A hazardous waste pollutant risk contribution identification method based on PBMT features

Abstract

The application provides a hazardous waste pollutant risk contribution identification method based on PBMT characteristics, and belongs to the technical field of hazardous waste environmental risk assessment. Through collection of hazardous waste pollutant detection data, a PBMT characteristic vector containing persistence, biological accumulation, migration and toxicity is constructed; relying on a four-level multi-medium fugacity model to simulate the cross-medium migration law of pollutants, medium concentration and flux data are output; a three-level risk transmission network of pollutant medium receptors is built. The improved entropy weight TOPSIS method is used to calculate the comprehensive risk value of the pollutant, the PMF model is combined to realize the risk tracing of the pollution source, the risk of the pollutant transformation product is iteratively calculated, and the risk contribution degree of the parent pollutant is reversely corrected, and finally a grading control list is output. The application effectively improves the risk quantization and tracing accuracy, can accurately identify high-risk pollutants and control sources, is suitable for risk identification and fine control of various hazardous waste scenes, and has high application value.

Description

Technical Field

[0001] This invention relates to the field of hazardous waste environmental risk assessment technology, and more specifically, to a method for identifying the risk contribution of hazardous waste pollutants based on PBMT characteristics. Background Technology

[0002] Hazardous wastes are complex in composition, typically containing multiple persistent and highly toxic organic or inorganic pollutants. After release, these pollutants migrate and transform across multiple media, including the atmosphere, water bodies, soil, and sediments, posing long-term, complex exposure risks to human health, aquatic ecosystems, and terrestrial ecosystems. Existing hazardous waste risk assessment technologies have the following limitations: Current risk assessments rely solely on pollutant toxicity parameters for risk scoring, failing to systematically integrate the four core environmental behavior characteristics: persistence (P), bioaccumulation (B), migration (M), and toxicity (T). This results in a one-sided assessment dimension, which cannot reflect the cumulative risks brought about by the cross-media migration of pollutants. Existing multi-media fugacity models can only simulate pollutant concentration distribution, but they do not construct a pollutant-media-ecological receptor coupled risk transmission network, and therefore cannot quantify the risk transmission efficiency of different exposure pathways. The existing entropy-weighted TOPSIS model directly uses the original indicators for weighting without standardizing and correcting for negative transferability indicators, resulting in dimensional bias in feature scores and distorted risk calculation results. Existing positive definite matrix factor decomposition (PMF) risk tracing relies solely on the contribution ratio of pollutant concentration spectrum decomposition, ignoring the PBMT environmental hazard attributes of pollutants. It can only identify concentration contributions and cannot represent the true environmental risk contributions. Existing methods ignore the derivative risks of secondary products from the environmental transformation of pollutants. The toxicity and mobility of transformation products are often higher than those of the parent pollutant, which will significantly underestimate the overall risk contribution of the parent pollutant. Current risk identification lacks quantitative uncertainty analysis, the interference of parameter fluctuations on risk contribution results cannot be quantified, and the reliability of control lists cannot be determined.

[0003] In summary, existing technologies struggle to accurately quantify the true risk contribution of various pollutants and their corresponding pollution sources in complex hazardous waste, and the classification of control lists relies on a single basis, leading to significant biases in assessment results. To address these shortcomings of existing technologies, this invention proposes a method for identifying the risk contribution of hazardous waste pollutants based on PBMT characteristics. Summary of the Invention

[0004] The purpose of this invention is to address the problems of existing hazardous waste risk assessment methods, such as single-dimensionality assessment, lack of coupling of multi-media migration and transformation, neglect of risks derived from transformation products, reliance on concentration for risk tracing, and lack of uncertainty quantification. This invention provides a method for identifying the risk contribution of hazardous waste pollutants based on PBMT characteristics. It fully integrates four-dimensional characteristics of persistence (P), bioaccumulation (B), migration (M), and toxicity (T), coupled with four-level multi-media fugacity simulation, three-level risk transmission network, improved entropy-weighted TOPSIS comprehensive risk calculation, PMF risk tracing, risk correction for transformation products, and Monte Carlo uncertainty analysis. This method accurately quantifies the risk contribution of each pollutant and each pollution source, and outputs a graded control list.

[0005] To achieve the above-mentioned objectives, the present invention provides the following technical solution: a method for identifying the risk contribution of hazardous waste pollutants based on PBMT characteristics, comprising the following steps: Step 1: Collect full-component detection data of the hazardous waste to be identified, construct an initial pollutant inventory, and extract four basic characteristic parameters for each pollutant in the inventory: persistence (P), bioaccumulation (B), mobility (M), and toxicity (T) to form a single pollutant PBMT feature vector. Step 2: Construct a multi-media environmental fugacity model, input the release scenario parameters of hazardous waste and the environmental media property parameters, simulate the migration and transformation paths of each pollutant among the four media of atmosphere, water, soil and sediment, and output the steady-state concentration distribution and cross-media flux of each pollutant in each medium. Step 3: Establish a four-dimensional feature classification benchmark for PBMT, normalize the dimensions of each feature parameter, and calculate the persistence score, bioaccumulation score, migration score and toxicity score in combination with the hazard threshold to obtain the standardized PBMT feature matrix. Step 4: Construct a three-level risk transmission network of pollutants-media-receptors with pollutants as nodes, cross-media migration paths as edges, and media flux as edge weights to determine the exposure transmission paths of each pollutant to different environmental receptors. Step 5: The weight coefficients of the four PBMT characteristics are objectively calculated using the improved entropy weight method, and the proximity of each pollutant in each medium is calculated using the TOPSIS method. The exposure concentration weights are then superimposed to obtain the comprehensive risk value of a single pollutant. Step 6: Based on the positive definite matrix factorization model, combined with the pollutant source fingerprint characteristics and risk transmission network, the comprehensive risk value is decomposed to different pollution sources, and the initial contribution ratio of each pollution source to the total risk is calculated. Step 7: Identify the environmental transformation products of each pollutant, collect the PBMT characteristic parameters of the transformation products and iteratively calculate their comprehensive risk value, trace back to the parent pollutant, and correct the risk contribution of the parent pollutant. Step 8: Sort the pollutants and their corresponding pollution sources according to the revised risk contribution, and output a list of high-risk contribution pollutants and a list of priority control sources to complete the risk contribution identification of hazardous waste pollutants.

[0006] As a preferred technical solution of the present invention, in step one, the persistence P parameter includes aerobic biodegradation half-life, anaerobic biodegradation half-life, hydrolysis half-life and photolysis half-life; the bioaccumulation B parameter includes bioaccumulation factor BCF and biomagnification factor BMF; the migration M parameter includes organic carbon adsorption factor Koc, Henry's law constant and diffusion coefficient; the toxicity T parameter includes acute toxicity LD50 / LC50, chronic toxicity baseline dose and carcinogenicity slope factor.

[0007] As a preferred technical solution of the present invention, in step two, the gas-water total transfer coefficient of the fourth-level fugacity model The transfer coefficient is calculated by combining the gas film transfer coefficient, liquid film transfer coefficient, and total suspended particulate matter adsorption transfer coefficient in series. The calculation formula is as follows: In the formula: This is the overall gas-water transfer coefficient, in units of... ; This is the air film transfer coefficient, in units of... ; The liquid film transfer coefficient is expressed in units of 1000 ppm. ; The adsorption transfer coefficient of the particulate phase is expressed in units of 1000 m³ / s. All transfer coefficients have the same unit, satisfying the series resistance calculation rules.

[0008] As a preferred technical solution of the present invention, in step three, the PBMT four-dimensional feature grading benchmark adopts a four-level grading system, corresponding to four levels: low hazard, medium hazard, high hazard, and extremely high hazard. The score intervals corresponding to each level are [0, 0.25), [0.25, 0.5), [0.5, 0.75), and [0.75, 1]. During normalization, negative indicators are converted using the reciprocal before being substituted into the S-shaped function for calculation. The organic carbon adsorption coefficient in the migration characteristics is a negative indicator, and the conversion formula is: In the formula: This is the organic carbon adsorption coefficient, in units of... ; The converted positive index value, in units of The score becomes dimensionless after subsequent normalization.

[0009] As a preferred technical solution of the present invention, in step four, the first level of the three-level risk transmission network is a hazardous waste release source node, containing n pollutant sub-nodes; the second level is an environmental medium node, containing four medium sub-nodes: atmosphere, water, soil, and sediment; the third level is a receptor node, containing three receptor sub-nodes: human health, terrestrial ecology, and aquatic ecology; the transmission efficiency of a single path also needs to be corrected by the distribution coefficient between media, and the correction formula is: In the formula: Let be the partition coefficient of pollutant i between media k and l, which is dimensionless; and be the corrected transfer efficiency. It is a dimensionless value.

[0010] As a preferred technical solution of the present invention, in step five, the calculation process of the improved entropy weight method is as follows: first, construct the PBMT feature judgment matrix, calculate the information entropy of each indicator, then introduce the deviation coefficient to correct the entropy weight, and finally obtain the persistence weight, bioaccumulation weight, migration weight and toxicity weight; the TOPSIS method uses the maximum value combination of each feature as the positive ideal solution and the minimum value combination as the negative ideal solution, and calculates the Euclidean distance of each pollutant to the positive and negative ideal solutions to obtain the proximity.

[0011] As a preferred technical solution of the present invention, in step six, the input of the positive definite matrix factor decomposition model includes pollutant concentration spectrum, PBMT characteristic spectrum and environmental medium distribution spectrum. The number of factors obtained by decomposition is determined by the silhouette coefficient method. Each factor corresponds to a type of pollution source, and the initial contribution ratio is the proportion of the risk value corresponding to the factor to the total risk value.

[0012] As a preferred technical solution of the present invention, step seven: identify the environmental transformation products of each pollutant, collect the PBMT characteristic parameters of the transformation products and substitute them into steps one to five to iteratively calculate their comprehensive risk value, and backtrack to the parent pollutant based on the transformation yield and path backtracking coefficient to correct the risk contribution of the parent pollutant. The corrected comprehensive risk value of the i-th parent pollutant is then calculated. The calculation formula is: In the formula: The total number of transformation products of the i-th parent pollutant, dimensionless; The molar yield of product q from parent i is dimensionless. The comprehensive risk value of the qth conversion product is expressed in units of... ; is the risk backtracking coefficient from product q to parent i, which is dimensionless; This is the risk attenuation coefficient for the transformation path, in units of... ; The environmental migration distance between the parent organism and the product, in units of Corrected composite risk value The unit is The units are consistent with those before the correction.

[0013] As a preferred technical solution of the present invention, in step eight, the list of high-risk contributing pollutants is sorted from high to low according to the degree of risk contribution, and the core PBMT dominant characteristics and main exposure media of each pollutant are marked; the list of priority control sources is marked with the risk contribution ratio of each pollution source and key control nodes.

[0014] As a preferred embodiment of the present invention, the method further includes an uncertainty analysis step, which uses Monte Carlo simulation to sample the fluctuations in the values ​​of the PBMT parameters and calculates the 95% confidence interval of the risk contribution; during the simulation, it is assumed that each PBMT parameter follows a log-normal distribution, and the sampled value of the j-th parameter is: In the formula: The logarithmic mean of the parameters is dimensionless. The logarithm and standard deviation of the parameter are dimensionless. These are dimensionless random numbers that are distributed according to a standard normal distribution. After performing N sampling operations, the 2.5% and 97.5% quantiles of the risk contribution are calculated as the upper and lower limits of the 95% confidence interval, and the coefficient of variation is output. As a quantitative indicator of the reliability of the identification results, among which The standard deviation of the risk value. The CV is the mean risk value, expressed in percent.

[0015] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention constructs a complete PBMT four-dimensional pollutant environmental characteristic evaluation system, simultaneously covering four core hazard attributes: persistence, bioaccumulation, migration, and toxicity. It breaks through the limitations of traditional assessments that rely solely on single toxicity indicators for scoring, comprehensively restoring the long-term, cross-media, and multi-receptor complex hazard nature of pollutants in the environment. The evaluation dimensions are more complete, and the theoretical basis is more aligned with the evolutionary laws of the entire hazardous waste pollution process. The self-coupled four-level multi-media fugacity model accurately characterizes the interphase diffusion, adsorption, and sedimentation processes of pollutants among the atmosphere, water bodies, soil, and sediments through the gas-water two-phase series resistance transfer coefficient. It can quantitatively output the steady-state concentration and cross-media flux of each medium, overcoming the shortcomings of traditional single-media models in simulating long-distance migration and diffusion of pollutants, and accurately capturing the overall environmental exposure level after hazardous waste release.

[0016] This invention designs a dedicated positive conversion formula for the negative attribute of the organic carbon adsorption coefficient (Koc) in mobility indicators. Combined with a unified four-level hazard classification standard and S-shaped function normalization, it eliminates calculation biases caused by differences in the dimensions and numerical ranges of different characteristic parameters. It uniformly quantifies the hazard scores of the four categories of indicators (P / B / M / T), and the standardized PBMT feature matrix is ​​comparable across pollutants and scenarios. The three-level risk transmission network topology of pollutant-media-receptor, with pollutants as source nodes, environmental media as transmission channel nodes, and human / ecological receptors as risk endpoint nodes, and using media allocation coefficients to correct path transmission efficiency, completely clarifies the entire exposure chain from each pollutant to different protection targets. It can intuitively locate high-risk transmission channels, providing a clear target basis for blocking pollution exposure.

[0017] This invention employs an improved entropy weighting method with deviation coefficient correction to objectively weight the four-dimensional PBMT indicators, completely avoiding the distortion problem caused by subjective scoring. It combines the TOPSIS method with Euclidean distance calculation to determine the proximity of pollutant risk to the medium, and then superimposes the medium exposure concentration weight to obtain the comprehensive risk value of a single pollutant. The calculation logic is objective and free from human intervention, significantly improving the accuracy of risk quantification results. By coupling the positive definite matrix factor decomposition (PMF) model with the PBMT comprehensive risk spectrum, unlike traditional methods that rely solely on concentration spectrum for source tracing, this invention directly uses the actual comprehensive risk as the decomposition input. It automatically determines the optimal number of pollution source factors using the profile coefficient method and outputs the actual risk contribution ratio of each pollution source, resolving the assessment misconception that "high concentration does not equal high risk" and achieving true risk source tracing.

[0018] This invention fully considers the derivative risks of secondary pollutants generated by the hydrolysis, photolysis, and biotransformation of pollutants. It designs an iterative backtracking correction formula, which integrates multiple parameters such as molar conversion yield, risk backtracking coefficient, migration attenuation coefficient, and migration distance to quantify the superimposed hazards of conversion products. This formula reverses the comprehensive risk of the parent pollutant and effectively solves the problems of existing technologies generally underestimating the risk of secondary pollution and having overly conservative and insufficient control measures.

[0019] This invention outputs a two-tiered hierarchical control list. The high-risk pollutant list simultaneously marks the dominant hazard characteristics and main exposure media of each pollutant, while the priority control source list clearly defines the risk proportion of each pollution source and key blocking nodes. The list is rich in information dimensions and clearly targeted, directly supporting the implementation of hierarchical control decisions for hazardous waste disposal, site remediation, and source reduction. Based on the log-normal distribution characteristics of PBMT parameters, large-scale random sampling is performed to quantify the 95% confidence interval and coefficient of variation of risk contribution. This intuitively demonstrates the degree of interference of parameter detection errors and fluctuations in literature values ​​on the assessment results, providing a reliable basis for risk control decisions and overcoming the shortcomings of existing technologies that lack error explanations and cannot verify the credibility of conclusions.

[0020] This invention is adaptable to various types of hazardous waste scenarios. It can be used for risk identification of organic hazardous waste, heavy metal hazardous waste, and mixed compound hazardous waste. It can also be applied to various release scenarios such as hazardous waste landfills, storage warehouses, incineration workshops, and integrated treatment plants. The method is highly versatile and has a wide range of applications. Attached image description: Figure 1 This is a schematic diagram of the method steps provided by the present invention; Figure 2 This is a schematic diagram of feature grading and scoring provided by the present invention; Figure 3 This is a schematic diagram of the computational logic provided by the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are specific implementations of the present invention and are not limited to all embodiments.

[0022] Therefore, the following detailed description of embodiments of the present invention is not intended to limit the scope of the claimed invention, but merely illustrates some embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0023] It should be noted that, in the absence of conflict, the embodiments and features and technical solutions in the embodiments of the present invention can be combined with each other. It should be noted that similar reference numerals and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0024] Example 1: A method for identifying the risk contribution of hazardous waste pollutants based on PBMT features, comprising the following steps: Step 1: Collect full-component detection data of the hazardous waste to be identified, construct an initial pollutant inventory, and extract four basic feature parameters for each pollutant in the inventory: persistence (P), bioaccumulation (B), mobility (M), and toxicity (T) to form a single pollutant PBMT feature vector. Step 2: Construct a multi-media environmental fugacity model, input the release scenario parameters of hazardous waste and the environmental media property parameters, simulate the migration and transformation paths of each pollutant among the four media of atmosphere, water, soil and sediment, and output the steady-state concentration distribution and cross-media flux of each pollutant in each medium. Step 3: Establish a four-dimensional feature classification benchmark for PBMT, normalize the dimensions of each feature parameter, and calculate the persistence score, bioaccumulation score, migration score and toxicity score in combination with the hazard threshold to obtain the standardized PBMT feature matrix. Step 4: Construct a three-level risk transmission network of pollutants-media-receptors with pollutants as nodes, cross-media migration paths as edges, and media flux as edge weights to determine the exposure transmission paths of each pollutant to different environmental receptors. Step 5: The weight coefficients of the four PBMT characteristics are objectively calculated using the improved entropy weight method, and the proximity of each pollutant in each medium is calculated using the TOPSIS method. The exposure concentration weights are then superimposed to obtain the comprehensive risk value of a single pollutant. Step 6: Based on the positive definite matrix factorization model, combined with the pollutant source fingerprint characteristics and risk transmission network, the comprehensive risk value is decomposed to different pollution sources, and the initial contribution ratio of each pollution source to the total risk is calculated. Step 7: Identify the environmental transformation products of each pollutant, collect the PBMT characteristic parameters of the transformation products and iteratively calculate their comprehensive risk value, trace back to the parent pollutant, and correct the risk contribution of the parent pollutant. Step 8: Sort the pollutants and their corresponding pollution sources according to the revised risk contribution, and output a list of high-risk contribution pollutants and a list of priority control sources to complete the risk contribution identification of hazardous waste pollutants.

[0025] In step one, the persistence P parameter includes the aerobic biodegradation half-life, anaerobic biodegradation half-life, hydrolysis half-life, and photolysis half-life; the bioaccumulation B parameter includes the bioaccumulation factor BCF and the biomagnification factor BMF; the mobility M parameter includes the organic carbon adsorption factor Koc, Henry's law constant, and diffusion coefficient; and the toxicity T parameter includes the acute toxicity LD50 / LC50, the baseline dose for chronic toxicity, and the carcinogenicity slope factor.

[0026] In step two, the overall gas-water transfer coefficient of the fourth-order fugacity model... The transfer coefficient is calculated by combining the gas film transfer coefficient, liquid film transfer coefficient, and total suspended particulate matter adsorption transfer coefficient in series. The calculation formula is as follows: In the formula: This is the overall gas-water transfer coefficient, in units of... ; This is the air film transfer coefficient, in units of... ; The liquid film transfer coefficient is expressed in units of 1000 ppm. ; The adsorption transfer coefficient of the particulate phase is expressed in units of 1000 m³ / s. All transfer coefficients have the same unit, satisfying the series resistance calculation rules.

[0027] In step three, the PBMT four-dimensional feature grading benchmark adopts a four-level grading system, corresponding to low hazard, medium hazard, high hazard, and extremely high hazard levels, with score intervals of [0, 0.25), [0.25, 0.5), [0.5, 0.75), and [0.75, 1] ​​for each level. During normalization, negative indicators are converted using a reciprocal before being substituted into the S-shaped function for calculation. The organic carbon adsorption coefficient in the migration characteristics is a negative indicator, and the conversion formula is: In the formula: This is the organic carbon adsorption coefficient, in units of... ; The converted positive index value, in units of The score becomes dimensionless after subsequent normalization.

[0028] In step four, the first level of the three-tiered risk transmission network consists of hazardous waste release source nodes, comprising n pollutant sub-nodes; the second level consists of environmental media nodes, comprising four media sub-nodes: atmosphere, water, soil, and sediment; and the third level consists of receptor nodes, comprising three receptor sub-nodes: human health, terrestrial ecology, and aquatic ecology. The transmission efficiency of a single path also needs to be adjusted by the distribution coefficient between media, and the adjustment formula is as follows: In the formula: Let be the partition coefficient of pollutant i between media k and l, which is dimensionless; and be the corrected transfer efficiency. It is a dimensionless value.

[0029] In step five, the calculation process of the improved entropy weight method is as follows: first, construct the PBMT feature judgment matrix, calculate the information entropy of each indicator, then introduce the deviation coefficient to correct the entropy weight, and finally obtain the persistence weight, bioaccumulation weight, migration weight and toxicity weight; the TOPSIS method uses the maximum value combination of each feature as the positive ideal solution and the minimum value combination as the negative ideal solution, and calculates the Euclidean distance of each pollutant to the positive and negative ideal solutions to obtain the proximity.

[0030] In step six, the inputs to the positive definite matrix factor decomposition model include pollutant concentration spectrum, PBMT characteristic spectrum and environmental media distribution spectrum. The number of factors obtained by decomposition is determined by the silhouette coefficient method. Each factor corresponds to a type of pollution source, and the initial contribution ratio is the proportion of the risk value corresponding to the factor to the total risk value.

[0031] Step 7: Identify the environmental transformation products of each pollutant, collect the PBMT characteristic parameters of the transformation products, and substitute them into the calculation of their comprehensive risk value in steps 1 to 5. Based on the transformation yield and path backtracking coefficient, backtrack to the parent pollutant to correct the risk contribution of the parent pollutant. The corrected comprehensive risk value of the i-th parent pollutant is then calculated. The calculation formula is: In the formula: The total number of transformation products of the i-th parent pollutant, dimensionless; The molar yield of product q from parent i is dimensionless. The comprehensive risk value of the qth conversion product is expressed in units of... ; is the risk backtracking coefficient from product q to parent i, which is dimensionless; This is the risk attenuation coefficient for the transformation path, in units of... ; The environmental migration distance between the parent organism and the product, in units of Corrected composite risk value The unit is The units are consistent with those before the correction.

[0032] In step eight, the list of high-risk pollutants is sorted from high to low according to their risk contribution, and the core PBMT dominant characteristics and main exposure media of each pollutant are marked; the list of priority control sources is marked with the risk contribution ratio of each pollution source and key control nodes.

[0033] The method also includes an uncertainty analysis step, using Monte Carlo simulation to sample the fluctuations in the PBMT parameters and calculate the 95% confidence interval for the risk contribution. During the simulation, it is assumed that each PBMT parameter follows a log-normal distribution, and the sampled value of the j-th parameter is: In the formula: The logarithmic mean of the parameters is dimensionless. The logarithm and standard deviation of the parameter are dimensionless. These are dimensionless random numbers that are distributed according to a standard normal distribution. After performing N sampling operations, the 2.5% and 97.5% quantiles of the risk contribution are calculated as the upper and lower limits of the 95% confidence interval, and the coefficient of variation is output. As a quantitative indicator of the reliability of the identification results, among which The standard deviation of the risk value. The CV is the mean risk value, expressed in percent.

[0034] This invention starts with measured component data of hazardous waste, first establishing a complete PBMT four-dimensional environmental characteristic system; then, it simulates the multi-media migration concentration and flux of pollutants using a four-level fugacity model, and characterizes the complete exposure path of pollutants to humans and ecological receptors using a three-level coupling network; it employs an improved entropy weight TOPSIS to eliminate interference from indicator dimensions and accurately quantify the comprehensive risk of single pollutants; it utilizes a PMF model to achieve pollution source tracing based on risk rather than concentration; further, it considers the risks derived from environmental transformation products, correcting the true risk contribution of the parent pollutant through an iterative backtracking formula; finally, it sorts and outputs a graded control list, and provides Monte Carlo simulation to quantitatively assess uncertainty. The entire method simultaneously considers the inherent environmental hazards of pollutants, cross-media migration behavior, secondary transformation risks, and the reliability of assessment results, addressing the pain points of existing hazardous waste risk identification methods, which are one-sided, biased, and unable to support precise control.

[0035] Although the invention has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made therein without departing from the spirit and scope of the invention. Accordingly, this specification and drawings are merely exemplary descriptions of the invention as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if such modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include such modifications and modifications.

Claims

1. A method for identifying the risk contribution of hazardous waste pollutants based on PBMT features, characterized in that, Includes the following steps: Step 1: Collect full-component detection data of the hazardous waste to be identified, construct an initial pollutant inventory, and extract four basic characteristic parameters for each pollutant in the inventory: persistence (P), bioaccumulation (B), mobility (M), and toxicity (T) to form a single pollutant PBMT feature vector. Step 2: Construct a multi-media environmental fugacity model, input the release scenario parameters of hazardous waste and the environmental media property parameters, simulate the migration and transformation paths of each pollutant in the four media of atmosphere, water, soil and sediment, and output the steady-state concentration distribution and cross-media flux of each pollutant in each medium. Step 3: Establish a four-dimensional feature classification benchmark for PBMT, normalize the dimensions of each feature parameter, and calculate the persistence score, bioaccumulation score, migration score and toxicity score in combination with the hazard threshold to obtain the standardized PBMT feature matrix. Step 4: Construct a three-level risk transmission network of pollutants-media-receptors with pollutants as nodes, cross-media migration paths as edges, and media flux as edge weights to determine the exposure transmission paths of each pollutant to different environmental receptors. Step 5: The weight coefficients of the four PBMT characteristics are objectively calculated using the improved entropy weight method, and the proximity of each pollutant in each medium is calculated using the TOPSIS method. The exposure concentration weights are then superimposed to obtain the comprehensive risk value of a single pollutant. Step 6: Based on the positive definite matrix factorization model, combined with the pollutant source fingerprint characteristics and risk transmission network, the comprehensive risk value is decomposed to different pollution sources, and the initial contribution ratio of each pollution source to the total risk is calculated. Step 7: Identify the environmental transformation products of each pollutant, collect the PBMT characteristic parameters of the transformation products and iteratively calculate their comprehensive risk value, trace back to the parent pollutant, and correct the risk contribution of the parent pollutant. Step 8: Sort the pollutants and their corresponding pollution sources according to the revised risk contribution, and output the list of high-risk contribution pollutants and the list of priority control sources to complete the risk contribution identification of hazardous waste pollutants.

2. The method for identifying the risk contribution of hazardous waste pollutants based on PBMT features according to claim 1, characterized in that, In step one, the persistence P parameter includes aerobic biodegradation half-life, anaerobic biodegradation half-life, hydrolysis half-life, and photolysis half-life; the bioaccumulation B parameter includes bioaccumulation factor BCF and biomagnification factor BMF; the mobility M parameter includes organic carbon adsorption factor Koc, Henry's law constant, and diffusion coefficient; and the toxicity T parameter includes acute toxicity LD50 / LC50, chronic toxicity baseline dose, and carcinogenicity slope factor.

3. The method for identifying the risk contribution of hazardous waste pollutants based on PBMT features according to claim 1, characterized in that, In step two, the overall gas-water transfer coefficient of the fourth-order fugacity model... The transfer coefficient is calculated by combining the gas film transfer coefficient, liquid film transfer coefficient, and total suspended particulate matter adsorption transfer coefficient in series. The calculation formula is as follows: In the formula: This is the overall gas-water transfer coefficient, in units of... ; This is the air film transfer coefficient, in units of... ; The liquid film transfer coefficient is expressed in units of 1000 ppm. ; The adsorption transfer coefficient of the particulate phase is expressed in units of 1000 m³ / s. All transmission coefficients have the same unit, satisfying the series resistance calculation rules.

4. The method for identifying the risk contribution of hazardous waste pollutants based on PBMT features according to claim 1, characterized in that, In step three, the PBMT four-dimensional feature grading benchmark adopts a four-level grading system, corresponding to low hazard, medium hazard, high hazard, and extremely high hazard levels, with score intervals of [0, 0.25), [0.25, 0.5), [0.5, 0.75), and [0.75, 1] ​​for each level. During normalization, negative indicators are converted using a reciprocal before being substituted into the S-shaped function for calculation. The organic carbon adsorption coefficient in the migration characteristics is a negative indicator, and the conversion formula is: In the formula: This is the organic carbon adsorption coefficient, in units of... ; The converted positive index value, in units of The score becomes dimensionless after subsequent normalization.

5. The method for identifying the risk contribution of hazardous waste pollutants based on PBMT features according to claim 1, characterized in that, In step four, the first level of the three-tiered risk transmission network consists of hazardous waste release source nodes, comprising n pollutant sub-nodes; the second level consists of environmental media nodes, comprising four media sub-nodes: atmosphere, water, soil, and sediment; and the third level consists of receptor nodes, comprising three receptor sub-nodes: human health, terrestrial ecology, and aquatic ecology. The transmission efficiency of a single path also needs to be adjusted by the distribution coefficient between media, and the adjustment formula is as follows: In the formula: Let be the partition coefficient of pollutant i between media k and l, which is dimensionless; and be the corrected transfer efficiency. It is a dimensionless value.

6. The method for identifying the risk contribution of hazardous waste pollutants based on PBMT features according to claim 1, characterized in that, In step five, the calculation process of the improved entropy weight method is as follows: first, construct the PBMT feature judgment matrix, calculate the information entropy of each indicator, then introduce the deviation coefficient to correct the entropy weight, and finally obtain the persistence weight, bioaccumulation weight, migration weight and toxicity weight. The TOPSIS method uses the maximum value combination of each feature as the positive ideal solution and the minimum value combination as the negative ideal solution, and calculates the Euclidean distance of each pollutant to the positive and negative ideal solutions to obtain the proximity.

7. The method for identifying the risk contribution of hazardous waste pollutants based on PBMT features according to claim 1, characterized in that, In step six, the inputs to the positive definite matrix factor decomposition model include pollutant concentration spectrum, PBMT characteristic spectrum and environmental medium distribution spectrum. The number of factors obtained by decomposition is determined by the silhouette coefficient method. Each factor corresponds to a type of pollution source, and the initial contribution ratio is the proportion of the risk value corresponding to the factor to the total risk value.

8. The method for identifying the risk contribution of hazardous waste pollutants based on PBMT features according to claim 1, characterized in that, Step 7: Identify the environmental transformation products of each pollutant, collect the PBMT characteristic parameters of the transformation products, and substitute them into the calculation of their comprehensive risk value in steps 1 to 5. Based on the transformation yield and path backtracking coefficient, backtrack to the parent pollutant to correct the risk contribution of the parent pollutant. The corrected comprehensive risk value of the i-th parent pollutant is then calculated. The calculation formula is: In the formula: The total number of transformation products of the i-th parent pollutant, dimensionless; The molar yield of product q from parent i is dimensionless. The comprehensive risk value of the qth conversion product is expressed in units of... ; is the risk backtracking coefficient from product q to parent i, which is dimensionless; This is the risk attenuation coefficient for the transformation path, in units of... ; The environmental migration distance between the parent organism and the product, in units of ; Corrected composite risk value The unit is The units are consistent with those before the correction.

9. The method for identifying the risk contribution of hazardous waste pollutants based on PBMT features according to claim 1, characterized in that, In step eight, the list of high-risk contributing pollutants is sorted from high to low according to their risk contribution, and the core PBMT dominant characteristics and main exposure media of each pollutant are marked. The priority control source list indicates the risk contribution ratio of each pollution source and key control points.

10. The method for identifying the risk contribution of hazardous waste pollutants based on PBMT features according to claim 1, characterized in that, The method also includes an uncertainty analysis step, which uses Monte Carlo simulation to sample the fluctuations in the PBMT parameters and calculates the 95% confidence interval of the risk contribution. During the simulation, it is assumed that each PBMT parameter follows a log-normal distribution, and the sampled value of the j-th parameter is: In the formula: The logarithmic mean of the parameters is dimensionless. The logarithm and standard deviation of the parameter are dimensionless. These are dimensionless random numbers that are distributed according to a standard normal distribution. After performing N sampling operations, the 2.5% and 97.5% quantiles of the risk contribution are calculated as the upper and lower limits of the 95% confidence interval, and the coefficient of variation is output. As a quantitative indicator of the reliability of the identification results, among which The standard deviation of the risk value. The CV is the mean risk value, expressed in percentiles.

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