Status, Sources, and Risks of Heavy Metals in Surface Sediments of Baiyangdian Lake and Inflow Rivers, North China

Abstract

Baiyangdian Lake, recognized as the largest freshwater body in northern China, plays a vital role in maintaining the regional eco-environment. Prior studies have pointed out the contamination of sediments with heavy metals, raising concerns about eco-environmental challenges. Therefore, it is imperative to evaluate the current pollution levels and ecological threats related to heavy metals found in the sediments of Baiyangdian Lake as well as in its inflow rivers. In May 2022, surface sediments with a depth of less than 20 cm were analyzed for Cu, Zn, Pb, Cr, Ni, As, Cd, and Hg to determine the pollution status, identify sources of pollution, and evaluate potential ecological risks. A range of evaluation methods used by predecessors such as geo-accumulation index (I_geo), enrichment factor (EF), ecological risk index (RI), sediment quality guidelines (SQGs), positive matrix factorization (PMF), absolute principal component score-multiple linear regression model (APCS-MLR), chemical mass balance (CMB), and UNMIX model were analyzed. After comparison, multi-methods including the geo-accumulation index (Igeo), absolute principal component score-multiple linear regression model (APCS-MLR), ecological risk index (RI), and sediment quality guidelines (SQGs) were utilized this time, leading to a better result. Findings reveal that pollution levels are generally low or non-existent, with only 1.64% of sampling sites showing close to moderate pollution levels for Cu, Pb, and Zn, and 4.92% and 1.64% of sites exhibiting close to moderate and moderate pollution levels for Cd, respectively. The main contributors to heavy metal presence are pinpointed as industrial wastewater discharge, particularly Cu, Zn, Pb, Cd, and Hg. The ecological risks are also relatively low, with 4.92%, 1.64%, and 1.64% of sampling sites demonstrating close to moderate, moderate, and strong risks in the inflow rivers, respectively. Additionally, only one site shows moderate potential biological toxicity, while the rest display non-toxicity. These findings will update our cognition and offer a scientific basis for pollution treatment and ecosystem enhancement for government management.

1. Introduction

Pollution from heavy metals has become a significant worldwide concern [1,2,3,4,5,6], with rivers, lakes, and oceans serving as primary repositories for these contaminants [7,8,9]. The heavy metals are discharged into water bodies through anthropogenic activities such as industrial, agricultural, and daily life practices [10,11,12], as well as natural processes like rock weathering [13,14,15]. Upon entering the water, heavy metals settle in sediments at the bottom [13,16], where they can persist for extended periods [17]. When disturbed, these contaminated sediments can act as secondary sources of pollution [9], negatively impacting water quality and ecological health. The long residual and chronic toxic nature of heavy metals in sediments will lead to environmental deterioration, the degradation of water quality [3,18,19], and pose a threat to human health and aquatic life by entering into the food chain and causing various diseases, including genetic mutations [20,21,22,23,24]. Assessing the pollution and identifying the driving factors of heavy metals in sediments can provide worthy insights for contamination mitigation and ecosystem enhancement [10,25]. As a result, the pollution of heavy metals in sediments has garnered global attention and has become a hotspot of academic research [26,27,28].

To date, pollution and ecological risk evaluation for heavy metals have primarily relied on methods such as geo-accumulation index (Igeo) [29,30], enrichment factor (EF) [31,32], ecological risk index (RI) [33,34], sediment quality guidelines (SQGs) [35,36], pollution index (RI) [37], and pollution load index (PLI) [37]. The comprehensive use of multiple methods is a clear trend in this field [38,39,40]. It is important to note that obtaining an objective background or reference value is crucial in determining the validity of evaluation results. Additionally, the quantitative identification of contaminant sources often involves receptor models [41] like positive matrix factorization (PMF) [42,43], absolute principal component score–multiple linear regression model (APCS-MLR) [44,45,46], chemical mass balance (CMB) [47,48], and UNMIX model [49,50]. Each of these models has its own strengths and weaknesses [51,52], and sometimes different models may yield similar results, such as APCS-MLR and PMF [53,54]. Recent studies suggest that APCS-MLR generally outperforms other common models [51].

In China, over the past few decades, heavy metal pollution has occurred in the sediments of some rivers and lakes due to human activities such as industrial production. In the past ten years, China has attached great importance to the protection of the ecological environment and the treatment of pollution and has adopted many remediation measures, which have improved the quality of surface water and sediment environment. Turning to Baiyangdian Lake and its inflow rivers, focusing on heavy metals, many scholars have conducted studies on the pollution status and ecological risks in surface sediments. Although previous research reveals that heavy metals such as As, Cd, Cr, Co, Cu, Fe, Pb, Hg, Mn, Ni, and Zn et al., when their concentration reaches a certain value, may have a negative impact on lakes and rivers [37], published papers have been concluded that heavy metals have presented eco-environmental challenges in both past and recent years in the surface sediments mainly due to Cu, Zn, Pb, Cr, Ni, As, Cd, and Hg in the study area [9,55,56]. Previous research indicates that heavy metals may derive from various sources including industrial wastewater discharge, agricultural and domestic sewage discharge, and rock weathering deposition [2,57,58]. Specifically, Cd, As, Hg, Pb, and Zn are closely linked to wastewater discharge from refining and electroplating activities [59,60], and, to a certain extent, domestic sewage discharge [61]. The As element is also influenced by rock weathering deposition [37], as well as agricultural and domestic sewage discharge [62]. Cu, Zn, and Cd are additionally sourced from rock weathering deposition [19], automobile exhaust emissions, and tire wear sediment [59,60], while Hg is also impacted by atmospheric deposition [63]. Moreover, Cr and Ni are notably also influenced by rocks weathering deposition [59,60,63]. From the perspective of the source location for heavy metals, the concentrations of Zn, Hg, Cu, Cd, Ni, and Cr are affected by external input from rivers [37], with Cu, Cd, and Zn showing significant changes due to this input [30]. Pb and Cr are also influenced by human activities [30,37], while As is significantly impacted by local human activities [30]. The sources and relationships among these heavy metals are complex, making traditional quantitative research methods like correlation analysis and hierarchical clustering less applicable. The above findings provide a solid foundation for current research efforts.

However, previous studies have primarily been concerned with the pollution of the surface sediments in Baiyangdian Lake, with limited attention given to pollution in the inflow rivers that recharge the lake. Only a few rivers, such as Fuhe, Tanghe, Xiaoyihe, and Zhulonghe, have been studied [37,57,58,61]. Additionally, previous research did not quantitatively identify the contribution of diverse pollution sources, such as industry and rock weathering, to heavy metals pollution in the surface sediments of Baiyangdian Lake and its inflow rivers. Building upon these gaps, this study aims to: (1) identify the current pollution status of heavy metals in surface sediments of Baiyangdian Lake and all its inflow rivers using the Igeo index; (2) determine pollution sources and respective contribution rates through the APCS-MLR model; and (3) recognize potential ecological risks and biological toxicity applying the RI and SQGs.

2. Study Area

Baiyangdian Lake, acknowledged as the largest freshwater body in Northern China [64], is situated in the central region of Hebei Province, with central coordinates around (116°00′ E, 38°52′ E) (Figure 1). It has the characteristics of a monsoon, continental, and semi-arid climate; 80% of precipitation in the year is concentrated from July to September, and the average water surface evaporation is 925 mm. The typical annual temperature fluctuates between 7.3 °C and 12.7 °C, with an average annual precipitation of approximately 563.9 mm [17]. Baiyangdian Lake is crucial for preserving the eco-environment of the surrounding area [8], with several inflow rivers including Fuhe, Nanpuhe, Pinghe, Baigouyinhe, Tanghe, Xiaoyihe, Zhulonghe, Renwenganqu, and Zhaowangxinqu within its watershed (Figure 1). Under natural conditions, the rivers of Fuhe, Baigouyinhe, and Xiaoyihe input water into the lake all year round, and other rivers run into the lake in wet seasons. In recent years, the annual ecological water replenishment through the rivers into Baiyangdian Lake is more than 4.0 × 10⁸ m³, and the water level of the lake is maintained at 6.5~7.4 m. The mean water storage capacity of Baiyangdian Lake reaches about 13.2 × 10⁸ m³, with a total area of 366 km². The surface ground around Baiyangdian Lake and its inflow rivers primarily consist of Quaternary alluvial, diluvial, and lacustrine deposits [65], while silt, sandy mud, and muddy sand are prevalent in the shallow sediments of the lake and its inflow rivers. Human activities during a historical period have led to heavy metals pollution in this region, deteriorating the water and sediment quality of Baiyangdian Lake [17,62]. The main sources of pollution include domestic waste, agricultural chemicals from residents, and industrial pollutants from both local and upstream areas carried by the inflowing rivers [66,67,68].

Baiyangdian Lake, acknowledged as the largest freshwater body in Northern China [64], is situated in the central region of Hebei Province, with central coordinates around (116°00′ E, 38°52′ E) (Figure 1). It has the characteristics of a monsoon, continental, and semi-arid climate; 80% of precipitation in the year is concentrated from July to September, and the average water surface evaporation is 925 mm. The typical annual temperature fluctuates between 7.3 °C and 12.7 °C, with an average annual precipitation of approximately 563.9 mm [17]. Baiyangdian Lake is crucial for preserving the eco-environment of the surrounding area [8], with several inflow rivers including Fuhe, Nanpuhe, Pinghe, Baigouyinhe, Tanghe, Xiaoyihe, Zhulonghe, Renwenganqu, and Zhaowangxinqu within its watershed (Figure 1). Under natural conditions, the rivers of Fuhe, Baigouyinhe, and Xiaoyihe input water into the lake all year round, and other rivers run into the lake in wet seasons. In recent years, the annual ecological water replenishment through the rivers into Baiyangdian Lake is more than 4.0 × 10⁸ m³, and the water level of the lake is maintained at 6.5~7.4 m. The mean water storage capacity of Baiyangdian Lake reaches about 13.2 × 10⁸ m³, with a total area of 366 km². The surface ground around Baiyangdian Lake and its inflow rivers primarily consist of Quaternary alluvial, diluvial, and lacustrine deposits [65], while silt, sandy mud, and muddy sand are prevalent in the shallow sediments of the lake and its inflow rivers. Human activities during a historical period have led to heavy metals pollution in this region, deteriorating the water and sediment quality of Baiyangdian Lake [17,62]. The main sources of pollution include domestic waste, agricultural chemicals from residents, and industrial pollutants from both local and upstream areas carried by the inflowing rivers [66,67,68].

3. Materials and Methods

3.1. Sampling and Analysis

In May 2022, 83 surface sediment samples were gathered from depths from 0 to 20 cm in the study area using a plastic columnar sampler. These samples were obtained from 61 sites across 9 rivers, namely, Fuhe (FHP), Nanpuhe (NBHP), Pinghe (PHP), Baigouyinhe (BJYP), Tanghe (THP), Xiaoyihe (XYP), Zhulonghe (ZLP), Renwenganqu (RWP), and Zhaowangxinqu (ZWXP), as well as 22 sites within Baiyangdian Lake (BYD) (Figure 1). Upon collection, the samples were meticulously labeled, numbered, refrigerated at 4 °C, and then transferred to a laboratory for heavy metal concentration measuring, including Cu, Zn, Pb, Cr, Cd, As, Ni, and Hg. Subsequently, the samples underwent natural drying at 20 °C until reaching a consistent weight. Following the manual removal of plant roots, shellfish residues, and gravel, the samples were crushed and sifted through a mesh made of nylon fiber filaments with a pore diameter of 2 mm to produce sediment powder, which was then stored in clean and dry glass bottles for further analysis.

After that, the powder samples were firstly dissolved in a mixture of HNO₃, HF, and HClO₄ inside a clean and dry Teflon crucible, and then a mixture of HCl and HNO₃ was introduced into the crucible for digestion, followed by evaporation under ventilated conditions. Subsequently, a certain amount of HNO₃ was added to the residue, which was then dissolved under heat. After cooling, the samples were transferred to a flask, diluted with ultrapure water, and prepared for testing using various instruments. The concentrations of Zn and Cr were measured using inductively coupled plasma atomic emission spectrometry (ICP-AES, iCAP6300, USA), whereas As and Hg concentrations were determined with an atomic fluorescence spectrophotometer (AFS-230, China). Cu, Pb, Cd, and Ni concentrations were analyzed using inductively coupled plasma mass spectrometry (ICP-MS, Thermo X-II, USA). To guarantee the reliability and accuracy of the test results, national standard materials [5], repeated samples, and blank samples were included in the analysis.

3.2. Pollution Levels of Heavy Metals

After obtaining the precise concentration data of heavy metals by integrated use of these automatic instruments mentioned in the last paragraph, the geo-accumulation index (I_geo) is utilized to estimate the pollution levels of heavy metals, which takes into account the effects of natural geological processes and human activities on heavy metals [5,13]. By comparing the concentration of heavy metals found in stream sediments to local background values, this index is expressed as [13,69]:

$$I_{geo}={log}_2(C_i/1.5B_i)$$

(1)

where C_i is the measured concentration of heavy metal i (mg/kg), and B_i represents the local background value of heavy metal i (mg/kg) (

Table 1. The background values of heavy metals for surface sediments in the study area.

Item	Cu	Zn	Pb	Cr	Cd	As	Ni	Hg
Background value/(mg·kg−1)	22.6	78.4	26	68.3	0.097	13.6	30.8	36
Data sources	[71]	[9,17]	[71]	[9,17]	[9,17,71]	[9,17]	[9]	[72]

). The pollution levels are classified as practically no pollution (Igeo ≤ 0), light pollution (0 < Igeo ≤ 1), close to moderate pollution (1 < Igeo ≤ 2), moderate pollution (2 < Igeo ≤ 3), close to strong pollution (3 < Igeo ≤ 4), strong pollution (4 < Igeo ≤5), and extreme pollution (Igeo > 5) [27,70].

3.3. Pollution Sources of Heavy Metals

The recognition of pollution sources of heavy metals can be achieved through APCS-MLR [73,74]. Initially, a concept of dimensionality reduction is applied to condense multiple indicators into a few comprehensive ones. Principal component analysis (PCA) serves to investigate the connections among various indicators and to derive a condensed set of representative indicators that encapsulate critical information [21,75]. Subsequently, the rotation indicator load matrix and eigenvalues are obtained through varimax rotation based on PCA results, leading to the calculation of the absolute principal component score (APCS). Finally, by integrating APCS with multiple linear regression (MLR), the mean contribution rate (F_j) of each pollution source to every indicator can be determined [76,77,78].

The MLR is expressed as [73,74]:

$$C_j=b_{j0}+{\sum }_{p=1}^m(b_{pj}{APCS}_p)$$

(2)

where C_j denotes the measured concentration of heavy metal j (mg/kg), b_j0 stands for the multiple regression constant term of heavy metal j, and b_pj is the regression coefficient of heavy metal j to pollution source p. m represents the number of pollution sources, APCS_p stands for the absolute principal component factor score, and b_pjAPCS_p indicates the contribution of pollution source p to C_j.

The F_j is expressed as [76,77,78]:

$$F_j=(\mid b_{pj}{{(APCS}_p)}_{mean}\mid )/(\mid b_{j0}\mid +{\sum }_{p=1}^m{\mid b}_{pj}{APCS}_p\mid )$$

(3)

3.4. Potential Ecological Risks of Heavy Metals Pollution

The potential ecological risks associated with heavy metal pollution can be assessed according to the comprehensive ecological risk index (RI) and the sediment quality guidelines (SQGs), respectively.

3.4.1. Comprehensive Ecological Risk Index (RI)

The potential ecological risks can be evaluated using RI by integrating the concentrations of heavy metals, alongside their ecological, environmental, and toxicological impacts, in accordance with sedimentology principles. The equations are expressed as [71,79]:

$$E_i=T_i(C_i/B_i)$$

(4)

$$RI={\sum }_{i=1}^n{E}_i$$

(5)

where C_i represents the measured concentration of heavy metal i (mg/kg), and B_i stands for the local background value of heavy metal i (mg/kg) (Table 1). T_i represents the toxicity parameter of heavy metal i, and the values are 30 (Cd), 5 (Cu, Pb, Ni), 2 (Cr), 1 (Zn), 10 (As), and 40 (Hg), respectively [59]. E_i denotes the ecological risk index to heavy metal i, n means the number of heavy metal species. The classification of RI is displayed in

Table 2. Grading criteria to pollution levels of heavy metals according to the ecological risk index.

Ecological Risk Index to Single Heavy Metal	Comprehensive Ecological Risk Index to Multiple Heavy Metals	Risk Levels
Ei < 40	RI < 150	light
40 ≤ Ei < 80	150 ≤ RI < 300	close to moderate
80 ≤ Ei < 160	300 ≤ RI < 600	moderate
160 ≤ Ei < 320	RI ≥ 600	strong
Ei ≥ 320	-	extreme

[9].

3.4.2. Sediment Quality Guidelines (SQGs)

The potential biological toxicity of heavy metals to benthic organisms or overlying aquatic organisms can be estimated by the sediment quality guidelines (SQGs). The equations are expressed as [17,80]:

$${TUs}_i=C_i/{PEC}_i$$

(6)

$$\sum TUs={\sum }_{i=1}^n{C}_i/{PEC}_i$$

(7)

$$I_{SQGs}=(\sum TUs)/n$$

(8)

where C_i is the measured concentration of heavy metal i (mg/kg), PEC_i stands for the probable effect concentration of heavy metal i (mg/kg) (Table 3), and n means the number of heavy metal species. TUs_i denotes the toxicity unit of heavy metal i, and I_SQGs is the sediment quality guidelines index.

When ΣTUs < 4, heavy metals in sediments can be deemed as non-toxic; when ΣTUs ≥ 6, it can be classified as having acute toxicity; and when 4 ≤ ΣTUs < 6, it can be categorized as having moderate toxicity [81,82,83]. Moreover, if the C_i is below the threshold effect concentration (TEC) (mg/kg) (Table 3), the toxic effect rarely occurs; if the C_i exceeds the PEC (mg/kg), the toxic effect will occur frequently; and if the C_i is within the range of TEC and PEC, the toxic effect will occasionally occur [84,85,86]. The classification of I_SQGs is displayed in Table 4 [16,87,88].

Table 3. Threshold criteria to TEC and PEC.

	Cu	Zn	Pb	Cr	Cd	As	Ni	Hg
TEC/(mg·kg−1)	31.6	121	35.8	43.4	0.99	9.79	22.7	0.18
PEC/(mg·kg−1)	149	459	128	111	4.98	33	48.6	1.06
Data sources	[9,84,89]	[84,89]	[84,89]	[9,84,89]	[9,84,89]	[9,84,89]	[9,84,89]	[84,89]

Table 3. Threshold criteria to TEC and PEC.

	Cu	Zn	Pb	Cr	Cd	As	Ni	Hg
TEC/(mg·kg−1)	31.6	121	35.8	43.4	0.99	9.79	22.7	0.18
PEC/(mg·kg−1)	149	459	128	111	4.98	33	48.6	1.06
Data sources	[9,84,89]	[84,89]	[84,89]	[9,84,89]	[9,84,89]	[9,84,89]	[9,84,89]	[84,89]

Table 4. Grading criteria to potential biological toxicity of heavy metals according to I_SQGs.

Item	ISQGs ≤ 0.1	0.1 < ISQGs ≤ 1	1 < ISQGs ≤ 5	ISQGs > 5
Probability of biological toxicity (PBT)	PBT < 14%	15% < PBT ≤ 29%	33% < PBT ≤ 58%	75% < PBT ≤ 81%
Risk levels	no	low	moderate	high

Table 4. Grading criteria to potential biological toxicity of heavy metals according to I_SQGs.

Item	ISQGs ≤ 0.1	0.1 < ISQGs ≤ 1	1 < ISQGs ≤ 5	ISQGs > 5
Probability of biological toxicity (PBT)	PBT < 14%	15% < PBT ≤ 29%	33% < PBT ≤ 58%	75% < PBT ≤ 81%
Risk levels	no	low	moderate	high

4. Results and Discussion

4.1. Pollution Levels of Heavy Metals

With the exception of Hg in inflow rivers and As and Hg in Baiyangdian Lake, the concentration of other heavy metals in certain sites, both in the surface sediments of inflow rivers and Baiyangdian Lake, was found to be higher than their background values based on statistical data analysis. Among the 61 sites in inflow rivers, the concentrations of Cu, Zn, Pb, Cr, Cd, As, and Ni were larger than their background values in 45, 32, 22, 33, 41, 51, and 5 sites, respectively, accounting for 73.77%, 52.46%, 36.07%, 54.10%, 67.21%, 83.61%, and 8.20% of the sites. In Baiyangdian Lake, out of the 22 sites, the concentrations of Cu, Pb, Zn, Cr, Ni, and Cd exceeded their background values in 16, 3, 8, 7, 11, and 17 sites, representing proportions of 72.73%, 13.64%, 36.36%, 31.82%, 50.00%, and 77.27%, respectively. Furthermore, the mean and median values of heavy metal concentrations in inflow rivers are almost all larger than those in Baiyangdian Lake, indicating a higher overall pollution level of heavy metals in the upstream region of the watershed (

Table 5. Concentration statistics of heavy metals at sampling sites.

Item	Cu	Zn	Pb	Cr	Cd	As	Ni	Hg
Max/(mg·kg−1)	81.200	372.000	123.000	92.600	2.250	28.700	54.200	0.200
Mean/(mg·kg−1)	27.746	82.849	30.269	69.931	0.207	9.544	33.669	0.042
Min/(mg·kg−1)	15.200	41.900	14.900	46.100	0.038	2.080	20.600	0.010
Standard deviation	9.796	38.495	14.308	9.855	0.294	4.073	6.226	0.034
Variation coefficient	35.305%	46.464%	47.270%	14.092%	141.765%	42.673%	18.493%	81.556%
Upper quartile (75%)	30.500	88.900	34.900	76.050	0.200	11.250	37.000	0.041
Median	26.000	72.700	26.900	70.300	0.150	8.880	33.600	0.029
Lower quartile (25%)	21.750	64.500	21.600	63.550	0.110	7.235	29.050	0.024
Max/(mg·kg−1)	33.100	91.300	30.500	75.300	0.220	13.500	40.700	0.074
Mean/(mg·kg−1)	25.159	72.918	22.768	62.927	0.136	8.191	31.800	0.035
Min/(mg·kg−1)	18.100	50.400	15.300	48.800	0.056	4.800	24.300	0.015
Standard deviation	3.887	12.457	3.755	8.474	0.051	1.778	4.767	0.015
Variation coefficient	15.450%	17.083%	16.493%	13.467%	37.623%	21.706%	14.990%	44.508%
Upper quartile (75%)	27.800	86.750	25.025	72.800	0.178	8.925	36.675	0.045
Median	24.100	72.900	23.150	62.250	0.130	8.255	30.750	0.030
Lower quartile (25%)	22.450	64.100	20.250	55.425	0.096	7.242	28.100	0.025

). Additionally, the variation coefficient of heavy metal concentrations in inflow rivers was also larger than that in Baiyangdian Lake, with a relatively large variation coefficient among different heavy metals, suggesting significant differences in pollution levels among various heavy metals in inflow rivers (Table 5). This phenomenon might be attributed to two primary factors: (1) the relatively large interval distance between sampling sites, and (2) the distinct locations of different sampling sites, which were significantly influenced by human activities and various pollution sources. This underscored the importance of identifying the sources of pollution.

The I_geo values calculated for both the surface sediment of the inflow rivers and Baiyangdian Lake suggest that the levels of heavy metal pollution were relatively low (Figure 2). Among the 63 sites in inflow rivers, negative values for Cr and Hg displayed no pollution from these heavy metals. However, Cu, Zn, Pb, Cd, As, and Ni were found in 8, 5, 9, 27, 1, and 1 sites, respectively, with proportions of 13.11%, 8.20%, 14.75%, 44.26%, 1.64%, and 1.64%, showing light pollution. Cu, Pb, Zn, and Cd were present in one, one, one, and three sites, respectively, with proportions of 1.64%, 1.64%, 1.64%, and 4.92%, indicating close to moderate pollution levels. Additionally, Cd was found in one site, accounting for 1.64%, showing moderate pollution. In Baiyangdian Lake, out of the 22 sites, Cu, Zn, Pb, Cr, As, Ni, and Hg were at no pollution level, while Cd was present in eight sites, representing 36.36% of places displaying light pollution. Furthermore, the median values of I_geo for Cu were −0.413 (no pollution), 0.204 (light pollution), and 1.260 (close to moderate pollution). For Pb, the median values of I_geo were −0.700 (no pollution), 0.219 (light pollution), and 1.496 (close to moderate pollution). The median values of I_geo for Zn were −0.716 (no pollution), 0.188 (light pollution), and 1.408 (close to moderate pollution). The median value of I_geo for Cr was −0.600 (no pollution). For Ni, the median values of I_geo were −0.013 (no pollution) and 0.230 (light pollution). The median values of I_geo for Cd were −0.404 (no pollution), 0.307 (light pollution), 1.918 (close to moderate pollution), and 2.563 (moderate pollution). The median values of I_geo for As were −1.271 (no pollution) and 0.492 (light pollution). Lastly, the median value of I_geo for Hg was −10.814 (no pollution). These values were crucial for enhancing our understanding of the average pollution levels associated with various heavy metals.

4.2. Pollution Sources of Heavy Metals

Principal component analysis (PCA) was performed on the surface sediments of inflow rivers and Baiyangdian Lake, revealing that the sum of the first three principal component eigenvalues for the eight heavy metals was 6.817 and 6.921, respectively (Figure 3). These values accounted for 84.214% and 86.517% of the total information, respectively. The main load heavy metals (

Table 6. PCA of heavy metals in surface sediment of inflow rivers.

Heavy Metal	Load Coefficient of PCA
	PC01	PC02	PC03
Cu	0.828	0.311	0.182
Zn	0.92	−0.322	−0.01
Pb	0.817	−0.303	0.184
Cr	0.615	0.695	−0.002
Cd	0.738	−0.583	−0.219
As	0.62	−0.12	−0.626
Ni	0.683	0.689	−0.113
Hg	0.533	−0.204	0.628

and

Table 7. PCA of heavy metals in surface sediment of Baiyangdian Lake.

Heavy Metal	Load Coefficient of PCA
	PC01	PC02	PC03
Cu	0.965	−0.083	−0.079
Zn	0.941	0.181	−0.047
Pb	0.787	0.476	−0.183
Cr	0.842	−0.383	−0.314
Cd	0.707	0.439	0.086
As	0.472	−0.522	0.587
Ni	0.889	−0.398	−0.129
Hg	0.493	0.299	0.62

) of the principal component PC01 for the inflow rivers were Cu, Zn, Pb, Cr, Cd, As, Ni, and Hg; the principal component PC02 consisted of Cr, Cd, and Ni, and the principal component PC03 included As and Hg. On the other hand, for Baiyangdian Lake, the principal component PC01 encompassed Cu, Zn, Pb, Cr, Cd, and Ni. PC02 included As, and PC03 was comprised of As and Hg.

Based on the APCS-MLR model, multiple linear regression equations were developed, and the constant terms and coefficients were presented in

Table 8. Regression constant terms and coefficients based on the APCS-MLR model in surface sediments of inflow rivers.

Heavy Metal	Constant Term	APCS01 Coefficient	APCS02 Coefficient	APCS03 Coefficient	R2
Cu	−8.439	5.799	\	−3.032	0.848
Zn	−8.118	27.557	−18.352	6.767	0.932
Pb	1.780	10.416	−8.126	−2.076	0.843
Cr	27.851	2.156	6.314	−1.425	0.868
Cd	−0.095	0.177	−0.177	0.116	0.829
As	0.962	0.983	0.436	3.835	0.986
Ni	5.567	1.611	3.973	−0.704	0.954
Hg	0.003	0.018	−0.017	−0.008	0.473

and

Table 9. Regression constant terms and coefficients based on the APCS-MLR model in surface sediments of Baiyangdian Lake.

Heavy Metal	Constant Term	APCS01 Coefficient	APCS02 Coefficient	APCS03 Coefficient	R2
Cu	9.315	1.693	\	\	0.930
Zn	24.835	5.297	2.121	\	0.919
Pb	11.746	1.335	1.686	−0.725	0.880
Cr	31.810	3.224	−3.060	−2.814	0.955
Cd	−0.002	0.016	0.021	\	0.693
As	3.579	0.379	−0.874	1.104	0.839
Ni	12.927	1.914	−1.788	−0.653	0.965
Hg	0.001	0.003	0.004	0.010	0.717

. The fitting coefficient of determination (R²) for the measured concentration of heavy metals and the model-calculated concentration mostly ranges from 0.693 to 0.984, indicating a strong level of agreement. This suggests that the model is dependable and appropriate for analyzing the sources of heavy metal pollution. The calculations reveal that (1) in the surface sediments of inflow rivers, heavy metals Cu, Zn, Pb, As, Cd, and Hg are predominantly attributed to industrial wastewater discharge, with mean contribution rates of 78.07%, 64.45%, 62.12%, 57.28%, 53.66%, and 57.27%, respectively. However, the primary source of Cr remains unidentified (

Table 10. Mean contribution rate of pollution sources to heavy metals in surface sediment of inflow rivers.

Pollution Sources	Cu	Zn	Pb	Cr	Cd	As	Ni	Hg
Industrial wastewater discharge	78.07%	64.45%	62.12%	19.00%	53.66%	57.28%	29.62%	57.27%
Rocks weathering deposition	\	31.16%	35.14%	39.99%	38.87%	18.58%	52.49%	39.18%
Agricultural and domestic sewage discharge	3.06%	1.22%	0.96%	1.00%	2.68%	14.78%	1.02%	1.96%
Unknown source	18.87%	3.17%	1.77%	40.02%	4.80%	9.36%	16.87%	1.59%

); (2) in the surface sediments of Baiyangdian Lake, heavy metals Cu, Zn, Pb, Cd, Ni, and Hg are primarily linked to industrial wastewater discharge, with mean contribution rates of 62.13%, 63.87%, 46.39%, 86.19%, 53.18%, and 68.12%, respectively. Nevertheless, the major contributor of Cr and As are yet to be determined (

Table 11. Mean contribution rate of pollution sources to heavy metals in surface sediment of Baiyangdian Lake.

Pollution Sources	Cu	Zn	Pb	Cr	Cd	As	Ni	Hg
Industrial wastewater discharge	62.13%	63.87%	46.39%	44.12%	86.19%	39.35%	53.18%	68.12%
Rocks weathering deposition	\	2.93%	6.54%	4.71%	12.59%	10.00%	5.59%	10.26%
Agricultural and domestic sewage discharge	\	\	2.26%	3.43%	\	9.94%	1.63%	19.07%
Unknown source	37.87%	33.21%	44.81%	47.74%	1.21%	40.71%	39.59%	2.55%

); (3) in contrast, the contribution of agricultural and domestic sewage discharge to heavy metals in the surface sediments of both inflow river and Baiyangdian Lake is relatively minimal (Table 10 and Table 11).

4.3. Potential Ecological Risks of Heavy Metals Pollution

4.3.1. Potential Ecological Risks of Heavy Metals Pollution Based on RI

The potential ecological risks linked to heavy metals in both the surface sediments of inflow rivers and Baiyangdian Lake are relatively low, with the exception of Cd, based on the calculated E_i (Figure 4). Among the 61 sites in inflow rivers, the ecological risk index was below 40 for Cu, Zn, Pb, Cr, As, Ni, and Hg, indicating a light level of potential ecological risk posed by these heavy metals. However, Cd showed levels ranging from close to moderate to extreme in 34, 3, 2, and 1 sites, respectively, accounting for 55.74%, 4.92%, 3.28%, and 1.64% of the total sites. In Baiyangdian Lake, only Cd in 13 sites (59.09%) exhibited a close to moderate level of risk, while the other heavy metals remained at a light level. The comprehensive ecological risk in most sites in both the surface sediment of inflow rivers and Baiyangdian Lake was also relatively low, as determined by the calculated RI (Figure 4). Out of the 61 sites in inflow rivers, the levels of risk were categorized as light, close to moderate, moderate, and strong in 56, 3, 1, and 1 sites, respectively, representing proportions of 91.80%, 4.92%, 1.64%, and 1.64%. On the other hand, all 22 sites in Baiyangdian Lake were classified as having a light level of risk.

4.3.2. Potential Ecological Risks of Heavy Metals Pollution Based on SQGs

The potential biological toxicity of heavy metals to benthic organisms or overlying aquatic organisms in both the surface sediment of inflow rivers and Baiyangdian Lake was found to be at a low-risk level based on the calculated I_SQGs. The values exhibited all less than 1 but more than 0.1 (Figure 5). Additionally, the probability of biological toxicity (PBT) ranged between 15% and 29% (Table 4). When looking at the calculated ΣTUs, 1 out of the 61 sites in inflow rivers indicated moderate toxicity, while the rest were considered non-toxic (Figure 6). In Baiyangdian Lake, all 22 sites were identified as non-toxic (Figure 6).

According to the values of C_i, TEC, and PEC, there were evident variations in the frequency of toxic effects among different heavy metals. Among the 61 sites in inflow rivers, Ni was found to frequently cause toxic effects in one site, while Cu, Zn, Pb, Cr, Cd, As, Ni, and Hg were associated with occasional toxic effects in 11, 14, 6, 61, 59, 1, 25, and 1 sites, respectively, with proportions of 18.03%, 22.95%, 9.84%, 100.00%, 96.72%, 1.64%, 40.98%, and 1.64%. Additionally, other sites exhibited rare occurrences of toxic effects from these heavy metals (Figure 7). In the case of the 22 sites in Baiyangdian Lake, Cu, Cr, Ni, and As were found to occasionally cause toxic effects in 2, 22, 22, and 2 sites, respectively, with proportions of 9.09%, 100.00%, 100.00%, and 9.09%, while other sites experienced rare toxic effects from these heavy metals (Figure 7).

4.4. Comparison of the Results with Published Study

Several published studies have reported the average concentration of different heavy metals in the sediments of Baiyangdian Lake at different times (

Table 12. Comparison of the average concentration of heavy metals in the sediments of Baiyangdian Lake (mg·kg⁻¹).

Sampling Time	Cu	Zn	Pb	Cr	Cd	As	Ni	Hg	Data Sources
2022	25.159	72.918	22.768	62.927	0.136	8.191	31.800	0.035	Present study
2020	31.02	93.06	39.8	82.56	0.32	18.96	\	\	[17]
2019–2020	37.43	102.43	27.78	75.46	0.33	9.91	37.22	0.054	[55]
2019	32.33	84.24	19.17	56.37	0.35	9.53	30.18	\	[9]
2019	28.79	56.77	19.01	59.68	0.25	\	28.85	\	[91]
2016	32.27	137.84	66.96	54.52	1.22	\	27.58	\	[92]
2010	28.19	150.88	33.50	41.34	0.80	32.08	35.04	\	[56]

). Nonetheless, owing to differences in the number and distribution of sampling sites, as well as sampling depth, accurately identifying changes in the average concentration of heavy metals is challenging. Subjectively, there appears to be a decreasing trend in the average concentration of heavy metals, aligning with the findings of Wei’s study [17]. This trend could be attributed to eco-environmental management and restoration efforts [9,90]. Comparing the results to previous studies since 2019, it is more consistent that the pollution levels of heavy metals are generally at a lower degree. However, some sites still exhibit relatively higher pollution levels for Cd [9,55], highlighting the need for attention from the ecological environment management department.

5. Conclusions

(1)

Pollution levels of heavy metals exhibited relatively low in both the surface sediments of inflow rivers and Baiyangdian Lake. However, it is important to note that Cu, Zn, Pb, and Cd, with proportions of 1.64%, 1.64%, 1.64%, and 4.92%, respectively, indicated close to moderate pollution levels. Specifically, Cd, with a proportion of 1.64%, displayed moderate pollution at sampling sites of inflow rivers. Compared with previous studies, it is evident that heavy metal pollution levels were generally low, but Cd showed relatively higher pollution degrees at some sites.

(2)

Heavy metals found in the surface sediments of inflow rivers and Baiyangdian Lake originated from various sources such as industrial wastewater discharge, rock weathering deposition, and agricultural and domestic sewage discharge during the historical period. In the case of inflow rivers, industrial wastewater discharge was identified as the primary source of Cu, Zn, Pb, As, Cd, and Hg, contributing mean rates of 78.07%, 64.45%, 62.12%, 57.28%, 53.66%, and 57.27%, respectively. Rock weathering deposition was determined to be the main source of Ni, with a mean contribution rate of 52.49%. However, the main contributor to Cr, accounting for 40.02%, was not definitively identified, while rock weathering deposition contributed 39.99%. As for heavy metals in Baiyangdian Lake, industrial wastewater discharge was the primary source of Cu, Zn, Pb, Cd, Ni, and Hg, with mean contribution rates of 62.13%, 46.39%, 63.87%, 53.18%, 86.19%, and 68.12%, respectively. The main sources of Cr (47.74%) and As (40.71%) were not clearly identified, although industrial wastewater discharge contributed 44.12% and 39.35%, respectively. Additionally, heavy metals from the water recharging of the nine inflow rivers and local industrial activities, particularly for Cr, Ni, Cd, and Hg, also contributed to the heavy metals content in Baiyangdian Lake.

(3)

The comprehensive ecological risk indicated relatively low in most sites, both in the surface sediment of inflow rivers and Baiyangdian Lake. However, it is important to highlight that a small proportion of sampling sites (4.92%, 1.64%, and 1.64%) showed varying degrees of risk in inflow rivers, ranging from close to moderate to strong. The potential biological toxicity of heavy metals to benthic organisms or overlying aquatic organisms at all sites in inflow rivers and Baiyangdian Lake was also low, with a probability of biological toxicity (PBT) between 15% and 29%. Only one site was identified as having moderate toxicity, while the rest were considered non-toxic. Furthermore, variations were observed in the occurrence frequency of toxic effects among different heavy metals. Specifically, Ni was found to frequently cause toxic effects in one site, whereas other heavy metals caused toxic effects rarely or occasionally.

(4)

This study offered a comprehensive understanding of the heavy metal pollution status, sources, and potential ecological risks associated with various heavy metals in the surface sediments of Baiyangdian Lake and its inflow rivers. The research findings provided a crucial basis for government departments to formulate effective ecological restoration measures. Notably, for certain inflow rivers, there had been limited research or reporting on sediment heavy metal pollution, and this study contributed valuable new insights. Although the overall pollution level of surface sediments was low and had demonstrated a decreasing trend over recent years, it was important to highlight that some areas still exhibited Cd pollution at relatively higher concentrations. Nevertheless, due to financial constraints, the sample quantity and depth in this study were limited. Future researchers should enhance sample density to improve the accuracy of the findings.