The Spatial Distribution of Trace Elements and Rare-Earth Elements in the Stream Sediments Around the Ikuno Mine Area in Hyogo Prefecture, Southwest Japan

Abstract

In the present study, major oxide, trace, and rare-earth element (REE) contents in the stream sediments of the Ikuno and surrounding areas of the central part of Hyogo Prefecture in the Kinki district in southwestern Japan were analyzed. Several abandoned mines that contain Au, Ag, Cu, Pb, Zn, Fe, W, and As exist in these areas, including the Ikuno and Akenobe mines, which are famous historical mines. A total of 156 stream sediments over approximately 1300 km² in these areas were analyzed using X-ray fluorescence (XRF) and inductively coupled plasma mass spectrometry (ICP-MS). The spatial distribution patterns of elemental concentrations in the stream sediments in the Ikuno area were determined by three primary factors: the surface geology, the localized deposition of ore minerals, and the influence of the sedimentation of heavy minerals in the basin on local distribution. The mean value of the spatial distributions of the ore deposits was greater than the median, primarily due to the presence of concentrated regions near the mining sites. A Kolmogorov–Smirnov test indicated abnormal distribution patterns of Pb, Zn, Cu, Cr, and Ni due to the presence of exceptionally high concentrations of these elements at the mine sites. The stream sediments showed higher levels of light REEs, mainly La, Ce, and Nd, in comparison with the heavy REEs. This pattern, deviating from the global abundance, suggests the dominating influence of mining sites on local REE distributions. These findings are essential for assessing the environmental impacts of historical mining and developing strategies for responsible resource management in the region. By understanding the geochemical signatures of mining-affected areas, these data could contribute to future environmental monitoring and mitigation efforts, enhancing our understanding of environmental sustainability and responsible resource utilization.

1. Introduction

Geochemical analysis plays a critical role in deciphering the Earth’s geological history and resource potential, and in monitoring its environment. Stream sediments serve as natural collectors of eroded materials from surrounding landscapes, providing significant insight into the unique chemical composition of a place. Stream sediments consist of a combination of rocks, sediments, and soils that originated from the drainage basin located upstream of the collecting location [1,2]. These stream sediments are useful for estimating the average chemical compositions of surface materials. However, when analyzing and interpreting related data, certain natural processes must be considered. This is because the chemical compositions of stream sediments can be altered by weathering and sedimentation processes originating from the parent rocks, as well as by bioactivities, which may include human activities.

Rare-earth elements (REEs), in addition to yttrium (Y), are often used to study the chemical processes that occur in the environment [3]. Despite the unique chemical and physical features of REEs, the demand for them is expected to significantly increase in the future, primarily due to their growing utilization. The demand for rare-earth elements (REEs) is projected to rise significantly due to their increasing applications in clean-energy technologies and electronics [4,5]. This surge is particularly driven by the growing electric vehicle and wind energy markets, both of which heavily rely on REEs [6].

The distribution of REEs in mining areas has also been widely studied to better understand the influence of mining processes on the environment. Generally, REEs are more mobile in acidic environments than in alkaline environments, as acidic environments dissolve the minerals that bind REEs to soil and rocks [7,8]. The accumulation of elements from various sources, including due to the weathering and erosion of rocks and minerals, as well as mining activities, can be localized in stream sediments.

The present paper presents representative geochemical maps of the Ikuno and surrounding areas with a high sampling density (one site per 8 km²). Geochemical maps display the spatial patterns of chemical elements on the Earth’s land surface and provide essential data on the natural and anthropogenic concentrations of these elements in the environment. Recent interest in environmental issues has prompted their use to evaluate the environment for human life by studying the spatial distribution of element concentrations and geochemical processes on the Earth’s surface [9,10]. In Japan, the Geological Survey of Japan began a geochemical mapping project in 1999 covering the entire area of Japan [11]. This project documents the elemental distribution on surface areas on a database for environmental assessment. For the baseline data, samples were collected from a sampling location unaffected by anthropogenic activities. About 150 stream sediment samples were collected at a low sampling density (one site per 120 km²). From this project, a basic geochemical atlas of Japan has been published [12]. In addition, geochemical maps for several regions with higher sampling densities have been constructed [13,14,15].

Despite these studies, the chemical and mineralogical contents of these sediments, as well as their sources, remain poorly understood. The main aim of the present study was to determine the influence of the lithological and petrological nature of these stream sediments within a mining area, with additional attention given to the behavior of REEs. The objectives of this study were to evaluate the concentrations of REEs in stream sediments from the Ikuno mine and the surrounding areas and to determine the major processes and factors controlling the levels of these elements in the stream sediments in these areas.

2. Study Area

The Ikuno mine is located in the Ikuno district, Hyogo Prefecture. It is one of the two largest historical mines in this area, the other being Akenobe. Many inactive mines in this area were previously operated for mining base and precious metals. The Ikuno mine and other mines in this district are hydrothermal veins genetically related to acidic igneous activities of the late Cretaceous to early Paleogene period. The Ikuno deposit has a history spanning more than 1200 years. The hydrothermal system responsible for the deposit has impressive dimensions, with a width of 4 × 5 km and a depth of more than 1.2 km. The Ikuno deposit is the most significant mineral resource in the region and is characterized by numerous productive mineral veins, contributing to a total ore production volume exceeding 1.2 million tons. The study area—namely, the Ikuno and surrounding areas—is located between longitudes 134°45′ and 135°00′ E and latitudes 35°00′ and 35°10′ N. As it is positioned between the Chugoku mountains and Tamba highland, its terrain is predominantly mountainous to hilly. It falls within the inner zone of southwest Japan and is geotectonically divided into the Maizuru, Ultra-Tamba, and Tamba terranes, from structurally upper to lower units [16,17,18]. The geological composition of this region is remarkably diverse (Figure 1), comprising volcanic rocks, plutonic rocks, accretionary complexes, sedimentary rocks, Late Pleistocene–Holocene deposits, and numerous mineral occurrences [16].

In terms of lithology, this region is divided into two sections: one from the south to the middle, and the other from the middle to the north. In the southern-to-middle section, felsic volcanic rocks (rhyolite) and sedimentary rocks dominate and are distributed from the eastern to the middle part of the study area. Some gabbroic rocks are also found at the center of the felsic volcanic rocks in the eastern part. Additionally, some of the mafic volcanic rocks, felsic plutonic rocks, and sedimentary rocks in the accretionary complex, and a small number of mafic plutonic rocks, are distributed from the western to the central part of this area. The second part, from the middle to the north, is more complex. All bedrock types are present in this area, including limestone of the Yakuno complex, granitic rocks, rhyolite, andesite, granodiorite, and sedimentary rocks [16].

3. Materials and Methods

A total of 156 active tributary streams from the drainage basins across the study area provided the fine-grained stream silt that was collected in wet conditions (Figure 2). The sampling sites were determined to represent the respective drainage areas, and most of the sites were located around the confluence, where they joined the main river. The samples were obtained from the outflows of the drainage basin in the area. Fine sediments were sieved with stream water at each sampling site using an 80-mesh (0.177 mm) stainless steel sieve. The collected sediments were cleansed with water to eliminate organic substances, and subsequently, the samples were separated using filter paper.

The collected samples were dried in an oven for approximately 24 h at a temperature of 110 °C. They were then homogenized and again filtered through an 80-mesh (0.177 mm) sieve to minimize the variance in the estimated element concentrations [20]. The next steps involved different treatments of major, trace, and rare-earth elements. For the major element analysis, around 1 g of the sediment sample was dried again at 110 °C in an oven for about 24 h. Then, a 0.7 g sample was mixed with 6.0 g of lithium tetraborate anhydrate to prepare a glass bead. The mixture was heated to 1000 °C in a platinum dish for around 12 min in a bead machine. The trace element analysis required around 2.5 g of sediment sample, which was ground with an agate mortar and pestle for around 20 min. Then, the sample was ignited at 1000 °C for 2 h and ground again with an agate mortar and pestle. Finally, 2.0 g of the fine sediment sample was mixed with 3.0 g of lithium tetraborate anhydrate to prepare a glass bead using the same method as for the measurement of major oxide.

The glass beads were analyzed via X-ray fluorescence (XRF) spectrometry using a Shimadzu XRF-1800 instrument equipped with a Rh tube. Ten major elements and their oxides, and 12 trace elements, were calibrated using reference samples. Based on reference rock studies, the analytical accuracy was calculated to be better than 2% for abundant oxides, such as SiO₂ and Al₂O₃, and better than 5% for less abundant oxides, such as TiO₂, MnO, and P₂O₅ [21]. Furthermore, loss-on-ignition was assessed by measuring the reduction in weight upon ignition to determine the proportion of volatile components. For the REE analysis, glass beads were pulverized into fine powder using an agate mortar and pestle. Approximately 12.5 mg of the substance was processed using ion exchange resin columns. The La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, and Y concentrations in the sediment samples were measured using an Agilent ICP-MS 7700 inductively coupled plasma mass spectrometer. The approach outlined in Yokoyama et al. (2017) was used to calculate and adjust the oxide and hydroxide formation rates for Ba, Ce, Pr, Nd, Sm, Eu, Gd, and Tb [22].

A comprehensive statistical approach was employed to interpret the distribution behaviors of elemental concentrations. Factor analysis, using principal component analysis (PCA) with varimax rotation, effectively revealed the underlying dominant factors influencing their spatial distribution. The interpretation of these factors was based on elemental loadings, which reflect correlations aligned with established geological processes. To further demonstrate anomalous elemental concentrations, thresholds for each element were established using box and whisker plots generated by MINITAB 15. In addition, as part of this study, the concentrations of REEs in the stream sediments were standardized using CI chondrite. Chondrite normalization is a commonly used method of standardizing the concentration of REEs based on the standard REE abundance on Earth, as seen in the Masuda–Coryell plot [23]. The normalization value can be calculated using chondrite data according to the formula provided by Anders and Grevesse [24]. The observed anomaly can be positive or negative based on whether the rock formation process involves the depletion or enrichment of plagioclase [25].

4. Results

4.1. Geochemical Maps

Geochemical maps of the major oxides and trace elements were prepared with OriginPro computer software. The geochemical maps displayed the contours of the chemical concentrations, as determined from the analysis findings. These contours can be shown by the coloring scale and/or size of the circle in each sampling position; in this study, we used the coloring scale.

4.2. Factor Analysis of Elemental Concentrations in the Stream Sediments

To interpret the distribution behaviors of the elemental concentrations, the dominant factors that influenced their distribution in the Ikuno and surrounding areas were identified using factor analysis (

Table 1. The results of the analysis of the stream sediments in the Ikuno and surrounding areas (n = 156).

Oxides and Elements	Unit	Min	Max	Mean	Median	SD	p	p-Log
Al2O3	wt%	10.81	17.42	18.24	14.25	1.12	0.935	0.786
CaO	wt%	0.36	6.11	1.48	1.04	1.11	<0.001	0.106
Fe2O3	wt%	2.13	22.01	6.95	6.48	3.29	0.008	0.91
K2O	wt%	0.91	4.35	2.71	2.85	0.68	0.202	0.002
MgO	wt%	0.25	5.22	1.48	1.1	1.05	<0.001	0.568
MnO	wt%	0.05	0.77	0.16	0.13	0.1	<0.001	0.087
Na2O	wt%	0.48	3.54	1.58	1.51	0.53	0.132	0.817
P2O5	wt%	0.02	0.27	0.09	0.08	0.04	0.06	0.033
SiO2	wt%	44.72	77.19	65.59	66.67	6.24	0.172	0.042
TiO2	wt%	0.21	6.64	0.94	0.79	0.63	<0.001	0.644
Pb	ppm	17	4623	153	45	472	<0.001	0.001
Ba	ppm	32	1071	515	516	130	0.231	<0.001
Zr	ppm	141	1231	413	366	199	0.003	0.811
Y	ppm	19	82	29	28	7	<0.001	0.016
Sr	ppm	37	301	133	129	51	0.591	0.308
Rb	ppm	43	267	136	133	37	0.428	0.031
Zn	ppm	50	13,152	419	179	1146	<0.001	0.002
Cu	ppm	2	2601	114	34	313	<0.001	0.041
Ni	ppm	2	190	31	20	30	<0.001	0.199
Co	ppm	6	210	36	28	29	<0.001	0.955
Cr	ppm	7	1837	113	60	205	<0.001	0.661
V	ppm	17	454	115	92	73	0.008	0.941

). The data distribution in this study was assumed to be either normal or Gaussian. Two types of distribution data were created: original data and log-transformed data. Trace element data are typically not normally distributed. Converting elemental concentrations to logarithmic values results in nearly normally distributed data. Trace element geochemical data are often distributed with a log-normal distribution [26,27]. A Kolmogorov–Smirnov test was used to assess whether the concentrations of the 22 elements had a normal distribution or not.

The element concentrations were considered normally distributed when their probability exceeded the 99% significance level. Table 1 shows that 11 elements from the original data were normally distributed, while 21 elements in the log-transformed data were normally distributed. However, in many cases, the log transformation reduced the skewness and kurtosis of the distribution of the element concentrations [27,28]. Consequently, all the elemental concentrations (except for Ba) were transformed into common logarithms for factor analysis.

For the factor analysis, principal factor analysis was used, following Ohta et al. (2005), as well as the variance maximum procedure for factor rotation (varimax rotation). Five factors were chosen based on Kaiser’s criterion method [27]. This method determines the number of factors based on the eigenvalue (Figure 3). The five factors with eigenvalues higher than 1 explained 82% of the total variability (

Table 2. The factor loadings of the factor analysis carried out with 24 variables.

Oxides and Elements	Communality	Factor
		1	2	3	4	5
TiO2	0.57	0.91	0.128	−0.059	0.103	−0.057
K2O	0.81	−0.885	0.178	−0.21	−0.059	−0.159
V	0.93	0.862	0.18	0.199	0.228	0.03
Fe2O3	0.83	0.858	0.377	0.078	0.207	0.006
SiO2	0.95	−0.819	−0.24	−0.097	−0.385	−0.154
MgO	0.79	0.782	0.105	0.463	0.273	0.151
Co	0.82	0.771	0.455	0.147	0.225	0.046
Rb	0.7	−0.748	0.337	−0.285	−0.086	0.105
CaO	0.88	0.72	−0.027	0.213	0.535	−0.069
MnO	0.85	0.684	0.484	0.008	0.114	0.12
Ba	0.73	−0.682	−0.19	−0.417	0.222	0.209
Y	0.88	0.53	−0.056	−0.129	−0.072	0.484
P2O5	0.62	0.486	0.372	0.162	0.43	0.358
Pb	0.48	0.02	0.949	−0.047	0.051	0.065
Zn	0.6	0.122	0.935	0.037	0.12	−0.007
Cu	0.4	0.143	0.831	0.334	0.06	−0.071
Na2O	0.88	−0.211	−0.564	0.393	0.351	−0.361
Ni	0.74	0.161	0.052	0.904	0.057	0.155
Cr	0.87	0.451	0.116	0.769	0.118	−0.016
Sr	0.78	0.4	−0.074	−0.02	0.839	−0.077
Al2O3	0.86	0.113	0.207	0.106	0.695	0.2
Zr	0.89	−0.063	−0.052	−0.15	−0.143	−0.863
Eigenvalue		10.2	3.3	1.7	1.6	1.2
Variance (%)		36	18	11	10	7

Boldface type means that the factor loading is larger than 0.5 and smaller than −0.5.

). The correlation of the elements in each factor will be discussed later alongside the spatial distribution patterns of the elemental concentrations in the geochemical maps.

4.3. The Spatial Distribution Patterns of the Elemental Concentrations in the Stream Sediments

The first and fourth factors explained that 46% are mostly controlled by the chemical composition of the parent lithology (Table 2). The first factor showed a negative association of the elemental distributions of potassium (K) and silicon (Si) with those of calcium (Ca), iron (Fe), magnesium (Mg), manganese (Mn), phosphorus (P), and titanium (Ti). The stream sediments derived from the non-alkali felsic volcanic rocks and the mafic plutonic rocks contained high amounts of Al, K, and Si, as well as low concentrations of Ca, Fe, Mg, Mn, P, and Ti. Conversely, the non-alkali mafic volcanic rocks, mafic plutonic rocks, and sedimentary rocks had abundant amounts of Ca, Fe, Mg, Mn, P, and Ti but lower levels of Al, K, and Si. The accretionary complexes present in some areas of the northern part (around the Akenobe mine and the northern part of the Tajima Takeda district) showed positive loadings for CaO and MgO. Some higher spot concentrations of CaO and MgO were also found, which are possibly related to the presence of limestone (Figure 4). The positive loadings for Al and Sr in the fourth factor were probably related to the abundance of feldspars in the accretionary complexes and felsic plutonic rocks.

The second factor, which explained 18% of the variance, was the significant concentrations of Pb, Zn, Cu, and Cr, which were strongly correlated with the mineral occurrences. The Ikuno and Akenobe mines had the highest spot concentrations of these elements in this area (Figure 5). Other inactive mines also showed positive loadings for element concentrations in this factor, but some did not. For example, the Koujiya mine (which produced sulfur, Pb, and Zn) in the southern part of the Ikuno district and the Chihara mine (which produced Cu) in the southeastern part of the Tajima Takeda district did not show positive loadings for element concentrations in this factor.

In the third factor, a combination of lithology and mineral occurrences was present. The element concentrations in this factor were higher in areas where accretionary complexes were abundant. Additionally, the Akenobe and Chihara mines had elevated Ni and Cr concentrations.

The last factor consisted of two elements: Zr and Y. Elevations in both elements were detected in the basin areas. In the Asago basin (in the northern part of the Tajima Takeda district), these elements were positively correlated. Conversely, they were negatively correlated in the Nishiwaki basin—Zr had a high concentration, and Y was depleted.

5. Discussion

5.1. The Influence of Lithology on the Stream Sediments

The results of the factor analysis indicated that the elemental distributions were predominantly influenced by the parent lithology. Three of the five factors that contributed more than 50% of the total variances were related to the parent lithology. A previous study on regional geochemical mapping in Japan also indicated that rock type, which encompassed over 50% of the drainage basin area, influenced the element concentrations found in the stream sediments [27]. To explore this influence, 156 stream sediment samples were classified into eight subgroups based on the dominant lithology (

Table 3. The medians of the elemental concentrations in the stream sediments based on lithology.

Parent Lithology a		Fel_V	Fel_P	Maf_V	Acc_P	Acc_Pl	Sed_1	Gab_Pl	Fel_Pp
Number		80	12	6	27	17	9	1	4
Al2O3	wt%	14.03	13.77	14.33	14.16	15.95	14.8	13.72	14.22
CaO	wt%	0.98	1.67	2.06	0.74	2.75	2.1	1.14	1.1
Fe2O3	wt%	5.87	5.27	9.63	5.28	9.88	10.32	5.96	7.27
K2O	wt%	2.95	3.41	1.86	2.88	1.72	2.21	2.81	2.61
MgO	wt%	0.99	1.32	1.54	1.02	3.49	2.63	0.93	1.81
MnO	wt%	0.13	0.1	0.26	0.12	0.2	0.17	0.13	0.18
Na2O	wt%	1.44	2.4	1.13	1.51	1.9	1.46	1.2	1.17
P2O5	wt%	0.08	0.08	0.12	0.07	0.12	0.11	0.05	0.1
SiO2	wt%	67.51	64.94	56.36	71.09	58.81	60.5	67.98	64.81
TiO2	wt%	0.74	0.61	1.29	0.76	1.16	1.02	0.8	0.84
Pb	ppm	47	42	67	40	59	40	43	158
Ba	ppm	546	526	483	527	380	371	624	446
Zr	ppm	376	430	369	401	260	224	340	267
Y	ppm	30	29	26	27	27	25	32	29
Sr	ppm	129	172	139	100	184	141	103	105
Rb	ppm	142	139	107	126	84	123	153	151
Zn	ppm	195	143	310	166	202	148	135	372
Cu	ppm	30	20	25	34	55	69	15	73
Ni	ppm	16	11	18	28	70	56	11	50
Co	ppm	24	23	53	18	58	69	24	43
Cr	ppm	43	51	72	73	189	146	23	206
V	ppm	84	97	211	80	196	166	119	138

^a Fel_V (felsic volcanic), Fel_P (felsic plutonic), Maf_V (mafic plutonic), Acc_P (accretionary complex Permian dominated by sedimentary rocks), Acc_P1 (accretionary complexes dominated by mafic plutonic rocks), Sed_1 (sedimentary rocks), Gab_PI (gabbroic), Fel_Pp (felsic plutonic rocks Permian).

).

The median comparison in Table 3 shows a strong relationship between lithology and elemental distribution in the first factor. These median data demonstrate two poles (highest and lowest), with mafic, felsic, accretionary complex, or sedimentary rocks at each pole. High medians of Al, Ca, Fe, Mg, Mn, and P appear to correspond to mafic and sedimentary rocks. On the other hand, high medians of K, Si, and Y are associated with felsic and accretionary complex rocks. Both sets of median values prove that the elemental distributions of the first factor elements, which are shown on the geochemical map, are strongly related to the parent lithology.

5.2. The Influence of Mineral Deposits on the Spatial Distribution Patterns of the Elements

The contribution of mineral deposits to the spatial distribution patterns of the elements is shown in the second factor in the factor analysis. These elements were not normally distributed. Figure 6 shows that the mean/median ratios of the elements in the second factor were higher than those of the other elements. This phenomenon was caused by high spot concentrations in several areas.

The high spot concentrations of the elements in the second factor, identified through principal component analysis (PCA), are related to mineral deposits. The thresholds for each element were determined using box and whisker plots generated by MINITAB 15 (see Materials and Methods for details on threshold determination).

Table 4. The thresholds and outlier numbers of the heavy elements.

Oxides and Elements	Threshold		Number of Outlier Data
Fe2O3	13.18	wt%	6
MnO	0.31	wt%	10
TiO2	1.83	wt%	6
Pb	135	ppm	24
Zn	495	ppm	20
Cu	127	ppm	18
Ni	27	ppm	15
Co	76	ppm	9
Cr	228	ppm	16
Zr	767	ppm	9
Sr	244	ppm	1
V	255	ppm	4

provides the threshold and the number of outlier data for each element. Significantly high element concentrations were found in the two largest mines, Ikuno and Akenobe. The numerous outliers for Pb, Zn, and Cu are consistent with the vein-type mineralization prevalent in the study area.

5.3. The Influence of Heavy Mineral Sedimentation in the Basin

The correlation of the two elements in the fifth factor is unique and, thus, interesting to analyze because it is both positive and negative. In the Asago basin (in the northern part of the Tajima Takeda district), Zr and Y showed a positive correlation. Their elevated concentrations were related to the occurrence of garnet.

Garnet exhibits a strong affinity for yttrium (Y), with studies demonstrating significant Y enrichment in various garnet types, including pegmatitic, granitic, and pelitic garnets, where concentrations can range from wt% to thousands of ppm [29,30,31,32]. This preferential partitioning of Y into garnet, often 100–1000 times greater than other major rock-forming minerals like quartz, biotite, and muscovite, highlights garnet’s critical role in Y fractionation within geological systems. Furthermore, Y enrichment can also be influenced by accessory rare-metals and fluorite present in felsic rocks, potentially contributing to the observed variations. On the other hand, a negative correlation was seen in the Nishiwaki basin—Zr had a high concentration, unlike Y (Figure 7). These different behaviors suggest that the higher Zr in the Nishiwaki basin was not influenced by garnet but probably by the presence of zircon (ZrSiO₄). The heavy minerals were transported by river streams from higher to lower areas until they were trapped in the basin.

5.4. Rare-Earth Elements (REEs)

The analysis of the REE data from the stream sediments in Ikuno, as shown in

Table 5. Analytical results of rare-earth elements (REEs) from stream sediments in Ikuno and surrounding areas (n = 156).

REEs	Median	Mean	Min	Max	SD
La	24.72	28.56	5.21	74.10	14.43
Ce	55.77	61.07	12.67	137.99	27.87
Pr	5.61	6.70	1.36	16.60	3.37
Nd	21.49	25.83	5.60	61.36	12.87
Sm	4.11	5.33	1.21	12.68	2.75
Eu	1.09	1.33	0.39	3.06	0.75
Gd	4.04	5.43	1.22	13.09	3.06
Tb	0.70	0.87	0.23	1.96	0.42
Dy	4.54	5.57	1.64	12.75	2.70
Ho	1.01	1.17	0.23	2.67	0.57
Er	3.23	3.68	1.38	8.13	1.63
Tm	0.48	0.54	0.23	1.21	0.24
Yb	3.31	3.76	1.56	8.42	1.68
Lu	0.50	0.58	0.23	1.29	0.26

, suggests higher concentrations of light REEs (LREEs) than of heavy REEs (HREEs). Notably, La, Ce, and Nd exhibited the highest concentrations. Ce is the most abundant of the naturally occurring REEs (with a crustal abundance of 60 ppm) [33]. Additionally, some elements displayed substantial deviations from the mean data, potentially indicating the influence of the surrounding mining areas [33]. While igneous rocks within the upper layer of the Earth’s crust contain significantly higher concentrations of cerium, lanthanum, yttrium, and neodymium than of lead, cobalt, tin, molybdenum, silver, and tungsten [34,35], this does not apply in this study because of the influence of heavy mineral deposits in the basin due to the weathering and oxidation process in the mining area. Jaireth et al. (2014) investigated the abundance of REEs in different rock types and observed that both felsic and alkaline rocks exhibited higher concentrations of REEs than of mafic rocks [36]. This finding suggests that the higher abundance of REEs could be a contributing factor to the elevated levels of LREEs in this area.

The chondrite-normalized analysis presented in Figure 8 reveals anomalies in Europium (Eu). The presence of an Eu anomaly can be attributed to the ease with which Eu²⁺ ions replace Ca²⁺ ions in minerals that substitute plagioclase. Eu behavior within felsic melts is primarily governed by feldspar. Unlike other trivalent REEs, Eu²⁺ readily integrates into feldspar structures [33]. This selective incorporation during feldspar crystallization and removal creates a negative Eu anomaly in the remaining melt. A similar negative anomaly can occur during the partial melting of materials rich in Eu²⁺. Due to its low field strength, this element behaves compatibly, favoring incorporation into the solid residue rather than the melt. Rare-earth elements (REEs) in general are lithophile, meaning they preferentially reside in silicate minerals like feldspar, rather than in metallic or sulfide minerals [34].

In addition, while Ce typically exhibits negative anomalies in oxidizing environments, the data presented show a positive anomaly. Negative Ce anomalies are commonly observed in oxidizing environments associated with mining activities. The negative Ce anomaly is likely related to the Ce³⁺/CeO₂ equilibrium under oxidizing conditions. The oxidation of Ce³⁺ to Ce⁴⁺ leads to the preferential removal of Ce as CeO₂, resulting in its depletion relative to other REEs. As highlighted by Seto and Akagi (2008), this process is particularly pronounced in environments with limited organic matter [37]. Positive anomalies can also occur under specific circumstances. Ratie et al. (2020) proposed that high Fe content, a low organic carbon/Fe ratio, and the presence of newly formed Fe oxyhydroxides can promote the adsorption of Ce⁴⁺, leading to a positive anomaly [38]. However, the source rock itself can also play a role. For instance, felsic igneous rocks often contain ZrSiO₄, a mineral that readily incorporates Ce⁴⁺ because the latter’s ionic radius (0.97 Å) is similar to that of Zr⁴⁺ (0.84 Å). This inherent ability of ZrSiO₄ to substitute Ce⁴⁺ for Zr⁴⁺ in its crystal structure can lead to a positive Ce anomaly, independent of external oxidizing conditions [39]. Therefore, interpreting positive Ce anomalies requires considering both the geochemical processes within the mining area and the natural composition of the source rock, particularly the presence of minerals such as ZrSiO₄, which have a high affinity for Ce⁴⁺.

6. Conclusions

A total of 156 stream sediment samples were collected and analyzed for 22 elements, generating geochemical maps and revealing spatial distribution patterns. Principal factor analysis identified three primary factors influencing elemental concentrations:

• Parent Lithology:

• Parent lithology exerts the strongest control over elemental distributions, explaining over 50% of the variance. Mafic, felsic, accretionary complex, and sedimentary rocks significantly influence major elements and specific trace elements (V, Cu, Rb, Ba, Ni, Cr, Sr).

• Influence of Mineral Deposits:

• Elemental concentrations influenced by mineral deposits exhibit higher mean/median ratios and extreme outliers due to ore deposits in the mining areas.

• Heavy Mineral Sedimentation:

• The spatial distribution of some elements is influenced by the sedimentation of heavy minerals within the basin, contributing to localized variations in elemental concentrations. LREEs (La, Ce, Nd) show higher concentrations compared to HREEs. Deviations from typical REE abundances, including negative Ce anomalies, suggest a potential influence of the surrounding mining areas on REE distributions.

These findings have implications for solving different challenges in the region, the environmental assessment of existing and abandoned mine sites, and geological mapping to better understand the regional geological effects on mineralization. Furthermore, by incorporating a sustainability perspective, this research contributes to the development of strategies for responsible resource utilization and environmental protection within the studied region. The data produced can be used to create baseline data for future environmental monitoring projects.