Micro-plastic soil ecological risk assessment method, equipment, medium and product

Abstract

The invention discloses a micro-plastic soil ecological risk assessment method and device, a medium and a product, and relates to the field of micro-plastic soil ecological effect assessment.The method comprises the steps that sampling is conducted in a to-be-assessed sample plot, and biological repeated samples are obtained; the method comprises the following steps: treating a biological repeated sample by using a co-extraction method to obtain total DNA of soil; performing high-throughput sequencing of amplicon genes on the total DNA of the soil to obtain original data; preprocessing the original data, and constructing a species abundance matrix; constructing a transboundary co-occurrence network according to the species abundance matrix; respectively calculating a first network topology index and a second network topology index; and according to the first network topology index and the second network topology index, determining a comprehensive ecological effect index of the to-be-evaluated sample plot by using a comprehensive ecological effect index determination model so as to evaluate the micro-plastic soil ecological risk. According to the invention, the integrity and accuracy of micro-plastic soil ecological risk assessment are improved.

Description

Technical Field

[0001] This application relates to the field of microplastic soil ecological effect assessment, and in particular to a method, equipment, medium and product for microplastic soil ecological risk assessment. Background Technology

[0002] Microplastic (MP) pollution has become a global environmental problem, with its accumulation in terrestrial ecosystems being particularly severe. Studies have shown that the amount of microplastics entering terrestrial ecosystems can be 4 to 23 times that of aquatic environments. Agricultural activities are one of the major sources of pollution; for example, the widespread use of plastic mulch films and the application of sewage sludge rich in microplastics, as well as particulate matter generated by tire wear during transportation, all contribute to the large-scale accumulation of microplastics in soil. These plastic particles, typically smaller than 5 millimeters in size, can further degrade into nanoplastics (NPs) smaller than 1 micrometer, enabling them to directly enter the interior of soil microorganisms.

[0003] Microplastic pollution significantly alters the physicochemical properties of soil, affecting its bulk density, porosity, water-holding capacity, and pH, thereby changing the microenvironment upon which microorganisms depend. Furthermore, as a persistent carbon source that is difficult to degrade, microplastics alter soil biogeochemical cycles. These physical and chemical changes bring complex biological effects to soil microbial communities. On the one hand, microplastic surfaces can form unique microbial niches, or "plastic spheres," which may stimulate specific microbial functions and enzyme activities in the short term. On the other hand, long-term accumulation leads to a decrease in total microbial biomass, reduced community richness (depending on the type of plastic polymer), and weakened stability of microbial co-occurrence networks. The complexity and contradiction of these effects indicate that simple, single-dimensional measurements (such as total biomass or the activity of a particular enzyme) are insufficient to comprehensively and accurately assess the true ecological impact of microplastics, urgently requiring a comprehensive assessment method capable of revealing system-level changes.

[0004] Current ecological risk assessment (ERA) frameworks, such as those adopted by a certain country's environmental protection agency, typically include standardized steps such as problem identification, exposure assessment, toxicity (effect) assessment, and risk characterization. In the field of soil pollution, existing assessment techniques cover multiple levels, including chemical analysis, physical remediation, and biological methods. However, these methods exhibit significant limitations when assessing the complex ecological effects of emerging pollutants like microplastics.

[0005] Traditional biological assessment methods, such as laboratory carbon fumigation or phospholipid fatty acid analysis (PLFA), while providing information on total microbial biomass or general community composition, are cumbersome, costly, and time-consuming (typically requiring 7 to 21 days to obtain results), demanding a high level of operator skill. Furthermore, bioassays relying on specific indicator species (such as springtails) are susceptible to interference from environmental variations, potentially leading to an underestimation of true ecological risks. A common drawback of these methods is that they only provide macroscopic or isolated biological indicators, failing to capture the complex interactions within the microbial community that forms the core of soil ecosystem function. Therefore, they cannot reveal the deeper impacts of pollutants on ecosystem stability and resilience. This creates a demand for novel assessment methods based on network theory.

[0006] The related technology discloses a method for evaluating agricultural ecosystem disturbance based on microbial community network attributes. The method's forward-looking aspect lies in its recognition that network topology, as a novel attribute, can surpass traditional species diversity indicators and more profoundly reflect the health and functional stability of the ecosystem. However, this method suffers from a fundamental and critical limitation: its analytical model is entirely based on the bacterial and fungal kingdoms, systematically neglecting other key members of the soil microbial food web, especially protists as primary predators. This neglect results in an incomplete ecological model, thus limiting the accuracy and depth of its assessment results. Summary of the Invention

[0007] The purpose of this application is to provide a method, equipment, medium, and product for assessing the ecological risk of microplastic soil, so as to improve the completeness and accuracy of the assessment.

[0008] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for assessing the ecological risk of microplastics in soil, including: Sampling was conducted in the plot to be evaluated to obtain a first biological replica of the microplastic contaminated area and a second biological replica of the clean control area; the microplastic contaminated area contained bacteria, fungi and protozoa; Using a co-extraction method, the first biological replicate sample and the second biological replicate sample were processed to obtain the first soil total DNA and the second soil total DNA. High-throughput sequencing of amplicon genes was performed on the first soil total DNA and the second soil total DNA to obtain first raw data and second raw data; the amplicon genes include 16S rRNA, ITS and 18S rRNA. The first and second raw data are preprocessed respectively, and a first species abundance matrix and a second species abundance matrix are constructed. The species abundance matrix represents biological replicates of different species, listed as the preprocessed raw data. The elements of the species abundance matrix are the relative abundance of the preprocessed raw data in each biological replicate. Based on the first species abundance matrix, a first cross-species co-occurrence network is constructed; the cross-species co-occurrence network is constructed with bacteria, fungi, and protists as nodes and significant relationships between bacteria, fungi, and protists as edges; Based on the second species abundance matrix, a second cross-species co-occurrence network is constructed; The first network topology index of the first cross-boundary co-occurrence network and the second network topology index of the second cross-boundary co-occurrence network are calculated respectively. The network topology index includes standard network indicators and novel network indicators. The standard network indicators include modularity, clustering coefficient, and average path length. The novel network indicators include protist-bacterial interaction ratio, cross-boundary connectivity index, trophic level modularity, and key predator index. Based on the first network topology index and the second network topology index, the comprehensive ecological effect index of the sample plot to be evaluated is determined using a comprehensive ecological effect index determination model to assess the ecological risk of microplastic soil; the comprehensive ecological effect index determination model is a weighted scoring system based on empirical data or a trained machine learning model.

[0009] In one embodiment, sampling is performed in the plot to be evaluated to obtain a first biological replica of the microplastic contaminated area and a second biological replica of the clean control area, specifically including: Multiple sampling points were randomly set up in the microplastic contaminated area and the clean control area of ​​the plot to be evaluated; At each sampling point, a soil core was collected at a predetermined depth using a soil auger. Multiple soil cores from the microplastic contaminated area were mixed to form the first biological replicate sample; Multiple soil cores from the clean control area were mixed to form a second biological replicate sample.

[0010] In one embodiment, the first raw data is preprocessed, and a first species abundance matrix is ​​constructed, specifically including: The first raw data is subjected to data quality control. Using Phred quality scores, sequences with quality scores less than Q30 are deleted to obtain the first raw data after quality control. The first raw data after quality control is subjected to noise reduction or clustering to generate a first amplicon sequence variant / first operational classification unit; The DADA2 algorithm is used to identify and remove chimeric sequences from the first amplicon sequence variant / first operational classification unit to obtain the first amplicon sequence variant / first operational classification unit after removing the chimeric sequences; Based on the first amplicon sequence variant / first operational taxonomic unit after removing the chimeric sequence, species annotation is performed using the SILVA and UNITE databases to obtain the first amplicon sequence variant / first operational taxonomic unit of the annotated species, which serves as the first raw data after preprocessing. Based on the preprocessed first raw data and the corresponding first biological replicate sample, a first species abundance matrix is ​​constructed.

[0011] In one embodiment, the second raw data is preprocessed, and a second species abundance matrix is ​​constructed, specifically including: The second raw data is subjected to data quality control. Using Phred quality scores, sequences with quality scores less than Q30 are deleted to obtain the quality-controlled second raw data. The second raw data after quality control is subjected to noise reduction or clustering to generate a second amplicon sequence variant / second operational classification unit; The DADA2 algorithm is used to identify and remove chimeric sequences in the second amplicon sequence variant / second operational classifier to obtain the second amplicon sequence variant / second operational classifier after removing the chimeric sequences; Based on the second amplicon sequence variant / second operational taxonomic unit after removing the chimeric sequence, species annotation is performed using the SILVA and UNITE databases to obtain the second amplicon sequence variant / second operational taxonomic unit after annotating the species, which serves as the second raw data after preprocessing. A second species abundance matrix is ​​constructed based on the preprocessed second raw data and the corresponding second biological replicate samples.

[0012] In one embodiment, a first cross-species co-occurrence network is constructed based on the first species abundance matrix, specifically including: Bacteria, fungi, and protists are used as nodes in the first cross-species co-occurrence network; The first species abundance matrix is ​​filtered to obtain the filtered first species abundance matrix. Using nonparametric correlation analysis, the correlation coefficients between the first amplicon sequence variants and the first operational taxonomic units of all annotated species in the filtered first species abundance matrix were determined. The first amplicon sequence variant / first operational taxonomic unit is retained after annotating species whose absolute correlation coefficient is greater than the correlation strength threshold; All correlation coefficients were subjected to p-value tests, and the p-values ​​were corrected using the Benjamini-Hochberg method with multiple tests to obtain corrected p-values. The first amplicon sequence variant / first operational taxonomic unit of the annotated species with corrected p-values ​​less than the statistical significance threshold was retained to obtain the first cross-species co-occurrence network.

[0013] In one embodiment, the protist-bacterial interaction ratio is the ratio of the number of significantly negatively correlated edges to the number of significantly positively correlated edges between protist nodes and detail nodes; the cross-kingdom connectivity index is the relative relationship between the connection density among bacteria, fungi, and protists and the internal connection density; the trophic level modularity is used to assess whether the community is segmented along the trophic level / functional group boundary; and the critical predator index is the negative correlation between the betweenness centrality of nodes and the connectivity of bacterial nodes.

[0014] In one embodiment, the comprehensive ecological effect index determination model is as follows: ; Among them, EEI is the comprehensive ecological effect index; w i Let ΔMetric represent the weight of the i-th network topology index in assessing the overall ecosystem stability, where i = 1, ..., n, and n is the total number of network topology indices; i This represents the percentage change of the i-th first network topology index relative to the i-th second network topology index.

[0015] In a second aspect, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the microplastic soil ecological risk assessment method described in any one of the above.

[0016] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the microplastic soil ecological risk assessment method described above.

[0017] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the microplastic soil ecological risk assessment method described above.

[0018] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a method, equipment, medium, and product for assessing the ecological risk of microplastic-contaminated soil. The method includes: sampling from the plot to be assessed to obtain a first biological replica sample from the microplastic-contaminated area and a second biological replica sample from the clean control area; processing the first and second biological replica samples using a co-extraction method to obtain a first total soil DNA and a second total soil DNA; performing high-throughput sequencing of amplicon genes on the first and second total soil DNA to obtain a first raw data and a second raw data; preprocessing the first and second raw data to construct a first species abundance matrix and a second species abundance matrix; and based on the first species... An abundance matrix is ​​used to construct a first cross-species co-occurrence network, which is built with bacteria, fungi, and protists as nodes and significant relationships among them as edges. A second cross-species co-occurrence network is constructed based on a second species abundance matrix. The first network topology index of the first cross-species co-occurrence network and the second network topology index of the second cross-species co-occurrence network are calculated respectively. The network topology indices include the protist-bacterial interaction ratio, cross-species connectivity index, trophic level modularity, and key predator index. Based on the first and second network topology indices, a comprehensive ecological effect index determination model is used to determine the comprehensive ecological effect index of the sample plots to be assessed, in order to evaluate the ecological risk of microplastic soils. This application integrates the key functions of the soil microbial food web with trophic level groups—bacteria, fungi, and protists—into a cross-species co-occurrence network, thereby enabling a truly comprehensive, system-level assessment of soil health under the stress of pollutants such as microplastics, improving the completeness and accuracy of microplastic soil ecological risk assessment. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating a method for assessing the ecological risk of microplastic soil, provided as an embodiment of this application; Figure 2 A flowchart illustrating the overall workflow of a microplastic soil ecological risk assessment method provided in one embodiment of this application; Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] This application aims to address the core shortcomings of related technologies in assessing the ecological effects of microplastics on soil. The technical problem is not simply adding data to existing models, but rather correcting a fundamentally flawed ecological model. Related technologies rely on a simplified model that does not reflect ecological reality for risk assessment; the purpose of this application is to provide a more complete and accurate assessment framework.

[0024] Current mainstream microbiome research, including the aforementioned methods for evaluating agricultural ecosystem disturbances based on microbial community network attributes, largely focuses on bacteria and fungi. However, this two-kingdom model inherently leads to a simplistic interpretation of ecological processes. In soil, bacteria and fungi primarily act as decomposers, with their interactions mainly driven by resource competition, supplemented by some synergistic effects. This model depicts a two-dimensional interactive network dominated by horizontal competition. However, real soil ecosystems are far more complex. Analyzing only bacteria and fungi is akin to assessing a grassland ecosystem by considering only competition among herbivores while completely ignoring the presence of carnivores. This model fails to capture the crucial "top-down" regulatory mechanisms within the ecosystem, thus its assessment of system stability is one-sided.

[0025] The core problem this application aims to address is the systematic neglect of the role of protists in related technologies. Soil protists are a highly diverse group in terms of phylogeny and function, and they are "major consumers of bacteria and fungi in the soil." In the context of microplastic pollution research, the role of protists remains an under-explored area.

[0026] Protists are not merely another group of nodes in the microbial community; they represent a unique, higher trophic level within the microbial food web. Through predation, they directly regulate the structure and abundance of bacterial and fungal communities, and this predator-prey relationship is a crucial feedback loop for maintaining microbial community homeostasis. Therefore, assessment models that do not include protists cannot answer the following key questions: Does microplastic pollution disrupt key predator-prey relationships in soil micro-food webs? Are apex predators in micro-ecosystems disappearing? Answers to these questions are essential for understanding the cascading effects of pollution and the long-term health of ecosystems, and all relevant techniques, including methods for assessing agricultural ecosystem disturbances based on microbial community network properties, cannot provide these answers.

[0027] The stability of an ecosystem depends not only on species diversity, but also on the complex network of interactions among all its organisms. A comprehensive understanding of community structure and function necessitates elucidating the relationships between all relevant taxa. The ultimate goal is to gain a holistic understanding of the ecology and evolution of microbial ecosystems.

[0028] In summary, the technical problem this application aims to solve is the current lack of a standardized, sensitive, and ecologically realistic assessment method. This method needs to integrate key functions and trophic level groups—bacteria (primary decomposers / preys), fungi (primary decomposers / competitors), and protozoa (primary predators)—into a unified network model, thereby enabling a truly comprehensive, system-level assessment of soil health under the stress of pollutants such as microplastics.

[0029] This application proposes a method called "Tri-Kingdom Ecological Assessment" to quantitatively assess the impact of microplastics on soil ecosystems by constructing and analyzing a comprehensive co-occurrence network of bacteria, fungi, and protists.

[0030] In one exemplary embodiment, such as Figure 1 As shown, a method for assessing the ecological risk of microplastics in soil is provided, including the following steps: S1: Samples are taken from the plot to be evaluated to obtain a first biological replica of the microplastic contaminated area and a second biological replica of the clean control area; the microplastic contaminated area contains bacteria, fungi and protozoa.

[0031] In this embodiment, as Figure 2 As shown, the technical solution of this application follows a standardized process from on-site sampling to final data reporting. This process aims to ensure the accuracy, repeatability, and interpretability of the evaluation results.

[0032] Figure 2 The main stages of the method described in this application are visually illustrated: 1. Standardized sampling: Collect samples from the target area (microplastic contamination area) and the control area (clean control area).

[0033] 2. Total DNA extraction from soil: DNA containing bacteria, fungi and protists is extracted simultaneously from a single soil sample.

[0034] 3. Amplicon sequencing of bacteria, fungi, and protozoa: Sequencing of the extracted DNA using three amplicon genes: 16S rRNA, ITS (Internal Transcribed Spacer), and 18S rRNA.

[0035] 4. Microbial information processing: The sequencing data of the three marker genes are processed and integrated into a unified species abundance matrix, and microorganisms are initially screened based on the frequency of species occurrence.

[0036] 5. Construction and analysis of cross-species networks: Construct cross-species co-occurrence networks based on species abundance matrices and calculate their network topology indices.

[0037] 6. Ecological Effect Index (EEI) Calculation: Integrates changes in network indices into a comprehensive score.

[0038] 7. Analysis of soil microplastic pollution.

[0039] 8. Soil microplastic risk assessment based on cross-border microbial networks: Output quantitative assessment results and diagnostic information.

[0040] In one embodiment, S1 specifically includes: S11: Multiple sampling points are randomly set up in the microplastic contaminated area and the clean control area in the plot to be evaluated.

[0041] S12: Use a soil auger to collect soil cores at a preset depth at each sampling point.

[0042] S13: Mix multiple soil cores from the microplastic contaminated area to form the first biological replicate sample.

[0043] S14: Mix multiple soil cores from the clean control area to form a second biological replicate sample.

[0044] In this embodiment, to minimize interference from environmental heterogeneity, the method emphasizes strict sampling specifications. The sampling protocol references established soil microbiology research designs. Specifically, multiple sampling points are randomly set up in each plot to be evaluated (e.g., a microplastic contaminated area and a clean control area with similar physicochemical properties). At each sampling point, a soil core is collected at a depth of 0-15 cm (preset depth) using a soil auger, and multiple cores (e.g., 5) are combined into a single biological replicate. At least three independent biological replicates are collected from each plot. All biological replicates are immediately placed on ice after collection and transported back to the laboratory for processing as soon as possible.

[0045] S2: Using a co-extraction method, the first biological duplicate sample and the second biological duplicate sample are processed respectively to obtain the first soil total DNA and the second soil total DNA.

[0046] S3: Perform high-throughput sequencing of amplicon genes on the first soil total DNA and the second soil total DNA respectively to obtain the first raw data and the second raw data; the amplicon genes include 16S rRNA, ITS and 18S rRNA.

[0047] In this embodiment, a key technical detail of the method of this application is the simultaneous co-extraction of high-quality total genomic DNA (total soil DNA) from a single biological replicate sample using an optimized kit or process. This co-extraction strategy avoids systematic bias that may be introduced by extracting different microbial taxa separately, ensuring the direct comparability of subsequent abundance data, since all data originate from the exact same microbial community snapshot.

[0048] After obtaining high-quality total genomic DNA, a three-marker gene amplicon sequencing strategy was employed: Bacteria: Amplify highly variable regions of the 16S rRNA gene, such as 338F (ACTCCTACGGGAGGCAGCAG)-806R (GGACTACHVGGGTWTCTAAT).

[0049] Fungi: Amplify the internal transcribed spacer (ITS), such as ITS1F (CTTGGTCATTTAGAGGAAGTAA)-ITS2R (CTTGGTCATTTAGAGGAAGTAA).

[0050] Protozoa: Amplify highly variable regions of the 18S rRNA gene (such as V4 or V9 regions, e.g., TAReuk454FWD1F(CCAGCASCYGCGGTAATTCC)-TAReuk454REV3R(ACTTTCGTTCTTGATYRA).

[0051] Parallel high-throughput sequencing of the three amplicon genes was performed on the total genomic DNA of the same biological replicate sample to obtain raw data characterizing three microbial kingdoms.

[0052] S4: Preprocess the first raw data and the second raw data respectively, and construct the first species abundance matrix and the second species abundance matrix; the species abundance matrix represents biological replicates with different species, listed as the preprocessed raw data; the elements of the species abundance matrix are the relative abundance of the preprocessed raw data in each biological replicate.

[0053] In one embodiment, the first raw data is preprocessed, and a first species abundance matrix is ​​constructed, specifically including: The first raw data is subjected to data quality control. Using Phred quality scores, sequences with quality scores less than Q30 are deleted to obtain the first raw data after quality control.

[0054] The first raw data after quality control is subjected to noise reduction or clustering to generate a first amplicon sequence variant / first operational classification unit.

[0055] The DADA2 algorithm is used to identify and remove chimeric sequences from the first amplicon sequence variant / first operational classification unit to obtain the first amplicon sequence variant / first operational classification unit after removing the chimeric sequences.

[0056] Based on the first amplicon sequence variant / first operational taxonomic unit after removing the chimeric sequence, species annotation is performed using the SILVA and UNITE databases to obtain the first amplicon sequence variant / first operational taxonomic unit of the annotated species, which serves as the first raw data after preprocessing.

[0057] Based on the preprocessed first raw data and the corresponding first biological replicate sample, a first species abundance matrix is ​​constructed.

[0058] In one embodiment, the second raw data is preprocessed, and a second species abundance matrix is ​​constructed, specifically including: The second raw data is subjected to data quality control. Using Phred quality scores, sequences with quality scores less than Q30 are deleted to obtain the quality-controlled second raw data.

[0059] The second raw data after quality control is subjected to noise reduction or clustering to generate a second amplicon sequence variant / second operational classification unit.

[0060] The DADA2 algorithm is used to identify and remove chimeric sequences in the second amplicon sequence variant / second operational classification unit to obtain the second amplicon sequence variant / second operational classification unit after removing the chimeric sequences.

[0061] Based on the second amplicon sequence variant / second operational taxonomic unit after removing the chimeric sequence, species annotation is performed using the SILVA and UNITE databases to obtain the annotated species' second amplicon sequence variant / second operational taxonomic unit, which serves as the preprocessed second raw data.

[0062] A second species abundance matrix is ​​constructed based on the preprocessed second raw data and the corresponding second biological replicate samples.

[0063] In this embodiment, a series of computational processes are performed on the raw data generated from sequencing. This workflow integrates standardized analysis modules for different marker genes. The main steps include: Data quality control: Remove low-quality sequencing reads.

[0064] The Phred Quality Score (Q-score) is used to measure the accuracy of sequences. Sequences with a Q-score less than Q30 are deleted. This step indicates that the base accuracy is above 99.9%, and software such as FastP and Trimmomatic are generally used.

[0065] Noise reduction and clustering: Noise reduction is performed using algorithms such as DADA2 to generate amplicon sequence variants (ASVs), or clustering is performed using traditional methods into operational taxonomic units (OTUs). ASVs have higher taxonomic resolution.

[0066] Chimera removal: Algorithms such as DADA2 are used to identify and remove chimeric sequences generated during PCR (Polymease Chain Reaction).

[0067] Species annotation: The obtained ASV / OTU sequences are compared with authoritative reference databases to determine their taxonomic classification. For example, the SILVA database is used to annotate 16S rRNA and 18S rRNA sequences, and the UNITE database is used to annotate ITS sequences.

[0068] The final and most crucial output of this process is a unified species abundance matrix. The rows of this matrix represent different biological replicates, while the columns represent all ASVs / OTUs from the three kingdoms of bacteria, fungi, and protists. The values ​​in the matrix represent the relative abundance of each ASV / OTU in each biological replicate. Relative abundance is calculated by dividing the number of sequences of a particular species' ASV in a biological replicate by the total number of sequences of that species in that biological replicate. For example, if the value of bacterial ASV1 in sample 1 is 300, and the total number of bacterial ASVs in sample 1 is 30,000, then the relative abundance of ASV1 is 1%.

[0069] This unified species abundance matrix serves as the direct input for subsequent network construction.

[0070] S5: Based on the first species abundance matrix, construct the first cross-species co-occurrence network; the cross-species co-occurrence network is constructed with bacteria, fungi and protists as nodes and significant relationships between bacteria, fungi and protists as edges.

[0071] In one embodiment, S5 specifically includes: S51: Bacteria, fungi, and protists are used as nodes in the first cross-species co-occurrence network.

[0072] S52: Filter the first species abundance matrix to obtain the filtered first species abundance matrix.

[0073] S53: Using nonparametric correlation analysis, determine the correlation coefficients between the first amplicon sequence variants / first operational taxonomic units after all annotated species in the filtered first species abundance matrix.

[0074] S54: The first amplicon sequence variant / first operational taxonomic unit after retaining annotated species whose absolute correlation coefficient is greater than the correlation strength threshold.

[0075] S55: Perform p-value tests on all the correlation coefficients and use the Benjamini-Hochberg method to perform multiple tests to correct the p-values, and retain the first amplicon sequence variant / first operational taxonomic unit after the annotated species whose corrected p-values ​​are less than the statistical significance threshold to obtain the first cross-species co-occurrence network.

[0076] In this embodiment, the construction of the cross-species co-occurrence network aims to infer potential interaction relationships between microorganisms from the species abundance matrix using statistical methods.

[0077] 1. Data filtering: To reduce noise and computational complexity, rare species are first removed from the species abundance matrix. For example, ASVs / OTUs with a total relative abundance of less than 0.01% in all samples are removed, or ASVs / OTUs with an occurrence rate of less than 50% are removed.

[0078] 2. Correlation Calculation: Microbiome data are typically sparse (containing a large number of zero values) and non-normally distributed. Therefore, this application employs a more robust non-parametric correlation analysis method for outliers and non-linear relationships, namely Spearman's rank correlation. The Spearman correlation coefficient ρ between all ASV / OTU pairs in the species abundance matrix is ​​calculated.

[0079] 3. Significance Threshold Setting: To build a reliable network, spurious or weak correlations must be filtered out. This application's method employs a strict dual threshold criterion to define "edges" (i.e., significant relationships) in the network: (1) Correlation strength threshold: Only connections with strong correlation are retained, for example, the absolute value of Spearman correlation coefficient |ρ|>0.7 (correlation strength threshold).

[0080] (2) Statistical significance threshold: p-value tests are performed on all calculated correlations, and the p-values ​​are corrected by multiple tests (False Discovery Rate, FDR) using methods such as Benjamini-Hochberg, retaining only connections with corrected p-values ​​less than the statistical significance threshold (such as 0.01 or 0.001).

[0081] ASV / OTU pairs that simultaneously satisfy both conditions are considered to have a significant co-occurrence relationship and are connected by an edge in the network. The resulting cross-kingdom co-occurrence network is a complex graph consisting of nodes representing microorganisms from different kingdoms and edges representing the significant relationships between them.

[0082] S6: Construct a second cross-species co-occurrence network based on the second species abundance matrix.

[0083] In practical applications, the principle of constructing the second cross-boundary co-occurrence network is the same as that of constructing the first cross-boundary co-occurrence network.

[0084] S7: Calculate the first network topology index of the first cross-boundary co-occurrence network and the second network topology index of the second cross-boundary co-occurrence network respectively; the network topology index includes standard network indicators and novel network indicators; the standard network indicators include modularity, clustering coefficient and average path length; the novel network indicators include protist-bacterial interaction ratio, cross-boundary connectivity index, trophic level modularity and key predator index.

[0085] In this embodiment, in addition to calculating some standard network metrics (used for baseline comparison) that are also used in related technologies, this application introduces a series of novel topological indices specifically designed for cross-boundary co-occurring networks. These new indices can capture unique ecological information unlocked by the introduction of protists as a trophic level, which cannot be calculated using existing technologies.

[0086] Standard network metrics (used for baseline assessment) include: Modularity: measures the degree to which nodes in a network are clustered into tightly connected "modules".

[0087] Clustering coefficient: measures the degree of interconnection between the "neighbors" of nodes in a network.

[0088] Average Path Length: The average of the shortest distances between any two nodes in a network.

[0089] Standard network metrics provide a macroscopic description of the overall network structure.

[0090] The novel network metrics unique to this application include: The Protist-Bacteria Interaction Ratio (PBI-Ratio) is defined as the ratio of the number of significantly negatively correlated (predation) edges between protist and bacterial nodes to the number of significantly positively correlated (other relationships) edges. A high PBI-Ratio indicates that the community structure is primarily shaped by top-down predation pressure; under pollution stress, a decrease in this ratio may signify the collapse of predation control.

[0091] The Inter-Kingdom Connectivity Index (IKI) quantifies the relative density of connectivity between the three kingdoms and within the microbial food web. This index reflects the degree of integration or isolation between different trophic levels / functional groups. Under microplastic stress, a decrease in the IKI index clearly indicates the discoupling of the microbial food web.

[0092] Trophic Level Modularity (TLM): An improved modularity algorithm specifically designed to assess whether a community is fragmented along kingdom (i.e., trophic level / functional group) boundaries. An increase in TLM indicates that different kingdoms are becoming increasingly isolated from each other, which is an important signal of ecosystem dysfunction.

[0093] The Keystone Predator Index (KPI) identifies key protozoan predators in a network by calculating the betweenness centrality of nodes and their negative correlation with bacterial nodes. The disappearance of these species can have a disproportionately large impact on the overall community structure.

[0094] S8: Based on the first network topology index and the second network topology index, the comprehensive ecological effect index of the sample plot to be evaluated is determined using the comprehensive ecological effect index determination model to assess the ecological risk of microplastic soil; the comprehensive ecological effect index determination model is a weighted scoring system based on empirical data or a trained machine learning model.

[0095] In this embodiment, the final step is to integrate all the changes in network topology indices observed between microplastic-contaminated soil and control soil into a single, actionable quantitative score.

[0096] This step employs an algorithmic model (e.g., a weighted scoring system based on empirical data, or a machine learning model trained on a large reference database) to calculate the Ecological Effect Index (EEI). The algorithm's input is the percentage change (Δ) of various network indicators in contaminated soil relative to control soil, such as Δ modularity, ΔPBI-Ratio, ΔIKI, etc.

[0097] The model for determining the comprehensive ecological effect index is as follows: .

[0098] Among them, EEI is the comprehensive ecological effect index; w i Let ΔMetric represent the weight of the i-th network topology index in assessing the overall ecosystem stability. This weight can be determined through experiments or model training, where i = 1, ..., n, and n is the total number of network topology indices. i This represents the percentage change of the i-th first network topology index relative to the i-th second network topology index.

[0099] The final output EEI is a standardized score (e.g., 0-10), where a low score indicates a weak ecological effect of microplastics, while a high score indicates severe damage to the structure and function of the microbial food web. This clear and easily communicated quantitative result provides a direct basis for characterizing ecological risks.

[0100] The method described in this application is used to assess the ecological risks posed by microplastic pollution in soil and has the following advantages: 1. Excellent ecological realism and sensitivity.

[0101] This application fundamentally transcends the decomposer-centric (bacteria / fungi) perspective of related technologies by incorporating protists into the analytical framework, constructing a multi-trophic-level ecological model that includes predator-prey relationships. This model more realistically reflects the complex structure of the soil microbial food web. This enhanced ecological realism directly translates into improved assessment sensitivity. The method in this application can detect subtle but crucial changes in predator-prey relationships that are completely invisible to existing techniques that only analyze bacteria and fungi. Therefore, this application can identify potential ecological damage earlier and more accurately.

[0102] 2. Enhanced diagnostic and mechanistic insights.

[0103] Existing methods for evaluating agricultural ecosystem disturbances based on microbial community network attributes can only provide a general score for "ecological disturbance" or "impact." In contrast, this application, through its unique analytical indicators (such as PBI-Ratio and IKI), can provide specific and in-depth mechanistic diagnoses. The method in this application not only determines "whether the soil is healthy," but also reveals "why the soil is unhealthy." For example, the assessment report can provide a diagnosis such as: "The soil ecosystem is in poor health, primarily due to the discoupling of fungal and bacterial communities (low IKI index), and the collapse of predation control by key protists (low PBI-Ratio)." This in-depth diagnostic information is invaluable for developing targeted and effective soil remediation strategies.

[0104] 3. Clear inventiveness relative to related technologies.

[0105] This application possesses significant novelty and inventiveness compared to related technologies (methods for evaluating agricultural ecosystem disturbances based on microbial community network attributes). Incorporating protists into network analysis is not merely an obvious, simple extension, but a qualitative leap, as it unlocks a completely new analytical dimension based on trophic level interactions. Table 1 clearly illustrates the essential differences between this application and related technologies, highlighting the inventiveness of this application.

[0106] Table 1. Comparison of Features between This Application and Related Technologies

[0107] 4. Broad potential for platform-based applications.

[0108] Although this application is optimized for assessing microplastic pollution, its core TKEA assessment framework exhibits "pollutant nonspecificity." The basic structure of the soil microbial food web is affected by various environmental stressors. Therefore, the method described in this application can be readily adapted and applied to assess the ecological risks of other types of soil pollutants, such as heavy metals, pesticides, herbicides, and emerging nanomaterials. This significantly expands the commercial value and market application scope of this application, making it a potential platform technology and new industry standard for next-generation soil health diagnostics.

[0109] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described microplastic soil ecological risk assessment method.

[0110] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the above-described microplastic soil ecological risk assessment method.

[0111] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the above-described microplastic soil ecological risk assessment method.

[0112] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 3 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for assessing the ecological risk of microplastic soil.

[0113] Those skilled in the art will understand that Figure 3The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0114] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0115] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0116] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0117] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0118] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for assessing the ecological risk of microplastics in soil, characterized in that, include: Samples were taken from the plots to be evaluated to obtain the first biological replica sample from the microplastic contaminated area and the second biological replica sample from the clean control area. The microplastic contamination zone contains bacteria, fungi, and protozoa; Using a co-extraction method, the first biological replicate sample and the second biological replicate sample were processed to obtain the first soil total DNA and the second soil total DNA. High-throughput sequencing of amplicon genes was performed on the first soil total DNA and the second soil total DNA to obtain first raw data and second raw data; the amplicon genes include 16S rRNA, ITS and 18S rRNA. The first raw data and the second raw data are preprocessed respectively, and a first species abundance matrix and a second species abundance matrix are constructed. The species abundance matrix consists of biological replicates that exhibit different behaviors, listed as the raw data after preprocessing; the elements of the species abundance matrix represent the relative abundance of the raw data in each biological replicate after preprocessing. Based on the first species abundance matrix, a first cross-species co-occurrence network is constructed; the cross-species co-occurrence network is constructed with bacteria, fungi, and protists as nodes and significant relationships between bacteria, fungi, and protists as edges; Based on the second species abundance matrix, a second cross-species co-occurrence network is constructed; The first network topology index of the first cross-boundary co-occurrence network and the second network topology index of the second cross-boundary co-occurrence network are calculated respectively. The network topology index includes standard network indicators and novel network indicators. The standard network indicators include modularity, clustering coefficient, and average path length. The novel network indicators include protist-bacterial interaction ratio, cross-boundary connectivity index, trophic level modularity, and key predator index. Based on the first network topology index and the second network topology index, the comprehensive ecological effect index of the sample plot to be evaluated is determined using a comprehensive ecological effect index determination model to assess the ecological risk of microplastic soil; the comprehensive ecological effect index determination model is a weighted scoring system based on empirical data or a trained machine learning model.

2. The method for assessing the ecological risk of microplastic soil according to claim 1, characterized in that, Sampling was conducted in the plots to be evaluated to obtain the first biological replica sample from the microplastic contaminated area and the second biological replica sample from the clean control area, specifically including: Multiple sampling points were randomly set up in the microplastic contaminated area and the clean control area of ​​the plot to be evaluated; At each sampling point, a soil core was collected at a predetermined depth using a soil auger. Multiple soil cores from the microplastic contaminated area were mixed to form the first biological replicate sample; Multiple soil cores from the clean control area were mixed to form a second biological replicate sample.

3. The method for assessing the ecological risk of microplastic soil according to claim 1, characterized in that, The first raw data is preprocessed, and a first species abundance matrix is ​​constructed, specifically including: The first raw data is subjected to data quality control. Using Phred quality scores, sequences with quality scores less than Q30 are deleted to obtain the first raw data after quality control. The first raw data after quality control is subjected to noise reduction or clustering to generate a first amplicon sequence variant / first operational classification unit; The DADA2 algorithm is used to identify and remove chimeric sequences from the first amplicon sequence variant / first operational classification unit to obtain the first amplicon sequence variant / first operational classification unit after removing the chimeric sequences; Based on the first amplicon sequence variant / first operational taxonomic unit after removing the chimeric sequence, species annotation is performed using the SILVA and UNITE databases to obtain the first amplicon sequence variant / first operational taxonomic unit of the annotated species, which serves as the first raw data after preprocessing. Based on the preprocessed first raw data and the corresponding first biological replicate sample, a first species abundance matrix is ​​constructed.

4. The method for assessing the ecological risk of microplastic soil according to claim 1, characterized in that, The second raw data is preprocessed, and a second species abundance matrix is ​​constructed, specifically including: The second raw data is subjected to data quality control. Using Phred quality scores, sequences with quality scores less than Q30 are deleted to obtain the quality-controlled second raw data. The second raw data after quality control is subjected to noise reduction or clustering to generate a second amplicon sequence variant / second operational classification unit; The DADA2 algorithm is used to identify and remove chimeric sequences in the second amplicon sequence variant / second operational classifier to obtain the second amplicon sequence variant / second operational classifier after removing the chimeric sequences; Based on the second amplicon sequence variant / second operational taxonomic unit after removing the chimeric sequence, species annotation is performed using the SILVA and UNITE databases to obtain the second amplicon sequence variant / second operational taxonomic unit after annotating the species, which serves as the second raw data after preprocessing. A second species abundance matrix is ​​constructed based on the preprocessed second raw data and the corresponding second biological replicate samples.

5. The method for assessing the ecological risk of microplastic soil according to claim 3, characterized in that, Based on the first species abundance matrix, a first cross-species co-occurrence network is constructed, specifically including: Bacteria, fungi, and protists are used as nodes in the first cross-species co-occurrence network; The first species abundance matrix is ​​filtered to obtain the filtered first species abundance matrix. Using nonparametric correlation analysis, the correlation coefficients between the first amplicon sequence variants and the first operational taxonomic units of all annotated species in the filtered first species abundance matrix were determined. The first amplicon sequence variant / first operational taxonomic unit is retained after annotating species whose absolute correlation coefficient is greater than the correlation strength threshold; All correlation coefficients were subjected to p-value tests, and the p-values ​​were corrected using the Benjamini-Hochberg method with multiple tests to obtain corrected p-values. The first amplicon sequence variant / first operational taxonomic unit of the annotated species with corrected p-values ​​less than the statistical significance threshold was retained to obtain the first cross-species co-occurrence network.

6. The method for assessing the ecological risk of microplastics in soil according to claim 1, characterized in that, The protist-bacterial interaction ratio is the ratio of the number of significantly negatively correlated edges to the number of significantly positively correlated edges between protist nodes and detail nodes; the cross-kingdom connectivity index is the relative relationship between the connection density among bacteria, fungi, and protists and the internal connection density; the trophic level modularity is used to assess whether the community is segmented along the trophic level / functional group boundary; the key predator index is the negative correlation between the betweenness centrality of nodes and the connectivity of bacterial nodes.

7. The method for assessing the ecological risk of microplastic soil according to claim 1, characterized in that, The model for determining the comprehensive ecological effect index is as follows: ； Among them, EEI is the comprehensive ecological effect index; w i Let ΔMetric represent the weight of the i-th network topology index in assessing the overall ecosystem stability, where i = 1, ..., n, and n is the total number of network topology indices; i This represents the percentage change of the i-th first network topology index relative to the i-th second network topology index.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the microplastic soil ecological risk assessment method according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the microplastic soil ecological risk assessment method according to any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the microplastic soil ecological risk assessment method according to any one of claims 1-7.

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