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Article

Pollution Characteristics of Heavy Metals in Surface Sediments of the Shuimo River in Urumqi, China

by
Huiying Ma
1,2,
Yidan Zhang
1,2,
Zhidong Liu
1,2,
Yue Chen
1,2 and
Guanghui Lv
1,2,3,*
1
College of Ecology and Environment, Xinjiang University, Urumqi 830017, China
2
Key Laboratory of Oasis Ecology, Urumqi 830017, China
3
Institute of Arid Ecology and Environment, Xinjiang University, Urumqi 830017, China
*
Author to whom correspondence should be addressed.
Metals 2023, 13(9), 1578; https://doi.org/10.3390/met13091578
Submission received: 13 July 2023 / Revised: 19 August 2023 / Accepted: 8 September 2023 / Published: 10 September 2023
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Abstract

:
Heavy metal pollution in the surface sediments of urban rivers has a significant influence on the safety of city residents. This study explores the features of heavy metal pollution in the surface sediments of the Shuimo River and provides a theoretical basis for decision makers regarding river management and restoration. This study uses principal component analysis and kriging interpolation to analyse the pH values and pollution characteristics of nine heavy metals (As, Pb, Zn, Cu, Ni, Fe, Mn, Cr, and V) in 23 surface sediments of the Shuimo River. The results showed that the pH value of the surface sediments along the direction of water flow had a quadratic curve trend. Kriging interpolation revealed consistency in the spatial distribution of heavy metals and Fe, and the peak value was from Qidaowan to Weihuliang. There were significant positive correlations (p < 0.05) between Fe and Pb; Mn, Cr, V, Cu, and Zn; and Mn, Cr, and V. The principal component analysis showed that the main heavy metals in the surface sediments of the Shuimo River were Fe, Zn, Cu, and As. The total amount of heavy metals was in the order of Fe > Mn > Zn > V > Cr > Ni > Cu > Pb > As, ranging from 11.27~18,760.97 mg·kg−1. The cluster analysis classified the nine heavy metals into four categories: Zn and Cu in the first category; Ni in the second; As and Pb in the third; and V, Cr, Mn, and Fe in the fourth.

1. Introduction

Aquatic organisms use sediments as nutrients and habitats. Heavy metals in the water column and suspended particulate matter can be enriched in sediments through processes such as adsorption, complexation, flocculation, and sedimentation and can be re-released into the overlying water when hydrological and environmental conditions change [1]. Because of its adsorption properties, the sediment can adsorb all types of pollutants (including heavy metals) entering the water body and become a sink for heavy metals in the water body, thus reflecting the pollution level of heavy metals in the water body [2,3]; at the same time, sediment is a source of heavy metals in the water body. Due to absorption and enrichment using aquatic plants and animals, heavy metals in surface sediments can also be transferred through the food chain and food web, ultimately threatening human health [4]. Particle size has an important influence on sediment characteristics such as specific surface area and surface free energy, which in turn affect the sorption, desorption, and transport of heavy metals.
Soil weathering and scouring in watersheds and the anthropogenic discharge of domestic and industrial effluents are the main sources of heavy metals in water bodies, with soil weathering being the source of background heavy metal concentrations in the environment [5,6]. Heavy metals entering the water column are reduced in solubility by sediment adsorption, reducing their toxicity [7,8]. When the redox conditions in the sediment itself, such as pH, Eh values, and natural complexing agents change, the bound heavy metals can again assume a dissolved state, and their complexed ions and free molecules are the objects of direct biological uptake. This cyclic sorption–desorption diffusion process causes secondary pollution of the aquatic environment [9,10]. The pollution of water bodies by sediments has become an object of research in environmental science in recent years.
Wang et al. [11] used the cumulative index method, potential ecological risk index method, and risk assessment code method to evaluate the degree of heavy metal pollution and biological effectiveness of sediments for risk assessment and analysed the total amounts of six heavy metals (As, Cd, Cr, Cu, Pb, and Zn) in sediments and their fugitive patterns. Qin et al. [12] found that riverbed sediments play a non-negligible role in the migration and transformation of secondary pollutants in rivers. In recent years, scholars have mostly focused on the sources, migration, characteristics, and biological effects of heavy metal pollution in river surface sediments [13,14,15]. In China, Zeng [16], Li [17], Shan [18], and Ulamu [19] evaluated the water quality status, ecological and environmental changes, and pollution management in the Shuimo River basin; however, studies on the characteristics of sediment heavy metal pollution are lacking. In this study, the characteristics of heavy metal pollution in the surface sediments of urban rivers are investigated using the Shuimo River as the research object, with the aim of providing a basis for the environmental restoration and ecological construction of urban rivers in Urumqi.

2. Overview of the Study Area

Urumqi, the capital of the Xinjiang Uyghur Autonomous Region, is located on the northern slope of the Tianshan Mountains, surrounded by desert, with its specific coordinates ranging from 42°45′32.4″–44°08′00″ N latitude and 86°37′33.3″–88° 58′24.4″ E longitude. The city of Urumqi is approximately 153 km long from north to south and 190 km wide from east to west, covering a total area of 12,000 km2.
The Shuimo River originates in the southwestern part of Fukang City, Changji Hui Autonomous Prefecture, China. The river is mainly located in the eastern suburbs of Urumqi, passing through the Shuimogou, Tianshan, and Midong districts of Urumqi before finally flowing into the Bayi Reservoir, a rocky fissure–gushing river mainly recharged using groundwater. The watershed ranges from E87°59′ to E88°09′ and N43°45′ to N44°15′. The river basin covers an area of 281.4 km2, the water source protection zone covers an area of 45.7 km2, and the river is 50 km long, of which 18 km lies within the territory of Urumqi City and is an important part of the surface water body. The northern part of the basin is a plains area, the central part has low hills, and the southeastern part has medium to low mountains with elevations between 600 and 1800 m [20]. The surface water resources of the Shuimo River are 21,608,900 m3/year, and the underground water resources are 34,475,900 m3/year. The annual runoff was relatively stable, and there were no obvious dry periods, with an average runoff of 39,670,000 m3/year (at the Weihuliang observation station) [21]. The Shuimo River flows through densely populated areas with developed industries and suburban areas with developed agriculture and animal husbandry. It is also an important scenic tourist area in Urumqi. The environmental quality of the river water in the Shuimo River has gradually deteriorated due to the yearly remittance of nearly 10 million m3 of production and domestic water along both sides of the river in the past 30 years. Through the analysis of heavy metals in the surface sediments of the river basin, reasonable measures were proposed to improve the environmental quality of the Shuimo River water. A detailed sampling site location map is shown in Figure 1.

3. Materials and Methods

3.1. Sample Collection and Preservation

Sediment samples were collected at each sampling point along the nearshore within a 10 m length of the parallel river direction, with gentle currents as far as possible, using the plume-mixed sampling method to randomly take sediment that is 0–10 cm deep five times for mixing as one sample. A total of 23 sediment samples were collected from the surface layer of the bottom sediment along the river channel [22].
The samples were collected in clean Teflon sealed bags, numbered, and stored at 4 °C in the laboratory, then stored at −20 °C in the refrigerator. The surface sediment samples were air dried under natural conditions for 3 days, crushed to remove foreign matter, baked at 105 °C for 24 h to a constant weight, and passed through a 2 mm (10 mesh) nylon sieve. The sediment samples were ground in a mortar, passed through a 0.075 mm (200 mesh) nylon sieve, and tested.

3.2. Analytical Methods

The pH was determined using an acidity meter, and the samples were digested with HNO3-HClO4-HF. Cu, Zn, Ni, and Fe were determined using flame atomic absorption spectrophotometry (GB/T17138-1997) [23] (Perkin Elmer AA900 Atomic Absorption Spectrometer, Waltham, MA, USA), Pb and Mn were determined using graphite furnace atomic absorption spectrophotometry (GB/T17141-1997) [24], and As was determined using the microwave digestion/atomic fluorescence method (Aurora Lumina Model 3400 Atomic Fluorescence Spectrometer, Aurora Instruments, Vancouver, BC, Canada) [25]. To ensure accuracy, the national-level standards GSS-25 and GSS-28 were used as quality control standards. Parallel samples were prepared for each sample, and the analytical error of the parallel samples was <5%.

3.3. Data Processing

The clustering analysis of heavy metals in the sediments was performed using SAS 9.4m6 (Statistical Analysis System) statistical analysis software, and the spatial analysis of heavy metal concentrations and enrichment coefficients in the surface sediments was performed using Surfer 24 software with kriging interpolation.

4. Results and Analysis

4.1. Surface Sediment pH Characteristics

The pH of the surface sediments in the Shuimo River Basin was alkaline, with a range of (7.5–8.5) and a quadratic curve of (R2 = 0.384, p < 0.01) in the direction of the water flow (Figure 2). The overall decreasing trend up to the 11th sample point (Liudaowan Mine) was followed by a gradual increase, probably owing to the high concentration of acidic water with sulphate and free sulphuric acid in the mine. The magnitude of the pH decrease varied between river sections, reflecting the diversity of pH variability in the surface sediments of the Shuimo River. Similar results were found in Zhang’s investigation of pollution in the Shuimo River, which found that water quality in the Shuimo River Basin was mainly influenced by alkaline substances discharged from paper, textiles, and factories [21].

4.2. Heavy Metal Content and Spatial Distribution of Surface Sediments

From the statistical results of all valid sampling points (Table 1), it can be seen that the coefficient of variation (50.57) of Zn was the largest, indicating that its distribution was the most dispersive; the variation range of Fe was 5670.47–25,729.74 mg·kg−1, with a mean value of 18,760.97 mg·kg−1; the standard deviation and maximum value of As and Pb were smaller, and the data distribution was relatively less dispersive. The standard deviations and maximum values of As and Pb were small, and the dispersion of the data distribution was relatively small; Cu, Ni, Mn, Cr, and V, the five heavy metals, had approximately the same degree of dispersion. The skewness and kurtosis of all nine heavy metals were greater than zero, indicating that the data distribution was more concentrated than normal.
Figure 3 shows the contour distribution of the nine heavy metals in the surface sediments of the Shuimo River. From Figure 3 and Table 1, it can be seen that the content of the heavy metal Zn gradually increases along the direction of the river, reaches the maximum value, and then decreases. Its coefficient of variation reaches 50.57%, and the maximum difference of heavy metals is 332.27 mg·kg−1 in the whole horizontal area. For the heavy metal element Cu content, the overall performance shows that with the increase in latitude, its content shows a trend of increasing and then decreasing, then increasing to reach the maximum, and then gradually decreasing again; Figure 3 shows that the maximum Zn and Cu contents both appear in the same area, which is consistent with the results of the cluster analysis (Figure 8). This may be because the confluence belongs to the administrative district, with a high population density and frequent human activities, and is downstream of the industrial area, where many kinds of domestic and industrial wastewater are injected into the Shuimo River, which is influenced by human factors.
The coefficient of variation (15.93) of the heavy metal element Ni is the smallest, and it can be seen from Figure 3 that most values appear in two places where the intersection occurs, likely because it forms differently than the three above. The results of the cluster analysis support this.
As and Pb increased along the course of the water flow, which may be due to the discharge of factory wastewater near the water source, causing the heavy metal content to increase downstream.
The coefficient of variation of Fe was relatively small, but its content was the largest (25,729.74 mg·kg−1), and its distribution tended to be homogeneous throughout the study area. The distribution of Mn, Cr, and V was not obvious in the whole area, and their contents varied from high to low. The coefficients of variation of all three elements were at a relatively medium level, which might be caused by belonging to one category of pollution sources, and they were consistent with the results of cluster analysis.
From the above analysis, it can be concluded that the main heavy metal pollution source was in the vicinity of Qidaowan to Weihuliang, likely because of the distribution of paper and plastic processing, plants, farms, bleaching and dyeing plants, and other pollution sources in the vicinity. The main pollutants affecting the water quality of this river section, such as suspended solids, significantly increased. In the vicinity of the coal mine in Weihuliang, river water is turbid and contains a large number of impurities such as plastic bags, pieces of cloth, and beverage bottles. As a surface river, many sections of the Shuimo River flow through residential areas, and the random disposal and accumulation of residents’ household wastes has a large impact [26].

4.3. Major Heavy Metal Contaminants in Surface Sediments

4.3.1. Correlation of Major Heavy Metal Pollutants

Nine heavy metal pollutants in the surface sediments of the Shuimo River were selected for analysis, and correlation matrices were established between the factors, with the scales between different environmental qualities reflecting the correlation between groups and factors. The correlation matrices for the evaluation factors are listed in Figure 4. There was a significant correlation with Pb (p < 0.05) (Figure 4) while it showed a weak correlation with the other heavy metals. This may be related to the main source of pollution in the Shuimo River, as the other heavy metals are mostly in the form of sulphate, while the heavy metal salts of sulphuric acid are not easily decomposed. Pb also showed a significant correlation with Fe (p < 0.05), a weak correlation with other heavy metals, and a highly significant correlation with Zn and Cu (p < 0.01), which was consistent with the results of the principal component analysis. Fe showed a highly significant correlation (p < 0.01) with Mn and V and a significant correlation (p < 0.05) with Cr, which may be because Fe and most sulphates tend to form coordination compounds and thus pollute water sources. Mn showed a significant correlation (p < 0.05) with Cr and V, while Cr showed the same positive correlation (p < 0.05) with V. Cr pollution in the Shuimo River Basin is mainly related to input in the river and may originate from the presence of heavy metal pollutants such as Mn, Cr, and V in adjacent industrial wastewater discharge.

4.3.2. Determination of Major Pollutants

Corresponding to the correlation coefficient matrix, the Jacobian method was used to determine the non-negative eigenroots of the characteristic equation. The eigenroots and unit eigenvectors are denoted as λ1 ≥ λ2 ≥ …… ≥ λp > 0 and u1 ≥ u2 ≥ …… ≥ up, respectively. The Jacobian method is a generalisation of the Lagrangian order Chapit method used to find first-order nonlinear equations with n independent variables. This method has a simple formulation: only one matrix and vector multiplication must be computed at each iteration, and the original matrix A is always constant during the computation, which is relatively easy to perform.
Figure 5a–i represent the eigenvectors corresponding to each metal element from the principal component (1–9), respectively. From Figure 5, it can be seen that the largest contributors to principal components 1–9, respectively, are Fe, Zn, and Cu; As, Ni, Pb, and Cr; Mn and V; Zn and Cu; Pb and Cr; and Fe.
Figure 6 shows the cumulative contribution of the variance, and Figure 7 shows the R eigenvalues of the nine factors. The eigenvectors corresponding to the main pollutants in Figure 6 were taken from the top m factors according to the requirement of the cumulative contribution of variance, i.e., ∑ i = 1 m λ i / ∑ i = 1 p λ i = 70 % . Figure 6 shows that the cumulative contribution of the first three principal factors, instead of the original nine factors, is 72.53%. Therefore, the information on the original factors can be largely retained by taking the first three principal components for factor loading analysis.
The results show that component 1 contributes the most to Fe, component 2 contributes mainly to Zn and Cu, and component 3 contributes the most to As. It can be seen that the surface heavy metal pollutants in the Shuimo River are mainly Fe, Zn, Cu, and As. This may be directly related to the industrial wastewater discharged by factories along the river, as there are several factories located near the watershed of the Shuimo River, including steel plate factories, furniture factories, antiseptic wood factories, the Nanhu paper industry, Weihuliang coal mines, Qidaowan Industrial Park, linoleum paper factories, and Midong Industrial Park. These include steel plate mills, furniture mills, antiseptic wood mills, Nanhu paper mills, Vikhuliang coal mines, Qidaowan Industrial Park, Linoleum paper mills, and Midong Industrial Park. Each factory produces highly toxic and difficult-to-treat wastewater. The cooling water discharged from the power industry can seriously affect the living environment of aquatic organisms in the river due to high water temperatures and can cause abnormal colours and odours in the river, while the paper and alkali processes also discharge coloured, odorous wastewater containing foam, oil, and floating matter. Industrial wastewater discharge is one of the main sources of pollution in the Shuimo River Basin [27].

4.4. Cluster Analysis of Total Surface Sediment Heavy Metals

The nine heavy metal elements in the surface sediments have similar combination characteristics and can be divided into four categories according to the results of cluster analysis (Figure 8).
The first category was Zn and Cu, the second was Ni, the third was As and Pb, and the fourth was V, Cr, Mn, and Fe. The heavy metal in the first category, due to the influence of the underground storage medium and the groundwater in the process of movement, dissolved into the medium. The high values of some heavy metals in the sediment, due to the influence of the underground storage medium and different combination characteristics of Ni and other heavy metals, indicate the possibility of anthropogenic pollution influence in addition to the natural sources of the river.

5. Discussion

5.1. Analysis of the Pollution Status and Sources of Heavy Metals in Surface Sediments

The content and distribution characteristics of heavy metals in urban river surface sediments are usually a comprehensive reflection or record of the relative enrichment of heavy metals on the surface formed by anthropogenic discharge and natural accumulation in the area, which is the main clue for the management of heavy metal pollution. Spatial differences in the discharge patterns and intensities of pollution sources can also lead to the uneven distribution of heavy metals in the surface sediments of urban rivers. The results showed that the total amount of each heavy metal ranked from largest to smallest was as follows: Fe > Mn > Zn > V > Cr > Ni > Cu > Pb > As. The principal component analysis (Figure 5) revealed that the heavy metal pollutants in the surface sediment of the Shuimo River were mainly Fe, Zn, Cu, and As. Among them, Fe was the largest contributing element to principal component 1, which may be directly related to the distribution of factories along the river. For example, iron and steel plants near the Shuimo River Basin may be the main source of Fe in sediments and may be the main cause of the highest Fe content among all elements, which is similar to the results of Luo [28]. Second, heavy metals discharged from furniture factories, paper mills, coal mines, and machinery factories during production and processing may enter the Shuimo River Basin with groundwater, precipitation, and surface runoff, thus causing Zn, Cu, and As to be the main pollutants in an area. The correlation coefficients of the nine heavy metals (Figure 4) revealed that Fe had a significant positive correlation with Pb, Mn, Cr, and V. However, the significant correlation between heavy metals in sediments reflects the similarity of metal element pollution sources to some extent. Therefore, Fe, Pb, Mn, Cr, and V in the surface sediments of the Shuimo River may share the same sources, such as iron and steel mills and machine shops. Previous studies have shown that the main metal pollutants generated during the production and processing of steel smelting and machinery are Fe, Mn, Cr, and Pb. Normally, heavy metal pollutants from industrial activities are deposited downstream with river flow and the deposition distance may vary with river volume, water velocity, and channel width. By comparing the spatial distribution of the heavy metal content in the surface sediment of the Shuimo River (Figure 3) with the industrial distribution in the field survey, it is easy to see that the enrichment of each metal occurred at or near downstream from the plant. This is consistent with the results of heavy metal pollution in Jiaozhou Bay reported by Liang et al. [29]. The presence of various types of factories causes heavy metals to enter the surrounding soil through gas emissions, rainfall, and free deposition. Soil particles contaminated with heavy metals converge with surface runoff into near-shore water bodies and eventually settle into the water sediments, resulting in the enrichment of heavy metals in the sediments [30].

5.2. Suggestions for the Control and Management of Heavy Metal Pollutants in Surface Sediments

This study provided a comprehensive investigation of heavy metal pollutants in the surface layer of the Shuimo River and favourable data to support pollution control. Therefore, the management of heavy metal pollution in this region is urgent. First, heavy metal pollution in river wetlands can enter plants and animals through the enrichment of the food chain, thus endangering the entire ecosystem function and process. Second, because the river eventually flows into the Taqiaowan Reservoir, heavy metal pollution in the upper reaches of the river poses a serious threat to the ecological safety of the reservoir. Therefore, the monitoring of the placement and discharge of the “three wastes” from the industrial areas around the Shuimo River basin should be strengthened and the distance between the various factories and the riparian zone should be controlled to reduce the amount of pollutants produced by industry entering the river through various means (such as precipitation and surface runoff). Vegetation has a certain absorption and adsorption effect on heavy metals; therefore, increasing the greening of the riparian zone can reduce the enrichment of heavy metals in river surface sediments.

6. Conclusions

This paper used principal component analysis and kriging interpolation to analyse the pH values of 23 surface sediments and the pollution characteristics of nine heavy metal elements in the Shuimo River Basin, and the main conclusions are as follows:
  • The pH values of the surface sediments along the direction of the water flow showed a quadratic curve trend, first decreasing and then increasing;
  • The kriging results showed that the spatial distribution of all heavy metals, except Fe, was consistent with the peaks occurring from Qidaowan to Weihuliang;
  • The correlation analysis revealed significant positive correlations between Fe and Pb; Mn, Cr, and V; Cu and Zn; and Mn, Cr, and V (p < 0.05);
  • The principal component analysis of nine heavy metal pollutants in 23 surface sediments showed that Fe, Zn, Cu, and As were the main heavy metal pollutants in the surface sediments of the Shuimo River. The ranking results of the total amount of each heavy metal were Fe > Mn > Zn > V > Cr > Ni > Cu > Pb > As;
  • The R-type clustering results showed that the nine heavy metal pollutants were classified into four categories: the first category was Zn and Cu, the second category was Ni, the third category was As and Pb, and the fourth category was V, Cr, Mn, and Fe. The two heavy elements of the first group may be the result of chemical reactions between domestic sewage and long-term biodegradable acid and alkaline salt pollutants in municipal waste, organic pollutants, carbonate soils, and sediments as environmental backgrounds. Ni and As have different chemical combinations with other metals, indicating that the river bottom sediments may be affected by human activities in addition to the natural materials of the river and contaminants.

Author Contributions

Conceptualization, H.M. and Y.Z.; Methodology, G.L.; Software, H.M., Y.Z. and G.L.; Validation, H.M. and Z.L.; Formal analysis, H.M., Y.Z., Z.L. and Y.C.; Investigation, Z.L. and Y.C.; Resources, H.M., Z.L. and Y.C.; Data curation, Z.L. and G.L.; Writing—original draft, H.M.; Writing—review & editing, H.M., Y.Z., Z.L., Y.C. and G.L.; Visualization, Y.Z. and G.L.; Supervision, Z.L., Y.C. and G.L.; Project administration, Y.C. and G.L. All authors have read and agreed to the published version of the manuscript.

Funding

Xinjiang University, School-level Doctoral Initiation: 620312339.

Data Availability Statement

The data presented in this study are available on request from the cor responding author. The data are not publicly available due to the data involve sensitive information such as the environmental status of Urumqi.

Acknowledgments

We would like to thank Li Ma from the Xinjiang Uygur Autonomous Region Grassland General Station for being instrumental in our use of the analytical methods. For this, we are very grateful.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Distribution of sampling sites.
Figure 1. Distribution of sampling sites.
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Figure 2. pH value of sampling point.
Figure 2. pH value of sampling point.
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Figure 3. Concentrations of heavy metals in surface sediments of Shuimo River.
Figure 3. Concentrations of heavy metals in surface sediments of Shuimo River.
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Figure 4. Correlation coefficient of the total amount of different heavy metals in sediments. (* Indicates a significant correlation at the 0.05 level (two-sided); ** means a significant correlation at the 0.01 level (two-sided)).
Figure 4. Correlation coefficient of the total amount of different heavy metals in sediments. (* Indicates a significant correlation at the 0.05 level (two-sided); ** means a significant correlation at the 0.01 level (two-sided)).
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Figure 5. Eigenvectors corresponding to the main pollutants. ((a–i) represent the eigenvectors corresponding to each metal element from the principal component 1–9, respectively.)
Figure 5. Eigenvectors corresponding to the main pollutants. ((a–i) represent the eigenvectors corresponding to each metal element from the principal component 1–9, respectively.)
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Figure 6. Contribution of R (relational clustering).
Figure 6. Contribution of R (relational clustering).
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Figure 7. Eigenvalues of R.
Figure 7. Eigenvalues of R.
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Figure 8. R-type cluster tree of heavy metals in surface sediments of the Shuimo River.
Figure 8. R-type cluster tree of heavy metals in surface sediments of the Shuimo River.
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Table 1. Statistics of heavy metals in surface sediments of the Shuimo River.
Table 1. Statistics of heavy metals in surface sediments of the Shuimo River.
ElementMean Value (mg/kg)Standard DeviationMin. Value (mg/kg)Maximum Value (mg/kg)Coefficient of Variation (%)SkewnessKurtosis
As11.272.676.1617.4123.712.476.34
Pb18.607.047.8943.0137.872.084.61
Zn116.6458.9862.70332.2750.572.586.88
Cu41.5312.9025.4480.5731.062.476.41
Ni45.217.2033.7060.9315.932.627.10
Fe18,760.974082.845670.4725,729.7421.762.707.43
Mn441.9899.51299.14641.0722.512.576.93
Cr54.0317.2221.1591.7231.882.586.88
V97.5226.1041.33136.2826.772.496.57
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Ma, H.; Zhang, Y.; Liu, Z.; Chen, Y.; Lv, G. Pollution Characteristics of Heavy Metals in Surface Sediments of the Shuimo River in Urumqi, China. Metals 2023, 13, 1578. https://doi.org/10.3390/met13091578

AMA Style

Ma H, Zhang Y, Liu Z, Chen Y, Lv G. Pollution Characteristics of Heavy Metals in Surface Sediments of the Shuimo River in Urumqi, China. Metals. 2023; 13(9):1578. https://doi.org/10.3390/met13091578

Chicago/Turabian Style

Ma, Huiying, Yidan Zhang, Zhidong Liu, Yue Chen, and Guanghui Lv. 2023. "Pollution Characteristics of Heavy Metals in Surface Sediments of the Shuimo River in Urumqi, China" Metals 13, no. 9: 1578. https://doi.org/10.3390/met13091578

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