and water bodies. Local farmers frequently use the water from

Dhalaibeel and Bangshi River for irrigation purposes.

Multivariate statistical techniques can consider a number of factors with control data variability simultaneously and therefore offer significant advantages over univariate techniques, where errors associated with repeated statistical testing can occur (Manly and

Alberto, 2016). In our study, multivariate analyses, namely principal component analysis

(PCA), cluster analysis (CA), and correlation analysis were used to interpret the trace element data in surface water samples collected from Dhalaibeel and Bangshi River at Savar,

Bangladesh. The intention underlying the use of multivariate analysis was to achieve more efficient data compression from the original data, and to obtain information that can help interpret the environmental geochemical origin.

The objectives of the present investigation were to: (i) determine the heavy metal concentrations in water samples; (ii) compare and contrast metal contents in Dhalaibeel and

BangshiACCEPTED River water during pre- and post-monsoon MANUSCRIPT conditions; (iii) compare the metal concentrations with guideline and literature values and (iv) identify different sources of contamination of heavy metals in water samples by applying multivariate statistical techniques.

2. Materials and methods

2.1. Sample collection and analysis

Water samples were collected during the pre-monsoon and post-monsoon seasons from four sampling spots located in Dhalaibeel and five sampling spots along Bangshi River, Savar

(Fig. 1). The plastic bottles were previously soaked with a mixture of distilled water, 2%

HNO3 and 0.5% H2O2 for ~24 hours and then washed several times with distilled water followed by ringing with double distilled water and oven drying (40-50o C). To avoid bacterial degradation, after being filtered (pore size = 0.45 µm), the samples were acidified immediately with the addition of 2 ml ultra-pure HNO3 per liter of water and then carefully preserved in a refrigerator at 4o C before laboratory analysis (Mastoi et al., 1997). Analysis of heavy metals (Pb, Cd, Ni, Cr, Cu, Zn, Mn, and As) was carried out by atomic absorption spectrophotometer (Model AA-6800, Shimadzu Corporation, Japan) in the Nuclear Analytical

Chemistry Laboratory, NRCD, Institute of Nuclear Science and Technology, Gonakbari,

Savar. Analytical conditions for measuring the metal concentrations are tabulated in Table 1.

In order to assure the precision of the measurement, standard reference solution (supplied by

Wako Pure Chemical Industry Ltd., Japan) with a known concentration of each measured element were used as control samples. The control sample was analyzed after every three water samples to check the accuracy of analysis. Each sample was measured at least three times in order to assess the repeatability of the measurement. Samples were reanalyzed if the relative standard deviation of the measurement exceeded 10%. The reagents were of analytical grade and double distilled water was used throughout the study. StatisticalACCEPTED software, SPSS (version 24) and Minitab MANUSCRIPT 16 were used for multivariate statistical analysis.

3. Results and discussion

3.1. Seasonal and spatial variation of metals

The concentration variations of heavy metals (Pb, Cd, Ni, Cr, Cu, Zn, Mn, and As) at each sampling station of Dhalaibeel and Bangshi River during the pre-monsoon and post-monsoon seasons are presented in Fig. 2. The levels of heavy metals were generally higher than the reference values suggested by WHO (1993) with the exception of Cu and Mn, which could be due to excessive amounts of industrial effluents ending up in the water bodies. However, the concentration of metals were close to (or lower than) the respective standard values recommended by Bangladesh (ECR, 1997) with the exception of Pb, Cd, and Cr concentrations (Fig. 2). A comparison of our values with the criterion continuous concentration (CCC) and criteria maximum concentration (CMC) values of USEPA (2009) water quality criteria revealed that all examined metals concentrations were higher than the

CCC and CMC values except Ni and As in both Dhalaibeel and Bangshi River water. The metal contents in Buriganga River, Bangladesh (Ahmad et al., 2010) were several times higher than our results, which is possibly due to dumping of human wastes and industrial effluents at a higher degree in the Buriganga River. The spatial distribution of surface water contaminants during pre- and post-monsoon indicated that the concentration of metals decreased as distance increased from the Dhaka Export Processing Zone (DEPZ) (Fig. 3).

Metal concentrations in Dhalaibeel water, where effluents were directly discharged from various industries around DEPZ, were higher than the Bangshi River water which was 2.5 km from Dhalaibeel. This decline in metal contents with distance may be attributed to the effect of dilution.

ACCEPTED3.2. Correlation analysis MANUSCRIPT Seasons may play an important role in relationships between different heavy metal concentrations. Figure 4 shows the correlations between heavy metal concentrations measured in Dhalaibeel and Bangshi River water during the pre- and post-monsoon periods. However,

no dramatic difference was observed in the slope of the pre- and post-monsoon data points with the exception of Pb-Cd, Pb-Cr, Zn-Cd, Ni-Zn, and Ni-Cr (Fig. 4). The study revealed a significant correlation among Pb, Ni, Cr, Cu, Zn, and As in the water samples. Pb showed a close relationship with Ni (r = 0.708), Cu (r = 0.704), and As (r = 0.509). Similarly, close relationships were noted for element pairs of Ni-Cr, Ni-Cu, Ni-As, Cr-Cu, Cr-As, Cu-Zn, and

Cu-As (r > 0.5), suggesting that these metals had a common source. Alternatively, Cd and Mn showed no significant correlations with other metals in water samples, indicating their different source of origin. One way ANOVA test revealed that variations of metal concentrations in Dhalaibeel and Bangshi River water were significant at the 95% significance level. However, seasonal variations of metal concentrations were not statistically significant.

3.3. Principal component analysis (PCA)

In order to obtain detailed information about the types of natural and anthropogenic sources responsible for enriching the heavy metals and their movement in water; the PCA with varimax rotation was carried out. Each dataset was subjected to PCA using the correlation matrix in order to standardize each variable, so that the analysis was not influenced by differences in data magnitude and measurement scales (Webster, 2001). Significant factors were selected based on the Kaiser principle of accepting factors with eigenvalues > 1. Factor loadings were considered significant if they were > 0.6 (DelValls et al., 1998). The results of

PCA (Table 2) showed that the three extracted independent principal components (PCs) accounted for 79.97% of the total variance, which was quite good and reliable to identify the main sources of variation in the hydrochemistry. The first component accounted for 41.36%, theACCEPTED second for 23.37%, and the third for 15.24% MANUSCRIPT of the total variance. The graphical representation of the first two components is shown in Figure 5, where the associations between these variances are observed. The first one shows high positive loading on Pb, Ni,

Cu, Zn, and As. Elements like Pb, Cu, Ni, Sn, Ba, and As are known as markers of paint

industries (Lin et al., 2002), many of which were present in the study area. Thus, the wastewater from dyeing, printing, leather, various chemicals, and metallurgical industries around the DEPZ could be the main sources of Pb, Ni, Cu, Zn, and As in water reservoirs. The second principal component showed positive loading on Mn and negative loading on Cr. This negative relationship between Mn and Cr was also observed correlation analysis (Fig. 4).

Substantial amounts of elements like Cr, Cu, and Mn are likely to enter into water reservoirs with municipal wastewater (Liu et al., 2003). The third principal component contained only one variable, Cd, with a high positive loading of 0.95. Cadmium is a naturally occurring element in the earth’s crust, but combustion of motor fuels (e.g., petrol) and coal can be considered as a source of anthropogenic Cd pollution. However, score plot of the first two principal components (explain 64.73% of the total variance) classified the two sampling zones

(Dhalaibeel and Bangshi River), wherein Bangshi River samples shows more variations relative to the samples from Dhalaibeel (Fig. 6).

3.4. Cluster analysis

Hierarchical cluster analysis using Ward’s agglomerative method with Pearson correlation was employed to group the heavy metals and sampling locations (Fig. 7 and 8). The distance axis represents the degree of association between groups of variables, i.e. the lower the value on the axis, the more significant the association. Three statistically significant clusters emerges from the grouping of eight metals (Fig. 7). Cluster one showed a close association between Cr and Cd, while cluster two consisted of Mn. Cluster three was formed by As, Zn,

Cu, Ni, and Pb that shows complete accordance with the PCA analysis. Figure 8 represents thatACCEPTED nine sampling stations were clustered into MANUSCRIPT three groups. Group one consisted of W8 to W5, group two consisted of W9 and W3, and group three comprised W7 to W1. The clustering procedure identifies the groups of similar sites in a quite convincing way.

4. Conclusion

The level of heavy metals recorded in Dhalaibeel and Bangshi River water clearly demonstrates the anthropogenic impacts on water contamination. The mean concentrations of heavy metals in Dhalaibeel and Bangshi River decreased in the order of Zn> Cu> Pb> Mn>

Cr> Ni> As> Cd and Zn> Cu> Cr> Pb> Mn> Ni> As> Cd, respectively. Analysis of metal concentrations data using the multivariate statistical techniques elicited some information that was not clearly visible from the analytical data. Correlation matrix among the heavy metal concentrations and in the selected sampling stations demonstrated a number of strong associations. The results indicated that, in general, correlations among the heavy metals were not influenced by the seasonal variations. The PCA reduced the original data matrix to three principal components explaining 79.97% of the total variance and identified three sources of metal contamination. The major cause of metal pollution was the discharge of untreated industrial effluents into the nearby water bodies. Moreover, municipal wastewater and atmospheric deposition of metals due to the combustion of fossil fuels were the major causes of the deteriorating water quality. Cluster analysis grouped nine sampling sites into two clusters of similar water quality characteristics. In order to protect the water quality around

Dhalaibeel and Bangshi River from farther deterioration, effluent treatment unit should be installed and properly used in every surrounding industry. The regulatory initiatives should be strictly enforced to ensure that industrial waters are properly treated before discharging to nearby water bodies. Furthermore, regular monitoring of the water bodies around the industrial area at Savar should be undertaken to identify the sources of water contamination.

ConflictsACCEPTED of interest MANUSCRIPT There is no conflict of interest

Acknowledgement

The authors thank the authority of Bangladesh Atomic Energy Commission for providing laboratory facilities to analyze the water samples using conventional techniques. The authors are also grateful to the authorities of the Dhaka Export Processing Zone (EPZ). Special thanks are accorded to Md. Abdus Sobhan, Assistant Manager (Commercial Operation) for his kind cooperation in providing critical information and helping to collect the samples. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for- profit sectors.

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Figure captions:

Fig. 1. Map of study area and location of sampling sites.

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Fig. 2. Seasonal changes in dissolved heavy metal concentrations (in ppm) in surface water of Dhalaibeel and Bangshi River collected from nine sampling stations (W1 to W4 for Dhalaibeel and W5 to W9 for Bangshi River). Comparison of analysed metal concentrations with WHO (1993) and Bangladesh (ECR 1997) drinking water standards.

Fig. 3. The changes in metal concentrations (ppm) (pre- and post-monsoon) as a function of distance (km) from the Dhaka Export Processing Zone (DEPZ).

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Fig. 4. Correlation between water-dissolved heavy metals shows the seasonal influence on their variability.

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Fig. 5. Contribution of each element to the PC loadings obtained by the principal component analysis.

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Fig. 6. Principal component scores for the water samples of Dhalaibeel and Bangshi River.

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Fig. 7. Dendrogram showing clustering of metals using Ward’s agglomerative method with Pearson correlation.

Fig. 8. Dendrogram showing clustering of sampling stations using Ward’s agglomerative method with Pearson correlation.

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Table 1 Analytical conditions for the measurement of heavy metal concentrations in aqueous solution using AAS.

Elements Wavelength Slit Lamp Current Mode Calibration Detection (nm) (nm) (mA) Range (mg/L) limit (mg/L) Pb 283.3 0.5 10 Flame 0.0 – 3.0 0.04 Cd 228.8 0.5 8 Flame 0.0 – 1.2 0.006 Ni 232.0 0.2 12 Flame 0.0 – 3.0 0.015 Cr 357.9 0.5 10 Flame 0.0 – 2.0 0.01 Cu 324.8 0.2 6 Flame 0.0 – 3.0 0.006 Zn 213.9 0.2 8 Flame 0.0 – 1.6 0.005 Mn 285.2 0.5 8 Flame 0.0 – 2.0 0.02 As 193.7 0.5 12 HVG 0.001 – 0.006 0.01

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Table 2 Total variance explained and component matrices for heavy metals in the water samples.

Extraction Sums of Squared Rotation Sums of Squared Component Initial Eigenvalues Loadings Loadings % of % of % of Vari Cumulative Vari Cumulative Vari Cumulative Total ance % Total ance % Total ance % 1 4.08 50.95 50.95 4.08 50.95 50.95 3.31 41.36 41.36 2 1.26 15.73 66.68 1.26 15.73 66.68 1.87 23.37 64.73 3 1.06 13.29 79.98 1.06 13.29 79.97 1.22 15.24 79.97 4 0.67 8.40 88.37 5 0.46 5.71 94.08 6 0.27 3.34 97.42 7 0.13 1.65 99.07 8 0.07 0.93 100.00

Component matrixa Rotated component matrixb PC 1 PC 2 PC 3 PC 1 PC 2 PC 3 Pb 0.81 - - 0.72 - - Cd - 0.80 - - - 0.95 Ni 0.87 - - 0.80 - - Cr -0.74 - - - -0.71 - Cu 0.91 - - 0.93 - - Zn 0.67 - - 0.85 - - Mn - - 0.62 - 0.86 - As 0.79 - - 0.61 - -

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization a 3 components extracted. b Rotation converged in 4 iterations.

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