Assessing Sustainable Management of a Plateau Lake: Adsorption and Integrated Risk of Sediment Pollutants

Abstract

As one of the nine largest plateau lakes in Yunnan Province, China, Qilu Lake is considered significantly affected by extensive anthropogenic pollution. However, the pollution status and integrated risks posed by organochlorine pesticides and heavy metals in the lake’s sediments remain poorly understood. This study analyzed the concentrations of organochlorine pesticides and heavy metals in 22 surface sediment samples from the Qilu Lake, and assessed their combined ecological and health risks. Results showed that the mean concentrations of five target organochlorine pesticides (α-hexachlorocyclohexane, β-hexachlorocyclohexane, γ-hexachlorocyclohexane, p,p′-dichlorodiphenyltrichloroethane, and o,p′-dichlorodiphenyltrichloroethane) were consistently low, whereas most heavy metals, except for arsenic, significantly exceeded Yunnan Province background values, with mercury and cadmium exhibiting the most pronounced enrichment. Source analysis indicated that the heavy metals mainly derived from a mixed agricultural-industrial-traffic source, a natural geogenic source, and industrial-traffic emissions, while the organochlorine pesticides originated from both historical residues and ongoing agricultural applications. A linear model was identified as optimal function for characterizing the adsorption-accumulation relationship between organochlorine pesticides and heavy metals. Ecological risks were dominated by heavy metals, especially cadmium, and the evaluated results showed that the health risks were higher for children than adults. Although non-carcinogenic risks were negligible, carcinogenic risks, particularly from chromium, warrant special attention, especially for children. This study enhances the understanding of combined pollution in rural plateau lakes and provides a scientific basis for achieving sustainable water environment management by (1) establishing an integrated risk assessment framework for pollutants, (2) identifying a priority control pollutant list, and (3) laying a theoretical foundation for targeted ecological restoration strategies, directly supporting the implementation of Sustainable Development Goal (SDG) 6 (clean water and sanitation).

1. Introduction

Lakes represent critical ecosystems on the Earth and constitute vital water resources for human society [1,2,3]. Importantly, sediments are ecologically valuable components of lake ecosystems, acting as both a sink and source of persistent toxic substances (PTSs) [4]. When introduced into lakes, a small proportion of PTS remains dissolved in the water column, whereas the majority, combining with suspended particulate matter and biological debris, accumulate in sediments. These sediments can later become secondary pollution sources, releasing PTSs back into the overlying water column through chemical, physical and biological processes [5]. Moreover, owing to their long residence time, lake sediments act as valuable archives of anthropogenic impacts and natural watershed processes [6]. Therefore, sediment analysis is a critical method to determine the pollution status and risks of PTSs in lakes. Complex PTS mixtures are of particular concern, and their environmental behavior and ecological effects have attracted growing scientific interest [7,8].

PTSs in lake sediments can cause progressive ecosystem deterioration [9,10] and various toxicological effects across multiple aquatic trophic levels [11,12]. Comprehensive risk assessments of lake sediments serve to quantify pollution severity and ecological impacts, thereby establishing a scientific foundation for targeted ecological restoration strategies. Empirical evidence indicates that such assessments can significantly improve water quality and promote the recovery of ecosystem integrity in polluted lakes [13,14]. Therefore, quantifying cumulative risks from complex PTSs in sediments emerges as a critical research priority.

As critical PTSs, organochlorine pesticides (OCPs) and heavy metals (HMs) have become priority environmental pollutants of global concern. OCPs, a class of synthetic chlorinated hydrocarbons, were historically applied for agricultural crop protection and public health interventions [15]. Due to their environmental persistence and toxicity, OCPs have been progressively phased out globally under the Stockholm Convention. HMs comprise metallic and metalloid elements with densities greater than 4 g/cm³ [16]. HM emissions originate from natural and anthropogenic sources. In lake sediments, OCPs mainly originate from agricultural and urban inputs [17], whereas HMs largely accumulate from industrial discharges and agricultural runoff [18].

Excessive PTSs in lake sediments not only degrade biodiversity, but can also bioaccumulate through the food chain, ultimately endangering human health. Exposure to OCPs is significantly associated with obesity, type 2 diabetes, Parkinson’s disease, and cancer [19]. Furthermore, by mimicking thyroid hormones, OCPs can trigger cascading detrimental effects on neurobehavioral and reproductive system function and development [20]. Certain HMs including zinc (Zn) and copper (Cu) serve as crucial dietary micronutrients; however, supra-nutritional exposure or contact with non-essential HMs like cadmium (Cd), lead (Pb), chromium (Cr), arsenic (As) and nickel (Ni) poses serious threats to human health [21]. HM exposure may induce various diseases, including cancer, retardation, hypertension, and multi-organ dysfunction [19,22].

Extensive HM and OCP pollution has been documented in the lake ecosystems of the Yunnan–Guizhou Plateau [2,23,24,25,26]. Nevertheless, critical research gaps persist, notably in source apportionment and health risk assessment. While risk assessment methods have advanced, the cumulative and interactive health and ecological effects of co-exposure to OCPs and HMs are still poorly quantified. Moreover, despite the availability of various remediation techniques for OCPs and HMs in the aquatic systems, significant uncertainties exist regarding sustainability and environmental effectiveness. Qilu Lake, one of the nine largest freshwater lakes in Yunnan Province, serves as a critical water resource for Tonghai County. Favorable climate conditions, characterized by warm temperatures, high humidity, and abundant sunlight, have made the region the largest vegetable production base in Yunnan Province. High-intensity agricultural practices are prevalent, with chemical fertilizer application rates ranging from 270 to 600 kg/mu per crop cycle, and annual pesticide usage increasing by approximately 300 tons [27,28]. Additionally, industrial activities such as ferrous metallurgy, manufacturing, printing, fertilizer production, and food processing have developed within the basin. As such, Qilu Lake represents an ideal site for investigating the combined effects of OCP and HM pollution. Studies have reported pollution by nitrogen, HMs and OCPs in the aquatic ecosystems of Qilu Lake, posing potential yet unpredictable ecological risks [23,25,29]. However, due to the complexity and variable nature of OCPs and HMs, critical research gaps remain regarding the synergistic effects of these pollutants, as well as the risks associated with combined exposure in multi-pollutant scenarios, particularly in high-altitude lake ecosystems such as those on the Yunnan–Guizhou Plateau. Therefore, the primary objectives of this study were to (1) conduct integrated assessment of HM and OCP pollution levels; (2) assess the potential ecological and human health risks under combined HM and OCP exposure scenarios; and (3) identify pollution sources of HMs and OCPs. This study establishes a theoretical foundation for the coordinated control and ecological risk management of HMs and OCPs in the Qilu Lake basin, while also provides scientific support for evidence-based governance strategies in agricultural lake ecosystems.

2. Materials and Methods

2.1. Study Area Overview and Sampling

Qilu Lake (102°43′49″–102°49′12″ E, 24°08′33″–24°13′57″ N; elevation: 1797 ± 5 m a.s.l) is a representative plateau freshwater lake located in Tonghai County, Yuxi City, Yunnan Province, China. As a critical water resource for the regional agricultural and domestic use, this semi-enclosed shallow lake has a surface area of 36 km² and a drainage basin of 359 km², with a mean depth of 4.5 m and a water volume of 1.676 × 10⁸ m³. The basin encompasses 7 towns and 20 villages, supporting a population of 275,300 and containing 15,173 hectares of farmland. Major inflow rivers include the Hongqi river, Daxin river, Zhewan river, and Zhonghe river (Figure 1), with a total annual runoff of 84.30 × 10⁶ m³. The Hongqi River alone contributes about 50% of this inflow. Seasonal monsoon rains from late May to mid-October account for 83% of the annual surface runoff.

In June 2022, we employed a Petersen grab (CN150, Guangzhou Ruibin Technology Co., Ltd., Guangzhou City, China) to collect surface sediments (0–10 cm) at 22 different points (Figure 1). Each composite sediment sample, formed from four subsamples collected from within a 2 m × 2 m area, was temporarily stored in a pre-cleaned amber glass bottle sealed under light-proof conditions at 4 °C for transport. Subsequently, samples were freeze-dried, grinded and sieved through a 100–mesh sieve. Then, duplicate samples were allocated for the analysis of HMs and OCPs, and stored at −20 °C until analysis.

2.2. Sample Processing and Testing

The pretreatment and analytical procedures for OCPs and HMs in sediment samples were performed in accordance with the standard methods prescribed by the Ministry of Ecology and Environment of China [30,31].

For HM analysis, 1.0 g of each pretreated sample was digested by microwave-assisted treatment with a mixture of HCl–HNO₃–HF–HClO₄. After digestion, residual HF was neutralized with saturated H₃BO₃, ensuring complete fluoride complexation. The HM concentrations were determined by Inductively Coupled Plasma Mass Spectrometry (ICP–MS, Thermo Fisher, X2, Waltham, MA, USA). Standard solutions with five concentration points, and internal standard solutions, were prepared to establish the calibration curve. As the concentration of the target element exceeded the range of the calibration curve, the sample was diluted tenfold with aqueous HNO₃.

For OCP analysis, 10 g of each pretreated sample was dried with anhydrous Na₂SO₄ before extraction. The detailed extraction, concentration, and purification procedures follow the Chinese National Environmental Standard [31]. The purified solution was again concentrated to near dryness under nitrogen flow. An appropriate volume of 500 mg/L internal standard solution was added, and the final volume was adjusted to 1.0 mL. After thorough mixing, the solution was transferred into a 2 mL chromatography vial for instrumental analysis. The OCP concentration was measured by gas chromatography–mass spectrometry (GC–MS, Clarus 600, 8547, PE, USA) with electron ionization and an HP–5MS capillary column (30 m × 0.25 mm × 0.25 µm). Helium (>99.999%) served as the carrier gas with a 1.0 mL/min flow rate and the injection volume was 1.0 µL in splitless injection mode. The injector and ion source temperatures were maintained at 280 °C and 230 °C, respectively. The oven program was initiated at 120 °C for 2 min; increased at 12 °C/min to 180 °C with 5 min hold time, followed by a further increase at 7 °C/min to 240 °C with 1 min hold time; subsequently increased at 7 °C/min to 250 °C with 2 min hold time; and finally increased to 280 °C for 2 min. Prior to OCP testing, we prepared calibration curves with standard and surrogate solutions.

2.3. Quality Control/Quality Assurance

To control analytic quality, procedural blanks and matrix-spiked samples were analyzed. Two mixture standard solutions were used: one for HMs, containing As, Cd, Cr, Hg, Pb, Cu, Ni and Zn at 100 mg/L each, and one for OCPs, containing α-HCH, β-HCH, γ-HCH, δ-HCH, o,p′-DDT, p,p′-DDT, p,p′-DDE, p,p′-DDD,HCB, Heptachlor, Aldrin, Heptachlor epoxide, cis-Chlordane, trans-Chlordane, α-Endosulfan, β-Endosulfan, Dieldrin, Endrin, Endrin aldehyde, Endosulfan sulfate, Endrin ketone, Methoxychlor, and Mirex at 1 mg/L each.

To ensure analytic accuracy, each batch of 20 samples included 1 reagent blank, 1 duplicate, 2 reference standard samples, and 1 matrix-spiked sample, resulting in 15 valid data points. The reagent blank was used to monitor possible pollution, and no target compounds were detected in it. The matrix-spiked sample was employed to evaluate matrix effects, with spiking levels of 3–5 times the expected concentration. All chemical reagents including acids, solvents, copper powder, and anhydrous sodium sulfate were checked for potential interference and pollution prior to use. The recoveries of HMs and OCPs in sediments ranged 75–109% and 93–105%, respectively, meeting the accepted criteria for trace analysis of these pollutants.

2.4. Ecological Risk Assessment

The potential ecological risk index (PERI) is used to assess the potential risks posed by HMs. PERI provides a comprehensive assessment by incorporating synergistic effects, hazard thresholds, HM concentrations, and environmental sensitivity [32,33]. The calculation follows the established approach [34]:

$$PERI = {\sum }_{i=1}^n{Er}_i = {\sum }_{i=1}^n{Tr}_i{\displaystyle \frac{C_i}{C_b}}$$

(1)

where Er_i represents the ecological risk factor of a single HM i, Tr_i denotes its toxic response coefficient (Cu = 5, Ni = 5, Pb = 5, As = 10, Cd = 30, Hg = 40, Cr = 2, and Zn = 1) [1], C_i is the measured concentration of HM i (mg/kg), and C_b is the soli background value of HM i in Yunnan Province (Cu = 46.3, Ni = 42.5, Pb = 40.6, As = 18.4, Cd = 0.218, Hg = 0.058, Cr = 65.2, and Zn = 89.7 mg/kg) [35]. Both PERI and Er_i values are classified into five risk levels, as detailed in Table S1.

The ecological risk of OCPs was evaluated using the risk quotient (RQ) method, calculated as follows:

$$RQ = {\displaystyle \frac{MC}{PNEC}}$$

(2)

where MC is the measured concentration of individual OCPs (ng/g); PNEC denotes the predicted no-effect concentration (ng/g). The PNEC values used in this study were obtained from a previous study [14].

For each sampling point, the total RQ (∑RQ) is calculated using the following equation:

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

(3)

The ecological risks are classified into four levels: negligible risk (RQ < 0.01), low risk (0.01 ≤ RQ < 0.1), moderate risk (0.1 ≤ RQ < 1), and high risk (RQ ≥ 1) [8].

2.5. Human Risk Assessment

Chronic exposure to OCPs and HMs poses significant health risks. According to the U.S. Environmental Protection Agency (US EPA) risk assessment model [36,37,38], this study quantified both carcinogenic and non-carcinogenic risks for children and adults via three primary exposure routes: ingestion (Ing), dermal contact (Der), and inhalation (Inh). The average daily doses (ADD) of OCPs and HMs for both groups via three routes were assessed using the flowing equations:

$${ADD}_{Ing} = {\displaystyle \frac{C_i \times IngR \times EF \times ED}{BW \times AT}} \times {10}^{-6}$$

(4)

$${ADD}_{Der}={\displaystyle \frac{C_i\times SA\times AF\times ABS\times EF\times ED}{BW\times AT}}\times {10}^{-6}$$

(5)

$${ADD}_{Inh}={\displaystyle \frac{C_i\times InhR\times EF\times ED}{BW\times AT}}\times {10}^{-6}$$

(6)

The total carcinogenic risk (ΣCR) and total hazard index (ΣHI) were employed to quantify the potential carcinogenic and non-carcinogenic risks based on the following equations:

$$\Sigma CR = {\sum }_{i =1}^n{ADD}_i \times {SF}_i$$

(7)

$$\Sigma HI={\sum }_{i=1}^n{\displaystyle \frac{{ADD}_i}{{RfD}_i}}$$

(8)

where C_i is the concentration of individual HMs or OCPs; n denotes the number of HMs and OCPs. Definitions and values of all other parameters used in calculating ADD, ΣCR, and ΣHI for OCPs and HMs are provided in Tables S2 and S3, respectively.

According to the US EPA guidelines [39], CR is classified as follows: negligible (ΣCR < 10⁻⁶), acceptable (10⁻⁶ < ΣCR < 10⁻⁴), and significant (ΣCR > 10⁻⁴). For non-carcinogenic risk, a ΣHI < 1 indicates no adverse health effects.

2.6. Statistical Data Analysis

Statistical analyses including ANOVA F-test, t-test statistics and Spearman correlation analysis were performed using SPSS 23. Coefficient of variation (CV) and enrichment factor (EF) of HMs and OCPs were calculated with Excel 2022. Geospatial visualizations depicting the study area, sampling point distribution and spatial patterns of HMs and OCPs for PERI and ∑RQ data were constructed using ArcMap 10.5. Origin 2022 was employed to generate box plots, bar charts, radar charts, and heat maps.

3. Results and Discussion

3.1. Descriptive Statistics and Pollution Degree Assessment for OCPs and HMs

Preliminary statistical analysis indicated the varying degrees of HM and OCP pollution in the surface sediments of Qilu Lake (Figure 2a,b). In this study, concentrations below the detection limit were assigned a value of zero. Among 23 target OCPs, only α-HCH, β-HCH, γ-HCH, o,p’-DDT and p,p′-DDT were detected, with mean concentrations of 5.72, 13.94, 2.40, 8.99 and 6.30 ng/g, respectively (Figure 2a). This congener profile aligns with those documented in sediments from Fuxian Lake and Yangzong Lake [24,26], suggesting similar source characteristics and environmental persistence. The mean concentrations of ΣHCHs (α + β + γ) and ΣDDTs (o,p’ + p,p′) were 22.07 and 15.29 ng/g, respectively, both well below China’s Soil Environment Quality Standards limit of 100 ng/g [40]. Furthermore, the mean ΣOCP concentration (37.36 ng/g) in this study is higher than in Baiyangdian Lake [41], Ashtamudi Wetland [42], and Zayandehrud River [43], but lower than those in the Yellow River [8], Karst Wetland [44], East Lake [45], and Fuxian Lake [24]. The regional heterogeneity reflects the synergistic effect of anthropogenic pressure and industrial development on natural ecosystems. Among HCH isomers, β-HCH predominated in both detection frequency and concentration, implying that environmental isomerization of α-HCH and γ-HCH to β-HCH. DDT concentrations were lower than those of HCHs, consistent with the historically greater production and application of HCHs in China.

The mean concentrations of HMs were as follows: Hg: 0.16; Cd: 1.34; As: 18.20; Ni: 51.67; Cu: 70.62; Pb: 54.00; Cr: 104.00; and Zn: 160.30 mg/kg (Figure 2b). These values represented 2.76, 6.15, 0.99, 1.22, 1.53, 1.33, 1.60, and 1.79 times the soil background values in Yunnan Province [35], respectively, indicating elevated accumulation for most HMs except As. Concentrations of Cd, Ni and Cu exceeded the National Soil Secondary Quality Standard values limits of 0.3, 40, and 50 mg/kg, respectively, whereas other HMs remained below their respective thresholds [46]. Moreover, all eight HMs significantly exceeded the mean concentrations reported in Chinese soils [47]. Compared with global sediment HM levels, Cd, Cu, Pb and Zn concentrations in this study exceed those reported in the Akyatan Lagoon [48], Edku Lake [49], Chembarambakkam [50], Jinmucuo Lake [51], and Poyang Lake [52].

The proportions of OCPs and HMs exhibited co-variation with pollution and background levels, ordered as follows: β-HCH (37.32%) > o,p′-DDT (24.06%) > p,p′-DDT (16.87%) > α-HCH (15.32%) > γ-HCH (6.43%) for OCPs and Zn (34.82%) > Cr (22.59%) > Cu (15.35%) > Pb (11.73%) > Ni (11.22%) > As (3.95%) > Cd (0.29%) > Hg (0.04%) for HMs. Although γ-HCH, Hg, Cd, and As existed at relatively low concentrations, their ecotoxicological effects and human health risks warrant serious concern.

The CV reflects both the spatial variability susceptibility and anthropogenic influence [53,54]. The significantly higher CV for OCPs compared to HMs (Figure 2a,b) indicates not only greater spatial heterogeneity in their distribution but also a stronger anthropogenic impact. This is consistent with the fact that OCPs are solely derived from anthropogenic sources, whereas HMs originate from both human and lithogenic origins.

EF value is widely used to assess pollution levels and anthropogenic impacts, providing a quantitative measure of relative pollution [55]. Calculated as the ratio of a pollutant’s measured concentration to its background value, the EF serves as a key indicator for differentiating between natural and anthropogenic origins [56]. EF values for OCPs were consistently below 0.5, whereas those for HMs significantly exceeded this threshold (Figure 2c,d). EF < 0.5 suggests a predominantly natural origin with minimal anthropogenic influence [57]; EF > 1.5 indicates a primarily anthropogenic source with negligible natural input [58]. Although OCPs are synthetic, their lower EF values may result from the adoption of reference background levels for construction land specified in China’s National Soil Environmental Quality Standards [59]. Most HMs exhibited EF values between 1.0 and 1.5 (Figure 2d), reflecting mixed anthropogenic and natural contributions. Notably, the elevated mean EF values of Cd (6.17) and Hg (2.74) are primarily attributed to their extensive application in industrial raw materials, agricultural fertilizers and pesticides. The surface sediments of Qilu Lake are polluted with considerable Cd, primarily originating from industrial and agricultural activities. Major contributors include wastewater discharge from smelting, printing, and chemical production, along with leaching of fertilizers and pesticides from surrounding farmland. This accords with Tonghai County’s role as a major vegetable cultivation base in Yunnan Province.

3.2. Source Identification for OCPs and HMs

Among HCH congeners, β-HCH showed the highest detection frequency (95.5%) and mean concentration (13.9 ± 4.6 ng/g) (Figure 2a), due to its environmental persistence and isomerization of α-HCH and γ-HCH into β–HCH [60], indicating a historical residue origin of HCHs.

The α-/β-HCH, β-/(α + γ)-HCH, and o,p′-/p,p′-DDT ratios were employed to identify potential sources of residual HCHs and DDTs in sediments (Figure 3a,b). Since technical HCHs and lindane are two major sources of HCHs in the environment, α-/β-HCH ratio is often employed as an indicator for distinguishing their sources. A high α-/β-HCH ratio signifies recent input of technical HCHs, whereas a low value points to historical residues [61]. Of the 22 sediment samples, both α-HCH and β-HCH were co-detected at 17 sampling points, with the α-/β-HCH ratio ranging from 0.30 to 1.10 and an average value of 0.54 (Figure 3b), significantly lower than that of technical HCHs (5–14). Only β-HCH was detected at four points, and only α-HCH was found at one point (Figure 3a). These results suggest that the HCHs in sediments primarily originate from historical residues. Additionally, a β-/(α + γ)-HCH ratio helps distinguish between recent and historical sources of HCHs. A ratio < 0.5 indicates recent inputs (e.g., lindane application or atmospheric deposition), whereas a ratio > 0.5 implies historical technical HCH or lindane use [62]. In this study, β-/(α + γ)-HCH ratios significantly exceeded 0.5 in 18 sampling points (Figure 3b), and a significant positive correlation was observed between β-HCH and ∑HCHs (R² = 0.579, p < 0.01), indicating that HCHs originated from historical residues. This finding aligns with previous studies demonstrating gradual degradation of α-HCH and γ-HCH into β-HCH over time [63] and further underscores the considerable effectiveness of China’s ban on the production and use of HCHs [64]. Currently, HCHs in most regions of China primarily originate from the gradual release and transport of historical pesticide residues [65].

Technical DDTs primarily consist of p,p′-DDT (75%) and o,p′-DDT (15%) [66]. Under aerobic and anaerobic conditions, DDT degrades to DDE and DDD, respectively [44]. Neither DDE nor DDD was detected in this study, while trace levels of p,p′-DDT and o,p′-DDT were found in 50% and 72.7% of the samples, respectively. This pattern indicates that DDT pollution mainly originates from recent technical DDT inputs associated with agricultural practices rather than historical residues. Since the ban of technical DDTs, dicofol has been become a major source of “new” DDT in the environment [67]. The o,p′-/p,p′-DDT ratio serves as an indicator of dicofol input, with technical DDTs ranging between 0.2 and 0.3, and dicofol ranging from 0.3 to 9.2 or higher [68]. Here, the o,p′-/p,p′-DDT ratio varied from 0.41 to 2.25 (mean: 1.03), signifying dicofol contributions.

Principal component analysis (PCA) is employed to identify potential sources of HMs [69]. Following varimax rotation, three principal components (PCs) with eigenvalues greater than 1 were derived from eight heavy metals (Figure 3c,d). These three PCs collectively accounted for 69.31% of the total variance (

Table 1. Factor loading matrix of heavy metals in surface sediments of Qilu Lake.

Element	PC1	PC2	PC3
As	0.243	0.595	0.134
Hg	0.086	−0.198	0.704
Ni	0.448	0.048	0.351
Cd	0.440	0.236	−0.327
Cu	0.423	−0.272	−0.067
Pb	0.532	0.028	−0.217
Cr	−0.147	0.644	0.296
Zn	0.237	−0.243	0.343
Eigenvalue	2.733	1.686	1.125
Percentage of variance (%)	34.16	21.08	14.06
Cumulative percentage of variance (%)	34.16	55.24	69.31

The heavy metals are denoted by their standard chemical symbols: zinc (Zn), copper (Cu), mercury (Hg), cadmium (Cd), lead (Pb), chromium (Cr), arsenic (As), and nickel (Ni). Principal component is abbreviated as PC.

and Table S4), effectively representing the source characteristics of these elements. PC1, accounting for 34.16% of the total variance, was dominated by positive loadings of Pb (0.532), Ni (0.448), Cd (0.440), and Cu (0.423). As mentioned above, the mean concentrations of Cd, Ni and Cu were well above the National Soil Secondary Quality Standard [46] and exhibited high CV (CV > 50%; Figure 2b), indicating an anthropogenic source. Furthermore, Pb showed strong positive correlations with Ni, Cu, and Cd (Figure 4), indicating they likely share the same pollution sources. Despite the ban on leaded gasoline, Pb continues to be released from fuel combustion and deposited into sediments via atmospheric deposition and runoff [70]. This is compounded by vehicle emissions, which contribute Cd, Ni, and Pb [23], as well as Cu from tires and braking systems [70]. Concurrently, mining and smelting activities are recognized sources of Pb and Cu [71,72]. Agricultural fertilizers and pesticides are recognized sources of HMs, containing a certain amount of Cu, Pb, Ni, and particularly Cd [1,54,73]. Notably, Cd pollution stems from multiple anthropogenic sources, including agricultural chemicals (herbicides, fungicides, and fertilizers) and industrial activities (electronics manufacturing, chemical production, and electroplating) [74]. Field surveys confirm the presence of extensive farmland dedicated to vegetable cultivation in the Qilu Lake basin. The area also hosts various industrial activities, including ferrous metallurgy, manufacturing, printing, fertilizer production, and food processing. Therefore, PC1 is a combination of sources of agricultural, industrial and traffic emissions. PC2 explained 21.08% of the total variance and exhibited high positive correlation for As (0.595) and Cr (0.644). The mean concentrations of As and Cr were below the National Soil Secondary Quality Standard [46] and exhibited a low CV (Figure 2b), suggesting minimal anthropogenic influence. Moreover, As and Cr showed a significant positive correlation (Figure 5). Given that Cr is a rock-forming element primarily derived from detrital material in surface runoff [1], the co-migration of both elements across the region can be attributed to natural geochemical processes such as rocking weathering and pedogenesis [75]. PC2 is therefore interpreted as comprising natural sources, including the erosion and transport of catchment soils and rocks into the lake. PC3 was dominated by a strong loading from Hg (0.704), while Ni and Zn showed moderate loadings (0.351 and 0.343, respectively); this component accounted for 14.06% of the total variance. As is known, these HMs are affected by anthropogenic activities to a large extent. Industrial production in the region relies heavily on coal combustion for power, and Hg is widely recognized as an indicator of coal combustion [76]. Zn is commonly found in automotive tires, lubricants, and anti-wear additives, and may be released through tire abrasion and lubricating processes [77]. Zn and Ni are associated with industrial processes such as metallurgy and petro-chemical plants [78]. Additionally, municipal discharge and vehicular emission was the main source of Zn and Ni [78]. The Qilu Lake basin contains extensive industrial and mining operations, as well as transportation infrastructure. Thus, PC3 represents industrial emissions and vehicular exhaust. In short, the three main sources of HMs in the Qilu Lake basin are identified as agricultural practices, natural processes, and industrial and traffic emissions.

Pearson’s correlation coefficient is employed to identify pollutant types and their potential sources [14]. Correlations between the different HMs and OCP congeners are presented in Figure 4. Notably, Pb exhibited strong positive correlations with Ni, Cd, and Cu, while Ni was also significantly correlated with Cu. Similarly, As was positively correlated with Cr. The significant positive correlations among most HMs suggest similar origins and transport patterns, largely driven by anthropogenic activities [79]. In contrast, Hg and Zn showed no significant correlations with other HMs, implying distinct accumulation mechanisms. These differences may arise from anthropogenic inputs, element-specific properties, organic matter content, sediment grain size, and competitive adsorption among elements, warranting further systematic investigation to elucidate the underlying biogeochemical processes. Meanwhile, no correlation or significant negative correlations were observed between HMs and OCPs, indicating influence from multiple sources within the Qilu Lake basin, such as agricultural activities, domestic sewage, industrial discharge, and traffic-related pollution. The absence of strong positive correlations among OCP congeners may also reflect the release of sediment OCP residues into the water column, indicative of dynamic sediment–water partitioning. It should be noted that the behavior and transport of OCPs in aquatic environment are influenced by multiple factors, including agricultural practices, hydrodynamics, and benthic processes. Therefore, these correlations should not be interpreted as sole determinants of pollutant dynamics [80,81].

3.3. Modeling of the HM Effects on OCP Co-Adsorption Mechanism

No correlations were found between three HMs (Hg, Cd, Cr) and any OCPs, while significant negative correlations were observed between Ni/Zn and α-HCH, Pb and β-HCH, As and γ-HCH, as well as Cu, Pb and o,p′-DDT (Figure 4). These results suggest that the presence of these HMs may inhibit the enrichment and accumulation of the corresponding OCPs. Specifically, elevated levels of Ni/Zn, Pb, As, and Cu/Pb appear to reduce the enrichment and accumulation of α-HCH, β-HCH, γ-HCH, and o,p′-DDT, respectively. This may be attributed to alterations in sedimentary redox conditions exerting negative feedback effect on pollution deposition, or to complex biogeochemical interactions among the pollutants. Therefore, further study should focus on the competitive interfacial behaviors underlying HM–OCP co-adsorption, and the mediating role of sediment physicochemical properties. To determine the optimal functional model, regression analysis was conducted using three candidate models: linear, quadratic, and power function. Fitted curves illustrating the adsorption levels of OCPs and HMs are presented in Figure 5. Model selection was based on goodness of fit (R²), adjusted R², significance of the ANOVA F-test, and regression coefficient t-test statistics. The linear model was determined as the optimal function for characterizing the adsorption–accumulation relationship between OCPs and HMs in surface sediments from the Qilu Lake (Table S5). However, the precise mechanisms driving their interactions require further elucidation.

3.4. Ecological Risks for HMs and OCPs

HMs and OCPs in sediments can reenter the water column through resuspension, leading to secondary pollution and posing threats to aquatic organisms [11,82]. Therefore, it is essential to assess ecological risks posed by HMs and OCPs in sediments. RQ and ∑RQ for OCPs and Er and PERI for HMs at each sampling point are listed in Table S6. As shown in Figure 6a, most box plots were incomplete due to the low detection frequencies of OCPs. The RQ values decreased in the order γ-HCH > p,p′-DDT > o,p′-DDT > α-HCH > β-HCH. Although β-HCH was detected frequently, γ-HCH and p,p′-DDT exhibited high ecological risk (RQ > 1), making them the primary drivers of ecotoxicological risk. In contrast, o,p′-DDT showed moderate risk (0.1 < RQ < 1) in 72.3% of the samples (Table S6), while α-HCH and β-HCH contributed minimally, indicating low or negligible risk. Environment pollutants typically coexist rather than occur in isolation. Similar structures and toxicodynamic mechanisms among OCP congeners can induce cumulative or synergistic ecotoxic effects, significantly increasing risks to ecosystems [83]. Previous studies have confirmed that co-exposure to multiple compounds can result in combined toxicity, with significant adverse effects even at low concentrations [84,85]. Therefore, ΣRQ was used to quantify integrated ecotoxicological risks from coexisting OCP congeners. Spatial analysis revealed high ecological risk (∑RQ > 1) across most regions of Qilu Lake (Figure 6b), primarily driven by anthropogenic activities, particularly intensified agricultural practices and increased pesticide application. Notably, γ-HCH (44.84%), p,p′-DDT (37.88%) and o,p′-DDT (16.21%) collectively accounted for 98.93% of ∑RQ, identifying them as major contributors to the elevated ecological risks. Pesticide residues from foliage and soil enter the hydrological cycle through rainfall leaching and surface runoff, eventually discharging into the lake. This process can lead to significant accumulation of OCPs across most areas of Qilu Lake, especially in low-lying zones and near sewage discharge points.

The ecological risks of individual HMs were quantified with the Er index, whereas the integrated risks from cumulative HM pollution with the PERI. Mean Er values followed the order Cd > Hg > As > Cu > Pb > Ni > Cr > Zn (Figure 6c). Cd showed high ecological risk (Er rang: 133.5–240.9; mean: 183.7) in 63.6% of the samples (Table S6). Hg exhibited considerable ecological risks (Er range: 82.8–132.4; mean: 110.5). These results indicate that Cd and Hg are the predominant ecological risk factors in the sediments, consistent with previous findings [25]. The other six HMs showed negligible or low ecological risks (Er < 40). This pattern aligns with HM pollution characteristics observed in other lakes on the Yunnan–Guizhou Plateau, where Cd also represents a major risk factor [86]. The PERI values of combined HM pollution varied between 266.8 and 401 (Figure 6c), indicating moderate-to-considerable ecological risks. Spatially, considerable risk levels were observed at 72.7% of sampling points across Qilu Lake, with the remainders exhibiting moderate risks (Figure 6d). Given that Cd and Hg were the dominant contributors to the PERI, accounting for 55.76% and 33.55%, respectively, targeted control of these metals forms the cornerstone of ecological management strategies for Qilu Lake basin.

3.5. Human Health Risks of OCPs and HMs

Chronic exposure to OCPs and HMs can induce various human diseases, even cancer [21,87]. Therefore, this study assessed carcinogenic and non-carcinogenic risks for local residents, with specific consideration of children and adults. For non-carcinogenic risks, ΣHI values for both OCPs and HMs were below 1 at all sampling points (Figure 7a), indicating negligible risks. Regarding carcinogenic risks, ΣCR values of As, Ni and Cd ranged 10^–4 to 10^–6 (Figure 7b), falling within acceptable levels. As, a toxic carcinogen, exists naturally via biogeochemical processes such as mineral decomposition, precipitation, and adsorption-desorption, as well as from anthropogenic activities like fertilizer and pesticide use. Chronic exposure to As has been linked to carcinogenic effects in multiple organs [88]. Similarly, environmental exposure to high levels of Ni may lead to various pathological outcomes, including lung fibrosis, kidney and heart diseases, and respiratory cancers [89]. Chronic exposure to elevated Cd concentrations has also been associated with increased lung cancer mortality [21]. In contrast, Cr exhibited ΣCR values substantially exceeding 10^–4 (Figure 7b), indicating a significant carcinogenic risk, consistent with previous findings [54,90]. Although Cr is essential in trace amounts for specific physiological functions, chronic exposure to elevated levels can induce cellular damage, genotoxicity, and cancer [91]. ΣCR values of Pb and OCPs were well below 10^–6 (Figure 7b), demonstrating a negligible carcinogenic risk. Overall, compared to OCPs, HMs, particularly Cr, were identified as the major contributors to both carcinogenic and non-carcinogenic risks for both adults and children. These results underscore the need for systematic monitoring and targeted control of Cr levels in Qilu Lake basin to mitigate human health risks.

This study evaluated human exposure to PTS in surface sediments from Qilu Lake based on USEPA guidelines for soil and dust assessment [36,37,38]. The quantitative health risk assessment was conducted for local residents, focusing on children, through three primary exposure routes: Ing, Der and Inh (Figure 6c,d). Both the HQ and CR values exhibited a consistent order across exposure routes: Ing > Der > Inh. These results clearly identify ingestion as the primary exposure route for both non-carcinogenic and carcinogenic effects of PTS, which aligns with previous findings [44,80]. Both types of risks were higher in children than in adults (Figure 6a,b), reflecting children’s greater vulnerability due to physiological traits, behavior patterns such as hand-to-mouth activities, and longer exposure duration during development [92].

4. Conclusions

Pollutants often coexist in lake sediments, and exposure to such complex mixtures poses potential risks to both human health and ecological integrity. This study was conducted to evaluate the pollution status and ecological and human health risks associated with the combined pollution of organochlorine pesticides and heavy metals in surface sediments of Qilu lake. The detected organochlorine pesticides, including α-hexachlorocyclohexane, β-hexachlorocyclohexane, γ-hexachlorocyclohexane, o,p′-dichlorodiphenyltrichloroethane, and p,p′-dichlorodiphenyltrichloroethane, had mean concentrations of 5.72, 13.94, 2.40, 8.99 and 6.30 ng/g, respectively. The mean concentrations of ΣHCHs and ΣDDTs were 22.07 and 15.29 ng/g, respectively, both significantly lower than China’s Soil Environment Quality Standards limit of 100 ng/g. Among heavy metals, the mean concentrations were as follows: mercury: 0.16; cadmium: 1.34; arsenic: 18.20; nickel: 51.67; copper: 70.62; lead: 54.00; chromium: 104.00; and zinc: 160.30 mg/kg. These values represented 2.76, 6.15, 0.99, 1.22, 1.53, 1.33, 1.60, and 1.79 times the soil background values in Yunnan Province, respectively, with mercury and cadmium showing the most pronounced enrichment. Source apportionment indicated that nickel, copper, lead, and cadmium originated from a combination of agricultural, industrial, traffic-related sources, whereas arsenic and chromium were derived from natural sources. Mercury and zinc were linked to traffic and industrial emissions. Analysis of organochlorine pesticide sources revealed that hexachlorocyclohexanes were largely derived from historical residues, whereas dichlorodiphenyltrichloroethanes originated from recent inputs of technical dichlorodiphenyltrichloroethanes and dicofol.

A significant negative correlation was observed between organochlorine pesticides and certain heavy metals, indicating distinct sources and potential interactive effects in terms of adsorption and accumulation. Hence, three models were employed to evaluate these interactions, among which the linear model was identified as the most suitable. In most samples, ecological risk posed by organochlorine pesticides was high. In contrast, the risk from heavy metals was considerable, with cadmium being a predominant contributor to the total risk. The health risks were generally consistent: both organochlorine pesticides and heavy metals posed higher risks to children than to adults. Although the combined pollutants posed no non-carcinogenic risks to local residents, their carcinogenic risks varied considerably. ΣCR values of lead and organochlorine pesticides were well below 10^–6, while those of arsenic, nickel and cadmium ranged 10^–4 to 10^–6. In contrast, chromium exhibited ΣCR values substantially exceeding 10^–4, indicating that carcinogenic risks, particularly from chromium, could not be ignored and require serious attention.

Overall, the results of this study elucidate the ecological and human health risks posed by the coexistence of organochlorine pesticide and heavy metal pollutants, emphasizing the effects of anthropogenic activities on Qilu Lake basin, which thus provide a useful scientific foundation for the management and restoration of agricultural lake ecosystems impacted by combined organochlorine pesticide and heavy metal pollution.