Accumulative substance water quality standard correction method based on BAF-BCF difference

Abstract

The invention discloses a cumulative substance water quality standard correction method based on BAF-BCF difference, and belongs to the technical field of environment standard formulation and environment risk management. The correction method comprises the following steps: respectively determining two exposure concentrations corresponding to BAF and BCF of a target cumulative pollutant; obtaining a biological concentration factor predicted value and a biological accumulation factor predicted value based on the quantitative model; calculating the ratio of the predicted value of the biological accumulation factor to the predicted value of the biological concentration factor under the nutritional level of each aquatic organism to obtain a correction factor; performing water quality standard correction according to the correction factor; according to the method, the multi-way exposure of the accumulative substances in the real environment is introduced into the water quality standard correction process, so that the conversion from single water phase exposure to comprehensive ecological exposure is realized, the ecological correlation and risk characterization accuracy of the aquatic organism water quality standard are improved, and a technical scheme with high operability is provided for the environment management of the accumulative pollutants.

Description

Technical Field

[0001] This invention relates to the field of environmental benchmarking and environmental risk management, specifically to a method for correcting water quality benchmarks based on the cumulative substance difference between BAF and BCF. Background Technology

[0002] Water Quality Criteria (WQCs) are crucial for protecting aquatic life in water environment management, and their development is typically based on toxicity data obtained under laboratory aquatic exposure conditions. However, in real aquatic ecosystems, pollutants enter organisms not only through water exposure but also through food intake, sediment contact, and other pathways. For substances with accumulative properties, they can continuously accumulate within organisms and be transferred through the food chain, indicating that non-aquatic exposure may be a significant, even dominant, source of exposure in real-world environments. Therefore, existing water quality criteria based solely on aquatic exposure may underestimate the actual exposure levels of accumulative substances in real ecosystems, thus impacting the effective protection of aquatic life to some extent.

[0003] Bioconcentration Factor (BCF) is commonly used to characterize the bioaccumulation of pollutants under laboratory aquatic exposure conditions, while Bioaccumulation Factor (BAF) better reflects the accumulation level under multi-pathway exposure scenarios in real environments. Both represent the levels at which pollutants reach their in vivo threshold concentrations under different exposure conditions. Based on this, this paper proposes constructing a trophic-level specific BAF / BCF ratio as a correction parameter for water quality benchmarks for accumulative substances by analyzing the differences between BAF and BCF, thus incorporating the bioaccumulation characteristics under multi-pathway exposure conditions into the benchmark development process. This approach enables the transformation of aquatic biological water quality benchmarks from single-aquatic exposure to comprehensive ecological exposure, thereby improving the ecological relevance and risk characterization accuracy of benchmark values ​​and providing a practically feasible technical solution for environmental management and ecological risk prevention of accumulative pollutants. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides a method for correcting water quality benchmarks based on the cumulative substance difference between BAF and BCF, thereby improving the accuracy and ecological relevance of aquatic biological water quality benchmarks.

[0005] A method for correcting water quality standards based on the cumulative substance difference between BAF and BCF includes the following steps: S101. Determine the correction factor; Determine the first exposure concentration of the target cumulative pollutant in laboratory water bodies and the second exposure concentration of the target cumulative pollutant in field water bodies, respectively; Based on the quantitative model, the predicted value of the bioconcentration factor is obtained by taking the first exposure concentration and the trophic level of the aquatic organism as input; the predicted value of the bioaccumulation factor is obtained by taking the second exposure concentration and the trophic level of the aquatic organism as input; wherein, the quantitative model is used to characterize the quantitative relationship between the bioconcentration factor, the bioaccumulation factor and multiple factors including the first exposure concentration, the second exposure concentration and the trophic level of the aquatic organism. Calculate the ratio of the predicted value of the bioaccumulation factor to the predicted value of the bioconcentration factor at each trophic level of aquatic organisms to obtain the correction factor corresponding to each trophic level of aquatic organisms; S102, Water Quality Standard Correction; Based on the trophic level of the aquatic organism to be studied, a corresponding correction factor is selected. The laboratory toxicity data is corrected using the correction factor, and the corrected water quality benchmark is determined based on the corrected laboratory toxicity data.

[0006] Explanation: The above method predicts the enrichment differences of organisms at different trophic levels in laboratory and real-world environments by constructing a quantitative model, and converts the BAF / BCF ratio into a targeted correction factor. During the benchmark development stage, the corresponding correction factor is dynamically selected based on the trophic level of the target organism to calibrate laboratory toxicity data, thereby significantly improving the ecological relevance and risk characterization accuracy of the water quality benchmark. This method effectively compensates for the underestimation of the ecological risk of highly bioaccumulative pollutants in traditional single-phase aquatic exposure benchmark systems, and more realistically reflects the multi-pathway exposure levels of pollutants in complex food webs, providing scientific and universal technical support for precise water environment management and proactive risk prevention and control.

[0007] Furthermore, the target cumulative pollutants include halogenated organic pollutants and organometallic compounds.

[0008] Note: For the pollutants mentioned above that are easily amplified through the food chain, this study effectively corrects the systematic underestimation of the real multi-pathway exposure risk (BAF) by traditional laboratory water phase exposure data (BCF). This makes the derived water quality benchmark more consistent with the hazard level of pollutants after bioaccumulation and transtrophic transfer in actual ecosystems, thus providing more scientific and reliable technical support for the water environment management and ecological protection of accumulated substances.

[0009] Further, determining the first exposure concentration of the target cumulative pollutant in laboratory water and the second exposure concentration of the target cumulative pollutant in field water includes: Obtain multiple sets of laboratory aqueous phase exposure data and field environmental monitoring data of the target cumulative pollutant; The first exposure concentration is determined by statistical measures based on the water exposure concentrations reported in the multiple sets of laboratory water exposure study data; the second exposure concentration is determined by statistical measures based on the water exposure concentrations reported in the multiple sets of field environmental monitoring study data; wherein the statistical measures are median or average.

[0010] Note: The above method ensures the representativeness of the exposure scenarios and the reliability of the data, providing a scientific and comparable data foundation for the subsequent accurate quantification of the BAF-BCF difference. It effectively avoids the bias of a single data source and significantly improves the objectivity of the correction factor and the scientific nature of the benchmark derivation process.

[0011] Furthermore, the multiple factors also include: chemical substance type, aquatic habitat type, and taxonomic information of organisms.

[0012] Note: The above additions to multiple factors fully consider the differentiated impact of three key factors on bioaccumulation behavior, enabling the generated BAF-BCF correction factor to more realistically reflect the multi-pathway exposure characteristics of specific pollutants in specific environments and biological groups.

[0013] Furthermore, the quantification model adopts a linear mixed-effects model.

[0014] Furthermore, the linear mixed-effects model is constructed by using exposure concentration, trophic level of aquatic organisms, and type of chemical substance as fixed effects, the taxonomic unit to which the species belongs as a random effect, and introducing interaction terms between exposure concentration and type of chemical substance, and between trophic level of aquatic organisms and type of chemical substance.

[0015] Note: The linear mixed-effects model can significantly improve the accuracy and robustness of BAF / BCF predictions: it not only fully captures the regularity of key driving factors, but also effectively corrects the structural differences of multi-source data, providing a statistically rigorous and ecologically meaningful modeling foundation for generating highly reliable correction factors.

[0016] Furthermore, the quantification model employs a quantile regression model, a Bayesian hierarchical model, or a machine learning model.

[0017] Explanation: By introducing quantile regression models, Bayesian hierarchical models, or machine learning models as quantitative modeling tools, the adaptability and accuracy of correction factor prediction are significantly enhanced: quantile regression accurately captures the asymmetric distribution characteristics of BAF / BCF in extreme exposure scenarios, avoiding the underestimation of high-risk situations by mean prediction; Bayesian hierarchical models effectively solve the data sparsity problem and output probabilistic correction factors by integrating prior knowledge and uncertainty propagation; and machine learning models can automatically identify complex nonlinear interaction effects between chemistry, habitat, and biology, improving the predictive generalization ability of differences in exposure through multiple pathways.

[0018] Furthermore, the quantification model is pre-trained using multiple sets of exposure concentrations, aquatic organism trophic levels, and corresponding bioconcentration factors or bioaccumulation factors.

[0019] Note: The above training ensures that the generated correction factors have sufficient ecological representativeness and statistical robustness.

[0020] Furthermore, the step of correcting the laboratory toxicity data using a correction factor and determining the corrected water quality standard based on the corrected laboratory toxicity data includes: Divide the laboratory toxicity endpoint data of the biological species under study by the corresponding correction factor to obtain the corrected toxicity data; The corrected toxicity data is used as input, fitted by a species sensitivity distribution model, and calculated based on a preset protection threshold to obtain the corrected freshwater aquatic organism water quality benchmark.

[0021] Explanation: By dynamically matching correction factors based on trophic levels and directly calibrating laboratory toxicity data, the core input parameters of the water quality benchmark were accurately corrected, and then a more scientific benchmark value was derived using the Species Sensitivity Distribution (SSD) model.

[0022] The beneficial effects of this invention are: This invention provides a method and framework for correcting water quality benchmarks for bioaccumulative substances based on BAF-BCF differences. By introducing a quantitative characterization of the systematic differences between laboratory aquatic exposure scenarios and real-world ecosystem multi-pathway exposure scenarios, this invention effectively addresses the problem that existing water quality benchmarks tend to underestimate ecological risks when facing highly bioaccumulative pollutants. Compared to traditional methods that rely solely on single-aquatic exposure data to develop water quality benchmarks, this invention can more realistically reflect the exposure levels of pollutants in the actual environment, improving the ecological relevance and risk characterization accuracy of aquatic biological water quality benchmarks. Furthermore, this invention transforms the differences in bioaccumulation under multi-pathway exposure conditions into operable technical parameters, providing a scientific and scalable technical approach for water environment management and ecological risk prevention of bioaccumulative pollutants, and contributing to improving the rationality and foresight of water quality management decisions. Attached Figure Description

[0023] Figure 1 This is a flowchart of the aquatic organism water quality benchmark correction method according to an embodiment of the present invention; Figure 2 This is a statistical graph showing the different types of substances, carbon chain lengths, tested biological phyla, and their corresponding BAF and BCF data in the embodiments of the present invention. Figure 3Meta-analysis results of BAF and BCF data for different substances in embodiments of the present invention and characteristics of effect size variation; (a) Meta-analysis results based on Hedges' g effect size; (b) Relationship between effect size and carbon chain length; (c) Relationship between effect size and log Kow; Figure 4 Modeling of BAF and BCF in embodiments of the present invention and factors influencing their differences; (a) the trend of BAF and BCF of different substances with trophic level and environmental concentration; (b) the environmental exposure concentration range of various substances; (c) the trophic level range of various substances. Figure 5 The following is a decomposition of the driving factor effect type of the BAF-BCF difference in this embodiment of the invention; (a) is the Blinder-Oaxaca decomposition result of PFOS; (b) is the Blinder-Oaxaca decomposition result of PFOA; Figure 6 The following is a construction of a trophic level-specific BAF / BCF ratio correction factor for embodiments of the present invention: (a) is the relationship between predicted BAF and BCF and trophic level changes for PFOS; (b) is the relationship between predicted BAF and BCF and trophic level changes for PFOA. Figure 7 The following are the changes in SSD curves and HC5 values ​​before and after correction in this embodiment of the invention: (a) PFOA data from the EPA of a first country; (b) PFOS data from the EPA of a first country; (c) PFOS data from a second country; (d) PFOS data from a third country. Figure 8 This is a flowchart illustrating the process of obtaining correction factors in the method for correcting water quality standards for cumulative substances in freshwater aquatic organisms according to an embodiment of the present invention. Detailed Implementation

[0024] To further illustrate the methods and effects of this invention, the technical solution of this invention will be clearly and completely described below in conjunction with experiments.

[0025] This invention aims to provide a method and framework for correcting water quality standards for accumulative substances in freshwater aquatic organisms based on the differences between BAF and BCF, and to correct water quality standards for typical accumulative substances (such as PFAS) based on the correction method. Specific details are provided in the following embodiments: Example: A method for correcting water quality standards based on the cumulative substance difference between BAF and BCF, comprising the following steps: S100. Obtain and analyze the key influencing factors of bioconcentration factor (BCF) and bioaccumulation factor (BAF); the key influencing factors obtained are exposure concentration and trophic level of aquatic organisms. Exposure concentration refers to the concentration of a target cumulative pollutant; the trophic level of aquatic organisms refers to the energy transfer level of an organism in the food web, with higher values ​​indicating a more apex predatory position. like Figure 8 As shown, the specific process of S100 includes the following steps A1 to A3; A1: Collect BAF and BCF data of target cumulative pollutants in freshwater aquatic organisms, and after quality control and standardization preprocessing, use meta-analysis to quantitatively determine the differences between the two. 1-1: Research literature on bioaccumulation factors and bioconcentration factors in aquatic organisms was retrieved through database systems. Keyword combinations were used for retrieval, and literature containing valid BAF or BCF data was obtained through step-by-step screening of abstracts and full texts. 1-2: Based on this, for each article, the publication year, species name, aquatic habitat type, sample tissue type, pollutant concentration in water and organisms, chemical substance name and corresponding BAF or BCF value are extracted, along with the substance category, carbon chain length and taxonomic information and trophic level information of the species, to construct a standardized original database. 1-3: The collected data underwent preprocessing and standardization, including unifying species names and taxonomic systems, removing data records below the detection limit, converting dry weight concentration data to wet weight data, and calculating the corresponding BAF or BCF values ​​based on the relationship between water and organism concentrations for literature that only reported water and organism concentrations without directly providing BAF or BCF. Simultaneously, the data sources were differentiated according to experimental conditions: data obtained from field sampling were classified as BAF, and data obtained under controlled laboratory conditions were classified as BCF, to improve the comparability of data between different studies. Figure 2 ); 1-4: The processed data were comprehensively evaluated using meta-analysis. Standardized effect size was selected as the statistical indicator to quantitatively assess the systematic differences between BAF and BCF among different substances and biological groups. The overall difference level was obtained by weighted averaging, thereby systematically exploring the differences and variation patterns of BAF and BCF under multi-pathway exposure conditions and single-aqueous phase exposure conditions. Figure 3 ); The above-mentioned meta-analysis to determine the differences between BAF and BCF specifically includes: Meta-analysis was used to systematically evaluate the differences between BCF and BAF data, and to... Hedges' g As an effect size, the standardized mean difference (SMD) between the two indicators is calculated. Hedges' g It is at Cohen's dIntroducing correction factors J The bias correction effect size is applicable to situations where the sample sizes of different studies or different compounds are inconsistent (Formula 1).

[0026] (1) in, and represents the mean of BAF and BCF, respectively.

[0027] To correct for bias introduced by differences in sample size among different compounds, a correction factor was used. J The calculation formula is as follows (Formula 2).

[0028] (2) in, and These represent the sample sizes of BAF and BCF, respectively.

[0029] Combined standard deviation ( pooled standard deviation Calculate according to formula (3): (3) in, This represents the standard deviation of BAF or BCF. For each... Hedges' g The value is further calculated to determine its variance. Var(g) ), used for subsequent weighted average and confidence interval estimation, and its calculation formula is as follows (4): (4) For PFAS, the overall effect size is calculated using the inverse variance weighting method, where the reciprocal of the variance of each effect size is used as the weight. Hedges' g A positive value indicates that BAF is greater than BCF, while a negative value indicates that BAF is less than BCF. All statistical analyses were performed in R 4.3.3 using the "metafor" package.

[0030] The above steps, through systematic retrieval, rigorous screening, and standardized preprocessing, constructed a high-quality, highly comparable aquatic organism accumulation database. Meta-analysis further provided quantitative evidence that BAF is generally higher than BCF, laying a solid empirical foundation for subsequent investigations into the reasons for the differences and ensuring the objectivity and universality of the analytical conclusions. A2: Based on the linear mixed-effects model, statistical fitting models of BAF and BCF were constructed respectively to identify the key influencing factors that lead to the difference between the two; the two key influencing factors of exposure concentration and trophic level of aquatic organisms were obtained. 2-1: Based on the collected BAF and BCF data, a linear mixed-effects model was used to statistically model the logarithmic values ​​of BAF and BCF respectively. Exposure concentration, trophic level and chemical type were used as fixed effect factors, and the taxonomic unit to which the species belonged was used as a random effect factor. Interaction terms between exposure concentration and chemical type, and between trophic level and chemical type were introduced to characterize the variation characteristics of BAF and BCF under different substances and different biological groups. 2-2: To improve the model, factors that are difficult to quantify directly, such as individual differences, changes in environmental conditions, and mixed exposures, are uniformly included in the random error term to characterize the potential impact of unobserved factors on BAF and BCF, thereby improving the robustness and applicability of the model. Figure 4 a) 2-3: For factor identification, based on the constructed statistical model, the effects of factors such as exposure concentration, trophic level, and species taxonomic differences are quantitatively analyzed to identify the key driving factors leading to the difference between BAF and BCF, thus achieving a systematic analysis of the formation mechanism of BAF-BCF differences. Figure 4 b-4c); A linear mixed-effects model (LMM) was used to analyze logBAF and logBCF data of PFAS to characterize the hierarchical structure and variability caused by species differences, exposure concentration, trophic level, individual differences, environmental conditions, and mixed exposure. To fully utilize observational information from multiple species across different compounds, chemical type was included as an interaction term in the model. Given the ongoing controversy surrounding the impact of organism size on bioaccumulation, and the generally weak and limited direct effects of environmental parameters and mixed exposure on BAF and BCF due to data availability, these unmeasured or unobservable factors were uniformly incorporated into the residual term (ε) to represent unexplained variability. The model also included exposure concentration, trophic level, and phylogenetic taxonomic unit family (…). Family This approach balances structural robustness with computational efficiency. Exposure concentration and trophic level are considered as fixed effects. Family As a random effect (Equation 7): (7) in, Represents logBAF or logBCF; For a fixed intercept; The fixed effect representing the exposure concentration; This indicates the fixation effect of a trophic level; Fixed effects indicating the type of chemical substance; This indicates the interaction effect between exposure concentration and chemical type; This indicates the interaction between trophic level and chemical type; Random effects at the scientific level; This represents the residual error term. All statistical analyses were performed in R 4.3.3 using the "lme4" software package.

[0031] Understandably, the use of a linear mixed-effects model (LMM) effectively addresses the complexity of the data. This model, while controlling for inherent species differences (random effects) and the influence of species type, quantitatively analyzes the independent effects and interactions of pre-defined factors such as exposure concentration and trophic level (fixed effects) on BAF and BCF. This precisely identifies exposure concentration and trophic level of aquatic organisms as the core key factors driving the differences between BAF and BCF, avoiding interference from other confounding variables. A3: Combining the Blinder–Oaxaca decomposition method, analyze the types of effects of different influencing factors on the differences between the two, including characteristic effects and coefficient effects; 3-1: Based on the key influencing factors identified in S2, a difference decomposition method is introduced to quantitatively decompose the overall difference between BAF and BCF. The contributions of different influencing factors in the formation of the difference are distinguished into the characteristic effect caused by the difference in factor values ​​and the coefficient effect caused by the difference in the intensity of factor action, so as to reveal the structural source of the BAF-BCF difference. 3-2: Evaluate the relative contributions of influencing factors to the characteristic effect and coefficient effect, respectively, and clarify the differences in the ways in which exposure concentration and trophic level affect bioaccumulation behavior under laboratory aqueous exposure scenarios and real-world multi-pathway exposure scenarios, so as to provide a quantitative basis for the subsequent construction of ecologically significant modified parameters. Figure 5 ).

[0032] Specifically, Blinder-Oaxaca decomposition identifies the effect types of influencing factors, providing a basis for constructing BAF / BCF factors of trophic level characteristics; To further quantify the sources of the differences between BAF and BCF, regression curves for BAF and BCF were constructed based on a linear mixed-effects model (LMM), and the predicted fixed effects were extracted from the model. Based on this, the Blinder–Oaxaca decomposition method was used to quantitatively decompose the overall differences between BAF and BCF (Equation 8), dividing them into two components: (1) endowment effect, which represents the difference caused by the different numerical distributions of explanatory variables (such as trophic level and exposure concentration) between the BAF and BCF groups; and (2) coefficient effect, which reflects the difference caused by the different strengths (regression coefficients) of the influence of the same explanatory variables on BAF and BCF.

[0033] (8) in, and These represent the average predicted values ​​of BAF and BCF, respectively. and This represents the average level of the explanatory variables in the BAF and BCF groups; and The regression coefficients represent the magnitude of the influence of the explanatory variables on the BAF and BCF; the interaction term characterizes the contribution of the interaction to the overall difference. All analyses were performed in R 4.3.3 using the "Oaxaca" software package. It should be understood that step A3 delves into the specific mechanisms by which key factors (exposure concentration, trophic level) lead to differences. This method decomposes the overall differences into "characteristic effects" (differences in factor levels themselves in BAF / BCF scenarios) and "coefficient effects" (differences in the strength of effects at the same factor level). In particular, it reveals a significantly stronger "coefficient effect" of trophic level through dietary enrichment in real multi-pathway exposure (BAF), thus clearly elucidating the structural source of the differences. Through step A3 above, the specific roles of trophic levels and exposure concentrations were explained and analyzed. The results show that the mechanism of action of exposure concentrations is that the concentration values ​​differ in the two cases, and the mechanism of action of trophic levels is that their corresponding degrees differ in the two cases. Figure 5 The red and blue colors in the image represent two different effects.

[0034] In summary, S100 ultimately identified exposure concentration and trophic level as key influencing factors. This not only confirms the core role of multi-pathway environmental exposure (food chain transmission) and highlights the limitations of laboratory BCF assessment (ignoring trophic level and complex exposure sources), but also, as mentioned above, the impact of exposure concentration is mainly due to the magnitude of the value. Therefore, this paper fixes the difference between BAF and BCF caused by exposure concentration at the mean difference between the two cases. As for the impact of trophic level, this paper elaborates on it according to different trophic levels, which can be seen in subsequent steps S101. like Figure 1 As shown, S101, determine the correction factor; (1) Determine the first exposure concentration of the target cumulative pollutant in laboratory water bodies and the second exposure concentration of the target cumulative pollutant in field water bodies respectively; The target cumulative pollutants include chemicals that are persistent, bioaccumulative, and ecotoxic in the environment, including but not limited to halogenated organic pollutants and organometallic compounds.

[0035] The determination of the first exposure concentration of the target cumulative pollutant in laboratory water and the second exposure concentration of the target cumulative pollutant in field water includes: Obtain multiple sets of laboratory aqueous phase exposure data and field environmental monitoring data of the target cumulative pollutant; The first exposure concentration is determined by statistical measures based on the water exposure concentrations reported in the multiple sets of laboratory water phase exposure study data; the second exposure concentration is determined by statistical measures based on the water exposure concentrations reported in the multiple sets of field environmental monitoring study data; wherein the statistical measure is the median.

[0036] (2) Based on the quantitative model, the predicted value of the bioconcentration factor is obtained by taking the first exposure concentration and the trophic level of the aquatic organism as input; the predicted value of the bioaccumulation factor is obtained by taking the second exposure concentration and the trophic level of the aquatic organism as input; wherein, the quantitative model is used to characterize the quantitative relationship between the bioconcentration factor, the bioaccumulation factor and multiple factors including the first exposure concentration, the second exposure concentration and the trophic level of the aquatic organism; specifically, the quantitative model adopts a linear mixed-effects model, and the establishment and training method is the same as that of the linear mixed-effects model in A2 above. The linear mixed-effects model is constructed by using exposure concentration, aquatic trophic level, and chemical substance type as fixed effects, and the taxonomic unit to which the species belongs as a random effect, and by introducing interaction terms between exposure concentration and chemical substance type, and between aquatic trophic level and chemical substance type. The quantification model is pre-trained using multiple sets of exposure concentration, aquatic trophic level, and corresponding bioconcentration factor or bioaccumulation factor data; it is the same as step A2 mentioned above, and will not be repeated here.

[0037] For example, in this embodiment, the R of BCF obtained by the above linear mixed-effects model is... 2 The R of BAF is 0.79. 2 It is 0.64; The multiple factors also include: chemical substance type, aquatic habitat type, and taxonomic information of organisms.

[0038] In other embodiments, the quantization model employs a quantile regression model, a Bayesian hierarchical model, or a machine learning model.

[0039] (3) Calculate the ratio of the predicted value of the bioaccumulation factor to the predicted value of the bioconcentration factor at each trophic level of aquatic organisms, and obtain the correction factor corresponding to each trophic level of aquatic organisms; S102, Water Quality Standard Correction; Based on the trophic level of the aquatic organism to be studied, a corresponding correction factor is selected. The laboratory toxicity data is corrected using the correction factor, and the corrected water quality benchmark is determined based on the corrected laboratory toxicity data.

[0040] The process of correcting laboratory toxicity data using a correction factor and determining the corrected water quality standard based on the corrected laboratory toxicity data includes: Divide the laboratory toxicity endpoint data of the biological species under study by the corresponding correction factor to obtain the corrected toxicity data; The corrected toxicity data is used as input, fitted by a species sensitivity distribution model, and calculated based on a preset protection threshold to obtain the corrected freshwater aquatic organism water quality benchmark. For example, a correction factor is matched to the corresponding trophic level for each laboratory-tested aquatic species (e.g., factor 4.0 for trophic level 3). Then, the raw toxicity endpoint data (e.g., median lethal concentration, LC50) for each species are analyzed. 50 Perform a division operation (corrected toxicity value = original LC) 50 (÷ Correction factor); This represents the organism's sensitivity to multi-pathway exposure in the wild, which is equivalent to four times that under laboratory testing conditions. After obtaining the corrected toxicity datasets for each species, they are substituted into the species sensitivity distribution model for standard calculations. The model fits the distribution curve of these data on logarithmic coordinates (such as normal or log-logarithmic distribution) and reads the corresponding concentration value from the curve according to a preset protection threshold (such as the water quality benchmark protecting 95% of species, i.e., HC5). For example, if the corrected toxicity values ​​for three species (trophic levels 2, 3, and 4) are 50, 10, and 2 μg / L, respectively, the HC5 might be calculated as 5 μg / L after fitting the model. This 5 μg / L is the final "corrected water quality benchmark." In contrast, if the original laboratory data (such as 100, 50, and 20 μg / L) are used, the traditional benchmark might be 15 μg / L. This demonstrates that by correcting the input data, this step systematically reduces the derived baseline value, thereby substantially incorporating the risk of bioaccumulation under multiple exposure pathways and making the baseline more accurate. For example, such as Figure 6 As shown, in this embodiment, the corrected baseline values ​​for PFOA and PFOS are reduced by 2.5 times and 3.2-4.4 times, respectively.

Claims

1. A method for correcting water quality standards based on the cumulative substance difference of BAF-BCF, characterized in that, Includes the following steps: S101. Determine the correction factor; Determine the first exposure concentration of the target cumulative pollutant in laboratory water bodies and the second exposure concentration of the target cumulative pollutant in field water bodies, respectively; Based on the quantitative model, the first exposure concentration and the trophic level of aquatic organisms are used as inputs to output the predicted value of the bioconcentration factor. Using the second exposure concentration and the trophic level of aquatic organisms as inputs, the predicted value of the bioaccumulation factor is output; wherein, the quantitative model is used to characterize the quantitative relationship between the bioconcentration factor, the bioaccumulation factor and multiple factors including the first exposure concentration, the second exposure concentration and the trophic level of aquatic organisms; Calculate the ratio of the predicted value of the bioaccumulation factor to the predicted value of the bioconcentration factor at each trophic level of aquatic organisms to obtain the correction factor corresponding to each trophic level of aquatic organisms; S102, Water Quality Standard Correction; Based on the trophic level of the aquatic organism to be studied, a corresponding correction factor is selected. The laboratory toxicity data is corrected using the correction factor, and the corrected water quality benchmark is determined based on the corrected laboratory toxicity data.

2. The method for correcting water quality standards based on the cumulative substance difference of BAF-BCF as described in claim 1, characterized in that, The target cumulative pollutants include halogenated organic pollutants and organometallic compounds.

3. The method for correcting water quality standards based on the cumulative substance difference of BAF-BCF as described in claim 1, characterized in that, The determination of the first exposure concentration of the target cumulative pollutant in laboratory water and the second exposure concentration of the target cumulative pollutant in field water includes: Obtain multiple sets of laboratory aqueous phase exposure data and field environmental monitoring data of the target cumulative pollutant; The first exposure concentration is determined by statistical measures based on the water exposure concentrations reported in the multiple sets of laboratory water exposure study data; the second exposure concentration is determined by statistical measures based on the water exposure concentrations reported in the multiple sets of field environmental monitoring study data; wherein the statistical measures are median or average.

4. The method for correcting water quality standards based on the cumulative substance difference of BAF-BCF as described in claim 1, characterized in that, The multiple factors also include: chemical substance type, aquatic habitat type, and taxonomic information of organisms.

5. The method for correcting water quality standards based on the cumulative substance difference of BAF-BCF as described in claim 1, characterized in that, The quantification model adopts a linear mixed-effects model.

6. The method for correcting cumulative substances in water quality standards based on BAF-BCF differences as described in claim 5, characterized in that, The linear mixed-effects model is constructed by using exposure concentration, trophic level of aquatic organisms, and type of chemical substance as fixed effects, taxonomic unit of species as random effect, and introducing interaction terms between exposure concentration and type of chemical substance, and between trophic level of aquatic organisms and type of chemical substance.

7. The method for correcting water quality standards based on the cumulative substance difference of BAF-BCF as described in claim 1, characterized in that, The quantification model employs a quantile regression model, a Bayesian hierarchical model, or a machine learning model.

8. The method for correcting water quality standards based on the cumulative substance difference of BAF-BCF as described in claim 1, characterized in that, The quantitative model was pre-trained using multiple sets of exposure concentrations, aquatic organism trophic levels, and corresponding bioconcentration and bioaccumulation factor data.

9. The method for correcting water quality standards based on the cumulative substance difference of BAF-BCF as described in claim 1, characterized in that, The process of correcting laboratory toxicity data using a correction factor and determining a corrected water quality standard based on the corrected laboratory toxicity data includes: Divide the laboratory toxicity endpoint data of the biological species under study by the corresponding correction factor to obtain the corrected toxicity data; The corrected toxicity data is used as input, fitted by a species sensitivity distribution model, and calculated based on a preset protection threshold to obtain the corrected freshwater aquatic organism water quality benchmark.

Images