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Journal of Arid Land
Journal of Arid Land  2023, Vol. 15 Issue (11): 1355-1375    DOI: 10.1007/s40333-023-0111-7
Research article     
Multiple assessments, source determination, and health risk apportionment of heavy metal(loid)s in the groundwater of the Shule River Basin in northwestern China
WEN Xiaohu1, LI Leiming2,*(), WU Jun3, LU Jian4, SHENG Danrui1
1Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
2Key Laboratory of Comprehensive and Highly Efficient Utilization of Salt Lake Resources, Qinghai Institute of Salt Lakes, Chinese Academy of Sciences, Xining 810008, China
3Yantai Research Institute, Harbin Engineering University, Yantai 264006, China
4Shandong Key Laboratory of Coastal Environmental Processes/CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Chinese Academy of Sciences, Yantai 264003, China
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Abstract  

Global ecosystems and public health have been greatly impacted by the accumulation of heavy metal(loid)s in water. Source-specific risk apportionment is needed to prevent and manage potential groundwater contamination with heavy metal(loid)s. The heavy metal(loid)s contamination status, water quality, ecological risk, and health risk apportionment of the Shule River Basin groundwater are poorly understood. Therefore, field sampling was performed to explore the water quality and risk of heavy metal(loid)s in the groundwater of the Shule River Basin in northwestern China. A total of 96 samples were collected from the study area to acquire data for water quality and heavy metal(loid)s risk. There was noticeable accumulation of ferrum in the groundwater of the Shule River Basin. The levels of pollution were considered to be moderately low, as evaluated by the degree of contamination, heavy metal evaluation index, heavy metal pollution index, and Nemerow pollution index. The ecological risks were also low. However, an assessment of the water quality index revealed that only 58.34% of the groundwater samples had good water quality. The absolute principal component scores-multiple linear regression model was more suited for this study area than the positive matrix factorization model. There were no obvious noncarcinogenic or carcinogenic concerns for all types of receptors according to the values of the total hazard index and total carcinogenic risk. The human activities and the initial geological environment factor (65.85%) was the major source of noncarcinogenic risk (residential children: 87.56%; residential adults: 87.52%; recreational children: 86.77%; and recreational adults: 85.42%), while the industrial activity factor (16.36%) was the major source of carcinogenic risk (residential receptors: 87.96%; and recreational receptors: 68.73%). These findings provide fundamental and crucial information for reducing the health issues caused by heavy metal(loid)s contamination of groundwater in arid areas.

Key words： groundwater      heavy metal(loid)s      ecological risk      health risk      Shule River Basin     
Received: 17 May 2023      Published: 30 November 2023
Corresponding Authors: * LI Leiming (E-mail: lileiming@isl.ac.cn)
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WEN Xiaohu
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SHENG Danrui
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WEN Xiaohu, LI Leiming, WU Jun, LU Jian, SHENG Danrui. Multiple assessments, source determination, and health risk apportionment of heavy metal(loid)s in the groundwater of the Shule River Basin in northwestern China. Journal of Arid Land, 2023, 15(11): 1355-1375.

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http://jal.xjegi.com/10.1007/s40333-023-0111-7     OR     http://jal.xjegi.com/Y2023/V15/I11/1355

Fig. 1 Distribution of sampling sites. DEM, digital elevation model.
Fig. 2 Variation of heavy metal(loid)s in groundwater samples of the Shule River Basin. Al, aluminum; Mn, manganese; Fe, ferrum; Co, cobalt; Ni, nickel; Cu, cuprum; Zn, zinc; As, arsenic; Cd, cadmium; Pb, lead; IQR, interquartile range.
Fig. 3 Spatial distribution of degree of contamination (DC; a), heavy metal evaluation index (HEI; b), heavy metal pollution index (HPI; c), and Nemerow pollution index (NP; d) for groundwater in the Shule River Basin
Fig. 4 Spatial distribution of water quality index (WQI; a) and ecological risk index (ERI; b) for groundwater in the Shule River Basin
Heavy metal(loid)s PMF model APCS-MLR model
Factor 1 (%) Factor 2 (%) Factor 3 (%) R2 Factor 1 (%) Factor 2 (%) Factor 3 (%) R2
Al 62.2 0.1 37.7 0.005 2.0 94.0 4.0 0.899
Mn 43.1 31.2 25.7 0.418 56.1 33.6 15.3 0.629
Fe 16.4 70.4 13.1 1.000 88.6 1.8 9.6 0.944
Co 21.1 65.0 13.9 0.916 89.0 0.6 10.3 0.926
Ni 18.3 66.8 14.8 0.983 88.2 1.8 10.0 0.938
Cu 12.0 48.9 39.1 0.907 90.0 0.4 9.6 0.906
Zn 17.9 51.7 30.4 0.073 14.5 1.3 84.2 0.987
As 18.4 25.6 56.0 1.000 93.3 0.3 6.4 0.876
Cd 59.2 26.4 14.4 1.000 75.9 10.3 13.8 0.834
Pb 38.3 50.4 11.4 0.765 64.1 20.3 15.6 0.882
Table 1 Contribution of each factor derived from positive matrix factorization (PMF) model and absolute principal component scores-multiple linear regression (APCS-MLR) model
Fig. 5 Scatter plot of observed data and predicted data derived from absolute principal component scores-multiple linear regression (APCS-MLR) model. (a), Al; (b), Mn; (c), Fe; (d), Co; (e), Ni; (f), Cu; (g), Zn; (h), As; (i), Cd; (j), Pb.
Fig. 6 Factor figure-prints of heavy metal(loid)s resulted from positive matrix factorization (PMF) model. Factor 1 represents human activities and the initial geological environment factor, Factor 2 represents industrial activity factor, and Factor 3 represents agricultural practices factor.
Fig. 7 Spearman correlation analysis among the groundwater heavy metal(loid)s (a) and component loading of the 10 measured heavy metal (loid)s on varimax rotated factors (b). ** indicates significant correlation at the level of 0.01, and * indicates significant correlation at the level of 0.05.
Fig. 8 Spatial distribution of normalized contribution for Factor 1 (a), Factor 2 (b), and Factor 3 (c)
Fig. 9 Percentage of source-specific health risks from different source by APCS-MLR model for residential and recreational receptors
Factor 1 Factor 2 Factor 3 Total Factor 1 Factor 2 Factor 3 Total
Noncarcinogenic risk for residential adults Noncarcinogenic risk for recreational adults
Al 1.84×10-6 8.63×10-5 3.67×10-6 9.18×10-5 4.20×10-8 1.97×10-6 8.40×10-8 2.10×10-6
Mn 2.74×10-4 1.64×10-4 7.46×10-5 4.88×10-4 4.05×10-5 2.43×10-5 1.10×10-5 7.22×10-5
Fe 1.20×10-2 2.44×10-4 1.30×10-3 1.35×10-2 2.75×10-4 5.58×10-6 2.98×10-5 3.10×10-4
Co 1.43×10-3 9.61×10-6 1.65×10-4 1.60×10-3 2.76×10-5 1.86×10-7 3.19×10-6 3.10×10-5
Ni 4.64×10-3 9.47×10-5 5.26×10-4 5.26×10-3 8.42×10-5 1.72×10-6 9.55×10-6 9.55×10-5
Cu 1.75×10-3 7.79×10-6 1.87×10-4 1.95×10-3 4.01×10-5 1.78×10-7 4.28×10-6 4.46×10-5
Zn 7.32×10-5 6.57×10-6 4.25×10-4 5.05×10-4 1.51×10-6 1.35×10-7 8.76×10-6 1.04×10-5
As 1.30×10-2 4.16×10-5 8.88×10-4 1.39×10-2 2.97×10-4 9.54×10-7 2.04×10-5 3.18×10-4
Cd 1.33×10-3 1.80×10-4 2.41×10-4 1.75×10-3 3.04×10-5 4.13×10-6 5.53×10-6 4.01×10-5
Pb 6.15×10-4 1.95×10-4 1.50×10-4 9.60×10-4 1.08×10-5 3.43×10-6 2.64×10-6 1.69×10-5
THI 3.50×10-2 1.03×10-3 3.96×10-3 4.00×10-2 8.07×10-4 4.25×10-5 9.52×10-5 9.41×10-4
Noncarcinogenic risk for residential children Noncarcinogenic risk for recreational children
Al 3.47×10-6 1.63×10-4 6.95×10-6 1.74×10-4 2.26×10-7 1.06×10-5 4.52×10-7 1.13×10-5
Mn 5.05×10-4 3.02×10-4 1.38×10-4 9.00×10-4 9.87×10-5 5.91×10-5 2.69×10-5 1.76×10-4
Fe 2.27×10-2 4.60×10-4 2.46×10-3 2.56×10-2 1.48×10-3 3.01×10-5 1.60×10-4 1.67×10-3
Co 2.70×10-3 1.82×10-5 3.12×10-4 3.03×10-3 1.66×10-4 1.12×10-6 1.93×10-5 1.87×10-4
Ni 8.78×10-3 1.79×10-4 9.95×10-4 9.95×10-3 5.32×10-4 1.09×10-5 6.03×10-5 6.03×10-4
Cu 3.31×10-3 1.47×10-5 3.53×10-4 3.68×10-3 2.16×10-4 9.60×10-7 2.30×10-5 2.40×10-4
Zn 1.39×10-4 1.24×10-5 8.04×10-4 9.55×10-4 8.71×10-6 7.81×10-7 5.06×10-5 6.01×10-5
As 2.45×10-2 7.87×10-5 1.68×10-3 2.62×10-2 1.60×10-3 5.13×10-6 1.09×10-4 1.71×10-3
Cd 2.51×10-3 3.40×10-4 4.56×10-4 3.31×10-3 1.64×10-4 2.22×10-5 2.98×10-5 2.16×10-4
Pb 1.16×10-3 3.69×10-4 2.83×10-4 1.82×10-3 6.99×10-5 2.21×10-5 1.70×10-5 1.09×10-4
THI 6.63×10-2 1.94×10-3 7.48×10-3 7.56×10-2 4.33×10-3 1.63×10-4 4.97×10-4 4.99×10-3
Carcinogenic risk for residential receptors Carcinogenic risk for recreational receptors
As 6.67×10-7 3.13×10-5 1.33×10-6 3.33×10-5 2.39×10-8 1.12×10-6 4.77×10-8 1.19×10-6
Cd 1.93×10-6 1.15×10-6 5.25×10-7 3.43×10-6 4.37×10-7 2.62×10-7 1.19×10-7 7.79×10-7
TCR 2.59×10-6 3.25×10-5 1.86×10-6 3.70×10-5 4.61×10-7 1.38×10-6 1.67×10-7 2.01×10-6
Table 2 Source-specific noncarcinogenic and carcinogenic risks of heavy metal(loid)s in groundwater of the Shule River Basin from different source for four demographic categories
Parameter Minimum Maximum Mean SD CV (%) Permissible value# Percentage of SER (%)
Al (μg/L) 0.1 44.6 3.5 6.3 182.0 200.0 0.0
Mn (μg/L) 0.1 4.7 0.4 0.6 140.0 100.0 0.0
Fe (μg/L) 70.9 1300.0 358.0 306.0 85.7 300.0 39.6
Co (μg/L) 0.0 0.6 0.2 0.1 79.1 50.0 0.0
Ni (μg/L) 1.0 13.4 4.0 3.2 79.1 20.0 0.0
Cu (μg/L) 0.4 14.1 2.9 2.9 97.0 1000.0 0.0
Zn (μg/L) 0.9 70.7 5.7 9.8 170.0 1000.0 0.0
As (μg/L) 0.3 5.7 1.6 1.2 74.5 10.0 0.0
Cd (μg/L) 0.0 0.2 0.0 0.0 76.3 5.0 0.0
Pb (μg/L) 0.0 0.2 0.1 0.1 91.1 10.0 0.0
TDS (mg/L) 301.0 7009.0 1731.0 1484.0 85.7 1000.0 49.0
Na+ (mg/L) 20.0 1358.0 243.0 244.0 100.0 200.0 38.5
Mg2+ (mg/L) 21.6 532.0 163.0 139.0 84.9 50.0 80.2
K+ (mg/L) 3.0 49.9 13.5 11.8 87.2 10.0 42.7
Ca2+ (mg/L) 30.6 382.0 102.0 76.2 74.9 75.0 44.8
Cl- (mg/L) 19.5 1155.0 262.0 263.0 101.0 250.0 36.5
NO3- (mg/L) 2.1 89.1 19.8 19.1 96.6 20.0 39.6
SO42- (mg/L) 76.0 3522.0 749.0 757.0 101.0 250.0 61.5
HCO3-/CO32- (mg/L) 61.0 293.0 152.0 55.7 36.7 - -
DO (mg/L) 2.8 18.5 7.4 1.9 25.5 - -
pH 7.2 9.4 8.0 0.4 5.2 6.5‒8.5 4.2
EC (μS/cm) 446.0 7480.0 2025.0 1527.0 75.4 - -
Temperature (°C) 9.8 24.8 14.3 2.9 20.4 - -
Table S1 Statistical summary of TDS, DO, pH, EC, temperature, and the concentrations of heavy metal(loid)s, anions, and cations of groundwater samples
Groundwater
or river		 Al (μg/L) Mn (μg/L) Fe (μg/L) Co (μg/L) Ni (μg/L) Zn (μg/L) As (μg/L) Cd (μg/L) Pb (μg/L) Reference
Shule River Basin 3.5 0.4 358.0 0.2 4.0 5.7 1.6 0.0 0.1 This study
Rivers in NQTP 1753.0 5.6 76.7 0.3 2.4 35.2 3.4 0.2 3.0 Li et al. (2022)
Heihe River 10.5 2.6 187.0 - - - 0.8 <0.1 <0.1 Qu et al. (2019)
Buh River 18.2 3.8 198.0 - - - 0.9 N.D. <0.1 Qu et al. (2019)
Yellow River 10.2 3.3 154.0 - - - 1.2 <0.1 0.1 Qu et al. (2019)
Za'gya Zangbo River 39.8 0.3 - - - - 5.7 N.D. 0.0 Qu et al. (2019)
Yangtze River - 30.7 - 1.9 2.8 20.5 - 3.6 15.8 Qu et al. (2017)
Lancang River (Mekong River) 14.8 1.7 2.6 - - - - - - Huang et al. (2009)
Salween River (Nujiang River) 20.7 - 19.7 - - - - - - Huang et al. (2009)
Yarlung Zangbo River 20.6 12.8 - - - - 10.5 1.0 5.6 Qu et al. (2015) and Zheng et al. (2010)
Ganges River <0.2 N.D. 0.4 - - - N.D. N.D. N.D. Zhang et al. (2015)
Indus River <8.8 0.4 N.D. - - - 13.7 N.D. - Zhang et al. (2015) and Qu et al. (2019)
Chinese Loess Plateau 18.1 58.2 67.0 7.3 13.2 46.8 15.2 0.0 0.5 Xiao et al. (2019)
Tarim River Basin 32.0 80.4 219.0 0.2 0.5 10.1 5.8 0.0 0.8 Xiao et al. (2014)
Zhangye Basin 3.6 6.3 181.0 0.1 2.5 1.9 1.3 0.0 0.1 Sheng et al. (2022)
Table S2 Comparison of the average concentrations of heavy metal(loid)s in groundwater or river
Fig. S1 Spatial distribution of the concentrations of heavy metal(loid)s in groundwater of the Shule River Basin. (a), aluminum (Al); (b), manganese (Mn); (c), ferrum (Fe); (d), cobalt (Co); (e), nickel (Ni); (f), copper (Cu); (g), zinc (Zn); (h), arsenic (As); (i), cadmium (Cd); (j), lead (Pb).
Fig. S2 Contributions from three sources by absolute principal component scores-multiple linear regression (APCS-MLR) model. Factor 1 represents human activities and the initial geological environment factor, Factor 2 represents industrial activity factor, and Factor 3 represents agricultural practices factor.
Fig. S3 Hazard index (HI; a-d) and carcinogenic risk (CR; e and f) from heavy metal(loid)s for residential and recreational (b, d and f) receptors in groundwater of the Shule River Basin. HI-RES-Adult, hazard index for residential adults; HI-REC-Adult, hazard index for recreational adults; HI-RES-Child, hazard index for residential children; HI-REC-Child, hazard index for recreational children; CR-RES, carcinogenic risk for residential receptors; CR-REC, carcinogenic risk for recreational receptors.
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