Pollutant analysis method, apparatus, and electronic device

Abstract

The application provides a pollution analysis method, device and electronic equipment, including: obtaining pollution sample data, grouping the pollution sample data based on a preset environmental medium and a preset sample extraction method to obtain pollution sample data sets of multiple different environmental media; determining distribution information of the pollution sample data according to the multiple pollution sample data sets; and determining an influencing factor and a risk type of the distribution of the pollution sample data according to the distribution information. The pollution analysis method can realize the mastery of the spatial distribution, influencing factor and ecological risk of the pollution in the water environment by obtaining the pollution sample data, so as to solve the problem that the distribution mechanism and risk of the reservoir pollution are unknown, strengthen the cognition of the overall reservoir pollution, and help the control and management of the reservoir pollution.

Description

Technical Field

[0001] This application relates to the field of water environment technology, and in particular to a pollutant analysis method, apparatus and electronic equipment. Background Technology

[0002] Pollutants are frequently detected in aquatic ecosystems, posing varying degrees of harm and negative impacts on both ecosystems and human health. Differences in spatial coverage and sampling conditions among reservoirs lead to diverse methods for pollutant detection and data analysis. Conclusions drawn from each case study of a reservoir may not be directly applicable to other reservoir studies, resulting in limited research on reservoirs and a lack of understanding of the potential risks they face and the overall impact of reservoir pollution. Summary of the Invention

[0003] In view of this, the purpose of this application is to propose a pollutant analysis method, apparatus and electronic equipment to solve the analysis of pollutants in reservoirs and enhance the overall understanding of reservoir pollution.

[0004] To achieve the above objectives, the first aspect of this application provides a method for analyzing pollutants, comprising:

[0005] Obtain pollutant sample data, and group the pollutant sample data to obtain multiple pollutant sample datasets;

[0006] Based on multiple pollutant sample datasets, determine the distribution information of the pollutant sample data;

[0007] Based on the distribution information, the pollution ecological information of the pollutant sample data distribution is determined.

[0008] Furthermore, the process of acquiring pollutant sample data involves grouping the pollutant sample data to obtain multiple pollutant sample datasets, including:

[0009] Based on a preset sample extraction method and a preset environmental medium, the pollutant sample data are grouped to obtain multiple pollutant sample datasets.

[0010] Furthermore, the pollutant sample data includes geographical environmental information of the sampling point and pollutant characteristic information;

[0011] The step of determining the distribution information of pollutant sample data based on multiple pollutant sample datasets includes:

[0012] Based on the geographical environment information of the sampling points, the pollutant characteristic information, and the preset environmental medium, statistical analysis is performed on multiple pollutant sample datasets to determine the distribution information of the pollutant sample data.

[0013] Furthermore, the pollution ecological information includes risk type, influencing factors, and target impact degree value corresponding to the influencing factors;

[0014] The step of determining the pollution ecological information of the pollutant sample data distribution based on the distribution information includes:

[0015] Based on the distribution information, determine the influencing factors of the pollutant sample data distribution and the target influence value corresponding to the influencing factors;

[0016] Based on the distribution information, determine the risk type of the pollutant sample data distribution.

[0017] Furthermore, the pollutant sample data also includes geographical environmental information of the sampling point and pollutant characteristic information, wherein the pollutant characteristic information includes pollutant abundance;

[0018] The step of determining the influencing factors of the pollutant sample data distribution and the target influence value corresponding to the influencing factors based on the distribution information includes:

[0019] The geographical environment information of the sampling points corresponding to the distribution information and the abundance of pollutants are input into a preset geographic detector model to obtain the initial impact value on the abundance of pollutants.

[0020] The initial impact value is compared with a first preset threshold. If the initial impact value is less than the first preset threshold, the geographical environment information is determined to be the influencing factor, and the initial impact value is used as the target impact value corresponding to the influencing factor.

[0021] Furthermore, the geographical environment information of the sampling point includes multiple environmental information items;

[0022] The step of inputting the geographical environment information and pollutant abundance of the sampling points into a preset geographical detector model to obtain an initial impact value on the pollutant abundance includes:

[0023] By inputting multiple environmental information items and pollutant abundance into the geographic detector model, a first influence value of each environmental information item on the pollutant abundance is obtained;

[0024] Multiple first influence values ​​are interactively calculated to obtain an initial influence value for the abundance of the pollutant.

[0025] Furthermore, the pollutant sample data also includes geographical environmental information of the sampling point and pollutant characteristic information, wherein the pollutant characteristic information includes pollutant morphological characteristic information;

[0026] The step of determining the influencing factors of the pollutant sample data distribution and the target influence degree value corresponding to the influencing factors based on the distribution information further includes:

[0027] Redundancy analysis is performed on the geographical environment information corresponding to the distribution information and the pollutant morphology characteristic information to determine a second impact degree value for the pollutant morphology characteristic information;

[0028] The second impact value is compared with a second preset threshold. If the second impact value is less than the second preset threshold, the pollutant morphology information is determined to be the influencing factor, and the second impact value is used as the target impact value corresponding to the influencing factor.

[0029] Furthermore, the risk types include pollution risk types, risk types corresponding to polymer types, and potential ecological risk types;

[0030] The pollutant sample data also includes various polymer types;

[0031] The step of determining the risk type of the pollutant sample data distribution based on the distribution information includes:

[0032] The pollutant abundance corresponding to the distribution information is calculated with a preset pollutant abundance to obtain a pollutant index;

[0033] The pollution risk type is determined based on the pollutant index;

[0034] The proportion of each polymer type among the various polymer types and the preset hazard score corresponding to each polymer type are calculated to obtain the type risk index for each polymer.

[0035] Based on the risk index, determine the risk type corresponding to each polymer type;

[0036] The potential ecological risk index is determined by calculating the pollution index, the proportion of each polymer type among all polymer types, and the preset hazard score corresponding to each polymer type.

[0037] The type of potential ecological risk is determined based on the potential ecological risk index.

[0038] A second aspect of this application provides a pollutant analysis apparatus, comprising:

[0039] The acquisition module is used to acquire pollutant sample data and group the pollutant sample data to obtain multiple pollutant sample datasets.

[0040] The first determining module is used to determine the distribution information of the pollutant sample data based on the multiple pollutant sample datasets;

[0041] The second determining module determines the pollution ecological information of the distribution of the pollutant sample data based on the distribution information.

[0042] A third aspect of this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described above.

[0043] As can be seen from the above, the pollutant analysis method provided in this application acquires pollutant sample data, groups the pollutant sample data to obtain multiple pollutant sample datasets, and facilitates the analysis of different pollutant sample data by grouping the pollutant sample data, making the analysis more accurate; based on the multiple pollutant sample datasets, the distribution information of the pollutant sample data is determined, and the distribution information in the pollutant sample datasets facilitates subsequent analysis of the water environment in different regions; based on the distribution information, the pollution ecological information of the distribution of the pollutant sample data is determined; through the distribution information, the analysis of pollutant sample data in the water environment of different distribution areas can be realized, so as to solve the analysis of reservoir pollutants, enhance the overall understanding of reservoir pollution, and help control and manage reservoir pollution. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a schematic diagram of a pollutant analysis method according to an embodiment of this application;

[0046] Figure 2 This is a schematic diagram of the pollutant distribution process according to an embodiment of this application;

[0047] Figure 3 This is a schematic diagram of the structure of a pollutant analysis device according to an embodiment of this application;

[0048] Figure 4 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0050] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0051] Microplastics, as a novel environmental pollutant threatening the health of reservoir ecosystems, have been detected in reservoirs on all five continents over the past six years. The problem of microplastic pollution in reservoirs is increasingly attracting global attention. To understand the factors driving the characteristics and distribution of microplastics in reservoirs, this study considered the impact of multiple variables on the heterogeneity of microplastic characteristics. Exemplary variables include: sampling point location relative to the dam, sediment properties, population density, land use type, and sampling tools. Furthermore, different microplastic sampling methods vary, and current research only considers the influence of single factors on microplastic abundance distribution, with little understanding of the relative importance of multiple potential factors in driving microplastic characteristic distribution, or the combined effect of these factors. Given the potential risks faced by reservoirs and the lack of overall understanding of reservoir microplastic pollution, there is an urgent need for a comprehensive analysis of the distribution characteristics, multi-factor influence mechanisms, and ecological risks of microplastics in reservoirs on a global scale.

[0052] In the embodiments of this application, data on microplastics in reservoirs from the literature are extracted and grouped, and statistical analysis is performed on the extracted and grouped data. The statistical analysis is used to assess the distribution characteristics, influencing factors, and ecological risks of microplastic pollution in reservoirs. A multivariate assessment index system is used to clarify the current status of microplastic pollution in reservoirs and its key drivers, providing scientific guidance for reservoir pollution control and management.

[0053] The embodiments of this application are described in detail below with reference to the accompanying drawings.

[0054] refer to Figure 1 One embodiment of this application provides a method for analyzing pollutants, including:

[0055] Step S101: Obtain pollutant sample data, and group the pollutant sample data to obtain multiple pollutant sample datasets;

[0056] Step S102: Determine the distribution information of the pollutant sample data based on the multiple pollutant sample datasets;

[0057] Step S103: Determine the pollution ecological information of the distribution of the pollutant sample data based on the distribution information.

[0058] Specifically, the pollutant sample data is grouped to obtain multiple pollutant sample datasets. Grouping the pollutant sample data facilitates the analysis of different pollutant sample data, making the analysis more accurate. Based on the multiple pollutant sample datasets, the distribution information of the pollutant sample data is determined. The distribution information in the pollutant sample datasets facilitates subsequent analysis of the water environment in different regions. Based on the distribution information, the pollution ecological information of the pollutant sample data distribution is determined. Through the distribution information, the analysis of pollutant sample data in the water environment of different distribution areas can be realized, thereby solving the analysis of reservoir pollutants, strengthening the overall understanding of reservoir pollution, and contributing to the control and management of reservoir pollution.

[0059] In some embodiments, step S101, which involves acquiring pollutant sample data and grouping the pollutant sample data to obtain multiple pollutant sample datasets, includes:

[0060] Based on a preset sample extraction method and a preset environmental medium, the pollutant sample data are grouped to obtain multiple pollutant sample datasets.

[0061] Specifically, by grouping pollutant sample data according to preset sample extraction methods and preset environmental media, different preset extraction methods can be obtained for different preset environmental media. This facilitates subsequent analysis of different preset extraction methods for the same preset environmental media and helps in the statistical analysis of the pollution status of pollutants on the reservoir.

[0062] For example, the preset sample extraction method includes digestion, filtration, sieving and density separation, and the preset environmental medium includes water, sediment and aquatic organisms.

[0063] Furthermore, exemplarily, refer to Figure 2 The dataset consists of 440 samples collected from 43 reservoirs.

[0064] Figure 2 In this context, environmental media include: water bodies, sediments, and aquatic organisms.

[0065] In some embodiments, in step S102, the pollutant sample data includes geographical environmental information of the sampling point and pollutant characteristic information;

[0066] The step of determining the distribution information of pollutant sample data based on multiple pollutant sample datasets includes:

[0067] Based on the geographical environment information of the sampling points, the pollutant characteristic information, and the preset environmental medium, statistical analysis is performed on multiple pollutant sample datasets to determine the distribution information of the pollutant sample data.

[0068] Specifically, for example, the geographical environmental information of the sampling points includes the latitude and longitude of the sampling points relative to the reservoir and the geographical location of the sampling points relative to the reservoir. The pollutant characteristic information includes microplastic morphological characteristics and microplastic abundance. By performing statistical analysis on multiple microplastic sample datasets, the location information of the distribution of microplastic sample data relative to latitude and longitude and the geographical location of the sampling points relative to the reservoir can be obtained. Furthermore, for different microplastic characteristic information and preset environmental media, the distribution of preset environmental media and the different microplastic characteristic information corresponding to preset environmental media can be obtained, which facilitates the analysis of microplastic sample data.

[0069] For example, 30 studies meeting the analytical requirements demonstrated the occurrence and temporal or spatial distribution characteristics of microplastics in 43 reservoirs. These reservoirs were located in 9 in North America, 1 in South America, 6 in Europe, 6 in South Africa, and 21 in Asia. The environmental media studied primarily consisted of water bodies (21 studies) and sediments (17 studies), with less focus on aquatic organisms (7 studies). Nearly 44% of these reservoirs are primarily used for drinking water supply.

[0070] The abundance of microplastics in the water ranged from 0.28 to 181,927.71 cells / m³, with a mean of 10,129.19 ± 25,272.92 cells / m³. Within each reservoir, the coefficient of variation for microplastic abundance ranged from 0 to 250%. The four reservoirs with coefficients of variation greater than 100% had sampling points located in the upper, middle, and lower reaches of the reservoir, spanning relatively large latitudinal ranges, or in different seasons. Most studies focused on investigating the abundance of microplastics in surface water. Stratified sampling was only conducted in a few reservoirs (e.g., Danjiangkou Reservoir), showing that the average abundance of microplastics in the middle layer (intermediate depth) was significantly higher than that in the surface layer (≤1 meter underwater) and the bottom layer (0.5 meters above the bottom of the reservoir). This indicates that reservoirs with higher average microplastic abundance are located in temperate and subtropical regions.

[0071] The abundance of microplastics in sediments ranged from 0.50 to 9677.00 particles / kg, with a mean of 1168.75 ± 2055.68 particles / kg. Within each reservoir, the coefficient of variation for sediment microplastic abundance ranged from 0 to 300%. The highest coefficient of variation was observed in the South Doni Reservoir (ND), with sampling points spanning three seasons and two population density gradients. Similarly, in some sediment sampling points located in the upper, middle, and lower reaches of the reservoirs, the coefficient of variation for microplastic abundance was greater than 100%.

[0072] The abundance of microplastics in aquatic organisms in the reservoir ranged from 0.20 to 51.70 per sample, with a mean of 7.60 ± 12.17 per sample. The surveyed aquatic organisms were mainly fish, followed by shellfish. Fish and shellfish samples were collected by netting or fishing. The average abundance of microplastics in shellfish was one order of magnitude higher than that in fish, reaching 51.70 per sample.

[0073] Microplastic particle sizes are mainly divided into two categories: ≤1 mm and 1-5 mm. Three studies detected particle sizes exceeding 5 mm. The minimum sample size depends on the selected sampling mesh, sieve mesh count, and filter membrane pore size, ranging from 0.45 to 355 micrometers. In reservoir water and sediments, microplastics with a diameter <2 mm were frequently detected, averaging 89% and 90%, respectively. Among these, small-diameter (<1 mm) microplastics, which pose a greater threat to aquatic ecosystems, accounted for an average of 75% and 64% of both water and sediment samples, respectively.

[0074] Transparent color was the most abundant color in both water and sediment samples, accounting for 33% and 24%, respectively. Other common colors were blue, black, and brown. The shape and polymer type composition and abundance of microplastics varied considerably across different environmental media. Although fibers were the dominant microplastic shape in water, sediment, and biological samples, their proportions varied, at 65%, 63%, and 90%, respectively. Fragments were the next most abundant, accounting for 27% and 20% in water and sediment, respectively. Polypropylene and polyethylene were the dominant polymer types of microplastics in both water and sediment, each accounting for more than 15%. Polyester had the highest proportion in biological samples, reaching 41%.

[0075] In some embodiments, in step S103, the pollution ecological information includes risk type, influencing factors, and target impact degree value corresponding to the influencing factors;

[0076] The step of determining the pollution ecological information of the pollutant sample data distribution based on the distribution information includes:

[0077] Step S1031: Based on the distribution information, determine the influencing factors of the pollutant sample data distribution and the target influence degree value corresponding to the influencing factors;

[0078] Step S1032: Determine the risk type of the pollutant sample data distribution based on the distribution information.

[0079] Specifically, determining the influencing factors of the pollutant sample data distribution and the target impact values ​​corresponding to these factors enables statistical analysis of these factors and their corresponding target impact values, providing a clearer understanding of the pollution factors and status of the water environment, which is helpful for the control and management of aquatic ecosystem pollution. Determining the risk type of the pollutant sample data distribution enables pollution risk assessment of the pollutant sample data distribution, providing a clearer understanding of the degree of water environment pollution, which is used for assessing aquatic ecosystem pollution.

[0080] In some embodiments, in step S1031, the pollutant sample data further includes geographical environmental information of the sampling point and pollutant characteristic information, wherein the pollutant characteristic information includes pollutant abundance.

[0081] The step of determining the influencing factors of the pollutant sample data distribution and the target influence value corresponding to the influencing factors based on the distribution information includes:

[0082] Step S10311: Input the geographical environment information of the sampling point corresponding to the distribution information and the pollutant abundance into the preset geographical detector model to obtain the initial influence value on the pollutant abundance;

[0083] Step S10312: Compare the initial impact value with a first preset threshold. In response to the initial impact value being less than the first preset threshold, determine the geographical environment information as the influencing factor, and use the initial impact value as the target impact value corresponding to the influencing factor.

[0084] Specifically, since the factors influencing pollutant abundance differ in different regions, when using a factor to assess pollutant abundance in other regions, the factor may not have any effect on pollutant abundance in those regions. Therefore, the target impact value corresponding to the factor is first obtained. By comparing the target impact value with a first preset threshold, if the value is less than the first preset threshold, the factor is considered to have an impact on pollutant abundance in that region. Based on the target impact value, it is possible to quickly and accurately determine whether the factor truly has an impact on pollutant abundance. At the same time, by obtaining the target impact value, the influencing factor and its corresponding impact value can be accurately determined, which can be used for the control and management of aquatic ecosystem pollution.

[0085] In some embodiments, in step S10311, the geographical environment information of the sampling point includes multiple environmental information items;

[0086] The step of inputting the geographical environment information and pollutant abundance of the sampling points into a preset geographical detector model to obtain an initial impact value on the pollutant abundance includes:

[0087] By inputting multiple environmental information items and pollutant abundance into the geographic detector model, a first influence value of each environmental information item on the pollutant abundance is obtained;

[0088] Multiple first influence values ​​are interactively calculated to obtain an initial influence value for the abundance of the pollutant.

[0089] Specifically, by inputting multiple environmental information and pollutant abundance values ​​into the geographic detector model, a first influence degree value for each influencing factor is obtained, which is the influence value of a single factor on pollutant abundance. Multiple first influence degree values ​​are interactively calculated to obtain an initial influence degree value for the pollutant abundance, which is the interaction of the influence degree values ​​of a single factor to obtain the influence degree of multiple factors. Through the analysis of the influence degree values ​​of multiple factors and the influence degree values ​​of single factors, a more accurate analysis of the influencing factors is achieved.

[0090] For example, the geographic detector model is adopted, wherein the formula for calculating the degree of influence is as follows:

[0091]

[0092] In the formula, L represents the classification of influencing factors; N h N and N are the number of categories of the h-th influencing factor and the total number of categories of influencing factors, respectively; and σ 2 q represents the variance of the h-th type of influencing factor and the variance of the pollutant abundance distribution of the total influencing factors, respectively; where q∈[0,1], the larger the value of q, the greater the influence of the influencing factor on the pollutant abundance.

[0093] The geographic detector model includes factor detectors, interaction detectors, and risk detectors. Factor detectors are used to quantify explanatory variables (i.e., classifications of influencing factors, hereinafter collectively referred to as X) such as the sampling point's geographical location relative to the reservoir, seasonal variations of the sampling point, land use type of the sampling point, and sampling extraction methods. i The influence of X on microplastic abundance and small-particle-size microplastic abundance (i.e., pollutant abundance, hereinafter collectively referred to as Y) is determined. The q-value described above is used to measure the influence of X on Y.

[0094] Use an interaction detector to evaluate whether there is an interaction between the classified X1 and X2 of the influencing factors or whether they independently affect the distribution of the microplastic abundance Y. By comparing the q-values of X1 and X2 with the q(X1∩X2) value of the interaction, the degree value of the influence of the enhanced or weakened interaction on the distribution of the microplastic abundance Y can be judged. When q(X1∩X2) < Min(q(X1), q(X2)), it indicates that the degree value of the two-factor interaction non-linearly weakens; when Min(q(X1), q(X2)) < q(X1∩X2) < Max(q(X1), q(X2)), it indicates that the degree value corresponding to the single factor non-linearly weakens; when q(X1∩X2) > Max(q(X1), q(X2)), it indicates that the degree value of the two-factor interaction increases; when q(X1∩X2) = q(X1) + q(X2), it indicates that the two factors act independently and the degree values are comparable; when q(X1∩X2) > q(X1) + q(X2), then the degree value of the two-factor influence non-linearly increases. The significance test of the q-value uses the F-test, where the F-test includes the significance level value p for testing the q-value, and its p is set to 0.05 (i.e., comparing the q-value with the first preset threshold). When the q-value is less than 0.05, it is considered that the influencing factor affects the distribution of the pollutant sample, and the q-value is used as the degree value of the influence. Use a risk detector to compare the degree value of the influence with the preset threshold range to judge the risk type of the microplastics. Among them, the risk types include high risk, medium risk, and low risk. When the q-value is greater than or equal to 0.05, it is considered that the influencing factor does not affect the distribution of the pollutant sample, that is, it is not considered.

[0095] In some embodiments, in step S1031, the pollutant sample data further includes the geographical environment information and pollutant characteristic information of the sampling point, where the pollutant characteristic information includes the pollutant morphological characteristic information;

[0096] The determining of the influencing factors of the pollutant sample data distribution and the target influence degree value corresponding to the influencing factors according to the distribution information further includes:

[0097] Perform redundancy analysis on the geographical environment information corresponding to the distribution information and the pollutant morphological characteristic information to determine the second influence degree value for the pollutant morphological characteristic information;

[0098] Compare the second influence degree value with a second preset threshold. In response to the second influence degree value being less than the second preset threshold, determine the pollutant morphological information as the influencing factor, and use the second influence degree value as the target influence degree value corresponding to the influencing factor.

[0099] Specifically, by performing redundancy analysis on geographic environmental information and pollutant morphological characteristic information, a second impact value of the pollutant morphological characteristic information is obtained. The second impact value is based on the degree of influence of pollutant morphological characteristics on geographic environmental information. Redundancy analysis visualizes the second impact value, which can be used by the aquatic ecosystem to control and manage pollution based on geographic environmental information. The second impact value is compared with a second preset threshold to determine whether the pollutant morphological characteristic information is an influencing factor. By verifying the second impact value, if it is confirmed that the impact value of the influencing factor exceeds the preset threshold, it is considered that the influencing factor has indeed affected the distribution of pollutant samples, and the second impact value is used as the target impact value of the influencing factor. The influencing factor is accurately identified and the corresponding impact value is obtained, and the results are visualized for researchers to view. The second preset threshold uses the same verification method as the first preset threshold, which will not be repeated here.

[0100] Furthermore, for example, pollutant morphology information includes the color, shape, and polymer type of microplastics; geographic environmental information includes the sampling month, sampling depth, and the relative geographical location of the sampling point to the reservoir; using pollutant morphology information as multiple corresponding variables for redundancy analysis and geographic environmental information as explanatory variables, the significance level values ​​of pollutant morphology characteristics information are determined through stepwise forward selection and Monte Carlo permutation tests; and the vegan package is used to perform permutation multivariate analysis of variance and similarity analysis to test the statistical differences in pollutant morphology characteristics between different extraction methods and environmental media.

[0101] The above-mentioned determination of influencing factors and their corresponding target influencing factor values ​​constitutes the identification method using Raman spectroscopy.

[0102] For example, the primary factor influencing the abundance distribution of microplastics in water bodies is the extraction method, while secondary factors include seasonal variation, geographical location, and land use type. When the q-values ​​for extraction method ∩ seasonal variation, extraction method ∩ land use type, extraction method ∩ geographical location, geographical location ∩ seasonal variation, and extraction method ∩ identification method are all greater than 0.90, it indicates that the interaction of these factors produces a nonlinear or linear mutually reinforcing effect.

[0103] In sediments, the main factors influencing microplastic abundance distribution are extraction method, sampling point location relative to the reservoir, and latitude; secondary factors include longitude, seasonal variation, identification method, and land use type. The interaction between extraction method and seasonal variation has the greatest impact on microplastic abundance distribution in sediments, accounting for 70%. Notably, these factors do not have independent effects on microplastic abundance distribution but exhibit a mutually reinforcing or nonlinear effect.

[0104] The distribution characteristics of the relative abundance of small-diameter microplastics (<1 mm) in water bodies were driven by sampling method and seasonal variation, accounting for up to 61% and 50% of the total, respectively; followed by extraction method, land use type, microplastic abundance, and the location of the sampling point relative to the reservoir. The interaction between seasonal variation and sampling method with other factors had an influence greater than 50%. In sediments, identification method and land use type were the main factors influencing small-diameter microplastics, followed by latitudinal differences. Clearly, compared to sediments, seasonal variation and land use type have a greater impact on small-diameter microplastics in water bodies.

[0105] Risk detection results: Microplastic abundance was highest in temperate reservoir waters, especially in Asia (see Table 1). In sediments, subtropical reservoirs in Asia showed relatively higher microplastic abundance. Regarding land use type, reservoirs near cities or industrial areas were more likely to show higher microplastic abundance. The location of the sample relative to the reservoir affected the distribution of microplastic abundance in sediments, with higher abundance in the reservoir area compared to above the reservoir. This phenomenon was not observed in the reservoir water, but small-diameter microplastics were more abundant in the reservoir water compared to above the reservoir (see Table 2). Regarding seasonal variation, microplastic abundance in water was relatively higher from December to February (June to August in the Southern Hemisphere), while in sediments, the risk was greater from June to August (December to February in the Southern Hemisphere). Furthermore, microplastic abundance was higher when using large-sample sampling, chemical digestion and density separation extraction, and Raman spectroscopy identification. Although Fourier transform infrared spectroscopy was used to detect higher abundance of microplastics in sediments, Raman spectroscopy was more effective in detecting small-particle microplastics.

[0106] Similarity analysis results: Significant differences in the morphological characteristics of microplastics were observed in different environmental media, with greater differences in water bodies than in sediments. In both water and sediment environments, the similarity of microplastic characteristics exhibited a distance-attenuation pattern; a linear distance-attenuation model showed that the similarity of microplastic characteristics in both water and sediments decreased with increasing geographical distance. Furthermore, the attenuation rate of microplastic characteristic similarity in sediments was greater than that in water bodies.

[0107] Table 1 Favorable range of microplasticity abundance distribution

[0108]

[0109] Table 2 Favorable range of small particle size microplastic distribution

[0110]

[0111] In some embodiments, in step S1032, the risk type includes pollution risk type, risk type corresponding to polymer type, and potential ecological risk type;

[0112] The pollutant sample data also includes various polymer types;

[0113] The step of determining the risk type of the pollutant sample data distribution based on the distribution information includes:

[0114] The pollutant abundance corresponding to the distribution information is calculated with a preset pollutant abundance to obtain a pollutant index;

[0115] The pollution risk type is determined based on the pollutant index;

[0116] The proportion of each polymer type among the various polymer types and the preset hazard score corresponding to each polymer type are calculated to obtain the type risk index for each polymer.

[0117] Based on the risk index, determine the risk type corresponding to each polymer type;

[0118] The potential ecological risk index is determined by calculating the pollution index, the proportion of each polymer type among all polymer types, and the preset hazard score corresponding to each polymer type.

[0119] The type of potential ecological risk is determined based on the potential ecological risk index.

[0120] Specifically, the pollution index is compared with a preset range corresponding to the pollutant risk type to obtain the pollutant risk type, and the pollution of the aquatic ecosystem can be controlled and managed according to the pollutant risk type; the type risk index of each polymer is compared with a preset risk type range corresponding to each polymer type to obtain the risk type corresponding to each polymer type, and the pollution of the aquatic ecosystem can be controlled and managed according to the risk type corresponding to each polymer type; the potential ecological risk index is compared with a preset range corresponding to the potential ecological risk type to obtain the potential ecological risk type, and the pollution of the aquatic ecosystem can be controlled and managed according to the potential risk type.

[0121] For example, a pollution index, a polymer risk assessment index, and a potential risk assessment index are used to assess microplastic pollution in reservoir water and sediments. These three indicators can take into account both the abundance of microplastics and the potential risks posed by their polymer types.

[0122] The pollution index, as an effective pollution assessment index for evaluating the risk level within a region, is widely used to reflect the overall pollution level of microplastics in environmental media such as water bodies and sediments. The calculation formula is as follows:

[0123] CFi = i / C0

[0124]

[0125]

[0126] In the formula, CF i Let C be the pollution coefficient of microplastics at sampling point i in reservoir r. i The measured concentration of microplastics is represented by C0, which is the background value. The lowest detected microplastic abundance value is considered the background value, for example: 0.28 microplastics / m³ in reservoir water and 0.50 microplastics / kg in sediment; n is the number of sampling points in reservoir r; PLI r For pollutant index; when PLI r When the values ​​are <10, 10~20, 20~30 and >30 respectively, the corresponding pollution risk types are low risk I, medium risk II, high risk III and extremely high risk IV respectively.

[0127] The polymer risk assessment index is calculated using the following formula:

[0128]

[0129] In the formula, H is the calculated polymer risk index; P n S represents the proportion of various polymers in the sampling points. n This refers to the hazard rating of the corresponding polymers in microplastics. Various polymers include: polypropylene, polyethylene terephthalate, polyethylene, polystyrene, polystyrene foam, polyamide, polycarbonate, and polyvinyl chloride, with corresponding hazard ratings of 1, 4, 11, 30, 44, 47, 610, and 5001, respectively. When the polymer risk index value is <10, 10–100, 100–1000, and >1000, the corresponding risk types are low risk I, medium risk II, high risk III, and extremely high risk IV, respectively.

[0130] The potential ecological risk index comprehensively considers the ecological, environmental, and toxicological effects of microplastics. Its calculation formula is as follows:

[0131] C f = i / C0

[0132]

[0133] RI = T i ×C f

[0134] In the formula, C f C is the enrichment coefficient of microplastics in sample i. iT represents the measured concentration of microplastics, C0 represents the background concentration; i It is the toxicity coefficient of microplastics, P n S represents the proportion of various polymers in the sampling points. n It is the hazard rating of the corresponding polymer in the microplastic; R I As a potential ecological risk index; when R I When the values ​​are <150, 150~300, 300~600, 600-1200 and >1200 respectively, the corresponding potential ecological risk types are low risk I, medium risk II, high risk III, medium-high risk IV and extremely high risk V respectively.

[0135] For example, in reservoir water, 18% of microplastic pollution was classified as lightly polluted, 8% as moderately polluted, 13% as highly polluted, and 61% as severely polluted. In sediments, 33% of reservoir microplastic pollution was classified as low-risk, 33% as moderate-risk, 5% as highly risky, and 29% as severely polluted. Within the same reservoir, the degree of microplastic pollution in water and sediments is not consistent. Except for a few reservoirs, the microplastic pollution index in water is generally higher than that in sediments.

[0136] The risk types of microplastic polymers in water bodies were classified into Level I, Level II, and Level III. For water bodies, 20% were classified as Level I, 20% as Level II, and 60% as Level III. For sediments, the respective risk levels were 40% Level I, 30% Level II, and 30% Level III. Reservoirs with high polymer risk indices do not necessarily have high pollution indices. In other words, reservoirs with high levels of microplastic pollution do not necessarily have high polymer risk. Of the reservoirs where microplastic abundance was measured, only about 50% had their microplastic polymer types analyzed. These reservoirs are mainly located in China, indicating that their microplastic polymer risk is not negligible.

[0137] The potential ecological risk index results show that the risk types of microplastics in the reservoir water are classified as I, III, and IV, with 35% at level I, 15% at level II, and 50% at level III. In contrast, the potential ecological risk of microplastic pollution in sediments is lower, with 70% classified as low-risk. Within the same reservoir, the potential ecological risk index of microplastics in the water is generally higher than that in the sediments. However, there is no necessary correlation between the level of potential ecological risk and the degree of microplastic pollution. That is to say, a reservoir with a high potential ecological risk may not necessarily have abundant microplastics.

[0138] It should be noted that the embodiments of this application can also be further described in the following ways:

[0139] Microplastic abundance in reservoirs exhibits significant variability, ranging from 2 to 6 orders of magnitude. To identify influencing factors on microplastic distribution, a geographic detector model was used to quantitatively investigate the effects of geographic location, seasonal variation, sampling depth, land use type, and extraction method on microplastic abundance and the distribution of microplastics with a particle size <1 mm. All factors interacted to enhance their influence on microplastic distribution. During the rainy season, sediments are more likely to become storage sites for terrestrial microplastics, while reservoir water bodies tend to trap small-diameter microplastics. Due to the multivariable nature of microplastic morphological characteristics, redundancy analysis, similarity analysis, permutation multifactor ANOVA, and distance decay models were employed to identify the main factors influencing microplastic dynamics. The morphological distribution of microplastics differs between water bodies and sediments, but within the same environmental medium, the morphological characteristics of microplastics all conform to the distance decay law. Relatively speaking, the heterogeneity of microplastic morphological characteristics in water bodies is more significant, indicating the diversity of its pollution sources.

[0140] Standardizing and specifying methods for microplastic sampling, extraction, and identification helps expand the scope of comparative studies and reduce uncertainty. A pollutant analysis method proposed in this application demonstrates that large-sample sampling, chemical digestion and density separation extraction, combined with Raman spectroscopy for microplastic identification, can improve the accuracy of detection results. Regarding ecological risk, assessments of pollution load index, polymer risk index, and potential ecological risk index indicate that the degree of microplastic pollution and the level of potential ecological risk in reservoirs are all relatively high. Therefore, it is necessary to expand the scale and scope of research on microplastic pollution in reservoirs.

[0141] It should be noted that the method in this embodiment can be executed by a single device, such as a computer or server. The method can also be applied in a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method in this embodiment, and the multiple devices will interact with each other to complete the method described.

[0142] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0143] Based on the same inventive concept, corresponding to any of the above-described embodiments, this application also provides a pollutant analysis device.

[0144] refer to Figure 3 The pollutant analysis device includes: an acquisition module 201, used to acquire pollutant sample data and group the pollutant sample data to obtain multiple pollutant sample datasets;

[0145] The first determining module 202 is used to determine the distribution information of the pollutant sample data based on the multiple pollutant sample datasets;

[0146] The second determining module 203 determines the pollution ecological information of the distribution of the pollutant sample data based on the distribution information.

[0147] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing this application, the functions of each module can be implemented in one or more software and / or hardware.

[0148] The apparatus described above is used to implement a corresponding pollutant analysis method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0149] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a pollutant analysis method as described in any of the above embodiments.

[0150] Figure 4 This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0151] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0152] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0153] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0154] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0155] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0156] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0157] The electronic devices described above are used to implement a corresponding pollutant analysis method in any of the foregoing embodiments and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0158] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides a non-transitory computer-readable storage medium that stores computer instructions for causing the computer to execute a pollutant analysis method as described in any of the above embodiments.

[0159] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0160] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute a pollutant analysis method as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0161] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0162] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0163] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0164] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

Claims

1. A method for analyzing pollutants, characterized in that, include: Obtain pollutant sample data, and group the pollutant sample data to obtain multiple pollutant sample datasets; Based on multiple pollutant sample datasets, determine the distribution information of the pollutant sample data; Based on the distribution information, the pollution ecological information of the pollutant sample data distribution is determined; The pollution ecological information includes risk type, influencing factors, and target impact level values ​​corresponding to the influencing factors; determining the pollution ecological information of the pollutant sample data distribution based on the distribution information includes: determining the influencing factors of the pollutant sample data distribution and the target impact level values ​​corresponding to the influencing factors based on the distribution information; and determining the risk type of the pollutant sample data distribution based on the distribution information. The pollutant sample data also includes geographical environmental information of the sampling points and pollutant characteristic information, wherein the pollutant characteristic information includes pollutant morphological characteristic information; the step of determining the influencing factors of the distribution of the pollutant sample data and the target influence degree value corresponding to the influencing factors based on the distribution information includes: performing redundancy analysis on the geographical environmental information corresponding to the distribution information and the pollutant morphological characteristic information to determine a second influence degree value for the pollutant morphological characteristic information; comparing the second influence degree value with a second preset threshold, and in response to the second influence degree value being less than the second preset threshold, determining the pollutant morphological characteristic information as the influencing factor, and using the second influence degree value as the target influence degree value corresponding to the influencing factor.

2. The method according to claim 1, characterized in that, The process of acquiring pollutant sample data involves grouping the pollutant sample data to obtain multiple pollutant sample datasets, including: Based on a preset sample extraction method and a preset environmental medium, the pollutant sample data are grouped to obtain multiple pollutant sample datasets.

3. The method according to claim 2, characterized in that, The pollutant sample data includes geographical environmental information of the sampling points and pollutant characteristic information; The step of determining the distribution information of pollutant sample data based on multiple pollutant sample datasets includes: Based on the geographical environment information of the sampling points, the pollutant characteristic information, and the preset environmental medium, statistical analysis is performed on multiple pollutant sample datasets to determine the distribution information of the pollutant sample data.

4. The method according to claim 1, characterized in that, The pollutant sample data also includes geographical environmental information of the sampling points and pollutant characteristic information, wherein the pollutant characteristic information includes pollutant abundance. Based on the distribution information, determine the influencing factors of the pollutant sample data distribution and the target influence values ​​corresponding to the influencing factors, including: The geographical environment information of the sampling points corresponding to the distribution information and the abundance of pollutants are input into a preset geographic detector model to obtain the initial impact value on the abundance of pollutants. The initial impact value is compared with a first preset threshold. If the initial impact value is less than the first preset threshold, the geographical environment information is determined to be the influencing factor, and the initial impact value is used as the target impact value corresponding to the influencing factor.

5. The method according to claim 4, characterized in that, The geographical environment information of the sampling points includes multiple environmental information items; The geographical environment information and pollutant abundance of the sampling points are input into a preset geographic detector model to obtain an initial impact value on the pollutant abundance, including: By inputting multiple environmental information items and pollutant abundance into the geographic detector model, a first influence value of each environmental information item on the pollutant abundance is obtained; Multiple first influence values ​​are interactively calculated to obtain an initial influence value for the abundance of the pollutant.

6. The method according to claim 4, characterized in that, The risk types include pollution risk types, risk types corresponding to polymer types, and potential ecological risk types; The pollutant sample data also includes various polymer types; The step of determining the risk type of the pollutant sample data distribution based on the distribution information includes: The pollutant abundance corresponding to the distribution information is calculated with a preset pollutant abundance to obtain a pollutant index; The pollution risk type is determined based on the pollutant index; The proportion of each polymer type among the various polymer types and the preset hazard score corresponding to each polymer type are calculated to obtain the type risk index for each polymer. Based on the risk index, determine the risk type corresponding to each polymer type; The potential ecological risk index is determined by calculating the pollutant index, the proportion of each polymer type among all polymer types, and the preset hazard score corresponding to each polymer type. The type of potential ecological risk is determined based on the potential ecological risk index.

7. A pollutant analysis device, applicable to the method described in any one of claims 1-6, characterized in that, include: The acquisition module is used to acquire pollutant sample data and group the pollutant sample data to obtain multiple pollutant sample datasets. The first determining module is used to determine the distribution information of the pollutant sample data based on the multiple pollutant sample datasets; The second determining module determines the pollution ecological information of the distribution of the pollutant sample data based on the distribution information.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 6.

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