Organic contaminated soil groundwater remediation monitoring method and system

Abstract

The invention relates to the technical field of groundwater treatment, and discloses an organic contaminated soil groundwater remediation monitoring method and system, and the system comprises a first collection module, a first processing module, a first calculation module, a first analysis module, a second collection module, a second processing module, a second calculation module and a second analysis module. And the precision and efficiency of pollution monitoring are effectively improved. Compared with the traditional technical means, the systematic data processing and analysis method significantly improves the real-time performance and accuracy of environment monitoring, realizes the conversion from static monitoring to dynamic management, not only enhances the recognition capability of pollution sources, but also optimizes the implementation of restoration measures, and the system can comprehensively integrate different types of environment data. The multi-dimensional reference coefficient enables treatment effectiveness evaluation to be more scientific, can effectively reflect the influence of different factors on the soil and water body remediation process, and enables judgment of the regional treatment state to be more objective.

Description

Technical Field

[0001] The present invention relates to the technical field of groundwater treatment, and in particular to a method and system for monitoring the restoration of groundwater in organically contaminated soil. Background Art

[0002] With the rapid development of industrial production, the problem of groundwater pollution has become increasingly prominent, and the impact of groundwater pollution on the environment and economic development has become increasingly apparent. At present, developed countries have invested a lot of manpower and material resources to repair polluted areas, and the in-situ remediation technology of groundwater has also been rapidly developed. In order to ensure the safety of the ecological environment system and the sustainable use of groundwater, it is necessary to repair the contaminated groundwater. Therefore, the research and development of related remediation technologies is imminent.

[0003] In the current situation of organically contaminated soil and groundwater remediation, traditional monitoring and treatment methods often have multiple shortcomings. Existing systems usually lack real-time monitoring and comprehensive evaluation capabilities, resulting in insufficient accuracy in pollution detection. In addition, many monitoring programs can only be limited to a single water quality or soil parameter and cannot fully reflect the overall health status of soil and water bodies. This reduces the effectiveness and pertinence of treatment measures, making it difficult to achieve the expected remediation effect.

[0004] Therefore, we proposed an organic contaminated soil and groundwater remediation monitoring system method and system to solve the above-mentioned problems. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for monitoring the remediation of organically contaminated soil and groundwater, so as to solve the problem that the existing systems for groundwater treatment proposed in the above background technology usually lack real-time monitoring and comprehensive evaluation capabilities, resulting in insufficient accuracy of pollution detection. In addition, many monitoring schemes can only be limited to a single water quality or soil parameter, and cannot fully reflect the overall health status of soil and water bodies. This reduces the effectiveness and pertinence of treatment measures, making it difficult to achieve the expected remediation effect.

[0006] To achieve the above-mentioned object, the present invention provides the following technical solutions: an organic contaminated soil and groundwater remediation monitoring system, comprising a first acquisition module, a first processing module, a first calculation module, a first analysis module, a second acquisition module, a second processing module, a second calculation module and a second analysis module;

[0007] The first acquisition module is used to divide the monitored area and collect data from the divided areas, thereby generating a first multi-source data set;

[0008] The first processing module is used to preprocess the first multi-source data set and reorganize the preprocessed first multi-source data set into a water quality data set and a soil data set;

[0009] The first calculation module is used to integrate and calculate the water quality data group and the soil data group, so as to generate a pollution anomaly coefficient WRX;

[0010] The first analysis module is used to compare the pollution anomaly coefficient WRX with a preset first threshold value Y, thereby generating a first comparison result, and determining whether there is pollution in the area according to the first comparison result;

[0011] The second acquisition module is used to continuously monitor the polluted area and generate a second multi-source data set from the monitored data;

[0012] The second processing module is used to preprocess the second multi-source data set to generate a physical data set, a chemical data set, and a biological data set;

[0013] The second calculation module is used to integrate the physical data set, the chemical data set and the biological data set to calculate the governance effectiveness coefficient ZLX;

[0014] The second analysis module is used to compare the governance effectiveness coefficient ZLX with a preset second threshold R to generate a second comparison result.

[0015] Preferably, the first acquisition module divides the monitoring area into 1, 2, 3, ..., i respectively;

[0016] The first acquisition module includes a water quality acquisition unit and a soil acquisition unit, wherein the water quality acquisition unit is used to acquire water quality data in different areas, and the soil acquisition unit is used to acquire soil data in different areas;

[0017] The water quality data and soil data constitute the first data set, including dissolved oxygen concentration, organic matter concentration, pH value, soil water content, soil organic content and volatile organic compound concentration;

[0018] The first processing module is used to preprocess and dimensionlessly transform the first multi-source data set, and organize the processed first multi-source data set into a water quality data set and a soil data set respectively;

[0019] The water quality data set includes dissolved oxygen concentration, organic matter concentration, and pH value;

[0020] The dissolved oxygen concentrations are marked as A1, A2, A3, ..., Ai according to the regional labels;

[0021] The concentration of organic matter is recorded as B1, B2, B3, ..., Bi according to the regional labels;

[0022] The pH values ​​are marked as C1, C2, C3, ..., Ci according to the area;

[0023] The soil data set includes soil water content, soil organic content, and volatile organic compound concentrations;

[0024] The soil water content is recorded as D1, D2, D3, ..., Di according to the regional marks;

[0025] Soil organic content is recorded as E1, E2, E3, ..., Ei according to the regional labels;

[0026] The concentrations of volatile organic compounds are marked as F1, F2, F3, ..., Fi according to the area.

[0027] Preferably, the first calculation module calculates and obtains the pollution anomaly coefficient WRX by the following formula:

[0028]

[0029] Wherein: a1 and a2 are weight values, and the values ​​of a1 and a2 are adjusted and set by the user, PSZ is the water quality reference coefficient, which is calculated from the water quality data group, and PTR is the soil reference coefficient, which is calculated from the soil data group.

[0030] Preferably, the water quality reference coefficient PSZ and the soil reference coefficient PTR are calculated and obtained by the following formulas:

[0031] PSZ=++;

[0032] PTR = ++;

[0033] Where: Ai, Bi, Ci, Di, Ei and Fi are the dissolved oxygen concentration, organic matter concentration, pH value, soil water content, soil organic content and volatile organic compound concentration in the area numbered i, respectively;

[0034] A1, B1, C1, D1, E1, and F1 are the dissolved oxygen concentration, organic matter concentration, pH value, soil water content, soil organic content, and volatile organic compound concentration in the area numbered 1, respectively;

[0035] n is the number of terms, and n=i.

[0036] Preferably, the first comparison result is as follows:

[0037] When WRX≤Y, it means that there is no pollution anomaly in the current area. When WRX≥Y, it means that there is pollution anomaly in the current area.

[0038] Preferably, the second acquisition module includes a physical parameter acquisition unit, a chemical parameter acquisition unit and a biological parameter acquisition unit;

[0039] The physical parameter acquisition unit is used to acquire physical parameters of different regions, the chemical parameter acquisition unit is used to acquire chemical parameters of different regions, and the biological parameter acquisition unit is used to acquire biological parameters of different regions;

[0040] The physical parameters, chemical parameters and biological parameters constitute a second data set, including soil particle density, soil water permeability, soil temperature, nitrogen content, phosphorus content, organic pollutant degradation rate, microbial activity, microbial quantity and soil enzyme activity;

[0041] The second processing module is used to preprocess and dimensionlessly process the second multi-source data set, and organize the processed second multi-source data set into a physical data set, a chemical data set, and a biological data set;

[0042] The physical data set includes soil particle density, soil water permeability, and soil temperature;

[0043] The soil particle density is recorded as G1, G2, G3, ..., Gi according to the regional labels;

[0044] Soil water permeability is recorded as H1, H2, H3, ..., Hi according to the regional marks;

[0045] The soil temperatures are recorded as J1, J2, J3, ..., Ji according to the regional labels;

[0046] The chemical data set includes nitrogen content, phosphorus content, and organic pollutant degradation rates;

[0047] The nitrogen content is recorded as K1, K2, K3, ..., Ki according to the regional mark;

[0048] The phosphorus content is recorded as L1, L2, L3, ..., Li according to the regional labels;

[0049] The degradation rates of organic pollutants are marked as M1, M2, M3, ..., Mi according to the regional labels;

[0050] Biological datasets included microbial activity, microbial abundance, and soil enzyme activity;

[0051] The dissolved oxygen concentrations are recorded as N1, N2, N3, ..., Ni according to the regional labels;

[0052] The concentration of organic matter is recorded as O1, O2, O3, ..., Oi according to the regional labels;

[0053] The pH values ​​are marked as P1, P2, P3, ..., Pi according to the area.

[0054] Preferably, the second calculation module calculates and obtains the governance effectiveness coefficient ZLX by the following formula:

[0055]

[0056] Wherein: b1, b2 and b3 are weight values, and the values ​​of b1, b2 and b3 are adjusted and set by the user, PWL is the physical reference coefficient, which is calculated from the physical data set, PHX is the chemical reference coefficient, which is calculated from the chemical data set, and PSW is the biological reference coefficient, which is calculated from the biological data set.

[0057] Preferably, the physical reference coefficient PWL, the chemical reference coefficient PHX and the biological reference coefficient PSW are calculated and obtained by the following formulas respectively:

[0058] PWL = ++;

[0059] PHX=++;

[0060] PSW = ++;

[0061] Where: Gi, Hi, Ji, Ki, Li, Mi, Ni, Oi and Pi are soil particle density, soil water permeability, soil temperature, nitrogen content, phosphorus content, organic pollutant degradation rate, microbial activity, microbial quantity and soil enzyme activity in the area numbered i, respectively;

[0062] G1, H1, J1, K1, L1, M1, N1, O1 and P1 are the soil particle density, soil water permeability, soil temperature, nitrogen content, phosphorus content, organic pollutant degradation rate, microbial activity, microbial quantity and soil enzyme activity in the area numbered 1, respectively.

[0063] Preferably, the second comparison result is as follows:

[0064] When ZLX≤R, it means that there is no abnormality in the repair process of the current area. When ZLX≥R, it means that there is an abnormality in the repair process of the current area.

[0065] This application also includes a method for monitoring the remediation of organically contaminated soil and groundwater, and the specific steps are as follows:

[0066] S1. Use the first acquisition module to spatially divide the monitoring area, set sampling points and perform data acquisition to generate a first multi-source data set, including preliminary data of water quality and soil samples;

[0067] S2. Preprocessing the first multi-source data set using the first processing module, including denoising, standardization, and missing value filling, and the sorted data is divided into a water quality data group and a soil data group;

[0068] S3, integrating and calculating the water quality data group and the soil data group through the first calculation module to generate a pollution anomaly coefficient WRX, using the first analysis module to compare WRX with a preset first threshold value Y to generate a first comparison result, and determining whether there is pollution in the area according to the comparison result;

[0069] S4. Continuously monitor the determined contaminated area through the second acquisition module, collect data regularly, generate a second multi-source data set, use the second processing module to pre-process the second multi-source data set to ensure the consistency and availability of the data, and generate a physical data set, a chemical data set, and a biological data set;

[0070] S5. The physical data set, the chemical data set and the biological data set are integrated and calculated by the second calculation module to generate the governance effectiveness coefficient ZLX. The second analysis module is used to compare ZLX with the preset second threshold R to generate a second comparison result, and the effectiveness of the remediation measures is evaluated based on the second comparison result.

[0071] Compared with the prior art, the present invention has the following beneficial effects:

[0072] 1. Through the collaborative work of multiple modules, the accuracy and efficiency of pollution monitoring are effectively improved. Compared with traditional technical means, the systematic data processing and analysis method significantly improves the real-time and accuracy of environmental monitoring, and realizes the transformation from static monitoring to dynamic management. Through continuous monitoring and data feedback, it not only enhances the ability to identify pollution sources, but also optimizes the implementation of remediation measures.

[0073] 2. The calculation formulas for the water quality reference coefficient PSZ and the soil reference coefficient PTR provide a detailed analysis of the key parameters of each region. This method can not only capture the specific pollution characteristics in the region, but also provide relative references for different regions, making the comparative analysis more accurate and scientific. The PSZ and PTR calculated by the formula provide a quantitative reference basis for water quality and soil pollution in different regions. This quantitative capability allows a more intuitive comparison of the pollution conditions in different regions, thereby providing specific goals and directions for environmental governance. The comprehensive calculation method reduces the impact of a single data on the overall assessment by considering the interrelationships of multiple parameters, thereby improving the accuracy of the pollution anomaly coefficient WRX. This improvement helps to identify potential pollution problems in a timely manner and avoids misjudgments caused by data bias. By providing detailed calculation methods and corresponding reference coefficients, the monitoring system provides a more scientific basis for governance decisions. This systematic data analysis and decision-making support capability can effectively guide subsequent governance measures and environmental management strategies and improve governance results.

[0074] 3. By combining the calculation of the physical reference coefficient PWL, the chemical reference coefficient PHX and the biological reference coefficient PSW, the system can comprehensively integrate different types of environmental data. This multi-dimensional reference coefficient makes the evaluation of governance effectiveness more scientific and can effectively reflect the impact of different factors on the soil and water restoration process. By obtaining the governance effectiveness coefficient ZLX, the system quantifies the monitoring results, making the judgment of the regional governance status more objective. This quantitative evaluation mechanism makes the results of environmental monitoring easier to understand and implement. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 It is a system step diagram of the present invention.

[0076] Figure 2 The present invention is a flow chart of the method.

[0077] In the figure: 1. first acquisition module; 11. water quality acquisition unit; 12. soil acquisition unit; 2. first processing module; 3. first calculation module; 4. first analysis module; 5. second acquisition module; 51. physical parameter acquisition unit; 52. chemical parameter acquisition unit; 53. biological parameter acquisition unit; 6. second processing module; 7. second calculation module; 8. second analysis module. DETAILED DESCRIPTION

[0078] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0079] Example 1: Please refer to Figure 1 , an organic contaminated soil and groundwater remediation monitoring system, comprising a first acquisition module 1, a first processing module 2, a first calculation module 3, a first analysis module 4, a second acquisition module 5, a second processing module 6, a second calculation module 7 and a second analysis module 8;

[0080] The first acquisition module 1 is used to divide the monitored area and collect data from the divided areas, thereby generating a first multi-source data set;

[0081] The first processing module 2 is used to preprocess the first multi-source data set and reorganize the preprocessed first multi-source data set into a water quality data set and a soil data set;

[0082] The first calculation module 3 is used to integrate and calculate the water quality data group and the soil data group, so as to generate the pollution anomaly coefficient WRX;

[0083] The first analysis module 4 is used to compare the pollution anomaly coefficient WRX with a preset first threshold value Y, thereby generating a first comparison result, and determining whether there is pollution in the area according to the first comparison result;

[0084] The second acquisition module 5 is used to continuously monitor the polluted area and generate a second multi-source data set from the monitored data;

[0085] The second processing module 6 is used for preprocessing the second multi-source data set to generate a physical data set, a chemical data set and a biological data set;

[0086] The second calculation module 7 is used to integrate the physical data set, the chemical data set and the biological data set to calculate the governance effectiveness coefficient ZLX;

[0087] The second analysis module 8 is used to compare the governance effectiveness coefficient ZLX with a preset second threshold R, thereby generating a second comparison result.

[0088] In this embodiment: the first acquisition module 1 is responsible for dividing the monitored area and collecting relevant data to form a first multi-source data set. By accurately dividing the area, potential pollution sources can be more effectively identified to ensure the pertinence and effectiveness of data collection. This process provides basic data for subsequent data analysis, making the monitoring work more systematic.

[0089] The first processing module 2 pre-processes the collected first multi-source data set and organizes it into a water quality data set and a soil data set. The quality and consistency of the data are ensured through denoising, standardization and missing value filling. This process improves the availability of the data, ensures the accuracy of subsequent analysis results, and lays a solid foundation for judging the pollution situation.

[0090] The first calculation module 3 integrates and calculates the water quality data set and the soil data set to generate the pollution anomaly coefficient WRX. Through this coefficient, the pollution degree of different areas can be quantified and provide a quantitative basis for decision-making. This calculation provides clear indicators for pollution monitoring, making the determination of pollution degree more intuitive and operational.

[0091] The first analysis module 4 compares the pollution anomaly coefficient WRX with the preset first threshold value Y to generate a first comparison result. Through comparative analysis, it is possible to quickly determine whether there is pollution in the area and provide a basis for subsequent governance measures. This process improves the efficiency of monitoring, timely discovers potential pollution problems, and reduces environmental risks.

[0092] The second acquisition module 5 is responsible for continuously monitoring the identified polluted areas and generating a second multi-source data set. Through regular monitoring, changes in the pollution status can be dynamically understood to ensure timely acquisition of the latest data. This continuous monitoring mechanism improves the system's responsiveness and helps to adjust control measures in a timely manner.

[0093] The second processing module 6 pre-processes the second multi-source data set to generate a physical data set, a chemical data set, and a biological data set. Through effective data organization, the integration and analysis of different types of data are ensured. This process improves the efficiency of data processing and makes multi-dimensional pollution monitoring more systematic and comprehensive.

[0094] The second calculation module 7 integrates the physical, chemical and biological data sets into a governance effectiveness coefficient ZLX. This coefficient can be used to evaluate the effectiveness of the restoration measures and the governance effect. This calculation provides an important basis for subsequent decision-making, can more intuitively reflect the results of the restoration work, and provide support for optimizing the governance plan.

[0095] The second analysis module 8 compares the governance effectiveness coefficient ZLX with the preset second threshold R to generate a second comparison result. Through analysis and comparison, the success of the governance measures can be effectively evaluated and deficiencies can be identified in a timely manner. This process not only improves the transparency of the repair work, but also provides feedback for continuous improvement.

[0096] The organic contaminated soil and groundwater remediation monitoring system effectively improves the accuracy and efficiency of pollution monitoring through the collaborative work of multiple modules. Compared with traditional technical means, the systematic data processing and analysis method significantly improves the real-time and accuracy of environmental monitoring, and realizes the transformation from static monitoring to dynamic management. Through continuous monitoring and data feedback, it not only enhances the ability to identify pollution sources, but also optimizes the implementation of remediation measures.

[0097] Example 2: Please refer to Figure 1 ,The first acquisition module 1 divides the monitoring area into ,1, 2, 3, ..., i;

[0098] The first acquisition module 1 includes a water quality acquisition unit 11 and a soil acquisition unit 12. The water quality acquisition unit 11 is used to collect water quality data in different areas, and the soil acquisition unit 12 is used to collect soil data in different areas.

[0099] The water quality data and soil data constitute the first data set, including dissolved oxygen concentration, organic matter concentration, pH value, soil water content, soil organic content and volatile organic compound concentration;

[0100] The first processing module 2 is used to preprocess and dimensionlessly transform the first multi-source data set, and organize the processed first multi-source data set into a water quality data set and a soil data set respectively;

[0101] The water quality data set includes dissolved oxygen concentration, organic matter concentration, and pH value;

[0102] The dissolved oxygen concentrations are marked as A1, A2, A3, ..., Ai according to the regional labels;

[0103] The concentration of organic matter is recorded as B1, B2, B3, ..., Bi according to the regional labels;

[0104] The pH values ​​are marked as C1, C2, C3, ..., Ci according to the area;

[0105] The soil data set includes soil water content, soil organic content, and volatile organic compound concentrations;

[0106] The soil water content is recorded as D1, D2, D3, ..., Di according to the regional marks;

[0107] Soil organic content is recorded as E1, E2, E3, ..., Ei according to the regional labels;

[0108] The concentrations of volatile organic compounds are marked as F1, F2, F3, ..., Fi according to the area.

[0109] In this embodiment, by dividing the area to be monitored into multiple independent small areas 1, 2, 3, ..., i, more refined monitoring can be achieved. This division method ensures that the pollution characteristics of each area can be identified separately, thereby improving the accuracy and pertinence of the data.

[0110] The first collection module 1 includes a water quality collection unit 11 and a soil collection unit 12, so that the collection of water quality and soil data complement each other to form a first data set. This comprehensive data collection method makes monitoring more comprehensive and can better reflect the pollution status in the area.

[0111] The first dataset contains a variety of important parameters, such as dissolved oxygen concentration, organic matter concentration, pH value, soil moisture content, soil organic content and volatile organic compound concentration. Through these diverse indicators, the pollution status of soil and water bodies can be more comprehensively assessed, providing richer information for subsequent analysis.

[0112] The first processing module 2 preprocesses and dimensionlessly transforms the first multi-source data set, which not only improves the quality and consistency of the data, but also makes it easier to compare different data types. This process ensures the effectiveness of subsequent analysis and avoids misjudgments caused by data quality issues.

[0113] The structured organization of water quality data sets and soil data sets makes subsequent data analysis and processing more efficient. Through clear marking, parameters of specific areas can be quickly located and identified, thereby improving the convenience of data processing.

[0114] Example 3: Please refer to Figure 1 The first calculation module 3 calculates the pollution anomaly coefficient WRX by the following formula:

[0115]

[0116] Wherein: a1 and a2 are weight values, and the values ​​of a1 and a2 are adjusted and set by the user, PSZ is the water quality reference coefficient, which is calculated from the water quality data group, and PTR is the soil reference coefficient, which is calculated from the soil data group.

[0117] The water quality reference coefficient PSZ and soil reference coefficient PTR are calculated by the following formulas:

[0118] PSZ=++;

[0119] PTR = ++;

[0120] Where: Ai, Bi, Ci, Di, Ei and Fi are the dissolved oxygen concentration, organic matter concentration, pH value, soil water content, soil organic content and volatile organic compound concentration in the area numbered i, respectively;

[0121] A1, B1, C1, D1, E1, and F1 are the dissolved oxygen concentration, organic matter concentration, pH value, soil water content, soil organic content, and volatile organic compound concentration in the area numbered 1, respectively;

[0122] n is the number of terms, and n=i.

[0123] In this embodiment, the first calculation module 3 adjusts the weight values ​​a1 and a2 so that the user can flexibly set the influence of each indicator according to specific environmental conditions and monitoring objectives. This flexibility improves the adaptability of the model, allowing the user to optimize the monitoring effect according to actual needs and ensure a more accurate decision-making process.

[0124] The calculation formula of the pollution anomaly coefficient WRX combines the water quality reference coefficient PSZ and the soil reference coefficient PTR, reflecting the relationship between water quality and soil data. By considering these two factors at the same time, the pollution status can be evaluated more comprehensively, enhancing the credibility of the monitoring results.

[0125] The calculation formulas for the water quality reference coefficient PSZ and the soil reference coefficient PTR provide a detailed analysis of the key parameters of each region. This method can not only capture the specific pollution characteristics in the region, but also provide relative references for different regions, making the comparative analysis more accurate and scientific. The PSZ and PTR calculated by the formula provide a quantitative reference basis for water quality and soil pollution in different regions. This quantitative capability allows a more intuitive comparison of the pollution conditions in different regions, thereby providing specific goals and directions for environmental governance. The comprehensive calculation method reduces the impact of a single data on the overall assessment by considering the interrelationship of multiple parameters, thereby improving the accuracy of the pollution anomaly coefficient WRX. This improvement helps to identify potential pollution problems in a timely manner and avoids misjudgments caused by data bias. By providing detailed calculation methods and corresponding reference coefficients, the monitoring system provides a more scientific basis for governance decisions. This systematic data analysis and decision support capability can effectively guide subsequent governance measures and environmental management strategies and improve governance results.

[0126] Example 4: Please refer to Figure 1 , the first comparison results are as follows:

[0127] When WRX≤Y, it means that there is no pollution anomaly in the current area. When WRX≥Y, it means that there is pollution anomaly in the current area.

[0128] In this embodiment: the judgment criteria of the first comparison result are clear and unambiguous, which helps the system to quickly respond to potential environmental risks.

[0129] Quantifying the judgment of pollution status into a specific numerical range makes the monitoring process more objective. Quantitative evaluation can reduce the interference of subjective judgment, improve the scientific nature of monitoring, and ensure that the results are highly reliable. By setting the first threshold value Y, the system can provide early warning when the pollution status changes. This timely feedback mechanism is very important for environmental management, which helps relevant departments to take prompt measures to prevent further deterioration of pollution problems. According to the comparison results, the monitoring system can quickly determine the pollution status of the region and formulate targeted governance strategies. When the monitoring results show that there are abnormal pollution, detailed investigations and governance work can be carried out immediately to improve the efficiency of environmental governance. Clear comparison results provide a reliable basis for decision-making, allowing relevant departments to formulate corresponding management and governance measures according to the specific pollution status. This data-driven decision-making support capability improves the scientificity and effectiveness of environmental governance.

[0130] Example 5: Please refer to Figure 1 , the second acquisition module 5 includes a physical parameter acquisition unit 51, a chemical parameter acquisition unit 52 and a biological parameter acquisition unit 53;

[0131] The physical parameter acquisition unit 51 is used to acquire physical parameters of different regions, the chemical parameter acquisition unit 52 is used to acquire chemical parameters of different regions, and the biological parameter acquisition unit 53 is used to acquire biological parameters of different regions;

[0132] Physical parameters, chemical parameters, and biological parameters constitute the second data set, including soil particle density, soil water permeability, soil temperature, nitrogen content, phosphorus content, organic pollutant degradation rate, microbial activity, microbial count, and soil enzyme activity;

[0133] The second processing module 6 is used to preprocess and dimensionlessly process the second multi-source data set, and organize the processed second multi-source data set into a physical data set, a chemical data set, and a biological data set;

[0134] The physical data set includes soil particle density, soil water permeability, and soil temperature;

[0135] The soil particle density is recorded as G1, G2, G3, ..., Gi according to the regional labels;

[0136] Soil water permeability is recorded as H1, H2, H3, ..., Hi according to the regional marks;

[0137] The soil temperatures are recorded as J1, J2, J3, ..., Ji according to the regional labels;

[0138] The chemical data set includes nitrogen content, phosphorus content, and organic pollutant degradation rates;

[0139] The nitrogen content is recorded as K1, K2, K3, ..., Ki according to the regional mark;

[0140] The phosphorus content is recorded as L1, L2, L3, ..., Li according to the regional labels;

[0141] The degradation rates of organic pollutants are marked as M1, M2, M3, ..., Mi according to the regional labels;

[0142] Biological datasets included microbial activity, microbial abundance, and soil enzyme activity;

[0143] The dissolved oxygen concentrations are recorded as N1, N2, N3, ..., Ni according to the regional labels;

[0144] The concentration of organic matter is recorded as O1, O2, O3, ..., Oi according to the regional labels;

[0145] The pH values ​​are marked as P1, P2, P3, ..., Pi according to the area.

[0146] In this embodiment, the second acquisition module 5 realizes comprehensive monitoring of soil and water bodies through the combination of the physical parameter acquisition unit 51, the chemical parameter acquisition unit 52 and the biological parameter acquisition unit 53. This multi-dimensional data acquisition method ensures that various influencing factors are fully considered, thereby more accurately reflecting the pollution status.

[0147] The clear division of physical, chemical and biological parameters into independent data sets facilitates the systematic management of data. For example, the physical data set includes soil particle density, water permeability and temperature, while the chemical and biological data sets cover key indicators such as nitrogen and phosphorus content and microbial activity. This detailed division facilitates subsequent analysis and comparison, making the monitoring results more targeted.

[0148] Each parameter is marked with a region, which enables quantitative evaluation and comparison. This method enhances the ability to analyze pollution characteristics in different regions, allowing for intuitive comparison of pollution conditions between regions and helping to formulate corresponding governance measures.

[0149] The second processing module 6 preprocesses and dimensionlessly converts the second multi-source data set to improve the quality and consistency of the data. This process ensures the comparability between different data types, lays the foundation for subsequent analysis and calculation, and improves the accuracy of overall monitoring.

[0150] By continuously collecting multiple types of parameters, the system can achieve dynamic monitoring. This real-time feedback mechanism enables the monitoring results to reflect environmental changes in a timely manner, adjust the governance strategy in a timely manner, and prevent the deterioration of pollution problems.

[0151] Example 6: Please refer to Figure 1 The second calculation module 7 calculates the governance effectiveness coefficient ZLX through the following formula:

[0152]

[0153] Wherein: b1, b2 and b3 are weight values, and the values ​​of b1, b2 and b3 are adjusted and set by the user, PWL is the physical reference coefficient, which is calculated from the physical data set, PHX is the chemical reference coefficient, which is calculated from the chemical data set, and PSW is the biological reference coefficient, which is calculated from the biological data set.

[0154] The physical reference coefficient PWL, chemical reference coefficient PHX and biological reference coefficient PSW are calculated by the following formulas:

[0155] PWL = ++;

[0156] PHX=++;

[0157] PSW = ++;

[0158] Where: Gi, Hi, Ji, Ki, Li, Mi, Ni, Oi and Pi are soil particle density, soil water permeability, soil temperature, nitrogen content, phosphorus content, organic pollutant degradation rate, microbial activity, microbial quantity and soil enzyme activity in the area numbered i, respectively;

[0159] G1, H1, J1, K1, L1, M1, N1, O1 and P1 are the soil particle density, soil water permeability, soil temperature, nitrogen content, phosphorus content, organic pollutant degradation rate, microbial activity, microbial quantity and soil enzyme activity in the area numbered 1, respectively.

[0160] The second comparison results are as follows:

[0161] When ZLX≤R, it means that there is no abnormality in the repair process of the current area. When ZLX≥R, it means that there is an abnormality in the repair process of the current area.

[0162] In this embodiment: the second calculation module 7 allows the user to adjust the weight values ​​b1, b2 and b3, so that the monitoring system can flexibly optimize the impact of each indicator according to the specific situation. This flexibility ensures that the evaluation of the governance effect can be closer to the actual environmental conditions and improves the adaptability of monitoring.

[0163] By combining the calculation of the physical reference coefficient PWL, the chemical reference coefficient PHX and the biological reference coefficient PSW, the system can comprehensively integrate different types of environmental data. This multi-dimensional reference coefficient makes the evaluation of governance effectiveness more scientific, and can effectively reflect the impact of different factors on the soil and water restoration process, and conduct quantitative analysis through specific formulas. This meticulous calculation method can provide more targeted evaluation results and help to accurately judge the governance effect.

[0164] By obtaining the governance effectiveness coefficient ZLX, the system quantifies the monitoring results, making the judgment of the regional governance status more objective. This quantitative evaluation mechanism makes the results of environmental monitoring easier to understand and implement.

[0165] By setting the second threshold R, the system can monitor abnormal situations during the repair process in real time and provide timely feedback, which helps relevant departments to take countermeasures quickly to ensure the smooth progress of the repair work and avoid waste of resources.

[0166] This application also includes a method for monitoring the remediation of organically contaminated soil and groundwater. Figure 2 , the specific steps are as follows:

[0167] S1, using the first acquisition module 1 to spatially divide the monitoring area, set sampling points and perform data acquisition, and generate a first multi-source data set, including preliminary data of water quality and soil samples;

[0168] S2, using the first processing module 2 to preprocess the first multi-source data set, including denoising, standardization and missing value filling, and the sorted data is divided into a water quality data group and a soil data group;

[0169] S3, integrating and calculating the water quality data group and the soil data group through the first calculation module 3 to generate a pollution anomaly coefficient WRX, and using the first analysis module 4 to compare WRX with a preset first threshold value Y to generate a first comparison result, and judging whether there is pollution in the area according to the comparison result;

[0170] S4, continuously monitoring the determined contaminated area through the second acquisition module 5, regularly collecting data to generate a second multi-source data set, using the second processing module 6 to pre-process the second multi-source data set to ensure data consistency and availability, and generate a physical data set, a chemical data set, and a biological data set;

[0171] S5. The physical data set, the chemical data set and the biological data set are integrated and calculated by the second calculation module 7 to generate the governance effectiveness coefficient ZLX. The second analysis module 8 is used to compare ZLX with the preset second threshold R to generate a second comparison result, and the effectiveness of the remediation measures is evaluated based on the second comparison result.

[0172] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

[0173] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An organic contaminated soil and groundwater remediation monitoring system, characterized by: It comprises a first acquisition module (1), a first processing module (2), a first calculation module (3), a first analysis module (4), a second acquisition module (5), a second processing module (6), a second calculation module (7) and a second analysis module (8); The first acquisition module (1) is used to divide the monitored area and collect data from the divided areas, thereby generating a first multi-source data set; The first processing module (2) is used to preprocess the first multi-source data set and reorganize the preprocessed first multi-source data set into a water quality data set and a soil data set; The first calculation module (3) is used to integrate and calculate the water quality data group and the soil data group, thereby generating a pollution anomaly coefficient WRX; The first analysis module (4) is used to compare the pollution anomaly coefficient WRX with a preset first threshold value Y, thereby generating a first comparison result, and determining whether the area is polluted according to the first comparison result; The second acquisition module (5) is used to continuously monitor the polluted area and generate a second multi-source data set from the monitored data; The second processing module (6) is used to pre-process the second multi-source data set to generate a physical data set, a chemical data set and a biological data set; The second calculation module (7) is used to integrate the physical data set, the chemical data set and the biological data set to calculate the governance effectiveness coefficient ZLX; The second analysis module (8) is used to compare the governance effectiveness coefficient ZLX with a preset second threshold R, thereby generating a second comparison result.

2. The organic contaminated soil and groundwater remediation monitoring system according to claim 1 is characterized by: The first acquisition module (1) divides the monitoring area into 1, 2, 3, ..., i respectively; The first acquisition module (1) comprises a water quality acquisition unit (11) and a soil acquisition unit (12), wherein the water quality acquisition unit (11) is used to acquire water quality data in different areas, and the soil acquisition unit (12) is used to acquire soil data in different areas; The water quality data and soil data constitute the first data set, including dissolved oxygen concentration, organic matter concentration, pH value, soil water content, soil organic content and volatile organic compound concentration; The first processing module (2) is used to preprocess and dimensionlessly transform the first multi-source data set, and organize the processed first multi-source data set into a water quality data set and a soil data set respectively; The water quality data set includes dissolved oxygen concentration, organic matter concentration, and pH value; The dissolved oxygen concentrations are marked as A1, A2, A3, ..., Ai according to the regional labels; The concentration of organic matter is recorded as B1, B2, B3, ..., Bi according to the regional labels; The pH values ​​are marked as C1, C2, C3, ..., Ci according to the area; The soil data set includes soil water content, soil organic content, and volatile organic compound concentrations; The soil water content is recorded as D1, D2, D3, ..., Di according to the regional marks; Soil organic content is recorded as E1, E2, E3, ..., Ei according to the regional labels; The concentrations of volatile organic compounds are marked as F1, F2, F3, ..., Fi according to the area.

3. The organic contaminated soil and groundwater remediation monitoring system according to claim 2 is characterized by: The first calculation module (3) calculates and obtains the pollution anomaly coefficient WRX by the following formula: Wherein: a1 and a2 are weight values, and the values ​​of a1 and a2 are adjusted and set by the user, PSZ is the water quality reference coefficient, which is calculated from the water quality data group, and PTR is the soil reference coefficient, which is calculated from the soil data group.

4. The organic contaminated soil and groundwater remediation monitoring system according to claim 3 is characterized by: The water quality reference coefficient PSZ and soil reference coefficient PTR are calculated by the following formulas: PTR = ++; Where: Ai, Bi, Ci, Di, Ei and Fi are the dissolved oxygen concentration, organic matter concentration, pH value, soil water content, soil organic content and volatile organic compound concentration in the area numbered i, respectively; A1, B1, C1, D1, E1, and F1 are the dissolved oxygen concentration, organic matter concentration, pH value, soil water content, soil organic content, and volatile organic compound concentration in the area numbered 1, respectively; n is the number of terms, and n=i.

5. The organic contaminated soil and groundwater remediation monitoring system according to claim 4 is characterized by: The first comparison result is as follows: When WRX≤Y, it means that there is no pollution anomaly in the current area. When WRX≥Y, it means that there is pollution anomaly in the current area.

6. The organic contaminated soil and groundwater remediation monitoring system according to claim 5 is characterized by: The second acquisition module (5) comprises a physical parameter acquisition unit (51), a chemical parameter acquisition unit (52) and a biological parameter acquisition unit (53); The physical parameter acquisition unit (51) is used to acquire physical parameters of different regions, the chemical parameter acquisition unit (52) is used to acquire chemical parameters of different regions, and the biological parameter acquisition unit (53) is used to acquire biological parameters of different regions; The physical parameters, chemical parameters and biological parameters constitute a second data set, including soil particle density, soil water permeability, soil temperature, nitrogen content, phosphorus content, organic pollutant degradation rate, microbial activity, microbial quantity and soil enzyme activity; The second processing module (6) is used to pre-process and dimensionlessly transform the second multi-source data set, and organize the processed second multi-source data set into a physical data set, a chemical data set, and a biological data set; The physical data set includes soil particle density, soil water permeability, and soil temperature; The soil particle density is recorded as G1, G2, G3, ..., Gi according to the regional labels; Soil water permeability is recorded as H1, H2, H3, ..., Hi according to the regional marks; The soil temperatures are recorded as J1, J2, J3, ..., Ji according to the regional labels; The chemical data set includes nitrogen content, phosphorus content, and organic pollutant degradation rates; The nitrogen content is recorded as K1, K2, K3, ..., Ki according to the regional mark; The phosphorus content is recorded as L1, L2, L3, ..., Li according to the regional labels; The degradation rates of organic pollutants are marked as M1, M2, M3, ..., Mi according to the regional labels; Biological datasets included microbial activity, microbial abundance, and soil enzyme activity; The dissolved oxygen concentrations are recorded as N1, N2, N3, ..., Ni according to the regional labels; The concentration of organic matter is recorded as O1, O2, O3, ..., Oi according to the regional labels; The pH values ​​are marked as P1, P2, P3, ..., Pi according to the area.

7. The organic contaminated soil and groundwater remediation monitoring system according to claim 6 is characterized by: The second calculation module (7) calculates the governance effectiveness coefficient ZLX by the following formula: Wherein: b1, b2 and b3 are weight values, and the values ​​of b1, b2 and b3 are adjusted and set by the user, PWL is the physical reference coefficient, which is calculated from the physical data set, PHX is the chemical reference coefficient, which is calculated from the chemical data set, and PSW is the biological reference coefficient, which is calculated from the biological data set.

8. The organic contaminated soil and groundwater remediation monitoring system according to claim 7 is characterized by: The physical reference coefficient PWL, the chemical reference coefficient PHX and the biological reference coefficient PSW are calculated and obtained by the following formulas: PWL = ++; PHX=++; PSW = ++; Where: Gi, Hi, Ji, Ki, Li, Mi, Ni, Oi and Pi are soil particle density, soil water permeability, soil temperature, nitrogen content, phosphorus content, organic pollutant degradation rate, microbial activity, microbial quantity and soil enzyme activity in the area numbered i, respectively; G1, H1, J1, K1, L1, M1, N1, O1 and P1 are the soil particle density, soil water permeability, soil temperature, nitrogen content, phosphorus content, organic pollutant degradation rate, microbial activity, microbial quantity and soil enzyme activity in the area numbered 1, respectively.

9. The organic contaminated soil and groundwater remediation monitoring system according to claim 8 is characterized by: The second comparison result is as follows: When ZLX≤R, it means that there is no abnormality in the repair process of the current area. When ZLX≥R, it means that there is an abnormality in the repair process of the current area.

10. A method for monitoring the remediation of organically contaminated soil and groundwater, characterized in that: The method is used to implement an organic contaminated soil and groundwater remediation monitoring system as described in any one of the above claims 1 to 9, and the specific steps are as follows: S1. Use the first acquisition module (1) to spatially divide the monitoring area, set sampling points and perform data acquisition to generate a first multi-source data set, including preliminary data of water quality and soil samples; S2, using the first processing module (2) to preprocess the first multi-source data set, including denoising, standardization and missing value filling, and the sorted data is divided into a water quality data group and a soil data group; S3, integrating and calculating the water quality data set and the soil data set through the first calculation module (3) to generate a pollution anomaly coefficient WRX, comparing WRX with a preset first threshold value Y through the first analysis module (4) to generate a first comparison result, and determining whether the area is polluted based on the comparison result; S4, continuously monitoring the determined contaminated area through the second acquisition module (5), regularly collecting data, generating a second multi-source data set, and pre-processing the second multi-source data set using the second processing module (6) to ensure the consistency and availability of the data, thereby generating a physical data set, a chemical data set, and a biological data set; S5. The physical data set, the chemical data set and the biological data set are integrated and calculated by the second calculation module (7) to generate a governance effectiveness coefficient ZLX. The second analysis module (8) is used to compare ZLX with a preset second threshold R to generate a second comparison result. The effectiveness of the remediation measures is evaluated based on the second comparison result.

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