Method for evaluating conversion coefficient of hydroxyl radicals (∙oh) produced by oxidation of soil minerals

Abstract

A method for evaluating a conversion coefficient of hydroxyl radicals (∙OH) produced by oxidation of soil minerals, aiming to solve the problem that it is difficult to completely simulate the complexity of an actual soil environment under laboratory conditions, and comprising preprocessing sample and experimental historical data, constructing a laboratory simulation model, and verifying the accuracy thereof. By simulating the oxidation process of soil minerals under different environmental conditions, a dynamics model is constructed to record the amount of production and rule of change of hydroxyl radicals. Actual soil environmental data is collected and substituted into the dynamics model, and a simulation result is compared with an actual monitoring result to evaluate the applicability of the dynamics model. Finally, preprocessed on-site monitoring data is substituted into the evaluation model to calculate a conversion coefficient.

Description

A method for evaluating the conversion coefficient of hydroxyl radicals (·OH) produced by soil mineral oxidation. Technical Field

[0001] This invention relates to the field of environmental and agricultural data processing technology, and provides a method for evaluating the conversion coefficient of hydroxyl radicals (·OH) produced by soil mineral oxidation. Background Technology

[0002] The assessment of the conversion coefficient for hydroxyl radical production from soil mineral oxidation involves multiple aspects, including mineral type, environmental conditions, and redox reaction mechanisms. Because the conversion coefficient is usually closely related to specific experimental conditions, mineral characteristics, and the kinetics of the oxidation reaction, it is difficult to provide a universally applicable fixed value. In the soil environment, the process of hydroxyl radical production from mineral oxidation is often closely related to the reaction between ferrous ions and oxygen molecules. This process occurs through abiotic pathways, such as ferrous ions activating oxygen molecules to produce superoxide anions, which in turn generate hydrogen peroxide, ultimately producing ·OH through Fenton or Fenton-like reactions.

[0003] Under laboratory conditions, the production of hydroxyl radicals and their changes over time were determined by simulating the oxidation process of soil minerals under different environmental conditions. This typically requires the use of specific chemical probes (such as benzoates) to capture and quantify hydroxyl radicals. Based on the results of laboratory simulation experiments and existing theoretical knowledge, a kinetic model for the production of hydroxyl radicals from soil mineral oxidation was constructed. This model can describe the process of hydroxyl radical production from mineral oxidation under different conditions and its main influencing factors. Field monitoring was conducted in actual soil environments to verify the effectiveness of the laboratory simulation experiments and the kinetic model. By comparing the field monitoring data with the model predictions, the model parameters were further adjusted and improved.

[0004] Soil minerals are diverse, and the ability of each mineral to produce hydroxyl radicals varies, significantly influenced by environmental conditions such as temperature, humidity, pH, and organic matter content. Redox reaction mechanisms are complex, involving multiple intermediate products and reaction pathways, making them difficult to accurately describe and predict. Laboratory conditions cannot fully simulate the complexity and dynamism of the actual soil environment, hindering the accurate assessment of the hydroxyl radical conversion coefficients produced by soil mineral oxidation. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for evaluating the conversion coefficient of hydroxyl radicals (·OH) produced by soil mineral oxidation. This method solves the problem that it is difficult to fully simulate the complexity and dynamics of the actual soil environment under laboratory conditions, making it impossible to accurately evaluate the conversion coefficient of hydroxyl radicals produced by soil mineral oxidation.

[0006] To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows:

[0007] This invention provides a method for evaluating the conversion coefficient of hydroxyl radicals (·OH) produced by soil mineral oxidation, comprising:

[0008] Step S101: Obtain sample data and experimental history data, preprocess the sample data and experimental history data to obtain preprocessed sample data and preprocessed experimental history data.

[0009] Step S102: Construct a laboratory simulation model using preprocessed historical experimental data, retrieve sample data from the database, substitute the sample data from the database into the preset laboratory simulation model to obtain experimental simulation results, compare the experimental simulation results with historical experimental data in the database, and if the data comparison results are consistent, the laboratory simulation model is qualified.

[0010] Step S103: Receive the expected sample data, substitute the expected sample data into the laboratory simulation model to generate the simulation experimental results of the expected sample, match the simulation experimental results of the expected sample with the expected sample data to obtain a simulation data set, and use the experimental history data and simulation data set to construct a kinetic model for the oxidation of hydroxyl radicals in soil minerals. The kinetic model for the oxidation of hydroxyl radicals in soil minerals is used to simulate the oxidation process of soil minerals under different environmental conditions in the laboratory. The control parameters under different environmental conditions include temperature, humidity, pH value, and added chemical reagents. Record the amount of hydroxyl radicals generated and the change law of hydroxyl radicals over time according to the oxidation process of soil minerals under different environmental conditions.

[0011] Step S104: Collect field monitoring data in the actual soil environment. The field monitoring data in the actual soil environment includes environmental parameters and data on hydroxyl radicals produced by soil mineral oxidation. Substitute the field monitoring data in the actual soil environment into the kinetic model of hydroxyl radicals produced by soil mineral oxidation, output the simulation results of hydroxyl radicals produced by soil mineral oxidation, and compare the simulation results of hydroxyl radicals produced by soil mineral oxidation with the field monitoring data in the actual soil environment. If the error of the comparison results is within the preset range, evaluate the field monitoring data in the actual soil environment.

[0012] Step S105: Preprocess the field monitoring data in the actual soil environment, and substitute the preprocessed field monitoring data in the actual soil environment into the preset evaluation model to obtain the evaluation result of the hydroxyl radical conversion coefficient of soil mineral oxidation.

[0013] Furthermore, the present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals (·OH) produced by soil mineral oxidation, wherein step S102 includes:

[0014] Receive the evaluation target, and determine the comparison indicators based on the evaluation target. The comparison indicators include the amount of hydroxyl radicals generated, the reaction rate, and the time dependence.

[0015] The comparison indicators in the experimental simulation results are numerically compared with the corresponding historical experimental data in the database to obtain the comparison results. The error between the experimental simulation results and the historical experimental data is calculated to obtain the error analysis results.

[0016] Based on the comparison results and error analysis results, it is determined whether the experimental simulation results are consistent with the historical experimental data in the database. If the error is within the preset range, the laboratory simulation model is considered to have passed the test.

[0017] Furthermore, the present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals (·OH) produced by soil mineral oxidation, wherein step S103 includes:

[0018] Chemical probes are used to capture and quantify hydroxyl radicals. The amount of hydroxyl radicals generated is calculated by measuring the concentration of the product after the chemical probe reacts with the hydroxyl radical. The chemical probes include benzoates.

[0019] Furthermore, the present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals (·OH) produced by soil mineral oxidation, wherein step S103 includes:

[0020] Obtain the soil sample data to be used, including the sample source, sample type, sample composition, laboratory simulation requirements, and experimental conditions;

[0021] According to the requirements of laboratory simulation, the pretreated sample data is substituted into the laboratory simulation model, the simulation conditions such as temperature, humidity, pH value, and added chemical reagents are set, and the simulation process is started.

[0022] The laboratory simulation model runs a simulation algorithm based on the input sample data and the set simulation conditions to generate the simulation results of the sample to be used. The simulation results of the sample to be used include the amount of hydroxyl radicals generated, the reaction rate, and the time dependence.

[0023] The simulation results are post-processed to extract the indicators corresponding to the expected sample data. The extracted indicators are then matched with the expected sample data. The matched simulation results and the expected sample data are integrated into a simulation dataset, which includes simulation results and sample information.

[0024] Furthermore, the present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals (·OH) produced by soil mineral oxidation, wherein step S104 includes:

[0025] Data on actual soil environmental parameters, including temperature, humidity, pH, and hydroxyl radical production from soil mineral oxidation, were collected.

[0026] And establish collection time and location tags for the collected data;

[0027] The field monitoring data collected in the actual soil environment were preprocessed, and the preprocessed data were substituted into the kinetic model of hydroxyl radical production by soil mineral oxidation. The model parameters were set and the model was run.

[0028] The kinetic model will run simulation algorithms based on the actual field monitoring data in the input soil environment and output the simulation results of soil mineral oxidation to produce hydroxyl radicals.

[0029] Furthermore, the present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals (·OH) produced by soil mineral oxidation, wherein step S104 includes:

[0030] The generation amount and time dependence of hydroxyl radicals in the simulation results were extracted. The simulation results were compared with the field monitoring data in the actual soil environment item by item to analyze the consistency and differences in key indicators.

[0031] Calculate the error of the comparison results. If the error of the comparison results is within the preset range, the dynamic model is considered to be qualified in the actual soil environment.

[0032] If the error of the comparison results exceeds the preset range, the dynamic model needs to be optimized.

[0033] If the kinetic model is suitable for the actual soil environment, then the field monitoring data in the actual soil environment will be evaluated.

[0034] Furthermore, the present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals (·OH) produced by soil mineral oxidation, wherein step S105 includes:

[0035] Based on the assessment objectives and the characteristics of the hydroxyl radical conversion coefficient produced by soil mineral oxidation, a suitable assessment model is constructed. The pre-processed field monitoring data is input into the assessment model. This pre-processed field monitoring data includes environmental parameters such as temperature, humidity, and pH value, as well as field monitoring data on hydroxyl radical production from soil mineral oxidation.

[0036] The evaluation model is activated to process the input field monitoring data. Based on the built-in algorithm and logic, the evaluation model calculates the evaluation result of the hydroxyl radical conversion coefficient produced by soil mineral oxidation.

[0037] The beneficial effects of this invention are mainly reflected in the following aspects:

[0038] By establishing laboratory simulation models and kinetic models, this invention can accurately simulate the oxidation process of soil minerals and the amount of hydroxyl radicals produced under different environmental conditions (such as temperature, humidity, pH value, etc.), thereby improving the accuracy of the assessment of the conversion coefficient of hydroxyl radicals produced by soil mineral oxidation.

[0039] Compared to field experiments, laboratory simulation experiments can significantly save time, manpower, and material costs. Model simulation avoids the complexities and uncontrollable factors inherent in field experiments, thus reducing overall experimental costs. The accuracy and reliability of the model are verified by comparing laboratory simulation results with historical experimental data. Furthermore, the applicability and reliability of the model in practical applications are further enhanced through the collection of field monitoring data in actual soil environments and model validation.

[0040] The assessment method provided by this invention not only yields specific numerical values ​​for the hydroxyl radical conversion coefficient produced by soil mineral oxidation, but also reveals the intrinsic mechanisms and key influencing factors of soil mineral oxidation processes, providing strong support for in-depth research in soil environmental science and agricultural technology. The assessment results can provide a scientific basis for agricultural production and soil management, guiding decisions on soil improvement, fertilizer application, and pollution control. By optimizing soil environmental management measures, crop yield and quality can be improved, promoting sustainable agricultural development.

[0041] The evaluation method and kinetic model provided by this invention can further guide the research and development and application of new technologies. For example, by simulating the soil mineral oxidation process under different management measures, the potential and effectiveness of new technologies in improving the soil environment can be evaluated, promoting the innovative development of soil environmental science and agricultural technology.

[0042] In summary, this invention, through a scientific and systematic evaluation method, not only improves the accuracy and reliability of evaluation results but also reduces experimental costs, enhances the applicability and reliability of the model, provides strong support for the optimization of agricultural production and soil management, and promotes scientific research and technological innovation in related fields. Attached Figure Description

[0043] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0044] Figure 1 is a schematic diagram of a method for evaluating the conversion coefficient of hydroxyl radicals (·OH) produced by soil mineral oxidation, provided by an embodiment of the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The technical solutions provided by various embodiments of this invention will be described in detail below with reference to the accompanying drawings.

[0046] To better understand the purpose of this invention, the invention will now be described in further detail.

[0047] This invention provides a method for evaluating the conversion coefficient of hydroxyl radicals (·OH) produced by soil mineral oxidation, comprising:

[0048] Step S101: Obtain sample data and experimental history data, preprocess the sample data and experimental history data to obtain preprocessed sample data and preprocessed experimental history data.

[0049] Obtain sample data and experimental history data:

[0050] Sample data includes specific information about the soil sample, such as its origin, type, and composition, which is crucial for understanding the characteristics and chemical properties of soil minerals.

[0051] Historical experimental data refers to past experimental data related to the production of hydroxyl radicals from soil mineral oxidation, including records of soil mineral oxidation processes under different conditions and measurements of hydroxyl radical production. Historical data provides valuable reference for building and validating models.

[0052] Preprocessing of sample data and experimental history data: removing or correcting errors, outliers, or missing values. For example, checking for unreasonable ranges or duplicate records, and ensuring data accuracy and consistency. Converting data to a format suitable for subsequent analysis and modeling. This includes converting text data to numerical data and standardizing units of measurement.

[0053] Data should be scaled as needed to eliminate the influence of different dimensions between features, ensuring that all features have equal importance in the model. Features that significantly impact the evaluation model should be selected from the raw data. These features are closely related to soil mineral types and environmental conditions (such as temperature, humidity, and pH).

[0054] After the above preprocessing steps, the sample data and experimental historical data will be organized into a well-structured, uniformly formatted, and reliable dataset. These datasets will be used to subsequently build laboratory simulation models and kinetic models.

[0055] Preprocessing can correct errors and outliers in the data, reduce noise and bias, thereby improving the accuracy and robustness of the model. Data standardization and normalization can accelerate model training and improve computational efficiency. Feature selection helps the model focus on features that have a significant impact on the evaluation results, avoiding overfitting and underfitting problems.

[0056] Step S101 provides high-quality data support for the subsequent construction of laboratory simulation and kinetic models. Through meticulous data preprocessing, the accuracy and reliability of the models are ensured, laying a solid foundation for subsequent analysis and evaluation.

[0057] Step S102: Construct a laboratory simulation model using preprocessed historical experimental data, retrieve sample data from the database, substitute the sample data from the database into the preset laboratory simulation model to obtain experimental simulation results, compare the experimental simulation results with historical experimental data in the database, and if the data comparison results are consistent, the laboratory simulation model is qualified.

[0058] After preprocessing, the historical experimental data includes high-quality information on the oxidation process of soil minerals under different conditions. This information forms the basis for constructing a laboratory simulation model. Based on this historical experimental data, appropriate mathematical modeling methods (such as regression analysis, machine learning algorithms, etc.) are used to construct a laboratory simulation model capable of simulating the soil mineral oxidation process.

[0059] The database contains information on various soil samples, which will be used to test the accuracy and reliability of the laboratory simulation model. The process involves inputting the sample data from the database into the pre-built laboratory simulation model and executing the simulation.

[0060] The laboratory simulation model generates corresponding simulation results based on the input sample data. These results include simulations of soil mineral oxidation processes under different environmental conditions (such as temperature, humidity, pH, etc.) and predicted values ​​of hydroxyl radical production.

[0061] The simulation results are compared with corresponding historical experimental data in the database. The comparison includes key indicators such as the amount of hydroxyl radicals generated, reaction rate, and time dependence. If the comparison results show that the simulation results are consistent with the historical experimental data or the error is within the preset range, the laboratory simulation model is considered to be able to accurately simulate the oxidation process of soil minerals, and the model is considered to have passed the test.

[0062] If the comparison results show significant differences or the error exceeds the preset range, the laboratory simulation model needs to be adjusted and optimized until satisfactory simulation results are achieved. By constructing a laboratory simulation model that can accurately simulate the soil mineral oxidation process, a reliable foundation can be provided for the subsequent construction and evaluation of kinetic models.

[0063] Compared to field experiments, laboratory simulations can significantly save time, manpower, and material costs, while avoiding the uncontrollable factors associated with field experiments. Laboratory simulation models can flexibly adjust environmental parameters to simulate soil mineral oxidation processes under different conditions, providing support for a deeper understanding of oxidation reaction mechanisms.

[0064] Step S102 is a crucial step in constructing and validating the laboratory simulation model, ensuring the accuracy and reliability of subsequent analysis and evaluation. Through a rigorous comparative validation process, the laboratory simulation model can accurately simulate the oxidation process of soil minerals, laying a solid foundation for subsequent kinetic model construction and conversion coefficient assessment.

[0065] Step S103: Receive the expected sample data, substitute the expected sample data into the laboratory simulation model to generate the simulation experimental results of the expected sample, match the simulation experimental results of the expected sample with the expected sample data to obtain a simulation data set, and use the experimental history data and simulation data set to construct a kinetic model for the oxidation of hydroxyl radicals in soil minerals. The kinetic model for the oxidation of hydroxyl radicals in soil minerals is used to simulate the oxidation process of soil minerals under different environmental conditions in the laboratory. The control parameters under different environmental conditions include temperature, humidity, pH value, and added chemical reagents. Record the amount of hydroxyl radicals generated and the change law of hydroxyl radicals over time according to the oxidation process of soil minerals under different environmental conditions.

[0066] The received sample data includes information on soil samples expected to be used in the assessment process, such as sample origin, type, and composition. This data will be used in subsequent simulation experiments.

[0067] The received sample data, intended for use, will be input into a validated laboratory simulation model. The model will then use this data to simulate the oxidation process of soil minerals under different conditions.

[0068] The laboratory simulation model runs simulation algorithms based on the input sample data to generate simulated experimental results. These results reflect the oxidation process of soil minerals and the amount of hydroxyl radicals generated under different environmental conditions (such as temperature, humidity, and pH). The simulation results are then matched with the expected sample data, and the correspondence between the simulation results and the sample data is accurate. This step facilitates subsequent data analysis and processing.

[0069] The matched simulation results and the expected sample data are integrated into a simulation dataset. This dataset contains various key indicators and sample information from the simulation experiments, providing necessary data support for the subsequent construction of the kinetic model.

[0070] Based on historical experimental data and simulation datasets, a kinetic model of hydroxyl radical production from soil mineral oxidation was constructed using appropriate mathematical modeling methods (such as differential equation models and machine learning methods). This model can describe the oxidation process of soil minerals under different environmental conditions and its main influencing factors.

[0071] Kinetic models were used to simulate the oxidation process of soil minerals under different environmental conditions in a laboratory setting. These environmental conditions included temperature, humidity, pH, and added chemical reagents. By adjusting these parameters, the model was able to predict the amount of hydroxyl radicals produced under different conditions and their changes over time.

[0072] Providing in-depth insights: Kinetic models can reveal the intrinsic mechanisms and key influencing factors of soil mineral oxidation processes, providing a scientific basis for soil environmental management and agricultural production. By simulating oxidation processes under different conditions, kinetic models can help decision-makers evaluate the effectiveness of different management measures and formulate scientifically sound soil management plans.

[0073] Predictions based on dynamic models can guide the research and development and application of new technologies, and promote the innovative development of soil environmental science and agricultural technology.

[0074] Step S104: Collect field monitoring data in the actual soil environment. The field monitoring data in the actual soil environment includes environmental parameters and data on hydroxyl radical production from soil mineral oxidation. Substitute the field monitoring data in the actual soil environment into the kinetic model of hydroxyl radical production from soil mineral oxidation, output the simulation results of hydroxyl radical production from soil mineral oxidation, and compare the simulation results of hydroxyl radical production from soil mineral oxidation with the field monitoring data in the actual soil environment. If the error of the comparison results is within the preset range, evaluate the field monitoring data in the actual soil environment.

[0075] Field monitoring data is collected in actual soil environments, including direct and indirect data on environmental parameters (such as temperature, humidity, and pH) and the production of hydroxyl radicals from soil mineral oxidation. These data provide a true reflection of the soil mineral oxidation process and its conversion coefficients.

[0076] The collected data is labeled with collection time and location tags, ensuring data traceability and accuracy. These tags facilitate subsequent data analysis and comparison.

[0077] Necessary preprocessing is performed on the collected field monitoring data, including data cleaning, outlier handling, missing value imputation, and ensuring data quality and consistency.

[0078] The pre-processed field monitoring data of the actual soil environment were then substituted into the previously constructed kinetic model of hydroxyl radical production from soil mineral oxidation. This step is crucial for verifying the applicability and accuracy of the model in real-world environments.

[0079] Set the model parameters and run the kinetic model to simulate the oxidation process of soil minerals based on the input actual environmental data, and output the simulation results of soil mineral oxidation producing hydroxyl radicals.

[0080] The simulation results of the kinetic model were compared item by item with field monitoring data in the actual soil environment. Special attention was paid to the consistency and differences of key indicators such as the amount of hydroxyl radicals generated and their time dependence.

[0081] Calculate the error of the comparison results and determine whether the error is within the preset range. Error analysis is an important means of evaluating the accuracy and reliability of the dynamic model prediction.

[0082] If the error of the comparison results is within the preset range, the kinetic model is considered to perform adequately in the actual soil environment and can be effectively evaluated based on field monitoring data in the actual soil environment. If the error of the comparison results exceeds the preset range, the kinetic model needs to be optimized and adjusted to improve the model's predictive accuracy and reliability.

[0083] Step S104 involves collecting field monitoring data in the actual soil environment and comparing it with the simulation results of the kinetic model to verify the effectiveness and accuracy of the kinetic model in practical applications. This process not only provides a reliable basis for assessing the conversion coefficient of hydroxyl radicals produced by soil mineral oxidation but also provides important feedback for subsequent optimization and application of the kinetic model. Through continuous model validation and optimization, the scientific rigor and practicality of the assessment method can be further improved.

[0084] Step S105: Preprocess the field monitoring data in the actual soil environment, and substitute the preprocessed field monitoring data in the actual soil environment into the preset evaluation model to obtain the evaluation result of the hydroxyl radical conversion coefficient of soil mineral oxidation.

[0085] Step S105 is the final step in the entire assessment process. It focuses on calculating the specific assessment results of the hydroxyl radical conversion coefficient produced by soil mineral oxidation using a pre-set assessment model after a series of preprocessing steps based on the field monitoring data in the actual soil environment. The following is a detailed explanation of this step:

[0086] Preprocessing is performed on field monitoring data collected from actual soil environments. Preprocessing includes data cleaning (removing noise, outliers, etc.), format conversion, standardization (such as normalization and standardization), and feature selection. The purpose of preprocessing is to improve the quality and consistency of the input data to meet the requirements of the evaluation model regarding input data format and quality.

[0087] Based on the assessment objectives and the characteristics of the hydroxyl radical conversion coefficient generated by soil mineral oxidation, a suitable assessment model is selected or constructed. This model is based on machine learning, statistical models, or other mathematical methods, depending on the complexity of the problem and the characteristics of the available data.

[0088] The pre-processed field monitoring data from the actual soil environment (including environmental parameters such as temperature, humidity, pH, and field monitoring data on hydroxyl radical production from soil mineral oxidation) is substituted into the pre-set evaluation model. This step is a key step in combining actual data with evaluation logic.

[0089] Launch the evaluation model and let it process the input field monitoring data. The evaluation model will analyze and calculate the data based on its built-in algorithms and logic.

[0090] After completing data processing, the evaluation model will output the evaluation result of the hydroxyl radical conversion coefficient produced by soil mineral oxidation. This result is a specific numerical value, as well as a range or probability distribution, depending on the output format of the evaluation model.

[0091] The assessment results should be interpreted, their meaning understood, and applied to practical soil management and agricultural production. The assessment results can guide decisions regarding soil improvement, fertilizer application, and pollution control.

[0092] Specifically, the present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals (·OH) produced by soil mineral oxidation, wherein step S102 includes:

[0093] Receive the evaluation target, and determine the comparison indicators based on the evaluation target. The comparison indicators include the amount of hydroxyl radicals generated, the reaction rate, and the time dependence.

[0094] The comparison indicators in the experimental simulation results are numerically compared with the corresponding historical experimental data in the database to obtain the comparison results. The error between the experimental simulation results and the historical experimental data is calculated to obtain the error analysis results.

[0095] Based on the comparison results and error analysis results, it is determined whether the experimental simulation results are consistent with the historical experimental data in the database. If the error is within the preset range, the laboratory simulation model is considered to have passed the test.

[0096] Specifically, the present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals (·OH) produced by soil mineral oxidation, wherein step S103 includes:

[0097] Chemical probes are used to capture and quantify hydroxyl radicals. The amount of hydroxyl radicals generated is calculated by measuring the concentration of the product after the chemical probe reacts with the hydroxyl radical. The chemical probes include benzoates.

[0098] Specifically, the present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals (·OH) produced by soil mineral oxidation, wherein step S103 includes:

[0099] Obtain the soil sample data to be used, including the sample source, sample type, sample composition, laboratory simulation requirements, and experimental conditions;

[0100] According to the requirements of laboratory simulation, the pretreated sample data is substituted into the laboratory simulation model, the simulation conditions such as temperature, humidity, pH value, and added chemical reagents are set, and the simulation process is started.

[0101] The laboratory simulation model runs a simulation algorithm based on the input sample data and the set simulation conditions to generate the simulation results of the sample to be used. The simulation results of the sample to be used include the amount of hydroxyl radicals generated, the reaction rate, and the time dependence.

[0102] The simulation results are post-processed to extract the indicators corresponding to the expected sample data. The extracted indicators are then matched with the expected sample data. The matched simulation results and the expected sample data are integrated into a simulation dataset, which includes simulation results and sample information.

[0103] Specifically, the present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals (·OH) produced by soil mineral oxidation, wherein step S104 includes:

[0104] Data on actual soil environmental parameters, including temperature, humidity, pH, and hydroxyl radical production from soil mineral oxidation, were collected.

[0105] And establish collection time and location tags for the collected data;

[0106] The field monitoring data collected in the actual soil environment were preprocessed, and the preprocessed data were substituted into the kinetic model of hydroxyl radical production by soil mineral oxidation. The model parameters were set and the model was run.

[0107] The kinetic model will run simulation algorithms based on the actual field monitoring data in the input soil environment and output the simulation results of soil mineral oxidation to produce hydroxyl radicals.

[0108] Specifically, step S104 of this invention details how to collect field monitoring data from the actual soil environment and apply it to a kinetic model of hydroxyl radical production from soil mineral oxidation to verify the model's effectiveness and output simulation results. The following is a detailed breakdown of this step:

[0109] First, it is necessary to collect a series of key environmental parameters in the actual soil environment, including temperature, humidity, and pH. These parameters are crucial for understanding the overall state of the soil environment.

[0110] Simultaneously, it is also necessary to collect direct or indirect data on the production of hydroxyl radicals from soil mineral oxidation. This data is obtained through specific sensors or chemical analysis methods to reflect the oxidation process of soil minerals under actual environmental conditions.

[0111] Time and location labels were created for the collected data. The time labels recorded the specific time points when the data was collected, which helps to consider the impact of time factors on the soil environment during subsequent data analysis.

[0112] Location tags mark the specific locations where data was collected, making it easier to identify and analyze differences in soil environment between different geographical locations.

[0113] Necessary preprocessing steps are performed on the collected field monitoring data, ensuring data quality and consistency. Preprocessing includes data cleaning (removing outliers, filling in missing values, etc.), data format conversion, and standardization or normalization.

[0114] Preprocessing steps help improve the accuracy of input data for dynamic models, thereby enhancing the model's predictive performance.

[0115] The pre-processed field monitoring data of the actual soil environment were then substituted into the previously constructed kinetic model of hydroxyl radical production from soil mineral oxidation. This step is a crucial link between actual observation data and theoretical models.

[0116] In the kinetic model, appropriate parameters are set, such as controlling temperature, humidity, and pH, to simulate conditions in the actual soil environment. Running the kinetic model, based on the input actual soil environment data, utilizes built-in simulation algorithms to calculate and output simulation results of hydroxyl radical production from soil mineral oxidation.

[0117] The simulation results output by the kinetic model will provide detailed information about the process of hydroxyl radical production from soil mineral oxidation, including the amount of hydroxyl radicals produced and their time dependence.

[0118] Step S104, through data collection in the actual soil environment, label establishment, data preprocessing, and the operation of the kinetic model, successfully combined actual observation data with the theoretical model, providing a scientific basis for simulating the process of soil mineral oxidation to produce hydroxyl radicals. This process not only verified the application effect of the kinetic model in the actual environment but also provided strong data support for subsequent soil management and agricultural production.

[0119] Specifically, the present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals (·OH) produced by soil mineral oxidation, wherein step S104 includes:

[0120] The generation amount and time dependence of hydroxyl radicals in the simulation results were extracted. The simulation results were compared with the field monitoring data in the actual soil environment item by item to analyze the consistency and differences in key indicators.

[0121] Calculate the error of the comparison results. If the error of the comparison results is within the preset range, the dynamic model is considered to be qualified in the actual soil environment.

[0122] If the error of the comparison results exceeds the preset range, the dynamic model needs to be optimized.

[0123] If the kinetic model is suitable for the actual soil environment, then the field monitoring data in the actual soil environment will be evaluated.

[0124] The kinetic model was validated and field monitoring data were evaluated. Key indicators, primarily the amount of hydroxyl radicals produced and their time dependence, were extracted from the simulation results. These indicators directly reflect the generation characteristics and dynamic changes of hydroxyl radicals during soil mineral oxidation. The key indicators, such as the amount of hydroxyl radicals produced and their time dependence, from the simulation results were compared item by item with the corresponding data from field monitoring in the actual soil environment. This step aims to verify whether the simulation results can accurately reflect the generation of hydroxyl radicals in the actual soil environment.

[0125] During the comparison process, it is necessary to carefully analyze the consistency and differences between the simulation results and the actual data on key indicators. Consistency indicates that the kinetic model can simulate the actual soil environment well, while differences indicate areas in the model that need improvement.

[0126] After comparing the results, it is necessary to calculate the error between the simulation results and the actual data. The error calculation method can include various forms such as absolute error and relative error, depending on the evaluation requirements and the characteristics of the data.

[0127] Based on the calculated error, the suitability of the kinetic model in the actual soil environment is determined. If the error is within the preset range, the model is considered to be able to adapt well to the actual environment and is considered qualified; otherwise, the model needs to be optimized.

[0128] If the model is unqualified (i.e., the error exceeds the preset range), the dynamic model needs to be optimized based on the comparison results and error analysis. Optimization involves multiple aspects, such as adjusting model parameters, improving simulation algorithms, and adding factors to be considered.

[0129] Assuming the kinetic model is valid, field monitoring data in actual soil environments can be evaluated. The evaluation includes analyzing the validity and reliability of the data, as well as the soil environmental conditions they reflect.

[0130] Specifically, the present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals (·OH) produced by soil mineral oxidation, wherein step S105 includes:

[0131] Based on the assessment objectives and the characteristics of the hydroxyl radical conversion coefficient produced by soil mineral oxidation, a suitable assessment model is constructed. The pre-processed field monitoring data is input into the assessment model. This pre-processed field monitoring data includes environmental parameters such as temperature, humidity, and pH value, as well as field monitoring data on hydroxyl radical production from soil mineral oxidation.

[0132] The evaluation model is activated to process the input field monitoring data. Based on the built-in algorithm and logic, the evaluation model calculates the evaluation result of the hydroxyl radical conversion coefficient produced by soil mineral oxidation.

[0133] Based on the assessment objectives and the characteristics of the hydroxyl radical conversion coefficient generated by soil mineral oxidation, a suitable assessment model is first constructed. This model needs to be able to process and analyze the complex data in the process of hydroxyl radical generation by soil mineral oxidation, including environmental parameters (such as temperature, humidity, and pH) and field monitoring data of hydroxyl radical generation by soil mineral oxidation.

[0134] The field monitoring data collected from the actual soil environment is preprocessed. The preprocessing steps include data cleaning (removing outliers, filling missing values, etc.), data standardization or normalization, and organizing environmental parameters (temperature, humidity, pH) and soil mineral oxidation hydroxyl radical production data into a format that the model can recognize.

[0135] Preprocessed field monitoring data (including environmental parameters and soil mineral oxidation hydroxyl radical production data) are input into the evaluation model. The accuracy and consistency of the data are crucial to the accuracy of the model output.

[0136] Launch the pre-built evaluation model and begin processing the input field monitoring data. The model will analyze and calculate the data based on its built-in algorithms and logic.

[0137] The evaluation model uses a built-in algorithm and input data to calculate the specific evaluation result of the hydroxyl radical conversion coefficient produced by soil mineral oxidation. This result reflects the conversion efficiency or capacity of soil mineral oxidation to produce hydroxyl radicals under specific environmental conditions.

[0138] The evaluation results are output and analyzed. The analysis helps to understand the oxidation behavior of soil minerals under different environmental conditions, as well as the generation patterns and conversion efficiency of hydroxyl radicals. This has important guiding significance for soil management and agricultural production.

[0139] Step S105 is the final step of the assessment method. By constructing a suitable assessment model and processing field monitoring data from the actual soil environment, the specific assessment results of the hydroxyl radical conversion coefficient produced by soil mineral oxidation are calculated. This process not only provides a scientific basis for in-depth research on soil mineral oxidation behavior but also provides strong support for practical applications in soil management and agricultural production. The accuracy and reliability of the assessment results are of great significance for guiding agricultural production practices and optimizing soil management measures.

[0140] This invention effectively solves the problem of accurately evaluating the hydroxyl radical conversion coefficient produced by soil mineral oxidation, which is difficult to fully simulate the complexity and dynamics of the actual soil environment under laboratory conditions. The specific solution includes the following aspects:

[0141] First, sample data and historical experimental data are acquired and preprocessed to construct a laboratory simulation model. Then, sample data from the database are substituted into this model for simulation, and the simulation results are compared with historical experimental data in the database to assess the model's accuracy and reliability. This step ensures that the laboratory simulation model can realistically reflect the oxidation process of soil minerals.

[0142] By receiving the sample data to be used and inputting it into a laboratory simulation model, simulated experimental results are generated. Subsequently, the simulated experimental results are matched with the sample data and combined with historical experimental data to construct a kinetic model of hydroxyl radical production from soil mineral oxidation. This model can simulate the oxidation process of soil minerals under different environmental conditions (such as temperature, humidity, pH, added chemical reagents, etc.) and record the amount of hydroxyl radicals produced and their changes over time.

[0143] Field monitoring was conducted in actual soil environments to collect data on environmental parameters and hydroxyl radical production from soil mineral oxidation. This data was then input into a previously constructed kinetic model, and simulation results were output and compared with actual monitoring data. Error analysis of the comparison results validated the applicability and accuracy of the kinetic model in actual soil environments. If significant errors were found, the model was optimized and adjusted.

[0144] After preprocessing the field monitoring data from the actual soil environment, the data was input into a pre-defined assessment model to calculate the assessment results of the hydroxyl radical conversion coefficient produced by soil mineral oxidation. This step, based on the validated kinetic model and field monitoring data, ensured the accuracy and reliability of the assessment results.

[0145] Through the above steps, this invention not only simulates the oxidation process of soil minerals under laboratory conditions, but also verifies the effectiveness of the simulation results and kinetic model using field monitoring data from actual soil environments. This solves the problem that laboratories cannot fully simulate the complexity and dynamics of actual soil environments, and enables accurate assessment of the hydroxyl radical conversion coefficient generated by soil mineral oxidation. This method improves the scientific rigor and practicality of the assessment, providing strong support for research in agriculture and the environment.

[0146] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations. The above-described embodiments of this invention do not constitute a limitation on the scope of protection of this invention.

Claims

1. A method for evaluating the conversion coefficient of hydroxyl radicals (·OH) produced by soil mineral oxidation, characterized in that, include: Step S101: Obtain sample data and experimental history data, preprocess the sample data and experimental history data to obtain preprocessed sample data and preprocessed experimental history data. Step S102: Construct a laboratory simulation model using preprocessed historical experimental data, retrieve sample data from the database, substitute the sample data from the database into the preset laboratory simulation model to obtain experimental simulation results, compare the experimental simulation results with historical experimental data in the database, and if the data comparison results are consistent, the laboratory simulation model is qualified. Step S103: Receive the expected sample data, substitute the expected sample data into the laboratory simulation model to generate the simulation experimental results of the expected sample, match the simulation experimental results of the expected sample with the expected sample data to obtain a simulation data set, and use the experimental history data and simulation data set to construct a kinetic model for the oxidation of hydroxyl radicals in soil minerals. The kinetic model for the oxidation of hydroxyl radicals in soil minerals is used to simulate the oxidation process of soil minerals under different environmental conditions in the laboratory. The control parameters under different environmental conditions include temperature, humidity, pH value, and added chemical reagents. Record the amount of hydroxyl radicals generated and the change law of hydroxyl radicals over time according to the oxidation process of soil minerals under different environmental conditions. Step S104: Collect field monitoring data in the actual soil environment. The field monitoring data in the actual soil environment includes environmental parameters and data on hydroxyl radicals produced by soil mineral oxidation. Substitute the field monitoring data in the actual soil environment into the kinetic model of hydroxyl radicals produced by soil mineral oxidation, output the simulation results of hydroxyl radicals produced by soil mineral oxidation, and compare the simulation results of hydroxyl radicals produced by soil mineral oxidation with the field monitoring data in the actual soil environment. If the error of the comparison results is within the preset range, evaluate the field monitoring data in the actual soil environment. Step S105: Preprocess the field monitoring data in the actual soil environment, and substitute the preprocessed field monitoring data in the actual soil environment into the preset evaluation model to obtain the evaluation result of the hydroxyl radical conversion coefficient of soil mineral oxidation. Step S102 includes: Receive the evaluation target, and determine the comparison indicators based on the evaluation target. The comparison indicators include the amount of hydroxyl radicals generated, the reaction rate, and the time dependence. The comparison indicators in the experimental simulation results are numerically compared with the corresponding historical experimental data in the database to obtain the comparison results. The error between the experimental simulation results and the historical experimental data is calculated to obtain the error analysis results. Based on the comparison results and error analysis results, determine whether the experimental simulation results are consistent with the historical experimental data in the database. If the error is within the preset range, the laboratory simulation model is considered to be qualified. Step S103 includes: Obtain the soil sample data to be used, including the sample source, sample type, sample composition, laboratory simulation requirements, and experimental conditions; According to the requirements of laboratory simulation, the pretreated sample data is substituted into the laboratory simulation model, the simulation conditions such as temperature, humidity, pH value, and added chemical reagents are set, and the simulation process is started. The laboratory simulation model runs a simulation algorithm based on the input sample data and the set simulation conditions to generate the simulation results of the sample to be used. The simulation results of the sample to be used include the amount of hydroxyl radicals generated, the reaction rate, and the time dependence. The simulation results are post-processed to extract the indicators corresponding to the expected sample data. The extracted indicators are then matched with the expected sample data. The matched simulation results and the expected sample data are integrated into a simulation dataset, which includes simulation results and sample information.

2. The method for evaluating the conversion coefficient of hydroxyl radicals (·OH) produced by soil mineral oxidation as described in claim 1, characterized in that, Step S103 includes: Chemical probes are used to capture and quantify hydroxyl radicals. The amount of hydroxyl radicals generated is calculated by measuring the concentration of the product after the chemical probe reacts with the hydroxyl radical. The chemical probes include benzoates.

3. The method for evaluating the conversion coefficient of hydroxyl radicals (·OH) produced by soil mineral oxidation as described in claim 1, characterized in that, Step S104 includes: Data on actual soil environmental parameters, including temperature, humidity, pH, and hydroxyl radical production from soil mineral oxidation, were collected. And establish collection time and location tags for the collected data; The field monitoring data collected in the actual soil environment were preprocessed, and the preprocessed data were substituted into the kinetic model of hydroxyl radical production by soil mineral oxidation. The model parameters were set and the model was run. The kinetic model will run simulation algorithms based on the actual field monitoring data in the input soil environment and output the simulation results of soil mineral oxidation to produce hydroxyl radicals.

4. The method for evaluating the conversion coefficient of hydroxyl radicals (·OH) produced by soil mineral oxidation as described in claim 3, characterized in that, Step S104 includes: The generation amount and time dependence of hydroxyl radicals in the simulation results were extracted. The simulation results were compared with the field monitoring data in the actual soil environment item by item to analyze the consistency and differences in key indicators. Calculate the error of the comparison results. If the error of the comparison results is within the preset range, the dynamic model is considered to be qualified in the actual soil environment. If the error of the comparison results exceeds the preset range, the dynamic model needs to be optimized. If the kinetic model is suitable for the actual soil environment, then the field monitoring data in the actual soil environment will be evaluated.

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