Method for screening of polycyclic aromatic hydrocarbon ecotoxicity biomarkers

Abstract

The present disclosure provides a screening method of polycyclic aromatic hydrocarbon ecological toxicity biomarker, and relates to the technical field of biology.The method of the present disclosure comprises the following steps: exposing a protozoan model to blank control conditions and different concentrations of polycyclic aromatic hydrocarbon or alkyl derivative pollutants, respectively, and synchronously determining the response data of the control group and the different concentration treatment groups at multiple toxicity endpoints; determining the effective response intensity of unit living cells; performing parallel fitting calculation on the effective response intensity by using a nonlinear dose-effect model and a linear dose-effect model, respectively, to determine the residual sum of squares of the two models; calculating a first characteristic parameter according to the ratio of the residual sum of squares of the nonlinear dose-effect model and the linear dose-effect model, and calculating a second characteristic parameter based on the predicted extreme value of the nonlinear dose-effect model; determining the comprehensive evaluation index of each toxicity endpoint according to the first characteristic parameter and the second characteristic parameter, and determining the polycyclic aromatic hydrocarbon ecological toxicity biomarker from the multiple toxicity endpoints.

Description

Technical Field

[0001] This disclosure relates to the field of biotechnology, and in particular to a method for screening ecotoxicological biomarkers of polycyclic aromatic hydrocarbons. Background Technology

[0002] Polycyclic aromatic hydrocarbons (PAHs) are a class of persistent organic pollutants that are widely present in the environment. Therefore, establishing rapid and sensitive ecotoxicity assessment methods for PAHs is of great significance for environmental monitoring and risk assessment.

[0003] In current technical approaches, ecotoxicological assessments of polycyclic aromatic hydrocarbons (PAHs) typically employ multiple toxicity endpoints, including cell membrane potential, reactive oxygen species (ROS) levels, adenosine triphosphate (ATP) content, superoxide dismutase (SOD) activity, and cell viability. These indicators reflect the toxic effects of pollutants from different perspectives: changes in cell membrane potential can characterize early disturbances in cellular function; ROS levels reflect the degree of oxidative stress; ATP content indicates energy metabolism status; SOD activity reflects antioxidant defense capabilities; and cell viability reflects cell survival.

[0004] However, when screening for the optimal biomarker from multiple candidate toxicity endpoints, the reliance on subjective experience or simple statistical significance analysis often leads to problems such as unclear dose-response relationships, distorted response curves, or low signal-to-noise ratios in the selected toxicity endpoints, making it difficult to establish stable and reproducible toxicity judgment criteria. Summary of the Invention

[0005] This disclosure provides a method for screening ecotoxicity biomarkers of polycyclic aromatic hydrocarbons.

[0006] According to one aspect of this disclosure, a method for screening ecotoxicological biomarkers of polycyclic aromatic hydrocarbons is provided, comprising: Protozoan models were exposed to blank control conditions and different concentrations of polycyclic aromatic hydrocarbons or their alkyl derivatives to obtain control groups and multiple concentration treatment groups. The response data of the control group and different concentration treatment groups at multiple toxic endpoints were measured simultaneously. The multiple toxic endpoints include at least two of the following: cell membrane potential, reactive oxygen species level, adenosine triphosphate content, superoxide dismutase activity, and cell activity. Based on the number of surviving cells in each concentration treatment group, the response data is normalized to determine the effective response intensity per unit of live cell. The effective response intensity was calculated by parallel fitting using both a nonlinear dose-response model and a linear dose-response model, and the sum of squared residuals of the nonlinear dose-response model and the linear dose-response model were determined based on the results of the parallel fitting calculations. Based on the ratio of the sum of squared residuals of the nonlinear dose-response model and the linear dose-response model, a first characteristic parameter is calculated to characterize the degree of conformity of the response data to the dose-response law, and a second characteristic parameter is calculated based on the predicted extreme value of the nonlinear dose-response model to characterize the amplitude of the signal response. Based on the first characteristic parameter and the second characteristic parameter, a comprehensive evaluation index for each of the toxicity endpoints is determined, and based on the comprehensive evaluation index for each of the toxicity endpoints, polycyclic aromatic hydrocarbon ecotoxicity biomarkers are determined from multiple toxicity endpoints.

[0007] According to the screening method for ecotoxicological biomarkers of polycyclic aromatic hydrocarbons according to at least one embodiment of this disclosure, the first characteristic parameter SFF is determined according to the following formula: in, and These are the sum of squared residuals for the nonlinear dose-response model and the linear dose-response model, respectively.

[0008] According to the screening method for ecotoxicological biomarkers of polycyclic aromatic hydrocarbons according to at least one embodiment of this disclosure, the second characteristic parameter is determined according to the following formula. : in, and These represent the maximum effect extreme value and the baseline value of extremely low concentration predicted by the nonlinear dose-response model, respectively.

[0009] According to the screening method for ecotoxicological biomarkers of polycyclic aromatic hydrocarbons according to at least one embodiment of this disclosure, a comprehensive evaluation index for each of the toxicity endpoints is determined based on the first characteristic parameter and the second characteristic parameter, including: The product of the first characteristic parameter and the second characteristic parameter corresponding to each of the aforementioned toxicity endpoints is used as the comprehensive evaluation index of that toxicity endpoint.

[0010] According to at least one embodiment of the method for screening polycyclic aromatic hydrocarbon ecotoxicity biomarkers of this disclosure, when the polycyclic aromatic hydrocarbon ecotoxicity biomarker is cell membrane potential, the method further includes: Slow-response membrane potential fluorescent probes were used to label the protozoan models in each concentration treatment group to determine the membrane potential response intensity of each concentration treatment group. Based on the membrane potential response intensity of each concentration treatment group and its corresponding pollutant concentration, a dose-response curve between the membrane potential response intensity and the pollutant concentration is constructed. Toxicity characteristic parameters are extracted from the dose-response curve, and the ecotoxicity of polycyclic aromatic hydrocarbons is determined based on the toxicity characteristic parameters.

[0011] According to the screening method for ecotoxicological biomarkers of polycyclic aromatic hydrocarbons according to at least one embodiment of this disclosure, toxicity characteristic parameters are extracted from the dose-response curve, including: The dose-response curve is fitted to obtain the corresponding fitted curve; Based on the fitted curve, corresponding toxicity characteristic parameters are extracted. The toxicity characteristic parameters include at least one of half-effect concentration and maximum response slope. The half-effect concentration is the pollutant concentration that causes a 50% maximum membrane potential response intensity. The maximum response slope is the absolute value of the derivative of the fitted curve at the central inflection point, which is used to characterize the severity of the sublethal toxicity of cells as the pollutant concentration increases.

[0012] The method for screening ecotoxicological biomarkers of polycyclic aromatic hydrocarbons according to at least one embodiment of this disclosure, which determines the ecotoxicity of polycyclic aromatic hydrocarbons based on the toxicity characteristic parameters, includes at least one of the following steps: Based on the maximum response slope corresponding to each polycyclic aromatic hydrocarbon or its alkyl derivative, the toxicity intensity of each polycyclic aromatic hydrocarbon or its alkyl derivative is ranked. The maximum response slope is positively correlated with the toxicity intensity of the polycyclic aromatic hydrocarbon or its alkyl derivative. Alternatively, based on the half-effect concentration of each polycyclic aromatic hydrocarbon or its alkyl derivative, the toxicity intensity of each polycyclic aromatic hydrocarbon or its alkyl derivative can be ranked, where the half-effect concentration is negatively correlated with the toxicity intensity of the polycyclic aromatic hydrocarbon or its alkyl derivative. or, The maximum response slope and half-effect concentration of each polycyclic aromatic hydrocarbon or its alkyl derivative were used as composite indicators to rank the toxicity intensity of each polycyclic aromatic hydrocarbon or its alkyl derivative.

[0013] The screening method for ecotoxicological biomarkers of polycyclic aromatic hydrocarbons according to at least one embodiment of this disclosure, after ranking the toxicity of various polycyclic aromatic hydrocarbons or their alkyl derivatives, further includes: Based on the ranking of toxicity intensity and the target classification ratio, each polycyclic aromatic hydrocarbon or its alkyl derivative is classified into risk levels, including high risk, medium risk or low risk. Alternatively, based on the response slope threshold and the half-effect concentration threshold, polycyclic aromatic hydrocarbons or their alkyl derivatives with a maximum response slope greater than or equal to the response slope threshold and a half-effect concentration less than or equal to the half-effect concentration threshold are classified as high-risk; polycyclic aromatic hydrocarbons or their alkyl derivatives that only satisfy the condition of a maximum response slope greater than or equal to the response slope threshold or a half-effect concentration less than or equal to the half-effect concentration threshold are classified as medium-risk; and polycyclic aromatic hydrocarbons or their alkyl derivatives with a maximum response slope less than the response slope threshold and a half-effect concentration greater than the half-effect concentration threshold are classified as low-risk.

[0014] According to at least one embodiment of the method for screening ecotoxicological biomarkers of polycyclic aromatic hydrocarbons (PAHs) of the present disclosure, before determining the ecotoxicity of PAHs based on the toxicity characteristic parameters, the method further includes: Based on the fluorescence response signals of the control group and each concentration treatment group, a significance analysis was performed to determine the significance level of each concentration treatment group relative to the control group, and the goodness of fit value of the fitting curve corresponding to each polycyclic aromatic hydrocarbon or its alkyl derivative was determined. Based on the significance level and the goodness-of-fit value, the target dose-effect curve is identified from the dose-effect curves corresponding to each polycyclic aromatic hydrocarbon or its alkyl derivative, and the target dose-effect curve and its corresponding toxicity characteristic parameters are used as reliable data for determining the ecotoxicity of polycyclic aromatic hydrocarbons.

[0015] The beneficial effects of this disclosure lie in the simultaneous measurement of response data from protozoan models at multiple toxic endpoints (including at least two of cell membrane potential, reactive oxygen species level, adenosine triphosphate content, superoxide dismutase activity, and cell viability). Normalization is performed based on the number of surviving cells in each concentration treatment group to extract the effective response intensity per unit of live cell, effectively eliminating the interference of cell death on the response signal and ensuring the authenticity and comparability of the data. Based on this, parallel fitting calculations of the effective response intensity are performed using both nonlinear and linear dose-response models. The ratio of the sum of squared residuals from the two models is used to calculate a first characteristic parameter (i.e., the S-type fidelity factor) characterizing the degree of conformity of the response data to the classical dose-response law. Simultaneously, a second characteristic parameter (i.e., the dynamic signal span) characterizing the signal response amplitude is calculated based on the predicted extreme values ​​of the nonlinear dose-response model. This allows for a quantitative evaluation of the response quality of each toxic endpoint from two dimensions: "normality of the dose-response curve morphology" and "signal dynamic range." Then, by fusing the first and second characteristic parameters, a comprehensive evaluation index is obtained. Based on this comprehensive evaluation index, the optimal biomarker, namely the polycyclic aromatic hydrocarbon ecotoxicity biomarker, is objectively screened from multiple toxicity endpoints. This overcomes the shortcomings of relying solely on experience to subjectively select toxicity biomarkers and lacking unified quantitative standards. It provides biomarkers with higher sensitivity, more regular dose-response relationships, and more significant signal responses for the ecotoxicity assessment of polycyclic aromatic hydrocarbons. Attached Figure Description

[0016] The accompanying drawings illustrate exemplary embodiments of the present disclosure and, together with the description thereof, serve to explain the principles of the present disclosure. These drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification.

[0017] Figure 1 This is a flowchart illustrating a method for screening ecotoxicological biomarkers of polycyclic aromatic hydrocarbons according to one embodiment of the present disclosure.

[0018] Figure 2 This is a schematic flowchart illustrating the process of determining the ecotoxicity of polycyclic aromatic hydrocarbons (PAHs) based on cell membrane potential in a screening method for ecotoxicological biomarkers of PAHs according to one embodiment of this disclosure.

[0019] Figure 3 This is a flowchart illustrating step S230 of a method for screening ecotoxicological biomarkers of polycyclic aromatic hydrocarbons according to an embodiment of the present disclosure.

[0020] Figure 4 This is a schematic flowchart illustrating the process of identifying the target dose-response curve in a screening method for ecotoxicological biomarkers of polycyclic aromatic hydrocarbons according to one embodiment of the present disclosure.

[0021] Figure 5-24 This is a schematic diagram of the dose-response curves of five detection indicators under exposure to different polycyclic aromatic hydrocarbons or their alkyl derivatives.

[0022] Figure 25 The median lethality (LD50) after 48 hours of exposure to different polycyclic aromatic hydrocarbons or their alkyl derivatives is... 50 Concentration comparison diagram.

[0023] Figure 26 This is a schematic diagram showing the comparison results of the comprehensive resolution index (i.e., comprehensive evaluation index) of cell membrane potential (DiBAC4), superoxide dismutase (SOD) activity and adenosine triphosphate (ATP) content, taking Nap as an example, according to one embodiment of this disclosure.

[0024] Figure 27-30 This is a comparative diagram of the comprehensive evaluation index of multiple pollutants and multiple toxicity indicators according to one embodiment of the present disclosure.

[0025] Figure 31 This is a schematic diagram of a two-dimensional mapping analysis of the robustness of naphthalene exposure to different toxic endpoints based on a dual penalty mechanism of a dose-effect fidelity resolution index (SFR) evaluation model according to one embodiment of the present disclosure. Detailed Implementation

[0026] The present disclosure will now be described in further detail with reference to the accompanying drawings and examples. It should be understood that the specific examples described herein are for illustrative purposes only and are not intended to limit the scope of the disclosure. Furthermore, it should be noted that, for ease of description, only the parts relevant to the present disclosure are shown in the accompanying drawings.

[0027] It should be noted that, where there is no conflict, the embodiments and features described in this disclosure can be combined with each other. The technical solutions of this disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0028] Existing technologies lack methods for objectively and quantitatively screening optimal ecotoxicological biomarkers from multiple toxicity endpoints, making it difficult to guarantee the comprehensive superiority of the selected toxicological biomarkers in terms of sensitivity, dose-response relationship, and signal response amplitude.

[0029] To address this, the present disclosure proposes the following technical solution, in which response data of a protozoan model at multiple toxic endpoints (including at least two of cell membrane potential, reactive oxygen species level, adenosine triphosphate content, superoxide dismutase activity, and cell viability) are simultaneously measured. Normalization is performed based on the number of surviving cells in each concentration treatment group to extract the effective response intensity per unit of live cell, effectively eliminating the interference of cell death on the response signal and ensuring the authenticity and comparability of the data. Based on this, parallel fitting calculations of the effective response intensity are performed using both nonlinear and linear dose-response models. A first characteristic parameter characterizing the degree of conformity of the response data to the classical dose-response law is calculated based on the ratio of the sum of squared residuals of the two models. Simultaneously, a second characteristic parameter characterizing the signal response amplitude is calculated based on the predicted extreme values ​​of the nonlinear dose-response model. Thus, the response quality of each toxic endpoint is quantitatively evaluated from two dimensions: "normality of the dose-response curve morphology" and "signal dynamic range." Then, by fusing the first and second characteristic parameters, a comprehensive evaluation index is obtained. Based on this comprehensive evaluation index, the optimal biomarker, namely the polycyclic aromatic hydrocarbon ecotoxicity biomarker, is objectively screened from multiple toxicity endpoints. This overcomes the shortcomings of relying solely on experience to subjectively select toxicity biomarkers and lacking unified quantitative standards. It provides biomarkers with higher sensitivity, more regular dose-response relationships, and more significant signal responses for the ecotoxicity assessment of polycyclic aromatic hydrocarbons.

[0030] Figure 1 This is a flowchart illustrating a method for screening ecotoxicological biomarkers of polycyclic aromatic hydrocarbons according to one embodiment of the present disclosure.

[0031] like Figure 1 As shown, the screening method for ecotoxicological biomarkers of polycyclic aromatic hydrocarbons preferably includes steps S110 to S150, which are described in detail below.

[0032] In step S110, protozoan models are exposed to blank control conditions and different concentrations of polycyclic aromatic hydrocarbons or their alkyl derivatives to obtain control groups and multiple concentration treatment groups. Response data of the control group and different concentration treatment groups at multiple toxic endpoints are simultaneously measured. These multiple toxic endpoints include at least two of the following: cell membrane potential, reactive oxygen species level, adenosine triphosphate content, superoxide dismutase activity, and cell activity.

[0033] In this context, the protozoan model refers to a single-celled eukaryotic organism used as the experimental subject. In the technical solution disclosed herein, ciliated protozoa (such as *Tetrahymena* or *Tetrahymena*) are preferably used as the experimental subject. As single-celled eukaryotic organisms, these organisms possess both cellular-level physiological sensitivity and are an important component of aquatic ecosystems; their toxicological responses can directly reflect the potential impact of pollutants on microbial ecology.

[0034] Polycyclic aromatic hydrocarbons or their alkyl derivatives are aromatic compounds formed by the fusion of two or more benzene rings or their alkyl-substituted products. Preferably, the polycyclic aromatic hydrocarbon or its alkyl derivatives include one or more of naphthalene (Nap), phenanthrene (Phe), 2-methylnaphthalene (2-MN), and 1-methylphenanthrene (1-MP).

[0035] A toxic endpoint is a quantitatively measurable biological response at the cellular, molecular, or biochemical level to an organism after exposure to a contaminant.

[0036] In some embodiments of this disclosure, protozoan models are exposed to blank control conditions and different concentrations of polycyclic aromatic hydrocarbons (PAHs) or their alkyl derivatives to obtain a control group and multiple concentration treatment groups. Preferably, this includes: preparing PAH or their alkyl derivative solutions of varying concentrations; inoculating healthy protozoan models into cell culture plates, wherein the number of protozoan models inoculated in each well of the cell culture plate is 1×10^3~10^4; adding different concentrations of PAH or their alkyl derivative solutions to the wells of the cell culture plates inoculated with protozoan models to obtain a control group with blank control conditions and multiple concentration treatment groups with different pollutant concentrations; and placing the cell culture plates after adding the PAH or their alkyl derivative solutions in a constant temperature incubator for 6-96 hours.

[0037] In this embodiment, solutions of polycyclic aromatic hydrocarbons (PAHs) or their alkyl derivatives at gradient concentrations are first prepared. Specifically, typical PAHs or their alkyl derivatives are selected, dissolved in DMSO (dimethyl sulfoxide, cell culture grade), and prepared into high-concentration stock solutions. Then, a series of PAH or their alkyl derivative solutions of different concentrations are obtained using a gradient dilution method. The concentration range should cover from the no-effect concentration to the complete-effect concentration to ensure that a complete dose-response curve can be constructed subsequently.

[0038] Next, after the contaminant solution is prepared, the protozoan model is inoculated. Healthy protozoan models (preferably *Pterocarya spp.*) cultured to the logarithmic growth phase are inoculated into multi-well cell culture plates. Preferably, 96-well or 24-well cell culture plates are used. The number of protozoan models per well is controlled during inoculation; preferably, each well should contain 1×10^3 to 1×10^4 protozoan models to ensure a relatively consistent cell count across wells and reduce experimental errors caused by individual differences. After inoculation, the cells are observed under a microscope to confirm they are in good condition and evenly distributed.

[0039] Then, the wells of the inoculated cell culture plates were divided into a control group and multiple treatment groups with different contaminant concentrations. Pre-prepared culture media containing different concentrations of polycyclic aromatic hydrocarbons (PAHs) or their alkyl derivatives were added to each treatment group, while an equal volume of blank culture media containing the same concentration of solvent but without contaminants was added to the control group. Preferably, at least three parallel replicate wells were set in each group to ensure the statistical reliability of the data.

[0040] The cell culture plates, after being filled with culture medium, are placed in a constant temperature incubator for exposure culture, with the exposure time selected within the range of 6 to 96 hours. Preferably, during the exposure period, the cell status in each well of the cell culture plate is observed and recorded regularly, including morphological changes and motility, in order to monitor for possible acute toxic effects in a timely manner.

[0041] Thus, by setting up a control group and multiple concentration treatment groups, the necessary data foundation can be provided for the subsequent construction of dose-response curves. Furthermore, the pollutant concentration gradient settings for multiple concentration treatment groups ensure that a complete response range covering no effect to full effect can be obtained, providing sufficient data points to support subsequent treatments.

[0042] Next, after exposure, samples from each concentration treatment group and control group were collected (or parallel wells were pre-set for the detection of different toxic endpoints), and response data for at least two of the above-mentioned toxic endpoints were simultaneously measured. Preferably, for cell membrane potential, the old culture medium was removed, a slow-response membrane potential fluorescent probe working solution was added, and after incubation in the dark for 30-60 minutes, the cells were centrifuged and washed to remove free probes. The fluorescence intensity was read as response data using a fluorescence detection device (such as a fluorescence microplate reader). For reactive oxygen species (ROS) levels, ROS-specific fluorescent probes (such as DCFH-DA) were used to label cells, which were then incubated in the dark and washed. The fluorescence intensity was read to reflect the intracellular oxidative stress level. For adenosine triphosphate (ATP) content, after cell lysis, the fluorescence value was measured using an ATP assay kit (such as a chemiluminescence method based on luciferase) to reflect the cellular energy metabolism status. For superoxide dismutase (SOD) activity, cellular proteins were extracted, and the absorbance at a specific wavelength was measured using an SOD activity assay kit to reflect the response of the antioxidant enzyme system. To assess cell viability, a cell metabolic activity assay reagent (such as CCK-8 reagent) is added, and the absorbance is measured after incubation to reflect the overall cell survival and metabolic status.

[0043] Please continue to refer to this. Figure 1 In step S120, the response data is normalized based on the number of surviving cells in the control group and each concentration treatment group to determine the effective response intensity per unit of living cell.

[0044] In this embodiment, after exposure and culture for a preset time, the actual number of surviving cells in the control group and each concentration treatment group is counted by microscopic sampling or cell counting instrument.

[0045] Next, the obtained response data were normalized based on the number of surviving cells in the control group and the concentration treatment group to determine the effective response intensity per unit of living cell.

[0046] Preferably, the normalization process includes two normalization steps: anti-lethal interference conversion and baseline normalization. Specifically, in the first normalization step, the response data (such as the average fluorescence intensity value obtained for cell membrane potential) of the control group and each concentration treatment group for different toxic endpoints are divided by the number of surviving cells in the corresponding concentration treatment group to obtain the response intensity per unit of living cell for each concentration treatment group for the toxic endpoint, thereby eliminating the signal attenuation caused by the massive cell death due to high concentrations of pollutants. In the second normalization step, based on the response intensity calculated in the first normalization step, the response intensity per unit of living cell of the control group is used as a baseline value (e.g., set to 1.0 or 100%). The ratio of the response intensity per unit of cell of each concentration treatment group to this baseline value is calculated to obtain the fold change or percentage response value of each concentration treatment group relative to the control group, which is taken as the final effective response intensity per unit of living cell for the toxic endpoint.

[0047] In step S130, the effective response intensity is calculated by parallel fitting using a nonlinear dose-response model and a linear dose-response model, respectively, and the sum of squared residuals of the nonlinear dose-response model and the linear dose-response model are determined based on the results of the parallel fitting calculation.

[0048] The nonlinear dose-response model is a mathematical model that can describe the characteristic S-shaped or saturated change of biological response with increasing pollutant concentration. Preferably, the nonlinear dose-response model adopts a four-parameter logistic model (4PL), which can accurately characterize the dose-response relationship of "no response at low concentration - rapid response at medium concentration - saturation at high concentration".

[0049] A linear dose-response model is a mathematical model that assumes a simple linear relationship between the biological response and the pollutant concentration. It is usually a univariate linear regression model and serves as a control model for nonlinear dose-response models, used to quantify the degree to which response data deviates from the classic S-shaped pattern.

[0050] The Residual Sum of Squares (RSS) is the sum of the squares of the differences between the observed effective response intensity data points and the response intensity values ​​predicted by the model. This value is used to quantify the deviation between the model's predictions and the actual observed values. The smaller the RSS value, the smaller the model's fitting error to the data, and the better the fitting effect.

[0051] Parallel fitting calculation is the process of simultaneously substituting the same set of experimental data (i.e., the horizontal axis is the logarithm of pollutant concentration, and the vertical axis is the effective response intensity per unit of living cell after double normalization) into two models to solve for parameters and fit curves.

[0052] In this embodiment, after determining the effective response intensity per unit of living cell for each concentration treatment group, the calculated effective response intensity per unit of living cell for each concentration treatment group is used as the ordinate (Y), and the logarithm of the corresponding polycyclic aromatic hydrocarbon pollutant concentration (e.g., log...) is used as the coordinate. 10 Using concentration as the x-axis (X), a two-dimensional discrete dataset is constructed for subsequent calculations.

[0053] Then, the above two-dimensional discrete dataset is substituted into the pre-defined nonlinear dose-response model. Through iterative calculation, the optimal parameters of the nonlinear dose-response model (including baseline response value, maximum response extreme value, half-effect concentration, and slope factor, etc.) are solved, and the residual sum of squares under the nonlinear dose-response model is calculated based on the above optimal parameters.

[0054] Simultaneously, the same set of two-dimensional discrete datasets is substituted into the linear dose-response model to solve for the optimal slope and intercept of the linear regression, thereby constructing the corresponding linear equation. Then, the sum of squared residuals under the linear dose-response model is calculated based on this linear equation.

[0055] Please continue to refer to this. Figure 1 In step S140, a first characteristic parameter is calculated to characterize the degree of conformity of the response data to the dose-response law based on the ratio of the sum of squared residuals of the nonlinear dose-response model and the linear dose-response model, and a second characteristic parameter is calculated to characterize the amplitude of the signal response based on the predicted extreme value of the nonlinear dose-response model.

[0056] Among them, the first characteristic parameter (i.e., S-type fidelity factor, SFF) is a numerical index used to quantify the degree to which the response data of a certain toxicity endpoint conforms to the dose-response law. The larger the value, the more the data conforms to the typical toxicological response law.

[0057] The second characteristic parameter (i.e., the dynamic signal span) is determined by the difference between the maximum effect extreme value predicted by the nonlinear dose-response model and the baseline value of the extremely low concentration. It is used to characterize the maximum signal response amplitude or dynamic range that the toxic endpoint can produce under pollutant exposure. The maximum effect extreme value is the limit value that the response intensity approaches as the pollutant concentration approaches infinity, as predicted by the nonlinear dose-response model; that is, the top plateau of the dose-response curve. The extremely low concentration baseline value is the baseline level that the response intensity approaches as the pollutant concentration approaches zero, as predicted by the nonlinear dose-response prediction model; that is, the bottom plateau of the dose-response curve.

[0058] In some embodiments of this disclosure, the first characteristic parameter is calculated according to the following formula. SFF : in, and These are the sum of squared residuals for the nonlinear dose-response model and the linear dose-response model, respectively.

[0059] Based on the above formula, obtain the residual sum of squares of the nonlinear dose-response model and the residual sum of squares of the linear dose-response model, calculate the ratio between the two residual sums of squares, subtract the ratio from the value 1 to obtain the difference, and then compare the difference with the value 0 and take the larger value as the first characteristic parameter.

[0060] The first characteristic parameter obtained in this way SFF If the response data perfectly conforms to the classic S-shaped dose-response law, the nonlinear dose-response model will fit the data extremely well, and the first characteristic parameter will be... SFF The value will be very large. Conversely, if the data exhibits irregular fluctuations, distorted patterns, or a simple linear relationship, the fitting advantage of the nonlinear model will be lost, and the RSS will be affected. 非线性量效模型 Approaching or even exceeding RSS 线性量效模型 This leads to the first characteristic parameter SFF The value is close to or less than 1. Therefore, when the response data is a non-S-shaped response or morphological distortion, this parameter will automatically trigger a decay penalty (i.e., the value becomes significantly smaller).

[0061] In some embodiments of this disclosure, the second characteristic parameter is determined according to the following formula. Span : in, and These represent the maximum effect extreme value and the baseline value of extremely low concentration predicted by the nonlinear dose-response model, respectively.

[0062] Based on the above formula, the maximum effect extreme value and the extremely low concentration baseline value are obtained from the fitting results of the nonlinear dose-response model. The absolute value of the difference between the maximum effect extreme value and the extremely low concentration baseline value is calculated, and then the ratio of this absolute value to the absolute value of the extremely low concentration baseline value is calculated to obtain the second characteristic parameter. Span .

[0063] The second characteristic parameter obtained in this way Span This reflects the factor by which the maximum effect is increased relative to the baseline value at extremely low concentrations, and is dimensionless. It is noteworthy that the second characteristic parameter... Span The calculation relies on the predicted extreme value of the nonlinear dose-response model, rather than the measured maximum value. This is because the measured maximum value may be affected by experimental limitations (such as insufficient pollutant concentration) or random errors, while the predicted extreme value of the nonlinear dose-response model is more robust and can reflect the theoretical maximum response capability of the indicator.

[0064] Please continue to refer to this. Figure 1 In step S150, a comprehensive evaluation index for each of the toxicity endpoints is determined based on the first characteristic parameter and the second characteristic parameter, and polycyclic aromatic hydrocarbon ecotoxicity biomarkers are determined from multiple toxicity endpoints based on the comprehensive evaluation index for each of the toxicity endpoints.

[0065] In this embodiment, for each toxic endpoint (such as cell membrane potential, reactive oxygen species level, adenosine triphosphate content, superoxide dismutase activity, or cell viability), the first and second characteristic parameters obtained through the aforementioned steps are fused together to calculate a comprehensive evaluation index (i.e., dose-response fidelity index) for that toxic endpoint. This comprehensive evaluation index can be used to comprehensively assess the merits of a toxic endpoint as a biomarker.

[0066] In some embodiments of this disclosure, the product of the first characteristic parameter and the second characteristic parameter corresponding to each toxic endpoint is used as the comprehensive evaluation index of that toxic endpoint. This allows for the simultaneous amplification of the comprehensive evaluation index of toxic endpoints that exhibit both high dose-response consistency and large signal response amplitude. Conversely, if a toxic endpoint performs poorly in either dimension (e.g., curve distortion leading to extremely low first characteristic parameter, or weak response leading to extremely low second characteristic parameter), its comprehensive evaluation index will be significantly reduced due to a double attenuation penalty.

[0067] After determining the comprehensive evaluation index corresponding to each toxicity endpoint, at least one of the multiple toxicity endpoints is selected as the ecotoxicological biomarker for polycyclic aromatic hydrocarbons based on the comprehensive evaluation index corresponding to each toxicity endpoint.

[0068] Preferably, based on the comprehensive evaluation index corresponding to each toxicity endpoint, the one with the highest comprehensive evaluation index is selected as the ecotoxicological biomarker for polycyclic aromatic hydrocarbons (PAHs). In other embodiments, the two or three toxicity endpoints with the highest comprehensive evaluation index may also be selected as the ecotoxicological biomarkers for PAHs.

[0069] In some embodiments of this disclosure, when the ecotoxicological biomarker of polycyclic aromatic hydrocarbons (PAHs) is cell membrane potential, the screening method for PAH ecotoxicological biomarkers preferably further includes steps S210 to S230. Please refer to [link / reference needed]. Figure 2 .

[0070] In step S210, a slow-response membrane potential fluorescent probe is used to label the protozoan models in each concentration treatment group to determine the membrane potential response intensity of each concentration treatment group.

[0071] In step S220, a dose-response curve between membrane potential response intensity and pollutant concentration is constructed based on the membrane potential response intensity of each concentration treatment group and its corresponding pollutant concentration.

[0072] In step S230, toxicity characteristic parameters are extracted from the dose-effect curve, and the ecotoxicity of polycyclic aromatic hydrocarbons is determined based on the toxicity characteristic parameters.

[0073] Among them, the slow-response membrane potential fluorescent probe is a voltage-sensitive dye that can generate a corresponding fluorescent signal according to the change of cell membrane potential. Preferably, the slow-response membrane potential fluorescent probe is DiBAC4(3) (bis-(1,3-dibarbituric acid)-trimethine oxonol), with an excitation wavelength of 490 nm and an emission wavelength of 525 nm.

[0074] Labeling protozoan models involves combining slow-response membrane potential fluorescent probes with the protozoan models, enabling them to respond to corresponding biological states through fluorescent signals.

[0075] In this embodiment, after exposure, the culture medium in each well of the cell culture plate is removed, and then working solution containing slow-response membrane potential fluorescent probes is added for incubation in the dark. This allows the probe molecules that have entered the protozoan model cells to bind to intracellular proteins, and removes unbound free probe molecules, ensuring that the fluorescent response signal detected subsequently comes only from probe molecules that have bound to intracellular proteins.

[0076] Specifically, the working solution for the slow-response membrane potential fluorescent probe is prepared according to the probe's instructions. Preferably, the final concentration of the prepared slow-response membrane potential fluorescent probe working solution is 20 to 40 μmol / L. After the preset exposure time, the cell culture plate is removed from the incubator, and the old culture medium in each well is removed. Preferably, after centrifuging the solution in each well at low speed, the supernatant in each well is aspirated to remove the old culture medium from the wells of the cell culture plate. During the aspiration process, care should be taken to avoid touching the ciliate cells at the bottom of the wells to prevent cell loss.

[0077] After removing the old culture medium, add the prepared slow-response membrane potential fluorescent probe working solution to each well of the cell culture plate, with the volume of the probe in each well being the same as the volume of the old culture medium. After adding the solution, gently agitate the multi-well plate or gently pipette to ensure that the probe working solution is thoroughly mixed with the ciliate cells. Incubate the cell culture plate in a constant temperature environment (e.g., 25°C) in the dark for 30 to 60 minutes. Do not wash the protozoan model cells during the incubation in the dark to maintain continuous contact between the probe and the cells and ensure that a sufficient number of probe molecules enter the cells.

[0078] After incubation in the dark, transfer the mixture containing the protozoan model and fluorescent probe from each well to centrifuge tubes (if using a centrifugable multi-well plate, centrifugation can be performed directly within the plate) and centrifuge at low speed. This low-speed centrifugation causes the protozoan cells to precipitate at the bottom of the tube. Carefully aspirate the supernatant after centrifugation. The supernatant mainly contains free probe molecules that have not entered the cells; removing these significantly reduces background fluorescence interference in subsequent detection.

[0079] Next, add a buffer solution of appropriate salinity (such as phosphate buffer) to the centrifuge tube, and gently pipette or shake to resuspend the precipitated ciliate cells evenly. At this point, the labeling step is complete, and the resulting cell suspension can be used for subsequent fluorescence response signal reading. If a multi-well plate that can be directly centrifuged is used, centrifugation, supernatant removal, and resuspension can be performed directly in the wells without transferring to centrifuge tubes, making the operation simpler and more suitable for high-throughput processing.

[0080] Thus, by using slow-response membrane potential fluorescent probes to label exposed protozoan models, the biological signal of cell membrane potential was transformed into a quantitatively detectable fluorescent response signal. Furthermore, DiBAC4(3), used as an anionic probe, exhibits transmembrane distribution strictly dependent on the membrane potential state; that is, the higher the degree of cell membrane depolarization, the more probe molecules enter the cell, and the stronger the fluorescent response signal. This characteristic allows the intensity of the fluorescent response signal to accurately reflect the amplitude of membrane potential changes, providing a reliable quantitative basis for the subsequent construction of dose-response curves.

[0081] Next, after labeling the protozoan models in each well of the cell culture plate, a fluorescence detection device was used to detect the fluorescence intensity of the control group and each concentration treatment group, thereby obtaining the fluorescence response signals of the control group and each concentration treatment group.

[0082] Specifically, first, the fluorescence detection device is started, preheated, and self-tested to ensure the light source is stable and the device is working properly. Next, the excitation and emission wavelengths of the fluorescence detection device are set. Taking DiBAC4(3) as an example, the excitation wavelength is set to 490nm and the emission wavelength is set to 525nm. The excitation wavelength of 490nm matches the maximum absorption peak of the fluorescent probe, which can excite the probe molecules to produce fluorescence with maximum efficiency. The emission wavelength of 525nm corresponds to the maximum emission peak of the fluorescent probe. Detection at this wavelength can obtain the strongest fluorescence signal and the best signal-to-noise ratio. Then, according to the type of cell culture plate used (such as a 96-well plate or a 24-well plate), the reading area is set to ensure that all test wells (including the control group and each concentration treatment group) are covered.

[0083] After the parameters are set, the fluorescence detection device will automatically irradiate each test well with excitation light and collect fluorescence signals in sequence. Preferably, the reading time for each well is set to 100 to 500 milliseconds to ensure that a stable and representative fluorescence intensity value (i.e., fluorescence response signal) is obtained.

[0084] In addition, for each experimental setup (including the control group and each concentration treatment group), each treatment group has at least three parallel replicate wells, and the fluorescence detection device will read the fluorescence intensity value of each parallel well. After reading, the fluorescence detection device automatically generates a raw data file containing the fluorescence intensity value of each well.

[0085] Export data from the fluorescence detection device to statistical analysis software (such as Microsoft Excel). For each treatment group, summarize the fluorescence intensity values ​​of its three (or more) parallel replicate wells and calculate the arithmetic mean, i.e., the average fluorescence intensity value. The calculation formula is: Average fluorescence intensity value = (Fluorescence intensity value of well 1 + Fluorescence intensity value of well 2 + Fluorescence intensity value of well 3) / Number of parallel wells.

[0086] After obtaining the average fluorescence intensity value of each treatment group, the average fluorescence intensity value of each concentration treatment group was compared one by one with the average fluorescence intensity value of the control group as the benchmark, and the comparison results were recorded. The comparison results can reflect the changes in fluorescence response of each concentration treatment group relative to the control group.

[0087] Therefore, setting at least three parallel replicates for each treatment group and calculating the average fluorescence intensity value can effectively reduce the errors caused by random factors such as uneven cell seeding, probe labeling differences, and well position effects. The average fluorescence intensity value reflects the true biological response level of the treatment group.

[0088] After collecting fluorescence response signals from the control group and each concentration treatment group, the fluorescence intensity values ​​of each concentration treatment group were normalized using the fluorescence response signal of the control group as a benchmark to obtain the membrane potential response intensity of each concentration treatment group relative to the control group. Then, a scatter plot was drawn with the logarithm of pollutant concentration as the abscissa and the membrane potential response intensity as the ordinate, and the plotted together to form a curve, thus constructing a complete dose-response curve.

[0089] In some embodiments of this disclosure, dedicated blank control wells are provided. These blank control wells undergo the same treatment process as the experimental wells (i.e., the control group and each concentration treatment group), including adding an equal volume of slow-response membrane potential fluorescent probe working solution, performing the same light-protected incubation and centrifugation washing operations, and adding an equal volume of phosphate buffer for resuspension. The only difference is that the blank control wells do not contain protozoan models.

[0090] Therefore, the fluorescence intensity of the blank control wells represents the sum of background signals in the entire detection system, including: the autofluorescence of the fluorescent probe in a cell-free state, the fluorescence generated by the non-specific binding of the fluorescent probe to non-cellular surfaces (such as the bottom of the well plate), the autofluorescence of the culture medium and buffer solution, the autofluorescence generated by the well plate material under excitation light, and the dark current and electronic noise of the fluorescence detection device.

[0091] The baseline correction formula is: Corrected fluorescence value = Original average fluorescence intensity value - Average fluorescence intensity value of blank control wells. This calculation is performed separately for the control group and each concentration treatment group. For example, if the original average fluorescence intensity value of the control group is 1500 units and the average fluorescence intensity value of the blank control wells is 200 units, then the corrected average fluorescence intensity value of the control group is 1300 units (i.e., 1500 units minus 200 units); if the original average fluorescence intensity value of a certain concentration treatment group is 2000 units, then its corrected average fluorescence intensity value is 1800 units.

[0092] By baseline correction, all average fluorescence intensity values ​​have had their common background signals removed, thus truly reflecting the fluorescence signals contributed by the protozoan model itself, namely the probe fluorescence corresponding to the cell membrane potential response.

[0093] After baseline correction, the corrected mean fluorescence intensity values ​​for the control group and each concentration treatment group were obtained. Normalization was then performed, with the baseline being the baseline-corrected mean fluorescence intensity value of the control group. This is because the control group represents the normal physiological state of the protozoan model when unexposed to pollutants, and its membrane potential is at baseline, reflecting the fluorescence signal level under the basal physiological state of the cells.

[0094] In some technical solutions of this disclosure, a dose-response curve between the membrane potential response intensity and the pollutant concentration is constructed based on the membrane potential response intensity and the pollutant concentration of each concentration treatment group. Preferably, this includes: plotting the data points corresponding to the control group and each concentration treatment group in a coordinate system with the logarithm of the pollutant concentration as the abscissa and the membrane potential response intensity as the ordinate, thereby obtaining the dose-response curve between the membrane potential response intensity and the pollutant concentration.

[0095] After obtaining the membrane potential response intensity of each concentration treatment group relative to the control group, these discrete data need to be converted into a visualized dose-response curve so as to intuitively observe the law of effect change with concentration and to carry out subsequent quantitative analysis.

[0096] In this embodiment, since the toxic effects of polycyclic aromatic hydrocarbons (PAHs) often occur within a concentration range of several orders of magnitude, directly using the original pollutant concentration as the x-axis would result in data points in the low-concentration region being highly concentrated on one side of the axis, while data points in the high-concentration region would be sparsely distributed, failing to clearly demonstrate the complete dose-response relationship. Therefore, it is necessary to perform a logarithmic transformation on the original pollutant concentration values. For example, the x-axis value = log 10 (Pollutant concentration). Through logarithmic transformation, the data points that were originally exponentially distributed on the number axis are transformed into a uniform linear distribution, making it easier to observe the complete effect change process from low concentration to high concentration.

[0097] Preferably, for the control group, the pollutant concentration is 0, making logarithmic transformation impossible. In actual plotting, the control group can be processed in either of the following ways: First, it can be separately marked as "Control" or "0" on the horizontal axis and placed in a suitable position on the far left of the coordinate system; second, when performing curve fitting, the pollutant concentration of the control group can be considered as a value far below the minimum detection limit, but its logarithmic value is usually not included in the concentration range labeling for curve fitting in the graph, but is presented separately as a reference point for the baseline level.

[0098] After data preparation, a coordinate system is plotted. Data points from the control group and each concentration treatment group are added to the coordinate system one by one: for each concentration treatment group, enter the corresponding x-axis value (logarithmic value of pollutant concentration) and y-axis value (membrane potential response intensity); for the control group, enter its x-axis label (e.g., separately labeled "Control") and y-axis value of 1.0. Then, connect the data points in ascending order of their x-axis values ​​(e.g., using a straight line or a smooth curve) to form a dose-response curve.

[0099] In this way, by plotting a dose-response curve with the logarithm of pollutant concentration on the x-axis and the membrane potential response intensity on the y-axis, the visualization, integration, and regularity presentation of discrete experimental data are achieved.

[0100] In some embodiments of this disclosure, step S230, extracting toxicity characteristic parameters from the dose-effect curve and determining the ecotoxicity of polycyclic aromatic hydrocarbons based on the toxicity characteristic parameters, preferably includes steps S231 to S232. Please refer to [reference needed]. Figure 3 .

[0101] In step S231, the dose-response curve is fitted to obtain the corresponding fitted curve.

[0102] In step S232, based on the fitted curve, the corresponding toxicity characteristic parameters are determined. The toxicity characteristic parameters include at least one of half-effect concentration and maximum response slope. The half-effect concentration is the pollutant concentration that causes a 50% maximum membrane potential response intensity. The maximum response slope is the absolute value of the derivative of the fitted curve at the central inflection point, which is used to characterize the severity of the sublethal toxicity of cells as the pollutant concentration increases.

[0103] In this embodiment, a four-parameter logistic model is used to fit each dose-response curve (one curve corresponds to one polycyclic aromatic hydrocarbon or its alkyl derivative), transforming discrete experimental data points into a continuous mathematical model, thus achieving a precise description and quantitative analysis of the dose-response relationship. This four-parameter logistic model can fully utilize data points from all concentration treatment groups, obtaining optimal parameter estimates through the least squares method, which is more accurate and reliable than traditional methods based on single-point or linear interpolation.

[0104] After obtaining the fitted curve, corresponding toxicity characteristic parameters can be extracted based on it. Preferably, the half-effect concentration or the maximum response slope can be extracted from the fitted curve. The half-effect concentration is the pollutant concentration that can elicit a 50% maximum membrane potential response. The smaller the half-effect concentration, the lower the concentration at which the pollutant can cause a significant effect, i.e., the higher the toxic potency and the stronger the toxicity. The maximum response slope is the absolute value of the derivative of the fitted curve at the central inflection point, i.e., the tangent slope, which reflects the rate at which the biological effect changes with the pollutant concentration. The larger the maximum response slope, the more dramatic the membrane potential change is within a small range of pollutant concentration changes; the steeper the curve, the stronger the pollutant's toxic effect. Conversely, the smaller the maximum response slope, the more slowly the effect increases with the pollutant concentration; the flatter the curve, the milder the pollutant's toxic effect.

[0105] Therefore, by extracting two toxicity characteristic parameters, half-effect concentration and maximum response slope, a more comprehensive and accurate description of toxicity characteristics is achieved. Half-effect concentration provides a benchmark for toxic potency (e.g., how low a pollutant concentration can produce an effect), while maximum response slope supplements the information on differences in toxicity action modes (e.g., how drastic the effect changes with pollutant concentration).

[0106] In some embodiments of this disclosure, the ecotoxicity of polycyclic aromatic hydrocarbons (PAHs) is determined based on the toxicity characteristic parameters. Preferably, this includes: ranking the toxicity intensity of each PAH or its alkyl derivative according to the maximum response slope corresponding to each PAH or its alkyl derivative, wherein the maximum response slope is positively correlated with the toxicity intensity of the PAH or its alkyl derivative.

[0107] In this embodiment, the maximum response slope reflects the rate at which the effect changes with the pollutant concentration. A larger maximum response slope indicates that a drastic change in membrane potential can be caused within a narrow range of pollutant concentration variations; a steeper curve suggests that the compound has a strong perturbation effect on the cell membrane potential. Therefore, the maximum response slope is positively correlated with the toxicity of the pollutant.

[0108] Therefore, based on the maximum response slope corresponding to each polycyclic aromatic hydrocarbon or its alkyl derivative, the toxicity intensity of each polycyclic aromatic hydrocarbon or its alkyl derivative can be ranked. The larger the maximum response slope, the higher the ranking, indicating greater toxicity intensity; conversely, the lower the maximum response slope, the lower the ranking, indicating lower toxicity intensity.

[0109] In some other embodiments of this disclosure, the ecotoxicity of polycyclic aromatic hydrocarbons (PAHs) is determined based on the toxicity characteristic parameters, preferably by ranking the toxicity intensity of each PAH or its alkyl derivative according to the half-effect concentration, wherein the half-effect concentration is negatively correlated with the toxicity intensity of the PAH or its alkyl derivative.

[0110] In this embodiment, the half-effect concentration refers to the pollutant concentration that elicits 50% of the maximum membrane potential response. The smaller the half-effect concentration value, the lower the concentration at which 50% of the maximum membrane potential response is elicited, indicating a higher toxic potency and stronger toxicity of the compound. Therefore, the half-effect concentration is negatively correlated with toxicity intensity.

[0111] Therefore, the toxicity of polycyclic aromatic hydrocarbons (PAHs) or their alkyl derivatives can be ranked according to their half-effect concentrations. A smaller half-effect concentration indicates that a significant effect can be caused at a lower pollutant concentration, and the toxicity is higher. Conversely, a larger half-effect concentration indicates that a significant effect can only be caused at a higher pollutant concentration, and the toxicity is lower.

[0112] In some further embodiments of this disclosure, the ecotoxicity of polycyclic aromatic hydrocarbons (PAHs) is determined based on the toxicity characteristic parameters, preferably by using the maximum response slope and half-effect concentration corresponding to each PAH or its alkyl derivative as a composite index to rank the toxicity intensity of each PAH or its alkyl derivative.

[0113] In this embodiment, a composite index is constructed based on the maximum response slope and half-effect concentration corresponding to each polycyclic aromatic hydrocarbon or its alkyl derivative. Preferably, the composite index is calculated according to the following formula: Composite Index = Maximum Response Slope / Half-Effect Concentration. Thus, this composite index reflects that the greater the maximum response slope and the smaller the half-effect concentration, the higher the toxicity. Combining the two allows for a more comprehensive toxicity assessment.

[0114] Therefore, the toxicity of each polycyclic aromatic hydrocarbon (PAH) or its alkyl derivative can be ranked according to the composite index corresponding to each PAH or its alkyl derivative. The smaller the composite index, the lower the toxicity; conversely, the larger the composite index, the higher the toxicity.

[0115] It should be noted that in other embodiments, other existing methods can also be used to calculate the composite index, such as summing the two, so as to rank the toxicity intensity of polycyclic aromatic hydrocarbons or their alkyl derivatives. This disclosure does not impose any special limitations on this.

[0116] In some embodiments of this disclosure, after ranking the toxicity intensity of each polycyclic aromatic hydrocarbon or its alkyl derivative, the method further includes: classifying each polycyclic aromatic hydrocarbon or its alkyl derivative into risk levels based on the toxicity intensity ranking results and the target classification ratio, wherein the risk levels include high risk, medium risk, or low risk.

[0117] In this embodiment, after determining the toxicity intensity ranking of each polycyclic aromatic hydrocarbon or its alkyl derivative, and obtaining the toxicity intensity ranking result, the toxicity intensity ranking result is divided into three risk levels (including high risk, medium risk, or low risk) according to a pre-set target division ratio (such as 30%, 40%, and 30%).

[0118] Taking the toxicity intensity ranking results as an example, which are arranged in order of toxicity intensity from high to low, the top 30% of polycyclic aromatic hydrocarbons or their alkyl derivatives can be identified as high risk, the middle 40% as medium risk, and the remaining 30% as low risk.

[0119] Thus, by using the target division ratio to classify the risk level of the toxicity intensity ranking results, continuous toxicity parameters can be transformed into discrete risk levels, providing a scientific basis for environmental management and decision-making applications of polycyclic aromatic hydrocarbon ecotoxicity.

[0120] In other embodiments of this disclosure, after ranking the toxicity of each polycyclic aromatic hydrocarbon or its alkyl derivative, the method further includes: classifying polycyclic aromatic hydrocarbons or their alkyl derivatives as high-risk based on a response slope threshold and a half-effect concentration threshold; classifying polycyclic aromatic hydrocarbons or their alkyl derivatives as medium-risk based on only satisfying either a maximum response slope threshold or a half-effect concentration threshold; and classifying polycyclic aromatic hydrocarbons or their alkyl derivatives as low-risk based on a maximum response slope threshold or a half-effect concentration threshold.

[0121] In this embodiment, by pre-setting a response slope threshold and a half-effect concentration threshold, the compounds are classified into three levels—high risk, medium risk, and low risk—based on the comparison between the maximum response slope and half-effect concentration of each polycyclic aromatic hydrocarbon or its alkyl derivative and these two thresholds, using a dual-condition judgment rule.

[0122] Specifically, if the maximum response slope of a certain polycyclic aromatic hydrocarbon or its alkyl derivative is greater than or equal to the response slope threshold, and at the same time its half-effect concentration is less than or equal to the half-effect concentration threshold, it is classified as a high-risk level.

[0123] If a polycyclic aromatic hydrocarbon or its alkyl derivative satisfies only one of the above two conditions, namely, the maximum response slope is greater than or equal to the response slope threshold but the half-effect concentration is greater than the half-effect concentration threshold, or the half-effect concentration is less than or equal to the half-effect concentration threshold but the maximum response slope is less than the response slope threshold, then it is determined to be of medium risk level.

[0124] If the maximum response slope of a certain polycyclic aromatic hydrocarbon or its alkyl derivative is less than the response slope threshold, and at the same time its half-effect concentration is greater than the half-effect concentration threshold, it is classified as a low-risk level.

[0125] In one embodiment, when determining the response slope threshold and the half-effect concentration threshold, one or more polycyclic aromatic hydrocarbons (PAHs) or their alkyl derivatives with clear toxicological characteristics or recognized risk levels can be selected as references, and their toxicity characteristic parameters can be used as the basis for setting the threshold. For example, the half-effect concentration and maximum response slope of a PAH or its alkyl derivative that has been identified as high-risk can be used as the half-effect concentration threshold and the response slope threshold, respectively. PAHs or their alkyl derivatives with toxicity greater than or equal to the response slope threshold are classified as high-risk (i.e., the maximum response slope is greater than or equal to the response slope threshold, and at the same time, their half-effect concentration is less than or equal to the half-effect concentration threshold); PAHs or their alkyl derivatives with slightly weaker toxicity are classified as medium-risk (i.e., the maximum response slope is greater than or equal to the response slope threshold but the half-effect concentration is greater than the half-effect concentration threshold, or the half-effect concentration is less than or equal to the half-effect concentration threshold but the maximum response slope is less than the response slope threshold); PAHs or their alkyl derivatives with significantly weaker toxicity are classified as low-risk (i.e., the maximum response slope is less than the response slope threshold, and at the same time, their half-effect concentration is greater than the half-effect concentration threshold).

[0126] Thus, the dual-condition judgment rule integrates information from both the half-effect concentration and the maximum response slope, achieving a more comprehensive risk assessment of toxicity characteristics. Both conditions must be met simultaneously for a compound to be classified as high-risk, ensuring that high-risk compounds possess both high potency and potent effects, representing the group with the strongest overall toxicity. Meeting only one condition results in a medium-risk classification; these compounds may be effective at low concentrations but with mild effects, or effective at high concentrations but with severe effects, exhibiting a singular risk characteristic, with an overall risk between high and low. Failing to meet either condition results in a low-risk classification; these compounds are not prominent in either toxicity dimension, and their potential harm is relatively low.

[0127] It should be noted that, since all toxicity indicators are converted into comparable numerical values ​​(response slope, half-effect concentration, etc.), the toxicity intensity ranking and risk level rules constructed based on these quantitative parameters are objective and universal. Through a unified statistical analysis process, consistent judgment standards can be maintained across different laboratories and experimental batches. This means that the method for judging toxic substance risk is not dependent on specific compound types; as long as the same quantitative threshold is met, classification can be applied, making risk level classification highly transferable and practical.

[0128] In some embodiments of this disclosure, before determining the ecotoxicity of polycyclic aromatic hydrocarbons (PAHs) based on the toxicity characteristic parameters, the screening method for PAH ecotoxicity biomarkers preferably further includes steps S410 to S420. Please refer to [link / reference needed]. Figure 4 .

[0129] In step S410, a significant difference analysis is performed on the fluorescence response signals of the control group and each concentration treatment group to determine the significance level value of each concentration treatment group relative to the control group, and the goodness of fit value of the fitting curve corresponding to each polycyclic aromatic hydrocarbon or its alkyl derivative is determined.

[0130] In step S420, based on the significance level value and the goodness-of-fit value, the target dose-effect curve is identified from the dose-effect curves corresponding to each polycyclic aromatic hydrocarbon or its alkyl derivative, and the target dose-effect curve and its corresponding curve characteristic parameters are used as reliable data for determining the ecotoxicity of polycyclic aromatic hydrocarbons.

[0131] The significance analysis refers to using statistical methods to determine whether the differences between different concentration treatment groups are caused by a true effect, rather than a random result due to random fluctuations. In this disclosure, the significance analysis involves statistically testing the fluorescence response signals of each concentration treatment group and the control group to determine whether polycyclic aromatic hydrocarbons or their alkyl derivatives caused significant changes in membrane potential at each pollution concentration.

[0132] In this embodiment, after obtaining the fluorescence response signal data of each concentration treatment group and constructing the dose-response curve fitting, statistical software is used to perform one-way ANOVA on each concentration treatment group and the control group. The result of the one-way ANOVA will give an overall p-value (i.e., significance level value) to determine whether there are significant differences among all groups.

[0133] If the p-value of the ANOVA is less than 0.05, it indicates that at least one concentration treatment group is significantly different from the control group, and subsequent multiple comparisons can be performed; if the p-value is greater than or equal to 0.05, it indicates that there is no significant difference among all groups. Even if the mean of some concentration treatment groups is later observed to be higher than that of the control group, such differences may be due to random fluctuations and should not be considered as a true effect.

[0134] If the analysis of variance (ANOVA) result is significant (p < 0.05), then Dunnett's multiple comparisons are performed. Dunnett's multiple comparisons is a method specifically designed for comparing multiple treatment groups with a control group. It allows for pairwise comparisons between each concentration treatment group and the control group while controlling for the overall error rate (typically 0.05). Statistical software automatically calculates the p-value for each concentration treatment group compared to the control group and identifies which concentration treatment groups show significant differences. After completing the significance analysis, the significance level p-value for each concentration treatment group relative to the control group is obtained. This significance level value is used for subsequent data reliability screening.

[0135] Meanwhile, when fitting the dose-response curve, the corresponding goodness-of-fit value is recorded. This value can be automatically calculated and output by the software. The closer the goodness-of-fit value is to 1, the higher the degree of agreement between the fitted curve and the original data points, the clearer the dose-response relationship, and the better the data quality.

[0136] After completing the difference significance analysis and goodness-of-fit assessment, the dose-response curves are screened based on the significance level and goodness of fit. Only dose-response curves that meet the requirements of a significance level less than the significance level threshold (e.g., 0.05) and a goodness of fit greater than the goodness of fit threshold (e.g., 0.9) are determined as target dose-response curves, indicating that the dose-response curve and its corresponding curve characteristic parameters can be used as reliable data for subsequent toxicity determination.

[0137] Thus, by introducing significance analysis and goodness-of-fit assessment, a rigorous data reliability screening mechanism was established to ensure that only high-quality data that meets statistical requirements can be included in the subsequent toxicity assessment, which significantly improves the scientific validity and credibility of the toxicity assessment results.

[0138] It should be noted that, in the embodiments provided in this disclosure, toxicity determination is achieved by constructing a dose-response relationship between pollutant concentration and biological effects, and extracting toxicity characteristic parameters from the dose-response curve. This method is not limited to the single indicator of cell membrane potential; that is, in addition to cell membrane potential, commonly used toxicity endpoints such as reactive oxygen species levels, adenosine triphosphate (ATP) content, superoxide dismutase (SOD) activity, and cellular metabolic activity are also applicable to the toxicity determination method of this disclosure. Specifically, under the same exposure experimental conditions, response data for each concentration treatment group at any of the above-mentioned toxicity endpoints can be acquired simultaneously. After normalization, a dose-response curve for that toxicity endpoint can be constructed by using the logarithm of the pollutant concentration as the abscissa and the normalized response intensity as the ordinate. Based on this, a four-parameter Logistic model is used to perform nonlinear fitting on the dose-response curve, and the half-maximum effective concentration (WIF) and the maximum response slope are extracted from the fitted curve as curve characteristic parameters. The WIF is used to characterize the toxic potency of the toxicity endpoint, and the maximum response slope is used to characterize the severity of the toxic effect. Therefore, the method for constructing dose-response curves and extracting toxicity characteristic parameters provided in this disclosure has broad applicability and can also be applied to other toxicity endpoints besides cell membrane potential, providing multi-dimensional quantitative judgment basis for the ecotoxicity assessment of polycyclic aromatic hydrocarbons.

[0139] Based on the screening method for ecotoxicological biomarkers of polycyclic aromatic hydrocarbons provided in this disclosure, the following parameters were simultaneously measured: cell membrane potential, reactive oxygen species (ROS) levels, adenosine triphosphate (ATP) content, superoxide dismutase (SOD) activity, cell viability (CCK8), and median lethality (LD50). 50 Using various biological indicators such as polycyclic aromatic hydrocarbons (PAHs), a multi-dimensional toxicity evaluation system was constructed, providing a direct comparative basis for the screening of ecotoxicological biomarkers of PAHs.

[0140] Figure 5-9 The dose-response curves and goodness-of-fit (R) of five indicators (cell membrane potential (DiBAC4), ATP, ROS, SOD, and CCK8) under naphthalene (Nap) exposure are shown in sequence. 2 The graph shows the membrane potential response curve (i.e., the vertical axis represents the values ​​of each detection indicator, and the horizontal axis represents the concentration of pollutants). Figure 4 The curve for the membrane potential index (CPI) is the steepest, with a high goodness of fit (the curve is almost linear), showing a significant concentration-dependent effect. This indicates that the membrane potential index is extremely sensitive to the toxicity response of naphthalene, producing significant changes even at low concentrations. In contrast, the curves for other detection indices are relatively flat, with lower goodness of fit and smaller response amplitudes.

[0141] Similarly, Figure 10-14 The dose-response curves and goodness of fit of five detection indicators under 2-methylnaphthalene (2-MN) exposure are shown in sequence. Figure 15-19 The dose-response curves and goodness of fit of the five detection indicators under phenotypic exposure are shown in sequence. Figure 20-24 The dose-response curves and goodness of fit of five detection indicators under 1-methylphenanthrene (1-MP) exposure are shown in sequence.

[0142] As can be seen from the above figure, the cell membrane potential index can show a significant response even at low concentrations when exposed to various polycyclic aromatic hydrocarbons or their alkyl derivatives, demonstrating its high sensitivity and consistency to the toxicity of various polycyclic aromatic hydrocarbons or their alkyl derivatives.

[0143] Figure 25 The comparison of the median lethal concentration (LD50) of *Pteranodon spp.* after 48 hours of exposure to the above four typical polycyclic aromatic hydrocarbons or their alkyl derivatives (phenanthrene, naphthalene, 1-methylphenanthrene, 2-methylnaphthalene) is shown.

[0144] Based on the dose-response curves of the membrane potential response indicators under exposure to the aforementioned four polycyclic aromatic hydrocarbons or their alkyl derivatives, the toxicity response characteristics reflected are... Figure 25 The 48-hour median lethal concentrations of various polycyclic aromatic hydrocarbons or their alkyl derivatives shown demonstrate good consistency in terms of toxicity.

[0145] Although different pollutants differ in the slope parameters and dynamic changes of the dose-response curves of membrane potential response indicators, highly toxic pollutants generally exhibit lower membrane potential response thresholds, more significant early functional perturbations, and more concentrated dose-response ranges, while less toxic pollutants show characteristics of slow response and smaller changes.

[0146] Therefore, cell membrane potential, as an ecotoxicological biomarker of polycyclic aromatic hydrocarbons (PAHs), can reflect the intensity of the toxic effects of PAHs or their alkyl derivatives on protozoan models from the perspective of membrane homeostasis perturbation. The toxicity trend it reveals is generally consistent with the lethal endpoint evaluation results based on the median lethality index, but at the same time it provides higher resolution information on the toxicity action mode and early response characteristics.

[0147] In addition, such as Figure 5 , Figure 10 , Figure 15 and Figure 20As shown, the membrane potential response data of each pollutant can be well fitted by the nonlinear dose-response model, with a goodness of fit R² greater than 0.93, indicating that cell membrane potential as a toxicity response indicator has high stability and repeatability. Different pollutants show significant differences in the onset threshold, slope of the dose curve, and trend of change in the high-dose range of the membrane potential response, reflecting their different abilities to disturb cell membrane homeostasis.

[0148] The aforementioned differences provide a basis for classifying and ranking the ecotoxicity of polycyclic aromatic hydrocarbons (PAHs) at environmental concentrations based on membrane potential response indicators. Highly toxic pollutants typically induce significant membrane potential perturbations at low concentrations, exhibiting a steep or threshold-type dose-response relationship, while relatively less toxic pollutants require higher concentrations to elicit detectable membrane potential changes, and their response curves are relatively flat. These results indicate that cell membrane potential response characteristics can not only reflect the toxicity of PAHs to protozoa but also reveal differences in the toxic action patterns of different pollutants. Therefore, based on the response threshold, slope, and overall trend of the membrane potential dose-response curve, the toxicity risk of PAHs can be rapidly ranked and classified, providing experimental evidence for establishing a rapid toxicity screening and risk classification method that does not rely on lethal endpoints.

[0149] Figure 26 This is a schematic diagram showing the comparison results of the comprehensive resolution index (i.e., comprehensive evaluation index) of cell membrane potential (DiBAC4), superoxide dismutase (SOD) activity and adenosine triphosphate (ATP) content, taking NAP as an example, according to one embodiment of this disclosure.

[0150] like Figure 26 As shown, Part A presents a high-fidelity, large-span example: the response curve of cell membrane potential (DiBAC4). It can be seen that as naphthalene concentration (x-axis) increases, the normalized response (y-axis) exhibits a typical S-shaped upward trend. Its S-fidelity factor (SFF, the first characteristic parameter) is 0.687, the dynamic signal span (Span, the second characteristic parameter) is as high as 2.963, and the comprehensive resolution index (SFR, the comprehensive evaluation index) reaches a maximum of 2.035. This indicates that this indicator not only conforms to the dose-response law but also has a very large signal response amplitude, making it an excellent biomarker.

[0151] Figure 26Part B illustrates an example of limited signal span, namely superoxide dismutase (SOD) activity. Although its S-fidelity factor (SFF) is high (0.983), the curve rapidly plateaus after a sharp drop at low concentrations, with a small overall signal variation (dynamic signal span of only 0.775), resulting in a reduced comprehensive resolution index (SFR) of 0.762. This indicates that although its regularity is acceptable, its sensitivity as an ecotoxicological biomarker of polycyclic aromatic hydrocarbons is insufficient due to limitations in signal response amplitude.

[0152] Figure 26 Section C illustrates an example of response morphology distortion, specifically adenosine triphosphate (ATP) content. The data points are extremely scattered, the curve shape is irregular, and it lacks typical S-type dose-response characteristics. Its S-fidelity factor (SFF) is only 0.508, the dynamic signal span is only 0.465, and the overall resolution index (SFR) is the lowest (only 0.236), indicating that the ATP index exhibits a chaotic response and low signal-to-noise ratio under naphthalene exposure, making it unsuitable as an ecotoxicological biomarker for polycyclic aromatic hydrocarbons (PAHs).

[0153] Figure 27-30 This is a comparative diagram of the comprehensive evaluation index of multiple pollutants and multiple toxicity indicators according to one embodiment of the present disclosure.

[0154] in, Figure 27-30 This section compares the dose-response fidelity (SFR) scores for different toxicity endpoints under exposure to 1-methylphenanthrene (1-DMPHE), 2-methylnaphthalene (DMNAP), naphthalene, and phenanthrene. The horizontal axis represents the SFR score, calculated as the product of the S-fidelity factor (SFF) and the signal span. The vertical axis represents toxicity indicators, including cell membrane potential (DiBAC4), superoxide dismutase (SOD) activity, cell viability (CCK8), adenosine triphosphate (ATP) levels, and reactive oxygen species (ROS) levels.

[0155] from Figure 27-30 As can be seen, the cell membrane potential response not only conforms to the classic S-shaped dose-response law (high SFF value) but also exhibits a significant signal response amplitude (large signal span), enabling it to sensitively and accurately reflect the intensity of the toxic effects of polycyclic aromatic hydrocarbons (PAHs) on protozoa from the perspective of membrane homeostasis perturbation. In contrast, other biochemical indicators either have distorted response curves that do not conform to the dose-response law, or have limited signal response amplitudes and insufficient sensitivity. Therefore, based on the calculation and verification of the dose-response fidelity resolution index, cell membrane potential can be identified as a biomarker of PAH ecotoxicity.

[0156] Figure 31 This is a schematic diagram of a two-dimensional mapping analysis of the robustness of naphthalene exposure to different toxic endpoints based on a dual penalty mechanism of a dose-effect fidelity resolution index (SFR) evaluation model according to one embodiment of the present disclosure.

[0157] like Figure 31 As shown in the figure, the horizontal axis represents the S-shape Fidelity Factor (SFF), and the vertical axis represents the Dynamic Signal Span (Span).

[0158] from Figure 31 As can be seen, the cell membrane potential (DiBAC4) is clearly located in the "optimal warning zone (high fidelity + large span)" in the upper right corner, and its SFR comprehensive resolution index is as high as 2.04, indicating that its response curve not only highly conforms to the S-type dose-response law, but also has a very large signal response amplitude, making it the most robust and sensitive ecotoxicity biomarker under naphthalene exposure. In contrast, superoxide dismutase (SOD) is located in the "restricted region: span penalty" in the lower right corner. Although its SFF value is high (consistent with the dose-response law), its dynamic signal span is small, and the weak signal amplitude is easily submerged in complex environmental backgrounds, resulting in an overall SFR score of only 0.76. Reactive oxygen species (ROS) is located in the "restricted region: fidelity penalty" on the left. Its response data lacks the typical S-shaped threshold law, and the curve shape is distorted, triggering the fidelity penalty mechanism, causing the SFR to drop to 0.00. Adenosine triphosphate (ATP) falls into the "double penalty failure region (morphological distortion + small amplitude)" in the lower left corner. It deviates significantly from classical toxicological laws and lacks sufficient response amplitude, resulting in an SFR of only 0.24. It should be noted that the CCK8 fitting exceeds the upper limit and is therefore ignored.

[0159] By objectively mapping each candidate toxic endpoint in a two-dimensional feature space, the technical solution disclosed herein can automatically identify and apply targeted penalties, effectively eliminating inferior biochemical indicators that are unsuitable due to morphological distortion, weak signals, or irregular fluctuations, and accurately locking in the optimal polycyclic aromatic hydrocarbon ecotoxicity biomarkers that combine high fidelity and wide range.

[0160] In the description of this specification, the references to terms such as "one embodiment / mode," "some embodiments / modes," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, or characteristic described in connection with that embodiment / mode or example is included in at least one embodiment / mode or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment / mode or example. Moreover, the specific features, structures, or characteristics described may be combined in any suitable manner in one or more embodiments / modes or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments / modes or examples described in this specification, as well as the features of different embodiments / modes or examples.

[0161] Those skilled in the art should understand that the above embodiments are merely for illustrating the present disclosure and are not intended to limit the scope of the disclosure. Those skilled in the art can make other changes or modifications based on the above disclosure, and these changes or modifications still fall within the scope of the present disclosure.

Claims

1. A method for screening ecotoxicological biomarkers of polycyclic aromatic hydrocarbons, characterized in that, include: Protozoan models were exposed to blank control conditions and different concentrations of polycyclic aromatic hydrocarbons or their alkyl derivatives to obtain control groups and multiple concentration treatment groups. The response data of the control group and different concentration treatment groups at multiple toxic endpoints were measured simultaneously. The multiple toxic endpoints include at least two of the following: cell membrane potential, reactive oxygen species level, adenosine triphosphate content, superoxide dismutase activity, and cell activity. Based on the number of surviving cells in the control group and each concentration treatment group, the response data were normalized to determine the effective response intensity per unit of live cell. The effective response intensity was calculated by parallel fitting using both a nonlinear dose-response model and a linear dose-response model, and the sum of squared residuals of the nonlinear dose-response model and the linear dose-response model were determined based on the results of the parallel fitting calculations. Based on the ratio of the sum of squared residuals of the nonlinear dose-response model and the linear dose-response model, a first characteristic parameter is calculated to characterize the degree of conformity of the response data to the dose-response law, and a second characteristic parameter is calculated based on the predicted extreme value of the nonlinear dose-response model to characterize the amplitude of the signal response. as well as Based on the first characteristic parameter and the second characteristic parameter, a comprehensive evaluation index for each of the toxicity endpoints is determined, and based on the comprehensive evaluation index for each of the toxicity endpoints, polycyclic aromatic hydrocarbon ecotoxicity biomarkers are determined from multiple toxicity endpoints.

2. The method for screening ecotoxicological biomarkers of polycyclic aromatic hydrocarbons as described in claim 1, characterized in that, The first characteristic parameter is determined according to the following formula. SFF : in, and These are the sum of squared residuals for the nonlinear dose-response model and the linear dose-response model, respectively.

3. The method for screening ecotoxicological biomarkers of polycyclic aromatic hydrocarbons as described in claim 1, characterized in that, The second characteristic parameter is determined according to the following formula. : in, and These represent the maximum effect extreme value and the baseline value of extremely low concentration predicted by the nonlinear dose-response model, respectively.

4. The method for screening ecotoxicological biomarkers of polycyclic aromatic hydrocarbons as described in claim 1, characterized in that, Based on the first characteristic parameter and the second characteristic parameter, a comprehensive evaluation index for each of the toxicity endpoints is determined, including: The product of the first characteristic parameter and the second characteristic parameter corresponding to each of the aforementioned toxicity endpoints is used as the comprehensive evaluation index of that toxicity endpoint.

5. The method for screening ecotoxicological biomarkers of polycyclic aromatic hydrocarbons as described in any one of claims 1-4, characterized in that, When the ecotoxicological biomarker of polycyclic aromatic hydrocarbons (PAHs) is cell membrane potential, the screening method for PAH ecotoxicological biomarkers further includes: Slow-response membrane potential fluorescent probes were used to label the protozoan models in each concentration treatment group to determine the membrane potential response intensity of each concentration treatment group. Based on the membrane potential response intensity of each concentration treatment group and its corresponding pollutant concentration, a dose-response curve between the membrane potential response intensity and the pollutant concentration was constructed; and Toxicity characteristic parameters are extracted from the dose-response curve, and the ecotoxicity of polycyclic aromatic hydrocarbons is determined based on the toxicity characteristic parameters.

6. The method for screening ecotoxicological biomarkers of polycyclic aromatic hydrocarbons as described in claim 5, characterized in that, Toxicity characteristic parameters are extracted from the dose-response curve, including: The dose-response curve is fitted to obtain the corresponding fitted curve; and Based on the fitted curve, corresponding toxicity characteristic parameters are extracted. The toxicity characteristic parameters include at least one of half-effect concentration and maximum response slope. The half-effect concentration is the pollutant concentration that causes a 50% maximum membrane potential response intensity. The maximum response slope is the absolute value of the derivative of the fitted curve at the central inflection point, which is used to characterize the severity of the sublethal toxicity of cells as the pollutant concentration increases.

7. The method for screening ecotoxicological biomarkers of polycyclic aromatic hydrocarbons as described in claim 6, characterized in that, The ecotoxicity of polycyclic aromatic hydrocarbons is determined based on the aforementioned toxicity characteristic parameters, including at least one of the following steps: Based on the maximum response slope corresponding to each polycyclic aromatic hydrocarbon or its alkyl derivative, the toxicity intensity of each polycyclic aromatic hydrocarbon or its alkyl derivative is ranked. The maximum response slope is positively correlated with the toxicity intensity of the polycyclic aromatic hydrocarbon or its alkyl derivative. Alternatively, based on the half-effect concentration of each polycyclic aromatic hydrocarbon or its alkyl derivative, the toxicity intensity of each polycyclic aromatic hydrocarbon or its alkyl derivative can be ranked, where the half-effect concentration is negatively correlated with the toxicity intensity of the polycyclic aromatic hydrocarbon or its alkyl derivative. or, The maximum response slope and half-effect concentration of each polycyclic aromatic hydrocarbon or its alkyl derivative were used as composite indicators to rank the toxicity intensity of each polycyclic aromatic hydrocarbon or its alkyl derivative.

8. The method for screening ecotoxicological biomarkers of polycyclic aromatic hydrocarbons as described in claim 7, characterized in that, After ranking the toxicity of various polycyclic aromatic hydrocarbons or their alkyl derivatives, the following are also included: Based on the ranking of toxicity intensity and the target classification ratio, each polycyclic aromatic hydrocarbon or its alkyl derivative is classified into risk levels, including high risk, medium risk or low risk. Alternatively, based on the response slope threshold and the half-effect concentration threshold, polycyclic aromatic hydrocarbons or their alkyl derivatives with a maximum response slope greater than or equal to the response slope threshold and a half-effect concentration less than or equal to the half-effect concentration threshold are classified as high-risk; polycyclic aromatic hydrocarbons or their alkyl derivatives that only satisfy the condition of a maximum response slope greater than or equal to the response slope threshold or a half-effect concentration less than or equal to the half-effect concentration threshold are classified as medium-risk; and polycyclic aromatic hydrocarbons or their alkyl derivatives with a maximum response slope less than the response slope threshold and a half-effect concentration greater than the half-effect concentration threshold are classified as low-risk.

9. The method for screening ecotoxicological biomarkers of polycyclic aromatic hydrocarbons as described in claim 5, characterized in that, Before determining the ecotoxicity of polycyclic aromatic hydrocarbons (PAHs) based on the aforementioned toxicity characteristic parameters, the screening method for PAH ecotoxicity biomarkers further includes: Based on the fluorescence response signals of the control group and each of the concentration treatment groups, a significance analysis was performed to determine the significance level of each concentration treatment group relative to the control group, and the goodness-of-fit value of the fitting curves corresponding to each polycyclic aromatic hydrocarbon or its alkyl derivative was determined; and Based on the significance level and the goodness-of-fit value, the target dose-effect curve is identified from the dose-effect curves corresponding to each polycyclic aromatic hydrocarbon or its alkyl derivative, and the target dose-effect curve and its corresponding toxicity characteristic parameters are used as reliable data for determining the ecotoxicity of polycyclic aromatic hydrocarbons.

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