Rare earth elements are widely used in the study of source tracing and geochemical characteristics, which can reflect the degree of influence of human activities on water environment. In order to reveal the additive effects of various human activities on the geochemical characteristics of rare earth elements in the surface water environment, the content of rare earth elements in the surface river water in the coal-grain complex area of eastern China was determined by inductively coupled plasma mass spectrometry (ICP-MS). The results showed that all water samples were alkaline, ΣREE content ranged from 0.18 ~ 0.88 µg·L-1, with an average value of 0.28 µg·L-1. All of the water samples showed significant enrichment of light rare earth elements due to a combination of natural processes and external inputs. The spatial distribution and fractionation characteristics of rare earth elements show that rare earth elements (especially Ce) are prone to adsorption/complexation reactions with colloidal particles, resulting in their removal from water, Eu anomaly is related to plagioclase crystallization and human activities (coal development, agricultural production, urbanization process), and Gd anomaly is only related to human activities. The research results can provide scientific reference for revealing the distribution of rare earth elements and geochemical environmental behavior in surface water environment under the superimposed influence of various human activities.
Research Article
Geochemical characteristics and influencing factors of dissolved rare earth elements in rivers of coal-grain composite areas in eastern China
https://doi.org/10.21203/rs.3.rs-3227184/v1
This work is licensed under a CC BY 4.0 License
Journal Publication
published 11 Jan, 2024
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Coal-grain compound zone
Rivers
Rare earth elements
Geochemical characteristics
Influencing factors
Rare earth elements (REEs) are a group of combined metallic elements that possess unique and similar geochemical properties (Wang et al., 2014). In hydrogeochemical research, they can be further classified into light rare earth elements (LREEs) with atomic numbers ranging from 57 to 63 and heavy rare earth elements (HREEs) with atomic numbers ranging from 64 to 71 (Louis et al., 2020). Rare earth elements exhibit high electropositivity and primarily exist in the form of REE3+ in water bodies. However, the chemical properties of two rare earth elements, Ce and Eu, are distinctive and sensitive. Under certain specific environmental conditions, they can exist in the chemical form of Ce4+ and Eu2+ respectively (Leybourne et al., 2000), exhibiting positive or negative anomalies in their concentrations. This phenomenon helps explain rock weathering and material transport processes (Ingri et al., 2000). Additionally, rare earth elements are always present in combination with geological materials, and their compositions remain unchanged throughout geochemical processes, including weathering, sedimentation, and diagenesis (Yang et al., 2002). Therefore, rare earth elements are widely applied in provenance tracing, water-rock interactions, identification of weathering processes, geochemical migration, and environmental research (Berglund et al., 2019; Pereto et al., 2020). Furthermore, the concentration of rare earth elements in water bodies can also reflect the cumulative effects of natural processes and human activities.
Rare earth elements (REEs), as trace elements, play an irreplaceable role in various fields such as military, industry, agriculture, and healthcare. In industry, rare earth elements are used in weapon systems, automotive manufacturing, glass and metallurgical additives, pigments, rechargeable batteries, color televisions, flat panel displays, mobile phones, optical fibers, and more (Arvidsson et al., 2017; Migaszewski et al., 2015; Savvilotidou et al., 2015). In agriculture, rare earth elements are used as fertilizers and feed additives to promote plant or livestock growth, increase crop yield and quality (Diatloff et al., 1999; Hu et al., 2004). In medicine, rare earth elements such as Gd are commonly used as contrast agents in clinical and diagnostic magnetic resonance imaging (MRI) (Goullé et al., 2012; Lerat-Hardy et al., 2019). Other rare earth elements like Yb are also utilized in the development of cancer treatment drugs (Sato et al., 2006; Lu et al., 2017).
In nature, rare earth elements primarily enter various surface water, groundwater, soil, and sediment through processes such as rock weathering, erosion, transportation, and adsorption complexation (Wilkin et al., 2021; Zhang and Liu, 2004). On the other hand, with the continuous development of industrial and agricultural production, an increasing amount of rare earth elements enters surface water environments through anthropogenic means. For example, large quantities of mine water and coal preparation wastewater generated from coal mining activities (Miranda et al., 2022; Pazand et al., 2015) and phosphorus-containing fertilizers used in agricultural activities (Diatloff et al., 1999; Bispo et al., 2021) contain rare earth elements. They can enter surface rivers through discharge and surface runoff, leading to the continuous accumulation of exogenous rare earth elements. Excessive rare earth elements have been identified as potential micropollutants in water bodies, particularly in rivers used as sources of drinking water (Atinkpahoun et al., 2020; Gwenzi et al., 2008).
In the northern region of Anhui Province, China, there are six large and medium-sized cities experiencing rapid urbanization. Simultaneously, this area has vast plains with fertile soil, and agricultural production is active. The cities of Huainan and Huaibei in the region possess high-quality underground coalfields with significant reserves. They were once one of China's five major coal production bases and have a mining history of nearly a century. Coal mining, agricultural production, and the urbanization process will have cumulative effects on the geochemical characteristics of rare earth elements in surface water environments. However, current research has not clearly revealed the characteristics and degree of influence of rare earth elements under the combined effects of multiple factors. Therefore, this study aims to investigate the rare earth elements in surface rivers in this region, analyze and explore the migration and differentiation of rare earth elements, and reveal the cumulative effects of multiple external factors on the chemical characteristics of rare earth elements. The research findings can be used to trace the geochemical behavior of surface water based on its geochemical characteristics and to monitor its concentration. This will provide fundamental data and scientific basis for comprehensive ecological and environmental management in the region.
2.1. Study area
The northern region of Anhui Province is located north of the Huai River and south of the Yellow River floodplain (114°55′E ~ 118°10′E, 32°25′N ~ 34°35′N). It is characterized by predominantly flat terrain, with thick Quaternary loose layers, a high water table, and a dense network of rivers. The western and southwestern parts of the region are low mountainous areas, while the rest is vast plains (Ju et al., 2012). The study area lies in the transitional zone of the north-south climate in China (Yang et al., 2012), with a warm temperate monsoon climate. The annual average temperature ranges from 14 ~ 15°C, and the annual average precipitation is between 750 and 900 mm. The annual average evaporation is 1000 to 1300 mm. The main sources of water supply are natural precipitation in the plains, river water, and groundwater. The surface rivers within the region belong to the Huai River system, with major tributaries including the Sui River, Ying River, Xifeng River, Ci River, Wo River, Hui River, and Tuo River. These rivers are distributed in the northern part of the Huai River and are characterized as original rain-fed rivers (Yu et al., 2015). The region is home to six large and medium-sized cities, with a total population of over 26 million. The plain land area in the northern part of Anhui covers a total area of 37,400 km2, with a cultivated land area of 20,178 km2, accounting for 36.3% of the province's cultivated land area. The main crops grown are wheat, corn, soybeans, and rice, making it an important base for commodity grain production. Additionally, the two coalfields in Huainan and Huaibei are important energy bases in China, with proven coal reserves of 30 billion tons. The gray area in Fig. 1 represents the coalfield, with a total of 35 underground coal mines. The main coal seams being mined are from the Carboniferous and Permian systems, with an annual output of over 100 million tons of high-quality coal. The coal industry is supported by coal preparation plants, coking plants, and coal-fired power plants. In terms of land use, the study area is predominantly comprised of farmland and cultivated land, followed by residential areas and forest vegetation, with the remaining portion covered by grassland.
2.2. Sample collection and analysis
Sample collection will be conducted during the high water period in August 2022. Considering both convenience and sample representativeness, a total of 32 water samples will be collected from rivers in the study area. The distribution of sampling points is shown in Fig. 1, 4 points along the Ying River (Yh1 ~ Yh4), 4 points along the Xifeng River (Xfh1 ~ Xfh4), 5 points along the Wo River (Gh1 ~ Gh5), 5 points along the Hui River (Hh1 ~ Hh5), 4 points along the Tuo River (Th1 ~ Th4), 3 points along the Xinbian River (Xbh1 ~ Xbh3), and 2 points along the Sui River (Sh1 ~ Sh2).Water samples will be collected near the centerline of the rivers using 1L glass water samplers. Prior to formal sample collection, the samplers will be rinsed with water three times. The subsequent water samples will be collected in pre-acid-washed polyethylene bottles and their corresponding physicochemical parameters, such as water temperature (T), pH, electrical conductivity (EC), dissolved oxygen (DO), oxidation-reduction potential (Eh), and total dissolved solids (TDS), will be measured on-site using portable instruments. The collected water samples will be filtered through cellulose acetate membranes with a pore size of 0.45µm and acidified with high-purity HNO3 to reach a pH of ≤ 2. The samples will then be sealed to isolate them from air and protected from light during transportation back to the laboratory. They will be stored at 4℃ until further testing.
Inductively Coupled Plasma Mass Spectrometry (ICP-MS) is known for its high sensitivity, fast detection speed, and capability to quantitatively analyze dozens of elements within minutes. It has the advantage of simple sample pre-treatment, minimal interference, and straightforward spectral lines (He et al., 2021). In this study, we used an Inductively Coupled Plasma Mass Spectrometer (ICP-MS, Agilent7500cx) to analyze rare earth elements and other cations dissolved in water samples. The ICP-MS is equipped with a Shield Torch, which significantly improves the analysis sensitivity. The experimental water used for testing was MilliQ ultrapure water with a resistance of 18.2 MΩ · cm. Nitric acid was a high-purity reagent, purified by sub-boiling distillation. The water samples to be tested were transferred to clean polyethylene terephthalate (PET) bottles, and indium (In) was added as an internal standard to achieve a concentration of 10 ng/mL in the water samples. The samples were then ready for analysis using the ICP-MS. To ensure the reliability of the test results and minimize the impact of sensitivity drift, a quality control (QC) standard solution of known concentration was measured every 10 samples during the testing process for monitoring and calibration.
2.3. Statistical analysis
The analysis of REEs concentrations in the water of the study area rivers was conducted. To determine the enrichment degree of REEs in the river water, the REEs concentrations were standardized against the average concentration in the North American Shale Composite. This standardization process resulted in REEs distribution patterns. Logarithmic transformations were applied to the REEs concentrations in the water samples before performing statistical analysis using multivariate statistical techniques to obtain distribution images. Eu and Ce anomalies were calculated using relevant formulas to describe their abnormality levels. To identify the controlling factors of REEs, linear regression equations were used for fitting to assess the correlation between different factors and REEs. Sampling point maps were generated using ArcGIS 10.7 software, while all statistical analyses and processing were performed using Origin 2022b software.
3.1. Content of rare earth elements
| µ | ||||||
|---|---|---|---|---|---|---|
| Mean | Min | Max | SD | CV(%) | China Rivera | |
| La | 0.0209 | 0.0145 | 0.0609 | 0.0094 | 44.93 | 0.0476 |
| Ce | 0.0095 | 0.0009 | 0.0933 | 0.0165 | 173.79 | 0.1000 |
| Pr | 0.0075 | 0.0062 | 0.0173 | 0.0019 | 25.78 | 0.0118 |
| Nd | 0.0081 | 0.0029 | 0.0435 | 0.0071 | 87.27 | 0.0478 |
| Sm | 0.0146 | 0.0083 | 0.0742 | 0.0115 | 78.64 | 0.0095 |
| Eu | 0.1984 | 0.0676 | 1.6230 | 0.2747 | 138.44 | 0.0098 |
| Gd | 0.0286 | 0.0111 | 0.0904 | 0.0175 | 61.13 | 0.0098 |
| Tb | 0.0004 | 0.0000 | 0.0027 | 0.0005 | 128.75 | 0.0018 |
| Dy | 0.0023 | 0.0007 | 0.0081 | 0.0016 | 70.00 | 0.0084 |
| Ho | 0.0030 | 0.0025 | 0.0056 | 0.0006 | 21.42 | 0.0024 |
| Er | 0.0031 | 0.0011 | 0.0097 | 0.0020 | 65.53 | 0.0094 |
| Tm | 0.0011 | 0.0006 | 0.0029 | 0.0005 | 49.00 | 0.0014 |
| Yb | 0.0101 | 0.0066 | 0.0190 | 0.0029 | 28.67 | 0.0089 |
| Lu | 0.0011 | 0.0000 | 0.0034 | 0.0009 | 80.68 | 0.0017 |
| LREE | 0.2278 | 0.1360 | 0.7677 | 0.1447 | 0.6351 | 0.23 |
| HREE | 0.0496 | 0.0276 | 0.1097 | 0.0197 | 0.3965 | 0.04 |
| ∑REE | 0.31 | 0.17 | 1.88 | 0.3047 | 98.65 | 0.27 |
| LREE/HREE | 4.97 | 2.30 | 16.12 | 2.54 | 51.19 | 5.17 |
| LaN/YbN | 0.21 | 0.12 | 0.43 | 0.09 | 0.41 | 0.52 |
| δEu | 38.68 | 15.21 | 87.01 | 14.73 | 38.09 | 4.46 |
| δCe | 0.13 | 0.02 | 0.43 | 0.12 | 95.59 | 0.92 |
| δGd | 6.27 | 1.46 | 18.83 | 4.19 | 66.90 | 0.96 |
| Gdanthr(ng·L− 1) | 23.61 | 8.80 | 55.80 | 0.0157 | 66.92 | 0.625 |
| Gdanthr% | 80.08 | 38.70 | 95.20 | 0.1114 | 13.91 | 6.38 |
| a Zhou et al., 2012 | ||||||
Table 1 summarizes the REEs concentrations in the rivers of the study area. From Table 1, it can be observed that the ∑REE concentrations in the rivers of the study area range from 0.18 to 0.88 µg·L-1, with an average of 0.28 µg·L-1. This overall average falls within the average level of Chinese rivers and is considerably higher than the average REEs concentration (∑REE = 27.90 ng·L-1) in the Yangtze River at the Wuhan section (Wang et al., 2021). It is also comparable to the average REEs concentration (∑REE = 0.27 µg·L-1) in eastern Chinese rivers (Zhou et al., 2012) and the average REE concentration (∑REE = 0.25 µg·L-1) in approximately 500 rivers in eastern Canada (Leybourne et al., 2008). However, it is lower than the average REEs concentration (∑REE = 0.51 µg·L-1) in the Amazon River (Gwénaël et al., 2006) and the average REEs concentration (∑REE = 0.69 µg·L-1) in the Baotou section of the Yellow River (He et al., 2009).
3.2. Spatial Distribution of Rare Earth Elements
As shown in Fig. 2 and Fig. 3, the mainstream (Anhui section of the Huai River) exhibits a decreasing trend in ∑REE concentrations from upstream to downstream, indicating a strong self-purification capability of rare earth elements in the mainstream. In contrast, the tributaries, including Ying River, XiFei River, Wo River, Hui River, Tuo River, XinBian River, and Sui River, generally show an increasing trend in ∑REE concentrations from upstream to downstream, with significant fluctuations. The trend of increasing concentrations is particularly evident for LREEs, indicating that human activities may contribute to the dissolved-phase REEs in the tributaries. Among all sampling points, the concentrations of HREEs fluctuate less, indicating their relatively stable chemical properties and weak influence from physical/chemical processes and anthropogenic emissions. On the other hand, the LREEs concentrations show a significant range of fluctuations, suggesting their sensitivity to both water environment and physical/chemical processes. Furthermore, some studies suggest that the unusually high concentrations of dissolved REEs in natural water bodies are related to human activities. For instance, Munemoto (Munemoto et al. 2020) proposed that REEs may be introduced into water bodies through industrial and domestic wastewater as well as pesticides and fertilizers. Costa (Costa et al. 2021) also indicated that the high REE concentrations in the Patox lake region in southern Brazil were due to the introduction of fertilizers through local fertilizer production industries. Hu (Hu et al. 2006) pointed out that coal combustion can lead to the accumulation of REEs in water bodies and soils through atmospheric deposition. Moreover, Munira (Munira et al. 2018) demonstrated the anomalous enrichment of REEs in the Huainan coalfield. Among the sampling points, Yh4, Gh5, and Hh5 stand out with significantly higher LREEs concentrations compared to other sampling points in the study area. Yh4 is located near the county town of Ying Shang, Hh5 is located in Guzhen County, both areas are densely populated and upstream of Huabei coalfield, where coal mining and open-air dumping of coal gangue can have a significant impact on REEs concentrations. Gh5 is located downstream in Mengcheng County, away from urban areas, and surrounded by large agricultural planting areas, indicating a possible association between the abnormal REEs concentrations and agricultural activities in the region.
3.3. Fractionation characteristics of rare earth elements
Table 1 presents the geochemical parameters of REEs fractionation in rivers of the study area. To eliminate the homogeneous effect caused by the Oddo-Harkins rule, the measured REEs concentrations were standardized using the Post-Archean Australian Shale (PAAS) (Taylor et al., 1985). The parameters δCe, δEu, and δGd (Song et al., 2017; Wang et al., 2019a) were calculated using the following formulas:
Where δ represents the degree of anomaly for the respective element, subscript N denotes the normalized value relative to the North American Shale, superscript * represents the background value for the geological setting, Gdanthr represents the concentration of anthropogenic Gd, Gdmeasured represents the measured value of Gd in the sample, and GdPAAS represents the corresponding Gd concentration in the Post-Archean Australian Shale (PAAS). In Eq. (1), δCe greater than 1 indicates enrichment of Ce, while values less than 1 indicate depletion of Ce. Similarly, in Eq. (2), δEu greater than 1 indicates enrichment of Eu, while values less than 1 indicate depletion of Eu.
3.3.1. Fractionation Characteristics of Light and Heavy Rare Earth Elements
The ratio of LREEs to HREEs can be used to reflect the fractionation of REEs in the surface river water of the study area. Figure 3, which combines a symmetric kernel density plot and a box plot, compares the median, range, and distribution pattern of different REEs components. The thin lines indicate the data range without outliers, gray dots represent the median values, gray lines represent the mean values, and the rectangular lengths represent the interquartile range. The violin plot shows the probability density of REEs data at different values, and the wider the plot, the more samples with that value (Galic et al., 2012). An important geochemical characteristic of river water in the study area is the significant enrichment of LREEs. Both Table 1 and Fig. 3 clearly show this feature, with LREEs accounting for over 83% of the ∑REE. The LREE/HREE ratio ranges from 2.3 to 6.99, with an average of 4.68. This is consistent with previous reports from other regions, such as Dongting Lake (Wang et al., 2019a), Poyang Lake (Wang et al., 2019b), the Baotou segment of the Yellow River (He et al., 2009), the Pearl River (Han et al., 2022), and the Gan River (Zhang et al., 2017), which all showed varying degrees of LREEs enrichment. The relatively low concentration of HREEs in the surface water can be attributed to their strong tendency to form complexes with inorganic anions and subsequently precipitate, thereby maintaining low HREEs concentrations in the water phase (Weltje et al., 2002; Williams-Jones et al., 2012). Therefore, the distribution pattern of dissolved REEs in the rivers of the study area shows relative LREEs enrichment. Figure 4 presents the normalized distribution pattern of REEs in the surface water of the study area compared to the Panasqueira Average Abundance Standard (PAAS). It shows that the distribution pattern of the study area's surface rivers is generally similar to the average pattern of Chinese rivers. The curve for HREEs is relatively smooth with small variations, while the curve for LREEs is more irregular with larger fluctuations.
3.3.2. Ce and Eu anomalies
In the surface water samples from the study area, the δCe values were all less than 1.0, ranging from 0.02 to 0.43, with an average of 0.13 (Table 1, Fig. 4, and Fig. 5). This indicates a strong negative Ce anomaly, which is significantly lower than the background δCe value for Chinese rivers (δCe = 0.92) (Zhou et al., 2012). Previous research suggests that the Ce anomaly in water may be related to the oxidation environment (Warke et al., 2020), where changes in pH have a significant impact on the valence state of Ce. Under alkaline conditions, Ce3+ is more susceptible to oxidation to Ce4+:
Ce3+ + 4 OH− − e− = CeO2 + 2 H2O (5)
According to Eq. (5), it can be observed that Ce is typically present in the form of Ce3+ at pH < 7. However, at pH > 7, Ce3+ is prone to be oxidized into poorly soluble tetravalent oxides (CeO2), leading to further precipitation or adsorption onto iron(Fe), manganese(Mn), and aluminum(Al) oxides and subsequently depositing on particles. As a result, a negative Ce anomaly appears in alkaline rivers, and the Ce anomaly is strongly dependent on pH (Qian et al., 2021; Han et al., 2007). In the study area, the measured data shows that the pH values of surface rivers range from 7.64 to 9.80, with an average of 7.78, indicating a weakly alkaline environment. Therefore, the negative Ce anomaly in the surface water of the study area can be attributed to its alkaline conditions. Notably, the δCe values in the main stream of the Huai River are significantly higher than those in other tributaries. The slight Ce anomaly in the main stream of the Huai River could be related to the similar pH levels with other tributaries. Additionally, the lower Fe content in the main stream of the Huai River compared to other tributaries indicates that the low concentration of Fe might not favor the removal or adsorption of Ce3+(RÖnnback et al., 2008; Koeppenkastrop et al., 1993).
The δEu values of surface river water in the study area are all greater than 1, ranging from 15.20 to 70.21, with an average of 37.74. This is much higher than the background value of δEu in Chinese rivers (δEu = 4.46) (Zhou et al., 2012). The anomaly of Eu is mainly controlled by plagioclase crystallization. During the process of water-rock interaction, Eu2+ and Ca2+ have similar chemical properties, and Eu enters calcium-bearing minerals (typically plagioclase) in its divalent form, leading to crystallization and precipitation, and distinct separation from other rare earth elements. When plagioclase undergoes dissolution, the surface water can exhibit a positive Eu anomaly (Qian et al., 2021; Noack et al., 2014). However, a significant positive Eu anomaly could also be related to anthropogenic inputs of Eu. Among the collected samples, the δEu values at Yh4, Gh5, and Hh5 are higher than those at other sampling points, indicating that the anomalous Eu content in these locations is likely due to anthropogenic inputs.
3.3.3. Gd anomaly and anthropogenic Gd (Gdanthr) in rivers
According to previous studies (Amorim et al., 2019), when δGd > 1.5, it indicates that Gd has exceeded natural levels. In the present study, the variation range of δGd in rivers is 1.46 to 18.83, with an average of 6.27 (Table 1). Therefore, the majority of surface water in the study area contains input of anthropogenic Gd. As shown in Fig. 6, significant anomalies in Gdanthr and δGa are observed at Yh1, Yh2, Yh3, Xbh2, and Xbh3, which are located in the middle to lower reaches of urban areas (as seen in Fig. 1). Hence, the high Gdanthr concentration at these points might be related to the presence of anthropogenic Gd in hospital wastewater or Gd discharge from wastewater treatment plants (Goullé et al., 2012; Lerat-Hardy et al., 2019). For the other rivers, most of them pass through agricultural areas, coal mining areas, or urban areas, and their δGd values are concentrated around 2 to 8. The concentrations of Gdanthr in these rivers mostly range from 8 to 30 ng•L− 1, slightly lower than those in Paranoá Lake, Brazil (δGd = 11.35 ~ 68.37) (Amorim et al., 2019). The Gdanthr concentrations (Gdanth = 7.76 ~ 20.70 ng·L− 1) are similar to those in Paranoá Lake (Gdanth = 8.8 ~ 33.6 ng·L− 1), indicating that these rivers are influenced by anthropogenic Gd, but the impact is relatively small. This may be related to the use of pesticides and fertilizers in the grain planting areas and the Gd emissions from the Huainan and Huaibei Coal-fields within the study area.
3.4 Control factors of rare earth elements
The redundancy analysis (RDA) indicated that the physicochemical parameters of the water had a significant influence on both the REEs content and the light-to-heavy REE ratio in the surface water (as shown in Fig. 7). The physicochemical parameters collectively contributed 59% to the variability in REEs content and ratio. Specifically, Al was found to be the most influential parameter, with a contribution rate of 37.1%, and its correlation was statistically significant with a P-value of 0.02 (P < 0.05). On the other hand, pH had a lower contribution rate of 9.1%, and its correlation was not statistically significant with a P-value greater than 0.05. Overall, the chemical characteristics of rare earth elements in the study area were influenced to a lesser extent by the physicochemical properties of the water.
The occurrence of dissolved REEs in aquatic environments is not stable and is susceptible to the influence of physical and chemical changes in the water, leading to variations in REEs concentrations. Processes such as the formation of complexes in water and the adsorption of REEs onto Fe, Mn oxides/hydroxides, and colloidal particles can cause fractionation between the dissolved phase and the solid phase at the solution/solid interface, and the adsorption of particles and colloids can significantly affect the concentration of dissolved REEs (RÖnnback et al., 2008; Koeppenkastrop et al., 1993). Numerous studies have shown that the geological background of the study area and the physicochemical properties of surface water (such as pH) as well as the presence of colloidal ions, organic matter, and suspended particulate matter can all act as control factors on the concentration of REEs in rivers (Elderfield et al., 1990; Zhou et al., 2012; Han et al., 2007; Wang et al., 2021).
The content and distribution characteristics of REEs vary among different rivers in the study area, and even within the same river, significant differences can be observed between different seasons and different sampling points over time (Bagard et al., 2011; Moeller et al., 2014). The pH of rivers in the study area does not show a significant correlation with the concentration of REEs, which could be attributed to the limited range of pH values (7.64 ~ 9.80) or the influence of anthropogenic inputs of rare earth elements into the water, causing fluctuations in the REEs content (Pourret et al., 2013; Schijf et al., 2011). The concentration of REEs shows varying degrees of positive correlation with iron(Fe) and Al contents, as shown in Fig. 8. After excluding extremely high outlier values of REEs, linear regression equations were established between the REEs concentration and the Fe and Al contents. The results indicate that the correlation coefficient (r) between LREEs and Fe is 0.44, between Eu and Fe is 0.58, between LREEs and Al is 0.66, and between HREEs and Al is 0.89. Colloidal forms of Fe, Mn, and Al hydroxides/oxides are prevalent in water environments, and their large surface areas efficiently adsorb and complex with REEs. The interaction between REEs and Fe, Mn oxides/hydroxides primarily occurs through complexation reactions with OH- unsaturated functional groups on their surfaces (Pourret et al., 2013; Schijf et al., 2011). In neutral to alkaline solutions, about 90% of REEs can be adsorbed by Fe, Mn, and Al oxides/hydroxides (Koeppenkastrop et al., 1993). Overall, the observed correlation between REEs (especially LREEs) and Fe, Al colloids in the study area reflects the occurrence of complexation reactions between REEs and Fe, Al colloids in the natural river system. Among these interactions, Fe colloids tend to complex more readily with Eu, while Al colloids tend to complex more easily with Ce.
The concentration distribution trend of REEs in the main rivers of northern Anhui Province shows a decreasing trend along the main stream, Huai River Anhui section, from upstream to downstream, while the 7 tributaries show an increasing trend from upstream to downstream. The REEs content in river water is jointly determined by geological background, physical and chemical properties of water, and anthropogenic inputs. High concentrations of REEs are significantly influenced by the combined effects of agricultural fertilization, urbanization processes, and coal mining. The REEs composition in the study area is characterized by a high enrichment of LREEs, accounting for more than 83% of the total REEs. Eu shows a significant positive anomaly and is positively correlated with the concentration of Fe, while Ce exhibits a strong negative anomaly and is positively correlated with the concentration of Al. Obvious Gd anomalies and anthropogenic Gd are observed in urban areas, while other areas show typical Gd anomalies. Based on the above findings and to minimize the potential harm from the release of REEs into the environment, the following recommendations are proposed: (1) Rational and gradual reduction of agricultural fertilizer use, with an emphasis on using organic fertilizers; (2) Strict adherence to discharge standards for domestic wastewater; and (3) Strict control of wastewater and gas emissions in coal mining areas, along with enhanced primary treatment of wastewater and gas at the source.
Foundation item: Project supported by the Natural Science Foundation of the Department of Education of Anhui Province (KJ2021A0085).
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No competing interests reported.