Soil micro-plastic ecological risk assessment method based on three-dimensional probability density distribution

Abstract

The invention discloses a soil micro-plastic ecological risk assessment method based on three-dimensional probability density distribution, and belongs to the technical field of environmental pollution prevention and control. According to the method, soil micro-plastic distribution characteristic data of a target site are collected and screened, statistical models such as power law distribution, bimodal distribution and normal-inverse Gaussian distribution are used for fitting the size, shape and density characteristics of micro-plastic, and a three-dimensional probability density distribution model of the size, shape and density of the micro-plastic is constructed. And optimizing the collected and screened micro-plastic toxicity data based on the constructed three-dimensional probability density distribution model. For special types of sites, customized toxicity data optimization can be carried out based on field sampling and detection results. And in combination with the optimized SSD curve, calculating HC5 and RQ, and carrying out ecological risk assessment on the micro-plastics. The implementation of the invention can provide a scientific basis for environmental ecological risk assessment, pollution prevention and control management and policy making of new pollutants in soil.

Description

Technical Field

[0001] The present invention belongs to the technical field of environmental pollution prevention and control, and specifically relates to a soil microplastic ecological risk assessment method based on three-dimensional probability density distribution. Background Art

[0002] As the problem of microplastic pollution becomes increasingly serious, its accumulation in the soil environment and its potential ecological impact have received widespread attention. Microplastics in the soil can enter the environment through various pathways such as agricultural film degradation, sludge application, and atmospheric deposition, and have multi-level impacts on soil structure, biological communities, and ecological functions. The species sensitivity distribution method was proposed by the United States Environmental Protection Agency (USEPA) in 1998 as a pollutant risk assessment method. It is a statistical modeling method based on the differences in sensitivity of multiple species to the same pollutant. By collecting toxicity threshold data of different species and fitting them into a probability density distribution curve, the concentration of pollutants (such as HC5) affected by a specific proportion of species in the ecosystem is estimated, which is then used to derive ecological safety thresholds and conduct environmental risk assessments. This method has the advantages of strong data integration and clear risk thresholds, and has been widely used in the ecological risk management of various pollutants. However, the traditional SSD construction method still has significant limitations when applied to microplastic ecological risk assessment, and it is difficult to accurately reflect the ecological effects of microplastic pollution in the actual environment. Specifically, it is reflected in the following aspects: (1) The toxicity data sources are wide and heterogeneous. Existing studies on the toxicity of microplastics are mostly based on different research backgrounds, experimental designs, exposure conditions and test organisms, resulting in large differences in toxicity endpoints, data units and microplastic properties (such as size, shape, type, etc.), making it difficult to directly compare and integrate different data, affecting the scientific nature and consistency of the SSD curve. (2) There is a lack of technical means to optimize modeling based on the distribution characteristics of microplastics in the target site. Currently, most SSD curves are constructed based on microplastic data of a single type or under laboratory conditions, without considering the complex composition of microplastic pollution in the actual environment, resulting in the lack of pertinence and representativeness of the constructed SSD curves, making it difficult to truly reflect the ecological risk level of the target site. (3) There is inconsistency between the laboratory toxicity threshold and the actual measured concentration in the environment in terms of units and exposure forms. Laboratories often determine toxicity thresholds using microplastics of a single particle size, while microplastic exposure in the environment is mostly manifested as a heterogeneous mixture with a variety of particle sizes, shapes and densities. This makes it impossible to directly use experimental results for environmental risk assessment. It is urgent to standardize toxicity data and enhance environmental relevance through methods such as particle size conversion, volume adjustment, and exposure concentration correction.

[0003] Therefore, there is an urgent need to construct a method that can take into account the three-dimensional characteristics of microplastic size, shape, and density, integrate multi-source toxicity data, and realize data standardization processing, so as to improve the applicability and scientificity of the SSD curve in microplastic ecological risk assessment, and provide more accurate and effective technical support for the ecological safety management of microplastic pollution in different types of sites. Summary of the Invention

[0004] The present invention aims to overcome the shortcomings of existing technologies and provide a soil microplastic ecological risk assessment method based on a three-dimensional probability density distribution. This method systematically integrates the distribution characteristics of microplastics at a target site with toxicity data to construct a representative microplastic SSD curve for the site, providing a scientific basis for soil microplastic risk management and decision-making under different pollution scenarios.

[0005] The specific technical solutions adopted in the present invention are as follows:

[0006] The present invention provides a soil microplastic ecological risk assessment method based on three-dimensional probability density distribution, which is as follows:

[0007] S1: Collect and screen soil microplastic distribution characteristic data; the soil microplastic distribution characteristic data includes the quantity abundance or mass abundance of microplastics in the soil and the size, shape and density of microplastics;

[0008] S2: Based on the valid data screened in S1, the size, shape and density data of microplastics were normalized and statistically analyzed. Then, the probability density distributions of microplastic size, shape and density were fitted using power law distribution, bimodal distribution and normal-inverse Gaussian distribution respectively. The three-dimensional joint probability density distribution characteristics of microplastics were obtained, and a multidimensional probability model reflecting the composition and distribution law of microplastics in the target site was constructed.

[0009] S3: Collect and screen soil microplastic toxicity data;

[0010] S4: Based on the three-dimensional probability density distribution characteristics of soil microplastics obtained in S2, the soil microplastic toxicity data screened in S3 are optimized;

[0011] S5: For soil microplastics in special sites, obtain their three-dimensional characteristic distribution through field sampling and analysis, and then conduct targeted toxicity data optimization;

[0012] S6: Based on the toxicity data optimized by S4 or S5, an SSD curve is fitted; the ecological safety threshold of soil microplastics is determined according to the obtained SSD curve, and then the risk quotient of soil microplastics is calculated based on the obtained ecological safety threshold to assess the ecological risk of soil microplastics.

[0013] Preferably, in S1, the sources of soil microplastic distribution characteristic data are as follows:

[0014] Through on-site collection and laboratory analysis of soil samples at the target site, data on the size, shape, density and abundance of microplastics are obtained; through literature retrieval of publicly published research results, reported soil microplastic distribution characteristic data are obtained; and domestic and foreign microplastic environmental databases are used to extract microplastic-related attribute data for specific areas.

[0015] Preferably, in S1, the screening criteria for soil microplastic distribution characteristic data are as follows:

[0016] Data from remote areas that were not target sites, data with significant detection bias or outliers, and data with no reported detection methods or too low particle size accuracy were removed.

[0017] Preferably, in S2, the fitting modeling method is as follows:

[0018] The size distribution of microplastics was fitted using a power-law distribution model to obtain its size probability density distribution parameter k; the shape distribution of microplastics was fitted using a bimodal distribution model based on the morphological classification ratio to obtain its shape probability density distribution parameters f1, f2, μ1, μ2 and σ1, σ2; the density distribution of microplastics was fitted using a normal-inverse Gaussian distribution model to obtain its density probability density distribution parameters μ, δ, α, and β;

[0019] Among them, k is the exponential parameter of the power-law distribution, which is used to describe the degree of inclination of the microplastic size distribution; f1 and f2 are the relative contributions of the two normal distributions; μ1 and μ2 are the means of the two normal distributions, σ1 and σ2 are the standard deviations of the two normal distributions; μ, δ, α and β represent the position, scale, tail weight and asymmetry of the density probability density distribution, respectively.

[0020] Preferably, in S3, the sources of soil microplastic toxicity data include domestic and foreign toxicity databases, publicly published document reports, and toxicity data obtained through microplastic toxicity experiments on soil organisms.

[0021] Preferably, in S3, the screening criteria for soil microplastic toxicity data are as follows:

[0022] Eliminate experimental data that do not provide specific information about the microplastics used in the experiment, including the size, shape, and type of microplastics; eliminate experimental data that do not have a blank control in the toxicity test or that have significant differences between the blank group and the treatment group; exclude non-representative species or biological test data with no obvious correlation with soil ecosystems; delete experimental data with insufficient sample size or large standard error.

[0023] Preferably, in said S4, the method for data optimization is to sequentially perform particle size distribution correction, number concentration and mass concentration conversion, monodisperse and polydisperse microplastic threshold concentration conversion, and actual microplastic effect threshold calculation in the environment.

[0024] Preferably, in S5, the data optimization method includes:

[0025] Soil samples were collected from special sites to analyze the distribution characteristics of microplastics. Based on the on-site detection data obtained from the analysis, a three-dimensional probability density distribution was fitted to obtain the fitting parameters of the target site. Based on the fitting results, an SSD curve that better conforms to the actual situation of the target site was constructed. The special sites mentioned above include historical non-standard landfills and industrial wastelands.

[0026] Preferably, in S6, the log-normal distribution model is used to fit the SSD curve obtained by fitting to determine the ecological safety threshold HC5; then the measured microplastic pollution concentration MEC is compared with the predicted no-effect concentration PNEC obtained according to the ecological safety threshold to calculate the risk quotient RQ; the ecological risk level is divided according to the obtained risk quotient to assess the ecological risk of soil microplastics.

[0027] Furthermore, the criteria for determining the ecological risk level are:

[0028] RQ < 0.01 means no risk; 0.01 ≤ RQ < 0.1 means low risk; 0.1 ≤ RQ < 1 means medium risk; RQ ≥ 1 means high risk.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1) Toxicity data optimization based on three-dimensional distribution characteristics: Incorporating three-dimensional properties of microplastics, such as size, shape, and density, into the SSD curve construction process improves the comparability and credibility of toxicity data;

[0031] 2) Support customized assessments for specific sites: Through sampling modeling and parameter optimization, targeted adjustments to the SSD curve are achieved, enhancing the practicality of risk assessment;

[0032] 3) The assessment results are more scientific and operational: HC5 and RQ are used as core indicators to quantify ecological risks, which has good policy support and management application prospects.

[0033] The method of the present invention provides a systematic, quantitative and operational solution for soil microplastic risk assessment. It is particularly suitable for specific areas with complex pollution sources or where long-term governance is required. It helps to promote the scientific and refined development of microplastic ecological risk assessment and prevention and control work. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments.

[0035] Figure 1 3 is a graph showing the fitting results of the probability density distribution of soil microplastic size in the embodiment.

[0036] Figure 2 3 is a graph showing the fitting results of the probability density distribution of soil microplastic shapes in the embodiment.

[0037] Figure 3 3 is a graph showing the fitting results of the probability density distribution of soil microplastic density in the embodiment.

[0038] Figure 4 3D probability density distribution fitting result of soil microplastics in the embodiment.

[0039] Figure 5 It is a soil microplastic SSD curve diagram drawn after optimizing toxicity data based on the fitting results of the three-dimensional probability density distribution of soil microplastics in the embodiment.

[0040] Figure 6 3. This is a graph showing the fitting results of the probability density distribution of soil microplastic shapes obtained from sampling and testing at an abandoned landfill in Jiaxing, Zhejiang Province in the embodiment.

[0041] Figure 7 3. This is a graph showing the probability density distribution fitting results of soil microplastic density obtained from sampling and testing at an abandoned landfill in Jiaxing, Zhejiang Province in the embodiment.

[0042] Figure 8 It is a soil microplastic SSD curve diagram drawn after optimizing toxicity data based on the three-dimensional probability density distribution fitting results of soil microplastics in abandoned landfills in the embodiment.

[0043] Figure 9 4 is an overall flow chart of the ecological risk assessment method in the embodiment.

[0044] Figure 10 3 is an uncertainty analysis diagram of the risk quotient RQ value of the soil above the landfill in the embodiment.

[0045] Figure 11 3 is an uncertainty analysis diagram of the risk quotient RQ value of farmland soil around an abandoned landfill in Jiaxing, Zhejiang Province in the embodiment. DETAILED DESCRIPTION

[0046] The present invention will be further described and illustrated below with reference to the accompanying drawings and specific embodiments. The technical features of each embodiment of the present invention may be combined accordingly, provided that there is no conflict between them.

[0047] like Figure 9The figure shows a soil microplastic ecological risk assessment method based on three-dimensional probability density distribution provided by the present invention. This method integrates soil microplastic three-dimensional probability density distribution modeling, toxicity data screening and effect correction, and ecological risk quantitative assessment based on the Species Sensitivity Distribution (SSD) model. It is applicable to the systematic ecological safety assessment probability density distribution of soil microplastic pollution in typical sites such as farmland, landfills, and industrial sites.

[0048] Each step of the method of the present invention will be described in detail below.

[0049] S1, Collection and screening of soil microplastic distribution characteristics data:

[0050] Collect and screen soil microplastic distribution characteristic data; among them, soil microplastic distribution characteristic data include the quantitative abundance or mass abundance of microplastics in the soil as well as the size, shape and density of microplastics.

[0051] As a preferred embodiment of the present invention, the collection of microplastic distribution characteristic data comes from channels such as field sampling and testing, publicly published literature reports, and environmental monitoring databases. The data content includes the quantity or mass abundance of microplastics in the soil, as well as attribute information such as size, shape, and density. After collection is completed, the raw data is evaluated and screened based on relevance and data quality. Data that is irrelevant to the target site, data with a large number of missing data or unclear detection methods, and data with significant deviations or outliers are eliminated to ensure the representativeness and validity of the data.

[0052] Specifically, the sources of soil microplastic distribution characteristic data are: obtaining microplastic size, shape, density and abundance data through on-site collection and laboratory analysis of soil samples at the target site; obtaining reported soil microplastic distribution characteristic data through literature retrieval of publicly published research results; and using domestic and foreign microplastic environmental databases to extract microplastic-related attribute data for specific areas.

[0053] Specifically, the screening criteria for soil microplastic distribution characteristic data are:

[0054] Data from remote areas that were not target sites, data with significant detection bias or outliers, and data with no reported detection methods or too low particle size accuracy were removed.

[0055] In other words, the literature in this step must report the distribution of microplastics by size, shape, and type in the soil. Size distributions where large-sized particles are more abundant than small-sized particles need to be removed based on the actual situation, as this is often caused by insufficient detection equipment accuracy. Furthermore, the literature must include data such as the sampling location and the proportion of microplastics by size, shape, and type.

[0056] S2, fitting of the three-dimensional (size, shape, density) probability density distribution characteristics of soil microplastics:

[0057] Based on the valid data screened in S1, the size, shape and density data of microplastics were normalized and statistically analyzed, and then the power law distribution, bimodal distribution and normal-inverse Gaussian distribution were used to fit the probability density distribution of microplastic size, shape and density, respectively, to obtain the three-dimensional joint probability density distribution characteristics of microplastics, and construct a multidimensional probability model reflecting the composition and distribution law of microplastics in the target site, which comprehensively reflects the composition characteristics of microplastics in the target site and provides a basis for the subsequent optimization of toxicity data and the construction of SSD curves.

[0058] As a preferred embodiment of the present invention, the fitting modeling method in this step is specifically as follows:

[0059] The size distribution of microplastics was fitted using a power-law distribution model to obtain its size probability density distribution parameter k; the shape distribution of microplastics was fitted using a bimodal distribution model based on the morphological classification ratio to obtain its shape probability density distribution parameters f1, f2, μ1, μ2 and σ1, σ2; the density distribution of microplastics was fitted using a normal-inverse Gaussian distribution model to obtain its density probability density distribution parameters μ, δ, α, and β;

[0060] Among them, k is the exponential parameter of the power-law distribution, which is used to describe the degree of inclination of the microplastic size distribution; f1 and f2 are the relative contributions of the two normal distributions; μ1 and μ2 are the means of the two normal distributions, σ1 and σ2 are the standard deviations of the two normal distributions; μ, δ, α and β represent the position, scale, tail weight and asymmetry of the density probability density distribution, respectively.

[0061] Specifically, the probability density distribution models of microplastic size, shape, and density are:

[0062] Size: Power-law distribution model

[0063] y=bx -k

[0064] Where x is the longest dimension of the microplastic particle size (micrometers), y is the abundance (percentage); b = (k-1)x min k-1, where x min is the minimum particle size for which the equation applies; k is the exponential parameter of the power-law distribution.

[0065] Shape: Bimodal distribution model

[0066]

[0067] Among them, f1 and f2 are the relative contributions of the two normal distributions; μ1 and μ2 are the means of the two normal distributions, σ1 and σ2 are the standard deviations of the two normal distributions; x is the Corey Shape Factor (CSF), Where L, W, and H are the length, width, and height of the microplastic particles, respectively; y is the probability density value corresponding to the CSF value.

[0068] Density: Normal-Inverse Gaussian (NIG) distribution model

[0069]

[0070] where μ, δ, α, and β represent the location, scale, tail weight, and asymmetry of the distribution, respectively. K1 is a modified Bessel function of the third kind, with order 1. x is the density of microplastics; y is the probability density value corresponding to the density value.

[0071] S3, collect and screen soil microplastic toxicity data:

[0072] As a preferred embodiment of the present invention, soil microplastic toxicity data is collected from domestic and foreign toxicity databases, publicly published document reports, and toxicity data obtained through microplastic toxicity experiments on soil organisms. After the data are collected, the obtained data are evaluated and screened to eliminate data on non-regional characteristic species, data without control and blank groups, and data with excessively large differences in the number of deaths in the control group to ensure the quality and applicability of the toxicity data.

[0073] Specifically, the screening criteria for soil microplastic toxicity data are:

[0074] Eliminate experimental data that do not provide specific information about the microplastics used in the experiment, including the size, shape, and type of microplastics; eliminate experimental data that do not have a blank control in the toxicity test or that have significant differences between the blank group and the treatment group; exclude non-representative species or biological test data with no obvious correlation with soil ecosystems; delete experimental data with insufficient sample size or large standard error.

[0075] In actual use, the literature of this step must report: (1) the species and taxonomic group studied; (2) the characteristics of microplastics (size, shape, type); and (3) the experimental design (exposure duration, experimental endpoint, toxicity value). In the same type of study, if there are multiple experimental endpoints for the same species, the chronic toxicity value takes precedence over the acute toxicity value. Among the multiple toxicity values, the value with the lowest abundance (i.e., the value corresponding to the highest microplastic toxicity) is selected and included in the database.

[0076] S4, Optimization of soil microplastic toxicity data:

[0077] Based on the three-dimensional probability density distribution characteristics of soil microplastics obtained in S2, the soil microplastic toxicity data screened in S3 were optimized to improve the matching degree between the toxicity data and the actual pollution characteristics of the site.

[0078] As a preferred embodiment of the present invention, the data optimization method in this step is to sequentially perform operations such as particle size distribution correction, number concentration and mass concentration conversion, monodisperse and polydisperse microplastic threshold concentration conversion, and actual microplastic effect threshold calculation in the environment.

[0079] Specifically, the method for optimizing soil microplastic toxicity data is as follows:

[0080]

[0081] Integrating formulas (1) to (4) we can obtain:

[0082]

[0083] Among them, EC X,mono V is the toxicity threshold concentration obtained from a single type of microplastic experiment; mono is the average volume of a single type of microplastic used in the experiment; V t is the total volume of microplastics within the size range that the test species can ingest in the natural environment; N is the total number of simulation examples; EC X,env is the actual environmental threshold concentration corresponding to the toxicity data obtained from the literature experiment, which will be used to draw the subsequent SSD curve; m is the mass concentration of microplastics used in the experiment; ρ is the density of microplastics used in the experiment; EC X,poly V is the toxicity threshold concentration of the tested species under mixed particle size conditions; poly is the average volume of microplastics within the size range that the experimental species can ingest in the natural environment; f available It is the ratio of the number of microplastics within the size range that the experimental test species can ingest to the total number of microplastics; as shown in Table 1, the corresponding extrapolation factor (EF) table is selected according to the number of experimental exposure days, and EF is determined according to the duration of the exposure test.

[0084] Table 1 Select the corresponding extrapolation factors according to the number of experimental exposure days

[0085]

[0086] S5, site-specific optimization methods:

[0087] For soil microplastics in special sites, their three-dimensional characteristic distribution is obtained through field sampling, analysis and testing, and then targeted toxicity data optimization is carried out to make the SSD curve more in line with the actual situation of the site.

[0088] As a preferred embodiment of the present invention, the data optimization method in this step includes:

[0089] Soil samples are collected from special sites (including historical non-standard landfills and industrial waste sites) to analyze the distribution characteristics of microplastics. Based on the on-site detection data obtained from the analysis, three-dimensional probability density distribution fitting is performed to obtain the fitting parameters of the target site. Based on the fitting results, an SSD curve that is more in line with the actual situation of the target site is constructed to achieve a more targeted ecological risk assessment.

[0090] In actual use, in order to more accurately describe the three-dimensional probability density distribution characteristics of microplastics in the target site, in theory, the more sampling points, the more accurate the actual situation can be reflected. Taking into account the complexity of the soil microplastic detection method, it is preferred that at least five sampling points are required to better reflect the overall situation of microplastics in the site.

[0091] S6, Risk Assessment Methodology:

[0092] Based on the toxicity data optimized by S4 or S5, the SSD curve is fitted; the ecological safety threshold of soil microplastics is determined according to the obtained SSD curve, and then the risk quotient of soil microplastics is calculated based on the obtained ecological safety threshold to evaluate the ecological risk of soil microplastics and provide a scientific basis for pollution prevention and control.

[0093] As a preferred embodiment of the present invention, in this step, the SSD curve obtained by fitting is fitted using a log-normal distribution model to determine the ecological safety threshold HC5; then the measured microplastic pollution concentration MEC (MEC is obtained through actual measurement, or through the known microplastic concentration measured in known literature) is compared with the predicted no-effect concentration PNEC obtained according to the ecological safety threshold (obtained by dividing HC5 by the assessment factor), and the risk quotient RQ is calculated (i.e., the risk quotient RQ of microplastic pollution in the assessment area is obtained by dividing MEC by PNEC); the ecological risk level is divided according to the obtained risk quotient to assess the ecological risk of soil microplastics. In actual use, the criteria for determining the ecological risk level are generally as follows:

[0094] RQ < 0.01 means no risk; 0.01 ≤ RQ < 0.1 means low risk; 0.1 ≤ RQ < 1 means medium risk; RQ ≥ 1 means high risk.

[0095] In actual use, in order to further improve the scientific nature and reliability of risk assessment results, uncertainty analysis can be conducted on the risk quotient (RQ). In the method of the present invention, uncertainty analysis mainly considers the sources of uncertainty in MEC. MEC values ​​are limited by the spatial distribution of sample points, sampling accuracy, and measurement methods, and there are certain measurement errors and spatial variability. Therefore, by using statistical methods such as confidence interval analysis and Monte Carlo simulation, the transmission and impact of MEC uncertainty on RQ values ​​can be evaluated, thereby obtaining the confidence range of the risk quotient, providing a more scientific reference for the classification and management decision-making of regional microplastic pollution ecological risk levels.

[0096] The method and effects of the present invention will be specifically described below through examples.

[0097] Example

[0098] Figure 9 The overall flow chart of the ecological risk assessment method in the embodiment is as follows:

[0099] 1. Collection of three-dimensional characteristic data of microplastics

[0100] This embodiment systematically collects relevant literature on the distribution characteristics of microplastics in soil published between 2021 and 2024. A literature search was performed using a combination of keywords ("soil"; "microplastic*"; "plasticparticle*"; "plastic pollution") in Web of Science. The study must report the size, shape, and type distribution of microplastics in soil or groundwater. For size distribution conditions where the abundance of large-sized particles is greater than the abundance of small-sized particles, they need to be removed according to actual conditions, because this situation is mostly caused by insufficient accuracy of the detection equipment. The literature must include data such as the sampling location, the proportion of microplastics of different sizes, shapes, and types. A total of 754 articles related to soil microplastics were retrieved, and 157 articles on soil microplastics remained after initial screening. After further detailed reading of the literature screening, 14 articles on soil microplastics remained. Image data was extracted using getdata software.

[0101] 2. Three-dimensional feature fitting of microplastics in soil

[0102] The collected three-dimensional characteristic data of microplastics are simplified and fitted.

[0103] like Figure 1As shown in the figure, the probability density distribution of soil microplastic size in the embodiment is fitted. From the figure, it can be seen that the probability density distribution parameter k of microplastic size in different soils. The average value of k of the microplastic size distribution in soil calculated from the size distribution fitting results is 1.1;

[0104] like Figure 2 As shown in the figure, it is a fitting result diagram of the probability density distribution of the shape of soil microplastics in the embodiment. From the figure, it can be seen that the shape distribution fitting results: the fitting parameters of the shape distribution of microplastics in soil are: f1=0.18, f2=0.82, σ1=0.03, σ2=0.20, μ1=0.07, μ2=0.50;

[0105] like Figure 3 As shown, it is a graph showing the fitting results of the probability density distribution of soil microplastic density in the embodiment. From the figure, it can be seen that the density distribution fitting results: the fitting parameters of the microplastic density distribution in the soil are: μ = 0.91, δ = 0.08, α = 1.94, β = 1.72.

[0106] like Figure 4 As shown in the figure, it is a fitting result diagram of the three-dimensional probability density distribution of soil microplastics in the embodiment. From the figure, we can see the variability of the main distribution patterns and characteristics of soil microplastics. The three-dimensional probability density distribution of size, shape and density can more intuitively and accurately visualize the distribution characteristics of microplastics in the soil environment.

[0107] 3. Data collection and screening of soil microplastic toxicity

[0108] This embodiment systematically collects relevant literature published between 2021 and 2024 on the effects of the size, shape, and type of microplastics in soil on their biological toxicity. Literature retrieval was performed using a combination of keywords ("microplastic*" OR "plastic particle*"; "size" OR "shape" OR "type"; "toxicity" OR "toxic effects") in Web of Science. The study must report the size, shape, and type of microplastics used in the experiment, and report the toxicity value. A total of 1,896 relevant documents were retrieved, and 108 documents on soil microplastic toxicity remained after initial screening. After further detailed reading of the literature screening, 17 documents with valid data remained, and 33 valid microplastic toxicity data were collected.

[0109] 4. Optimization of soil microplastic toxicity data

[0110] The existing SSD curve drawing method needs two corrections to make it consistent with the concentration of microplastics in the environment: first, the microplastic toxicity data obtained from different experiments need to be converted, and the toxicity value of the single particle size microplastic used in the experiment needs to be converted into a toxicity value that conforms to the particle size distribution law of microplastics in the environment; second, the microplastic toxicity data obtained from laboratory tests needs to be corrected to the toxicity data corresponding to the actual environmental concentration.

[0111]

[0112] The average volume V of microplastics within the size range that the experimental species can ingest in the natural environment poly The ratio of the number of microplastics within the size range that the experimental species can ingest to the total number of microplastics f available The Python code is used to simulate and determine the distribution of soil microplastics in three dimensions. For example, for species with an ingestible size of 20-500 μm, V poly The value is 1418651.03μm 3 , f available The value is 0.6473; for species with an ingestible size of 20-1000 μm, V poly The value is 8945859.48μm 3 , f available The value is 0.7633.

[0113] 5. Draw a new SSD curve based on the optimized toxicity data

[0114] like Figure 5 As shown in the figure, the soil microplastic SSD curve is drawn after optimizing the toxicity data according to the fitting results of the three-dimensional probability density distribution of soil microplastics in the embodiment. It can be seen from the figure that the environmental microplastic threshold concentration (EC X，env ) draws the SSD curve, and the resulting HC5 value has environmental reference significance. The HC5 value changes from the original 38.5 pieces / kg to 5587.824 pieces / kg.

[0115] 6. Optimize toxicity data for special contaminated sites

[0116] This example conducted a sampling survey on an abandoned landfill in Jiaxing, Zhejiang. A total of 9 sampling points were selected on the landfill, and 15 sampling points were selected in the farmland around the landfill. After analyzing all the sampling points, Figure 6 As shown in the figure, it is a fitting result of the probability density distribution of the shape of microplastics in the soil obtained by sampling and testing at a certain abandoned landfill in Jiaxing, Zhejiang Province in the embodiment. From the figure, it can be seen that the shape distribution fitting result of microplastics in the soil of the abandoned landfill is obtained. Figure 7As shown in the figure, the probability density distribution fitting result of soil microplastic density obtained from sampling and testing at an abandoned landfill in Jiaxing, Zhejiang Province in the embodiment can be seen from the figure. The density distribution fitting result of microplastics in the soil of the abandoned landfill can be readjusted according to the fitting result, as shown in the figure. Figure 8 The following is a graph showing the SSD curve of soil microplastics, obtained by optimizing toxicity data based on the three-dimensional probability density distribution of soil microplastics from abandoned landfills. The graph shows that the resulting new SSD curve better reflects the ecological risk of the target site to the surrounding environment than the unoptimized curve. The HC5 value is 9489.54 n / kg.

[0117] 7. Risk Assessment

[0118] Based on the HC5 value of 9489.54 n / kg, the risk quotients (RQs) for nine soil sites above the abandoned landfill were calculated to be: 1.4130; 1.6248; 1.3627; 0.9843; 2.2453; 1.5240; 1.4058; 0.7825; and 1.0501. Therefore, seven of the nine soil sites above the landfill were classified as high risk, and two as medium risk. The RQs for 15 soil sites in the surrounding farmland were: 0.0315; 0.1557; 0.1052; 0.0737; 0.0730; 0.0525; 0.0418; 0.0210; 0.0626; 0.0525; 0.1576; 0.3023; 0.2619; 0.2519; and 0.0839. Therefore, among the 15 soil points in the surrounding farmland, 9 are low-risk and 6 are medium-risk.

[0119] 8. Uncertainty Analysis

[0120] The uncertainty analysis results of the risk quotient RQ show that the 95% confidence interval of the RQ value at each assessment point has a small fluctuation range, indicating that the HC5 value fitting and the actual microplastic concentration measurement results have high stability and accuracy, and the risk assessment results have good credibility and stability.

[0121] Specifically, if Figure 10 As shown in the figure, the uncertainty analysis diagram of the soil risk quotient RQ value above the landfill in the embodiment can be seen from the figure. The confidence interval of the RQ value of the soil points above the landfill is generally at the medium risk level or above. More than 95% of the simulation results fall into the medium risk (0.1≤RQ<1) or high risk (RQ≥1) interval, indicating that the ecological risk level of microplastic pollution in this area is high, and the risk level judgment is highly robust. In contrast, Figure 11The figure below shows an uncertainty analysis of the risk quotient (RQ) value for farmland soil surrounding an abandoned landfill in Jiaxing, Zhejiang Province, as described in the examples. The figure shows that the overall risk level of the surrounding farmland soil is low, with approximately 20% of the simulation results falling into the low-risk range (0.01 ≤ RQ < 0.1), and the remaining approximately 80% falling into the medium-risk range. This indicates that the ecological risk of microplastic pollution in farmland soil is within a controllable range, but there is still the possibility of increased risk under certain circumstances. This uncertainty analysis further verifies the scientific nature of the risk classification and provides data support and decision-making reference for the identification and subsequent management of high-risk areas.

[0122] It can be seen from this that the present invention collects and screens the soil microplastic distribution characteristic data of the target site, and uses statistical models such as power law distribution, bimodal distribution and normal-inverse Gaussian distribution to fit the size, shape and density characteristics of microplastics, respectively, to construct a three-dimensional probability density distribution model of microplastic size, shape and density. Based on the constructed three-dimensional probability density distribution model, the collected and screened microplastic toxicity data are optimized by adopting methods such as particle size distribution correction, number concentration and mass concentration conversion, monodisperse and polydisperse concentration conversion, and actual environmental effect threshold adjustment to improve the adaptability between toxicity data and the actual microplastic exposure characteristics of the target site. For special types of sites (such as landfills, etc.), customized toxicity data optimization can be carried out based on field sampling and test results to further enhance the site adaptability and scientificity of the SSD curve. Combined with the optimized SSD curve, the ecological risk assessment of microplastics is carried out by calculating the ecological safety threshold (HC5) and risk quotient (RQ). The implementation of the present invention can provide a scientific basis for environmental ecological risk assessment, pollution prevention and control management, and policy formulation of new pollutants in soil.

[0123] The embodiment described above is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Persons skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, any technical solution obtained by equivalent substitution or equivalent transformation falls within the scope of protection of the present invention.

Claims

1. A soil microplastic ecological risk assessment method based on three-dimensional probability density distribution, characterized in that: The details are as follows: S1: Collect and screen soil microplastic distribution characteristic data; the soil microplastic distribution characteristic data includes the quantity abundance or mass abundance of microplastics in the soil and the size, shape and density of microplastics; S2: Based on the valid data screened in S1, the size, shape and density data of microplastics were normalized and statistically analyzed. Then, the probability density distributions of microplastic size, shape and density were fitted using power law distribution, bimodal distribution and normal-inverse Gaussian distribution respectively. The three-dimensional joint probability density distribution characteristics of microplastics were obtained, and a multidimensional probability model reflecting the composition and distribution law of microplastics in the target site was constructed. S3: Collect and screen soil microplastic toxicity data; S4: Based on the three-dimensional probability density distribution characteristics of soil microplastics obtained in S2, the soil microplastic toxicity data screened in S3 are optimized; S5: For soil microplastics in special sites, obtain their three-dimensional characteristic distribution through field sampling and analysis, and then conduct targeted toxicity data optimization; S6: Based on the toxicity data optimized by S4 or S5, an SSD curve is fitted; the ecological safety threshold of soil microplastics is determined according to the obtained SSD curve, and then the risk quotient of soil microplastics is calculated based on the obtained ecological safety threshold to assess the ecological risk of soil microplastics.

2. The soil microplastic ecological risk assessment method based on three-dimensional probability density distribution according to claim 1 is characterized in that: In S1, the sources of soil microplastic distribution characteristic data are as follows: Through on-site collection and laboratory analysis of soil samples at target sites, data on the size, shape, density, and abundance of microplastics were obtained. Data on the distribution characteristics of soil microplastics were obtained through literature searches of published research results. Microplastic-related property data of specific areas are extracted using domestic and international microplastic environmental databases.

3. The soil microplastic ecological risk assessment method based on three-dimensional probability density distribution according to claim 1 is characterized in that: In S1, the screening criteria for soil microplastic distribution characteristic data are as follows: Data from remote areas that were not target sites, data with significant detection bias or outliers, and data with no reported detection method or too low particle size accuracy were removed.

4. The soil microplastic ecological risk assessment method based on three-dimensional probability density distribution according to claim 1, characterized in that: In S2, the fitting modeling method is as follows: The size distribution of microplastics was fitted using a power-law distribution model to obtain its size probability density distribution parameter k; the shape distribution of microplastics was fitted using a bimodal distribution model based on the morphological classification ratio to obtain its shape probability density distribution parameters f1, f2, μ1, μ2 and σ1, σ2; the density distribution of microplastics was fitted using a normal-inverse Gaussian distribution model to obtain its density probability density distribution parameters μ, δ, α, and β; Among them, k is the exponential parameter of the power-law distribution, which is used to describe the degree of inclination of the microplastic size distribution; f1 and f2 are the relative contributions of the two normal distributions; μ1 and μ2 are the means of the two normal distributions, σ1 and σ2 are the standard deviations of the two normal distributions; μ, δ, α and β represent the position, scale, tail weight and asymmetry of the density probability density distribution, respectively.

5. The soil microplastic ecological risk assessment method based on three-dimensional probability density distribution according to claim 1 is characterized in that: In S3, the sources of soil microplastic toxicity data include domestic and foreign toxicity databases, publicly published document reports, and toxicity data obtained through microplastic toxicity experiments on soil organisms.

6. The soil microplastic ecological risk assessment method based on three-dimensional probability density distribution according to claim 1, characterized in that: In S3, the screening criteria for soil microplastic toxicity data are as follows: Eliminate experimental data that do not provide specific information about the microplastics used in the experiment, including the size, shape, and type of microplastics; eliminate experimental data that do not have a blank control in the toxicity test or that have significant differences between the blank group and the treatment group; exclude non-representative species or biological test data with no obvious correlation with soil ecosystems; delete experimental data with insufficient sample size or large standard error.

7. The soil microplastic ecological risk assessment method based on three-dimensional probability density distribution according to claim 1, characterized in that: In S4, the data optimization method is to sequentially perform particle size distribution correction, number concentration and mass concentration conversion, monodisperse and polydisperse microplastic threshold concentration conversion, and actual microplastic effect threshold calculation in the environment.

8. The soil microplastic ecological risk assessment method based on three-dimensional probability density distribution according to claim 1 is characterized in that: In S5, the data optimization method includes: Soil samples were collected from special sites to analyze the distribution characteristics of microplastics. Based on the on-site detection data obtained from the analysis, a three-dimensional probability density distribution was fitted to obtain the fitting parameters of the target site. Based on the fitting results, an SSD curve that better conforms to the actual situation of the target site was constructed. The special sites mentioned above include historical non-standard landfills and industrial wastelands.

9. The soil microplastic ecological risk assessment method based on three-dimensional probability density distribution according to claim 1, characterized in that: In S6, the log-normal distribution model is used to fit the SSD curve obtained by fitting to determine the ecological safety threshold HC5; then the measured microplastic pollution concentration MEC is compared with the predicted no-effect concentration PNEC obtained according to the ecological safety threshold to calculate the risk quotient RQ; the ecological risk level is divided according to the obtained risk quotient to assess the ecological risk of soil microplastics.

10. The soil microplastic ecological risk assessment method based on three-dimensional probability density distribution according to claim 9, characterized in that: The criteria for determining the ecological risk level are: RQ < 0.01 means no risk; 0.01 ≤ RQ < 0.1 means low risk; 0.1 ≤ RQ < 1 means medium risk; RQ ≥ 1 means high risk.

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