Next Article in Journal
Humin as an External Electron Mediator for Microbial Pentachlorophenol Dechlorination: Exploration of Redox Active Structures Influenced by Isolation Methods
Next Article in Special Issue
Contamination Evaluation and Source Identification of Heavy Metals in the Sediments from the Lishui River Watershed, Southern China
Previous Article in Journal
Occurrence and Ecological and Human Health Risk Assessment of Polycyclic Aromatic Hydrocarbons in Soils from Wuhan, Central China
Previous Article in Special Issue
Spatial Characteristics of Heavy Metals in Street Dust of Coal Railway Transportation Hubs: A Case Study in Yuanping, China
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 15
Issue 12
10.3390/ijerph15122752
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Distribution, Sources, and Water Quality Assessment of Dissolved Heavy Metals in the Jiulongjiang River Water, Southeast China

by
Bin Liang
,
Guilin Han
*,
Man Liu
,
Kunhua Yang
,
Xiaoqiang Li
and
Jinke Liu
School of Scientific Research, China University of Geosciences (Beijing), Beijing 100083, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2018, 15(12), 2752; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph15122752
Submission received: 25 October 2018 / Revised: 29 November 2018 / Accepted: 3 December 2018 / Published: 5 December 2018
(This article belongs to the Collection Environmental Risk Assessment)
Browse Figures
Versions Notes

Abstract

:
In this study, the concentration of eight dissolved heavy metals (Ti, Cr, Mn, Fe, Ni, Mo, Sb, and Ba) in 42 water samples from the Jiulongjiang River, southeast China, were determined by inductively coupled plasma mass spectrometry (ICP-MS). Multivariate statistical methods, including correlation analysis (CA) and factor and principal component analysis (FA/PCA), were analyzed to identify the sources of the elements. Water quality index (WQI) and health risk assessment, including hazard quotient (HQ) and hazard index (HI), were used to evaluate water quality and the impacts on human health. Our results were compared with the drinking water guidelines reported by China, the World Health Organization (WHO), and the United States Environmental Protection Agency (US EPA), revealing that Ti, Mn, and Sb were not within approved limits at some sites and might be the main pollutants in the drainage basin. Based on the spatial distributions, Ti, Mn, Fe, Ni, and Mo showed good similarity, indicating that they might come from similar sources along the river. The CA results also showed that Ti, Mn, Fe, Ni, and Mo had a high correlation coefficient. The FA/PCA results identified three principal components (PC) that accounted for 79.46% of the total variance. PC 1 suggested that a mixed lithogenic and urban land source contributed to Ti, Mn, Fe, Ni, and Mo; PC 2 showed that Cr, Ni, and Mo were influenced by the discharge of industrial effluents; Sb had a strong loading on PC 3, which was controlled by mining activities. The results of the WQI indicated that the water in the Jiulongjiang River was basically categorized as excellent water, but the water quality levels in site W5 and N4 were poorer due to urban land use. Hazard quotient and HI values showed that Sb was a potential threat to human health, indicating that preventive actions should be considered in regard to mining activities in the upper reaches of Beixi stream.

1. Introduction

Globally, heavy metals are one of the most hazardous pollutants in water [1,2,3,4,5,6], and can be observed in dissolved phase [7,8,9,10], suspended particle phase [11,12,13], and sedimentary phases [14,15] in water systems. Dissolved heavy metals are generally more toxic than other phases [7,16]. Many heavy metal elements, such as Ti, Cr, Mn, Fe, Ni, Cu, Zn, Mo, Cd, Sb, Ba, Pb, etc., used to be detected in dissolved form due to industrial and domestic discharge without treatments [17,18,19]. Due to the toxicity, non-degradation, and bio-accumulation of heavy metals, an overbalance of concentrations can make water unsuitable for drinking and even cause severe risks to human bodies [4,20,21,22]. Statistically, about 80% of human disease is caused by water pollution, according to the World Health Organization (WHO) [23]. Therefore, continued research on dissolved heavy metals may be key in order to shed further light on some of the effects of pollution. Moreover, evaluating the effects of dissolved heavy metals on human health and water systems is considered as one of the most direct and important ways in which to judge the pollution present in water [1,10,23,24,25]. Thus, it is of great significance to understand the concentration, distribution, and sources of dissolved heavy metals, to assess water quality and health risks, to control water pollution, and to protect water resources [25].
Dissolved heavy metals can be released into the aqueous system by natural processes, such as bedrock weathering or volcanism [26,27], and element concentrations can be largely increased through anthropogenic factors, such as industrial wastes, sewage discharge, coal combustion, mining, and vehicle transportation [24,28,29]. Unfortunately, it is almost inevitable that water quality will be degraded with the rapid development of urban, industrial, and agricultural activities in a city. The Jiulongjiang River is the second longest river in Fujian Province, southeast China, and is also the source of irrigation and drinking water for many riverside towns [30]. In recent years, the drainage basin has received considerable attention in regard to its environmental problems, such as: (i) the discharge of industrial and domestic sewage [31]; (ii) the effects of local mining activities, given that mining and coal resources are widely distributed in the upper reaches of Beixi stream [32]; (iii) the inputs of chemical fertilizers and nutrients [32]; (iv) the extensive development of hydro power plants in the drainage basin [8,33]; (v) the effects of a large-scale livestock operation on the bank of the river [34], etc. These situations may have contributed to the increasing metal pollution of the Jiulongjiang River, and appropriate measures must be taken in order to ensure the protection of local human and environmental health. However, information on source identification and assessment of water quality has rarely been discussed in previous studies.
In this study, a geochemical survey on eight selected elements (Ti, Cr, Mn, Fe, Ni, Mo, Sb, and Ba) in 42 water samples has been conducted with the objectives to (i) investigate the spatial distribution characteristics of dissolved heavy metals in the river water; (ii) identify the sources of dissolved heavy metals with multivariate statistical methods, including correlation analysis (CA) and factor and principal component analysis (FA/PCA); (iii) evaluate water quality and distinguish the hazard impacts on human health by calculating the water quality index (WQI) as well as the hazard quotient (HQ) and hazard index (HI). This paper is aiming to help develop local water management strategies for preventing hazardous contamination.

2. Materials and Methods

2.1. Regional Geography

The Jiulongjiang River is located in the south of Fujian Province (24°13′–25°51′ N, 116°47′–118°02′ E) (Figure 1), and consists of the Beixi stream, Xixi stream, Nanxi stream, and the regional estuary. The Beixi stream is the main stream in the drainage basin, and the Xixi stream and Nanxi stream flow towards the estuary. The drainage basin has an area of 14,700 km2 [35], and the length of the river is approximately 1723 km, with an average annual runoff of 1.4 × 1010 km3 [33,36]. The study area is situated in the subtropical oceanic monsoon climate zone, with an average annual temperature around 21 °C and annual precipitation of 1200–2000 mm [31,33].
The drainage basin is an important area where the Eurasian plate interacted with the Pacific plate [37]. Multi-stage magmatic activities took place in the Mesozoic, and the medium-acidic magmatic rocks were widely distributed. Intrusive rocks and volcanic rocks are mainly distributed in the central and southern parts of the basin, accounting for about two-thirds of the total drainage area [33]. Sedimentary rocks and coal-bearing interlayers are exposed in the northeastern part of the drainage basin and the source of the Xixi stream. The downstream of the Xixi stream deposits a series of alluvial stratum. Carbonates are distributed in the upper reaches of the Beixi stream [38].

2.2. Sample Collection and Preparation

Sampling was conducted from downstream to upstream of each stream on the basis of hydrological conditions, lithology, and land use. Water samples were collected from 42 sites along the Beixi stream, Xixi stream, Nanxi stream, and estuary of the Jiulongjiang River, labeled N1–23, W1–10, S1–4, and E1–5 (Figure 1), respectively, during 24–29 July 2017. Before collection, all instruments were rinsed three times with river water. Samples were collected using a water sampler and stored in pre-cleaned high-density polyethylene (HDPE) plastic bottles. For subsequent determination of dissolved heavy metals, water samples were filtered through a 0.22-μm filter membrane on site. Then, water temperature and pH were immediately determined using a YSI water quality monitoring meter (Xylem Inc., Yellow Springs, OH, USA). Finally, the filtrate was acidified to pH ≤ 2 using ultra-pure concentrated nitric acid and stored at approximately 4 °C before element determination.

2.3. Dissolved Heavy Metals Determination

The dissolved heavy metals, including Ti, Cr, Mn, Fe, Ni, Mo, Sb, and Ba, were analyzed using inductively coupled plasma-mass spectrometry (ICP-MS, Elan 9000, Perkin Elmer Optima, Waltham, MA, USA) in Tianjin Key Laboratory of Water Resources and Environment, China. The uncertainty of the standard reference material (GSB 04-1767-2004) was below 0.7%. Procedural blanks were determined between every 10 water samples to control the accuracy. Method precision was controlled by re-determining randomly replicate samples between every 10 water samples. Good agreement was obtained between the determined concentrations and standard reference material. The relative standard deviations (RSDs) of elements were below 6.1%. For comparing the distribution and exploring the possible sources of dissolved heavy metals in this study, all of the data were processed using Microsoft Office 2019 (Microsoft Corporation, Redmond, Seattle, WA, USA) and SPSS 19.0 (IBM Corporation, Armonk, NY, USA).

2.4. Water Quality Index

The WQI has been emerging as a powerful tool to assess water quality. The impacts caused by different water quality variables were calculated with the following equation:
WQI = Σ [Wi × (Ci/Si)] × 100,
where Wi represents the weight of each parameter and is calculated on the basis of the eigenvalues for each principal component and factor loading for each parameter from the FA/PCA results [29,39]; Ci represents the concentrations of each dissolved heavy metal; and Si represents the guideline values of Chinese drinking water (GB 5749-2006) for each element. The WQI values were classified into five categories: (i) excellent water quality (0 ≤ WQI < 50); (ii) good water quality (50 ≤ WQI < 100); (iii) poor water quality (100 ≤ WQI < 200); (iv) very poor water quality (200 ≤ WQI < 300); and (v) water unsuitable for drinking (WQI ≥ 300) [24].

2.5. Health Risk Assessment

Hazard quotient and HI are calculated to evaluate the toxicity of hazardous substances and factors in aqueous systems with quantitative assessments [40,41]. The ingestion of elements through the mouth and nose and absorption through the skin are the three most common exposure pathways [16]. Hazard quotient is the ratio between exposure through individual pathways and the reference dose [42]. Hazard index is the sum of the HQs for individual elements from both the applicable pathways mentioned previously, which is used to analyze the total potential non-carcinogenic risk [43,44]. Non-carcinogenic risk or adverse effects on human health exist when HQ or HI is ≥ 1; no deleterious effects exist when HQ or HI is < 1. Equations to calculate HQ and HI values are as follows:
ADDingestion = (Cw × IR × EF × ED)/(BW × AT),
ADDdermal = (Cw × SA × Kp × ET × EF × ED × 10−3)/(BW × AT),
HQ = ADD/RfD,
RfDdermal = RfD × ABSGI,
HI = ΣHQs,
where the meanings and their units in these equations are as follows: ADDingestion and ADDdermal represents the average daily dose from ingestion and dermal absorption, respectively (μg/kg/day); Cw is the average concentration of each element in water (μg/L); BW is the average body weight (70 kg for adults and 15 kg for children); IR is the ingestion rate (2 L/day for adults and 0.64 L/day for children); EF is the exposure frequency (350 days/year); ED is the exposure duration (30 years for adults and 6 years for children); AT is the average time (=ED × 365 days/year); SA represents the exposed skin area (18,000 cm2 for adults and 6600 cm2 for children); ET is the exposure time (0.58 h/day for adults and 1 h/day for children); Kp represents dermal permeability coefficient in water (cm/h); RfD is the reference dose (μg/kg/day); ABSGI is the gastrointestinal absorption factor (dimensionless). The reference values were obtained from the United States Environmental Protection Agency (US EPA) [42].

3. Results and Discussion

3.1. Water Parameters and Distribution of Dissolved Heavy Metals

The parameters of water samples and concentrations of dissolved heavy metals are listed in Table 1. The Kolmogorov-Smirnov (K-S) test was used to compare a sample with a reference probability distribution, and the results showed that temperature, pH, Ti, and Fe were normally distributed (bilateral significance > 0.100). The coefficient of variation (CV) for the elements indicated that the mean concentration values might have been affected by abnormal interference, resulting in extremely high values. Thus, median values would be better than arithmetic mean values in this study. However, the results reported in the guidelines and other rivers were given as mean values for comparison.
The temperature of water ranged from 21.5 °C to 31.8 °C, with an average of 26.0 °C. Samples collected from Beixi stream had a lower temperature than those from the Nanxi stream and Xixi stream. The pH values varied between weak acidic and weak alkaline (6.42 to 7.60), basically within the guideline (6.5–8.5) reported by GB 5749-2006 [45]. The values of dissolved heavy metals decreased in the following order: Ti > Fe > Ba > Mn > Sb > Mo > Ni > Cr. Based on the average concentrations, the selected elements in water were classified into three categories: (1) Cr, Ni, and Mo (<1 μg/L); (2) Mn and Sb (1–10 μg/L); (3) Ti, Fe, and Ba (>10 μg/L) (Table 1). Cr, Fe, Ni, Mo, and Ba complied with guidelines for drinking water recommended by China (GB 5749-2006), the WHO, and the US EPA, while Ti, Mn, and Sb were not within approved limits at some sites and might be affected by intensive anthropogenic activities along the river. Based on the heavy metals in major world rivers, Ti and Sb were relatively higher, and Ni and Ba were similar to the mean values [46]. Compared with recent published works, the results of Cr, Mn, and Ni were similar to those from the Yangtze River [9] and slightly lower than those from the Pearl River [47]; the concentrations of Ba were similar to those from the Douro River, Portugal [48]; the concentrations of Mo were similar to the water from the Calore River, southern Italy [19]; and the value of Fe in the Jiulongjiang River was similar to that in the Han River [16].
The distribution of each element is individually plotted in Figure 2, and the original data is shown in the Supplementary Materials (Table S1). Based on the distribution characteristics of eight elements, we concluded that the pattern of Ti concentrations were highly similar with that of Fe concentrations in terms of spatial distribution across the whole drainage basin. Ba was slightly similar with Ti and Fe in the Xixi stream. In the Beixi stream, Mn, Ni, and Mo showed a good homogeneity in the upper and middle reaches (N1–17), similar to Ti and Fe. In the Nanxi stream, Ti, Fe, and Mo were distributed in a similar pattern, and Cr, Mn, and Ni were distributed in another pattern with a peak value in the sample from site S4. Sb had a flat pattern, except for sample W1. Overall, the distributions of Ti, Mn, Fe, Ni, and Mo showed a good similarity, indicating that they might be affected by similar natural and anthropogenic activities along the river.

3.2. Source Identification of Dissolved Heavy Metals

3.2.1. Correlation Analysis

A Pearson correlation matrix was used to investigate the relations and interactions of dissolved heavy metals in the Jiulongjiang River water [19,25,29]. Strong correlation coefficients might result from similar hydro-geochemical characteristics in the different sites of the study area [51]. The results are shown in Table 2. Strong positive correlation coefficients were observed ranging from 0.630 to 0.996 between each pair of Ti, Mn, Fe, Ni, and Mo, at the 0.01 level, where Ti and Fe were highly correlated, corresponding to the homogeneous spatial distribution of these two elements in Figure 2. Moreover, Mn, Ni, and Mo add might be distributed throughout the Jiulongjiang River in a similar way with Ti and Fe. Cr and Ni had a strong positive correlation coefficient of 0.393 at the 0.05 level, indicating similar sources of Cr and Ni. Ba had a low correlation with other elements. The strong correlations at different levels among the metals indicated multiple sources could be responsible for the dissolved heavy metals present in water of the Jiulongjiang River.

3.2.2. Factor and Principal Component Analysis

Factor and Principal Component Analysis is a statistical method to explain the underlying structure of a set of variables and interpret the relationships between variables by converting a large number of potentially related variables into a set of linearly unrelated variables using a dimensionality reduction technique [25,52]. The data needed to avoid numerical ranges of the original variables by z-scale standardization [53]. The reliability of the data for FA/PCA was under 0.001, as calculated by Kaiser-Meyer-Olkin and Bartlett’s sphericity test. The results of FA/PCA for metal concentrations are listed in Table 3. In order to identify the sources of the dissolved heavy metals, the factor scores for each element are plotted in Figure 3.
Three principal components (PC) with eigenvalues were extracted accounting for 79.46% of the total variance. The factor loadings were classified into three groups: strong, moderate, and weak, with the loading values of >0.75, 0.75–0.50, and 0.50–0.30 [52], respectively. PC 1 had strong loadings of Ti (0.90), Mn (0.90), Fe (0.90), Ni (0.88), and Mo (0.85), with a variance of 50.03% (Figure 1), indicating a mixed source of weathering rocks and urban land. Ti and Fe, which are the major elements in crustal materials, were major components with relatively high values [27,54], and the low concentrations of Mn, Ni, and Mo were also representative of their background levels. Therefore, a lithogenic origin might be presumed, since these heavy metals are generally present in weathered rocks [41,55]. Moreover, Ti, Mn, Fe, Ni, and Mo were enriched in site W5 and N4, as well as their nearby area, which were defined as urban land [31], indicating distinct urban land pollution. PC 2, responsible for 16.88% of the variance, was correlated with Cr (0.71), Ni (0.37), and Mo (0.30), which are associated with the discharge of industrial effluents from the factories. It has been reported that some factories are located in the estuary wharf [52,56]. PC 3 had a strong positive loading of Sb (0.95), with a variance of 12.56%. Mine deposits and coal areas are distributed in the upper reaches of Beixi stream (Figure 1) [32], which might be the main factor to cause high loading of Sb in the drainage basin [57,58].

3.3. Water Quality Index and Health Risk Assessment

3.3.1. Water Quality Index

The weights of each parameter for calculating the WQI were obtained on the basis of the FA/PCA, and the weights are summarized in Table 4. The values of WQI were calculated with Equation (1), and the WQI results of each sampling site are shown in Figure 4 and in the Supplementary Materials (Table S1). The WQI values varied from 4.23 to 97.65, among which water samples at site N4 (WQI = 97.65) were categorized as good water, and water samples at W5 had a value near the good water limit (WQI = 47.33). In these two sites, water might have been polluted by anthropogenic activities. Water in other sites of this study area fell into the category of excellent water, with WQI values less than 50, indicating the natural water was suitable for drinking. In general, the water quality of Jiulongjiang River was good, but more attention should be paid to sites N4 and W5. On the basis of source identification analyzed by multivariate statistics, Ti, Mn, Fe, and Ni were controlled factors in these two sites, and thus, our study results suggest that the local government should introduce policies to control urban land pollution and industrial emissions.

3.3.2. Health Risk Assessment

Based on the risk assessment guidance [42], HQ and HI values for Cr, Mn, Fe, Ni, Mo, Sb, and Ba by ingestion and dermal pathways for adults and children are shown in Figure 5, and the calculation processes are provided in Supplementary Materials (Tables S2–S6), where the HI values were the sum of HQingestion and HQdermal. The missing values of Ti were caused by the lack of RfDingestion and RfDdermal values. For adults, the HQingestion and HQdermal for all elements were smaller than 1, indicating these elements presented little hazard through ingestion and dermal absorption in the whole drainage area. Comparatively, the HQingestion and HQdermal for children were higher, implying children were more sensitive to the dissolved heavy metals in the water. A health risk assessment showed that the HI values of Sb were closer to 1 (0.09 for adults and 0.17 for children) than the other elements, indicating that Sb might represent a potential threat to the water quality of the river. Therefore, we also suggest that more attention should be paid towards controlling mining activities in order to decrease the levels of Sb inputted into the Jiulongjiang River.

4. Conclusions

This study presented data of dissolved heavy metals (Ti, Cr, Mn, Fe, Ni, Mo, Sb, and Ba) in the Jiulongjiang River water in order to analyze the distribution and sources of pollution in the water, by using multivariate statistical methods, including CA and FA/PCA. We also provided important information that is relevant to local water management officials by assessing water quality and health risk. Our results arrive at the following conclusions:
(i)
Three groups of dissolved heavy metals were categorized: (1) Cr, Ni, and Mo (<1 μg/L); (2) Mn and Sb (1–10 μg/L); (3) Ti, Fe, and Ba (>10 μg/L), in which Ti, Mn, Sb were not within accepted limits at some sites on the basis of guidelines established by the WHO, the US EPA, and China, indicating that they might be the main pollutants of concern in the drainage basin.
(ii)
Based on the spatial distributions and CA results, Ti, Mn, Fe, Ni, and Mo all had a high correlation coefficient, indicating a similar source. The FA/PCA results identified three components that accounted for 79.46% of the total variance. PC 1 suggested that a mixed lithogenic and urban land source contributed to Ti, Mn, Fe, Ni, and Mo; PC 2 explained that Cr, Ni, and Mo were also influenced by the discharge of industrial effluents; Sb had a strong loading on PC 3, which was controlled by mining activities.
(iii)
The results of WQI indicated that the water in the Jiulongjiang River was basically categorized as excellent water, and water in sites W5 and N4 were poorer because of urban land use. HQ and HI values indicated that Sb was a potential threat to human health. Mining activities in the upper reaches of Beixi stream should be closely scrutinized, with preventive actions taken, if needed.

Supplementary Materials

The following are available online at https://0-www-mdpi-com.brum.beds.ac.uk/1660-4601/15/12/2752/s1, Table S1. Original data and water quality index (WQI) results of dissolved heavy metals in the Jiulongjiang River. Table S2. Hazard quotient by ingestion pathway of dissolved heavy metals for adults. Table S3. Hazard quotient by ingestion pathway of dissolved heavy metals for children. Table S4. Hazard quotient by dermal pathway of dissolved heavy metals for adults. Table S5. Hazard quotient by dermal pathway of dissolved heavy metals for children. Table S6. Hazard index of dissolved heavy metals in the Jiulongjiang River.

Author Contributions

Conceptualization: B.L. and G.H.; methodology: B.L.; software: B.L.; validation: G.H.; investigation: G.H., M.L., K.Y., X.L., and J.L.; writing—original draft preparation: B.L.; writing—review and editing: G.H.; supervision: G.H.; project administration: G.H.; funding acquisition: G.H.

Funding

This work was supported jointly by the National Natural Science Foundation of China (No. 41325010; 41661144029).

Acknowledgments

The authors gratefully acknowledge Yupeng Tian for his assistance with field sampling. The authors also thank Jun Li for laboratory assistance. The authors thank the three anonymous reviewers and the editors for the valuable comments and suggestions that greatly improved the quality of this manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Islam, M.S.; Ahmed, M.K.; Raknuzzaman, M.; Habibullah-Al-Mamun, M.; Islam, M.K. Heavy metal pollution in surface water and sediment: A preliminary assessment of an urban river in a developing country. Ecol. Indic. 2015, 48, 282–291. [Google Scholar] [CrossRef]
  2. Kominkova, D.; Nabelkova, J. Effect of urban drainage on bioavailability of heavy metals in recipient. Water Sci. Technol. 2007, 56, 43–50. [Google Scholar] [CrossRef] [PubMed]
  3. Kaushik, A.; Kansal, A.; Meena, S.; Kumari, S.; Kaushik, C.P. Heavy metal contamination of river Yamuna, Haryana, India: Assessment by Metal Enrichment Factor of the Sediments. J. Hazard. Mater. 2009, 164, 265–270. [Google Scholar] [CrossRef] [PubMed]
  4. Chowdhury, S.; Mazumder, M.A.J.; Al-Attas, O.; Husain, T. Heavy metals in drinking water: Occurrences, implications, and future needs in developing countries. Sci. Total Environ. 2016, 569–570, 476–488. [Google Scholar] [CrossRef]
  5. Wu, Q.; Leung, J.Y.S.; Geng, X.; Chen, S.; Huang, X.; Li, H.; Huang, Z.; Zhu, L.; Chen, J.; Lu, Y. Heavy metal contamination of soil and water in the vicinity of an abandoned e-waste recycling site: Implications for dissemination of heavy metals. Sci. Total Environ. 2015, 506–507, 217–225. [Google Scholar] [CrossRef]
  6. Xiao, J.; Wang, L.; Deng, L.; Jin, Z. Characteristics, sources, water quality and health risk assessment of trace elements in river water and well water in the Chinese Loess Plateau. Sci. Total Environ. 2019, 650, 2004–2012. [Google Scholar] [CrossRef]
  7. Shotyk, W.; Bicalho, B.; Cuss, C.W.; Donner, M.W.; Grant-Weaver, I.; Haas-Neill, S.; Javed, M.B.; Krachler, M.; Noernberg, T.; Pelletier, R.; et al. Trace metals in the dissolved fraction (<0.45 μm) of the lower Athabasca River: Analytical challenges and environmental implications. Sci. Total Environ. 2017, 580, 660–669. [Google Scholar] [CrossRef]
  8. Yang, X.; Wang, Z. Distribution of dissolved, suspended, and sedimentary heavy metals along a salinized river continuum. J. Coast. Res. 2017, 33, 1189–1195. [Google Scholar] [CrossRef]
  9. Li, S.; Xu, Z.; Cheng, X.; Zhang, Q. Dissolved trace elements and heavy metals in the Danjiangkou Reservoir, China. Environ. Geol. 2008, 55, 977–983. [Google Scholar] [CrossRef]
  10. Jiang, Y.; Xie, Z.; Zhang, H.; Xie, H.; Cao, Y. Effects of land use types on dissolved trace metal concentrations in the Le’an River Basin, China. Environ. Monit. Assess. 2017, 189, 633. [Google Scholar] [CrossRef]
  11. Rügner, H.; Schwientek, M.; Milačič, R.; Zuliani, T.; Vidmar, J.; Paunović, M.; Laschou, S.; Kalogianni, E.; Skoulikidis, N.T.; Diamantini, E.; et al. Particle bound pollutants in rivers: Results from suspended sediment sampling in Globaqua River Basins. Sci. Total Environ. 2019, 647, 645–652. [Google Scholar] [CrossRef] [PubMed]
  12. Li, Y.; Hisayuki, T.; Yang, T.; Chen, J. The elemental composition of suspended particles from the Yellow and Yangtze Rivers. Geochim. Cosmochim. Acta 1984, 48, 1561–1564. [Google Scholar] [CrossRef]
  13. Salim, R. Adsorption of lead on the suspended particles of river water. Water Res. 1983, 17, 423–429. [Google Scholar] [CrossRef]
  14. Li, R.; Tang, C.; Li, X.; Jiang, T.; Shi, Y.; Cao, Y. Reconstructing the historical pollution levels and ecological risks over the past sixty years in sediments of the Beijiang River, South China. Sci. Total Environ. 2019, 649, 448–460. [Google Scholar] [CrossRef] [PubMed]
  15. Zhang, Z.; Lu, Y.; Li, H.; Tu, Y.; Liu, B.; Yang, Z. Assessment of heavy metal contamination, distribution and source identification in the sediments from the Zijiang River, China. Sci. Total Environ. 2018, 645, 235–243. [Google Scholar] [CrossRef] [PubMed]
  16. Li, S.; Zhang, Q. Spatial characterization of dissolved trace elements and heavy metals in the upper Han River (China) using multivariate statistical techniques. J. Hazard. Mater. 2010, 176, 579–588. [Google Scholar] [CrossRef]
  17. Chirila, E.; Draghici, C.; Puhacel, A. Total and dissolved metals occurrence in municipal wastewater treatment plant effluents. Environ. Eng. Manag. J. 2014, 13, 2218. [Google Scholar] [CrossRef]
  18. Gurung, S.B.; Geronimo, F.K.; Hong, J.; Kim, L. Application of indices to evaluate LID facilities for sediment and heavy metal removal. Chemosphere 2018, 206, 693–700. [Google Scholar] [CrossRef]
  19. Zuzolo, D.; Cicchella, D.; Catani, V.; Giaccio, L.; Guagliardi, I.; Esposito, L.; De Vivo, B. Assessment of potentially harmful elements pollution in the Calore River basin (Southern Italy). Environ. Geochem. Health 2017, 39, 531–548. [Google Scholar] [CrossRef]
  20. Carlson, R.W. Thermal Ionization Mass Spectrometry. In Treatise on Geochemistry; Elsevier: Amsterdam, The Netherlands, 2014; pp. 337–354. ISBN 9780080983004. [Google Scholar]
  21. Bullen, T.D. 7.10—Metal Stable Isotopes in Weathering and Hydrology. In Treatise on Geochemistry, 2nd ed.; Holland, H.D., Turekian, K.K., Eds.; Elsevier: Oxford, UK, 2014; pp. 329–359. ISBN 978-0-08-098300-4. [Google Scholar]
  22. Duruibe, J.O.; Ogwuegbu, M.O.C.; Egwurugwu, J.N. Heavy metal pollution and human biotoxic effects. Int. J. Phys. Sci. 2007, 2, 112–118. [Google Scholar] [CrossRef]
  23. Zhang, Y.; Chen, J.; Wang, L.; Zhao, Y.; Ou, P.; Shi, W. Establishing a method to assess comprehensive effect of gradient variation human health risk to metal speciation in groundwater. Environ. Pollut. 2018, 241, 887–899. [Google Scholar] [CrossRef] [PubMed]
  24. Meng, Q.; Zhang, J.; Zhang, Z.; Wu, T. Geochemistry of dissolved trace elements and heavy metals in the Dan River Drainage (China): Distribution, sources, and water quality assessment. Environ. Sci. Pollut. Res. 2016, 23, 8091–8103. [Google Scholar] [CrossRef] [PubMed]
  25. Sun, W.; Xia, C.; Xu, M.; Guo, J.; Sun, G. Application of modified water quality indices as indicators to assess the spatial and temporal trends of water quality in the Dongjiang River. Ecol. Indic. 2016, 66, 306–312. [Google Scholar] [CrossRef]
  26. Bu, H.; Song, X.; Guo, F. Dissolved trace elements in a nitrogen-polluted river near to the Liaodong Bay in Northeast China. Mar. Pollut. Bull. 2017, 114, 547–554. [Google Scholar] [CrossRef]
  27. Kumar, M.; Ramanatahn, A.L.; Tripathi, R.; Farswan, S.; Kumar, D.; Bhattacharya, P. A study of trace element contamination using multivariate statistical techniques and health risk assessment in groundwater of Chhaprola Industrial Area, Gautam Buddha Nagar, Uttar Pradesh, India. Chemosphere 2017, 166, 135–145. [Google Scholar] [CrossRef] [PubMed]
  28. Hahn, J.; Opp, C.; Evgrafova, A.; Groll, M.; Zitzer, N.; Laufenberg, G. Impacts of dam draining on the mobility of heavy metals and arsenic in water and basin bottom sediments of three studied dams in Germany. Sci. Total Environ. 2018, 640–641, 1072–1081. [Google Scholar] [CrossRef]
  29. Wang, J.; Liu, G.; Liu, H.; Lam, P.K.S. Multivariate statistical evaluation of dissolved trace elements and a water quality assessment in the middle reaches of Huaihe River, Anhui, China. Sci. Total Environ. 2017, 583, 421–431. [Google Scholar] [CrossRef]
  30. Yang, K.; Han, G.; Liu, M.; Li, X.; Liu, J.; Zhang, Q. Spatial and Seasonal Variation of O and H Isotopes in the Jiulong River, Southeast China. Water 2018, 10, 1677. [Google Scholar] [CrossRef]
  31. Zhang, X.; Zheng, Q.; Zhou, L.; Wei, J. Nonpoint pollution source-sink landscape pattern change analysis in a coastal river basin in southeast China. Int. J. Environ. Res. Public Health 2018, 15, 2115. [Google Scholar] [CrossRef]
  32. Tian, Y. The Geochemical Characteristics of Nutrient Elements in River Water in Jiulongjiang Basin, China; University of Geosciences: Beijing, China, 2018. [Google Scholar]
  33. Li, X.; Han, G.; Dong, A.; Liu, M.; Yang, K. Hydrogeochemical characteristics and controlling factors in Jiulong River (Fujian Province) during the flood season. Chin. J. Ecol. 2018, 37, 697–706. [Google Scholar]
  34. Zhang, Y.; Hong, H.; Zeng, Y.; Huang, J.; Huang, Y.; Lin, Y. The environmental problems of livestock raising and its countermeasures in Jiulongjing watershed. Chongqing Environ. Sci. 2003, 25, 29–34. [Google Scholar]
  35. Chen, N.; Wang, L.; Lin, H.; Wu, J.; Liu, T. A spatio-temporal correlation analysis of water quality and economic growth in the Jiulong River Basin. J. Ecol. Rural Environ. 2012, 28, 19–25. [Google Scholar]
  36. Lin, C. Distribution Characteristics and Source Analysis of Trace Elements in Offshore Surface Sediments from Jiulong River; Huaqiao University: Quanzhou, China, 2017. [Google Scholar]
  37. Mao, J.; Xie, G.; Li, X.; Zhang, C.; Mei, Y. Mesozoic large-scale metallogenic pulses in North China and corresponding geodynamic settings. Earth Sci. Front. 2004, 11, 45–55. [Google Scholar]
  38. Zhang, Z.; Zuo, R. Tectonic evolution of southwestern Fujian Province and spatial-temporal distribution regularity of mineral deposits. Acta Petrol. Sin. 2015, 31, 217–229. [Google Scholar]
  39. Topalian, M.L.; Castane, P.M.; Rovedatti, M.G.; Salibian, A. Principal component analysis of dissolved heavy metals in water of the Reconquista river (Buenos Aires, Argentina). Bull. Environ. Contam. Toxicol. 1999, 63, 484–490. [Google Scholar] [CrossRef] [PubMed]
  40. Lemly, A.D. Evaluation of the hazard quotient method for risk assessment of selenium. Ecotoxicol. Environ. Saf. 1996, 35, 156–162. [Google Scholar] [CrossRef]
  41. Tepanosyan, G.; Sahakyan, L.; Belyaeva, O.; Asmaryan, S.; Saghatelyan, A. Continuous impact of mining activities on soil heavy metals levels and human health. Sci. Total Environ. 2018, 639, 900–909. [Google Scholar] [CrossRef]
  42. US EPA. Risk Assessment Guidance for Superfund Volume I: Human Health Evaluation Manual (Part E. Supplemental Guidance for Dermal Risk Assessment); US EPA: Washington, DC, USA, 2004; ISBN EPA/540/1-89/002.
  43. Cai, L.M.; Wang, Q.S.; Luo, J.; Chen, L.G.; Zhu, R.L.; Wang, S.; Tang, C.H. Heavy metal contamination and health risk assessment for children near a large Cu-smelter in central China. Sci. Total Environ. 2019, 650, 725–733. [Google Scholar] [CrossRef]
  44. Zhang, T.; Xu, W.; Lin, X.; Yan, H.; Ma, M.; He, Z. Assessment of heavy metals pollution of soybean grains in North Anhui of China. Sci. Total Environ. 2019, 646, 914–922. [Google Scholar] [CrossRef]
  45. Chinese Ministry of Health. Environmental Quality Standards for Drinking Water Quality (GB 5749-2006); Standards Press of China: Beijing, China, 2006.
  46. Li, Y. A brief discussion on the mean oceanic residence time of elements. Geochim. Cosmochim. Acta 1982, 46, 2671–2675. [Google Scholar] [CrossRef]
  47. Wang, Y. Characteristics of Water Quality and Heavy Metals in Guangdong Section of the Pearl River Basin; South China University of Technology: Guangzhou, China, 2012. [Google Scholar]
  48. Ribeiro, C.; Couto, C.; Ribeiro, A.R.; Maia, A.S.; Santos, M.; Tiritan, M.E.; Pinto, E.; Almeida, A.A. Distribution and environmental assessment of trace elements contamination of water, sediments and flora from Douro River estuary, Portugal. Sci. Total Environ. 2018, 639, 1381–1393. [Google Scholar] [CrossRef] [PubMed]
  49. WHO. Guidelines for Drinking-Water Quality: Fourth Edition Incorporating the First Addendum; WHO: Geneva, Switzerland, 2017; ISBN 978-92-4-154995-0. [Google Scholar]
  50. US EPA. 2012 Edition of the Drinking Water Standards and Health Advisories; US EPA: Washington, DC, USA, 2012.
  51. Helena, B.; Pardo, R.; Vega, M.; Barrado, E.; Fernandez, J.M.; Fernandez, L. Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga River, Spain) by principal component analysis. Water Res. 2000, 34, 807–816. [Google Scholar] [CrossRef]
  52. Gao, L.; Wang, Z.; Shan, J.; Chen, J.; Tang, C.; Yi, M.; Zhao, X. Distribution characteristics and sources of trace metals in sediment cores from a trans-boundary watercourse: An example from the Shima River, Pearl River Delta. Ecotoxicol. Environ. Saf. 2016, 134, 186–195. [Google Scholar] [CrossRef] [PubMed]
  53. Simeonov, V.; Stratis, J.A.; Samara, C.; Zachariadis, G.; Voutsa, D.; Anthemidis, A.; Sofoniou, M.; Kouimtzis, T. Assessment of the surface water quality in Northern Greece. Water Res. 2003, 37, 4119–4124. [Google Scholar] [CrossRef]
  54. Yu, M.D.; He, X.S.; Xi, B.D.; Gao, R.T.; Zhao, X.W.; Zhang, H.; Huang, C.H.; Tan, W. Investigating the composition characteristics of dissolved and particulate/colloidal organic matter in effluent-dominated stream using fluorescence spectroscopy combined with multivariable analysis. Environ. Sci. Pollut. Res. 2018, 25, 9132–9144. [Google Scholar] [CrossRef]
  55. Franco-Uría, A.; López-Mateo, C.; Roca, E.; Fernández-Marcos, M.L. Source identification of heavy metals in pastureland by multivariate analysis in NW Spain. J. Hazard. Mater. 2009, 165, 1008–1015. [Google Scholar] [CrossRef]
  56. Lin, C.Q.; Hu, G.R.; Yu, R.L. Lead pollution and isotopic tracing in intertidal sediments of Jiulong River downstream. China Environ. Sci. 2015, 35, 2503–2510. [Google Scholar]
  57. Huang, Y.; Wu, X.; Zhou, Y.; Lu, Y.; Wang, Z. Research progress of antimony contamination in water environment and remediation techniques. J. Nanjing Norm. Univ. 2015, 38, 122–128. [Google Scholar]
  58. Zhang, D.; Pan, X.; Mu, G. Antimony pollution in water and soil and its ecotoxicological effects on plants and microorganisms. Chin. J. Appl. Environ. Biol. 2010, 16, 891–894. [Google Scholar]
Ijerph 15 02752 g001 550
Figure 1. Regional lithology, mining distribution and sampling sites in the Jiulongjiang River.
Figure 1. Regional lithology, mining distribution and sampling sites in the Jiulongjiang River.
Ijerph 15 02752 g001
Ijerph 15 02752 g002 550
Figure 2. Spatial distributions of dissolved heavy metals in the Jiulongjiang River.
Figure 2. Spatial distributions of dissolved heavy metals in the Jiulongjiang River.
Ijerph 15 02752 g002
Ijerph 15 02752 g003 550
Figure 3. Loading plot of factors or dissolved heavy metals in the Jiulongjiang River.
Figure 3. Loading plot of factors or dissolved heavy metals in the Jiulongjiang River.
Ijerph 15 02752 g003
Ijerph 15 02752 g004 550
Figure 4. Water quality index (WQI) values of water in the Jiulongjiang River.
Figure 4. Water quality index (WQI) values of water in the Jiulongjiang River.
Ijerph 15 02752 g004
Ijerph 15 02752 g005 550
Figure 5. Hazard quotient and hazard index for each element of the Jiulongjiang River.
Figure 5. Hazard quotient and hazard index for each element of the Jiulongjiang River.
Ijerph 15 02752 g005
Table
Table 1. Physicochemical parameters and concentrations of dissolved heavy metals in the Jiulongjiang River and from other places.
Table 1. Physicochemical parameters and concentrations of dissolved heavy metals in the Jiulongjiang River and from other places.
MinMaxMedianMeanSD 1CV 2K-S Test 3Guidelines for Drinking WaterWorldDouroCaloreLe’anPearlYangtzeHan
China 5WHO 6US PEA 7River 8River 9River 10River 11River 12River 13River 14
Water parameters
T (°C)21.531.826.026.52.170.080.452
pH6.427.607.197.160.270.040.9856.50–8.50
Dissolved heavy metals (μg/L)
Tind 4135.0347.3444.3924.760.560.487100 3 2.69
Cr0.056.250.170.451.082.410505010012.1219.7 1.700.268.14
Mn0.06473.701.4240.2586.592.150100400 7 1035 1.062.5330.72
Fe0.8386.0328.1827.4715.730.570.653300300 40 12.5330.64
Ni0.0510.050.381.171.931.650.0012070 0.35.9324.6 1.890.181.71
Mo0.224.600.79 0.850.710.830.01570 615.090.89
Sb0.259.671.091.261.341.06052060.071.980.33 41.58
Ba4.14231.4220.1229.6435.201.190700130020002019.2419444.55 87.47
1 SD: standard deviation; 2 CV: the coefficient of variation; 3 K-S test: the Kolmogorov-Smirnov (K-S) test; 4 nd: no data. Data sources: 5 Chinese Ministry of Health [45]; 6 WHO (the World Health Organization) [49]; 7 US EPA (the United States Environmental Protection Agency) [50]; 8 Li [46]; 9 Ribeiro et al. [48]; 10 Zuzolo et al. [19]; 11 Jiang et al. [10]; 12 Wang [47]; 13 Li et al. [9]; 14 Li and Zhang [16].
Table
Table 2. Pearson correlation matrix of dissolved heavy metals in the Jiulongjiang River.
Table 2. Pearson correlation matrix of dissolved heavy metals in the Jiulongjiang River.
TiCrMnFeNiMoSbBa
Ti1
Cr−0.0141
Mn0.698 **−0.0181
Fe0.996 **0.0100.696 **1
Ni0.630 **0.393 *0.857 **0.632 **1
Mo0.649 **0.2660.726 **0.653 **0.802 **1
Sb−0.1510.032−0.029−0.1110.003−0.0731
Ba0.248−0.0470.2430.257−0.036−0.034−0.0161
*: strong positive correlation coefficients at the 0.05 level; **: strong positive correlation coefficients at the 0.01 level.
Table
Table 3. Varimax rotated component matrix of dissolved heavy metals in the Jiulongjiang River.
Table 3. Varimax rotated component matrix of dissolved heavy metals in the Jiulongjiang River.
VariablesPC 1PC 2PC 3Communalities
Ti0.90−0.24−0.140.88
Cr0.170.710.200.58
Mn0.90−0.080.060.81
Fe0.90−0.23−0.100.87
Ni0.880.370.100.92
Mo0.850.30−0.030.82
Sb−0.080.010.950.91
Ba0.22−0.690.210.57
Eigenvalues (%)4.001.351.00
Variance (%)50.0316.8812.56
Cumulative (%)50.0366.9079.46
PC: principal component. Significance of Kaiser-Meyer-Olkin and Bartlett’s sphericity test is <0.001. Extraction method: Principal component analysis. Rotation method: Varimax with Kaiser normalization. Rotation converges after four iterations.
Table
Table 4. Weights for the variables in the water samples from the Jiulongjiang River.
Table 4. Weights for the variables in the water samples from the Jiulongjiang River.
PCEigenvalue (%)Relative EigenvalueVariableLoading ValueRelative Loading Value on Same PCWeight 1
14.000.63Ti0.900.200.13
Mn0.900.200.13
Fe0.900.200.13
Ni0.880.200.13
Mo0.850.190.12
Total4.431.000.63
21.350.21Cr0.710.510.11
Ni0.370.270.06
Mo0.300.220.05
Total1.381.000.21
31.000.16Sb0.951.000.16
Total0.951.000.16
Total6.36 1.00
1 Weight = relative eigenvalue × relative loading value.

Share and Cite

MDPI and ACS Style

Liang, B.; Han, G.; Liu, M.; Yang, K.; Li, X.; Liu, J. Distribution, Sources, and Water Quality Assessment of Dissolved Heavy Metals in the Jiulongjiang River Water, Southeast China. Int. J. Environ. Res. Public Health 2018, 15, 2752. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph15122752

AMA Style

Liang B, Han G, Liu M, Yang K, Li X, Liu J. Distribution, Sources, and Water Quality Assessment of Dissolved Heavy Metals in the Jiulongjiang River Water, Southeast China. International Journal of Environmental Research and Public Health. 2018; 15(12):2752. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph15122752

Chicago/Turabian Style

Liang, Bin, Guilin Han, Man Liu, Kunhua Yang, Xiaoqiang Li, and Jinke Liu. 2018. "Distribution, Sources, and Water Quality Assessment of Dissolved Heavy Metals in the Jiulongjiang River Water, Southeast China" International Journal of Environmental Research and Public Health 15, no. 12: 2752. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph15122752

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop