A method and system for identifying new pollutants in water sources with high sand content

Abstract

The present invention discloses a method and system for identifying new pollutants in water sources with high sand content, which relates to the technical field of water pollution investigation, including: collecting surface water samples from water sources through organic glass water collectors, and collecting sediment samples from water sources through Peterson mud collectors; using density-based clustering algorithms to process and analyze microplastic content and organic pollutant concentrations, and identify outliers and potential new pollutants; by analyzing the changes in the proportion of new pollutants in water bodies, the sources and migration paths of new pollutants are obtained; risk assessment experiments are conducted on new pollutants, and new pollutant data are collected; feature extraction and classification of new pollutant data are performed to identify the types of new pollutants. By setting a sample processing module and a sample analysis module based on the interaction mechanism between sediment particles and new pollutants, an environmental risk assessment method for the input of new pollutants in water bodies with high sand content is established.

Description

Technical Field

[0001] The present invention relates to the technical field of water pollution investigation, and in particular to a method and system for identifying new pollutants in a water source with high sand content. Background Art

[0002] High-sediment water sources usually refer to those water sources with high concentrations of sediment. Sediment in river water is a pollutant that not only pollutes the water, but also serves as an effective carrier and adsorbent for the absorption of new pollutants in the water, playing an important role in the migration of new pollutants. These water sources may have high sediment content due to various factors such as geology, climate, and human activities. Water bodies with high sediment content not only affect water quality, but may also damage water treatment facilities, increasing the difficulty and cost of water treatment.

[0003] With the acceleration of industrialization and urbanization, a large number of new pollutants are continuously discharged into the environment. These new pollutants may have characteristics such as toxicity, persistence, and bioaccumulation, posing a serious threat to the environment and human health. Therefore, timely and accurate identification of new pollutants is of great significance for protecting water quality in water sources, maintaining ecological balance and human health. Summary of the invention

[0004] In order to solve the above technical problems, a method and system for identifying new pollutants in water sources with high sand content are provided. This technical solution solves the problems raised in the above background technology.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] A method for identifying new pollutants in a water source with high sand content, comprising:

[0007] Surface water samples were collected from the water source using a plexiglass water sampler, and sediment samples were collected from the water source using a Peterson mud sampler;

[0008] Surface water samples were filtered using glass fiber filters, and the membranes retained the suspended matter samples. Microplastics on the membranes were identified, photographed, and counted using a stereo microscope;

[0009] The sediment sample is freeze-dried, ground, sieved, centrifuged, extracted, dried, eluted and filtered in sequence to obtain an organic pollutant sample to be tested;

[0010] Using density-based clustering algorithms, microplastic content and organic pollutant concentrations were processed and analyzed to identify outliers and potential new pollutants;

[0011] Using isotope tracing method, by analyzing the changes in the proportion of new pollutants in water bodies, the sources and migration paths of new pollutants can be obtained;

[0012] Collect new pollutant data by conducting risk assessment experiments on new pollutants, including migration and transformation capabilities, ecotoxicity, and binding capabilities to human cells;

[0013] The random forest algorithm is used to extract and classify features of new pollutant data and identify the types of new pollutants.

[0014] Preferably, the step of sequentially freeze-drying, grinding, sieving, centrifuging, extracting, drying, eluting and filtering the sediment sample to obtain the organic pollutant sample to be tested specifically comprises:

[0015] The sediment samples are placed in a freeze dryer, where the water in the sediment samples sublimates in the form of ice crystals by lowering the temperature and increasing the vacuum degree;

[0016] The dried sediment samples were ground into a fine powder;

[0017] Large particles of impurities are removed from the ground sediment sample by sieving;

[0018] The sieved sediment sample is placed in a centrifuge tube and centrifuged using a centrifuge;

[0019] The supernatant after centrifugation is mixed with an extractant to perform an extraction operation;

[0020] The extract is concentrated by using a rotary evaporator for evaporation and concentration, and the concentrated sediment sample is further dried;

[0021] After drying, the sediment sample is eluted with ammonia methanol solution and methanol in sequence, and the eluted sediment sample is filtered to obtain an organic pollutant sample to be tested.

[0022] Preferably, the use of a density-based clustering algorithm to process and analyze microplastic content and organic pollutant concentrations to identify outliers and potential new pollutants specifically includes:

[0023] S101: Build a system with microplastic content as the horizontal axis and organic pollutant concentration as the vertical axis, and input the data into the coordinate system for punctuation;

[0024] S102: Preset the neighborhood distance and set the initial value of N to 1;

[0025] S103: Select a data point that has not been visited;

[0026] S104: Determine whether the number of data points in the neighborhood of the data point is higher than a preset threshold. If so, mark the point as visited and the Nth cluster, and the points within its neighborhood distance range are marked as the same cluster. If not, mark the point as visited and noise.

[0027] S105: Determine whether there is no data point marked as unvisited and clustered. If so, set the value of N to N plus one. If not, select a data point marked as unvisited and clustered, and return to step S104;

[0028] S106: Determine whether there are no data points marked as unvisited, if so, output each cluster, if not, return to step S103;

[0029] S107: Identify abnormal clusters from each cluster and output them as new pollutants.

[0030] Preferably, the isotope tracer method is used to analyze the changes in the proportion of new pollutants in water bodies to obtain the sources and migration paths of new pollutants, which specifically include:

[0031] Deuterium is selected as an isotope to label the new pollutant, thereby obtaining a labeled new pollutant;

[0032] The marked new pollutants were introduced into the water source with high sediment content through simulation experiments;

[0033] Regularly collect water quality samples from the water sources in the simulation experiment;

[0034] Conduct isotope analysis on the collected water samples to determine the changes in concentration and proportion of new pollutants marked in the simulation experiment;

[0035] Determine whether the concentration of the marked new pollutant is higher than the preset concentration threshold. If so, output that the location is an important source of the new pollutant. If not, no output is made.

[0036] By analyzing the changes in the proportion of marked new pollutants at different times and locations, the migration speed and direction of new pollutants in the water body can be obtained.

[0037] Preferably, the collecting of new pollutant data by conducting risk assessment experiments on new pollutants specifically includes:

[0038] Fill the silt into the plexiglass leaching column with sand and gravel at the bottom, tap the column wall while filling to mix the silt thoroughly;

[0039] After the filling of the silt is completed, a layer of filter paper is placed on top of the filled silt, and then covered with sand and gravel;

[0040] The soil column is pre-saturated with calcium chloride solution, and after being allowed to stand for equilibrium, the sediment contaminated to a certain degree with new pollutants is filled above the sand and gravel at the top of the soil column;

[0041] The leaching experiment was carried out using calcium chloride solution as the leaching solution. After the leaching solution completely flowed out of the soil column, the soil from each layer of the soil column was collected and freeze-dried for testing.

[0042] Determine the change in the ratio of the concentration of new pollutants in sediment to the leaching time, and output it as the migration and transformation capacity of the new pollutants;

[0043] The solution after the new pollutant is degraded is measured by high-resolution mass spectrometry, and the mass spectrum is analyzed, identified and inferred as the product after the new pollutant is degraded;

[0044] The organic toxicity prediction program is used to predict the ecotoxicity of each degradation product to aquatic organisms and output the ecotoxicity of new pollutants;

[0045] Select cell lines for culture based on potential target organs or cell types of new contaminants;

[0046] The new pollutants were combined with fluorescent dyes and the uptake and distribution of fluorescent markers by cells were observed using fluorescence microscopy;

[0047] The binding ability of new pollutants to human cells was evaluated by analyzing cell viability, pollutant uptake, and fluorescence intensity.

[0048] Preferably, the use of the random forest algorithm to extract features and classify new pollutant data, and identifying the types of new pollutants specifically includes:

[0049] S201: setting the historical pollutant database and the potential pollutant database as model training data sets;

[0050] S202: Randomly select a data subset that has not been selected from the model training data set as the root node of the decision tree;

[0051] S203: establishing a first leaf node at the root node according to the difference in migration and conversion capabilities as a splitting criterion;

[0052] S204: establishing a second leaf node at the first leaf node according to the difference in ecotoxicity as a splitting criterion;

[0053] S205: establishing a third leaf node at the second leaf node according to the difference in binding ability to human cells as a splitting criterion;

[0054] S206: Summarize the root node and all its leaf nodes and output them as a decision tree;

[0055] S207: Determine whether corresponding decision trees are established for all data subsets in the model training data set. If so, output all decision trees as a random forest model of pollutant data. If not, return to step S202;

[0056] S208: Input the new pollutant data into the pollutant data random forest model, and output the type of the new pollutant.

[0057] Furthermore, a system for identifying new pollutants in a water source with high sand content is proposed, which is used to implement the above-mentioned method for identifying new pollutants in a water source with high sand content, including:

[0058] A sample processing module, wherein the sample processing module is used to collect surface water samples from a water source through an organic glass water collector, collect sediment samples from a water source through a Peterson mud sampler, filter the surface water samples through a glass fiber filter membrane, retain the filter membrane as a suspended matter sample, use a stereo microscope to identify, photograph and count microplastics on the filter membrane, and freeze-dry, grind, sieve, centrifuge, extract, dry, elute and filter the sediment samples in sequence to obtain an organic pollutant sample to be tested;

[0059] The sample analysis module is used to process and analyze the microplastic content and organic pollutant concentration using a density-based clustering algorithm, identify outliers and potential new pollutants, use isotope tracing to analyze the changes in the proportion of new pollutants in water bodies, obtain the source and migration path of new pollutants, conduct risk assessment experiments on new pollutants, collect new pollutant data, use a random forest algorithm to extract features and classify new pollutant data, and identify the types of new pollutants.

[0060] Optionally, the sample processing module specifically includes:

[0061] A sample collection unit, the sample collection unit is used to collect surface water samples from a water source through a plexiglass water sampler, and to collect sediment samples from a water source through a Peterson mud sampler;

[0062] A first sample pretreatment unit, wherein the first sample pretreatment unit is used to filter the surface water sample using a glass fiber filter membrane, the filter membrane retains the suspended matter sample, and a stereo microscope is used to identify, photograph and count the microplastics on the filter membrane;

[0063] The second sample pretreatment unit is used to sequentially perform freeze drying, grinding, sieving, centrifuging, extracting, drying, eluting and filtering operations on the sediment sample to obtain an organic pollutant sample to be tested.

[0064] Optionally, the sample analysis module specifically includes:

[0065] A sample identification unit, wherein the sample identification unit is used to process and analyze the microplastic content and organic pollutant concentration using a density-based clustering algorithm to identify outliers and potential new pollutants;

[0066] A migration analysis unit, which is used to obtain the source and migration path of new pollutants by analyzing the change in the proportion of new pollutants in the water body using an isotope tracer method;

[0067] A risk assessment unit, the risk assessment unit is used to collect new pollutant data by conducting risk assessment experiments on new pollutants;

[0068] The type identification unit is used to use the random forest algorithm to extract and classify the new pollutant data and identify the type of the new pollutant.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] The rapid and accurate identification of new pollutants in water sources with high sand content not only improves the efficiency of water quality monitoring, but also ensures the accuracy of monitoring results, providing reliable data support for subsequent water quality management and protection work. It can effectively prevent the spread and accumulation of these pollutants in the environment, thereby protecting the ecological environment and human health, and ensuring the sustainable use of water resources. It is of great significance to alleviate the problem of water shortage and promote sustainable economic and social development. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 The flowchart of the method for identifying new pollutants in a water source with high sand content of the present invention is as follows;

[0072] Figure 2 The present invention is a flow chart of the sediment sample processing method;

[0073] Figure 3 A flow chart of the method for identifying outliers and potential new contaminants of the present invention;

[0074] Figure 4 This is a flow chart of the method for analyzing the source and migration path of new pollutants using isotope tracing method of the present invention;

[0075] Figure 5 This is a flow chart of the experimental method for risk assessment of new pollutants of the present invention;

[0076] Figure 6 This is a flow chart of the method for extracting features and classifying new pollutant data and identifying the types of new pollutants according to the present invention. DETAILED DESCRIPTION

[0077] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0078] Reference Figure 1 As shown, a method for identifying new pollutants in a water source with high sediment content includes:

[0079] Surface water samples were collected from the water source using a plexiglass water sampler, and sediment samples were collected from the water source using a Peterson mud sampler;

[0080] Surface water samples were filtered using glass fiber filters, and the membranes retained the suspended matter samples. Microplastics on the membranes were identified, photographed, and counted using a stereo microscope;

[0081] The sediment sample is freeze-dried, ground, sieved, centrifuged, extracted, dried, eluted and filtered in sequence to obtain an organic pollutant sample to be tested;

[0082] Using density-based clustering algorithms, microplastic content and organic pollutant concentrations were processed and analyzed to identify outliers and potential new pollutants;

[0083] Using isotope tracing method, by analyzing the changes in the proportion of new pollutants in water bodies, the sources and migration paths of new pollutants can be obtained;

[0084] Collect new pollutant data by conducting risk assessment experiments on new pollutants, including migration and transformation capabilities, ecotoxicity, and binding capabilities to human cells;

[0085] The random forest algorithm is used to extract and classify features of new pollutant data and identify the types of new pollutants.

[0086] As shown in FIG2 , the sediment sample is freeze-dried, ground, sieved, centrifuged, extracted, dried, eluted and filtered in sequence to obtain the organic pollutant sample to be tested, which specifically includes:

[0087] The sediment samples are placed in a freeze dryer, where the water in the sediment samples sublimates in the form of ice crystals by lowering the temperature and increasing the vacuum degree;

[0088] The dried sediment samples were ground into a fine powder;

[0089] Large particles of impurities are removed from the ground sediment sample by sieving;

[0090] The sieved sediment sample is placed in a centrifuge tube and centrifuged using a centrifuge;

[0091] The supernatant after centrifugation is mixed with an extractant to perform an extraction operation;

[0092] The extract is concentrated by using a rotary evaporator for evaporation and concentration, and the concentrated sediment sample is further dried;

[0093] After drying, the sediment sample is eluted with ammonia methanol solution and methanol in sequence, and the eluted sediment sample is filtered to obtain an organic pollutant sample to be tested.

[0094] Freeze drying can remove moisture from sediment samples to prevent chemical reactions or sample deterioration during subsequent processing. During the extraction process, attention should be paid to controlling conditions such as temperature, time and stirring speed to improve the extraction efficiency. Elution can remove interfering substances that may exist in the sample and improve the accuracy of the analysis.

[0095] As shown in Figure 3, the density-based clustering algorithm is used to process and analyze the microplastic content and organic pollutant concentrations, and outliers and potential new pollutants are identified, including:

[0096] S101: Build a system with microplastic content as the horizontal axis and organic pollutant concentration as the vertical axis, and input the data into the coordinate system for punctuation;

[0097] S102: Preset the neighborhood distance and set the initial value of N to 1;

[0098] S103: Select a data point that has not been visited;

[0099] S104: Determine whether the number of data points in the neighborhood of the data point is higher than a preset threshold. If so, mark the point as visited and the Nth cluster, and the points within its neighborhood distance range are marked as the same cluster. If not, mark the point as visited and noise.

[0100] S105: Determine whether there is no data point marked as unvisited and clustered. If so, set the value of N to N plus one. If not, select a data point marked as unvisited and clustered, and return to step S104;

[0101] S106: Determine whether there are no data points marked as unvisited, if so, output each cluster, if not, return to step S103;

[0102] S107: Identify abnormal clusters from each cluster and output them as new pollutants.

[0103] Density based clustering has some advantages over other clustering algorithms, it does not require a pre-set number of clusters, it also identifies outliers as noise unlike mean shift clustering algorithm it puts data points into one cluster even if they are very different, also it works well in finding clusters of arbitrary size and shape.

[0104] As shown in Figure 4, by using the isotope tracer method and analyzing the changes in the proportion of new pollutants in water bodies, the sources and migration paths of new pollutants are obtained, including:

[0105] Deuterium is selected as an isotope to label the new pollutant, thereby obtaining a labeled new pollutant;

[0106] The marked new pollutants were introduced into the water source with high sediment content through simulation experiments;

[0107] Regularly collect water quality samples from the water sources in the simulation experiment;

[0108] Conduct isotope analysis on the collected water samples to determine the changes in concentration and proportion of new pollutants marked in the simulation experiment;

[0109] Determine whether the concentration of the marked new pollutant is higher than the preset concentration threshold. If so, output that the location is an important source of the new pollutant. If not, no output is made.

[0110] By analyzing the changes in the proportion of marked new pollutants at different times and locations, the migration speed and direction of new pollutants in the water body can be obtained.

[0111] Isotope tracer is a trace analysis method that uses radioactive nuclides or rare stable nuclides as tracers to mark the research object. This method can track the migration and transformation of substances or elements in biological systems, geological systems or water bodies. When applied to the identification of new pollutants in water sources with high sand content, the isotope tracer method can infer the source and migration path of new pollutants by analyzing the changes in the proportion of new pollutants in the water body.

[0112] As shown in Figure 5, by conducting risk assessment experiments on new pollutants, the data collected on new pollutants specifically include:

[0113] Fill the silt into the plexiglass leaching column with sand and gravel at the bottom, tap the column wall while filling to mix the silt thoroughly;

[0114] After the filling of the silt is completed, a layer of filter paper is placed on top of the filled silt, and then covered with sand and gravel;

[0115] The soil column is pre-saturated with calcium chloride solution, and after being allowed to stand for equilibrium, the sediment contaminated to a certain degree with new pollutants is filled above the sand and gravel at the top of the soil column;

[0116] The leaching experiment was carried out using calcium chloride solution as the leaching solution. After the leaching solution completely flowed out of the soil column, the soil from each layer of the soil column was collected and freeze-dried for testing.

[0117] Determine the change in the ratio of the concentration of new pollutants in sediment to the leaching time, and output it as the migration and transformation capacity of the new pollutants;

[0118] The solution after the new pollutant is degraded is measured by high-resolution mass spectrometry, and the mass spectrum is analyzed, identified and inferred as the product after the new pollutant is degraded;

[0119] The organic toxicity prediction program is used to predict the ecotoxicity of each degradation product to aquatic organisms and output the ecotoxicity of new pollutants;

[0120] Select cell lines for culture based on potential target organs or cell types of new contaminants;

[0121] The new pollutants were combined with fluorescent dyes and the uptake and distribution of fluorescent markers by cells were observed using fluorescence microscopy;

[0122] The binding ability of new pollutants to human cells was evaluated by analyzing cell viability, pollutant uptake, and fluorescence intensity.

[0123] In experiments to evaluate the ecotoxicity of new pollutants, predicting the ecotoxicity of each degradation product to aquatic organisms can further assess whether the environmental risk of the degradation products is increasing or decreasing, and identify degradation products with greater environmental risks; in experiments to evaluate the binding ability of new pollutants to human cells, the fluorescent labeling method used is more intuitive and visual than other methods, and can be used for real-time monitoring.

[0124] As shown in Figure 6, the random forest algorithm is used to extract and classify the features of new pollutant data, and the types of new pollutants are specifically identified as follows:

[0125] S201: setting the historical pollutant database and the potential pollutant database as model training data sets;

[0126] S202: Randomly select a data subset that has not been selected from the model training data set as the root node of the decision tree;

[0127] S203: establishing a first leaf node at the root node according to the difference in migration and conversion capabilities as a splitting criterion;

[0128] S204: establishing a second leaf node at the first leaf node according to the difference in ecotoxicity as a splitting criterion;

[0129] S205: establishing a third leaf node at the second leaf node according to the difference in binding ability to human cells as a splitting criterion;

[0130] S206: Summarize the root node and all its leaf nodes and output them as a decision tree;

[0131] S207: Determine whether corresponding decision trees are established for all data subsets in the model training data set. If so, output all decision trees as a random forest model of pollutant data. If not, return to step S202;

[0132] S208: Input the new pollutant data into the pollutant data random forest model, and output the type of the new pollutant.

[0133] Random forest regression is an algorithm based on ensemble learning. It performs regression tasks by constructing multiple decision trees and integrating their prediction results. In a random forest, each decision tree is independent and trained on a randomly selected subsample, which can effectively reduce the risk of overfitting. Random forest obtains the final regression result by averaging or weighted averaging the prediction results of multiple decision trees.

[0134] Furthermore, based on the same inventive concept as the above-mentioned method for identifying new pollutants in a water source with high sand content, the present solution also proposes a system for identifying new pollutants in a water source with high sand content, comprising:

[0135] A sample processing module, wherein the sample processing module is used to collect surface water samples from a water source through an organic glass water collector, collect sediment samples from a water source through a Peterson mud sampler, filter the surface water samples through a glass fiber filter membrane, retain the filter membrane as a suspended matter sample, use a stereo microscope to identify, photograph and count microplastics on the filter membrane, and freeze-dry, grind, sieve, centrifuge, extract, dry, elute and filter the sediment samples in sequence to obtain an organic pollutant sample to be tested;

[0136] The sample analysis module is used to process and analyze the microplastic content and organic pollutant concentration using a density-based clustering algorithm, identify outliers and potential new pollutants, use isotope tracing to analyze the changes in the proportion of new pollutants in water bodies, obtain the source and migration path of new pollutants, conduct risk assessment experiments on new pollutants, collect new pollutant data, use a random forest algorithm to extract features and classify new pollutant data, and identify the types of new pollutants.

[0137] The sample processing module specifically includes:

[0138] A sample collection unit, the sample collection unit is used to collect surface water samples from a water source through a plexiglass water sampler, and to collect sediment samples from a water source through a Peterson mud sampler;

[0139] A first sample pretreatment unit, wherein the first sample pretreatment unit is used to filter the surface water sample using a glass fiber filter membrane, the filter membrane retains the suspended matter sample, and a stereo microscope is used to identify, photograph and count the microplastics on the filter membrane;

[0140] The second sample pretreatment unit is used to sequentially perform freeze drying, grinding, sieving, centrifuging, extracting, drying, eluting and filtering operations on the sediment sample to obtain an organic pollutant sample to be tested.

[0141] The sample analysis module specifically includes:

[0142] A sample identification unit, wherein the sample identification unit is used to process and analyze the microplastic content and organic pollutant concentration using a density-based clustering algorithm to identify outliers and potential new pollutants;

[0143] A migration analysis unit, which is used to obtain the source and migration path of new pollutants by analyzing the change in the proportion of new pollutants in the water body using an isotope tracer method;

[0144] A risk assessment unit, the risk assessment unit is used to collect new pollutant data by conducting risk assessment experiments on new pollutants;

[0145] The type identification unit is used to use the random forest algorithm to extract and classify the new pollutant data and identify the type of the new pollutant.

[0146] Furthermore, the present solution also proposes a computer-readable storage medium on which a computer-readable program is stored. When the computer-readable program is called, the above-mentioned method for identifying new pollutants in water sources with high sand content is executed.

[0147] It is understandable that the storage medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid state drive (SSD).

[0148] To sum up, the advantages of the present invention are: it can realize the rapid and accurate identification of new pollutants in water sources with high sand content, which not only improves the efficiency of water quality monitoring, but also ensures the accuracy of the monitoring results, and provides reliable data support for subsequent water quality management and protection work. It can effectively prevent the spread and accumulation of these pollutants in the environment, thereby protecting the ecological environment and human health, and ensuring the sustainable use of water resources. It is of great significance to alleviate the problem of water shortage and promote the sustainable development of the economy and society.

[0149] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for identifying new pollutants in a water source with high sand content, characterized in that: include: Surface water samples were collected from the water source using a plexiglass water sampler, and sediment samples were collected from the water source using a Peterson mud sampler; Surface water samples were filtered using glass fiber filters, and the membranes retained the suspended matter samples. Microplastics on the membranes were identified, photographed, and counted using a stereo microscope; The sediment sample is freeze-dried, ground, sieved, centrifuged, extracted, dried, eluted and filtered in sequence to obtain an organic pollutant sample to be tested; Using density-based clustering algorithms, microplastic content and organic pollutant concentrations were processed and analyzed to identify outliers and potential new pollutants; Using isotope tracing method, by analyzing the changes in the proportion of new pollutants in water bodies, the sources and migration paths of new pollutants can be obtained; Collect new pollutant data by conducting risk assessment experiments on new pollutants, including migration and transformation capabilities, ecotoxicity, and binding capabilities to human cells; Use the random forest algorithm to extract features and classify new pollutant data to identify the types of new pollutants; The density-based clustering algorithm is used to process and analyze the microplastic content and organic pollutant concentrations, and outliers and potential new pollutants are identified, including: S101: Build a system with microplastic content as the horizontal axis and organic pollutant concentration as the vertical axis, and input the data into the coordinate system for punctuation; S102: Preset the neighborhood distance and set the initial value of N to 1; S103: Select a data point that has not been visited; S104: Determine whether the number of data points in the neighborhood of the data point is higher than a preset threshold. If so, mark the point as visited and the Nth cluster, and the points within its neighborhood distance range are marked as the same cluster. If not, mark the point as visited and noise. S105: Determine whether there is no data point marked as unvisited and clustered. If so, set the value of N to N plus one. If not, select a data point marked as unvisited and clustered, and return to step S104; S106: Determine whether there are no data points marked as unvisited, if so, output each cluster, if not, return to step S103; S107: identifying abnormal clusters from each cluster and outputting them as new pollutants; The collection of new pollutant data by conducting risk assessment experiments on new pollutants specifically includes: Fill the silt into the plexiglass leaching column with sand and gravel at the bottom, tap the column wall while filling to mix the silt thoroughly; After the filling of the silt is completed, a layer of filter paper is placed on top of the filled silt, and then covered with sand and gravel; The soil column is pre-saturated with calcium chloride solution, and after being allowed to stand for equilibrium, the sediment contaminated to a certain degree with new pollutants is filled above the sand and gravel at the top of the soil column; The leaching experiment was carried out using calcium chloride solution as the leaching solution. After the leaching solution completely flowed out of the soil column, the soil from each layer of the soil column was collected and freeze-dried for testing. Determine the change in the ratio of the concentration of new pollutants in sediment to the leaching time, and output it as the migration and transformation capacity of the new pollutants; The solution after the new pollutant is degraded is measured by high-resolution mass spectrometry, and the mass spectrum is analyzed, identified and inferred as the product after the new pollutant is degraded; The organic toxicity prediction program is used to predict the ecotoxicity of each degradation product to aquatic organisms and output the ecotoxicity of new pollutants; Select cell lines for culture based on potential target organs or cell types of new contaminants; The new pollutants were combined with fluorescent dyes and the uptake and distribution of fluorescent markers by cells were observed using fluorescence microscopy; The binding ability of new pollutants to human cells was evaluated by analyzing cell viability, pollutant uptake, and fluorescence intensity.

2. A method for identifying new pollutants in a water source with high sand content according to claim 1, characterized in that: The process of sequentially freeze-drying, grinding, sieving, centrifuging, extracting, drying, eluting and filtering the sediment sample to obtain the organic pollutant sample to be tested specifically includes: The sediment samples are placed in a freeze dryer, where the water in the sediment samples sublimates in the form of ice crystals by lowering the temperature and increasing the vacuum degree; The dried sediment samples were ground into a fine powder; Large particles of impurities are removed from the ground sediment sample by sieving; The sieved sediment sample is placed in a centrifuge tube and centrifuged using a centrifuge; The supernatant after centrifugation is mixed with an extractant to perform an extraction operation; The extract is concentrated by using a rotary evaporator for evaporation and concentration, and the concentrated sediment sample is further dried; After drying, the sediment sample is eluted with ammonia methanol solution and methanol in sequence, and the eluted sediment sample is filtered to obtain an organic pollutant sample to be tested.

3. A method for identifying new pollutants in a water source with high sand content according to claim 2, characterized in that: The isotope tracer method is used to analyze the changes in the proportion of new pollutants in water bodies to obtain the sources and migration paths of new pollutants, including: Deuterium is selected as an isotope to label the new pollutant, thereby obtaining a labeled new pollutant; The marked new pollutants were introduced into the water source with high sediment content through simulation experiments; Regularly collect water quality samples from the water sources in the simulation experiment; Conduct isotope analysis on the collected water samples to determine the changes in concentration and proportion of new pollutants marked in the simulation experiment; Determine whether the concentration of the marked new pollutant is higher than the preset concentration threshold. If so, output that the location is an important source of the new pollutant. If not, no output is made. By analyzing the changes in the proportion of marked new pollutants at different times and locations, the migration speed and direction of new pollutants in the water body can be obtained.

4. A method for identifying new pollutants in a water source with high sand content according to claim 3, characterized in that: The random forest algorithm is used to extract and classify the features of new pollutant data and identify the types of new pollutants, specifically including: S201: setting the historical pollutant database and the potential pollutant database as model training data sets; S202: Randomly select a data subset that has not been selected from the model training data set as the root node of the decision tree; S203: establishing a first leaf node at the root node according to the difference in migration and conversion capabilities as a splitting criterion; S204: establishing a second leaf node at the first leaf node according to the difference in ecotoxicity as a splitting criterion; S205: establishing a third leaf node at the second leaf node according to the difference in binding ability to human cells as a splitting criterion; S206: Summarize the root node and all its leaf nodes and output them as a decision tree; S207: Determine whether corresponding decision trees are established for all data subsets in the model training data set. If so, output all decision trees as a random forest model of pollutant data. If not, return to step S202; S208: Input the new pollutant data into the pollutant data random forest model, and output the type of the new pollutant.

5. A system for identifying new pollutants in a water source with high sand content, used to implement the method for identifying new pollutants in a water source with high sand content as described in any one of claims 1 to 4, characterized in that: include: A sample processing module, wherein the sample processing module is used to collect surface water samples from a water source through an organic glass water collector, collect sediment samples from a water source through a Peterson mud sampler, filter the surface water samples through a glass fiber filter membrane, retain the filter membrane as a suspended matter sample, use a stereo microscope to identify, photograph and count microplastics on the filter membrane, and freeze-dry, grind, sieve, centrifuge, extract, dry, elute and filter the sediment samples in sequence to obtain an organic pollutant sample to be tested; A sample analysis module, which is used to process and analyze the microplastic content and organic pollutant concentration using a density-based clustering algorithm, identify outliers and potential new pollutants, use isotope tracing to analyze the changes in the proportion of new pollutants in water bodies, obtain the source and migration path of new pollutants, conduct risk assessment experiments on new pollutants, collect new pollutant data, and use a random forest algorithm to extract features and classify new pollutant data to identify the types of new pollutants; The density-based clustering algorithm is used to process and analyze the microplastic content and organic pollutant concentrations, and outliers and potential new pollutants are identified, including: S101: Build a system with microplastic content as the horizontal axis and organic pollutant concentration as the vertical axis, and input the data into the coordinate system for punctuation; S102: Preset the neighborhood distance and set the initial value of N to 1; S103: Select a data point that has not been visited; S104: Determine whether the number of data points in the neighborhood of the data point is higher than a preset threshold. If so, mark the point as visited and the Nth cluster, and the points within its neighborhood distance range are marked as the same cluster. If not, mark the point as visited and noise. S105: Determine whether there is no data point marked as unvisited and clustered. If so, set the value of N to N plus one. If not, select a data point marked as unvisited and clustered, and return to step S104; S106: Determine whether there are no data points marked as unvisited, if so, output each cluster, if not, return to step S103; S107: identifying abnormal clusters from each cluster and outputting them as new pollutants; The collection of new pollutant data by conducting risk assessment experiments on new pollutants specifically includes: Fill the silt into the plexiglass leaching column with sand and gravel at the bottom, tap the column wall while filling to mix the silt thoroughly; After the filling of the silt is completed, a layer of filter paper is placed on top of the filled silt, and then covered with sand and gravel; The soil column is pre-saturated with calcium chloride solution, and after being allowed to stand for equilibrium, the sediment contaminated to a certain degree with new pollutants is filled above the sand and gravel at the top of the soil column; The leaching experiment was carried out using calcium chloride solution as the leaching solution. After the leaching solution completely flowed out of the soil column, the soil from each layer of the soil column was collected and freeze-dried for testing. Determine the change in the ratio of the concentration of new pollutants in sediment to the leaching time, and output it as the migration and transformation capacity of the new pollutants; The solution after the new pollutant is degraded is measured by high-resolution mass spectrometry, and the mass spectrum is analyzed, identified and inferred as the product after the new pollutant is degraded; The organic toxicity prediction program is used to predict the ecotoxicity of each degradation product to aquatic organisms and output the ecotoxicity of new pollutants; Select cell lines for culture based on potential target organs or cell types of new contaminants; The new pollutants were combined with fluorescent dyes and the uptake and distribution of fluorescent markers by cells were observed using fluorescence microscopy; The binding ability of new pollutants to human cells was evaluated by analyzing cell viability, pollutant uptake, and fluorescence intensity.

6. A system for identifying new pollutants in a water source with high sand content according to claim 5, characterized in that: The sample processing module specifically includes: A sample collection unit, the sample collection unit is used to collect surface water samples from a water source through a plexiglass water sampler, and to collect sediment samples from a water source through a Peterson mud sampler; A first sample pretreatment unit, wherein the first sample pretreatment unit is used to filter the surface water sample using a glass fiber filter membrane, the filter membrane retains the suspended matter sample, and a stereo microscope is used to identify, photograph and count the microplastics on the filter membrane; The second sample pretreatment unit is used to sequentially perform freeze drying, grinding, sieving, centrifuging, extracting, drying, eluting and filtering operations on the sediment sample to obtain an organic pollutant sample to be tested.

7. A system for identifying new pollutants in a water source with high sand content according to claim 6, characterized in that: The sample analysis module specifically includes: A sample identification unit, wherein the sample identification unit is used to process and analyze the microplastic content and organic pollutant concentration using a density-based clustering algorithm to identify outliers and potential new pollutants; A migration analysis unit, which is used to obtain the source and migration path of new pollutants by analyzing the change in the proportion of new pollutants in the water body using an isotope tracer method; A risk assessment unit, the risk assessment unit is used to collect new pollutant data by conducting risk assessment experiments on new pollutants; The type identification unit is used to use the random forest algorithm to extract and classify the new pollutant data and identify the type of the new pollutant.

Images