Ecological and health risk assessment of heavy metals bioaccumulation in Ganges fish near Varanasi, India

doi:10.21203/rs.3.rs-3340793/v1

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Ecological and health risk assessment of heavy metals bioaccumulation in Ganges fish near Varanasi, India

https://doi.org/10.21203/rs.3.rs-3340793/v1

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published 26 Dec, 2023

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Heavy metal contamination in river Ganga is one of the factor for deterioration in its water quality, also adds to human health risks. We designed our study to achieve a holistic approach by not only estimating the concentration of heavy metals (Lead, Manganese, Chromium and Cadmium) in the river water at different sites based on human anthropogenic activities but also in the fishes residing in the same sites that are collected for human consumption on daily basis. We found, Ganga river in Varanasi is highly loaded with metals (PLI = 6.698) Mean concentration in water was: Pb 1.29 mg/L, Mn 1.325 mg/L, Cr 0.169 mg/L and Cd 0.161mg/L, which were above the permissible limits stated by Environment Protection Agency (EPA) in drinking water. Fishes including exotic and invasive species were collected from the wild and were processed for the presence of these metals in their living tissues. Degree of heavy metal concentration followed liver > gills > muscles. Highest accumulation of Pb was observed in Carpio (Cyprinus carpio) liver (8.86 µg/g) and lowest in Baikari (Clupisoma garua) muscles (0.07 µg/g). Total Target Hazard Quotient (THQ) value i.e. hazard index (HI) showed values in following sequence: C.carpio > O. nilotus > C.punctatus > J.coitor > M.armatus > M.tengara > Baikari. Maximum HI was recorded in Carpio, which is highly consumed fish by human, hence may be harmful to them.

Ganga

heavy metals

pollution

fishes

health risk assessment

THQ

The worldwide issue of heavy metal pollution poses a significant threat to both aquatic and terrestrial ecosystems. Several rivers are known to be affected by this pollution with particular concern for those that hold sacred significance, as they are often subjected to extensive human activities and interventions. Our very prestigious and holy river Ganga is the largest river (travels 2525 km and covers 8,61,404 km² of the drainage basin, travelling through Uttarakhand, Uttar Pradesh, Bihar, Jharkhand and West Bengal, finally discharges its water into the Bay of Bengal) of the Indian subcontinent, with spiritual significance in Indian culture. Unfortunately, in recent times, the Ganga River has encountered significant sequential and spatial challenges concerning its water quality and biodiversity, primarily due to poor sanitation, agricultural runoff, household waste, and rapid anthropogenic activities. Among these challenges, heavy metal pollutants stand out as exceptionally persistent and long-lasting, remaining in the ecosystem and impacting various trophic levels (Maurya et al., 2016). Aquatic organisms including fish, zooplankton, benthic algae etc. are frequently exposed to heavy metals and show significant variance in metal accumulation. Furthermore, it adversely impacts the diversity of flora and fauna, leading to alterations in the physical, chemical, and biological properties of the river (Usmani et al., 2017). Various site and season specific studies have consistently found that carcinogenic metals, such as Arsenic (As), Cadmium (Cd), Chromium (Cr), Mercury (Hg), Nickel (Ni), and Lead (Pb), exceed the World Health Organization's (WHO) permissible limit for drinking water in the Ganga River (Pandey et al., 2010; Katiyar, 2011) and concluded that Ganga water is unfit for human consumption even for household works.

As the River Ganga is home to about 140 species of fishes including exotic varieties, numerous hazardous elements bio-accumulate and biomagnifies in the fishes of Ganga (Mitra et al., 2012; Sudhakar et al., 2014). These fish species play a crucial role as prominent bio-indicators, reflecting the extent of metal pollution present in the ecosystem (Sarkar et al., 2012). Gills and oral ingestion of contaminated food and sediment particles are the main sources of entry, while the liver, kidneys, gills, muscles, skin and brain are the main organs of their accumulation in fishes (Vaseem et al., 2013). As a potential source of protein, essential minerals, vitamins, and unsaturated fatty acids in the human diet, about 20 kg year^− 1 per capita, Indian consumers alone account for 50% of this global consumption, contributing around 8–9 kg per year for those who include fish in their diet. (Medeirost al., 2012; FAO, 2022). From the contaminated fishes, these metals can enter the human system and contribute to a serious health risk to a person through contaminated environmental and food exposure.

In 2010, the World Health Organization (WHO) identified lead (Pb), cadmium (Cd), and mercury (Hg) as three of the top ten chemicals with significant public health implications. These metals can adversely affect critical organs such as the kidneys, liver, and brain, leading to nephrotoxicity, hepatotoxicity, and neurotoxicity, respectively. Once inside the body, some metals have the ability to bind to enzymes containing sulfur, disturbing their normal functioning. Additionally, certain metals can induce oxidative stress by generating free radicals (Jaishankar et al., 2014; Maurya et al., 2016). The amount of heavy metals present in the water bodies reaches to human beings via consumable fishes present in them. Therefore, fishes are one of the primary source of entry of these metals.

In the past few years, several studies in various regions across the globe has highlighted, about the detrimental impacts of metals on both the water ecosystems and human health. Matta G et al, 2022 has studied the Himalayan region of Ganga river and its health risk for resident population, using Hazard Quotient (HQ) and Hazard index (HI). Han et al, 2021 investigated the levels of As, Cd, Cr, Hg and Pb in marine fishes of Zhejiang province, which is a coastal area of East China Sea and evaluated the health risk based on daily consumption of these fishes. The metals in question originate from both natural and anthropogenic sources. According to Hossain et al. 2023, Pb, As, Co, and Ni primarily stem from natural sources, while Cu, Mn, Zn, and Cr are mainly linked to anthropogenic activities, including industrial effluents, domestic wastewater, agricultural practices, vessel emissions, and river confluences. Nevertheless, some water bodies like Lake Hawassa, Ethiopia are free from metal pollution to the extent that consuming fish from them poses no health risk to humans (Melake et al, 2022). Thus regularly conducting studies on major river bodies is crucial to understand the potential harmful effects of heavy metals in river water and its associated health risks to human beings. The Minamata catastrophe in the 1950s in Japan, due to mercury poisoning, serves as a stark reminder of the devastating consequences of such contamination. Itai Itai disease is way similar which has also affected the mass population.

The current study was conducted in the Varanasi district of Uttar Pradesh, which is one of the busiest industrialized cities in India. The primary objective of the present study is to assess the presence of heavy metals especially Pb, Mn, Cr and Cd in river sediment, water and in tissues (gills, liver and muscle) of seven different consumable fish species, collected directly from river Ganga. This comprehensive study is the first of its kind conducted in the Ganga River in Varanasi, providing insights into the extent of heavy metal pollution in the environment and its potential impact on human health. Furthermore, the study aimed to determine the ecological risk using sediment quality indices and assess the potential health risk for individuals who consume these fish contaminated with metals.

2.1 Study area

Present study is done in Varanasi (25° 20’N and 83° 00’E) an industrially and spiritually important resident hub of north India. The district is registered with several small and large-scale industries including carpet, textiles, Diesel and locomotive workshops, paper, food, medical and rubber-plastic, and glass industries whose effluents are either partially or without treatment discharged into the river. Ten different study sites (Fig. 1) were selected based on severe anthropogenic activities like ritual bathing, religious offerings, cremation of dead bodies and the places where city’s heavy discharge of sewage and waste water takes place.

2.2. Collection of samples

2.2.1. Sediment

Sediment sample from the bank of river Ganga with the help of core sampler (0–10 cm depth) were collected from the mentioned 10 stations (Fig. 1) labelled and transported to the laboratory in airtight plastic bags at 4°C.

2.2.2 Water

Water samples were collected from approximately 50–100 cm from the bank of each collection sites (Fig. 1) in 1000 ml pre-sterilized bottles from 10 different locations. Sampling was done at the early morning because morning hours are with least human interventions, water flow is stable and its contents are uniformly distributed. The water was collected in plastic bottles and immediately kept in an ice box (4°C) and transported to the laboratory.

2.2.3 Fish

Fishes were collected directly from the wild with the help of local fishermen. Gill net was used for capturing the fishes. Fishes were captured consecutively for 3 years from 2019 to 2021, except in rainy season. Seven species of fishes were randomly selected from the wild, from site no.1 to 10 (Fig. 1) on the basis of their availability. Collected samples were bought to the laboratory in water filled container. Identification of fishes were done by key identifying features, based on scales, fins, presence of barbles, body shape and size (Srivastava, 2019).

. Within 2 hours of collection, biometrics study was done and fishes were dissected to collect their tissues (gills, liver and muscles). The tissues were stored at -20°C until further processing.

Table 1

Biometric observations in fish species.
S.N.	Fish species			Inhabit stratum	Feeding habit	Conservation status	No. of samples	Weight (gm)	Length (cm)
	Local name	Family	Scientific name
1	Sauri	Channidae	Channa gachua	Middle and lower Ganga surface	Carnivorous	Least concern	6	7–12	15–20
2	Baikari	Schilbeidae	Clupisomagarua	demarsal	Insects,shrimp,crustaceans	Least concern	6	3–5	15–18
3	Bam	Congridae	Mastacembelus armatus	demersal	Insect larvae and blackworms	Least concern	6	10–15	18–25
4	Tengra	Bagridae	Mystus tengara	demarsal	zooplanktons	Least concern	7	23–35	20–25
5	Carpio	Cyprinidae	Cyprinus carpio	Benthopelagic	detri-omnivore	Critically endangered	6	25–32	20–30
6	Pathri	Sciaenidae	Johnius coitor	demersal	Cephalopods, crustaceans	Least concern	5	8–10	5–8
7	Tilapia	Cichlidae	Oreochromis niloticus	Benthopelagic	Benthic algae, insect larvae	Least concern	6	22–27	15–20

2.3 Sample preparation

2.3.1 Physicochemical parameters of water

Physicochemical parameters studied were pH, TDS, BOD, DO and fecal coliform. pH and Total Dissolved Solid (TDS) of water was measured by digital pH meter and probe method respectively by dipping the instrument into water. Biological Oxygen Demand (BOD) was calculated by measuring initial dissolved oxygen (DO) by titration method and then incubating the sample at 20°C for 5 days in dark followed by final DO measurement. The final DO was subtracted by the initial DO to obtain BOD.

Fecal coliform was measured by Plate count method, where 0.1–0.2 ml of water was dropped in agar medium in petri plates and incubated in bacteriological incubator at 37°C for 24 hours. Bacterial colony was counted and calculated by Eq. 1.

\(\frac{\mathbf{n}\mathbf{o}.\mathbf{o}\mathbf{f} \mathbf{c}\mathbf{o}\mathbf{l}\mathbf{o}\mathbf{n}\mathbf{i}\mathbf{e}\mathbf{s} \times 100}{\mathbf{d}\mathbf{i}\mathbf{l}\mathbf{u}\mathbf{t}\mathbf{i}\mathbf{o}\mathbf{n}}\) ………… Eq. 1

2.3.2 Processing of samples for heavy metals detection

Sediment

The sediments collected were air dried for 72 hours at room temperature, crushed with mortar and pestle and sieved to obtain fine particles. Then samples were kept in small plastic bags at 25–28°C till further analysis. APHA, 2017 method was followed for total acid (ternary acid) digestion of samples. Samples were then heated at 60°C on hot plate for 160 minutes and filtered with Whatmann-42 filter paper followed by cooling and further analyzed by Atomic absorption spectrometer (AAS).

Water

Each 80 ml water sample was filtered to remove unwanted macroscopic substances and digested with aqua regia, HCl: HNO₃ (3:1) following APHA, 2017 method (APHA, 2017). Acid mixed samples were subjected to thermostatically controlled Hot plate digestion up to 60°C for 15 minutes. Allowed to cool, filtered through Whatmann-42 filter paper and analyzed in AAS.

Fish

Dissected tissues were dried in incubator at 60°C for 36 hours, then were digested in conc. HNO₃ and H₂O₂ at 1:1 ratio in microwave digestion system at 130°C, diluted with Milli Q water and filtered with Whatmann-42 filter paper. The samples were further diluted with Milli Q water for analysis via AAS technique.

2.4. Atomic Absorption Spectrometer (AAS) technique

A Perkin Elmer, PinAAcle Atomic Absorption Spectrometer (AAS) with Zeeman background correction system equipped with a flame furnace was used to measure Mn, Pb, Cd and Cr in the samples using an external standard method. Detection limit of the instrument was 0.127 mg/L for Mn, 0.18 mg/L for Pb, 0.052 mg/L for Cd and 0.096 mg/L for Cr.

2.4.1. Quality assurance and quality control (QA/QC) of AAS

The quality assurance and quality control (QA/QC) Analytical estimation was performed using NIST traceable Certified Reference Material of Multielement standard with purity of more than 99.99%. AAS, Perkin Elmer, PinAAcle 900F was calibrated with three-point calibration in triplicate. The sample were analyzed in triplicate with a mandatory blank sample for all the estimations during study. The QA/QC data is depicted in Table 2.

Table 2

QA/QC results of analytical methods for heavy metal concentrations.
Element	Detection Limit, mg/L	Recovery, %	RSD*, %
Mn	0.127	103.8-106.5	1.8
Pb	0.180	93.6-106.7	10.2
Cd	0.052	95.6-108.7	9.5
Cr	0.096	88.4-105.3	13.2
*Relative standard deviation (RSD)

2.5 Heavy metal pollution assessment in sediment

2.5.1 Contamination factor

Contamination factor (CF) represents the concentration of selected metal higher than the baseline concentration of same metal in an uncontaminated site. CF in this study is calculated by following equation (Fukue et al., 2006).

\(\mathbf{C}\mathbf{F}=\frac{\mathbf{C}\mathbf{o}\mathbf{n}\mathbf{c}\mathbf{e}\mathbf{n}\mathbf{t}\mathbf{r}\mathbf{a}\mathbf{t}\mathbf{i}\mathbf{o}\mathbf{n} \mathbf{o}\mathbf{f} \mathbf{m}\mathbf{e}\mathbf{t}\mathbf{a}\mathbf{l} \mathbf{i}\mathbf{n} \mathbf{s}\mathbf{e}\mathbf{d}\mathbf{i}\mathbf{m}\mathbf{e}\mathbf{n}\mathbf{t} \mathbf{s}\mathbf{a}\mathbf{m}\mathbf{p}\mathbf{l}\mathbf{e}}{\mathbf{B}\mathbf{a}\mathbf{s}\mathbf{e}\mathbf{l}\mathbf{i}\mathbf{n}\mathbf{e} \mathbf{c}\mathbf{o}\mathbf{n}\mathbf{c}\mathbf{e}\mathbf{n}\mathbf{t}\mathbf{r}\mathbf{a}\mathbf{t}\mathbf{i}\mathbf{o}\mathbf{n} \mathbf{o}\mathbf{f} \mathbf{m}\mathbf{e}\mathbf{t}\mathbf{a}\mathbf{l}}\) ………….Eq. 2

Baseline concentration was taken as per reference no (Salomons et al., 1984).

The CF is divided into these categories as (Patel et al., 2018).

CF < 1 is low

CF = 1–3 is Moderate

CF = 3–6 is Considerable

CF > 6 is very high contamination

2.5.2 Enrichment Factor

Enrichment Factor (EF) denotes the anthropogenic and geogenic influx of metals in the water (Salomons et al., 1984). Eq. 3 is used to calculate EF (Delgado et al., 2010).

\(\mathbf{E}\mathbf{F}=\frac{(\mathbf{C}\mathbf{o}\mathbf{n}\mathbf{c}\mathbf{e}\mathbf{n}\mathbf{t}\mathbf{r}\mathbf{a}\mathbf{t}\mathbf{i}\mathbf{o}\mathbf{n} \mathbf{o}\mathbf{f} \mathbf{m}\mathbf{e}\mathbf{t}\mathbf{a}\mathbf{l} \mathbf{i}\mathbf{n} \mathbf{s}\mathbf{e}\mathbf{d}\mathbf{i}\mathbf{m}\mathbf{e}\mathbf{n}\mathbf{t} \times \mathbf{B}\mathbf{a}\mathbf{s}\mathbf{e}\mathbf{l}\mathbf{i}\mathbf{n}\mathbf{e} \mathbf{c}\mathbf{o}\mathbf{n}\mathbf{c}\mathbf{e}\mathbf{n}\mathbf{t}\mathbf{r}\mathbf{a}\mathbf{t}\mathbf{i}\mathbf{o}\mathbf{n} \mathbf{o}\mathbf{f} \mathbf{F}\mathbf{e})}{(\mathbf{B}\mathbf{a}\mathbf{s}\mathbf{e}\mathbf{l}\mathbf{i}\mathbf{n}\mathbf{e} \mathbf{c}\mathbf{o}\mathbf{n}\mathbf{c}\mathbf{e}\mathbf{n}\mathbf{t}\mathbf{r}\mathbf{a}\mathbf{t}\mathbf{i}\mathbf{o}\mathbf{n} \mathbf{o}\mathbf{f} \mathbf{m}\mathbf{e}\mathbf{t}\mathbf{a}\mathbf{l} \times \mathbf{C}\mathbf{o}\mathbf{n}\mathbf{c}\mathbf{e}\mathbf{n}\mathbf{t}\mathbf{a}\mathbf{t}\mathbf{i}\mathbf{o}\mathbf{n} \mathbf{o}\mathbf{f} \mathbf{F}\mathbf{e} \mathbf{i}\mathbf{n} \mathbf{s}\mathbf{e}\mathbf{d}\mathbf{i}\mathbf{m}\mathbf{e}\mathbf{n}\mathbf{t})}\) ………….Eq. 3

Here, Fe is used as conservative metal and its baseline was obtained from (Muller, 1979). The value of enrichment factor if estimated close to or < 1, reflects the primary source of trace elements from natural source like crustal or marine environments and if > 1 shows the source is from anthropogenic activities.

Enrichment factor is categorized as

EF = 1.5–3.0: minor enrichment

EF = 3.0–5.0: moderate

EF = 5.0–10.0: severe

EF > 10: very severe enrichment.

2.5.3 Pollution Load Index

Pollution Load Index (PLI) gives the overall picture of pollution load at a particular site (Angulo, 1996) PLI was calculated as:

\({\mathbf{P}\mathbf{L}\mathbf{I}=(\mathbf{C}\mathbf{F}1 \times \mathbf{C}\mathbf{F}2\dots \dots ..\mathbf{C}\mathbf{F}\mathbf{n})}^{1/\mathbf{n}}\) …………Equation 4

PLI if obtained 1 indicates baseline levels and > 1 indicates progressive alteration of toxicity.

2.5.4 Geo-accumulation Index

Geo-accumulation Index (Igeo) suggested by Muller represents the degree of metal pollution in terms of metal accumulation and considers anthropogenic input in pollution load (Muller, 1979). It is given by following formula:

\(\mathbf{I}\mathbf{g}\mathbf{e}\mathbf{o}=\mathbf{l}\mathbf{o}\mathbf{g}2\frac{\mathbf{C}\mathbf{n}}{1.5 \times \mathbf{B}\mathbf{n}}\) …………Equation 5

Where, Cn = concentration of element n, in sediment in mg/kg

Bn = background concentration

1.5 represents a constant, which compensates for weathering and lithogenic effects (Huang et al., 2020).

Table 3 shows the seven classification levels of heavy metal pollution in sediment, with standard values of Igeo as defined by Muller, 1979.

Table 3

Pollution level classification of heavy metals in sediment.
Igeo	Level	Pollution Level
< 0	0	Unpolluted
> 0 to 1	1	Unpolluted to Moderately polluted
> 1 to 2	2	Moderately polluted
> 2 to 3	3	Moderately to heavily polluted
> 3 to 4	4	Heavily polluted
> 4 to 5	5	Heavily to extremely polluted
> 5	6	Extremely polluted

2.5.5 Ecological risk level

The potential ecological risk index from heavy metals to the aquatic ecosystem is assessed by calculating ecological risk level. This index was developed to evaluate the potential risk of heavy metal contamination in sediments by (Hakanson, 1980). The final value of RI is obtained by calculating using following formula:

\(RI=\sum {Er}^{Me}\) …………(Eq. 6)

\({Er}^{Me}={Tr}^{Me}\times {CF}^{Me}\) …………(Eq. 7)

RI is the sum of potential risk of individual heavy metal. Er is the potential ecological risk of individual metal. Tr denotes toxicity response index. Tr values for Pb, Mn, Cr, and Cd are 5, 1, 2, and 30 respectively (Hakanson, 1980) and CF is the contamination factor of individual metal.

2.6. Heavy metal concentration assessment in water and tissues

AAS readings obtained for the different heavy metals present in the water samples and the fish tissue analyzed was then used for further estimations.

The Heavy metals concentration in water samples from river Ganga was calculated by the formula given below:

\(\mathbf{H}\mathbf{e}\mathbf{a}\mathbf{v}\mathbf{y} \mathbf{m}\mathbf{e}\mathbf{t}\mathbf{a}\mathbf{l} \mathbf{c}\mathbf{o}\mathbf{n}\mathbf{c}\mathbf{e}\mathbf{n}\mathbf{t}\mathbf{r}\mathbf{a}\mathbf{t}\mathbf{i}\mathbf{o}\mathbf{n}=\frac{\mathbf{A}\mathbf{A}\mathbf{S} \mathbf{r}\mathbf{e}\mathbf{a}\mathbf{d}\mathbf{i}\mathbf{n}\mathbf{g}}{\mathbf{V}\mathbf{o}\mathbf{l}\mathbf{u}\mathbf{m}\mathbf{e} \mathbf{o}\mathbf{f} \mathbf{t}\mathbf{h}\mathbf{e} \mathbf{s}\mathbf{a}\mathbf{m}\mathbf{p}\mathbf{l}\mathbf{e}}\) …………(Eq. 8)

The concentration of heavy metals in fish tissue was calculated as:

\(\mathbf{H}\mathbf{e}\mathbf{a}\mathbf{v}\mathbf{y} \mathbf{m}\mathbf{e}\mathbf{t}\mathbf{a}\mathbf{l} \mathbf{c}\mathbf{o}\mathbf{n}\mathbf{c}\mathbf{e}\mathbf{n}\mathbf{t}\mathbf{r}\mathbf{a}\mathbf{t}\mathbf{i}\mathbf{o}\mathbf{n}=\frac{\mathbf{A}\mathbf{A}\mathbf{S} \mathbf{r}\mathbf{e}\mathbf{a}\mathbf{d}\mathbf{i}\mathbf{n}\mathbf{g}}{\begin{array}{c}weight of the sample \left(\mathbf{g}\mathbf{m}\right)\\ \end{array}}\) …………(Eq. 9)

2.7. Bioaccumulation factor (BCF)

BCF is the ratio of the contaminant in an organism to the concentration in the ambient environment in the steady state, where the organism can take in the contaminant through ingestion with its food as well as through direct contact. BCF was calculated using the formula suggested by Lau et al., 1998.

\(\mathbf{B}\mathbf{i}\mathbf{o}\mathbf{a}\mathbf{c}\mathbf{c}\mathbf{u}\mathbf{m}\mathbf{u}\mathbf{l}\mathbf{a}\mathbf{t}\mathbf{i}\mathbf{o}\mathbf{n} \mathbf{f}\mathbf{a}\mathbf{c}\mathbf{t}\mathbf{o}\mathbf{r}=\frac{\mathbf{C}\mathbf{o}\mathbf{n}\mathbf{c}\mathbf{e}\mathbf{n}\mathbf{t}\mathbf{r}\mathbf{a}\mathbf{t}\mathbf{i}\mathbf{o}\mathbf{n} \mathbf{o}\mathbf{f} \mathbf{h}\mathbf{e}\mathbf{a}\mathbf{v}\mathbf{y} \mathbf{m}\mathbf{e}\mathbf{t}\mathbf{a}\mathbf{l}\mathbf{s} \mathbf{i}\mathbf{n} \mathbf{f}\mathbf{i}\mathbf{s}\mathbf{h} \mathbf{t}\mathbf{i}\mathbf{s}\mathbf{s}\mathbf{u}\mathbf{e}}{\mathbf{C}\mathbf{o}\mathbf{n}\mathbf{c}\mathbf{e}\mathbf{n}\mathbf{t}\mathbf{r}\mathbf{a}\mathbf{t}\mathbf{i}\mathbf{o}\mathbf{n} \mathbf{o}\mathbf{f} \mathbf{h}\mathbf{e}\mathbf{a}\mathbf{v}\mathbf{y} \mathbf{m}\mathbf{e}\mathbf{t}\mathbf{a}\mathbf{l}\mathbf{s} \mathbf{i}\mathbf{n} \mathbf{s}\mathbf{u}\mathbf{r}\mathbf{r}\mathbf{o}\mathbf{u}\mathbf{n}\mathbf{d}\mathbf{i}\mathbf{n}\mathbf{g} \mathbf{w}\mathbf{a}\mathbf{t}\mathbf{e}\mathbf{r}}\) …………(Eq. 10)

2.8. Estimated daily intake

Fish muscles are the major source of food for 50% of the human population. Therefore, fish muscles are used for calculating the human health risk through an estimated daily intake (EDI) of metals. EDI is calculated by the following equation as per Song et al., 2009.

\(\text{E}\text{D}\text{I} (\text{m}\text{g}/\text{k}\text{g} \text{b}\text{o}\text{d}\text{y} \text{w}\text{e}\text{i}\text{g}\text{h}\text{t} \text{o}\text{f} \text{c}\text{o}\text{n}\text{s}\text{u}\text{m}\text{e}\text{r}/\text{d}\text{a}\text{y}) =\frac{(\text{C}\text{o}\text{n}\text{c}\text{e}\text{n}\text{t}\text{r}\text{a}\text{t}\text{i}\text{o}\text{n} \text{o}\text{f} \text{m}\text{e}\text{t}\text{a}\text{l} \times \text{W}\text{e}\text{i}\text{g}\text{h}\text{t} \text{o}\text{f} \text{f}\text{i}\text{s}\text{h} \text{c}\text{o}\text{n}\text{s}\text{u}\text{m}\text{e}\text{d} \text{p}\text{e}\text{r} \text{d}\text{a}\text{y})}{\text{B}\text{o}\text{d}\text{y} \text{w}\text{e}\text{i}\text{g}\text{h}\text{t} \text{o}\text{f} \text{c}\text{o}\text{n}\text{s}\text{u}\text{m}\text{e}\text{r}}\) …..(Eq. 11)

Where, the concentration of metals in muscles was converted into dry weight by dividing with a concentration factor 4.8 (Rahman et al., 2012). The average weight of fish consumed per day is 25 g as suggested by a North India survey study by Kumar et al, 2020 when average body weight of consumer is 52 (for Indian men) (Jain et al., 1995).

2.9. Human health risk assessment

In order to assess the health risk due to consumption of metal-loaded fishes, we calculated EDI, THQ and HI which gives an account of potential risk on health due to heavy metals consumption through contaminated food. (Chary et al., 2008; Hough et al., 2004) and calculated as

\(\mathbf{T}\mathbf{H}\mathbf{Q}=\frac{\mathbf{E}\mathbf{D}\mathbf{I}}{\mathbf{R}\mathbf{f}\mathbf{D}}\) …………(Eq. 12)

Where EDI is calculated as per Eq. 5. RfD is the standard dose intake of a particular metal in a day (mg/kg body weight/day) that is under tolerable and healthy range (USEPA, 2011) given in table no 10. A THQ more than 1 indicates deleterious health effect due to contaminated food exposure in the population.

HI indicates risk due to multiple metals present in contaminated food and calculated as (USEPA, 2011):

\(\mathbf{H}\mathbf{I}=\sum \mathbf{T}\mathbf{H}\mathbf{Q}\) …………(Eq. 13)

2.10 Statistical analysis:

The significant difference between heavy metal contaminations at different sampling sites were compared using one-way ANOVA. The significant difference between concentration of heavy metals and their bioaccumulation in various fish tissues were compared using Two-Way ANOVA test and correlation test was done by Pearson’s correlation matrix to show the inter elemental relation.

3.1 Analysis of physicochemical properties of water in Varanasi

The physicochemical qualities of river water samples collected from ten different sites of Varanasi are summarized in Table 4. The temperature of the river water in the studied period ranged between 18 and 30°C with an average temperature of 27°C, which was stable over the period. pH was within the permissible range, comparatively lower towards the drainage site (7.5) than other river sites (8.1). The alkalinity of water observed in other river sites was due to dissolved carbonates, bicarbonates and hydroxides ions most probably due to increases in pollution load in the river from upstream to downstream, say from Kanpur to Varanasi (Maurya et al., 2019; Gupta et al., 2013). However, according to European Union, for fisheries and aquatic life, pH of 6.0 to 9.0 is permissible. (USEPA, 1986; USEPA. 1999a). Although Ganga water holds buffering capacity, nevertheless this pH is unsuitable for human consumption.

Table 4

Physicochemical parameters of sampled water.
Parameters	Drainage site	Other river site	Bureau of Indian Standard (BIS) Permissible limit
TDS	545 ± 28.47	302.58 ± 47.14	2000 mg/L
pH	7.45 ± 0.41	8.05 ± 0.05	6.5–8.5
DO (mg/L)	3.04 ± 0.84	7.02 ± 0.78	4–6 mg/L
BOD (mg/L)	53.77 ± 9.21	4.72 ± 2.04	3.0 mg/l
Fecal coliform	23x 10⁶-60x10⁶/ 100 ml	0.13 x 10⁶-0.012 x 10⁶/100 ml	Nil/100ml
Pb	0.955	0.142	0.01 mg/L
Mn	0.581	0.159	0.1 mg/L
Cr	0.159	0.148	O.05 mg/L
Cd	0.149	0.139	0.01 mg/L
Drainage sites are site no.1, 2 and 10 as per mentioned in Fig. 1. Other river sites are site no.3–9 as per mentioned in Fig. 1.

DO determines the purity of water and its fitness for the survival of life within the water body. It is seen that the observed DO (Table 4) is below the permissible limit at the drainage site and within range at other river sites, indicating poor water quality at the drainage site, which might be caused due to heavy sewage discharge from the city. Aquatic aerobic bacteria consume oxygen from water for the decomposition of wastes, thus increasing BOD. In the present study, BOD was found to be 53.77 mg/L at the drainage site (Table 4). This could be due to untreated domestic sewage, agriculture runoff, and residual fertilizers. Other sites of the river also showed increase in the BOD as compared to the permissible limit stated by BIS. Notably, in Table 4, TDS, BOD and fecal coliform are increased with higher metal concentration whereas pH and DO is decreased with increased metal concentration at drainage site and vice versa for other river sites. These physicochemical parameter indicates the deterioration of the quality of river water at drainage site due to increasing metal pollution load. Besides these the addition of non-biodegradable pollutants keep on adding to the associated problems. Aquatic species thriving in such waters are subjected to poor quality of environment as well as bioaccumulation of several pollutants in their body tissues. (fecal coliforms in heavy metals)

3.2 Heavy metal assessment in sediment

Sediment sample collected from different sites mentioned in Fig. 1 were screened for the occurrence of heavy metals in it. Result obtained is summarised in Table 5. Mean metal concentration in sediment followed the trend as follows Mn > Pb > Cr > Cd. The mean concentration of Pb in sediment was 158.455 mg/kg, CF is 8.33 and EF is 40.21 indicating very high lead contamination in sediment. Geo accumulation factor (GAF) 1.66 indicates Class 2 category (Table 3) with moderately polluted intensity and ecological risk level of 41.695 (Table 5) reveals moderate ecological risk due to lead. In a similar study lead accumulation was found from moderate to strong in river Ganga in Varanasi (Pandey et al., 2015). The primary sources of Pb contamination in the river Ganga in Varanasi are metal processing and battery manufacturing industries, the use of lead-based paints, pesticides, and fertilizers in agriculture, and gold extraction processes involving lead in amalgamation techniques. These activities result in the leaching of Pb into the soil, which eventually gets transported into the river. However, the mean Mn concentration in sediment was found to be 355.622 mg/kg, CF is 0.46 and EF is 2.25 which indicates low contamination, GAF is 0.092, depicts Class 1 category (Table 3), i.e, unpolluted to modrately polluted, Er: 0.461 indicate low ecological risk due to manganese. This indicates that river is safe with Mn concentration.

Table 5

Assessment of heavy metal pollution indices in sediment.
Heavy metals	Mean Concentration (mg/kg)	Contamination factor (CF) Eq.No. 2	Enrichment factor (EF) Eq.No.3	Geo accumulation factor (GAF) Eq.No.5	Ecological risk level (Er) Eq.No.7	Pollution Load Index (PLI) Eq.No.4
Lead	158.455	8.33	40.21	1.667	41.695	6.698
Manganese	355.622	0.46	2.25	0.092	0.461
Chromium	154.328	2.14	10.45	0.428	4.286
Cadmium	46.717	274.96	1285.67	55.177	8248.8

Mean chromium concentration in sediment was 154.328 mg/kg, CF is 2.14, EF is 10.45, which indicates moderate contamination of chromium in sediment and GAF is 0.428, depicts class 1 category (Table 3), i.e. unpolluted to moderately polluted pollution intensity, Er is 4.286 showing low ecological risk.

Mean cadmium concentration in sediment was found to be 46.717 mg/kg, with CF is 274.96, EF is 1285.67 depicting very high contamination in the river sediment. GAF is 55.177 indicates alarming pollution intensity (Table 3) whereas Er is 8248.8 shows very high ecological risk due to cadmium. Cadmium toxicity is highest in river sediment and has surpassed safe concentration limit. The elevated Cd level in the river Ganga is likely to stem from different tanning, electroplating and dye industries in Varanasi district and several domestic channels induced in the Ganga River may be the source of Cd contamination. Additionally, improper disposal and recycling of electronic wastes might also contribute to the presence of Cd in the river.

Overall pollution load index (PLI) of heavy metals was 6.698 which clearly indicates that river Ganga is polluted with heavy metals beyond the permissible limits in Varanasi district and people living in its vicinity are subjected to health risk.

3.3 Heavy metal analysis in water

The heavy metals concentrations in Ganga water in Varanasi at ten selected sites are recorded and presented in Table 6. All the four metals surpassed the safe concentration limit set by EPA at all the ten studied sites. Concentration of Pb, Mn, Cd and Cr was recorded highest at Varuna Ganga confluence point, which were 1.29 mg/L, 1.32 mg/L, 0.169 mg/L and 0.161 mg/L respectively followed by Nagwa and Raj ghat. These points are noted to have highest sewage discharge from the city and are considered among drainage sites (Table 6). These metal contaminations were statistically significant at different sampling sites with p < 0.5. It was found that the concentration of heavy metals at all the sites studied were beyond the permissible limits of this heavy metals in water given by EPA posing the threat to the aquatic life. Similar study was conducted at Kanpur, Allahabad, Mirzapur and Varanasi districts of Uttar Pradesh and observed that water of river Ganga was loaded with Pb 0.24 mg/l, Cd 0.85 mg/l and Cr 0.45 mg/l concentrations in Varanasi in the year 2019 (Maurya et al., 2019). The presence of heavy metals in water, sediment and aquatic lives is responsible for their entry in the food chain ultimately reaching the human population (Sarkar et al., 2016). The results of the present study indicates that, industrial effluent discharge and agricultural runoff, released into the Ganga River in Varanasi is polluted with heavy metals and unfit for human consumption.

Table 6

Heavy metal contamination in mg/L at different sampling sites. Statistical significance of heavy metal contamination at different sampling sites was done using One Way Anova and the differences were significant at p < 0.5, f = 1.6309.
Sites	Pb	Mn	Cr	Cd
Permissible limit as per EPA^*	0.05 mg/L	0.05 mg/L	0.1 mg/L	0.005 mg/L
Nagwa	1.093 ± 0.054	0.221 ± 0.011	0.164 ± 0.008	0.156 ± 0.007
Samne ghat	0.477 ± 0.023	0.200 ± 0.010	0.146 ± 0.007	0.131 ± 0.006
Assi ghat	0.140 ± 0.007	0.141 ± 0.007	0.144 ± 0.007	0.130 ± 0.006
Tulsi ghat	0.017 ± 0.00	0.128 ± 0.006	0.140 ± 0.007	0.124 ± 0.006
Harishchandra ghat	0.158 ± 0.007	0.153 ± 0.007	0.157 ± 0.007	0.155 ± 0.007
Shivala	0.075 ± 0.003	0.143 ± 0.007	0.143 ± 0.007	0.123 ± 0.006
Dassaswamedh ghat	0.146 ± 0.007	0.193 ± 0.009	0.147 ± 0.007	0.140 ± 0.007
Manikarnika ghat	0.181 ± 0.009	0.175 ± 0.008	0.156 ± 0.007	0.152 ± 0.007
Raj ghat	0.281 ± 0.014	0.181 ± 0.009	0.153 ± 0.007	0.153 ± 0.007
Varuna ganga confluence	1.297 ± 0.064	1.325 ± 0.06	0.169 ± 0.008	0.161 ± 0.008
*EPA: Environment Protection Agency

3.4. Detection of heavy metals in fish tissues.

Both indigenous and exotic fishes were investigated for the potential accumulation of heavy metals in their tissues. It was revealed that all the four heavy metals examined were accumulated in the gills, liver, and muscles of every fish species investigated (Table 7).

In all the seven fish species, the degree of heavy metal concentration followed liver > gills > muscles. Liver being an important organ for detoxification as well as for protein synthesis may be a possible reason for having the highest metal affinity (Fernandes et al., 2008). Gills have a large surface area, and are in continuous contact with the aquatic environment, therefore are the second most important site for metal concentration. Another reason may be due to the increased number of chloride cells that pick up metal ions from contaminated water (Mazon et al., 1999; Costa et al., 2002). Although fish muscles are consumed as protein source all over the globe, it is a metabolically less active tissue (Adhikari et al., 2009; Radhakrishnan, 2010) and thus, the reason for least accumulation of metals in muscles. Less extensive blood circulation in muscles in comparison to other vital organs like liver, kidney and gills is also a major factor.

In our field study, heavy metal trend was Mn > Cr > Pb > Cd in almost all the species and tissues. Probable reason for more Mn concentration could be cumulative role of water contamination and essential elemental nature of Mn in enzymatic activity (Altaf et al., 2016). Cr enters the aquatic system via multiple industrial sources from where hexavalent form of chromium is reported to diffuses readily in the fish tissue and penetrates cell membrane (Ghosh, 2002; Bagchi et al., 2001) Report shows that Cd was accumulated highest in liver tissue of bottom feeder followed by column and surface feeder fishes (Delgado et al., 2010).

In the present study, the highest concentration of Pb and Cd was observed in Carpio liver (8.86 µg/g and 3.27 µg/g respectively) while lowest Pb in Baikari muscles (0.07 µg/g) and Cd remains undetected in tengra, bam and pathari. The Food and Agricultural Organisation (FAO) proposed a limit of 0.5 µg/g for Pb in food (FAO, 1983) while Food and Environment Protection Act (FEPA,2003) set this value to 2.0 µg/g. Being an invasive and larger size species, Cyprinus carpio and exhibits aggressive behavior and is a bottom feeder, which increases their exposure to higher concentrations of contaminants, such as lead, found in the sediment. Likewise, Tilapia is an omnivorous and opportunistic feeder, which could be attributed to high Pb accumulation in its liver. However small size of pathari fish gains importance because smaller body size reduces the metal accumulation through surface action. In a similar previous study, in delta region of Nigeria, Pb was detected higher than acceptable limit of 0.5 µg/ in the fishes collected from Finima creek. The reason was due to crude oil spills in that area (Abarshi et al, 2017). Other studies showed higher concentrations of Cd and Pb compared to Cr in fish tissues (Javed et al, 2013; Begum e al, 2013). These results imply that these external sources have a more significant influence on the bioaccumulation process in fish. Mn was recorded highest in Pathari liver (53.19 µg/g) and lowest in Baikari muscles (1.10 µg/g). Cr was estimated highest in Pathari liver whereas lowest in Bam muscles. European Union Commission (EUC) suggested the daily tolerable chromium concentration to be 1 µg/g, while the FEPA,2003 suggested 0.15 µg/g and WHO suggested 0.15 µg/g.. The concentration level of each metal in fish tissue was statistically significant at p < 0.05

In another study of pelagic and benthic fishes of Ogbese River, Ondo State, South-Western Nigeria, Cd was detected as 0.001 ppm in the heart of only one benthic species C. gariepinus (Josephine Omowumi Olayinka-Olagunju et al,2021). However even a little Cd concentration is hazardous.

Table 7

Concentration of heavy metals in different fish tissue (µg/g) and their permissible range by FAO. Statistical significance of heavy metal concentration in various fish tissues was done using Two Way ANOVA test and the differences were significant at p < 0.05, F_tissue = 10.144, F_metals 14.339 and F_{tissue × metals} = 2.312.
Species	Tissue	Pb	Mn	Cr	Cd
	Permissible Limit as per FAO^*	0.2 µg/g	0.98 µg/g	0.05 µg/g	0.02 µg/g
Sauri	Gills	2.905 ± 0.14	17.829 ± 0.891	2.046 ± 0.102	0.427 ± 0.021
	Liver	3.248 ± 0.16	19.858 ± 0.992	3.978 ± 0.198	0.452 ± 0.022
	Muscles	2.778 ± 0.13	12.419 ± 0.620	2.896 ± 0.144	0.275 ± 0.013
Baikari	Gills	1.284 ± 0.06	4.129 ± 0.206	3.165 ± 0.158	0.075 ± 0.003
	Liver	1.449 ± 0.07	4.328 ± 0.216	3.706 ± 0.185	0.120 ± 0.006
	Muscles	0.079 ± 0.003	1.106 ± 0.055	3.145 ± 0.157	0.025 ± 0.001
Carpio	Gills	4.146 ± 0.20	8.842 ± 0.442	5.115 ± 0.255	1.906 ± 0.095
	Liver	8.868 ± 0.44	15.614 ± 0.780	25.704 ± 1.285	3.274 ± 0.163
	Muscles	2.316 ± 0.11	3.157 ± 0.157	4.993 ± 0.249	2.487 ± 0.124
Tilapia	Gills	3.876 ± 0.19	6.872 ± 0.343	9.094 ± 0.454	0.143 ± 0.007
	Liver	5.025 ± 0.25	8.432 ± 0.421	9.845 ± 0.492	0.158 ± 0.007
	Muscles	0.784 ± 0.04	1.7380 ± 0.086	2.363 ± 0.118	0.129 ± 0.006
Tengra	Gills	6.435 ± 0.32	13.344 ± 0.667	1.689 ± 0.084	0.681 ± 0.034
	Liver	7.920 ± 0.39	19.166 ± 0.958	2.371 ± 0.118	ND
	Muscles	0.614 ± 0.03	1.766 ± 0.088	1.281 ± 0.064	ND
Bam	Gills	5.342 ± 0.267	17.884 ± 0.894	4.656 ± 0.232	ND
	Liver	7.651 ± 0.382	24.076 ± 1.203	5.467 ± 0.273	ND
	Muscles	1.684 ± 0.084	3.513 ± 0.175	0.489 ± 0.024	ND
Pathari	Gills	5.904 ± 0.295	25.425 ± 1.271	2.577 ± 0.128	ND
	Liver	7.354 ± 0.367	53.193 ± 2.659	29.467 ± 1.473	ND
	Muscles	1.886 ± 0.094	6.288 ± 0.314	0.701 ± 0.035	ND
*FAO: Food and Agriculture Organisation

Variation of heavy metal concentration in fish tissues also depend on age because the time they spend in water decide the concentration of metals in their body since fishes are captured at their different life period.

3.5. Correlation analysis of heavy metal in fish tissue

Inter elemental relation is shown by Pearson's correlation matrix (Table 8). The correlation coefficient ranges between − 1 to + 1. A positive correlation between two metals indicate that for every positive increase in one, there is a positive increase of a fixed proportion in the other, whereas a negative correlation indicates that for every positive increase in one metal, there is a negative decrease of a fixed proportion in the other.

Table 8

Shows inter elemental relation through Pearson's correlation matrix.
Heavy metals	Pb	Mn	Cr	Cd
Pb	1
Mn	0.1975	1
Cr	0.2727	0.2952	1
Cd	0.7205	-0.2414	0.3649	1

In our study, we found notable correlation between Pb and Cd (r = 0.72, p < 0.05). The probable reason is due to the high concentration of these two elements in Carpio and Tengra in all the selected fish organs. Cd and Pb is reported to occur from the common sources like leaded petrol, coal combustion, smelting and old pre industrial lead. Similar result was observed in one study, where positive correlation between Cd and Pb was observed by C. catla and C. mrigala (Dhanakumar et al., 2015). The negative correlation was seen in case of Cd and Mn, while in case of Pb to Mn, Pb to Cr, Mn to Cr, and Cr to Cd, there were non-significant positive correlations (p < 0.05 and r is < 0.5).

3.6. Determination of Bio-accumulation factor

Metal bioaccumulation in fishes depend upon affinity of metals to fish tissues, variations in uptake, deposition, and excretion processes (Jezierska, B. and M. Witeska, 2001). Higher metal concentrations in the environment tend to lead to increased uptake and accumulation in fish and may differ in fishes living in the same water stream. This relationship has earlier been observed in both field and laboratory studies (Hongjun W. et al, 2013). As shown in Table 9, at statistical significant p < 0.05, BAF in each fish species followed the same trend in organs i.e., liver > gills > muscles and all metals had tissue concentrations higher than their corresponding concentrations in water. Figure 3 shows that Bioaccumulation Factor (BAF) for all four metals is consistently higher in metabolically active tissues such as the liver and gills as compared to less active tissues like the muscles in both indigenous and invasive fish species. This indicates that the uptake and accumulation of these metals are more pronounced in the liver and gills due to their physiological roles and higher metabolic activity, while the muscles tend to exhibit lower levels of metal bioaccumulation.

The carp species showed the highest concentration of all metals in its liver, may be due to its opportunistic feeding behavior and potentially higher metabolic rates. This suggests that carp may have a greater efficiency in taking up heavy metals from the environment. Indigenous species such as Tengra, Bam, and Pathri also exhibited relatively higher metal concentrations compared to other fish species. These heavy metals has the capacity to form harmful soluble compounds (Amin et al, 2021) and higher metal retention could be due to the formation of metal-protein complexes, which might impede the excretion process within the fish body. The retention of these metals within fish can lead to substantial changes, causing them to endure stressful conditions and potentially disrupting their hematological and biochemical indices (M Salaah et al, 2020; Elhaddad et al., 2022).

Table 9

Bio-accumulation factor in different fish tissues. Statistical significance of heavy metal accumulation in various fish tissues was done using Two Way ANOVA test and the differences were significant at p < 0.05, F_tissue = 9.052, F_metals = 10.82 and F_{tissue × Metals} = 2.2664.
Species	Tissue	Pb	Mn	Cr	Cd
Sauri	Gills	7.506 ± 0.37	63.676 ± 3.18	13.417 ± 0.67	2.985 ± 0.14
	Liver	8.392 ± 0.42	70.924 ± 3.54	26.083 ± 1.30	3.160 ± 0.15
	Muscles	7.178 ± 0.35	44.356 ± 2.21	18.987 ± 0.94	1.928 ± 0.09
Baikari	Gills	3.318 ± 0.16	14.749 ± 0.73	20.752 ± 1.03	0.530 ± 0.02
	Liver	3.744 ± 0.18	15.458 ± 0.77	24.295 ± 1.21	0.842 ± 0.04
	Muscles	0.205 ± 0.01	3.950 ± 0.19	20.620 ± 1.03	0.178 ± 0.00
Carpio	Gills	10.714 ± 0.53	31.578 ± 1.57	33.535 ± 1.67	13.324 ± 0.66
	Liver	22.914 ± 1.14	55.765 ± 2.78	168.506 ± 8.42	22.887 ± 1.14
	Muscles	5.985 ± 0.29	11.276 ± 0.56	32.735 ± 1.63	17.391 ± 0.86
Tilapia	Gills	10.015 ± 0.50	24.543 ± 1.22	59.620 ± 2.98	0.999 ± 0.05
	Liver	12.984 ± 0.64	30.116 ± 1.50	64.545 ± 3.22	1.107 ± 0.05
	Muscles	2.026 ± 0.10	6.207 ± 0.31	15.491 ± 0.77	0.905 ± 0.04
Tengra	Gills	16.626 ± 0.83	47.657 ± 2.38	11.076 ± 0.55	4.764 ± 0.23
	Liver	20.464 ± 1.02	68.451 ± 3.42	15.544 ± value	0
	Muscles	1.587 ± 0.08	6.308 ± 0.31	8.401 ± 0.42	0
Bam	Gills	13.803 ± 0.69	63.874 ± 3.19	30.523 ± 1.52	0
	Liver	19.7683 ± 0.98	85.988 ± 4.29	35.841 ± 1.79	0
	Muscles	4.352 ± 0.21	12.548 ± 0.62	3.209 ± 0.16	0
Pathari	Gills	15.254 ± 0.76	90.805 ± 4.54	16.897 ± 0.84	0
	Liver	19 ± 0.95	189.97 ± 9.49	193.176 ± 9.65	0
	Muscles	4.873 ± 0.24	22.457 ± 1.12	4.594 ± 0.23	0

3.7. Health risk assessment.

The findings of our study indicate a substantial decline in the sediment and water quality of the Ganga river, primarily attributed to the elevated concentrations of heavy metals. This contamination poses a potential risk to human health to the local residents, especially concerning the direct consumption of contaminated fishes caught from the river. Figure 4 represents species wise EDI of metal concentration (mg per kg body weight) for consumers. The EDI for Pb was measured higher than the recommended daily allowance in Sauri, Carpio, Bam and Pathari as mentioned in Table.10. EDI for Mn was estimated highest in Sauri followed by Pathari. For Cr, it was higher than the recommended daily allowance in all the species, highest in Carpio followed by Baikari and Sauri, while Cd was highest in Common carp, followed by Sauri and Tilapia. The study clearly demonstrates that among the fish species examined, Cyprinus carpio displayed the highest concentrations of Cr and Cd, whereas Sauri showed the highest levels of Pb and Mn in terms of EDI. Being at higher trophic level fish, common carp may accumulate Cd and Cr through biomagnification (Riede, 2004). This observation indicates that individuals who regularly consume fish may experience the greatest intake of these metals by consuming common Carp and Sauri fish species. The THQ estimated for individual heavy metals through consumption of different fish species are presented in Table 10. The acceptance value for THQ is 1. In our study, we found that every metal was below the hazard quotient except Cd in common Carp. This finding highlights the potential health risk associated with Cd exposure through the consumption of common Carp.

Numerous scientists across the globe have experienced similar results. In an Indian study at Damodar River basin, Burnpur, West Bengal, children were found to be at higher risk of Cd toxicity when consuming three fish species, including Clupisoma garua, along with Puntius ticto and Labeo bata (Mohantaa et al., 2018). However, other studies have shown that the muscles of fish from various regions were within permissible limits for human consumption, with THQ values less than 1 (Maurya et al., 2019; Ahmed et al., 2022; Ali et al., 2018). On the contrary, Yi et al. 2011 reported higher THQ values (THQ > 1) in the lower reaches of the Yangtze River basin in China. In the Karnaphuli River, Bangladesh, the THQ value for H. nehereus exceeded the reference value (higher than 1) (Ali MM et al., 2020).

In our study, the total THQ value i.e. HI of metals was recorded in following sequence: common Carp > Tilapia > Sauri > Pathari > Bam > Tengra > Baikari. The study found that the average HI (hazard index) value for common Carp and Tilapia exceeded 1, indicating a potential health hazard for humans who consume these contaminated fish. Although, Tilapia consumption has been found to pose a non-carcinogenic risk to human health (Hong-Giang Hoang, 2021). Maximum HI was recorded in common Carp, implying that its consumption may pose a health risk to humans. Common carp and Tilapia, being invasive species with opportunistic feeding behavior, can inadvertently consume various flora and fauna, including microplastics. In urban areas, the disposal of colored microplastics into water bodies can attract these voracious eaters, causing them to ingest the microplastics and potentially be exposed to heavy metals (Adji BK et al., 2022)

To safeguard against excessive metal concentration in humans through the food chain, regular monitoring of heavy metals in fishes is crucial.

Table 10

Showing Estimated daily intake (EDI), Recommended dose (RfD) established by USEPA, Target Hazard Quotient (THQ), Hazard index (HI)
Fish species	Heavy metals	Recommended daily allowance mg day-¹ kg^− 1 body weight	RfD mg kg^− 1 day^− 1	EDI mg kg^− 1 day^− 1	THQ	HI
Sauri	Pb Mn Cr Cd	0.25 2.5-3 0.23 0.07	0.004 0.140 1.500 0.001	0.719 4.444 1.902 0.193	0.179 0.031 0.012 0.193	0.417
Baikari	Pb Mn Cr Cd	0.25 2.5-3 0.23 0.07	0.004 0.140 1.50 0.001	0.020 0.395 2.066 0.017	0.005 0.002 0.013 0.017	0.039
Carpio	Pb Mn Cr Cd	0.25 2.5-3 0.23 0.07	0.004 0.140 1.500 0.001	0.599 1.129 3.280 1.742	0.149 0.008 0.021 1.742	1.922
Tilapia	Pb Mn Cr Cd	0.25 2.5-3 0.23 0.07	0.004 0.14 1.500 0.001	0.203 0.622 1.552 0.090	0.050 0.004 0.010 0.091	1.560
Tengra	Pb Mn Cr Cd	0.25 2.5-3 0.23 0.07	0.004 0.140 1.500 0.001	0.159 0.632 0.841 0.000	0.039 0.004 0.005 0.000	0.049
Bam	Pb Mn Cr Cd	0.25 2.5-3 0.23 0.07	0.004 0.14 1.5 0.001	0.436 1.257 0.321 0.000	0.109 0.008 0.002 0.000	0.120
Pathari	Pb Mn Cr Cd	0.25 2.5-3 0.23 0.07	0.004 0.140 1.500 0.001	0.488 2.250 0.460 0.000	0.122 0.016 0.003 0.000	0.141

In this research, the presence of heavy metals was observed in the river water, sediment, and edible fishes of the Ganga river in Varanasi district. Concentrations of studied heavy metals, namely Pb, Mn, Cr, and Cd, were found to exceed the permissible limits set by international standards (BIS and WHO) for drinking water. The sediment was particularly polluted with Cd, posing a potential threat to bottom-dwelling species in the river. Although the levels of heavy metals in the fish muscles were below the hazard quotient, it is evident that prolonged consumption of such contaminated fishes could lead to bioaccumulation in the food chain. This, in turn, may result in the accumulation of heavy metals in humans, surpassing the hazard index and causing health risks to the local population.

This study is of great importance because regular assessments aids in identifying early signs of contamination, implementing necessary measures, and raising awareness about the dangers posed by toxic substances in water sources.

AAS Atomic absorption spectrophotometer

GAI Geo Accumulation Index

PLI Pollution Load Index

BAF Bioaccumulation factor

EDI Estimated daily intake

THQ Target hazard quotient

HI Hazard Index

Pb Lead

Mn Manganese

Cr Chromium

Cd Cadmium

Acknowledgements

The authors would like to thank CSIR-Indian Institute of Toxicology Research, Lucknow and Department of Chemical Engineering, IIT, BHU for providing the facilities required to perform this study.

Author Contribution

Vijay Nath Mishra is behind the idea of this study. Bhargawi Mishra did the material preparation, experiments, data collection and wrote the first draft of manuscript. Nasreen Ghazi Ansari has performed some of the experimental part. Geeta J. Gautam helped in data analysis, interpretation and finalizing the manuscript. Rajnish Chaturvedi commented on previous version of the manuscript. All authors read and approved the final manuscript.

Funding: There is no funding agency for this study.

Ethical declaration: This study involved collection of fishes from wild and direct processing for the estimation of heavy metals in different tissues. The study protocol was assessed and approved by the BHU ethics committee (542/GO/ReBi/S/02/CPCSEA dated 26.05.2017)

Data Availability The generated and analyzed datasets during the study are available per request from the corresponding author.

Conflict of Interest The authors declare no conflict of interests

Consent for Publication All authors have read and approved the final manuscript.

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No competing interests reported.

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Ecological and health risk assessment of heavy metals bioaccumulation in Ganges fish near Varanasi, India

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Abstract

Figures

1. Introduction:

2. Material and Methods:

2.1 Study area

2.2. Collection of samples

2.2.1. Sediment

2.2.2 Water

2.2.3 Fish

2.3 Sample preparation

2.3.1 Physicochemical parameters of water

2.3.2 Processing of samples for heavy metals detection

2.4. Atomic Absorption Spectrometer (AAS) technique

2.4.1. Quality assurance and quality control (QA/QC) of AAS

2.5 Heavy metal pollution assessment in sediment

2.5.1 Contamination factor

2.5.2 Enrichment Factor

2.5.3 Pollution Load Index

2.5.4 Geo-accumulation Index

1.5 represents a constant, which compensates for weathering and lithogenic effects (Huang et al., 2020).

2.5.5 Ecological risk level

2.6. Heavy metal concentration assessment in water and tissues

2.7. Bioaccumulation factor (BCF)

2.8. Estimated daily intake

2.9. Human health risk assessment

2.10 Statistical analysis:

3. Results and Discussion:

3.1 Analysis of physicochemical properties of water in Varanasi

3.2 Heavy metal assessment in sediment

3.3 Heavy metal analysis in water

3.4. Detection of heavy metals in fish tissues.

3.5. Correlation analysis of heavy metal in fish tissue

3.6. Determination of Bio-accumulation factor

3.7. Health risk assessment.

4. Conclusion:

Abbreviations

Declarations

References

Additional Declarations

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