Distribution, Source Identification, Ecological and Health Risks of Heavy Metals in Surface Sediments of the Rupsa River, Bangladesh

Toxin Reviews

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Distribution, source identification, ecological and health risks of heavy metals in surface sediments of the Rupsa River, Bangladesh

Ram Proshad, Tapos Kormoker & Saiful Islam

To cite this article: Ram Proshad, Tapos Kormoker & Saiful Islam (2019): Distribution, source identification, ecological and health risks of heavy metals in surface sediments of the Rupsa River, Bangladesh, Toxin Reviews, DOI: 10.1080/15569543.2018.1564143 To link to this article: https://doi.org/10.1080/15569543.2018.1564143

Published online: 21 Jan 2019.

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RESEARCH ARTICLE Distribution, source identification, ecological and health risks of heavy metals in surface sediments of the Rupsa River, Bangladesh

Ram Proshada , Tapos Kormokerb and Saiful Islama,c aDepartment of Soil Science, Patuakhali Science and Technology University, Dumki, Patuakhali, Bangladesh; bDepartment of Emergency Management, Patuakhali Science and Technology University, Dumki, Patuakhali, Bangladesh; cGraduate School of Environment and Information Sciences, Yokohama National University, Yokohama, Kanagawa, Japan

ABSTRACT ARTICLE HISTORY The present study was conducted to assess the pollution level and distribution of heavy metals Received 26 September 2018 and their ecological and health risk in sediments of the Rupsa River, Bangladesh. Heavy metals Accepted 16 December 2018 were measured by using inductively coupled plasma mass spectrometer. The mean concentra- KEYWORDS tion of Cr, Ni, Cu, As, Cd, and Pb in sediments was 25.26, 42.40, 68.81, 9.31, 3.78, and 32.57 mg/ Surface sediment; heavy kg, respectively. Metals in sediment during winter were higher than summer season. metals; source Multivariate principal component analysis revealed significant anthropogenic contributions of Cr, identification; ecological Ni, As and Cd in sediment. The enrichment factor (EF), geoaccumulation index (Igeo), pollution risk; health risk; Bangladesh i load index (PLI), and contamination factor (C f) demonstrated that most of the sediment samples were heavily contaminated by Cd. The sum of toxic units in most of the sampling sites during winter season was greater than 4, indicating a moderate to serious toxicity of heavy metals in sediment. In view of the potential ecological risk, sediments of Rupsa River showed low to very high potential ecological risk. The chronic daily intake of metals were higher than oral RfD which was the result of metal contamination. The total hazard index for adult and children, about 100% sampling location possess (HI > 1) indicating pose non-cancer risk. The carcinogenic risks from As and Pb at all sites via ingestion and dermal contact were higher than acceptable ranges (104). This study suggested that more attention should be directed to the comprehensive risk assessment of heavy metals of this riverine aquatic environment.

Introduction agricultural, and different textile industries discharge untreated wastes into the river directly without any In recent years, contamination of heavy metals in the treatment of wastage resulting metal pollution in the aquatic environment has attracted global attention river environment (Sekabira et al. 2010, Bhuiyan et al. owing to its persistence and environmental toxicity 2011, Zhang et al. 2011, Grigoratos et al. 2014). (Armitage et al. 2007, Yuan et al. 2011, Ahmed et al. Contamination of water bodies by heavy metals ranks 2015, Islam et al. 2015a, Ali et al. 2016, Islam et al. among the major environmental problems, with many 2017). Highly toxic heavy metals such as arsenic, cad- issues accompanying rapid economic development in mium, and lead are considered as serious contami- both developed and developing countries (Wu et al. nants in the aquatic environment (Islam and Al- 2011, Islam et al. 2014, 2018). Recently, the pollution Mamun 2017, Proshad et al. 2018). The huge amount of aquatic environments has been regarded as a topic of heavy metals is discharged by anthropogenic activ- of much discussion; and the issue of sediment con- ities (Gao et al. 2010, Nduka and Orisakwe 2011)as tamination by heavy metals has received more atten- well as by natural actions that contribute metal con- tion due to the toxic and persistent characters tamination in the aquatic environments (Wilson and (Armitage et al. 2007, Zhan et al. 2010, Yuan et al. Pyatt 2007, Khan et al. 2008, Sekabira et al. 2010, Bai 2011, Raknuzzaman et al. 2016, Islam et al. 2018). et al. 2011, Zhang et al. 2011, Martin et al. 2015, Sediment is an essential and dynamic part of the Bhuyan et al. 2017). Usually heavy metals sources are riverine ecosystems with a variety of habitats and varied for country to country but notably include min- environments. Sediments usually provide useful infor- ing, landfill, tanning, chemical, pharmaceutical, mation for environmental and geochemical pollution

CONTACT Ram Proshad [email protected] Department of Soil Science, Patuakhali Science and Technology University, Dumki, Patuakhali 8602, Bangladesh ß 2019 Informa UK Limited, trading as Taylor & Francis Group 2 R. PROSHAD ET AL. status (Uluturhan et al. 2011, Islam et al. 2015b). In 2015b, Islam et al. 2018). As a developing country like sediment, heavy metals are present in a number of Bangladesh is facing this environmental problem and chemical forms and undergo various changes in their the aquatic environment is continuously being conta- speciation due to dissolution, precipitation, sorption, minated by heavy metals. Rupsa River is the largest and complexation phenomena during their transporta- and important river in the Khulna City area of tion through the river system (Mohiuddin et al. 2012, Bangladesh. Because of the industrially developed Islam et al. 2018). The concentrations of heavy metals area, the heavy metal pollution of the Rupsa River is are extremely high in sediment than the water column increasing day by day. The study river recently has because of metals tend to amass in bottom deposits been raised attention to public concern due to its (He et al. 2009, Sultan and Shazili 2009, Nobi et al. extreme pollution. To date, no scientific research 2010, Rezayi et al. 2011, Bhuyan et al. 2017). In the regarding heavy metal pollution in sediment of the riverine ecosystems, sediments can be polluted with Rupsa River has been conducted so far. Therefore, the various kinds of hazardous substances and heavy met- objectives of this study are to assess the pollution sta- als through several pathways such as disposal of liquid tus of the Rupsa River by estimating the levels of effluents, traffic emissions, terrestrial runoff, brick kilns, heavy metals (Cr, Ni, Cu, As, Cd, and Pb) in surface and leachates carrying chemicals originating from sediments in two different seasons; to observe the numerous urban, industrial, and agricultural activities level of ecological risk of heavy metals in sediments; (Ahmad et al. 2010, Chen et al. 2012, Shikazono et al. and also to evaluate the noncarcinogenic and carcino- 2012, Varol and Sen 2012). Various indexes, such as genic health risk that the heavy metals may pose contamination factor (CF), enrichment factor (EF), geo- to humans. accumulation index (Igeo), and pollution load index (PLI), have been developed to evaluate the environ- Materials and methods mental risk of heavy metals in surface sediment based on their total content, bioavailability, and toxicity Description of the study area (Yang et al. 2009,Yuet al. 2011). The enrichment fac- The area of Khulna district is 4389.11 km2 and located tor, geoaccumulation index, and contamination factor at the southern in Bangladesh and third largest indus- of individual heavy metal in sediment are calculated trialist area of the country after Dhaka and using its total content and sediment quality guideline Chittagong. The Khulna city is situated on the bank of value (Hou et al. 2013, Zhang et al. 2013, Islam et al. Rupsa River and this river play a significant role in 2018). The pollution load index (PLI) and potential development and industrialization of Khulna city and ecological risk index (PER) have also been developed also economy of Bangladesh. Rupsa River is 50 km to assess the combined risk of multiple heavy metals away from the largest mangrove Sundarban on the in sediment (Yang et al. 2009, Huang et al. 2013). To upstream of the Rupsa (Samad et al. 2015). The lower assess the carcinogenic and noncarcinogenic health part of the Vhairab River near Khulna city is known as risk of heavy metals, several methods have also been Rupsa River. Khulna is an industrial area of developed such as chronic daily intake (CDI) estima- Bangladesh, which is highly susceptible to environ- tion, target hazard quotient (THQ), and hazard index mental pollution over the last decade. Several types of (HI). Therefore, it is essential to measure the concen- industries were set up on the bank of Rupsa River like trations of heavy metals in sediments of any polluted fish processing industry, cement industry, shipyard, riverine ecosystem. tannery industries, packaging industry, dyeing, brick Bangladesh is one of the largest deltas in the world kiln, metal workshops, battery manufacturing indus- which formed by the Ganges, Brahmaputra and tries, textile industries, pesticide and fertilizer indus- Meghna and spreading over five countries, namely tries, different food processing industries and other Bangladesh, Bhutan, China, India, and Nepal (Islam factories produce huge volumes of effluents that con- et al. 2015a, Islam and Al-Mamun 2017). In tain heavy metals. Now a days, this river is continu- Bangladesh, a huge amount of untreated industrial ously polluted due to discharge of untreated wastes wastes have been discharged into open water bodies and effluents from these industries and also house- everyday (Ahmad et al. 2010, Islam et al. 2015b). hold and municipal wastes throw randomly to the Again, a large amount of heavy metal with suspended river. Rupsa River has great importance on agriculture solids is coming down from neighboring country like and household activities and daily uses. The location India through the Teesta and the Brahmaputra Rivers of the river is shown in the following map (Figure 1). (Haque et al. 2007, Mohiuddin et al. 2011, Islam et al. The indiscriminate dumping of domestic, urban, and TOXIN REVIEWS 3

Figure 1. Map showing the study area of Rupsa River, Bangladesh. industrial wastes, combined with the failure of author- sampling point and 21 pairs of composite sediment ities to protect the ecological health of the river has samples were collected. Sediment samples were then aggravated the situation to the point where this river freeze-dried to obtain constant weight. The samples is dying biologically and hydrologically. The wastes are were homogenized by grinding in an agate mortar, mixed with sediment and water and continuously pol- sieved through 106 mm aperture nylon sieve and luted by heavy metals cause river pollution. For this, stored in labeled glass bottles until chemical analyses we have selected Rupsa River for determining toxic (Liang et al. 2015). metal contamination with ecological and human health risk. Analytical methods for chemical parameters The pH of sediments was measured in 1:2.5 sediment Sediment sample collection and preparation to water ratio. The suspension was allowed to stand Sediment samples were collected by random method overnight prior to pH determination (Abollino et al. from different sampling location in the river. Two sea- 2002). The pH was measured using a pH meter with sons summer (August–September, 2016) and winter the calibration of pH 4, pH 7 and pH 9 standards. For (January–February, 2017) was selected for sediment electrical conductivity (EC) determination, 5.0 g of sedi- sample collection. Composite sediment samples were ment was taken in 50-ml polypropylene tubes. Then, collected by using standard procedure from every 30 ml of distilled water was added to the tube. The lid sampling location (USEPA 2001a). About 21 sampling was closed properly and was shaken for 5 min. After sites of Rupsa River (Figure 1) were selected and 200 g that, EC was measured using an EC meter (Horiba sediment collected at a depth of 0 to 5 cm using a D-52). Percent N and C of sediment was measured portable Ekman grab sampler. Sediment was taken by using elemental analyzer (model type: vario EL III, means of a percussion hammer corer at a length of Elen-emtar, Germany). The textural classes for different 50–80 cm for determining background value of prein- soil samples were then determined by plotting the dustrial sample (Schottler and Engstrom 2006). To results on a triangular diagram designed by Marshall determine the age and sediment accumulation rates, (1947) followed USDA system. About 50 g of oven- Lead-210 dating by alpha spectrometry method was dried soil was taken in a dispersion cup and 10 ml of applied. Each sediment sample was obtained by mix- 5% calgon solution was added to the samples and ing sediments randomly collected (3 times) at each allowed to soak for 15 min. Then, 90 ml distilled water 4 R. PROSHAD ET AL. was added to the cup. The suspension was then each experiment, a run included blank, certified refer- stirred with an electrical stirrer for 10 min. The content ence materials (CRM) and samples were analyzed in of the dispersion cup was then transferred to a liter duplicate to eliminate any batch-specific error. sedimentation cylinder and distilled water was added to make the volume up to the mark. A cork was Sediment quantitative ecological assessment placed on the mouth of the cylinder and the cylinder was inverted several times until the whole sediment Enrichment factor (EF) mass appeared in the suspension. The cylinder was set Enrichment factor (EF) is considered as an effective upright and the hydrometer readings were taken at tool to evaluate the magnitude of contaminants in the 40 s and 2 h of sedimentation. The corrections of environment (Franco-Uria et al. 2009). The EF for each hydrometer readings were made as the hydrometer element was calculated to evaluate anthropogenic was calibrated at 68 F. The percentage of sand, silt influences on heavy metals in sediments using the fol- and clay were calculated as follows: lowing formula (Selvaraj et al. 2004): %ðÞ¼þ ð Silt Clay Corrected hydrometer reading at ¼ ðÞ= =ðÞ= EF CM CAl sample CM CAl background (1) 40 s=Oven dry weight of sedimentÞ100 where (C /C ) is the ratio of concentration of %ðÞClay ¼ ðCorrected hydrometer reading M Al sample heavy metal (CM) to that of aluminum (CAl) in the soil = Þ after 2h Oven dry weight of sediment 100 sample, and (CM/CAl)background is the same reference SandðÞ% ¼ 100% ratio in the background sample. The background values of Cr, Ni, Cu, As, Cd and Pb in sediments were 41, ðÞþ ðÞ% ¼ %ðÞþ % Silt Clay Silt Silt Clay Clay 39, 35, 8.5, 0.92 and 23 mg/kg, respectively and the background value of Al was taken from another study (Islam et al. 2015a). Generally, an EF value of about 1 Heavy metal assessment from sediment suggests that a given metal may be entirely from All chemicals were analytical grade reagents and Milli- crustal materials or natural weathering processes Q (ElixUV5 and MilliQ, Millipore, USA) water was used (Zhang and Liu 2002). Samples having enrichment fac- for solution preparation. The Teflon vessel and poly- tor >1.5 was considered indicative of human influence propylene containers were cleaned, soaked in 5% and (arbitrarily) an EF of 1.5–3, 3–5, 5–10 and >10 is

HNO3 for more than 24 h, then rinsed with Milli-Q considered the evidence of minor, moderate, severe, water and dried. For metal analysis, 20 ml water sam- and very severe modification, respectively (Birch and ple and 0.5 g of sediment sample was treated with Olmos 2008). 5 ml 69% HNO3 acid and 2 ml 30% H2O2 in a closed Teflon vessel and was digested in a Microwave Geoaccumulation index (I ) Digestion System. The digested solution was then fil- geo tered by using syringe filter (DISMICVR – 25HP PTFE, The degree of contamination from the trace metals pore size ¼ 0.45 lm) Toyo Roshi Kaisha, Ltd., Japan could be assessed by determining the geoaccumula- and stored in 50-ml polypropylene tubes (Nalgene, tion index (Igeo) proposed by Muller (1969). The index New York). Afterwards, the vessels were cleaned using of geoaccumulation (Igeo) has been widely applied to Milli-Q water and dried with air. Finally, blank digestion the assessment of soil contamination (Santos Bermejo with 5 ml 69% HNO3 following the said digestion proce- et al. 2003). In order to characterize the level of pollu- dures were carried out to clean up the digestion ves- tion in the sediment, geoaccumulation index (Igeo) val- sels. For heavy metals, samples were analyzed by using ues were calculated using the equation: inductively coupled plasma mass spectrometry (ICPMS, Igeo ¼ log 2ðÞCn=1:5Bn (2) Agilent 7700 series). Multi-element Standard XSTC-13 (SpexCertiPrepVR , USA) solutions was used to prepare where Cn is the measured concentration of metal n calibration curve. The calibration curves with R2> 0.999 in the soil and Bn is the geochemical background were accepted for concentration calculation. Multi value of element n in the background sample element solution (Agilent Technologies, USA) 1.0 lg/L (Turekian and Wedepohl 1961, Rudnick and Gao was used as tuning solution covering a wide range of (2003). The factor 1.5 is introduced to minimize the masses of elements. All test batches were evaluated possible variations in the background values which using an internal quality approach and validated if they may be attributed to lithogenic effects (Nikolaidis satisfied the defined internal quality controls (IQCs). For et al. 2010). Geoaccumulation index (Igeo) values were TOXIN REVIEWS 5

interpreted as: Igeo 0 – practically uncontaminated; equations which were used to calculate PER and are 0 Igeo 1 – uncontaminated to moderately contami- as follows: nated; 1 I 2 – moderately contaminated; geo Ci Xn 2 I 3 – moderately to heavily contaminated; i ¼ ¼ i geo Cf i Cd Cf (5) Cn ¼ 3 Igeo 4 – heavily contaminated; 4 Igeo 5 – i 1 heavily to extremely contaminated; and 5 < I – Xm geo i ¼ i i ¼ i extremely contaminated. Er Tr Cf PER Er (6) i¼1

i where Cf is the single-element contamination factor, Contamination factor i i C is the content of the element in samples and Cn is The contamination factor (CF) is the ratio obtained by the background value of the element. The background dividing the concentration of each metal in the soil by value of Cr, Ni, Cu, As, Cd and Pb in soils were 90, 68, the baseline or background value (hazardous elements 45, 13, 0.3 and 20 mg/kg, respectively (pre-industrial in the pre-industrial soil of the study area). samples of the study area) (Turekian and Wedepohl 1961,Yiet al. 2011). The sum of Ci for all metals rep- i Cheavymetal f Cf ¼ (3) Cbackground resent the integrated pollution degree (Cd) of the environment. Ei is the potential ecological risk index The contamination factor being classified into four r and is the biological toxic factor of an individual elem- grades for monitoring the pollution of one single ent. The toxic-response factors for Cr, Ni, Cu, As, Cd, metal over a period of time (Turekian and Wedepohl and Pb were 2, 6, 5, 10, 30, and 5, respectively 1961, Hakanson 1980). The contamination levels may (Hakanson 1980, Luo et al. 2007, Gong et al. 2008, be classified based on their intensities on a scale rang- Madiseh et al. 2009, Guo et al. 2010,Wuet al. 2010, ing from 1 to 6: low degree (C i < 1), moderate f Jintao et al. 2011, Amuno 2013). PER is the compre- degree (1 C i < 3), considerable degree (3 C i < f f hensive potential ecological risk index, which is the 6), and very high degree (C i 6) (Luo et al. 2007) f sum of Ei . Sensitivity of the biological community is (Table 8). Thus, the values can monitor the enrichment r represented by it to the toxic substance and indicates of one given metal in sediments over a period the potential ecological risk caused by the overall of time. contamination.

Pollution load index (PLI) Toxic unit analysis Pollution load index act as an integrated approach The sum of toxic units (RTUs) is considered as poten- which assess sediment quality of heavy metals. tial acute toxicity of hazardous elements in sediment According to Suresh et al. (2011) pollution load index may be assessed from six hazardous elements (Cr, Ni, samples. Toxic unit analysis is stated as the ratio of Cu, As, Cd and Pb). Pollution load index can be deter- the assessed concentration of hazardous elements in mined for six toxic metals like Cr, Ni, Cu, As, Cd and sediment to probable effect level (PELs) (Zheng et al. Pb. Pollution load index was measured by using the 2008). A moderate to serious toxicity of hazardous ele- following formula (Tomlison et al. 1980): ments remain in sediment when the sum of toxic units for all sediment samples is more than 4 (Bai 1=n PLI ¼ ðÞCF1 CF2 CF3 ... CFn (4) et al. 2011). The TU for each metal was calculated using the following formula: where CF is contamination factor for each single metal ¼ ðÞ= and n is number of heavy metals. The overall toxicity TU CM PEL (7) status of heavy metals in sediment may be assessed where CM is the concentration of heavy metal (CM)in from Pollution load index (PLI) calculation. PLI is the sediment and PEL is the probable effect levels value share of conclusion of six heavy metals. of heavy metals in sediment. X TUs ¼ TU is TU TU TU Potential ecological risk (PER) metal1 metal2 metal3 metal4 ...... TUmetaln ð8Þ The degrees of hazardous elements contamination in P agricultural soils are determined by PER index. Guo where TUs is the product of toxic units for heavy et al. (2010) and Luo et al. (2007) proposed metals in sediments. 6 R. PROSHAD ET AL.

Noncarcinogenic risk assessment where THQ is the target hazard quotient, CDI is the chronic daily intake of heavy metal (mg/kg) and RfD is Intake of heavy metals through ingestion and reference dose (mg/kg/day). The RfD for Cr, Ni, Cu, As, dermal contact pathway Cd, and Pb were 0.003, 0.02, 0.04, 0.0003, 0.0005, and Ingestion and dermal contact of heavy metals from 0.0035 mg/kg/day, respectively (USEPA 2002, USDOE polluted river sediment have great importance in 2011). The reference dose (RfD) (mg/kg/day) is an esti- potential exposure pathways. Out of several exposure mation of maximum permissible risk on human popula- pathways, ingestion of metals from sediment is the tion through daily exposure, taking into consideration most common exposure pathway for Cr, Ni, Cu, As, Cd, sensitive group (children) during the lifetime. If the CDI and Pb. The chronic daily intake (CDI) of heavy metals is higher than RfD (HQ > 1), there will be a severe through oral ingestion was calculated in case of inges- health hazard to human, whereas CDI is less than RfD tion exposure pathway – considering chronic exposure (HQ 1), there will be no severe human health effects – (Zabin et al. 2008). Heavy metal intake through inges- (USEPA 1989, USEPA 2001b). The health risk guidelines tion was assessed through following equation: determination of chemical mixtures defined that “simultaneous sub-threshold exposures to several CS IRS EF ED ” CDIingest-sediment ¼ CF (9) chemicals may result in an adverse health effect and BW AT “the magnitude of the adverse effect will be propor- where CDIingest-sediment—chronic daily intake of heavy tional to the sum of the ratios of the sub-threshold metals from sediments via ingestion: mg/kg; CS— exposures to acceptable exposures” (USEPA 1986). exposure-point concentration: mg/kg; IRS—ingestion Again, hazard index (HI) can be generated from the rate of sediment: 100 for adult male and female where hazard quotient to calculate the combined risk of indi- 200 mgd1 for children respectively (USEPA 2011); EF vidual heavy metals in the form of mix contaminates — exposure frequency: 350 days/year (USEPA 2011); (USEPA 1989, Qing et al. 2015). In order to assess the ED — exposure duration: 30 years for adult male and overall potential for noncarcinogenic effects from female where 6 years for children (USEPA 2011); BW — more than one heavy metal, a hazard index (HI) has body weight: 70 kg for adult male, 65 kg for adult been formulated based on the guidelines for health female and 15 kg for children; AT — averaging time risk assessment of chemical mixtures (USEPA 1999). for non-carcinogens: 365 ED (USEPA 2011); CF— The hazard index (HI) from THQs is expressed as the units conversion factor: 106 kg/mg (USEPA 2002). sum of the hazard quotients (USEPA 2011). The equa- The chronic daily intake of sediment through ingestion tion used for estimating the hazard index is as follows: was assessed separately for adult and children as body- HIingestsediment ¼ RTHQn (11) weight and ingestion rate of metals was different for adult and children. In the present study, long-term expos- ¼ THQ element 1 þ THQ element 2 þ ...... : ure(30yearsforadultmaleandadultfemalewhere þ THQ elements n ð12Þ 6 years for children) was considered for determining CDI The guidelines also state that any single metal with via heavy metal ingestion from sediment. an exposure level greater than the toxicity value will The potential noncarcinogenic risks for each indi- cause the hazard index to exceed unity, for multiple vidual heavy metal (Cr, Ni, Cu, As, Cd and Pb) through metal exposures the HI can also exceed unity even if ingestion were assessed by the target hazard quotient no single metal exposure exceeds its RfD. (THQ) (USEPA 1989). The methodology for the estima- Dermal contact is an important exposure pathway tion of noncarcinogenic risks was applied in accord- of heavy metals from sediments. Chronic daily intake ance with that provided by the U.S. Environmental via dermal contact (CDI ) is appropriately Protection Agency (USEPA) Region III’s risk-based con- dermal-sediment assessed using site-specific information for population centration table (USEPA 2011). Hazard quotient (HQ) at risk. The mathematical equation is for assessing was determined on the basis of chronic daily intake heavy metal intake through dermal contact exposure from ingestion (CDIingest-sediment) and it was calculated pathway to metals in sediments (USEPA 2007; Qing by dividing the average daily dose to a specific refer- et al. 2015) through following equation: ence dose (RfD) (USEPA 1989,Quet al. 2012, Genthe et al. 2013). The equation used for estimating the tar- CSSAAFABSIRSEFED CDIdermal-sediment ¼ CF get hazard quotient is as follows: BW AT CDI (13) THQ ¼ (10) ingest sediment RfD TOXIN REVIEWS 7

where CDIdermal-sediment—chronic daily intake of heavy metals from dermal contact of sediments; CS — heavy metals from sediment through dermal contact: mg/kg; metal concentration in sediments: mg/kg; AF — sedi- CS — heavy metal concentration in sediments: mg/kg; ment-to-skin adherence factor: 0.7 mg/cm2 for adult SA — exposure skin surface area available for contact: and 0.2 mg/cm2 for children (USEPA 2011); IngR— 2 2 5700 cm for adult and 1600 cm for children (USEPA ingestion rate of sediment: 100 and 200 mgd 1 for 2011); AF — sediment-to-skin adherence factor: adult and children respectively (USEPA 2011); EF — 2 2 0.7 mg/cm for adult and 0.2 mg/cm for children exposure frequency: 350 days/year (USEPA 2011); (USEPA 2011); ABS — dermal absorption fraction: 0.01 ED — exposure duration: 30 years for adult and 6 years — for adult and 0.001 for children (USEPA 2011); IRS for children (USEPA 2011); BW — body weight: 70 kg ingestion rate of sediment: 100 and 200 mgd 1 for for adult and 15 kg for children; AT — averaging time — adult and children respectively (USEPA 2011); EF for noncarcinogens: 365 ED (USEPA 2011); CF—units exposure frequency: 350 days/year (USEPA 2011); ED conversion factor: 106 kg/mg (USEPA 2002); — exposure duration: 30 years for adult and 6 years CSFingest—Chronic oral slope factor: 1.5 for As and 8.5 for children (USEPA 2011); BW — body weight: 70 kg 10 3 for Pb (USDOE 2011, USEPA 2011); SA — for adult male, 65 kg for adult female and 15 kg for exposure skin surface area available for contact: children; AT — averaging time for non-carcinogens: 5700 cm2 for adult and 1600 cm2 for children (USEPA 365 ED (USEPA 2011); CF—units conversion factor: 2011); ABS — dermal absorption fraction: 0.01 for 10 6 kg/mg (USEPA 2002) d adult and 0.001 for children (USEPA 2011); HQ for dermal exposure route was calculated using ABS —Gastrointestinal absorption factor: 0.41 and 1 the following equation (Qing et al. 2015): GI for As and Pb respectively (USEPA 2011). CDI THQdermalsediment ¼ (14) In present study, we calculated carcinogenic risk RfD for arsenic and lead as they are classified as probably The RfD for Cr, Ni, Cu, As, Cd, and Pb were 6.00E- carcinogenic to humans (ASTDR 2007, ATSDR 2012). 05, 5.60E-03, 1.20E-02, 3.00E-04, 5.00E-04, and 5.25E- The excess cancer risks lower than 106 (a probability 04 mg/kg/day, respectively (USEPA 2002; USDOE 2011). of 1 chance in 1,000 000 of an individual developing Assuming additive effect, the hazard index (HI) for cancer) are considered to be negligible, cancer risks dermal exposure route was calculated using the fol- above 104 are considered unacceptable by most lowing equation: international regulatory agencies (USEPA 1989, Guney 6 4 HIdermalsediment ¼ RTHQn (15) et al. 2010) and risks lying between 10 and 10 are generally considered an acceptable range, ¼ THQ element1 þ THQ element 2 þ ...... : depending on the situation and circumstances of þ ð Þ THQ elements n 16 exposure (Fryer et al. 2006,Huet al. 2012). The value 106 is also considered the carcinogenic target risk by USEPA (2011). Carcinogenic risk assessment Carcinogenic risk is considered as the probability of an Statistical analysis individual developing any type of cancer in the whole lifetime due to exposure to carcinogenic hazards (Li The data were statistically analyzed by using the et al. 2014, Emenike et al. 2017,Suet al. 2017). statistical package SPSS 20.0 (International Business Carcinogenic risk expressed as the total cancer risk Machines Corporation [IBM] Armonk, NY). The means Equation (19). and standard deviations of the metal concentrations ðÞin sediment were calculated. Multivariate methods in CRingest-sediment ¼ ðÞCSAFIngREFED = BWAT terms of principal component analysis (PCA) were CFCSFingest ð17Þ used to interpret the potential sources of heavy ðÞ CRdermal-sediment¼ ðÞCSSAAFABSdEFED = BWAT metal in sediment (Liang et al. 2015). A Pearson CFCSF ABS ð18Þ bivariate correlation was used to evaluate the inter- X ingest GI element relationship in sediment. Surfer 15.0 soft- Total cancer risk¼ Cancer risk ¼ Risk þRisk ingestion dermal ware was applied to make counter graph to show (19) the distribution of several parameters related to where CRingest-sediment— cancer risk of metals from metals. Microsoft Excel 2013 was used for other ingestion of sediments CRdermal-sediment— cancer risk of calculations. 8 R. PROSHAD ET AL.

Results and discussion system can be considered the main source of organic carbon (Islam and Al-Mamun 2017, Jain et al. 2008). Physicochemical properties and heavy metals According to the USDA soil texture classification, the concentration in sediment textural analysis revealed that the sediments of the The physicochemical properties of sediments in the studied river were loam and silt loam (Table 1). Fine- study sites during the summer and winter seasons are sediment textures are essential for geochemical car- presented in Table 1. The pH of the sediments was riers, which can control metal bioavailability for the alkaline for all the sites. Due to the variation in topog- aquatic organisms exposed through dissolved metals raphy, hydrology, and geology within the catchment (Islam et al. 2015a). areas, as well as differences in precipitation and local The mean concentrations of Cr, Ni, Cu, As, Cd, climate, the chemical properties such as pH, alkalinity, and Pb in sediments of Rupsa River are presented in and concentration of heavy metals may differ substan- Table 2. The distributions of these heavy metals are tially between streams even within small distances presented in Figures 2 and 3. A wide range of values (Gundersen and Steinnes 2001). Electrical conductivity for metal concentrations was observed among the in sediments ranged from 2.99 to 4.90 (dS/m) during sampling sites. Factors such as salinity, geomorpho- summer and 3.59 to 5.88 (dS/m) during winter. Total logical setup and land runoff might have played a nitrogen content was ranged from 0.07 to 0.26% dur- vital role in the variation of metals. Metals concentra- ing summer and 0.13 to 0.26% during winter tions in sediment during winter season were slightly (Table 1). The organic carbon composition in the river- higher than summer due to the lower water flow dur- ine sediments is varying due to its origin in the ing season winter which could support to accumulate aquatic environment. Phytoplankton and zooplankton the heavy metals in sediment (Mohiuddin et al. 2011, are the most abundant sources of the organic material Ali et al. 2016). The concentrations of heavy metals at in the sediments (El-Gohary et al. 2012). The organic R8 and followed by R18, R9 and R7 sites were much carbon in sediments was ranged from 0.13 to 2.82% higher than others sites indicated that downstream and 0.26 to 3.64% during summer and winter season, river flow and rapid urbanization activities drove respectively (Table 1). The highest percentage of heavy metals contamination in surface sediment (Yang organic carbon might be attributed to the high et al. 2009,Liet al. 2012). The urban activities like amount of municipal and industrial sewage at R21 industrial discharges, municipal waste water, house- site. The high rate of organic growth together with hold garbage, and urban runoff of Khulna city are the the organic detritus introduced by the drainage main causes of higher metal input at R8 and R18

Table 1. Physicochemical properties of sediment collected from Rupsa River, Khulna, Bangladesh. Sediment texture (mg/kg) pH EC( dS/m) %N %OC Sand Silt Clay Sites Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer winter Textural class R1 8.24 8.44 2.99 3.59 0.12 0.18 0.37 0.76 256 281.6 567 584.01 177 134.39 Silt loam R2 8.18 8.38 3.01 3.61 0.09 0.13 0.23 0.69 324 356.4 510 525.3 166 118.3 Silt loam R3 8.42 8.62 3.04 3.65 0.16 0.15 0.16 1.21 278 305.8 532 547.96 190 146.24 Silt loam R4 8.41 8.61 4.90 5.88 0.11 0.17 2.14 1.97 312 343.2 571 588.13 117 68.67 Silt loam R5 8.36 8.56 4.30 5.16 0.15 0.24 1.85 3.26 482 530.2 353 363.59 165 106.21 Silt loam R6 8.56 8.77 4.60 5.52 0.07 0.15 0.13 0.35 219 240.9 576 593.28 205 165.82 Silt loam R7 8.49 8.69 3.10 3.72 0.16 0.25 0.42 0.78 256 281.6 524 539.72 220 178.68 Silt loam R8 8.45 8.65 3.98 4.78 0.18 0.23 0.26 0.49 310 341 604 622.12 86 36.88 Silt loam R9 8.53 8.73 3.97 4.76 0.15 0.24 1.02 1.46 296 325.6 548 564.44 156 109.96 Silt loam R10 8.13 8.33 4.20 5.04 0.08 0.13 0.31 0.66 337 370.7 532 547.96 131 81.34 Silt loam R11 8.41 8.61 4.13 4.96 0.1 0.18 0.18 0.31 410 451 450 463.5 140 85.5 Loam R12 8.31 8.51 4.10 4.92 0.21 0.2 0.26 0.83 234 257.4 618 636.54 148 106.06 Silt loam R13 8.43 8.63 4.60 5.52 0.13 0.18 1.34 2.45 198 217.8 602 620.06 200 162.14 Silt loam R14 7.92 8.11 4.40 5.28 0.12 0.16 2.31 2.56 167 183.7 575 592.25 258 224.05 Silt loam R15 8.45 8.65 4.55 5.46 0.09 0.14 0.13 0.26 214 235.4 578 595.34 208 169.26 Silt loam R16 8.18 8.38 4.20 5.04 0.10 0.17 1.24 2.07 237 260.7 490 504.7 273 234.6 Silt loam R17 8.46 8.66 4.15 4.98 0.12 0.24 0.38 0.94 276 303.6 522 537.66 220 158.74 Silt loam R18 7.87 8.06 4.18 5.02 0.26 0.26 0.22 0.44 343 377.3 565 581.95 92 40.75 Silt loam R19 8.45 8.65 3.79 4.55 0.16 0.24 0.32 1.18 242 266.2 525 540.75 233 193.05 Silt loam R20 8.36 8.56 3.98 4.78 0.15 0.21 1.63 3.48 278 305.8 510 525.3 212 168.9 Silt loam R21 8.34 8.54 4.20 5.04 0.11 0.18 2.82 3.64 310 341 570 587.1 120 71.9 Silt loam According to the United States Department of Agriculture soil classification system. TOXIN REVIEWS 9 sampling sites. The average concentration of heavy season, respectively (Table 2). The highest concentra- metals in sediments were in the decreasing order of tion of Ni was observed at R8 site (103.76 and Cu > Ni > Pb > Cr > As > Cd. 151.48 mg/kg, winter and summer season, respect- In the present study, the highest level of Cr was ively). Actually, this R8 sampling site was municipal observed in sediment collected from L8 site (30.70 waste throwing area in BIWTA ghat area in Rupsa. and 52.19 mg/kg, summer and winter season, respect- Higher amount of Ni was found at the site near to the ively) (Table 2). Chromium present in the Rupsa River district urban area, which indicates the higher input of sediment could have been caused by two reasons: (1) Ni in sediment that might be originated from urban natural: concentration of Cr-bearing minerals; and (2) and industrial wastes (Mohiuddin et al. 2011). Nickel anthropogenic: industrial activities such as tanneries concentration in sediments of the present study was and textile factories which are discharging Cr based higher than the other studies (Datta and Subramanian oxidants (chromate, dichromate, etc.) (Facetti et al. 1998, Singh et al. 2005, Gupta et al. 2009, Liu et al. 1998;). Hence, the waste discharged from such indus- 2009, Raphael et al. 2011, Rahman et al. 2014). Ni con- tries was most probably responsible for elevated Cr centration of present study also compared to several level in the exposed sediment (Mohiuddin et al. sediment standard quality guidelines and it was found 2011). The Cr concentrations of present study were that Ni concentration level for present study was compared to other study conducted in Bangladesh higher than TRV, LEL, TEL, PEL and ERL (Persuad et al. and other countries. Chromium concentration in sedi- 1993, USEPA 1999, MacDonald et al. 2000) and lower ments of the present study was found higher than than ASV, CUC, SEL and ERM (Turekian and Wedepohl the other studies (Singh et al. 2005,Guptaet al. 1961, Persuad et al. 1993, MacDonald et al. 2000, 2009,Raphaelet al. 2011)(Table 3). Chromium con- Rudnick and Gao 2003)(Table 3). centration of present study also compared to several The mean concentration of Cu was found 48.28 sediment standard quality guidelines and it was and 89.32 mg/kg, respectively, for summer and winter. cleared that Cr concentration level was lower than The highest concentration of Cu was observed at R13 TRV,ASV,LEL,TEL,CUC,SEL,PEL,ERLandERM site (82.08 and 151.84 mg/kg) in summer and winter (Turekian and Wedepohl 1961,Persuadet al. 1993, season, respectively (Table 2). The higher level of Cu USEPA 1999,MacDonaldet al. 2000, Rudnick and Gao was found in the study area which is may be the 2003)(Table 3). result of anthropogenic activities such as vehicle and The mean concentration of Ni in sediment was coal combustion emissions (Zhu et al. 2011,Liet al. found as 34.47 and 50.33 mg/kg in summer and winter 2012), car lubricants (Al-Khashman 2007,Fuet al.

Table 2. Total concentration of heavy metals (mg/kg dw) in sediment collected from Rupsa River, Bangladesh. Metal concentration (mg/kg)

Cr Ni Cu As Cd Pb

Sites Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter R1 12.69 21.58 8.68 12.67 21.54 39.86 3.89 6.43 1.09 1.94 10.71 19.74 R2 18.40 31.28 4.42 6.45 19.21 35.55 3.96 6.54 1.27 2.26 5.48 10.11 R3 16.92 28.77 7.52 10.98 18.66 34.51 4.13 6.81 1.27 2.25 6.21 11.46 R4 18.56 31.55 34.08 49.76 63.53 117.53 5.94 9.80 3.71 6.60 16.48 30.39 R5 19.50 33.15 40.62 59.30 54.78 101.35 5.81 9.58 4.07 7.24 18.13 33.43 R6 18.35 31.20 36.72 53.62 51.37 95.03 5.46 9.00 3.73 6.62 15.92 29.36 R7 27.37 46.53 87.95 128.40 41.81 77.35 8.73 14.41 1.89 3.35 9.63 17.75 R8 30.70 52.19 103.76 151.48 32.97 61.00 8.58 14.15 1.44 2.56 11.82 21.80 R9 27.74 47.16 94.30 137.68 37.24 68.89 14.68 24.21 1.55 2.75 13.40 24.70 R10 13.86 23.56 15.01 21.92 37.79 69.90 3.61 5.96 3.28 5.83 30.74 56.68 R11 13.67 23.23 15.87 23.16 40.46 74.85 3.71 6.13 3.28 5.83 31.72 58.49 R12 13.69 23.28 23.60 34.46 57.69 106.72 3.77 6.22 3.26 5.79 30.62 56.46 R13 13.71 23.30 24.96 36.44 82.08 151.84 11.29 18.63 2.02 3.59 33.06 60.96 R14 14.76 25.10 17.94 26.19 69.54 128.64 9.51 15.70 1.27 2.26 32.13 59.25 R15 13.60 23.11 30.57 44.63 70.30 130.05 9.91 16.36 1.18 2.09 43.74 80.67 R16 25.23 42.90 42.92 62.66 68.11 126.01 6.26 10.33 2.47 4.40 39.67 73.15 R17 24.13 41.03 35.06 51.19 58.30 107.86 10.91 18.00 0.94 1.66 40.00 73.77 R18 29.47 50.10 34.07 49.75 60.70 112.29 12.32 20.33 2.19 3.89 37.85 69.79 R19 12.82 21.80 21.22 30.98 37.48 69.34 5.10 8.42 7.46 13.25 16.32 30.10 R20 14.37 24.44 24.23 35.37 51.77 95.78 5.06 8.34 6.28 11.17 22.01 40.59 R21 13.44 22.84 20.48 29.90 38.64 71.49 4.97 8.21 3.50 6.22 15.35 28.30 Ranges 12.69-30.70 21.58-52.19 4.42-103.76 6.45-151.48 18.66-82.08 34.51-151.84 3.61-14.68 5.96-24.21 0.94-7.46 1.66-13.25 5.48-43.74 10.11-80.67 Mean ± SD 18.71 ± 6.1 31.81 ± 10.4 34.47 ± 27.7 50.33 ± 40.4 48.28 ± 17.7 89.32 ± 32.9 7.02 ± 3.3 11.59 ± 5.44 2.72 ± 1.7 4.83 ± 3.0 22.90 ± 12.1 42.23 ± 22.4 10 R. PROSHAD ET AL.

Figure 2. Heavy metals distribution in sediment collected from Rupsa River, Bangladesh (summer season).

2014), and natural phenomenon such as metal con- sediment excavation that alters the hydraulic regime tents of rocks and parent materials, processes of soil and/or arsenic source material increased the rate of formation (Li et al. 2008, Yang et al. 2009,Yiet al. discharge into freshwater habitat (Baeyens et al. 2007, 2011). Copper concentration in sediments of the pre- Pravin et al. 2012). Arsenic concentration in sediments sent study was higher than the other studies (Datta of the present study were compared to other study and Subramanian 1998, Singh et al. 2005, Gupta et al. conducted in Bangladesh and other countries and 2009, Ahmad et al. 2010, Raphael et al. 2011, Rahman found that As concentration in present study was et al. 2014). Cu concentration of present study also higher than the other studies (Singh et al. 2005, Gupta compared to several sediment standard quality guide- et al. 2009, Ahmad et al. 2010, Raphael et al. 2011, lines and it was found that Cu concentration level for Rahman et al. 2014). Arsenic concentration of present present study was higher than TRV, ASV, LEL, TEL, CUC study also compared to several sediment standard and ERL (Turekian and Wedepohl 1961, Persuad et al. quality guidelines and it was found that As concentra- 1993, USEPA 1999, MacDonald et al. 2000, Rudnick tion level for present study was higher than TRV, LEL, and Gao 2003) and lower than SEL, PEL and ERM TEL and CUC (Persuad et al. 1993, USEPA 1999, (Persuad et al. 1993, MacDonald et al. 2000)(Table 3). MacDonald et al. 2000, Rudnick and Gao 2003) and The highest concentration of As was observed at lower than ASV, SEL and PEL (Turekian and Wedepohl R9 site (14.68 mg/kg in summer and 24.21 mg/kg in 1961, Persuad et al. 1993, MacDonald et al. 2000) winter) followed by R18 (12.32 mg/kg in summer sea- (Table 3). son and 20.33 mg/kg in winter season). In the present The highest concentration of Cd was observed at study, the average concentration of As in sediment R19 site (7.46 mg/kg in summer and 13.25 mg/kg in was observed 7.02 and 11.59 mg/kg during summer winter) followed by R20 (6.28 mg/kg in summer sea- and winter season, respectively (Table 2). Recently, the son and 11.17 mg/kg in winter season). In the present anthropogenic activities such as treatment of agricul- study, the average concentration of Cd in sediment tural land with arsenical pesticides (Fu et al. 2014), was observed 2.72 and 4.83 mg/kg during summer treating of wood using chromated copper arsenate, and winter season, respectively (Table 2). Higher Cd burning of coal in thermal plants power stations, and concentration in sediment of Rupsa River might be TOXIN REVIEWS 11

Figure 3. Heavy metals distribution in sediment collected from Rupsa River, Bangladesh (winter season). related to industrial activity, atmospheric emission, MacDonald et al. 2000, Rudnick and Gao 2003) and Cd plated items. Slightly higher Cd levels during (Table 3). winter might be attributed to the variation in water In present study, the highest level of Pb was capacity of the river, where water input to the river is observed at R15 site (43.74 and 80.67 mg/kg) during generally limited in winter, resulting in the precipita- winter and summer season, respectively (Table 2). The tion of contaminants in the sediment (Ali et al. 2016). highest level of Pb in sediments at R15 site can be Cadmium concentration in sediments of the present due to the effect from point and nonpoint sources; study were compared to other study conducted in such as municipal runoffs, atmospheric deposition and Bangladesh and other countries and found that Cd leaded gasoline, chemical manufacturing, and steel concentration in present study was higher than the works in urban area of Khulna city (Mohiuddin et al. other studies (Datta and Subramanian 1998,Singh 2011, Shikazono et al. 2012, Islam and Al-Mamun et al. 2005,Guptaet al. 2009,Liuet al. 2009,Ahmad 2017). In sediment, Pb was observed notably higher in et al. 2010,Raphaelet al. 2011, Rahman et al. 2014, winter season compared to summer which indicates Islam et al. 2015a). Cadmium concentration of pre- that there is a considerable change in organic profile sent study also compared to several sediment stand- by resuspension and/or deposition or by changes in ard quality guidelines and it was found that As redox and pH conditions (ElNemr et al. 2007, Bastami concentration level for present study was higher than et al. 2012, Islam et al. 2015b). Lead concentration in TRV,ASV,LEL,TEL,CUC,PEL,andERL(Turekianand present study was found higher than some other stud- Wedepohl 1961,Persuadet al. 1993,USEPA1999, ies (Datta and Subramanian 1998, Gupta et al. 2009, 12 R. PROSHAD ET AL.

Raphael et al. 2011). According to sediment quality guideline, Pb concentration of the present study was higher than TRV, ASV, LEL and CUC (Turekian and ) ) ) Wedepohl 1961, Persuad et al. 1993, USEPA 1999, 2000 2000 2000 .( .( .( MacDonald et al. 2000, Rudnick and Gao 2003) and et al et al et al lower than TEL, SEL, PEL, ERL and ERM (Persuad et al. ) ) 1993, MacDonald et al. 2000)(Table 3). 1961 1998 ) ) ) ) ) ), MacDonald ), MacDonald ), MacDonald

) Source analysis of heavy metals in sediment 2003 2000 2000 2000 ) ) a) ) .( .( .( 2014 1993 1993 1993 ) 2010 .( 2009 . (2011) .( .( .( Statistical analyses were performed to elucidate the 2005 ) 2015 2018 .( et al et al et al .( .( .( .( 2009

et al associations among heavy metals in sediment and to et al et al et al et al .( 1999 et al et al et al et al et al identify the important factors involved in controlling et al

resent study the transport and distribution of metal contaminants (Liu et al. 2003, Chen et al. 2012, Islam et al. 2015b), for example, Pearson’s correlation. Inter-metal interac- 83) Islam 3312) Islam – 78 Liu 8.4 Gupta – – – tions may illustrate the sources and pathways of the 19 Datta and Subramanian ( metals present in the particulate media (Raknuzzaman et al. 2016). Pearson’s correlation (PC) matrix for analyzed sediment parameters was calculated to see if 2.8) 58 (36 19) 731 (43 some of the parameters interrelated with each other – – 1.4 4.3 –

1.29.6 46.7 218and MacDonald the Persuad results are presented in Table 4. A clear pattern of significant positive correlation was found among physicochemical properties and metals in the sediment samples: Silt-Sand, Ni-Cr, Cu-EC, As-Cr, As-Ni, 52) 1.2 (0.26 67) 7.7 (2.0 – –

48 NA 26 As-Cu, Pb-EC, and Pb-Cu during summer season. –– – – – However, in winter season, significant positive correlation was found among Cr-N, Ni-N, Ni-Cr, Cu-EC, As-N, As-Cr, As-Ni, As-Cu, Pb-EC, and Pb-Cu; negative correlation was found among Silt-Sand and Clay-Sand. 712) 21 (7.6

118) 25 (2.6 Higher correlation coefficient between the metals indi- 4.4 NA 0.14 – – 102 14 – – cated common sources, mutual dependence and iden- tical behavior during their transport (Chen et al. 2012, Islam et al. 2014, Ali et al. 2016). To identify the source of heavy metals in sediment of different sites of Rupsa River, a principal component 539) 280 (62 163) 76 (35 – – analysis (PCA) was conducted, which has been considered to be an effective tool for source identification (Bai et al. 2011, Anju and Banerjee 2012, Islam et al. 2018). PCA analysis incorporated the six metal concentration data of the studied River and explored the pos- 183) 95 (37 841) 240 (62 – – 6.4 NA 0.98 128 NA 30 – – sible similar distribution pattern of metals. The principal component analysis was performed on the tabular and standardized forms of data set and is presented in Tables 5, 6, and Figure 4. The extraction method was performed to find out the principal com- ponents (PC) in PCA analysis that was Eigen values. Multivariate principal component analysis (PCA) of heavy metals in the samples explaining about Comparison of metals in sediment (mg/kg dw) with some reference values and some reported values. 94.956% (summer season) and 93.813% (winter season) cumulative variance of the data. In this study, Okumeshi River, NigeriaTRV (Toxicity reference value) 26 0.87 1.21 16 NA 16 NA 6 1.32 0.6 0.45 31 Raphael USEPA ( Jamuna River, BangladeshBuriganga River, BangladeshGomti River, India 110 178ASV(Average shale value) 200 33 8.15 90 28 28 16 68 NA 5.0 45 3.3 NA 13 70 2.4 0.3 Ahmad 40 20 Singh Turekian and Wedepohl ( LEL (Lowest effect level) 26 16 16 6 0.6 31 Persuad Yellow River, China 41 Bangshi River, Bangladesh 98 26 31 1.9 0.61 60 Rahman SEL (Severe effect Level) 110 75 110 33 10 250 Persuad TEL (Threshold effect level)CUC (Continental upper crust) 92 37 47 18 28 36 5.9 5 0.59 0.09 35 17 MacDonald Rudnick and Gao ( Ganges River, India 1.8 PEL (Probable effect level)ERL (Effect range low)ERM (Effect range medium) 90 370 81 36 51.6 20.9 197 270 34 17 3.5 91 MacDonald Korotoa River, Bangladesh 109 (55 Table 3. Study locationRupsa River, Bangladesh 25.26 (12.69-52.19) 42.40 (4.42-151.48) 68.81 (18.66-151.84) 9.31 (3.61-24.21) Cr 3.78 (0.94-13.25) 32.57 (95.48-80.67) P Ni Cu As Cd Pb References Buriganga River, Bangladesh 297 (17 two PCs were computed and the variances explained TOXIN REVIEWS 13

Table 4. Pearson correlation coefficient matrix for heavy metals in the sediment of Rupsa River, Bangladesh. Summer pH EC N OC Sand Silt Clay Cr Ni Cu As Cd Pb pH 1 EC 0.016 1 N 0.224 0.168 1 OC 0.154 0.407 0.138 1 Sand 0.017 0.046 0.110 0.036 1 Silt 0.029 0.131 0.147 0.102 0.711 1 Clay 0.163 0.044 0.108 0.115 0.095 0.083 1 Cr 0.002 0.082 0.417 0.165 0.176 0.033 0.175 1 Ni 0.379 0.043 0.288 0.047 0.045 0.083 0.013 0.789 1 Cu 0.136 0.791 0.017 0.355 0.317 0.138 0.159 0.010 0.024 1 AS 0.008 0.276 0.308 0.057 0.219 0.247 0.267 0.580 0.566 0.447 1 Cd 0.165 0.224 0.059 0.207 0.164 0.266 0.224 0.357 0.180 0.040 0.411 1 Pb 0.328 0.597 0.012 0.017 0.182 0.057 0.338 0.064 0.176 0.76 0.323 0.086 1 Winter pH 1 EC 0.015 1 N 0.131 0.066 1 OC 0.073 0.250 0.081 1 Sand 0.017 0.046 0.249 0.072 1 Silt 0.029 0.131 0.176 0.256 0.711 1 Clay 0.055 0.077 0.162 0.176 0.644 0.079 1 Cr 0.001 0.082 0.569 0.222 0.176 0.033 0.213 1 Ni 0.375 0.043 0.577 0.113 0.045 0.083 0.154 0.789 1 Cu 0.138 0.791 0.058 0.281 0.317 0.138 0.299 0.010 0.024 1 AS 0.132 0.275 0.471 0.029 0.219 0.247 0.042 0.580 0.566 0.447 1 Cd 0.167 0.225 0.193 0.352 0.164 0.266 0.057 0.357 0.180 0.040 0.411 1 Pb 0.330 0.598 0.031 0.055 0.182 0.057 0.197 0.064 0.176 0.760 0.323 0.086 1 Correlation is significant at the 0.05 level (2-tailed). Correlation is significant at the 0.01 level (2-tailed).

Figure 4. Principal component analysis (PCA) of heavy metals in sediment (first in summer and second in winter season) collected from Rupsa River, Bangladesh. Considering the highest component loading, first PC exhibited elevated loadings of Cr, Ni, As, and Cd. Second PC exhibited elevated loadings of Cu and Pb. by them were 62.365% and 32.591% for sediment col- Islam and Al-Mamun 2017). Overall, our study results lected during summer season and 51.593% and indicated that the sources of individual heavy metals 42.221. % for sediment collected during winter season, in surface sediments of the Rupsa River were complex. respectively (Figure 4). Overall, the PCA revealed two major groups of the metals in sediments for both sea- Assessment of metal pollution in sediment son, where one group consisted of Cr, Ni, As, and Cd which were predominantly contributed by anthropo- For the assessment of heavy metals contamination in genic activities (Iqbal and Shah 2011, Islam et al. sediment, an enrichment factor (EF) has been applied 2018). Second group consisted of Cu and Pb which by several researchers (Han et al. 2006, Cevik et al. were contributed by lithogenic sources or by industrial 2009, Bastami et al. 2014). The EF is a normalization emissions in the sampling sites (Islam et al. 2015c, technique that is being widely used to categorize the 14 R. PROSHAD ET AL.

Figure 5. Enrichment factor value of metals in sediment collected from Rupsa River, Bangladesh.

Figure 6. Geoaccumulation index (Igeo) of heavy metals in sediment collected from Rupsa River, Bangladesh. metal fractions that is associated with sediments study area. This might have happened due to higher (Huang et al. 2014). The calculated values of EF for concentration in sediment and lower geochemical each of the studied metals are presented in Figure 5. background values resulting to higher Igeo values of Taking as a whole, the mean EF values of all the Cd (Islam et al. 2015a). The highest values of Cd might studied metals suggested their enrichments in surface be due to contributions from atmospheric emission, sediments of the Rupsa River in Bangladesh. The EF leachates from defuzed batteries and Cd-plated items values of heavy metals for most sites were higher (Islam et al. 2014). This is of particular importance as than 1.5 suggested that these metals might be deliv- the concentrations of Cd could be detrimental to the ered from non-crustal materials, or nonnatural weath- majority of benthic organisms. The Igeo values for the ering processes (Gao and Chen 2012, Islam et al. all other studied metals indicated practically uncon-

2015a). The mean enrichment factor values of Cr, Ni, taminated. The Igeo values of Cu during winter season Cu, As, Cd, and Pb were 0.26, 0.50, 0.77, 0.44, 1.66, indicated uncontaminated to moderately contami- and 0.56 during summer and 0.44, 0.72, 1.43, 0.73, nated of sediment in the study area (Figure 6). The 2.95, and 1.03 during winter season, respectively. As a pollution load index values of heavy metals in sedi- whole, the enrichment factor of all the studied metals ments are presented in Figure 7. Pollution load index for all sampling sites were in the descending order of (PLI) value equal to zero indicates perfection; value of Cd > Cu > Pb > Ni > As > Cr. The higher EF of Cd in one indicates the presence of only baseline level of sediments indicated higher accumulation, which might pollutants and values above one indicates progressive be due to the sub-oxic or anoxic conditions of sedi- deterioration of the site and estuarine quality ment rather than human perturbation (Islam (Mohiuddin et al. 2011, Suresh et al. 2011). The values et al. 2018). of PLI were ranged from 0.40 to 1.35 during summer

The values of geoaccumulation index (Igeo) of the and 0.68 to 2.31 during winter season, which con- studied heavy metals are presented in Figure 6. firmed that the sediment of the studied river was con-

Among the studied metals, the Igeo values showed the taminated (PLI > 1). Among the studied metals, the decreasing order of Cd > Cu > Pb > Ni > As > Cr. The higher PLI values indicated that Cd and Cu are the highest Igeo values obtained for Cd were (0.165 major contributors to the sediment pollution. to 0.735 and 0.083 to 0.985 during summer and Comparing both season, PLI values of winter slightly winter season, respectively) indicated uncontaminated higher than summer season, which was consistent to moderately contaminated of sediment in the with the metal concentration in sediment for TOXIN REVIEWS 15

Table 5. Total variance explained and component matrices (summer season) for the heavy metals in sediment collected from Rupsa River, Bangladesh. Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Component Total % of variance Cumulative % Total % of variance Cumulative % Total % of variance Cumulative % 1 800.019 62.365 62.365 800.019 62.365 62.365 796.557 62.095 62.095 2 418.077 32.591 94.956 418.077 32.591 94.956 421.539 32.861 94.956 3 44.732 3.487 98.443 4 13.314 1.038 99.481 5 4.845 0.378 99.858 6 1.817 0.142 100.000

Elements Component matrix Rotated Component Matrix Raw component Rescaled component Raw component Rescaled component PC1 PC2 PC1 PC2 PC1 PC2 PC1 PC2 Cr 4.903 0.801 4.898 0.800 Ni 27.677 0.999 27.636 0.998 Cu 17.458 0.981 17.389 0.978 As 1.844 1.577 0.559 0.478 1.986 1.394 0.602 0.422 Cd Pb 10.487 0.863 10.681 0.879 KMO and Bartlett’s Test of Sphericity Kaiser–Meyer–Olkin Measure of Sampling Adequacy. 0.582 Bartlett’s Test of Sphericity Approx. Chi-Square 51.14 df 15 Significance 0.00 Extraction Method: Principal Component Analysis.

Table 6. Total variance explained and component matrices (winter season) for the heavy metals in sediment collected from Rupsa River, Bangladesh. Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Component Total % of variance Cumulative % Total % of variance Cumulative % Total % of variance Cumulative % 1 1737.698 51.593 51.593 1737.698 51.593 51.593 1724.898 51.212 51.212 2 1422.039 42.221 93.813 1422.039 42.221 93.813 1434.840 42.601 93.813 3 151.011 4.484 98.297 4 38.187 1.134 99.430 5 13.614 0.404 99.835 6 5.570 0.165 100.000

Elements Component matrix Rotated Component Matrix Raw component Rescaled component Raw component Rescaled component PC1 PC2 PC1 PC2 PC1 PC2 PC1 PC2 Cr 8.288 0.796 8.360 0.803 Ni 40.023 0.990 40.334 0.997 Cu 32.109 0.976 32.175 0.978 As 2.758 2.909 0.506 0.534 3.287 2.294 0.604 0.421 Cd Pb 6.805 18.698 0.304 0.835 19.686 0.879 KMO and Bartlett’s Test of Sphericity Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.585 Bartlett’s Test of Sphericity Approx. Chi-Square 55.18 df 15 Significance 0.00 Extraction Method: Principal Component Analysis. both seasons. The PLI can provide some understand- The contamination factor values of heavy metals in ing about the quality of the aquatic environment that sediments are presented in Figure 8. The value of con- affects the inhabitants (Ali et al. 2016). In addition, it tamination factor (CF) for all metals showed low also provides valuable information about the pollution degree to moderate degree of contamination, whereas status of the study area that helps the decision-makers Cd showed very considerable degree of contamination to take any decision (Suresh et al. 2011). For both sea- during winter season (Figure 8). Overall, the CF for all sons, higher pollution load index values were per- metals were the descending order of ceived in sampling sites R16 to L18, which might be Cd > Cu > Pb > As > Ni > Cr. The mean CF values of Cr, due to the effects of various industries. Ni, Cu, As, Cd, and Pb were 0.45, 0.88, 1.37, 0.82, 2.95 16 R. PROSHAD ET AL.

Figure 7. Pollution load index (PLI) value of heavy metals in sediment of Rupsa River, Bangladesh. (Dot line of the horizontal axis indicates the baseline level of pollutants).

Figure 8. Contamination factor (CF) of heavy metals in sediment of Rupsa River, Bangladesh. and 0.99 during summer and 0.77, 1.29, 2.55, 1.36, indicating considerable degree contamination of the 5.25 and 1.83 during winter season, respectively. sediment environment. The ecological risk factor for i individual element (E r) and the potential ecological risk index (PER) are presented in Table 9. The poten- Ecological risk assessment tial ecological risk factor of heavy metals in sediment Hakanson (1980) developed a methodology to assess were in the descending order of Cd > As > Cu > Pb > ecological risks for aquatic pollution control. The Ni > Cr. Considering the potential ecological risk fac- i methodology is based on the assumption that the tor (E r) for the individual element, Cd showed very i sensitivity of the aquatic system depends on its prod- high potential ecological risk with the E r factor rang- i uctivity. The contamination factor (C f), degree of con- ing from 30.49 to 432.11 (Table 9). The sites R5, R19, i tamination (Cd), ecological risk (E r), and risk index (RI) and R20 showed a considerable to very high eco- classes are shown in Table 7. The contamination fac- logical risk for Cd which might be due to the applica- i tor (C f) for individual metal and degree of contamin- tion of phosphate fertilizers to the agricultural field ation (Cd) are presented in Table 8. The assessment beside the river, dumping of oily materials from of integrated contamination degree of sediments is water vehicles, and waste disposal from the town based on the degree of contamination (Cd). The (ATSDR 2008,Islamet al. 2018). The potential eco- ranges of Cd were 3.21–20 with an average 10.29, logical risk index (PER) in the sampling sites were TOXIN REVIEWS 17

Table 7. Indices and grades of potential ecological risk of heavy metal pollution (Luo et al. 2007). Contamination Contamination degree Degree of Contamination degree of Grade of ecological risk i i factor (Cf ) of individual metal contamination (Cd) the environment Er of individual metal Risk index (PER) i i Cf <1 Low Cd <5 Low contamination Er<40 Low risk RI < 65 Low risk i i 1Cf <3 Moderate 5 Cd <10 Moderate contamination 40Er <80 Moderate risk 65 RI < 130 Moderate risk i i 3Cf <6 Considerable 10 Cd <20 Considerable contamination 80Er<160 Considerable risk 130 RI < 260 Considerable risk i i Cf 6 High Cd 20 High contamination 160Er <320 High risk RI 260 Very high risk i Er320 Very high risk

Table 8. Contamination factors, degree of contamination, contamination level, and pollution load of heavy metals in sediment collected from Rupsa River, Bangladesh. i Contamination factors (Cf )

Sites Season Cr Ni Cu As Cd Pb Degree of contamination (Cd) Contamination level R1 Summer 0.31 0.22 0.62 0.46 1.19 0.47 3.27 Low contamination Winter 0.53 0.32 1.14 0.76 2.11 0.86 5.72 Moderate contamination R2 Summer 0.45 0.11 0.55 0.47 1.39 0.24 3.21 Low contamination Winter 0.76 0.17 1.02 0.77 2.46 0.44 5.62 Moderate contamination R3 Summer 0.41 0.19 0.53 0.49 1.38 0.27 3.27 Low contamination Winter 0.70 0.28 0.99 0.80 2.45 0.50 5.72 Moderate contamination R4 Summer 0.45 0.87 1.82 0.70 4.04 0.72 8.6 Moderate contamination Winter 0.77 1.28 3.36 1.15 7.17 1.32 15.05 Considerable contamination R5 Summer 0.48 1.04 1.57 0.68 4.43 0.79 8.99 Moderate contamination Winter 0.81 1.52 2.90 1.13 7.87 1.45 15.68 Considerable contamination R6 Summer 0.45 0.94 1.47 0.64 4.05 0.69 8.24 Moderate contamination Winter 0.76 1.37 2.72 1.06 7.20 1.28 14.39 Considerable contamination R7 Summer 0.67 2.26 1.19 1.03 2.05 0.42 7.62 Moderate contamination Winter 1.13 3.29 2.21 1.70 3.64 0.77 12.74 Considerable contamination R8 Summer 0.75 2.66 0.94 1.01 1.56 0.51 7.43 Moderate contamination Winter 1.27 3.88 1.74 1.66 2.78 0.95 12.28 Considerable contamination R9 Summer 0.68 2.42 1.06 1.73 1.68 0.58 8.15 Moderate contamination Winter 1.15 3.53 1.97 2.85 2.99 1.07 13.56 Considerable contamination R10 Summer 0.34 0.38 1.08 0.42 3.57 1.34 7.13 Moderate contamination Winter 0.57 0.56 2.00 0.70 6.34 2.46 12.63 Considerable contamination R11 Summer 0.33 0.41 1.16 0.44 3.56 1.38 7.28 Moderate contamination Winter 0.57 0.59 2.14 0.72 6.33 2.54 12.89 Considerable contamination R12 Summer 0.33 0.61 1.65 0.44 3.54 1.33 7.9 Moderate contamination Winter 0.57 0.88 3.05 0.73 6.29 2.45 13.97 Considerable contamination R13 Summer 0.33 0.64 2.35 1.33 2.20 1.44 8.29 Moderate contamination Winter 0.57 0.93 4.34 2.19 3.90 2.65 14.58 Considerable contamination R14 Summer 0.36 0.46 1.99 1.12 1.38 1.40 6.71 Moderate contamination Winter 0.61 0.67 3.68 1.85 2.46 2.58 11.85 Considerable contamination R15 Summer 0.33 0.78 2.01 1.17 1.28 1.90 7.47 Moderate contamination Winter 0.56 1.14 3.72 1.92 2.28 3.51 13.13 Considerable contamination R16 Summer 0.62 1.10 1.95 0.74 2.69 1.72 8.82 Moderate contamination Winter 1.05 1.61 3.60 1.22 4.78 3.18 15.44 Considerable contamination R17 Summer 0.59 0.90 1.67 1.28 1.02 1.74 7.2 Moderate contamination Winter 1.00 1.31 3.08 2.12 1.81 3.21 12.53 Considerable contamination R18 Summer 0.72 0.87 1.73 1.45 2.38 1.65 8.8 Moderate contamination Winter 1.22 1.28 3.21 2.39 4.23 3.03 15.36 Considerable contamination R19 Summer 0.31 0.54 1.07 0.60 8.11 0.71 11.34 Considerable contamination Winter 0.53 0.79 1.98 0.99 14.40 1.31 20.00 Considerable contamination R20 Summer 0.35 0.62 1.48 0.59 6.83 0.96 10.83 Considerable contamination Winter 0.60 0.91 2.74 0.98 12.14 1.76 19.13 Considerable contamination R21 Summer 0.33 0.53 1.10 0.59 3.80 0.67 7.02 Moderate contamination Winter 0.56 0.77 2.04 0.97 6.76 1.23 12.33 Considerable contamination

47.57 to 464.29, which indicate low to very high eco- 2000, Zheng et al. 2008, IslamPet al. 2015b). Toxic unit logical risk (Table 9). (TU) and sum of toxic units ( TUs) for heavy metals in surface sediments of Rupsa River are presented in Figure 10. Toxic units of heavy metals in the studied Toxic unit analysis river decreased in the order of Ni > Cd > As > Pb > Potential acute toxicity of pollutants in sediment sam- Cu > Cr. The average toxic unit values of Cr, Ni, Cu, As, ples can be estimated as the sum of the toxic units, Cd, and Pb in sediments were 0.207, 0.957, 0.245, defined as the ratio of the determined concentration 0.413, 0.777, and 0.251 during summer, whereas they to probable effect levels (PELs) value (MacDonald et al. were 0.353, 1.398, 0.453, 0.682, 1.138, and 0.564 18 R. PROSHAD ET AL.

i Table 9. Potential ecological risk factors (Er) and potential ecological risk indexes (PER) of heavy metals in sediments. i Potential ecological risk factors (Er) Sites Season Cr Ni Cu AS Cd Pb Risk index (PER) Pollution degree R1 Summer 0.62 1.33 3.08 4.58 35.63 2.33 47.57 Low risk Winter 1.05 1.95 5.69 7.56 63.31 4.29 83.86 Moderate risk R2 Summer 0.90 0.68 2.74 4.66 41.56 1.19 51.74 Low risk Winter 1.53 0.99 5.08 7.69 73.85 2.20 91.34 Moderate risk R3 Summer 0.83 1.16 2.67 4.85 41.29 1.35 52.14 Low risk Winter 1.40 1.69 4.93 8.01 73.37 2.49 91.90 Moderate risk R4 Summer 0.91 5.24 9.08 6.99 121.09 3.58 146.89 Considerable risk Winter 1.54 7.66 16.79 11.53 215.18 6.61 259.31 Considerable risk R5 Summer 0.95 6.25 7.83 6.83 132.86 3.94 158.66 Considerable risk Winter 1.62 9.12 14.48 11.27 236.10 7.27 279.86 Very high risk R6 Summer 0.90 5.65 7.34 6.42 121.52 3.46 145.29 Considerable risk Winter 1.52 8.25 13.58 10.59 215.95 6.38 256.27 Considerable risk R7 Summer 1.34 13.53 5.97 10.27 61.49 2.09 94.69 Moderate risk Winter 2.27 19.75 11.05 16.95 109.26 3.86 163.14 Considerable risk R8 Summer 1.50 15.96 4.71 10.09 46.92 2.57 81.74 Moderate risk Winter 2.55 23.30 8.71 16.65 83.37 4.74 139.32 Considerable risk R9 Summer 1.35 14.51 5.32 17.26 50.45 2.91 91.81 Considerable risk Winter 2.30 21.18 9.84 28.49 89.65 5.37 156.83 Considerable risk R10 Summer 0.68 2.31 5.40 4.25 107.07 6.68 126.38 Moderate risk Winter 1.15 3.37 9.99 7.01 190.26 12.32 224.10 Considerable risk R11 Summer 0.67 2.44 5.78 4.37 106.95 6.90 127.10 Moderate risk Winter 1.13 3.56 10.69 7.21 190.05 12.71 225.36 Considerable risk R12 Summer 0.67 3.63 8.24 4.44 106.22 6.66 129.85 Moderate risk Winter 1.14 5.30 15.25 7.32 188.74 12.27 230.02 Considerable risk R13 Summer 0.67 3.84 11.73 13.28 65.87 7.19 102.57 Moderate risk Winter 1.14 5.61 21.69 21.91 117.04 13.25 180.64 Considerable risk R14 Summer 0.72 2.76 9.93 11.19 41.47 6.99 73.06 Moderate risk Winter 1.22 4.03 18.38 18.47 73.68 12.88 128.66 Moderate risk R15 Summer 0.66 4.70 10.04 11.66 38.41 9.51 74.99 Moderate risk Winter 1.13 6.87 18.58 19.24 68.26 17.54 131.61 Considerable risk R16 Summer 1.23 6.60 9.73 7.37 80.65 8.62 114.21 Moderate risk Winter 2.09 9.64 18.00 12.15 143.32 15.90 201.11 Considerable risk R17 Summer 1.18 5.39 8.33 12.83 30.49 8.70 66.92 Moderate risk Winter 2.00 7.87 15.41 21.17 54.18 16.04 116.68 Moderate risk R18 Summer 1.44 5.24 8.67 14.49 71.41 8.23 109.49 Moderate risk Winter 2.44 7.65 16.04 23.91 126.90 15.17 192.13 Considerable risk R19 Summer 0.63 3.26 5.35 6.00 243.17 3.55 261.96 Very high risk Winter 1.06 4.77 9.91 9.90 432.11 6.54 464.29 Very high risk R20 Summer 0.70 3.73 7.40 5.95 204.89 4.78 227.45 Considerable risk Winter 1.19 5.44 13.68 9.82 364.10 8.82 403.05 Very high risk R21 Summer 0.66 3.15 5.52 5.85 114.13 3.34 132.64 Considerable risk Winter 1.11 4.60 10.21 9.65 202.81 6.15 234.54 Considerable risk during winter season. The sum of toxic units in most hazard quotients (HQs), hazard index (HI), and carcino- of the sampling sites during winter season was greater genic risk of the studied metals were estimated for than 4, indicating a moderate to serious toxicity of adult male, adult female, and children and the results heavy metals to the sediment-dwelling fauna of the are presented hereby. The exposure scenario sug- study river (Wang et al. 2011, Xiao et al. 2012, Islam gested in this study that exposure through ingestion et al. 2018). The sum of toxic units for the studied pathway considered as the most common pathway heavy metals for sampling sites R7, R8, and R9 was and the dermal contact as another significant expos- higher than the other sites, which was in the similar ure pathway for both adult and children population. trends of metal concentrations in sediments (Figure 9 and Table 2). Noncarcinogenic risk assessment Ingestion is one of the main route of exposure and Health risk assessment most important source of exposure to heavy metals Human may be exposed to toxic heavy metals and from sediments. Metal specific information was used face several health problems. According to the risk to determine chronic daily intake of heavy metals assessment approach, noncarcinogenic risks of heavy from ingestion (CDI). We assume long-term exposure metals through ingestion and dermal contact path- for 30 years for adult male and adult female and ways were characterized in this study. In order to 6 years for children as the worst-case default assump- evaluate the risk, the chronic daily intakes (CDIs), tion. The health risk determinations from ingestion are TOXIN REVIEWS 19

Figure 9. Toxic unit values of heavy metals in sediment (summer and winter season) collected from Rupsa River, Bangladesh.

Table 10. Calculated CDI (mg/kg/day) values of metals from ingestion and dermal contact in sediment of Rupsa River, Bangladesh. Metals Adult male Adult female Children Oral RfD (mg/kg/day) Contamination of concern CDI (mg/kg/day) from ingestion Cr 0.727 0.783 3.392 0.003 Yes Ni 1.220 1.314 5.693 0.02 Yes Cu 1.979 2.132 9.237 0.04 Yes As 0.268 0.289 1.250 0.0003 Yes Cd 0.109 0.117 0.507 0.0005 Yes Pb 0.937 1.009 4.372 0.0035 Yes CDI (mg/kg/day) from dermal contact Cr 0.029 0.031 0.135 6.00E-05 Yes Ni 0.049 0.052 0.227 5.60E-03 Yes Cu 0.079 0.085 0.369 1.20E-02 Yes As 0.011 0.012 0.050 3.00E-04 Yes Cd 0.004 0.005 0.020 5.00E-04 Yes Pb 0.037 0.040 0.174 5.25E-04 Yes

presented in Table 10. The estimated non-carcinogenic exposure of studied metals for adult male, adult oral CDI values for all studied toxic metals were female and children. remaining much higher than the standard guideline The potential noncarcinogenic toxic effects posed values. On the basis of ingestion daily exposure, there by heavy metals are usually characterized by calculat- is a great chance to pose public health effect concerns ing HQ. The Hazard quotients (HQs) of individual to both adult and children from sediment ingestion. metal due to ingestion for the present study are pre- Again, chronic daily intake for ingestion was found sented in Table 11. The noncancer health risks related highest in children than adult male and children. This to individual element exposure through soil ingestion may be the result of lower body weight of children was low for all investigated elements resulted in a than the adult. It is indicated that the estimated non- HQ < 1, indicating low risk for both adults and chil- carcinogenic oral CDI values for all metals are more dren. If the HQ value is higher than one then there than the guideline values and are likely to pose public will be adverse health effects associated with over health effect concerns from ingestion of sediments exposure (USEPA 1989, 2001a, Zabin et al. 2008,Qu under investigation. The dermal contact of heavy met- et al. 2012, Genthe et al. 2013). The calculated HQ val- als from sediment is considered another important ues for the measured heavy metals assumed that the pathway of exposure. There may be several ways to HQ values for Cr, Ni, Cu, As, Cd, and Pb for adult male, expose metals due to dermal contact from sediment adult female and children were higher than 1 (HQ > like working, wading, playing etc. The calculated 1) which is considered not to be safe for a lifetime of chronic daily intakes of studied metals via dermal con- exposure and may cause adverse health effects in case tact are presented in Table 10. It is proved from Table of adult male, adult female and children. The HQ val- 10 that CDI from dermal contact for Cr, Ni, Cu, As, Cd, ues are much above unity indicating that there is and Pb were higher than dermal RfD values in case of adverse health effects associated with ingestion adult male, adult female and children. There is possi- exposure to the sediments of Rupsa River. The bility to arise non-carcinogenic risk due to dermal estimated HQ values for dermal contact are presented 20 R. PROSHAD ET AL.

Figure 10. Hazard index (HI) of heavy metals in sediment of Rupsa River, Bangladesh.

Table 11. Calculated HQ values of metals from ingestion and The combined effects of exposed metals and metal- dermal contact of sediments. loids were calculated as hazard index (HI) (Qing et al. Metals Adult male Adult female Children 2015). The data indicated that the HI values were HQ from ingestion of sediment Cr 242.25 260.89 1130.52 higher than one. However, when considering the total Ni 60.99 65.68 284.63 exposure HI of ingestion and dermal contact there Cu 49.48 53.29 230.92 As 893.05 961.74 4167.55 was chance of having noncancer risk for all metals on Cd 217.40 234.12 1014.53 adult and children health. HI can serve as a conserva- Pb 267.70 288.29 1249.25 HQ from dermal contact of sediment tive assessment tool to estimate high-end-risk rather Cr 483.30 520.48 2255.39 than low-end risk to protect the public (Qu et al. Ni 8.69 9.36 40.56 > Cu 6.58 7.09 30.71 2012). If the HI 1, then a chronic noncancer effect is As 35.63 38.37 166.29 likely to occur and probability increases with the Cd 8.67 9.34 40.48 < Pb 71.21 76.68 332.30 increase of HI values. Again the value of HI 1, then Hazard index (ingestion þ 2344.95 2525.33 10943.13 no risk of noncarcinogenic effects is believed to occur dermal contact) (Paustenbach 2002, Qing et al. 2015). The total hazard index for children and adult are presented in Figure 10. About 100% sampling location possess HI > 1 haz- in Table 11.ItisclearfromTable 11 that all studied ard risk index values for adult male, adult female, and metals (Cr, Ni, Cu, As, Cd, and Pb) have HQ values children indicating pose non-cancer risk. From the HI, higher than 1 (HQ > 1) for adult male, adult female values for all populations are much higher than unity and children. This suggests that there may be non- indicating a chronic noncarcinogenic effect likely to carcinogenic adverse health effect to adult male, occur due to ingestion and dermal contact exposure adult female, and children through dermal absorption to the studied sediments. route on over exposure of studied metals indicating noncarcinogenic risk to adult male, adult female and Carcinogenic risk assessment children. HQ value for children was higher in present study than adult male and adult female. This may be Carcinogenic risk is regarded as the probability of an due to lower body weight in case of children com- individual developing any type of cancer in the whole pared to adult (Zabin et al. 2008,Quet al. 2012, lifetime due to exposure to carcinogenic hazards (Li Genthe et al. 2013). et al. 2014). In the present study, carcinogenic risk (RI) TOXIN REVIEWS 21

Table 12. Carcinogenic risk (RI) of arsenic and lead due to heavy metals. The chronic daily intake of adult male, ingestion and dermal contact of sediment in Rupsa adult female, and children were higher than the RfD River, Bangladesh. value. The non-carcinogenic estimations for ingestion Exposure pathways Arsenic (As) Lead (Pb) and dermal exposure to individual metal (Cr, Ni, Cu, Adult male Ingestion 2.93E-01 5.0E-03 Dermal contact 6.5E-02 3.0E-03 As, Cd, and Pb) in all sampling sites cause non-cancer Adult female Ingestion 3.15E-01 6.0E-03 risk to people. According to health risk assessment, Dermal contact 7.07E-02 3.0E-03 Children Ingestion 1.02E þ 01 2.0E-02 total hazard index in adult male, adult female, and Dermal contact 1.0E-03 8.91E-05 children (HI > 1) pose non-cancer risk. The carcino- Total risk (adult male) 3.58E-01 8.00E-03 Total risk (adult female) 3.86E-01 9.00E-03 genic risk assessment for arsenic and lead assumed Total risk (adult children) 1.02E þ 01 2.01E-02 that RI values are in a very high risk (>104) resulting cancer risk to human living in the study area spe- was calculated for ingestion and dermal contact of cially children. exposure for As and Pb as they are classified as probably carcinogenic to adult and children (ASTDR 2007, Acknowledgements 2012). The carcinogenic risk for adult and children are presented in Table 12. If the value of RI < 106, the The authors thank the authority of Patuakhali Science and carcinogenic risk from exposure to the sediment can Technology University (PSTU), Bangladesh and Yokohama be negligible and if the value of RI > 106 the risk of National University, Japan for providing laboratory facilities to complete this study. Furthermore, authors are thankful for developing cancer in human beings likely high. An RI 6 4 the kind help from the members Patuakhali Science and value within a range from 10 to 10 indicates an Technology University, Bangladesh during the acceptable or tolerable risk to social stability and to field sampling. human health (Wu et al. 2015). Cancer risk was negligible as cancer risk value for all sites were lower than Disclosure statement target value 10 6 (USEPA 2011). The carcinogenic risks from As and Pb at all sites via ingestion and dermal The authors declare that there are no conflicts of interest. contact were higher than acceptable ranges. Total carcinogenic risk of As in present study for adult male, ORCID adult female, and children were 3.58E-01, 3.86E-01 and 1.02E þ 01, respectively. Again, total carcinogenic risk Ram Proshad http://orcid.org/0000-0002-4878-9616 of Pb in present study for adult male, adult female, and children were 8.00E-03, 9.00E-03 and 2.01E-02, respectively. The carcinogenic risks of As and Pb due References to exposure from studied sediment via ingestion and Abollino, O., et al., 2002. Heavy metals in agricultural soils dermal contact pathways were so much higher than from Piedmont, Italy. Distribution, speciation and chemo- standard value that there could be cancer risk to peo- metric data treatment. Chemosphere, 49 (6), 545–557. ple (adult male, adult female, and children) in the Ahmad, M.K., et al., 2010. Heavy metals in water, sediment study area. and some fishes of Buriganga River, Bangladesh. International journal of environmental research, 4, 321–332. Ahmed, M.K., et al., 2015. Human health risk assessment of Conclusions heavy metals in tropical fish and shell fish collected from the river Buriganga, Bangladesh. Environmental science The study conclude that the mean concentration of and pollution research. 22(20), 15880–15890. heavy metals in sediments of Rupsa River were in the Alghamdi, B.A., Mannoubi, I.E., and Zabin, S.A., 2018. Heavy decreasing order of Cu > Ni > Pb > Cr > As > Cd. There metals’ contamination in sediments of Wadi Al-Aqiq water found strong correlation coefficients matrix at signifi- reservoir dam at Al-Baha region, KSA: Their identification cance levels of p < 0.01 and p < 0.05 indicating similar and assessment. Human and ecological risk assessment: an source of contamination. Multivariate principal compo- international journal,1. nent analysis showed significant anthropogenic contri- Ali, M.M., et al., 2016. Preliminary assessment of heavy metals in water and sediment of Karnaphuli River, butions of Cr, Ni, As and Cd in sediment. According to Bangladesh. Environmental nanotechnology monitoring and ecological risk, enrichment factor, contamination fac- management,5,27–35. tor, geoaccumulation index, pollution load index, toxic Al-Khashman, O.A., 2007. Determination of metal accumula- unit analysis, and potential ecological risk showed low tion in deposited street dusts in Amman, Jordan. to high contamination of sediment by the studied Environmental geochemistry and health, 29 (1), 1–10. 22 R. PROSHAD ET AL.

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