A method and system for detecting heavy metal speciation in a soil remediation process

Abstract

The application relates to the technical field of soil remediation detection, and discloses a heavy metal form analysis and detection method and system for a soil remediation process. The method collects a soil sample to be detected and uniformly processes soil particles to obtain the soil particles, places the soil particles in a continuous extraction device, adds different chemical reagents according to a preset program, and monitors the concentration of heavy metal ions in real time to generate a dynamic dissolution curve. Based on the curve, five form intervals such as exchangeable state and carbonate combined state are divided, and the form proportion distribution is calculated according to the peak area of each interval. A heavy metal form migration model is established, the proportion distribution is matched with the time sequence of soil remediation stage parameters, and each form conversion path is output. According to the form conversion path, the dosage of a remediation agent and reaction time are adjusted, the adjusted parameters are fed back to a remediation equipment control terminal, the soil remediation efficiency and precision can be effectively improved, and reliable technical support is provided for soil remediation process control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of soil remediation detection, in particular to a method and system for analyzing and detecting heavy metal forms in soil remediation process. BACKGROUND

[0002] In current soil remediation operations, heavy metal pollution treatment is a common task, and the existence form of heavy metals in soil is directly related to their migration and bioavailability. Different forms of heavy metals have significant differences in their effects on the ecological environment and human health. The existing analysis and detection methods for heavy metal forms in soil remediation process mostly use the mode of offline sampling and laboratory analysis. This kind of method needs to transport the samples collected on the soil remediation site to the laboratory after sampling, and then perform a series of complex pretreatment operations before using detection instruments for form analysis. The whole process is time-consuming and difficult to reflect the dynamic changes of heavy metal forms in soil remediation process in real time, which makes the remediation operators unable to grasp the transformation rules of heavy metal forms in the remediation process in time, and they can only adjust the remediation parameters according to experience or preset schemes, which may lead to unreasonable remediation agent dosage and improper reaction time control.

[0003] The existing analysis and detection methods often have inconsistent division standards and fuzzy division intervals for heavy metal forms. Some methods can only distinguish a few heavy metal forms, and cannot comprehensively cover the five key forms of exchangeable state, carbonate combined state, iron and manganese oxide combined state, organic matter combined state and residual state, which makes the judgment of the migration characteristics of heavy metals in soil not comprehensive and accurate enough. In addition, the existing methods lack effective means to associate the heavy metal form analysis results with the soil remediation stage parameters, making it difficult to establish a direct link between heavy metal form changes and remediation operations, and unable to dynamically adjust the remediation strategy according to the real-time changes of heavy metal forms, which may lead to low remediation efficiency, even substandard remediation or over-remediation, increasing the cost and time consumption of soil remediation.

[0004] In actual soil remediation engineering, the physicochemical properties of soil at different remediation stages change, and the form of heavy metals also changes. However, the existing detection methods cannot capture this dynamic change in real time, making the remediation process in a relatively passive state, and it is difficult to achieve precise and efficient soil remediation operations. Moreover, some detection methods are complex in data processing and analysis, requiring high professional skills of the operators, and the operation process is complex, which is not conducive to widespread application in actual remediation engineering, and cannot meet the demand for rapid and accurate detection of heavy metal forms in large-scale soil remediation operations. SUMMARY

[0005] The present application aims to provide a method and system for analyzing and detecting heavy metal forms in soil remediation process to solve the problems raised in the background.

[0006] To achieve the above object, the application provides a heavy metal form analysis and detection method for soil remediation process, which comprises the following steps:

[0007] Collecting a soil sample to be tested and pretreating the same to obtain homogenized soil particles;

[0008] Placing the homogenized soil particles in a continuous extraction device and adding different chemical reagents in sequence according to a preset program;

[0009] Monitoring the elution concentration of heavy metal ions in the continuous extraction device in real time to generate a dynamic elution curve;

[0010] Based on the dynamic elution curve, five form intervals of heavy metals are divided, which are exchangeable state, carbonate-bound state, iron-manganese oxide-bound state, organic matter-bound state and residual state, respectively;

[0011] According to the elution peak area corresponding to each form interval, the proportion distribution of each form of heavy metal is calculated;

[0012] Establishing a heavy metal form migration model, and time-matching the proportion distribution with soil remediation stage parameters;

[0013] Outputting the conversion path of each form of heavy metal in the remediation process through the heavy metal form migration model;

[0014] Adjusting the remediation agent dosage and reaction time parameters according to the conversion path;

[0015] Feeding the adjusted parameters back to the control terminal of the soil remediation equipment.

[0016] Preferably, the pretreatment comprises:

[0017] Air-drying the collected soil sample under constant temperature conditions until the moisture content is less than 5%;

[0018] Mechanically sieving the air-dried soil using a nylon screen to retain particles with a particle size of less than 2 mm;

[0019] Dividing the sieved particles into three samples using the quartering method to obtain the homogenized soil particles.

[0020] Preferably, the preset program comprises:

[0021] In the first stage, magnesium chloride solution is used as the extraction agent and oscillation is performed at 25°C for two hours;

[0022] In the second stage, sodium acetate buffer solution is used to adjust the pH to 5, and oscillation is performed at the same temperature for four hours;

[0023] The third stage uses hydroxylamine hydrochloride solution to react in a water bath at 85 degrees Celsius for six hours;

[0024] The fourth stage uses hydrogen peroxide and nitric acid mixture to heat to 95 degrees Celsius twice;

[0025] The fifth stage uses a mixture of hydrofluoric acid and perchloric acid for microwave digestion.

[0026] Preferably, the generation of the dynamic dissolution curve includes:

[0027] The concentration of heavy metals in the extraction solution is collected by inductively coupled plasma mass spectrometry at a frequency of once per minute;

[0028] Perform three repeated measurements on the concentration data of each sampling point and take the arithmetic mean;

[0029] The arithmetic mean is fitted as a Gaussian distribution curve in time sequence, and the dissolution time points corresponding to each peak are marked.

[0030] Preferably, the division of the morphological interval includes:

[0031] Identify the first peak in the Gaussian distribution curve appearing in the interval of zero to thirty minutes as the exchangeable state;

[0032] Identify the second peak appearing in the interval of thirty minutes to three hours as the carbonate-bound state;

[0033] Identify the wide peak appearing in the interval of three to eight hours as the iron-manganese oxide-bound state;

[0034] Identify the multi-peak superposition region appearing in the interval of eight to twenty hours as the organic matter-bound state;

[0035] Subtract the sum of the first four forms from the total amount of heavy metals detected in the digestion stage as the residual state.

[0036] Preferably, the establishment of the heavy metal form migration model includes:

[0037] Construct a three-dimensional matrix with repair days as the horizontal axis and the proportion of each form as the vertical axis;

[0038] Mark the time node of the remediation agent dosing event in the three-dimensional matrix;

[0039] Calculate the correlation coefficient of the proportion of each form and the remediation agent dosage by partial least squares method;

[0040] Define the dominant migration path according to the form whose correlation coefficient is greater than zero point eight.

[0041] Preferably, the generation of the conversion path includes:

[0042] When the dominant migration path shows carbonate-bound state transforming into exchangeable state, it is determined that the morphological release is caused by pH value drop;

[0043] When the iron-manganese oxide-bound state and the organic matter-bound state decrease simultaneously, it is determined that the desorption is caused by oxidation-reduction potential rise;

[0044] The determination result is cross-verified with real-time working condition data of the remediation equipment.

[0045] Preferably, the adjustment of the remediation agent dosage includes:

[0046] When the exchangeable state proportion is detected to be more than thirty percent of the threshold value, the passivation agent dosage is increased by twenty percent;

[0047] When the residual state proportion increases by less than five percent for three consecutive days, the operation time of the electric remediation equipment is extended by one hour;

[0048] The adjustment period is dynamically updated according to the morphological transformation rate in the transformation path.

[0049] Preferably, the parameter feedback of the control terminal includes:

[0050] The instruction of increasing the passivation agent dosage is converted into a pulse type delivery control signal;

[0051] The instruction of extending the operation time of the electric remediation equipment is superimposed on the original timing program of the equipment, and the original timing program of the equipment refers to the timing program for controlling the basic operation time of the electric remediation equipment per day, which is provided by the equipment manufacturer or set during previous debugging.

[0052] After the feedback parameter takes effect, the monitoring cycle of the dynamic dissolution curve is restarted.

[0053] Preferably, the present application further includes a heavy metal morphological analysis and detection system for soil remediation process, which comprises a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor realizes the steps of the heavy metal morphological analysis and detection method for soil remediation process when executing the computer program.

[0054] Compared with the prior art, the present application has the following beneficial effects:

[0055] The soil remediation process heavy metal form analysis detection method can ensure the consistency and representativeness of subsequent detection samples, avoid detection result deviation caused by uneven samples, and make the subsequent heavy metal form analysis more reliable. The homogenized soil particles are placed in a continuous extraction device, different chemical reagents are added in sequence according to a preset program, the continuous extraction operation of heavy metals in the soil is realized, the sample does not need to be transferred multiple times, the loss and pollution of the sample in the processing process are reduced, and the application of the preset program ensures the standardization and stability of the extraction process, so that different batches and different operators can follow unified standards when performing extraction operations, and the repeatability of the detection process is improved.

[0056] Real-time monitoring of the dissolution concentration of heavy metal ions in the continuous extraction device and generating a dynamic dissolution curve can capture the change of the dissolution concentration of heavy metals in the soil remediation process in real time, intuitively reflect the dissolution rule of heavy metals in different extraction stages, break the limitation that the traditional offline detection method cannot obtain data in real time, and enable the remediation operator to timely understand the dynamic change trend of the heavy metal form, thereby providing real-time data reference for subsequent form division and parameter adjustment. Based on the dynamic dissolution curve, five form intervals of heavy metals are divided, the exchangeable state, the carbonate combined state, the iron and manganese oxide combined state, the organic matter combined state and the residual state are clearly distinguished, the main existing forms of heavy metals in the soil are comprehensively covered, the analysis of the heavy metal form is more comprehensive, and the existing state and migration characteristics of the heavy metals in the soil can be more accurately mastered.

[0057] According to the dissolution peak area corresponding to each form interval, the proportion distribution of each form heavy metal is calculated, the proportion distribution of different form heavy metals in the soil is presented in a quantitative manner, the operator can clearly understand the relative content of each form heavy metal, and specific data support is provided for subsequent analysis of the migration and transformation of heavy metals. A heavy metal form migration model is established, the proportion distribution is time-matched with the soil remediation stage parameters, the organic combination of the heavy metal form change and the remediation stage is realized, the change rule of the heavy metal form in different remediation stages is clearly displayed, and the influence of the remediation operation on the heavy metal form is clearly displayed, thereby providing a basis for subsequent adjustment of the remediation strategy.

[0058] The transformation path of each form of heavy metal in the remediation process is output by the heavy metal form migration model, which can intuitively present the form transformation direction and process of heavy metals in the remediation process, so that the operator can clearly understand how the heavy metals are transformed from one form to another form and which remediation factors affect the transformation process. According to the transformation path, the dosage of the remediation agent and the reaction time parameter can be adjusted, precise adjustment measures can be taken according to the transformation of the heavy metal form, blind adjustment of the remediation parameters can be avoided, the remediation operation is more targeted, and the transformation of heavy metals to a form with less environmental hazards can be effectively promoted.

[0059] The adjusted parameters are fed back to the control terminal of the soil remediation equipment, realizing real-time linkage of the detection results and equipment control, eliminating manual adjustment of equipment parameters, reducing human operation errors, improving the timeliness and accuracy of remediation equipment parameter adjustment, ensuring that the remediation equipment can operate according to the optimal parameters, and further promoting the orderly and efficient development of soil remediation operation, adapting to the demand for precise and efficient remediation in actual soil remediation engineering, and at the same time, the method has a standard operation process and does not require too complex operation steps, facilitating the popularization and application in soil remediation engineering of different scales. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 A working principle diagram of the soil remediation process heavy metal form analysis and detection method is provided.

[0061] Figure 2 A working principle diagram for soil sample pretreatment is provided.

[0062] Figure 3 A working principle diagram for generating a dynamic dissolution curve is provided. DETAILED DESCRIPTION

[0063] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0064] Please refer to Figure 1The application provides a heavy metal form analysis and detection method and system for soil remediation process, which comprises dynamic tracking of heavy metal form transformation through continuous extraction technology combined with real-time monitoring, and optimizing remediation process parameters based thereon. The specific implementation of the method comprises sample pretreatment, continuous extraction, real-time monitoring, form division, model establishment and parameter feedback. After collecting the soil sample to be tested, the sample is first pretreated to obtain homogenized soil particles, which ensures the representativeness and repeatability of the sample. The homogenized soil particles are placed in a special continuous extraction device, and different chemical property extraction reagents are sequentially added according to a preset timing program. The selection of the reagents is designed for different binding forms of heavy metals. During the extraction process, the concentration change of heavy metal ions in the solution is monitored in real time by a high-precision sensor to generate a time-concentration dynamic curve. Based on the characteristic peaks of the dynamic dissolution curve, five form intervals of heavy metals are identified and divided, including exchangeable state, carbonate combined state, iron and manganese oxide combined state, organic matter combined state and residual state. The proportion of each form is calculated by integrating the corresponding peak area. Subsequently, a heavy metal form migration model is established, the form proportion data is matched with the time sequence parameters (such as remediation agent dosing time and equipment operation state) in the remediation process, and the transformation path between the forms is analyzed. The model output is used to guide the remediation operation, such as adjusting the amount of passivation agent or prolonging the reaction time, and the optimized parameters are automatically fed back to the control terminal of the remediation equipment to realize closed-loop control. The whole method emphasizes real-time and adaptability, which can effectively improve the remediation efficiency and reduce the risk of secondary pollution.

[0065] Example 1: see Figure 2 The pretreatment process is carried out in a constant temperature laboratory, and the laboratory environment temperature is maintained at 20±2 degrees Celsius, which aims to minimize the interference of temperature fluctuations on the stability of heavy metal forms in the soil. The original soil sample collected is evenly spread on a clean enamel plate, and the spreading thickness is strictly controlled below 2 centimeters to ensure uniform evaporation of water. The sample is placed in a ventilated environment without direct light for natural air drying, and it is turned over every 12 hours during this period to ensure that the drying degree of the soil is consistent. The end of the drying process is determined by weight method, that is, the sample is weighed continuously for two times at an interval of 24 hours, and when the sample mass change is less than 0.1%, it is considered that the moisture content has been reduced to below 5%, reaching the predetermined standard.

[0066] The air-dried soil sample is then subjected to mechanical sieving, which is performed using nylon sieves that are chemically inert to prevent metal contamination. The sieves have a 2 mm mesh size, and the sieving is performed on a standard shaker. The shaker oscillates at a fixed frequency of 200 times per minute for 10 minutes. After the oscillation, the impurities such as stones and plant roots that remain on the sieve are discarded, and the fine particles that pass through the sieve, which have a particle size of less than 2 mm, are collected as the material for subsequent experiments. This step effectively removes heterogeneous components and improves the uniformity of the sample. After obtaining the sieved particles, a classical quartering method is used to further ensure the representativeness of the sample. The entire sieved soil particles are stacked into a cone, and then a presser is used to gently flatten the top of the cone to form a circular thin layer with uniform thickness. Then, a cross-shaped divider is used to accurately divide the thin layer into four equal parts, and the two opposite parts are combined together, while the remaining two parts are discarded. The combined sample is mixed again, and the above process of stacking, flattening, dividing, and sampling is repeated three times. After three times of quartering, the final soil particles have high uniformity, and the chemical composition has minimal spatial distribution differences, which meets the stringent requirements of the continuous extraction experiment for sample uniformity.

[0067] The preset program of the continuous extraction is executed on an automated continuous extraction device with multi-channel reagent addition and precise temperature control functions. The extraction program is divided into five stages with specific purposes, and the parameters of reagents, temperature, and time for each stage are strictly set. The first stage aims to extract exchangeable heavy metals, and 1.0 mol / L magnesium chloride solution is used as the extractant. The mass-to-volume ratio of the soil sample to the extractant is controlled at 1:20. The mixed suspension is transferred to a constant-temperature oscillator, which is oscillated at 25°C and 200 rpm for 2 hours. The relatively mild conditions in this stage aim to desorb heavy metal ions adsorbed on the surface of soil particles by electrostatic attraction. The second stage targets carbonate-bound heavy metals, and the extractant is changed to 1.0 mol / L sodium acetate buffer solution adjusted to pH 5.0 with acetic acid. This stage is also performed at 25°C, but the oscillation time is extended to 4 hours. The slightly acidic environment simulates the dissolution process of carbonates under natural conditions when the pH slightly decreases, thereby releasing the heavy metals bound to them. The third stage aims to dissociate iron and manganese oxide-bound states, and 0.1 mol / L hydroxylamine hydrochloride solution is used as the extractant. The reaction is performed in a constant-temperature water bath at 85°C with continuous magnetic stirring, and the reaction time is set to 6 hours. The high temperature and reducing environment work together to effectively release the heavy metals wrapped or co-precipitated in iron and manganese oxides.

[0068] The fourth stage treats heavy metals combined with organic matter, using a mixture of hydrogen peroxide and concentrated nitric acid in a volume ratio of 2:1. The operation is divided into two steps, first, a portion of the mixture is added at room temperature, and the reaction is allowed to proceed for 1 hour to preliminarily oxidize the organic matter; then the remaining mixture is added, and the mixture is heated to 95 degrees Celsius, and the reaction is allowed to proceed for 2 hours to completely decompose the organic matter by strong oxidation and release the heavy metals fixed therein. The fifth stage is directed to residual heavy metals present in the crystal lattice of primary or secondary minerals, and a mixture of hydrofluoric acid and perchloric acid in a volume ratio of 3:1 is used in a high-pressure resistant sealed microwave digestion tank. The digestion program is set to heat to 180 degrees Celsius and maintain for 30 minutes, and the strong corrosion of the mixed acid and the high temperature and high pressure conditions are used to completely decompose stable minerals such as silicates, so that the total amount of heavy metals contained therein can be measured. After each extraction stage, the reaction mixture is subjected to high-speed centrifugation to achieve solid-liquid separation, and the supernatant is collected for measurement, and the residual solid precipitate is washed with deionized water and centrifuged, and then enters the next stage of the extraction process, to ensure the specificity and completeness of each form of extraction.

[0069] Example 2: see Figure 3 The acquisition of the dynamic dissolution curve relies on a set of high time resolution online monitoring systems, which uses an inductively coupled plasma mass spectrometer (ICP-MS) as the core detection unit, which has extremely high sensitivity and multi-element simultaneous detection capability. After the extraction liquid flows out of the continuous extraction device, it is introduced through a precisely designed flow injection analysis module, which is equipped with a quantitative sample ring and a continuous flow of carrier liquid, and can titrate the measured liquid into the atomizer of the ICP-MS at a stable flow rate of 1 milliliter per minute. The instrument method is optimized, and the best signal-to-noise ratio parameters are set for the target heavy metal elements (such as lead, cadmium, chromium, copper, zinc), and the data acquisition frequency is fixed at once per minute, that is, at the end of each minute of each extraction stage, the instrument automatically completes a spectral scan and records the characteristic ion intensity values of each element. In order to maximize the control of analysis errors, each time point measurement is not a single reading, but is automatically executed by the instrument internal control software three times of continuous repeated acquisition, and the three ion intensity values obtained are first converted into corresponding mass concentration values according to the multi-point calibration curve established in advance, and then the arithmetic mean of the three concentration values is calculated, which is taken as the final heavy metal concentration value at the specific minute time point. This set of processes from sample introduction, signal acquisition to data preprocessing runs through the entire continuous extraction process, from the zero point when no extractant is added, to the end of the fifth stage microwave digestion and detection, to generate a complete time series concentration data set, which clearly depicts the dynamic trajectory of heavy metals as they are gradually dissolved by different chemical reagents.

[0070] After obtaining the original time-concentration data sequence, it needs to be mathematically processed to generate a smooth dynamic dissolution curve that can be used for peak analysis. Data processing is carried out in dedicated chemometrics software, and the operator draws all data points on a scatter plot with time points as independent variables (abscissa) and corresponding average heavy metal concentrations as dependent variables (ordinate). Due to the kinetic characteristics of the extraction process, the data points usually show a peak-shaped trend of rising first and then falling, but there will be certain random fluctuations. In order to reveal its inherent law, a nonlinear curve fitting algorithm based on Gaussian function is used to fit the scatter points, and Gaussian function can well describe the natural distribution form of concentration in many physical and chemical processes. The software adjusts the parameters of the Gaussian function (such as peak height, peak position, peak width) through iterative calculation, so that the residual sum of squares between the fitted curve and all measured data points reaches a minimum, usually requiring a goodness of fit (R²) greater than 0.99 to confirm that the curve can highly restore the actual dissolution process. After successful fitting, a continuous and smooth dynamic dissolution curve is generated.

[0071] The software uses numerical calculation method to calculate the first derivative of the fitted curve, and the point where the first derivative is equal to zero corresponds to the peak point (maximum value point) on the curve. The abscissa value corresponding to each peak point is the time when the heavy metal dissolution concentration of the form reaches the maximum value. These key time points are accurately marked and recorded as important basis for dividing the form interval. The height of the peak reflects the maximum dissolution concentration of the heavy metal of the form, and the width of the peak reflects the speed of dissolution and the heterogeneity of binding strength to some extent. The division of the form interval is carried out immediately after the generation of the dynamic dissolution curve, and its core is to establish a scientific correspondence between the characteristic peaks appearing in different time intervals on the curve and the specific chemical forms of heavy metals in soil. The main basis for division is the chemical extraction characteristics of each form and its dissolution sequence on the time axis. The exchangeable state heavy metal is adsorbed on the surface of soil particles by weak electrostatic force, and it is the easiest and fastest to be desorbed when it contacts the neutral salt extractant (such as magnesium chloride solution), so it appears as the first significant peak on the dynamic dissolution curve. This peak is usually very sharp, with a fast dissolution rate, and the time interval of the peak appearance is defined within 0 to 30 minutes after the start of extraction. The carbonate-bound state heavy metal is associated with carbonate precipitates or co-precipitates in soil, and when the pH value of the extraction system is slightly reduced to weak acidity (such as sodium acetate buffer environment), the carbonate begins to dissolve and release the metal, which appears as the second peak on the curve. The time interval of this peak appearance is usually between 30 minutes and 3 hours, and the peak shape is slightly wider than that of the exchangeable state, indicating that it needs a certain reaction time for release.

[0072] The iron and manganese oxide-bound heavy metals refer to the part wrapped in or amorphous iron and manganese oxide lattice, which requires stronger reducing conditions and longer action time to release (such as the action of hydroxylamine hydrochloride solution at higher temperature), so in the dynamic dissolution curve, it shows a wide peak shape appearing in the interval of 3 hours to 8 hours, the peak value may not be so prominent, but the duration is longer, forming an obvious "bulge" area, which reflects the slow and complex characteristics of the oxide reduction and dissolution process. The release of organic matter-bound heavy metals depends on the oxidative decomposition of organic matter (such as the action of hydrogen peroxide / nitric acid mixed solution), due to the complex composition of soil organic matter and different oxidation difficulty, the release of heavy metals is often not synchronized, so in the curve, corresponding to the interval of 8 hours to 20 hours, it is often observed that a complex peak group formed by the superposition of multiple small peaks or a wide peak with an asymmetric "shoulder peak", which embodies the characteristics of step-by-step release of organic-bound heavy metals. For residual state heavy metals, they exist in the stable lattice of primary or secondary minerals such as silicates, and are not extracted by the first four reagents in the conventional continuous extraction procedure, and can only be completely released in the last strong acid digestion stage (hydrofluoric acid / perchloric acid mixed solution, microwave digestion). Therefore, the residual state does not directly show an independent dissolution peak in the dynamic dissolution curve, and the determination of its content needs to be indirectly obtained by measuring the total amount of heavy metals in the solution after the fifth stage digestion, and then subtracting the sum of the heavy metal contents calculated by integrating the areas of the corresponding dissolution peaks in the first four form intervals (i.e. exchangeable state, carbonate-bound state, iron and manganese oxide-bound state, organic matter-bound state). The quantification of each form is realized by integrating the peak area of the dynamic dissolution curve in the corresponding interval, and the area value is automatically calculated by the software. Finally, the contents of all forms are normalized, and the percentage distribution of each form of heavy metals in the soil can be obtained.

[0073] The starting point of the model construction is the systematic organization of data, and the core structure is a three-dimensional data matrix. The abstract space of this matrix is defined by three dimensions. The first dimension is the time axis of the repair process, marked in days, starting from the zeroth day of the repair project and recording until the current analysis day, with each day as an independent time layer. The second dimension is the daily percentage data of the five heavy metal forms (exchangeable state, carbonate-bound state, iron and manganese oxide-bound state, organic matter-bound state, residual state), which are derived from the continuous extraction and form analysis of soil samples collected from representative sites in the repair site every day. The third dimension is a set of work condition parameters related to the repair operation, including but not limited to the specific dosage (usually in kilograms per hectare) of various repair agents (such as passivators, oxidizing agents, nutrient salts, etc.), real-time pH value, oxidation-reduction potential (Eh value), soil humidity, and other environmental indicators. Each repair day corresponds to a slice in the matrix, which contains all the form percentages and all the work condition parameter values for that day.

[0074] In the three-dimensional matrix, it is necessary to specially mark the time nodes of key remediation events, such as when a specific type and specific dose of remediation agent was added, when an electric remediation device was started or a leaching system was opened. These events are marked as specific markers in the matrix, usually in the form of event markers and time stamps associated with the basic data. After completing the construction of the data matrix and event marking, the next step is to analyze the quantitative relationship between the morphological proportion and the remediation operation, and here the partial least squares method (PLS) is used for modeling. PLS regression is especially suitable for handling multiple collinearity between independent variables (such as remediation agent dosage, pH value, etc. Working condition parameters) and the sample size (days) may be less than the number of variables. The analysis process is carried out for each heavy metal form respectively, for example, taking the "exchangeable state proportion" as the dependent variable Y, and the daily "remediation agent A dosage", "remediation agent B dosage", "pH value", "Eh value" and other working condition parameters as the independent variable matrix X. PLS algorithm extracts latent variables (principal components) in X and Y, maximizes the explanation of Y variation while considering the correlation with X, and establishes a linear regression model between X and Y.

[0075] For the established PLS model, it is necessary to evaluate the significant degree of the influence of each independent variable (working condition parameter) on the dependent variable (morphological proportion), which is usually realized by calculating the variable importance projection (VIP) value. The VIP value quantifies the contribution of each independent variable to the explanation of the variation of the dependent variable, which is based on the principal components extracted by the PLS model. The larger the VIP value of a certain independent variable, the more important it is to explain the change of the morphological proportion. The calculation formula of the VIP value can be expressed as:

[0076]

[0077] Where: represents the variable importance projection (VIP) value of the th independent variable, represents the total number of principal components extracted by the PLS regression model, is the index in the summation formula, representing the th principal component that is currently being calculated, represents the variation (sum of squares) of the dependent variable (heavy metal morphological proportion) that can be explained by the th principal component, represents the square of the loading of the th independent variable on the th principal component, reflecting the contribution weight of the independent variable to the principal component.

[0078] To simplify the model output and focus on the main contradictions, a threshold of VIP value is set, for example, 1.0. When the VIP value of a working condition parameter is greater than or equal to 1.0, it is generally considered that the parameter has a significant influence on the change of the form. After the model runs, for example, the VIP value of "pH value" for "exchangeable state proportion" is 1.85, and the VIP value of "passivator dosage" for the same form is 1.72, both of which exceed the threshold, so their relationship with the exchangeable state is identified as a relationship that needs to be focused on. After identifying important variables, the model further investigates the partial correlation coefficient between these important variables and the form proportion, which reflects the linear correlation strength and direction between the two after excluding the influence of other variables. A threshold of correlation coefficient is set, for example, 0.8. When the absolute value of the partial correlation coefficient between an important working condition parameter and a certain form proportion is greater than 0.8, the relationship is defined as a "dominant migration path". For example, the analysis result may show that "pH value" is significantly negatively correlated with "exchangeable state proportion" (partial correlation coefficient <-0.8), and "passivator dosage" is also significantly negatively correlated with "exchangeable state proportion" (partial correlation coefficient <-0.8). Then, "pH value decreases → exchangeable state increases" and "passivator dosage → exchangeable state decreases" are identified as two dominant migration paths that act on the same form but have opposite directions.

[0079] Based on the identified dominant migration paths and the positive and negative signs of their correlation coefficients, the model can be qualitatively interpreted. For example, when "pH value" and "exchangeable state proportion" show a significant negative correlation (i.e. pH decreases accompanied by exchangeable state increases), the model can infer that "soil acidification causes part of the bound heavy metals to be released and converted into more active exchangeable state". When "oxidation-reduction potential Eh" and "iron-manganese oxide bound state proportion" show a significant negative correlation (i.e. Eh increases accompanied by a decrease in the proportion of this form), the model can infer that "increased oxidation conditions promote the dissolution of part of the iron-manganese oxides, releasing the heavy metals bound to them". These inferences link abstract mathematical correlations to specific soil chemical principles. Finally, the output of the model is a semi-quantitative description of the migration and transformation of heavy metal forms during the remediation process, which systematically shows which operating parameters are the main driving factors and which forms they drive to transform in which direction. This output can be represented as a directed network graph, where nodes are forms or working condition parameters, edges represent dominant paths with significant correlations, and edges can be labeled with VIP values, partial correlation coefficients, and inferred transformation mechanisms.

[0080] Example 4: The generation of a transformation pathway is not a simple data mapping process, but a closed-loop analysis of data relationships based on the principles of soil chemistry, which involves mechanism inference and empirical verification. This will be illustrated with a specific data sequence. Suppose that in a period of remediation of cadmium-contaminated soil by stabilization technology, the heavy metal speciation migration model analyzes the data of the past seven days by partial least squares method and identifies a dominant migration pathway: there is a strong positive correlation between the proportion of carbonate-bound cadmium and soil pH (correlation coefficient > 0.8), that is, as the pH value decreases, the proportion of carbonate-bound cadmium shows a downward trend. At the same time, the model monitors that during this process, the proportion of exchangeable cadmium has increased accordingly. Based on this statistical phenomenon, the system preliminarily determines that there is a migration pathway of "transformation of carbonate-bound state to exchangeable state", and speculates that the driving force is "dissolution of carbonates and release of heavy metals caused by pH decrease". However, simple statistical correlation is not enough to confirm the chemical mechanism, and it must be cross-verified with real-time working condition sensor data on the remediation site.

[0081] The soil pH sensor deployed on site returns data at a frequency of once an hour, and the system retrieves the pH record corresponding to the time period of speciation analysis. Suppose it is found that just a day before the proportion of carbonate-bound cadmium began to decrease significantly, the pH value of the soil decreased from the initial 7.2 to 6.5 due to the infiltration of an acidic precipitation or the slight over-dosage of some acidifying remediation agent, and fluctuated at this low level. This independent pH monitoring data is highly consistent with the trigger condition (pH decrease) inferred by the model, thus strongly supporting the determination that "pH decrease is the main reason for the transformation of this speciation". See Table 1 for a set of simulated time series data summaries that support the determination process.

[0082] Table 1: Time series data summary of cadmium speciation proportion and key working condition parameters during remediation

[0083]

[0084] From the table data, it can be seen directly that from the 2nd day to the 4th day, the soil pH value decreased from 7.1 to 6.5, while the proportion of exchangeable cadmium increased from 13.1% to 18.9%, and the proportion of carbonate-bound cadmium decreased from 34.8% to 28.7%. The trend of this increase and decrease is clear, and the change mainly occurs between the two of the first four speciations, and the proportions of other speciations remain relatively stable, which is consistent with the characteristics of the "carbonate-bound state to exchangeable state" pathway. Another common type of transformation pathway determination involves changes in redox conditions. The model may identify another dominant pathway: there is a negative correlation between the proportion of iron-manganese oxide-bound cadmium and the oxidation-reduction potential (Eh) (correlation coefficient <-0.8), and the proportion of organic matter-bound cadmium also shows a similar negative trend with Eh, that is, the proportion of both decreases as Eh increases.

[0085] The system will determine that this phenomenon is "desorption caused by the synergistic effect of the reduction of iron and manganese oxides and the oxidation of organic matter caused by the increase of redox potential". To verify this path, the system will call the historical data of the on-site redox potential sensor. Assuming that the data record shows that on the 10th day of repair, in order to activate local microorganisms, slow-release oxygen agent was added, causing the soil Eh to gradually increase from the original 150mV to more than 400mV in the following days. Query the morphological analysis data of the corresponding time period, it is found that it is just after the Eh breaks through 300mV that the proportion of iron and manganese oxide combined state and organic matter combined state begins to appear continuous and synchronous decline. The consistency of Eh sensor data and morphological changes in time node and trend. The generation of the transformation path also needs to be cross-verified with the real-time working condition data of the repair equipment to exclude other interference factors or confirm the synergistic effect.

[0086] In the above case where pH decline leads to the transformation of carbonate combined state, the system will simultaneously query irrigation records, stirring equipment operation logs, etc. If the data shows that during the pH decline period, the soil stirring intensity also increased significantly, then the system needs to further analyze whether the pH decline alone dominates the transformation, or the strong mechanical stirring accelerates the contact and reaction of acidified soil particles, and jointly promotes the transformation rate. Through multivariate time series comparison, the contribution of each operating parameter to the morphological transformation can be more accurately defined. Finally, after cross-verification confirms the effective transformation path, it will be converted into a directed network graph element. Each path contains several key elements: the starting morphological node, the terminal morphological node (or intermediate morphological), the main working condition parameter driving the path (such as pH, Eh), the path intensity (based on correlation coefficient or change magnitude), and the time window of the path effective. This set of empirical path network dynamically depicts the geochemical behavior evolution of heavy metals in the repair process.

[0087] Example 5: Assume that in a site using stabilization technology to repair cadmium contaminated soil, the system is being continuously monitored. Daily morphological analysis data shows that the exchangeable state of cadmium in the soil has been rising for the past three days, at 28%, 31%, and 33% respectively. The system has a pre-set threshold value that when the exchangeable state exceeds 30%, it is determined that the active migration risk of heavy metals in the soil has increased, and intervention measures need to be taken. Therefore, when the third day's data is entered, the system immediately triggers the adjustment logic and generates an instruction: increase the dosage of the current phosphite passivator by 20%. The determination of the increase is based on a historical data model, which shows that under such soil conditions, a 20% increase can usually suppress the exchangeable state below the threshold value, while avoiding excessive dosage that causes cost waste or secondary pollution. The instruction clearly indicates the type of remediation agent, the percentage increase, and the specific dosage value.

[0088] Another adjustment scenario focuses on the final effect of remediation, i.e. the conversion efficiency of heavy metals to stable residual state. The system monitors that the proportion of residual state cadmium has changed in the past three days: 18.5% on the first day, 18.7% on the second day, and 18.8% on the third day. Although the trend is rising, the cumulative growth rate for three consecutive days is less than 0.5%, which is far below the threshold condition set by the system that "a continuous three-day increase of less than 5% is determined as remediation stagnation". After this condition is triggered, the system generates another instruction: extend the daily operating time of the electric remediation device by 1 hour. Extending the operating time aims to promote the conversion of heavy metals with deeper or more stable binding to soluble or exchangeable state by enhancing the effect of electric field, thereby providing substrates for subsequent stabilization reactions. The adjustment instruction also clearly specifies the device object and the specific time unit of extension. The key advanced function lies in the dynamic update of the adjustment period, which enables the system to have a certain adaptive ability. The adjustment period is not fixed, but is dynamically calculated according to the rate of morphological transformation. The system will periodically calculate the transformation rate of the main morphologies in recent days (such as the transformation of exchangeable state to carbonate-bound state, or the transformation of iron and manganese oxide-bound state to residual state). The transformation rate is obtained by linear regression analysis of the proportion data of the last five time points. The system sets a benchmark transformation rate V0. If the calculated current actual transformation rate V is greater than 1.5 times V0, it indicates that the remediation reaction is intense and the morphological change is fast, and the system will automatically shorten the adjustment period, for example from evaluating once a week to evaluating once a day, in order to respond more agilely to changes. Conversely, if V is less than 0.5 times V0, it indicates that the process is slow, and the adjustment period can be extended to twice a week to avoid unnecessary frequent disturbance.

[0089] The generated adjustment instructions need to be accurately converted into signals that the control terminal can recognize and execute. For instructions such as "increase the dosage of passivator by 20%", the control software will encode it into a set of pulse control signals. This set of signals contains two key parameters: pulse frequency and pulse width. The pulse frequency corresponds to the reference flow rate of the remediation equipment (such as a metering pump), and the pulse width corresponds to the dosage that needs to be increased. After the instruction is issued, the metering pump controller will receive these pulse signals and drive the actuator to deliver the passivator at a faster pace or longer opening time, ensuring the accurate implementation of the incremental dosage. This pulse delivery method helps to achieve uniform distribution of the agent in the soil. For instructions such as "extend the running time of the electric remediation equipment by 1 hour", the system uses the instruction superposition method to integrate into the original control program of the equipment. The running of the remediation equipment is usually controlled by a programmable logic controller (PLC) according to a preset schedule. The system does not directly modify the core timing program, but superimposes a delay instruction on it. The instruction takes effect at the original shutdown time of the equipment, forcing the equipment to continue running for 60 minutes and automatically shutting down after the extended period expires. This method reduces the impact on the stability of the original program and achieves seamless runtime extension. After all feedback parameters take effect, the system does not stop working, but immediately restarts a new monitoring cycle. This means that in the next sampling period after the instruction is issued, the system will collect soil samples again, perform a new round of continuous extraction and morphological analysis, generate a new dynamic leaching curve, and re-evaluate the proportion of each form. This new evaluation result will be used to determine whether the previous adjustment instruction is effective.

[0090] It should be noted that the relational terms herein such as first and second and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any such actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0091] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for analyzing and detecting heavy metal speciation in soil remediation processes, characterized in that, Includes the following steps: Soil samples were collected and pretreated to obtain homogenized soil particles. The homogenized soil particles are placed in a continuous extraction device, and different chemical reagents are added sequentially according to a preset program; The preset procedure includes: a first stage using magnesium chloride solution as an extractant, a second stage using sodium acetate buffer solution as an extractant, a third stage using hydroxylamine hydrochloride solution as an extractant, a fourth stage using a mixture of hydrogen peroxide and nitric acid as an extractant, and a fifth stage using a mixture of hydrofluoric acid and perchloric acid for microwave digestion. The concentration of heavy metal ions leached in the continuous extraction device is monitored in real time to generate a dynamic leaching curve; The generation of the dynamic dissolution curve includes: collecting the heavy metal concentration in the extract at a frequency of once per minute using an inductively coupled plasma mass spectrometer; performing three repeated measurements on the concentration data at each sampling point and taking the arithmetic mean; fitting the arithmetic mean into a Gaussian distribution curve according to the time series, and marking the dissolution time points corresponding to each peak. Based on the dynamic leaching curve, heavy metals are divided into five state ranges: exchangeable state, carbonate-bound state, iron-manganese oxide-bound state, organic matter-bound state, and residue state. The division of the morphological intervals includes: identifying the first peak in the Gaussian distribution curve appearing in the interval from 0 to 30 minutes as the exchangeable state; identifying the second peak appearing in the interval from 30 minutes to 3 hours as the carbonate-bound state; identifying the broad peak appearing in the interval from 3 to 8 hours as the iron-manganese oxide-bound state; identifying the multi-peak superposition region appearing in the interval from 8 to 20 hours as the organic matter-bound state; and subtracting the sum of the first four morphologies from the total amount of heavy metals detected in the digestion stage to obtain the residue state. The proportion of heavy metals in each form is calculated based on the dissolution peak area corresponding to each form range. A heavy metal speciation migration model was established, and the aforementioned proportion distribution was time-series matched with soil remediation stage parameters. The establishment of the heavy metal morphology migration model includes: constructing a three-dimensional matrix with the number of remediation days as the horizontal axis and the proportion of each heavy metal morphology as the vertical axis, the third dimension of the three-dimensional matrix being the working condition parameters related to the remediation operation; marking the time nodes of the remediation agent addition events in the three-dimensional matrix; calculating the correlation coefficient between the proportion of each morphology and the amount of remediation agent added using partial least squares method; and defining the dominant migration path based on the morphology with the correlation coefficient greater than a set threshold. The transformation paths of each form of heavy metal during the repair process are output through the heavy metal morphology migration model. The generation of the transformation pathway includes: when the dominant migration pathway shows the transformation of carbonate-bound state to exchangeable state, it is determined to be a form release caused by a decrease in pH value; when the iron-manganese oxide-bound state and the organic matter-bound state decrease simultaneously, it is determined to be desorption caused by an increase in redox potential; the determination results are cross-validated with the real-time operating data of the repair equipment. Adjust the dosage of the repair agent and the reaction time parameters according to the described conversion pathway; The adjusted parameters are fed back to the control terminal of the soil remediation equipment.

2. The method for heavy metal speciation analysis and detection in soil remediation process according to claim 1, characterized in that, The preprocessing includes: The collected soil samples were air-dried under constant temperature conditions until the moisture content was less than 5%. The air-dried soil was mechanically sieved using a nylon screen to retain particles with a diameter of less than two millimeters. The sieved particles were divided into three samples using a quartering method to obtain homogenized soil particles.

3. The method for heavy metal speciation analysis and detection in soil remediation process according to claim 1, characterized in that, The preset program includes: In the first stage, magnesium chloride solution was used as the extractant, and the mixture was shaken at 25 degrees Celsius for two hours. In the second stage, the pH was adjusted to 5.0 using sodium acetate buffer solution, and the mixture was shaken at 25 degrees Celsius for four hours. The third stage involves reacting a hydroxylamine hydrochloride solution in an 85-degree Celsius water bath for six hours. The fourth stage involves heating the solution of hydrogen peroxide and nitric acid to 95 degrees Celsius in two stages. The fifth stage involves microwave digestion using a mixture of hydrofluoric acid and perchloric acid.

4. The method for heavy metal speciation analysis and detection in soil remediation process according to claim 1, characterized in that, The set threshold is 0.

8.

5. The method for heavy metal speciation analysis and detection in soil remediation process according to claim 1, characterized in that, The adjustment of the dosage of the repair agent includes: When the proportion of exchangeable states exceeds the threshold of 30%, the amount of passivating agent added is increased by 20%. When the proportion of residue is less than 5% for three consecutive days, extend the running time of the electric repair equipment by 1 hour. The cycle is dynamically updated and adjusted based on the morphological transformation rate in the transformation path.

6. The method for heavy metal speciation analysis and detection in soil remediation process according to claim 5, characterized in that, The parameter feedback from the control terminal includes: The instruction to increase the amount of passivating agent added is converted into a pulse delivery control signal; The instruction to extend the running time of the electric repair equipment is superimposed on the original timing program of the equipment. The original timing program of the equipment refers to the timing program that comes with the equipment at the factory or is set in the early stage of debugging to control the daily basic running time of the electric repair equipment. The monitoring cycle of the dynamic dissolution curve is restarted after the feedback parameters take effect.

7. A heavy metal speciation analysis and detection system for soil remediation processes, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for heavy metal speciation analysis and detection in soil remediation process as described in any one of claims 1 to 6.

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