Ecological risk assessment method for toxic element pollution based on soil nematode ecological index

Abstract

The application provides a toxic element pollution ecological risk evaluation method based on a soil nematode ecological index, and belongs to the technical field of toxic element pollution ecological risk evaluation; a technical problem to be solved is to provide a toxic element pollution ecological risk evaluation method based on a soil nematode ecological index; a technical scheme is that sampling is carried out on soils in a region polluted by toxic elements in different degrees in multiple time periods and multiple sites, and the sampled soils are respectively used for soil potential toxic element content detection, soil physical and chemical property detection and soil nematode community structure analysis; characteristic indexes of potential toxic element pollution in the soils are respectively calculated, including a pollution factor, a Nemerow index, a pollution load index and a potential ecological risk index; soil nematode community structure is analyzed, and evaluation indexes of the soil nematode community structure are respectively calculated; and the application is applied to toxic element pollution ecological risk evaluation.

Description

TECHNICAL FIELD

[0001] The application provides a toxic element pollution ecological risk evaluation method based on a soil nematode ecological index, and belongs to the technical field of toxic element pollution ecological risk evaluation. BACKGROUND

[0002] In recent years, the problem of potential toxic element pollution of soil is prominent. The potential toxic element pollution of soil can threaten the stability of the ecological system and human health through the coupling cascade reaction of soil-food-environment-health, and thus it is necessary to pay high attention to the prevention and early warning of potential toxic element pollution of soil, and actively carry out the ecological risk assessment of soil pollution, and establish a more effective ecological risk assessment method.

[0003] At present, the evaluation method for the ecological risk of potential toxic element pollution is to evaluate the potential toxic element pollution level and ecological risk by the content of potential toxic elements in soil, such as single factor pollution index, geoaccumulation index, Nemerow index, pollution load index and potential ecological risk index. The risk calculation is based on the content of potential toxic elements in soil, and the biological toxicity of potential toxic elements and the influence on biological community structure are less considered or not considered. The existing method cannot capture all ecological risks and threats at the ecosystem level, and sometimes it may overestimate the risk. In addition, the traditional physical and chemical indicators have the problems of lagging, accumulation, limitation of single pollutant evaluation, cost efficiency, limitation of unknown chemical substance monitoring and the like in soil pollution monitoring and evaluation, which leads to the fact that they cannot comprehensively, timely and accurately reflect the ecological effects and comprehensive risks of soil pollution.

[0004] It is found that soil nematodes are sensitive to environmental changes such as potential toxic element pollution of soil, have rich species, short generation time, occupy multiple trophic niches and are easy to separate and identify, and are considered as a good indicator animal group. Research shows that potential toxic element pollution of soil can significantly change the community structure of soil nematodes. The use of soil animals to monitor and evaluate environmental quality is more comprehensive and accurate than the traditional soil potential toxic elements and soil physical and chemical indicators. The new development of biological technology of soil animals is helpful to establish the evaluation index of soil and environmental health.

[0005] However, the soil ecosystem is quite complex, and the current research mainly focuses on the toxicology of soil nematodes and the changes in the community structure under potential toxic element pollution. The soil nematode community structure is easily affected by environmental factors. Although some studies have found soil nematode ecological indexes that are significantly correlated with potential toxic element pollution, the dose-response relationship between the two in the field environment is not clear (not linear), and no relevant research has been found on the establishment of a systematic potential toxic element pollution ecological risk model using soil nematodes. The soil nematode community structure is affected by various environmental factors other than potential toxic element stress, such as soil properties, climate conditions, crops, and the like. In addition, there are often multiple potential toxic elements with high concentrations, and the mutual influence between potential toxic elements and potential toxic elements and between potential toxic elements and environmental factors, which makes it impossible to find and determine the accurate dose-response relationship between potential toxic elements and soil nematodes from the complex environmental factors, and further judge the ecological risk of potential toxic element pollution. SUMMARY

[0006] The present application aims to overcome the deficiencies in the prior art and solve the technical problem of providing a toxic element pollution ecological risk evaluation method based on soil nematode ecological indexes.

[0007] To solve the above technical problems, the technical scheme adopted by the present application is as follows: a toxic element pollution ecological risk evaluation method based on soil nematode ecological indexes, comprising the following evaluation steps:

[0008] Step 1: For the soil in the region that is contaminated by potential toxic elements to different degrees, multi-time and multi-site sampling is carried out, and the sampled soil is used for soil potential toxic element content detection, soil physical and chemical property detection, and soil nematode community structure analysis, respectively;

[0009] Step 2: Detect the content of potential toxic elements in the soil;

[0010] Step 3: Calculate the characteristic indexes of potential toxic element pollution in the soil, including pollution factor, Nemerow index, pollution load index, and potential ecological risk index, respectively;

[0011] Step 4: Analyze the soil nematode community structure and calculate the evaluation indexes of the soil nematode community structure, including nematode abundance, number proportion of different soil nematodes, Shannon-Wiener diversity index, soil nematode evenness index, nematode maturity index, nematode channel index, enrichment index, and structure index, respectively;

[0012] Step 5: Use Bayesian kernel machine regression to analyze the dose-response relationship among the soil potential toxic element content, soil nematode community structure, and pollution characteristic indexes;

[0013] Step six: Based on the Bayesian kernel machine regression analysis, the nematode ecological index and pollution characteristic evaluation index sensitive to the potential toxic elements are obtained, and multiple calculation models are used to construct the potential toxic element pollution ecological risk prediction model based on the soil nematode ecological index, including linear regression model, ridge regression model, random forest model, gradient boosting regression tree model, decision tree model and extreme random tree model.

[0014] Step seven: The constructed ecological risk prediction models are evaluated, and the optimal fitting model is selected.

[0015] The potential toxic elements in the sampled contaminated soil in step one include Zn, Cu, Cr, Cd, Ni, Mn, Hg, Pb, As, Sb and Se.

[0016] The specific method for detecting the content of potential toxic elements in soil in step two is as follows:

[0017] Weigh 0.2g of soil sample dried and passed through a 100 mesh sieve into a polytetrafluoroethylene digestion tube TF-36, add 10mL of hydrochloric acid, and insert into the digestion hole;

[0018] Set the temperature of the digestion instrument to 100℃, heat until the sample remains a small amount, and then remove and cool;

[0019] Add 5mL of nitric acid, 5mL of hydrofluoric acid and 1mL of perchloric acid, and cover;

[0020] Set the temperature of the digestion instrument to 150℃, and digest for 120min, then cool and uncover;

[0021] Warm up to 200℃, and wait until the thick white smoke stops;

[0022] Evaporate until the digestion solution is thick, and repeat the above digestion process;

[0023] Remove the digestion tube and cool it, add 2mL of hydrochloric acid to dissolve the soluble residue, rinse the inner wall with deionized water, and transfer the whole amount to a 50mL volumetric flask for constant volume, and shake well;

[0024] The detection is carried out by inductively coupled plasma mass spectrometry, in which Hg is not chased by acid during digestion to prevent Hg volatilization, and atomic absorption spectrometry is used to detect the content of potential toxic elements in soil.

[0025] The specific method for calculating the potential toxic element pollution characteristic index in step three is as follows:

[0026] In order to evaluate the pollution degree of potential toxic elements, pollution factor CF is used for evaluation, and the calculation formula of the factor is:

[0027]

[0028] Where: Ci represents the concentration of potentially toxic elements at sampling point element i, C o is the background value;

[0029] The grading criteria for the pollution factor CF are defined as:

[0030] When CF < 1, it indicates mild soil pollution;

[0031] When 1 ≤ CF < 3, it indicates moderate soil pollution;

[0032] When 3 ≤ CF < 6, it indicates high soil pollution;

[0033] When CF > 6, it indicates extremely high soil pollution;

[0034] To evaluate the comprehensive index of the combined potentially toxic element pollution, the Nemerow index NSPI is used for evaluation. The calculation formula of this index is:

[0035]

[0036] where: CF imax is the maximum value among all single pollution indices CF of a certain potentially toxic element in a certain area; CF iave is the average value of all single pollution indices CF of a certain potentially toxic element in a certain area;

[0037] The grading criteria for the Nemerow index NSPI are defined as:

[0038] When NSPI ≤ 0.7, the pollution of potentially toxic elements in this area is within the safe range;

[0039] When 0.7 < NSPI ≤ 1.0, the pollution of potentially toxic elements in this area is within the warning range;

[0040] When 1.0 < NSPI ≤ 2.0, the pollution of potentially toxic elements in this area is at a slight pollution level;

[0041] When 2.0 < NSPI ≤ 3.0, the pollution of potentially toxic elements in this area is at a moderate pollution level;

[0042] When NSPI > 3.0, the pollution of potentially toxic elements in this area is at a severe pollution level;

[0043] To evaluate the pollution load of combined potentially toxic elements in the study area according to the toxicity of each potentially toxic element, the pollution load index PLI is used for evaluation. The calculation formula of this index is:

[0044]

[0045] where, CF imis the maximum value among all single pollution indices CF of a certain potentially toxic element, PLI i is the pollution load index of a certain sampling point, and n is the number of potentially toxic elements to be evaluated, PLI zone is the pollution load index of a certain sampling area;

[0046] The grading standard for the pollution load index PLI is defined as:

[0047] When PLI ≤ 1, it is pollution-free;

[0048] When 1 < PLI ≤ 2, it is slightly polluted;

[0049] When 2 < PLI ≤ 3, it is moderately polluted;

[0050] When PLI > 3, it is highly polluted;

[0051] To evaluate the synergistic effect and potential ecological harm degree among multiple potentially toxic elements, the potential ecological risk index RI is used for evaluation. The calculation formula of this index is:

[0052] E i = T i × CF i ;

[0053]

[0054] Among them: E i is the ecological hazard coefficient of a certain potentially toxic element in a certain sampling point, and T i is the toxicity response coefficient of a certain potentially toxic element;

[0055] The potential ecological risk index is graded, and the grading rule is:

[0056] When E i < 40 or RI < 150, it is defined as a low risk;

[0057] When 40 ≤ E i < 80 or 150 ≤ RI < 300, it is defined as a medium risk;

[0058] When 80 ≤ E i < 160 or 300 ≤ RI < 600, it is defined as a relatively high risk;

[0059] When 160 ≤ E i < 320 or RI ≥ 600, it is defined as a high risk;

[0060] When E i ≥ 320, it is defined as an extremely high risk.

[0061] The specific method for calculating the evaluation index of the soil nematode community structure in step 4 is as follows:

[0062] Nematode abundance is expressed as the total number of nematodes per 100g of dry soil.

[0063] The percentages of plant-parasitic nematodes, bacteriophages, fungiophages, and predatory nematodes in the total number of nematodes were calculated per 100g of dry soil, with the relative abundance of each nutrient group being [missing information].

[0064] The Shannon-Wiener diversity index H is calculated using the following formula:

[0065] H=-∑p i (lnP i );

[0066] Among them, P i The proportion of individuals in the i-th taxonomic unit to the total number of nematodes;

[0067] To illustrate the spatial characteristics of nematode distribution in the soil, the soil nematode evenness E is calculated using the following formula:

[0068] E = H / lnS;

[0069] The maturity index (MI) of free-living nematodes is calculated using the following formula:

[0070] MI=∑v i ×f i ;

[0071] Among them, v i f represents the cp value assigned to each of the different life strategies of free-living nematodes in ecological succession. i denoted as the proportion of the number of individuals of the i-th nematode species to the total number of individuals in the community;

[0072] The plant parasitic nematode maturity index (PPI) is calculated using the following formula:

[0073] PPI = ∑v i ×f i ;

[0074] Among them, v i Based on the cp value of plant parasitic nematodes, f i denoted as the proportion of the number of individuals of the i-th nematode species to the total number of individuals in the community;

[0075] The ratio of bacteriophages to micro-nematodes is calculated using the formula for the Nematode Channel Index (NCR):

[0076] NCR = Ba / (Ba+Fu);

[0077] To assess the response of a food web to available resources, the enrichment index EI is calculated using the following formula:

[0078] EI = 100 × e / (e + b);

[0079] In this context, based on the cp value of free-living nematodes, b represents the basic components of the food web, referring to the two major groups Ba2 and Fu2; e represents the enriched components of the food web, referring to the two groups Ba1 and Fu2.

[0080] The formulas for calculating the values ​​of b and e are as follows:

[0081] b=∑k b n b ;

[0082] e = ∑k e n e ;

[0083] Where: k b k e n represents the weighted value corresponding to each group. b n e For the abundance of each population group;

[0084] To reflect changes in soil food web structure during human disturbance or ecological restoration, the structure index SI is calculated using the following formula:

[0085] SI = 100 × s / (s + b);

[0086] In the formula: s represents the structural components in the food web, including Ba3-Ba5, Fu3-Fu5, Om3-Om5 and Ca3-Ca5 groups;

[0087] The formula for calculating the s-value is:

[0088] s=∑k s n s ;

[0089] Where: k s n represents the weighted value corresponding to each group. s The abundance of each population group.

[0090] The specific method for analyzing the dose-response relationship among the potential toxic element content in soil, soil nematode community structure, and pollution characteristic index in step five is as follows:

[0091] By analyzing the dose-response relationship among the content of potentially toxic elements in the soil, the structure of soil nematode communities, and pollution characteristic indices, the ecological index of nematodes sensitive to potentially toxic elements and the evaluation index of pollution characteristics are determined. The calculation formula is as follows:

[0092] Y i =h(z) i1 , ..., zim )+βx i +e i ;

[0093] Where: Y i This is the soil nematode ecological index for the i-th sample; z i1 ,...,z im These are the m potentially toxic elements in the soil corresponding to the i-th sample; x i It is a set of potential confounding factors; h (·) It is the exposure-response function; β is a function of covariance x. i The effect; e i It is a residual.

[0094] The specific method for constructing the potential toxic element pollution ecological risk prediction model based on the soil nematode ecological index in step six is ​​as follows:

[0095] The dataset was randomly divided into two parts, with 70% used for model training and 30% used for model testing. The parameters of each model were set to default settings.

[0096] A predictive model is constructed using a linear regression model. The expression for the linear regression model is as follows:

[0097]

[0098] Where n is the dimension of the sampling points, m is the number of the sampling points, θ0 is the intercept of the regression equation, and θ j These are the coefficients corresponding to the characteristics in the regression equation, X. ij It is the feature vector of the sampling point, y i This is the actual value. L is the predicted value, and L is the value of the loss function.

[0099] A prediction model is constructed using a ridge regression model. The expression for the ridge regression model is as follows:

[0100]

[0101] Where: n is the dimension of the sampling points; m is the number of the sampling points; θ0 is the intercept of the regression equation; θ j These are the coefficients corresponding to the characteristics in the regression equation; X ij It is the feature vector of the sampling point; y i This is the actual value; These are the predicted values; α is the structural characteristic coefficient; L is the value of the loss function;

[0102] A prediction model was constructed using a random forest model, with the nematode ecological index as the predictor variable and ecological risk as the outcome variable. The model was implemented using the randomForest package in R software.

[0103] A predictive model was constructed using a gradient boosting regression tree model, with the nematode ecological index as the predictor variable and ecological risk as the outcome variable. The model was implemented using the caret package in R software.

[0104] A prediction model was constructed using a decision tree model, with the nematode ecological index as the predictor variable and ecological risk as the outcome variable. The model was implemented using the rpart package in R software.

[0105] An extreme random tree model was used to construct a prediction model, with the nematode ecological index as the predictor variable and ecological risk as the outcome variable. The model was implemented using the extraTrees package in R software.

[0106] The specific method for selecting the optimal fitting model in step seven is as follows:

[0107] The mean absolute error (MAE), root mean square error (RMSE), and goodness of fit (R²) were used. 2 The accuracy of each prediction model is evaluated using the following formula:

[0108]

[0109] In the formula, m is the number of sample sites, and y i It outputs the actual value of the label. It is the predicted value of the output label. It is the average value of the output labels;

[0110] According to the calculation results, the smaller the MAE and RMSE, the higher the prediction accuracy. The modeling set R... 2 The validation set R is used to evaluate the fitting accuracy of the model. 2 Used to evaluate prediction accuracy and model generalization ability.

[0111] The beneficial effects of this invention compared to the prior art are as follows: This invention uses Bayesian kernel machine regression analysis to analyze the dose-response relationship among the content of potential toxic elements in soil, the structure of soil nematode communities, and the pollution characteristic index, to obtain the nematode ecological index and pollution characteristic index sensitive to potential toxic elements. The nematode ecological index sensitive to potential toxic elements is used as the predictor variable, and the Nemerow index, pollution load index, and potential ecological risk index are used as the outcome variables. Various machine learning methods are used to construct an ecological risk assessment model for toxic element pollution based on the soil nematode ecological index. The model is then validated, its accuracy is evaluated, and the optimal model is selected, thereby achieving the purpose of using the soil nematode ecological index to assess the ecological risk of potential toxic elements. Attached Figure Description

[0112] The present invention will be further described below with reference to the accompanying drawings:

[0113] Figure 1This is a flowchart illustrating the steps of the ecological risk assessment method for toxic element pollution according to the present invention. Detailed Implementation

[0114] like Figure 1 As shown, this invention assesses the risk of potential toxic element pollution of the ecosystem based on the detection and analysis of soil nematode ecological indices. The specific method for risk assessment includes the following steps:

[0115] Soil samples were collected:

[0116] Sampling was conducted on soil samples with varying degrees of potential toxic element contamination in coal mining areas, along coal transport roads, and in environments far from other industrial pollution sources. Eight to ten sampling points were collected from each area, with each point yielding a zigzag composite sample and five replicates. Sampling points in each area included farmland samples from wheat, corn, and vegetable fields, with soil samples collected from the same points in spring and autumn. Before sampling, surface plant debris and other contaminants were removed. At sites with vegetation, the soil was loosened and the plants and their roots removed. Gravel and other foreign objects were removed from the soil sample using a bamboo shovel. Sampling tools were promptly cleaned to avoid cross-contamination. Bamboo shovels and bamboo strips were used to collect samples directly whenever possible. Soil samples were brought back to the laboratory for processing within 12 hours of collection. Fresh soil samples were divided into three portions for analysis of potential toxic element content, soil physicochemical properties, and soil nematode community structure.

[0117] Determining the content of potentially toxic elements Zn, Cu, Cr, Cd, Ni, Mn, Hg, Pb, As, Sb, and Se in soil:

[0118] This invention specifically employs microwave digestion, inductively coupled plasma mass spectrometry (ICP-MS), and atomic absorption spectrometry to detect the content of potentially toxic elements in soil.

[0119] Weigh 0.2 g of air-dried soil sample (passed through a 100-mesh sieve) into a PTFE digestion tube TF-36, add 10 mL of hydrochloric acid (HCl), and insert the tube into the digestion well. Set the digestion apparatus temperature to 100℃ and heat until a small amount of sample remains, then remove and cool. Add 5 mL of nitric acid (HNO3), 5 mL of hydrofluoric acid (HF), and 1 mL of perchloric acid (HClO4), and cap. Set the digestion apparatus temperature to 150℃ and digest for 120 min. After cooling, open the cap. Raise the temperature to 200℃ until thick white fumes are completely emitted. Evaporate until the digestion solution becomes viscous, and repeat the above digestion process. Remove the digestion tube and cool. Add 2 mL of hydrochloric acid to dissolve the soluble residue, rinse the inner wall with deionized water, and transfer the entire volume to a 50 mL volumetric flask and dilute to volume. Shake well. Detect using inductively coupled plasma mass spectrometry (ICP-MS). Hg is not removed during digestion to prevent volatilization, and is determined using atomic absorption spectrometry.

[0120] Calculate the characteristic index of potential toxic element pollution in soil, including:

[0121] (1) The contamination factor (CF) is widely used to evaluate the degree of contamination by potentially toxic elements and is considered the basis for environmental quality assessment. The calculation formula is as follows:

[0122]

[0123] C i represents the concentration of potentially toxic element i at the sampling point (mg / kg), and C o is the background value (in this invention, the concentrations of various potentially toxic elements in the control area are used as the background value). CF < 1 indicates mild soil pollution; 1 ≤ CF < 3 indicates moderate soil pollution; 3 ≤ CF < 6 indicates high soil pollution; CF > 6 indicates extremely high soil pollution.

[0124] (2) The Nemerow synthetic pollution index (NSPI) is used as a comprehensive index for composite pollution by potentially toxic elements. The calculation formula is as follows:

[0125]

[0126] CF imax is the maximum value among all single pollution indices CF of a certain potentially toxic element in a certain area; CF iave is the average value of all single pollution indices CF of a certain potentially toxic element in a certain area. For the Nemerow index NSPI ≤ 0.7, it indicates that the pollution by potentially toxic elements in this area is within the safe range; 0.7 < NSPI ≤ 1.0 is within the warning range; 1.0 < NSPI ≤ 2.0 is at a mild pollution level; 2.0 < NSPI ≤ 3.0 is at a moderate pollution level; NSPI > 3.0 is at a severe pollution level.

[0127] (3) The metalion load index (PLI) is used to evaluate the composite pollution load of potentially toxic elements in the study area according to the toxicity of each potentially toxic element. It is expressed by the formula:

[0128]

[0129] CF im is the maximum value among all single pollution indices CF of a certain potentially toxic element, PLI i is the pollution load index of a certain sampling point, n is the number of types of potentially toxic elements to be evaluated, PLI zoneis the pollution load index of a certain sampling area. The grading standard of the pollution load index is: PLI ≤ 1, no pollution; 1 < PLI ≤ 2, slight pollution; 2 < PLI ≤ 3, moderate pollution; PLI > 3, heavy pollution.

[0130] (4) The potential ecological risk index (RI) can be used to evaluate the synergistic effect between multiple potentially toxic elements and the degree of potential ecological hazard. The calculation formula is:

[0131] E i = T i × CF i ;

[0132]

[0133] E i is the ecological hazard coefficient of a certain potentially toxic element at a certain sampling point, and T i is the toxicity response coefficient of a certain potentially toxic element. The present invention adopts the toxicity response coefficients in the study of Hakanson: Ti = 1; V = 2; Cr = 2; Mn = 1; Ni = 5; Cu = 5; Zn = 1; As = 10; Cd = 30; Hg = 40; Tl = 40; Pb = 5. The grading of the potential ecological risk index is: E i < 40 or RI < 150, slight risk; 40 ≤ E i < 80 or 150 ≤ RI < 300, medium risk; 80 ≤ E i < 160 or 300 ≤ RI < 600, relatively high risk; 160 ≤ E i < 320 or RI ≥ 600, high risk; E i ≥ 320, extremely high risk.

[0134] Analysis is carried out on the soil nematode community structure:

[0135] Firstly, the isolation, collection and counting of soil nematodes are carried out. Specifically, the nematodes in 200 g of soil are isolated and collected by sucrose centrifugation method, and the total number of nematodes is counted through an Olympus ZX10 stereomicroscope.

[0136] Then, the evaluation indexes of the nematode community structure are calculated, including:

[0137] (1) The nematode abundance is expressed by the total number of nematodes per 100 g of dry soil.

[0138] (2) The relative abundances of each trophic group are respectively the percentages of plant-parasitic nematodes, bacterivorous nematodes, fungivorous nematodes, and predatory omnivorous nematodes in the total number of nematodes per 100 g of dry soil.

[0139] (3) Shannon-Wiener diversity index H:

[0140] H=-∑p i (lnP i );

[0141] Where P i denoted as the proportion of individuals in the i-th taxonomic unit to the total number of nematodes.

[0142] (4) Soil nematode evenness can reflect the composition, structure, and stability of nematode communities, and can illustrate the spatial characteristics of nematode distribution in the soil, E (Pielou evenness index). Its calculation formula is as follows:

[0143] E = H / lnS;

[0144] (5) The maturity index (MI) of free-living nematodes is calculated using the following formula:

[0145] MI=∑v i ×f i ;

[0146] Among them, v i f represents the cp value assigned to each of the different life strategies of free-living nematodes in ecological succession. i denoted as , representing the proportion of individuals of the i-th nematode species to the total number of individuals in the community.

[0147] (6) The plant parasite index (PPI) is calculated using the following formula:

[0148] PPI = ∑v i ×f i ;

[0149] Among them, v i Based on the cp value of plant parasitic nematodes, f i Same as above.

[0150] (7) The Nematode channel ratio (NCR) is the ratio of the number of bacteriophages to micro-nematodes (bacteriophages + fungiophages), and its calculation formula is as follows:

[0151] NCR = Ba / (Ba+Fu);

[0152] (8) The enrichment index (EI) can assess the response of a food web to available resources. Its calculation formula is as follows:

[0153] EI = 100 × e / (e + b);

[0154] Based on the cp value of free-living nematodes, b (basal) represents the basic components of the food web, usually referring to the two major groups Ba2 and Fu2; e (enchment) represents the enriched components of the food web, referring to the two groups Ba1 and Fu2. b and e are calculated as ∑k b n b ,∑k e n e , where k b k e n represents the weighted value corresponding to each group (its value is between 0.8 and 5.0). b n e The abundance of each population group.

[0155] (9) The structure index (SI) can reflect changes in the soil food web structure during human disturbance or ecological restoration. Its calculation formula is as follows:

[0156] SI = 100 × s / (s + b);

[0157] s (structure) represents the structural components of the food web, including Ba3-Ba5, Fu3-Fu5, Om3-Om5, and Ca3-Ca5 groups. The value of s is calculated as ∑k s n s , where k s n represents the weighted value corresponding to each group. s The abundance of each population group.

[0158] This invention specifically employs Bayesian nuclear machine regression (BKMR) to elucidate the dose-response relationship among potential toxic elements in soil, nematode community structure, and pollution characteristic indices. BKMR reveals the different effects of individual potential toxic elements on nematode populations and the potential synergistic or antagonistic interactions between potential toxic elements. The contribution of pollutants to ecological impacts was assessed using five strategies of the BKMR: (1) posterior inclusion probabilities (PIPs) were calculated to represent the relative importance of each potential toxic element to the soil nematode index; (2) a mixed exposure-effect plot was used to analyze the overall association between potential toxic elements and the soil nematode index; (3) univariate exposure-response curves were generated to elucidate the potential nonlinear association between potential toxic elements and the soil nematode index when all other potential toxic elements were fixed at the 50th percentile; (4) the association between individual potential toxic elements and the soil nematode index was assessed when other potential toxic elements were fixed at the 25th, 50th, or 75th percentile; and (5) bivariate exposure-response curves of potential toxic elements and the soil nematode index were plotted to explore potential interactions between potential toxic elements. Furthermore, cross-validation kernel ensemble (CVEK) was used to demonstrate the interaction effect value by utilizing cross-validation to determine the most suitable kernel function. These five strategies of the BKMR were also used to analyze the dose-response relationship between the soil nematode index and the soil potential toxic element pollution index. The 'bkmr' package is used for rigorous 10,000-iteration simulations to ensure the robustness of statistical results. Hypothesis testing for interactions of potentially toxic elements is performed using the CVEK package in R 4.1.0 software. The BKMR calculation formula is:

[0159] Y i =h(z) i1 , ..., z im )+βx i +e i ;

[0160] Where: Y i This is the soil nematode ecological index for the i-th sample; z i1 , ..., z im These are the m potentially toxic elements in the soil corresponding to the i-th sample; x i It is a set of potential confounding factors; h (·) It is the exposure-response function; β is a function of covariance x. i The effect; e i It is a residual.

[0161] This invention constructs an ecological risk assessment model using six computational models, specifically including:

[0162] (1) Construct a soil nematode ecological index prediction model to predict the ecological risk of potential toxic elements using LR:

[0163] Linear regression (LR) is a statistical method used to establish a linear relationship model between one or more independent variables and a dependent variable. A linear regression model finds the optimal linear relationship by minimizing the sum of squared errors. In R software, the `lm()` function can be used to fit a linear regression model. Using the nematode ecological index as the predictor variable and ecological risk as the outcome variable, a linear regression model is constructed. The calculation formula is:

[0164]

[0165] n is the dimension of the sampling points, m is the number of the sampling points, θ0 is the intercept of the regression equation, and θ j These are the coefficients corresponding to the characteristics in the regression equation, X. ij It is the feature vector of the sampling point, y i This is the actual value. L is the predicted value, and L is the value of the loss function.

[0166] (2) Constructing a soil nematode ecological index prediction model using Ridge to predict the ecological risk of potential toxic elements:

[0167] Ridge regression builds upon linear regression by introducing an L2 regularization term to address collinearity and improve the model's generalization ability. Ridge regression reduces coefficients and prevents overfitting by adding a penalty term proportional to the square of the coefficients to the loss function. In R, the `ridge()` function can be used to implement a ridge regression model. Choosing an appropriate regularization parameter `lambda`, using the nematode ecological index as the predictor variable and ecological risk as the outcome variable, the calculation formula is as follows:

[0168]

[0169] n is the dimension of the sampling points; m is the number of the sampling points; θ0 is the intercept of the regression equation; θ j These are the coefficients corresponding to the characteristics in the regression equation; X ij It is the feature vector of the sampling point; y i This is the actual value; α is the predicted value; L is the structural characteristic coefficient; L is the value of the loss function.

[0170] (3) Construct a soil nematode ecological index prediction model using RF to predict the ecological risk of potential toxic elements:

[0171] Random Forest (RF) is a machine learning model that uses multiple decision trees to train and predict data. RF can handle both classification and regression problems; if the target variable is categorical, it solves classification problems; if it's a continuous variable, it handles regression problems. The basic principle of random forest regression is: repeatedly and with replacement, randomly select n samples from the original sample to form x regression trees (xtree), with the samples not selected each time forming out-of-bag (OOB) data; then select m variables (mtry) that best split the data, determining the mtry value based on the principle of minimizing out-of-bag error; finally, combine the resulting multiple regression trees into a random forest and calculate the average value for output. In R software, the randomForest package can be used to build an RF model, using the nematode ecological index as the predictor variable and ecological risk as the outcome variable to construct a random forest prediction model.

[0172] (4) Construct a soil nematode ecological index prediction model using GBRT to predict the ecological risk of potential toxic elements:

[0173] Gradient Boosting Regression Tree (GBRT) is an ensemble learning algorithm based on decision trees that iteratively trains decision trees to minimize the gradient of the loss function. Each tree is a refinement of the previous one, thus progressively improving the model's performance. In R, the `train()` function from the `caret` package can be used with the `gbrt` parameter to build a GBRT model, using the nematode ecological index as the predictor variable and ecological risk as the outcome variable.

[0174] (5) Construct a soil nematode ecological index prediction model to predict the ecological risk of potential toxic elements using DT:

[0175] Decision trees (DT) are an intuitive classification and regression method that uses a tree-like structure to make decisions. In a decision tree, each internal node represents a judgment on an attribute, each branch represents a judgment outcome, and each leaf node represents a decision result. In R software, the rpart package can be used to build decision tree models. The model is constructed by recursively selecting the optimal split point, using the nematode ecological index as the predictor variable and ecological risk as the outcome variable.

[0176] (6) Construct a soil nematode ecological index prediction model using ERT to predict the ecological risk of potential toxic elements:

[0177] Extremely randomized trees (Extra Trees) are an ensemble learning method that makes predictions by constructing multiple extremely randomized decision trees. Unlike random forests, Extremely randomized trees randomly select split points rather than choosing the optimal ones. This approach increases the randomness of the model and reduces the risk of overfitting. In R software, the `extraTrees` package can be used to construct Extremely Randomized Tree models, using the nematode ecological index as the predictor variable and ecological risk as the outcome variable.

[0178] The dataset was randomly divided into two parts, with 70% used for model training and 30% for model testing. This invention uses the glmnet, rpart, gbm, and randomForest functions in R 4.1.0 software, and the parameters for the six models are set to default.

[0179] In evaluating the model, this invention uses mean absolute error (MAE), root mean square error (RMSE), and goodness of fit (R²). 2 To evaluate the accuracy of the prediction model, use MAE, RMSE, and R. 2 The calculation formula is as follows:

[0180]

[0181] In the formula, m is the number of sample sites, and y i It outputs the actual value of the label. It is the predicted value of the output label. This represents the average value of the output labels. Smaller MAE and RMSE values ​​indicate higher prediction accuracy. The modeling set R... 2 The validation set R is used to evaluate the fitting accuracy of the model. 2 Used to evaluate prediction accuracy and model generalization ability.

[0182] The embodiments of the present invention include the following verification experiments:

[0183] (1) The dose-response relationship between potential toxic elements in the soil and the ecological index of nematodes was verified by experiments:

[0184] Soil nematode structure index, nematode channel index, and maturity index showed a linear negative correlation with exposure to multiple potential toxic elements within the 25th to 75th percentile range. However, soil nematode diversity index, enrichment index, and evenness index showed a linear positive correlation with exposure to multiple potential toxic elements. Furthermore, the total abundance of soil nematodes exhibited a J-shaped curve correlation with exposure to multiple potential toxic elements.

[0185] Univariate dose-response results with 95% confidence intervals (CI) showed:

[0186] When the Cu concentration ranged from 1.30 mg / kg [95% CI: (-51.61, -4.25)] to 1.50 mg / kg [95% CI: (-30.65, -5.62)], the total abundance of soil nematodes showed a negative dose-response relationship with Cu.

[0187] Ni concentrations ranging from 1.45 mg / kg [95% CI: (-37.63, -5.57)] to 1.73 mg / kg [95% CI: (-39.90, -0.10)] were negatively correlated with the total abundance of soil nematodes.

[0188] When the Pb concentration ranged from 1.33 mg / kg [95% CI: (-31.35, -7.00)] to 1.56 mg / kg [95% CI: (-39.04, -6.56)], the total abundance of soil nematodes was negatively correlated with Pb.

[0189] Ni concentrations ranged from 1.42 mg / kg [95% CI: (-8.17, -0.13)] to 1.67 mg / kg [95% CI: (-8.89, -0.58)], and the soil nematode enrichment index was negatively correlated with Ni.

[0190] When the Mn concentration ranged from 2.24 mg / kg [95% CI: (-55.47, -7.88)] to 2.49 mg / kg [95% CI: (-16.62, -0.08)], the soil nematode enrichment index was negatively correlated with Mn.

[0191] Pb concentrations ranged from 1.43 mg / kg [95% CI: (-0.06, -0.00)] to 1.85 mg / kg [95% CI: (-0.21, -0.06)], and Pb was negatively correlated with the soil nematode passage index.

[0192] When the As concentration ranged from 1.31 mg / kg [95% CI: (-11.83, -1.35)] to 1.26 mg / kg [95% CI: (-50.43, -21.34)], As was negatively correlated with the soil nematode structure index.

[0193] (2) The dose-response relationship between the soil nematode ecological index and the characteristic index of potential toxic element pollution was verified by experiments:

[0194] Nemerow index, pollution load index and mixed soil nematode index showed a negative linear correlation, while potential ecological risk index showed a positive linear correlation.

[0195] Univariate dose-response results with a 95% CI showed:

[0196] The soil nematode structure index, ranging from 0.09 [95% CI: (-1.05, -0.92)] to 2.16 [95% CI: (-0.13, -0.05)], showed a negative dose-response relationship with the Nemerow index.

[0197] The soil nematode enrichment index was negatively correlated with the Nemerow index, when the enrichment index ranged from 0.09 [95% CI: (-0.56, -0.45)] to 2.16 [95% CI: (-0.06, -0.03)].

[0198] When the soil nematode maturity index is between 0.34 [95% CI: (-0.17, -0.14)] and 0.96 [95% CI: (-0.02, -0.01)], the maturity index is negatively correlated with the Nemerow index.

[0199] Soil nematode channel index was negatively correlated with Nemerow index when the channel index ranged from 0.01 [95% CI: (-0.08, -0.07)] to 0.30 [95% CI: (-0.02, -0.01)].

[0200] The total abundance of soil nematodes ranged from 3.72 [95% CI: (-0.05, -0.04)] to 5.69 [95% CI: (-2.00, -1.97)] and was negatively correlated with the Nemerow index.

[0201] When the soil nematode diversity index was between 0.87 [95% CI: (-0.04, -0.02)] and 1.25 [95% CI: (-0.42, -0.40)], the diversity index was negatively correlated with the Nemerow index.

[0202] Soil nematode evenness indices ranging from 0.50 [95% CI: (-0.03, -0.01)] to 0.69 [95% CI: (-0.21, -0.20)] were negatively correlated with the Nemerow index.

[0203] (3) Result prediction based on the potential toxic element ecological risk model of soil nematode index:

[0204] The decision tree model had the highest MAE (NSPI = 3.05, PLI = 0.498, RI = 177.217) and RMSE (NSPI = 16.583, PLI = 0.221, RI = 7645.183) values. Among the two linear regression-based algorithms, the ridge regression model had the highest NSPI (R... 2 =0.106, MAE=3.009, RMSE=4.53) and RI(R 2The model with a prediction score of 0.124, MAE of 181.734, and RMSE of 272.34 achieved the best prediction performance. However, the random forest model showed the best performance on PLI (R² = 0.124, MAE = 181.734, RMSE = 272.34). 2 =0.144, MAE=0.427, RMSE=0.515). In this invention, the extreme random tree model ranks third, after ridge regression and random forest models. The specific prediction data are shown in Table 1 below.

[0205]

[0206] Table 1. Performance prediction data of the potential toxic element ecological risk model based on soil nematode index.

[0207] This invention employs Bayesian kernel machine regression analysis to examine the intrinsic dose-response relationship among potential toxic elements in soil, nematode ecological indices, and pollution characteristic indices under complex field conditions. It confirms a negative linear dose-response relationship between soil nematode structure index, nematode channel index, maturity index, and potential toxic element concentration. Simultaneously, six prediction models—linear regression (LR), ridge regression (Ridge), random forest (RF), gradient boosting regression tree (GBRT), decision tree (DT), and extreme random tree (ERT)—are used to predict the ecological risk of potential toxic element pollution based on soil nematode ecological indices. The generalization and accuracy of the models are compared, and experiments verify that the Ridge model performs best for the Nemerow index and potential ecological risk index, while the RF model performs excellently for the pollution load index. Therefore, the most suitable evaluation model is selected.

[0208] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating ecological risk of toxic element pollution based on soil nematode ecological index, characterized in that: The evaluation steps include the following: Step 1: Multi-period and multi-site sampling is performed on the soil in the region that is contaminated by different degrees of potential toxic elements, and the sampled soil is used for detection of the content of potential toxic elements in the soil, detection of the physical and chemical properties of the soil, and analysis of the soil nematode community structure; Step 2: The content of potential toxic elements in the soil is detected; Step 3: The characteristic indexes of potential toxic element pollution in the soil are calculated, including pollution factor, Nemerow index, pollution load index, and potential ecological risk index; Step 4: The soil nematode community structure is analyzed, and the evaluation indexes of the soil nematode community structure are calculated, including nematode abundance, the proportion of the number of different soil nematodes, Shannon-Wiener diversity index, soil nematode evenness index, nematode maturity index, nematode channel index, enrichment index, and structure index; Step 5: The dose-response relationship among the content of potential toxic elements in the soil, the soil nematode community structure, and the pollution characteristic indexes is analyzed by using Bayesian kernel machine regression, and the specific method is as follows: By analyzing the dose-response relationship among the potential toxic element content in soil, soil nematode community structure and pollution characteristic index, the nematode ecological index sensitive to potential toxic elements and pollution characteristic evaluation index were determined, and the calculation formula was Y i = h(z i1 ,..., z im )+βx i +e i ; where: Y i is the soil nematode ecological index of the ith sample; z i1 ,..., z im is the mth potential toxic element of soil corresponding to the ith sample; x i is a set of potential confounding factors; h (·) is the exposure-response function; β is the effect on the covariate x i ; e i is the residual error; Step 6: Based on the analysis by the Bayesian kernel machine regression, the nematode ecological indexes sensitive to potential toxic elements and the pollution characteristic evaluation indexes are obtained, and a potential toxic element pollution ecological risk prediction model based on the soil nematode ecological index is constructed by using multiple calculation models, including linear regression model, ridge regression model, random forest model, gradient boosting regression tree model, decision tree model, and extreme random tree model; Step 7: The ecological risk prediction models constructed are evaluated, and the optimal fitting model is selected.

2. The method for evaluating ecological risk of toxic element pollution based on soil nematode ecological index according to claim 1, characterized in that: The potential toxic elements in the contaminated soil sampled in Step 1 include Zn, Cu, Cr, Cd, Ni, Mn, Hg, Pb, As, Sb, and Se.

3. The method according to claim 1, wherein the method is characterized by: The specific method for detecting the content of potential toxic elements in the soil in Step 2 is as follows: 0.2g of air-dried soil sample sieved to 100 mesh is weighed into a polytetrafluoroethylene digestion tube TF-36, 10mL of hydrochloric acid is added, and the digestion hole is inserted; The temperature of the digestion instrument is set to 100℃, and the sample is heated until there is a small amount of residue left, and then it is removed and cooled; 5mL of nitric acid, 5mL of hydrofluoric acid, and 1mL of perchloric acid are added and covered; The temperature of the digestion instrument is set to 150℃, and the digestion is performed for 120min, and after cooling, the cover is opened; The temperature is raised to 200℃, and when the thick white smoke stops, the digestion is continued; The digestion process is repeated until the digestion liquid is thick, and then the digestion tube is removed and cooled; 2mL of hydrochloric acid is added to dissolve the soluble residue, the inner wall is washed with deionized water, and the whole amount is transferred to a 50mL volumetric flask for constant volume, and then it is shaken well; The content of potential toxic elements in the soil is detected by inductively coupled plasma mass spectrometry, and Hg is not chased by acid during digestion to prevent Hg from volatilizing, and atomic absorption spectrometry is used to detect the content of potential toxic elements in the soil.

4. The method for evaluating ecological risk of toxic element pollution based on soil nematode ecological index according to claim 1, characterized in that: The specific method for calculating the potential toxic element pollution characteristic indexes in Step 3 is as follows: In order to evaluate the degree of potential toxic element pollution, the pollution factor CF is used for evaluation, and the calculation formula of the pollution factor CF is as follows: where: CF i represents the pollution factor value of a single potentially toxic element in soil, C i is the concentration of each element in the soil sample, C o is the corresponding background concentration of the potentially toxic element in soil; The classification standard of the pollution factor CF is defined as follows: When CF<1, it indicates that the soil is slightly polluted; When 1≤CF<3, it indicates that the soil is moderately polluted; When 3≤CF<6, it indicates that the soil is highly polluted; When CF>6, it indicates that the soil is extremely highly polluted; In order to evaluate the comprehensive index of the composite potential toxic element pollution, the Nemerow index NSPI is used for evaluation, and the calculation formula of the index is: where: CF imax is the maximum value of the pollution factor of a single potentially toxic element in the soil; CF iave is the average value of the pollution factor of a single potentially toxic element in the soil; The grading standard of the Nemerow index NSPI is defined as: When NSPI≤0.7, the potential toxic element pollution in the region is in a safe range; When 0.7<NSPI≤1.0, the potential toxic element pollution in the region is in a pre-warning range; When 1.0<NSPI≤2.0, the potential toxic element pollution in the region is in a slight pollution level; When 2.0<NSPI≤3.0, the potential toxic element pollution in the region is in a moderate pollution level; When NSPI>3.0, the potential toxic element pollution in the region is in a severe pollution level; According to the toxicity of each potential toxic element, the pollution load index PLI is used for evaluation, and the calculation formula of the index is: wherein PLI i is the pollution load index of a certain sampling point, n is the number of potential toxic element species to be evaluated; The grading standard of the pollution load index PLI is defined as: When PLI≤1, it is non-pollution; When 1<PLI≤2, it is slight pollution; When 2<PLI≤3, it is moderate pollution; When PLI>3, it is high pollution; To evaluate the synergistic effect of multiple potentially toxic elements and the degree of potential ecological harm, the potential ecological risk index (RI) was used for evaluation. The calculation formula of the index is E i = T i × CF i ; Wherein: E i is the ecological harm coefficient of a potential toxic element in a certain sampling point, T i is the toxicity response coefficient of a potential toxic element; The potential ecological risk index is graded, and the grading rule is: When E i <40 or RI < 150, defined as low risk; When 40 < E i <80 or 150 < RI < 300, defined as moderate risk; When 80 < E i <160 or 300 < RI < 600, defined as high risk; When 160 < E i <320 or RI > 600, defined as high risk; When E i ≥ 320, defined as very high risk.

5. The method according to claim 1, wherein the method is characterized by: The specific method for calculating the evaluation index of soil nematode community structure in step four is: When calculating the abundance of nematodes, the total number of nematodes per 100 g of dry soil is used to express; The relative abundance of each nutritional group is calculated respectively per 100 g of dry soil, and the percentage of plant parasitic nematodes, bacterivorous nematodes, fungivorous nematodes and predatory omnivorous nematodes in the total number of nematodes is calculated respectively; The Shannon-Wiener diversity index H is calculated, and the calculation formula is: H = -∑p i (lnP i ); wherein P i is the proportion of individuals in the i-th classification unit that are nematodes; In order to illustrate the spatial characteristics of the distribution of nematodes in the soil, the evenness E of soil nematodes is calculated, and the calculation formula is: E=H / lnS; Wherein, S is the number of nematode groups in each soil sample; The maturity index MI of free-living nematodes is calculated, and the calculation formula is: MI =∑v i x f i ; wherein v i is the c-p value of the i-th nematode, f i is the proportion of the i-th nematode to the total number of individuals in the community. The maturity index PPI of plant parasitic nematodes is calculated, and the calculation formula is: PPI =∑v i ×f i ; wherein v i is the c-p value of the i-th nematode, f i is the proportion of the i-th nematode to the total number of individuals in the community; The ratio of bacterivorous nematodes to microbivorous nematodes is calculated, and the calculation formula of the nematode channel index NCR is: NCR=Ba / (Ba+Fu); Wherein, Ba is the abundance of bacterivorous nematodes, and Fu is the abundance of fungivorous nematodes; In order to evaluate the response of food web to available resources, the enrichment index EI is calculated, and the calculation formula is: EI=100×e / (e+b); Wherein, b represents the basic component in the food web, which refers to Ba2 and Fu2 two groups; e represents the enrichment component in the food web, which refers to Ba1 and Fu2 two groups; The calculation formula of b and e values is: b =∑k b n b ; e = ∑k e n e ; wherein: k b , k e are the weighting values corresponding to each taxon, n b , n e are the abundances of each taxon. In order to reflect the changes of soil food web structure in the process of human disturbance or ecological restoration, the structure index SI is calculated, and the calculation formula is: SI=100×s / (s+b); In the formula: s represents the structural component in the food web, including bacterivorous nematodes, fungivorous nematodes and predatory omnivorous nematodes with c-p value between 3-5; The calculation formula of s value is: s =∑k s n s ; wherein: k s is a weighting value corresponding to each class, n s is the abundance of each class.

6. The method according to claim 4, wherein the method is characterized by: The specific method for constructing the potential toxic element pollution ecological risk prediction model based on soil nematode ecological index in step six is: The data set is randomly divided into two parts, 70% of which is used for model training and 30% of which is used for model testing, and the parameter selection of each model is the default parameter setting; A linear regression model is used to construct a prediction model, and the expression of the linear regression model is: where t is the number of explanatory variables used to build the model, m is the number of samples, θ0is the intercept of the regression equation, θ j is the coefficient corresponding to the feature in the regression equation, X ij is the feature vector of the sampling point, y i is the actual observed value of NSPI, PLI and RI, is the predicted value of NSPI, PLI and RI, and L is the value of the loss function. A ridge regression model is used to construct a prediction model, and the expression of the ridge regression model is: Wherein: α is the structure characteristic coefficient; A random forest model is used to construct a prediction model, the nematode ecological index is taken as a prediction variable, and the ecological risk is taken as an outcome variable, and the randomForest package in R software is used to realize the random forest model; A gradient boosting regression tree model is used to construct a prediction model, the nematode ecological index is taken as a prediction variable, and the ecological risk is taken as an outcome variable, and the caret package in R software is used to realize the gradient boosting regression tree model; A decision tree model is used to construct a prediction model, the nematode ecological index is taken as a prediction variable, and the ecological risk is taken as an outcome variable, and the rpart package in R software is used to realize the decision tree model; An extreme random tree model is used to construct a prediction model, the nematode ecological index is taken as a prediction variable, and the ecological risk is taken as an outcome variable, and the extraTrees package in R software is used to realize the extreme random tree model.

7. The method according to claim 6, wherein the method is characterized by: The specific method for selecting the optimal fitting model in step seven is: The mean absolute error MAE, the root mean square error RMSE and the goodness of fit R 2 The accuracy of each prediction model is evaluated, and the calculation formula is: wherein is the average of NSPI, PLI and RI; According to the calculation results, the smaller the MAE and RMSE, the higher the prediction accuracy, the modeling set R 2 For evaluating the fitting accuracy of modeling, the validation set R 2 For evaluating the prediction accuracy and model generalization ability.

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