Multi-level ecological risk assessment method and device for soil pollution

Abstract

The invention relates to a multilevel ecological risk assessment method and device for soil pollution, and belongs to the technical field of soil pollution risk assessment, and the method comprises the following steps: carrying out hazard identification: collecting and analyzing source-path-sink information of soil pollution, and carrying out risk screening when the risk exists through assessment; performing risk probability quantification on the optimal control pollutants, quantifying the risk probability by establishing a joint probability curve, and performing risk causal relationship judgment on the optimal control pollutants by evaluating the existence of the risk; and determining statistical causal association of pollution-ecological effect, obtaining a risk conclusion and other risk results of each level, and ending evaluation. The invention provides a multi-level progressive ecological risk assessment method, a soil pollution ecological risk conclusion with local representativeness and ecological robustness is formed, and safe utilization of a site soil ecological environment is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of soil pollution risk assessment, and in particular to a multi-level ecological risk assessment method and device for soil pollution, and in particular to a hierarchical ecological risk assessment method and device for soil pollution based on a comprehensive multidisciplinary approach. Background Art

[0002] At present, the adjustment of industrial structure has produced a large number of legacy and abandoned sites. The soil pollution problem in and around the sites has become increasingly prominent. Various pollutants will harm the function of the soil ecosystem through surface runoff, atmospheric deposition and groundwater migration. Carrying out soil ecological risk assessment of contaminated sites can play an important guiding role in the management of soil environment on the site. Ecological risk assessment is to assess the possibility of harmful substances causing adverse effects on the ecosystem and its components. It can provide a reference for pollution prevention and control around the site, and also support the establishment of contaminated site remediation goals and the inspection of remediation results. Therefore, carrying out soil ecological risk assessment of contaminated sites is of great significance to the protection of soil ecological environment.

[0003] The commonly used ecological risk assessment method is the quotient method, which requires sampling and analysis of the soil environmental media on the site and comparison with the corresponding standards to determine the size and level of risk. It is a semi-quantitative evaluation method.

[0004] For example, CN114049037A discloses a method for assessing the ecological risk of soil with complex contamination on a site, which includes a rapid risk identification stage, a first-stage preliminary assessment, a second-stage detailed assessment, and a third-stage characterization of complex pollution risks. Screening values ​​are derived based on ecological receptors, exposure pathways, toxicity endpoints, and protection levels of different land types. The screening values ​​are compared with the pollutant concentrations obtained from the survey. Based on pollutant concentration and morphology surveys, pollutant bioavailability survey tests, pollutant biotoxicity tests, and ecological surveys, the quotient method, index method (weight of evidence method), and probability method are used to assess the ecological risk of soil pollution on the site, and the ecological risk of complex contaminated soil is characterized.

[0005] CN111922068A discloses a method for risk assessment of petroleum hydrocarbons in soil and groundwater of industrial contaminated sites, including the following steps: step 1. collecting soil and groundwater samples from suspected contaminated plots; step 2. pre-treating soil and groundwater samples respectively, analyzing the composition and reference content of petroleum hydrocarbons in the samples; step 3. determining the main user population of the suspected contaminated plots and the exposure pathways of petroleum hydrocarbons to the population according to the use of the suspected contaminated plots, wherein the exposure pathways are the process of petroleum hydrocarbon pollutants in soil and groundwater migrating to and entering sensitive receptors; step 4. conducting toxicity assessment according to the types of petroleum hydrocarbon composition; step 5. calculating the non-carcinogenic hazard quotient of petroleum hydrocarbon pollutants and performing risk characterization according to the detection data of petroleum hydrocarbons, and judging whether the risk is acceptable. The scheme makes the scope of treatment and restoration accurate, the type of pollutants accurate, the economic investment reduced, and the environmental management cost reduced by analyzing the composition and content of petroleum hydrocarbons.

[0006] However, these existing assessment methods still have problems such as low assessment accuracy, insufficient correlation between pollution and local ecological effects, and poor ecological robustness, which lead to large deviations between assessment results and actual results. Summary of the invention

[0007] In view of the problems existing in the prior art, the purpose of the present invention is to provide a multi-level ecological risk assessment method and device for soil pollution, so as to solve the problems of low assessment accuracy, insufficient correlation between pollution and local ecological effects, and poor ecological robustness in the current assessment method, which lead to large deviations between the assessment results and the actual situation.

[0008] To achieve this object, the present invention adopts the following technical solutions:

[0009] In a first aspect, the present invention provides a multi-level ecological risk assessment method for soil pollution, the multi-level ecological risk assessment method comprising:

[0010] Hazard identification: collect and analyze the “source-path-sink” information of soil pollution at the target site to determine the risk association. If it is determined that there is a risk, then conduct pollution risk screening, otherwise end;

[0011] Pollution risk screening: soil sampling and analysis of target sites to obtain the concentration of characteristic pollutants, soil pollutant source analysis of characteristic pollutants, and calculation of risk index of characteristic pollutants to comprehensively screen priority pollutants. If the risk index of all priority pollutants shows no risk, it is assessed that there is no risk. Otherwise, the risk probability is quantified.

[0012] Quantification of risk probability: For the priority controlled pollutants that are shown as risky by the risk index, establish the priority controlled pollutant exposure probability distribution curve and the species sensitivity distribution curve, and form a joint probability curve to calculate the overall risk probability value. If the overall risk probability value of all priority controlled pollutants is ≤9.82%, it is assessed that there is no risk, otherwise a causal relationship determination is made;

[0013] Causal relationship determination: Conduct ecological analysis on the site to determine the environmental factors and sensitive receptor effects, and compare the differences in sensitive receptor ecological effects under different characteristic pollutant levels and / or different environmental factor groupings. Then, conduct correlation analysis and causal relationship statistical analysis in turn to obtain ecological risk conclusions.

[0014] The multi-level ecological risk assessment method provided by the present invention integrates multidisciplinary perspectives, provides a localized ecological risk evidence chain, and uses qualitative, semi-quantitative, quantitative and statistically significant multi-level risk characterization methods to form more locally representative and ecologically robust field pollution ecological risk conclusions to ensure the safe use of the site's ecological environment.

[0015] As a preferred technical solution of the present invention, the collection and analysis of soil pollution "source-path-sink" information includes: obtaining risk source information, obtaining risk path information and obtaining risk receptor information.

[0016] Preferably, the obtaining of risk source information includes: obtaining basic site information and potential pollutant information.

[0017] Preferably, the obtaining of risk path information includes: obtaining potential pollution areas and risk migration pathways.

[0018] Preferably, the obtaining of risk receptor information includes: obtaining ecological protection targets.

[0019] Preferably, the obtaining of basic site information includes: obtaining the geographical location, hydrogeology, climate conditions, land area, production process characteristics and production history of the target site.

[0020] Preferably, the obtaining of potential pollutant information includes: obtaining potential characteristic pollutants and potential pollution functional zoning of the target site.

[0021] Preferably, the obtaining of potential contaminated areas includes: obtaining potential characteristic pollutants and potential contaminated functional zoning of the target site, and identifying potential contaminated areas accordingly.

[0022] Preferably, the risk migration pathway includes: clarifying the exposure mode of soil pollutants in the target site, understanding the production method, production process and pollution-generating links, as well as the environmental protection level of the comprehensive treatment of wastewater, waste gas and waste rock.

[0023] Preferably, the acquisition of ecological protection goals includes: clarifying the main terrestrial ecosystem types and potential ecological risk receptors of the target site, and understanding the environmental factors that may affect the biological toxicity of pollutants.

[0024] As a preferred technical solution of the present invention, the method for determining risk association includes: collecting and analyzing the pollution indicators in the soil pollution "source-path-sink" information according to the "GB 15618-2018 Soil Environmental Quality Agricultural Land Soil Pollution Risk Control Standard", "HJ 25.1-2019 Technical Guidelines for Soil Pollution Status Investigation in Construction Land" or expert review results. One or a combination of at least two are determined.

[0025] Preferably, in the risk association determination, if the pollutant content in the soil is greater than the risk screening value in GB 15618-2018 Soil Environmental Quality Agricultural Land Soil Pollution Risk Management and Control Standard, it is determined that there is a risk.

[0026] Preferably, if the soil environmental assessment category in the risk association determination is level I or II of the soil environmental impact assessment project category in the "HJ 25.1-2019 Technical Guidelines for Investigation of Soil Pollution Status of Construction Land", it is determined that there is a risk.

[0027] Preferably, in the risk association determination, if the expert review result is that there is a risk, then it is assessed that there is a risk.

[0028] As a preferred technical solution of the present invention, the soil pollutant source analysis includes: conducting source analysis of characteristic pollutants based on the indicators of characteristic pollutants obtained from soil sampling and analysis, determining the source pathways of characteristic pollutants and quantifying the pollution contribution of different characteristic pollution sources, and screening priority pollutants.

[0029] Preferably, the source analysis method includes: one or a combination of at least two of principal component analysis, cluster analysis, factor analysis, chemical mass balance method, positive definite matrix factor method or isotope tracer method.

[0030] Preferably, if a pollution source has the largest contribution to the exposure concentration of a specific pollutant in the source analysis, then the pollutant is the priority controlled pollutant of the corresponding pollution source.

[0031] As a preferred technical solution of the present invention, the calculation of the risk index of characteristic pollutants includes: calculating the risk index with reference to the soil background value or ecological benchmark value of the target site and the environmental exposure concentration of the characteristic pollutants.

[0032] Preferably, the risk index comprises: one or a combination of at least two of a geoaccumulation index, a potential ecological risk index, a risk quotient, a pollution load index or a Nemerow risk index.

[0033] Preferably, if the risk index matches the range of risk in the risk level range, it is determined that the corresponding element is at risk, and then cross-combined with the source analysis results to determine the priority pollutants.

[0034] As a preferred technical solution of the present invention, the establishment of the exposure probability distribution curve of the priority controlled pollutants includes: using the log-normal distribution function to fit the distribution function of the soil exposure concentration of the detected priority controlled pollutants, and constructing a cumulative probability curve of the exposure concentration distribution of the soil of the target site.

[0035] Preferably, the establishment of the species sensitivity distribution curve includes: conducting a receptor effect analysis of ecological protection targets around the target site, using species data in the ecotoxicology database to perform a species effect analysis, and then using the collected ecospecies toxicology data for the target site to construct a species sensitivity distribution curve that conforms to the actual situation of the target site.

[0036] As a preferred technical solution of the present invention, the forming of the joint probability curve includes: coupling the pollutant exposure probability distribution curve and the species sensitivity distribution curve corresponding to the priority pollutant concentration to obtain the joint probability curve.

[0037] Preferably, the overall risk probability value includes a closed area formed by the joint probability curve, the x-axis and the y-axis.

[0038] As a preferred technical solution of the present invention, the ecological analysis includes: conducting ecological surveys and / or biological experiments on the soil environment around the target site and local species, key species or related ecological processes in the soil to determine the ecological effects of environmental factors and sensitive receptors.

[0039] Preferably, the environmental factors include: one or a combination of at least two of land use type, soil type, soil texture, soil organic matter content, pH value or cation exchange capacity.

[0040] Preferably, the sensitive receptor ecological effect includes: soil PLFA abundance and / or soil PLFA structure.

[0041] Preferably, the ecological survey conducted in the ecological analysis includes: soil animal survey, plant survey and microbial survey.

[0042] Preferably, the biological experiments conducted in the ecological analysis include: soil animal experiments, plant experiments and microbial experiments.

[0043] As a preferred technical solution of the present invention, the correlation analysis includes: performing correlation analysis on characteristic pollutant concentrations, environmental factors and sensitive receptor ecological effects, and screening relevant characteristic pollutants and environmental factors that are statistically significant with sensitive receptor ecological effects.

[0044] Preferably, the correlation analysis method includes: one or a combination of at least two of Pearson correlation analysis, Spearman correlation analysis, Mantel correlation analysis or principal component analysis.

[0045] Preferably, the causal association statistical analysis includes: performing causal association statistical analysis on the relevant characteristic pollutants and environmental factors that have statistically significant ecological effects on sensitive receptors obtained through screening to quantitatively analyze the degree of explanation and statistical significance of the characteristic pollutants and environmental factors on the ecological effects of sensitive receptors.

[0046] Preferably, the causal relationship statistical analysis method includes: structural equation model, redundancy analysis or canonical correlation analysis, or a combination of at least two of them.

[0047] Preferably, if the model fit p-value in the causal association statistical analysis is statistically significant, the model has a good fit, and the output model reflects the results as the ecological risk conclusion.

[0048] In a second aspect, the present invention provides a multi-level ecological risk assessment device for soil pollution, the multi-level ecological risk assessment device comprising:

[0049] The hazard identification module is used to collect and analyze the "source-path-sink" information of soil pollution at the target site to determine the risk association. If it is determined that there is a risk, pollution risk screening will be carried out, otherwise the process will end;

[0050] The pollution risk screening module is used to conduct soil sampling and analysis on the target site, obtain the concentration of characteristic pollutants, perform soil pollutant source analysis on characteristic pollutants, calculate the risk index of characteristic pollutants, and comprehensively screen the priority controlled pollutants. If the risk index of all priority controlled pollutants shows no risk, it is assessed that there is no risk. Otherwise, the risk probability is quantified.

[0051] The risk probability quantitative module is used to establish the exposure probability distribution curve of the priority controlled pollutants and the species sensitivity distribution curve for the priority controlled pollutants that are shown as risky by the risk index, and form a joint probability curve to calculate the overall risk probability value. If the overall risk probability value of all priority controlled pollutants is ≤9.82%, it is assessed that there is no risk, otherwise a causal relationship judgment is made;

[0052] The causal relationship determination module is used to conduct ecological analysis of the site, determine the environmental factors and sensitive receptor effects, and compare the differences in sensitive receptor ecological effects under different characteristic pollutant levels and / or different environmental factor groupings. It then performs correlation analysis and causal relationship statistical analysis in turn to obtain ecological risk conclusions.

[0053] Compared with the prior art solutions, the present invention has the following beneficial effects:

[0054] The assessment method provided by the present invention completes a complete ecological risk assessment process at each disciplinary level by constructing a multidisciplinary hierarchical framework, gradually improves the accuracy and ecological relevance of the assessment results, and has good effectiveness and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a flow chart of a multi-level ecological risk assessment method for soil pollution provided by an embodiment of the present invention;

[0056] Figure 2 is a schematic diagram of a soil pollution ecological risk assessment device provided by an embodiment of the present invention;

[0057] Figure 3 This is a schematic diagram of the research area and the investigated soil sampling points in Example 1 of the present invention;

[0058] Figure 4 It is a schematic diagram of the main mining process and pollution-generating links in the mining area in Example 1 of the present invention;

[0059] Figure 5 This is a PCA analysis result diagram of the source apportionment of heavy metals in soil in Example 1 of the present invention;

[0060] Figure 6 This is a PMF analysis result diagram of the source apportionment of heavy metals in soil in Example 1 of the present invention;

[0061] Figure 7 It is a violin plot of the potential ecological risk Er values ​​of 8 heavy metals in Example 1 of the present invention;

[0062] Figure 8 is a spatial regression analysis diagram of soil PERI value and mining area point distance in Example 1 of the present invention;

[0063] Fig. 9 It is the SSD curve of heavy metal Pd in ​​Example 1 of the present invention and the derived ecological reference value (SEC) diagram of different land use types;

[0064] Fig.10 It is the SSD curve of heavy metal Cd in Example 1 of the present invention and the derived ecological reference value (SEC) diagram of different land use types;

[0065] Fig.11 It is the SSD curve of heavy metal Cu in Example 1 of the present invention and the derived ecological reference value (SEC) diagram of different land use types;

[0066] Fig.12It is the SSD curve of heavy metal Zn in Example 1 of the present invention and the derived ecological reference value (SEC) diagram of different land use types;

[0067] Fig.13 It is the SSD curve of heavy metal Hg in Example 1 of the present invention and the derived ecological reference value (SEC) diagram of different land use types;

[0068] Fig.14 is the risk quotient RQ and P of the five screened heavy metals in Example 1 of the present invention N Violin plot of the values;

[0069] Fig.15 The heavy metal content in the overall P in Example 1 of the present invention is N Joint probability curve JCP of risk group and overall risk probability value ORP diagram;

[0070] Fig.16 The heavy metals in Example 1 of the present invention are low risk P N Joint probability curve JCP of risk group and overall risk probability value ORP diagram;

[0071] Fig.17 The heavy metal in Example 1 of the present invention is at medium risk P N Joint probability curve JCP of risk group and overall risk probability value ORP diagram;

[0072] Fig.18 The heavy metals in Example 1 of the present invention are in high risk P N Joint probability curve JCP of risk group and overall risk probability value ORP diagram;

[0073] Fig.19 is different from P in Example 1 of the present invention N Content and structure diagram of various PLFA characterizing microorganisms in soil under risk levels;

[0074] Fig. 20 It is a graph showing the content and structure of various PLFA characterizing microorganisms in soil under different land use types in Example 1 of the present invention;

[0075] Fig.21 is a graph showing the results of the Spearman correlation analysis between the environmental factors in Example 1 of the present invention;

[0076] Fig. 22 is the correlation analysis between each environmental variable and soil PLFA abundance and structure in Example 1 of the present invention;

[0077] Fig.23 This is a redundancy analysis result diagram of soil PLFA abundance and structure in Example 1 of the present invention;

[0078] Fig.24 This is a graph showing the hierarchical segmentation analysis results of soil PLFA abundance and structure in Example 1 of the present invention;

[0079] Fig.25 It is used to describe P in Example 1 of the present invention. N Figure 2 Structural equation model simulation results of risk, land use type and soil properties on PLFA abundance;

[0080] Fig.26 It is used to describe P in Example 1 of the present invention. N Figure 2 Structural equation model simulation results of the effects of risk, land use type and soil properties on PLFA structure.

[0081] In the figure: 100-hazard identification module, 200-pollution risk screening module, 300-risk probability quantification module, 400-causal relationship determination module.

[0082] The present invention is further described in detail below. However, the following examples are only simplified examples of the present invention and do not represent or limit the scope of protection of the present invention. The scope of protection of the present invention shall be subject to the claims. DETAILED DESCRIPTION

[0083] To better illustrate the present invention and facilitate understanding of the technical solution of the present invention, typical but non-limiting embodiments of the present invention are as follows:

[0084] This embodiment provides a multi-level ecological risk assessment method for soil pollution. The ecological risk assessment of soil pollution is mainly based on the "pollution source-pollution pathway-risk receptor" as the main assessment line, in which it is determined whether there is a significant "pollution-ecological effect" relationship between the environmental exposure of pollutants and the specific ecological effects of the ecological protection target receptors. If so, it is necessary to further determine the corresponding risk size, degree and action process. Therefore, when constructing a site ecological risk assessment method, by integrating the assessment methods and contents from different disciplinary perspectives, an ecological risk characterization method and results with differentiated assessment accuracy are formed, which helps to improve the cost-effectiveness of the assessment work and the accuracy of the results.

[0085] By building a multidisciplinary hierarchical framework, a complete ecological risk assessment process is completed at each disciplinary level, namely "problem formation" - "exposure and ecological effect assessment" - "risk characterization", and the accuracy and ecological relevance of the assessment results are gradually improved through hierarchical progression. Although the information materials, basis and complexity of the assessment work required for the assessment will increase with the progression of the assessment level, and the investment in assessment funds, labor costs, etc. will increase accordingly, the accuracy and credibility of the assessment results will also be enhanced.

[0086] The overall framework of the multi-level ecological risk assessment method for site soil constructed in this embodiment based on the risk characteristics of site soil pollution is as follows: Figure 1 As shown, the specific process is as follows:

[0087] Hazard identification: collect and analyze the “source-path-sink” information of soil pollution at the target site to determine the risk association. If it is determined that there is a risk, then conduct pollution risk screening, otherwise end;

[0088] Pollution risk screening: soil sampling and analysis of target sites to obtain the concentration of characteristic pollutants, soil pollutant source analysis of characteristic pollutants, and calculation of risk index of characteristic pollutants to comprehensively screen priority pollutants. If the risk index of all priority pollutants shows no risk, it is assessed that there is no risk. Otherwise, the risk probability is quantified.

[0089] Quantification of risk probability: For the priority controlled pollutants that are shown as risky by the risk index, establish the priority controlled pollutant exposure probability distribution curve and the species sensitivity distribution curve, and form a joint probability curve to calculate the overall risk probability value. If the overall risk probability value of all priority controlled pollutants is ≤9.82%, it is assessed that there is no risk, otherwise a causal relationship determination is made;

[0090] Causal relationship determination: Conduct ecological analysis on the site to determine the environmental factors and sensitive receptor effects, and compare the differences in sensitive receptor ecological effects under different characteristic pollutant levels and / or different environmental factor groupings. Then, conduct correlation analysis and causal relationship statistical analysis in turn to obtain ecological risk conclusions.

[0091] Among them, the collection and analysis of soil pollution "source-path-sink" information includes: obtaining risk source information, obtaining risk path information and obtaining risk receptor information.

[0092] Wherein, obtaining risk source information includes: obtaining basic site information and potential pollutant information.

[0093] Wherein, obtaining risk path information includes: obtaining potential pollution areas and risk migration pathways.

[0094] Wherein, the obtaining of risk receptor information includes: obtaining ecological protection targets.

[0095] The basic site information acquisition includes: acquiring the geographical location, hydrogeology, climate conditions, land area, production process characteristics and production history of the target site.

[0096] Wherein, the obtaining of potential pollutant information includes: obtaining potential characteristic pollutants and potential pollution functional zoning of the target site.

[0097] Wherein, the obtaining of potential pollution areas includes: obtaining potential characteristic pollutants and potential pollution functional zoning of the target site, and identifying potential pollution areas accordingly.

[0098] Among them, the risk migration pathways include: clarifying the exposure methods of soil pollutants in the target site, understanding the production methods, production processes and pollution-generating links, as well as the environmental protection level of the comprehensive treatment of wastewater, waste gas and waste rock.

[0099] Among them, the ecological protection goals include: clarifying the main terrestrial ecosystem types and potential ecological risk receptors of the target site, and understanding the environmental factors that may affect the biological toxicity of pollutants.

[0100] Furthermore, the main channels for collecting information in hazard identification include statistical data from various institutions, online databases, literature research, department visits, telephone consultations, and field investigations.

[0101] Among them, the basic information of the site, potential pollutant information, ecological protection goals, potential pollution areas, and risk migration pathways obtained will be integrated to form a qualitative risk evidence chain to provide material and evidence support for subsequent assessments. At the same time, a conceptual model of site ecological risk will be preliminarily established, that is, whether it is possible to establish a "pollution-ecological effect" relationship between characteristic pollutants and surrounding soil ecological receptors in or around the site area, and to determine the relevant characteristic pollutants.

[0102] Among them, the method of determining risk association includes: collecting and analyzing the pollution indicators in the soil pollution "source-path-sink" information according to the "GB 15618-2018 Soil Environmental Quality Agricultural Land Soil Pollution Risk Control Standard", "HJ 25.1-2019 Technical Guidelines for Soil Pollution Status Investigation in Construction Land" or expert review results, or a combination of at least two of them.

[0103] Among them, if the pollutant content in the soil in the risk association determination is greater than the risk screening value in GB 15618-2018 Soil Environmental Quality Agricultural Land Soil Pollution Risk Management Standard, it is determined that there is a risk.

[0104] Among them, if the soil environmental assessment category in the risk association determination is Level I or II of the soil environmental impact assessment project category in the "HJ 25.1-2019 Technical Guidelines for Investigation of Soil Pollution Status of Construction Land", it is determined that there is a risk.

[0105] Wherein, if the expert review result in the risk association determination is that there is a risk, then it is assessed that there is a risk.

[0106] In the present invention, the expert review result refers to the result of the expert group forming a unified opinion after reviewing according to the review process known in the art.

[0107] Among them, if multiple determination methods are used in determining risk association, it can only be determined that there is a risk if they all meet the corresponding determination principles, that is, if they all believe that there is a risk; if there is only a single determination, it is determined that there is a risk.

[0108] Among them, the soil pollutant source analysis includes: conducting source analysis of characteristic pollutants based on the indicators of characteristic pollutants obtained from soil sampling and analysis, determining the source pathways of characteristic pollutants and quantifying the pollution contribution of different characteristic pollution sources, and screening priority pollutants.

[0109] The source analysis method includes: one or a combination of at least two of principal component analysis, cluster analysis, factor analysis, chemical mass balance, positive definite matrix factor method or isotope tracer method.

[0110] Preferably, if a pollution source has the largest contribution to the exposure concentration of a specific pollutant in the source analysis, then the pollutant is the priority controlled pollutant of the corresponding pollution source.

[0111] For example, if the exposure concentrations of pollutants A, B, C, and D contribute the most to pollution source 1, pollution source 2, and pollution source 3, then pollutants A, pollutants B, pollutants C, and pollutants D are all priority controlled pollutants from pollution source 1.

[0112] Among them, sampling and analysis of target sites can be carried out according to the requirements of "HJ 25.1-2019 Technical Guidelines for Investigation of Soil Pollution Status of Construction Land", and a sampling plan can be formulated using the professional judgment method to analyze the soil environmental concentrations of characteristic pollutants.

[0113] Among them, the method for soil pollutant source analysis of characteristic pollutants is shown in Table 1.

[0114] Table 1

[0115]

[0116]

[0117] Among them, when conducting source analysis of soil pollutants, a variety of source analysis methods can be used for cross-comparison analysis, which can improve the accuracy and efficiency of screening and help reduce invalid characteristic pollutants in subsequent analysis processes.

[0118] The step of calculating the risk index of characteristic pollutants includes: calculating the risk index with reference to the soil background value or ecological benchmark value of the target site and using the environmental exposure concentration of the characteristic pollutants.

[0119] The risk index includes one or a combination of at least two of the following: geoaccumulation index, potential ecological risk index, risk quotient, pollution load index or Nemerow risk index.

[0120] Among them, if the risk index matches the range of risk in the risk level range, it is determined that the corresponding element is at risk, and then cross-combined with the source analysis results to determine the priority pollutants.

[0121] For example, commonly used risk index methods and their risk level classification standards are shown in Table 2 below.

[0122] Table 2

[0123]

[0124]

[0125] Furthermore, cross-comparison of multiple risk indices can be used to improve the accuracy of analysis and evaluation at this stage and avoid the generation of invalid analysis objects.

[0126] Furthermore, the results of risk index screening and source analysis are cross-compared together, and the intersection of pollutants determined by the two is selected to determine the priority controlled pollutants. For example, when cross-comparing, if the contribution of the pollution source of the element to the pollutant exposure concentration exceeds 50% and the risk index matches the range of risk in the risk level range, the corresponding element is determined to be the priority controlled pollutant; if the two are carried out separately, it meets the corresponding judgment principle, and it can be determined that the corresponding element is polluted and should be treated as a priority controlled pollutant.

[0127] Wherein, the establishment of the exposure probability distribution curve of the priority controlled pollutants includes: using the log-normal distribution function to fit the distribution function of the soil exposure concentration of the detected priority controlled pollutants, and constructing a cumulative probability curve of the exposure concentration distribution of the soil of the target site.

[0128] Among them, when constructing the probability distribution curve of pollutant exposure, the exposure assessment is no longer a conservative assessment of the concentration of pollutants, but to reflect the concentration range of pollutants in the site soil as comprehensively as possible, that is, to construct a cumulative distribution curve of pollutant exposure concentration, and to evaluate the actual site pollutant exposure level. Therefore, the sampling will be classified and evaluated for the overall site coverage, or divided into different functional zonings. According to the sampling specification requirements, a grid sampling plan will be formulated and surface soil samples will be collected to analyze the exposure concentration of the priority controlled pollutants determined at the previous level. According to the pollution characteristics, the sampling log-normal distribution function is used to fit the distribution function of the environmental exposure concentration data of the priority controlled pollutants to construct the cumulative probability curve of the exposure concentration distribution of the site soil.

[0129] Among them, the establishment of the species sensitivity distribution curve includes: conducting receptor effect analysis of ecological protection targets around the target site, using species data in the ecotoxicology database to conduct species effect analysis, and then using the collected ecological species toxicology data for the target site to construct a species sensitivity distribution curve that conforms to the actual situation of the target site.

[0130] Among them, when constructing the species sensitivity distribution curve, the receptor effect analysis of the ecological protection targets around the site was carried out, and the species effect analysis was carried out using the relevant species data in the ecotoxicology database. The collected ecological species toxicity data for the assessment area was used to construct a species sensitivity distribution curve (SSD) that conforms to the local reality. In the construction of the species sensitivity distribution curve, toxicity data were mainly obtained from the ECOTOX ecotoxicology database of the US Environmental Protection Agency and literature published at home and abroad. Indoor ecotoxicology experiments were carried out using standard toxicological experimental methods. The toxicological data used included representative effect endpoints and measurement endpoints of terrestrial ecosystems, mainly including: 1) biomass, yield, root elongation, etc. of terrestrial plants; 2) reproduction rate, population size and growth rate of soil invertebrates; 3) soil microbial biomass, soil nitrification, and inhibition rate of soil respiration of soil microorganisms and soil ecological processes dominated by microorganisms. Different types of toxic effect endpoints were selected to determine different toxic effect levels to meet the actual soil protection level and management requirements. Specifically, it can be divided into toxicological data of low effect levels (Level 1) with sublethal or chronic toxic effects as effect endpoints, such as NOEC, LOEC, EC10, etc. In addition, when the same species has toxicological data with multiple measurement endpoints, the most sensitive toxicological data shall be taken. When the same species has the same toxic effect index, the geometric mean of these toxic effect concentrations shall be taken. Using the above-collected and processed pollutant toxicity data, distribution functions such as Burr III function, Gamma function, Gumbel function, Log-logistic function, Log-normal function and Weibull function are selected for data fitting, and the optimal fitting model is selected for SSD curve construction.

[0131] The forming of the joint probability curve comprises: coupling the pollutant exposure probability distribution curve and the species sensitivity distribution curve corresponding to the priority pollutant concentration to obtain the joint probability curve.

[0132] The overall risk probability value includes a closed area formed by the joint probability curve, the x-axis and the y-axis.

[0133] Among them, the joint probability curve (JCP) is constructed by integrating the cumulative distribution curve of exposure and the species sensitivity curve. The probability coupling is carried out by the same exposure or effect concentration, and the JCP is constructed for probability assessment. Finally, the overall risk probability value (ORP) of each pollutant is calculated using the area under the curve formed by the established JPC and the xy axis. Ecological risk characterization is quantitatively determined by the size and range of ORP: when ORP < 1.75%, it indicates that the probability risk is very small and can be ignored; when 1.75% ≤ ORP < 9.82%, it indicates a low probability risk; when 9.82% ≤ ORP < 33%, it indicates a medium probability risk; when 33% ≤ ORP, it indicates a high probability risk.

[0134] Based on the above probability risk grouping, we can classify pollutants with ORP lower than 9.82% as low-risk pollutants. When the probability risk of all priority controlled pollutants is lower than this value, the assessment work can be terminated; however, if there are still pollutants with ORP exceeding 9.82%, we will enter the next level and conduct a more accurate and practical ecological level risk assessment.

[0135] If there are still pollutants showing medium or high risks after risk probability quantification, causal relationship determination will be carried out. The main work content of this level includes two aspects: ecological investigation or biological experiment, and risk causal relationship determination. Through the implementation of this level, the assessment will objectively reflect a more realistic "pollution-ecological effect" assessment around the mining area, and reflect the significant correlation between the two through statistical methods, thereby forming the final risk assessment results, and based on this, give targeted site contaminated soil ecological risk control strategies and suggestions.

[0136] The ecological analysis includes: conducting ecological surveys and / or biological experiments on the soil environment around the target site and the native species, key species or related ecological processes in the soil to determine the ecological effects of environmental factors and sensitive receptors.

[0137] In the present invention, the perimeter division of the soil environment around the target site in the ecological analysis can be selected by referring to the relevant range outside the occupied area in the current status survey range in Table 5 given in 7.2.2 of "HJ 25.1-2019 Technical Guidelines for Soil Pollution Status Investigation of Construction Land".

[0138] The environmental factors include: one or a combination of at least two of land use type, soil type, soil texture, soil organic matter content, pH value or cation exchange capacity.

[0139] Wherein, the sensitive receptor ecological effect includes: soil PLFA abundance and / or soil PLFA structure.

[0140] The ecological survey conducted in the ecological analysis includes soil animal survey, plant survey and microbial survey.

[0141] The biological experiments conducted in the ecological analysis include soil animal experiments, plant experiments and microbial experiments.

[0142] Among them, the ecological survey and / or biological experiment is to conduct field assessment on the local species, key species or related ecological processes around the site. In order to improve the accuracy and practical guidance of the assessment results, at this level, the assessor needs to conduct field ecological surveys or biological experiments, and detect environmental factors that may affect these effects (such as the physical and chemical properties of the soil, soil total carbon (Total carbon, TC) and organic carbon (Total organic carbon, TOC), soil total nitrogen (Total nitrogen, TN), soil pH value, cation exchange capacity (Cation exchange capacity, CEC), etc.), in order to understand the actual ecological effects and environmental conditions of the assessment area, and then integrate all the previous assessment evidence, and associate the actual pollution status with the actual ecological effects and environmental factors of the assessment area, and finally determine whether the "pollution-ecological effect" connection of the assessment mining area is established through a series of statistical analysis methods.

[0143] Table 3 below provides some examples of standardized methods for soil ecotoxicology and biological testing at home and abroad. Evaluators can choose appropriate methods to conduct ecological surveys or biological experiments based on local native species, dominant species or key species.

[0144] Table 3

[0145]

[0146]

[0147] Among them, the correlation analysis includes: conducting correlation analysis on characteristic pollutant concentrations, environmental factors and sensitive receptor ecological effects, and screening relevant characteristic pollutants and environmental factors that are statistically significant with sensitive receptor ecological effects.

[0148] Among them, if the ecological effect of the sensitive receptor corresponding to the environmental factor is statistically significant in the correlation analysis, it is considered to be correlated.

[0149] The correlation analysis method includes: one or a combination of at least two of Pearson correlation analysis, Spearman correlation analysis, Mantel correlation analysis or principal component analysis.

[0150] Among them, the causal association statistical analysis includes: conducting causal association statistical analysis on the relevant characteristic pollutants and environmental factors that have statistically significant ecological effects on sensitive receptors to quantitatively analyze the degree of explanation and statistical significance of the characteristic pollutants and environmental factors on the ecological effects of sensitive receptors.

[0151] The causal relationship statistical analysis method includes: structural equation model, redundancy analysis or canonical correlation analysis, or a combination of at least two of them.

[0152] Among them, if the model fit p value in the causal association statistical analysis is statistically significant, the model has a good fit, and the output model reflects the results as the ecological risk conclusion.

[0153] In the present invention, being statistically significant means meeting the basic requirements of the corresponding analysis method. According to the null hypothesis of the model of each multivariate statistical method, 0.05 is used as the criterion for determining whether the statistical model is statistically significant.

[0154] Furthermore, when performing correlation analysis and / or causal relationship statistical analysis, cross-evaluation can be performed selectively to enhance the accuracy of the analysis.

[0155] For example, the sensitive receptor ecological effect is to divide the microorganisms characterized by different PLFA into four categories: Gram-positive bacteria (G+), Gram-negative bacteria (G-), actinomycetes (A) and fungi (F). The sum of the PLFA content representing the same type of microorganisms is used to represent the abundance of the corresponding microorganisms, in units of nmol / g; the ratio of the content of different types of microorganisms is used to represent the structure of PLFA, such as the ratio of fungi to bacteria (F / B), the ratio of Gram-positive bacteria to Gram-negative bacteria (G- / G+), and the total content of each type of microorganism.

[0156] After the actual ecological effects of the assessment area are determined through ecological surveys or biological experiments, some causal judgment analysis models or attribution analysis models, such as structural equation models (SEM), multivariate correlation or regression analysis models (redundancy analysis RDA, canonical correlation analysis CCA), etc., are used to improve the Hill causal relationship judgment criteria for ecological risk assessment. Hill's causal relationship criterion provides a method to establish the strength of the causal relationship between environmental pressure (such as environmental pollution) and biological effects (based on ecological surveys or biological experiments). By establishing judgment criteria, it is clear that the harm in the ecosystem is caused by pollutants in the mining area and possibly related environmental factors, or the strength of evidence that may be attributed to these pollutants is increased. The specific judgment principles include: (1) Try to choose criteria with higher credibility and statistical significance for judgment. If the analysis results meet the requirements of the criteria, it is reasonable to believe that there is a causal relationship between pollutant exposure and ecological receptor response. (2) If there is an opposite judgment result (for example, there is a negative correlation between pollutant exposure and receptor response), then the causal relationship between pollutants and ecological receptor response cannot be established, and other possible causes should be analyzed. (3) If there are contradictions in the analysis results (for example, some results demonstrate a link between pollutant exposure and receptor response, while other results suggest that there is no link), further work is needed to explain this.

[0157] Furthermore, during the evaluation process, in order to ensure the accuracy of the evaluation, the analysis results between each step can be cross-compared and verified to improve the accuracy of the evaluation.

[0158] Furthermore, this embodiment provides a soil pollution ecological risk assessment device, such as Figure 2 As shown, the pollution ecological risk assessment device includes:

[0159] The hazard identification module 100 is used to collect and analyze the “source-path-sink” information of soil pollution at the target site to determine the risk association. If it is determined that there is a risk, pollution risk screening is performed, otherwise the process ends;

[0160] The pollution risk screening module 200 is used to perform soil sampling and analysis on the target site, obtain the concentration of characteristic pollutants, perform soil pollutant source analysis on characteristic pollutants, calculate the risk index of characteristic pollutants, and comprehensively screen the priority controlled pollutants. If the risk index of all priority controlled pollutants shows no risk, it is assessed that there is no risk. Otherwise, the risk probability is quantified.

[0161] The risk probability quantification module 300 is used to establish a priority controlled pollutant exposure probability distribution curve and a species sensitivity distribution curve for priority controlled pollutants that are shown as risky by the risk index, and form a joint probability curve to calculate the overall risk probability value. If the overall risk probability value of all priority controlled pollutants is ≤9.82%, it is assessed that there is no risk, otherwise a causal relationship determination is performed;

[0162] The causal relationship determination module 400 is used to perform ecological analysis on the site, determine environmental factors and sensitive receptor effects, and compare the differences in sensitive receptor ecological effects under different characteristic pollutant levels and / or different environmental factor groupings, and then perform correlation analysis and causal relationship statistical analysis in turn to obtain ecological risk conclusions.

[0163] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0164] Further, in order to illustrate the good assessment effect that can be achieved by the multi-level ecological risk assessment method for soil pollution provided by the present invention, the following actual examples are used for illustration, as follows:

[0165] Example 1

[0166] This example analyzes and evaluates the mining area in the northwest of Inner Mongolia Autonomous Region, which is located in Urad Rear Banner, Bayannur City, Inner Mongolia. Figure 3 As can be seen from the figure, the mining area is connected to the Yinshan Mountains in the north and the Hetao Plain planting area in the south, with complex topography and landforms around it. The mining area is located in the temperate zone and has a plateau continental climate, with an average annual temperature of 3.8 degrees, an average precipitation of 96-105.9 mm, an average frost-free period of 130 days, and an average annual frozen soil depth of 2.3 meters. The main characteristics of the climate are: cold and dry with much wind and sand in winter, dry and little rainfall in summer, strong sunshine, and large evaporation.

[0167] The evaluation mining area was established in 2000. It is a large-scale copper, sulfur and silver polymetallic deposit dominated by lead and zinc. The mine was originally affiliated to the Petrochemical Department of the Autonomous Region. The production scale was originally designed to be 35,000 tons / year, and the mining and dressing capacity was later expanded to 600,000 t / a. After restructuring and development, the mining area was jointly developed by multiple units. It is now mainly operated by two large mining units. The annual mining and dressing capacity is 1.8 million tons / year and 3.3 million tons / year respectively. The total area of ​​the mining area is about 2.28 square kilometers. The products are mainly lead and zinc concentrates, and sulfur is a by-product.

[0168] S1. Hazard Identification

[0169] Through information collection and preliminary research on the target mining area, it was determined that there was serious soil heavy metal pollution around the study area, and the characteristic pollutants were determined to include As, Cd, Cr, Cu, Hg, Ni, Pb and Zn. The assessed pollution range is concentrated about 10 kilometers from the center of the mining area. The environmental impact report of the mining company shows that the emissions from the mining area are recycled during the production process and are not discharged. Some of the waste rock is backfilled underground and some is stored in the stone storage yard. The main production process and product nodes of the mining area can be found in Figure 4 , the main route of heavy metal exposure is through the air transfer of particles or dust generated during mining, grinding and ore transportation, and the discharge of underground water and overflow wastewater from the beneficiation process. Although the study area is located in a typical ecologically fragile zone in northwest my country, the survey area is close to the Yellow River and the Hetao Plain, and is located in the agricultural and pastoral transition zone. There are various soil receptors in the soil of different land use types, including crops, vegetables and invertebrates in agricultural areas, and invertebrates in greening soils. In addition, there are some ferns on the exposed ground. Therefore, various heavy metals in mining activities may accumulate in the surrounding soil through atmospheric deposition and may have toxic effects on these soil receptors. Therefore, according to the requirements of the hierarchical ecological risk assessment technical framework, enter the next level of risk screening.

[0170] S2. Risk Screening

[0171] Sampling, testing, and data analysis steps will be carried out. On-site sampling will be carried out in the mining area and its surrounding areas. Based on previous data, the initial scope of this assessment is defined as a 10km radius radiation range centered on the mining area. The sampling points are concentrated in various functional units of the mining area, 1km, 2.5km, 5km, and 10km. A total of 38 sampling points were collected, and the land use type of each point was recorded. For details, see Figure 3 The sampling operation is carried out in accordance with the requirements of "HJ 25.1-2019 Technical Guidelines for Investigation of Soil Pollution Status of Construction Land", and a sampling plan is formulated using the professional judgment method.

[0172] The risk index used in this mining area is the potential ecological risk index PERI, risk quotient RQ, and Nemerow P listed in Table 2. NThe PERI uses the background value as the reference value. The natural background value can be used as the necessary content to maintain the species diversity and ecological function of the local soil ecosystem, and the biologically effective toxicity to local species can be ignored. Therefore, the selection of the PERI index can be used as a more stringent preliminary screening of this mining area. The reference benchmark for the RQ risk quotient considers the ecological functions of different land use types in this mining area and gives differentiated protection levels. The ecological risk screening values ​​under different land use types in this mining area are re-derived using the benchmark derivation method. Therefore, it can be used as a fine screening of pollutants in this mining area, and the pollutants and the corresponding ecological risks can be more accurately determined through preliminary screening and fine screening. The source apportionment method is carried out in this mining area by combining qualitative PCA with quantitative PMF. In addition, spatial regression analysis and Kriging interpolation are also used to characterize different risk levels and distribution ranges.

[0173] Heavy metal testing and analysis of the soil in the mining area and pollution source analysis were carried out. The soil heavy metal concentrations (As, Cd, Cr, Cu, Hg, Ni, Pb and Zn, in mg / kg) are shown in Table 4.

[0174] Table 4

[0175]

[0176]

[0177] Figure 5 PCA analysis of the concentrations of eight heavy metals in the study area revealed two main sources.

[0178] The mining risk sources represented by soil Pb, Cd, Cu, Zn and Hg pollution gradually changed to natural background sources represented by As, Ni and Cr.

[0179] Further adopting the PMF model ( Figure 6 ) confirmed the results of the two contributing sources of PCA analysis, and further quantified the contribution of each risk source to heavy metal emissions. The results showed that the main heavy metals contributed by soil parent material were Cr, Ni, and As, which contributed 77.8%, 81.7% and 78.1% of the total amount of the three heavy metals, respectively; and the representative pollutants brought by mining activities were Cd, Cu, Pb, and Zn, which contributed 83.3%, 64.6%, 87.2% and 86.5% of pollutant emissions, respectively. The two sources contributed to Hg emissions basically the same. Therefore, through the source analysis of the two methods, I judged from both qualitative and quantitative perspectives that the pollutants that need to be further controlled in this mining area are Cd, Cu, Pb, Zn and Hg, which can exclude further evaluation of Cr, Ni and As. Of course, Cr, Ni and As can also continue to be evaluated for subsequent risk indexes.

[0180] After that, the ecological risk characterization and spatial analysis of pollutants were carried out, and the potential ecological risk Er values ​​of 8 heavy metals were calculated based on the local soil background value ( Figure 7 ), the results showed that Pb, Cd, Cu, Zn and Hg in the soil of the study area had moderate to extremely high potential ecological risks, and Cd had the highest potential ecological risk. Compared with the limit of low potential ecological risk (Er = 40), soil Cr, Ni and As showed no ecological risk, which indicated that these three elements did not have a high potential ecological risk, and the results were consistent with the above source apportionment results.

[0181] The Er values ​​of the eight heavy metals were further combined to calculate the overall PERI analysis, and spatial regression analysis was performed with different distances from mining areas. The results are as follows: Figure 8 As shown, the PERI analysis was significantly regressed with the distance to the different sites (p < 0.001), and at the same time, the very high risk sites were mainly located in the main production area of ​​the mining area and within a range of nearly 1 km.

[0182] Since the use of background values ​​for assessment is relatively conservative, in order to reflect the real ecological risks, we used the baseline value derivation method and ecotoxicological data, removed the ecological species data that could not exist in the area, and combined the differentiated protection levels corresponding to different land use types and local background values ​​to construct the SSD curve of the assessment area and derive the ecological baseline values ​​of different local land use types. The results are as follows: Fig. 9 , Fig.10 , Fig.11 , Fig.12 , Fig.13 As shown in the figure, the benchmark for protecting agricultural land is 95% species protection level, while the benchmark for protecting forest land and wasteland or construction land is 80% and 50% species protection level respectively. This is mainly because the protection level of these two lands can be lowered relative to the ecological function of agricultural land, so the benchmark is more relaxed than the agricultural land benchmark.

[0183] The ecological benchmark values ​​and exposure concentrations for the application site types were further used to calculate the risk quotients (RQs) and Nemerow comprehensive index (P) of the five preliminarily screened heavy metals. N Value, the result is Fig.14 shown.

[0184] Unlike the PERI analysis, which lists Cd as the pollutant with the highest risk, the RQ values ​​based on the ecological benchmark values ​​of different land use types show that soil Zn has the highest risk, with 26.32% of samples exceeding the high risk level, 34.21% at medium risk, and 39.47% at low risk, followed by soil Pb and Cd, with 10.53% and 5.26% exceeding the high risk level, respectively. There are no high-risk samples for soil Cu and Hg, and there is only one medium-risk sample for Hg, and the rest are low risks, so their concern can be further reduced. N The values ​​showed that 50%, 23.68%, and 26.32% of the soil samples were classified as low risk, medium risk, and high risk levels, respectively. N The high-risk sites are all located in the main production areas of the mining area and farmland within 1 km, which is consistent with the results of the PERI analysis ( Figure 8 ).

[0185] Based on all the evidence at this level, Cd, Pb, Zn, Cu and Hg in the soil of the mining area and its surrounding areas were preliminarily screened as the main pollutants in the mining area through sampling analysis, source analysis and PERI index analysis. The Er index showed that Cd had the highest potential risk, with 26% of the sampling points exceeding the extremely high risk level. The RQ and P values ​​based on the ecological benchmark values ​​of land types were N The index calculation further screens out the ecological risk of soil Hg, and gives a more accurate spatial distribution compared to the PERI index, excluding some medium and high risk areas, thereby reducing unnecessary risk management investment. In addition, atmospheric deposition of pollutants with prevailing winds is the main way of risk transmission, and the overall ecological risk is mainly concentrated within a range of nearly 2.5 kilometers from the center of the mining area. Therefore, the four priority controlled pollutants, Cd, Pb, Zn, and Cu, can be further quantitatively evaluated for ecological risk probability.

[0186] S3. Quantification of risk probability

[0187] Construct a joint probability curve JCP. In order to carry out a more refined evaluation, this embodiment is based on P N The risk level divides the sample points into risk groups and constructs the exposure probability curves of the corresponding groups. Fig. 9 , Fig.10 , Fig.11 , Fig.12 , Fig.13 The SSD curve constructed finally formed the JCP of each pollutant and calculated the corresponding ORP. The results are as follows Fig.15 , Fig.16 , Fig.17 , Fig.18The probability method quantifies more accurate risk probability values ​​for the pollutants of concern in the survey area. The priority control order is: Zn>Pb>Cu>Cd. According to the classification standard of probability risk, Zn is a high-risk pollutant, Pb is a medium-risk pollutant, and Cu and Cd are classified as low-risk. N When evaluating samples by risk level, different heavy metals should be classified and controlled according to the risk level of the area they are in. N In the high-risk zone, Zn, Pb, and Cu pose high ecological risks to the local soil ecosystem, and Cd poses a moderate risk; in the moderate-risk zone, only Zn is at a high risk level, while the others are at low or minimal risk, and in the low-risk zone, all four HMs are below the low-risk limit.

[0188] Therefore, the study area should classify and manage different priority heavy metal types in different risk areas. The characteristics of probabilistic ecological risk quantify the risk probability of the corresponding pollutants and reduce the uncertainty of environmental exposure and ecological effects, thus giving more reliable risk results.

[0189] S4. Determination of causality

[0190] Conduct ecological surveys and determine causal relationships. Although the previous level provides ecotoxicity evidence for ecological receptors around the mining area, the ecotoxicity data may be obtained under experiments with soil conditions different from those in the study area. Therefore, in order to understand the true ecological effects of key receptors under local soil conditions, this level will conduct ecological surveys around the mining area, conduct field tests on the soil environment around the mining area and possible local ecological effects, and further verify the "pollution-ecological effect" connection under actual pollution conditions in the mining area. Since soil PLFA can conveniently, economically and quickly reflect the composition of the microbial community in the in-situ soil, in this mining area, soil PLFA will be used as a species ecological effect indicator to reflect the changes in the field soil microbial community around the mining area, and it will be associated with previous pollution exposure evidence and related environmental factors to determine a more realistic ecological risk status in this mining area. The final causal relationship is characterized by a series of multivariate statistical methods, and all exposure assessment results of the previous layers of the study area are combined to link them with the effect changes of soil PLFA abundance and structure.

[0191] One-way analysis of variance (ANOVA) was used to compare the different P NThe abundance and structure of PLFA in different risk levels and land use types were analyzed. The Spearman test was used to explore the correlation between different environmental variables and PLFA effects. The Mantel test was used to study the correlation between each PLFA species and environmental variables. Redundancy analysis with hierarchical partitioning (hp.RDA) was used for attribution analysis to determine the contribution of environmental variables to PLFA abundance and structure.

[0192] In addition, structural equation modeling (SEM) was constructed by screening out important factors affecting PLFA to determine the direct and indirect effects of heavy metal ecological risk on soil PLFA abundance and structure. Fisher's C and P values ​​were used to evaluate the overall goodness of fit of the model. Finally, the causal relationship between real pollution and local effects was determined in a statistical sense.

[0193] First, soil environmental factor analysis was performed to obtain the land use type, soil pH, TOC, TC, TN and CEC as shown in Table 5.

[0194] Table 5

[0195]

[0196]

[0197] Then soil PLFA analysis was performed. Fig.19 and Fig. 20 Shows different P N Soil risk levels and land use type groupings

[0198] PLFA abundance and structure. N Risk level comparison Fig.19 ), except for the significant difference in PLFA abundance of indicator fungi between high-risk and low-risk groups, there was no significant difference in the abundance of PLFA indicating other microorganisms at different risk levels; in addition, the values ​​of G- / T, F / T, G- / G+ and F / B reflecting the PLFA composition structure were significantly higher in the low-risk group than in the high-risk group, while G+ / T was significantly higher in the high-risk group than in the medium or low-risk group. All indicators indicating PLFA structure showed a gradient change of pollution effect. These results indicate that the high pollution risk brought by mining activities significantly reduced the abundance of soil fungi and changed the structure of soil PLFA, significantly increased G+ / T, but reduced G- / T, F / T, G- / G+ and F / B values.

[0199] Previous studies have shown that increased levels of heavy metal pollution will reduce the indicator PLFAs of bacteria, fungi and actinomycetes in the soil. Other studies have found that heavy metal pollution increases the abundance of fungal and actinomycete PLFAs. In this mining study, except for changes in fungi, changes in other microbial species with increased pollution were not significant. These may be caused by a combination of factors such as experimental conditions, soil physical and chemical properties, and land use type.

[0200] We further compared the differences in soil PLFA abundance and structure under different land use types ( Fig. 20 ), the results showed that the PLFA abundance of all microbial species in forest soil was significantly higher than that in other land use types, while the bacterial PLFA abundance in farmland was significantly higher than that in wasteland. For PLFA structure, only the F / T and F / B of forest land were significantly higher than those of industrial land, and other structural indicators did not show significant differences under different land use patterns. This shows that different land use patterns bring about significant differences in microbial abundance and structure. Forest soil has a higher microbial content and fungal-bacterial ratio. In addition, the results also show that carrying out afforestation projects in ecologically fragile areas can effectively improve the microbial ecology of the soil.

[0201] Then we carried out an analysis of influencing factors and causal determination. As the ecological environment in this area is fragile and soil microorganisms are easily affected by a variety of environmental factors, it is necessary to integrate information from all aspects in order to find the actual "pollution-ecological effect" connection in the mining area.

[0202] Soil PLFA and environmental factors were further analyzed by Spearman and Mantel correlation analysis ( Fig. 22 , the significance is indicated by *p<0.05, **<0.01, ***<0.001 in the figure). The results showed that the abundance of PLFA representing various microorganisms was significantly correlated with land use and some soil physical and chemical factors, but had no significant correlation with heavy metal content. In addition, the PLFA ratio index representing the composition and structure of microorganisms had no significant correlation with all environmental factors except A / T. The other ratio indexes were significantly correlated with the heavy metal content in the mining area, P N The index, as well as pH and TC, were significantly correlated;

[0203] Furthermore, the soil PLFA profile matrix was subjected to a Mantel correlation test with the environmental factor matrix, and the results showed only significant associations between land use type, TOC, and TN on the abundance of PLFAs representing G+, G-, and actinomycetes.

[0204] The above correlation analysis shows that the impact of heavy metal pollution risk on the abundance of various PLFA in soil does not seem to be significant, but it can change the structural composition of soil PLFA. The relative abundance of G+ in highly polluted areas will increase, while the relative abundance of G- and fungi will decrease, resulting in a negative correlation between G- / G+ and F / B indicators and pollution risk values. In addition, soil land use type seems to be a key factor in determining soil PLFA content, because land use affects soil TOC and TN content, and these two indicators provide direct nutrient sources for microbial growth.

[0205] To further explore the causal relationship between environmental factors and soil PLFA effects, a redundancy analysis method based on hierarchical partitioning was used to quantify the degree to which each environmental factor (explanatory variable) explained PLFA abundance and structure (response variable).

[0206] The hierarchical segmentation method can eliminate the collinearity problem between explanatory variables, such as the high autocorrelation between different soil physical and chemical properties and between different heavy metals ( Fig.21 ), so as to more accurately determine the contribution of each environmental factor to PLFA abundance and structural variation, and then rank the degree of influence. The results of redundancy analysis showed that ( Fig.23 , the shape and color of the legend indicate that the corresponding point belongs to the corresponding land use type and the corresponding pollution level at the same time). All explanatory variables explain 52.42% of the overall variation of the soil PLFA abundance and structure indicator matrix, of which the first constraint axis explains 66.64%, and the second constraint axis explains 24.39%. The two axes together account for 91% of the explained part. The high-risk points (circular points) in the figure are close to the industrial land type and the five heavy metal content indicators, while the medium- and low-risk points (triangles) are close to agricultural land. In addition, the angles between different explanatory variables and the corresponding ones also reflect the correlation between the indicators. The results are consistent with Fig. 22 The results are consistent.

[0207] The contribution of all explanatory variables to the response variable was quantified using the hierarchical partitioning method ( Fig.24 ), sorted by individual factors, land use had the highest contribution value (18.95%), followed by soil pH value (7.29%), both of which had significant effects, and the contributions of the remaining explanatory factors were all less than 5%.

[0208] Overall, soil physicochemical factors, land use type and heavy metal pollution explained 21.84%, 20.16% and 10.42% of the total variation in soil PLFA abundance and structure matrix, respectively. It can be seen from the above that the contribution of heavy metal pollution to soil PLFA effect near this mining area is smaller than the influence of the physical and chemical properties of the soil itself and the land use type, and does not show significance.

[0209] To further clarify the direct and indirect associations between various environmental factors and PLFA effects, we used structural equation modeling (SEM) to explore the causal relationships between key environmental variables and soil PLFA abundance and structure. Fig.25 and Fig.26 As shown in the figure, the numbers next to the arrows are the standardized path coefficients, which are represented by the width of the arrows. Red and blue arrows represent positive and negative relationships, respectively. Solid and dashed arrows represent significant and insignificant relationships, respectively. Significance is indicated by *p<0.05, **<0.01, and ***<0.001. R 2 It indicates the amount of variance explained by the exogenous variables in the model for the endogenous variables. Fisher's C statistic indicates the test of the fit of the entire model, and a high P value indicates the rationality of the entire model. PLFA abundance and structure are represented by the first principal component (PC1) score of the corresponding variable matrix. Both constructed models have good model fit (p>0.05, indicating that the model is statistically significant) and explanation rate (R 2 ), which proves the effectiveness of the model.

[0210] Fig.25 Shows P N The causal relationship between the pollution risk index, land use type and soil physical and chemical properties TOC, TN and PLFA abundance was analyzed. The results showed that P N The value can directly and significantly reduce the PLFA abundance, especially the PLFA content of indicator fungi ( Fig.19 ), can also significantly increase the content of soil TOC by affecting the way microorganisms utilize soil carbon, thereby increasing the abundance of PLFA.

[0211] As mentioned above, the increase in heavy metal pollution risk leads to an increase in TOC content, which may be due to the decrease in the proportion of inorganic carbon, or because the pollution risk changes the way soil carbon is used, reducing the efficiency of microbial use of organic carbon, resulting in higher soil TOC content in high-risk areas. Combining the positive and negative effects of the two, the combined effect value is -0.06, indicating that although the risk of heavy metal pollution can reduce the abundance of PLFA, compared with the significant positive effect of land use (comprehensive effect value of 0.76), the impact of heavy metal pollution on the abundance of PLFA is limited.

[0212] Fig.26 It shows P N The relationship between the comprehensive pollution risk index, land use type, soil physical and chemical properties TOC, TN, pH and PLFA structure composition showed that P NIt did not directly affect the structure of PLFA, but it did change the structure of PLFA by significantly reducing pH, which was the only significant effect pathway in the model.

[0213] In summary, heavy metal pollution indirectly changes the structure of PLFA by directly reducing the pH of the soil. As the pollution worsens, the soil pH becomes lower, so G+ / T increases, while G- / T, F / T, G- / G+ and F / B values ​​decrease. This result is consistent with the results of previous studies. The worsening of pollution reduces the relative content of fungi.

[0214] Based on the above results, it can be seen that heavy metal pollution in mining areas can significantly change the physical and chemical properties of soil, increase soil TOC content and reduce TC content and pH value. With the decrease of soil pH value, heavy metal pollution in mining areas will indirectly change the structural composition of soil PLFA and reduce soil F / B and G- / G+ indicators; at the same time, heavy metal pollution in mining areas can also directly affect or indirectly affect the abundance of PLFA in the soil around the mining area by affecting TOC, but the comprehensive effect is very limited. The results also show that afforestation work in the area can also increase the abundance of microorganisms in the soil, thereby improving the soil microecological environment.

[0215] It can be seen from the above examples that the ecological risk assessment technology method provided by the present invention can realize effective and reliable implementation of ecological risk assessment, and provide effective support for the localized, hierarchical and refined ecological risk assessment research of soil around contaminated sites.

[0216] The preferred embodiments of the present invention are described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, a variety of simple modifications can be made to the technical solution of the present invention, and these simple modifications all belong to the protection scope of the present invention.

[0217] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0218] In addition, various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

Claims

1. A multi-level ecological risk assessment method for soil pollution, characterized in that: The multi-level ecological risk assessment method includes the following process: Hazard identification: collect and analyze the "source-path-sink" information of soil pollution at the target site to determine the risk association. If it is determined that there is a risk, then conduct pollution risk screening, otherwise end; Pollution risk screening: soil sampling and analysis of target sites to obtain the concentration of characteristic pollutants, soil pollutant source analysis of characteristic pollutants, and calculation of risk index of characteristic pollutants to comprehensively screen priority pollutants. If the risk index of all priority pollutants shows no risk, it is assessed that there is no risk. Otherwise, the risk probability is quantified. Quantification of risk probability: For the priority controlled pollutants that are shown as risky by the risk index, establish the priority controlled pollutant exposure probability distribution curve and the species sensitivity distribution curve, and form a joint probability curve to calculate the overall risk probability value. If the overall risk probability value of all priority controlled pollutants is ≤9.82%, it is assessed that there is no risk, otherwise a causal relationship determination is made; Causal relationship determination: Conduct ecological analysis on the site to determine the environmental factors and sensitive receptor effects, and compare the differences in sensitive receptor ecological effects under different characteristic pollutant levels and / or different environmental factor groupings. Then, conduct correlation analysis and causal relationship statistical analysis in turn to obtain ecological risk conclusions.

2. The multi-level ecological risk assessment method according to claim 1, characterized in that: The collection and analysis of soil pollution "source-path-sink" information includes: obtaining risk source information, obtaining risk path information and obtaining risk receptor information; Preferably, the obtaining of risk source information includes: obtaining basic site information and potential pollutant information; Preferably, the obtaining of risk path information includes: obtaining potential pollution areas and risk migration pathways; Preferably, the obtaining of risk receptor information includes: obtaining ecological protection targets; Preferably, the obtaining of basic site information includes: obtaining the geographical location, hydrogeology, climate conditions, floor area, production process characteristics and production history of the target site; Preferably, the obtaining of potential pollutant information includes: obtaining potential characteristic pollutants and potential pollution functional zoning of the target site; Preferably, the obtaining of potential pollution areas includes: obtaining potential characteristic pollutants and potential pollution functional zoning of the target site, and identifying potential pollution areas accordingly; Preferably, the risk migration pathway includes: clarifying the exposure mode of soil pollutants in the target site, understanding the production mode, production process and pollution-generating links, and the environmental protection level of comprehensive treatment of wastewater, waste gas and waste rock; Preferably, the acquisition of ecological protection goals includes: clarifying the main terrestrial ecosystem types and potential ecological risk receptors of the target site, and understanding the environmental factors that may affect the biological toxicity of pollutants.

3. The multi-level ecological risk assessment method according to claim 1 or 2, characterized in that: The method of determining risk association includes: collecting and analyzing the pollution indicators in the soil pollution "source-path-sink" information according to the "GB 15618-2018 Soil Environmental Quality Agricultural Land Soil Pollution Risk Control Standard", "HJ 25.1-2019 Construction Land Soil Pollution Status Survey Technical Guidelines" or expert review results, or a combination of at least two of them; Preferably, in the risk association determination, if the pollutant content in the soil is greater than the risk screening value in GB 15618-2018 Soil Environmental Quality Agricultural Land Soil Pollution Risk Management and Control Standard, it is determined that there is a risk; Preferably, in the risk association determination, if the soil environmental assessment category is level I or II of the soil environmental impact assessment project category in the "HJ 25.1-2019 Technical Guidelines for Investigation of Soil Pollution Status of Construction Land", it is determined that there is a risk; Preferably, in the risk association determination, if the expert review result is that there is a risk, then it is assessed that there is a risk.

4. The multi-level ecological risk assessment method according to any one of claims 1 to 3, characterized in that: The soil pollutant source analysis includes: conducting source analysis of characteristic pollutants based on the indicators of characteristic pollutants obtained from soil sampling and analysis, determining the source pathways of characteristic pollutants and quantifying the pollution contribution of different characteristic pollution sources, and screening priority controlled pollutants; Preferably, the source analysis method includes: one or a combination of at least two of principal component analysis, cluster analysis, factor analysis, chemical mass balance, positive definite matrix factor method or isotope tracing method; Preferably, if a pollution source has the largest contribution to the exposure concentration of a specific pollutant in the source analysis, then the pollutant is the priority controlled pollutant of the corresponding pollution source.

5. The multi-level ecological risk assessment method according to any one of claims 1 to 4, characterized in that: The calculation of the risk index of characteristic pollutants includes: calculating the risk index by using the soil background value or ecological benchmark value of the target site as a reference and the environmental exposure concentration of the characteristic pollutants; Preferably, the risk index comprises: one or a combination of at least two of a geoaccumulation index, a potential ecological risk index, a risk quotient, a pollution load index or a Nemerow risk index; Preferably, if the risk index matches the range of risk in the risk level range, it is determined that the corresponding element is at risk, and then cross-combined with the source analysis results to determine the priority pollutants.

6. The multi-level ecological risk assessment method according to any one of claims 1 to 5, characterized in that: The establishment of the exposure probability distribution curve of the priority controlled pollutants includes: using a log-normal distribution function to fit the distribution function of the detected soil exposure concentration of the priority controlled pollutants, and constructing a cumulative probability curve of the exposure concentration distribution of the soil of the target site; Preferably, the establishment of the species sensitivity distribution curve includes: conducting a receptor effect analysis of ecological protection targets around the target site, using species data in the ecotoxicology database to perform a species effect analysis, and then using the collected ecospecies toxicology data for the target site to construct a species sensitivity distribution curve that conforms to the actual situation of the target site.

7. The multi-level ecological risk assessment method according to any one of claims 1 to 6, characterized in that: The forming of the joint probability curve comprises: coupling the pollutant exposure probability distribution curve and the species sensitivity distribution curve corresponding to the priority controlled pollutant concentration to obtain the joint probability curve; Preferably, the overall risk probability value includes a closed area formed by the joint probability curve, the x-axis and the y-axis.

8. The multi-level ecological risk assessment method according to any one of claims 1 to 7, characterized in that: The ecological analysis includes: conducting ecological surveys and / or biological experiments on the soil environment around the target site and the native species, key species or related ecological processes in the soil to determine the ecological effects of environmental factors and sensitive receptors; Preferably, the environmental factors include: one or a combination of at least two of land use type, soil type, soil texture, soil organic matter content, pH value or cation exchange capacity; Preferably, the sensitive receptor ecological effects include: soil PLFA abundance and / or soil PLFA structure; Preferably, the ecological survey conducted in the ecological analysis includes: soil animal survey, plant survey and microbial survey; Preferably, the biological experiments conducted in the ecological analysis include: soil animal experiments, plant experiments and microbial experiments.

9. The multi-level ecological risk assessment method according to any one of claims 1 to 8, characterized in that: The correlation analysis includes: performing correlation analysis on characteristic pollutant concentrations, environmental factors and sensitive receptor ecological effects, and screening out relevant characteristic pollutants and environmental factors that are statistically significant with sensitive receptor ecological effects; Preferably, the correlation analysis method includes: one or a combination of at least two of Pearson correlation analysis, Spearman correlation analysis, Mantel correlation analysis or principal component analysis; Preferably, the causal association statistical analysis includes: performing causal association statistical analysis on the relevant characteristic pollutants and environmental factors that have statistical significance in the ecological effects on sensitive receptors obtained by screening to quantitatively analyze the degree of explanation and statistical significance of the characteristic pollutants and environmental factors on the ecological effects of sensitive receptors; Preferably, the causal relationship statistical analysis method includes: one or a combination of at least two of structural equation model, redundancy analysis or canonical correlation analysis; Preferably, if the model fit p-value in the causal association statistical analysis is statistically significant, the model has a good fit, and the output model reflects the results as the ecological risk conclusion.

10. A multi-level ecological risk assessment device for soil pollution, characterized in that: The pollution ecological risk assessment device comprises: The hazard identification module is used to collect and analyze the "source-path-sink" information of soil pollution at the target site to determine the risk association. If it is determined that there is a risk, pollution risk screening will be carried out, otherwise the process will end; The pollution risk screening module is used to conduct soil sampling and analysis on the target site, obtain the concentration of characteristic pollutants, perform soil pollutant source analysis on characteristic pollutants, calculate the risk index of characteristic pollutants, and comprehensively screen the priority controlled pollutants. If the risk index of all priority controlled pollutants shows no risk, it is assessed that there is no risk. Otherwise, the risk probability is quantified. The risk probability quantitative module is used to establish the exposure probability distribution curve of the priority controlled pollutants and the species sensitivity distribution curve for the priority controlled pollutants that are shown as risky by the risk index, and form a joint probability curve to calculate the overall risk probability value. If the overall risk probability value of all priority controlled pollutants is ≤9.82%, it is assessed that there is no risk, otherwise a causal relationship judgment is made; The causal relationship determination module is used to conduct ecological analysis of the site, determine the environmental factors and sensitive receptor effects, and compare the differences in sensitive receptor ecological effects under different characteristic pollutant levels and / or different environmental factor groupings. It then performs correlation analysis and causal relationship statistical analysis in turn to obtain ecological risk conclusions.

Images