Karst region groundwater dynamic long-term monitoring and early warning method

Abstract

The present application relates to the technical field of groundwater pollution investigation and evaluation, and discloses a long-term monitoring and early warning method for groundwater dynamics in karst areas; the present application adopts high-density resistivity imaging technology to detect target karst areas, synchronously delimits the abnormal range of groundwater pollution and identifies the triple medium structure of karst pipes, fissures and matrix; a three-level targeted point distribution system is constructed, including main pipe main control points, fissure zone encryption points and matrix area supplementary points; combined with laser-induced fluorescence in-situ rapid detection and hydrogeochemical sampling analysis means, the heavy metal concentration and spatial and temporal distribution data of groundwater are obtained; a coupling evaluation model is constructed to complete the pollution degree grading and health risk dynamic evaluation and output the grading early warning results; the present application solves the problems of insufficient adaptability of the point distribution system and the triple medium structure, difficulty in balancing efficiency and accuracy of the detection means, and insufficient fusion of karst hydrogeological and geochemical characteristics in the evaluation model.

Description

Technical Field

[0001] This invention relates to the field of groundwater pollution investigation and assessment technology, and in particular to a method for long-term dynamic monitoring and early warning of groundwater in karst areas. Background Technology

[0002] Groundwater in karst areas is one of my country's important water sources. Karst hydrological systems are characterized by the coexistence of conduits, fissures, and matrix media, exhibiting extremely high heterogeneity. Groundwater flow paths are complex and variable, and pollutants such as heavy metals easily migrate and diffuse rapidly along karst channels, with significant spatiotemporal heterogeneity in the extent and form of pollution distribution. Conducting long-term dynamic monitoring and precise early warning of groundwater pollution in karst areas is a core technical step in ensuring regional drinking water safety and controlling groundwater environmental risks, and it has significant practical implications for the ecological environmental protection and sustainable development of karst regions.

[0003] Currently, groundwater monitoring in karst areas largely follows the conventional approach of uniform placement. Some optimized schemes focus only on key nodes of the main karst conduit, failing to fully consider the differences in pollution migration across the three media to construct a hierarchical and tiered placement system. This easily leads to gaps in monitoring key pollution migration zones or redundancy in background areas, resulting in low targeting and utilization efficiency of monitoring points. Chinese invention patent application CN115684326A discloses a rapid monitoring method for karst groundwater pollution. However, this method only places monitoring sections along the main karst flow channel, without designing differentiated placement rules for fracture development zones and matrix areas. Furthermore, water quality data acquisition relies solely on laboratory sampling and analysis, lacking supplementary in-situ rapid detection methods. This makes it difficult to simultaneously meet the temporal resolution and detection accuracy requirements of large-scale monitoring, and fails to efficiently capture the dynamic changes in heavy metal pollution in karst areas.

[0004] At the level of pollution assessment and early warning technology, existing groundwater pollution risk assessment models mostly rely on water quality index concentrations as the core calculation basis, generally failing to incorporate the inherent vulnerability of karst systems and the dynamic fluctuation characteristics of hydrology into the evaluation system. This makes it difficult to reflect the amplification or regulatory effect of karst hydrogeological conditions on pollution risk, resulting in discrepancies between the assessment results and the actual pollution risk level in karst areas. Chinese invention patent application CN114862189A discloses a groundwater pollution health risk assessment method. However, this method only calculates human health risk values ​​based on water quality testing data, failing to integrate the vulnerability of karst media and hydrological dynamic fluctuation factors to construct a coupled assessment model adapted to the karst environment. This leads to insufficient accuracy in pollution degree classification and risk warning results, making it unable to support long-term dynamic early warning and precise prevention and control of groundwater pollution in karst areas.

[0005] In summary, existing technologies for groundwater monitoring and early warning in karst areas suffer from several technical deficiencies, including insufficient compatibility of the monitoring point system with the triple medium structure, difficulty in balancing efficiency and accuracy in detection methods, and inadequate integration of karst hydrogeological and geochemical characteristics in the assessment model. These deficiencies prevent the targeted monitoring and dynamic risk assessment of heavy metal pollution and fail to meet the practical needs for long-term, refined management of groundwater environment in karst areas. Summary of the Invention

[0006] The present invention aims to provide a method for long-term dynamic monitoring and early warning of groundwater in karst areas, in order to solve the problems in the existing technology, such as insufficient compatibility of the sampling system with the triple medium structure, difficulty in balancing efficiency and accuracy of detection methods, and insufficient integration of karst hydrogeological and geochemical characteristics in the evaluation model.

[0007] To achieve the above objectives, the present invention provides the following method:

[0008] This invention provides a method for long-term dynamic monitoring and early warning of groundwater in karst areas:

[0009] S1: High-density resistivity imaging technology is used to detect the target karst area and obtain the spatial distribution data of the formation resistivity of the target karst area; based on the spatial distribution data of the formation resistivity, the range of groundwater pollution anomalies in the target karst area is preliminarily delineated; at the same time, the karst medium structure of the target karst area is identified by combining the spatial distribution data of the formation resistivity, and the identification results of the triple medium structure composed of karst conduits, fissures and matrix are obtained.

[0010] S2: Combining the abnormal groundwater pollution range with the triple medium structure identification results, a three-level targeted deployment system is constructed, consisting of the main pipeline control point, the fracture zone densification point, and the matrix zone supplementary point, to determine the deployment location and monitoring frequency of each monitoring point;

[0011] S3: According to the three-level targeted deployment system, the monitoring points are deployed in the target karst area, and the spatiotemporal distribution data of the concentration and speciation of heavy metals in groundwater are obtained by combining laser-induced fluorescence in situ rapid detection technology with hydrogeochemical sampling and analysis methods.

[0012] S4: Construct a coupled assessment model that integrates the karst vulnerability index, hydrological dynamic fluctuation coefficient, and geochemical sensitivity factor. Input the spatiotemporal distribution data into the coupled assessment model to complete the classification of groundwater pollution level and dynamic assessment of health risks, and output the corresponding dynamic monitoring and early warning results.

[0013] Further, in step S1, the specific process of using high-density resistivity imaging technology for detection is as follows: multiple parallel survey lines are laid out along the target karst area, with the direction of these parallel survey lines perpendicular to both the groundwater flow direction and the dominant direction of the karst conduit. Raw resistivity data of the formation is collected point by point along each of the parallel survey lines according to the electrode point spacing determined by the detection depth and resolution requirements. The raw resistivity data of the formation is then subjected to noise removal processing, including bad point removal, terrain correction, and drift correction. A continuous formation resistivity profile is obtained through least squares constrained inversion calculation. Formation resistivity data of the uncontaminated background area within the target karst area is collected, and the background resistivity value of the uncontaminated formation is statistically obtained. A set multiple of the background resistivity value of the uncontaminated formation is used as the resistivity anomaly threshold. The set multiple is determined according to the hydrogeological conditions and pollution control requirements of the target karst area. The range of groundwater pollution anomalies is delineated based on the resistivity anomaly threshold.

[0014] Furthermore, the specific process for identifying the triple-medium structure is as follows: combining the spatial distribution data of formation resistivity, drilling data of the target karst area, and on-site hydrogeological test results, the formation resistivity value, borehole karst rate, and aquifer permeability coefficient are used as classification indicators to identify three types of media according to a three-level threshold classification rule; the threshold values ​​in the three-level threshold classification rule are determined based on the hydrogeological survey data and lithological test results of the target karst area; wherein the formation resistivity value of the karst conduit is lower than the first threshold, the borehole karst rate is higher than the first karst rate threshold, and the aquifer permeability coefficient is higher than the first permeability coefficient threshold; the formation resistivity value of the fracture zone is between the first threshold and the second threshold, and the borehole karst rate is between the first karst rate threshold and the second threshold. The second karst rate threshold is between 2, the aquifer permeability coefficient is between the first and second permeability coefficient thresholds, the matrix area has a formation resistivity value higher than the second threshold, the borehole karst rate is lower than the second karst rate threshold, and the aquifer permeability coefficient is lower than the second permeability threshold; the spatial distribution range and flow path of the karst conduits, the development density and distribution area of ​​the fracture zone, the lithological parameters of the matrix area and the aquifer permeability coefficient are delineated; the groundwater depth parameters of the target karst area are obtained simultaneously through drilling water level measurement, and the borehole karst rate is used as the karst development degree parameter of the target karst area to obtain the triple medium structure identification results; the borehole karst rate is the ratio of the total height of the karst space exposed by the borehole to the corresponding total borehole footage.

[0015] Furthermore, the deployment rules of the three-level targeted deployment system are as follows: The main control points of the main karst pipeline are deployed at the branch intersections, runoff collection and distribution nodes, underground river outlets, and karst spring discharge outlets; areas with densely developed fractures are determined based on the fracture development density in the triple medium structure identification results, where the fracture development density is the cumulative length of fractures per unit area; the fracture zone densification points are deployed in the areas with densely developed fractures that fall within the groundwater pollution anomaly range; supplementary points in the matrix area are deployed in a uniform square grid pattern, with the grid side length determined based on the area of ​​the matrix area and the uniformity of hydrogeological conditions.

[0016] Furthermore, in step S2, the corresponding monitoring frequency is set according to the medium type and pollution anomaly degree of each monitoring point. The pollution anomaly degree is divided by the deviation multiple of the ratio of the background resistivity value of the uncontaminated stratum to the resistivity value of the stratum at the monitoring point. The larger the deviation multiple, the higher the pollution anomaly degree. The monitoring frequency of the main pipeline control point is higher than that of the fracture zone densification point, and the monitoring frequency of the fracture zone densification point is higher than that of the matrix zone supplementary point. For monitoring points of the same medium type, the higher the pollution anomaly degree, the higher the corresponding monitoring frequency.

[0017] Furthermore, in step S3, the specific implementation method of joint detection is as follows: Laser-induced fluorescence in-situ rapid detection equipment is used to perform continuous in-situ detection at each monitoring point to quickly obtain in-situ detection data of the total heavy metal concentration in groundwater; simultaneously, groundwater samples are collected at each monitoring point and sent to the laboratory, where hydrogeochemical sampling analysis methods are used to determine the precise concentration and content of each heavy metal species in the groundwater; a linear regression calibration equation is constructed using the synchronously collected precise concentration data from the laboratory as the true value and the in-situ detection data as the independent variable to calibrate and correct the concentration of all in-situ detection data; based on the proportion of each active heavy metal species determined in the laboratory, the time-series data of the calibrated total heavy metal concentration is decomposed into speciations, and integrated with the spatial location of each monitoring point to obtain the spatiotemporal distribution data of the heavy metal concentration and speciation in groundwater.

[0018] Furthermore, in step S4, the calculation formulas for the karst vulnerability index and the hydrological dynamic fluctuation coefficient are as follows:

[0019] ;

[0020] ;

[0021] In the formula, The karst vulnerability index The parameters representing the degree of karst development in the target karst region. The aquifer permeability coefficient is... Due to the depth of groundwater, , , These are the weighting coefficients for parameters related to karst development degree, aquifer permeability coefficient, and groundwater depth, respectively, with the sum of all weighting coefficients being 1; This is the hydrological dynamic fluctuation coefficient. This represents the maximum groundwater level at the corresponding monitoring point within the monitoring period. This represents the minimum groundwater level at the corresponding monitoring point during the monitoring period. The average groundwater level at the corresponding monitoring point during the monitoring period is used. The weight coefficients corresponding to the karst vulnerability index are calculated using the analytic hierarchy process (AHP) to construct a judgment matrix based on scores from regional hydrogeological experts. The objective weights are calculated using the entropy weight method based on multiple sets of hydrogeological monitoring sample data of the target karst area. The subjective weights and objective weights corresponding to each indicator are multiplied to obtain the product value. The product value of each indicator is divided by the sum of the product values ​​of all indicators, and the combined weights are synthesized through multiplication normalization.

[0022] Furthermore, in step S4, the geochemical sensitive factor includes the heavy metal toxicity coefficient and the proportion of active heavy metal forms. The comprehensive value calculation formula for the geochemical sensitive factor is as follows:

[0023] ;

[0024] In the formula, This is the comprehensive value of geochemical sensitive factors. For the first The concentration percentage of each heavy metal, wherein the concentration percentage is the first... The ratio of the concentration of a specific heavy metal to the total concentration of all monitored heavy metals. Let be the toxicity coefficient of the i-th heavy metal. For the first The proportion of active forms of heavy metals. The heavy metal is identified by its type number. The active forms of the heavy metal include exchangeable and carbonate-bound forms. Following the standard experimental procedure for heavy metal speciation analysis in groundwater, the exchangeable and carbonate-bound forms are extracted sequentially using a stepwise continuous extraction method, and their corresponding concentrations are measured. The ratio of the active form concentration to the total heavy metal concentration is calculated to obtain the proportion of the active form of the heavy metal. The heavy metal toxicity coefficient adopts the recommended value specified in the technical standards for health risk assessment of groundwater pollution issued by the national ecological and environmental authorities.

[0025] Furthermore, in step S4, the comprehensive evaluation value of the coupled assessment model constructed based on the karst vulnerability index, the hydrological dynamic fluctuation coefficient, and the geochemical sensitivity factor is calculated using the following formula:

[0026] ;

[0027] In the formula, For the coupled evaluation of the comprehensive value, These are the weighting coefficients corresponding to the karst vulnerability index. The weighting coefficients corresponding to the hydrological dynamic fluctuation coefficients. These are the weighting coefficients corresponding to the comprehensive value of geochemical sensitivity factors, and the sum of all weighting coefficients is 1; The karst vulnerability index is... The hydrological dynamic fluctuation coefficient is mentioned above. The weighting coefficients are calculated using the analytic hierarchy process (AHP) to obtain subjective weights. The weighting coefficients are based on a judgment matrix constructed from expert scores in the fields of karst hydrogeology and environmental risk. Objective weights are calculated using the entropy weighting method based on the long-term monitoring sample dataset of the target karst region. The subjective and objective weights corresponding to each indicator are multiplied to obtain a product value. The product value of each indicator is divided by the sum of all indicator product values, and the combined weights are synthesized using a multiplication normalization method.

[0028] Further, in step S4, the groundwater pollution level is classified according to the multiple of the coupled assessment comprehensive value relative to the benchmark comprehensive value. The benchmark comprehensive value is the coupled assessment comprehensive value corresponding to the unpolluted background area of ​​the target karst area. The classification threshold is adjusted in conjunction with the heavy metal concentration limit in the current groundwater quality standard to obtain three levels: light pollution, moderate pollution, and heavy pollution. Using the oral exposure route, combined with the concentration data of heavy metals in the groundwater and exposure parameters such as daily water intake, exposure frequency, exposure period, and average human body weight, the exposure parameters adopt the regional general default values ​​specified in the current groundwater pollution health risk assessment technical standard. According to the calculation model specified in the standard, two types of human health risk values ​​are calculated: non-carcinogenic hazard quotient and carcinogenic risk value. A two-dimensional matching matrix between the groundwater pollution level and the human health risk value is constructed. The groundwater pollution level and the human health risk value are input into the two-dimensional matching matrix to obtain four warning levels: blue, yellow, orange, and red. The corresponding dynamic monitoring and warning results are generated and output.

[0029] The beneficial effects of this invention are as follows: This invention simultaneously delineates the pollution anomaly range and identifies the karst triple-media structure through high-density resistivity imaging. Based on this, a three-level targeted monitoring system is constructed, consisting of main pipeline control points, fracture zone densification, and matrix zone supplementation. This effectively improves the targeting and utilization efficiency of monitoring point deployment, avoiding the problems of missing key migration zone monitoring and redundant points inherent in traditional uniform point deployment. By combining laser-induced fluorescence in-situ rapid detection with hydrogeochemical sampling analysis, and after precise laboratory data calibration, spatiotemporal distribution data of heavy metal concentration and speciation are obtained. This simultaneously meets the temporal resolution and detection accuracy requirements of large-scale dynamic monitoring, efficiently capturing the dynamic changes in heavy metal pollution in karst groundwater. Furthermore, a coupled assessment model integrating the karst vulnerability index, hydrological dynamic fluctuation coefficient, and geochemical sensitivity factors is constructed. Combined with pollution degree classification and human health risk assessment, a graded early warning system is achieved, fully adapting to the hydrogeological characteristics of karst and effectively improving the accuracy of pollution risk assessment and early warning results. This provides reliable technical support for the long-term refined management of groundwater pollution in karst areas. Attached Figure Description

[0030] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0031] Figure 1 This is a schematic diagram of a method for long-term dynamic monitoring and early warning of groundwater in karst areas, provided as an embodiment of the present invention. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0034] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0035] Existing technologies for groundwater monitoring and early warning in karst areas suffer from several technical deficiencies, including insufficient compatibility of the monitoring point system with the triple medium structure, difficulty in balancing efficiency and accuracy in detection methods, and inadequate integration of karst hydrogeological and geochemical characteristics in the assessment model. These deficiencies prevent the targeted monitoring and dynamic risk assessment of heavy metal pollution and fail to meet the practical needs for long-term, refined management of groundwater environment in karst areas.

[0036] The present invention aims to provide a method for long-term dynamic monitoring and early warning of groundwater in karst areas, in order to solve the problems in the existing technology, such as insufficient compatibility of the sampling system with the triple medium structure, difficulty in balancing efficiency and accuracy of detection methods, and the fact that the evaluation model does not fully integrate the hydrogeological and geochemical characteristics of karst.

[0037] like Figure 1 As shown in the figure, a specific embodiment of the present invention provides a method for long-term dynamic monitoring and early warning of groundwater in karst areas, the specific steps of which are as follows:

[0038] S1: High-density resistivity imaging technology is used to detect the target karst area and obtain the spatial distribution data of the stratigraphic resistivity of the target karst area; based on the spatial distribution data of stratigraphic resistivity, the abnormal range of groundwater pollution in the target karst area is initially delineated; at the same time, the karst media structure of the target karst area is identified by combining the spatial distribution data of stratigraphic resistivity, and the identification results of the triple media structure composed of karst conduits, fissures and matrix are obtained.

[0039] In this embodiment of the invention, a typical karst basin in Southwest China was selected as the target karst region. Twelve parallel survey lines were laid out, with the direction of multiple parallel survey lines perpendicular to the regional groundwater flow direction and the dominant orientation of the karst conduits. The spacing between adjacent survey lines was 50m. Raw resistivity data of the formation was collected point by point along each parallel survey line at an electrode point spacing of 5m. After noise removal processing, including defect removal, topographic correction, and drift correction, the raw resistivity data of the formation was processed using a least squares constrained inversion algorithm to obtain a continuous two-dimensional formation resistivity profile. This profile was then stitched together to form the spatial distribution data of formation resistivity for the entire region. During the inversion calculation, karst development information from boreholes was introduced as a geological constraint. The measured karst development location and lithological parameters from the boreholes were used as inversion control nodes to reduce the ambiguity of the resistivity inversion results and simultaneously improve the accuracy of identifying the pollution anomaly range and media structure. Three unpolluted background areas undisturbed by human activities were selected within the target karst region. Corresponding stratigraphic resistivity data were collected and the average value was calculated. The unpolluted stratigraphic background resistivity value was 220 Ω·m. 0.7 times the unpolluted stratigraphic background resistivity value was used as the resistivity anomaly threshold. The multiple was determined comprehensively based on the hydrogeological conditions of the target karst region and the regional groundwater pollution prevention and control requirements. That is, areas with stratigraphic resistivity values ​​below 154 Ω·m were identified as groundwater pollution anomaly ranges. When conducting triple-medium structure identification, the spatial distribution data of formation resistivity, drilling data from 15 boreholes in the target karst area, and pumping test results are combined. Formation resistivity, borehole karst rate, and aquifer permeability coefficient are used as classification indicators. The threshold values ​​in the three-level threshold classification rule are determined based on hydrogeological survey data and lithological test results of the target karst area. The first threshold corresponds to formation resistivity, borehole karst rate, and aquifer permeability coefficient values ​​of 80 Ω·m, 30%, and 10 m / d, respectively. The second threshold corresponds to values ​​of 200 Ω·m, 10%, and 0.1 m / d, respectively. Only areas that simultaneously meet the threshold requirements of all three indicators can be identified as the corresponding medium type. Among these, karst... The formation resistivity corresponding to the pipeline is below the first threshold, the borehole karst rate is above the first karst rate threshold, and the aquifer permeability coefficient is above the first permeability coefficient threshold. The formation resistivity corresponding to the fracture zone is between the first and second thresholds, the borehole karst rate is between the first and second karst rate thresholds, and the aquifer permeability coefficient is between the first and second permeability coefficient thresholds. The formation resistivity corresponding to the matrix zone is above the second threshold, the borehole karst rate is below the second karst rate threshold, and the aquifer permeability coefficient is below the second permeability coefficient threshold. This data is used to delineate the spatial distribution range and flow path of the karst pipeline, the development density and distribution area of ​​the fracture zone, and the lithological parameters and aquifer permeability coefficient of the matrix zone. Simultaneously, groundwater depth parameters for each area are obtained through drilling water level measurements. The borehole karst rate is calculated as the ratio of the total height of the karst space revealed by the borehole to the corresponding total borehole footage, serving as a parameter for the degree of karst development in the target karst area.After completing the initial media classification, the media classification threshold is verified and corrected using the measured results of field tracer tests and supplementary single-hole pumping tests to ensure that the triple media structure identification results match the actual hydrogeological parameters, and the complete triple media structure identification results are integrated and output.

[0040] S2: Combining the abnormal range of groundwater pollution with the identification results of the triple medium structure, a three-level targeted deployment system is constructed, consisting of the main control point of the main pipeline, the densified points in the fracture zone, and the supplementary points in the matrix zone, to determine the deployment location and monitoring frequency of each monitoring point.

[0041] In this embodiment of the invention, based on the triple media structure identification results, eight main control points are set up at the branch intersections and inflection points of the karst main pipeline, runoff collection and distribution nodes, and the outlets of underground rivers and karst springs, covering the main runoff and discharge channels of pollutants. Based on the fracture development density value, i.e., the cumulative length of fractures per unit area, densely developed fracture areas are delineated. Sixteen additional fracture zone monitoring points are set up in these densely developed fracture areas falling within the groundwater pollution anomaly range, with an average spacing of 200m, to achieve high-density monitoring of the pollution migration transition zone. Twelve supplementary matrix zone monitoring points are set up in the matrix area using a uniform square grid with a side length of 800m to obtain regional groundwater background values. A numerical model of karst groundwater pollution migration is constructed based on the triple media structure identification results and measured hydrogeological parameters. The initially set up monitoring points are optimized and adjusted based on the pollution migration numerical simulation results. Additional fracture zone monitoring points are added at the locations of simulated pollution priority migration paths and abrupt concentration gradient changes to further enhance the ability to capture dynamic pollution processes. The monitoring frequency is set according to the medium type and pollution anomaly level of each monitoring point. The pollution anomaly level is divided by the deviation multiple, which is the ratio of the background resistivity value of the uncontaminated stratum to the resistivity value of the stratum at the monitoring point. The larger the deviation multiple, the higher the pollution anomaly level. The default monitoring frequency for the main pipeline control points is once a day, the default monitoring frequency for the fracture zone densified points is once every 3 days, and the default monitoring frequency for the matrix supplementary points is once every 15 days. For high-anomaly points with a deviation multiple of more than 2 times, the monitoring frequency is doubled from the default to ensure the temporal resolution of high-risk areas. A dynamic adaptive adjustment mechanism for monitoring frequency is established. When the groundwater level fluctuation of a monitoring point exceeds 20% of the average groundwater level for the month, or when the heavy metal concentration continues to rise for three consecutive monitoring cycles with a cumulative increase of more than 10%, the monitoring frequency of that point is automatically doubled. After the water level and concentration stabilize for three consecutive monitoring cycles, the frequency gradually returns to the default monitoring frequency, realizing the dynamic optimization of monitoring resources.

[0042] S3: Based on the three-level targeted deployment system, monitoring points are set up in the target karst area. Combined with laser-induced fluorescence in situ rapid detection technology and hydrogeochemical sampling and analysis methods, the spatiotemporal distribution data of heavy metal concentration and speciation in groundwater are obtained.

[0043] In this embodiment of the invention, laser-induced fluorescence in-situ rapid detection equipment is installed at all monitoring points. Continuous in-situ detection is conducted according to the corresponding monitoring frequency to rapidly obtain in-situ detection data on the total concentration of four heavy metals (lead, cadmium, chromium, and arsenic) in groundwater. Simultaneously, groundwater samples are collected at each monitoring point once a month and sent to the laboratory for inductively coupled plasma mass spectrometry (ICP-MS) to determine the precise total concentration of each heavy metal. A stepwise continuous extraction method is used to sequentially extract exchangeable and carbonate-bound heavy metals and determine their corresponding concentrations, yielding heavy metal speciation analysis results. During the in-situ detection process, three quality control parameters are simultaneously collected: equipment operating voltage, water sample ambient temperature, and light source intensity. If the equipment operating voltage deviation exceeds ±5% of the rated value or the ambient temperature exceeds the equipment's applicable temperature range, it is determined to be outside the quality control allowable range. In-situ detection data outside the quality control allowable range are automatically marked and discarded, retaining only qualified data for subsequent calibration processing to ensure the reliability of the basic data.

[0044] Twenty sets of synchronously collected in-situ and laboratory detection data were selected. A univariate linear regression calibration equation was constructed with the precise concentration measured in the laboratory as the true value and the in-situ detection data as the independent variable. The calibration coefficient and intercept for each heavy metal were determined, and all in-situ detection data were calibrated and corrected. The relative error is the absolute value of the ratio of the difference between the calibrated in-situ detection concentration and the laboratory measured concentration to the laboratory measured concentration. The relative error of in-situ detection was controlled within 10% through calibration. Based on the proportion of active forms of each heavy metal measured in the laboratory, the time series data of the total concentration of heavy metals after calibration were morphologically decomposed. The basic spatiotemporal dataset was obtained by integrating the spatial coordinates and time series of each monitoring point.

[0045] After completing the concentration calibration and speciation at each monitoring point, based on the detection results of each monitoring point, the ordinary Kriging spatial interpolation method is used to spatially interpolate the heavy metal concentration and speciation ratio of the entire area. Combined with the continuous time series detection data, a three-dimensional spatiotemporal distribution raster dataset is generated to finely depict the spatiotemporal evolution characteristics of heavy metal pollution, and finally obtain complete spatiotemporal distribution data of heavy metal concentration and speciation in groundwater.

[0046] S4: Construct a coupled assessment model that integrates the karst vulnerability index, hydrological dynamic fluctuation coefficient, and geochemical sensitivity factors. Input spatiotemporal distribution data into the coupled assessment model to complete the classification of groundwater pollution level and dynamic assessment of health risks, and output the corresponding dynamic monitoring and early warning results.

[0047] In this embodiment of the invention, when calculating the karst vulnerability index, three indicators are selected: karst development degree parameter, aquifer permeability coefficient, and groundwater depth. A judgment matrix is ​​constructed by scoring by five experts in the field of karst hydrogeology, and the subjective weight is calculated using the analytic hierarchy process (AHP). Thirty sets of hydrogeological sample data from the study area are selected, and the objective weight is calculated using the entropy weight method. The subjective weight and objective weight corresponding to each indicator are multiplied to obtain a product value. Then, the product value of each indicator is divided by the sum of the product values ​​of all indicators, and the combined weight is obtained by multiplication normalization. The weight coefficients of the three indicators are 0.45, 0.35, and 0.2, respectively. These are substituted into the formula to calculate the karst vulnerability index of each monitoring point.

[0048] The hydrological dynamic fluctuation coefficient is calculated using a complete hydrological year as the monitoring cycle. The maximum, minimum and average groundwater levels at each point within the statistical cycle are then substituted into the formula to obtain the hydrological dynamic fluctuation coefficient for the corresponding point.

[0049] The formulas for calculating the karst vulnerability index and the hydrological dynamic fluctuation coefficient are as follows:

[0050] ;

[0051] ;

[0052] In the formula, The karst vulnerability index The parameters representing the degree of karst development in the target karst region. The aquifer permeability coefficient is... Due to the depth of groundwater, , , These are the weighting coefficients for parameters related to karst development degree, aquifer permeability coefficient, and groundwater depth, respectively, with the sum of all weighting coefficients being 1; This is the hydrological dynamic fluctuation coefficient. This represents the maximum groundwater level at the corresponding monitoring point within the monitoring period. This represents the minimum groundwater level at the corresponding monitoring point during the monitoring period. This represents the average groundwater level at the corresponding monitoring point within the monitoring period.

[0053] When calculating the comprehensive value of geochemical sensitivity factors, the above four heavy metals are used as the evaluation objects. The heavy metal toxicity coefficient adopts the recommended value specified in the technical standard for health risk assessment of groundwater pollution issued by the national ecological and environmental authorities. The concentration ratio and active form ratio of each heavy metal are calculated separately and substituted into the summation formula to obtain the comprehensive value of geochemical sensitivity factors.

[0054] Geochemical sensitivity factors include the heavy metal toxicity coefficient and the proportion of active heavy metal forms. The comprehensive value of geochemical sensitivity factors is calculated using the following formula:

[0055] ;

[0056] In the formula, This is the comprehensive value of geochemical sensitive factors. For the first The concentration percentage of each heavy metal, the concentration percentage of which is the first The ratio of the concentration of a specific heavy metal to the total concentration of all monitored heavy metals. Let be the toxicity coefficient of the i-th heavy metal. For the first The proportion of active forms of heavy metals. This is the type number of the heavy metal.

[0057] The concentration ratio is the first The ratio of the concentration of a specific heavy metal to the total concentration of all monitored heavy metals was used. The active forms of heavy metals include exchangeable and carbonate-bound forms. Following the standard experimental procedure for heavy metal speciation analysis in groundwater, a stepwise continuous extraction method was used to extract and measure the corresponding concentrations sequentially. The ratio of the concentration of active forms to the total heavy metal concentration was calculated to obtain the proportion of active forms. When constructing the coupled assessment model, the same analytic hierarchy process (AHP) combined with the entropy weight method was used to determine the combined weights of the three types of indicators. The weight coefficients for the karst vulnerability index, hydrological dynamic fluctuation coefficient, and geochemical sensitivity factor comprehensive value were 0.30, 0.25, and 0.45, respectively. These values ​​were then substituted into the formula to calculate the coupled assessment comprehensive value for each location.

[0058] The formula for calculating the comprehensive evaluation value is:

[0059] ;

[0060] In the formula, For the coupled evaluation of the comprehensive value, These are the weighting coefficients corresponding to the karst vulnerability index. The weighting coefficients corresponding to the hydrological dynamic fluctuation coefficients. These are the weighting coefficients corresponding to the comprehensive value of geochemical sensitivity factors, and the sum of all weighting coefficients is 1; The karst vulnerability index This is the hydrological dynamic fluctuation coefficient. The comprehensive value of geochemical sensitive factors; the weighting coefficients are based on the judgment matrix constructed by experts in the fields of karst hydrogeology and environmental risk, and the subjective weights are calculated by the analytic hierarchy process.

[0061] For three different media zones—karst conduits, fissure zones, and matrix zones—corresponding coupled assessment model index weights were calculated to adapt model parameters to the pollution migration and risk transmission characteristics of different media, further improving the regional adaptability of the assessment results. The coupled assessment comprehensive value of the uncontaminated background area of ​​the target karst region was used as the baseline comprehensive value. The grading thresholds were adjusted based on the heavy metal concentration limits in the current groundwater quality standards. A coupled assessment comprehensive value of 1.0 to 2.0 times the baseline comprehensive value was classified as lightly polluted, 2.0 to 5.0 times as moderately polluted, and above 5.0 times as heavily polluted. Health risk assessment adopted the oral exposure route. Exposure parameters used the universally accepted default values ​​for adults as specified in the current technical standards for health risk assessment of groundwater pollution: daily water intake of 2 liters, exposure frequency of 365 days per year, exposure period of 30 years, and average body weight of 60 kg. Two types of human health risk values—non-carcinogenic hazard quotient and carcinogenic risk value—were calculated according to the calculation model specified in the standard. A two-dimensional matching matrix was constructed to correlate groundwater pollution levels with human health risk values. By inputting these values ​​into the matrix, four warning levels—blue, yellow, orange, and red—were generated. The hierarchical mapping rules for the two-dimensional matching matrix were defined: groundwater pollution level as the row dimension and human health risk level as the column dimension. A blue warning corresponds to mild pollution with acceptable health risk; a yellow warning corresponds to mild pollution with health risk exceeding acceptable levels, or moderate pollution with acceptable health risk; an orange warning corresponds to moderate pollution with health risk exceeding acceptable levels, or severe pollution with health risk within acceptable levels; and a red warning corresponds to severe pollution with health risk significantly exceeding acceptable levels. The acceptable threshold for the non-carcinogenic hazard quotient was set to 1, and the acceptable starting threshold for the carcinogenic risk value was set to [missing value]. Non-carcinogenic hazard quotient greater than 10 or carcinogenic risk value greater than If a level is deemed to be significantly beyond acceptable levels, all thresholds are determined based on current technical standards for health risk assessment of groundwater pollution, and dynamic monitoring and early warning results of the corresponding level are generated and output.

[0062] The beneficial effects of this invention are as follows: This invention simultaneously delineates the pollution anomaly range and identifies the karst triple-media structure through high-density resistivity imaging. Based on this, a three-level targeted monitoring system is constructed, consisting of main pipeline control points, fracture zone densification, and matrix zone supplementation. This effectively improves the targeting and utilization efficiency of monitoring point deployment, avoiding the problems of missing key migration zone monitoring and redundant points inherent in traditional uniform point deployment. By combining laser-induced fluorescence in-situ rapid detection with hydrogeochemical sampling analysis, and after precise laboratory data calibration, spatiotemporal distribution data of heavy metal concentration and speciation are obtained. This simultaneously meets the temporal resolution and detection accuracy requirements of large-scale dynamic monitoring, efficiently capturing the dynamic changes in heavy metal pollution in karst groundwater. Furthermore, a coupled assessment model integrating the karst vulnerability index, hydrological dynamic fluctuation coefficient, and geochemical sensitivity factors is constructed. Combined with pollution degree classification and human health risk assessment, a graded early warning system is achieved, fully adapting to the hydrogeological characteristics of karst and effectively improving the accuracy of pollution risk assessment and early warning results. This provides reliable technical support for the long-term refined management of groundwater pollution in karst areas.

[0063] The above descriptions are merely embodiments of the present invention. Commonly known technical solutions or characteristics are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A method for long-term monitoring and early warning of groundwater dynamics in karst areas, characterized in that, The method includes: S1: High-density resistivity imaging technology is used to detect the target karst area and obtain the spatial distribution data of the formation resistivity of the target karst area; based on the spatial distribution data of the formation resistivity, the range of groundwater pollution anomalies in the target karst area is preliminarily delineated; at the same time, the karst medium structure of the target karst area is identified by combining the spatial distribution data of the formation resistivity, and the identification results of the triple medium structure composed of karst conduits, fissures and matrix are obtained. S2: Combining the abnormal groundwater pollution range with the triple medium structure identification results, a three-level targeted deployment system is constructed, consisting of the main pipeline control point, the fracture zone densification point, and the matrix zone supplementary point, to determine the deployment location and monitoring frequency of each monitoring point; S3: According to the three-level targeted deployment system, the monitoring points are deployed in the target karst area, and the spatiotemporal distribution data of the concentration and speciation of heavy metals in groundwater are obtained by combining laser-induced fluorescence in situ rapid detection technology with hydrogeochemical sampling and analysis methods. S4: Construct a coupled assessment model that integrates the karst vulnerability index, hydrological dynamic fluctuation coefficient, and geochemical sensitivity factor. Input the spatiotemporal distribution data into the coupled assessment model to complete the classification of groundwater pollution level and dynamic assessment of health risks, and output the corresponding dynamic monitoring and early warning results.

2. The method for long-term monitoring and early warning of groundwater dynamics in karst areas according to claim 1, characterized in that: In step S1, the specific process of using high-density resistivity imaging technology for detection is as follows: multiple parallel measuring lines are laid out along the target karst area. The layout direction of the multiple parallel measuring lines is perpendicular to the groundwater flow direction and the main direction of the karst conduit. Raw data of the formation resistivity are collected point by point along each of the parallel measuring lines according to the electrode point spacing determined by the detection depth and resolution requirements. The raw formation resistivity data is subjected to noise removal processing, including bad pixel removal, topographic correction, and drift correction, and then a continuous formation resistivity profile is obtained by least squares constrained inversion calculation. Resistivity data of the uncontaminated background area within the target karst region is collected, and the background resistivity value of the uncontaminated stratum is statistically obtained. A set multiple of the background resistivity value of the uncontaminated stratum is used as the resistivity anomaly threshold. The set multiple is determined according to the hydrogeological conditions and pollution control requirements of the target karst region. The range of groundwater pollution anomaly is delineated based on the resistivity anomaly threshold.

3. The method for long-term monitoring and early warning of groundwater dynamics in karst areas according to claim 1, characterized in that, The specific process for identifying triple dielectric structures is as follows: Combining the spatial distribution data of the formation resistivity, the drilling data of the target karst area and the results of on-site hydrogeological tests, the formation resistivity value, borehole karst rate and aquifer permeability coefficient are used as classification indicators, and three types of media are identified according to the three-level threshold classification rule. The thresholds in the three-level threshold division rule are determined based on the hydrogeological survey data and lithological test results of the target karst area. Wherein, the karst conduit corresponds to a formation resistivity value lower than a first threshold, a borehole karst rate higher than a first karst rate threshold, and an aquifer permeability coefficient higher than a first permeability coefficient threshold; the fracture zone corresponds to a formation resistivity value between a first threshold and a second threshold, a borehole karst rate between a first karst rate threshold and a second karst rate threshold, and an aquifer permeability coefficient between a first permeability coefficient threshold and a second permeability coefficient threshold; the matrix zone corresponds to a formation resistivity value higher than a second threshold, a borehole karst rate lower than a second karst rate threshold, and an aquifer permeability coefficient lower than a second permeability coefficient threshold. The spatial distribution range and flow path of the karst conduit, the development density and distribution area of ​​the fracture zone, and the lithological parameters and aquifer permeability coefficient of the matrix zone are obtained. Simultaneously, the groundwater depth parameters of the target karst area are obtained through drilling water level measurement. The borehole karst rate is used as the karst development degree parameter of the target karst area, and the triple medium structure identification results are integrated to obtain the triple medium structure identification results. The borehole karst rate is the ratio of the total height of the karst space exposed by the borehole to the total drilling footage of the corresponding borehole.

4. The method for long-term monitoring and early warning of groundwater dynamics in karst areas according to claim 1, characterized in that, The deployment rules for the three-level targeted deployment system are as follows: The main control points of the main karst pipeline are set up at the branch intersections, runoff collection and distribution nodes, underground river outlets, and karst spring discharge outlets. Based on the fracture development density in the triple medium structure identification results, a region with dense fracture development is determined. The fracture development density is the cumulative length of fractures per unit area. Fracture zone densification points are set up in the region with dense fracture development and at locations falling within the groundwater pollution anomaly range. Supplementary points in the matrix area are arranged in a uniform square grid pattern, and the side length of the grid is determined based on the area of ​​the matrix area and the uniformity of hydrogeological conditions.

5. The method for long-term monitoring and early warning of groundwater dynamics in karst areas according to claim 4, characterized in that: In step S2, the corresponding monitoring frequency is set according to the medium type and pollution anomaly degree of each monitoring point. The pollution anomaly degree is divided by the ratio of the background resistivity value of the uncontaminated stratum to the resistivity value of the stratum at the monitoring point as the deviation factor. The larger the deviation factor, the higher the pollution anomaly degree. The monitoring frequency of the main pipeline control points is higher than that of the fracture zone densification points, and the monitoring frequency of the fracture zone densification points is higher than that of the matrix zone supplementary points; for monitoring points of the same medium type, the higher the degree of pollution anomaly, the higher the corresponding monitoring frequency.

6. The method for long-term monitoring and early warning of groundwater dynamics in karst areas according to claim 1, characterized in that, In step S3, the specific implementation method of joint detection is as follows: Laser-induced fluorescence in situ rapid detection equipment was used to conduct in situ continuous detection at each of the monitoring points to quickly obtain in situ detection data of total heavy metal concentration in groundwater; Groundwater samples were collected simultaneously at each of the monitoring points and sent to the laboratory. Hydrogeochemical sampling and analysis methods were used to determine the precise concentration and content of each heavy metal in the groundwater. A linear regression calibration equation was constructed using synchronously collected laboratory precision concentration data as the true value and in-situ detection data as the independent variable to calibrate and correct the concentration of all in-situ detection data. Based on the proportion of each active form of heavy metal determined in the laboratory, the time series data of the calibrated total heavy metal concentration was decomposed into forms, and the spatiotemporal distribution data of the concentration and forms of heavy metals in groundwater were obtained by combining the spatial location of each monitoring point.

7. The method for long-term monitoring and early warning of groundwater dynamics in karst areas according to claim 1, characterized in that: In step S4, the calculation formulas for the karst vulnerability index and the hydrological dynamic fluctuation coefficient are as follows: ； ； In the formula, The karst vulnerability index The parameters representing the degree of karst development in the target karst region. The aquifer permeability coefficient is... Due to the depth of groundwater, , , These are the weighting coefficients for parameters related to karst development degree, aquifer permeability coefficient, and groundwater depth, respectively, with the sum of all weighting coefficients being 1; This is the hydrological dynamic fluctuation coefficient. This represents the maximum groundwater level at the corresponding monitoring point within the monitoring period. This represents the minimum groundwater level at the corresponding monitoring point during the monitoring period. The average groundwater level at the corresponding monitoring point during the monitoring period; the weight coefficients corresponding to the karst vulnerability index are based on a judgment matrix constructed by regional hydrogeological experts, and the subjective weights are calculated using the analytic hierarchy process. Based on multiple sets of hydrogeological monitoring sample data of the target karst area, the objective weights are calculated using the entropy weight method; the subjective weights corresponding to each indicator are multiplied by the objective weights to obtain the product value; the product value of each indicator is divided by the sum of the product values ​​of all indicators, and the combined weights are synthesized through multiplication normalization.

8. The method for long-term monitoring and early warning of groundwater dynamics in karst areas according to claim 1, characterized in that: In step S4, the geochemical sensitive factor includes the heavy metal toxicity coefficient and the proportion of active heavy metal forms. The comprehensive value of the geochemical sensitive factor is calculated using the following formula: ； In the formula, This is the comprehensive value of geochemical sensitive factors. For the first The concentration percentage of each heavy metal, wherein the concentration percentage is the first... The ratio of the concentration of a specific heavy metal to the total concentration of all monitored heavy metals. Let be the toxicity coefficient of the i-th heavy metal. For the first The proportion of active forms of heavy metals. This refers to the type number of the heavy metal; The active forms of heavy metals include exchangeable and carbonate-bound forms. According to the standard test procedure for heavy metal speciation analysis in groundwater, the exchangeable and carbonate-bound forms are extracted sequentially using a stepwise continuous extraction method, and the corresponding concentrations are measured. The ratio of the concentration of active forms to the total concentration of heavy metals is calculated to obtain the proportion of active forms of heavy metals. The heavy metal toxicity coefficient adopts the recommended value specified in the technical standards for health risk assessment of groundwater pollution issued by the national ecological and environmental authorities.

9. A method for long-term monitoring and early warning of groundwater dynamics in karst areas according to claim 1, characterized in that: In step S4, the comprehensive evaluation value of the coupled assessment model constructed based on the karst vulnerability index, the hydrological dynamic fluctuation coefficient, and the geochemical sensitivity factor is calculated using the following formula: ； In the formula, For the coupled evaluation of the comprehensive value, These are the weighting coefficients corresponding to the karst vulnerability index. The weighting coefficients corresponding to the hydrological dynamic fluctuation coefficients. These are the weighting coefficients corresponding to the comprehensive value of geochemical sensitivity factors, and the sum of all weighting coefficients is 1; The karst vulnerability index is... The hydrological dynamic fluctuation coefficient is mentioned above. This is the comprehensive value of the aforementioned geochemical sensitivity factors; The weighting coefficients are based on a judgment matrix constructed by expert scores in the field of karst hydrogeology and environmental risk, and subjective weights are calculated using the analytic hierarchy process. Based on the long-term monitoring sample dataset of the target karst area, the objective weights are calculated using the entropy weight method. The subjective weights corresponding to each indicator are multiplied by the objective weights to obtain the product value. The product value of each indicator is divided by the sum of the product values ​​of all indicators, and the combined weights are synthesized through multiplication normalization.

10. A method for long-term monitoring and early warning of groundwater dynamics in karst areas according to claim 9, characterized in that: In step S4, the groundwater pollution level is classified according to the multiple of the coupled assessment comprehensive value relative to the benchmark comprehensive value. The benchmark comprehensive value is the coupled assessment comprehensive value corresponding to the unpolluted background area of ​​the target karst area. The classification threshold is adjusted in combination with the heavy metal concentration limit in the current groundwater quality standard to obtain three levels: light pollution, moderate pollution, and heavy pollution. The oral exposure route was used, combined with the concentration data of heavy metals in the groundwater and the exposure parameters of daily water intake, exposure frequency, exposure period, and average human body weight. The exposure parameters adopted the regional general default values ​​specified in the current technical standard for health risk assessment of groundwater pollution. Two types of human health risk values, namely non-carcinogenic hazard quotient and carcinogenic risk value, were calculated according to the calculation model specified in the standard. A two-dimensional matching matrix is ​​constructed between the groundwater pollution level and the human health risk value. The groundwater pollution level and the human health risk value are input into the two-dimensional matching matrix to obtain four warning levels: blue, yellow, orange, and red. The corresponding dynamic monitoring and warning results are generated and output.

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