Method and system for evaluating risk of groundwater pollution to human health

Abstract

The invention discloses a method and a system for evaluating the risk of groundwater pollution to human health, and particularly relates to the technical field of groundwater pollution. Groundwater samples near pollution sources are collected, basic parameters are measured by combining field equipment, pollutants are preliminarily identified, the groundwater samples are divided into novel pollutants and common pollutants, and for the novel pollutants, the common pollutants are classified into the groundwater samples; based on the microbial degradation frequency change and concentration detection accuracy, the identification difficulty is evaluated, the pollutants are divided into high, medium and low difficulty identification types, the pollutants with low difficulty identification are directly subjected to health risk assessment, and the pollutants with high difficulty identification utilize a structure-activity relation model to predict the threat of the pollutants with high difficulty identification to human health. For the pollutants with medium difficulty in identification, whether the monitoring strategy needs to be adjusted or not is determined by further analyzing the identification difficulty and health risk influence of the pollutants, the accuracy of novel pollutant identification is effectively improved, the health risk assessment process is optimized, and the scientificity and accuracy of the monitoring strategy are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of groundwater pollution, and in particular to a method and system for assessing the risk of groundwater pollution to human health. Background Art

[0002] Groundwater pollution risk assessment for human health refers to the analysis and prediction of potential hazards of pollutants in groundwater to human health through scientific methods. This assessment process usually includes identifying harmful substances in groundwater, such as heavy metals, chemical waste or microorganisms, determining the concentrations of these substances, and evaluating the ways in which humans may be exposed to these pollutants (such as drinking water, skin contact or ingestion through the food chain). The goal is to determine whether the pollutants pose a health threat to the human body and quantify the severity of this threat. However, some emerging pollutants, such as pharmaceutical waste, endocrine disruptors, microplastics, etc., may not be effectively identified by traditional detection technologies. These substances may be in extremely low concentrations, but they are still potentially harmful to the human body. Due to their complex characteristics and the lack of standardized detection methods and health risk assessment standards, they are difficult to detect or ignored, which increases the blind spots of the assessment. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for assessing the risk of groundwater pollution to human health, so as to overcome the shortcomings of the background technology.

[0004] In order to achieve the above object, the present invention provides the following technical solution: a method for assessing the risk of groundwater pollution to human health, comprising the following steps:

[0005] S1: Collect water samples near the pollution source and measure the basic parameters of groundwater through on-site equipment. At the same time, conduct preliminary pollutant identification and classify them into new pollutants and common pollutants;

[0006] S2: Mark the identified new pollutants and determine the difficulty of identifying them based on the changes in the frequency of degradation of new pollutants by natural microorganisms and the accuracy of new pollutant concentration detection;

[0007] S3: The difficulty of identifying new pollutants is divided into different levels, namely high difficulty identification, medium difficulty identification and low difficulty identification;

[0008] S4: New pollutants with low identification difficulty are directly evaluated for health risks; for new pollutants with high identification difficulty, the severity of their threat to human health is predicted by using the structure-activity relationship model based on the similarity of their chemical structure with known harmful substances;

[0009] S5: For emerging pollutants with medium difficulty to identify, further predictive analysis will be conducted on their identification difficulty, and based on the degree of impact of emerging pollutants with medium difficulty to human health risk assessment, it will be determined whether the monitoring strategy needs to be adjusted.

[0010] Preferably, in S2, the identified new pollutants are marked, and the difficulty of identifying the new pollutants is determined according to the change in the frequency of degradation of the new pollutants by natural microorganisms and the accuracy of the detection of the concentration of the new pollutants, specifically:

[0011] The cumulative degradation efficiency fluctuation index is generated according to the change in the frequency of degradation of new pollutants by natural microorganisms. The method for obtaining the cumulative degradation efficiency fluctuation index is:

[0012] Different environmental conditions are set on site to regularly monitor the degradation of pollutants and record the degradation efficiency of pollutants in the H time period, that is, the change of pollutant concentration over time, expressed as: In the formula, CDE(t i ) indicates that at time t i The degradation efficiency at t is , C0 is the initial concentration, C(t i ) is time t i The pollutant concentration at that time; the degradation rate k is calculated based on the degradation data of each time interval i , that is, the rate of change of pollutant concentration over time, expressed as: Among them, k i is the degradation rate in the i-th time interval, Δt i =t i+1 -t i is the time interval; the standard deviation of the degradation rate in each time period is calculated to reflect the fluctuation of the degradation process, and the cumulative degradation efficiency fluctuation index is calculated by the ratio of the standard deviation to the average degradation efficiency. The expression is: In the formula, σk i is the standard deviation of the degradation rate at each time period, is the average degradation efficiency, and CD is the cumulative degradation efficiency fluctuation index.

[0013] Preferably, in S2, a detection repeatability error interference index is generated according to the accuracy of the new pollutant concentration detection, and the method for obtaining the detection repeatability error interference index is:

[0014] Obtain the true concentration value C of new pollutants in n tests in real time true and the measured value C for each test meas,i , and calculate the error ΔC of its i-th detection i , the expression is: ΔC i =C meas,i -Ctrue ; The detection error is calculated by minimizing the sum of squares of the errors between the measured value and the actual value, and the expression is: Where S is the detection error, and the standard range of the detection error under normal conditions in historical data is obtained: S min ~S max ; Calculate the detection repeatability error interference index. If the detection error is greater than S max , the expression is: DF=|SS max |; If the detection error is less than S min , the expression is: DF=|SS min |; In the formula, DF is the detection repeatability error interference index.

[0015] Preferably, the cumulative degradation efficiency fluctuation index and the detection repeatability error interference index are converted into a first eigenvector, and the first eigenvector is used as the input of the machine learning model. The machine learning model uses each group of first eigenvectors to predict the identification difficulty analysis value label of the new pollutant as the prediction target, and takes minimizing the sum of the prediction errors of the identification difficulty analysis value labels of all new pollutants as the training target. The machine learning model is trained until the sum of the prediction errors converges, and the model training is stopped. The identification difficulty analysis value of the new pollutant is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

[0016] Preferably, in S3, the difficulty of identifying new pollutants is divided into different levels, namely, high difficulty identification, medium difficulty identification and low difficulty identification, specifically:

[0017] The obtained identification difficulty analysis value of the new pollutant is compared with the gradient standard threshold, the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and the identification difficulty analysis value of the new pollutant is compared with the first standard threshold and the second standard threshold respectively;

[0018] If the identification difficulty analysis value of the new pollutant is greater than the second standard threshold, it means that the identification difficulty of the new pollutant is high, and a high-difficulty identification signal is generated at this time, and it is classified as a high-difficulty identification type;

[0019] If the identification difficulty analysis value of the new pollutant is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it means that the identification difficulty of the new pollutant is average, and a medium difficulty identification signal is generated, and it is classified as a medium difficulty identification type;

[0020] If the identification difficulty analysis value of the new pollutant is less than the first standard threshold, it means that the identification difficulty of the new pollutant is low. At this time, a low-difficulty identification signal is generated and it is classified as a low-difficulty identification type.

[0021] Preferably, in S5, for new pollutants with medium difficulty in identification, further predictive analysis is performed on the difficulty of identification, and according to the degree of impact of new pollutants with medium difficulty in identification on human health risk assessment, it is determined whether the monitoring strategy needs to be adjusted, specifically:

[0022] For new pollutants with medium difficulty in identification, that is, the identification difficulty analysis value of the new pollutants generated in a fixed time period is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the identification difficulty analysis values ​​generated in the subsequent fixed time period that are greater than or equal to the first standard threshold and less than or equal to the second standard threshold are collected, and a corresponding data set is established, and the mean and standard deviation of the data set are calculated. After analysis, the impact of the new pollutants on human health risk assessment is predicted, and it is determined whether the monitoring strategy needs to be adjusted.

[0023] Preferably, if the mean of the identification difficulty analysis values ​​in the data set is greater than or equal to the reference threshold of the identification difficulty analysis value mean, and the standard deviation of the identification difficulty analysis value is less than the reference threshold of the standard deviation of the identification difficulty analysis value, it indicates that the identification process of the new pollutant is stable, and the detection and identification errors are small. At this time, no warning signal is generated, and the existing monitoring strategy is maintained without adjustment;

[0024] If the mean of the recognition difficulty analysis value is greater than or equal to the reference threshold of the recognition difficulty analysis value mean, and the standard deviation of the recognition difficulty analysis value is greater than or equal to the reference threshold of the standard deviation of the recognition difficulty analysis value, although the recognition difficulty is high, the detection result fluctuates greatly, indicating that the recognition process is unstable. At this time, a first-level warning signal is generated and the monitoring frequency is increased to improve the accuracy and stability of the detection;

[0025] If the mean of the recognition difficulty analysis value is less than the reference threshold of the recognition difficulty analysis value mean, and the standard deviation of the recognition difficulty analysis value is greater than or equal to the reference threshold of the recognition difficulty analysis value standard deviation, the overall recognition difficulty is low, but the error fluctuation is large. At this time, a secondary warning signal is generated to further investigate the cause of the recognition error. The monitoring strategy needs to be adjusted to avoid missing potential risks.

[0026] If the mean of the identification difficulty analysis value is less than the reference threshold of the mean of the identification difficulty analysis value, and the standard deviation of the identification difficulty analysis value is less than the reference threshold of the standard deviation of the identification difficulty analysis value, the identification difficulty is low and the fluctuation is small, indicating that the identification process of the pollutant is stable. At this time, a third-level warning signal is generated to reduce the monitoring frequency and reduce resource consumption.

[0027] The present invention also provides a system for assessing the risk of groundwater pollution to human health, including a pollutant collection module, an identification difficulty analysis module, an identification difficulty classification module, a health threat prediction module, and a monitoring strategy adjustment module;

[0028] Pollutant collection module: collect water samples from near the pollution source, measure the basic parameters of groundwater through on-site equipment, and conduct preliminary pollutant identification, dividing them into new pollutants and common pollutants;

[0029] Identification difficulty analysis module: Mark the identified new pollutants and determine the identification difficulty of new pollutants based on the changes in the frequency of degradation of new pollutants by natural microorganisms and the accuracy of new pollutant concentration detection;

[0030] Identification difficulty grading module: the identification difficulty of new pollutants is divided into different levels, namely high difficulty identification, medium difficulty identification and low difficulty identification;

[0031] Health threat prediction module: New pollutants with low identification difficulty are directly evaluated for health risks; for new pollutants with high identification difficulty, the structure-activity relationship model is used to predict the severity of their threat to human health based on the similarity between their chemical structure and known harmful substances;

[0032] Monitoring strategy adjustment module: For new pollutants with medium difficulty to identify, further predictive analysis is conducted on their identification difficulty, and based on the degree of impact of new pollutants with medium difficulty to human health risk assessment, it is determined whether the monitoring strategy needs to be adjusted.

[0033] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0034] 1. The present invention collects water samples from the vicinity of the pollution source and uses the cumulative degradation efficiency fluctuation index and the detection repeatability error interference index for comprehensive evaluation. The scheme can accurately quantify the difficulty of identifying new pollutants and use machine learning models for predictive analysis. This method can not only conduct targeted risk assessments on pollutants with low, medium and high identification difficulties, but also further analyze the potential health risks of pollutants with medium difficulty through the mean and standard deviation of the data set, thereby ensuring the scientific nature of the monitoring strategy.

[0035] 2. The present invention can automatically adjust the monitoring frequency and strategy according to the different identification difficulties and effectively allocate resources. For example, for pollutants that are difficult to identify, the system strengthens monitoring through early warning signals, while for pollutants that are easy to identify and stable, the monitoring frequency can be appropriately reduced to optimize detection efficiency. In addition, by combining the structure-activity relationship model, the health risk prediction of pollutants that are difficult to identify is more accurate, ensuring that potential threats to public health are effectively evaluated and controlled. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0037] Figure 1 The present invention is a flow chart of the method.

[0038] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Example 1, please refer to Figure 1 As shown, the method for assessing the risk of groundwater pollution to human health described in this embodiment includes the following steps:

[0041] S1: Collect water samples near the pollution source and measure the basic parameters of groundwater through on-site equipment. At the same time, conduct preliminary pollutant identification and classify them into new pollutants and common pollutants;

[0042] S2: Mark the identified new pollutants and determine the difficulty of identifying them based on the changes in the frequency of degradation of new pollutants by natural microorganisms and the accuracy of new pollutant concentration detection;

[0043] S3: The difficulty of identifying new pollutants is divided into different levels, namely high difficulty identification, medium difficulty identification and low difficulty identification;

[0044] S4: New pollutants with low identification difficulty are directly evaluated for health risks; for new pollutants with high identification difficulty, the severity of their threat to human health is predicted by using the structure-activity relationship model based on the similarity of their chemical structure with known harmful substances;

[0045] S5: For emerging pollutants with medium difficulty to identify, further predictive analysis will be conducted on their identification difficulty, and based on the degree of impact of emerging pollutants with medium difficulty to human health risk assessment, it will be determined whether the monitoring strategy needs to be adjusted.

[0046] In S1, water samples are collected from the vicinity of the pollution source, and the basic parameters of groundwater are measured by on-site equipment. At the same time, preliminary pollutant identification is performed and they are divided into new pollutants and common pollutants, specifically:

[0047] Select sampling points of groundwater wells, rivers, or surface water close to the pollution source to ensure that the collected water samples can represent the groundwater pollution near the pollution source. Determine the appropriate sampling depth based on the layers and geological structure of the groundwater. Deep groundwater may have different pollutant distributions than shallow water bodies. Use professional sampling equipment, such as groundwater sampling pumps or water extractors, to ensure that the water samples will not be secondary polluted by the external environment during the sampling process.

[0048] Field equipment measures basic parameters of groundwater, including: pH: A portable pH meter is used to measure the acidity and alkalinity of water samples. Changes in pH can reflect whether the groundwater is affected by acidic or alkaline pollutants. Temperature: A thermometer is used to measure the temperature of groundwater. Temperature anomalies may reflect chemical reactions or the presence of pollutants in the groundwater. Dissolved oxygen (DO): A dissolved oxygen meter is used to measure the oxygen content in water samples. Low dissolved oxygen may mean organic pollutants or increased biological activity. Conductivity: A conductivity meter is used to measure the conductivity of water. Changes in conductivity are usually related to the concentration of dissolved ions in the water and can indirectly reflect the concentration of pollutants. Oxidation-reduction potential (ORP): Measuring the redox potential of water can help determine the redox environment of groundwater and indirectly reflect the chemical properties of pollutants.

[0049] Use portable testing instruments (such as heavy metal detectors and volatile organic compound detectors) to conduct preliminary identification of common pollutants (such as heavy metals such as lead, arsenic, and mercury, and organic compounds such as benzene and toluene). Compare the test results with the pollutant limits of national or local standards to identify pollutants that exceed the standards. Based on the test results, the identified pollutants are divided into common pollutants and new pollutants. Common pollutants include heavy metals, nitrogen and phosphorus compounds, etc., and new pollutants include medical waste, endocrine disruptors, microplastics, etc.

[0050] S2: Mark the identified new pollutants and determine the difficulty of identifying them based on the changes in the frequency of degradation of new pollutants by natural microorganisms and the accuracy of new pollutant concentration detection.

[0051] New pollutants are labeled according to their source, type and chemical structure. Common new pollutants include endocrine disruptors, pharmaceutical waste, microplastics, personal care product residues, etc. These pollutants can be preliminarily classified based on known information in the database and given specific labels for subsequent analysis. Each new pollutant is labeled with its main chemical properties (such as polarity, molecular weight, solubility, degradation products, etc.). This information can help determine the migration behavior of pollutants and their potential impact on the environment. Pollutants are labeled for their potential toxicity based on existing toxicological data, especially their impact on human health, such as whether they are carcinogenic, mutagenic or reproductive toxic.

[0052] Evaluate the rate at which new pollutants are degraded by microorganisms in the natural environment through laboratory or field experiments. Experimental methods may include observing the degree of decomposition of pollutants under the action of microorganisms under controlled conditions (such as temperature, pH, and oxygen content). Common methods include: Degradation experiments that simulate groundwater environments: Under simulated groundwater conditions, monitor changes in pollutant concentrations and collect samples regularly to evaluate degradation rates. Analysis of degradation intermediates: By detecting the intermediates produced during the degradation process, we can further understand the degradation path of pollutants.

[0053] The cumulative degradation efficiency fluctuation index is generated according to the change in the frequency of degradation of new pollutants by natural microorganisms. The method for obtaining the cumulative degradation efficiency fluctuation index is:

[0054] Different environmental conditions (such as temperature, pH, dissolved oxygen content, microbial community type, etc.) are set on site to monitor the degradation of pollutants regularly. The degradation efficiency of pollutants in the H time period is recorded, that is, the change of pollutant concentration over time, expressed as: In the formula, CDE(t i ) indicates that at time t i The degradation efficiency at t is , C0 is the initial concentration, C(t i ) is time t i The pollutant concentration at that time; the degradation rate k is calculated based on the degradation data of each time interval i , that is, the rate of change of pollutant concentration over time, expressed as: Among them, k i is the degradation rate in the i-th time interval, Δt i =t i+1 -t i is the time interval; the standard deviation of the degradation rate in each time period is calculated to reflect the fluctuation of the degradation process, and the cumulative degradation efficiency fluctuation index is calculated by the ratio of the standard deviation to the average degradation efficiency. The expression is: In the formula, σk i is the standard deviation of the degradation rate at each time period, is the average degradation efficiency, and CD is the cumulative degradation efficiency fluctuation index.

[0055] The larger the cumulative degradation efficiency fluctuation index, the greater the volatility of the degradation process of the new pollutant, showing unstable degradation efficiency under different environmental conditions. This means that the pollutant may degrade faster under certain conditions, but degrade slowly or almost not at all under other conditions. This uncertainty increases the difficulty of understanding its degradation behavior. Due to the large fluctuations in degradation efficiency, pollutants may be difficult to accurately identify in certain environments.

[0056] A high cumulative degradation efficiency fluctuation index also indicates that the degradation pathways of new pollutants are more complex and may produce a variety of degradation byproducts or different chemical forms. These products and forms may exhibit different toxicity or environmental behaviors, further increasing the difficulty of detecting and analyzing pollutants. This complex degradation process requires more advanced monitoring methods and multi-dimensional analysis to accurately assess the actual presence of pollutants and their impact. Therefore, the larger the cumulative degradation efficiency fluctuation index, the more difficult it is to identify new pollutants. The uncertainty and complexity of its degradation process not only increase the detection challenges, but may also affect pollution.

[0057] Select appropriate detection technology based on the physical and chemical properties of new pollutants. Commonly used detection methods for new pollutants include high-sensitivity technologies such as gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). The accuracy of detection mainly depends on the sensitivity of the instrument, the sample processing method and the characteristics of the pollutant.

[0058] The detection repeatability error interference index is generated according to the accuracy of the new pollutant concentration detection. The method for obtaining the detection repeatability error interference index is:

[0059] Obtain the true concentration value C of new pollutants in n tests in real time true and the measured value C for each test meas,i , and calculate the error ΔC of its i-th detection i , the expression is: ΔC i =C meas,i -C true ; The detection error is calculated by minimizing the sum of squares of the errors between the measured value and the actual value, and the expression is: Where S is the detection error, and the standard range of the detection error under normal conditions in historical data is obtained: S min ~S max ; Calculate the detection repeatability error interference index. If the detection error is greater than S max , the expression is: DF=|SS max |; If the detection error is less than S min , the expression is: DF=|SSmin |; In the formula, DF is the detection repeatability error interference index.

[0060] The larger the test repeatability error interference index, the more difficult it is to identify new pollutants. This is because a high test repeatability error interference index reflects large fluctuations in results over multiple tests, indicating that the test system has significant errors and instabilities when measuring pollutant concentrations. Bias and inconsistency in test results make it difficult to accurately determine the actual concentration of pollutants, thereby increasing the challenge of identifying pollutants.

[0061] High detection repeatability error interference index also means that existing detection technology may not be able to effectively capture the true concentration of pollutants, especially when the pollutant concentration is very low or exists in a complex matrix. Due to poor detection repeatability, systematic errors and random errors make it difficult to reliably identify pollutants in different environments or conditions, complicating the detection and tracking of pollutants. This makes it difficult for scientists and engineers to assess the long-term behavior and health risks of pollutants.

[0062] Therefore, as the detection repeatability error interference index increases, the difficulty of identifying new pollutants also increases. This requires the use of more accurate detection technology to reduce error interference, and may require multiple repeated tests and cross-validation to improve the reliability and accuracy of the detection.

[0063] The cumulative degradation efficiency fluctuation index and the detection repeatability error interference index are converted into the first eigenvector, and the first eigenvector is used as the input of the machine learning model. The machine learning model predicts the identification difficulty analysis value label of the new pollutant for each group of first eigenvectors as the prediction target, and minimizes the sum of the prediction errors of the identification difficulty analysis value labels of all new pollutants as the training target. The machine learning model is trained until the sum of the prediction errors converges, and the model training is stopped. The identification difficulty analysis value of the new pollutant is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

[0064] The method for obtaining the identification difficulty analysis value of the new pollutant is: from the first eigenvector training data of the trained machine learning model, obtain the corresponding function expression: CR=F(CD, DF); wherein F is the output function of the model, CD is the cumulative degradation efficiency fluctuation index, DF is the detection repeatability error interference index, and CR is the identification difficulty analysis value of the new pollutant.

[0065] S3: The difficulty of identifying new pollutants is divided into different levels, namely high difficulty identification, medium difficulty identification and low difficulty identification.

[0066] The obtained identification difficulty analysis value of the new pollutant is compared with the gradient standard threshold, the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and the identification difficulty analysis value of the new pollutant is compared with the first standard threshold and the second standard threshold respectively;

[0067] If the identification difficulty analysis value of the new pollutant is greater than the second standard threshold, it means that the identification difficulty of the new pollutant is high, and a high-difficulty identification signal is generated at this time, and it is classified as a high-difficulty identification type;

[0068] If the identification difficulty analysis value of the new pollutant is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it means that the identification difficulty of the new pollutant is average, and a medium difficulty identification signal is generated, and it is classified as a medium difficulty identification type;

[0069] If the identification difficulty analysis value of the new pollutant is less than the first standard threshold, it means that the identification difficulty of the new pollutant is low. At this time, a low-difficulty identification signal is generated and it is classified as a low-difficulty identification type.

[0070] In this application, the polynomial regression model of machine learning is used to predict the identification difficulty analysis value of new pollutants using the cumulative degradation efficiency fluctuation index and the detection repeatability error interference index. The prediction results are classified according to the set gradient standard threshold and divided into three types: high difficulty, medium difficulty and low difficulty. This process uses machine learning models to capture complex nonlinear relationships, thereby more accurately identifying the difficulty of pollutants and providing corresponding classification signals for subsequent processing and management.

[0071] S4: New pollutants that are easy to identify are directly subject to health risk assessment. For new pollutants that are difficult to identify, the structure-activity relationship model is used to predict the severity of their threat to human health based on the similarity between their chemical structure and known harmful substances.

[0072] For new pollutants that have been classified as low difficulty to identify, their detection process is stable, the concentration is accurate, and the cumulative degradation efficiency and detection repeatability error interference index indicate that their identification difficulty is low. For such pollutants, health risk assessment can be carried out directly, including assessing the possible exposure pathways of the pollutants, including drinking water, skin contact, air inhalation, etc. For pollutants in groundwater, the most important pathway is usually drinking water intake. Calculate daily exposure based on the concentration of pollutants in water and the amount of water ingested by humans. Look for toxicological data in existing databases, such as reference dose (Reference Dose, RfD) or no observable adverse effect level (NOAEL). These data are used to assess the potential harm of pollutants to human health. Compare the exposure dose with the toxicity threshold. If the exposure dose is lower than the reference dose or NOAEL, the health risk is low.

[0073] For new pollutants classified as difficult to identify, direct health risk assessment is difficult due to their degradation complexity, large detection errors, etc. Therefore, the structure-activity relationship model is used to predict the severity of their threat to human health.

[0074] Through the chemical structure of the new pollutant, chemical structure databases (such as PubChem, ChemSpider, etc.) are used to search for known hazardous substances with similar structures. Similarity can be measured by algorithms such as the Tanimoto coefficient. Extract key structural features of pollutants (such as functional groups, bonding characteristics, molecular size, polarity, etc.), which are important inputs in the structure-activity relationship model. Use QSAR models with known toxicity data and structural information to predict the potential toxicity of new pollutants. The QSAR model predicts the activity of target substances based on known toxicological data of similar chemicals through statistical or machine learning algorithms. The model outputs predicted values, including potential acute toxicity, chronic toxicity, carcinogenicity or endocrine disruption. Usually, the model outputs a risk level or value to reflect the health threat of pollutants to the human body. Use the toxicity prediction values ​​generated by the model (such as LD50, carcinogenicity coefficient, etc.) to estimate the degree of threat of pollutants to the human body. Based on these predicted toxicity values, combined with the exposure pathways and exposure amounts of pollutants, assess their potential threats to the population. According to the risk results output by the model, pollutants are divided into high, medium and low risk categories, and combined with their exposure data in the environment, their potential threats to public health are further confirmed.

[0075] S5: For emerging pollutants with medium difficulty to identify, further predictive analysis will be conducted on their identification difficulty, and based on the degree of impact of emerging pollutants with medium difficulty to human health risk assessment, it will be determined whether the monitoring strategy needs to be adjusted.

[0076] For new pollutants with medium difficulty in identification, that is, the identification difficulty analysis value of the new pollutants generated in a fixed time period is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the identification difficulty analysis values ​​generated in the subsequent fixed time period that are greater than or equal to the first standard threshold and less than or equal to the second standard threshold are collected, and a corresponding data set is established, and the mean and standard deviation of the data set are calculated. After analysis, the impact of the new pollutants on human health risk assessment is predicted, and it is determined whether the monitoring strategy needs to be adjusted.

[0077] If the mean of the identification difficulty analysis values ​​in the data set is greater than or equal to the reference threshold of the identification difficulty analysis value mean, and the standard deviation of the identification difficulty analysis value is less than the reference threshold of the identification difficulty analysis value standard deviation, it means that the identification process of the new pollutant is stable, and the detection and identification errors are small, indicating that the monitoring status of the pollutant is good. At this time, no warning signal is generated, and the existing monitoring strategy is maintained without adjustment;

[0078] If the mean of the identification difficulty analysis value is greater than or equal to the reference threshold of the identification difficulty analysis value mean, and the standard deviation of the identification difficulty analysis value is greater than or equal to the reference threshold of the identification difficulty analysis value standard deviation, although the identification difficulty is high, the detection result fluctuates greatly, indicating that the identification process is unstable and the pollutant behavior is complex. At this time, a first-level warning signal is generated, and it is necessary to increase the monitoring frequency or improve the monitoring technology to improve the accuracy and stability of the detection;

[0079] If the mean of the recognition difficulty analysis value is less than the reference threshold of the recognition difficulty analysis value mean, and the standard deviation of the recognition difficulty analysis value is greater than or equal to the reference threshold of the recognition difficulty analysis value standard deviation, the overall recognition difficulty is low, but the error fluctuation is large. There may be occasional detection problems or changes in environmental conditions that affect the detection stability. At this time, a secondary warning signal is generated to further investigate the cause of the recognition error. It may be necessary to appropriately adjust the monitoring strategy to avoid missing potential risks.

[0080] If the mean of the identification difficulty analysis value is less than the reference threshold of the mean of the identification difficulty analysis value, and the standard deviation of the identification difficulty analysis value is less than the reference threshold of the standard deviation of the identification difficulty analysis value, the identification difficulty is low and the fluctuation is small, indicating that the identification process of the pollutant is stable and the risk is controllable. At this time, a third-level warning signal is generated, and the monitoring strategy can be moderately simplified or the monitoring frequency can be reduced to reduce resource consumption.

[0081] It should be noted here that the importance of the first-level warning signal is greater than that of the second-level warning signal, and the importance of the second-level warning signal is greater than that of the third-level warning signal. Relevant personnel can take corresponding adjustment measures according to different warning signal levels.

[0082] In this embodiment, preliminary pollutant identification is performed by collecting water samples from near the pollution source and using field equipment to measure the basic parameters of groundwater, and classifying them into new pollutants and common pollutants. For new pollutants, the difficulty of identification is evaluated based on the changes in the frequency of microbial degradation and the accuracy of concentration detection in nature, and they are divided into high, medium and low difficulties. For new pollutants with low difficulty identification, health risk assessment is carried out directly; for new pollutants with high difficulty identification, the structure-activity relationship model (QSAR) is used to predict their health threats by similarity with known harmful substances. New pollutants with medium difficulty identification require further prediction analysis of identification difficulty, combined with their impact on health risk assessment, to determine whether to adjust the monitoring strategy.

[0083] Example 2, please refer to Figure 2 As shown, a groundwater pollution risk assessment system for human health described in this embodiment includes a pollutant collection module, an identification difficulty analysis module, an identification difficulty classification module, a health threat prediction module and a monitoring strategy adjustment module;

[0084] Pollutant collection module: collect water samples from near the pollution source, measure the basic parameters of groundwater through on-site equipment, and conduct preliminary pollutant identification, dividing them into new pollutants and common pollutants;

[0085] Identification difficulty analysis module: Mark the identified new pollutants and determine the identification difficulty of new pollutants based on the changes in the frequency of degradation of new pollutants by natural microorganisms and the accuracy of new pollutant concentration detection;

[0086] Identification difficulty grading module: the identification difficulty of new pollutants is divided into different levels, namely high difficulty identification, medium difficulty identification and low difficulty identification;

[0087] Health threat prediction module: New pollutants with low identification difficulty are directly evaluated for health risks; for new pollutants with high identification difficulty, the structure-activity relationship model is used to predict the severity of their threat to human health based on the similarity between their chemical structure and known harmful substances;

[0088] Monitoring strategy adjustment module: For new pollutants with medium difficulty to identify, further predictive analysis is conducted on their identification difficulty, and based on the degree of impact of new pollutants with medium difficulty to human health risk assessment, it is determined whether the monitoring strategy needs to be adjusted.

[0089] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0090] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0091] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0092] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0093] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

Claims

1. A method for assessing the risk of groundwater pollution to human health, characterized by: The following steps are involved: S1: Collect water samples near the pollution source and measure the basic parameters of groundwater through on-site equipment. At the same time, conduct preliminary pollutant identification and classify them into new pollutants and common pollutants; S2: Mark the identified new pollutants and determine the difficulty of identifying them based on the changes in the frequency of degradation of new pollutants by natural microorganisms and the accuracy of new pollutant concentration detection; S3: The difficulty of identifying new pollutants is divided into different levels, namely high difficulty identification, medium difficulty identification and low difficulty identification; S4: New pollutants with low identification difficulty are directly evaluated for health risks; for new pollutants with high identification difficulty, the severity of their threat to human health is predicted by using the structure-activity relationship model based on the similarity of their chemical structure with known harmful substances; S5: For emerging pollutants with medium difficulty to identify, further predictive analysis will be conducted on their identification difficulty, and based on the degree of impact of emerging pollutants with medium difficulty to human health risk assessment, it will be determined whether the monitoring strategy needs to be adjusted.

2. The method for assessing the risk of groundwater pollution to human health according to claim 1, characterized in that: In S2, the identified new pollutants are marked, and the difficulty of identifying the new pollutants is determined based on the changes in the frequency of degradation of new pollutants by natural microorganisms and the accuracy of new pollutant concentration detection, specifically: The cumulative degradation efficiency fluctuation index is generated according to the change in the frequency of degradation of new pollutants by natural microorganisms. The method for obtaining the cumulative degradation efficiency fluctuation index is: Different environmental conditions are set on site to regularly monitor the degradation of pollutants and record the degradation efficiency of pollutants in the H time period, that is, the change of pollutant concentration over time, expressed as: In the formula, CDE(t i ) indicates that at time t i The degradation efficiency at t is , C0 is the initial concentration, C(t i ) is time t i The pollutant concentration at that time; the degradation rate k is calculated based on the degradation data of each time interval i , that is, the rate of change of pollutant concentration over time, expressed as: Among them, k i is the degradation rate in the ith time interval, Δt i =t i+1 -t i is the time interval; the standard deviation of the degradation rate in each time period is calculated to reflect the fluctuation of the degradation process, and the cumulative degradation efficiency fluctuation index is calculated by the ratio of the standard deviation to the average degradation efficiency. The expression is: In the formula, σk i is the standard deviation of the degradation rate at each time period, is the average degradation efficiency, and CD is the cumulative degradation efficiency fluctuation index.

3. The method for assessing the risk of groundwater pollution to human health according to claim 2, characterized in that: In S2, the detection repeatability error interference index is generated according to the accuracy of the new pollutant concentration detection, and the method for obtaining the detection repeatability error interference index is: Obtain the true concentration value C of new pollutants in n tests in real time true and the measured value C for each test meas,i , and calculate the error ΔC of its i-th detection i , the expression is: ΔC i =C meas,i -C true ; The detection error is calculated by minimizing the sum of squares of the errors between the measured value and the actual value, and the expression is: Where S is the detection error, and the standard range of the detection error under normal conditions in historical data is obtained: S min ~S max ; Calculate the detection repeatability error interference index. If the detection error is greater than S max , the expression is: DF=|SS max |; If the detection error is less than S min , the expression is: DF=|SS min |; In the formula, DF is the detection repeatability error interference index.

4. A method for assessing the risk of groundwater pollution to human health according to claim 3, characterized in that: The cumulative degradation efficiency fluctuation index and the detection repeatability error interference index are converted into the first eigenvector, and the first eigenvector is used as the input of the machine learning model. The machine learning model predicts the identification difficulty analysis value label of the new pollutant for each group of first eigenvectors as the prediction target, and minimizes the sum of the prediction errors of the identification difficulty analysis value labels of all new pollutants as the training target. The machine learning model is trained until the sum of the prediction errors converges, and the model training is stopped. The identification difficulty analysis value of the new pollutant is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

5. A method for assessing the risk of groundwater pollution to human health according to claim 4, characterized in that: In S3, the difficulty of identifying new pollutants is divided into different levels, namely high difficulty identification, medium difficulty identification and low difficulty identification, as follows: The obtained identification difficulty analysis value of the new pollutant is compared with the gradient standard threshold, the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and the identification difficulty analysis value of the new pollutant is compared with the first standard threshold and the second standard threshold respectively; If the identification difficulty analysis value of the new pollutant is greater than the second standard threshold, it means that the identification difficulty of the new pollutant is high, and a high-difficulty identification signal is generated at this time, and it is classified as a high-difficulty identification type; If the identification difficulty analysis value of the new pollutant is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it means that the identification difficulty of the new pollutant is average, and a medium difficulty identification signal is generated, and it is classified as a medium difficulty identification type; If the identification difficulty analysis value of the new pollutant is less than the first standard threshold, it means that the identification difficulty of the new pollutant is low. At this time, a low-difficulty identification signal is generated and it is classified as a low-difficulty identification type.

6. A method for assessing the risk of groundwater pollution to human health according to claim 5, characterized in that: In S5, for new pollutants with medium difficulty identification, further predictive analysis is conducted on their identification difficulty, and based on the impact of new pollutants with medium difficulty identification on human health risk assessment, it is determined whether the monitoring strategy needs to be adjusted, specifically: For new pollutants with medium difficulty in identification, that is, the identification difficulty analysis value of the new pollutants generated in a fixed time period is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the identification difficulty analysis values ​​generated in the subsequent fixed time period that are greater than or equal to the first standard threshold and less than or equal to the second standard threshold are collected, and a corresponding data set is established, and the mean and standard deviation of the data set are calculated. After analysis, the impact of the new pollutants on human health risk assessment is predicted, and it is determined whether the monitoring strategy needs to be adjusted.

7. A method for assessing the risk of groundwater pollution to human health according to claim 6, characterized in that: If the mean of the identification difficulty analysis values ​​in the data set is greater than or equal to the reference threshold of the identification difficulty analysis value mean, and the standard deviation of the identification difficulty analysis value is less than the reference threshold of the identification difficulty analysis value standard deviation, it means that the identification process of the new pollutant is stable, and the detection and identification errors are small. At this time, no warning signal is generated, and the existing monitoring strategy is maintained without adjustment; If the mean of the recognition difficulty analysis value is greater than or equal to the reference threshold of the recognition difficulty analysis value mean, and the standard deviation of the recognition difficulty analysis value is greater than or equal to the reference threshold of the standard deviation of the recognition difficulty analysis value, although the recognition difficulty is high, the detection result fluctuates greatly, indicating that the recognition process is unstable. At this time, a first-level warning signal is generated and the monitoring frequency is increased to improve the accuracy and stability of the detection; If the mean of the recognition difficulty analysis value is less than the reference threshold of the recognition difficulty analysis value mean, and the standard deviation of the recognition difficulty analysis value is greater than or equal to the reference threshold of the recognition difficulty analysis value standard deviation, the overall recognition difficulty is low, but the error fluctuation is large. At this time, a secondary warning signal is generated to further investigate the cause of the recognition error. The monitoring strategy needs to be adjusted to avoid missing potential risks. If the mean of the identification difficulty analysis value is less than the reference threshold of the mean of the identification difficulty analysis value, and the standard deviation of the identification difficulty analysis value is less than the reference threshold of the standard deviation of the identification difficulty analysis value, the identification difficulty is low and the fluctuation is small, indicating that the identification process of the pollutant is stable. At this time, a third-level warning signal is generated to reduce the monitoring frequency and reduce resource consumption.

8. A system for assessing the risk of groundwater pollution to human health, used to implement the method for assessing the risk of groundwater pollution to human health as claimed in any one of claims 1 to 7, characterized in that: It includes pollutant collection module, identification difficulty analysis module, identification difficulty classification module, health threat prediction module and monitoring strategy adjustment module; Pollutant collection module: collect water samples from near the pollution source, measure the basic parameters of groundwater through on-site equipment, and conduct preliminary pollutant identification, dividing them into new pollutants and common pollutants; Identification difficulty analysis module: Mark the identified new pollutants and determine the identification difficulty of new pollutants based on the changes in the frequency of degradation of new pollutants by natural microorganisms and the accuracy of new pollutant concentration detection; Identification difficulty grading module: the identification difficulty of new pollutants is divided into different levels, namely high difficulty identification, medium difficulty identification and low difficulty identification; Health threat prediction module: New pollutants with low identification difficulty are directly evaluated for health risks; for new pollutants with high identification difficulty, the structure-activity relationship model is used to predict the severity of their threat to human health based on the similarity between their chemical structure and known harmful substances; Monitoring strategy adjustment module: For new pollutants with medium difficulty to identify, further predictive analysis is conducted on their identification difficulty, and based on the degree of impact of new pollutants with medium difficulty to human health risk assessment, it is determined whether the monitoring strategy needs to be adjusted.

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