Pollution characteristics and risk assessment of 15 organic phosphorus flame retardants in the Ili River Basin

Pollution characteristics and risk assessment of 15 organic phosphorus flame retardants in the Ili River Basin | 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 Pollution characteristics and risk assessment of 15 organic phosphorus flame retardants in the Ili River Basin feng hu, Xinglei WANG, SiYu WANG, EPTIHAR•Jappar EPTIHAR•Jappar, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4593271/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract A solid-phase extraction-gas chromatography-pulsed flame photometric detector (SPE-GC-PFPD) was developed to determine 15 organophosphate flame retardants (OPFRs) in surface water of the Ili River basin. The recovery ranged from 76.4% to 98.6%, showing a good linear relationship. Four OPFRs were identified among them, with mean concentrations of 20, 28, 36, and 21 ng/L. The ecological risk of flame retardants in water was assessed using the risk entropy method. The ecological risk quotient (RQ) was calculated as 0.1, indicating a moderate to low level of risk. The noncarcinogenic and carcinogenic risks associated with the four OPFRs were found to be below their respective risk thresholds, suggesting negligible health risks from drinking water intake. The frequent detection of TiBP, TCEP, TCPP, and TDCPP warrants further attention. Organophosphorus flame retardant Purification concentrate Risk evaluation Gas chromatography Exposure level Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Flame retardants are widely employed in electronics, textiles, and home decor materials [1–3] to enhance fire resistance. Organophosphate flame retardants (OPFRs) are prominent among these and have emerged as novel pollutants, garnering increased attention. They can enter various media such as water [4] , dust [5] , air, [6] and sediment through [7] processes like volatilization, article wear, and liquid leaching. OPFRs are known for their bioaccumulation and environmental toxicity, impacting human health by disrupting the endocrine, immune, and nervous systems. Two primary methods are utilized for OPFR determination: GC/MS [8–9] and LC/MS [10–12] . GC/MS offers a higher detection limit compared to LC/MS, with peaks exhibiting varying degrees of tailing. LC/MS/MS offers high sensitivity but faces challenges in distinguishing trio-toluene phosphate, trim-toluene phosphate, and trip-toluene phosphate due to identical retention times and quantitative ions. Despite its advantages, LC/MS also presents drawbacks, such as high cost and operational expenses. Literature and data concerning direct OPFR determination via GC-FPD remain limited. The Ili River, a landlocked river in central Asia, flows through China and Kazakhstan and hosts numerous concentrated drinking water sources. Recent decades have witnessed rapid agricultural, livestock, industrial, and economic growth in the basin, significantly impacting its ecosystem. OPFRs are prevalent water pollutants whose concentrations can potentially affect human health and the environment. Notably, there has been no prior research on OPFRs in the Ili River basin. This study optimized solid-phase extraction (SPE) conditions and employed GC-PFPD, which offers increased sensitivity and selectivity compared to conventional flame photometric detectors (FPD). Fifteen methods were established for qualitative and quantitative analysis of OPFRs, enabling detection in surface water within the Ili River Basin. Additionally, an ecological risk assessment of OPFRs was conducted for the first time. Concurrently, a human health risk assessment was performed using the United States Environmental Protection Agency (USEPA) recommended model, serving as a benchmark for OPFR content and risk evaluation in the Ili River basin. Materials and methods 1.1 Instruments and reagents The instruments and equipment used in this study included gas chromatography (GC2014-PFPD, Shimizu, Japan), Rtx-5 (30 m´0.250 mm´0.25 µm), a solid phase extractor (RECO Plus), a nitrogen blowing concentrator (GM-AUTO-12S, Shanghai Ganmin), a 0.45 µm glass fiber filter membrane, and an HLB solid phase extraction column (the column volume was 6 mL, and the packing mass was 500 mg), which were purchased from Shanghai Anpu Company. Fifteen OPFRs were analyzed, including triethyl phosphate (TEP), triisopropyl phosphate (TiPP), triisobutyl phosphate (TnPP), triisobutyl phosphate (TiBP), tributyl phosphate (TnBP), tri(2-chloroethyl) phosphate (TCEP), tri(2-chloropropyl) phosphate (TCPP), tri(1,3-dichloroisopropyl) phosphate (TDCPP), triphenyl phosphate (TPhP), tri(butoxyethyl) phosphate (TBEP), 2-ethylhexyl diphenyl phosphate (EHDPP), tri(2-ethylhexyl) phosphate (TEHP), tri-o-toluene phosphate (o-TTP), tri-M-toluene phosphate (m-TTP), and tri-p-toluene phosphate (p-TTP). These standard blends were sourced from Bellingville. Methanol and ethyl acetate of HPLC grade were procured from Merck. 1.2 Sample collection and pretreatment From August to November 2023, surface water samples (labeled A1 ~ A5) were collected from five points across the Ili River basin. These samples were stored in brown glass bottles and transported to the laboratory under subdued light conditions. In the lab, each 1 L water sample underwent pretreatment by adding 3–5 g of NaCl and adjusting the pH to < 2. The HLB column was conditioned with 10 mL of methanol followed by ultrapure water prior to use. The water sample was then passed through the HLB column at a flow rate of 8 mL/min for enrichment. Following this, elution was performed using 10 mL of ethyl acetate twice at a flow rate of 1 mL/min. The eluate was dehydrated with 10 g of anhydrous sodium sulfate and concentrated to 0.5 mL using a nitrogen blower at 35°C. The concentrated solution was stored in a refrigerator at 4°C until analysis. 1.3 Instrument Analysis Gas chromatographic conditions: The column temperature was initially set to 100°C, then ramped up to 150°C at a rate of 10°C/min (maintained for 2 min), followed by a further increase to 280°C at 8°C/min (maintained for 15 min). During the experiment, mixed standard solutions with varying concentrations were prepared and diluted with ethyl acetate to achieve concentrations of 100, 200, 400, 600, and 800 µg/L. The peak area of the OPFRs was used as the x-axis coordinate, while their concentrations were plotted on the y-axis to generate the standard curve. 1.4 Quality control and quality assurance Throughout this experiment, all procedures, including glassware cleaning, sampling, preservation, and extraction were strictly adhered to according to the QA/QC guidelines recommended by the US Environmental Protection Agency (EPA). Utensils and solvents were also utilized as blanks. For each batch of samples (≤ 10), a laboratory blank was included alongside known concentrations of standard materials. The relative recovery rate for the targeted OPFRs ranged between 76% and 98%. Ecological risk assessment The ecological risk of flame retardants in water was assessed using the risk quotient (RQ) method, as depicted in formula (1) [13] . According to ECOTOX, a toxicity database maintained by the US EPA, 10 out of the 15 OPFRs have PNEC values. An RQ < 0.1 indicates a low risk, suggesting negligible ecological impact. For RQ values between 0.1 and 1.0, the risk is considered medium. RQ values exceeding 1.0 signify significant ecological risks, warranting increased attention and management. $$\:\text{R}\text{Q}\:=\frac{C}{\text{P}\text{N}\text{E}\text{C}}$$ 1 where RQ is a dimensionless constant; C is the exposure concentration of pollutants in water, ng/L; and PNEC predicts no effect on concentration, ng/L. Human health risk assessment 3.1 Health risk assessment methods Several researchers have devised their own models and methodologies for assessing health risks from pollution in drinking water sources, albeit with similar foundational principles, including carcinogenic and noncarcinogenic risk evaluation models. This study employs the US EPA model in conjunction with the Monte Carlo method to conduct an initial evaluation of human health risks posed by 12 OPFRs in water bodies. Specific parameters of the model were adjusted to align with the local socioeconomic context in the Yili region. The health risk assessment model endorsed by the US EPA [14] was applied to assess the health risks associated with OPFRs in the Ili River basin. The calculation of the average daily exposure of the population to OPFRs through drinking water is outlined as follows: \(\:\:\:\:\:\:\:\:\:\:\:\:\:\:\text{S}\text{W}\text{S}\text{C}=\times\:\text{Q}\text{R}\times\:\frac{\text{T}}{\text{B}\text{W}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\) (2) where SWSC represents the daily exposure dose per unit body weight of OPFRs through drinking, measured in ng/(kg•d); ci denotes the concentration of OPFRs in water (ng/L); T stands for the absorption rate of drinking water intake, set at 100%; QR represents the average daily water intake for individuals of varying ages; and BW refers to the body weight across different age groups in Xinjiang. The HQ represents the noncarcinogenic risk of a population ingesting OPFRs through drinking water and is calculated as follows: $$\:\text{H}\text{Q}\:=\frac{\text{S}\text{W}\text{S}\text{C}}{\text{R}\text{f}\text{D}}$$ 3 where HQ is the noncarcinogenic risk value of OPFR ingestion through drinking water, and RfD is the US EPA's reference dose value for each OPFR [15] , ng/(kg•d). It is generally believed that when HQ > 1, there is a noncarcinogenic health risk. When HQ < 0.1, the noncarcinogenic risk is considered to be small or negligible. \(\:\text{H}\text{C}\text{R}=\text{s}\text{w}\text{s}\text{c}\times\:\text{s}\text{f}\text{o}\) where HCR denotes the carcinogenic risk associated with the population ingesting OPFRs through drinking water, while Sfo represents the carcinogenic slope factor indicating the maximum risk probability from ingesting contaminants through drinking, measured in (kg•d)/ng. Typically, if HCR is less than 1.0 × 10 − 6 , the cancer risk is considered low. Conversely, an HCR exceeding 1.0 × 10 –6 indicates a potential cancer risk [16] . 3.2 Uncertainty of health risk assessment The health risk analysis process involves numerous uncertainties, including uneven pollutant distribution in the water environment due to limited sampling point concentration data, variations in individual human physiology, and differences in pollutant migration levels. Methods for evaluating uncertainty include the Monte Carlo method, Taylor's simplification method, the probability tree method, and expert judgment. In this study, the uncertainty of OPFR health risk assessment in the Ili River basin was examined using the Monte Carlo method. Results and discussion 4.1 Optimization of the sample pretreatment method Gas chromatography equipped with a PFPD detector demonstrates excellent sensitivity to OPFRs, allowing complete separation of all 15 types within a 30-min timeframe. Notably, it achieves effective separation of three isomers: tri-o-toluene phosphate (o-TTP), tri-M-toluene phosphate (m-TTP), and tri-p-toluene phosphate (p-TTP (Fig. 1 ). In this study, the SPE column utilized is HLB, known for its superior extraction efficiency with strong polarity OPFRs. The recovery rates for the first 12 out of the 15 OPFRs ranged from 70.5–108.4%, while the last 3 OPFRs exhibited lower recovery rates, 50%. To optimize recovery, a 1 L water sample was treated with both acid and salt, showing improved recovery rates of 76.4–77.8% compared to treatments with acid or salt alone. Overall, the total recovery of all 15 OPFRs ranged from 76.4–98.6% ( Fig. 2 ). All target compounds exhibited linearity within this range, with correlation coefficients (R2) exceeding 0.995. The method's detection limits (LOD) were calculated using a signal-to-noise ratio (S/N) of 3, resulting in LODs ranging from 20 to 40 ng/L for the 15 OPFRs. The relative standard deviation (RSD) ranged from 4.4–10.7%. 4.2 Detection of OPFR concentrations in water Following sample collection, 15 types of OPFRs were detected in the water samples using the optimized SPE method. Four substances were identified: TiBP, TCEP, TCPP, and TDCPP, with their concentrations detailed in Table 1 . The total (Ʃ) minimum, maximum, and mean concentrations of OPFRs were 0, 193, and 105 ng/L, respectively. Among these, TiBP and TCPP exhibited the highest maximum concentrations at 46 ng/L and 61 ng/L, respectively, followed by TCEP at 45 ng/L and TDCPP at 41 ng/L. Regarding sampling points, A5, located in the upper reaches of the basin with sparse population, did not show any detected OPFRs. A2, situated at the confluence of three rivers, exhibited the highest OPFR concentrations. The A1 and A4 sampling sites, characterized by lower water flow rates, experienced significant dilution effects on OPFRs due to high water volumes, and were less influenced by human activities. In contrast, A3, closer to human activity zones like sewage treatment plant discharges and domestic sewage, showed slightly elevated OPFR concentrations, though overall levels remained low without a clear trend over the sampling period from August to November. This suggests minimal changes in mass concentrations, likely due to stable water volumes and limited external influences. Table 1 Exposure levels of OPFRs in surface water at different sampling sites compound minimum (ng/L) maximum (ng/L) average (ng/L) relevance ratio(%) TEP ND ND ND 0 TiPP ND ND ND 0 TnPP ND ND ND 0 TiBP ND 46 20 70 TnBP ND ND ND 0 TCEP ND 45 28 80 TCPP ND 61 36 80 TDCPP ND 41 21 60 TPhP ND ND ND 0 TBEP ND ND ND 0 EHDPP ND ND ND 0 TEHP ND ND ND 0 o-TTP ND ND ND 0 m-TTP ND ND ND 0 p-TTP ND ND ND 0 ƩOPFRs ND 193 105 - Note: ND indicates not detected The concentration of TiBP ranged from 0 to 46 ng/L, reflecting its relatively high mass concentration despite being difficult to dissolve in water, consistent with findings by Han Chao [17] . TCEP, with concentrations ranging from 0 to 45 ng/L, exhibits low hydrophobicity (lgKOW = 1.44) and high solubility in water [18] . TCPP and TDCPP, widely distributed in nature, are highly polar and easily soluble in water. Compositionally, TCPP and TCEP dominate the water bodies in this study, similar to conclusions drawn by Shi et al. [19] in their study of various surface water bodies in Beijing. Comparing ΣOPFR results with other regions, the concentrations observed here are lower than those found in the Jinjiang River in Chengdu (689.09-10623.94 ng/L) [20] , Nanjing section of the Yangtze River (85.21-1557.96 ng/L) [21] , Luoma Lake surface water (97.1 ~ 1066.00 ng/L) [22] , and lower reaches of the Yangtze River (55.6 ~ 5071 ng/L) [23] . They are comparable to the lower concentrations (37.2–510 ng/L) observed in rivers in the New York area [24] . The sampling site in this study is located at a distance from industrial zones, with surrounding residents primarily engaged in herding activities, resulting in relatively low human activity intensity. This suggests minimal impact from industrial production and residential activities, contributing to a relatively good quality of the water environment. 4.3 Ecological Risk Assessment In this study, four flame retardants were detected, among which TCEP, TCPP, and TDCPP were assessed for acute biological toxicity using PNEC. Tables 2 and 3 show that for aquatic organisms, the Risk Quotient (RQ) values for TCEP ranged from 0 to 1.5 × 10–3, for TCPP from 0 to 5.0 × 10–2, and for TDCPP from 0 to 9.7 × 10–3. According to the species-specific risk values, TCPP posed the greatest risk to algae, TDCPP to crustaceans, and also to fish, with all RQ values below 0.1, indicating low ecological risk. Notably, 15 monomers, including m-TTP and p-TTP, which are more toxic OPFRs, were not detected in this study. These research findings contrast with ecological risk studies in the middle and upper reaches of the Beijiang River basin [25], where higher concentrations of electronic enterprises discharge pollutants such as m-TTP, p-TTP, and other OPFRs. In contrast, the Ili River basin is primarily influenced by traditional processing enterprises, contributing to differing ecological risk profiles. Table 2 Biotoxicity data for TCEP, TCPP, and DCPP [26] Species name compound L(E)C50 (mg/L) PNEC (ng/L) RQ algae TCEP 51 51 000 0 ~ 8.8×10 –4 TCPP 45 45 000 0 ~ 1.3×10 –3 TDCPP 39 39 000 0 ~ 1.0×10 –3 crustacea TCEP 330 330 000 0 ~ 1.3×10 –4 TCPP 91 91 000 0 ~ 6.7×10 –4 TDCPP 4.2 4 200 0 ~ 9.7×10 –3 fish TCEP 30 30 000 0 ~ 1.5×10 –3 TCPP 1.2 1 200 0 ~ 5.0×10 –2 TDCPP 5.1 5 100 0 ~ 8.0×10 –3 The application of the risk entropy method in ecological risk assessment introduces certain uncertainties. Toxicological data referenced are often based on foreign non-native species, lacking specific data for local species, leading to potential differences in toxicity assessments. The Ili River basin offers abundant fish resources and diverse rare fish species. Wei Lili's study [27] identified 145 algae species from 41 genera in the basin, using L(E)C50 data for RQ calculations, but this approach has inherent limitations. Studies by both domestic and international scholars indicate that OPFR concentrations in sediments typically exceed those found in surface water. Compounds like EHDPP and TEHP tend to adsorb onto organic matter in sediments [28] , increasing ecological risks for organisms such as algae and crustaceans over prolonged exposure periods. While this study did not analyze OPFR levels in sediments, future research will focus on comprehensive ecological risk assessments in the basin, emphasizing the identification and monitoring of flame retardant pollutants in water bodies. 4.4 Health Risk Assessment of OPFRs Currently, RfD values are available for 12 OPFRs, including TEP, TnBP, TDCPP, TPHP, EHDPP, o-TTP, m-TTP, p-TTP, TCEP, TCPP, TBEP, and TEHP. SFO values are available for four OPFRs: TCEP, TCPP, TnBP, and TEHP [11]. Health risk assessment parameters for different age groups and genders are presented in Table 3 , while RfD and SFO values for OPFRs ingested via drinking water are detailed in Table 4 . In Chinese practice, drinking water is typically boiled before consumption, resulting in the volatilization of some organic compounds during the process [29]. Studies indicate that boiling for 10 min can reduce the content of semivolatile organic compounds by 20–30% [30]. In this study, a boiling residue ratio coefficient of 0.7 was applied for health risk assessment to ensure the results are more objective and practical. Table 3 Health risk assessment parameters for different populations [31–32] crowd man woman adult children adult children water intake (L/d) 2.29 0.81 1.76 0.76 weight (kg) 72.00 24.00 56.80 23.00 absorptivity (%) 100 100 100 100 Table 4 Reference dose values and slope carcinogens of OPFRs target object RfD ng/(kg·d) SFO (kg·d)/ng Boil residue ratio TEP 125 000 - 0.7 TnBP 10 000 9.00×10 –9 TCEP 7 000 2.0×10 –8 TCPP 10 000 - TDCPP 20 000 3.10×10 –8 TPhP 7 000 - TBEP 15 000 - EHDPP 15 000 - TEHP 100 000 3.10×10 –8 o-TTP 20 000 - m-TTP 20 000 - p-TTP 20 000 - For assessing the risks associated with 15 types of OPFRs in water bodies, exposure levels vary among different demographic groups. Health risks were evaluated for male children, male adults, female children, and female adults using formulas (1) to (3), calculating both carcinogenic and noncarcinogenic risks, illustrated in Figs. 3 and 4 , respectively. Noncarcinogenic risk values (HQ) for OPFRs in male children, male adults, female children, and female adults ranged from 0 to 1.43 × 10 –4 , 0 to 1.35 × 10 –4 , 0 to 1.51 × 10 –4 , and 0 to 1.39 × 10 –4 , respectively. HQ values below 0.1 indicate low noncarcinogenic risk associated with OPFRs in the water of the Ili River basin across all four demographic groups. TCPP poses the highest noncarcinogenic risk among the substances studied, predominantly used as a modifier and plasticizer in rubber products. Its consistent detection at all four sampling points (A1 to A4) suggests challenges in its removal by sewage treatment plants, consistent with observations by Sun Jia [33] regarding TCPP's frequent detection in surface water. Figure 3 Noncarcinogenic risk distribution of surface water flame retarding 4.5 Uncertainty analysis of health risk assessment This study primarily utilizes Monte Carlo simulation with 10,000 iterations to capture parameters' uncertainty and variability. It calculates carcinogenic risk values and corresponding cumulative probabilities of OPFRs from drinking water, revealing significant variability in cancer risk levels. The probabilities of cancer risk (50%) and cancer risk (90%) for male children, male adults, female children, and female adults can vary by 5 to 12 times. Factors such as seasonal variations, production processes and cycles of industries, water consumption patterns among different populations, and residents' activities can influence carcinogenic risk values of OPFRs. The uncertainty in exposure concentrations, duration, water consumption patterns, and other factors contributes to substantial differences in cumulative statistical outcomes for health risk probabilities. Regarding the perspective of carcinogenic risk probability (100%), the carcinogenic risk values associated with TECP and TDCPP were found to be below the acceptable threshold of carcinogenic risk set by the US EPA (1 × 10 –6 ). Table 5 Cumulative statistical results of the health risk probability for OPFRs among different populations crowd Probabilistic risk (1 × 10 –6 ) TECP TDCPP 10% 30% 50% 70% 90% 100% 10% 30% 50% 70% 90% 100% Male child 0 0 0.0071 0.024 0.074 0.92 0 0.0000 0.0088 0.030 0.098 0.14 Male adult 0 0.0030 0.0081 0.017 0.043 0.47 0 0.0034 0.0100 0.021 0.055 0.95 Female child 0 0.0027 0.0071 0.016 0.041 0.40 0 0.0034 0.0095 0.021 0.051 0.54 Female adult 0 0.0028 0.0079 0.019 0.042 0.47 0 0.0027 0.0037 0.010 0.055 0.49 A sensitivity analysis was performed on each parameter to further assess the health risks of OPFRs across different populations. The analysis reveals that water consumption has the most significant impact on carcinogenic sensitivity, accounting for approximately 45.3%, as illustrated in Fig. 5 . Conversely, body weight exhibited the least influence on sensitivity, with a negligible correlation observed. In practical scenarios, residents often consume filtered or boiled water, potentially mitigating the sensitivity of drinking water to cancer risks to some extent. However, as the industry and economy continue to develop in the Ili River basin, the health risks associated with OPFRs may fluctuate. Further investigation is required to determine if changes in OPFR concentrations correspondingly alter carcinogenic sensitivity levels. Although the carcinogenic risk and noncarcinogenic risk of 15 OPFRs in the Yili River Basin are lower than the risk threshold and the water environment quality is relatively good, the detection rates of TCPP and TCEP are high, and OPFRs should receive increased attention. Conclusions 1) Fifteen OPFRs were detected in the water samples using the SPE-GC-PFPD detector. Upon adjusting the pH of the water samples to below 2 and adding NaCl, the detection limit improved to 20 ~ 40 ng/L, and the recovery rate increased to between 76.4% and 98.6%, fulfilling the requirements for surface water analysis. The linear relationship was strong, with correlation coefficients (R2) all exceeding 0.995. 2) At the five sampling sites, four types of OPFRs were detected in varying amounts. The average concentrations of TiBP, TCEP, TCPP, and TDCPP were 20, 28, 36, and 21 ng/L, respectively. Despite the low concentrations of TCPP and TCEP, their high detection frequencies should be considered. 3) The RQ value for ecological risk was less than 0.1; however, the ecological risk has not been assessed. Future research will address the ecological risks associated with sediments in the Ili River basin. The noncarcinogenic risk values for 15 OPFRs ranged from 0 to 1.51 × 10 –4 , with HQ below 0.1, and the carcinogenic risk values for 4 OPFRs varied from 0 to 4.16 × 10 –8 , below the established risk threshold of 1 × 10 –6 . The influence of drinking water on population health is considered negligible. 4) Uncertainty in the health risk assessment was analyzed through Monte Carlo simulations, which showed a wide variation in carcinogenic risk levels. However, these values remained below the US EPA's acceptable carcinogenic risk level of 1 × 10 –6 . Sensitivity analysis indicated that the volume of consumed water significantly influenced carcinogenic sensitivity. Declarations Author Contributions Conceptualization: Feng HU has read and agreed to the published version of the manuscript. E-mail： [email protected] Funding Open Project of Ili Kazakh Autonomous Prefecture key laboratory of normal school pollutants chemistry and environmental governance (2023HJYB03) Institutional Review Board Statement The study did not require ethical approval. Informed Consent Statement Not applicable. Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request Conflicts of Interest The authors declare no conflict of interest. References XUE Qianqian,WEI Yang,TIAN Yingze, et al. Characteristics,Gas-Particle Partitioning, and Health Risks of PM2.5-Bound PAHs and OPEs from 2019 to 2020 in Jinnan District, Tianjin. [J]. Research in Environmental Scientific,2022,35(1):30-39. MARKLUND A,ANDERSSON B,HAGLUND P.Screening of organophosphorus compo-unds and their distribution in various indoorenvironments[J].Chemosphere,2003,53:1137-1146. BRANDSMA S H, BOER J D, LEONARDS P E G, et al. 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Chinese Population Exposure Parameter Manual (adult volume) [M]. Beijing: China Environmental Science Press, 2014:47–748. Duan Xiaoli, Ed. Chinese Population Exposure Parameter Manual (Children's Volume) [M]. Beijing: China Environmental Press, 2016. Sun Jia, Ding Weinan, Zhang Zhanen, et al., Pollution characteristics of organophosphate flame retardants in wastewater treatment plants [J]. Environmental Science,2018,39(5):2230-2238. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. 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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-4593271","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":329787984,"identity":"80f77cbe-992d-4eb8-8bd4-09771f7734d4","order_by":0,"name":"feng hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYHCCxAcJFRJyDOyMDcSp52FgSDZ4cMbCmIGZBC1skg/bKhIbmIl1lb10w2ODhDMS6f3NzG3SvDsY5PnFDhCwReYA2C+5Mw4zArWcYTCcOTuBgBaJhGSQLbkNYC1tDAkGtwlrSZNIbJNIlydZS4IB8VpuQBxmuPEwY7Pl3DYJwn5hn5GT+PBHRZ283PH2hzfettnI80sT0AK0B66CRYKBQYKQcrA9B2As5g/EqB8Fo2AUjIKRBwD8HkCPLKV5YgAAAABJRU5ErkJggg==","orcid":"","institution":"Ili Normal University","correspondingAuthor":true,"prefix":"","firstName":"feng","middleName":"","lastName":"hu","suffix":""},{"id":329787985,"identity":"a6292c7d-e5d5-45ba-9a72-a0f74607cf78","order_by":1,"name":"Xinglei WANG","email":"","orcid":"","institution":"Ili Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xinglei","middleName":"","lastName":"WANG","suffix":""},{"id":329787986,"identity":"97897061-a75e-446d-afbf-5d2a920cf9e9","order_by":2,"name":"SiYu WANG","email":"","orcid":"","institution":"Ili Ecological Environment Monitoring Station","correspondingAuthor":false,"prefix":"","firstName":"SiYu","middleName":"","lastName":"WANG","suffix":""},{"id":329787987,"identity":"9754a8ca-caba-40e0-afd7-fff9eb77a37d","order_by":3,"name":"EPTIHAR•Jappar EPTIHAR•Jappar","email":"","orcid":"","institution":"Ili Ecological Environment Monitoring Station","correspondingAuthor":false,"prefix":"","firstName":"EPTIHAR•Jappar","middleName":"","lastName":"EPTIHAR•Jappar","suffix":""},{"id":329787988,"identity":"a467cb69-57a4-4964-b3e8-54eceabee3a9","order_by":4,"name":"Shihui LIU","email":"","orcid":"","institution":"Ili Ecological Environment Monitoring Station","correspondingAuthor":false,"prefix":"","firstName":"Shihui","middleName":"","lastName":"LIU","suffix":""}],"badges":[],"createdAt":"2024-06-17 09:44:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4593271/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4593271/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62002812,"identity":"ee76ccce-0204-4904-af81-b0f2b5fadff8","added_by":"auto","created_at":"2024-08-08 06:14:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":25521,"visible":true,"origin":"","legend":"\u003cp\u003eChromatograms of 15 kinds of OPFRs\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4593271/v1/304bace9e9a0c3c1885f353d.png"},{"id":62002813,"identity":"b5893584-96e5-44bf-8f4f-7a11d2c7282a","added_by":"auto","created_at":"2024-08-08 06:14:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41982,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of adding acid and salt on the recovery of OPFRs\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4593271/v1/e5db178ab5f51aad6c5d9ae8.png"},{"id":62002809,"identity":"8df46c53-c719-4f68-bf5b-ef068696aa24","added_by":"auto","created_at":"2024-08-08 06:14:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61985,"visible":true,"origin":"","legend":"\u003cp\u003eNoncarcinogenicrisk distribution of surface water flame retarding\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4593271/v1/98c0740b71fb56fd45db5048.png"},{"id":62002810,"identity":"31b494f7-6336-4dc5-8028-b58558520a63","added_by":"auto","created_at":"2024-08-08 06:14:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":51283,"visible":true,"origin":"","legend":"\u003cp\u003eCancer risk distribution of surface water flame retardants\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4593271/v1/1ffd3477415b437516797922.png"},{"id":62002811,"identity":"2a2df443-f65a-487b-8a17-7015821fb18e","added_by":"auto","created_at":"2024-08-08 06:14:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":16854,"visible":true,"origin":"","legend":"\u003cp\u003eHealth risk parameter sensitivity analysis\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4593271/v1/b41b7f6d6be4c6c6f55e6064.png"},{"id":64523541,"identity":"c1f83888-62d2-4464-8bb4-060cd393af85","added_by":"auto","created_at":"2024-09-14 12:46:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2293657,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4593271/v1/78d76d30-3055-4406-982c-800fe0f28db4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pollution characteristics and risk assessment of 15 organic phosphorus flame retardants in the Ili River Basin","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFlame retardants are widely employed in electronics, textiles, and home decor materials\u003csup\u003e\u0026nbsp;[1\u0026ndash;3]\u003c/sup\u003e to enhance fire resistance. Organophosphate flame retardants (OPFRs) are prominent among these and have emerged as novel pollutants, garnering increased attention. They can enter various media such as water\u003csup\u003e\u0026nbsp;[4]\u003c/sup\u003e, dust\u003csup\u003e\u0026nbsp;[5]\u003c/sup\u003e, air,\u003csup\u003e\u0026nbsp;[6]\u0026nbsp;\u003c/sup\u003eand sediment through\u003csup\u003e\u0026nbsp;[7]\u0026nbsp;\u003c/sup\u003eprocesses like volatilization, article wear, and liquid leaching. OPFRs are known for their bioaccumulation and environmental toxicity, impacting human health by disrupting the endocrine, immune, and nervous systems. Two primary methods are utilized\u003ca id=\"_anchor_1\" href=\"#_msocom_1\" language=\"JavaScript\" name=\"_msoanchor_1\"\u003e\u003c/a\u003e for OPFR determination: GC/MS\u003csup\u003e\u0026nbsp;[8\u0026ndash;9]\u003c/sup\u003e and LC/MS\u003csup\u003e\u0026nbsp;[10\u0026ndash;12]\u003c/sup\u003e. GC/MS offers a higher detection limit compared to LC/MS, with peaks exhibiting varying degrees of tailing.\u0026nbsp;LC/MS/MS offers high sensitivity but faces challenges in distinguishing trio-toluene phosphate, trim-toluene phosphate, and trip-toluene phosphate due to identical retention times and quantitative ions. Despite its advantages,\u0026nbsp;LC/MS\u0026nbsp;also presents drawbacks, such as high cost and operational expenses. Literature and data concerning direct OPFR determination via\u0026nbsp;GC-FPD\u0026nbsp;remain limited.\u003c/p\u003e\n\u003cp\u003eThe Ili River, a landlocked river in central Asia, flows through China and Kazakhstan and hosts numerous concentrated drinking water sources. Recent decades have witnessed rapid agricultural, livestock, industrial, and economic growth in the basin, significantly impacting its ecosystem. OPFRs are prevalent water pollutants whose concentrations can potentially affect human health and the environment. Notably, there has been no prior research on OPFRs in the Ili River basin. This study optimized solid-phase extraction (SPE) conditions and employed GC-PFPD, which offers increased sensitivity and selectivity compared to conventional flame photometric detectors (FPD). Fifteen methods were established for qualitative and quantitative analysis of OPFRs, enabling detection in surface water within the Ili River Basin. Additionally, an ecological risk assessment of OPFRs was conducted for the first time. Concurrently, a human health risk assessment was performed using the United States Environmental Protection Agency (USEPA) recommended model, serving as a benchmark for OPFR content and risk evaluation in the Ili River basin.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Instruments and reagents\u003c/h2\u003e \u003cp\u003e \u003cb\u003eThe instruments and equipment used in this study included gas chromatography (GC2014-PFPD, Shimizu, Japan), Rtx-5 (30 m´0.250 mm´0.25 µm), a solid phase extractor (RECO Plus), a nitrogen blowing concentrator (GM-AUTO-12S, Shanghai Ganmin), a 0.45 µm glass fiber filter membrane, and an HLB solid phase extraction column (the column volume was 6 mL, and the packing mass was 500 mg), which were purchased from Shanghai Anpu Company.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFifteen OPFRs were analyzed, including triethyl phosphate (TEP), triisopropyl phosphate (TiPP), triisobutyl phosphate (TnPP), triisobutyl phosphate (TiBP), tributyl phosphate (TnBP), tri(2-chloroethyl) phosphate (TCEP), tri(2-chloropropyl) phosphate (TCPP), tri(1,3-dichloroisopropyl) phosphate (TDCPP), triphenyl phosphate (TPhP), tri(butoxyethyl) phosphate (TBEP), 2-ethylhexyl diphenyl phosphate (EHDPP), tri(2-ethylhexyl) phosphate (TEHP), tri-o-toluene phosphate (o-TTP), tri-M-toluene phosphate (m-TTP), and tri-p-toluene phosphate (p-TTP). These standard blends were sourced from Bellingville. Methanol and ethyl acetate of HPLC grade were procured from Merck.\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Sample collection and pretreatment\u003c/h2\u003e \u003cp\u003e \u003cb\u003eFrom August to November 2023, surface water samples (labeled A1 ~ A5) were collected from five points across the Ili River basin. These samples were stored in brown glass bottles and transported to the laboratory under subdued light conditions. In the lab, each 1 L water sample underwent pretreatment by adding 3–5 g of NaCl and adjusting the pH to \u0026lt; 2. The HLB column was conditioned with 10 mL of methanol followed by ultrapure water prior to use. The water sample was then passed through the HLB column at a flow rate of 8 mL/min for enrichment. Following this, elution was performed using 10 mL of ethyl acetate twice at a flow rate of 1 mL/min. The eluate was dehydrated with 10 g of anhydrous sodium sulfate and concentrated to 0.5 mL using a nitrogen blower at 35°C. The concentrated solution was stored in a refrigerator at 4°C until analysis.\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Instrument Analysis\u003c/h2\u003e \u003cp\u003e \u003cb\u003eGas chromatographic conditions: The column temperature was initially set to 100°C, then ramped up to 150°C at a rate of 10°C/min (maintained for 2 min), followed by a further increase to 280°C at 8°C/min (maintained for 15 min). During the experiment, mixed standard solutions with varying concentrations were prepared and diluted with ethyl acetate to achieve concentrations of 100, 200, 400, 600, and 800 µg/L. The peak area of the OPFRs was used as the x-axis coordinate, while their concentrations were plotted on the y-axis to generate the standard curve.\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.4 Quality control and quality assurance\u003c/h2\u003e \u003cp\u003e \u003cb\u003eThroughout this experiment, all procedures, including glassware cleaning, sampling, preservation, and extraction were strictly adhered to according to the QA/QC guidelines recommended by the US Environmental Protection Agency (EPA). Utensils and solvents were also utilized as blanks. For each batch of samples (≤ 10), a laboratory blank was included alongside known concentrations of standard materials. The relative recovery rate for the targeted OPFRs ranged between 76% and 98%.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eEcological risk assessment\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe ecological risk of flame retardants in water was assessed using the risk quotient (RQ) method, as depicted in formula (1) \u003csup\u003e[13]\u003c/sup\u003e. According to ECOTOX, a toxicity database maintained by the US EPA, 10 out of the 15 OPFRs have PNEC values. An RQ \u0026lt; 0.1 indicates a low risk, suggesting negligible ecological impact. For RQ values between 0.1 and 1.0, the risk is considered medium. RQ values exceeding 1.0 signify significant ecological risks, warranting increased attention and management.\u003c/p\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\text{R}\\text{Q}\\:=\\frac{C}{\\text{P}\\text{N}\\text{E}\\text{C}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e \u003cp\u003ewhere RQ is a dimensionless constant; C is the exposure concentration of pollutants in water, ng/L; and PNEC predicts no effect on concentration, ng/L.\u003c/p\u003e \u003c/div\u003e\n\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":" Human health risk assessment","content":"\u003ch2\u003e3.1 Health risk assessment methods\u003c/h2\u003e\u003cp\u003eSeveral researchers have devised their own models and methodologies for assessing health risks from pollution in drinking water sources, albeit with similar foundational principles, including carcinogenic and noncarcinogenic risk evaluation models. This study employs the US EPA model in conjunction with the Monte Carlo method to conduct an initial evaluation of human health risks posed by 12 OPFRs in water bodies. Specific parameters of the model were adjusted to align with the local socioeconomic context in the Yili region. The health risk assessment model endorsed by the US EPA \u003csup\u003e[14]\u003c/sup\u003e was applied to assess the health risks associated with OPFRs in the Ili River basin. The calculation of the average daily exposure of the population to OPFRs through drinking water is outlined as follows:\u003c/p\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\text{S}\\text{W}\\text{S}\\text{C}=\\times\\:\\text{Q}\\text{R}\\times\\:\\frac{\\text{T}}{\\text{B}\\text{W}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\)\u003c/span\u003e \u003c/span\u003e(2)\u003c/p\u003e\u003cp\u003ewhere SWSC represents the daily exposure dose per unit body weight of OPFRs through drinking, measured in ng/(kg•d); ci denotes the concentration of OPFRs in water (ng/L); T stands for the absorption rate of drinking water intake, set at 100%; QR represents the average daily water intake for individuals of varying ages; and BW refers to the body weight across different age groups in Xinjiang.\u003c/p\u003e\u003cp\u003eThe HQ represents the noncarcinogenic risk of a population ingesting OPFRs through drinking water and is calculated as follows:\u003c/p\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\text{H}\\text{Q}\\:=\\frac{\\text{S}\\text{W}\\text{S}\\text{C}}{\\text{R}\\text{f}\\text{D}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cp\u003ewhere HQ is the noncarcinogenic risk value of OPFR ingestion through drinking water, and RfD is the US EPA's reference dose value for each OPFR \u003csup\u003e[15]\u003c/sup\u003e, ng/(kg•d). It is generally believed that when HQ \u0026gt; 1, there is a noncarcinogenic health risk. When HQ \u0026lt; 0.1, the noncarcinogenic risk is considered to be small or negligible. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{H}\\text{C}\\text{R}=\\text{s}\\text{w}\\text{s}\\text{c}\\times\\:\\text{s}\\text{f}\\text{o}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003ewhere HCR denotes the carcinogenic risk associated with the population ingesting OPFRs through drinking water, while Sfo represents the carcinogenic slope factor indicating the maximum risk probability from ingesting contaminants through drinking, measured in (kg•d)/ng. Typically, if HCR is less than 1.0 × 10\u003csup\u003e− 6\u003c/sup\u003e, the cancer risk is considered low. Conversely, an HCR exceeding 1.0 × 10\u003csup\u003e–6\u003c/sup\u003e indicates a potential cancer risk \u003csup\u003e[16]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e \u003cb\u003e3.2 Uncertainty of health risk assessment\u003c/b\u003e \u003c/p\u003e\u003cp\u003e \u003cb\u003eThe health risk analysis process involves numerous uncertainties, including uneven pollutant distribution in the water environment due to limited sampling point concentration data, variations in individual human physiology, and differences in pollutant migration levels. Methods for evaluating uncertainty include the Monte Carlo method, Taylor's simplification method, the probability tree method, and expert judgment. In this study, the uncertainty of OPFR health risk assessment in the Ili River basin was examined using the Monte Carlo method.\u003c/b\u003e \u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Optimization of the sample pretreatment method\u003c/h2\u003e \u003cp\u003eGas chromatography equipped with a PFPD detector demonstrates excellent sensitivity to OPFRs, allowing complete separation of all 15 types within a 30-min timeframe. Notably, it achieves effective separation of three isomers: tri-o-toluene phosphate (o-TTP), tri-M-toluene phosphate (m-TTP), and tri-p-toluene phosphate (p-TTP (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eIn this study, the SPE column utilized is HLB, known for its superior extraction efficiency with strong polarity OPFRs. The recovery rates for the first 12 out of the 15 OPFRs ranged from 70.5\u0026ndash;108.4%, while the last 3 OPFRs exhibited lower recovery rates, 50%. To optimize recovery, a 1 L water sample was treated with both acid and salt, showing improved recovery rates of 76.4\u0026ndash;77.8% compared to treatments with acid or salt alone. Overall, the total recovery of all 15 OPFRs ranged from 76.4\u0026ndash;98.6% (\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAll target compounds exhibited linearity within this range, with correlation coefficients (R2) exceeding 0.995. The method's detection limits (LOD) were calculated using a signal-to-noise ratio (S/N) of 3, resulting in LODs ranging from 20 to 40 ng/L for the 15 OPFRs. The relative standard deviation (RSD) ranged from 4.4\u0026ndash;10.7%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Detection of OPFR concentrations in water\u003c/h2\u003e \u003cp\u003eFollowing sample collection, 15 types of OPFRs were detected in the water samples using the optimized SPE method. Four substances were identified: TiBP, TCEP, TCPP, and TDCPP, with their concentrations detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The total (Ʃ) minimum, maximum, and mean concentrations of OPFRs were 0, 193, and 105 ng/L, respectively. Among these, TiBP and TCPP exhibited the highest maximum concentrations at 46 ng/L and 61 ng/L, respectively, followed by TCEP at 45 ng/L and TDCPP at 41 ng/L. Regarding sampling points, A5, located in the upper reaches of the basin with sparse population, did not show any detected OPFRs. A2, situated at the confluence of three rivers, exhibited the highest OPFR concentrations. The A1 and A4 sampling sites, characterized by lower water flow rates, experienced significant dilution effects on OPFRs due to high water volumes, and were less influenced by human activities. In contrast, A3, closer to human activity zones like sewage treatment plant discharges and domestic sewage, showed slightly elevated OPFR concentrations, though overall levels remained low without a clear trend over the sampling period from August to November. This suggests minimal changes in mass concentrations, likely due to stable water volumes and limited external influences.\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\u003e\u003cb\u003eExposure levels of OPFRs in surface water at different sampling sites\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eminimum (ng/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emaximum (ng/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaverage (ng/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003erelevance ratio(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTEP\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\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTiPP\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\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTnPP\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\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTiBP\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\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTnBP\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\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCEP\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\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCPP\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\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTDCPP\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\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPhP\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\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBEP\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\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEHDPP\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\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTEHP\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\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eo-TTP\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\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em-TTP\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\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-TTP\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\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eƩOPFRs\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\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: ND indicates not detected\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe concentration of TiBP ranged from 0 to 46 ng/L, reflecting its relatively high mass concentration despite being difficult to dissolve in water, consistent with findings by Han Chao \u003csup\u003e[17]\u003c/sup\u003e. TCEP, with concentrations ranging from 0 to 45 ng/L, exhibits low hydrophobicity (lgKOW\u0026thinsp;=\u0026thinsp;1.44) and high solubility in water \u003csup\u003e[18]\u003c/sup\u003e. TCPP and TDCPP, widely distributed in nature, are highly polar and easily soluble in water. Compositionally, TCPP and TCEP dominate the water bodies in this study, similar to conclusions drawn by Shi et al. \u003csup\u003e[19]\u003c/sup\u003e in their study of various surface water bodies in Beijing. Comparing ΣOPFR results with other regions, the concentrations observed here are lower than those found in the Jinjiang River in Chengdu (689.09-10623.94 ng/L) \u003csup\u003e[20]\u003c/sup\u003e, Nanjing section of the Yangtze River (85.21-1557.96 ng/L) \u003csup\u003e[21]\u003c/sup\u003e, Luoma Lake surface water (97.1\u0026thinsp;~\u0026thinsp;1066.00 ng/L) \u003csup\u003e[22]\u003c/sup\u003e, and lower reaches of the Yangtze River (55.6\u0026thinsp;~\u0026thinsp;5071 ng/L) \u003csup\u003e[23]\u003c/sup\u003e. They are comparable to the lower concentrations (37.2\u0026ndash;510 ng/L) observed in rivers in the New York area \u003csup\u003e[24]\u003c/sup\u003e. The sampling site in this study is located at a distance from industrial zones, with surrounding residents primarily engaged in herding activities, resulting in relatively low human activity intensity. This suggests minimal impact from industrial production and residential activities, contributing to a relatively good quality of the water environment.\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.3 Ecological Risk Assessment\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eIn this study, four flame retardants were detected, among which TCEP, TCPP, and TDCPP were assessed for acute biological toxicity using PNEC.\u003c/b\u003e Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003eshow that for aquatic organisms, the Risk Quotient (RQ) values for TCEP ranged from 0 to 1.5 \u0026times; 10\u0026ndash;3, for TCPP from 0 to 5.0 \u0026times; 10\u0026ndash;2, and for TDCPP from 0 to 9.7 \u0026times; 10\u0026ndash;3. According to the species-specific risk values, TCPP posed the greatest risk to algae, TDCPP to crustaceans, and also to fish, with all RQ values below 0.1, indicating low ecological risk. Notably, 15 monomers, including m-TTP and p-TTP, which are more toxic OPFRs, were not detected in this study. These research findings contrast with ecological risk studies in the middle and upper reaches of the Beijiang River basin [25], where higher concentrations of electronic enterprises discharge pollutants such as m-TTP, p-TTP, and other OPFRs. In contrast, the Ili River basin is primarily influenced by traditional processing enterprises, contributing to differing ecological risk profiles.\u003c/b\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\u003e\u003cb\u003eBiotoxicity data for TCEP, TCPP, and DCPP\u003c/b\u003e \u003csup\u003e\u003cb\u003e[26]\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL(E)C50 (mg/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePNEC (ng/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRQ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ealgae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;8.8\u0026times;10\u003csup\u003e\u0026ndash;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;1.3\u0026times;10\u003csup\u003e\u0026ndash;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDCPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;1.0\u0026times;10\u003csup\u003e\u0026ndash;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ecrustacea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e330 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;1.3\u0026times;10\u003csup\u003e\u0026ndash;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;6.7\u0026times;10\u003csup\u003e\u0026ndash;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDCPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;9.7\u0026times;10\u003csup\u003e\u0026ndash;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003efish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;1.5\u0026times;10\u003csup\u003e\u0026ndash;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;5.0\u0026times;10\u003csup\u003e\u0026ndash;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDCPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;8.0\u0026times;10\u003csup\u003e\u0026ndash;3\u003c/sup\u003e\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\u003eThe application of the risk entropy method in ecological risk assessment introduces certain uncertainties. Toxicological data referenced are often based on foreign non-native species, lacking specific data for local species, leading to potential differences in toxicity assessments. The Ili River basin offers abundant fish resources and diverse rare fish species. Wei Lili's study \u003csup\u003e[27]\u003c/sup\u003e identified 145 algae species from 41 genera in the basin, using L(E)C50 data for RQ calculations, but this approach has inherent limitations. Studies by both domestic and international scholars indicate that OPFR concentrations in sediments typically exceed those found in surface water. Compounds like EHDPP and TEHP tend to adsorb onto organic matter in sediments \u003csup\u003e[28]\u003c/sup\u003e, increasing ecological risks for organisms such as algae and crustaceans over prolonged exposure periods. While this study did not analyze OPFR levels in sediments, future research will focus on comprehensive ecological risk assessments in the basin, emphasizing the identification and monitoring of flame retardant pollutants in water bodies.\u003c/p\u003e \u003cp\u003e4.4 Health Risk Assessment of OPFRs\u003c/p\u003e \u003cp\u003eCurrently, RfD values are available for 12 OPFRs, including TEP, TnBP, TDCPP, TPHP, EHDPP, o-TTP, m-TTP, p-TTP, TCEP, TCPP, TBEP, and TEHP. SFO values are available for four OPFRs: TCEP, TCPP, TnBP, and TEHP [11]. Health risk assessment parameters for different age groups and genders are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, while RfD and SFO values for OPFRs ingested via drinking water are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In Chinese practice, drinking water is typically boiled before consumption, resulting in the volatilization of some organic compounds during the process [29]. Studies indicate that boiling for 10 min can reduce the content of semivolatile organic compounds by 20\u0026ndash;30% [30]. In this study, a boiling residue ratio coefficient of 0.7 was applied for health risk assessment to ensure the results are more objective and practical.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eHealth risk assessment parameters for different populations\u003c/b\u003e \u003csup\u003e\u003cb\u003e[31\u0026ndash;32]\u003c/b\u003e\u003c/sup\u003e\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ecrowd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eman\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ewoman\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eadult\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003echildren\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eadult\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003echildren\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewater intake (L/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eweight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e23.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eabsorptivity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eReference dose values and slope carcinogens of OPFRs\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003etarget object\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRfD ng/(kg\u0026middot;d)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSFO (kg\u0026middot;d)/ng\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBoil residue ratio\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTnBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.00\u0026times;10\u003csup\u003e\u0026ndash;9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u0026times;10\u003csup\u003e\u0026ndash;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTDCPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.10\u0026times;10\u003csup\u003e\u0026ndash;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPhP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEHDPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTEHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.10\u0026times;10\u003csup\u003e\u0026ndash;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eo-TTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em-TTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-TTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\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\u003eFor assessing the risks associated with 15 types of OPFRs in water bodies, exposure levels vary among different demographic groups. Health risks were evaluated for male children, male adults, female children, and female adults using formulas (1) to (3), calculating both carcinogenic and noncarcinogenic risks, illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, respectively. Noncarcinogenic risk values (HQ) for OPFRs in male children, male adults, female children, and female adults ranged from 0 to 1.43 \u0026times; 10\u003csup\u003e\u0026ndash;4\u003c/sup\u003e, 0 to 1.35 \u0026times; 10\u003csup\u003e\u0026ndash;4\u003c/sup\u003e, 0 to 1.51 \u0026times; 10\u003csup\u003e\u0026ndash;4\u003c/sup\u003e, and 0 to 1.39 \u0026times; 10\u003csup\u003e\u0026ndash;4\u003c/sup\u003e, respectively. HQ values below 0.1 indicate low noncarcinogenic risk associated with OPFRs in the water of the Ili River basin across all four demographic groups. TCPP poses the highest noncarcinogenic risk among the substances studied, predominantly used as a modifier and plasticizer in rubber products. Its consistent detection at all four sampling points (A1 to A4) suggests challenges in its removal by sewage treatment plants, consistent with observations by Sun Jia \u003csup\u003e[33]\u003c/sup\u003e regarding TCPP's frequent detection in surface water.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e Noncarcinogenic risk distribution of surface water flame retarding\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e4.5 Uncertainty analysis of health risk assessment\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study primarily utilizes Monte Carlo simulation with 10,000 iterations to capture parameters' uncertainty and variability. It calculates carcinogenic risk values and corresponding cumulative probabilities of OPFRs from drinking water, revealing significant variability in cancer risk levels. The probabilities of cancer risk (50%) and cancer risk (90%) for male children, male adults, female children, and female adults can vary by 5 to 12 times. Factors such as seasonal variations, production processes and cycles of industries, water consumption patterns among different populations, and residents' activities can influence carcinogenic risk values of OPFRs. The uncertainty in exposure concentrations, duration, water consumption patterns, and other factors contributes to substantial differences in cumulative statistical outcomes for health risk probabilities. Regarding the perspective of carcinogenic risk probability (100%), the carcinogenic risk values associated with TECP and TDCPP were found to be below the acceptable threshold of carcinogenic risk set by the US EPA (1 \u0026times; 10\u003csup\u003e\u0026ndash;6\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eCumulative statistical results of the health risk probability for OPFRs among different populations\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecrowd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"12\" nameend=\"c13\" namest=\"c2\"\u003e \u003cp\u003eProbabilistic risk (1\u0026nbsp;\u0026times;\u0026nbsp;10\u003csup\u003e\u0026ndash;6\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eTECP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003eTDCPP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale adult\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale adult\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.49\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\u003eA sensitivity analysis was performed on each parameter to further assess the health risks of OPFRs across different populations. The analysis reveals that water consumption has the most significant impact on carcinogenic sensitivity, accounting for approximately 45.3%, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Conversely, body weight exhibited the least influence on sensitivity, with a negligible correlation observed. In practical scenarios, residents often consume filtered or boiled water, potentially mitigating the sensitivity of drinking water to cancer risks to some extent. However, as the industry and economy continue to develop in the Ili River basin, the health risks associated with OPFRs may fluctuate. Further investigation is required to determine if changes in OPFR concentrations correspondingly alter carcinogenic sensitivity levels.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAlthough the carcinogenic risk and noncarcinogenic risk of 15 OPFRs in the Yili River Basin are lower than the risk threshold and the water environment quality is relatively good, the detection rates of TCPP and TCEP are high, and OPFRs should receive increased attention.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003e \u003cb\u003e1) Fifteen OPFRs were detected in the water samples using the SPE-GC-PFPD detector. Upon adjusting the pH of the water samples to below 2 and adding NaCl, the detection limit improved to 20\u0026thinsp;~\u0026thinsp;40 ng/L, and the recovery rate increased to between 76.4% and 98.6%, fulfilling the requirements for surface water analysis. The linear relationship was strong, with correlation coefficients (R2) all exceeding 0.995.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e2) At the five sampling sites, four types of OPFRs were detected in varying amounts. The average concentrations of TiBP, TCEP, TCPP, and TDCPP were 20, 28, 36, and 21 ng/L, respectively. Despite the low concentrations of TCPP and TCEP, their high detection frequencies should be considered.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3) The RQ value for ecological risk was less than 0.1; however, the ecological risk has not been assessed. Future research will address the ecological risks associated with sediments in the Ili River basin. The noncarcinogenic risk values for 15 OPFRs ranged from 0 to 1.51 \u0026times;\u003c/b\u003e 10\u003csup\u003e\u0026ndash;4\u003c/sup\u003e, \u003cb\u003ewith HQ below 0.1, and the carcinogenic risk values for 4 OPFRs varied from 0 to 4.16 \u0026times; 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026ndash;8\u003c/b\u003e\u003c/sup\u003e, \u003cb\u003ebelow the established risk threshold of 1 \u0026times;\u003c/b\u003e 10\u003csup\u003e\u0026ndash;6\u003c/sup\u003e. \u003cb\u003eThe influence of drinking water on population health is considered negligible.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003e4) Uncertainty in the health risk assessment was analyzed through Monte Carlo simulations, which showed a wide variation in carcinogenic risk levels. However, these values remained below the US EPA's acceptable carcinogenic risk level of 1 \u0026times; 10\u003c/b\u003e \u003csup\u003e \u003cb\u003e\u0026ndash;6\u003c/b\u003e \u003c/sup\u003e. \u003cb\u003eSensitivity analysis indicated that the volume of consumed water significantly influenced carcinogenic sensitivity.\u003c/b\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Feng\u0026nbsp;HU\u0026nbsp;has read and agreed to the published version of the manuscript. E-mail：[email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOpen Project of Ili Kazakh Autonomous Prefecture key laboratory of normal school pollutants chemistry and environmental governance (2023HJYB03)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study did not require ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eData Availability\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eXUE Qianqian,WEI Yang,TIAN Yingze, et al. Characteristics,Gas-Particle Partitioning, and Health Risks of PM2.5-Bound PAHs and OPEs from 2019 to 2020 in Jinnan District, Tianjin. [J]. 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Environmental Science,2018,39(5):2230-2238.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Organophosphorus flame retardant, Purification concentrate, Risk evaluation, Gas chromatography, Exposure level","lastPublishedDoi":"10.21203/rs.3.rs-4593271/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4593271/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA solid-phase extraction-gas chromatography-pulsed flame photometric detector (SPE-GC-PFPD) was developed to determine 15 organophosphate flame retardants (OPFRs) in surface water of the Ili River basin. The recovery ranged from 76.4% to 98.6%, showing a good linear relationship. Four OPFRs were identified among them, with mean concentrations of 20, 28, 36, and 21 ng/L. The ecological risk of flame retardants in water was assessed using the risk entropy method. The ecological risk quotient (RQ) was calculated as 0.1, indicating a moderate to low level of risk. The noncarcinogenic and carcinogenic risks associated with the four OPFRs were found to be below their respective risk thresholds, suggesting negligible health risks from drinking water intake. The frequent detection of TiBP, TCEP, TCPP, and TDCPP warrants further attention.\u003c/p\u003e","manuscriptTitle":"Pollution characteristics and risk assessment of 15 organic phosphorus flame retardants in the Ili River Basin","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-08 06:14:24","doi":"10.21203/rs.3.rs-4593271/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"92242a24-6263-4eb2-89ff-98eea3b996d9","owner":[],"postedDate":"August 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-15T11:23:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-08 06:14:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4593271","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4593271","identity":"rs-4593271","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}