Groundwater pollution early warning index determination method and system for sea-land interaction zone chemical industry park

Abstract

The invention discloses a sea-land interaction zone chemical industry park groundwater pollution early warning index determination method and system, and the method comprises the steps: building a pollutant directory according to the hydrogeological conditions of a chemical industry park, and dividing the pollutant directory into inorganic pollutants and organic pollutants; constructing an entropy weight method-mathematical fuzzy evaluation model to carry out comprehensive evaluation on the inorganic pollutants to obtain an index sequence of the inorganic pollutants; evaluating the exposure potential EP and the toxicity potential HP of the organic pollutants according to the actually measured data of the organic pollutants and considering various evaluation parameters of the organic pollutants; obtaining final organic pollution indexes according to the EP and the HP of the organic indexes, and sorting the final organic pollution indexes; and according to the sorting result of the inorganic pollution indexes and the organic pollution indexes, determining the early warning indexes of the inorganic and organic pollutants in the underground water in the chemical industry park by combining the underground water quality standard. According to the method, objectivity and scientificity of pollutant identification are improved, and the problems that current pollution index selection is high in subjectivity, single in evaluation dimension, poor in regional applicability and the like are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of environmental pollution early warning, and in particular relates to a method and system for determining groundwater pollution early warning indicators in a chemical park in a sea-land interaction zone. Background Art

[0002] The land-sea transition zone, where marine and terrestrial systems interact most frequently, possesses unique geological, geomorphological, and hydrological characteristics. Because its groundwater systems are frequently influenced by complex factors such as tides, water-level fluctuations, and seawater intrusion, hydrodynamic conditions fluctuate dramatically, and pollutant migration and transformation behaviors exhibit distinct patterns compared to inland areas. In coastal areas, groundwater recharge, runoff, and discharge patterns are often controlled by multiple media—surface water, groundwater, and seawater—resulting in highly spatially heterogeneous and temporally unstable pollutant sources and distributions.

[0003] With the rapid development of coastal economic zones, a large number of chemical companies have gradually concentrated along the coast, forming dense chemical industrial parks. Chemical parks are highly susceptible to complex and multi-faceted groundwater pollution during processes such as raw material transportation, product storage, production emissions, and accidental leaks. This is particularly true of persistent and toxic organic micropollutants, which pose a potential pollution risk in complex hydrogeological environments. Currently, most groundwater pollution incidents are directly linked to areas with dense chemical plant concentrations, making early warning and prevention difficult.

[0004] When it comes to pollution identification and risk assessment, traditional methods rely primarily on static judgments about whether groundwater pollutant concentrations exceed limits, or on evaluating the extent of pollution using a single-factor pollution index. While simple and easy to use, these methods generally suffer from the following flaws: First, they fail to fully integrate the region's actual geological background, pollution source structure, and pollutant environmental behavior, lacking specificity; second, the selection of pollution indicators is subjective and ignores the synergistic relationship between the exposure probability and toxic effects of the pollutants themselves; and third, they lack a systematic indicator optimization method for the coexistence of multiple pollutants, making it difficult to support accurate early warning, prevention, and control of groundwater pollution.

[0005] On the other hand, some current pollution identification research has introduced multi-indicator, multi-dimensional comprehensive assessment approaches, attempting to rank and screen pollutants through statistical models, entropy weighting methods, fuzzy mathematics, and other methods. However, most methods fail to effectively distinguish between inorganic substances with large fluctuations in natural background values ​​and organic pollutants primarily derived from human activities. Furthermore, the identification of organic micropollutants focuses more on determining if concentrations exceed standards, with less consideration given to organic pollutants at lower concentrations, as well as the combined impact of exposure intensity (such as detection rate) and toxic effects (such as bioaccumulation, in vitro and in vivo toxicity, etc.), making it difficult to develop a forward-looking early warning indicator system.

[0006] Therefore, there is an urgent need to develop a multi-parameter hybrid model approach that integrates the characteristics of the land-sea interaction zone, incorporates the differences between organic and inorganic pollution, and takes into account multi-dimensional characteristics such as pollution concentration, exposure probability, and toxic effects. This approach should be able to comprehensively identify potential groundwater pollutants, especially in land-sea interaction zones where chemical parks are concentrated. This approach should provide a theoretical basis and decision-making tools for the scientific screening and priority control of early warning pollution indicators, and provide a theoretical basis for subsequent ecological and environmental risk assessments. Summary of the Invention

[0007] The purpose of the present invention is to address the shortcomings of the existing technology and provide a method for determining groundwater pollution warning indicators in chemical parks in the sea-land interaction zone. The method comprehensively considers multiple information such as pollutant concentration, source attributes, persistence, toxicity and detection frequency, and integrates a multi-indicator evaluation system under different data dimensions to improve the objectivity and scientificity of pollutant identification. It overcomes the problems of strong subjectivity in indicator selection, single evaluation dimension, and poor regional applicability in the existing groundwater pollution identification methods.

[0008] In order to solve the above technical problems, the present invention adopts the following technical solutions: A method for determining groundwater pollution early warning indicators in a chemical park in a sea-land interaction zone comprises the following steps: Step 1: Establish a pollutant inventory based on the hydrogeological conditions of the chemical park and divide it into inorganic pollutants and organic pollutants; Step 2: Construct an entropy weight method-mathematical fuzzy evaluation model to comprehensively evaluate inorganic pollutants and obtain the ranking results of various inorganic pollutant indicators; Step 3: Based on the actual measured organic pollutant data and considering various evaluation parameters, the exposure potential (EP) of the organic pollutants is evaluated, and priority-controlled organic pollutants are screened out based on the exposure potential (EP). The toxicity potential (HP) of the selected priority-controlled organic pollutants is then evaluated. Step 4: Obtain the final pollution index based on the exposure potential EP and toxicity potential HP of the organic indicators, and rank the pollution indexes of the organic indicators to obtain the organic pollution ranking results; Step 5: Based on the ranking results of inorganic pollution indicators and organic pollution indicators and combined with groundwater quality standards, determine the early warning indicators of inorganic and organic pollutants in the chemical park groundwater.

[0009] Furthermore, the method for constructing the entropy weight method-mathematical fuzzy evaluation model in step 2 includes: The inorganic pollutant indicators are divided into pollution levels, and the occurrence frequency of different inorganic pollutants in different pollution intervals is counted to construct a standardized frequency matrix; Based on the above, the entropy weight method is introduced to calculate the pollution level weight and obtain the level weight vector W; After obtaining the pollution level weight, the membership function is constructed by combining the concentration value of each inorganic pollution index with the pollution level boundary, and the membership degree of each sample point under each pollution level is calculated to generate the fuzzy membership matrix R; Finally, the pollution level weight vector W and the fuzzy membership matrix R are weighted to obtain the entropy weight-mathematical fuzzy comprehensive evaluation model.

[0010] Furthermore, the fuzzy membership matrix R is constructed as follows: Determine the factor set based on the selected inorganic pollution index, use the pollution level classification as the evaluation set, and determine the fuzzy comprehensive judgment matrix R based on the membership function between the factor set and the evaluation set;

[0011] in, r ij Indicates the membership of the i-th sample point under the j-th pollution level; the calculation of the membership is based on the position of the pollutant concentration value x in the level boundary value interval. For negative indicators, the membership function belongs to the first level:

[0012] The membership function of the intermediate level, i.e., the 2nd to k-1th level, is:

[0013] The membership function belonging to the kth level, which is the worst level, is: ; in, c k Inorganic index in the k Pollution degree limit.

[0014] Furthermore, the exposure potential assessment methods for organic indicators include: First, the concentration and detection rate data of each pollutant were standardized to make them comparable under the same dimension; Subsequently, principal component analysis (PCA) was used to reduce the dimensionality of the two standardized indicators. The first principal component PC1 was extracted as the main axis to explain the variability in the data to the greatest extent. The PC1 score was normalized to obtain the EP value of each pollutant.

[0015] Furthermore, methods for screening out priority-controlled organic pollutants include: The normalized median EP value of 0.5 was used as the basic threshold, and pollutants with EP ≥ 0.5 were defined as having a high exposure potential; Secondly, the 67th percentile values ​​of pollutant concentrations and detection rates are further referenced as auxiliary screening criteria, that is, organic pollutants with an EP value greater than or equal to 0.5 and a concentration or detection rate that at least meets their respective 67th percentile standards are regarded as priority pollutants.

[0016] Further, methods for assessing the toxicity potential of HP include: HP assessment is based on four toxicity-related indicators: persistence, bioaccumulation, in vivo toxicity, and in vitro toxicity. The data for these indicators are standardized and then subjected to dimensionality reduction analysis using the PCA method. The first two principal components, PC1 and PC2, are extracted to comprehensively represent the toxicity of the pollutants. The scores of each pollutant on the PC1 and PC2 axes are multiplied by the proportion of explained variance corresponding to the principal component, and then a weighted average is taken to obtain the toxic potential value HP of the pollutant. The calculation formula is as follows: HP= ; in, and are the scores of pollutants on PC1 and PC2 respectively, and is the variance contribution rate of the corresponding principal component.

[0017] Furthermore, the final pollution index is calculated in step 4 as follows: Multiply the EP value and HP value of the selected priority organic pollutants to obtain the priority index PI that comprehensively reflects the exposure level and toxicity characteristics of the pollutants. The calculation formula is: PI=EP*HP; Organic pollutants are sorted according to PI values ​​to identify organic pollution of key concern.

[0018] The present invention also provides a system for implementing the above-mentioned method for determining pollution early warning indicators in chemical parks, comprising: The pollutant collection module is used to establish a pollutant list and classify it into inorganic pollutants and organic pollutants based on the hydrogeological conditions of the chemical park; The inorganic pollutant pollution level classification module is used to construct an entropy weight method-mathematical fuzzy evaluation model to conduct a comprehensive assessment of inorganic pollutants and obtain the ranking results of various inorganic pollutant indicators; The organic pollutant exposure and toxicity potential assessment module is used to assess the exposure potential (EP) of organic pollutants based on the actual measured organic pollutant data and taking into account various assessment parameters. It is also used to screen out priority-controlled organic pollutants based on the exposure potential (EP) and then assess the toxicity potential (HP) of the selected priority-controlled organic pollutants. The organic pollutant early warning indicator identification module is used to obtain the final pollution index based on the exposure potential EP and toxicity potential HP of the organic indicators, and to sort the pollution index of the organic indicators to obtain the organic pollution index ranking result; The groundwater pollutant index early warning module is used to determine the early warning indicators of inorganic and organic pollutants in groundwater of chemical parks based on the ranking results of inorganic pollution indicators and organic pollution indicators and combined with groundwater quality standards.

[0019] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0020] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0021] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention integrates the entropy weight method with the fuzzy mathematical model, principal component analysis, and toxicity potential comprehensive assessment method to establish a multi-parameter, multi-dimensional pollution identification system for inorganic and organic pollutants, which can systematically and scientifically identify representative and early warning groundwater pollution indicators; (2) The entropy weight method is used to objectively extract the information differences of inorganic pollutants at each pollution level, and the level attribution judgment is realized by combining the membership calculation; at the same time, the exposure potential and toxicity weight parameters are used to quantify the risk of organic pollutants to ensure that the results are both objective and environmental risk-oriented; (3) The method is closely integrated with the hydrogeological background of the land-sea interaction zone, the pollution source characteristics of chemical industrial parks, and the differences in pollutant behavior, and is effectively applicable to the screening of groundwater pollution early warning indicators in such complex areas; (4) Compared with the traditional method based on a single concentration threshold or empirical judgment, the present invention uses a joint model of multivariate statistics and weight evaluation model to reduce human subjective interference while improving the accuracy of index screening and evaluation efficiency, and has good engineering practicality; (5) The pollution index identification method established in this invention provides a quantitative basis for groundwater pollution risk warning, and can provide scientific support for the subsequent optimization of groundwater pollution monitoring points, risk assessment modeling, and pollution control strategy formulation, which will help to improve the efficient management and risk prevention and control capabilities of the groundwater environment in the land-sea interaction zone. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of a method for determining pollution warning indicators in a chemical park according to an embodiment of the present invention; Figure 2 This is a classification chart of inorganic pollutant indicators according to an embodiment of the present invention; Figure 3 This is a diagram of the analysis structure for analyzing and evaluating organic pollution pollutants according to an embodiment of the present invention; Figure 4 This is a diagram showing the calculation results of the organic pollutant priority index PI according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0024] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0025] The present invention will be further described below with reference to specific examples, but they are not intended to limit the present invention.

[0026] like Figure 1 As shown, the embodiment of the present invention discloses a method for determining groundwater pollution early warning indicators in a chemical park in a sea-land interaction zone, comprising the following steps: Step 1: Establish a comprehensive pollutant inventory based on the hydrogeological conditions of the chemical park and classify it into inorganic and organic pollutants; This example uses an active chemical park in the land-sea interface as an example. By investigating the company's raw material usage, emission inventories, and historical groundwater monitoring data, combined with a hydrogeological profile of the area, systematic sampling and analysis of organic and inorganic indicators were conducted. This led to the establishment of a preliminary pollutant inventory consisting of 64 indicators, including 16 inorganic pollutants (including nutrients, heavy metals, and inorganic anions) and 48 organic pollutants (including SVOCs and VOCs).

[0027] Step 2: Construct an entropy weight method-mathematical fuzzy evaluation model to comprehensively evaluate inorganic pollutants and obtain the ranking results of various inorganic pollutant indicators; In this step, the method of constructing the entropy weight method-mathematical fuzzy evaluation model includes: First, inorganic pollutants are classified into pollution levels based on their regional background values ​​and the Groundwater Quality Standard (GB / T 14848-2017). The frequency of occurrence of different inorganic pollutants in different pollution intervals is calculated to obtain a frequency matrix. The frequency matrix is ​​then standardized to obtain a standardized matrix: (1) is the number of occurrences of inorganic indicators in different pollution levels, is the maximum number of occurrences of different pollution levels, is the minimum value occurring at different pollution levels.

[0028] Further convert the normalized frequency matrix into a scale matrix: (2) Where, is the total number of occurrences of different inorganic indicators at the same pollution level. After obtaining the ratio matrix, the entropy weight method is introduced based on this to calculate the pollution level weight. Specifically, according to the information entropy theory, the information entropy value of the jth pollution level is calculated. H j : , (3) Among them, n is the number of evaluation indicators, k is the normalization constant; then according to the information entropy value H j Calculating weights W j : (4) Where, W j represents the weight of the jth pollution level, and m is the total number of pollution levels.

[0029] After obtaining the pollution level weights, the concentration values ​​of each pollution indicator and the pollution level boundaries are combined to construct a membership function, calculate the membership of each sample point under each pollution level, and generate a fuzzy membership matrix. Specifically, the factor set is determined based on the selected inorganic pollution indicators, and the pollution level classification is used as the evaluation set. The fuzzy comprehensive judgment matrix R is determined based on the membership function between the factor set and the evaluation set: (5) in, r ij Indicates the membership of the i-th sample point under the j-th pollution level. The calculation of membership is based on the position of the pollutant concentration value x in the level boundary value interval. For negative indicators, the membership function belongs to the first level: (6) The membership function for the intermediate levels (2nd to k-1th levels) is: (7) The membership function belonging to the kth level (the worst level) is: (8) in, c k Inorganic index in thek Pollution degree limit.

[0030] Finally, the fuzzy comprehensive judgment matrix R is multiplied by the weight set W of each pollution grade to perform a weighted operation to obtain the entropy weight-fuzzy comprehensive evaluation model. The entropy weight-fuzzy comprehensive evaluation model can be used to calculate the pollution identification value of each inorganic indicator at each sampling point, thereby performing a comprehensive ranking evaluation of the inorganic pollution indicators: (9) Among them, 𝑊 is the pollution level weight vector, and 𝑅 is the fuzzy membership matrix.

[0031] In this embodiment, a pollution level classification is established for the 16 inorganic pollutants obtained in step 1. The classification results are shown in Table 1.

[0032] Table 1 Classification of pollution levels of inorganic pollution indicators

[0033] Note: X is the measured inorganic index content; GBV is the background value of groundwater in the study area; GQⅢ and GQⅣ are the limits according to the groundwater quality standard (GB / T 14848-2017) Calculate the 16 inorganic pollutants obtained in step 1 according to the above method PI The final calculation result is as follows Figure 2 As shown. Based on the frequency distribution of inorganic pollutant indicators in 5 pollution levels, the entropy weight method was used to calculate the weights of the five pollution levels. It is worth noting that the entropy weight method uses information entropy as the basis to measure the amount of information contained in the pollution indicators of each level. When the distribution difference of a certain pollution level in different indicators is significant, its information entropy is smaller and the discrimination is stronger, so it is given a higher weight; otherwise, the weight is lower. The calculation results show that the weights of the five pollution levels are 0.09, 0.12, 0.28, 0.24 and 0.27, respectively, indicating that pollution levels III, IV and V contribute relatively more to pollution identification. In order to further realize the quantitative identification of pollution levels, the fuzzy mathematical evaluation method was further introduced. The pollution level of each inorganic indicator at each sample point was determined according to the principle of maximum membership, and finally the pollution index of each inorganic indicator in the study area was calculated. Among the 16 inorganic indicators, -N has the highest PI value of 5.52, which is much higher than other factors, followed by fluoride (PI=5.08) and chloride (PI=4.50). As and Mn also show relatively high pollution indexes, with PI values ​​of 4.66 and 4.21 respectively. The PI values ​​of the remaining inorganic indicators are relatively low, indicating relatively low environmental risks.

[0034] Step 3: Based on the actual measured organic pollutant data and considering various evaluation parameters, the exposure potential (EP) of the organic pollutants is evaluated, and priority-controlled organic pollutants are screened out based on the exposure potential (EP). The toxicity potential (HP) of the selected priority-controlled organic pollutants is then evaluated. In this step, the organic pollutant screening system is integrated with six criteria: pollutant content, detection rate, persistence, bioaccumulation, in vivo toxicity and in vitro toxicity. The first two reflect the exposure level and widespread occurrence of organic micropollutants in the study area, while the last four characterize the standards of the hazardous characteristics of organic micropollutants. When evaluating the exposure potential EP of organic pollutants, organic micropollutants are preliminarily screened based on the two indicators of pollutant concentration and detection rate, and their overall exposure potential (EP) is evaluated accordingly. Figure 3 As shown, specifically, first, the concentration and detection rate data of each pollutant are standardized to make them comparable under the same dimension; Subsequently, principal component analysis (PCA) was used to reduce the dimensionality of the two standardized indicators. The first principal component PC1 was extracted as the main axis to explain the variability in the data to the greatest extent. The PC1 score was normalized to obtain the EP value of each pollutant.

[0035] After the EP value calculation is completed, in order to further screen out representative organic micropollutants with high exposure risks, this study adopted a "double threshold screening method". First, the normalized median value of the EP value (0.5) is used as the basic threshold, and pollutants with EP ≥ 0.5 are defined as having a high exposure potential. Secondly, in order to take into account the concentration levels and detection frequencies of pollutants in different sampling points, the 67th percentile values ​​of pollutant concentrations and detection rates are further referenced as auxiliary screening criteria. Therefore, only when the EP value is greater than or equal to 0.5 and the concentration or detection rate at least meets their respective 67th percentile standards can it enter the next stage of the priority control pollutant list. This screening strategy effectively avoids misjudgment caused by a single indicator being too high, and more robustly reflects the true environmental risk of pollutants.

[0036] Further toxic potential (HP) analysis of organic pollutants screened through EP is a key step in selecting optimal control indicators. HP assessment is based on four toxicity-related indicators: persistence, bioaccumulation, in vivo toxicity, and in vitro toxicity. After these indicator data are standardized, the above indicator data are standardized again and the PCA method is used for dimensionality reduction analysis. The first two principal components, PC1 and PC2, are extracted to comprehensively represent the toxic performance of the pollutants; the scores of each pollutant on the PC1 and PC2 axes are multiplied by the proportion of explained variance corresponding to the principal components, and then weighted averaged to obtain the toxic potential value HP of the pollutant. The calculation formula is as follows: HP= ; in, and are the scores of pollutants on PC1 and PC2 respectively, and is the variance contribution rate of the corresponding principal component.

[0037] Step 4: Obtain the final pollution index based on the exposure potential EP and toxicity potential HP of the organic indicators, and rank the pollution indexes of the organic indicators to obtain the ranking results of the organic pollution indicators; Based on step 3, the EP value and HP value of the selected priority organic pollutants are multiplied to obtain the priority index PI that comprehensively reflects the exposure level and toxicity characteristics of the pollutants. The calculation formula is: PI=EP*HP; Organic pollutants are sorted according to PI values ​​to identify organic pollution of key concern.

[0038] In this example, the environmental concentration of organic pollution indicators is low, usually at the ng / L-μg / L level. Therefore, the concentration values ​​are transformed by log10 to avoid excessive data dispersion and unreasonable distribution. Organic micropollutants are preliminarily screened based on the two indicators of pollutant concentration and detection rate, and their overall exposure potential (EP) is evaluated accordingly. After normalizing the pollutant concentration and detection rate, principal component analysis (PCA) dimensionality reduction is used, in which the first principal component (PC1) is used as the main axis reflecting the data variability, which can effectively integrate the key information of exposure potential. Finally, the EP value of each pollutant is obtained by normalizing the score on the PC1 axis. Finally, the organic micropollutants are preliminarily screened by combining the EP threshold (0.5) and the 67th percentile standard of pollutant concentration and detection rate. (3) Similarly, PCA dimensionality reduction is used to integrate the four standards of toxic potential (HP), and the normalized score of the pollutant along the coordinate axis is defined as the HP for the comprehensive evaluation of the four toxicity standards. Specifically, the pollutant scores on PC1 and PC2 were used as “complex” variables, representing the four toxicity criteria that were highly correlated with them. The PC1 and PC2 scores were weighted by their corresponding explanatory proportions to calculate the HP value. Finally, the priority index PI of organic micropollutants was calculated using EP and HP. Finally, the R package changepoint was used to identify short interval points, and the organic pollutants ranked before the first discontinuity point were used as early warning indicators of organic pollution. Figure 4Using the R package changepoint, 35 organic micropollutants were classified into four groups based on the identification of three structural mutation points. The first three groups consisted of phthalates and polycyclic aromatic hydrocarbons (PAHs). The group before the first structural mutation point consisted of di(2-ethylhexyl) phthalate (DEHP), benzo[k]fluoranthene (BkF), chrysene (Chr), benzo[b]fluoranthene (BbF), and benz[a]anthracene (BaA). Phthalates, such as DEHP, DnOP, and DEP, showed significant concentrations and detection rates, while PAHs exhibited even higher toxicity levels. PAEs are widely used in industrial and consumer products, such as plastics, coatings, and personal care products. They are easily released into the environment during production, use, and disposal, and are particularly prone to accumulation in areas of high human activity, such as chemical industrial parks. PAHs are primarily derived from anthropogenic activities, such as biomass burning and petrochemical industry activities, which are closely related to the primary industrial activities in the study area. Therefore, the two major pollutants, PAEs and PAHs, are taken as key focus categories, and the priority organic micropollutants DEHP, BkF, Chr, BbF and BaA are included in the priority control organic pollution indicators to achieve more targeted groundwater pollution identification and risk control.

[0039] Step 5: Based on the ranking results of inorganic pollution indicators and organic pollution indicators, combined with the groundwater quality standard (GB / T14848-2017), determine the early warning indicators of inorganic and organic pollutants in the chemical park groundwater; the differences in the sources of pollutants determine that their environmental behavior and risk paths are essentially different. Inorganic indicators such as -N, Cl⁻, F⁻, Mn and As, etc., are partly derived from geological background processes (such as mineral dissolution, water-rock interaction), and partly related to agricultural activities or seawater intrusion. Their pollution characteristics are usually controlled by hydrogeochemical processes and regional stratigraphic structures. Organic micropollutants such as phthalates and polycyclic aromatic hydrocarbons basically come from emissions from related industrial activities, and their migration characteristics are more controlled by physical and chemical properties such as polarity, volatility, and adsorption. Secondly, there are significant differences in the content of inorganic pollutants and organic pollutants in groundwater in the land-sea interaction zone, which further strengthens the necessity of classification and evaluation. For example, -N, Cl⁻, and NH⁻ ... This causes the pollution to become "hidden" and "long-term". Once such pollutants enter the aquifer, they may continue to affect groundwater quality at low concentrations and accumulate risks. Therefore, incorporating organic micropollutants into the priority pollutant evaluation system is a necessary supplement to achieve early identification of pollution and forward-looking risk management. Compared with the traditional concentration-driven screening approach, the multidimensional optimization evaluation system that integrates exposure potential and toxicity potential constructed in this study shows greater scientificity and sensitivity in identifying low-concentration but high-risk organic micropollutants. This method can not only systematically quantify the actual exposure level and health hazard characteristics of pollutants in the environment, but also clarify their priority control level, thereby providing solid data support and decision-making basis for source analysis, migration path identification and ecological health risk assessment of groundwater pollutants.

[0040] The pollutant identification method for groundwater in the terrestrial-marine transition zone developed in this study prioritizes a weighting strategy, which directly impacts the scientific and stable nature of pollutant ranking. For the identification of inorganic pollution indicators, the weights assigned to each pollution level in the model are objectively determined based on the frequency of occurrence. Pollution levels are categorized according to national groundwater quality standards and regional background values. The information provided by each level for pollution identification is measured by statistically analyzing the frequency of occurrence of each indicator within each level. The advantages of the entropy weighting method include quantifying the information entropy of different pollution levels and effectively avoiding bias caused by subjective weighting. This method ensures the rationality of pollution level classification while more objectively reflecting the relative contribution of pollution levels in the identification model. This model has strong applicability and practical value in the terrestrial-marine transition zone. Based on preliminary research and existing hydrogeological data, regional background values ​​for relevant indicators can be systematically obtained, providing a scientific basis for determining classification thresholds. Furthermore, the confluence of freshwater and saltwater in the terrestrial-marine transition zone and the impact of industrial activities significantly enhance the spatial variability of pollution levels, further improving the model's ability to distinguish between different levels. This study introduced two dimensions, exposure potential (EP) and toxicity potential (HP), into the organic indicator evaluation model. Standardization and principal component analysis (PCA) were used to extract relevant weights to calculate the final priority index (PI). In prioritizing pollution indicators, we first considered the exposure potential of pollutants, which depends on the occurrence of pollutants in the groundwater of the study area. Using pollution concentration and detection frequency as two exposure parameters is a reasonable approach. However, factors such as the limited scale of groundwater sampling and industrial point source emissions introduce certain uncertainties, and the assessment results should be interpreted with caution and used for screening. Therefore, pollutants with high concentrations and high detection frequencies should be assigned high EP values ​​and attract attention. Prioritizing organic pollutants based solely on a single toxicity indicator is extremely limited. This study collected data on multiple toxicity parameters, used principal component analysis (PCA) to comprehensively assess the toxicity hazards of organic micropollutants, and calculated the contribution of these evaluation parameters to the final priority pollution index (PI). For example, taking DEHP, which ranked first, as an example, although it did not show extreme values ​​in toxicity parameters, it ultimately ranked first in the PI ranking after a comprehensive evaluation due to its high detection rate, high concentration performance, and moderate to high toxicity potential. However, it should be noted that the assessment of toxicity potential still has certain uncertainties. This is mainly because the data used are mainly derived from public datasets and literature, and there are certain differences in experimental conditions, species coverage, and exposure time. It may be difficult to fully reflect the toxicological effects of the target pollutants in the real environment of the land-sea interface zone. This is mainly reflected in the construction of the SSD model. The limited amount of toxicity data for some organic micropollutants may cause certain fitting errors, thereby affecting the calculation of subsequent toxicity characteristic values.Furthermore, there may be a certain degree of information overlap between the multiple evaluation dimensions of toxicity potential, and the presence of collinearity also warrants attention. In this study, to verify the independence of model parameters, the variance inflation factor (VIF) was calculated. The results showed that it did not meet the contribution assessment criteria (VIF < 5), which would not interfere with the subsequent PCA or multivariate linear regression results. This statistically supports the rationality of the model structure. Furthermore, in constructing the SSD model, the unique ecological and environmental context of the land-sea interaction zone was fully considered. Where available, toxicity data from representative species or those more closely resembling groundwater system exposure scenarios were prioritized to reduce uncertainty introduced by exogenous data bias.

[0041] The embodiment of the present invention further provides a system for implementing the above-mentioned method for determining pollution warning indicators of a chemical park, comprising: The pollutant collection module is used to establish a pollutant list and classify it into inorganic pollutants and organic pollutants based on the hydrogeological conditions of the chemical park; The inorganic pollutant pollution level classification module is used to construct an entropy weight method-mathematical fuzzy evaluation model to conduct a comprehensive assessment of inorganic pollutants and obtain the ranking results of various inorganic pollutant indicators; The organic pollutant exposure and toxicity potential assessment module is used to assess the exposure potential (EP) of organic pollutants based on the actual measured organic pollutant data and taking into account various assessment parameters. It is also used to screen out priority-controlled organic pollutants based on the exposure potential (EP) and then assess the toxicity potential (HP) of the selected priority-controlled organic pollutants. The organic pollutant early warning indicator identification module is used to obtain the final pollution index based on the exposure potential EP and toxicity potential HP of the organic indicators, and to sort the pollution index of the organic indicators to obtain the organic pollution index ranking result; The groundwater pollutant index early warning module is used to determine the early warning indicators of inorganic and organic pollutants in groundwater of chemical parks based on the ranking results of inorganic pollution indicators and organic pollution indicators and combined with groundwater quality standards.

[0042] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0043] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0044] The above are only preferred embodiments of the present invention and do not limit the implementation mode and protection scope of the present invention. For those skilled in the art, it should be aware that all solutions obtained by equivalent substitutions and obvious changes made using the contents of the present invention specification should be included in the protection scope of the present invention.

Claims

1. A method for determining groundwater pollution early warning indicators in a chemical park in a sea-land interaction zone, characterized in that: The following steps are involved: Step 1: Establish a pollutant inventory based on the hydrogeological conditions of the chemical park and divide it into inorganic pollutants and organic pollutants; Step 2: Construct an entropy weight method-mathematical fuzzy evaluation model to comprehensively evaluate inorganic pollutants and obtain the ranking results of various inorganic pollutant indicators; Step 3: Based on the actual measured organic pollutant data and considering various evaluation parameters, the exposure potential (EP) of the organic pollutants is evaluated, and priority-controlled organic pollutants are screened out based on the exposure potential (EP). The toxicity potential (HP) of the selected priority-controlled organic pollutants is then evaluated. Step 4: Obtain the final pollution index based on the exposure potential EP and toxicity potential HP of the organic indicators, and rank the pollution indexes of the organic indicators to obtain the organic pollution ranking results; Step 5: Based on the ranking results of inorganic pollution indicators and organic pollution indicators and combined with groundwater quality standards, determine the early warning indicators of inorganic and organic pollutants in the chemical park groundwater.

2. The groundwater pollution early warning indicator for chemical parks in the sea-land interaction zone according to claim 1 is characterized in that: The construction method of the entropy weight method-mathematical fuzzy evaluation model in step 2 includes: The inorganic pollutant indicators are divided into pollution levels, and the occurrence frequencies of different inorganic pollutants in different pollution intervals are counted to construct a standardized frequency matrix; Based on the above, the entropy weight method is introduced to calculate the pollution level weight and obtain the level weight vector W; After obtaining the pollution level weight, the membership function is constructed by combining the concentration value of each inorganic pollution index with the pollution level boundary, and the membership degree of each sample point under each pollution level is calculated to generate the fuzzy membership matrix R; Finally, the pollution level weight vector W and the fuzzy membership matrix R are weighted to obtain the entropy weight-mathematical fuzzy comprehensive evaluation model.

3. The groundwater pollution early warning indicator for chemical parks in the sea-land interaction zone according to claim 2 is characterized in that: The construction method of the fuzzy membership matrix R is: The factor set is determined based on the selected inorganic pollution index, and the pollution level classification is used as the evaluation set. The fuzzy comprehensive judgment matrix R is determined based on the membership function between the factor set and the evaluation set: in, r ij Indicates the membership of the i-th sample point under the j-th pollution level; the calculation of the membership is based on the position of the pollutant concentration value x in the level boundary value interval. For negative indicators, the membership function belongs to the first level: The membership function of the intermediate level, i.e., the 2nd to k-1th level, is: The membership function belonging to the kth level, which is the worst level, is: ； in, c k Inorganic index in the k The pollution degree limit.

4. The method for determining groundwater pollution early warning indicators in a chemical park in a sea-land interaction zone according to claim 1, characterized in that: Methods for assessing exposure potential for organic indicators include: First, the concentration and detection rate data of each pollutant were standardized to make them comparable under the same dimension; Subsequently, principal component analysis (PCA) was used to reduce the dimensionality of the two standardized indicators. The first principal component PC1 was extracted as the main axis to explain the variability in the data to the greatest extent. The PC1 score was normalized to obtain the EP value of each pollutant.

5. The groundwater pollution early warning indicator for chemical parks in the sea-land interaction zone according to claim 1 is characterized in that: Methods for screening out priority organic pollutants include: The normalized median EP value of 0.5 was used as the basic threshold, and pollutants with EP ≥ 0.5 were defined as having a high exposure potential; Secondly, the 67th percentile values ​​of pollutant concentrations and detection rates are further referenced as auxiliary screening criteria, that is, organic pollutants with an EP value greater than or equal to 0.5 and a concentration or detection rate that at least meets their respective 67th percentile standards are regarded as priority controlled pollutants.

6. The groundwater pollution early warning indicator for chemical parks in the sea-land interaction zone according to claim 1 is characterized in that: Methods for assessing HP for its toxic potential include: HP assessment is based on four toxicity-related indicators: persistence, bioaccumulation, in vivo toxicity, and in vitro toxicity. The data for these indicators are standardized and then analyzed using the PCA method for dimensionality reduction. The first two principal components, PC1 and PC2, are extracted to comprehensively represent the toxicity of the pollutants. The scores of each pollutant on the PC1 and PC2 axes are multiplied by the proportion of explained variance corresponding to the principal component, and then a weighted average is taken to obtain the toxic potential value HP of the pollutant. The calculation formula is as follows: HP= ; in, and are the scores of pollutants on PC1 and PC2 respectively, and is the variance contribution rate of the corresponding principal component.

7. The groundwater pollution early warning indicator for chemical parks in the sea-land interaction zone according to claim 1 is characterized in that: The final pollution index calculation method in step 4 is: Multiply the EP value and HP value of the selected priority organic pollutants to obtain the priority index PI that comprehensively reflects the exposure level and toxicity characteristics of the pollutants. The calculation formula is: PI=EP*HP; Organic pollutants are sorted according to PI values ​​to identify organic pollution of key concern.

8. A system for realizing the groundwater pollution early warning indicator of a chemical park in a sea-land interactive zone according to any one of claims 1 to 7, characterized in that: include: The pollutant collection module is used to establish a pollutant list and classify it into inorganic pollutants and organic pollutants based on the hydrogeological conditions of the chemical park; The inorganic pollutant pollution level classification module is used to construct an entropy weight method-mathematical fuzzy evaluation model to conduct a comprehensive assessment of inorganic pollutants and obtain the ranking results of various inorganic pollutant indicators; The organic pollutant exposure and toxicity potential assessment module is used to assess the exposure potential (EP) of organic pollutants based on the actual measured organic pollutant data and taking into account various assessment parameters. It is also used to screen out priority-controlled organic pollutants based on the exposure potential (EP) and then assess the toxicity potential (HP) of the selected priority-controlled organic pollutants. The organic pollutant early warning indicator identification module is used to obtain the final pollution index based on the exposure potential EP and toxicity potential HP of the organic indicators, and to sort the pollution index of the organic indicators to obtain the organic pollution index ranking result; The groundwater pollutant index early warning module is used to determine the early warning indicators of inorganic and organic pollutants in groundwater of chemical parks based on the ranking results of inorganic pollution indicators and organic pollution indicators and combined with groundwater quality standards.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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