Method for determining industry soil and groundwater preferential management and control pollutants through multi-stage screening

Abstract

The invention discloses a method for determining industrial soil and underground water preferential management and control pollutants through multistage screening, and belongs to the technical field of environment management and pollutant risk assessment.The method comprises the steps that an industrial pollutant database of an area to be managed and controlled is constructed; forming a specific pollutant list; quantitative indexes are determined, a TOPSIS method is adopted to sort the pollutants, and preferential control pollutants are determined; according to the method, a three-stage progressive screening framework of'suspected pollutant library-particular pollutant-preferential control pollutant 'is carried out, a multi-criterion comprehensive evaluation system of a weight and TOPSIS sorting model is fused, multi-dimensional risk quantitative sorting is carried out on the pollutants, and the pollutants needing preferential control are accurately locked; the defects that a traditional screening technology is high in subjectivity, single in consideration dimension, lack of industry pertinence and the like are overcome, the comprehensiveness, scientificity and accuracy of pollutant screening are remarkably improved, and effective technical support is provided for environmental risk management and control.

Description

Technical Field

[0001] This invention relates to the field of environmental management and pollutant risk assessment technology, specifically to a method for determining priority pollutants for control in industry soil and groundwater through multi-level screening. Background Technology

[0002] Currently, pollutant screening mainly relies on general inventories. Existing technologies have explored techniques such as comprehensive scoring methods, potential hazard index methods, multi-criteria screening, and non-targeted analysis to identify and prioritize pollutants. For example, some studies have improved the accuracy of screening by constructing industry-specific pollutant databases, multi-criteria screening models, and the analytic hierarchy process (AHP)-entropy joint weighting method; others focus on the application of non-targeted analysis techniques in the identification of trace organic matter. These methods have promoted the development of pollutant screening technologies to some extent, but they are still mainly general-purpose and lack in-depth integration with regionally specific industrial structures and pollution characteristics.

[0003] Therefore, existing screening methods have significant limitations: First, they fail to fully consider the complex pollution characteristics of the region, including the intertwining of industrial, agricultural, and domestic pollution sources and the synergistic pollution of soil and groundwater, neglecting industry-specific pollution profiles and spatial heterogeneity; second, they lack a tiered screening system (suspected case investigation - feature identification - priority control), resulting in incomplete pollutant coverage, insufficient targeting, and a high risk of missed or incorrect diagnoses; third, traditional technical weighting is subjective and heavily reliant on data, making it difficult to dynamically adapt to regional industrial and hydrogeological conditions. With increasing policy requirements for source control, there is an urgent need to construct a regionally specific screening system centered on multi-criteria comprehensive screening technology and encompassing a three-tiered list, in order to achieve precise risk management and support the ecological security and green transformation of the Pearl River Delta. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides a method for multi-level screening to identify priority pollutants for control in industry soil and groundwater.

[0005] A method for identifying priority pollutants for control in industry soil and groundwater through multi-level screening includes the following steps: Establish a database of industry pollutants in areas to be controlled; Characteristic pollutants that can be actually detected are selected from the industry pollutant database to form a list of characteristic pollutants; Determine quantitative indicators to describe the hazards of pollutants in the characteristic pollutant inventory, and assign weights to the quantitative indicators; Based on quantitative indicators and weights, the TOPSIS method is used to rank the pollutants in the characteristic pollutant list, and priority pollutants for control are determined according to the ranking results.

[0006] Explanation: The above method integrates multi-source information such as raw materials, processes, and emissions to construct a dynamically updated industry pollutant database, realizing full-chain pollutant traceability from source to end. By combining subjective weighting using AHP with objective weighting using entropy weighting, and applying the TOPSIS ideal point ranking method, the complex environmental behavior, ecotoxicology, and health risk attributes of pollutants are transformed into measurable and comparable calculation results. This scientifically quantifies the comprehensive environmental risk priority of pollutants, enabling the decision-making process to shift from qualitative to quantitative. It can efficiently and accurately screen out a few key pollutants that urgently need priority control from a massive amount of pollutants, greatly improving the accuracy and efficiency of soil and groundwater risk management.

[0007] Furthermore, the method for constructing the industry pollutant database for the area to be controlled is as follows: Obtain environmental sequences of all or part of the industries in the area to be controlled, wherein the environmental sequences consist of raw material data, process metabolic pathways and emission result data; Based on the environmental sequence, all pollutants generated by all or some industries in the area to be controlled are identified, an industry pollutant database is generated and updated in real time.

[0008] Note: The above method takes the environmental sequence as its starting point, comprehensively considering everything from the source of pollutants to their final emission. It generates a database through simulation, rather than relying on limited on-site monitoring or experience-based predictions. This allows it to cover all pollutants generated by all or some industries in the area to be controlled, providing comprehensive and accurate data. The real-time update mechanism ensures that the database keeps pace with industry dynamics, providing the latest and most accurate data support for subsequent pollution control, and helping to formulate more targeted and timely pollution prevention and control strategies.

[0009] Furthermore, the environmental sequence is obtained through historical environmental monitoring data of all or part of the industries in the area to be controlled, enterprise full-process pollution discharge data, and existing standards and literature data.

[0010] Note: The historical environmental monitoring data mentioned above provides a long-term record of pollution in the region, reflecting the distribution and change patterns of pollutants at different times; the enterprise's full-process pollution discharge data goes deep into the production process, accurately presenting the source and process of pollutant generation; existing standards and literature data provide authoritative basis and theoretical support for the assessment.

[0011] Furthermore, the step of screening out detectable characteristic pollutants from the industry pollutant database to form a list of characteristic pollutants includes: Pollutants from historical environmental monitoring data were selected from the industry pollutant database; Based on the enterprise's full-process pollution discharge data, pollutants in existing standards and literature data, sampling and testing are carried out in the area to be controlled to obtain detectable pollutants; the pollutants in the historical environmental monitoring data and the detectable pollutants constitute the characteristic pollutant list.

[0012] Note: The above method can make full use of monitoring data, and at the same time, it can conduct sampling and testing based on the enterprise's full-process pollution discharge data, existing standards and literature data to obtain detectable pollutants. It can accurately locate the pollutants that actually exist at present from the perspective of production source and theoretical norms, and combine on-site testing to ensure the authenticity and reliability of the data. This makes the list both historically continuous and practically relevant, providing a solid, comprehensive and accurate data basis for the subsequent precise control of soil and groundwater pollutants.

[0013] Furthermore, the quantitative indicators are obtained by quantifying the primary indicators; the primary indicators include: endowment indicators, migration indicators, persistence indicators, bioaccumulation indicators, acute toxicity indicators, and health impact indicators.

[0014] Furthermore, the quantitative indicators include: groundwater detection rate, groundwater exceedance rate and degree of groundwater exceedance among the occurrence indicators, soil detection rate, soil exceedance rate and degree of groundwater exceedance; adsorption factors among the mobility indicators; biodegradation and hydrolysis rates among the persistence indicators; bioaccumulation factors, bioaccumulation factors, and biotransformation half-life factors among the bioaccumulation indicators; pseudogib, water fleas, and zebrafish among the acute toxicity indicators; and carcinogenicity, teratogenicity, mutagenicity, and endocrine disruptors among the health impact indicators.

[0015] Note: The above method refines primary indicators into specific quantitative indicators covering multiple dimensions such as occurrence, mobility, persistence, bioaccumulation, acute toxicity, and health effects, comprehensively and accurately characterizing the features of pollutants. This provides rich and detailed data support for accurately assessing the hazards of pollutants, helping to scientifically and systematically identify priority pollutants for control and improving the accuracy and effectiveness of soil and groundwater pollution prevention and control.

[0016] Furthermore, assigning weights to the quantitative indicators includes: The AHP weight scaling method is used to obtain the first weight parameter of each quantitative indicator; The second weighting parameter of each quantitative indicator is calculated using the entropy weighting method. The first weight parameter and the second weight parameter are combined by multiplication synthesis method to calculate the comprehensive weight of each quantitative indicator. The comprehensive weight is used to assign weight to the quantitative indicator.

[0017] Explanation: The above method uses the AHP weight scaling method to obtain the first weight parameter, which fully considers expert experience and subjective judgment, hierarchicallyizes complex problems, and determines weights based on relative importance, ensuring that the weight settings align with actual decision-making logic. The entropy weight method calculates the second weight parameter, which objectively reflects the information content of each indicator based on the dispersion of the data itself, avoiding subjective bias. Then, the two are combined through a multiplicative synthesis method to obtain a comprehensive weight, which integrates subjective and objective factors and fully leverages the advantages of both methods, making the weight assignment of quantitative indicators more scientific, reasonable, and comprehensive, providing a solid and reliable basis for the subsequent accurate screening of priority pollutants for control.

[0018] Furthermore, the multiplicative synthesis method is shown in equation (1) below: (1) In equation (1), for n The first of the quantitative indicators j The comprehensive weight corresponding to each quantitative indicator for n The first of the quantitative indicators j The first weight parameter corresponding to each quantitative indicator, for n The first of the quantitative indicators j The second weight parameter corresponding to each quantitative indicator.

[0019] Note: The above formula makes the final calculated comprehensive weight more scientific, comprehensive and reasonable, and can more accurately reflect the relative importance of each quantitative indicator in assessing the harm of pollutants, providing a reliable basis for determining the priority control of pollutants in the industry's soil and groundwater.

[0020] Furthermore, the ranking of pollutants in the characteristic pollutant list based on quantitative indicators and weights using the TOPSIS method includes: A decision matrix is ​​constructed using pollutants from the characteristic pollutant list as rows and quantitative indicators as columns. The decision matrix is ​​standardized to obtain a standardized matrix; the standardization process uses vector normalization or range standardization. The standardized matrix is ​​weighted and then weighted to obtain the weighted matrix; Based on the weighted matrix, the relative closeness between each pollutant and its ideal solution is calculated; the ideal solution of the pollutant represents the best or worst value of each quantitative index of the pollutant in the weighted matrix. Pollutants are prioritized based on their relative proximity; the higher the relative proximity, the higher the priority of the pollutant. The method of determining priority pollutants based on the ranking results is as follows: pollutants that rank in the top 30% of the pollutant priority ranking are designated as priority pollutants.

[0021] Explanation: The above scheme constructs a decision matrix that maps pollutants to quantitative indicators; it processes the decision matrix using vector normalization or range standardization to eliminate the influence of different indicator dimensions; it assigns weights and calculates a weighted matrix to strengthen the role of key indicators; it calculates relative proximity to measure the distance between pollutants and the ideal solution, intuitively reflecting the degree of pollution hazard; it ranks pollutants according to relative proximity to scientifically and accurately determine their priority; finally, it prioritizes the top 30-50% of pollutants as high-risk pollutants, which helps to rationally allocate resources and improve the efficiency and effectiveness of soil and groundwater pollution prevention and control.

[0022] Furthermore, the calculation of the relative proximity between each pollutant and its ideal solution based on the weighted normalization matrix includes: Determine the positive and negative ideal solutions for each pollutant in the weighted normalization matrix; Calculate the Euclidean distance between each pollutant and the positive and negative ideal solutions, respectively; Based on Euclidean distance, the relative proximity is calculated using the following formula (2); (2); In equation (2), For relative closeness, Let Euclidean distance be the pollutant to the positive ideal solution. Let be the Euclidean distance from the pollutant to the negative ideal solution, where The value range is 0~1. The closer it is to 1, the higher the relative similarity. The closer it is to 0, the lower the relative similarity.

[0023] Explanation: The above method establishes clear optimal and worst-case reference standards for assessing the degree of pollution hazard by determining positive and negative ideal solutions; calculating the Euclidean distance between each pollutant and the positive and negative ideal solutions can intuitively and quantitatively reflect the degree of deviation of pollutants from the ideal state; and using a specific formula to calculate the relative closeness based on Euclidean distance makes the pollutant hazard assessment more scientific and accurate, providing solid and reliable data support for rationally determining pollutant priorities and priority control targets.

[0024] The beneficial effects of this invention are: This invention integrates multi-source information such as raw materials, processes, and emissions to construct a dynamically updated industry pollutant database, enabling full-chain pollutant traceability from source to end. By combining subjective weighting using AHP with objective weighting using entropy weighting, and employing the TOPSIS ideal point ranking method, the complex environmental behavior, ecotoxicology, and health risk attributes of pollutants are transformed into measurable and comparable calculation results. This scientifically quantifies the comprehensive environmental risk priority of pollutants, enabling the decision-making process to shift from qualitative to quantitative. It can efficiently and accurately screen out a few key pollutants that urgently need priority control from a massive amount of pollutants, greatly improving the accuracy and efficiency of soil and groundwater risk management. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating an embodiment of the present invention; Figure 2 This is a schematic diagram of the technical analysis route of an embodiment of the present invention; Figure 3 This is the source of the electroplating industry survey data in the embodiments of the present invention; Figure 4 This is a diagram showing the percentage of suspected pollutants in the electroplating industry in an embodiment of the present invention; Figure 5 This is a diagram showing the percentage of characteristic pollutants in the soil of the electroplating industry in this embodiment of the invention; Figure 6 This is a diagram showing the percentage of characteristic pollutants in groundwater from the electroplating industry, as described in this invention. Figure 7 This is a schematic diagram of the electroplating industry judgment matrix in an embodiment of the present invention. Detailed Implementation

[0026] To further illustrate the methods and effects of this invention, the technical solution of this invention will be clearly and completely described below in conjunction with experiments.

[0027] Example: A method for identifying priority pollutants for control in industrial soil and groundwater through multi-level screening, comprising the following steps: Figure 1 , Figure 2 As shown; S1. Construct a database of industry pollutants for the areas to be controlled; The industry pollutant database includes pollutant name, CAS number, type, and main source information; the construction method includes S1-1 to S1-2. S1-1. Obtain the environmental sequence of all or part of the industries in the area to be controlled, wherein the environmental sequence is composed of raw material data, process metabolic pathways and emission result data arranged in chronological order; Specifically, raw material data is obtained through bill of materials management and chemical composition analysis; this includes collecting enterprise raw material safety data sheets and purchase lists, and using standard databases such as the "Chemical Classification and Labelling Specification" for impurity identification.

[0028] The metabolic pathways of the process were realized through chemical process simulation and reaction mechanism studies. Specifically, process simulation software such as Aspen Plus was used, combined with knowledge of chemical reaction equations and catalytic mechanisms from the literature, to construct a chemical transformation network from raw materials to products.

[0029] Emissions data are obtained through multi-media environmental models. Specifically, assessment models such as the EPI Suite from the US EPA are used to predict the distribution coefficients and degradation half-lives of pollutants in air, water, soil, and sediment.

[0030] S1-2. Based on the environmental sequence, determine all pollutants generated by all or part of the industries in the area to be controlled, generate an industry pollutant database and update it in real time; For example, this step utilizes cheminformatics tools such as ChemAxon to automatically enumerate all possible reaction intermediates, byproducts, and degradation products through algorithms, generating a list containing all theoretically possible chemical substances. The environmental sequence is obtained through historical environmental monitoring data of all or some industries in the area to be controlled, full-process pollution discharge data of enterprises, and existing standards and literature data; specifically, it includes the following steps a~e: a. Collect historical data: Collect soil and groundwater environmental survey data from the past 5 years in relevant industrial clusters in the Pearl River Delta to obtain pollutant indicators and concentrations.

[0031] b. Enterprise Full-Process Pollution Discharge Analysis: Conduct a full-chain investigation of "raw materials-production-emission" for three key industries: electroplating, leather making, and ceramics. Combine the enterprise's environmental impact assessment report, cleaner production audit report, and pollution discharge permit information to count the types, quantities, and discharge destinations of pollutants emitted by each enterprise and establish an enterprise pollutant emission database.

[0032] c. Analyze the raw material usage list, intermediate products and waste composition, add any missing pollutants to the suspected pollutant list, and specify the pollutant detection value, detection limit, emission standard and degree of exceedance.

[0033] d. Find local standards and other information: collect local standards and other information on air, water, and solid waste in the electroplating, leather making, and ceramics industries, and sort out information on suspected pollutants.

[0034] e. Review literature and research findings: Review domestic and international literature on soil pollution from similar industries, and draw on the types and characteristics of typical pollutants generated by similar industries as a selection reference.

[0035] For example, consider a gas-liquid process in an electroplating industry in southern China; the source of the land data is [see details]. Figure 3 The pollutant percentage chart in the database can be found here. Figure 4 The above S1 steps identified a total of 124 pollutants, including 26 metals and inorganic substances: fluoride, cyanide, tin, cadmium, lead, chromium, copper, zinc, nickel, mercury, arsenic, manganese, hexavalent chromium, iron, aluminum, silver, ammonia, selenium, vanadium, beryllium, boron, antimony, thallium, cobalt, molybdenum, and palladium. 24 halogenated hydrocarbons (including halogenated benzenes) are included, including trichloroethylene, chloroform, tetrachloroethylene, 1,2-dichloroethane, dichloromethane, carbon tetrachloride, chloromethane, 1,1-dichloroethane, 1,1-dichloroethylene, cis-1,2-dichloroethylene, trans-1,2-dichloroethylene, 1,2-dichloropropane, vinyl chloride, 1,2,3-trichloropropane, chlorobenzene, 1,2,3-trichlorobenzene, 1,2,4-trichlorobenzene, o-dichlorobenzene, m-dichlorobenzene, p-dichlorobenzene, 1,1,1,2-tetrachloroethane, 1,1,2,2-tetrachloroethane, 1,1,1-trichloroethane, and 1,1,2-trichloroethane. Seventeen perfluorinated compounds are listed, including perfluorooctanoic acid, perfluorovaleric acid, perfluorohexanoic acid, perfluorononanoic acid, perfluorodecanoic acid, perfluoroheptanoic acid, perfluorobutyric acid, perfluoroundecanoic acid, perfluorohexanesulfonic acid, 6:2 chloropolyfluoroether sulfonic acid, perfluorobutane sulfonate, perfluorododecanoic acid, sodium perfluorononenoxybenzenesulfonate, hexafluoropropylene oxide dimer acid, 6:2 fluorosulfonic acid, potassium 11-chloroeicosicofluoro-3-oxoundecane-1-sulfonate, and perfluorooctanesulfonyl compounds; 16 polycyclic aromatic hydrocarbons are listed, including benzo(… a) Pyrene, benzo(b)fluoranthene, benzo(g,h,i)pyrene, benzo[k]fluoranthene, phenanthrene, chrysene, fluorene, dibenzo(a,h)anthene, acenaphthene, acenaphthene, anthracene, inden(1,2,3-cd)pyrene, fluoranthene, benzo[k]anthene, pyrene, naphthalene; 9 benzene series compounds, including: benzene, toluene, o-xylene, m-xylene, p-xylene, styrene, ethylbenzene, 1,2,4-trimethylbenzene, 1,3,5-trimethylbenzene; 6 polychlorinated biphenyls, including 2,2',5,5'-tetrachlorobiphenyl, 3 The following compounds are listed: 3',4,4'-tetrachlorobiphenyl, 2,3',4,4',5-pentachlorobiphenyl, 2,3,3',4,4'-pentachlorobiphenyl, 3,3',4,4',5-pentachlorobiphenyl, and 2,4,4'-trichlorobiphenyl; 4 ketones, including acetone, 2-butanone, 4-methyl-2-pentanone, and cyclohexanone; and 4 phthalate (titanium ester) compounds, including di(2-ethylhexyl) phthalate, dibutyl phthalate, dimethyl phthalate, and phthalic acid. Dioctyl ester; 3 hydrocarbons: dodecane, hexadecane, octadecane; 3 ethers: propylene glycol monomethyl ether, diethylene glycol monobutyl ether, nonylphenol polyoxyethylene ether; 2 phenols: phenol, 2-chlorophenol; 2 esters: n-butyl acetate, isoamyl acetate; 2 anilines: aniline, 2,2'-dihydroxydiethylamine; 1 alcohol: n-butanol; 1 aldehyde: formaldehyde; 5 other pollutants: acrylic acid, petroleum hydrocarbons, 2-butoxyethanol, 2-acrylonitrile, nitrobenzene.

[0036] S2. Select detectable characteristic pollutants from the industry pollutant database to form a list of characteristic pollutants; specifically including: Pollutants from historical environmental monitoring data were selected from the industry pollutant database; Based on the enterprise's full-process pollution discharge data, pollutants in existing standards and literature data, sampling and testing are carried out in the area to be controlled to obtain detectable pollutants; the pollutants in the historical environmental monitoring data and the detectable pollutants constitute the characteristic pollutant list.

[0037] Sampling and testing specifically involves analyzing on-site samples using instruments such as GC-MS / LS-MS, and performing non-targeted and suspicious screening analyses. Specifically, mass spectrometry data processing software (such as MS-DIAL and Compound Discoverer) is used for peak detection, deconvolution, and molecular formula prediction to analyze and assign structures to mismatched chromatographic peaks.

[0038] For example, by combining soil pollution survey data from key industry enterprises, self-monitoring data of soil and groundwater from these enterprises, and monitoring data of soil and groundwater surrounding these enterprises, soil monitoring data and groundwater pollution survey data from the electroplating, leather, and ceramics industries over the past five years were obtained, and the types of metals and organic compounds detected were summarized. Through targeted and non-targeted data monitoring at the project site, pollution factors specific to the electroplating, leather, and ceramics industries were extracted and added to the characteristic pollutant list. Based on the above methods, a characteristic pollutant list for the electroplating, leather, and ceramics industries in the industrial cluster area was selected and determined. This list includes pollutant monitoring values, detection limits, evaluation standards, degrees of exceedance, and source information, providing a foundation for subsequent screening of priority control pollutants. ① Screening of characteristic pollutants in soil; After screening, such as Figure 5 As shown, a total of 64 soil pollutants were identified, as detailed below: The list includes 18 halogenated hydrocarbons (including halogenated benzenes), namely 1,1,2,2-tetrachloroethane, 1,1,2-trichloroethane, 1,1-dichloroethane, 1,2,3-trichloropropane, 1,2-dichlorobenzene, 1,2-dichloropropane, 1,2-dichloroethane, dichloromethane, trichloroethylene, carbon tetrachloride, tetrachloroethylene, cis-1,2-dichloroethylene, trans-1,2-dichloroethylene, chlorobenzene, chloroform, chloromethane, and vinyl chloride.

[0039] Fifteen metals and inorganic compounds, including total fluoride, cyanide, manganese, copper, zinc, lead, nickel, vanadium, cadmium, chromium, mercury, cobalt, arsenic, total chromium, and hexavalent chromium. Eight polychlorinated biphenyls, including PCB-114, PCB-118, PCB-123, PCB-156, PCB-157, PCB-189, PCB-77, and PCB-81. Eight polycyclic aromatic hydrocarbons, including benzo[a]pyrene, benzo[a]anthracene, benzo[b]fluoranthracene, benzo[k]fluoranthracene, dibenzo[a,h]anthracene, indo[1,2,3-cd]pyrene, naphthalene, and chloroform. Seven benzene compounds, including benzene, toluene, ethylbenzene, styrene, o-xylene, m-xylene, and p-xylene. One ketone, 4-methyl-2-pentanone. One aniline, aniline. One phenol, phenol. One aldehyde was listed: formaldehyde. One phthalate (titanium ester) was listed: di(2-ethylhexyl) phthalate. The other three items were: petroleum hydrocarbons (C10-C40), petroleum hydrocarbons (C6-C9), and nitrobenzene.

[0040] ② Screening of characteristic pollutants in groundwater; After screening, such as Figure 6 As shown, a total of 31 pollutants were identified in the groundwater, specifically: 16 metals and inorganics, including fluorides, cyanides, hexavalent chromium, vanadium, cadmium, chromium, mercury, cobalt, manganese, nickel, lead, arsenic, copper, zinc, silver, and total chromium; 5 benzene compounds, including benzene, toluene, ethylbenzene, m-xylene, and p-xylene; 4 halogenated hydrocarbons, including 1,2-dichloroethane, dichloromethane, 1,2-dichloropropane, and trichloroethylene; 2 phthalates (titanium esters), dibutyl phthalate and dioctyl phthalate; and 1 aldehyde, formaldehyde.

[0041] Table 1. List of Characteristic Pollutants in the Electroplating Industry

[0042] S3. Determine quantitative indicators for describing the hazards of pollutants in the characteristic pollutant list, and assign weights to the quantitative indicators. The quantitative indicators are obtained by quantifying the primary indicators; the primary indicators include: endowment indicators, migration indicators, persistence indicators, bioaccumulation indicators, acute toxicity indicators, and health impact indicators.

[0043] The quantitative indicators include: groundwater detection rate, groundwater exceedance rate, and groundwater exceedance degree among the occurrence indicators; soil detection rate, soil exceedance rate, and groundwater exceedance degree among the soil exceedance indicators; adsorption factors among the mobility indicators; biodegradation and hydrolysis rates among the persistence indicators; bioaccumulation factors, bioaccumulation factors, and biotransformation half-life factors among the bioaccumulation indicators; Pseudomonas aeruginosa, Daphnia davidii, and zebrafish among the acute toxicity indicators; and carcinogenicity, teratogenicity, mutagenicity, and endocrine disruptors among the health impact indicators. The specific explanation of the quantitative indicators is as follows: (1) O: includes six secondary indicators: groundwater detection rate O11, groundwater exceedance rate O12 and groundwater exceedance degree O13, soil detection rate O14, soil exceedance rate O15 and groundwater exceedance degree O16. The detection rate is calculated by dividing the number of tests by the total number of samples at all monitoring locations and time periods. The exceedance rate is determined by dividing the number of exceedances by the total number of samples. The exceedance degree is calculated by using the 95th percentile of the detected concentration to reduce the impact of outliers. The concentration of undetected (ND) values ​​is estimated by half of the method detection limit (MDL). (2) Migration (M): The adsorption factor (M11) is used as the evaluation index, and the adsorption factor Koc measures the strength of pollutant adhesion to the soil. The higher the value, the lower the migration rate. Koc is estimated using the KOCWIN v4.11 module in EPA EPI Suite v4.11; (3) Persistence (P): This includes two secondary indicators: biodegradation (P11) and hydrolysis rate (P12). Biodegradation was quantified using the Biowin4 results from the BIOWIN v4.10 module in EPA EPISuite v4.11. The hydrolysis rate was represented by estimating the pollutant hydrolysis half-life at pH=8 using the HYDROWIN v2.00 module in EPAEPI Suite v4.11. (4) Bioaccumulation (B): includes three secondary indicators: bioaccumulation factor (B11), bioaccumulation factor (B12), and biotransformation half-life factor (B13). All bioaccumulation values ​​(BCF, BAF, and BTF) were estimated using the BCFBAF v3.01 module in EPA EPI Suite v4.11. (5) Acute toxicity (T): Three secondary indicators were used to measure the toxicity: Pseudomonas sylvestris T11 (96h EC50), Daphnia T12 (48h EC50), and Zebrafish T13 (96h LC50). The US EPA Ecological Structure Activity Relationship (ECOSAR, v1.11) model in EPI Suite v4.11 was used for prediction (this may not be applicable to heavy metal pollutants). (6) Health effects (H), including four secondary indicators: carcinogenicity (H11), teratogenicity (H12), mutagenicity (H13), and endocrine disruptors (H14).

[0044] Specifically, the weight of each quantitative indicator is calculated using the AHP-entropy weight method, including the following steps I to IV; I. The pairwise comparison judgment matrix is ​​constructed by comparing each pair of quantitative indicators using the AHP scaling method; the comparison table is shown in Table 2; the judgment matrix for the electroplating industry is as follows: Figure 7 As shown; Table 2. Relative Importance Scale for Comparison of Each Pair of Quantitative Indicators Using the AHP Scale

[0045] II. Perform eigenvector calculation on the judgment matrix to obtain the first weight parameter of each quantification index; III. Analyze the distribution of historical environmental monitoring data for each pollutant on the quantitative indicators using the entropy weight method, and calculate the second weight parameter for each quantitative indicator based on the dispersion of the historical environmental monitoring data distribution. IV. The first weight parameter and the second weight parameter are combined by multiplication synthesis method to calculate the comprehensive weight of each quantitative indicator. The comprehensive weight is used to assign weight to the quantitative indicator.

[0046] The multiplicative synthesis method is shown in equation (1) below: (1) In equation (1), for n The first of the quantitative indicators j The comprehensive weight corresponding to each quantitative indicator for n The first of the quantitative indicators j The first weight parameter corresponding to each quantitative indicator, for n The first of the quantitative indicators j The second weight parameter corresponding to each quantitative indicator.

[0047] For this study, the screening criteria were used as the overall objective, i.e., the first level; the first-level indicators were used as the second level; and the second-level indicators were used as the third level. Judgment matrices were constructed for each level. The relative weights between two indicators at the same level were determined by statistical scoring from several experts using a 1-9 scale based on Table 4.4-9. Because the indicators are essentially the same across different environmental media, the constructed judgment matrices are also essentially the same. Based on the constructed judgment matrix, the eigenvectors of each indicator are calculated using the square root method. Then, the indicators are weighted according to the relative magnitude of the eigenvectors. The weighting results of the analytic hierarchy process (AHP) are basically the same in different environmental media. (2) Entropy weight method weight: The entropy weight method primarily relies on information entropy for weight calculation. According to the definition of information entropy, the entropy value can be used to judge the dispersion of a certain indicator. The smaller the information entropy, the more concentrated and stable the distribution of the indicator's values; relatively speaking, its corresponding weight should be larger in the comprehensive evaluation. Conversely, the larger the information entropy, the smaller the corresponding weight should be. This study mainly uses normalized indicators for entropy weight calculation.

[0048] (3) Overall weight; The overall weight is calculated using the formula (1) above. The final overall weight is shown in Tables 3 and 4: Table 3. Weights and Comprehensive Weights for Soil Hierarchy Analysis Method and Entropy Weight Method in the Electroplating Industry

[0049] Table 4. Weights and Comprehensive Weights of Groundwater Hierarchical Analysis Method, Entropy Weight Method, and Groundwater Analysis in the Electroplating Industry

[0050] The data above shows that the ranking of certain indicators is relatively stable under different weighting methods. For example, biodegradation (P1) and biotransformation half-life factor (B3) have relatively low values ​​under all three weighting methods, indicating that these two indicators are relatively unimportant in the overall importance assessment.

[0051] The hydrolysis rate (P2) has a moderate weight under the analytic hierarchy process (AHP), but a relatively high weight under the entropy weight method. This indicates that although some indicators are subjectively undervalued, they exhibit a relatively concentrated distribution in the data and require a larger weight to reflect the differences between different pollutants. The exceedance level (O3) has a high weight under the AHP, but a low weight under the entropy weight method. This suggests that although some indicators are subjectively highly valued, their distribution within the normalized interval is highly dispersed, easily influencing the screening results. Therefore, their weights need to be appropriately reduced to neutralize this influence. Further optimization of the weight allocation system is possible.

[0052] Comprehensive ranking of priority pollutants: To determine the ranking of priority pollutants, the evaluation index data of each pollutant were normalized and positiveized, transforming the sub-standard data into dimensionless values ​​within the range of 0-100 for subsequent calculations. For some indicators spanning multiple orders of magnitude, a logarithmic transformation with a suitable base was performed during normalization to prevent significant data dispersion. The specific normalization and positiveization formulas for each indicator are shown in Table 5. Table 5 lists the normalization and positive transformation formulas for each indicator.

[0053] The Top-to-Best Solution Distance (TOPSIS) method is a commonly used within-group comprehensive evaluation method that fully utilizes the information in the original data, and its results accurately reflect the differences between the evaluation schemes. Using the TOPSIS method based on the AHP-entropy weighting method, pollutants are ranked according to their scores and their proximity to the ideal solution.

[0054] The ranking of pollutant index scores and the distances between positive and negative ideal solutions in different media are shown in Tables 6 and 7. Table 6. Ranking of Soil Pollutants by TOPSIS Score in the Electroplating Industry

[0055] Table 7. Ranking of TOPSIS Scores for Groundwater Pollutants in the Electroplating Industry

[0056] S4. Based on quantitative indicators and weights, the TOPSIS method is used to rank the pollutants in the characteristic pollutant list, and the priority pollutants for control are determined according to the ranking results. include: S4-1. Construct a decision matrix using pollutants from the characteristic pollutant list as rows and quantitative indicators as columns. S4-2. Standardize the decision matrix to obtain a standardized matrix; the standardization process uses vector normalization or range standardization. The data for the 19 sub-indicators were summarized and standardized to ensure consistency and comparability. The sub-indicator data were normalized to dimensionless values ​​in the range of 0-100 using the following formula (3): (3) In the formula, U represents the normalized data, X represents the data before normalization, Xmax represents the highest detection value selected in the indicator data, and Xmin represents the lowest detection value selected in the indicator data. Values ​​exceeding Xmax are assigned 100 points, and values ​​below Xmin are assigned 0 points. Some indicators have a wide distribution, spanning multiple orders of magnitude. Logarithmic transformation is applied to prevent significant data dispersion and ensure the relative distinction between the values ​​of the indicators. For example, formula (4) is used. (4) S4-3. Assign weights to the standardized matrix and perform weighted calculations to obtain the weighted standardized matrix; S4-4. Based on the weighted standardization matrix, calculate the relative closeness between each pollutant and its ideal solution; the ideal solution of the pollutant represents the best and worst values ​​of each quantitative indicator of the pollutant in the weighted standardization matrix. The calculation of the relative proximity between each pollutant and its ideal solution based on a weighted normalization matrix includes: Determine the positive and negative ideal solutions for each pollutant in the weighted normalization matrix; Calculate the Euclidean distance between each pollutant and the positive and negative ideal solutions, respectively; Based on Euclidean distance, the relative proximity is calculated using the following formula (2); (2); In equation (2), For relative closeness, Let Euclidean distance be the pollutant to the positive ideal solution. Let be the Euclidean distance from the pollutant to the negative ideal solution, where The value range is 0~1. The closer it is to 1, the higher the relative similarity. The closer it is to 0, the lower the relative similarity.

[0057] S4-5. Based on relative proximity, prioritize the pollutants; wherein, the higher the relative proximity, the higher the priority of the pollutant. The method of determining priority pollutants based on the ranking results is as follows: pollutants that rank in the top 30-50% of the pollutant priority ranking are designated as priority pollutants.

[0058] In this embodiment of the invention, a list of priority pollutants is determined: the list of priority pollutants consists of the top 15 pollutants in the TOPSIS calculation score and the priority soil pollutants in the priority pollutant calculation list. Finally, 21 priority pollutants in the soil of the electroplating industry and 19 priority pollutants in the groundwater are selected.

[0059] The lists of priority pollutants for the electroplating industry in different environmental media are shown in Tables 8 and 9: Table 8. List of Priority Pollutants for Soil Control in the Electroplating Industry

[0060] Table 9. List of Priority Pollutants Controlled in Groundwater from the Electroplating Industry

[0061] In summary, this invention has developed a high-precision pollutant screening technology based on a multi-criteria comprehensive screening technique, comprising a "database of suspected pollutants - list of characteristic pollutants - priority control pollutants." Suspected pollutants are primarily listed and screened from historical site survey data, literature, and local standards. Characteristic pollutants are mainly obtained from historical and supplementary monitoring data, screening for detectable indicators within the region and excluding inorganic acids, alkalis, and salts. Priority control pollutants are screened based on existing characteristic pollutants, using 6 primary indicators and 19 secondary indicators (occurrence, migration, persistence, bioaccumulation, acute toxicity, and health effects). The weights of the indicators are determined using the analytic hierarchy process (AHP) and entropy weighting method with expert scoring, and the pollutants are ranked using the TOPSIS method to comprehensively determine the list of priority control pollutants. After screening, 124 suspected pollutants from the electroplating industry were identified, with the main pollutants being 26 metals and inorganic substances, 24 halogenated hydrocarbons, 17 perfluorinated compounds, and 16 polycyclic aromatic hydrocarbons, among others. The electroplating industry identified 64 characteristic pollutants in soil, primarily 18 halogenated hydrocarbons, 15 metals and inorganic compounds, and 8 polychlorinated biphenyls (PCBs). The electroplating industry also identified 31 characteristic pollutants in groundwater, primarily 16 metals and inorganic compounds, 5 benzene compounds, and 4 halogenated hydrocarbons. The electroplating industry also identified 21 priority pollutants for soil and 19 priority pollutants for groundwater, with the main pollutant types being metals and inorganic compounds, halogenated hydrocarbons, and benzene compounds.

[0062] In other embodiments of the present invention, taking the leather industry as an example, the leather industry has 224 suspected pollutants, mainly including 26 metals and inorganic substances, 25 halogenated hydrocarbons, and 22 titanates. The leather industry has 70 characteristic pollutants in its soil, mainly including 18 metals and inorganic substances, 15 halogenated hydrocarbons, and 12 polycyclic aromatic hydrocarbons; the leather industry has 88 characteristic pollutants in its groundwater, mainly including 18 metals and inorganic substances, 16 halogenated hydrocarbons, and 12 benzene compounds. The leather industry has 18 priority-controlled pollutants in its soil and 19 priority-controlled pollutants in its groundwater, mainly including metals and inorganic substances, halogenated hydrocarbons, and titanates.

[0063] In other embodiments of the present invention, taking the ceramics industry as an example, there are 94 suspected pollutants in the ceramics industry, with the main pollutant types being 28 metals and inorganic substances, 18 polychlorinated biphenyls (PCBs), and 14 halogenated hydrocarbons. There are 34 characteristic pollutants in the soil of the ceramics industry, with the main pollutant types being 13 polycyclic aromatic hydrocarbons (PAHs), 10 metals and inorganic substances, and 5 benzene series compounds, etc.; there are 22 characteristic pollutants in the groundwater of the ceramics industry, with the main pollutant types being 17 metals and inorganic substances and 2 benzene series compounds, etc. There are 21 priority pollutants for soil and 19 priority pollutants for groundwater in the ceramics industry, with the main pollutant types being PAHs and halogenated hydrocarbons.

[0064] After review and verification by this invention, the data screening of characteristic pollutants in the electroplating, leather making, and ceramics industries is accurate. The electroplating industry needs to supplement its monitoring with indicators such as silver and perfluorinated compounds in the soil; the prioritized pollutants for electroplating are basically consistent with the regional industry type. The leather making industry needs to supplement its monitoring with indicators such as AOX and PAEs in the soil, and DMF indicators in soil and groundwater; the prioritized pollutants are basically consistent with the regional industry type. The ceramics industry needs to supplement its monitoring with indicators such as thallium, boron, cobalt, barium, antimony, vanadium, beryllium, molybdenum, and phenols; the prioritized pollutants are basically consistent with the regional industry type.

Claims

1. A method for determining priority pollutants for soil and groundwater management in an industry through multi-level screening, characterized in that, Includes the following steps: Establish a database of industry pollutants in areas to be controlled; Detectable characteristic pollutants are screened from the industry pollutant database. Create a list of characteristic pollutants; Determine quantitative indicators to describe the hazards of pollutants in the characteristic pollutant inventory, and assign weights to the quantitative indicators; Based on quantitative indicators and weights, the TOPSIS method is used to rank the pollutants in the characteristic pollutant list, and priority pollutants for control are determined according to the ranking results.

2. The method for determining priority pollutants for soil and groundwater management in an industry through multi-level screening as described in claim 1, characterized in that, The method for constructing the industry pollutant database for the area to be controlled is as follows: Obtain the environmental sequence of all or part of the industries in the area to be controlled, wherein the environmental sequence consists of raw material data, process metabolic pathways and emission result data arranged in chronological order; Based on the environmental sequence, all pollutants generated by all or some industries in the area to be controlled are identified, an industry pollutant database is generated and updated in real time.

3. The method for determining priority pollutants for soil and groundwater management in an industry through multi-level screening as described in claim 1, characterized in that, The environmental sequence is obtained through historical environmental monitoring data of all or part of the industries in the area to be controlled, enterprise full-process pollution discharge data, and existing standards and literature data.

4. The method for determining priority pollutants for soil and groundwater management in an industry through multi-level screening as described in claim 1, characterized in that, The process involves selecting detectable characteristic pollutants from the industry pollutant database. A list of characteristic pollutants has been compiled, including: Pollutants from historical environmental monitoring data were selected from the industry pollutant database; Based on the enterprise's full-process pollution discharge data, pollutants in existing standards and literature data, sampling and testing are carried out in the area to be controlled to obtain detectable pollutants; the pollutants in the historical environmental monitoring data and the detectable pollutants constitute the characteristic pollutant list.

5. The method for determining priority pollutants for soil and groundwater management in an industry through multi-level screening as described in claim 1, characterized in that, The quantitative indicators are obtained by quantifying the primary indicators; the primary indicators include: endowment indicators, migration indicators, persistence indicators, bioaccumulation indicators, acute toxicity indicators, and health impact indicators.

6. The method for determining priority pollutants for soil and groundwater management in an industry through multi-level screening as described in claim 5, characterized in that, The quantitative indicators include: groundwater detection rate, groundwater exceedance rate and degree of groundwater exceedance among the occurrence indicators, soil detection rate, soil exceedance rate and degree of groundwater exceedance; adsorption factors among the mobility indicators; biodegradation and hydrolysis rates among the persistence indicators; bioaccumulation factors, bioaccumulation factors, and biotransformation half-life factors among the bioaccumulation indicators; Pseudomonas, Daphnia, and Zebrafish among the acute toxicity indicators; and carcinogenicity, teratogenicity, mutagenicity, and endocrine disruptors among the health impact indicators.

7. The method for determining priority pollutants for industrial soil and groundwater through multi-level screening as described in claim 1, characterized in that, The process of assigning weights to the quantitative indicators includes: The AHP weight scaling method is used to obtain the first weight parameter of each quantitative indicator; The second weighting parameter of each quantitative indicator is calculated using the entropy weighting method. The first weight parameter and the second weight parameter are combined by multiplication synthesis method to calculate the comprehensive weight of each quantitative indicator. The comprehensive weight is used to assign weight to the quantitative indicator.

8. The method for determining priority pollutants for industrial soil and groundwater through multi-level screening as described in claim 7, characterized in that, The multiplicative synthesis method is shown in equation (1) below: (1) In equation (1), for n The first of the quantitative indicators j The comprehensive weight corresponding to each quantitative indicator for n The first of the quantitative indicators j The first weight parameter corresponding to each quantitative indicator, for n The first of the quantitative indicators j The second weight parameter corresponding to each quantitative indicator.

9. The method for determining priority pollutants for soil and groundwater management in an industry through multi-level screening as described in claim 1, characterized in that, The ranking of pollutants in the characteristic pollutant list based on quantitative indicators and weights using the TOPSIS method includes: A decision matrix is ​​constructed using pollutants from the characteristic pollutant list as rows and quantitative indicators as columns. The decision matrix is ​​standardized to obtain a standardized matrix; the standardization process uses vector normalization or range standardization. The standardized matrix is ​​weighted and then weighted to obtain the weighted matrix; Based on the weighted matrix, the relative closeness between each pollutant and its ideal solution is calculated; the ideal solution of the pollutant represents the best or worst value of each quantitative index of the pollutant in the weighted matrix. Pollutants are prioritized based on their relative proximity; the higher the relative proximity, the higher the priority of the pollutant. The method of determining priority pollutants based on the ranking results is as follows: pollutants that rank in the top 30-50% of the pollutant priority ranking are designated as priority pollutants.

10. The method for determining priority pollutants for industrial soil and groundwater through multi-level screening as described in claim 9, characterized in that, The calculation of the relative proximity between each pollutant and its ideal solution based on a weighted normalization matrix includes: Determine the positive and negative ideal solutions for each pollutant in the weighted normalization matrix; Calculate the Euclidean distance between each pollutant and the positive and negative ideal solutions, respectively; Based on Euclidean distance, the relative proximity is calculated using the following formula (2); （2）； In equation (2), For relative closeness, Let Euclidean distance be the pollutant to the positive ideal solution. Let be the Euclidean distance from the pollutant to the negative ideal solution, where The value range is 0~1. The closer it is to 1, the higher the relative similarity. The closer it is to 0, the lower the relative similarity.

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