A method for determining the toxicity threshold of pioneer plants in ionic rare earth ore to pollutants

Abstract

The present invention belongs to the technical field of ecological environment protection. The present invention provides a method for determining the toxicity threshold of pioneer plants in ionic rare earth ores to pollutants. The method of the present invention mainly includes measuring the toxicity effect of ionic rare earth ore pollutants on pioneer plants, screening indicators significantly related to the pollutants, selecting the optimal dose-effect model for each indicator for fitting, calculating the toxicity threshold of each evaluation endpoint indicator according to the fitting function, calculating the weight of each evaluation endpoint indicator by using the principal component analysis method, and calculating the comprehensive toxicity threshold of the plant to the pollutants according to the toxicity threshold and weight of the evaluation endpoint indicator. The present invention provides a reference value for judging the toxicity threshold of pioneer plants to pollutants in the ecological restoration of ionic rare earth ores and the screening of pioneer plants in the vegetation restoration of rare earth ore areas.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological environment protection, and particularly to a method for determining the toxicity threshold of pioneer plants in ionic rare earth ores to pollutants. Background Art

[0002] Ionic rare earth resources are an important source of world heavy rare earths, characterized by large reserves, wide distribution, high content of medium and heavy rare earths, good ratio, excellent mining and metallurgical properties, and low radioactivity. With the rapid development of science and technology, the demand for rare earth elements has increased sharply, and the mining process of rare earth elements has caused many environmental problems.

[0003] The pollution of rare earth yards is a process in which a large amount of toxic and harmful substances such as residual chemical agents enter the surrounding environment through different channels and accumulate. Due to the relatively backward pool leaching and heap leaching processes used in the early stage, the depth of a large amount of chemical reagents such as ammonium sulfate and ammonium bicarbonate used in the rare earth smelting process is 3%, and the soaking time is about 150 - 400 days. Due to the high concentration and long time, the soil in the mining area is acidified and compacted, and the organic matter is lost. The lateral seepage of the leaching solution and capillary action damage the plant roots, and many herbaceous plants on the ground die, causing relatively serious damage to the vegetation. It has caused great harm to the ecological environment of the local mining area.

[0004] Protecting the ecology and restoring the environment while exploiting and utilizing rare earth resources is an inevitable requirement in the future. Using pioneer plants for mine ecological restoration is a low-cost and long-term sustainable measure. Pioneer plants have extremely tenacious vitality and can adapt to the extreme conditions of ionic rare earth mines. In order to restore the mine vegetation and improve the ecological environment, pioneer grasses with strong tolerance, such as ryegrass, miscanthus, bermudagrass, bahiagrass, vetiver grass, alfalfa, etc., should be planted first, so that the bare land can be quickly covered by plants, forming a grass community, gradually improving the soil, and thus gradually establishing a stable and high-yield ecological system in the mining area.

[0005] However, the high ammonia nitrogen, sulfate and heavy metal pollution in the mining area often causes ecological and physiological stress on pioneer plants, and has a negative impact on the survival and growth of plants, thus affecting the effect of using pioneer plants for ecological restoration. Therefore, determining the toxicity threshold of pioneer plants to mine pollutants is of great significance for the selection of pioneer plants for ionic rare earth mine ecological restoration. Summary of the Invention

[0006] To overcome the above-mentioned defects existing in the prior art, the present invention provides a method for determining the toxicity threshold of pioneer plants in ionic rare earth ores to pollutants, so as to determine the toxicity threshold of pioneer plants to pollutants, which is of great significance for the selection of pioneer plants for ionic rare earth mine ecological restoration.

[0007] In order to achieve the above-mentioned invention purpose, the present invention provides the following technical solutions:

[0008] The present invention provides a method for determining the toxicity threshold of pioneer plants in ionic rare earth ores to pollutants, comprising the following steps:

[0009] (1) Determining the toxicity effect of ionic rare earth ore pollutants on pioneer plants;

[0010] (2) Based on the toxicity effect determination results obtained in step (1), performing a correlation analysis with the pollutant concentration, and screening out the indicators significantly correlated with the pollutants;

[0011] (3) Taking the pollutant concentration as the independent variable x and the significantly correlated indicators screened in step (2) as the dependent variable y, and selecting the optimal dose - effect model for each indicator for fitting;

[0012] (4) Screening out the indicators that are significantly correlated with the pollutant concentration and have a coefficient of determination > 0.8 in the dose - effect relationship as the comprehensive toxicity threshold evaluation end - point indicators, calculating the toxicity threshold of each evaluation end - point indicator according to the fitting function, and calculating the weight of each evaluation end - point indicator using the principal component analysis method;

[0013] (5) Calculating the comprehensive toxicity threshold of the plant to the pollutant based on the toxicity threshold and weight of the evaluation end - point indicators.

[0014] Preferably, step (1) specifically includes:

[0015] (1.1) Filling pollution - free soil from ionic rare earth ore areas with equal weights into flowerpots, dividing the flowerpots into different gradients, setting a sufficient number of biological replicates for each gradient, adding pollutant solutions with gradient concentrations and the same volume, and mixing evenly to obtain polluted soils with different concentrations;

[0016] (1.2) Sowing seeds with the same mass at a suitable and same depth in each flowerpot. When the plants grow to the rapid growth stage, sampling is carried out to measure the plant growth and physiological indicators.

[0017] Preferably, the plant growth and physiological indicators in step (1.2) are specifically: plant height and above - ground and underground biomass as growth indicators, and chlorophyll a, chlorophyll b, carotene, soluble sugar, proline, hydrogen peroxide, malondialdehyde, superoxide dismutase, peroxidase, glutathione peroxidase, and glutathione as physiological indicators.

[0018] Preferably, the step of selecting the optimal dose - effect model for each indicator for fitting in step (3) is specifically:

[0019] (3.1) Adding the model formulas included in the "dose - response curve" package of R language software to the non - linear fitting custom function of origin;

[0020] (3.2) Use the Rank models App in the origin software to select the optimal model for each indicator and establish a relationship model between the indicator and the pollutant.

[0021] Preferably, the model formula described in step (3.1) includes:

[0022] log-logistic: y = c + (d - c) / (1 + exp(b * (log(x) - log(e)))) f ;

[0023] Brain-Cousens: y = c + (d + f * x - c) / (1 + exp(b * (log(x) - log(e)))) ;

[0024] Cedergreen-Ritz-Streibig: y = c + (d + f * exp(-1 / x) - c) / (1 + exp(b * (log(x) - log(e)))) ;

[0025] Weibull I: y = c + (d - c)exp(-exp(b(log(x) - log(e)))),

[0026] The parameterization of the above model formula adopts a unified structure. The coefficient b represents the slope of the dose-response curve, c and d represent the lower and upper limits of the response, and e represents the effective dose EC 50 .

[0027] Preferably, the steps for selecting the optimal model described in step (3.2) are specifically as follows:

[0028] Use the Rank models App in the origin software, input the pollutant concentration and the corresponding indicator data, check the dose-effect curve model added to the origin software in step (3.1), and the software will output the goodness-of-fit (GoF) indices of different models, including the Akaike information criterion (AIC), Bayesian information criterion (BIC), coefficient of determination (R 2 ), residual sum of squares (RSS), root mean square error (RSS / dof). The smaller the values of AIC, BIC, RSS, and RSS / dof, and the closer R 2 is to 1, the better the model fits the data. The software will sort the models according to the goodness-of-fit indices, and the models ranked at the top are the best-fitting models.

[0029] Preferably, the method for calculating the toxicity threshold of each evaluation endpoint indicator according to the fitting function described in step (4) is specifically as follows:

[0030] (4.1) Calculate that the pollutant stress causes an x% adverse effect on the index relative to the control group:

[0031] b = a * (1 ± x%),

[0032] Where: a represents the index value corresponding to the control group when the pollutant concentration is 0, and "(1 ± x%)" means that if the stress causes the index value to decrease to "(1 - x%)", and if the stress causes the index value to increase to "(1 + x%)";

[0033] (4.2) Substitute the calculated b into the dose-effect relationship curve of the index and the pollutant, and calculate the pollutant concentration corresponding to an x% adverse effect caused by the pollutant stress on each index relative to the control group as the toxicity threshold EC x .

[0034] Preferably, the method for calculating the weight of each evaluation endpoint index by using the principal component analysis method in step (4) is specifically as follows: First, standardize the selected index data by the range method, and then use the principal component analysis to obtain the variance interpretation table and the component matrix table, and extract the principal components with a cumulative contribution rate > 80% to calculate the weight.

[0035] Preferably, the formula for calculating the comprehensive toxicity threshold of the plant to the pollutant according to the toxicity threshold and weight of the evaluation endpoint index in step (5) is specifically as follows:

[0036] ;

[0037] Where: EC x represents the comprehensive toxicity threshold, ECxi represents the threshold of the evaluation endpoint index i, and Wi represents the weight of the evaluation endpoint index i.

[0038] Preferably, the pioneer plants include gramineous or leguminous plants, and the pollutants include NH 4 + , SO 4 2- or heavy metal pollutants.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0040] The present invention screens the plant growth and physiological indicators significantly affected by the pollutants in ionic rare earth ores as the evaluation endpoint indicators, taking into account the specificity of different pollutants to different plants. Reasonably selecting the toxicity threshold evaluation indicators helps to calculate the threshold more accurately. At the same time, in order to avoid the poor fitting effect of using a single dose-effect model to fit multiple indicators, the multi-model fitting effect evaluation of the indicators is carried out, so as to select the best fitting model and improve the accuracy of the measurement. The present invention provides a set of feasible method guidance for judging the toxicity threshold of pioneer plants to pollutants in the ecological restoration of ionic rare earth ores, and also provides a reference value for the screening of pioneer plants in the vegetation restoration of rare earth mining areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0042] Figure 1 It is a schematic diagram of the technical route of the determination method of the present invention;

[0043] Figure 2 It is the dose-effect curve graph of the evaluation endpoint indicators in Example 1 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] The present invention provides a method for determining the toxicity threshold of pioneer plants in ionic rare earth ores to pollutants, which includes the following steps:

[0045] (1) Measuring the toxicity effect of the pollutants in ionic rare earth ores on pioneer plants;

[0046] (2) Based on the toxicity effect measurement results obtained in step (1), performing a correlation analysis with the pollutant concentration, and screening the indicators significantly correlated with the pollutants;

[0047] (3) Taking the pollutant concentration as the independent variable x and the significantly correlated indicators screened in step (2) as the dependent variable y, selecting the optimal dose-effect model for each indicator for fitting;

[0048] (4) Screening the indicators significantly correlated with the pollutant concentration and with a coefficient of determination > 0.8 in the dose-effect relationship as the comprehensive toxicity threshold evaluation endpoint indicators, calculating the toxicity threshold of each evaluation endpoint indicator according to the fitting function, and calculating the weight of each evaluation endpoint indicator using the principal component analysis method;

[0049] (5) Calculating the comprehensive toxicity threshold of the plant to the pollutant based on the toxicity threshold and weight of the evaluation endpoint indicators.

[0050] In the present invention, step (1) preferably specifically includes:

[0051] (1.1) Fill non-polluted soils from ionic rare earth ore areas of equal weight into pots respectively, divide the pots into different gradients, set a sufficient number of biological replicates for each gradient, add pollutant solutions with gradient concentrations and the same volume, and then mix evenly to obtain polluted soils with different concentrations;

[0052] (1.2) Sow seeds of the same mass at a suitable and same depth in each pot. When the plants grow to the rapid growth stage, sample them and measure the physiological indexes of plant growth.

[0053] In the present invention, the physiological indexes of plant growth described in step (1.2) are preferably specifically: plant height and aboveground and underground biomass as growth indexes, and chlorophyll a, chlorophyll b, carotene, soluble sugar, proline, hydrogen peroxide, malondialdehyde, superoxide dismutase, peroxidase, glutathione peroxidase and glutathione as physiological indexes.

[0054] In the present invention, the step of selecting the optimal dose-effect model for fitting for each index in step (3) is preferably specifically:

[0055] (3.1) Add the model formulas included in the "dose-response curve" package of R language software to the non-linear fitting custom function of origin;

[0056] (3.2) Use the Rank models App in origin software to select the optimal model for each index to establish the relationship model between the index and the pollutant.

[0057] In the present invention, the model formulas described in step (3.1) include:

[0058] log-logistic: y = c + (d - c) / (1 + exp(b * (log(x) - log(e)))) f ;

[0059] Brain-Cousens: y = c + (d + f * x - c) / (1 + exp(b * (log(x) - log(e))));

[0060] Cedergreen-Ritz-Streibig: y = c + (d + f * exp(-1 / x) - c) / (1 + exp(b * (log(x) - log(e))));

[0061] Weibull I: y = c + (d - c)exp(-exp(b(log(x) - log(e)))),

[0062] The parameterization of the above model formula adopts a unified structure. The coefficient b represents the slope of the dose-response curve, c and d represent the lower and upper limits of the response, and e represents the effective dose EC 50 .

[0063] In the present invention, the step of selecting the optimal model described in step (3.2) is specifically preferably:

[0064] Using the Rank models App in the origin software, input the pollutant concentration and the corresponding index data, check the dose-effect curve model added to the origin software in step (3.1), and the software will output the goodness-of-fit (GoF) indices of different models, including the Akaike information criterion (AIC), Bayesian information criterion (BIC), coefficient of determination (R 2 ), residual sum of squares (RSS), root mean square error (RSS / dof). The smaller the values of AIC, BIC, RSS, and RSS / dof, and the closer R 2 is to 1, the better the model fits the data. The software will sort the models according to the goodness-of-fit indices, and the models ranked at the top are the best-fitting models.

[0065] In the present invention, the method of calculating the toxicity threshold of each evaluation endpoint index according to the fitting function described in step (4) is specifically preferably:

[0066] (4.1) Calculate the x% adverse effect of the index caused by the pollutant stress relative to the control group:

[0067] b = a*(1 ± x%),

[0068] In the formula: a represents the index value corresponding to the control group when the pollutant concentration is 0, and "(1 ± x%)" means that if the stress causes the index value to decrease to "(1 - x%)", and if the stress causes the index value to increase to "(1 + x%)";

[0069] (4.2) Substitute the calculated b into the dose-effect relationship curve between the index and the pollutant, and calculate the pollutant concentration corresponding to the x% adverse effect of each index caused by the pollutant stress relative to the control group as the toxicity threshold EC x .

[0070] In the present invention, the method of calculating the weight of each evaluation endpoint index by using the principal component analysis method is specifically preferably: First, standardize the selected index data by the range method, and then use the principal component analysis to obtain the variance interpretation table and the component matrix table, and extract the principal components with a cumulative contribution rate > 80% to calculate the weights.

[0071] In the present invention, the formula for calculating the comprehensive toxicity threshold of plants to pollutants based on the toxicity threshold and weight of the evaluation endpoint index in step (5) is preferably specifically:

[0072] ;

[0073] In the formula: EC x represents the comprehensive toxicity threshold, ECxi represents the threshold of evaluation endpoint index i, and Wi represents the weight of evaluation endpoint index i.

[0074] In the present invention, the pioneer plants preferably include Gramineae or Leguminosae plants, and more preferably ryegrass and alfalfa; the pollutants preferably include NH 4 + , SO 4 2- or heavy metal pollutants.

[0075] The technical solutions provided by the present invention will be described in detail below in conjunction with the embodiments, but they cannot be understood as limiting the protection scope of the present invention. Example 1

[0076] Taking the determination of the toxicity threshold concentration of ammonium by the pioneer plant ryegrass of ionic rare earth ore as an example, the specific steps are as follows:

[0077] (1) Determining the toxicity effect of ionic rare earth ore pollutants on pioneer plants:

[0078] Put 1 kg of pollution-free soil from the ionic rare earth ore area into flowerpots respectively, divide the flowerpots into different gradients, set 3 biological replicates for each gradient, add ammonium chloride solutions with gradient concentrations and the same volume (100 ml) respectively and mix evenly to obtain 0, 36, 90, 180, 360, 540 and 720 mg·kg -1 NH 4 + -polluted soil;

[0079] Plant 50 seeds at a depth of 1.5 cm in each flowerpot. When the plants grow to the rapid growth stage, sample them and measure the plant growth physiological indexes: plant height, aboveground and underground biomass, chlorophyll a, chlorophyll b, carotene, soluble sugar, proline, hydrogen peroxide, malondialdehyde, superoxide dismutase (SOD), peroxidase (POD), glutathione peroxidase (GPX) and glutathione (GSH).

[0080] (2) Based on the toxicity effect measurement results obtained in step (1), conduct a correlation analysis with the pollutants to determine the indexes significantly correlated with the pollutants: Use SPSS software to conduct a correlation analysis on the data of each index and the ammonium concentration. The results show that NH 4+ With proline, H 2 O 2 , MDA, POD, GPX and GSH have significant positive correlations, and have significant negative correlations with plant height, root dry weight, soluble sugar and NH 4 + .

[0081] (3) Taking the pollutant concentration as the independent variable x and the significantly correlated indicators screened in step (2) as the dependent variable y, fit the optimal dose-effect model for the indicators: Add the model formula included in the dose-response curve (drc) package in R language to the nonlinear fitting custom function of origin; Use the Rank models App of origin to calculate the GoF index of the same indicator under different model fittings to judge the overall fitting degree of the model. The larger the GoF index, the better the model fits the data.

[0082] (4) Take the indicators that are significantly correlated with the pollutant concentration and have a good fitting effect of the dose-effect relationship (R 2 > 0.8) as the comprehensive toxicity threshold evaluation endpoint indicators. The results are obtained with the root dry weight, soluble sugar, proline, H 2 O 2 , MDA and GSH as the comprehensive toxicity threshold evaluation endpoint indicators. The fitting curves of the dose-effect curves of the indicators are as Figure 2 shown. The dose-effect curves of the root dry weight, soluble sugar, MDA, H 2 O 2 of ryegrass and the ammonium concentration conform to the logistics model, and the dose-effect curves of proline and GSH and the ammonium concentration conform to the DoseResp model. Using the dose-effect relationship curves of each indicator and the pollutant, calculate the pollutant concentrations corresponding to 5%, 10%, 25%, and 50% adverse effects of the plant relative to the control group (0 mg·kg -1 NH 4 + ) as the toxicity thresholds EC 5 , EC 10 , EC 25 , EC 50 . At the same time, use the range method to standardize the selected indicator data, then perform principal component analysis to obtain the variance explanation table and the component matrix table, and extract the principal components with a cumulative contribution rate greater than 80% to calculate the weights. The toxicity thresholds EC 2 O 2 of the root dry weight, soluble sugar, proline, H 5 of ryegrass, MDA, and GSH are 3.9, 321.2, 101.6, 37.5, 484.8, and 43.1 mg·kg -1 (EC10 , EC 25 , EC 50 As shown in Table 1, the corresponding index weights are 0.1194, 0.1656, 0.1803, 0.1779, 0.1763, 0.1806 (as shown in Table 2).

[0083]

[0084]

[0085]

[0086] (5) Calculate the comprehensive toxicity threshold of plants to pollutants based on the thresholds and weights of the evaluation endpoint indicators. Through the formula:

[0087] ;

[0088] In the formula: EC x represents the comprehensive toxicity threshold, ECxi represents the threshold of evaluation endpoint indicator i, and Wi represents the weight of evaluation endpoint indicator i.

[0089] Finally, the comprehensive toxicity threshold EC 5 of ryegrass to ammonium is 171.9 mg·kg -1 , and by the same token, EC 10 is 207.8 mg·kg -1 , EC 25 is 286.6 mg·kg -1 , EC 50 is 382.3 mg·kg -1 .

[0090] To sum up, the present invention screens the plant growth and physiological indicators significantly affected by ionic rare earth ore pollutants as evaluation endpoint indicators, takes into account the specificities of different pollutants to different plants, and reasonably selects toxicity threshold evaluation indicators to help calculate the threshold more accurately; at the same time, in order to avoid the poor fitting effect of a single dose-effect model fitting multiple indicators, the multi-model fitting effect evaluation of the indicators is carried out, so as to select the best fitting model and improve the accuracy of the measurement. The present invention provides a set of feasible method guidance for judging the toxicity threshold of pioneer plants to pollutants in the ecological restoration of ionic rare earth ores, and also provides a reference value for the screening of pioneer plants in the vegetation restoration of rare earth mining areas.

[0091] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for determining the toxicity threshold of pioneer plants in ionic rare earth ore to pollutants, characterized in that, it comprises the following steps: (1) Determining the toxicity effect of ionic rare earth ore pollutants on pioneer plants; (2) Based on the toxicity effect measurement results obtained in step (1), performing a correlation analysis with the pollutant concentration, and screening out the indicators significantly correlated with the pollutants; (3) Taking the pollutant concentration as the independent variable x and the significantly correlated indicators screened in step (2) as the dependent variable y, and fitting the optimal dose-effect model for each indicator; specifically: (3.1) Adding the model formulas included in the "Dose-Response Curve" package of the R language software to the nonlinear fitting custom function of origin; the model formulas are: log-logistic: y = c + (d - c) / (1 + exp(b * (log(x) - log(e)))) f ; Brain-Cousens: y = c + (d + f * x - c) / (1 + exp(b * (log(x) - log(e)))); Cedergreen-Ritz-Streibig: y = c + (d + f * exp(-1 / x) - c) / (1 + exp(b * (log(x) - log(e)))); Weibull I: y = c + (d - c)exp(-exp(b(log(x) - log(e)))), The parameterization of the above model formula adopts a unified structure. The coefficient b represents the slope of the dose-response curve, c and d represent the lower and upper limits of the response, and e represents the effective dose EC 50 ; (3.2)Use the Rank models App in the origin software to evaluate the goodness of fit of each model, and select the optimal model based on the Akaike information criterion AIC, Bayesian information criterion BIC, coefficient of determination R 2 value, residual sum of squares RSS, root mean square error RSS / dof value index, and establish a relationship model between the index and the pollutant; (4) Screening out the indicators that are significantly correlated with the pollutant concentration and have a coefficient of determination > 0.8 in the dose-effect relationship as the comprehensive toxicity threshold evaluation endpoint indicators, calculating the toxicity threshold of each evaluation endpoint indicator according to the fitting function, and calculating the weight of each evaluation endpoint indicator using the principal component analysis method; The method for calculating the toxicity threshold of each evaluation endpoint indicator according to the fitting function is specifically: (4.1) Calculating the adverse effect of the pollutant stress on the indicator relative to the control group by x%: b = a * (1 ± x%), where: a represents the indicator value corresponding to the control group when the pollutant concentration is 0, and "(1 ± x%)" means that if the stress causes the indicator value to decrease to "(1 - x%)", and if the stress causes the indicator value to increase to "(1 + x%)"; (4.2) Substitute the calculated b into the dose-effect relationship curve between the index and the pollutant, and calculate the pollutant concentration corresponding to each index when the pollutant stress causes an x% adverse effect relative to the control group as the toxicity threshold EC x ; (5) Calculating the comprehensive toxicity threshold of the plant to the pollutant according to the toxicity threshold and weight of the evaluation endpoint indicator.

2. The method for determining the toxicity threshold of pioneer plants in ionic rare earth ore to pollutants according to claim 1, characterized in that, step (1) specifically includes: (1.1) Filling pollution-free soil from ionic rare earth ore areas with equal weight into flowerpots respectively, dividing the flowerpots into different gradients, setting a sufficient number of biological replicates for each gradient, adding pollutant solutions with gradient concentrations and the same volume respectively, and mixing evenly to obtain polluted soils with different concentrations; (1.2) Sowing seeds with the same mass at an appropriate and same depth in each flowerpot, sampling when the plants grow to the rapid growth stage, and measuring the plant growth physiological indicators.

3. The method for determining the toxicity threshold of pioneer plants in ionic rare earth ore to pollutants according to claim 2, characterized in that, The specific plant growth physiological indexes described in step (1.2) are as follows: plant height and aboveground and underground biomass are used as growth indexes, and chlorophyll a, chlorophyll b, carotenoid, soluble sugar, proline, hydrogen peroxide, malondialdehyde, superoxide dismutase, peroxidase, glutathione peroxidase and glutathione are used as physiological indexes.

4. A method for determining the toxicity threshold of pioneer plants of ionic rare earth ore to pollutants according to claim 1, characterized in that the specific steps of selecting the optimal model described in step (3.2) are as follows: Using the Rank models App in the origin software, input the pollutant concentration and the corresponding index data, and check the dose-effect curve model added to the origin software in step (3.1). The software will output the goodness-of-fit GoF index of different models, including the Akaike information criterion AIC, the Bayesian information criterion BIC, the coefficient of determination R 2 , the residual sum of squares RSS, the root mean square error RSS / dof. The smaller the values of AIC, BIC, RSS, and RSS / dof, and the closer R 2 is to 1, the better the model fits the data. The software will sort the models according to the goodness-of-fit index, and the models ranked at the top are the best-fitting models.

5. A method for determining the toxicity threshold of pioneer plants of ionic rare earth ore to pollutants according to claim 1, characterized in that the method for calculating the weight of each evaluation endpoint index by using the principal component analysis method described in step (4) is as follows: first, standardize the selected index data by using the range method, and then use the principal component analysis to obtain the variance explanation table and the component matrix table, and extract the principal components with a cumulative contribution rate > 80% to calculate the weight.

6. A method for determining the toxicity threshold of pioneer plants of ionic rare earth ore to pollutants according to claim 1, characterized in that the formula for calculating the comprehensive toxicity threshold of plants to pollutants based on the toxicity threshold and weight of the evaluation endpoint index described in step (5) is as follows: ； Where: EC x represents the comprehensive toxicity threshold, ECxi represents the threshold of evaluation endpoint index i, and Wi represents the weight of evaluation endpoint index i.

7. A method for determining the toxicity threshold of pioneer plants of ionic rare earth ore to pollutants according to any one of claims 1 to 6, characterized in that The pioneer plants include gramineous or leguminous plants, and the pollutants include NH 4 + , SO 4 2- or heavy metal pollutants.

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