Heavy metals in surface sediments in the trans-Himalayan Koshi River catchment: Distribution, source identification and pollution assessment

Journal Pre-proof Heavy metals in surface sediments in the trans-Himalayan Koshi River catchment: Distribution, source identification and pollution assessment Mingyue Li, Qianggong Zhang, Xuejun Sun, Kabita Karki, Chen Zeng, Aastha Pandey, Bakhat Rawat, Fan Zhang PII:

S0045-6535(19)32650-5

DOI:

https://doi.org/10.1016/j.chemosphere.2019.125410

Reference:

CHEM 125410

To appear in:

ECSN

Received Date: 31 July 2019 Revised Date:

6 November 2019

Accepted Date: 18 November 2019

Please cite this article as: Li, M., Zhang, Q., Sun, X., Karki, K., Zeng, C., Pandey, A., Rawat, B., Zhang, F., Heavy metals in surface sediments in the trans-Himalayan Koshi River catchment: Distribution, source identification and pollution assessment, Chemosphere (2019), doi: https://doi.org/10.1016/ j.chemosphere.2019.125410. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

Graphical Abstract

Heavy metals in surface sediments in the trans-Himalayan Koshi River Catchment: distribution, source identification and pollution assessment Mingyue Li1,3, Qianggong Zhang1,2*, Xuejun Sun1,3, Kabita Karki1,3, Chen Zeng1, Aastha Pandey1,3, Bakhat Rawat1,3, Fan Zhang1,2,3

1. Key Laboratory of Tibetan Environmental Changes and Land Surface Process, Institute of Tibetan Plateau Research, Chinese Academy of Sciences (CAS), Beijing 100101, China 2. CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China 3. University of Chinese Academy of Sciences, Beijing 100049, China

Corresponding author: Qianggong Zhang Email: [email protected]

1

1

Abstract

2

Rivers flowing across the Himalayas are important water resources and deliver

3

large amounts of sediment to regional and downstream ecosystems. However, the

4

geochemistry of Himalayan river sediments has been less studied. Surface sediment

5

samples collected from a typical trans-Himalayan river, the Koshi River (KR), were

6

used to investigate the distribution, pollution status and potential sources of heavy

7

metals. Heavy metals did not show significant spatial differences between the

8

upstream and downstream areas of the river, but Cd and Pb displayed higher values in

9

the upstream area. The average heavy metal concentrations in the KR sediments are

10

comparable to the natural background values and are lower than the sediment

11

guidelines. Pollution assessment using the geo-accumulation index (Igeo), enrichment

12

factor (EF) and pollution load index (PLI) suggested negligible anthropogenic

13

disturbances except for slight contamination by Cd, Pb and Cu at a few sites. Principal

14

component analysis revealed that Cr, Co, Ni and Zn were primarily from the parent

15

rock and that Cu, Cd and Pb were derived from both natural and anthropogenic

16

sources. Despite contrasting environmental settings and human activities in the upper

17

and lower reaches of the river, the heavy metals concentrations in the KR sediments

18

showed consistency with natural backgrounds and negligible contamination. The

19

geochemistry of river sediments is a useful indicator of environmental changes, and

20

long-term observations of the geochemistry of trans-Himalayan river sediments are 2

21

needed to understand the impacts of intensified climate change and human activities

22

on the Himalayan environment.

23

Keywords: Trans-Himalayan river, heavy metals, sediments, spatial distribution,

24

pollution assessment, sources

3

25

1. Introduction

26

With the recent boom in industrialization and socio-economic development,

27

heavy metal contamination of river systems has become a global issue and has

28

received considerable attention because of the toxicity and persistence of heavy

29

metals in aquatic system (Ferati et al., 2015; Islam et al., 2015; Sun et al., 2019).

30

River sediments are an important carrier and sink for heavy metals ( Murray et al.,

31

1999; Zhang et al., 2016; Guo et al., 2018). Heavy metals in river sediments mainly

32

originated from the weathering of bedrock, runoff from agriculture, sewage treatment

33

and atmospheric deposition ( Li et al., 2011; Varol, 2011; Guo et al., 2018). When

34

environmental conditions (e.g., redox potential, pH, bioturbation, organic matter, and

35

other conditions.) change, heavy metals may be released from river sediments into

36

water (Davutluoglu et al., 2011; Superville et al., 2014); which could affect water

37

environmental safety and cause heavy metals to enter the food chain, creating a health

38

risk to living organisms (Raut et al., 2017; Strady et al., 2017; Xu et al., 2017b).

39

Hence, understing about heavy metals in river sediments is essencial for the

40

evaluation of aquatic environment safety. Studies have been performed around the

41

globe to understand the provenance, transport and accumulation of heavy metals in

42

river sediments ( Upadhyay et al., 2006; Jain et al., 2008; Ma et al., 2016; Maharana

43

et al., 2018; Nawab et al., 2018). Several empirical tools have been developed for use

44

in assessing potential environmental risks due to the presence heavy metals in river 4

45

sediments (Loska and Wiechula, 2003; Islam et al., 2018), and these tools provide an

46

important scientific basis for water and environmental management (Zahra et al.,

47

2014; Ji et al., 2019).

48

The Himalayas and the Tibetan Plateau, known as the Asian Water Tower, are the

49

sources of many major Asian rivers that support a huge and diverse ecosystem and

50

provide water resources to more than one billion people (Ives and Messerli, 1989; Xu

51

et al., 2009). The rivers flowing across the Himalayas not only distribute fresh water

52

to local and distant ecosystems but also deliver large amounts of chemicals and

53

sediment via pronounced erosion, contributing as much as 20% of the global sediment

54

input to the world’s oceans ( Subramanian et al., 1985; Singh et al., 2007). The

55

Himalayas are highly sensitive to global climate change and anthropogenic pollution

56

(Guzzella et al., 2011; Pant et al., 2018). In the past decade, studies of Himalayan

57

rivers have focused on the impacts of climate change on glaciers and hydrological

58

regimes (Immerzeel et al., 2010; Immerzeel et al., 2012; Lutz et al., 2014; Nepal et al.,

59

2014; Nepal and Shrestha, 2015). Water solutes have also been evaluated and

60

discussed in terms of their sources and as an indication of water quality (Pant et al.,

61

2018; Qu et al., 2019). However, the geochemistry of trans-Himalayan river

62

sediments has been less studied (Singh, 2009).

63

The Koshi River (KR) flows through the Himalayan mountains between China

64

and Nepal and discharges into the alluvial plains of northern Bihar in India. The KR

65

has experienced environmental problems over the past several decades due to its 5

66

extremely dynamic channels and frequent flooding (Hu et al., 2012; Khanal et al.,

67

2015; Azam et al., 2018). The change of topography and climate from north to south

68

are contrasting, resulting in substantial physical weathering and sediment mixing

69

throughout the basin (Wolff-Boenisch et al., 2009; Gonga-Saholiariliva et al., 2016).

70

Few inhabitants live in the upper reache of the Tibet, whereas densely populated areas

71

exist in its lower reach in Nepal, and most livelihoods in this region are based on

72

agriculture and livestock. Transportation in this region is higher in China than in

73

Nepal due to restricted economic development in some areas. The KR supplies most

74

of the sources of drinking and irrigation water for indigenous people (Bastakoti et al.,

75

2017). The comprehensive effects of human activities and natural processes could

76

cause heavy metal pollution, affecting the fragile ecology and human health in the KR

77

basin (Ansari et al., 2000; Paul, 2017). Studies of heavy metals in the KR sediment

78

can provide a reference for securing water quality in this region. In this study, surface

79

sediments in the KR across the Himalayas were collected and used to (1) determine

80

the spatial distributions of heavy metals (Cr, Co, Ni, Cu, Zn, Cd and Pb); (2) evaluate

81

the level of heavy metal contamination using 3 different methods including geological

82

accumulation index, enrichment factor and pollution load index; and (3) explore the

83

potential sources of heavy metals in KR sediments and their environmental

84

significance.

6

85

2. Materials and methods

86

2.1 Study area

87

The KR (Fig. 1), which flows through China, Nepal and India before entering the

88

Ganges, is one of the most typical trans-Himalayan rivers. The KR has a catchment

89

area of approximately 87,311 km2; with 32% of this area is in China, 45% is in Nepal

90

and 23% is in India (Angeli et al., 2019). The main stream has a total length of 730

91

km (Sinha et al., 2019), 560 km of which is included in this study. Different tributaries

92

traverse different lithologies (Fig. S1-S2). The headwaters of the KR lie in the

93

Tethyan sedimentary sequence (TSS), and then the KR passes through the Greater

94

Himalayan sequence (GHS), the Lesser Himalayan sequence (GHS), and finally the

95

Quaternary alluvium sediments (Azam et al., 2018). The elevation decreases from

96

8844 m at the peak of Mount Everest to 30 m in the plains in India (Shrestha et al.,

97

2017). The climate, which is determined by geomorphological features, is arid and

98

frigid in the northern part of the KR basin and humid tropical in the south, where it is

99

affected by the South Asian monsoon. The mean annual temperature ranges from -5°C

100

to 30°C from north to south. The annual precipitation increases from the

101

trans-Himalaya region (207 mm) to the mid-mountains of Nepal (more than 3000 mm)

102

(Shrestha et al., 2017). Most of the precipitation occurs during the monsoon season

103

from June to September (Bastola et al., 2018). Approximately 6.2 billion tonnes of

104

water and 19.5 million tonnes of sediment are transported to the Ganges River every 7

105

year (Paul, 2017; Sinha et al., 2019), forming the largest river-built alluvial fan in the

106

world (Chinnasamy, 2017).

107

2.2 Sampling and laboratory analysis

108

Field sampling was conducted in November of 2017. Taking into consideration

109

both the road accessibility and regional representativeness, a set of sampling sites that

110

included both the main stream and major tributaries was chosen. In this study, to

111

compare the differences of heavy metals in sediments between Nepali and Chinese

112

regions, we refer to upper reach in China as upstream and lower reach in Nepal as

113

downstream. In total, 6 sites in the upstream and 17 sites in the downstream were

114

sampled (Fig. 1). There are relatively fewer sampling sites in the upstream, due to the

115

inaccessible conditions and the shorter river course, as well as the relatively uniform

116

geographic conditions and the low human population density in the upstream regions.

117

At each site, onshore sediments (0-10 cm) were randomly collected from at least 4

118

locations (a total of 1-2 kg) using a clean plastic sampler, well mixed, stored in

119

polyethylene bags, transported to the laboratory under refrigeration, and kept under

120

refrigeration until testing. Detailed information on the sample sites is provided in the

121

supplemental file (Table S1).

122

Prior to measurement, the stored samples were air-dried, roots were removed,

123

and samples were sieved through a 200-mesh screen. The sample pretreatment

124

method was described in detail in an earlier study (Wang et al., 2017). The prepared 8

125

samples were weighed, and 20 mg of each sample was digested in an oven by a

126

mixture of HF/HNO3. The concentration of heavy metals was determined by

127

inductively coupled plasma-mass spectrometry (ICP-MS, X-7 Thermo Elemental) in

128

the Key Laboratory of Environmental Change and Surface Processes of the Institute

129

of Tibetan Plateau Research Chinese Academy of Sciences. The detection limits (µg/g)

130

were 0.42 for Cr, 0.15 for Co, 0.89 for Ni, 0.24 for Cu, 0.24 for Zn, 0.01 for Cd, 0.12

131

for Pb and 0.14 for Sc. During the digestion and test procedures, standard reference

132

samples (GSS-1), blank samples, parallel samples and the study samples were run in

133

the same way to control the quality of the entire analytical procedure and ensure

134

comparable detection results (Li et al., 2011; Paudyal et al., 2016b; Tripathee et al.,

135

2016a). The relative standard deviation (RSD) values of all elements were found to be

136

less than 5%. The detail test results were given in the supplemental file (Table. S2)).

137

2.3 Statistical analysis

138

SPSS 20.0 and Origin 9.0 software were used to perform all of the statistical

139

analyses. The Mann-Whitney nonparametric test was used to identify significant

140

differences in heavy metal content between the upstream and downstream regions.

141

Because the Shapiro-Wilk test showed that the data do not follow a normal

142

distribution, the Spearman correlation analysis matrix was employed to investigate the

143

relationships among the measured levels of heavy metals in the sediments. Principal

144

component analysis (PCA) was used for potential source identification; elements 9

145

loaded in the same PC indicate their similar provanence. Kaiser-Meyer-Olkin values

146

and Bartlett sphericity tests were used to examine the reliability of PCA and Varimax

147

rotation to minimize the number of variables with high loading on each factor (Chai et

148

al., 2017).

149

3. Results and discussion

150

3.1 Heavy metal concentrations and distribution

151

3.1.1 Heavy metal concentration

152

The concentrations of heavy metals varied over a wide range (Fig. 1); the values

153

(in µg/g) were Cr: 24.89~123.05, Co: 4.23~19.59, Ni: 10.42~52.65, Cu: 11.21~43.63,

154

Zn: 33.34~93.36, Cd: 0.07~0.59, and Pb: 12.96~36.17 (Table 1). The average contents

155

of these metals in the sediment samples followed the order Zn > Cr > Ni > Cu > Pb >

156

Co > Cd, similar to the order of metal concentrations reported in Tibetan top-soil (Li

157

et al., 2009), Nepalese Himalayan soils and the upper continental crust (UCC) (Taylor

158

and McLennan, 1985) except in the case of Cu, which was present at higher

159

concentrations than Ni in the UCC and lower concentrations than Pb in soils

160

fromTibet and Nepal. The different relative amounts of Cu, Ni and Pb in the KR,

161

UCC, and soils in Tibetan and Nepalese Himalaya may be attributed to the similar

162

abundance levels of the three metals and the different lithologies regions (Singh and

163

Rajamani, 2001b).

10

164 165

3.1.2 Spatial distribution Spatially,

all

heavy

metals

showed

large

variations

(coefficient

of

166

variation >15%). This variation might be due by to the complex geological and

167

geographic characteristics at different sites (Guo et al., 2018). The highest

168

concentrations (in µg/g) of Cr (123.05), Co (19.59), Ni (52.65) and Zn (93.36) were

169

found at site P2 (Fig. 1). Cu (43.63) exhibited the highest concentration at site K5, an

170

extremely high Cd (0.59) concentration was detected at site P3, and the highest Pb

171

concentration was found at

172

heavy metals were higher in the upstream sediments than in the downstream

173

sediments (Fig. 2). However, no obvious differences were detected using the

174

Mann-Whitney (M-W) nonparametric test except in the case of Cd and Pb, which

175

showed significantly higher values in the upstream sediments than in the downstream

176

sediments (n = 23, p < 0.05). Higher average concentrations of Cd and Pb were found

177

in the upstream sediments, and all heavy metals showed higher concentrations at site

178

P2, possibly resulting from the presence of source rocks. Large cooper ores deposits

179

along the faulted tectonic zone in Tibet where rivers flow across it (Qu et al., 2019);

180

therefore, it can be inferred that these heavy metal-enriched ores might contribute to

181

the higher heavy metal concentrations observed in the rivers in Tibet. It should be

182

pointed out that site P2 were the sampling site closest to the 318 National Highway of

183

China; therefore, the higher concentrations of heavy metals at site P2 could also be

184

related to traffic emissions due to the abrasion of brake linings, emission of engine oil

site P1 (Fig. 2). The average concentrations of most

11

185

and tearing of tires. Site K5 has the highest content of Cu, possibly resulting from

186

agricultural emissions as the land use type at K5 is farmland.

187

3.1.3 Comparison of heavy metal concentrations in sediments from other large

188

Asian rivers

189

The concentration of heavy metals in the sediments of the KR were compared

190

with those of other large Asian rivers; data from Tibetan top-soil, the UCC and

191

sediment quality guidelines (SQGs) were also included (Table 1). In general, the

192

heavy metals concentrations in the sediments of the KR were lower than those found

193

in the sediments of large rivers that drain populous areas, such as the Pearl River, the

194

Ganges River, the Yangtze River and the Yellow River; exceptions were that the

195

concentration of Cd was comparable to the level in the Pearl River, Cd and Pb were

196

higher than that in the Yellow River. For those found in other remote regions of large

197

rivers, the concentrations of heavy metal in the sediments of the KR were lower than

198

those in Yarlung Tsangbo River, generally comparable to the Yamuna River and were

199

higher than those in the Mekong River but displayed differences in terms of

200

individual heavy metals; for example, the Cd level in river sediments was higher in

201

the KR than in the Yarlung Tsangbo River, Cu, Zn and Pb levels were higher than

202

those found in the Yamuna River, and the concentrations of Cd were lower than that

203

found in the Mekong River. Studies of sediments from the Yarlung Tsangbo River and

204

the Yamuna River suggested that heavy metals were generally associated with the

205

characteristics of bedrock and had almost no anthropogenic sources except in the case 12

206

of slight contamination by Cd, Cu and Pb, that might have resulted from disturbance

207

by human activities (Dalai et al., 2004; Li et al., 2011). Similarly, the heavy metals in

208

KR sediments generally fall within the range found as natural background in remote

209

rivers, although elevated levels of Cd were found compared with Tibetan topsoil and

210

Nepalese Himalayan soil.

211

SQGs are often used to assess sediment quality and to designate tolerable

212

concentrations of sediment-bound pollutants (Zahra et al., 2014). In the SQGs, “effect

213

range low” (ERL) represents the critical value below which the concentration of a

214

chemical has no adverse biological effects. Effect range median (ERM) indicates the

215

chemical content below which adverse biological effects are expected to occur ((Long

216

et al., 1998). Compared with the SQGs, the Co, Zn, Cd and Pb levels in all sites are

217

lower than the ERLs, suggesting that the levels of Co, Zn, Cd and Pb in the surface

218

sediments of the KR pose no risk to the ecosystem. However, the concentrations of Cr

219

at 2 sites (P2, K15), Ni at 7 sites (P1, P2, P3, K1, K2, K13, K15) and Cu at 4 sites (P2,

220

K2, K5, K10, K17) exceeded the ERL values and were lower than the ERM values,

221

suggesting that these metals could pose a potential biological threat to local

222

organisms.

223

3.2 Assessment of heavy metal contamination

224

Igeo, EF and PLI were introduced as a means of accurately and comprehensively

225

evaluating the pollution status of sediments. In this study, the abundances of specific 13

226

elements in the Tibetan Plateau topsoil (Li et al., 2009) were chosen as background

227

references for the assessments considering that (1) the use of regional background

228

values is more appropriate than the use of average crust or average shale data (Blaser

229

et al., 2000) and (2) the elemental abundance of Nepalese Himalayan soils is

230

comparable to that of Tibetan topsoil (Tripathee et al., 2016a).

231

3.2.1 Geo-accumulation index

232

The geo-accumulation index (Igeo) proposed by (Muller, 1979) was used to

233

quantify metals contamination caused by both natural geological and geographical

234

processes and human activities. Igeo is calculated according to the following formula: Igeo = log2[Cm/(1.5Bm)]

235

(1)

236

where Cm is the concentration of metals in the examined samples and Bm is the

237

regional background level of the evaluated metal. The factor 1.5 is used to adjust for

238

lithospheric effects. The Igeo divides heavy metal contamination into seven levels

239

(Muller, 1981): Class 0 is practically no pollution (Igeo ≤ 0); Class 1 indicates no

240

pollution to moderate pollution (0 < Igeo < 1); Class 2 stands for moderate pollution

241

(1 < Igeo < 2); Class 3 is defined as moderate to heavy pollution (2 < Igeo < 3); Class

242

4 indicates heavy pollution (3 < Igeo < 4); Class 5 represents heavy to extremely

243

heavy pollution (4 < Igeo < 5); and Class 6 indicates extremely heavy pollution (Igeo >

244

5).

245

The calculated Igeo values for Cr, Co, Ni, Cu, Zn and Pb fluctuated from

246

practically no pollution to no-to-moderate pollution (Fig. 3), whereas the Igeo values 14

247

for Cd range from -1.26 to 1.83, indicating practically no pollution to moderate

248

pollution. The heavy metals tested in this study showed the following average Igeo

249

values: Cr (-0.92), Co (-0.59), Ni (-0.88), Cu (-0.60), Zn (-0.85), Pb (-0.58) and Cd

250

(0.16). The Igeo values for all of the tested metals except Cd fell into Class 0,

251

demonstrating that there is no contamination by these metals in general. It is worth

252

noting that the Igeo of Cd fell into Class 1, indicative of slight Cd pollution; in

253

particular, the Igeos of Cd at P1 (1.13), P3 (1.83), and K2 (1.39) indicated moderate

254

Cd pollution at these sites.

255

3.2.2 Enrichment factor

256

The enrichment factor (EF) is commonly used to determine the degree of

257

anthropogenic heavy metal pollution (Atiemo et al., 2012). The EF is computed

258

according to the following equation: EF = [(CE/CR) Sample]/[(CE/CR) Background]

259

(2)

260

where (CE/CR)

261

and the level of a reference element in the river sediment and (CE/CR) Background is the

262

ratio of two elements in the Tibetan Plateau topsoil (Li et al., 2009). Al, Fe, Mn, Sc

263

and Ti are usually used as reference elements (Han et al., 2006). Because these major

264

elements were not included in this study, Sc was used as the reference element for

265

geochemical normalization (Salati and Moore, 2010). Sc is chemically stable, and its

266

concentrations are close to the regional background values of the topsoil, showing

267

little difference at different sites. Generally, EF < 1.5 suggests that an element is

Sample

represents the ratio between the level of the examined element

15

268

entirely controlled by natural processes, and 1.5 < EF < 3, 3 < EF < 5 and 5 < EF < 10

269

are interpreted as minor, moderate, severe, and very severe sediment contamination,

270

respectively (Loska and Wiechula, 2003; Sutherland, 2000; Xu et al., 2017b).

271

The average EF values of the heavy metals tested in this study exhibited the

272

order Cd (1.38) > Cu (0.85) > Pb (0.83) > Co (0.81) > Ni (0.68) > Zn (0.67) > Cr

273

(0.66). None of the average EF values exceeded 1.5, indicating that the KR sediments

274

are generally not polluted by these heavy metals. A few relatively higher EF values (>

275

1.5) were found for several heavy metals at a few sites, such as for Cd in P1 (2.23), P3

276

(3.40), P5 (2.03), P6 (3.05), K5 (1.64) and K12 (1.56), for Cu in K5 (1.96) and K10

277

(1.73), and for Pb in P5 (2.00). However, none of these values are higher than 10,

278

suggesting that there is only minor anthropogenic impact from Cd, Cu and Pb at some

279

sites.

280

3.2.3 The pollution load index (PLI)

281

The pollution load index (PLI) was proposed by (Tomlinson et al., 1980) to

282

quantitatively evaluate the integrated pollution degree of heavy metals. The PLI is

283

calculated based on the contamination factor of the metals (CF):

284

CF = CM/CB

(3)

285

PLI = (CF1 ×CF2 ×CF3 ×…×CF n)1/n

(4)

286

where CM/CB is the ratio of the content of the examined metals to the background

287

value. This empirical index provides a simple, comparative means for assessing the

288

level of heavy metal pollution. PLI values are divided into two levels: 0 < PLI < 1 16

289

indicates the presence of no metal contaminants, and PLI > 1 indicates polluted

290

sediment (Tomlinson et al., 1980).

291

The PLI values in surface sediments of the KR ranged from 0.64 to 1.76 with an

292

average of 1.01, indicating almost no heavy metal pollution of the surface sediments

293

of the KR. Seven of the 23 sampling sites have PLI values > 1 (Table 2); the highest

294

PLI value was found at site P2 (1.87), followed by sites P3 (1.54), K2 (1.51), P1

295

(1.34), K15 (1.27), K5 (1.07) and K1(1.06). Sites P1, P2 and P3 are close to the 318

296

National Highway of China, and sites K1, K2, K5 and K15 have the land use type of

297

farmland (Table S1); thus, proximity to anthropogenic activities might explain the

298

higher PLIs at these sites. At site P2, except for Cd, Pb and Cu, the other heavy metals,

299

including Cr, Co, Ni and Zn, also showed higher concentrations than were found at

300

the other sites. As discussed earlier, the concentrations of Cr, Co and Ni showed no

301

evidence of pollution and were similar to the background values; thus, the higher

302

concentrations of heavy metals at site P2 may be largely attributed to the presence of

303

heavy metal-enriched minerals such as biotite and chalcopyrite in the TSS and GHS

304

in central Himalaya (Carosi et al., 2018; Ghezzi et al., 2019; Larson, 2012), and

305

biotites are visible in the samples collected from these sites.

306

3.2.4 Overall assessment of heavy metal contamination

307

Overall, all the assessment methods revealed generally low levels of heavy metal

308

pollution in KR sediments, with slight to no contamination by Cr, Co, Ni and Zn. This

309

indicates that the concentrations and distributions of these heavy metals are mainly 17

310

controlled by the geological background of the river basin. A study of the water of the

311

Dudhkoshi River in Nepal also found low concentrations of heavy metals, and it was

312

suggested that the heavy metals in this basin originate mainly from natural weathering

313

(Paudyal et al., 2016b); this conclusion is consistent with our overall assessment of

314

heavy metals in river sediments in the KR basin. It is notable that our study revealed

315

signs of pollution by Cd, Pb and Cu, as shown by their consistently high Igeo, EF and

316

CF values. Interestingly, some studies conducted in other regions of central Himalaya

317

also found higher concentrations of Cd, Cu and Pb that might be derived from

318

agricultural runoff and from the long-range transport of atmospheric pollutants based

319

on comparisons with elemental data from neighbouring regions and on correlation

320

coefficients (Rupakheti et al., 2017; Shah et al., 2012).

321

3.3 Potential sources of heavy metals

322

To analyse the potential sources of heavy metals, Spearman correlation analysis

323

(Table 3) and PCA (Table 4) were performed. The Kaiser-Meyer-Olkin (KMO)

324

measurement was 0.801, which was higher than 0.5 (the recommended KMO value),

325

and the PCA results passed the Bartlett sphericity tests (P < 0.001), indicating that the

326

application of PCA is appropriate for assessing heavy metals in KR sediments. Two

327

PCs were revealed (Fig. 5) with eigenvalues >1; these PCs explained 74.49% of the

328

total variance in the heavy metal dataset.

329

The first principal component (PC1), which explains 48.92% of the total 18

330

variance, was positively loaded (> 0.70) with Cr, Co, Ni, Zn and Sc. The Spearman

331

correlation analysis coefficients showed that there were significantly positive

332

correlations (P < 0.01) between Sc and other metals, including Cr (r = 0.73, p < 0.01),

333

Co (r = 0.72, p < 0.01), Ni (r = 0.64, p < 0.01) and Zn (r = 0.88, p < 0.01), suggesting

334

that Sc and these heavy metals may have the same source. Considering that Sc is an

335

immobile element present in natural sources, that the metals loaded on PC1 have

336

lower concentrations and that no signs of pollution were found, as discussed in section

337

3.2, it can be concluded that PC1 represents the lithogenic sources that control the

338

characteristics of these heavy metals.

339

PC2, which accounts for 25.57% of the total variance, was mainly characterized

340

by weak positive loading of Cu and strong positive loading of Cd and Pb, indicating

341

that Cu, Cd and Pb might be derived from similar sources. Cu, Cd and Pb pollution

342

was found at some sites, as mentioned above; therefore, we suggest that PC2 might

343

represent the anthropogenic sources of heavy metals. Specifically, Cu had a relatively

344

smaller loading in PC2 and did not show a correlation with other metals. The highest

345

concentrations of Cu were found at K2 and at K5 and K10 downstream of Nepal (Fig.

346

2); these sites show a land use type of farmland. We argue that the use of pesticides

347

and fertilizers in agriculture might disperse Cu into the KR via surface runoff and that

348

chemicals from these sources may eventually accumulate in sediments (Zhang et al.,

349

2018). This is supported by a previous study in which it was found that discharge

350

from agriculture resulted in heavy metal pollution in a Nepali river (Yadav et al., 19

351

2014). It should, however, be noted that even the highest concentration of Cu (43.63

352

µg/g) found in this study did not exceed the ERM. Therefore, naturally occurring Cu

353

remained the dominant source of this metal (Maharana et al., 2018), and this may also

354

be the case for some sites such as P2, in which copper-rich rocks form the lithological

355

background (Huang et al., 2010). This is further supported by the moderate EF value

356

of Cu of 0.96 despite its highest concentration at P2.

357

Cd and Pb showed strong positive loading on PC2. Except for the correlation

358

between Cd and Pb (r = 0.60, p < 0.05), Pb showed significant correlation with Cr (r =

359

0.61, p < 0.01) and Zn (r = 0.59, p <0.01); this might be because Cd and Pb are

360

generally associated with lead-zinc ore (Xu et al., 2017a), indicating that the parent

361

rock influenced the concentrations of heavy metals (Huang et al., 2010). Surprisingly,

362

higher Cd levels were found at sites P1 and P3, and higher Pb levels were found at

363

sites P1, P2 and P5. These sites are located in the upper reaches of the river, which is

364

usually considered a pristine region due to its sparse surrounding population. Previous

365

studies of atmospheric heavy metal deposition in the Himalayas revealed that Cd and

366

Pb could be transported long distances from South Asia (Cong et al., 2015a; Sharma

367

et al., 2015). Thus, the higher Cd and Pb levels found in the upper reaches of the KR

368

might be partially attributable to long-range transport and to the deposition of

369

atmospheric pollutants (Raut et al., 2017).

370

The highest concentrations of Cd, Pb and Cu were found at site P1, P2 and which

371

were close to the 318 National Highway in China. Similarly, heavy metals were 20

372

higher at site K2, which is also near the highway in Nepal. Therefore, another

373

possible source of Cu, Cd and Pb in sediments of the KR might be traffic activity. It

374

was demonstrated by (Wang et al., 2017) that Cu, Cd and Pb in soils along the

375

highway in the Tibetan Plateau were mainly derived from traffic sources.

376

3.4 Trans-Himalayan river sediment geochemistry as indicators

377

of regional environmental changes

378

As presented and discussed earlier in this work, heavy metals in sediments of the

379

KR showed insignificant or negligible contamination, and generally uniform sediment

380

quality was observed in both upstream and downstream areas. The trans-Himalayan

381

river basins feature contrasting environmental settings and human activities and are

382

characterized by sparse populations in the upper reaches and intensified human

383

activity in the lower reaches. Therefore, one would expect to see higher contamination

384

in the lower reaches of the river, but our results did not show this pattern. This

385

indicates that the KR basin remains unpolluted in terms of heavy metals in river

386

sediments and that the chemical features of the river sediments have mainly been

387

influenced by watershed lithology, with only a minor contribution from local human

388

activities. An earlier study of sediments in Himalayan river systems also showed

389

negligible pollution and little impact from anthropogenic activities (Ramesh et al.,

390

2000), and our study performed in 2017 again revealed the natural background levels

391

of heavy metals in the KR sediments. It is should be noted, however, that recent 21

392

studies of the water in some lower reaches of the KR have revealed heavy metal

393

pollution resulting from anthropogenic factors such as changes in land use, industrial

394

emissions and agricultural activities (Paudyal et al., 2016a; Tripathee et al., 2016b).

395

Furthermore, anthropogenic emissions transported over long distances can be

396

deposited into and accumulate in remote Himalayan environments, representing an

397

additional source of pollutants in the rivers (Cong et al., 2015b; Kang et al., 2016).

398

In addition to anthropogenic activities, climate change is also recognized as a

399

prominent driver of regional environmental changes. Climate change could affect the

400

river flow (Salik et al., 2016) and water-sediment regulation scheme (Liu et al., 2019),

401

thereby influencing and in turn being reflected in the chemical properties of river

402

sediments (Galy and France-Lanord, 2001). Studies have shown that increasing

403

climate extremes and rapid glacier melt have influenced river hydrology in the central

404

Himalaya, including that of the KR catchment (Jin et al., 2005; King et al., 2017;

405

Shrestha et al., 2017). These changes will further affect the aquatic environment by

406

causing erosion and weathering change features within the river basins (Liu et al.,

407

2005). Studies of the concentrations of elements in the headwaters of the Yarlung

408

Tsangbo, Indus and Ganges Rivers showed that the melting of glaciers has already

409

influenced the heavy metal concentrations in these river regions (Zhang et al., 2015).

410

A case study conducted at a typical glacial basin in the inland Tibetan Plateau also

411

suggested the presence of high levels of mercury in the glacial runoff (Sun et al.,

412

2017). 22

413

River sediments are carriers of mixed natural and anthropogenic information that

414

is reflected in their geochemical characteristics (Olivares-Rieumont et al., 2005; Singh

415

and Rajamani, 2001a; Wang et al., 2014). For example, Papastergios and his co-works

416

(2009) studied the sediment geochemistry of the Nestos River and found that the

417

highest elemental concentrations were mainly due to the natural and agricultural

418

mobilization of fine particles. An 8-year observation of water and river sediments in

419

South Korea indicated that the increasing trend in heavy metal concentration was

420

associated with the expansion of urbanization and industrialization (Pandey et al.,

421

2019), a study of the Himalayan river system illustrated that the accumulation of

422

heavy metals in sediment was influenced by finer grain sizes and high contents of clay

423

minerals that are mainly related to the physical weathering process (Ramesh et al.,

424

2000), and a case study of the River Soan in Pakistani Himalaya found relatively

425

higher levels of heavy metals in post-monsoon sediments due to input of terrestrial

426

contaminants during monsoon flooding (Nazeer et al., 2019). These examples

427

demonstrate that study of the geochemistry of river sediments can shed some light on

428

the effects of climate change and anthropogenic activities on the regional environment

429

and potentially provide valuable information regarding environmental changes within

430

the basin. The Himalayan region, with its complicated and fragile ecosystems that are

431

fundamentally significant for human well-being, is an area of concern. However,

432

studies of the geochemistry of Himalayan river sediments and attempts to use it as a

433

tool for the understanding and assessment of basin-scale environmental changes 23

434

remain insufficient. We suggest that long-term observations of the river sediment

435

geochemistry of trans-Himalayan rivers are needed in the future and that such

436

observations will help deepen the understanding of the combined impacts of climate

437

change and human activities on the environment in this unique high-mountain region.

438

4. Conclusion

439

Sediment samples were collected from the trans-Himalayan Koshi River and

440

assessed to determine the concentrations of heavy metals, including Cr, Co, Ni, Cu,

441

Zn, Cd and Pb. The results revealed a large variation in heavy metal concentrations,

442

with average values in the order Zn > Cr > Ni > Cu > Pb > Co > Cd. The spatial

443

distribution of heavy metals did not differ significantly between the upstream and

444

downstream areas except that the levels of Cd and Pb were slightly higher in upstream

445

than downstream. In comparison with other rivers, the average heavy metal

446

concentrations in the KR sediments are lower than those in the sediments of large

447

rivers draining populous areas, the Yarlung; as well as the sediment guidelines.

448

However, the concentration are comparable to the Yamuna River and higher than the

449

Mekong river which are large rivers in remote regions, and showed elevated Cd

450

compared with Tibetan topsoil and Nepalese Himalayan soil. Multiple approaches

451

were used to assess the heavy metal pollution of the sediments. The geo-accumulation

452

index revealed no contamination of heavy metals at almost any of the sites except for

453

a few sites that showed slightly high concentrations of Cd. EFs demonstrated low 24

454

values of <10, indicating minor anthropogenic impact in this region. The PLI revealed

455

a low degree of pollution at 7 of the 23 sites examined. PCA revealed the potential

456

sources of heavy metals in the sediments: Cr, Co, Ni and Zn were derived from the

457

weathering of parent rock; Cu was mainly attributed to the parent rock but was

458

slightly influenced by agricultural activity; and Cd and Pb were derived both from

459

natural sources and from atmospheric transport from areas with air pollution. Cu, Cd

460

and Pb levels might also be influenced by traffic emissions at a few sites. Our study

461

provides fundamental information on heavy metal pollution in the sediments of an

462

important and typical trans-Himalayan river and suggests that the sediments of the

463

whole river remain dominated by its lithological background and by natural processes.

464

Himalayan ecosystems are highly sensitive to climate change and anthropogenic

465

activities, the KR basin and central Himalaya is undergoing dramatic climate change,

466

and intensified human activities in surrounding areas have led to heavy metal

467

aboundances in aquatic system. River sediments may carry integrated information on

468

environmental changes within the basin, hence, long-term observations on the

469

geochemistry of river sediments are needed as a means of studying the combined

470

effects of climate change and human activities on regional environmental changes in

471

the Himalayas.

472

Acknowledgments

473

This work was supported by the National Natural Science Foundation of China 25

474

(Grant Nos. 41761144078, 41671074, and 41630754). The fieldwork was supported

475

by the Kathmandu Center for Research and Education, Chinese Academy of

476

Sciences-Tribhuvan University, and the Central Department of Environmental Science,

477

Tribhuvan University. Q.G. Zhang acknowledges financial support from the Youth

478

Innovation Promotion Association of CAS (Grant No. 2016070).

479

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782 783 784 785

Fig. 1. Map showing the study area and sampling sites in the Koshi River basin

Fig. 2. Contents of heavy metals in sediments of the Koshi River. a and b represent insignificant and significant differences of heavy metals concentrations in the up and down stream, respectively.

786

41

787 788

Fig. 3. Igeo of heavy metals in surface sediments from different Koshi River sites.

789

790 791

Fig. 4. Enrichment factor of heavy metals in surface sediments from different Koshi River sites.

42

792 793

Fig. 5. Loading plot showing loading of the two principal components in the principal component

794

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43

795

Table 1. Heavy metal concentrations in the sediment samples of the Koshi River and other selected rivers from the references Cr

Co

Ni

Cu

Zn

Cd

Pb

μg/ g

μg/ g

μg/ g

μg/ g

μg/ g

μg/ g

μg/ g

24.89~123.05

4.23~19.59

10.42~52.65

12.44~36.03

33.41~93.36

0.16~0.59

17.52~36.17

(59.57)

(10.05)

(25.81)

(21.88)

(58.20)

(0.30)

(27.01)

26.26~100.90

8.374~13.52

12.69~49.54

11.21~43.63

33.34~61.94

0.07~0.43

12.96~28.22

(42.52)

(9.59)

(20.64)

(20.76)

(46.18)

(0.16)

(19.24)

24.89~123.0

4.23~19.59

10.42~52.65

11.21~43.63

33.34~93.36

0.07~0.59

12.96~36.17

(47.18)

(9.72)

(22.05)

(21.06)

(49.46)

(0.20)

(21.36)

134

24

133

-

206

0.36

23

(Ramesh et al., 2000)

Yarlung Tsangbo River

97.51

13.39

50.55

30.15

84.83

0.13

27.29

(Li et al., 2011)

Yamuna River

25.0

24.3

23.8

14.2

43.3

-

14.9

(Dalai et al., 2004)

Mekong River

10.47

4.90

26.81

15.88

31.93

1.48

25.05

(Strady et al., 2017)

Yangtze River

79.1

-

22.42

24.7

82.9

-

23.8

(Wang et al., 2015)

Yellow River

62.4

-

23.6

40.7

68.4

0.085

15.2

(Li et al., 2016)

-

14.7

-

40.80

109.09

0.19

40.51

(Zhang et al., 2013)

ERL

81

-

20.9

34

150

1.2

46.7

ERM

370

-

51.6

270

410

9.6

218

Tibet Plateau

54.68

9.39

24.82

20.11

57.19

0.11

20.73

(Li et al., 2009)

Nepal

38.83

7.92

17.31

19.51

66.87

0.12

21.20

(Tripathee et al., 2016a)

UCC

35.00

10

20.00

25.00

71.00

0.10

20.00

(Taylor and McLennan, 1985)

River Upstream (mean) Koshi

Downstream

River

(mean)

Reference

Present study Whole Basin (mean) Ganges River

Pearl River SQGs

(Long et al., 1998)

44

796

Table 2. CF and PLI assessment data of heavy metals in Koshi River sediments CF Site

PLI Cr

Co

Ni

Cu

Zn

Cd

Pb

P1

1.07

1.10

0.98

0.90

1.32

3.29

1.74

1.34

P2

2.25

2.09

2.12

1.79

1.63

1.82

1.52

1.87

P3

1.33

1.14

1.28

1.39

1.20

5.33

1.20

1.54

P4

0.71

0.92

0.70

0.87

0.77

1.72

1.08

0.92

P5

0.46

0.45

0.42

0.62

0.58

1.44

1.42

0.67

P6

0.72

0.73

0.74

0.95

0.59

2.84

0.85

0.91

K1

0.79

1.17

0.86

0.92

1.04

1.70

1.12

1.06

K2

1.08

1.44

1.13

1.83

1.05

3.95

1.36

1.51

K3

0.65

0.89

0.69

0.66

0.75

0.85

0.63

0.73

K4

0.61

0.91

0.66

0.82

0.69

0.63

0.77

0.72

K5

0.75

1.03

0.75

2.17

0.71

1.82

1.02

1.07

K6

0.62

0.97

0.64

0.64

0.70

1.39

0.98

0.81

K7

0.68

1.02

0.73

0.81

0.73

1.50

0.85

0.87

K8

0.48

0.97

0.58

0.56

0.58

1.16

0.85

0.70

K9

0.72

0.95

0.80

0.75

0.74

1.29

0.74

0.84

K10

0.66

0.92

0.70

1.94

0.85

1.36

0.91

0.98

K11

0.54

1.18

0.51

0.67

0.71

0.75

0.75

0.71

K12

0.60

0.99

0.67

1.46

0.76

1.57

0.76

0.91

K13

0.87

0.90

0.99

0.71

0.81

1.30

0.99

0.92

K14

0.67

0.92

0.84

0.91

0.84

1.05

0.99

0.88

K15

1.85

1.07

2.00

0.74

0.87

1.83

1.12

1.27

K16

0.87

1.00

0.78

0.90

1.08

1.01

1.00

0.94

K17

0.81

0.96

0.68

1.78

0.83

0.78

1.08

0.94

797

Table 3. Spearman's correlation matrix for heavy metal concentrations. Cr

798 799

Co

Ni

Cu

Zn

Cd

Pb

Cr

1.00

Co

0.51*

1.00

Ni

0.92**

0.46**

1.00

Cu

0.48*

0.36

0.44*

1.00

Zn

0.79**

0.57**

0.78**

0.51*

1.00

Cd

0.56**

0.43*

0.60**

0.45*

0.39

1.00

Pb

0.61**

0.42*

0.51*

0.33

0.59**

0.60*

1.00

Sc

0.73**

0.72**

0.64**

0.49*

0.88**

0.35

0.60**

Sc

1.00

** Correlation is significant at the 0.01 level. * Correlation is significant at the 0.05 level.

800 45

801

Table 4. Rotated component matrix of heavy metals. Component

Heavy metal 1

2

Cr

0.879

0.301

Co

0.903

0.161

Ni

0.856

0.240

Cu

0.293

0.458

Zn

0.802

0.472

Cd

0.084

0.896

Pb

0.376

0.734

Sc

0.846

0.310

Eigenvalues

4.96

1.00

% of variance

48.92%

25.57%

% of cumulative

48.92%

74.49%

802

46

Highlights:

(1) First evaluation of heavy metals pollution in Koshi River sediments were conducted. (2) The distribution of heavy metals in the up- and downstream portions were uniform. (3) Only Cd, Cu and Pb indicated potential anthropogenic disturbances at a few sites. (4) Heavy metals in the surface sediments were mainly related to watershed lithology. (5) Trans-Himalayan river sediments may indicate regional environmental changes.

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: