Evaluating heavy metal levels and their toxicity risks in an urban lake in Chennai, India

1 Evaluating heavy metal levels and their toxicity 1 risks in an urban lake in Chennai, India 2 Daniel Rosado1,2,3*, Franco Castillo1, Indumathi Nambi2, Rajagopal Sadhasivam2, 3 HimaBindu Valleru2, Nicola Fohrer1 4 1 Department of Hydrology and Water Resources Management, Institute for Natural Resource 5 Conservation, Kiel University, 24118 Kiel, Germany. 6 2 Department of Civil Engineering, Indian Institute of Technology Madras, 600036 Chennai, India. 7 3 Indo-German Centre for Sustainability, Indian Institute of Technology Madras, 600036 Chennai, 8 India. 9 10 HIGHLIGHTS: 11  Pb, Cr and Cu in water of the Sembakkam lake pose a threat to biota 12  Ni, Cr and Cu in sediment pose the highest risk for the biota of the lake 13  Untreated wastewater is likely most relevant pollution sources 14 15 16 CORRESPONDING AUTHOR: 17 * Department of Hydrology and Water Resources Management, Institute for Natural Resource 18 Conservation, Kiel University, Olshausenstr. 75, D-24118 Kiel, Germany. Tel.: 0049 431 880 4889; 19 E-mail address: [email protected] (Daniel Rosado). 20 21 22 Acknowledgements 23 The authors thank the financial support of the Deutsche Hydrologische Gesellschaft 24 (DHG). 25 26 27 Title Page 1 Abstract 1 Many urban water bodies in Chennai, India receive untreated sewage that pollutes 2 their waters. An example is the Sembakkam lake, which waters reach the 3 Pallikaranai marshland, a proposed Ramsar site. In 2019, the city experienced the 4 worst water crisis in 30 years, and many lakes were extremely dry, favoring peaks 5 of heavy metals. Therefore, this study focuses on analysing heavy metal pollution 6 and evaluating its potential effects on biota. 7 In situ parameters were measured and water, sediment, and water hyacinth 8 samples were collected during four campaigns. Al, As, Cr, Cu, Fe, Mn, Ni, Pb, and 9 Zn were measured in all samples. Digestions for total metal content were performed 10 in solid samples and acetic acid extractions only in sediments. 11 The average pH (7.89-8.46) was neutral-alkaline and electrical conductivities (155912 2864 µS/cm) were high. In water, Pb (average: 2.59 µg/l) posed the highest toxicity 13 risk according to the United Nations Economic Commission for Europe, followed by 14 Cu and Cr. In sediment, Cu and Cr reached severe enrichment with respect to 15 continental crust (averages: 19.46 and 13.65) followed by Ni and Zn with moderately 16 severe enrichment. Ni produced the highest toxicity risk (average: 76.18 mg/kg), 17 above the effects range-median, followed by Cr and Cu, between the effects range18 low and effects range-median. The highest bioaccumulation factors in the water 19 hyacinth were in the roots. Translocation factors showed similar concentrations in 20 stems and leaves. Proper management of sewage is necessary to diminish the 21 potential deleterious effects of metals on aquatic life and by extension, human 22 health. 23 24 Keywords 25 Trace element; Lake ecosystem pollution; Heavy metals; BCR sequential extraction; 26 water hyacinth; South India 27 Blinded Manuscript Click here to view linked References 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 2 1 Introduction 28 The United Nations defined the sustainable development goal number 6 “ensuring 29 availability and sustainable management of water and sanitation for all” as one of 30 the 17 Sustainable Development Goals to be achieved by 2030 (United Nations 31 2015). It includes the target 6.6 “protect and restore water-related ecosystems, 32 including mountains, forests, wetlands, rivers, aquifers and lakes” (United Nations 33 2015). However, aquatic ecosystems are threatened by a variety of pressures such 34 as water abstraction, pollution, invasive species, pathogens, geomorphological 35 alterations, encroachment, and climate change, among others due to population 36 growth and industrialization (Pistocchi et al. 2017; Udias et al. 2020). 37 This is the situation of many urban water bodies in developing countries, including 38 in megacities, where larger numbers of humans are exposed to pollutants. A 39 common situation in these countries is that untreated sewage is discharged into 40 urban water bodies because of non-existing or non-properly-enforced regulations 41 (Fraga et al. 2020; Kumar et al. 2022; Das et al. 2022). In this context, heavy metal 42 concentrations can increase in water, sediments and living beings due to 43 bioconcentration and biomagnification and become a risk for human communities 44 nearby. In fact, some studies have alerted about this situation in many developing 45 countries, like Egypt (Abu El-Magd et al. 2021), Malaysia (Prasanna et al. 2012) and 46 China (Jiang et al. 2018). However, information on possible heavy metal related 47 risks for humans in the urban context is still scarce. 48 India has experienced a rapid increase in population, a significant economic growth 49 and an intense industrialization. After a protectionist period, the economy of India 50 was liberalized in the 1990’s and it became the world's fastest growing major 51 economy between 2014 and 2018 (International Monetary Fund 2019). This 52 situation together with frequent discharges of untreated wastewater due to a lack of 53 sewage treatment and control over the industrial sector caused many aquatic 54 ecosystems to be polluted and their aquatic life to be reduced (Kelkar et al. 2011; 55 Chaturvedi 2012; Hossain et al. 2013; Sharma et al. 2018). As an example, the 56 sewage generation in the country was estimated to be around 62000 million liters 57 per day (MLD) in 2015 while the installed sewage treatment capacity was only 58 23277 MLD (Sharawat et al. 2019). Therefore, discharge of untreated sewage into 59 water bodies may be responsible for polluting three quarters of surface water 60 resources (Kumar and Tortajada 2020). 61 Chennai, the capital of the state of Tamil Nadu, has also experienced intense 62 population and industrial growth. Untreated domestic and industrial wastewater 63 discharges (Arappor Iyakkam 2017), garbage dumping, and others are threatening 64 the biodiversity of the city's large number of water bodies (Lan et al. 2014). They 65 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 3 include the Pallikaranai marshland, a proposed Ramsar site that was drained to 66 expand the urban area of the city and, consequently, reduced from around 50 km2 67 in 1980 to only 6 km2 today (Vencatesan 2007; Steinbruch and Hörmann 2015; Sree 68 Sharmila and Swathika 2016). In addition, many artificial lakes, locally called tanks, 69 are located within Chennai. The tanks are traditional retention storages that 70 accumulated water during the monsoon season that was used for irrigation during 71 the dry season (Devi et al. 2020). The tanks were usually constructed by damming 72 intermittent streams using crescent-shaped earthen bunds in a cascade down the 73 axes of shallow inland valleys (Massuel et al. 2014; Devi et al. 2020). However, 74 water tanks became ornamental rather than functional when water provision shifted 75 to groundwater, resulting in their abandonment and degradation (Palanisami et al. 76 2008; Adelina 2015). It also affected the tanks in the Pallikaranai catchment, 77 including the Sembakkam lake, which waters reach the Pallikaranai Marshland. In 78 the Adyar catchment, siltation diminished water storage capacity of their tanks by 79 15% (Massuel et al. 2014). 80 Some studies have reported on the worrying levels of heavy metals (in this article, 81 the term heavy metal includes metals like Al, As, Cr, Cu, Fe, Mn, Ni, Pb, and Zn, 82 measured in this work, and metalloids, i.e. As, etc.) in the water bodies of Chennai. 83 In Chemberambakkam lake, where water is collected for drinking water supply, Cd, 84 Pb, Fe, Co, and Ni in water were higher than the World Health Organization 85 thresholds for drinking water (Prabhu et al. 2015). Water in some lakes of south 86 Chennai has high concentrations of Cu and Pb (Lakshmi et al. 2018). In the Ennore 87 creek, abnormalities attributed to heavy metals were found on the Asian green 88 mussel Perna viridis (Vasanthi et al. 2017) and high concentrations of Fe, Mn, Zn, 89 Cu, Pb, and Cd were found in the flathead grey mullet fish Mugil cephalus (Arockia 90 Vasanthi et al. 2013). Jayaprakash et al. (2010) studied the sediments of the 91 Pallikaranai marshland and found they are more heavily contaminated with Cd, Hg, 92 Cr, Cu, Ni, Pb, and Zn than in other regions on the southeast coast of India. In beach 93 sediments of Chennai, Pb and Ni were over the lowest effect level (LEL) and effects 94 range low (ERL) in several locations (Santhiya et al. 2011). The water hyacinth 95 Pontederia crassipes (formerly Eichhornia crassipes) is a plant that can concentrate 96 heavy metals in its tissues and it is even used in constructed wetlands (Newete et 97 al. 2016). Therefore, it can be used as an indicator or heavy metal pollution in waters 98 (Eid et al. 2020). 99 Heavy metals in water bodies can reach higher concentrations than usual during 100 drought events. Chennai suffers from drought regularly and it is ranked as the fourth 101 most vulnerable city to climate change in India (Kelkar et al. 2011). In the intense 102 drought event of 2019, many tanks were extremely dry and showed the lowest water 103 area of the period 2015-2020. The surface of the largest tanks shrank significantly 104 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 4 and some small tanks lost all their water (Jayaraman, 2019; “Water crisis in 105 Chennai", 2019). 106 Hence, this study aims to analyze the heavy metal pollution in water, sediments, 107 and water hyacinths of the Sembakkam lake and to evaluate their potential toxicity 108 risks on the biota during 2019, including an extreme drought event. Therefore, this 109 study is a valuable example for other urban lakes in Chennai and Asia that suffer 110 from similar pollution and climatic problems and contributes to a better 111 understanding of the current water challenges in the region, and helps stakeholders 112 to implement management strategies to comply with the Sustainable Development 113 Goals. 114 2 Materials and methods 115 2.1 Study area 116 Chennai is the capital of the south Indian state of Tamil Nadu. According to the 2011 117 Indian census, the Chennai district had 7,088,000 inhabitants in an area of 426 km2 118 and the agglomeration area had 8.70 million inhabitants in 1,189 km2, i.e. the fourth 119 biggest in India (Directorate of Census Operations Tamil Nadu 2011). The city is 120 located on the southeastern coast of India (13o05′N and 80o18′E), on a flat area 121 known as the Eastern Coastal Plains with an average elevation of 6.7 m.a.s.l. and 122 the highest point of 60 m.a.s.l. (Pulikesi et al. 2006). 123 The city has a tropical wet and dry climate (Köppen: Aw) with little changes in 124 temperature throughout the year (Rajanikanth and Rajini Kanth 2020). Highest and 125 lowest average monthly temperatures in the period 1971-2000 ranged from 28.9 to 126 37.1oC and from 21.2 to 28.0oC (Selvaraj et al. 2016). The highest temperatures 127 usually correspond to late May and early June and the lowest to January (Prakash 128 and Punyaseshudu 2015). The influence of the northeast monsoon provides a high 129 average annual precipitation of around 1,400 mm to Chennai, reaching 2570 mm in 130 extreme years, like 2005 (Rajanikanth and Rajini Kanth 2020). Most of the rain takes 131 place during the monsoon season, from mid–October to mid–December, and it is 132 followed by a dry season (Selvaraj et al. 2016). 133 The two major rivers of the city are the Cooum River and the Adyar River. Both flow 134 from west to east and drain into the Bay of Bengal. The rivers are connected by the 135 Buckingham Canal, an artificial waterway that goes parallel to the coast (Mariappan 136 2014). The Pallikaranai catchment plays a relevant role because of its 137 environmental value and the pressures that it is suffering. This catchment contains 138 seven lakes, including the Pallikaranai marshland, a proposed Ramsar site 139 (Shekhar 2020), and Sembakkam lake. The catchment was first comprised of vast 140 agricultural lands and the Pallikaranai marshland before they were urbanized. 141 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 5 Construction reduced the extension of the Pallikaranai marshland to only 6 km2 from 142 around 50 km2 in former times (Vencatesan 2007; Steinbruch and Hörmann 2015; 143 Sree Sharmila and Swathika 2016). In 2007, 3.17 km2 of the wetland was declared 144 a Reserve Forest (Vencatesan 2007). However, there are two garbage-dump sites 145 on its outskirts and in 2006, a sewage treatment plant was built near the marshland. 146 Since then, the processed effluent is discharged into the water body (Steinbruch 147 and Hörmann 2015). 148 Traditionally, the Sembakkam lake (12.9321° N, 80.1543° E) and other tanks in the 149 catchment recharged its water during the monsoon season and provided water for 150 irrigation during the dry season. Due to urbanization, the lake became ornamental 151 and has suffered from degradation and abandonment. Currently, the lake is 152 recharged with untreated sewage throughout the year. Until 2018, there was a 153 landfill in the southwestern corner of the lake and still today, there are pumping 154 groundwater that is later sold as drinking water. The lakes suffer from heavy 155 pollution and eutrophication, making the water unsuitable for drinking, fishing, or 156 recreational purposes because of high levels of total dissolved solids, biological 157 oxygen demand, chemical oxygen demand, etc. (Raveen et al. 2008). Unfortunately, 158 compromised contaminated water is not only an issue in Sembakkam Lake, but also 159 in lakes such as Rajakilpakkam, Madipakkam, and Medavakkam (Raveen et al. 160 2008). 161 2.2 In situ parameters, sampling and sample pre-treatment 162 Twenty-two sampling points (Table S1) were selected throughout Sembakkam Lake 163 covering the whole lake area (Figure 1). Samples of water and water hyacinth were 164 collected in three sampling campaigns during the dry season (campaign 1, 165 04/07/2019; campaign 2, 07/08/2019; campaign 3, 04/09/2019). Sediments were 166 collected in campaigns 1 and 3 and in an extra fourth campaign (20/09/2019) during 167 the wet (monsoon) season, i.e. approximately every two months due to more stable 168 concentrations compared to water. Due to the severe drought of 2019 and the 169 subsequent reduction of the surface of the lake, some sampling points were 170 unavailable in campaign 1. 171 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 6 172 Figure 1. Sampling points in the Sembakkam lake, Chennai, India. 173 After collection, water, sediment, and water hyacinth samples were transported in 174 less than 3 hours to the laboratories of the Civil Engineering Department of the India 175 Institute of Technology Madras in a dark cooler (4°C). 176 Some water quality parameters were measured in situ (pH, electrical conductivity, 177 and temperature) at each sampling point with a WTW 3630 IDS portable 178 multiparameter meter (Xylem Analytics, Germany). Water samples were collected 179 in 250 ml polyethylene bottles previously washed with 10% HNO3 and Milli-Q water 180 in the laboratory and rinsed three times with lake water afterwards. In the laboratory, 181 water samples were filtered through 0.45 μm pore Teflon filters, acidified with HNO3 182 to pH 2 or lower, and stored at 4°C until analysis. These methods are in accordance 183 with ISO standard 5667 parts 1, 3, and 4 and with common procedures in scientific 184 publications (Villa-Achupallas et al. 2018; Gemeda et al. 2021). 185 Sediment samples were collected with a Van Veen grab at each sampling point. 186 Only grabs that showed adequate penetration were retained. The sediment material 187 collected with five full grabs was collected and later mixed and homogenized to 188 obtain a representative sample from each point that was stored in a dark cooler at 189 4°C. In the laboratory, the samples were dried at 60°C in an oven, disaggregated 190 with an agate mortar, and sieved to fraction <63 μm (Jones and Turki 1997; Ouyang 191 et al. 2002). Concentration of metals in sediments were referred to dry weight 192 sediment (105°C). These methods are in accordance with ISO standard 5667 parts 193 1, 12, and 15, and with common procedures in scientific publications (Rosado et al. 194 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 7 2015; Naifar et al. 2018). 195 Water hyacinth samples were collected in the Sembakkam lake manually from a 196 boat in the locations where it was present. Once in the laboratory, plant samples 197 were properly washed with Milli-Q water, and dried in an oven at 60°C until constant 198 weight. Dried samples were mechanically ground using a stainless-steel grinder 199 (particle diameter of 100 µm). The resulting powder form of the plant sample was 200 stored at room temperature until further analysis (Elmorsi et al. 2019). 201 2.3 Sediment and water hyacinth digestions 202 Sediment and water hyacinth samples underwent digestion in Teflon vessels in a 203 PicoTrace® digestion block with 5 ml Suprapur nitric acid (HNO3) 65% (Merck, 204 Germany) and 0.2 g of sediment or water hyacinth at 140°C for 16 hours. After the 205 digestion phase, the block was cooled down to room temperature. The digestates 206 were filtered, made up to 50 ml with Milli-Q water, and stored in polypropylene bottle 207 in a fridge at 4°C before the metal analysis. For quality control purposes, all 208 digestion batches included a blank vessel with 5 ml of HNO3 only and a reference 209 material vessel with reference soil SO-4 from the Canadian Certified Reference 210 Materials Project. Recoveries were greater than 90% with the reference material for 211 each metal and therefore considered satisfactory. 212 Sediments (1 g) underwent a extraction with 40 ml of 0.11 mol/L acetic acid prepared 213 from Suprapur acetic acid at room temperature as described in the first step of the 214 BCR-701 sequential extraction procedure (Pueyo et al. 2001) to obtain the acid 215 extractable fraction, the most labile fraction of metals in sediments. After the 216 extraction, the suspension was filtered and the filtrate was stored in a polypropylene 217 bottle in a fridge at 4°C prior to metal analysis. For quality control purposes, all 218 extraction batches included a blank vessel with 40 ml of acetic acid only and a 219 reference material vessel with reference material BCR-701 from the catalogue for 220 certified reference materials of the European Commission's Joint Research Centre 221 (JRC), achieving recoveries greater than 85% for all the elements and therefore 222 considered acceptable. 223 2.4 Heavy metals measurements 224 In water samples as well as digestates of sediment and water hyacinth samples, Al, 225 As, Cr, Cu, Fe, Mn, Ni, Pb, and Zn were measured using an ICP-OES (Thermo 226 Scientific iCAP 6000) with axial and radial view. 227 2.5 Data analysis 228 The enrichment factor (EF) of heavy metals in sediments was calculated to assess 229 the magnitude of enrichment and the potential anthropogenic involvement (Buat230 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 8 Menard and Chesselet 1979; Aung et al. 2019). The following equation was 231 employed: EF=(Csample/Alsample)/(Ccrust/Alcrust), in which Csample is the heavy metal 232 concentration in the sample; Ccrust is the average heavy metal concentration in the 233 upper continental crust according to Wedepohl (1995) in mg/kg: Al, 77440; As, 2; 234 Cr, 35; Cu, 14.3; Fe, 30890; Mn, 728; Ni, 18.6; Pb, 17; Zn, 52; Alsample is the Al 235 content in the sample; and Alcrust is the Al content in the continental crust (Wedepohl 236 1995). Aluminum was chosen as a normalization element due to its uniquely 237 lithospheric origin (Thiombane et al. 2019). The EF was interpreted as follows, no 238 enrichment (< 1), minor (1 – 3), moderate (3 – 5), moderately severe (5 – 10), severe 239 (10 – 25), very severe (25 – 50), and extremely severe (>50) (Amin et al. 2008; 240 Rastegari Mehr et al. 2021). 241 The bioaccumulation factor (BF) was employed to show how different water hyacinth 242 tissues bioaccumulate heavy metals. Thus, it was calculated as the ratio between 243 the element concentrations across three plant tissues (root, stem, leaves) and those 244 in its corresponding water: BF=Ctissue/Cwater (Carrillo-González and González245 Chávez 2006; Chamba et al. 2017). The translocation factor (TF) was utilized to 246 assess the transference of heavy metals from the roots to aerial parts (leaves and 247 stem) in the water hyacinth. Therefore, TF was calculated as the ratio between the 248 element concentrations in the aerial parts (stem, leaves) and those in the roots: TF 249 = Caerial/Croots (Conesa et al. 2006; Chopin et al. 2008; Chamba et al. 2017). The BF 250 and TF are relevant factors when assessing the phytoremediation capacity of a 251 given species (Carrillo-González and González-Chávez 2006; Chopin et al. 2008). 252 R studio software version 1.4.1106 (R Development Core Team 2021) was used to 253 carry out analysis of variance (ANOVA) with Tukey's post hoc test to check 254 significant differences between temporal and spatial averages of heavy metals. 255 Also, a Pearson correlation test was performed to look for heavy metals with similar 256 behavior. 257 3 Results and discussion 258 3.1 In situ parameters 259 The average pH values recorded were 8.34 in campaign 1, 7.89 in campaign 2, and 260 8.46 in campaign 3. Hence, the pH was around neutral-alkaline. Although, the 261 interval between averages can be considered moderate (0.45 pH units), the ANOVA 262 test showed a statistically significant difference across sampling campaigns 263 (p<0.05). The Sembakkam lake receives a remarkable amount of sewage and 264 wastewater that can influence the pH of the lake as well as the higher influence of 265 rainwater throughout the year. These values fall within the range defined by the 266 United States Environmental Protection Agency (USEPA) as suitable for aquatic life 267 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 15 403 Figure 5. Distribution (percentage) of the concentrations of heavy metals in the 404 sediments of Sembakkam Lake, Chennai, India according to the effects range low 405 (ERL) and the effects range median (ERM) intervals defined by Long et al. (1995). 406 Correlated metals suggest a similar source of pollution (Zhang et al. 2018). The 407 results of the Pearson’s correlation matrix for concentrations in the Sembakkam lake 408 sediment (Table 4) showed that across campaigns only Cu and Cr (r=0.72), As and 409 Fe (r=0.70), and Ni and Cr (r=0.94) had an r≥0.7. These significant correlations 410 suggest that they might be derived from untreated discharged sewage, leachates 411 from the landfill, or other common sources. 412 Table 4. Pearson correlation matrix of metal variables in Sembakkam Lake across 413 sampling campaigns. 414 Al As Cr Cu Fe Mn Ni Pb Zn Al 1 0.22 -0.08 0.26 0.51 0.26 -0.07 -0.28 -0.26 As 1 0.50 0.31 0.70 0.25 0.37 0.41 -0.10 Cr 1 0.72 0.40 0.16 0.94 0.61 -0.14 Cu 1 0.40 0.30 0.68 0.58 0.00 Fe 1 0.17 0.19 0.20 -0.24 Mn 1 0.13 -0.09 -0.07 Ni 1 0.58 -0.10 Pb 1 0.32 Zn 1 415 3.3.2 Acid extractable fraction 416 The average labile fraction of heavy metals followed a decreasing order of Mn 417 (43.69%) > As (15.63%) > Zn (13.24%) > Pb (4.78%) > Ni (3.43%) > Cu (1.51%) > 418 Cr (0.13%) > Al (0.05%) > Fe (0.04%) in Sembakkam Lake. A box-plot 419 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 16 representation can be found in Figure 6. Averages of every sampling campaign can 420 be found in the supplementary material (Table S3). The results obtained in this study 421 show similarities with those found by Chandra Sekhar et al. (2004) regarding the 422 high labile fraction of Zn, and with Wang et al. (2016) and El Nemr (2003) concerning 423 the highest labile fraction for Mn. Chandra Sekhar et al. (2004) reported that Zn, Cu, 424 and Pb were the most labile metals in the sediments of Kolleu Lake, India. The 425 averages of the labile metal concentrations were found to be as follows: Zn > Cu > 426 Pb > Cr > Ni. Wang et al. (2016) reported that the average labile fraction of metals 427 in the sediments of Lake Taihu, China, followed Mn > Zn > Cu > Ni > Pb > Fe. El 428 Nemr (2003) studied surface sediments of Lake Urullus, Egypt, and reported the 429 labile fraction of metals in the lake in decreasing order of Mn > Cu > Ni > Pb > Zn > 430 Cr. 431 432 Figure 6. Ratio labile/total metal concentrations in the sediments of the Sembakkam 433 Lake, Chennai, India. 434 An ANOVA test was conducted between the north and south areas of the lake. It 435 confirmed that there was no significant spatial difference (p>0.05) for metals Al, As, 436 Cr, Cu, Fe, Ni, Pb, and Zn. Only in the case of Mn, a significant difference was 437 found. 438 3.4 Water Hyacinth samples 439 Regarding bioaccumulation factors, water hyacinth showed BF>1 values for all 440 heavy metals, which implies that the plant has an efficient bioaccumulation system 441 in the roots and can be considered an accumulator instead of an excluder (BF<1) 442 (Yanqun et al. 2005). BF values are shown in Figure 7 and BF averages can be 443 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 17 found in the supplementary material (Table S4). The average concentrations of 444 heavy metals (mg/kg) in the tissues of the water hyacinth are also shown in the 445 supplementary material (Table S4). 446 The highest BF values recorded in this study were for Al followed by Fe, Mn, Cr, Cu, 447 Zn, Pb, Ni, and As (Figure 7). These results suggest that water hyacinth does not 448 absorb heavy metals homogeneously, and corroborate that absorption is not 449 concentration-dependent. The high BF values of Mn, Fe, Cu, and Zn correspond to 450 be essential plant macronutrients. Factors affecting heavy metal uptake and storage 451 by aquatic plants could be either biological, e.g. species, age, and physiology, or 452 non-biological (water physicochemical characteristics), e.g. temperature, salinity, 453 and pH (Bonanno and Lo Giudice 2010). Specifically, heavy metal uptake by plants 454 is influenced by heavy metal speciation such as humic complexes and free ions 455 (Bonanno and Lo Giudice 2010). 456 457 Figure 7. Bioaccumulation factor (BF) values in the leaves, stems and roots of the 458 water hyacinth Pontederia crassipes (formerly Eichhornia crassipes) across all 459 sampling campaigns in the Sembakkam Lake, Chennai, India. 460 The BF values of all metals were greater in the roots than in the aerial parts (stems 461 and leaves) and they differ significantly according to the ANOVA and Tukey post 462 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 18 hoc test results (p<0.05). In the roots, the average BF of every metal can be grouped 463 according to the degree of accumulation in millions of times for Al (2,285,239), 464 hundreds of thousands of times for Fe (790,708), Mn (457,747) and Cr (353,451), 465 tens of thousands of times for Cu (19,281), Zn (17,030) and Pb (11,227), and 466 thousands of times for Ni (5,162), and As (2,311). This implies a limited internal 467 transport of heavy metals from the roots to the leaves and stem. Ephraim et al. 468 (2018) confirmed the bioaccumulation potential of the water hyacinth finding BF>1 469 values and significant differences between the roots and the leaves for Ni, Cu, Pb 470 and Zn. The mentioned author found the highest BF for Ni and the lowest for Zn. In 471 a similar study, Eid et al. (2019) reported BF>1 values and significant differences in 472 the roots compared to the leaves and stems for Mn, Fe, Cu, Zn, Cr, Pb, and Ni. The 473 authors found the highest BF for Mn and the lowest for Ni. Other studies also proved 474 the ability of water hyacinth to concentrate heavy metals in their roots (Kamari et al. 475 2017; Saha et al. 2017). The high capacity of the water hyacinth to store heavy 476 metals in their roots could be contributing to a depuration process in the lake and a 477 lower concentration of metals in the water. 478 Being an aquatic floating plant, water hyacinth absorbs heavy metals present in the 479 water through the roots (Téllez et al. 2008). Physiological barriers against metal 480 transport to the aerial parts are frequent in plants (Bose et al. 2008; Rahman et al. 481 2008; Saha et al. 2017). Metal binding proteins and the different biochemistry for 482 accumulation between the root and stem could explain the absence of translocation 483 to aerial parts (Lytle et al. 1998). Smaller and harder cations usually bind to the 484 smaller atoms like N and O in the roots, while when transferred to the leaves and 485 stems they bind to more complex composites such as oxalates and phytochelatins 486 (Lytle et al. 1998). 487 The differences between roots and aerial tissues can also be seen in the 488 translocation factor (TF) in Figure 8. In the present study, all of the heavy metals 489 recorded TF<1 values, corroborating the water hyacinth’s low translocation 490 capacity. Across all sampling campaigns, the highest average amount transferred 491 from the roots to the leaves came from Zn (0.49) followed by Cu (0.30), Cr (0.26), 492 Mn (0.14), Pb (0.12), As (0.11), Ni (0.11), Fe (0.04), and Al (0.04). The highest 493 average amount transferred from the roots to the stems came from Zn (0.27) 494 followed by Mn (0.25), Cu (0.24), Pb (0.12), Cr (0.11), As (0.11) Ni (0.11), Fe (0.04), 495 and Al (0.03). All the TF averages are depicted in the supplementary material (Table 496 S4). 497 In previous studies, Ephraim et al., (2018) recorded TF<1 values for Cu, Ni, and Pb, 498 and only TF>1 for Zn, Eid et al. (2019) reported TF<1 values for Cr, Cu, Fe, Mn, Ni, 499 and Zn, and only a TF>1 for Pb and Du et al. (2020) reported TF<1 values for all 500 analyzed metals in their study: Cu, Pb, and Zn. These results coincide with the 501 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 19 present study regarding metals with TF<1. However, they are in disagreement with 502 the metals with TF>1. 503 Transport from the roots to the leaves and stem was mostly in a uniform manner. 504 Thus, most of the metals showed no significant difference (p>0.05) between Stem505 Leaves, with the exception of Mn, Zn, and Cr. 506 507 Figure 8. Translocation factor (TF) values in the leaves and stems of the water 508 hyacinth Pontederia crassipes (formerly Eichhornia crassipes) across all sampling 509 campaigns in the Sembakkam Lake, Chennai, India. 510 4 Conclusions 511 In this study, the heavy metal pollution in water, sediments, and water hyacinths of 512 the Sembakkam lake was evaluated. It was found that Pb and, to a lesser extent, 513 Cr and Cu in the water of the lake can pose a threat to biota due to toxicity. In 514 sediments, Ni poses the highest risk of frequent chronic toxic exposure to biota while 515 Cu and Cr pose a probability of occasional chronic toxic exposure. Also in 516 sediments, Cu and Cr showed severe enrichment, Ni and Zn moderately severe 517 enrichment and As, Fe, Mn, and Pb mostly minor enrichment. The severe and 518 moderately severe enrichments could be explained by anthropogenic sources of 519 pollution, like untreated wastewater as well as leachate produced in the closed 520 dumpsite in the southwestern corner of the lake. Water hyacinths are abundant and 521 their roots show concentrations up to hundreds of thousands of times higher than 522 the water, contributing to a depuration of heavy metals. However, stems and leaves 523 have concentrations significantly lower than the roots. 524 Proper management of sewage and waste is necessary to diminish the potential 525 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 20 deleterious effects of metals on aquatic life and by extension, human health. Further 526 research is needed to elucidate if the potential toxic effects identified in the 527 Sembakkam lake are similar in other lakes of the catchment and the Pallikaranai 528 catchment, and also in the fisheries and the surrounding groundwater that are 529 consumed by local communities. 530 Statements & Declarations 531 - Competing Interests 532 The authors have no relevant financial or non-financial interests to disclose 533 References 534 Abu El-Magd SA, Taha TH, Pienaar HH, et al (2021) Assessing heavy metal 535 pollution hazard in sediments of Lake Mariout, Egypt. J African Earth Sci 536 176:104116. https://doi.org/10.1016/j.jafrearsci.2021.104116 537 Adelina C (2015) Internship Report IGCS. Chennai 538 Altindaǧ A, Yiǧit S (2005) Assessment of heavy metal concentrations in the food 539 web of lake Beyşehir, Turkey. 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Concentration, bioaccumulation factors and translocation factors in roots, 902 stem and leaves of the water hyacinth of the Sembakkam lake, Chennai, India 903 following a digestion with commercial nitric acid (HNO3). 904 905 906 Sampling campaign Average 1 2 3 Water Hyacinth Concentration (mg/l) Leaves Al 280.6 702.6 234.3 439.6 As 0.17 0.39 0.15 0.24 Cr 5.83 39.48 5.05 14.96 Cu 14.64 15.82 14.38 15.42 Fe 232 304 450 386 Mn 149.1 117.5 138.3 146.5 Ni 2.83 3.75 3.09 3.74 Pb 0.65 1.96 9.22 4.12 Zn 24.95 62.48 35.21 41.85 Stem Al 315.7 340.0 203.8 375.4 As 0.24 0.21 0.20 0.26 Cr 6.58 9.17 3.34 10.79 Cu 7.09 32.19 4.85 16.19 Fe 218.5 398.9 277.8 420.3 Mn 244.4 248.7 271.1 265.1 Ni 2.08 5.21 2.19 4.78 Pb 0.40 3.58 8.19 4.14 Zn 15.01 27.69 22.71 25.29 Roots Al 7770 13319 9328 8211 As 1.70 2.50 1.83 1.62 Cr 44.98 70.61 50.78 49.98 Cu 34.40 75.66 52.29 61.79 Fe 5648 10550 7885 6519 Mn 640 1136 1675 1060 Ni 22.69 34.18 28.25 25.41 Pb 6.52 25.52 48.69 60.04 Zn 57.72 95.37 92.67 89.26 Bioaccumulation Factor Leaves Al 26007 214694 51890 97530 As 151 543 166 286 Cr 27434 321653 27187 125425 Cu 4229 5791 5561 5194 Fe 13096 33113 51611 32607 Mn 44508 44628 61989 50375 Ni 507 709 543 587 Pb 624 572 3774 1657 Zn 5837 7805 9755 7799 Stem Al 29266 103890 45135 59430 As 217 285 211 238 Cr 30962 74715 17976 41218 Cu 2049 11783 1875 5236 Fe 12326 43470 31871 29222 Mn 72963 94501 121491 96318 Ni 372 985 384 581 Pb 390 1046 3354 1597 Zn 3511 3459 6292 4421 Roots Al 720298 4069577 2065840 2285239 As 1514 3436 1982 2311 Cr 211689 575316 273348 353451 Cu 9936 27695 20212 19281 Fe 318672 1149601 904450 790908 Mn 191008 431722 750511 457747 Ni 4061 6460 4966 5162 Pb 6284 7455 19940 11227 Zn 13502 11914 25675 17030 Translocation factor Leaves Al 0.04 0.05 0.03 0.04 As 0.10 0.16 0.09 0.11 Cr 0.13 0.56 0.10 0.26 Cu 0.43 0.21 0.28 0.30 Fe 0.04 0.03 0.06 0.04 Mn 0.23 0.10 0.08 0.14 Ni 0.13 0.11 0.11 0.11 Pb 0.10 0.08 0.19 0.12 Zn 0.43 0.65 0.38 0.49 Stem Al 0.04 0.03 0.02 0.03 As 0.15 0.08 0.11 0.11 Cr 0.15 0.13 0.07 0.11 Cu 0.21 0.42 0.09 0.24 Fe 0.04 0.04 0.04 0.04 Mn 0.38 0.22 0.16 0.25 Ni 0.09 0.15 0.08 0.11 Pb 0.06 0.14 0.16 0.12 Zn 0.26 0.29 0.25 0.27 907 908 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65