A method for determining the distribution concentration of arsenate in soil solid and liquid phases and its application

Abstract

The invention discloses a method for determining the distribution concentration of arsenate in soil solid-liquid phases, and belongs to the field of soil pollution analysis. The invention is based on a surface coordination model calculation method for the chemical equilibrium of arsenate in an aqueous solution and the distribution equilibrium in soil solid-liquid phases, performs soil pretreatment by different experimental methods, and constructs a thermodynamic equilibrium model for the adsorption and distribution of As(V) in a soil system by screening multiple groups of model architecture prediction results. At the same time, a batch adsorption test is carried out on the soil for verification, and the distribution concentration of As(V) in soil solid-liquid phases can be effectively predicted, so as to provide a reference for soil heavy metal ecological risk assessment and soil pollution control.

Description

Technical Field

[0001] The present invention belongs to the field of soil pollution analysis, and more specifically, relates to a method for determining the distribution concentration of arsenate in the solid-liquid phase of soil and its application, which is a surface coordination model calculation method based on the chemical equilibrium of arsenate in aqueous solution and the distribution equilibrium of arsenate in the solid-liquid phase of soil. Background Art

[0002] Arsenic (As) is a highly toxic metal and a carcinogen. Arsenic exists in two main oxidation forms in the environment: trivalent arsenic (AsO3 3- ) and pentavalent arsenic (AsO4 3- ), the toxicity of arsenic depends on the form of arsenic, and the toxicity law is: inorganic arsenic> organic arsenic, trivalent arsenic> pentavalent arsenic. Soil arsenic pollution is a global problem, and there are countless reports on soil arsenic pollution at home and abroad. There are many ways for arsenic in the soil to enter the human body and affect human health. Dietary contact is one of the main ways. Excess arsenic in the soil can be transported upward to the edible part of crops, and eating this part can allow arsenic to enter the human body. It poses a serious threat to human health and food security, and effective governance and restoration are urgent issues to be addressed.

[0003] The adsorption-desorption of arsenic on the surface of solid colloids is one of the most important processes that control its distribution between solid and liquid phases in soil. In addition to the physical and chemical properties of arsenic, the adsorption capacity also depends on the number and type of effective adsorption sites in the soil, soil composition, pH, redox potential and other chemical properties. The study of the arsenic adsorption process in soil is conducive to further analyzing the environmental behavior of arsenic in soil and providing a theoretical basis and analytical means for scientifically evaluating the migration behavior and environmental risks of As(V) in the environment.

[0004] In recent years, geochemical morphological models are usually used to study the distribution of heavy metals in solid / liquid phases. Stern, Stumm and others developed a surface complexation model (Surface Complexation Model, SCM) to describe the adsorption behavior of ions on the mineral surface. SCM regards the adsorption sites on the adsorbent surface as functional groups, regards the adsorption process as a surface reaction between functional groups and ions, and describes the adsorption behavior of ions on the mineral surface as one or more surface coordination reactions. Common surface coordination models that describe the mineral adsorption process include the constant capacitance model (ConstantCapacityModel), the diffusion layer model (DiffuseLayerModel), the double layer model (DoubleLayerModel) and the triple layer model (TripleLayerModel).

[0005] The CD-MUSIC model was proposed by Hiemstra and van Riemsdijk (Hiemstra and van Riemsdijk, 1996; Hiemstra et al., 1996; Hiemstra and van Riemsdijk, 1999) on the basis of the three-layer model (TLM) and the MUSIC model. It is an SCM model used to describe the adsorption behavior of metal ions and non-metallic anions on the surface of soil oxides. The CD-MUSIC model fully considers the spatial distribution of the charge on the adsorbed ions at the solid-liquid interface, rather than treating them as point charges as in previous models. In the CD-MUSIC model, CD (charge distribution) refers to the distribution of the charge of the adsorbed molecules in different electrical layers of the charged surface (Hiemstra and van Riemsdijk, 1996; Hiemstra et al., 1996; Hiemstra and van Riemsdijk, 1999, 2006); and MUSIC (Multi-site surface complexity model) refers to the treatment of the structural heterogeneity of the mineral surface, that is, the use of multiple sites to describe the different coordination forms of the mineral surface, which can more accurately describe the chemical properties of various sites on the mineral surface than the previous single site method (Hiemstra et al., 1989). Today, the CD-MUSIC model has been widely used to study and analyze the adsorption behavior and surface morphology distribution of metal ions, non-metallic anions and organic molecules on soil, and plays an important role in predicting the migration laws of various pollutants in soil.

[0006] So far, there have been many advances in the study of adsorption models on soil oxide minerals, and some research has also been carried out on aluminosilicate clay minerals. For a variety of heavy metals that exist in the liquid phase in the form of cations, the surface complexation model can better describe their adsorption behavior on the mineral surface. For the existing calculation methods for predicting the distribution of heavy metals between the solid and liquid phases in soil, the calculation of anion AsO4 3- The adsorption prediction results in soil are much worse than those of heavy metals in the form of cations. There are defects such as outdated parameters, less consideration of soil environmental factors, and inaccurate calculations. The adsorption-desorption of arsenic on the surface of various solid colloids is one of the most important processes that control its distribution between solid and liquid phases in soil. The surface of iron oxides in soil is the main adsorption surface of As(V) and one of the important factors affecting the migration of As(V) in the environment. Summary of the invention

[0007] In order to solve the above technical problems, the present invention provides a method for determining the distribution concentration of arsenate in the solid and liquid phases of soil, comprising the following steps:

[0008] S1. Soil pretreatment

[0009] Taking soil that has not been contaminated by arsenic, air-drying it at room temperature to remove impurities, grinding it with a ceramic mortar, and passing it through a 60-mesh sieve, then removing organic matter from the soil to form inorganic soil, and measuring the physical and chemical properties of the inorganic soil; the impurities include sand particles and plant residues;

[0010] S2. Arsenic adsorption experiment

[0011] Selecting multiple concentrations of arsenate initial dosage to perform a batch adsorption experiment on the inorganic soil obtained in step S1, measuring the pH of the inorganic soil after equilibrium and the arsenate concentration in the liquid phase, and obtaining As(V) adsorption data;

[0012] S3. Establish allocation model and calculate

[0013] According to the inorganic soil physical and chemical properties measured in step S1 and the As(V) adsorption data obtained in step S2, a distribution model of As(V) in the soil is established, and then the adsorption result of As(V) on the soil is calculated by the distribution model;

[0014] Note: Through the establishment of the above method, the thermodynamic equilibrium model can be established using arsenic adsorption data and soil physical and chemical properties. The parameters and model framework can be extrapolated to a variety of soil types. The method is simple and reliable with a wide range of applications. It can better predict the morphological distribution of As(V) in the soil and the distribution between solid and liquid phases, providing a reference for actual soil risk assessment and soil heavy metal pollution control.

[0015] Furthermore, in step S1, the step of removing organic matter is as follows: 40 g of soil after passing through a 60-mesh sieve is placed in a 2-L beaker, and 800 mL of 1 mol·L -1 The NaClO solution was stirred at 25°C for 6 h, then centrifuged at 10,000 rpm for 30 min, the supernatant was discarded, and the mixture was repeated 3 times. The mixture was freeze-dried at -5°C for later use.

[0016] Description: By adding NaClO solution, it can act as an oxidant to remove organic matter from the soil.

[0017] Furthermore, in step S1, determining the physical and chemical properties of inorganic soil includes: determining the content of amorphous iron oxides in the soil; determining the effective PO4 3- content; determine the cation content in the soil; among them, the total iron oxide content in the soil was determined by the sodium dithionite-sodium citrate-sodium bicarbonate extraction method; the amorphous iron oxide content in the soil was determined by the oxalic acid-ammonium oxalate extraction method; the effective PO4 in the soil was determined by the sodium dithionite-sodium citrate-sodium acetate extraction method 3-Content: 0.43 mol·L -1 Determination of large amounts of cations such as Na in soil by HNO3 extraction + , Ca 2+ , K + Mg 2+ Content;

[0018] It shows that the above method can be used to determine the physical and chemical properties of soil more accurately.

[0019] Furthermore, in step S1, a composite method is used to remove organic matter, the steps are:

[0020] Take 40g of soil after passing through a 60-mesh sieve, stir and wash the soil sample with 800mL of 1mol·L-1 NaClO solution for 1.5h, then dry at 50℃ for 2h, and then freeze at -70℃ for 10min to form cooling soil, add the cooling soil to 500mL of H2O2 with a mass concentration of 30% at room temperature to obtain a H2O2 solution of cooling soil, and then gradually heat the H2O2 solution of cooling soil. When foam appears, heat it in a 70℃ constant temperature water bath and start to stir continuously. When the foam subsides, continue to add H2O2 until the soil color becomes lighter and there is no reaction. After cooling, pour off the supernatant and dry it for later use;

[0021] Note: The above method can remove organic matter to the greatest extent possible without affecting the adsorption capacity of various mineral ions, and has little effect on the physical and chemical properties of the soil and the adsorption performance of arsenate, which can improve the accuracy of model prediction.

[0022] Furthermore, the steps of the batch adsorption experiment in step S2 are:

[0023] Add 0.1 g of inorganic soil and 10 mL of 0.01 mol·L -1 NaNO3 was used as the electrolyte solution, and an appropriate amount of As(V) stock solution was added. A trace amount of 0.1 mol·L-1 HNO3 or 0.05 mol·L-1 NaOH solution was added to adjust the pH to 3-10. The centrifuge tube was oscillated and balanced at 25°C for 24 hours, and then centrifuged at 4000 rpm for 5 minutes. The supernatant was filtered through a 0.22 μm filter membrane, and the As(V) concentration in the filtrate was determined by ICP-OES to form adsorption data.

[0024] Note: By carrying out the As(V) batch adsorption experiment through the above steps, the As(V) concentration in the filtrate can be determined and the adsorption data can be formed more accurately.

[0025] Furthermore, the distribution model of As(V) in step S3 uses two iron oxides, goethite and ferrihydrite, as representatives, and the contents of the two iron oxides are converted from the total iron oxide content and the amorphous iron oxide content in the soil;

[0026] Description: Iron oxides have the strongest adsorption effect on As(V) and are one of the important factors affecting the migration of As(V) in the environment. The use of goethite and ferrihydrite can effectively represent the adsorption surface of various iron oxides in the soil.

[0027] Furthermore, the distribution model in step S3 uses ECOSAT4.9 software to calculate the experimental data of batch adsorption of As(V) on soil. The parameters that need to be input in ECOSAT4.9 software are: system pH value, total As(V) concentration, iron oxide concentration, competitive ion effective state PO4 3- The concentration of Ca + concentration and its adsorption parameters on various surfaces;

[0028] Description: This model can significantly increase the anion AsO4 3- Accuracy of predictions from adsorption calculations in soil.

[0029] Furthermore, the method for establishing the distribution model of As(V) is:

[0030] S3-1, respectively select the concentration of goethite, the concentration of ferrihydrite, and the effective state of competing ions PO4 3- The concentration of Ca + One or more parameters of concentration form a set of model frameworks;

[0031] S3-2, using the model framework in step S3-1, the correlation coefficient R and root mean square error RMSE value of the actual value and the predicted value of the As(V) adsorption experiment in step S2;

[0032] According to the correlation coefficient R and root mean square error RMSE value between the calculation results of multiple groups of model architectures and the actual values, the optimal allocation prediction model architecture is obtained; among them, the closer the correlation coefficient R is to 1, the better the simulation effect is, and the smaller the root mean square error RMSE value is, the smaller the error is and the better the prediction effect is.

[0033] S3-3, using the optimal distribution prediction model framework obtained in step S3-2, after inputting the considered component concentrations and adsorption parameters into the ECOSAT4.9 software, the concentrations of each form of As(V) at adsorption equilibrium on the soil surface are calculated;

[0034] Note: Since phosphate usually exists in the form of oxygen-containing anions, which is the same as the form of arsenic in solution, its adsorption behavior is similar to that of arsenic, and it will compete with arsenic for adsorption sites, thereby inhibiting the adsorption of arsenic. Therefore, the competitive ion effective state PO4 is considered. 3- and Na in soil + , Ca + The influence of environmental factors makes the prediction model architecture more accurate.

[0035] Furthermore, a method for determining the distribution concentration of arsenate in the solid and liquid phases of soil is applied in the field of soil pollution analysis and ecological risk assessment and prediction; soil samples from ten regions including Kunming, Quanzhou, Qinglong, Yingtan, Jishou, Nanning, Nanjing, Taiyuan, Yixing and Xihe are selected for As(V) distribution model adsorption prediction, and the predicted results are compared with the experimental values.

[0036] The beneficial effects of the present invention are:

[0037] (1) The present invention uses the adsorption data of representative soils from all over the country as the basis, combines the chemical equilibrium model in the solution and the adsorption parameters of As(V) on the surfaces of goethite and ferrihydrite in the relevant literature, and constructs a multi-surface distribution model of As(V) on the soil surface. It has been verified that the distribution of As(V) between the solid and liquid phases in the soil can be effectively predicted; on the basis of using goethite or ferrihydrite as the single adsorption surface in the As(V) soil distribution model in the past, goethite and ferrihydrite are selected as two adsorption surfaces and the relevant parameters are adjusted, and the competitive ion effective state PO4 is also considered. 3- The prediction results are more accurate than those of other model frameworks due to the influence of environmental factors such as large amounts of cations in the soil.

[0038] (2) The present invention is based on a thermodynamic equilibrium model, and the parameters and model framework can be extrapolated to a variety of soil types. The method is simple and reliable, with a wide range of applications. It can better predict the morphological distribution of As(V) in the soil and the distribution between the solid and liquid phases, providing a reference for actual soil risk assessment and soil heavy metal pollution control. At the same time, the organic matter in the soil is removed by a composite method, so that the organic matter can be removed to a large extent without affecting the adsorption capacity of each mineral ion, and the physical and chemical properties of the soil and the adsorption performance of arsenate are less affected, which can improve the accuracy of model prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a comparison diagram of the adsorption experiment and calculation results of As(V) on Kunming soil at various pH and As(V) initial concentrations in Example 1 of the present invention (the ordinate is the solid phase concentration of As(V) on the soil surface (μmol·L-1), the background solution is 0.01MNaNO3, the soil suspension concentration is 10g·L-1, the icon is the experimental data, and the line is the calculation result);

[0040] Figure 2 It is a comparison diagram of the adsorption experiment and calculation results of As(V) on Quanzhou soil at various pH and As(V) initial concentrations in Example 1 of the present invention (the ordinate is the solid phase concentration of As(V) on the soil surface (μmol·L-1), the background solution is 0.01MNaNO3, the soil suspension concentration is 10g·L-1, the icon is the experimental data, and the line is the calculation result);

[0041] Figure 3 It is a comparison diagram of the adsorption experiment and calculation results of As(V) on Qinglong soil at various pH and As(V) initial concentrations in Example 1 of the present invention (the ordinate is the solid phase concentration of As(V) on the soil surface (μmol·L-1), the background solution is 0.01MNaNO3, the soil suspension concentration is 10g·L-1, the icon is the experimental data, and the line is the calculation result);

[0042] Figure 4 It is a comparison diagram of the adsorption experiment and calculation results of As(V) on Yingtan soil at various pH and As(V) initial concentrations in Example 1 of the present invention (the ordinate is the solid phase concentration of As(V) on the soil surface (μmol·L-1), the background solution is 0.01MNaNO3, the soil suspension concentration is 10g·L-1, the icon is the experimental data, and the line is the calculation result);

[0043] Figure 5 It is a comparison diagram of the adsorption experiment and calculation results of As(V) in Jishou soil at various pH and As(V) initial concentrations in Example 1 of the present invention (the ordinate is the solid phase concentration of As(V) on the soil surface (μmol·L-1), the background solution is 0.01MNaNO3, the soil suspension concentration is 10g·L-1, the icon is the experimental data, and the line is the calculation result);

[0044] Figure 6 It is a comparison diagram of the adsorption experiment and calculation results of As(V) on Nanning soil at various pH and As(V) initial concentrations in Example 1 of the present invention (the ordinate is the solid phase concentration of As(V) on the soil surface (μmol·L-1), the background solution is 0.01MNaNO3, the soil suspension concentration is 10g·L-1, the icon is the experimental data, and the line is the calculation result);

[0045] Figure 7 It is a comparison diagram of the adsorption experiment and calculation results of As(V) on Nanjing soil at various pH and As(V) initial concentrations in Example 1 of the present invention (the ordinate is the solid phase concentration of As(V) on the soil surface (μmol·L-1), the background solution is 0.01MNaNO3, the soil suspension concentration is 10g·L-1, the icon is the experimental data, and the line is the calculation result);

[0046] Figure 8 It is a comparison diagram of the adsorption experiment and calculation results of As(V) on Taiyuan soil at various pH and As(V) initial concentrations in Example 1 of the present invention (the ordinate is the solid phase concentration of As(V) on the soil surface (μmol·L-1), the background solution is 0.01MNaNO3, the soil suspension concentration is 10g·L-1, the icon is the experimental data, and the line is the calculation result);

[0047] Fig. 9 It is a comparison diagram of the adsorption experiment and calculation results of As(V) on Yixing soil at various pH and As(V) initial concentrations in Example 1 of the present invention (the ordinate is the solid phase concentration of As(V) on the soil surface (μmol·L-1), the background solution is 0.01MNaNO3, the soil suspension concentration is 10g·L-1, the icon is the experimental data, and the line is the calculation result);

[0048] Fig.10 It is a comparison diagram of the adsorption experiment and calculation results of As(V) on soil at various pH values ​​and As(V) initial concentrations in Example 1 of the present invention (the ordinate is the solid phase concentration of As(V) on the soil surface (μmol·L-1), the background solution is 0.01MNaNO3, the soil suspension concentration is 10g·L-1, the icons are experimental data, and the lines are calculation results);

[0049] Fig.11 This is a verification diagram of the adsorption experiment and model calculation results of Example 1As(V) of the present invention on ten kinds of soils (the vertical axis is the model prediction value (mol·L-1), and the horizontal axis is the actual measured value (mol·L-1). In the figure, RMSE and R are the root mean square error and correlation coefficient between the model prediction value and the actual observation. The solid line is the 1:1 line).

[0050] Fig.12 This is a verification diagram of the adsorption experiment and model calculation results of Example 3As(V) of the present invention on ten kinds of soils (the vertical axis is the model prediction value (mol·L-1), and the horizontal axis is the actual measured value (mol·L-1). In the figure, RMSE and R are the root mean square error and correlation coefficient between the model prediction value and the actual observation. The solid line is the 1:1 line). DETAILED DESCRIPTION

[0051] The present invention is further described in detail below in conjunction with specific implementation methods to better reflect the advantages of the present invention.

[0052] 1. Comparison of experimental adsorption results of As(V) in soil and the results of distribution model calculation and prediction

[0053] Example 1

[0054] Soil from ten regions including Kunming, Quanzhou, Qinglong, Yingtan, Jishou, Nanning, Nanjing, Taiyuan, Yixing and Xihe was selected; the soil was air-dried at room temperature to remove impurities such as sand and plant residues, and then ground with a ceramic mortar to crush the soil and pass it through a 60-mesh sieve.

[0055] 1. Use the composite method to remove organic matter. Take 40g of soil from Kunming, Quanzhou, Qinglong, Yingtan, Jishou, Nanning, Nanjing, Taiyuan, Yixing, and Xihe, and use 800mL of 1mol·L -1 Stir and wash the soil sample with NaClO solution for 1.5h, then dry it at 70℃ for 2h, and then freeze it at -70℃ for 10min to form cooling soil. Add cooling soil to 500mL H2O2 with a mass concentration of 30% at room temperature, and then gradually heat the H2O2 solution of cooling soil. When foam appears, heat it in a 70℃ constant temperature water bath and start stirring continuously. When the foam subsides, continue to add H2O2 until the soil color becomes lighter and there is no reaction. After cooling, pour off the supernatant and dry it for later use.

[0056] 2. Arsenic adsorption experiment: select 845μmol / L, 222μmol / L, 205μmol / L, 445μmol / L, 493μmol / L, 209μmol / L, 422μmol / L, 275μmol / L, 53μmol / L, 56μmol / L of arsenate as initial dosage for batch adsorption experiment, measure the pH of the sample after equilibrium and the arsenate concentration in the liquid phase, and obtain As(V) adsorption data; specifically: add 0.1g of inorganic soil and 10mL of 0.01mol·L in 10 15mL centrifuge tubes respectively. -1 NaNO3 was used as the electrolyte solution, the above initial concentration of As(V) was added, and a trace amount of 0.1mol·L-1HNO3 or 0.05mol·L-1NaOH solution was added to adjust the pH to 3-10. The centrifuge tube was oscillated and balanced at 25°C for 24 hours, and then centrifuged at 4000rpm for 5 minutes. The supernatant was filtered through a 0.22μm filter membrane, and the As(V) concentration in the filtrate was determined by ICP-OES to form adsorption data. The experimental results are shown in Figure 2. Figure 1-10 shown.

[0057] Example 2

[0058] Soil from ten regions including Kunming, Quanzhou, Qinglong, Yingtan, Jishou, Nanning, Nanjing, Taiyuan, Yixing and Xihe was selected. The soil was air-dried at room temperature to remove impurities such as sand and plant residues. The soil was then ground with a ceramic mortar. The soil was ground and passed through a 60-mesh sieve to remove organic matter from the soil to form inorganic soil. The physical and chemical properties of the inorganic soil were measured and the distribution model was predicted.

[0059] 1. Use a composite method to remove organic matter. Take 40g of soil from Kunming, Quanzhou, Qinglong, Yingtan, Jishou, Nanning, Nanjing, Taiyuan, Yixing, and Xihe respectively. Stir and wash the soil samples with 800mL of 1mol·L-1 NaClO solution for 1.5h, then dry at 70℃ for 2h, and then freeze at -70℃ for 10min to form cooling soil. Add cooling soil to 500mL of H2O2 with a mass concentration of 30% at room temperature, and then gradually heat the H2O2 solution of cooling soil. When foam appears, heat it in a 70℃ constant temperature water bath and start stirring continuously. When the foam subsides, continue to add H2O2 until the soil color becomes lighter and there is no reaction. After cooling, pour off the supernatant and dry it for later use.

[0060] 2. Determination of amorphous iron oxide content in inorganic soil by oxalic acid-ammonium oxalate extraction method (ox method); take 700mL 0.2mol·L -1 of ammonium oxalate solution and 535 mL of 0.2 mol·L -1 oxalic acid solution into a 2L beaker and mix, then add 0.2mol·L -1 The pH value was adjusted to 3.0 by adding ammonium oxalate or oxalic acid solution, 0.50 g soil sample was weighed into a 15 mL centrifuge tube, 10 mL oxalic acid-ammonium oxalate buffer solution was added, and the mixture was shaken for 4 h in the dark; centrifuged at 3000 rpm for 15 min, the supernatant was collected after filtration and stored in the dark, the Fe concentration in the supernatant was determined by ICP-OES, and the amorphous iron oxide content in the inorganic soil was calculated using the following formula:

[0061]

[0062] Wherein, w(ox-Fe) is the content of amorphous iron oxides in inorganic soil (g kg -1 soil); c is the measured Fe concentration (mg·L -1 ); V is the volume of the solution (mL), which is 10 mL under the experimental conditions; m is the mass of the soil sample (g), which is 0.50 g under the experimental conditions;

[0063] 3. The content of iron oxides in inorganic soil was determined by sodium dithionite-sodium citrate-sodium bicarbonate extraction method (DCB method); 0.30 g of inorganic soil was weighed into a 15 mL centrifuge tube and 0.3 mol·L -1 10mL of sodium citrate solution, 1mol·L -1 1.25 mL of NaHCO3 solution and 0.2 g of Na2S2O4 powder were placed in a water bath at 80°C for 20 min with constant stirring. After cooling, the mixture was centrifuged at 3000 rpm for 15 min and the supernatant was collected. This process was repeated once. Then 10 mL of 0.1 mol·L-1 The centrifuge tube residue was washed twice with sodium chloride solution, and the two supernatants and washings were transferred to a 50 mL volumetric flask. After the volume was fixed, the Fe content in the solution was determined by ICP-OES, and the total iron oxide content in the inorganic soil was calculated using the following formula;

[0064]

[0065] Wherein, w(DCB-Fe) is the total iron oxide content in the inorganic soil (g kg -1 soil), c is the measured Fe concentration (mg·L -1 ); V is the volume of the solution (mL), which is 50 mL under the experimental conditions; m is the mass of the inorganic soil (g), which is 0.3 g under the experimental conditions;

[0066] 4. Determine the available phosphorus content by sodium dithionite-sodium citrate-sodium acetate extraction method; weigh 0.2g of inorganic soil into a 15mL polypropylene centrifuge tube, add 1.25mL of 0.3mol·L -1 Sodium acetate solution, 10mL 0.2mol·L -1 NaHCO3 solution and 0.65g Na2S2O4 powder; incubate in a water bath at 60℃ for 3.5h, centrifuge at 3000rpm for 15min, filter, and determine the PO4 content of the supernatant by UV spectrophotometry. 3- content;

[0067] 5. Determination of large amounts of cationic Na in inorganic soils using 0.43 mol·L-1 HNO3 extraction method + , Ca 2+ , K + Mg 2+ Weigh 1.00 g of soil sample into a 15 mL centrifuge tube and add 10 mL of 0.43 mol·L -1 HNO3 solution; after shaking in a constant temperature shaking box at 25°C for 2 hours, centrifuge at 4000 rpm for 5 minutes, save the supernatant, and measure the concentration of each ion in the solution by ICP-OES;

[0068] Soil pH, total As(V) concentration, iron oxide concentration, available PO4 3- The concentration of Ca + Concentration parameters: ECOSAT4.9 software was used to calculate the adsorption concentration of As(V) on the solid phase and obtain the dissolved concentration of As(V) under this condition;

[0069] The concentration of goethite in the As(V) distribution model was converted by using the DCB-Fe content in the soil. -1 ) = [DCB-Fe content in soil - ox-Fe content] (g kg -1 )*Soil suspension concentration (g·L -1 )*1000*107 / 54, where 107 is the molar mass of goethite Fe(OH)3;

[0070] The ferrihydrite concentration was converted using the ox-Fe content in the soil. -1 ) = ox-Fe content in soil (g kg -1 )*Soil suspension concentration (g·L -1 )*1000*107 / 54;

[0071] Effective PO4 3- The concentration (g·L -1 ) = Available PO4 in soil 3- Content (g·kg -1 )*Soil suspension concentration (g·L -1 )*1000;

[0072] After the parameters are entered, click the "RUN" button to calculate the concentration of each form of As(V) at adsorption equilibrium on the soil surface. The final output results are saved in the OUTMAN file for viewing. The model calculation results are as follows: Figure 1-10 As shown;

[0073] Comparing the experimental data obtained in Example 1 with the calculated data obtained in Example 2, Figure 1-10 As shown; the circle and square points are experimental data, and the solid and dotted lines are calculated results (I = 0.01 mol·L -1 NaNO3); By comparing the model calculation results with the adsorption experimental results, it can be seen that the soil prediction results in Kunming, Quanzhou, Qinglong, Jishou, Nanning, Nanjing, Taiyuan, Xihe and Yixing are relatively close to the experimental results, and the predicted concentration of arsenate ions in Yingtan is relatively small. In general, the As(V) distribution model has a good prediction effect on As(V).

[0074] 2. Establish the optimal model based on multiple group allocation model selection

[0075] Example 3

[0076] Targeting various adsorption surfaces and competing ions PO4 3- As well as the influence of environmental factors on Ca, soils from ten regions including Kunming, Quanzhou, Qinglong, Yingtan, Jishou, Nanning, Nanjing, Taiyuan, Yixing, and Xihe were selected as the research objects, and S1 to S12 models were set up for each.

[0077] The predicted values ​​of soil As(V) in ten regions under each model were calculated using the ECOSAT4.9 software in Example 2, and the actual adsorption amount was obtained using the batch adsorption experiment of As(V) in Example 1; the covariance of the predicted adsorption amount and the actual adsorption amount was calculated by the distribution model to obtain the correlation coefficient (R) and the root mean square error (RMSE) under the model, and then a group of models with the best prediction effect were selected; as shown in Table 1, where a check mark indicates that the influence of the concentration of the component is taken into account and participates in the calculation, and a cross indicates that the corresponding component is not considered;

[0078] Table 1 Conditions and prediction results of As(V) adsorption on soil under multiple model frameworks

[0079]

[0080]

[0081] As shown in Table 1, the correlation coefficient R and root mean square error RMSE values ​​of the calculated results and the actual values ​​according to different model frameworks can be obtained. Model S9 (i.e., taking the two active surfaces of goethite and ferrihydrite as the main adsorption surfaces of As(V), and considering the effective PO4 3- The correlation coefficient (R) of the prediction effect is closest to 1 and the root mean square error (RMSE) is the smallest, which is the optimal result;

[0082] The results of model S9 are as follows Fig.11 As shown, compared with the experimental values, the data points of the predicted values ​​of As(V) adsorption on soil by the As(V) distribution model are almost all concentrated within the ±1 standard error range of the 1:1 line, proving that the As(V) distribution model has a good prediction effect on As(V);

[0083] Among them, the charge distribution-multisite surface coordination (CD-MUSIC) model was used for As(V) on the surface of soil iron oxides; the corresponding aqueous phase morphological reactions and their equilibrium constants are shown in Table 2, and the adsorption model parameters of As(V) on the surface of goethite and ferrihydrite (HFO) are shown in Table 3;

[0084] Table 2 Water phase morphology reaction and its equilibrium constant table

[0085]

[0086]

[0087]

[0088] Table 3 Adsorption model parameters

[0089]

[0090]

[0091] 3. Explore the accuracy of the preprocessing method on the prediction results of the allocation model

[0092] Example 4

[0093] This example uses the method of Example 2 to obtain the predicted adsorption amount. The difference is that, unlike the method of removing organic matter in Example 2, 40 g of the ten kinds of soil in Example 1 are respectively added to a 2 L beaker, and 800 mL of 1 mol·L -1 The NaClO solution was stirred at 25°C for 6 hours, then centrifuged at 10000 rpm for 30 minutes, the supernatant was discarded, and the mixture was repeated 3 times, and freeze-dried for later use; the prediction results were generated under the conditions of As(V) distribution model S9 in Example 3. Fig.12 As shown, compared to Fig.11 The prediction results of this embodiment have a larger error range. It can be seen that the method for removing organic matter in Example 2 is more preferred.

Claims

1. A method for determining the distribution concentration of arsenate in the solid and liquid phases of soil, characterized in that: The following steps are involved: S1. Obtain the concentration of goethite, ferrihydrite and effective PO4 of competitive ions detected in the soil 3- concentration and calcium ion concentration; during the test, take soil that has not been contaminated by arsenic, first remove organic matter and measure the physical and chemical properties of inorganic soil; wherein, a composite method is used to remove organic matter, the steps are: take 40g of soil that has not been contaminated by arsenic after passing through a 60-mesh sieve, stir and rinse with 800mL of a 1mol·L-1 NaClO solution for 1.5h, then dry at 50℃ for 2h, and then freeze at -70℃ for 10min to form cooling soil, add cooling soil to 500mL of H2O2 with a mass concentration of 30% at room temperature to obtain a H2O2 solution of cooling soil, and then gradually heat the H2O2 solution of cooling soil, heat it in a 70℃ constant temperature water bath when foam appears, and start continuous stirring, and when the foam subsides, continue to drop H2O2 until the soil color becomes lighter and there is no reaction, and after cooling, pour off the supernatant and dry it for later use; S2, then the concentration of goethite, ferrihydrite and the effective state of competitive ions PO4 3- The concentration of pentavalent arsenic and the concentration of calcium ions are used as inputs, and a model calculation is performed based on a pentavalent arsenic distribution model, and the model calculation obtains the solid phase concentration adsorption value of pentavalent arsenic on the soil surface; the pentavalent arsenic distribution model uses ECOSAT4.9 software to predict the batch adsorption experimental results of pentavalent arsenic on soil; Wherein, the method for establishing the distribution model of pentavalent arsenic is: S3-1, respectively select the concentration of goethite, the concentration of ferrihydrite and the effective state of competing ions PO4 3- The concentration of and optionally the concentration of calcium ions are used as parameters to form a set of model frameworks; S3-2, using the model framework in step S3-1, performing model calculation to obtain a predicted adsorption value of the solid phase concentration of pentavalent arsenic on the soil surface; S3-3. The actual adsorption value of the solid phase concentration of pentavalent arsenic on the soil surface is obtained by conducting a batch adsorption test on the soil; then, according to the correlation coefficient R and the root mean square error RMSE value between the predicted adsorption value and the actual adsorption value, the parameters of the model are adjusted to establish a distribution model; wherein, the closer the correlation coefficient R is to 1, the better the simulation effect is, and the smaller the root mean square error RMSE value is, the smaller the error is and the better the prediction effect is.

2. A method for determining the distribution concentration of arsenate in soil solid and liquid phases according to claim 1, characterized in that: In step S1, determining the physical and chemical properties of inorganic soil includes: determining the content of amorphous iron oxides in the soil, determining the effective PO4 3- content, determination of calcium ion content in soil; among them, the sodium dithionite-sodium citrate-sodium bicarbonate extraction method was used to determine the total iron oxide content in soil; the oxalic acid-ammonium oxalate extraction method was used to determine the amorphous iron oxide content in soil; the sodium dithionite-sodium citrate-sodium acetate extraction method was used to determine the effective PO4 3- Content: 0.43 mol·L -1 The HNO3 extraction method was used to determine the calcium ion content in soil.

3. The method for determining the distribution concentration of arsenate in the solid and liquid phases of soil according to claim 1, characterized in that: The steps of the batch adsorption test in step S3-3 are as follows: 0.1 g of inorganic soil and 10 mL of 0.01 mol·L -1 NaNO3 was used as the electrolyte solution, and pentavalent arsenic solution of multiple concentrations was added. A trace amount of 0.1 mol·L-1 HNO3 or 0.05 mol·L-1 NaOH solution was added to adjust the pH to 3-10. The centrifuge tube was oscillated and balanced at 25°C for 24 hours, and then centrifuged at 4000 rpm for 5 minutes. The supernatant was filtered through a 0.22 μm filter membrane, and the pentavalent arsenic concentration in the filtrate was determined by ICP-OES to obtain adsorption data.

4. The method for determining the distribution concentration of arsenate in the solid and liquid phases of soil according to claim 1, characterized in that: In the distribution model of pentavalent arsenic in step S2, goethite and ferrihydrite are used as representatives of the adsorption surfaces of various iron oxides in the soil, and the contents of the two iron oxides are calculated from the total iron oxide content and the amorphous iron oxide content in the soil.

5. According to the method for determining the distribution concentration of arsenate in the solid and liquid phases of soil as described in claim 1, the method is applied in the fields of soil pollution analysis and ecological risk assessment and prediction.

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