Industrial site environment risk judgment method and system, electronic equipment and storage medium

Abstract

The invention provides an industrial site environment risk judgment method and system, electronic equipment and a storage medium, and relates to the technical field of soil volatile organic compound pollution. The method comprises the following steps: acquiring geographic data and initial pollutant distribution data of a to-be-judged area, and performing sampling and point distribution according to the geographic data and the initial pollutant distribution data to obtain sampling point data; and carrying out field sampling according to the sampling point data to obtain a sampled sample. And carrying out physicochemical property analysis and occurrence form analysis on the sampled sample to obtain physicochemical property data and occurrence form data of the volatile organic compound. The method comprises the following steps: extracting a microbial sample in a sampled sample, obtaining microbial population data and functional gene data through gene analysis, and inputting a preset microbial volatile organic compound metabolism simulation model to obtain volatile organic compound metabolism data and environment fate data. And obtaining the comprehensive risk level based on the occurrence form data, the physicochemical property data and the environment fate data. Through the scheme, the accuracy of industrial site environment risk judgment is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of soil volatile organic compound pollution, and in particular to an industrial site environmental risk determination method, system, electronic equipment and storage medium. Background Art

[0003] At present, environmental risk assessment of volatile organic compound pollution in industrial sites mainly adopts traditional pollutant concentration detection and physical and chemical property analysis methods. These methods measure the concentration level of volatile organic compounds in media such as soil, water and air, and infer the possible environmental risks based on their physical and chemical properties such as solubility, volatility, toxicity, etc. However, this risk assessment method based only on pollutant concentration and physical and chemical properties has certain limitations and cannot accurately assess environmental risks. Summary of the invention

[0004] The present application provides an industrial site environmental risk determination method, system, electronic device and storage medium, which are used to improve the accuracy of industrial site environmental risk determination.

[0005] In a first aspect, the present application provides a method for determining environmental risks of industrial sites, the method comprising: Obtaining geographic data and initial pollutant distribution data of the area to be determined, and performing layered point distribution according to the geographic data and the initial pollutant distribution data to obtain sampling point data; According to the sampling point data, sampling is performed on the area to be determined to obtain a sampling sample; Performing a physical and chemical property analysis on the sampled sample to obtain the physical and chemical property data of the volatile organic compounds in the sampled sample, and performing a morphological analysis on the sampled sample to obtain the occurrence morphological data of the volatile organic compounds in the sampled sample; Extracting a microbial sample from the sampled sample, and performing gene analysis on the microbial sample to obtain microbial group data in the microbial sample and functional gene data of each microbial group; Inputting the biogroup data and the functional gene data of each microbial group into a preset microbial volatile organic compound metabolism simulation model to obtain the metabolic data of the microbial volatile organic compounds, and determining the environmental fate data of the volatile organic compounds based on the metabolic data; The comprehensive risk level is obtained by weighted processing based on the occurrence form data, the physical and chemical property data and the environmental fate data.

[0006] In the above technical solution, scientific sampling points, systematic sample analysis, comprehensive microbial metabolism simulation and quantitative risk index integration have established a full-chain technical system of "site investigation-sample analysis-process simulation-risk determination". This method not only takes into account the static distribution characteristics of volatile organic compound pollution, but also the dynamic migration and transformation process of volatile organic compounds in the environment. In particular, the introduction of the microbial metabolism module enhances the long-term and accuracy of risk prediction.

[0007] In a second aspect of the present application, a system for determining environmental risks of industrial sites is provided, the system comprising: A layered point distribution module is used to obtain geographic data and initial pollutant distribution data of the area to be determined, and to perform layered point distribution according to the geographic data and the initial pollutant distribution data to obtain sampling point data; A sampling module, used for sampling the area to be determined according to the sampling point data to obtain a sampling sample; A sample analysis module, used to perform a physical and chemical property analysis on the sampled sample to obtain the physical and chemical property data of the volatile organic compounds in the sampled sample, and to perform a morphological analysis on the sampled sample to obtain the occurrence morphological data of the volatile organic compounds in the sampled sample; A microbial analysis module, used to extract microbial samples from the sampled samples, and perform gene analysis on the microbial samples to obtain microbial group data in the microbial samples and functional gene data of each microbial group; An environmental fate determination module, used to input the biota data and the functional gene data of each microbial group into a preset microbial volatile organic compound metabolism simulation model to obtain the metabolic data of the microbial volatile organic compounds, and determine the environmental fate data of the volatile organic compounds based on the metabolic data; The risk level determination module is used to perform weighted processing based on the occurrence morphology data, the physical and chemical property data and the environmental fate data to obtain a comprehensive risk level.

[0008] In a third aspect of the present application, a computer storage medium is provided, wherein the computer storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the above method steps.

[0009] In the fourth aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the above method.

[0010] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The present invention has established a full-chain technical system of "site investigation-sample analysis-process simulation-risk determination" through scientific sampling points, systematic sample analysis, comprehensive microbial metabolism simulation and quantitative risk index integration. This method not only takes into account the static distribution characteristics of volatile organic compound pollution, but also the dynamic migration and transformation process of volatile organic compounds in the environment. In particular, the introduction of the microbial metabolism module enhances the long-term and accuracy of risk prediction.

[0011] 2. This application can comprehensively characterize the physicochemical properties and existence forms of volatile organic compounds in the sampled samples by analyzing the physicochemical properties and occurrence forms of the sampled samples. The physicochemical property data reflect the basic characteristics of volatile organic compounds such as the total amount, valence state, solubility, and toxicity, while the occurrence form data reveal key information such as the binding state, migration ability, and bioavailability of volatile organic compounds in soil or sediment. The combination of the two can more accurately assess the distribution, migration, and risk exposure of volatile organic compounds in the environment.

[0012] 3. After obtaining the physicochemical property data, occurrence form data and microbial metabolism data of the sampled samples, this application can scientifically integrate multi-source data and objectively judge the comprehensive risk level of the contaminated site by constructing a comprehensive indicator system and adopting a weighted processing method. The occurrence form data reflects the mobility and bioavailability risk of volatile organic compounds, the physicochemical property data reflects the toxicity and exposure risk of volatile organic compounds, and the environmental fate data predicts the cumulative risk of volatile organic compounds in the long-term environmental process. By reasonably setting the weight coefficients of each indicator and weighted summing up each risk index, a quantitative and comparable comprehensive risk index can be obtained, and the pollution risk level of the site can be intuitively determined according to the preset risk level threshold. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A schematic diagram of a process for determining environmental risk of an industrial site provided in an embodiment of the present application; Figure 2 An architectural diagram of an industrial site environmental risk assessment system provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of an electronic device provided in this application. DETAILED DESCRIPTION

[0014] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0015] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0016] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0018] At present, the environmental risk assessment of volatile organic compound pollution in industrial sites mainly adopts traditional pollutant concentration detection and physical and chemical property analysis methods. These methods measure the concentration level of volatile organic compounds in environmental media such as soil, water and air, and infer the possible environmental risks based on the physical and chemical properties of volatile organic compounds such as solubility, volatility and toxicity. However, this risk assessment method that only relies on pollutant concentration and physical and chemical properties has certain limitations. It ignores the changes in the occurrence form of volatile organic compounds in the environment and the biogeochemical cycle process, and cannot accurately predict the migration and transformation behavior and final fate of volatile organic compounds under complex environmental conditions, resulting in a deviation between the risk estimation results and the actual situation. In particular, some low-concentration and highly toxic occurrence forms of volatile organic compounds may be ignored by traditional methods, but in fact they pose a major hidden danger to the ecological environment and human health. In addition, traditional risk assessment methods lack analysis of the structure and function of microbial communities in contaminated sites, ignoring the important role of microorganisms in the migration, transformation and toxic effects of volatile organic compounds, making the risk assessment results not comprehensive and accurate.

[0019] After the background introduction of the above content, those skilled in the art can understand the problems existing in the prior art. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0020] Based on the above background technology, please refer to Figure 1 , Figure 1A flow chart of an industrial site environmental risk determination method provided in an embodiment of the present application. The system can be implemented by a computer program or can be run as an independent tool application. Specifically, in an embodiment of the present application, the method can be applied on a server, but can also be applied to electronic devices such as servers. An industrial site environmental risk determination method includes the following steps: S101, obtaining geographic data and initial pollutant distribution data of the area to be determined, and performing layered point distribution according to the geographic data and the initial pollutant distribution data to obtain sampling point data; Specifically, when implementing the method for determining the environmental risk of volatile organic compound pollution in industrial sites of the present invention, it is first necessary to obtain the geographic data and initial pollutant distribution data of the area to be determined. Geographic data include natural geographic information such as topography, landform, hydrology, geology, etc. of the area to be determined, as well as human geographic information such as land use, population distribution, and transportation network. These geographic data can be obtained through field surveys, remote sensing image interpretation, geographic information system analysis, and other methods. The initial pollutant distribution data reflects the known volatile organic compound pollution sources, pollution range, and pollution degree in the area to be determined. These data can be obtained by collecting historical data, on-site investigations, preliminary sampling and testing, and other methods.

[0021] After obtaining the geographic data and initial pollutant distribution data of the area to be determined, it is necessary to carry out scientific and reasonable sampling point layout on this basis. The purpose of sampling point layout is to determine representative sampling locations and quantities to ensure that the collected samples can fully and accurately reflect the spatial distribution characteristics of volatile organic compound pollution in the area to be determined. According to the geographic data, the area to be determined can be divided into several relatively homogeneous zones, such as highlands, lowlands, and riverbanks, by comprehensively considering factors such as topography, landforms, hydrology, and geology. In each zone, based on the initial pollutant distribution data, sampling points are focused on key areas such as near pollution sources, on the migration path of pollution plumes, and at pollution boundaries. At the same time, sampling points should also be arranged in control areas far away from pollution sources to obtain background value data.

[0022] On the basis of determining the location of the sampling points, it is also necessary to reasonably set the number and density of sampling points according to the physical and chemical properties of the pollutants and the characteristics of the environmental medium. For areas where the pollutant concentration changes dramatically and the pollution range is small, the number and density of sampling points should be appropriately increased to improve the spatial resolution of sampling. For areas where the pollutant concentration is relatively uniform and the pollution range is large, the number and density of sampling points can be appropriately reduced to improve the efficiency and economy of sampling. By comprehensively balancing the representativeness, accuracy and cost of the sampling points, the reasonable location and number of sampling points are finally determined to form the sampling point data.

[0023] Based on the above embodiment, as an optional embodiment, the step of performing layered point distribution according to the geographic data and the initial pollutant distribution data to obtain sampling point data includes: S201, determining distribution characteristic data of pollutants according to the geographic data and the initial pollutant distribution data; Specifically, in the process of layered deployment based on geographic data and initial pollutant distribution data, it is first necessary to determine the distribution characteristic data of pollutants. The distribution characteristic data of pollutants reflects key information such as the spatial distribution pattern, concentration gradient, and migration trend of pollutants in the area to be determined, and is an important basis for scientific deployment.

[0024] In order to determine the distribution characteristic data of pollutants, it is necessary to comprehensively analyze the geographical data and the initial pollutant distribution data. First, use tools such as geographic information systems (GIS) to spatially overlay and correlate the geographical data and initial pollutant distribution data. By overlaying pollutant concentration data with geographical elements such as topography, landforms, hydrology, and geology, the distribution differences and concentration gradients of pollutants in different geographical units can be identified. For example, by analyzing the relationship between pollutant concentration and terrain elevation, the enrichment trend of pollutants in low-lying areas can be discovered; by analyzing the relationship between pollutant concentration and water systems, the migration paths of pollutants in surface water and groundwater can be inferred.

[0025] On the basis of overlay analysis, it is also necessary to use spatial interpolation, spatial statistics and other methods to perform spatial modeling and prediction of pollutant concentration data. Using methods such as Kriging interpolation and inverse distance weighted interpolation, the pollutant concentration in the unsampled area can be estimated based on the pollutant concentration values ​​at known sampling points to form a spatial distribution surface of pollutants. Through spatial autocorrelation analysis, such as the Moran index and the Geary coefficient, the spatial aggregation characteristics and heterogeneity of pollutant distribution can be quantified. Through trend surface analysis, multiple regression analysis and other methods, the quantitative relationship between pollutant concentration and various geographical elements can be revealed, and a spatial prediction model for pollutant distribution can be constructed.

[0026] Through the above comprehensive analysis, we can obtain the distribution characteristic data of pollutants, including the spatial distribution map of pollutants, concentration contour map, spatial autocorrelation index, influencing factors of pollutant distribution, etc. These distribution characteristic data intuitively show the spatial variation law of pollutants in the area to be determined, reveal the high-risk areas and sensitive and vulnerable areas of pollutant distribution, and provide important decision-making basis for subsequent stratified point distribution.

[0027] S202, determining the soil type in the area to be determined according to the geographic data, and dividing the area to be determined into layers according to the soil type to obtain regional layered data; Specifically, on the basis of determining the pollutant distribution characteristic data, in order to further improve the scientificity and representativeness of the sampling points, it is also necessary to determine the soil type in the area to be determined based on the geographical data, and stratify and divide it accordingly to obtain regional stratification data. Soil type is an important factor affecting the occurrence, migration and risk exposure of pollutants. Different soil types have significant differences in their ability to adsorb, transform and release pollutants. Therefore, it is necessary to fully consider the spatial differentiation characteristics of soil types when arranging sampling points.

[0028] In order to determine the soil type in the area to be determined, it is necessary to collect and analyze the geographical data of the area in detail, especially the topography, geomorphology, parent material, climate, vegetation and other data related to soil formation and evolution. Using remote sensing image interpretation, digital elevation model analysis and other technologies, basic geographical data such as topography and geomorphology maps, slope and aspect maps, and geological type maps of the area to be determined can be obtained. Combining field soil surveys and indoor soil physical and chemical property analysis data, and referring to existing soil survey data and soil classification systems, the distribution of soil types in the area to be determined can be comprehensively determined. For example, by analyzing topography and geological conditions, different soil types such as mountain brown soil, alluvial plain tidal soil, and river terrace sandy ginger black soil can be identified; by analyzing soil profile characteristics and physical and chemical properties, different subclasses and soil units can be further subdivided.

[0029] On the basis of determining the distribution of soil types, it is necessary to stratify the area to be judged according to the soil type. The purpose of stratification is to classify areas with similar soil characteristics into the same evaluation unit to facilitate targeted sampling and risk assessment. On the GIS platform, the soil type map is superimposed and analyzed with other geographic data, and the spatial continuity and heterogeneity of soil types are considered to stratify the area to be judged. Within each stratification unit, the soil type composition is relatively uniform, while the soil types vary greatly between different stratification units. As a result of stratification, regional stratification data is formed, including the boundary vector data of each stratification unit and the soil attribute table data.

[0030] S203, determining the sampling point data according to the distribution characteristic data of the pollutants and the regional stratification data.

[0031] Specifically, in stratified units with high pollutant concentrations and greater soil environmental sensitivity, the density and number of sampling points should be appropriately increased to improve the spatial resolution and representativeness of the sampling. These stratified units usually include areas near pollution sources, pollutant concentration areas, areas with high soil clay content and rich organic matter, etc. In these areas, the layout of sampling points should be as uniform as possible and cover different pollution gradients to accurately characterize the spatial variation characteristics of pollutants. At the same time, considering the environmental sensitivity of these areas, the location selection of sampling points should also avoid sensitive receptors such as densely populated areas and water source protection areas to reduce the impact of sampling activities on the surrounding environment.

[0032] In contrast, in stratified units with low pollutant concentrations and low soil environmental sensitivity, the density and number of sampling points can be relatively reduced to improve the economy and efficiency of sampling. These stratified units usually include areas far from pollution sources, areas with sparse distribution of pollutants, areas with high soil sand content and lack of organic matter, etc. In these areas, the layout of sampling points can be relatively sparse, but a certain degree of spatial representativeness must still be ensured to accurately grasp the background level and change trend of pollutants. At the same time, in the layout of sampling points in these areas, effective connection with sampling points in high-risk areas should also be considered to achieve full coverage sampling of the entire area to be determined.

[0033] Based on the above embodiment, as an optional embodiment, the determining of the sampling point data according to the distribution characteristic data of the pollutants and the regional stratification data includes: S301, dividing the area to be determined into a plurality of stratified areas according to the regional stratification data, and determining the stratified pollution distribution characteristics of each stratified area according to the distribution characteristic data of the pollutants; Specifically, regional stratification can be done with the help of tools such as GIS software, based on the CAD drawings or satellite remote sensing images of the site, and with reference to relevant industry standards, the site can be zoned according to certain stratification principles (such as land use type, surface cover type, etc.). For the analysis of pollution distribution characteristics in stratified areas, geostatistical methods such as Kriging interpolation and Moran's I index can be used to calculate the mean, variance, variogram and other parameters of pollutant concentrations in each area, and draw spatial distribution maps of pollutants. In the process of stratification and pollution distribution characteristics analysis, attention should be paid to the differences in spatiotemporal scales and quality of different data sources, and preprocessing such as data standardization and calibration is required when necessary.

[0034] Through regional stratification and stratified pollution distribution characteristics analysis, the pollution distribution parameters of different areas of the site were finally obtained. Based on these stratified pollution distribution parameters, the layout of sampling points can be further optimized, such as increasing the density of sampling points in areas with high pollution concentration and large variation, and reducing the number of sampling points in areas with low and uniform pollution concentration. Compared with the simple grid sampling point layout method, this stratified sampling optimization method based on pollution distribution can more specifically capture the spatial variation characteristics of pollution in the site, reduce the blindness and randomness of sampling, and obtain more representative sample data with the same sampling workload.

[0035] S302, determining the number of sampling points and spatial distribution data of each of the stratified regions according to the stratified pollution distribution characteristics of each of the stratified regions, and using the number of sampling points and the spatial distribution data as the sampling point data.

[0036] Specifically, the optimization of sampling points in stratified areas can make use of relevant research results and industry standards and specifications of predecessors, and refer to the survey sampling experience of similar sites. For example, the sample size calculation formula given in the "Guidelines for Data Quality Assessment of Contaminated Sites" of the US EPA, and the point distribution rules in the "Technical Guidelines for Soil Sampling" of the European Committee for Standardization. At the same time, the optimization of sampling points should also fully listen to the opinions and suggestions of the owner, regulatory authorities and other stakeholders, comprehensively consider factors such as the purpose of the pollution status investigation, the needs of site redevelopment and utilization, and risk control requirements, and balance between scientificity and operability. After the number and location of the sampling points are determined, a sampling point distribution map should also be compiled to clearly mark the number, coordinates, elevation and other information of each sampling point to guide subsequent on-site sampling work.

[0037] Through the above-mentioned optimization process of sampling points in the stratified area, the number of sampling points and spatial distribution data of each area of ​​the contaminated site are finally determined. The number of sampling points in each stratified area and the corresponding spatial distribution coordinates are summarized together to form a complete sampling point layout plan data, that is, the sampling point data in the method of the present invention. Compared with the homogenized grid layout, this stratified heterogeneous sampling point optimization layout can better conform to the spatial distribution law of site pollution, and more comprehensively capture the key areas and sensitive areas of pollution at the same sampling density. By reasonably controlling the sampling density of different pollution areas, it not only reduces the blindness of sampling, improves the efficiency of pollution detection, but also saves sampling costs and improves the economy of the investigation work. Therefore, the obtained sampling point data can more comprehensively and accurately represent the overall pollution status of the site, and provide high-quality data guarantee for the subsequent environmental risk assessment.

[0038] S102, sampling the area to be determined according to the sampling point data to obtain a sampling sample; Specifically, for soil sampling, it is necessary to use soil drills and other equipment to perform stratified sampling according to the sampling depth determined by the sampling point data. In each soil layer, a sufficient amount of soil samples should be collected and placed in clean polyethylene ziplock bags or glass bottles, labeled with the sampling point number, date, depth and other information. During the sampling process, care should be taken to prevent cross-contamination of samples, and the samples should be placed in a low-temperature storage environment in a timely manner to prevent changes in the properties of the samples. For groundwater sampling, it is necessary to select appropriate well locations or monitoring wells according to the sampling point data, and use a dedicated water sampler for sampling. Before sampling, the well should be washed and a certain amount of water should be drained to ensure that the collected water samples can represent the actual conditions of the groundwater. The collected water samples should be placed in clean glass bottles or polyethylene bottles, and appropriate protective agents such as nitric acid should be added in a timely manner to prevent the transformation or precipitation of ionic pollutants in the water samples. For sediment sampling, it is necessary to use mud samplers and other equipment to collect a certain amount of sediment samples at designated locations, and pay attention to maintaining the original structure and vertical sequence of the samples. After sediment samples are collected, they should be placed in clean stainless steel or plastic containers and stored at low temperatures or freeze-dried as soon as possible.

[0039] S103, performing a physical and chemical property analysis on the sampled sample to obtain physical and chemical property data of volatile organic compounds in the sampled sample, and performing a morphological analysis on the sampled sample to obtain occurrence morphological data of volatile organic compounds in the sampled sample; Specifically, the physical and chemical properties of the sampled samples are analyzed, mainly to determine the basic physical and chemical parameters of the samples, such as pH value, redox potential, water content, organic matter content, etc. These parameters can reflect the environmental conditions of the samples and have an important impact on the migration, transformation and bioavailability of volatile organic compounds. At the same time, the total concentration of volatile organic compounds in the samples needs to be determined to evaluate the occurrence level of pollutants. The total concentration of volatile organic compounds is usually determined by analytical methods such as atomic fluorescence spectrometry and inductively coupled plasma mass spectrometry. The choice of specific methods depends on factors such as the type of sample, the concentration level of volatile organic compounds and laboratory conditions. During the determination process, the operating procedures of the analytical method should be strictly followed, and necessary quality control measures should be taken, such as parallel sample determination and spike recovery determination, to ensure the accuracy and reliability of the data.

[0040] While obtaining the total amount of volatile organic compounds data, it is also necessary to conduct a special analysis of the occurrence forms of volatile organic compounds in the sampled samples. Volatile organic compounds can exist in the environment in a variety of forms, such as inorganic volatile organic compounds, organic volatile organic compounds, soluble volatile organic compounds, insoluble volatile organic compounds, etc. The migration ability, bioaccessibility and toxicity of different forms of volatile organic compounds in the environment vary greatly, so they must be distinguished and quantitatively analyzed. The morphological analysis of volatile organic compounds usually adopts a method that combines morphological separation with morphological detection, such as continuous extraction method, chromatography-mass spectrometry, etc. In the extraction and separation stage, according to the differences in the physical and chemical properties of different volatile organic compound forms, selective extractants and extraction conditions are used to gradually separate volatile organic compounds into different extraction components; in the detection and analysis stage, the volatile organic compounds in each extraction component are qualitatively and quantitatively analyzed, and finally the content data of various volatile organic compound forms are obtained. The entire morphological analysis process requires strict control of the consistency of extraction conditions and analysis conditions, and the use of standard substances, morphological balance and other means for quality control to ensure the accuracy and comparability of the morphological analysis data.

[0041] Through the analysis of the physical and chemical properties and morphological analysis of the sampled samples, the physical and chemical properties data and the occurrence morphological data of volatile organic compounds were finally obtained. The physical and chemical properties data reflect the environmental conditions and pollution levels of the occurrence of volatile organic compounds, while the morphological data reveal the migration ability and bioavailability of volatile organic compounds in the environment.

[0042] Based on the above embodiment, as an optional embodiment, the morphological analysis of the sample is performed to obtain the occurrence form data of the volatile organic compounds in the sample, including: The volatile organic compounds in the sample are separated and extracted according to different binding forms or chemical valence states by continuous extraction or selective extraction to obtain extracted samples, and the morphology of the extracted samples is measured by atomic absorption spectroscopy to obtain the occurrence form data of the volatile organic compounds in the sample.

[0043] Specifically, according to the characteristics of the pollutants and site conditions, choose the appropriate morphological extraction method. For example, for sites where the pollutants are mainly inorganic volatile organic compounds, the Tessier continuous extraction method can be used to divide the volatile organic compounds into exchangeable states, carbonate-bound states, iron-manganese oxide-bound states, organic-bound states, and residual states; for sites where the pollution is mainly organic volatile organic compounds, a specific organic solvent extraction method can be used to extract different organic volatile organic compounds. During the extraction process, the reagent ratio, reaction time, temperature and other conditions of each step must be strictly controlled to avoid mutual conversion between forms. After the extraction is completed, the extracted sample is immediately subjected to atomic absorption spectroscopy. Before the measurement, the instrument calibration and matrix matching are done well, and quality control measures such as parallel samples and spike recovery are adopted. If necessary, the total volatile organic content can also be determined at the same time, and the accuracy of the morphological analysis results can be verified by mass balance calculation.

[0044] Through the above-mentioned morphological extraction and measurement process, the content data of volatile organic compounds in different forms in the sampled samples, that is, the occurrence form data of volatile organic compounds, are finally obtained. According to the relative content of each form of volatile organic compounds, the percentage of morphological composition can be calculated to intuitively understand the existence mode of volatile organic compounds in the polluted medium. According to the binding strength and environmental release characteristics of each form of volatile organic compounds, the activity and mobility of volatile organic compounds under current environmental conditions can be judged. According to the morphological transformation mechanism of volatile organic compounds, the dynamic change trend of the morphological composition of volatile organic compounds can also be predicted. Compared with a single total concentration data, the occurrence form data of volatile organic compounds can reveal the dynamic distribution and environmental behavior of volatile organic compounds in complex environmental media more comprehensively and deeply.

[0045] S104, extracting a microbial sample from the sampled sample, and performing gene analysis on the microbial sample to obtain microbial group data in the microbial sample and functional gene data of each microbial group; Specifically, after obtaining the enriched microbial samples, it is necessary to perform genetic analysis on them to obtain the composition and functional information of the microbial community. Microbial gene analysis usually uses high-throughput sequencing technology, such as 16SrRNA gene amplicon sequencing, metagenomic sequencing, etc. Among them, 16SrRNA gene amplicon sequencing is a mature and economical method. By amplifying and sequencing specific regions of the microbial 16SrRNA gene, the microbial groups in the sample can be identified and the species composition and diversity information of the microbial community can be obtained. On the basis of 16S sequencing, some bioinformatics tools, such as PICRUSt, can also be combined to predict the metabolic functional characteristics of the microbial community. Metagenomic sequencing is a more direct and comprehensive method. By sequencing the total DNA of the microbial sample, all the genetic information of the microbial community can be obtained, and its metabolic function and ecological function can be directly analyzed. In the process of sequencing analysis, the operating procedures and data quality control standards of the sequencing platform should be strictly followed, and appropriate bioinformatics processes, such as sequence splicing, quality control, OTU clustering, species annotation, functional annotation, etc., should be adopted to ensure the accuracy and repeatability of data analysis.

[0046] Through genetic analysis of microbial samples, we finally obtained the group composition data and functional gene data of the microbial community. The group composition data reflects which microbial groups exist in the sample and the relative abundance of each group, revealing the species diversity and community structure characteristics of the microbial community. The functional gene data reflects the metabolic potential and ecological functions of the microbial community, such as the gene abundance and distribution characteristics in energy metabolism, carbon and nitrogen cycle, and pollutant degradation. Based on the above embodiment, as an optional embodiment, the gene analysis of the microbial sample is performed to obtain the microbial group data in the microbial sample and the functional gene data of each microbial group, including: S401, obtaining the microbial community data by performing metagenomic sequencing on the biological sample; Specifically, an appropriate microbial DNA extraction kit is used to extract total microbial DNA from environmental samples such as soil and sediment, and library construction steps such as DNA fragmentation, end repair, and connection adapters are performed. Then, the library is double-end sequenced using a second-generation high-throughput sequencing platform, such as Illumina HiSeq, BGISEQ, etc., to obtain a large amount of original genome sequence data. Next, the original data is subjected to bioinformatics analysis such as splicing and assembly to obtain a gene sequence set of the microbial community. Based on this gene set, Metaphlan2, Kraken and other software are used to quantitatively analyze the relative abundance of each microbial group to obtain the species composition information of the community. Finally, GraPhlan and other visualization tools are used to draw the composition structure diagram of the microbial community at different classification levels such as phylum, class, order, family, and genus.

[0047] Through the above-mentioned metagenomic sequencing and analysis process, the community composition data of the microbial sample, i.e., the microbial community data in the method of the present invention, is finally obtained. The community data generally includes the classification name, relative abundance, evolutionary status and other information of each microbial community. Based on the community data, the species diversity index of the microbial community, the proportion of dominant bacterial genera and other indicators can be calculated to evaluate the richness, uniformity and stability of the community. Through the dynamic comparative analysis of the community structure, the succession law of the microbial community under the stress of volatile organic compound pollution can be revealed. Through the association analysis with environmental factors, sensitive indicator genera of volatile organic compound pollution can be screened. Compared with a single culture identification method, metagenomic sequencing can more comprehensively reveal the microbial diversity of volatile organic compound contaminated sites, and clarify the interaction mechanism between volatile organic compound pollution and microbial communities, providing a more intuitive and quantitative basis for judging the ecological risk of volatile organic compound pollution.

[0048] S402, analyzing the expression levels of the functional genes of each of the microbial groups through transcriptome sequencing to obtain functional gene data of each of the microbial groups.

[0049] Specifically, when conducting transcriptome sequencing analysis on microbial samples, the total RNA of the sample is first extracted using RNeasy, TRIzol and other kits, and the concentration and quality of the RNA are detected. Then, a large amount of ribosomal RNA in the RNA sample is removed using rRNA removal reagent to enrich the target RNA such as mRNA. Next, the enriched RNA is used as a template to synthesize cDNA by reverse transcription, and library construction and quality control are performed. The cDNA library is sequenced at both ends using high-throughput sequencing platforms such as Illumina to obtain a large amount of original transcriptome sequence data. After quality control, removal of adapters, and filtering of the original data, cleanreads are aligned to the reference genome or transcriptome using software such as Tophat2 and HISAT2. Based on the alignment results, the expression FPKM value of each gene is calculated using software such as Cufflinks and HTSeq. Finally, differential expression analysis is performed using software packages such as DESeq2 and edgeR to obtain differentially expressed genes in the contaminated group relative to the control group. For these differential genes, bioinformatics analysis such as GO functional enrichment and KEGG pathway enrichment is further performed.

[0050] Through the above-mentioned transcriptome sequencing analysis, the functional gene expression data of the microbial community in the contaminated site, i.e., the microbial functional gene data in the present invention, are obtained. Functional gene data generally include information such as gene name, functional annotation, expression level, difference multiple, significance level, etc. Based on these data, volatile organic compound metabolism genes with significantly upregulated expression levels in the polluted environment can be screened out, such as volatile organic compound methylation gene arsM, volatile organic compound reduction gene arsC, volatile organic compound pump gene acr3, etc., revealing the metabolic conversion ability of the microbial community to volatile organic compounds. Combined with the expression pattern analysis of functional genes, the activity level and metabolic pathways of microbial volatile organic compound metabolism under different pollution levels can be clarified, and the biogeochemical cycle process of volatile organic compounds mediated by microorganisms can be predicted.

[0051] S105, inputting the biota data and the functional gene data of each microbial group into a preset microbial volatile organic compound metabolism simulation model to obtain metabolic data of microbial volatile organic compounds, and determining environmental fate data of volatile organic compounds based on the metabolic data; Specifically, after obtaining the data of microbial community group composition and functional gene abundance of each sample, these data need to be standardized and converted into an input format acceptable to the model. Then, the processed data are input into the preset microbial volatile organic compound metabolism model, and the physical and chemical property parameters such as pH value, redox potential, and organic matter content of the sample are input at the same time. According to the input microbial community composition and functional data, the model will simulate the volatile organic compound metabolism process of the microbial community under the physical and chemical conditions of the sample, quantitatively calculate the reaction rate and product generation of each metabolic process, and predict the morphological distribution, migration and diffusion, and environmental fate of volatile organic compounds within a certain time scale. By comparing the simulation results of different samples, the influence of microbial community composition and pollution degree on the metabolic fate of volatile organic compounds is analyzed, and the environmental risk of volatile organic compound pollution caused by microbial metabolism is judged. The entire simulation process is usually implemented by computer programs. Attention should be paid to the setting of calculation parameters and the quality control of simulation results. If necessary, sensitivity analysis, uncertainty analysis and other methods can be used to evaluate the reliability and applicability of the model.

[0052] Through the simulation analysis of microbial volatile organic compound metabolism, we finally obtained the morphological transformation data, migration accumulation flux data and environmental fate data of volatile organic compounds in the microbial metabolism process. These metabolic data quantitatively describe the biogeochemical cycle of volatile organic compounds mediated by microorganisms and reveal the microbial driving mechanism of the environmental risk of volatile organic compound pollution. Based on the metabolic simulation results, we can judge the contribution of microbial metabolic processes to the environmental risk of volatile organic compound pollution and predict the long-term environmental fate of volatile organic compound pollution.

[0053] Based on the above embodiment, as an optional embodiment, determining the environmental fate data of volatile organic compounds according to the metabolic data includes: S501, determining a potential value of morphological transformation of volatile organic compounds according to the metabolic data; Specifically, in order to determine the morphological transformation potential of volatile organic compound pollution, it is necessary to estimate the relative intensity of the volatile organic compound metabolic process using a quantitative calculation method based on the expression data of microbial metabolic functional genes. Specifically, first, based on the results of transcriptome analysis, the expression data of volatile organic compound reduction genes and volatile organic compound methylation genes are extracted. Taking the arsC gene as an example, the higher the expression level of this gene, the greater the metabolic flux of As(V) reduction to As(III) in the microbial community. After obtaining the expression levels of key functional genes, the potential value of volatile organic compound morphological transformation is calculated using the following formula: Volatile organic compound form transformation potential value = (Σmethylation gene expression - Σreduction gene expression) / (Σmethylation gene expression + Σreduction gene expression) The calculation result of this formula is a value between -1 and 1. When the potential value of volatile organic compound form transformation is greater than 0, it indicates that the methylation metabolic flux of volatile organic compounds is higher than the reduction metabolic flux of volatile organic compounds, indicating that volatile organic compounds will mainly transform from inorganic state to organic state, and the toxicity risk will gradually decrease; when the potential value of volatile organic compound form transformation is less than 0, it indicates that the reduction metabolic flux of volatile organic compounds is higher than the methylation metabolic flux of volatile organic compounds, indicating that volatile organic compounds will mainly be reduced from pentavalent inorganic state to trivalent inorganic state, and the toxicity risk will gradually increase; when the potential value of volatile organic compound form transformation is equal to 0, it indicates that the reduction and methylation metabolic fluxes of volatile organic compounds are in dynamic equilibrium, and the form composition of volatile organic compounds remains relatively stable.

[0054] S502: predicting migration distribution trend data of volatile organic compounds according to the morphological transformation potential value, and performing quantitative analysis according to the migration distribution trend data to determine environmental fate data of the volatile organic compounds.

[0055] Specifically, in order to predict the environmental migration and distribution trends of volatile organic compounds, it is necessary to use a mathematical model to dynamically simulate the migration and distribution process of volatile organic compounds in a multi-media environment based on the potential value of volatile organic compound form transformation. First, based on the environmental condition parameters of the contaminated site, such as soil physical and chemical properties, hydrogeological conditions, etc., a conceptual model of the migration and transformation of volatile organic compounds in the soil-water-sediment system is constructed. On the basis of the conceptual model, combined with the potential value of volatile organic compound form transformation, a mathematical model of the migration and distribution process of volatile organic compounds is established using partial differential equations to quantitatively describe the migration, exchange, adsorption, analysis, precipitation and dissolution of various forms of volatile organic compounds in multiple media. The migration and distribution model of volatile organic compounds is as follows: ∂C / ∂t=D(∂^2C) / (∂x^2)-v(∂C) / (∂x)+r Among them, C is the concentration of volatile organic compounds, t is time, x is spatial position, D is the hydrodynamic diffusion coefficient, v is the seepage velocity, and r is the biogeochemical reaction term. Introducing the potential value of volatile organic compound form transformation into the kinetic equation can quantitatively characterize the contribution of microbial metabolism to the migration and transformation of volatile organic compounds.

[0056] By solving the kinetic equation of volatile organic compound migration and distribution using numerical simulation methods, the distribution of volatile organic compounds in multi-media environments under different spatial and temporal conditions can be quantitatively predicted. By comparing and analyzing the simulation results under different morphological transformation potential value scenarios, the influence of microbial metabolic processes on the environmental fate of volatile organic compounds can be revealed. For example, when the morphological transformation potential value of volatile organic compounds is high, it indicates that the trend of volatile organic compounds transforming into low-toxic methylated volatile organic compounds is significant. The simulation results show that volatile organic compounds will mainly migrate from soil to surface water and groundwater, and the accumulation in sediments will gradually decrease; when the morphological transformation potential value of volatile organic compounds is low, it indicates that the trend of volatile organic compounds transforming into highly toxic trivalent inorganic volatile organic compounds is significant. The simulation results show that volatile organic compounds will mainly be enriched in soil and sediments, and diffuse through surface runoff and groundwater runoff, and the scope of pollution will continue to expand.

[0057] Based on the simulation prediction results of the migration and distribution trend of volatile organic compounds, statistical methods are used to quantitatively analyze the environmental fate of volatile organic compounds and characterize the risks. Statistical tests and regression analysis are performed on the simulated spatiotemporal distribution data to quantitatively reveal the accumulation trend, diffusion trend and fate distribution ratio of volatile organic compounds in environmental media, and calculate the exposure level and health risk of volatile organic compound pollution under different scenarios. Taking the risk of excessive concentration of volatile organic compounds in groundwater as an example, based on the simulated predicted value of volatile organic compound concentration in groundwater, the Monte Carlo method can be used to calculate the probability that the concentration of volatile organic compounds exceeds the water quality standard limit, and the risk level of groundwater pollution can be quantitatively evaluated.

[0058] S106, performing weighted processing based on the occurrence form data, the physical and chemical property data and the environmental fate data to obtain a comprehensive risk level.

[0059] Specifically, the calculation of risk scores and weight assignment of each data element need to follow certain principles and methods. For example, for the occurrence form data of volatile organic compounds, the risk score of each form can be given according to the bioavailability and environmental toxicity of each form of volatile organic compounds, and then the form risk score can be obtained by weighted average according to the relative content ratio of each form. For physical and chemical property data, the risk score of acidic, alkaline, oxidized, reduced and other conditions can be given according to the degree to which the physical and chemical parameter values ​​deviate from the appropriate range of pollutant migration and transformation. For environmental fate data, the risk score of the fate process can be given according to the flux size of the process of migration, accumulation and release of volatile organic compounds in the environment. In the process of determining the risk score and weight of each element, we should fully draw on the existing risk assessment standards and literature reports, and combine expert experience and judgment to improve the reliability and comparability of risk analysis results. If necessary, methods such as sensitivity analysis and uncertainty analysis can also be used to evaluate the robustness of risk analysis results.

[0060] Based on the above embodiment, as an optional embodiment, the weighted processing based on the occurrence form data, the physical and chemical property data and the environmental fate data to obtain the comprehensive risk level includes: S601, standardizing the occurrence form data, the physicochemical property data and the environmental fate data to obtain occurrence form index data, physicochemical property index data and environmental fate index data, and scoring the occurrence form index data, the physicochemical property index data and the environmental fate index data according to preset benchmark data to obtain occurrence form risk index, physicochemical property index and environmental fate risk index; Specifically, in order to conduct a comprehensive assessment of the risk of volatile organic compound pollution, it is first necessary to standardize the data on the occurrence forms of volatile organic compounds, the data on the physical and chemical properties of soil, and the environmental fate of volatile organic compounds. Taking the occurrence forms of volatile organic compounds as an example, the maximum and minimum value standardization method is used to convert the content data of volatile organic compounds in each form. The calculation formula is as follows: X'=(X-X_min) / (X_max-X_min) Among them, X is the original measured value of a certain form of volatile organic matter content, X_max and X_min are the maximum and minimum values ​​of the volatile organic matter content of this form, respectively, and X' is the dimensionless index value after standardization, ranging from 0 to 1. The same method is used to standardize the environmental fate index of volatile organic matter and the soil physical and chemical property index to obtain the occurrence form index data, physical and chemical property index data, and environmental fate index data with unified dimensions.

[0061] After completing data standardization, the data of each indicator are scored with reference to the preset evaluation benchmark value. The evaluation benchmark is generally determined according to the relevant environmental quality standards and ecological risk screening values, corresponding to different risk levels. For example, based on the WHO-specified volatile organic compound health risk value of 10 μg / L, the concentration of volatile organic compounds in water bodies is divided into four levels: 0.50 μg / L is a mild risk level, 50-100 μg / L is a moderate risk level, and >100 μg / L is a high risk level, and the risk indexes of 0, 1, 2, and 3 are assigned respectively. Similar hierarchical scoring matrices are used to determine the risk index according to the standardized values ​​of each indicator. Taking the volatile organic compound form index as an example, the higher the content of exchangeable and carbonate-bound volatile organic compounds after standardization, the higher the form risk index; the higher the content of residual volatile organic compounds after standardization, the lower the form risk index. By grading and assigning values ​​to the data of each indicator, quantitative occurrence form risk index, physical and chemical property risk index, and environmental fate risk index are obtained.

[0062] S602, weighting the morphological risk index, the physicochemical property index and the environmental fate risk index according to a preset weighting coefficient to obtain a comprehensive risk index, and determining the comprehensive risk level according to the comprehensive risk index and a preset index threshold.

[0063] Specifically, in order to calculate the comprehensive risk index and determine the risk level, it is first necessary to give the corresponding weight coefficient according to the ecological and environmental significance of each risk indicator and the size of the risk contribution. Generally, the expert scoring method is used to determine the indicator weight, that is, organize experts in related fields to score the importance of each indicator, calculate the mean of the score of each indicator, and normalize the mean as the weight coefficient. Taking the volatile organic compound form risk index, soil physical and chemical property risk index, and environmental fate risk index as examples, experts judged that the impact of soil physical and chemical properties on the risk of volatile organic compound pollution is relatively small, while the occurrence form of volatile organic compounds and environmental migration fate are the main factors determining the risk level. The weight coefficient of the soil physical and chemical property index can be set to 0.2, and the weight coefficients of the volatile organic compound form index and the environmental fate index can be set to 0.4 respectively. On the basis of determining the weights of each risk index, the weighted average model is used to calculate the comprehensive risk index of volatile organic compound pollution. The formula is as follows: Comprehensive risk index = 0.4 × volatile organic compound occurrence form risk index + 0.2 × soil physical and chemical properties risk index + 0.4 × environmental fate risk index The value range of the comprehensive risk index is 0~3. The larger the value, the higher the ecological risk of volatile organic compound pollution.

[0064] After obtaining the comprehensive risk index, the volatile organic compound pollution risk in the study area is divided into different levels with reference to the preset index threshold. The setting of the index threshold needs to comprehensively consider factors such as regional environmental background values, environmental quality standards, ecological protection goals, and the acceptable level of risk management. Generally, the comprehensive risk index is divided into 4 levels: 02 for medium risk, 2~3 for high risk, and >3 for extremely high risk.

[0065] On the other hand, the present invention also provides an industrial site environmental risk determination system, such as Figure 2 , the system comprises: The layered point distribution module 1 is used to obtain the geographic data and initial pollutant distribution data of the area to be determined, and perform layered point distribution according to the geographic data and the initial pollutant distribution data to obtain sampling point data; Sampling module 2, used for sampling the area to be determined according to the sampling point data to obtain a sampling sample; The sample analysis module 3 is used to perform a physical and chemical property analysis on the sampled sample to obtain the physical and chemical property data of the volatile organic compounds in the sampled sample, and to perform a morphological analysis on the sampled sample to obtain the occurrence morphological data of the volatile organic compounds in the sampled sample; A microbial analysis module 4 is used to extract a microbial sample from the sampled sample and perform gene analysis on the microbial sample to obtain microbial group data and functional gene data of each microbial group in the microbial sample; The environmental fate determination module 5 is used to input the biota data and the functional gene data of each microbial group into a preset microbial volatile organic compound metabolism simulation model to obtain the metabolic data of the microbial volatile organic compounds, and determine the environmental fate data of the volatile organic compounds according to the metabolic data; The risk level determination module 6 is used to perform weighted processing based on the occurrence morphology data, the physical and chemical property data and the environmental fate data to obtain a comprehensive risk level.

[0066] Please refer to Figure 3 The application also discloses an electronic device. Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0067] The communication bus 302 is used to realize the connection and communication between these components.

[0068] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0069] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0070] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field~Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processor (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0071] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read~Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally be at least one storage system located away from the aforementioned processor 301. Refer to Figure 3, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of an industrial site environmental risk determination method.

[0072] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program of the industrial site environmental risk determination method stored in the memory 305. When executed by one or more processors 301, the electronic device 300 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the described action sequence, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application. In the above embodiments, the description of each embodiment has its own emphasis. For the parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0073] In the several implementations provided in this application, it should be understood that the disclosed system can be implemented in other ways. For example, the system embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of the system or unit can be electrical or other forms.

[0074] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0075] The present application also provides a computer storage medium that can store multiple instructions, which are suitable for being loaded and executed by a processor as described above. Figure 1 The road assessment method of the embodiment shown in the figure can be found in the specific implementation process. Figure 1 The specific description of the illustrated embodiment will not be repeated here.

[0076] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0077] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0078] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0079] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for determining environmental risks of industrial sites, characterized in that: The method comprises: Obtaining geographic data and initial pollutant distribution data of the area to be determined, and performing layered point distribution according to the geographic data and the initial pollutant distribution data to obtain sampling point data; According to the sampling point data, sampling is performed on the area to be determined to obtain a sampling sample; Performing a physical and chemical property analysis on the sampled sample to obtain the physical and chemical property data of the volatile organic compounds in the sampled sample, and performing a morphological analysis on the sampled sample to obtain the occurrence morphological data of the volatile organic compounds in the sampled sample; Extracting a microbial sample from the sampled sample, and performing gene analysis on the microbial sample to obtain microbial group data in the microbial sample and functional gene data of each microbial group; Inputting the biogroup data and the functional gene data of each microbial group into a preset microbial volatile organic compound metabolism simulation model to obtain the metabolic data of the microbial volatile organic compounds, and determining the environmental fate data of the volatile organic compounds based on the metabolic data; The comprehensive risk level is obtained by weighted processing based on the occurrence form data, the physical and chemical property data and the environmental fate data.

2. The method according to claim 1, characterized in that The step of performing layered point distribution according to the geographic data and the initial pollutant distribution data to obtain sampling point data includes: Determining distribution characteristic data of pollutants based on the geographic data and the initial pollutant distribution data; Determine the soil type in the area to be determined according to the geographic data, and divide the area to be determined into layers according to the soil type to obtain regional layered data; The sampling point data is determined according to the distribution characteristic data of the pollutants and the regional stratification data.

3. The method according to claim 2, characterized in that The step of determining the sampling point data according to the distribution characteristic data of the pollutants and the regional stratification data comprises: Dividing the area to be determined into a plurality of stratified areas according to the regional stratification data, and determining the stratified pollution distribution characteristics of each of the stratified areas according to the distribution characteristic data of the pollutants; According to the stratified pollution distribution characteristics of each stratified area, the number of sampling points and the spatial distribution data of each stratified area are determined, and the number of sampling points and the spatial distribution data are used as the sampling point data.

4. The method according to claim 1, characterized in that: The performing of morphological analysis on the sample to obtain the occurrence morphological data of volatile organic compounds in the sample includes: The volatile organic compounds in the sample are separated and extracted according to different binding forms or chemical valence states by continuous extraction or selective extraction to obtain extracted samples, and the morphology of the extracted samples is measured by atomic absorption spectroscopy to obtain the occurrence form data of the volatile organic compounds in the sample.

5. The method according to claim 1, characterized in that The gene analysis of the microbial sample to obtain the microbial group data in the microbial sample and the functional gene data of each microbial group includes: Obtaining the microbiome data by performing metagenomic sequencing on the biological sample; Through transcriptome sequencing, the expression levels of the functional genes of each of the microbial groups are analyzed to obtain the functional gene data of each of the microbial groups.

6. The method according to claim 1, characterized in that Determining the environmental fate data of volatile organic compounds according to the metabolic data includes: Determining the potential value of the morphological transformation of volatile organic compounds according to the metabolic data; According to the morphological transformation potential value, the migration distribution trend data of the volatile organic compounds are predicted, and according to the migration distribution trend data, a quantitative analysis is performed to determine the environmental fate data of the volatile organic compounds.

7. The method according to claim 1, characterized in that The weighted processing based on the occurrence form data, the physical and chemical property data and the environmental fate data to obtain a comprehensive risk level includes: Standardizing the occurrence form data, the physicochemical property data and the environmental fate data to obtain occurrence form index data, physicochemical property index data and environmental fate index data, and scoring the occurrence form index data, the physicochemical property index data and the environmental fate index data according to preset benchmark data to obtain occurrence form risk index, physicochemical property index and environmental fate risk index; The morphological risk index, the physicochemical property index and the environmental fate risk index are weighted according to a preset weighting coefficient to obtain a comprehensive risk index, and the comprehensive risk level is determined according to the comprehensive risk index and a preset index threshold.

8. An industrial site environmental risk assessment system, characterized in that: include: A layered point distribution module is used to obtain geographic data and initial pollutant distribution data of the area to be determined, and to perform layered point distribution according to the geographic data and the initial pollutant distribution data to obtain sampling point data; A sampling module, used for sampling the area to be determined according to the sampling point data to obtain a sampling sample; A sample analysis module, used to perform a physical and chemical property analysis on the sampled sample to obtain the physical and chemical property data of the volatile organic compounds in the sampled sample, and to perform a morphological analysis on the sampled sample to obtain the occurrence morphological data of the volatile organic compounds in the sampled sample; A microbial analysis module, used to extract microbial samples from the sampled samples, and perform gene analysis on the microbial samples to obtain microbial group data in the microbial samples and functional gene data of each microbial group; An environmental fate determination module, used to input the biota data and the functional gene data of each microbial group into a preset microbial volatile organic compound metabolism simulation model to obtain the metabolic data of the microbial volatile organic compounds, and determine the environmental fate data of the volatile organic compounds based on the metabolic data; The risk level determination module is used to perform weighted processing based on the occurrence morphology data, the physical and chemical property data and the environmental fate data to obtain a comprehensive risk level.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes a processor, a memory and a transceiver, the memory is used to store instructions, the transceiver is used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1 to 7.

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