Dominant Riverine Input of Legacy Organochlorine Pesticides to a Semi- Enclosed Plateau Lake: Distribution, Source Apportionment and Bioaccumulation

Dominant Riverine Input of Legacy Organochlorine Pesticides to a Semi- Enclosed Plateau Lake: Distribution, Source Apportionment and Bioaccumulation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dominant Riverine Input of Legacy Organochlorine Pesticides to a Semi- Enclosed Plateau Lake: Distribution, Source Apportionment and Bioaccumulation Xixiang Qiu, Lei Sun, Xiaohui Zhao, Jianhui Liu, Kun Zhang, Yinfeng Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7587667/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Nov, 2025 Read the published version in Environmental Monitoring and Assessment → Version 1 posted 10 You are reading this latest preprint version Abstract Sources apportionment and clarification of migration pathways of organochlorine pesticides (OCPs) are critical yet unsolved scientific issues for sustainable development of plateau lakes. Here, we integrated the distribution pattern, sources analysis and bioaccumulation process of OCPs in a typical semi-enclosed plateau lake, representative aquatic ecosystem of the Tibetan and Yunnan-Guizhou plateau, to address these scientific issues. The δ-hexachlorocyclohexane (δ-HCH), α-hexachlorocyclohexane (α-HCH) and endrin aldehyde were the predominant OCPs in sediments, with their contents ranging from nd (not detectable) to 459.89 ng∙g − 1 (δ-HCH), nd to 450.87 ng∙g − 1 (endrin aldehyde) and 11.00 to 236.95 ng∙g − 1 (α-HCH) respectively. Notably, the main OCPs in lake sediments were linearly related to total organic carbon (TOC), indicating the homologous relationship between OCPs and TOC. Moreover, the behaviors of OCPs were governed by partitioning process regulated by TOC and pH of sediments. Integrating the OCPs distribution, stable carbon isotope (δ 13 C) mixing model and geo-graphical features, we identified inflow rivers (87.1%) and farmland (6.3%) as the main sources. While the bioaccumulation of OCPs in fish was moderate, long-term hjkealth risks of OCPs by consumption of fish were considerable. The findings of this study highlight the priority of basin-wide source control method for sustainable management of such semi-enclosed environment. organochlorine pesticides plateau lake persistent organic pollutants source apportionment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Persistent organic pollutants (POPs), including organochlorine pesticides (OCPs), continue to pose severe threats to sustainable utilization and management of global aquatic ecosystems due to their characteristics of semi-volatility, bioaccumulation, high toxicity, and resistance to degradation (de Boer et al., 2022 ; Jiang et al., 2024 ). Of the 23 POPs identified under the Stockholm Convention (Liu et al., 2016 ), 13 of them are organochlorine pesticides (OCPs). These include well-known compounds such as aldrin, lindane, heptachlor, endrin, and hexachlorocyclohexane, which have been recognized globally as priority contaminants ( http://www.pops.int ). Historically, valued for their effectiveness as insecticides and herbicides, OCPs have been extensively used on a global scale for several decades, persisting in aquatic ecosystem and dispersing into broader environments through air dispersion (Girones et al., 2020 ; González-Rubio et al., 2021 ; Jiang et al., 2024 ). Sediments, a critical compartment of aquatic eco-system such as lakes, play a dual role as both a sink and a delayed source of OCPs (Barletta et al., 2019 ). On the one hand, sediments can effectively sequester OCPs from water over decadal timescales due to the hydrophobic nature of OCPs through adsorption or allocation process (Şimşek Uygun & Albek, 2024 ). On the other hand, this apparent stability masks a latent environmental threat, which is the shifts in water environment can trigger the remobilization of historically buried OCPs from sediments, posing significant risks to drinking water safety and aquatic biodiversity (Rex et al., 2024 ). Therefore, studying the residual levels and environmental behavior of OCPs in sediments is critical for sustainable management of aquatic ecosystem. From the 1950s to 1980s, about 18000 tons of organochlorines (including polychlorinated biphenyls and OCPs) were produced or imported as dielectric fluids in electrical appliances or additives in paints in China (Jiang et al., 1997 ). In past two decades, while the Chinese government has implemented series of activities to restrict the circulation, use and production of OCPs-related products since becoming a signatory to the Stockholm Convention in 2004, residual OCPs continue to pose threats to aquatic ecosystem and human health due to their prolonged environmental persistence, high toxicity, and long-range transportation characteristics (Grung et al., 2015 ; Liu et al., 2016 ). As a result, extensive studies have systematically documented the types, concentrations and distribution of OCPs in various regions across China (Grung et al., 2015 ; Han & Currell, 2017 ; Li et al., 2017 ; Liu et al., 2016 ). Although these studies provided key information on the characteristics of OCPs in different regions of China, they however tend to focus on the central and eastern China due to the fast development and thus possible more usage of OCPs in these areas. As a result, a critical knowledge gap regarding the behavior pattern, sources and health risks of OCPs in those more enclosed agricultural or pastoral areas persisted. Meanwhile, enclosed agricultural or pastoral regions, particularly those in ecologically fragile plateau areas, are characterized by limited pollutant dispersion and heightened susceptibility to bioaccumulation (Machate et al., 2023 ); this makes such type of aquatic ecosystems more likely to accumulate OCPs and suffer greater ecological harms (Kallenborn, 2006 ; Pastorino et al., 2024 ). Consequently, effective prevention of OCPs in such enclosed plateau lakes necessitates source-directed intervention; scientific source apportionment and clarification of migration pathways of OCPs are fundamental yet unsolved scientific issues for targeted conservation strategies. However, due to the absence of systematic investigations, both the origins and transportation pathways of OCPs within these lake systems remain unresolved, presenting a major theoretical barrier to ecological conservation and restoration. The plateau lakes of northwest Yunnan, situated at the tectonic boundary between the Tibetan and Yunnan-Guizhou Plateau, present a representative yet understudied aquatic ecosystem characterized by exceptional biodiversity and ecological significance (Sun et al., 2023 ). In addition, the majority of these plateau lakes are typical fault-depression lakes with enclosed or semi-enclosed geographical features, resulting in lower environmental capacity and more fragile ecological environments (Hou et al., 2024 ). Moreover, compared with lakes in the Tibetan Plateau, where few agricultural or industrial sources existed and the main source of OCPs is the long-range transportation through monsoons (Ding et al., 2023 ; Ren et al., 2017 ; Sun et al., 2018 ), the plateau lakes in Yunnan-Guizhou Plateau are typically surrounded by agricultural activities, raising concerns about the types, accumulation characteristics, sources and the potential health risks of OCPs in these plateau lakes from agriculture-dominated area. To address these knowledge gaps, this study selected Lashihai Lake, a representative semi-enclosed plateau lake in the agricultural zone of northwest Yunnan, China, to: 1) analyze the types and concentrations of OCPs in surface sediments, revealing their residual levels, distribution patterns and influencing factors; 2) identify OCPs sources and migration pathways based on distribution pattern and stable carbon isotope (δ 13 C) mixing model analysis; 3) assess the bioaccumulation of OCPs in edible fish and the associated health risks. Materials and Methods Study area The Lashihai Wetland Nature Reserve, established in 1998, is located in the transition zone of the Tibetan-Yunnan Plateau, China (Fig. 1 ), and it was designated as a Wetland of International Importance in 2004 due to its critical role in the protection of rare migratory birds. The Lashihai Lake (26°44'-27°00'N, 100°05'-100°13'E) is the core area of this reserve. It is a typical semi-enclosed plateau lake with a watershed area of 265.6 km², a lake basin area of 5337.8 hectares, an average surface elevation of 2,438 meters, and an average water depth of 4.5 meters. Hydrologically, Lashihai Lake is situated at the lowest point of the watershed, surrounded by mountains with elevations ranging from 3,000 to 4,000 meters. The lake relies on seasonal precipitation and inflow from surrounding rivers for water replenishment, but lacks a stable outflow channel, forming a semi-enclosed catchment basin. From the perspective of land use, the watershed is primarily composed of forest (41.2%), meadow (18.5%), and marshland (12.3%). Besides, human activities are concentrated along the lakeshore, with farmlands accounting for 23.9% of the area. High-value economic crops, such as apples, pears and vegetables, are predominantly cultivated, with agricultural output contributing 54.9% of the region's total production value. Residential and tourism-related land uses make up 4.1%, with urbanization and tourism development showing an increasing trend (Dou et al., 2022 ). The relatively enclosed geographical characteristics, combined with the high proportion of agricultural activities in the surrounding areas, render this region a representative plateau lake for investigating the behavioral patterns and aquatic risks of OCPs. Sampling The selection of lake sediment sampling sites took into account hydrodynamic conditions, lakebed morphology, inflow rivers and potential pollution sources. A total of 42 surface sediment sampling points were set in the lake basin, 7 in the inflow rivers, 18 in the farming area around lake and 4 in forest (Fig. 1 ). For sediments of lake and inflow rivers, a grabbing sampler was used to collect the top 10 cm of sediment. At each point, 2–3 subsamples were collected and mixed as one final sample (about 1000 g). For soils of farming area and forest, we collected surface soils with a stainless-steel shovel with same process as that of sediments. All collected samples were sealed in self-sealing bags, transported to the laboratory in ice-packed containers, and stored at -20°C until analysis. Three edible fish species from Lashihai Lake were obtained with the assistance of local fishermen; the species include Carassius auratus , Cyprinus carpio and Pelteobagrus fulvidraco . Briefly, around 1000 ~ 2000 g of each species was collected and preserved at -20℃ after transportation to laboratory in ice-packed foam box. The measurements of body length for the two most common species, Carassius auratus and Cyprinus carpio , were performed with a vernier caliper. The fish were then subdivided according to body length as follows: Carassius auratus (S) (100.00 ~ 140.00 mm), Carassius auratus (L) (140.00 ~ 160.00 mm), Cyprinus carpio (S) (170.00 ~ 200.00 mm) and Cyprinus carpio (L) (200.00 ~ 230.00 mm). Determination methods Determination of OCPs The procedures for sample (sediment, soil and fish) pretreatment and OCPs determination followed those reported by Sun et al. ( 2023 ), and are briefly summarized here. Sediment and soil samples were freeze-dried, ground to 200 mesh, and extracted with n-hexane-dichloromethane (1:1) using ultrasonic-assisted extraction. Extracts were purified on Florisil columns, concentrated under nitrogen. Fish samples were rinsed with ultrapure water, and non-edible parts (bones, tails, scales) were discarded. Individuals of the same size and species were pooled, dried with ~ 20 g of anhydrous sodium sulfate, homogenized, and then subjected to the same extraction procedure as sediment. OCPs concentrations in all solid samples (sediment, soil and fish) were expressed on a dry-weight basis (ng∙g − 1 dw). A Gas chromatography–mass spectrometry (GC-MS, Thermo Scientific TRACE 1310 coupled with ISQ-LT) was used to quantify OCPs. A TG-5MS capillary column (30 m × 0.25 mm × 0.25 µm, Thermo Scientific) was used for separation, with high-purity helium as carrier gas. A 1 µL aliquot of each extract was injected directly, with inlet and detector temperatures set to 250°C and 300°C, respectively. Full-scan mode was used for qualitative identification based on retention time and diagnostic ions, while quantification relied on a six-point calibration curve (0, 0.1, 0.5, 1.0, 5.0, and 10.0 mg∙L − 1 ; R² >0.99). A standard reference soil (TMQC0227, J&K Scientific) containing 23 OCPs, including α-HCH, δ-HCH, and endrin aldehyde, was used for quality assurance and control. Specifically, five replicates of the standard soil were processed in parallel using the same extraction and analysis procedures as the samples. The average measured values were compared with the certified reference values to calculate correction coefficients, thereby ensuring analytical accuracy. Determination of TOC and stable carbon isotope ratios (δC) The methods for TOC and δ 13 C determination also followed Sun et al. ( 2023 ), with a concise description provided below. For TOC, ~ 0.008 g of sample was treated with several drops of 1% HCl to remove inorganic carbon, dried at 105°C, and ~ 0.004 g was wrapped in tin foil for measurement. TOC was analyzed with a German Element Vario analyzer (error < 0.2%), using CaCO₃ (ultra-pure, preheated at 280°C for 1 h) as the control. For δ 13 C, ~ 15 mg of dried sample was placed in a tin cup and measured using an elemental analyzer (Picarro TOC-CRDS, USA). The δ 13 C values were expressed in per mil (‰) relative to the Vienna Pee Dee Belemnite (VPDB) standard and calculated following Hoffman & Rasmussen ( 2022 ). Two laboratory standards (δ 13 C = 11.71 ± 0.03‰ and − 8.02 ± 0.05‰) were run with each batch to calibrate the results, with analytical precision of 0.03‰. Data analysis Bio-sediment accumulation factor (BSAF) To evaluate the accumulation ability of OCPs from sediments to fish, the calculation of BSAF was applied following the Eq. 1–1 (Akinsanya et al., 2015 ), where C f is the concentration of OCPs in fish on a dry-weight basis (ng∙g − 1 dw), and C s is the mean OCPs concentration in the surface sediments (ng∙g − 1 dw). \(\:BSAF={C}_{f}/{C}_{s}\) (1–1) Health risk assessment The EPA method of chemical pollution assessment from the US EPA fish survey was used to evaluate the potential health risks to local residents (EPA, 2000 ). Considering that the daily fish consumption of each person is crucial in health risk assessment, two main factors were considered: (1) Lashihai Lake is an inland plateau lake, and the total fish consumption is much lower than those in coastal cities; (2) fish from Lashihai is only one of the food sources for local residents; other sources like artificial fish ponds and external sources are available. Therefore, compared with previous studies, we considered it appropriate to set the daily fish consumption per person in this study at 25 g∙kg − 1 ∙d − 1 , assuming an average body weight of 60 kg for each adult (Sun et al., 2020 ; Sun et al., 2014 ). On this basis, the carcinogenic risk index (CRI) and exposure risk index (Papadopoulou et al., 2013 ) of OCPs in various fish species were determined according to Eq. 1–2 and 1–3, except for endosulfan and dieldrin, which were classified by the US EPA as pesticides without carcinogenic risks (EPA, 2000 ). In these two equations, C i is the mean concentration of OCPs in fish; CW is the daily consumption amounts of fish (g∙kg − 1 ∙d − 1 ); CSF is carcinogenic slope factor (mg∙kg − 1 ∙d − 1 ) −1 ; RHD is reference harm dosage (µg∙kg − 1 ∙d − 1 ); the detailed reference values of CSF and RHD can be achieved in previous studies (Sun et al.,2023; Watanabe et al., 2003 ). Mixing model of stable isotope analysis A Bayesian stable isotope mixing model was applied to δ 13 C data to estimate the relative contributions of different sources (e.g., farming area, inflow river, and forest) to lake sediments, following the approach described in Sun et al., 2023 . The analysis was conducted using the package “simmr” implemented in R 4.3.0 (The R Core Team, 2013). Briefly, outliers of δ 13 C data were first removed using interquartile range method prior to model-analysis. Then all the δ 13 C data were grouped into source group (farming area, inflow river and forest) and consumer group (lake sediment). Last, the grouped data were imported into the “simmr” package for model running. Detailed information and algebra equations of the mixing model can be acquired in this study (Parnell et al., 2010 ). Other data handling and visualization steps included preliminary data collation in Excel 2016, plotting with the “ggplot2” package in R, and spatial interpolation of OCPs concentrations in ArcGIS 10.2. Since the datasets did not follow normal distributions, non-parametric Wilcoxon tests were employed in R to evaluate differences between groups. Results and discussions Distribution of OCPs in lake sediments Multiple OCPs, including hexachlorocyclohexanes (HCHs), endrin aldehyde, endrin ketone, aldrin, p,p’-dichlorodiphenyltrichloroethane (p,p’-DDT), p,p’-dichlorodiphenyldichloroethylene (p,p’-DDE) and endosulfan were detectable in the surface sediments of Lashihai Lake. Among them, the α-HCH, δ-HCH and endrin aldehyde were prevalent at various sampling points with detection rates of 100% (α-HCH), 60% (δ-HCH) and 60% (endrin aldehyde) (Table 1 ). Apart from these three main OCPs, other OCPs had much lower detection rates and their medians were all below the limit of detection, indicating their limited representativeness (Table 1 ). Therefore, subsequent analysis in this paper primarily focuses on the three main OCPs (α-HCH, δ-HCH and endrin aldehyde). Overall, the concentrations of the three main OCPs in the surface sediments ranged from nd (not detectable) to 459.89 ng∙g − 1 (δ-HCH) (median of 142.45 ng∙g⁻¹), nd to 450.87 ng∙g − 1 (endrin aldehyde) (median of 68.05 ng∙g⁻¹) and 11.00 to 236.95 ng∙g − 1 (α-HCH) (median of 37.70 ng∙g⁻¹) respectively. On average, the δ-HCH accounted for 52% of the total contents of the three main OCPs (∑ 3 OCPs), while the concentration of α-HCH was lower than that of endrin aldehyde, accounting for only 17% of ∑ 3 OCPs (Fig. 2 , Table 1 ). These three major OCPs all showed high standard deviations in their concentrations, indicating that there was considerable heterogeneity in the distribution of OCPs within the sediments of the study area. The OCPs concentrations in this region are comparable to or lower than those in multiple wetland basins worldwide (Buah-Kwofie & Humphries, 2017 ; Buah-Kwofie & Humphries, 2021 ). For example, the total OCPs concentrations in sediments from Lake St. Lucia in South Africa and its two major inflow rivers were 370 ± 150 ng∙g⁻¹, 260 ± 110 ng∙g⁻¹, and 240 ± 120 ng∙g⁻¹, respectively, where the γ-HCH dominated, suggesting recent use of γ-HCH (Buah-Kwofie & Humphries, 2021 ). In contrast, the HCHs in this study were primarily composed of δ-HCH and α-HCH with almost no γ-HCH. It has been reported that γ-HCH can be rapidly degraded by microorganisms while δ-HCH and α-HCH can persist for a longer period (Tzanetou & Karasali, 2022 ; Willett et al., 1998 ). These together indicate that the HCHs in study area are more related to historical residues rather than recent usage. Table 1 Statistical values of OCPs (ng∙g − 1 dw) in sediments of Lashihai Lake OCPs min median max mean sd average contribution (%) a Detection rate (%) α-HCH 11.00 37.70 236.95 49.17 48.07 17 100 δ-HCH nd b 142.45 459.89 151.74 154.15 52 60 Endrin aldehyde nd 68.05 450.87 93.72 115.88 31 60 ∑ 3 OCPs c nd 248.13 1021.95 294.63 297.16 - 100 p,p’-DDT nd nd 4.68 0.65 1.42 - 19 p,p’-DDE nd nd 6.42 0.30 1.18 - 7 Endrin ketone nd nd 340.26 22.70 67.08 - 14 Aldrin nd nd 6.24 0.34 1.27 - 7 Endosulfan nd nd 12.62 1.13 2.64 - 19 a the average contribution of each of α-HCH, δ-HCH, endrin aldehyde to the∑ 3 OCPs b “nd” represents not detectable, below limit of detection c the total contents of the three major OCPs (α-HCH, δ-HCH and endrin aldehyde) Specifically, the highest concentration of α-HCH in the surface sediments of Lashihai Lake occurred in the center area (S17, 236.95 ng∙g ⁻¹ ), with the next highest concentrations located in the northern part of the lake area (S8, 199.11 ng∙g⁻¹, S7, 98.02 ng∙g⁻¹). The highest δ-HCH was also found in the northern part of the lake area (S8, 459.89 ng∙g⁻¹), while the area with the highest concentration of endrin aldehyde was in the northwest part of the lake (S17, 450.87 ng∙g⁻¹, S11, 369.18 ng∙g⁻¹), with the next highest concentrations appearing in the northeast part (S14, 359.43 ng∙g⁻¹, S9, 223.36 ng∙g⁻¹) (Fig. 3 ). Importantly, the higher values of these OCPs were predominantly found in the northeastern parts of the lake, partially consistent with the distribution patterns of inflow rivers and farmlands (Fig. 3 ). This preliminary evidence based on distribution patten suggests that farmlands and inflow rivers are sources of OCPs in study area, which will be further discussed in section 3.3. Influencing factors for OCPs distribution in sediments The behavior of persistent organic pollutants at the water-solid interface is mainly controlled by adsorption and allocation, which are commonly influenced by TOC, pH and grain size of sediments (Duodu et al., 2017 ; Gandla et al., 2023 ; Gao et al., 2013 ; Lv et al., 2020 ). Herein, we investigated the relationships between these influencing factors and the three main OCPs in sediments. The α-HCH (r = 0.5, p < 0.001), δ-HCH (r = 0.76, p < 0.001) and endrin aldehyde (r = 0.64, p < 0.001) were all positively correlated with TOC (Fig. 4 a), proving that organic matter of sediments is the main storage medium for these OCPs, which has also been observed in other area across the world (Duodu et al., 2017 ; Jiang et al., 2024 ; Mishra et al., 2012 ). Moreover, their relationship with TOC could be further described using linear models (Fig. 4 b-d). These together made us believe that the OCPs were enriched in sediments mainly through the allocation process, given that the allocation process is commonly marked by the joint increase between organic matter and organic pollutants. In contrast, the α-HCH (r = -0.52, p < 0.001), δ-HCH (r = -0.75, p < 0.001) and endrin aldehyde (r = -0.63, p < 0.001) were all negatively correlated with pH of sediments (Fig. 4 a). This is due to that the pH of soil or sediment can affect the microbial activity, thus affecting the accumulation of OCPs. For example, study reported that the lower pH can reduce microbial activity and thus lead to a lower degradation of OCPs in soil (Wenzel et al., 2002 ). Furthermore, lower pH can support the accumulation of organic matter which is beneficial for the accumulation of OCPs by ether adsorption or allocation process (Mishra et al., 2012 ; Škrbić & Đurišić-Mladenović, 2007 ); this also agrees with our conclusion that TOC is the main sink for OCPs in study area. For the influence of grain size, although the three main OCPs were negatively correlated with grain size of sediments (Fig. 4 a), the influence was not as significant as that of TOC and pH, suggesting limited influence of grain size on the distribution of OCPs, and that adsorption process, which is commonly affected by grain size (Amutova et al., 2023 ; Qian et al., 2020 ), may not be the dominated process. Analysis of Sources of OCPs Characteristics of OCPs in potential sources Farmlands are widespread around Lashihai Lake, and there are several inflow rivers that pass through residential and farmland area (Fig. 1 ); this makes them the potential local sources of OCPs in study area. To address this, we analyzed the types and contents of OCPs in soils or sediments of the farmlands and inflow rivers around Lashihai Lake, and compared them with the OCPs in surface sediments. Both farmlands and inflow rivers contained certain amounts of OCPs, with same species as those in the lake sediments. As for OCPs species, the three OCPs (α-HCH, δ-HCH and endrin aldehyde) commonly found in the lake sediments were also widely detected in the inflow rivers and farmland. In comparison, the concentrations of ∑ 3 OCPs in the lake sediments, inflow rivers and farmland were 294.63 ± 297.16 ng∙g − 1 , 53.39 ± 12.08 ng∙g − 1 and 46.47 ± 15.97 ng∙g − 1 respectively (Fig. 5 a, Table 1 ). For farmland soils, the residual contents of OCPs in this study were comparable to the average levels of farmland soils of China (Wang et al., 2016 ), but much lower than those agricultural soil from India or northwestern Spain (Calvelo Pereira et al., 2010 ; Mishra et al., 2012 ). In addition, the OCPs in the forest soils of study area were generally undetectable, suggesting that forest soils were not the main source of OCPs. This is consistent with the conclusion that the OCPs in the forest soil of the Tibetan Plateau mainly originate from deposition and their contents are generally low (Wang et al., 2016 ). Importantly, although the Wilcoxon test results indicated no significant differences in ∑ 3 OCPs between the lake sediments and the inflow rivers and farmlands ( p > 0.05, Fig. 5 b), the average ∑ 3 OCPs in the sediments were higher than those in farmlands and inflow rivers, further indicating that lake sediments are the ultimate sink for OCPs in this type of plateau lakes. It is also worth noting that the levels of OCPs of inflow rivers were generally higher than those of farmlands but lower than those of lake sediments, indicating the dominant role of inflow rivers as the main pathway for the migration of OCPs in this type of plateau lakes. Source analysis of OCPs based on stable carbon isotopes The three major OCPs in Lashihai sediments were significantly positively correlated with TOC and exhibited certain linear relationships, proving homology between OCPs and TOC. Based on this homology and the comparative analysis above, we further employed a Bayesian based stable carbon isotope mixing model to quantitatively analyze the sources of OCPs/TOC. The stable carbon isotope (δ 13 C) combined with mixing model has been proved to provide quantitative information about the organic sources (Cooper et al., 2014 ; Cooper et al., 2015 ; Liu et al., 2020 ; Upadhayay et al., 2020 ). In general, the δ 13 C in different potential sources and sediments of Lashihai were − 24.63 ± 3.29‰ (inflow rivers), -22.93 ± 2.19‰ (farmland), -22.43 ± 4.85‰ (sediments) and − 21.84 ± 4.6‰ (forest) respectively. The δ 13 C of lake basin surface sediments (-22.43 ± 4.85‰) did not show significant differences from those of farmland, inflow rivers and forest ( p > 0.05), suggesting that the organic sources in sediments are homologous to these sources (Fig. 6 a). Specifically, the δ 13 C of farmland (-22.93 ± 2.19‰) was closest to that of the sediments (-22.43 ± 4.85‰), suggesting farmlands as one important source. Furthermore, the lake sediments had larger range of δ 13 C-TOC compared with source areas such as inflow river, farmlands and forest (Fig. 6 b), suggesting that the organic sources of sediments are more diversified, and further emphasizing the role of sediments as the final reservoir of organics within the watershed; this is consistent with the geographical feature of this type of plateau lakes. Based on above analysis and the semi-enclosed geographical features of study area, we hypothesized that lake sediments are the ultimate sink, while farmlands, inflow rivers and surrounding forests are contributors of OCPs/TOC. The mixing model results indicated that the average contributions of different sources to lake sediments were 87.1 ± 0.06% (inflow rivers), 6.6%±4.3% (forests) and 6.3%±5.5% (farmland) (Fig. 6 c). The model results combined with the distribution characteristics of OCPs in different source areas further quantitatively proved that inflow rivers (87.1%) were the most significant source of OCPs. Moreover, considering the land use characteristics around Lashihai Lake (Fig. 1 ) where farmlands are dominant, and five inflow rivers all flow through agricultural area, the inflow rivers play important role in transporting OCPs from farmland into the lake basin. It is worth noting that while forests also contribute certain amounts of organics (6.6%), given the fact that the OCPs in forests soils of study area were undetectable, forests were thus not identified as the main source of OCPs but as significant contributor of TOC alone. Conclusively, integrating the OCPs distribution characteristics, carbon isotope mixing model analysis and geographical features, we proved that residual OCPs in agricultural soils can migrate into inflow rivers, which facilitates the transportation of OCPs into the lake basin where the OCPs are allocated into organic matter of lake sediments. Given the enclosed topography of this type of plateau lakes and the dominant role of inflow rivers in transportation of OCPs, the sustainable proactive mitigation strategies for similar aquatic ecosystems across the Tibetan-Yunnan-Guizhou Plateau should prioritize basin-wide source control method, with especial emphasis on the regulation of inflow rivers. This includes regulating OCPs reservoirs in upstream agricultural zones and surrounding inflow rivers to prevent long-term sediment sequestration and associated aquatic risks. Bioaccumulation of OCPs in fish and health risk assessment Bioaccumulation of OCPs in fish Fish, which is rich in lipids, serve as significant reservoirs of OCPs within lake ecosystems. Moreover, as one of food resources for local residents, the OCPs present in fish may enter human body through the food chain, posing health risks to local communities (Abayi et al., 2021 ; Buah-Kwofie & Humphries, 2021 ; Hasan et al., 2022 ; Riaz et al., 2021 ; van der Oost et al., 2003 ). Therefore, we selected commonly found fish species in Lashihai Lake to determine the types and levels of OCPs in fish and conducted health risk assessment. In total, seven kinds of OCPs (α-HCH, endrin aldehyde, endrin ketone, endosulfan, aldrin, p,p’-DDT and p,p’-DDE) can be detected in fish. The total contents of the seven OCPs (∑ 7 OCPs) in all fish species ranged from 27.76 to 388.76 ng∙g⁻¹, with a mean value of 167.02 ± 133.40 ng∙g⁻¹ (Fig. 7 a, Table 2 ). Specifically, the ∑ 7 OCPs of varying fish species ranked from highest to lowest as follows: 388.76 ng∙g⁻¹ ( Cyprinus carpio L), 154.09 ng∙g⁻¹ ( Cyprinus carpio S), 136.43 ng∙g⁻¹ ( Carassius auratus S), 128.10 ng∙g⁻¹ ( Carassius auratus L), and 27.76 ng∙g⁻¹ ( Pelteobagrus fulvidraco ) (Fig. 7 a, Table 2 ). Compared with fish from aquatic environment worldwide, the OCPs concentrations of fish in this study are at a moderate level. For example, the average contents of OCPs of varying fish species in the River Chenab, Pakistan were 117 ± 43 ng∙g⁻¹, ranging from 5.09 to 414 ng∙g⁻¹ (Siddique et al., 2023 ). A study from Wuhan, China reported that the OCPs in fish from Yangtze River basin were in the range of 2.04 to 189.04 ng∙g⁻¹ (wet weight) (Cui et al., 2015 ). In an estuarine wetland of South Africa, the OCPs in sediments ranged from 74 to 510 ng∙g⁻¹, while the OCPs in fish were between 30 and 175 ng∙g⁻¹ (Buah-Kwofie & Humphries, 2021 ). It is worth noting that those OCPs species with low detection rates (Table 1 ) in sediments were widely detectable in fish, such as p,p’-DDT, aldrin and endosulfan (Table 2 ). This is because fish can selectively accumulate specific types of OCPs rather than fully reflecting the composition of OCPs in sediments. For example, DDT, due to its high lipophilicity and lower metabolic stability, is more likely to accumulate in fish tissues than sediments (Jayaraj et al., 2016 ). In addition, bio-transformation of OCPs between varying species were also observed during bioaccumulation from sediment to fish in some studies (Oliva et al., 2022 ). Consequently, ether selective bioaccumulation or bio-transformation can trigger the differences in OCPs species or contents between fish and sediments. Furthermore, we differentiated body sizes of the most common edible fish, Carassius auratus and Cyprinus carpio , to explore the influence of growth time on the bioaccumulation of OCPs. The larger individuals of Cyprinus carpio (L) contained significantly higher ∑ 7 OCPs (388.76 ng∙g⁻¹) than the smaller ones (154.09 ng∙g⁻¹) ( p 0.05) (Fig. 7 a). This indicates that the accumulation of OCPs in the Cyprinus carpio is related to size and growth time. Commonly, the phenomenon of biomagnification is observed and used to explain the increase in the contents of persistent organic pollutants as compounds propagate up the food chain (Daley et al., 2014 ). However, the situation in this study is different because the growth process of the same species ( Cyprinus carpio ) does not involve the changes at the food chain level; we therefore attribute this phenomenon solely to the growth time. The BSAF values were determined to further evaluate bioaccumulation effect of OCPs from sediments to fish. The BSAF values based on total OCPs ranged from 0.08 ( Pelteobagrus fulvidraco ) to 1.06 ( Cyprinus carpio ) (Table 2 ); this is at a relatively low level according to the national range of BSAF reported for fish in the United States (0.7–8.6) (Wong et al., 2001 ), indicating a relatively low level of bioaccumulation effect in study area. Another study from China that calculated BSAF based on total OCPs reported BSAF ranging from 3.8 (Gray mullet) to 56 (Tilapia), with a BSAF of 6.6 ± 1.3 for the Carassius auratus (Kwok et al., 2013 ). In contrast, the BSAF of Carassius auratus in this study ranged from 0.35 to 0.37 (Table 2 ). This is because there are many factors affecting bioaccumulation effect, including fish species, environmental factors, diet, etc., leading to significant deviations in BSAF values across different regions (Akinsanya et al., 2015 ; Zhang et al., 2016 ; Zhou et al., 2008 ). More importantly, compared with countries with existing national OCPs data for fish or other aquatic organisms (Wong et al., 2001 ), China still lacks sufficient OCPs data for fish species across different regions due to significant geographical differences. Therefore, collecting OCPs data for fish species from different regions can provide crucial information for the formulation of national standards of China. Table 2 Statistical values of OCPs (ng∙g − 1 dw) and bio-sediment accumulation factor (BSAF) in fish of Lashihai Lake Parameters Carassius auratus (S) Carassius auratus (L) Cyprinus carpio (S) Cyprinus carpio (L) Pelteoebagrus fulvidraco Diet type omnivore omnivore omnivore omnivore omnivore Length a 100 ~ 140 140 ~ 160 190 ~ 200 200 ~ 230 180 ~ 210 α⁃HCH nd b 2.60 nd nd nd Aldrin 40.69 38.64 26.02 101.91 nd Endosulfan nd nd 89.60 nd nd p, p’-DDE nd 73.18 nd 260.01 nd Endrin aldehyde nd nd nd nd 12.20 p, p’- DDT nd 13.68 14.65 7.27 15.56 Endrin ketone 95.74 0.00 23.82 19.57 nd ∑ 7 OCPs c 136.43 128.10 154.09 388.76 27.76 BSAF d 0.37 0.35 0.42 1.06 0.08 a Body length of fish expressed in “mm” b below detection limit c the total contents of the seven OCPs d Bio-sediment accumulation factor based on Eq. (1–1) Health risk assessment OCPs can enter human body through food chain, thus posing health risks to local residents. Although the bioaccumulation effect of OCPs in study area is at a moderate level, the potential health risks cannot be ignored considering the long-term effects. Therefore, we assessed the potential health risks of OCPs in common edible fish based on CRI and ERI. In general, the ERI and CRI of most OCPs of varying fish species indicated limited exposure and carcinogenic risks through consuming fish, except for the aldrin. For ERI, only that of aldrin in Carassius auratus (L) exceeded 1000×10 − 3 (Fig. 7 b), the reference value for safety, suggesting a watchful exposure risk. In contrast, the ERI of other OCPs (DDT, endrin, α-HCH, endosulfan) across all kinds of fish was below the reference value, indicating an acceptable exposure risk on a larger scale. As for CRI, the CRI of aldrin in all kinds of fish, except for the Pelteobagrus fulvidraco , exceeded the reference value of 100×10 − 6 (184.29×10 − 6 ~721.90×10 − 6 ) (Fig. 7 c), suggesting certain carcinogenic risks when exposing to aldrin by consuming fish. Other OCPs (DDT, endrin, α-HCH, endosulfan) across all fish exhibited minimal carcinogenic risks, with CRI ranging from 0.00 to 37.86×10 − 6 . This is consistent with the fact that all kinds of fish, except for the Pelteobagrus fulvidraco , contain certain amounts of aldrin (Fig. 7 a). Additionally, although aldrin was not the most abundant OCPs in fish, its highest toxicity indicated by the highest CSF value of 17 (Dougherty et al., 2000 ; Watanabe et al., 2003 ) accounted for its considerable exposure and carcinogenic risks through fish. Another thing worth noting is that although the detection rate of aldrin in sediments was low, it was quite common in fish. This is due to the selective amplification during the bioaccumulation process from sediments to fish, a phenomenon observed in other species and regions as well (Alharbi et al., 2018 ). This further indicates that health risk assessments based solely on the OCPs contents in inorganic substances such as water, sediments or soils are not comprehensive enough, while health risk assessment based on food chain is more recommended. On the other hand, some studies have directly determined the contents of OCPs in humans and their health symptoms to reflect the health effects of OCPs (Papadopoulou et al., 2013 ). Although this kind of approach can most directly achieve the objectives of health risk assessment, the need for biological samples of human may also limit this approach. Above all, by analyzing the residual levels of OCPs in edible fish species and their potential intake in the Lashihai Lake area, the health risks of OCPs through fish consumption to the local population were at an acceptable level, but long-term accumulation may also pose considerable risks. Conclusions Organochlorine pesticides (OCPs) continue to pose significant ecological threats to global aquatic ecosystems due to their high toxicity and environmental persistence. As representative aquatic ecosystem across the Tibetan and Yunnan-Guizhou Plateau, enclosed plateau lakes provide crucial aquatic ecological services, yet understanding on sources and migration pathway of OCPs in these unique ecosystems remains unclear, hindering aquatic security management. This study reveals that under the combined influence of agricultural activities and the lakes' inherent hydrological closure, sediments serve as primary reservoirs for historical OCPs residues, with their distribution patterns being regulated by total organic carbon (TOC) and pH of sediments. Crucially, inflow rivers have been identified as the predominant carrier for OCPs transportation, emphasizing the necessity for basin-wide pollution source control strategies in sustainable management for this type of aquatic ecosystems. Although bioaccumulation from sediments to fish was moderate, the long-term exposure through food chains still warrants health risks. Given that China still lacks a national health risk assessment standard based on trophic measure for OCPs, more OCPs data of aquatic organisms from regions with varying geological features are imperative. Overall, this study enhances the understanding in the behavior patten and bioaccumulation process of OCPs in plateau lakes, offering valuable insights into sustainable utilization and management for other analogous aquatic environments. Declarations Ethical approval All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements We extend our sincere gratitude to all participants involved in this study for their invaluable contributions to data collection, fieldwork, and discussions. Special thanks are given to the College of Ecology and Environment, Southwest Forestry University, and the National Plateau Wetland Research Center for providing laboratory facilities, experimental equipment, and technical support. Their expertise and resources were instrumental in advancing the research objectives. We also acknowledge the contributions from colleagues and institutions that supported this project indirectly. Finally, we thank the anonymous reviewers for their constructive feedback on the manuscript. Authorship contribution Xixiang Qiu: Data curation, Investigation, Methodology, Supervision, Writing - original draft. Lei Sun: Conceptualization, Data curation, Formal analysis, Writing - review & editing. Xiaohui Zhao: Data curation, Formal analysis, Writing - review & editing. Jianhui Liu: Data curation, Supervision, Writing - review & editing. Kun Zhang: Formal analysis, Funding acquisition, Project administration, Writing - review & editing. Yinfeng Zhang: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing - review & editing. All authors reviewed the manuscript. Funding declaration This work was supported by the National Natural Science Foundation of China (Grant#32360291) and the Natural Science Foundation of Yunnan province, China (Grant #202101AT070036). There were no any conflicts of interests. Data availability Data is provided within the manuscript or supplementary information files. References Abayi, J. J. M., Gore, C. T., Nagawa, C., Bandowe, B. A. M., Matovu, H., Mubiru, E., Ngeno, E. C., Odongo, S., Sillanpää, M., & Ssebugere, P. (2021). Polycyclic aromatic hydrocarbons in sediments and fish species from the White Nile, East Africa: Bioaccumulation potential, source apportionment, ecological and health risk assessment. Environment Pollution , 278, 116855. https://doi.org/10.1016/j.envpol.2021.116855. Akinsanya, B., Alani, R., Ukwa, U. D., Bamidele, F., & Saliu, J. 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Cite Share Download PDF Status: Published Journal Publication published 19 Nov, 2025 Read the published version in Environmental Monitoring and Assessment → Version 1 posted Editorial decision: Revision requested 11 Oct, 2025 Reviews received at journal 01 Oct, 2025 Reviews received at journal 28 Sep, 2025 Reviewers agreed at journal 26 Sep, 2025 Reviewers agreed at journal 26 Sep, 2025 Reviewers agreed at journal 26 Sep, 2025 Reviewers invited by journal 25 Sep, 2025 Editor assigned by journal 24 Sep, 2025 Submission checks completed at journal 24 Sep, 2025 First submitted to journal 11 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7587667","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":525430533,"identity":"8142af8a-e0e0-4e49-a02e-9762d857a599","order_by":0,"name":"Xixiang Qiu","email":"","orcid":"","institution":"Yunnan Key Laboratory of Plateau Wetland Conservation, Restoration and Ecological Services, Southwest Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Xixiang","middleName":"","lastName":"Qiu","suffix":""},{"id":525430534,"identity":"bafb996d-03b4-4687-8b8c-df4d4b1ea4f3","order_by":1,"name":"Lei 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2","display":"","copyAsset":false,"role":"figure","size":284904,"visible":true,"origin":"","legend":"\u003cp\u003eStacked histogram of the contents of the three major OCPs in surface sediments\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7587667/v1/d8335fcd5cd389b00c047234.png"},{"id":93014007,"identity":"a1e80475-27d9-42fb-b81f-b113f6c117e9","added_by":"auto","created_at":"2025-10-08 07:33:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":466259,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of the three OCPs (\u003cstrong\u003ea\u003c/strong\u003e α-HCH, \u003cstrong\u003eb\u003c/strong\u003e δ-HCH, \u003cstrong\u003ec\u003c/strong\u003e endrin aldehyde) in sediments of Lashihai Lake\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7587667/v1/854a4758c075888da3680f05.png"},{"id":93014009,"identity":"e103eda4-0e1e-476c-b290-74b973d50b6e","added_by":"auto","created_at":"2025-10-08 07:33:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":466112,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Correlations between OCPs and influencing factors (pH, TOC and grain size) in surface sediments; linear relationship between \u003cstrong\u003eb\u003c/strong\u003eα-HCH, \u003cstrong\u003ec\u003c/strong\u003e δ-HCH,\u003cstrong\u003e d\u003c/strong\u003e endrin aldehyde and TOC\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7587667/v1/4c87099f4bf959bce0d59016.png"},{"id":93014004,"identity":"6e8b429c-4712-4b3b-b75d-924fc0ae8836","added_by":"auto","created_at":"2025-10-08 07:33:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":256873,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Stacked histogram of the contents of the three major OCPs in soils/sediments of farmlands and inflow rivers (“N” represents farmlands, “R” represents inflow rivers); \u003cstrong\u003eb\u003c/strong\u003e the average total OCPs contents (∑\u003csub\u003e3\u003c/sub\u003eOCPs) of various land types (the same letter “a” indicates no significant differences in ∑\u003csub\u003e3\u003c/sub\u003eOCPs, \u003cem\u003ep \u003c/em\u003e\u0026gt; 0.05)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7587667/v1/d237a43032448f18106cf839.png"},{"id":93014008,"identity":"682e8e83-7ff3-401a-91a4-13f8f37ba0d2","added_by":"auto","created_at":"2025-10-08 07:33:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":227372,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003eδ\u003csup\u003e13\u003c/sup\u003eC values of different land types;\u003cstrong\u003e b\u003c/strong\u003e relationships between total organic carbon (TOC) and δ\u003csup\u003e13\u003c/sup\u003eC of different land types; \u003cstrong\u003ec\u003c/strong\u003e the contribution ratios of different sources to OCPs of sediments in Lashihai Lake based on the stable carbon isotope (δ\u003csup\u003e13\u003c/sup\u003eC) mixing model\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7587667/v1/879596628716b50115ef4f75.png"},{"id":93014010,"identity":"3e853683-f9d9-4df3-bc9f-ad9b577e1698","added_by":"auto","created_at":"2025-10-08 07:33:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":389433,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Contents of OCPs in fish (ng∙g\u003csup\u003e-1\u003c/sup\u003e dry weight); \u003cstrong\u003eb \u003c/strong\u003eexposure risk index (ERI), and \u003cstrong\u003ec \u003c/strong\u003ecarcinogenic risk index (CRI) of OCPs in three fish species from the Lashihai Lake\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7587667/v1/5eafb47ddb7024be3913a3f7.png"},{"id":96650162,"identity":"61f57a25-8c33-4950-ac14-6c23c824aafc","added_by":"auto","created_at":"2025-11-24 16:09:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3848215,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7587667/v1/2bf08f1a-d69b-487e-95a0-dee8bb47d46e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dominant Riverine Input of Legacy Organochlorine Pesticides to a Semi- Enclosed Plateau Lake: Distribution, Source Apportionment and Bioaccumulation","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePersistent organic pollutants (POPs), including organochlorine pesticides (OCPs), continue to pose severe threats to sustainable utilization and management of global aquatic ecosystems due to their characteristics of semi-volatility, bioaccumulation, high toxicity, and resistance to degradation (de Boer et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Of the 23 POPs identified under the Stockholm Convention (Liu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), 13 of them are organochlorine pesticides (OCPs). These include well-known compounds such as aldrin, lindane, heptachlor, endrin, and hexachlorocyclohexane, which have been recognized globally as priority contaminants (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.pops.int\u003c/span\u003e\u003cspan address=\"http://www.pops.int\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Historically, valued for their effectiveness as insecticides and herbicides, OCPs have been extensively used on a global scale for several decades, persisting in aquatic ecosystem and dispersing into broader environments through air dispersion (Girones et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gonz\u0026aacute;lez-Rubio et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Sediments, a critical compartment of aquatic eco-system such as lakes, play a dual role as both a sink and a delayed source of OCPs (Barletta et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). On the one hand, sediments can effectively sequester OCPs from water over decadal timescales due to the hydrophobic nature of OCPs through adsorption or allocation process (Şimşek Uygun \u0026amp; Albek, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). On the other hand, this apparent stability masks a latent environmental threat, which is the shifts in water environment can trigger the remobilization of historically buried OCPs from sediments, posing significant risks to drinking water safety and aquatic biodiversity (Rex et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, studying the residual levels and environmental behavior of OCPs in sediments is critical for sustainable management of aquatic ecosystem.\u003c/p\u003e\u003cp\u003eFrom the 1950s to 1980s, about 18000 tons of organochlorines (including polychlorinated biphenyls and OCPs) were produced or imported as dielectric fluids in electrical appliances or additives in paints in China (Jiang et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). In past two decades, while the Chinese government has implemented series of activities to restrict the circulation, use and production of OCPs-related products since becoming a signatory to the Stockholm Convention in 2004, residual OCPs continue to pose threats to aquatic ecosystem and human health due to their prolonged environmental persistence, high toxicity, and long-range transportation characteristics (Grung et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). As a result, extensive studies have systematically documented the types, concentrations and distribution of OCPs in various regions across China (Grung et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Han \u0026amp; Currell, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Although these studies provided key information on the characteristics of OCPs in different regions of China, they however tend to focus on the central and eastern China due to the fast development and thus possible more usage of OCPs in these areas. As a result, a critical knowledge gap regarding the behavior pattern, sources and health risks of OCPs in those more enclosed agricultural or pastoral areas persisted. Meanwhile, enclosed agricultural or pastoral regions, particularly those in ecologically fragile plateau areas, are characterized by limited pollutant dispersion and heightened susceptibility to bioaccumulation (Machate et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); this makes such type of aquatic ecosystems more likely to accumulate OCPs and suffer greater ecological harms (Kallenborn, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Pastorino et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Consequently, effective prevention of OCPs in such enclosed plateau lakes necessitates source-directed intervention; scientific source apportionment and clarification of migration pathways of OCPs are fundamental yet unsolved scientific issues for targeted conservation strategies. However, due to the absence of systematic investigations, both the origins and transportation pathways of OCPs within these lake systems remain unresolved, presenting a major theoretical barrier to ecological conservation and restoration.\u003c/p\u003e\u003cp\u003eThe plateau lakes of northwest Yunnan, situated at the tectonic boundary between the Tibetan and Yunnan-Guizhou Plateau, present a representative yet understudied aquatic ecosystem characterized by exceptional biodiversity and ecological significance (Sun et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, the majority of these plateau lakes are typical fault-depression lakes with enclosed or semi-enclosed geographical features, resulting in lower environmental capacity and more fragile ecological environments (Hou et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, compared with lakes in the Tibetan Plateau, where few agricultural or industrial sources existed and the main source of OCPs is the long-range transportation through monsoons (Ding et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ren et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the plateau lakes in Yunnan-Guizhou Plateau are typically surrounded by agricultural activities, raising concerns about the types, accumulation characteristics, sources and the potential health risks of OCPs in these plateau lakes from agriculture-dominated area. To address these knowledge gaps, this study selected Lashihai Lake, a representative semi-enclosed plateau lake in the agricultural zone of northwest Yunnan, China, to: 1) analyze the types and concentrations of OCPs in surface sediments, revealing their residual levels, distribution patterns and influencing factors; 2) identify OCPs sources and migration pathways based on distribution pattern and stable carbon isotope (δ\u003csup\u003e13\u003c/sup\u003eC) mixing model analysis; 3) assess the bioaccumulation of OCPs in edible fish and the associated health risks.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eStudy area\u003c/p\u003e\u003cp\u003eThe Lashihai Wetland Nature Reserve, established in 1998, is located in the transition zone of the Tibetan-Yunnan Plateau, China (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and it was designated as a Wetland of International Importance in 2004 due to its critical role in the protection of rare migratory birds. The Lashihai Lake (26\u0026deg;44'-27\u0026deg;00'N, 100\u0026deg;05'-100\u0026deg;13'E) is the core area of this reserve. It is a typical semi-enclosed plateau lake with a watershed area of 265.6 km\u0026sup2;, a lake basin area of 5337.8 hectares, an average surface elevation of 2,438 meters, and an average water depth of 4.5 meters. Hydrologically, Lashihai Lake is situated at the lowest point of the watershed, surrounded by mountains with elevations ranging from 3,000 to 4,000 meters. The lake relies on seasonal precipitation and inflow from surrounding rivers for water replenishment, but lacks a stable outflow channel, forming a semi-enclosed catchment basin. From the perspective of land use, the watershed is primarily composed of forest (41.2%), meadow (18.5%), and marshland (12.3%). Besides, human activities are concentrated along the lakeshore, with farmlands accounting for 23.9% of the area. High-value economic crops, such as apples, pears and vegetables, are predominantly cultivated, with agricultural output contributing 54.9% of the region's total production value. Residential and tourism-related land uses make up 4.1%, with urbanization and tourism development showing an increasing trend (Dou et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The relatively enclosed geographical characteristics, combined with the high proportion of agricultural activities in the surrounding areas, render this region a representative plateau lake for investigating the behavioral patterns and aquatic risks of OCPs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSampling\u003c/p\u003e\u003cp\u003eThe selection of lake sediment sampling sites took into account hydrodynamic conditions, lakebed morphology, inflow rivers and potential pollution sources. A total of 42 surface sediment sampling points were set in the lake basin, 7 in the inflow rivers, 18 in the farming area around lake and 4 in forest (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For sediments of lake and inflow rivers, a grabbing sampler was used to collect the top 10 cm of sediment. At each point, 2\u0026ndash;3 subsamples were collected and mixed as one final sample (about 1000 g). For soils of farming area and forest, we collected surface soils with a stainless-steel shovel with same process as that of sediments. All collected samples were sealed in self-sealing bags, transported to the laboratory in ice-packed containers, and stored at -20\u0026deg;C until analysis.\u003c/p\u003e\u003cp\u003eThree edible fish species from Lashihai Lake were obtained with the assistance of local fishermen; the species include \u003cem\u003eCarassius auratus\u003c/em\u003e, \u003cem\u003eCyprinus carpio\u003c/em\u003e and \u003cem\u003ePelteobagrus fulvidraco\u003c/em\u003e. Briefly, around 1000\u0026thinsp;~\u0026thinsp;2000 g of each species was collected and preserved at -20℃ after transportation to laboratory in ice-packed foam box. The measurements of body length for the two most common species, \u003cem\u003eCarassius auratus\u003c/em\u003e and \u003cem\u003eCyprinus carpio\u003c/em\u003e, were performed with a vernier caliper. The fish were then subdivided according to body length as follows: \u003cem\u003eCarassius auratus\u003c/em\u003e (S) (100.00\u0026thinsp;~\u0026thinsp;140.00 mm), \u003cem\u003eCarassius auratus\u003c/em\u003e (L) (140.00\u0026thinsp;~\u0026thinsp;160.00 mm), \u003cem\u003eCyprinus carpio\u003c/em\u003e (S) (170.00\u0026thinsp;~\u0026thinsp;200.00 mm) and \u003cem\u003eCyprinus carpio\u003c/em\u003e (L) (200.00\u0026thinsp;~\u0026thinsp;230.00 mm).\u003c/p\u003e\u003cp\u003eDetermination methods\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eDetermination of OCPs\u003c/h2\u003e\u003cp\u003eThe procedures for sample (sediment, soil and fish) pretreatment and OCPs determination followed those reported by Sun et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and are briefly summarized here. Sediment and soil samples were freeze-dried, ground to 200 mesh, and extracted with n-hexane-dichloromethane (1:1) using ultrasonic-assisted extraction. Extracts were purified on Florisil columns, concentrated under nitrogen. Fish samples were rinsed with ultrapure water, and non-edible parts (bones, tails, scales) were discarded. Individuals of the same size and species were pooled, dried with ~\u0026thinsp;20 g of anhydrous sodium sulfate, homogenized, and then subjected to the same extraction procedure as sediment. OCPs concentrations in all solid samples (sediment, soil and fish) were expressed on a dry-weight basis (ng∙g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e dw).\u003c/p\u003e\u003cp\u003eA Gas chromatography\u0026ndash;mass spectrometry (GC-MS, Thermo Scientific TRACE 1310 coupled with ISQ-LT) was used to quantify OCPs. A TG-5MS capillary column (30 m \u0026times; 0.25 mm \u0026times; 0.25 \u0026micro;m, Thermo Scientific) was used for separation, with high-purity helium as carrier gas. A 1 \u0026micro;L aliquot of each extract was injected directly, with inlet and detector temperatures set to 250\u0026deg;C and 300\u0026deg;C, respectively. Full-scan mode was used for qualitative identification based on retention time and diagnostic ions, while quantification relied on a six-point calibration curve (0, 0.1, 0.5, 1.0, 5.0, and 10.0 mg∙L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; R\u0026sup2; \u0026gt;0.99). A standard reference soil (TMQC0227, J\u0026amp;K Scientific) containing 23 OCPs, including α-HCH, δ-HCH, and endrin aldehyde, was used for quality assurance and control. Specifically, five replicates of the standard soil were processed in parallel using the same extraction and analysis procedures as the samples. The average measured values were compared with the certified reference values to calculate correction coefficients, thereby ensuring analytical accuracy.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDetermination of TOC and stable carbon isotope ratios (δC)\u003c/h3\u003e\n\u003cp\u003eThe methods for TOC and δ\u003csup\u003e13\u003c/sup\u003eC determination also followed Sun et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), with a concise description provided below. For TOC, ~\u0026thinsp;0.008 g of sample was treated with several drops of 1% HCl to remove inorganic carbon, dried at 105\u0026deg;C, and ~\u0026thinsp;0.004 g was wrapped in tin foil for measurement. TOC was analyzed with a German Element Vario analyzer (error\u0026thinsp;\u0026lt;\u0026thinsp;0.2%), using CaCO₃ (ultra-pure, preheated at 280\u0026deg;C for 1 h) as the control.\u003c/p\u003e\u003cp\u003eFor δ\u003csup\u003e13\u003c/sup\u003eC, ~\u0026thinsp;15 mg of dried sample was placed in a tin cup and measured using an elemental analyzer (Picarro TOC-CRDS, USA). The δ\u003csup\u003e13\u003c/sup\u003eC values were expressed in per mil (\u0026permil;) relative to the Vienna Pee Dee Belemnite (VPDB) standard and calculated following Hoffman \u0026amp; Rasmussen (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Two laboratory standards (δ\u003csup\u003e13\u003c/sup\u003eC = 11.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u0026permil; and \u0026minus;\u0026thinsp;8.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u0026permil;) were run with each batch to calibrate the results, with analytical precision of 0.03\u0026permil;.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003eBio-sediment accumulation factor (BSAF)\u003c/h2\u003e\u003cp\u003eTo evaluate the accumulation ability of OCPs from sediments to fish, the calculation of BSAF was applied following the Eq.\u0026nbsp;1\u0026ndash;1 (Akinsanya et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), where C\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e is the concentration of OCPs in fish on a dry-weight basis (ng∙g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e dw), and C\u003cem\u003es\u003c/em\u003e is the mean OCPs concentration in the surface sediments (ng∙g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e dw).\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:BSAF={C}_{f}/{C}_{s}\\)\u003c/span\u003e\u003c/span\u003e (1\u0026ndash;1)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eHealth risk assessment\u003c/h3\u003e\n\u003cp\u003eThe EPA method of chemical pollution assessment from the US EPA fish survey was used to evaluate the potential health risks to local residents (EPA, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Considering that the daily fish consumption of each person is crucial in health risk assessment, two main factors were considered: (1) Lashihai Lake is an inland plateau lake, and the total fish consumption is much lower than those in coastal cities; (2) fish from Lashihai is only one of the food sources for local residents; other sources like artificial fish ponds and external sources are available. Therefore, compared with previous studies, we considered it appropriate to set the daily fish consumption per person in this study at 25 g∙kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e∙d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, assuming an average body weight of 60 kg for each adult (Sun et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). On this basis, the carcinogenic risk index (CRI) and exposure risk index (Papadopoulou et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) of OCPs in various fish species were determined according to Eq.\u0026nbsp;1\u0026ndash;2 and 1\u0026ndash;3, except for endosulfan and dieldrin, which were classified by the US EPA as pesticides without carcinogenic risks (EPA, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"273\" height=\"102\"\u003e\u003c/p\u003e\n\u003cp\u003eIn these two equations, C\u003csub\u003ei\u003c/sub\u003e is the mean concentration of OCPs in fish; CW is the daily consumption amounts of fish (g∙kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e∙d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e); CSF is carcinogenic slope factor (mg∙kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e∙d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003csup\u003e\u0026minus;1\u003c/sup\u003e; RHD is reference harm dosage (\u0026micro;g∙kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e∙d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e); the detailed reference values of CSF and RHD can be achieved in previous studies (Sun et al.,2023; Watanabe et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eMixing model of stable isotope analysis\u003c/h2\u003e\u003cp\u003eA Bayesian stable isotope mixing model was applied to δ\u003csup\u003e13\u003c/sup\u003eC data to estimate the relative contributions of different sources (e.g., farming area, inflow river, and forest) to lake sediments, following the approach described in Sun et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e. The analysis was conducted using the package \u0026ldquo;simmr\u0026rdquo; implemented in R 4.3.0 (The R Core Team, 2013). Briefly, outliers of δ\u003csup\u003e13\u003c/sup\u003eC data were first removed using interquartile range method prior to model-analysis. Then all the δ\u003csup\u003e13\u003c/sup\u003eC data were grouped into source group (farming area, inflow river and forest) and consumer group (lake sediment). Last, the grouped data were imported into the \u0026ldquo;simmr\u0026rdquo; package for model running. Detailed information and algebra equations of the mixing model can be acquired in this study (Parnell et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOther data handling and visualization steps included preliminary data collation in Excel 2016, plotting with the \u0026ldquo;ggplot2\u0026rdquo; package in R, and spatial interpolation of OCPs concentrations in ArcGIS 10.2. Since the datasets did not follow normal distributions, non-parametric Wilcoxon tests were employed in R to evaluate differences between groups.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results and discussions","content":"\u003cp\u003eDistribution of OCPs in lake sediments\u003c/p\u003e\u003cp\u003eMultiple OCPs, including hexachlorocyclohexanes (HCHs), endrin aldehyde, endrin ketone, aldrin, p,p\u0026rsquo;-dichlorodiphenyltrichloroethane (p,p\u0026rsquo;-DDT), p,p\u0026rsquo;-dichlorodiphenyldichloroethylene (p,p\u0026rsquo;-DDE) and endosulfan were detectable in the surface sediments of Lashihai Lake. Among them, the α-HCH, δ-HCH and endrin aldehyde were prevalent at various sampling points with detection rates of 100% (α-HCH), 60% (δ-HCH) and 60% (endrin aldehyde) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Apart from these three main OCPs, other OCPs had much lower detection rates and their medians were all below the limit of detection, indicating their limited representativeness (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Therefore, subsequent analysis in this paper primarily focuses on the three main OCPs (α-HCH, δ-HCH and endrin aldehyde). Overall, the concentrations of the three main OCPs in the surface sediments ranged from nd (not detectable) to 459.89 ng∙g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (δ-HCH) (median of 142.45 ng∙g⁻\u0026sup1;), nd to 450.87 ng∙g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (endrin aldehyde) (median of 68.05 ng∙g⁻\u0026sup1;) and 11.00 to 236.95 ng∙g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (α-HCH) (median of 37.70 ng∙g⁻\u0026sup1;) respectively. On average, the δ-HCH accounted for 52% of the total contents of the three main OCPs (\u0026sum;\u003csub\u003e3\u003c/sub\u003eOCPs), while the concentration of α-HCH was lower than that of endrin aldehyde, accounting for only 17% of \u0026sum;\u003csub\u003e3\u003c/sub\u003eOCPs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These three major OCPs all showed high standard deviations in their concentrations, indicating that there was considerable heterogeneity in the distribution of OCPs within the sediments of the study area. The OCPs concentrations in this region are comparable to or lower than those in multiple wetland basins worldwide (Buah-Kwofie \u0026amp; Humphries, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Buah-Kwofie \u0026amp; Humphries, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For example, the total OCPs concentrations in sediments from Lake St. Lucia in South Africa and its two major inflow rivers were 370\u0026thinsp;\u0026plusmn;\u0026thinsp;150 ng∙g⁻\u0026sup1;, 260\u0026thinsp;\u0026plusmn;\u0026thinsp;110 ng∙g⁻\u0026sup1;, and 240\u0026thinsp;\u0026plusmn;\u0026thinsp;120 ng∙g⁻\u0026sup1;, respectively, where the γ-HCH dominated, suggesting recent use of γ-HCH (Buah-Kwofie \u0026amp; Humphries, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In contrast, the HCHs in this study were primarily composed of δ-HCH and α-HCH with almost no γ-HCH. It has been reported that γ-HCH can be rapidly degraded by microorganisms while δ-HCH and α-HCH can persist for a longer period (Tzanetou \u0026amp; Karasali, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Willett et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). These together indicate that the HCHs in study area are more related to historical residues rather than recent usage.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStatistical values of OCPs (ng∙g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e dw) in sediments of Lashihai Lake\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOCPs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003emin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003emedian\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003emax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003emean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003esd\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eaverage contribution (%) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDetection rate (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eα-HCH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e236.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e49.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e48.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eδ-HCH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003end \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e142.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e459.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e151.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e154.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEndrin aldehyde\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e450.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e93.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e115.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026sum;\u003csub\u003e3\u003c/sub\u003eOCPs \u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e248.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1021.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e294.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e297.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep,p\u0026rsquo;-DDT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep,p\u0026rsquo;-DDE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEndrin ketone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e340.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e22.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e67.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAldrin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEndosulfan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e the average contribution of each of α-HCH, δ-HCH, endrin aldehyde to the\u0026sum;\u003csub\u003e3\u003c/sub\u003eOCPs\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e \u0026ldquo;nd\u0026rdquo; represents not detectable, below limit of detection\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e the total contents of the three major OCPs (α-HCH, δ-HCH and endrin aldehyde)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSpecifically, the highest concentration of α-HCH in the surface sediments of Lashihai Lake occurred in the center area (S17, 236.95 ng∙g\u003csup\u003e⁻\u0026sup1;\u003c/sup\u003e), with the next highest concentrations located in the northern part of the lake area (S8, 199.11 ng∙g⁻\u0026sup1;, S7, 98.02 ng∙g⁻\u0026sup1;). The highest δ-HCH was also found in the northern part of the lake area (S8, 459.89 ng∙g⁻\u0026sup1;), while the area with the highest concentration of endrin aldehyde was in the northwest part of the lake (S17, 450.87 ng∙g⁻\u0026sup1;, S11, 369.18 ng∙g⁻\u0026sup1;), with the next highest concentrations appearing in the northeast part (S14, 359.43 ng∙g⁻\u0026sup1;, S9, 223.36 ng∙g⁻\u0026sup1;) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Importantly, the higher values of these OCPs were predominantly found in the northeastern parts of the lake, partially consistent with the distribution patterns of inflow rivers and farmlands (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This preliminary evidence based on distribution patten suggests that farmlands and inflow rivers are sources of OCPs in study area, which will be further discussed in section 3.3.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eInfluencing factors for OCPs distribution in sediments\u003c/p\u003e\u003cp\u003eThe behavior of persistent organic pollutants at the water-solid interface is mainly controlled by adsorption and allocation, which are commonly influenced by TOC, pH and grain size of sediments (Duodu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gandla et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gao et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lv et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Herein, we investigated the relationships between these influencing factors and the three main OCPs in sediments. The α-HCH (r\u0026thinsp;=\u0026thinsp;0.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), δ-HCH (r\u0026thinsp;=\u0026thinsp;0.76, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and endrin aldehyde (r\u0026thinsp;=\u0026thinsp;0.64, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were all positively correlated with TOC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), proving that organic matter of sediments is the main storage medium for these OCPs, which has also been observed in other area across the world (Duodu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mishra et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Moreover, their relationship with TOC could be further described using linear models (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb-d). These together made us believe that the OCPs were enriched in sediments mainly through the allocation process, given that the allocation process is commonly marked by the joint increase between organic matter and organic pollutants. In contrast, the α-HCH (r = -0.52, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), δ-HCH (r = -0.75, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and endrin aldehyde (r = -0.63, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were all negatively correlated with pH of sediments (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). This is due to that the pH of soil or sediment can affect the microbial activity, thus affecting the accumulation of OCPs. For example, study reported that the lower pH can reduce microbial activity and thus lead to a lower degradation of OCPs in soil (Wenzel et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Furthermore, lower pH can support the accumulation of organic matter which is beneficial for the accumulation of OCPs by ether adsorption or allocation process (Mishra et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Škrbić \u0026amp; Đurišić-Mladenović, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2007\u003c/span\u003e); this also agrees with our conclusion that TOC is the main sink for OCPs in study area. For the influence of grain size, although the three main OCPs were negatively correlated with grain size of sediments (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), the influence was not as significant as that of TOC and pH, suggesting limited influence of grain size on the distribution of OCPs, and that adsorption process, which is commonly affected by grain size (Amutova et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Qian et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), may not be the dominated process.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAnalysis of Sources of OCPs\u003c/p\u003e\n\u003ch3\u003eCharacteristics of OCPs in potential sources\u003c/h3\u003e\n\u003cp\u003eFarmlands are widespread around Lashihai Lake, and there are several inflow rivers that pass through residential and farmland area (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e); this makes them the potential local sources of OCPs in study area. To address this, we analyzed the types and contents of OCPs in soils or sediments of the farmlands and inflow rivers around Lashihai Lake, and compared them with the OCPs in surface sediments. Both farmlands and inflow rivers contained certain amounts of OCPs, with same species as those in the lake sediments. As for OCPs species, the three OCPs (α-HCH, δ-HCH and endrin aldehyde) commonly found in the lake sediments were also widely detected in the inflow rivers and farmland. In comparison, the concentrations of \u0026sum;\u003csub\u003e3\u003c/sub\u003eOCPs in the lake sediments, inflow rivers and farmland were 294.63\u0026thinsp;\u0026plusmn;\u0026thinsp;297.16 ng∙g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 53.39\u0026thinsp;\u0026plusmn;\u0026thinsp;12.08 ng∙g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 46.47\u0026thinsp;\u0026plusmn;\u0026thinsp;15.97 ng∙g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For farmland soils, the residual contents of OCPs in this study were comparable to the average levels of farmland soils of China (Wang et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), but much lower than those agricultural soil from India or northwestern Spain (Calvelo Pereira et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Mishra et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In addition, the OCPs in the forest soils of study area were generally undetectable, suggesting that forest soils were not the main source of OCPs. This is consistent with the conclusion that the OCPs in the forest soil of the Tibetan Plateau mainly originate from deposition and their contents are generally low (Wang et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Importantly, although the Wilcoxon test results indicated no significant differences in \u0026sum;\u003csub\u003e3\u003c/sub\u003eOCPs between the lake sediments and the inflow rivers and farmlands (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), the average \u0026sum;\u003csub\u003e3\u003c/sub\u003eOCPs in the sediments were higher than those in farmlands and inflow rivers, further indicating that lake sediments are the ultimate sink for OCPs in this type of plateau lakes. It is also worth noting that the levels of OCPs of inflow rivers were generally higher than those of farmlands but lower than those of lake sediments, indicating the dominant role of inflow rivers as the main pathway for the migration of OCPs in this type of plateau lakes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eSource analysis of OCPs based on stable carbon isotopes\u003c/h2\u003e\u003cp\u003eThe three major OCPs in Lashihai sediments were significantly positively correlated with TOC and exhibited certain linear relationships, proving homology between OCPs and TOC. Based on this homology and the comparative analysis above, we further employed a Bayesian based stable carbon isotope mixing model to quantitatively analyze the sources of OCPs/TOC. The stable carbon isotope (δ\u003csup\u003e13\u003c/sup\u003eC) combined with mixing model has been proved to provide quantitative information about the organic sources (Cooper et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Cooper et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Upadhayay et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn general, the δ\u003csup\u003e13\u003c/sup\u003eC in different potential sources and sediments of Lashihai were \u0026minus;\u0026thinsp;24.63\u0026thinsp;\u0026plusmn;\u0026thinsp;3.29\u0026permil; (inflow rivers), -22.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2.19\u0026permil; (farmland), -22.43\u0026thinsp;\u0026plusmn;\u0026thinsp;4.85\u0026permil; (sediments) and \u0026minus;\u0026thinsp;21.84\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u0026permil; (forest) respectively. The δ\u003csup\u003e13\u003c/sup\u003eC of lake basin surface sediments (-22.43\u0026thinsp;\u0026plusmn;\u0026thinsp;4.85\u0026permil;) did not show significant differences from those of farmland, inflow rivers and forest (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting that the organic sources in sediments are homologous to these sources (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Specifically, the δ\u003csup\u003e13\u003c/sup\u003eC of farmland (-22.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2.19\u0026permil;) was closest to that of the sediments (-22.43\u0026thinsp;\u0026plusmn;\u0026thinsp;4.85\u0026permil;), suggesting farmlands as one important source. Furthermore, the lake sediments had larger range of δ\u003csup\u003e13\u003c/sup\u003eC-TOC compared with source areas such as inflow river, farmlands and forest (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb), suggesting that the organic sources of sediments are more diversified, and further emphasizing the role of sediments as the final reservoir of organics within the watershed; this is consistent with the geographical feature of this type of plateau lakes.\u003c/p\u003e\u003cp\u003eBased on above analysis and the semi-enclosed geographical features of study area, we hypothesized that lake sediments are the ultimate sink, while farmlands, inflow rivers and surrounding forests are contributors of OCPs/TOC. The mixing model results indicated that the average contributions of different sources to lake sediments were 87.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06% (inflow rivers), 6.6%\u0026plusmn;4.3% (forests) and 6.3%\u0026plusmn;5.5% (farmland) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). The model results combined with the distribution characteristics of OCPs in different source areas further quantitatively proved that inflow rivers (87.1%) were the most significant source of OCPs. Moreover, considering the land use characteristics around Lashihai Lake (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) where farmlands are dominant, and five inflow rivers all flow through agricultural area, the inflow rivers play important role in transporting OCPs from farmland into the lake basin. It is worth noting that while forests also contribute certain amounts of organics (6.6%), given the fact that the OCPs in forests soils of study area were undetectable, forests were thus not identified as the main source of OCPs but as significant contributor of TOC alone.\u003c/p\u003e\u003cp\u003eConclusively, integrating the OCPs distribution characteristics, carbon isotope mixing model analysis and geographical features, we proved that residual OCPs in agricultural soils can migrate into inflow rivers, which facilitates the transportation of OCPs into the lake basin where the OCPs are allocated into organic matter of lake sediments. Given the enclosed topography of this type of plateau lakes and the dominant role of inflow rivers in transportation of OCPs, the sustainable proactive mitigation strategies for similar aquatic ecosystems across the Tibetan-Yunnan-Guizhou Plateau should prioritize basin-wide source control method, with especial emphasis on the regulation of inflow rivers. This includes regulating OCPs reservoirs in upstream agricultural zones and surrounding inflow rivers to prevent long-term sediment sequestration and associated aquatic risks.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBioaccumulation of OCPs in fish and health risk assessment\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eBioaccumulation of OCPs in fish\u003c/h2\u003e\u003cp\u003eFish, which is rich in lipids, serve as significant reservoirs of OCPs within lake ecosystems. Moreover, as one of food resources for local residents, the OCPs present in fish may enter human body through the food chain, posing health risks to local communities (Abayi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Buah-Kwofie \u0026amp; Humphries, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hasan et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Riaz et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; van der Oost et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Therefore, we selected commonly found fish species in Lashihai Lake to determine the types and levels of OCPs in fish and conducted health risk assessment.\u003c/p\u003e\u003cp\u003eIn total, seven kinds of OCPs (α-HCH, endrin aldehyde, endrin ketone, endosulfan, aldrin, p,p\u0026rsquo;-DDT and p,p\u0026rsquo;-DDE) can be detected in fish. The total contents of the seven OCPs (\u0026sum;\u003csub\u003e7\u003c/sub\u003eOCPs) in all fish species ranged from 27.76 to 388.76 ng∙g⁻\u0026sup1;, with a mean value of 167.02\u0026thinsp;\u0026plusmn;\u0026thinsp;133.40 ng∙g⁻\u0026sup1; (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Specifically, the \u0026sum;\u003csub\u003e7\u003c/sub\u003eOCPs of varying fish species ranked from highest to lowest as follows: 388.76 ng∙g⁻\u0026sup1; (\u003cem\u003eCyprinus carpio\u003c/em\u003e L), 154.09 ng∙g⁻\u0026sup1; (\u003cem\u003eCyprinus carpio\u003c/em\u003e S), 136.43 ng∙g⁻\u0026sup1; (\u003cem\u003eCarassius auratus\u003c/em\u003e S), 128.10 ng∙g⁻\u0026sup1; (\u003cem\u003eCarassius auratus\u003c/em\u003e L), and 27.76 ng∙g⁻\u0026sup1; (\u003cem\u003ePelteobagrus fulvidraco\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared with fish from aquatic environment worldwide, the OCPs concentrations of fish in this study are at a moderate level. For example, the average contents of OCPs of varying fish species in the River Chenab, Pakistan were 117\u0026thinsp;\u0026plusmn;\u0026thinsp;43 ng∙g⁻\u0026sup1;, ranging from 5.09 to 414 ng∙g⁻\u0026sup1; (Siddique et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A study from Wuhan, China reported that the OCPs in fish from Yangtze River basin were in the range of 2.04 to 189.04 ng∙g⁻\u0026sup1; (wet weight) (Cui et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In an estuarine wetland of South Africa, the OCPs in sediments ranged from 74 to 510 ng∙g⁻\u0026sup1;, while the OCPs in fish were between 30 and 175 ng∙g⁻\u0026sup1; (Buah-Kwofie \u0026amp; Humphries, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It is worth noting that those OCPs species with low detection rates (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) in sediments were widely detectable in fish, such as p,p\u0026rsquo;-DDT, aldrin and endosulfan (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This is because fish can selectively accumulate specific types of OCPs rather than fully reflecting the composition of OCPs in sediments. For example, DDT, due to its high lipophilicity and lower metabolic stability, is more likely to accumulate in fish tissues than sediments (Jayaraj et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In addition, bio-transformation of OCPs between varying species were also observed during bioaccumulation from sediment to fish in some studies (Oliva et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, ether selective bioaccumulation or bio-transformation can trigger the differences in OCPs species or contents between fish and sediments.\u003c/p\u003e\u003cp\u003eFurthermore, we differentiated body sizes of the most common edible fish, \u003cem\u003eCarassius auratus\u003c/em\u003e and \u003cem\u003eCyprinus carpio\u003c/em\u003e, to explore the influence of growth time on the bioaccumulation of OCPs. The larger individuals of \u003cem\u003eCyprinus carpio\u003c/em\u003e (L) contained significantly higher \u0026sum;\u003csub\u003e7\u003c/sub\u003eOCPs (388.76 ng∙g⁻\u0026sup1;) than the smaller ones (154.09 ng∙g⁻\u0026sup1;) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that body size is one factor governing the bioaccumulation of OCPs in \u003cem\u003eCyprinus carpio\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, this phenomenon was not obvious in the \u003cem\u003eCarassius auratus\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). This indicates that the accumulation of OCPs in the \u003cem\u003eCyprinus carpio\u003c/em\u003e is related to size and growth time. Commonly, the phenomenon of biomagnification is observed and used to explain the increase in the contents of persistent organic pollutants as compounds propagate up the food chain (Daley et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, the situation in this study is different because the growth process of the same species (\u003cem\u003eCyprinus carpio\u003c/em\u003e) does not involve the changes at the food chain level; we therefore attribute this phenomenon solely to the growth time.\u003c/p\u003e\u003cp\u003eThe BSAF values were determined to further evaluate bioaccumulation effect of OCPs from sediments to fish. The BSAF values based on total OCPs ranged from 0.08 (\u003cem\u003ePelteobagrus fulvidraco\u003c/em\u003e) to 1.06 (\u003cem\u003eCyprinus carpio\u003c/em\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e); this is at a relatively low level according to the national range of BSAF reported for fish in the United States (0.7\u0026ndash;8.6) (Wong et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), indicating a relatively low level of bioaccumulation effect in study area. Another study from China that calculated BSAF based on total OCPs reported BSAF ranging from 3.8 (Gray mullet) to 56 (Tilapia), with a BSAF of 6.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 for the \u003cem\u003eCarassius auratus\u003c/em\u003e (Kwok et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In contrast, the BSAF of \u003cem\u003eCarassius auratus\u003c/em\u003e in this study ranged from 0.35 to 0.37 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This is because there are many factors affecting bioaccumulation effect, including fish species, environmental factors, diet, etc., leading to significant deviations in BSAF values across different regions (Akinsanya et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). More importantly, compared with countries with existing national OCPs data for fish or other aquatic organisms (Wong et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), China still lacks sufficient OCPs data for fish species across different regions due to significant geographical differences. Therefore, collecting OCPs data for fish species from different regions can provide crucial information for the formulation of national standards of China.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStatistical values of OCPs (ng∙g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e dw) and bio-sediment accumulation factor (BSAF) in fish of Lashihai Lake\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCarassius auratus (S)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eCarassius auratus (L)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eCyprinus carpio (S)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eCyprinus carpio (L)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ePelteoebagrus fulvidraco\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiet type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eomnivore\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eomnivore\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eomnivore\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eomnivore\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eomnivore\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLength \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100\u0026thinsp;~\u0026thinsp;140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140\u0026thinsp;~\u0026thinsp;160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e190\u0026thinsp;~\u0026thinsp;200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e200\u0026thinsp;~\u0026thinsp;230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e180\u0026thinsp;~\u0026thinsp;210\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eα⁃HCH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003end\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAldrin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e101.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEndosulfan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep, p\u0026rsquo;-DDE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e260.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEndrin aldehyde\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep, p\u0026rsquo;- DDT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEndrin ketone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003end\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026sum;\u003csub\u003e7\u003c/sub\u003eOCPs \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e136.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e128.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e154.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e388.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBSAF \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Body length of fish expressed in \u0026ldquo;mm\u0026rdquo;\u003c/p\u003e\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e below detection limit\u003c/p\u003e\u003cp\u003e\u003csup\u003ec\u003c/sup\u003e the total contents of the seven OCPs\u003c/p\u003e\u003cp\u003e\u003csup\u003ed\u003c/sup\u003e Bio-sediment accumulation factor based on Eq.\u0026nbsp;(1\u0026ndash;1)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eHealth risk assessment\u003c/h2\u003e\u003cp\u003eOCPs can enter human body through food chain, thus posing health risks to local residents. Although the bioaccumulation effect of OCPs in study area is at a moderate level, the potential health risks cannot be ignored considering the long-term effects. Therefore, we assessed the potential health risks of OCPs in common edible fish based on CRI and ERI.\u003c/p\u003e\u003cp\u003eIn general, the ERI and CRI of most OCPs of varying fish species indicated limited exposure and carcinogenic risks through consuming fish, except for the aldrin. For ERI, only that of aldrin in \u003cem\u003eCarassius auratus\u003c/em\u003e (L) exceeded 1000\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb), the reference value for safety, suggesting a watchful exposure risk. In contrast, the ERI of other OCPs (DDT, endrin, α-HCH, endosulfan) across all kinds of fish was below the reference value, indicating an acceptable exposure risk on a larger scale. As for CRI, the CRI of aldrin in all kinds of fish, except for the \u003cem\u003ePelteobagrus fulvidraco\u003c/em\u003e, exceeded the reference value of 100\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e (184.29\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e ~721.90\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec), suggesting certain carcinogenic risks when exposing to aldrin by consuming fish. Other OCPs (DDT, endrin, α-HCH, endosulfan) across all fish exhibited minimal carcinogenic risks, with CRI ranging from 0.00 to 37.86\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e. This is consistent with the fact that all kinds of fish, except for the \u003cem\u003ePelteobagrus fulvidraco\u003c/em\u003e, contain certain amounts of aldrin (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). Additionally, although aldrin was not the most abundant OCPs in fish, its highest toxicity indicated by the highest CSF value of 17 (Dougherty et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Watanabe et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) accounted for its considerable exposure and carcinogenic risks through fish. Another thing worth noting is that although the detection rate of aldrin in sediments was low, it was quite common in fish. This is due to the selective amplification during the bioaccumulation process from sediments to fish, a phenomenon observed in other species and regions as well (Alharbi et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This further indicates that health risk assessments based solely on the OCPs contents in inorganic substances such as water, sediments or soils are not comprehensive enough, while health risk assessment based on food chain is more recommended. On the other hand, some studies have directly determined the contents of OCPs in humans and their health symptoms to reflect the health effects of OCPs (Papadopoulou et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Although this kind of approach can most directly achieve the objectives of health risk assessment, the need for biological samples of human may also limit this approach. Above all, by analyzing the residual levels of OCPs in edible fish species and their potential intake in the Lashihai Lake area, the health risks of OCPs through fish consumption to the local population were at an acceptable level, but long-term accumulation may also pose considerable risks.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOrganochlorine pesticides (OCPs) continue to pose significant ecological threats to global aquatic ecosystems due to their high toxicity and environmental persistence. As representative aquatic ecosystem across the Tibetan and Yunnan-Guizhou Plateau, enclosed plateau lakes provide crucial aquatic ecological services, yet understanding on sources and migration pathway of OCPs in these unique ecosystems remains unclear, hindering aquatic security management. This study reveals that under the combined influence of agricultural activities and the lakes' inherent hydrological closure, sediments serve as primary reservoirs for historical OCPs residues, with their distribution patterns being regulated by total organic carbon (TOC) and pH of sediments. Crucially, inflow rivers have been identified as the predominant carrier for OCPs transportation, emphasizing the necessity for basin-wide pollution source control strategies in sustainable management for this type of aquatic ecosystems. Although bioaccumulation from sediments to fish was moderate, the long-term exposure through food chains still warrants health risks. Given that China still lacks a national health risk assessment standard based on trophic measure for OCPs, more OCPs data of aquatic organisms from regions with varying geological features are imperative. Overall, this study enhances the understanding in the behavior patten and bioaccumulation process of OCPs in plateau lakes, offering valuable insights into sustainable utilization and management for other analogous aquatic environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e All authors have read, understood, and have complied as applicable with the statement on\u0026nbsp;“Ethical responsibilities of Authors”\u0026nbsp;as found in the Instructions for Authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003eWe extend our sincere gratitude to all participants involved in this study for their invaluable contributions to data collection, fieldwork, and discussions. Special thanks are given to the College of Ecology and Environment, Southwest Forestry University, and the National Plateau Wetland Research Center for providing laboratory facilities, experimental equipment, and technical support. Their expertise and resources were instrumental in advancing the research objectives. We also acknowledge the contributions from colleagues and institutions that supported this project indirectly. Finally, we thank the anonymous reviewers for their constructive feedback on the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship contribution\u0026nbsp;\u003c/strong\u003eXixiang Qiu: Data curation, Investigation,\u0026nbsp;Methodology,\u0026nbsp;Supervision, Writing - original draft.\u0026nbsp;Lei Sun:\u0026nbsp;Conceptualization, Data curation,\u0026nbsp;Formal analysis, Writing -\u0026nbsp;review \u0026amp; editing.\u0026nbsp;Xiaohui Zhao:\u0026nbsp;Data curation, Formal analysis, Writing - review \u0026amp; editing.\u0026nbsp;Jianhui Liu:\u0026nbsp;Data curation, Supervision, Writing - review \u0026amp; editing. Kun Zhang: Formal analysis, Funding acquisition, Project administration, Writing - review \u0026amp; editing. Yinfeng Zhang: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing - review \u0026amp; editing. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding declaration\u003c/strong\u003e This work was supported by the National Natural Science Foundation of China (Grant#32360291) and the Natural Science Foundation of Yunnan province, China (Grant #202101AT070036). There were no any conflicts of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbayi, J. J. M., Gore, C. T., Nagawa, C., Bandowe, B. A. M., Matovu, H., Mubiru, E., Ngeno, E. C., Odongo, S., Sillanp\u0026auml;\u0026auml;, M., \u0026amp; Ssebugere, P. (2021). 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Levels and distribution of organochlorine pesticides in shellfish from Qiantang River, China. \u003cem\u003eJournal of Hazardous Materials\u003c/em\u003e, 152(3), 1192-1200. https://doi.org/10.1016/j.jhazmat.2007.07.103.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"organochlorine pesticides, plateau lake, persistent organic pollutants, source apportionment","lastPublishedDoi":"10.21203/rs.3.rs-7587667/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7587667/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSources apportionment and clarification of migration pathways of organochlorine pesticides (OCPs) are critical yet unsolved scientific issues for sustainable development of plateau lakes. Here, we integrated the distribution pattern, sources analysis and bioaccumulation process of OCPs in a typical semi-enclosed plateau lake, representative aquatic ecosystem of the Tibetan and Yunnan-Guizhou plateau, to address these scientific issues. The δ-hexachlorocyclohexane (δ-HCH), α-hexachlorocyclohexane (α-HCH) and endrin aldehyde were the predominant OCPs in sediments, with their contents ranging from nd (not detectable) to 459.89 ng∙g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (δ-HCH), nd to 450.87 ng∙g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (endrin aldehyde) and 11.00 to 236.95 ng∙g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (α-HCH) respectively. Notably, the main OCPs in lake sediments were linearly related to total organic carbon (TOC), indicating the homologous relationship between OCPs and TOC. Moreover, the behaviors of OCPs were governed by partitioning process regulated by TOC and pH of sediments. Integrating the OCPs distribution, stable carbon isotope (δ\u003csup\u003e13\u003c/sup\u003eC) mixing model and geo-graphical features, we identified inflow rivers (87.1%) and farmland (6.3%) as the main sources. While the bioaccumulation of OCPs in fish was moderate, long-term hjkealth risks of OCPs by consumption of fish were considerable. The findings of this study highlight the priority of basin-wide source control method for sustainable management of such semi-enclosed environment.\u003c/p\u003e","manuscriptTitle":"Dominant Riverine Input of Legacy Organochlorine Pesticides to a Semi- Enclosed Plateau Lake: Distribution, Source Apportionment and Bioaccumulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 07:33:45","doi":"10.21203/rs.3.rs-7587667/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-11T13:22:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-01T07:25:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-28T11:28:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"130630387981917572278864926577023128410","date":"2025-09-27T02:10:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164821634903268947063300884477379727277","date":"2025-09-26T17:52:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"323414900454135494949911443177411403958","date":"2025-09-26T05:38:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-25T15:06:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-24T10:52:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-24T10:51:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2025-09-11T04:23:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"71d4f104-7f18-4b66-b344-5e669c66f3b8","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T16:02:46+00:00","versionOfRecord":{"articleIdentity":"rs-7587667","link":"https://doi.org/10.1007/s10661-025-14808-7","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2025-11-19 15:57:23","publishedOnDateReadable":"November 19th, 2025"},"versionCreatedAt":"2025-10-08 07:33:45","video":"","vorDoi":"10.1007/s10661-025-14808-7","vorDoiUrl":"https://doi.org/10.1007/s10661-025-14808-7","workflowStages":[]},"version":"v1","identity":"rs-7587667","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7587667","identity":"rs-7587667","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}