Environ Monit Assess (2015) 187:322 DOI 10.1007/s10661-015-4558-7

Risk assessment of heavy metal contamination in sediments of a tropical lake K. Swarnalatha & J. Letha & S. Ayoob & Ajith G. Nair

Received: 20 December 2014 / Accepted: 21 April 2015 # Springer International Publishing Switzerland 2015

Abstract The risk assessment of heavy metal contamination was carried out in sediments of an urban tropical lake system (Akkulam-Veli) under threat from rapid unplanned urbanization and poor sewage management. Heavy metals were selected due to their persistent and bioaccumulative nature. Sequential extraction of the metals was carried out to resolve the sediments to their component phases. Well-established models were employed for risk analysis. The two pathways of contamination—ingestion and dermal contact—were considered for assessing risk. Risk Assessment Code of each metal was determined based on the lability of it in the different component phases. Cd was found to be the most hazardous metal by virtue of its high concentration in exchangeable and carbonate phases. Hazard indices of the metals were determined based on their K. Swarnalatha (*) Environmental Engineering Division, Department of Civil Engineering, College of Engineering, Trivandrum, Kerala 695 016, India e-mail: [email protected] J. Letha Cochin University of Science and Technology, Kochi, Kerala 695 023, India e-mail: [email protected] S. Ayoob TKM College of Engineering, Kollam, Kerala 691 005, India e-mail: [email protected] A. G. Nair Department of Civil Engineering, College of Engineering, Trivandrum, Kerala 695 016, India e-mail: [email protected]

total concentration in Akkulam-Veli (AV) Lake sediments. All heavy metals studied fall well below the threshold limit. However, Cr, Pb, and As, on account of their known toxicity, need to be monitored. Ni content in the lake system could potentially cause cancer to 134 adults in a population of one million. Concentrations of other metals are at carcinogenically safe limits. The study stresses the looming hazard faced by the Akkulam-Veli Lake system by heavy metal contaminants and the urgency in formulating remedial management plans. Keywords Heavy metal . Sediment . Risk assessment . Noncarcinogenic risk . Carcinogenic risk . Akkulam-Veli Lake

Introduction Heavy metal contamination of urban lakes is a serious pollution problem due to their persistent and bioaccumulative nature (Miguel et al. 2007; Ramesh et al. 2012; Sun et al. 2010). Heavy metals like Cr, Ni, Pb, Cd, and As in sediments are known to exhibit extreme toxicity even at trace levels (Rai 2009; Adakole and Abolude 2012). Further, Cd and As are carcinogenic and are a serious potential threat to aquatic systems (Li and Zhang 2010; Peng et al. 2009; Wei et al. 2010). Most of these metals deposited in the aquatic environment are biomagnifying in nature. Their accumulation in the food web poses a serious threat to human health (Swarnalatha et al. 2013a, b, 2014a, b).

322

Environ Monit Assess (2015) 187:322

Page 2 of 14

Metals are bound to different phases of sediment such as hydrated oxides, sulfides, organic compounds, and clay minerals. Their behavior varies in different sedimentary and diagenetic environments with change in remobilization capacity and biological uptake. Direct determination of specific chemical forms is difficult due to their differing capacity for binding metals (Wu et al. 2011). The identification of the exact chemical phase and nature in which metals occur in sediments and working out their quantitative distribution is necessary to assess their potential risk. This is necessary for chalking out suitable remedial steps. The sequential extraction procedure, which defines the metal activity by the analytical schemes, is the often preferred method to study the mobility and binding patterns of sediments with metals (Tessier et al. 1979). Human risk assessment is an extremely significant tool for decision-making factors in a lake management program. Human exposure to heavy metals may occur mainly through three different pathways, i.e., ingestion, inhalation, and dermal contact. In water bodies, ingestion occurs through consumption of contaminated sediment, water, or biota. Dermal contact is by direct absorption through the skin by contact with contaminated sediments and water. Inhalation of contaminants volatilized from surface water and sediment is of minor importance (Albering et al. 1999). Population groups that tend to be more susceptible to chemical contamination are infants and children, the elderly, pregnant women, those with chronic illnesses, and those who tend to them. The Akkulam-Veli (AV) Lake is a well-known tourist center in Thiruvananthapuram City that offers various recreational activities. However, the unplanned, rampant urbanization and a burgeoning population coupled with poor and obsolete waste management practices have direct repercussions in the lake environment. A significant portion of the sewage/waste flux of the city is channeled to the AV through two small streams. Thus, the pollution levels of the lake may pose a potential risk to human health. Though heavy metal distribution of AV Lake is available (Swarnalatha et al. 2013a, b, 2014a, b), studies on their potential health risk are lacking. In fact, investigations on the risk assessment of aquatic systems are relatively recent and few (Mahmood and Malik 2014; Liu et al. 2013; Khairy et al. 2009; Wu et al. 2009; De Miguel et al. 2007). This is likely due to the complexity of factors involved thereof. In this study, an attempt is made to understand the hazard posed by

heavy metal contamination in sediments of AV Lake. Risk assessment studies demonstrate the gravity of pollution, which is otherwise not assessable. The method involves sequential extraction techniques to assess the contamination in the various component phases of sediment. Since risk assessment models for tropical estuarine lakes are not available, models adapted from the US Environmental Protection Agency (US EPA 1996; 1997) are used. It is hoped that the present study would highlight the threat caused by heavy metal pollution in coastal aquatic systems.

Materials and methods Study area The AV Lake (8° 25′ N–8° 35′ N and 76° 50′ E–76° 58′ E) extends to about 3.2 km with an average depth less than 4 m (Fig. 1) and separated from the sea by a sand bar. The AV Lake system (∼1 km2) is set among laterite hillocks in a linear fashion and is fed by two streams (Kulathur and Kannamoola). These act as conduits for pollutants associated with sewage and significantly contribute to the heavy metal burden of the lake (Swarnalatha et al. 2013a). In addition, the lake also receives industrial wastes. The weak and narrow tidal range and linear nature of the lake inhibit any effective flushing activity. Sampling and analysis Sample preparation and analysis Fifteen sampling points (Fig. 1) were representatively selected in the AV Lake system for collecting bottom sediments. Sampling stations 1 to 7 were on the Akkulam region of the lake and 8 to 15 on the Veli stretch. The upper sediments (0–15 cm depth) of the lake were collected using a Van Veen grab in three consecutive seasons from 2011 to 2012, i.e., May 2011 (n=14), September 2011 (n=12), and January 2012 (n= 14). A total of 40 samples were thus obtained representing monsoon and pre-, post-, and monsoon seasons. At each station, three sediment samples were collected and mixed on site, to get representative sampling. Samples were then placed in polyethylene bags and transported to the laboratory (UNEP 1985). The collected samples were air dried, sealed in clean

Environ Monit Assess (2015) 187:322

Page 3 of 14 322

Fig. 1 Map showing the location and sampling stations of AV Lake

polythene bags, and stored in a refrigerator for characterization studies (Singh et al. 2005). Deionized water was used for chemical procedures. All glassware and polyethylene bottles were washed with metal-free soap, rinsed thoroughly, and soaked overnight in 50 % nitric acid solution to prevent metal contamination. All chemicals and standard solutions used were of analytical grade. The pH of sediments was determined (Mc Clean 1982) using a Systronicsmade pH meter. Total organic carbon (TOC) of sediments was found using chromic acid digestion and back titration (El Wakeel and Riley 1957). Loss on ignition (LOI) was measured as the percentage weight loss at 450 °C. The metals taken up for risk analysis in the present study are Cr, Ni, Cu, Zn, Pb, As, and Cd. The average concentration of Hg in sediments was found to be barely detectable and hence is not considered for this work. The dried sediment samples were further ignited at 950 °C for 1 h in a muffle furnace, and the residue was ground into microscopic particles (0.5 μm>d1 were only considered for interpreting the results. Sequential extraction studies For a comprehensive picture of sediment contamination by heavy metals to emerge, the data of element concentration in bulk sediment alone is not sufficient. However, the heavy metal contents in different component phases indicate their pathways in natural (sedimentary, biological, and hydrological) processes. The principle of trace metal sequential extraction is based on dissolving the heavy metals of a specific phase using reagents but leaving intact these metals contained in other component phases (Bilali et al. 2002). The five phases thus extracted in the different stages are the exchangeable phase (EXC), carbonate phase (CAR), Fe-Mn oxide phase (OXD), organic matter and sulfide phase (OMS), and residual phase (RES) in that order. The five-step sequential extraction procedure as given by Tessier et al. (1979) is followed in this study. One gram of the sediment is initially taken and subjected to the different extraction procedures. After every extraction, the extracts are centrifuged and the supernatants are filtered through a 0.45-μm membrane filter. The filtrate is tested for analyzing the concentrations of metals such as Cr, Ni, Cu, Zn, Pb, and Cd using ICP AES (STIC 2012). The reactivity of the sediments is evaluated by applying the criterion of Risk Assessment Code (RAC) as proposed by Perin et al. (1985). According to this criterion, when the total metal content bound to the EXC and CAR phases together is less than 1 % of its total concentration, it is considered safe or Bno risk^ for the environment. Risk increases with increase of metals in these two fractions. Assessment of risk to human health Risk from metal exposure to human health can be noncarcinogenic and/or carcinogenic. Further, sensitive populations (especially children) are most vulnerable

to risk. The risk assessment in the present work takes into consideration the potential contamination of humans by heavy metals in sediments. Only two main pathways of contamination—ingestion and dermal contact—are considered here. Due to lack of data and parameters at the national, regional, and local levels, the risk assessment was done based on models of US EPA (1989, 1996, 1997, 2006). These models express human health risk as a numeric quantity and are thus more easily comprehended. The susceptibility of child and adult is separately considered. The factors used in the models applied are given in Table 1. These may differ from place to place depending on exposure duration, body weight, lifestyles, individual habits, and tolerance levels. In this study, the threshold values of factors specified as by US EPA are made use of. Assessment of noncarcinogenic risk For assessing the noncarcinogenic risk, the average daily dose (ADD) is first determined. This represents the daily exposure levels of specified parameters (for human population), without an appreciable risk of their deleterious effects during a lifetime. Thus, ADDi is estimated by ADDi ¼

M  IRS  E F  ED BW  AT

ADDd ¼

M  CF  A F  ABSd  EF  ED  EV  SA BW  AT

ð1Þ

ð2Þ

where ADDi is the average daily dose by ingestion for a particular metal (ADDi) and average daily dose by dermal absorption (ADDd) (mg/kg/day); M is the average concentration of the heavy metal (mg/kg); IRS is the ingestion rate of the contaminated sediment (kg/day); EF is the exposure frequency (days/year); ED is the exposure duration (years); BW is the body weight (kg); AT is the averaging time (years); CF is the conversion factor (10−6 kg/mg); AF is the adherence factor to the skin (mg/cm2); ABSd is the dermal absorption factor; EV is the event frequency (event/day); and SA is the surface area (cm2). The parameters used in the model and their values adopted for this study are summarized in Table 1. Based on ADD values, hazard quotients (HQ) were calculated. Thus, HQi is the ratio of ADDi (from ingestion) to its corresponding reference dose

Environ Monit Assess (2015) 187:322

Page 5 of 14 322

Table 1 Parameters used in the exposure model for sediments Parameter with abbreviations Units

Child

Adult

Sources

Ingestion rate, IRS

kg/day

0.001

0.0035

Albering et al. (1999)

Exposure frequency, EF

30 (site specific)

30 (site specific)

Assumed

Exposure duration, ED

days/ year years

6

30

US EPA (2004)

Body weight, BW

kg

15

70

Albering et al. (1999)

Averaging time (for noncarcinogens), AT Averaging time (for carcinogens), AT

days

ED*365

ED*365

US EPA (1989)

days

70*365

70*365

US EPA (1989); De Miguel et al. (2007)

Constant For all elements except As 0.001, for As 0.03 mg/cm2 0.2

For all elements except As 0.001, for As 0.03 0.07

De Miguel et al. (2007) US EPA (1989)

cm2

Dermal absorption factor, ABSd Adherence factor of soil to skin, AF Surface area, SA

2800

5700

Wu et al. (2009)

Reference dose for ingestion, μg/kg/ RfDi day

Metal specific

Metal specific

US EPA (2006); Wu et al. (2009)

Reference dose for dermal absorption, RfDd

μg/kg/ day

Metal specific

Metal specific

US EPA (2006); Wu et al. (2009)

Dermal absorption factor

ABSd

Constant

For all elements except As 0.001, for As 0.03

For all elements except As 0.001, for As 0.03

(RfDi) and HQd is the ratio of ADDd (from dermal contact) to its corresponding reference dose (RfDd). RfD values for ingestion (RfDi) and dermal contact (RfDd) are presented in Table 2. HQ>1 indicates the potential of an element to adversely affect human health by noncarcinogenic effects. To evaluate the total potential of noncarcinogenic risks posed by more than one pathway, the hazard index (HI) is introduced, which is the sum of the HQ values from all applicable pathways. Considering the two pathways of ingestion and dermal contact in this study, the HI for a particular metal is the sum of its HQi (hazard quotient from ingestion) and HQd (hazard quotient from dermal contact) values. HI ¼ HQi þ HQd

ð3Þ

related exposure. Cancer risks are estimated as the incremental probability of an individual, developing cancer, over a lifetime as a result of exposure to a potential carcinogen (De Miguel et al. 2007; Li and Zhang 2010; Wu et al. 2009). In order to estimate the cancer risk, the ADD values (calculated for carcinogenic effects for ingestion and dermal contact) of each metal are multiplied by the slope factors (SF) of the corresponding metal (Eq. 6) (Li and Zhang 2010). The SF is a plausible upper-bound estimate of the probability of a response per unit intake of a metal over a lifetime (US EPA 1989). SF values (Table 3) are available in literature only for carcinogenic metals Ni, Pb, Cd, and As. Hence, carcinogenic risk is found out for these metals only. Carcinogenic Risk ¼ ADDc  S F

ð6Þ

where where HQi represents the hazard quotient through ingestion and HQd represents the hazard quotient through dermal contact. HI>1 represents the risk involved by the contamination of heavy metals. Assessment of carcinogenic risk Carcinogenic effects are quantified by estimating the probability of contracting cancer based on a site-

ADDc SF

Average daily dose for carcinogenic effects (mg/kg/day) Slope factor (mg/kg/day)

For estimating ADDc, the equations (Eqs. 1 and 2) used for estimating noncarcinogenic effects are adopted with carcinogenic parameters (Table 1). US EPA uses a risk level of 10−6 (i.e., one cancer case in among one

322

Environ Monit Assess (2015) 187:322

Page 6 of 14

Table 2 Reference dose (RfD) for ingestion and dermal contact (μg/kg/day) Element

Cr

Co

Ni

RfDi

3

0.3

20

RfDd

0.015

0.06

5.4

Cu

Zn

Pb

As

Cd

40

300

1.4

0.3

0.5

12

60

0.42

0.123

0.005

RfDi reference dose for ingestion, RfDd reference dose for dermal contact (US EPA 2004)

million persons) as the point at which risk management decisions need to be considered. The acceptable range of carcinogenic risk as per US EPA is in the range of 10−6 to 10−4 (US EPA 1996).

Results and discussion Analysis of sediments The pH is a key parameter controlling the behavior of heavy metal transfer in sediment. The pH of sediment ranges from 6.4 to 8.1 and had an overall average value of 7.1. Total organic carbon (TOC) has a significant role in the geochemical cycles of major and trace elements accumulated in sediments including metal mobility. A high value of TOC was found in sediments with an average value of 5.5 % (Fig. 2). The summary statistics of heavy metal distribution (Cr, Co, Ni, Cu, Zn, Pb, As, and Cd) in the surface sediments of AV Lake and their corresponding average shale values (Turekian and Wedepohl 1961) are presented in Table 4. The average concentrations of all metallic pollutants considered in this study (except As and Cd) are found to be exceeding the average shale values indicating the heavy metal pollution of AV Lake sediments. Statistical analysis Interelemental relationships of variables can indicate possible sources of metals. Correlation analysis of heavy Table 3 Slope factors (SF) in (mg/kg/day) for estimating carcinogenic risk SF

Ni

Pb

Cd

As

SFi

0.91

0.042

15

1.50

SFd

0.91

0.042

15

3.66

SFi slope factor for ingestion, SFd slope factor for dermal contact

metals (Cr, Co, Ni, Cu, Zn, Pb) with major elements (Si, Al, Fe, Mn, Ca, Mg, K, P) and different geochemical parameters (LOI, pH, TOC) is presented in Table 5. A high positive correlation between metals (Cr-Ni, Cr-Zn, Cr-Pb, Ni-Pb, Cu-Zn) indicates similar sources for them. Metals except Ni and Pb were found well related (r>0.61) to Fe. Among major elements, Si was found to be well but negatively related to Cr, Cu, and Zn, which indicates anthropogenic sources for these metals. Moderate to good relations of Mg to metals may be indicating the effect of the lake’s proximity to the sea. All metals were found to be having no relation with TOC, whereas Cu and Zn were having better relations, indicating the effect of organic sources. The poor relation of metals with TOC may be due to the influence of numerous factors such as multiple sources, occasional salinity intrusion, anoxic conditions of sediment, proximity to the sea, unauthorized/ authorized discharge of pollutants, nonuniform texture, shallow waters resulting in poor mixing, and increased silting due to developmental activities. The complexation reaction between heavy metals and organic complexants is usually recognized as the most important reaction pathway. However, in severely polluted waters, due to the complexity of organic matter, the reaction types between organic complexes and metals are difficult to predict. Habes and Nigem (2006) had also reported lack of strong relations between organic matter and heavy metals. Although organic matter, being a major binding phase for heavy metals, is true for AV Lake also, the complexity of the system due to the involvement of numerous geochemical and environmental factors may be responsible for not revealing the actual relations. HCA analysis was done on sediment data using Ward’s algorithm and the dendrogram obtained is presented in Fig. 3. Two main clusters are observed with significant linkage distance, indicating their clear independency. Cr, Ni, and Pb are found to be having close associations with Si and hence seem to be more linked to lithogenic sources.

Environ Monit Assess (2015) 187:322

Page 7 of 14 322

Fig. 2 General properties of sediments

TOC- Total organic carbon, LOI-Loss on ignition

This may be indicating an anthropogenic origin. The second cluster is further subdivided into two subclusters. Fe and Mn are having close associations in the first subcluster, and Cu, Zn, and Co in the second one. The association of Cu, Zn, and Co with total organic carbon may be indicating the organic sources of these metals. The relation of Cu and Zn was evident from the results of correlation analysis (Table 5) also. Sequential extraction studies The exchangeable phase in sediments is the most loosely bound, labile, highly toxic, and mostly bioavailable

fraction (Tessier et al. 1979; Wang et al. 2010). It can be observed from Fig. 4 that metals in the exchangeable phase of sediments are in the concentrated order Cd > Zn > Pb > Cr > As > Ni > Cu. Thus, Cd is the most labile as about 70 % of it is in the exchangeable phase. Cu with only 0.7 % in this phase shows the minimum mobility among the metals studied. The exchangeable phase can release significant amounts of metals into the water column of the lake. Metals bound to carbonate minerals are bioavailable for the gut environment of benthic organisms. The carbonate phase is generally considered as a poor carrier of metals (Surija and Branica 1995).

Table 4 Heavy metal concentrations (mg/kg) in lake sediments used for risk analysis Metal

Minimum

Maximum

Cr

49

642

183.17

133.90

17928

1.73

2.90

90

Ni

5

259

83.77

63.21

3996

1.35

1.27

68

Cu

3

126

53.80

32.63

1065

0.57

−0.45

45

Zn

19

279

123.40

68.33

4669

0.81

0.08

95

Pb

18

103

59.05

18.95

359

0.04

0.57

20

As

0

7.66

1.43

2.25

5

1.50

1.18

13

Cd

0

0.95

0.27

0.23

0

0.90

0.85

0.3

SD standard deviation a

Turekian and Wedepohl, 1961

Mean

SD

Variance

Skewness

Kurtosis

Average shale valuea

322

Environ Monit Assess (2015) 187:322

Page 8 of 14

Table 5 Correlation analysis between heavy metals, major elements and geochemical parameters of Akkulam Veli lake sediments Cr

Co

Ni

Cu

Zn

Pb

LOI

pH

Si

Al

Ca

Mg

K

Cr

1

Co

0.35

Ni

0.85

0.09

1

Cu

0.50

0.49

0.25

1

Zn

0.61

0.36

0.29

0.90

Pb

0.68

0.16

0.70

0.33

0.29

1

LOI

0.60

0.15

0.36

0.43

0.51

0.11

1

pH

0.24

−0.15 0.23

0.06

0.14

0.24

0.39

Si

−0.70 −0.50 −0.33 −0.70 −0.69 −0.18 −0.84 −0.26 1

Al

0.59

0.58

0.50

0.26

Ca

−0.14 −0.14 −0.16 0.26

0.22

−0.03 −0.01 −0.29 −0.18 0.40 1

Mg

0.34

0.63

0.02

0.68

0.62

0.05

0.44

0.36

−0.71 0.41 0.01

K

0.72

0.59

0.31

0.45

0.52

0.11

0.45

0.33

−0.68 0.51 −0.17 0.69 1

P

0.56

0.30

0.35

0.68

0.79

0.15

0.40

−0.03 −0.60 0.55 0.36

P

Fe

Mn

TOC

1

0.45

0.28

1

0.41

1 −0.09 −0.77 1 1 0.36 0.51 1

Fe

0.64

0.82

0.27

0.63

0.61

0.15

0.54

0.20

−0.81 0.57 −0.13 0.82 0.85 0.51 1

Mn

0.49

0.45

0.25

0.26

0.36

0.15

0.59

0.15

−0.66 0.46 −0.01 0.47 0.68 0.31 0.68 1

TOC 0.02

0.20

−0.20 0.26

0.35

−0.28 0.15

−0.08 −0.30 0.22 0.35

0.36 0.21 0.40 0.28 0.36 1

r≥0.6 are in italics TOC total organic carbon, LOI loss on ignition

Pb (17 %) shows the highest affinity to carbonates, and Cr (2.7 %) the least. Thus, the carbonate phase is not very significant as a metal adsorber in AV Lake sediments. Pb, As, Cr, and Zn exhibit the highest affinity (20– 39 %) to the Fe-Mn oxide phase (OXD) than to the other phases. Cu (2.7 %) and Cd (8.8 %) are found to be less adsorbed to this phase. The affinity of metals in AV Lake sediments to the Fe/Mn phase was reported earlier by Swarnalatha et al. (2013a). The Fe-Mn phase was found to be an important binding phase in the sediments of AV Lake. Due to their large surface area, Fe and Mn oxide phase impact the mobility and behavior of trace metals. They occur as cement-binding sediment particles or as coating of particles. These oxides are also excellent scavengers for trace metals and are mobilized under the reducing and acidic conditions (Stumm and Morgan 1996; Wu et al. 2011). The higher concentration of Fe (average of 6.32 %) as reported earlier by the authors (Swarnalatha et al. 2013a) too indicates that Fe could likely be a major adsorption phase in AV sediments. Fe and Mn oxides highly affect trace metal cycling in lake sediments by their increasing dissolution with increase in anoxic conditions (Whiteley and Pearce

2003). The anoxic conditions prevailing in the lake (Swarnalatha et al. 2013a, 2014b) are highly significant in this scenario, since such situations fix and release heavy metals to the water column with change in the redox environment (Weis and Weis 2004). The phase of organic matter and sulfides (OMS) is also important in controlling the mobility and bioavailability of heavy metals. A major portion of Cu is bound to the organic fraction (52.4 %) compared to other metals. The association of Cu with organic-rich fractions is well established (Pagnanelli et al. 2004; Li et al. 2001). Our results too conform to this observation. Cu exists in the form of stable organic complexes and metal sulfides in the lake sediments. It has a strong affinity to form organometal complexes which can be aerobically degraded (Whiteley and Pearce 2003). Other metals also having considerable affinity with the organic phase in decreasing order are Zn > As > Cr > Cd > Ni > Pb. These relevant aspects are discussed in our previous work (Swarnalatha et al. 2013a). Though the organic phase is relatively stable in nature, the metal ions in them could be mobilized under strong oxidizing conditions (when organic matter is metabolized away). Such observations were made by Tessier et al. (1979) and Wu

Environ Monit Assess (2015) 187:322

Page 9 of 14 322

Fig. 3 Hierarchical clustering analysis using Ward’s method of analysis, squared Euclidean distance, and Z transformation condition on AV Lake sediments

et al. (2011). The residual phase (RES) contains mainly primary and secondary minerals, which hold trace metals within their crystal structure. This phase is very refractory in the sense that the metals in them are not easily leached away from the mineral lattices (Wu et al. 2011). Thus, metals attached to residual phases are generally less toxic to biota. The dominant fraction of Ni (42.6 %) is found to be in the residual fraction. The contents of Cr and Cu are also significantly large. Cr is well known for its low mobility. It substitutes for Fe in primary minerals (Mason and Moore 1982). When these are oxidized, they are retained in the secondary mineral structure, even though Fe is leached out. Cu, though easily leached away in normal natural environments,

would have only a restricted mobility at the reducing conditions of the lake. Hence, it is retained considerably in RES. Since the Cd content is negligibly low in the residual phase, their source is likely to be anthropogenic. Risk Assessment Code Risk Assessment Code (RAC) is computed as follows: 1–10 %—low risk; 11–30 %—medium risk; 31– 50 %—high risk; and >50 %—very high risk. The results are presented in Table 6. The metals studied arranged in the decreasing order of RAC are Cd > Pb > Zn > As > Ni > Cr > Cu. The RAC value of Cd is highest and falls in the very high

322

Environ Monit Assess (2015) 187:322

Page 10 of 14

Fig. 4 Metal contents in different component phases of sediment

As Cd EXC Pb CAR Zn

OMS

Cu

RES

Ni Cr 0

20

40

60

80

100

Metal concentration (%)

risk category. The risk potential of Cd is maximum (RAC—73.4 %) among the heavy metals studied, though its total concentration is lowest in sediments. Cu dominantly attached to the organic portion is the only low risk metal, Cr and Ni fall in the medium risk category, while others are of high risk. Correlation studies of metals in different fractions with total metal concentrations in sediments An attempt is made to determine the interrelationship between total concentration of each metal and its content in different component phases. The results of correlation studies are given in Table 7. It is seen that the metal contents in the RES phase exhibit a marked sympathetic relationship with the total metal concentration, indicating the considerable lithogenic contribution of heavy metals. Similarly, concentrations of metals attached to the organic phase are well related with their corresponding total metal

concentrations. The strong positive correlation of OMS with total metal concentration (0.55–0.98) indicates that OM progressively adsorbs heavy metals with increase of their supply to the lake environment. This is consistent with earlier observations (Swarnalatha et al. 2013a). The correlation analysis for the oxide fraction is Cu (0.92)>Ni (0.79), Cr (0.67)>Zn (0.65)>Cd (0.59)>Pb (0.14). Similarly, residual fraction is well related with all metals (0.66–0.97) except Cd. This shows the significance of lithogenic sources of pollutants in AV Lake. The results of correlation analysis agree with the results of sequential extraction studies. The lack of Cd fraction in the residual phase clearly indicates its release as a contaminant in this aquatic system. Further, Cd for the most part (69 %) occurs in the exchangeable phase, confirming its anthropogenic origin. The exchangeable portions are the most bioavailable fraction as the metal

Table 7 Correlation between concentration of metal in bulk sediment and its component phases Metal

EXC

CAR

OXD

Cr

0.50

0.72

0.67

0.98

0.97

Ni

0.40

0.74

0.79

0.55

0.90

Cu

0.66

−0.09

0.92

0.96

0.77

Zn

0.56

0.06

0.65

0.73

0.66

Pb

0.80

0.36

0.14

0.88

0.89

Cd

_

0.72

0.59

0.96

–

Table 6 Risk Assessment Codes (RAC) of heavy metals Heavy metal

RAC

Risk assessment

Cr

25.6

Medium risk

Ni

27.9

Medium risk

Cu

6.4

Low risk

Zn

41.1

High risk

Pb

44.5

High risk

Cd

73.4

Very high risk

As

33.2

High risk

OMS

RES

EXC exchangeable fraction, CAR carbonate fraction, OXD Fe/Mn oxide fraction, OMS organic matter/sulfide fraction, RES residual fraction. r ≥ 0.6 is boldfaced

Environ Monit Assess (2015) 187:322

Page 11 of 14 322

Table 8 Average daily dose (ADD), hazard quotient (HQ), and hazard index (HI) of sediments of AV Lake Heavy metal ADDi (mg/kg/day) Child

Adult

ADDd (mg/kg/day)

HQi

Child

Child

Adult

HQd Adult

Child

HI Adult

Child

Adult

Cr

1.00E−03

7.53E−04

5.63E−07

8.58E−08

3.35E−01

2.51E−01

3.75E−02

5.72E−03

3.72E−01

2.57E−01

Ni

4.59E−04

3.44E−04

2.57E−07

3.92E−08

2.30E−02

1.72E−02

4.76E−05

7.27E−06

2.30E−02

1.72E−02

Cu

2.95E−04

2.21E−04

1.65E−07

2.52E−08

7.37E−03

5.53E−03

1.38E−05

2.10E−06

7.38E−03

5.53E−03

Zn

6.76E−04

5.07E−04

3.79E−07

5.78E−08

2.25E−03

1.69E−03

6.31E−06

9.64E−07

2.26E−03

1.69E−03

Pb

3.24E−04

2.43E−04

1.81E−07

2.77E−08

2.31E−01

1.73E−01

4.31E−04

6.59E−05

2.32E−01

1.73E−01

Cd

1.48E−06

1.11E−06

8.28E−10

1.26E−10

2.96E−03

2.22E−03

1.66E−04

2.53E−05

3.12E−03

2.24E−03

As

7.84E−06

5.88E−06

1.32E−07

2.01E−08

2.61E−02

1.96E−02

1.07E−03

1.63E−04

2.72E−02

1.98E−02

ADDi average daily dose by ingestion, ADDd average daily dose by dermal contact, HQi hazard quotient by ingestion, HQd hazard quotient by dermal contact, HI hazard index

from the sediment dissolves in water under slight changes of pH, redox potential, anoxic conditions, etc. This directly translates as the bioavailability potential. Since Cd is a carcinogenic element, a detailed investigation on Cd fractioning in the sediments of AV Lake would be useful. Pb+ goes into solution very easily under reducing conditions (Krauskopf 1979). This accounts for its high content (27.4 %) in the EXC phase. The significant content of Pb in the OXD phase is likely the more oxidized form of this metal contained in lithogenic sediments. The contribution through sewage is highly possible as Pb is a known component of urban sewage. Pb sequesters in the organic matter and clays remaining in the environment for a long time. Whatever be the source, around 27.4 % of Pb in EXC makes AV Lake a hot spot of heavy metal pollution. Significant portions of Ni, Cu, and Zn are also found in the residual phase fractions, indicating the lithogenic contribution of these pollutants. In polluted soil, Zn is generally associated with Fe and Mn oxides but can even form complexes with organic compounds. The results of the correlation studies are also agreeing with the comparable results of experimental studies establishing the significance of oxide, organic, and residual phases in AV Lake sediments.

values. The contaminants pose risk when ingested than through dermal contact. The hazard quotients are for all the metals studied and in their present concentration do not endanger human health (Table 8). However, Cr and Pb present a potential risk in the future by their highest values of HI for both children and adults. Their HI for children and adults are 0.37 and 0.26 respectively for Cr. These values are markedly less for Pb (0.23, 0.17). This index is exponentially less for other metals. However, in view of the burgeoning population and rampant urbanization of Thiruvananthapuram City, their prospective threat could not be underestimated. For instance, for As as a well-known carcinogenic element, sources ought to be constantly monitored even though its presence in AV Lake now is not threatening. HI values of other metals—As, Ni, Cu, Cd, and Zn— are comparatively lesser but should be regularly monitored in view of increasing population and urbanization. Being a carcinogenic element, As has to be given due importance in lake management programs, even though the risk associated is found to be lesser and not serious for the time being (0.0262). Table 9 Carcinogenic risks estimated for sediments

Metal

Child

Adult

Ni

3.58E−05

1.34E−04

Pb

1.17E−06

4.37E−06

Cd

1.90E−06

7.13E−06

As

1.05E−06

3.81E−06

Estimation of noncarcinogenic risk It can be observed from Table 8 that the ADDi values for all the studied metals are higher than their corresponding ADDd values for both children and adults. Obviously, similar trends hold true in the case of HQi and HQd

Sediment

322

Page 12 of 14

Environ Monit Assess (2015) 187:322

Estimation of carcinogenic risk

Conclusion

Ni is the only carcinogenic risky heavy metal (1.34E −04, for adults) in AV Lake sediments (Table 9) as it exceeds the threshold limit of 1E−04. All other metals studied are found to be within carcinogenically safe limits for both children and adults. Accordingly, risk values of Ni in sediments explain that in a population of 100,000 people, 3.58 children and 13.4 adults likely have cancer-related diseases by contact with sediment of the lake. As explained earlier, the assessment of risk posed by sediments of AV Lake to human health is attempted on the basis of US EPA exposure models. However, certain uncertainties exist that are emphasized by the US EPA and other studies. These relate to certain methodological aspects such as dermal absorption, ingestion rate, and varied exposure condition due to different ages and receptors. In addition, the parameters employed in the study might not be fully applicable to AV Lake. Further, only two modes of exposure, albeit the most common, i.e., ingestion and dermal contact, are here considered. The other modes of contact such as inhalation and oral intake from contaminated fish are not considered. Thus, this study may be considered as one that investigates the general risk associated with heavy metal contamination in a lake consequent to rapid urbanization and poor waste disposal practices. Further, in-depth studies are required to identify and delineate the pathways of each contaminant metal to the lake environment and adopt suitable remedial measures. Risk assessment studies indicate that the exposure of metals through ingestion is more risky than through skin contact. None of the metal contents at present are in the noncarcinogenic category since their hazard indices are less than 1. However, their potential threat over time by biomagnifications has to be considered and suitably monitored. Their HI values (between 0 and 1) bear this out and should be seriously looked into. Cd next follows Pb in toxicity. Carcinogenic risk from lake sediments is evaluated for the metals Ni, Pb, Cd, and As. This risk represents the probability of having cancer over a lifetime. Their carcinogenic risk in the AV system is in the order Ni > Cd > Pb > As. However, more detailed integrated studies on water, sediment, and biota are required before final conclusions are arrived at.

Risk assessment of heavy metals in the Akkulam-Veli Lake system reveals that the affinity of these to the component phases of sediments varies from one another. The metal contents in the exchangeable and carbonate phases have a bearing on their toxic potential. The organic matter-sulfide phase is found to be an important sequestrator of heavy metals. The lithogenic contribution of heavy metals to the lake sediment is considerable except for Cd, which hints at an anthropogenic origin. Evaluation based on lability of heavy metals indicates that Cd is found to be the most hazardous metal contaminant of sediments in spite of its lowest content in AV Lake sediments. However, when their total contents were taken into account, none of the metals studied were found to be hazardous at present, falling well below the threshold limit of 1. Cr and Pb, though, exhibit the highest hazard indices and hence need to be closely monitored. Ni reaches carcinogenically unsafe limits (for adults) in the lake sediments. Concentrations of other metals are currently at noncarcinogenic levels. Acknowledgments The authors thank the Director, Centre for Earth Science Studies (CESS), Thiruvananthapuram, and the Sophisticated Test and Instrumentation Centre, Cochin University of Science and Technology, CUSAT, Kochi, for extending laboratory facilities. The first author greatly acknowledges the financial assistance from the Kerala State Council for Science, Technology and Environment (KSCSTE), Government of Kerala, India

References Adakole, J. A., & Abolude, D. S. (2012). Pollutional status of Kubanni lake through metal concentrations in water and sediment columns, Nigeria. Research Journal of Environmental and Earth Sciences, 4(4), 424–427. Albering, H. J., Rila, J. P., Moonen, E. J. C., Hoogewerff, J. A., & Kleinjans, J. C. S. (1999). Human health risk assessment in relation to environmental pollution of two artificial freshwater lakes in the Netherlands. Environmental Health Pespectives, 107(1), 27–34. Bilali, L.E., Rasmussen, P.E., Hall, G.E.M. & Fortin, D. (2002). Role of sediment composition in trace metal distribution in lake sediments. Appl. Geochem, 17, 1171–1181. CESS (2009) Centre for Earth Science Studies, Trivandrum. Preparation of samples for XRF studies. www.cess.res.in Accessed 12 Jan 2009. De Miguel, E., Iribarren, I., Chaco’n, E., Ordon˜ez, A., & Charlesworth, S. (2007). Risk-based evaluation of the exposure of children to trace elements in playgrounds in Madrid (Spain). Chemosphere, 66, 505–513.

Environ Monit Assess (2015) 187:322 El Wakeel, S. K., & Riley, J. P. (1957). The determination of organic carbon in marine sediments. Journal du Conseil / Conseil Permanent International pour l’Exploration de la Mer, 22, 180–183. Habes, G., & Nigem, Y. (2006). Assessing Mn, Fe, Cu, Zn, and Cd pollution in bottom sediments of Wadi Al-Arab Dam, Jordan. Chemosphere, 65(11), 2114–2121. Khairy, M. A., Kolb, M., Mostafa, A. R., El-Fiky, A., & Bahadir, M. (2009). Risk assessment of polycyclic aromatic hydrocarbons in a Mediterranean semi-enclosed basin affected by human activities. Journal of Hazardous Materials, 170, 389–397. Krauskopf, K. B. (1979). Introduction to geochemistry (2nd ed.). Newyork: Mc Grawhill. Li, X., Shen, Z., Wai. O.W.H., & Li, Y.S. (2001) Chemical forms of Pb, Zn and Cu in Pearl river estuary. Marine pollution Bulletin, 42(3), 215–223. Li, S., & Zhang, Q. (2010). Risk assessment and seasonal variations of dissolved trace elements and heavy metals in the Upper Han River, China. Journal of Hazardous Materials, 181, 1051–1058. Liu, X., Song, Q., Tang, Y., Li, W., Xu, J., & Wu, J. (2013). Human health risk assessment of heavy metals in soil- vegetable system: a multi medium analysis. Science of the Total Environment, 463–464, 530–540. Mahmood, A., & Malik, R. N. (2014). Human health risk assessment of heavy metals via consumption of contaminated vegetables collected from different irrigation sources in Lahore, Pakistan. Arabian Journal of Chemistry, 7(1), 91–99. Mason, B., & Moore, C. B. (1982). Principles of geochemistry (4th ed.). New York: John Wiley and Sons. Mc Clean, E. O. (1982). Soil pH and lime requirement. Methods of soil analysis. Part 2. Agronomy (Vol. 9, pp. 199–224). Madison: Am. Soc. Agronomy Inc. Pagnanelli, F., Moscardini, E., Giuliano, V., & Toro, L. (2004). Sequential extraction of heavy metals in river sediments of an abandoned pyrite mining area: pollution detection and affinity series. Environmental Pollution, 132, 189–201 Peng, J.F., Song, Y.H., Yuan, P., Cui, X.Y., & Qiu, G.L. (2009). The remediation of heavy metals contaminated sediment. Journal of Hazardous materials, 161, 633–640. Perin, G., Craboledda, L., Lucchese, M., Cirillo, R., Dotta, L., Zanetta, M. L., & Oro, A. A. (1985). Heavy metal speciation in the sediments of northern Adriatic sea. A new approach for environmental toxicity determination. In T. D. Lakkas (Ed.), Heavy metals in the environment (Vol. 2). Edinburgh: CEP Consultants. Rai, P.K. (2009). Heavy Metal Phytoremediation from Aquatic Ecosystems with Special Reference to Macrophytes. Critical Reviews in Environmental Science and Technology, 39(9), 697–753. Ramesh, S. T., Rameshbabu, N., Gandhimathi, R., Nidheesh, P. V., & Srikanth Kumar, M. (2012). Kinetics and equilibrium studies for the removal of heavy metals in both single and binary systems using hydroxyapatite. Applied Water Science, 2, 187–197. STIC. (2012). Sophisticated Test and Instrumentation Centre, www.sticindia.com Sun, Y., Zhou, Q., Xie, X., & Liu, R. (2010). Spatial, sources and risk assessment of heavy metal contamination of urban soils

Page 13 of 14 322 in typical regions of Shenyang, China. Journal of Hazardous Materials, 174, 455–462. Swarnalatha, K., Letha, J., & Ayoob, S. (2013a). An investigation into the heavy metal burden of Akkulam–Veli Lake in south India. Environmental Earth Sciences, 68(3), 795–806. Swarnalatha, K., Letha, J., & Ayoob, S. (2013b). Ecological risk assessment of a tropical lake system. Journal of Urban and Environmental Engineering, 7(2), 323–329. Swarnalatha, K., Letha, J., Ayoob, S., & Sheela, A. M. (2014a). Identification of silicon as an appropriate normaliser for estimating the heavy metals enrichment of an urban lake system. Journal of Environmental Management, 129, 54–61. Swarnalatha, K., Letha, J., & Ayoob, S. (2014b). Effect of seasonal variations on the surface sediment heavy metal enrichment of a lake system in South India. Environmental Monitoring and Assessment. doi:10.1007/s10661-014-3687-8. Singh, K.P., Malik, A., Sinha, S., Singh, V.K., & Murthy, R.C. (2005). Estimation of source of heavy metal contamination in sediments of Gomti river (India) using principal Component analysis. Water, Air, and Soil Pollution, 166, 321–341. Stumm, W., & Morgan, J.J. (1996). Aquatic Chemistry: Chemical Equilibria and Rates in Natural Waters. Wiley, New York. Surija, B., & Branica, M. (1995). Distribution of Cd, Pb, Cu and Zn in carbonate sediments from the Krka river estuary obtained by sequential extraction. Science of the total environment, 170, 101–118. Tessier, A., Campbell, P. G. C., & Bisson, M. (1979). Sequential extraction procedure for the speciation of particulate trace metals. Analytical Chemistry, 51(7), 844–851. Turekian, K. K., & Wedopohl, K. H. (1961). Distribution of the elements in some major units of the earths crust. Geol Soc Am, 72, 175–192. UNEP (1985). Reference methods for marine pollution studies. United Nations Environment Program Regional seas, pp. 31– 39. US EPA. (1989). Risk assessment guidance for superfund, volume I, human health evaluation manual. (part A) Interim Final EPA/540/l-89/002., Office of Emergency and Remedial Response U.S. Environmental Protection Agency, Washington, DC 20450. US EPA. (1996). Soil screening guidance: technical background document. EPA/540/R-95/128. Office of Solid Waste and Emergency Response. US Environmental Protection Agency. Washington, DC. Available from: http://www.epa. gov/superfund/resources/soil/toc.htm#p1>. US EPA. (1997). Exposure factors handbook—general factors. EPA/600/P-95/002Fa, vol. I. Office of Research and Development. National Center for Environmental Assessment. US Environmental Protection Agency. Washington, DC. Available from: http://www.epa.gov/ncea/ pdfs/efh/front.pdf. US EPA. (2004). Risk assessment guidance for superfund volume i: human health evaluation manual (part E, supplemental guidance for dermal risk assessment). Office of Superfund R e m e d i a t i o n a n d Te c h n o l o g y I n n o v a t i o n U . S . Environmental Protection Agency, Washington, DC, EPA/ 540/R/99/005. US EPA. (2005). Integrated Risk Information System (IRIS). Available from: http://www.epa.gov/iris/subst/0278. htm#carc.

322

Page 14 of 14

US EPA. (2006). National recommended water quality criteria. Office of Water and Office of Science and Technology. 24 pp. Wang, S., Jia, Y., Wang, S., Wang, X., Wang, H., Zhao, Z., & Liu, B. (2010). Fractionation of heavy metals in shallow marine sediments from Jinzhou Bay, China. Journal of Environmental Sciences, 22(1), 23–31. Wei, L., Yonglong, L., Tieyu, W., Wenyou, H., Wentao, J., Jonathan, E. N., Jong, S. K., & John, P. G. (2010). Ecological risk assessment of arsenic and metals in sediments of coastal areas of northern Bohai and Yellow Seas, China. Ambio, 39, 367–375. doi:10.1007/ s13280-010-0077-5. Weis, J. S., & Weis, P. (2004). Metal uptake, transport and release by wetland plants: implications for phytoremediation and remediation. Environment International, 30, 685–700.

Environ Monit Assess (2015) 187:322 Whiteley, J. D., & Pearce, N. J. G. (2003). Metal distribution during diagenesis in the contaminated sediments of Dulas Bay, Anglesey, N. Wales, UK. Applied Geochemistry, 18, 901–913. Wu, B., Zhao, D. Y., Jia, H. Y., Zhang, Y., Zhang, X. X., & Cheng, S. P. (2009). Preliminary risk assessment of trace metal pollution in surface water from Yangtze River in Nanjing section, China. Bulletin of Environmental Contamination and Toxicology, 82, 405–409. doi:10.1007/s00128-008-9497-3. Wu, Z., He, M., & Lin, C. (2011). Environmental impacts of heavy metals (Co, Cu, Pb, Zn) in surficial sediments of estuary in Daliao River and Yingkou Bay (northeast China): concentration level and chemical fraction. Environmental Earth Science, 66, 2417–2430. doi:10. 1007/s12665-011-1466-1.