CN114997580A - Method for evaluating occurrence form and pollution risk of toxic elements in mining solid waste - Google Patents
Method for evaluating occurrence form and pollution risk of toxic elements in mining solid waste Download PDFInfo
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Abstract
The invention discloses a method for rapidly evaluating toxic element occurrence forms and pollution risks in mining solid wastes, which comprises the following specific steps: collecting samples and determining physicochemical properties; carrying out extraction of the total amount and occurrence form of toxic elements in the mining solid waste, and knowing the biological effectiveness of the toxic elements in the mining solid waste; forming a rapid prediction method for occurrence forms of toxic elements in the mining solid waste based on an artificial intelligence algorithm; forming a mining solid waste toxic element pollution risk assessment method. The method utilizes the relationship between the occurrence forms and the chemical compositions of the toxic elements in the mining solid waste to establish a machine learning model, so that the corresponding content of each occurrence form of the toxic elements in the mining solid waste is predicted and obtained, and the pollution risk of the toxic elements in the mining solid waste is further evaluated. The method has the advantages of high accuracy of judging the occurrence form of the toxic elements, simple process and low cost, has great significance for evaluating the potential pollution risk of the mining solid waste, and simultaneously provides scientific basis for reasonable treatment and utilization of the mining solid waste.
Description
Technical Field
The invention relates to the technical field of pollution risk assessment, in particular to an intelligent analysis method for pollution risk of toxic elements in mining solid waste.
Background
In recent years, environmental pollution in mine areas occurs frequently, and the remediation of harmful element pollution is always concerned by society. In the previous mining pollution research, environmental hazards caused by mining solid wastes are always paid attention. After many mining operations, soil, water, air and biota pollution is caused over time under the influence of climatic factors (such as rain and wind) due to the fact that mining solid wastes are not treated in time or any environmental control measures are not taken. The key for disposing and utilizing the mining solid waste is to efficiently and reasonably evaluate the potential environmental risk of the mining solid waste.
At present, the potential environmental risk of mining solid waste is mostly carried out through environmental indexes, scholars at home and abroad propose pollution evaluation methods of various toxic elements from different angles, for example, a potential ecological index method proposed by Hakanson of the famous geochemistry in Sweden is one of the most commonly used methods for evaluating the pollution degree of the toxic elements at present, and one of the key points of the method is to determine the toxicity coefficient of the toxic elements. However, the current environmental pollution evaluation generally focuses on the total content of toxic elements in the mining solid waste, and the influence of occurrence forms of the toxic elements on the environmental risks of the toxic elements is not considered; meanwhile, most of the evaluation methods are time-consuming and labor-consuming, the operation is complex, professional knowledge is required, and comprehensive treatment of solid wastes in mining industry is seriously hindered. Therefore, a rapid and efficient mining solid waste pollution evaluation method is needed to provide a certain guiding significance for analysis and treatment of mining solid waste.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a method for rapidly evaluating occurrence forms and pollution risks of toxic elements in mining solid waste.
The purpose of the invention is realized by the following technical scheme:
a method for evaluating occurrence forms and pollution risks of toxic elements in mining solid waste comprises the following steps:
s1, collecting a sample and determining the physicochemical property of solid waste in mining industry;
s2, extracting the total amount and occurrence form of toxic elements in the mining solid waste sample, collecting the obtained occurrence form of the toxic elements, the physicochemical properties of the mining solid waste and the properties of the toxic elements into a data set, and collecting the bioavailability indexes of the toxic elements in the mining solid waste;
s3, preprocessing the data set, and selecting an artificial intelligence algorithm to perform super-parameter optimization to obtain a final mining solid waste toxic element occurrence form prediction model;
s4, evaluating the pollution risk of the toxic elements in the mining solid waste based on the content of the toxic elements in the mining solid waste, the occurrence form of the toxic elements, the leaching risk coefficient of the toxic elements and the toxicity response coefficient of the toxic elements, wherein the calculation formula is as follows:
wherein PR represents the pollution risk value of the mining solid waste, n represents n toxic elements in the mining solid waste, m represents the occurrence form type of the toxic elements, and F ji Is the percentage of i occurrence forms of the jth toxic element, M ji Leaching risk coefficient of i occurrence forms of j toxic elements, M ji Between 0 and 1, W j The content of the jth toxic element in the mining solid waste,
is the poison of the jth toxic elementA coefficient of sexual response; the leaching risk factor in the formula is based on the state of the art extraction and is derived by referring to expert opinions.
Further, in step S1, the physicochemical properties of the mining solid waste are determined through laboratory experiments, including density, water content, compressibility, ignition loss, particle size grading, porosity, permeability coefficient, element or oxide content, phase composition, toxic element concentration, organic matter content, PH, alkalinity, redox property, clay mineral content, and biological properties, and the physicochemical properties of the mining solid waste directly affect the occurrence form of toxic elements in the mining solid waste.
Furthermore, the properties of the toxic elements comprise atomic number, relative atomic mass, ionic radius and outermost layer electron number; the biological effectiveness index refers to the toxicity response coefficient T of toxic elements r 。
The invention also provides electronic equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the program to realize the steps of the method for evaluating the occurrence form and the pollution risk of the toxic elements in the mining solid waste.
The invention also provides a computer readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the method for evaluating the occurrence of toxic elements and the risk of pollution in the solid waste of mining industry.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
1. the method is based on an artificial intelligence method, the known characteristic values influencing the occurrence forms of the toxic elements in the mining solid waste are trained, the prediction model is established, and the prediction model is applied to practice, so that the occurrence forms of the toxic elements in the mining solid waste from different sources can be predicted accurately, efficiently and quickly, and the potential pollution risk of the toxic elements in the mining solid waste is evaluated. The method has a guiding effect on the comprehensive treatment and utilization field of the mining solid waste.
2. The method has the advantages of high efficiency, economy, environmental protection and high accuracy, and overcomes the defects of complex operation and long time consumption of a Tessier continuous extraction method and a BCR continuous extraction method. The method has strong practical applicability and quick and convenient operation, can establish a high-accuracy and high-efficiency prediction model only by training the training set, and has remarkable economical efficiency and practicability.
3. At present, the data for evaluating the pollution of the toxic elements is the total amount data of the toxic elements obtained by chemical full analysis, however, the toxic elements existing in a residue state are difficult to be absorbed by plants, and the influence on a food chain is limited. Therefore, the content of toxic elements existing in the mining solid waste in different occurrence forms is respectively tested, so that the pollution degree of the mining solid waste is analyzed, and the polluted mining solid waste is further classified and treated. Therefore, the method establishes an effective method for evaluating the pollution cause and degree of various occurrence forms of toxic elements.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention.
FIG. 2 is the laboratory experiment result of the occurrence form of toxic elements of V, Cr and As in solid waste of certain mining industry.
FIG. 3 is a graph showing the fitting results between the actual and predicted values of the proportions of the toxic element-forming forms in example 1.
FIG. 4 is a graph showing the fitting result between the actual value and the predicted value of the proportion of the toxic element appearance in example 2.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
A method for rapidly evaluating the occurrence form and pollution risk of toxic elements in mining solid waste is disclosed, and is shown in figure 1, and comprises the following steps:
1. the mining solid waste samples were collected and physicochemical properties determined, and the basic characteristics and compositional information of the mining solid waste evaluated in this example included toxic element concentrations (including V, Cr, As, measured by inductively coupled plasma atomic emission spectroscopy in mg/kg), particle size grading (D10, D30, D50, D60, D90, measured by laser granulometer in um), loss on ignition (measured by thermogravimetry in%) and oxide content (MgO, Fe2O3, SO2, SiO2, CaO, P2O5, K2O, TiO2, Al2O3, measured by X-ray fluorescence spectroscopy in units%). FIG. 2 is the laboratory experiment result of the occurrence form of toxic elements of V, Cr and As in solid waste of certain mining industry.
2. According to the characteristics determined in the step 1, 10 samples of the mining solid waste are collected, and the toxic elements in the mining solid waste are extracted by a continuous extraction method, wherein the continuous extraction method comprises the following five steps:
step 201, taking 1g of mining solid waste, putting the mining solid waste into a 100mL centrifuge tube, adding 40mL of pure water, oscillating the mining solid waste for 16 hours at 25 ℃ through a mechanical shaker, and separating solid residues from an extracting solution to obtain an extracting solution B0 of the mining solid waste.
Step 202, putting the solid residue obtained in the step 201 into a centrifuge tube, and adding 40ml of CH3COOH (0.11 mol. L) -1 ) The extraction process was performed in the same manner as described in step 201 under acid-soluble conditions at 25 c to obtain extract B1 of the mining solid waste.
Step 203, putting the solid residue obtained in the step 202 into a centrifuge tube, and adding 40ml of NH2OH & HCl (0.5mol & L) -1 ) The solution pH was adjusted to 1.5 with HNO3, and the extraction process was performed at a reducible temperature of 25 ℃ in the same manner as described in step 201, to obtain extract B2 of the mining solid waste.
Step 204, 10mL of hydrogen peroxide (30%, pH 2.0) was carefully added to the residue of step 203, and the mixture was digested at room temperature for 1 hour, occasionally shaken up by hand. The vessel was heated to 85 ℃ in a water bath for 1 hour of digestion and then heated to evaporate to near dryness. 10mL of hydrogen peroxide (30% and pH 2.0) was added and the digestion was repeated. Then, 50mL of CH3COONH4(1.0 mol. L) was added -1 ) Added to the residue and the extraction process was performed at 25 c (pH adjusted to 2.0 with HNO 3) in the same manner as described in step 201 to obtain extract B3 of the mining solid waste.
Step 205, adding 7ml of HNO3 (60%), 2.5ml of HF (46%) and 0.5ml of H2O2 (30%) into a polytetrafluoroethylene container in a microwave oven at 220 ℃, continuously digesting for 20 minutes, and filtering to obtain the extracting solution B4 of the mining solid waste. Measuring the content of each toxic element in five extracting solutions of each mining solid waste by an inductively coupled plasma atomic emission spectrometry and calculating the percentage of each toxic element, wherein the content of each toxic element in different extracting solutions respectively corresponds to the occurrence form of each toxic element in the mining solid waste, and B0-B4 solutions respectively correspond to a water extractable state, an acid extractable state, a ferro-manganese oxide combined state, an organic matter and sulfide combined state and a residue lattice state. FIG. 2 shows laboratory experimental results of the occurrence of toxic elements of V, Cr and As in the first mining solid waste.
Based on the experiment, basic characteristics and component information of mining solid wastes, toxic element properties (represented by atomic volume, electron affinity, covalent atomic radius, relative atomic mass, electronegativity and atomic number in the example), total concentration of each toxic element in the sample and occurrence form of the toxic element are used as input, and the occurrence form ratio is used as output to construct a data set.
The data of the biological effectiveness of the toxic elements are collected, and the toxicity response coefficients of V, Cr and As in the toxic elements tested in the example are 2, 2 and 10 respectively.
3. The sample data collected in steps 1 and 2 need to be subjected to data normalization preprocessing to improve the performance of the model. The convergence test determines that the division ratio of the training set and the test set is 7:3, that is, the division standard of the data set in this example is that the training set occupies 70% of the size, and the test set occupies 30% of the size. The example selects a neural network regression algorithm as the machine learning algorithm model. In order to optimize the algorithm, the particle swarm algorithm is adopted to optimize the hyper-parameters, the size of the population is selected to be 100, and the final evolution algebra of the particle swarm algorithm is 100. To avoid randomness of the results, 10-fold cross-validation was used. Substituting the optimized hyper-parameters into a neural network algorithm, constructing a final neural network model by using a training set, and evaluating the generalization ability of the neural network model through a test set. In the embodiment, R is selected to evaluate the generalization ability of the neural network model, and is a correlation coefficient, and the value of the correlation coefficient reflects important statistics of linear correlation degree between the neural network model variables. The range of normal values is [ -1, 1], the closer the absolute value is to 1, the better the model fits. R can be calculated from the following formula.
n is the number of samples, y i Is the actual value of the one or more parameters,
is a predicted value of the number of the frames,
is the average of the actual values of the samples,
is the average of the sample predictions.
Fig. 3 is a fitting graph between the actual value and the predicted value of the occurrence form ratio of the toxic element in example 1, and the fitting effect of the real value and the predicted value is good in both the training set and the test set, the R value in the training set is 0.989, and the R value in the test set is 0.955. Therefore, the model has better prediction effect.
4. Based on the content of toxic elements in the mining solid waste, the occurrence form of the toxic elements, the leaching risk coefficient and the toxic element response coefficient, the toxic element pollution risk in the mining solid waste is evaluated, and the calculation formula is as follows:
wherein PR represents the pollution risk value of the mining solid waste, n represents n toxic elements in the mining solid waste, m represents that the occurrence form of the toxic elements is divided into m by a continuous extraction method, and F ji Is the percentage of i occurrence forms of the jth toxic element, M ji The leaching risk coefficient of i occurrence forms of the jth toxic element (between 0 and 1), W j The content of the jth toxic element in the mining solid waste,
the toxicity response coefficient of the jth toxic element. The leaching risk factor in the formula is based on the state of the art extraction and is derived with reference to expert opinions.
The leaching risk coefficient in the formula is based on the prior extraction technology level, and the difficulty degree of toxic elements obtained by direct soaking extraction in water and by adding chemical components such as acid, alkali and the like for adsorption reaction extraction is greatly different. In this example, the leaching risk coefficients of B0 to B4 were determined to be 1, 0.7, 0.5, 0.3, 0.1, and the toxic response coefficient T of the toxic element obtained in step 2 based on the above conceptual analysis r j The pollution risk value of No. 1 mining solid waste can be calculated to be 298.4753 by combining the calculation formulas of Table 1 and PR, and the pollution risk values of 10 mining solid wastes can be calculated by combining the calculation formulas of V2, Cr 2 and As 10, and the pollution risk value of No. 9 mining solid waste is the largest and the pollution risk of No. 3 mining solid waste is the smallest As shown in Table 1.
TABLE 110 values of risk of contamination of mining solid wastes
Example 2
The embodiment provides a method for rapidly evaluating the occurrence form and pollution risk of toxic elements in mining solid waste, and is shown by referring to fig. 1, and the specific implementation method comprises the following steps:
1. mining solid waste sample collection and physicochemical property determination, the mining solid waste base characteristics and component information evaluated in this example include toxic element concentrations (including Zn, Cd, Cu, Ni, Pb, measured by inductively coupled plasma atomic emission spectroscopy in mg/kg), particle size grading (D10, D30, D50, D60, D90, measured by laser granulometry in um), loss of ignition (measured by thermogravimetry in unit%), and oxide content (MgO, Fe2O3, SO2, SiO2, CaO, P2O5, K2O, TiO2, Al2O3, measured by X-ray fluorescence spectroscopy in unit%).
2. According to the characteristics determined in the step 1, 2 samples of the mining solid waste are collected, and toxic elements in the mining solid waste are extracted by a Tessier continuous extraction method, wherein the continuous extraction method comprises the following five steps.
Accurately weighing 2g of mining solid waste sample, carefully placing the sample into a 100mL rigid plastic round-bottom centrifuge tube with a cover, and carrying out step-by-step extraction operation.
(1) 16mL of a 1mol/L MgCl2 solution is added, the pH value is 7.0, the mixture is continuously shaken for 1h at 25 ℃, the centrifugation is carried out for 20min, and the supernatant is taken out and fixed to a volume of 25mL volumetric flask to be measured. The residue was washed with deionized water, centrifuged, and the toxic element concentration was determined after all supernatant was filtered.
(2) Carbonate bound state-16 mL of 1mol/L NaAc solution is added into the residue in the step (1), the residue is continuously shaken for 8h under the pH value of 5.0 and the temperature of 25 +/-1 ℃, centrifuged for 20min, and the supernatant liquid is sucked out and is added into a 25mL volumetric flask to be used as the liquid to be detected for atomic absorption. The residue was washed with deionized water, centrifuged, and the toxic element concentration was determined after all supernatant was filtered.
(3) Adding 16mL of 0.04mol/L NH2OH & HCl 25% HAc solution into the residue of the previous step, shaking intermittently at constant temperature of 96 +/-3 ℃ for 4h, centrifuging for 20min, taking out the supernatant, and metering the volume to a 25mL volumetric flask for atomic absorption to be used as a liquid to be detected. The residue was washed with ionic water, centrifuged, and the toxic element concentration was determined after all supernatants were filtered.
(4) Organic binding state and sulfide binding state, namely adding 3mL of 0.01mol/L HNO3 and 5mL of 30% H2O2 into the residue in the previous step, adjusting the pH value to 2 by using HNO3, heating the mixture to (85 +/-2) DEG C in a water bath, intermittently shaking the mixture for 2H, adding 5mL of H2O2 to adjust the pH value to 2, placing the mixture at (85 +/-2) DEG C, heating the mixture for 2H, intermittently shaking the mixture, cooling the mixture to (25 +/-1) DEG C, adding 5mL of 3.2 mol/L of 20% HNO3 solution of NH4Ac, diluting the mixture to 20mL, continuously shaking the mixture for 30min, centrifuging the mixture for 20min, taking out the supernatant, and fixing the volume to a 25mL volumetric flask to be used as atomic absorption liquid to be detected. Adding deionized water to wash the residue, centrifuging, and filtering all supernatant to determine the concentration of toxic elements.
(5) The residual state comprises quartz, clay minerals and the like, and is digested by HCl + HNO3+ HF + HClO 4. The procedure for digestion of the residual state is the same as that of the full-scale extraction method. And transferring the solution to a 50mL volumetric flask for constant volume to serve as a solution to be detected by a flame atomic absorption spectrometer. Blank samples and standard samples are adopted in the test to control the quality of the test data.
The proportion of toxic elements in exchangeable state, carbonate combined state, iron-manganese oxide combined state, organic combined state, sulfide combined state and residual state can be obtained through the above 5 steps, and the five occurrence states are recorded as F1-F5.
Based on the experiment, basic characteristics and component information of mining solid wastes, toxic element properties (represented by atomic volume, electron affinity, covalent atomic radius, relative atomic mass, electronegativity and atomic number in the example), total concentration of each toxic element in the sample and occurrence form of the toxic element are used as input, and the occurrence form ratio is used as output to construct a data set.
The biological effectiveness of the toxic elements is collected, and the toxicity response coefficients of Zn, Cd, Cu, Ni and Pb in the toxic elements tested in the example are 1, 30, 5 and 40 respectively.
3. The sample data collected in steps 1 and 2 need to be subjected to data normalization preprocessing to improve the performance of the model. The convergence test determines that the division ratio of the training set and the test set is 7:3, that is, the division standard of the data set in this example is that the training set occupies 70% of the size, and the test set occupies 30% of the size. The example selects a neural network regression algorithm as the machine learning algorithm model. In order to optimize the algorithm, the particle swarm algorithm is adopted to optimize the hyper-parameters, the size of the population is selected to be 100, and the final evolution algebra of the particle swarm algorithm is 100. To avoid randomness of the results, 10-fold cross-validation was used. Substituting the optimized hyper-parameters into a neural network algorithm, constructing a final neural network model by using a training set, and evaluating the generalization ability of the neural network model through a test set. In the embodiment, R is selected to evaluate the generalization ability of the neural network model, and is a correlation coefficient, and the value of the correlation coefficient reflects important statistics of linear correlation degree between model variables. The normal range is [ -1, 1], and the closer the absolute value is to 1, the better the neural network model fits. R can be calculated from the following formula.
n is the number of samples, y i Is the actual value of the,
is a predicted value of the number of the frames,
is the average of the actual values of the samples,
is the average of the sample predictions.
Fig. 4 is a fitting graph between the actual value and the predicted value of the occurrence form proportion of the toxic element in this embodiment 2, and the fitting effect of the true value and the predicted value is better in the training set and the test set, where the R value in the training set is 0.969 and the R value in the test set is 0.977. Therefore, the model has better prediction effect.
4. Based on the content of toxic elements in the mining solid waste, the occurrence form of the toxic elements, the leaching risk coefficient and the toxic element response coefficient, the toxic element pollution risk in the mining solid waste is evaluated, and the calculation formula is as follows:
wherein PR represents the pollution risk value of the mining solid waste, n represents n toxic elements in the mining solid waste, m represents that the occurrence forms of the toxic elements are divided into m by a continuous extraction method, and F ji Is the percentage of i occurrence forms of the jth toxic element, M ji Leaching risk coefficient (between 0 and 1) of i occurrence forms of j toxic elements)，W j The content of the jth toxic element in the mining solid waste,
the toxicity response coefficient of the jth toxic element.
The leaching risk coefficient in the formula is based on the prior extraction technology level, and the difficulty degree of toxic elements obtained by direct soaking extraction in water and by adding chemical components such as acid, alkali and the like for adsorption reaction extraction is greatly different. According to the above concept analysis, the leaching risk coefficients of F1-F5 are set as 1, 0.7, 0.5, 0.3 and 0.1, the toxic response coefficients of the toxic elements obtained from step 2 are Zn ═ 1, Cd ═ 30, Cu ═ 5, Ni ═ 5 and Pb ═ 40, and the pollution risk values of the two kinds of mining solid wastes can be calculated and obtained as 3932.469 and 630.1092 respectively by combining the calculation formulas of table 1 and PR. This indicates that the first mining solid waste contamination risk is much higher than the second.
By the method provided by the embodiment of the invention, the mining solid waste with pollution risk can be selected from various mining solid wastes; the metal recovery potential of the single mining solid waste can also be evaluated, for example, a threshold value is set, and when the pollution risk value of the metal is greater than or equal to the threshold value, the mining solid waste is indicated to have the pollution risk; in addition, a plurality of threshold ranges can be set, and the pollution risks corresponding to the threshold ranges are respectively as follows: no pollution risk, low pollution risk, medium pollution risk and high pollution risk; for example, setting the threshold range to 0-50, 50-200, 200-300, > 300; when the recovery potential value is between 0 and 50, the mining solid waste has no pollution risk; when the pollution risk value is 50-200, the pollution risk of the mining solid waste is low; when the pollution risk is 200-300, indicating the pollution risk of the mining solid waste; when the pollution risk is more than 300, the pollution risk of the mining solid waste is high.
Finally, it should be pointed out that: the above examples are merely illustrative of the computational process of the present invention and are not limiting thereof. Although the present invention has been described in detail with reference to the foregoing examples, it should be understood by those skilled in the art that the calculation processes described in the foregoing examples can be modified or equivalent substitutions for some of the parameters may be made without departing from the spirit and scope of the calculation method of the present invention.
The present invention is not limited to the above-described embodiments. The foregoing description of the specific embodiments is intended to describe and illustrate the technical solutions of the present invention, and the above specific embodiments are merely illustrative and not restrictive. Those skilled in the art can make many changes and modifications to the invention without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (5)
1. A method for evaluating toxic element occurrence form and pollution risk in mining solid waste is characterized by comprising the following steps:
s1, collecting a sample and determining the physicochemical property of solid waste in mining industry;
s2, extracting the total amount and occurrence form of toxic elements in the mining solid waste sample, collecting the obtained occurrence form of the toxic elements, the physicochemical properties of the mining solid waste and the properties of the toxic elements into a data set, and collecting the bioavailability indexes of the toxic elements in the mining solid waste;
s3, preprocessing the data set, and selecting an artificial intelligence algorithm to perform super-parameter optimization to obtain a final mining solid waste toxic element occurrence form prediction model;
s4, evaluating the pollution risk of the toxic elements in the mining solid waste based on the content of the toxic elements in the mining solid waste, the occurrence form of the toxic elements, the leaching risk coefficient of the toxic elements and the toxicity response coefficient of the toxic elements, wherein the calculation formula is as follows:
wherein PR represents the pollution risk value of the mining solid waste, n represents n toxic elements in the mining solid waste, m represents the occurrence form type of the toxic elements, and F ji I being a toxic element of the jth kindPercentage of species occurrence, M ji Leaching risk coefficient of i occurrence forms of j toxic elements, M ji Between 0 and 1, W j The content of the jth toxic element in the mining solid waste, T r j The toxicity response coefficient of the jth toxic element; the leaching risk factor in the formula is based on the state of the art extraction and is derived by referring to expert opinions.
2. The method of claim 1, wherein in step S1, physicochemical properties of the mining solid waste are determined by laboratory experiments, and include density, water content, compressibility, ignition loss, particle size distribution, porosity, permeability coefficient, element or oxide content, phase composition, toxic element concentration, organic matter content, PH, alkalinity, redox property, clay mineral content, and biological property, and the physicochemical properties of the mining solid waste directly affect the occurrence morphology of the toxic elements in the mining solid waste.
3. The method for rapidly evaluating the occurrence form and the pollution risk of the toxic elements in the mining solid waste according to claim 1, wherein the properties of the toxic elements comprise atomic number, relative atomic mass, ionic radius and outermost layer electron number; the biological effectiveness index refers to the toxicity response coefficient T of toxic elements r 。
4. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for evaluating the occurrence and pollution risk of toxic elements in solid waste of mining industry according to any one of claims 1 to 3 when executing the program.
5. A computer-readable storage medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, implements the steps of the method for evaluating the occurrence of toxic elements and the risk of pollution in the solid waste of mining industry according to any one of claims 1 to 3.
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