Method and device for in-situ sampling and dynamic monitoring of shallow damaged groundwater stratification

Abstract

The present application provides a shallow damaged groundwater stratified in-situ sampling and dynamic monitoring method and device, relates to the technical field of groundwater sampling and monitoring, and comprises the following steps: arranging monitoring wells in a target monitoring area and setting a plurality of monitoring points, collecting water quality parameters of each point in-situ at a preset time interval, and forming a water quality parameter sequence of each point changing with time; extracting the dispersion degree and deviation degree of each point to determine the stability coefficient of the point, and then screening out the point meeting the calibration condition as a target sampling depth; collecting samples and performing laboratory analysis on the target sampling depth, calculating a water quality state index in combination with the water quality parameter sequence; comparing the water quality state index of each target sampling depth with a preset risk level threshold value, dividing the water quality risk level, and statistically calculating the current highest risk level and the area risk rising proportion of the area, thereby generating early warning information of the target monitoring area, and the present application provides intelligent decision support for early intervention of groundwater pollution.

Description

Technical Field

[0001] This invention relates to the field of groundwater sampling and monitoring technology, specifically to a method and apparatus for in-situ sampling and dynamic monitoring of shallow damaged groundwater. Background Technology

[0002] Shallow groundwater is shallow, rapidly replenished, and closely connected to surface water, making it susceptible to human activities. Its pollution is characterized by its concealment, complexity, and long-term nature. Traditional groundwater monitoring methods often rely on single-point or mixed water sample collection and laboratory analysis, which makes it difficult to accurately characterize the distribution characteristics of pollutants on the vertical profile of the aquifer and to achieve real-time dynamic tracking of water quality parameters. Therefore, developing monitoring technologies that can achieve stratified, in-situ, and dynamic integrated monitoring is of great practical significance for accurately identifying pollution plumes, assessing pollution risks, and guiding remediation projects.

[0003] In the prior art, a method for stratified in-situ sampling and dynamic monitoring of shallow groundwater, disclosed in CN119756958A, obtains stratified water samples by deploying monitoring wells at different depths and using a method of layer-by-layer dewatering and water level restoration. It also combines automatic water level and temperature recorders for dynamic monitoring. Although this method achieves stratified sampling, its sampling process relies on manual dewatering and water level restoration, which is complex and time-consuming. Furthermore, it cannot obtain in-situ water quality parameters in real time during the sampling process to guide sampling decisions. In addition, this method lacks analysis and mining of time-series data of water quality parameters, cannot automatically identify abnormal layers and determine sampling targets based on dynamic changes in water quality, and is also difficult to quantitatively assess water quality risks and provide trend warnings. It has significant deficiencies in terms of real-time monitoring, adaptability, and intelligent decision support.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method and apparatus for in-situ sampling and dynamic monitoring of shallow damaged groundwater stratification, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for in-situ sampling and dynamic monitoring of shallow damaged groundwater stratification, including the following steps: Step 1: Install monitoring wells within the target monitoring area, and set up multiple monitoring points with different water depths at vertical intervals within the monitoring wells; Step 2: During the monitoring period, water quality parameters are collected in situ at each monitoring point during continuous sampling, and the collected data are matched with the water depth of the corresponding monitoring point to form a sequence of water quality parameters changing over time at each monitoring point. Step 3: For any monitoring point, determine its adjacent monitoring points based on the location relationship. Based on the water quality parameter sequence of each monitoring point and its adjacent monitoring points, determine the dispersion and deviation of the water quality parameters of each monitoring point. Based on the dispersion and deviation, evaluate the fluctuation stability of each monitoring point during the sampling process, screen out the monitoring points whose fluctuation stability meets the calibration conditions, and determine the water immersion depth of the monitoring points that meet the calibration conditions as the target sampling depth. Step 4: Collect groundwater samples at the target sampling depth and detect the types and concentrations of pollutants to obtain laboratory analysis data. Input the water quality parameter sequence and laboratory analysis data into a preset water quality status assessment function to obtain the water quality status index for each target sampling depth. The laboratory analysis data includes the detected concentration of pollutants, the number of pollutant types, and the corresponding toxicity weights. Step 5: Compare the water quality status index corresponding to each target sampling depth with the preset water quality risk level threshold, classify the groundwater water quality risk level of each target sampling depth, and count the highest risk level and the regional risk increase ratio within the target monitoring area to determine the early warning information for the target monitoring area.

[0007] Furthermore, monitoring wells are deployed in the target monitoring area, and multiple independent monitoring points are then set at vertical intervals within the monitoring wells. Each monitoring point corresponds to a different water depth to cover the vertical distribution of the shallow aquifer. Next, at preset fixed time intervals, water quality parameters at each monitoring point are collected synchronously using in-situ sensors deployed at each monitoring point. These water quality parameters include pH value, conductivity, dissolved oxygen, oxidation-reduction potential, temperature, nitrate concentration, ammonia nitrogen concentration, and heavy metal ion concentration. After each data collection, the obtained water quality parameters are matched with the corresponding water depth identifier of the monitoring point and the data collection timestamp, thereby forming a water quality parameter sequence that changes continuously over time for each monitoring point. The water quality parameter sequence includes the time series of pH value, conductivity, dissolved oxygen, oxidation-reduction potential, temperature, nitrate concentration, ammonia nitrogen concentration and heavy metal ion concentration.

[0008] Furthermore, for the water quality parameter sequence at each monitoring point, the following specific process is performed: First, extract eight water quality parameters from the water quality parameter sequence, namely pH value, conductivity, dissolved oxygen, oxidation-reduction potential, temperature, nitrate concentration, ammonia nitrogen concentration, and heavy metal ion concentration, collected at each sampling point during the current monitoring period. Calculate the coefficient of variation of each water quality parameter over time. Specifically, calculate the ratio of the standard deviation to the arithmetic mean of the same water quality parameter across all sampling points. The standard deviation characterizes the fluctuation range of the parameter over time, while the arithmetic mean is used to dimensionlessly process the fluctuation range, thereby eliminating the incomparability caused by differences in dimensions and orders of magnitude among different water quality parameters. Calculate the arithmetic mean of the coefficients of variation of the eight water quality parameters, which is then used as the degree of dispersion of the monitoring point. For each monitoring point, the deviation of its eight water quality parameters relative to adjacent monitoring points is calculated. Specifically, for each water quality parameter, at each sampling point, the value of that water quality parameter at that monitoring point is compared with the arithmetic mean of the same water quality parameter at all its adjacent monitoring points, and the absolute value of the relative deviation of the former relative to the latter is calculated. Then, the arithmetic mean of the absolute values ​​of the relative deviations of that water quality parameter at all sampling points is taken to obtain the interlayer deviation of that water quality parameter. Among these, for any monitoring point, the monitoring points are sorted from smallest to largest according to their corresponding water depth, and the monitoring point closest to that monitoring point in the depth direction is determined as the adjacent monitoring point. When the depth distance from the monitoring point above and the monitoring point below to the monitoring point is equal, both are considered as adjacent monitoring points of the monitoring point. The arithmetic mean of the interlayer deviation of the eight water quality parameters is taken as the degree of deviation of the monitoring point. For each monitoring point, a stability coefficient is determined based on its dispersion and deviation. Specifically, the stability contribution value in the time dimension is obtained by summing the dispersion with a unit constant and taking the reciprocal; the stability contribution value in the spatial dimension is obtained by summing the deviation with a unit constant and taking the reciprocal; then, preset weights are assigned to the stability contribution values ​​in the time and spatial dimensions respectively, and the two are summed with weights. The result is the stability coefficient of that monitoring point. The magnitude of the stability coefficient is negatively correlated with the dispersion and deviation, that is, the greater the dispersion or deviation, the smaller the stability coefficient, indicating that the overall stability of the monitoring point is worse.

[0009] Furthermore, the calibration conditions include instability calibration conditions and over-calibration calibration conditions; Stability coefficient Compared with the preset stability threshold If a comparison is made, If so, the monitoring point is determined to meet the instability calibration conditions; The eight water quality parameters at the monitoring point are judged one by one. The number of water quality parameters whose values ​​exceed the upper limit of their corresponding preset warning values ​​during the current monitoring period is counted. If the number is greater than or equal to one, the monitoring point is determined to meet the conditions for exceeding the standard. If a monitoring point meets any of the above conditions, its immersion depth is calibrated as the target sampling depth.

[0010] Furthermore, for each target sampling depth, after obtaining the laboratory analysis data of its groundwater sample, the water quality status index of that target sampling depth is calculated by combining the laboratory analysis data and the water quality parameter sequence. The specific method used to calculate the water quality status index is as follows: First, for each type of pollutant detected at the depth, the ratio of its detected concentration to the corresponding water quality standard limit is calculated, and then multiplied by the toxicity weight of the pollutant. The sum of the above calculation results for each type of pollutant is divided by the total number of detected pollutant types to obtain the comprehensive chemical pollution score. At the same time, the absolute value of the relative deviation between the average temperature at this depth during the current monitoring period and the average temperature of the reference layer during the same period is calculated as the temperature anomaly degree, and then multiplied by the preset temperature adjustment coefficient to obtain the comprehensive temperature anomaly score. The comprehensive chemical pollution score and the comprehensive temperature anomaly score are added together to obtain the water quality status index for the target sampling depth. The reference layer is determined based on long-term monitoring data from the monitoring well, and is selected as the depth layer where all water quality parameters have not exceeded the natural background value range and no target pollutants have been detected during the preset historical period.

[0011] Furthermore, the water quality risk level threshold includes a low-risk threshold. Medium risk threshold and high risk threshold And satisfy ; The water quality status index at each target sampling depth is compared with the water quality risk level threshold, and the water quality risk level of the groundwater at that target sampling depth is classified according to the following rules: like If so, the sampling depth of the target is determined to be of low risk level; like If so, the sampling depth of the target is determined to be of medium risk level; like If so, the sampling depth of the target is determined to be of a high-risk level; like If the sampling depth of the target is determined to be at a severe risk level, then the target sampling depth is determined to be at a severe risk level.

[0012] Furthermore, for each target sampling depth, the water quality parameter sequence corresponding to the three most recent samples within the current monitoring period is extracted. Based on the water quality parameter sequence, a comprehensive water quality index value is calculated. A comprehensive water quality index value sequence is constructed based on the three corresponding comprehensive water quality index values, denoted as […]. ,in, , , They represent the first The comprehensive water quality index values ​​at each sampling point, the first The comprehensive water quality index values ​​at each sampling point and the first Comprehensive water quality index values ​​at each sampling point; For each target sampling depth, the comprehensive water quality index value is calculated based on its water quality parameter sequence. Specifically, for each of the eight water quality parameters, namely pH, conductivity, dissolved oxygen, oxidation-reduction potential, temperature, nitrate concentration, ammonia nitrogen concentration, and heavy metal ion concentration, the deviation between the measured value and the corresponding water quality standard value is first calculated, and the deviation is squared to amplify the abnormal signal. Then, it is divided by the water quality standard value for dimensionless processing. The result is summed with the unit constant and the natural logarithm is taken. Finally, it is multiplied by the preset weight corresponding to the water quality parameter. The results of the eight water quality parameters after the above processing are summed, and the sum is the comprehensive water quality index value for the target sampling depth. A sequence is constructed based on the comprehensive water quality index values ​​calculated from the three most recent samples within the current monitoring period, and the trend slope of the sequence is calculated. ; like ,and If so, it is determined that the water quality risk at the target sampling depth is showing a continuously increasing trend; The highest water quality risk level at the current sampling time for all target sampling depths within the statistical target monitoring area is recorded as the current highest risk level of the area; the proportion of water quality risk showing a continuous upward trend among all target sampling depths within the statistical target monitoring area is recorded as the area risk increase proportion.

[0013] Furthermore, based on the following combinations of conditions, different levels of early warning information are generated and output: If the highest risk level in the target monitoring area is "severe risk level", a red alert will be generated, indicating that there is a severely polluted layer in the target monitoring area; If the highest risk level in the target monitoring area is "high risk level" and the risk increase rate in the area exceeds the preset threshold, an orange alert will be generated, indicating that the pollution risk in the target monitoring area is spreading rapidly. If the highest risk level in the target monitoring area is "medium risk" or below, and no red or orange alert has been generated, then no alert will be triggered.

[0014] This invention also provides a device for in-situ sampling and dynamic monitoring of shallow damaged groundwater stratification. This device is used to perform the aforementioned method for in-situ sampling and dynamic monitoring of shallow damaged groundwater stratification, including: The well-layout module is used to lay out monitoring wells within the target monitoring area, and to set up multiple monitoring points with different water depths at intervals along the vertical direction within the monitoring wells; The sequence building module is used to collect water quality parameters in situ at each monitoring point during the monitoring period and to match the collected data with the water depth of the corresponding monitoring point to form a sequence of water quality parameters of each monitoring point over time. The analysis and calibration module is used to determine the adjacent monitoring points based on the location relationship of any monitoring point. It determines the dispersion and deviation of the water quality parameters at each monitoring point based on the water quality parameter sequence of each monitoring point and its adjacent monitoring points. Based on the dispersion and deviation, it evaluates the fluctuation stability of each monitoring point during the sampling process, selects the monitoring points whose fluctuation stability meets the calibration conditions, and determines the water immersion depth of the monitoring points that meet the calibration conditions as the target sampling depth. The sampling and quality assessment module is used to collect groundwater samples at the target sampling depth and detect the types and concentrations of pollutants to obtain laboratory analysis data. The water quality parameter sequence and laboratory analysis data are input into a preset water quality status assessment function to obtain the water quality status index for each target sampling depth. The laboratory analysis data includes the detected concentration of pollutants, the number of pollutant types, and the corresponding toxicity weights. The early warning analysis module is used to compare the water quality status index corresponding to each target sampling depth with the preset water quality risk level threshold, classify the water quality risk level of groundwater at each target sampling depth, and count the highest risk level and the regional risk increase ratio within the target monitoring area to determine the early warning information for the target monitoring area.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This invention analyzes the time dispersion and spatial deviation of water quality parameters at each monitoring point in real time, dynamically calculates the stability coefficient of each monitoring point, and intelligently calibrates the target sampling depth that needs to be focused on and sampled by combining the stability coefficient with the real-time exceedance of various water quality parameters. This process overcomes the drawbacks of traditional methods that rely on fixed depth or experience sampling, and realizes the precise deployment of monitoring resources to abnormal layers, significantly improving the targeting and efficiency of sampling. Secondly, this invention creatively integrates the water quality parameter sequence obtained from continuous in-situ monitoring with laboratory analysis data to establish a water quality status index calculation formula. This index not only quantifies the degree of pollutant exceedance and toxicity impact, but also reflects the thermal anomaly signal that may be caused by pollution activities by introducing temperature anomaly, thereby achieving a more comprehensive and refined assessment of groundwater quality risk. Furthermore, this invention establishes a hierarchical dynamic early warning system. On the one hand, by continuously tracking the continuous change trend of comprehensive water quality index values ​​calculated entirely based on in-situ parameters, it can identify the continuous upward trend of water quality risk in the early stage and achieve trend early warning. On the other hand, the system generates early warning information of different levels based on the overall risk status of the region, providing intelligent decision support for early intervention and precise control of pollution. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the overall method flow of the present invention; Figure 2 A dual Y-axis image representing the degree of dispersion, deviation, and stability coefficient; Figure 3 A parallel coordinate graph of dispersion, deviation, and stability coefficient; Figure 4 This is a schematic diagram of the overall device module of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0018] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0019] Example: Please see Figures 1-3 The present invention provides a technical solution: A method for in-situ sampling and dynamic monitoring of shallow damaged groundwater stratification, including the following steps: Step 1: Install monitoring wells within the target monitoring area, and set up multiple monitoring points with different water depths at vertical intervals within the monitoring wells; In this embodiment, after drilling and completing the monitoring well in the target monitoring area, multiple independent monitoring points are set at different depths along the vertical direction of the monitoring well. The water depth interval between adjacent monitoring points is set as follows: Specifically, a combined layered monitoring and sampling device is installed inside the monitoring well. This device consists of multiple monitoring units connected in series along the axial direction. Each monitoring unit corresponds to an independent monitoring point. The units are isolated from each other by water-stopping expansion bladders or sealing rings to prevent vertical cross-flow of groundwater at different depths. Each monitoring unit is equipped with an independent water inlet channel and an in-situ sensor interface. The sensor is connected to the ground data acquisition terminal via a cable.

[0020] Step 2: During the monitoring period, water quality parameters are collected in situ at each monitoring point during continuous sampling, and the collected data are matched with the water depth of the corresponding monitoring point to form a sequence of water quality parameters changing over time at each monitoring point. In this embodiment, the current monitoring period is set to 20 days, and the sampling time interval is set to 24 hours. Water quality parameters at each monitoring point are collected synchronously using in-situ sensors deployed at each monitoring point. The water quality parameters include pH value, conductivity, dissolved oxygen, oxidation-reduction potential, temperature, nitrate concentration, ammonia nitrogen concentration, and heavy metal ion concentration. This setup ensures that the water samples and water quality parameters collected from each monitoring point truly represent the groundwater conditions at that depth through physical isolation, avoiding the vertical information confusion caused by traditional mixed sampling. At the same time, multiple independent points cover the complete vertical profile of the shallow aquifer, which can accurately depict the vertical distribution characteristics and migration patterns of pollutants, providing high-resolution basic data support for subsequent dynamic selection of target sampling depths and assessment of stratified water quality risks.

[0021] After each data collection, the obtained water quality parameters are matched with the corresponding water depth identifier of the monitoring point and the data collection timestamp, thereby forming a water quality parameter sequence that changes continuously over time for each monitoring point. The water quality parameter sequence includes the time series of pH value, conductivity, dissolved oxygen, oxidation-reduction potential, temperature, nitrate concentration, ammonia nitrogen concentration and heavy metal ion concentration.

[0022] In steps 1 and 2, pH, conductivity, dissolved oxygen, redox potential, temperature, nitrate concentration, ammonia nitrogen concentration, and heavy metal ion concentration were selected as monitoring parameters. This is because pH and conductivity reflect the basic chemical properties and total dissolved solids changes of groundwater, serving as fundamental indicators for judging the acid-base balance and mineralization degree of water bodies; dissolved oxygen and redox potential reveal the redox environment of groundwater, indicating potential conditions for organic matter degradation or pollutant migration and transformation; temperature, as a natural tracer, can help identify groundwater recharge sources and possible pollution thermal anomalies; nitrate and ammonia nitrogen are typical anthropogenic pollution indicators, directly reflecting the impact of agricultural activities or domestic sewage on groundwater; and heavy metal ion concentration is a key factor in assessing industrial pollution and potential toxicity. By simultaneously monitoring these eight parameters, the characteristics of groundwater quality can be comprehensively characterized from multiple dimensions, including physicochemical properties, redox state, nutrient pollution, and toxicity risk. This provides a data foundation for subsequent calculations of dispersion, deviation, and stability coefficients, thereby enabling accurate identification and dynamic tracking of anomalous strata.

[0023] Step 3: For any monitoring point, determine its adjacent monitoring points based on the location relationship. Based on the water quality parameter sequence of each monitoring point and its adjacent monitoring points, determine the dispersion and deviation of the water quality parameters of each monitoring point. Based on the dispersion and deviation, evaluate the fluctuation stability of each monitoring point during the sampling process, screen out the monitoring points whose fluctuation stability meets the calibration conditions, and determine the water immersion depth of the monitoring points that meet the calibration conditions as the target sampling depth. In this embodiment, the following specific process is performed for the water quality parameter sequence at each monitoring point: First, eight water quality parameters are extracted from the water quality parameter sequence, namely pH value, conductivity, dissolved oxygen, oxidation-reduction potential, temperature, nitrate concentration, ammonia nitrogen concentration, and heavy metal ion concentration, collected at each sampling point during the current monitoring period. The coefficient of variation of each water quality parameter over time is calculated using the following formula: In the formula, Indicates the first The coefficient of variation of the water quality parameters An index for water quality parameters; This refers to the number of sampling points, i.e., the number of sampling times. Indicates the first The first sample collected at the sampling point Water quality parameter values; The index of the sampling point; Indicates the first The average value of each water quality parameter at all sampling points; For this formula, the coefficient of variation of the dependent variable Used to characterize the The relative fluctuation of a water quality parameter over time, the magnitude of which reflects the stability of the parameter at different sampling times at the same monitoring point: The smaller the value, the more stable the water quality parameter changes over time, and the more stable the hydrodynamic conditions and hydrochemical environment are. The larger the value, the more drastic the fluctuation of the parameter, which may be affected by intermittent pollution input, changes in hydrogeological conditions, or disturbances in local hydrochemical reactions. It is an important basis for identifying anomalous strata. Indicates the first The square of the deviation of the water quality parameter value at each sampling point from its average value over a given period is calculated. A larger squared value indicates a more significant difference between the monitored value at that moment and the average level. This deviation may originate from sudden pollution events, changes in hydrological conditions, or local chemical reactions. Summing these squared deviations amplifies the contribution of outliers. It is more sensitive to drastic fluctuations, thus capturing abrupt changes in water quality parameters more accurately; This formula uses the ratio of standard deviation to mean, where standard deviation is the standard deviation. It quantifies the absolute fluctuation of the parameter around the average, while dividing by the average. This achieves dimensionless processing, eliminating the comparability barriers caused by differences in dimensions and orders of magnitude among different water quality parameters. At the same time, the coefficient of variation can reflect both the absolute amplitude and relative level of fluctuations. When the average value is low, even small absolute fluctuations will produce a large coefficient of variation. This is consistent with the actual law that low concentration parameters are more sensitive to pollution input in groundwater monitoring. Therefore, this formula can scientifically and reasonably characterize the stability characteristics of each water quality parameter in the time dimension.

[0024] The arithmetic mean of the coefficients of variation of the eight water quality parameters As a measure of the dispersion of the monitoring point; For each monitoring point, the deviation of its eight water quality parameters relative to adjacent monitoring points is calculated. Specifically, for each water quality parameter, at each sampling point, the difference between the value of the water quality parameter at that monitoring point and the arithmetic mean of the same water quality parameter at all adjacent monitoring points is divided by the difference, and the absolute value of the quotient is taken as the absolute value of the relative deviation. Then, the arithmetic mean of the absolute values ​​of the relative deviations of the water quality parameter at all sampling points is taken to obtain the interlayer deviation of the water quality parameter. Among them, for any monitoring point, the monitoring points are sorted from smallest to largest according to the water depth corresponding to each monitoring point, and the monitoring point closest to the monitoring point in the depth direction is determined as the adjacent monitoring point. When the depth distance from the monitoring point above and the monitoring point below to the monitoring point is equal, both are considered as adjacent monitoring points of the monitoring point. The arithmetic mean of the interlayer deviation of the eight water quality parameters is taken as the degree of deviation of the monitoring point. The degree of deviation is used to characterize the difference in water quality between a monitoring point and its adjacent layers in the spatial dimension. Its value reflects the significance of the anomaly of the point in the vertical profile: the smaller the degree of deviation, the closer the water quality parameters of the point are to its adjacent layers, the better the vertical distribution continuity, and the relatively homogeneous hydrochemical environment; the larger the degree of deviation, the more obvious the difference in water quality between the point and its upper and lower layers, which may indicate local pollution input, mixing of groundwater from different sources, or vertical hydrochemical stratification. It is a key indicator for identifying spatially anomalous layers and locating the core area of ​​pollution plumes.

[0025] The stability coefficient of the monitoring point is determined based on the aforementioned dispersion and deviation, using the following formula: In the formula, Indicates the first Stability coefficient of each monitoring point; For the first The degree of dispersion of each monitoring point; For the first The degree of deviation of each monitoring point; An index for the monitoring points; , These are the preset weights for the corresponding indicators. , ;set up The reason for this is that fluctuations in the time dimension often reflect the dynamic response characteristics and pollution development trends of groundwater systems better than static differences in space. The degree of dispersion characterizes the magnitude of water quality changes at different sampling times at the same monitoring point. Its increase usually means that the layer is affected by dynamic processes such as intermittent pollution input, changes in hydrodynamic conditions, or disturbances in hydrochemical reactions. These processes often indicate real-time changes in pollution risk and potential deterioration trends, and are key signals for early warning. On the other hand, the degree of deviation reflects the spatial difference between the point and adjacent layers. Although it can identify vertical anomalous distributions, it focuses more on the static characterization of pollution plumes. Therefore, a higher weight is given to the degree of dispersion, which makes the stability coefficient more sensitive to dynamic evolution signals and helps to prioritize the capture of anomalous layers that are changing, so as to achieve timely response to pollution risks.

[0026] For this formula, the stability coefficient Used to characterize the overall stability of a monitoring point in both time and space dimensions, its value reflects the degree of fluctuation and the significance of anomalies in the water quality parameters at that point: The larger the value, the more stable the location is in terms of time fluctuations and spatial differences with adjacent layers, indicating that the hydrodynamic conditions and hydrochemical environment are relatively stable and there is no obvious pollution disturbance. The smaller the value, the more drastic the temporal fluctuations and the more significant the spatial differences with the surrounding strata. It may be affected by intermittent pollution input, changes in hydrogeological conditions, or disturbances in local hydrochemical reactions, and is an important basis for identifying potential pollution strata. Dispersion and degree of deviation The stability coefficient is affected by both time and space dimensions. In real physical processes, An increase usually means that the location is affected by dynamic processes such as sudden pollution events, seasonal hydrological changes, or fluctuations in hydrochemical reactions, resulting in drastic changes in water quality parameters over time; An increase in the value indicates a significant difference in water quality between the point and its adjacent layers, potentially indicating localized pollution input, mixing of groundwater from different sources, or vertical water chemical stratification. The two are related by the reciprocal form in the formula. Establish a negative correlation: or The larger, the corresponding or The smaller the term, the lower the stability coefficient; The formula uses a weighted summation to separate the degree of dispersion and the degree of deviation. and Convert to a dimensionless stability contribution value, and then use weighted coefficients. , The comprehensive approach has clear physical and mathematical significance: First, the addition of one term avoids the case where the denominator is zero, ensuring that the formula can still be calculated stably even when the degree of dispersion or deviation is extremely small; second, the use of the reciprocal form to construct a negative correlation between the index value and the stability coefficient is consistent with the intuitive physical understanding that the more drastic the time fluctuations and the more significant the spatial differences, the worse the stability of the monitoring point; finally, by setting the weight coefficients, the degree of influence of time and space dimensions on stability can be adjusted according to actual needs, giving the formula good flexibility and adaptability, and enabling it to scientifically and reasonably quantify the comprehensive stability characteristics of the monitoring points.

[0027] Table 1: Stability Coefficient Statistics As shown in Table 1, based on the 15 sets of data, the stability coefficient of the monitoring points shows a continuous downward trend as the dispersion in the time dimension and the deviation in the spatial dimension gradually increase. When both the dispersion and deviation are at a low level (as in the first set of data), the stability coefficient is close to the maximum value, indicating that the water quality parameters of the monitoring point are highly consistent in both time and space, the hydrodynamic conditions are stable, and there is no obvious pollution disturbance. As both increase simultaneously, the stability coefficient gradually decreases, which means that the water quality fluctuations at the point are intensified and the differences with the surrounding strata are widened, reflecting an increased possibility of local pollution or hydrogeochemical anomalies. Figure 2 The image shows a dual Y-axis plot of dispersion, deviation, and stability coefficient based on the data in Table 1. As can be seen from the figure, the stability coefficient decreases as the dispersion increases, indicating that the more drastic the fluctuation of the monitoring point in the time dimension, the worse its stability, and the more likely it is to meet the instability calibration conditions and be selected as the target sampling depth. Figure 3 The graph shows the parallel coordinates of dispersion, deviation, and stability coefficient based on the data in Table 1. As can be seen from the graph, the greater the deviation, the lower the stability coefficient. This indicates that the more significant the difference in water quality between the monitoring point and its neighboring points, the higher the degree of spatial anomaly and the lower the stability. Therefore, the stability coefficient can effectively quantify the comprehensive stability of the monitoring point in both time and space, providing a scientific basis for the subsequent dynamic selection of target sampling depth and the accurate allocation of monitoring resources.

[0028] The calibration conditions include instability calibration conditions and over-calibration calibration conditions; Stability coefficient Compared with the preset stability threshold If a comparison is made, If so, the monitoring point is determined to meet the instability calibration conditions; The eight water quality parameters at the monitoring point are judged one by one. The number of water quality parameters whose values ​​exceed the upper limit of their corresponding preset warning values ​​during the current monitoring period is counted. If the number is greater than or equal to one, the monitoring point is determined to meet the conditions for exceeding the standard. If a monitoring point meets any of the above conditions, its immersion depth is calibrated as the target sampling depth. The preset stability threshold This threshold is used to determine whether a monitoring point meets the instability calibration conditions; its range is typically set to [value range missing]. The specific value depends on the hydrogeological background of the target monitoring area, the degree of natural fluctuation in water quality, and the stability requirements of the monitoring data; determine The methodology includes: First, based on continuous monitoring data (no less than 7 sampling periods) from the initial deployment of monitoring wells, the dispersion and deviation of each monitoring point are calculated to obtain their respective stability coefficients. Then, the stability coefficients of each monitoring point are statistically analyzed, and the median or lower quartile is used as the basic reference value. For areas with good water quality background conditions and small natural parameter fluctuations, the threshold can be appropriately increased (e.g., 0.60-0.65) to enhance sensitivity to early unstable signals. For areas with large natural water quality fluctuations or significant historical disturbances, a lower threshold (e.g., 0.35-0.45) is used to avoid over-screening. Alternatively, the depth of historical pollution events in the region can be considered, and a layer-by-layer empirical calibration method can be used, with the maximum stability coefficient corresponding to the known pollution layer as the lower limit of the threshold. If long-term monitoring data is lacking, a default threshold of 0.50 is recommended, as this value is in the middle of the recommended range and balances the sensitivity and specificity of anomaly identification.

[0029] The logic behind setting instability calibration conditions and over-calibration conditions is as follows: Instability calibration conditions are determined by the stability coefficient. Quantify the overall stability of monitoring points in both time and space dimensions. This means that the water quality parameters at this location fluctuate drastically over time or differ significantly from those of adjacent layers, and may be in a pollution precursor, mixing transition zone, or subject to dynamic disturbance. It is an important indicator for identifying potential pollution spread risks. The exceedance of calibration conditions directly reflects the objective fact that the pollutant concentration at this location exceeds the standard, and is direct evidence to confirm that pollution has occurred. Combining the two can both capture pollution events that have already occurred and provide early warning of potential pollution risks. The purpose of this setup is to filter out two types of target sampling depths: one is contaminated layers exceeding the standard, meaning at least one water quality parameter exceeds the preset warning value, requiring immediate sampling to confirm the contamination status; the other is potentially risky layers, meaning those that, while not exceeding the standard, exhibit poor water quality stability, significant dynamic changes, or prominent spatial anomalies, potentially located at the edge of a pollution plume, a migration channel, or in the early stages of pollution, requiring intensive monitoring to track their evolution. This dual screening ensures that limited sampling resources are precisely allocated to the depths of greatest concern. Calibrating the target sampling depth allows monitoring resources to be precisely focused on layers with water quality anomalies, avoiding indiscriminate sampling at all depths, significantly improving sampling efficiency and the targeted nature of pollution risk identification.

[0030] Step 4: Collect groundwater samples at the target sampling depth and detect the types and concentrations of pollutants to obtain laboratory analysis data. Input the water quality parameter sequence and laboratory analysis data into a preset water quality status assessment function to obtain the water quality status index for each target sampling depth. The laboratory analysis data includes the detected concentration of pollutants, the number of pollutant types, and the corresponding toxicity weights. In this embodiment, after obtaining the laboratory analysis data of groundwater samples at each target sampling depth, the water quality status index at that target sampling depth is calculated by combining the laboratory analysis data and the water quality parameter sequence. The formula used to calculate the water quality status index is as follows: In the formula, For the first Water quality status index at each target sampling depth The index for the target sampling depth; Indicates the first The set of pollutant species detected in groundwater samples at a target sampling depth. express The number of elements in the first element, i.e., the number of elements in the second element. The number of pollutant species detected in groundwater samples at a target sampling depth, i.e., the number of pollutant species with a detection concentration greater than 0. An index for the types of pollutants; For the first At the target sampling depth, the first The detection concentration of the pollutants, For the first Water quality standard limits for various pollutants; For the first The toxicity weight of each pollutant is based on its toxicological data (such as median lethal dose, carcinogenicity classification, etc.) and pre-defined environmental hazards, with a value range of [value missing]. The higher the value, the stronger the toxicity or harm. Temperature anomaly; Indicates the first The average temperature of each target sampling depth during the current monitoring period; The reference layer is determined by the following method: based on the long-term monitoring data of the monitoring well, a set of background value ranges and target pollutants representing the natural background water quality are set, and a certain depth layer in the monitoring well in which all water quality parameters have not exceeded the background value range and no target pollutants have been detected in the preset historical period is selected as the reference layer. This is the temperature regulation coefficient. It is calibrated using historical monitoring data.

[0031] For this formula, Used to characterize the overall risk level of groundwater quality at a target sampling depth, its value reflects the severity of the contamination impact at that depth: The smaller the value, the better the groundwater quality at that stratum, with low pollutant concentrations, weak toxicity, and temperature conditions close to the natural background level. The higher the value, the more significant the pollution problem in that layer, with high levels of pollutants exceeding standards, many types, and strong toxicity, or accompanied by abnormal temperature signals. It is a core quantitative indicator for assessing pollution risk and classifying risk levels. and It affects the environment from two dimensions: the intensity of chemical pollution and physical anomaly signals. , An increase directly means that the content of this pollutant in the water body is higher, and the greater the degree to which it exceeds the water quality standard limit, the more serious the potential harm to the ecological environment and human health. An increase in temperature reflects a significant deviation of the water temperature at that stratum from the natural background value of the reference stratum. This thermal anomaly may originate from processes such as industrial wastewater discharge, changes in groundwater recharge conditions, or intensified microbial metabolic activity, and is often a concomitant signal of pollution activity; the two are expressed through a weighted summation in the formula. Establish a positive correlation: or The larger, The higher; This formula uses a weighted summation method to combine chemical pollution indicators and physical anomaly indicators. The first term in the formula... The detected pollutants were averaged, taking into account both the types and quantities of pollutants. The impact of [the pollutant] was further differentiated by toxicity weights to determine the degree of harm of different pollutants, while also using [other methods]... This format achieves dimensionless representation of the degree of exceedance, making different pollutants comparable; secondly, the second item... Temperature anomalies are introduced as a supplementary indicator, which can capture early signals of pollution activity even when chemical indicators have not changed significantly. Finally, by setting a temperature adjustment coefficient, the relative importance of chemical and physical indicators in the comprehensive assessment can be balanced, making the formula adaptable and flexible, and enabling it to scientifically and comprehensively quantify the comprehensive risk of groundwater quality at the target sampling depth.

[0032] Step 5: Compare the water quality status index corresponding to each target sampling depth with the preset water quality risk level threshold, classify the water quality risk level of groundwater at each target sampling depth, and count the highest risk level and the regional risk increase ratio within the target monitoring area to determine the early warning information for the target monitoring area. In this embodiment, the water quality risk level threshold includes a low-risk threshold. Medium risk threshold and high risk threshold And satisfy Among them, the low-risk threshold Medium risk threshold and high risk threshold The determination was made based on a combination of historical monitoring data of the region and expert experience.

[0033] The water quality status index at each target sampling depth is compared with the water quality risk level threshold, and the water quality risk level of the groundwater at that target sampling depth is classified according to the following rules: like If so, the sampling depth of the target is determined to be of low risk level; like If so, the sampling depth of the target is determined to be of medium risk level; like If so, the sampling depth of the target is determined to be of a high-risk level; like If the sampling depth of the target is determined to be at a severe risk level, then the target sampling depth is determined to be at a severe risk level.

[0034] For each target sampling depth, the water quality parameter sequence corresponding to the three most recent samples within the current monitoring period is extracted. Based on the water quality parameter sequence, a comprehensive water quality index value is calculated. A comprehensive water quality index value sequence is constructed based on the three corresponding comprehensive water quality index values, denoted as […]. ,in, , , They represent the first The comprehensive water quality index values ​​at each sampling point, the first The comprehensive water quality index values ​​at each sampling point and the first Comprehensive water quality index values ​​at each sampling point; The formula for calculating the comprehensive water quality index value is as follows: In the formula, Indicates the first Comprehensive water quality index values ​​at each target sampling depth; An index for water quality parameters; For the first The preset weights of the water quality parameters meet the requirements. ; Indicates the first Water quality parameter values; Indicates the first The water quality standard values ​​corresponding to each water quality parameter; Among them, the preset weights of each water quality parameter The weights are positive numbers between 0 and 1, with the sum of the eight weights equal to 1. The specific weights are determined comprehensively based on the importance, toxicity, and environmental sensitivity of each water quality parameter to groundwater quality assessment, as well as the hydrogeological characteristics and pollution source types of the target monitoring area. Generally, heavy metal ions are given higher weights due to their high toxicity, strong environmental persistence, and significant harm to human health; nitrate and ammonia nitrogen concentrations, as typical anthropogenic pollution indicators, directly reflect the impact of agricultural activities or domestic sewage on groundwater, and thus have lower weights; pH, conductivity, dissolved oxygen, redox potential, and temperature, as basic physicochemical indicators, mainly reflect the basic chemical properties and redox environment of groundwater, and thus have relatively lower weights. The weight coefficients can be calibrated using expert scoring, analytic hierarchy process (AHP), entropy weighting, or principal component analysis based on historical monitoring data, and can be dynamically adjusted based on feedback from monitoring results in practical applications.

[0035] For this formula, Used to characterize the overall deviation of groundwater quality at the target sampling depth from the standard value, its magnitude reflects the overall anomaly of the water quality at that stratum: The smaller the value, the closer each water quality parameter is to its corresponding water quality standard value, indicating that the groundwater quality is good. The larger the value, the more significantly multiple water quality parameters in that layer deviate from the standard values, which may indicate pollution input or abnormal water chemical environment. Indicates the first The square of the difference between the measured value of a water quality parameter and its standard value reflects the degree to which the parameter deviates from the standard value. The formula is... The form achieves dimensionless representation of the degree of deviation, eliminating the comparability barrier caused by the difference in dimensions and order of magnitude of standard values ​​between different water quality parameters; secondly, the introduction of a logarithmic function It can smoothly handle small deviations and avoid noise interference, while maintaining the monotonicity of growth when there are large deviations, which conforms to the actual law of the nonlinear relationship between the degree of deviation of water quality parameters and risk; finally, through weighting coefficients The settings can differentiate the importance of each parameter in water quality assessment, making the formula highly adaptable.

[0036] Calculate the trend slope of the water quality comprehensive index value series. ; like ,and If so, it is determined that the water quality risk at the target sampling depth is showing a continuously increasing trend; The highest water quality risk level at the current sampling time for all target sampling depths within the target monitoring area is recorded as the current highest risk level of the area. The proportion of water quality risk showing a continuous upward trend among all target sampling depths within the target monitoring area is recorded as the regional risk increase ratio. The regional risk increase ratio refers to the percentage of target sampling depths within the target monitoring area that are determined to have a continuously upward trend in water quality risk at the current sampling time. This ratio is used to quantify the overall diffusion trend of pollution risk within the target monitoring area and is one of the key indicators for triggering an orange alert.

[0037] By comprehensively utilizing the identification of continuous upward trends and regional risk statistics, the characteristics of groundwater pollution risk can be fully depicted from three levels: dynamic changes at single points, qualitative change signals, and the overall regional situation. The identification of continuous upward trends can effectively filter out random fluctuations and capture the gradual deterioration process of risk. The statistics on the proportion of regional risk increase and the highest risk level provide a quantitative basis for assessing the scope of pollution spread and the overall risk level, laying a scientific foundation for graded early warning and precise control.

[0038] Based on the following combinations of conditions, generate and output different levels of early warning information: If the highest risk level in the target monitoring area is "severe risk level", a red alert will be generated, indicating that there is a severely polluted layer in the target monitoring area; If the highest risk level in the target monitoring area is "high risk level" and the risk increase rate in the area exceeds the preset threshold, an orange alert will be generated, indicating that the pollution risk in the target monitoring area is spreading rapidly. If the highest risk level in the target monitoring area is "medium risk" or below, and no red or orange alert has been generated, then no alert will be triggered.

[0039] Step 5 firstly, based on the comparison between the water quality status index and preset thresholds, the sampling depth of each target is divided into four risk levels: low, medium, high, and severe, providing a clear grading basis for subsequent early warnings. Secondly, by continuously tracking the continuous change trend of the comprehensive water quality index value calculated entirely based on in-situ parameters, and combining the trend slope with strict incremental conditions, random fluctuation interference can be effectively filtered out, and the gradual deterioration process of water quality risk can be accurately identified. On this basis, the highest risk level and the risk increase ratio of the region are statistically analyzed, comprehensively depicting the pollution risk characteristics from three levels: single-point dynamics, qualitative change signals, and regional situation. Finally, different levels of early warning information are automatically generated according to preset condition combinations, providing intelligent decision support for early intervention and precise control of pollution.

[0040] Please see Figure 4 A shallow damaged groundwater stratification in-situ sampling and dynamic monitoring device, including: The well-layout module is used to lay out monitoring wells within the target monitoring area, and to set up multiple monitoring points with different water depths at intervals along the vertical direction within the monitoring wells; The sequence building module is used to collect water quality parameters in situ at each monitoring point during the monitoring period and to match the collected data with the water depth of the corresponding monitoring point to form a sequence of water quality parameters of each monitoring point over time. The analysis and calibration module is used to determine the adjacent monitoring points based on the location relationship of any monitoring point. It determines the dispersion and deviation of the water quality parameters at each monitoring point based on the water quality parameter sequence of each monitoring point and its adjacent monitoring points. Based on the dispersion and deviation, it evaluates the fluctuation stability of each monitoring point during the sampling process, selects the monitoring points whose fluctuation stability meets the calibration conditions, and determines the water immersion depth of the monitoring points that meet the calibration conditions as the target sampling depth. The sampling and quality assessment module is used to collect groundwater samples at the target sampling depth and detect the types and concentrations of pollutants to obtain laboratory analysis data. The water quality parameter sequence and laboratory analysis data are input into a preset water quality status assessment function to obtain the water quality status index for each target sampling depth. The laboratory analysis data includes the detected concentration of pollutants, the number of pollutant types, and the corresponding toxicity weights. The early warning analysis module is used to compare the water quality status index corresponding to each target sampling depth with the preset water quality risk level threshold, classify the water quality risk level of groundwater at each target sampling depth, and count the highest risk level and the regional risk increase ratio within the target monitoring area to determine the early warning information for the target monitoring area.

[0041] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0042] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0043] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0044] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for in-situ sampling and dynamic monitoring of shallow damaged groundwater stratification, characterized in that, Specifically, it includes: Step 1: Install monitoring wells within the target monitoring area, and set up multiple monitoring points with different water depths at vertical intervals within the monitoring wells; Step 2: During the monitoring period, water quality parameters are collected in situ at each monitoring point during continuous sampling, and the collected data are matched with the water depth of the corresponding monitoring point to form a sequence of water quality parameters changing over time at each monitoring point. Step 3: For any monitoring point, determine its adjacent monitoring points based on the location relationship. Based on the water quality parameter sequence of each monitoring point and its adjacent monitoring points, determine the dispersion and deviation of the water quality parameters of each monitoring point. Based on the dispersion and deviation, evaluate the fluctuation stability of each monitoring point during the sampling process, screen out the monitoring points whose fluctuation stability meets the calibration conditions, and determine the water immersion depth of the monitoring points that meet the calibration conditions as the target sampling depth. Step 4: Collect groundwater samples at the target sampling depth and detect the types and concentrations of pollutants to obtain laboratory analysis data. Input the water quality parameter sequence and laboratory analysis data into a preset water quality status assessment function to obtain the water quality status index for each target sampling depth. The laboratory analysis data includes the detected concentration of pollutants, the number of pollutant types, and the corresponding toxicity weights. Step 5: Compare the water quality status index corresponding to each target sampling depth with the preset water quality risk level threshold, classify the groundwater water quality risk level of each target sampling depth, and count the highest risk level and the regional risk increase ratio within the target monitoring area to determine the early warning information for the target monitoring area.

2. The method for in-situ sampling and dynamic monitoring of shallow damaged groundwater stratification according to claim 1, characterized in that: Monitoring wells are deployed in the target monitoring area, and then multiple independent monitoring points are set at vertical intervals within the monitoring wells. Each monitoring point corresponds to a different water depth to cover the vertical distribution of the shallow aquifer. Then, water quality parameters at each monitoring point are collected synchronously at preset fixed time intervals. The water quality parameters include pH value, conductivity, dissolved oxygen, oxidation-reduction potential, temperature, nitrate concentration, ammonia nitrogen concentration, and heavy metal ion concentration. After each data collection, the obtained water quality parameters are matched with the corresponding water depth identifier of the monitoring point and the data collection timestamp, thereby forming a water quality parameter sequence that changes continuously over time for each monitoring point. The water quality parameter sequence includes the time series of pH value, conductivity, dissolved oxygen, oxidation-reduction potential, temperature, nitrate concentration, ammonia nitrogen concentration and heavy metal ion concentration.

3. The method for in-situ sampling and dynamic monitoring of shallow damaged groundwater stratification according to claim 2, characterized in that: For each monitoring point's water quality parameter sequence, the following specific process is performed: First, extract eight water quality parameters from the water quality parameter sequence, collected at each sampling point during the current monitoring period: pH value, conductivity, dissolved oxygen, oxidation-reduction potential, temperature, nitrate concentration, ammonia nitrogen concentration, and heavy metal ion concentration. Calculate the coefficient of variation of each water quality parameter over time. Specifically, calculate the ratio of the standard deviation to the arithmetic mean of the same water quality parameter across all sampling points. The standard deviation characterizes the fluctuation range of the parameter over time, while the arithmetic mean is used to dimensionlessly process the fluctuation range, thereby eliminating the incomparability caused by differences in dimensions and orders of magnitude among different water quality parameters. Then, calculate the arithmetic mean of the coefficients of variation of the eight water quality parameters, which serves as the dispersion of the monitoring point. For each monitoring point, the deviation of its eight water quality parameters relative to adjacent monitoring points is calculated. Specifically, for each water quality parameter, at each sampling point, the value of that water quality parameter at that monitoring point is compared with the arithmetic mean of the same water quality parameter at all its adjacent monitoring points, and the absolute value of the relative deviation of the former relative to the latter is calculated. Then, the arithmetic mean of the absolute values ​​of the relative deviations of that water quality parameter at all sampling points is taken to obtain the interlayer deviation of that water quality parameter. Among these, for any monitoring point, the monitoring points are sorted from smallest to largest according to their corresponding water depth, and the monitoring point closest to that monitoring point in the depth direction is determined as the adjacent monitoring point. When the depth distance from the monitoring point above and the monitoring point below to the monitoring point is equal, both are considered as adjacent monitoring points of the monitoring point. The arithmetic mean of the interlayer deviations of the eight water quality parameters is taken as the degree of deviation at the monitoring point.

4. The method for in-situ sampling and dynamic monitoring of shallow damaged groundwater stratification according to claim 3, characterized in that: For each monitoring point, a stability coefficient is determined based on its dispersion and deviation. Specifically, the stability contribution value in the time dimension is obtained by summing the dispersion with a unit constant and taking the reciprocal; the stability contribution value in the spatial dimension is obtained by summing the deviation with a unit constant and taking the reciprocal; then, preset weights are assigned to the stability contribution values ​​in the time and spatial dimensions respectively, and the two are summed with weights. The result is the stability coefficient of that monitoring point. The magnitude of the stability coefficient is negatively correlated with the dispersion and deviation, that is, the greater the dispersion or deviation, the smaller the stability coefficient, indicating that the overall stability of the monitoring point is worse.

5. The method for in-situ sampling and dynamic monitoring of shallow damaged groundwater stratification according to claim 4, characterized in that: The calibration conditions include instability calibration conditions and over-calibration calibration conditions; Stability coefficient Compared with the preset stability threshold If a comparison is made, If so, the monitoring point is determined to meet the instability calibration conditions; The eight water quality parameters at the monitoring point are judged one by one. The number of water quality parameters whose values ​​exceed the upper limit of their corresponding preset warning values ​​during the current monitoring period is counted. If the number is greater than or equal to one, the monitoring point is determined to meet the conditions for exceeding the standard. If a monitoring point meets any of the above conditions, its immersion depth is calibrated as the target sampling depth.

6. The method for in-situ sampling and dynamic monitoring of shallow damaged groundwater stratification according to claim 2, characterized in that: For each target sampling depth, after obtaining the laboratory analysis data of its groundwater sample, the water quality status index of that target sampling depth is calculated by combining the laboratory analysis data and the water quality parameter sequence. The specific method used to calculate the water quality status index is as follows: First, calculate the ratio of the detected concentration to the corresponding water quality standard limit for each type of pollutant detected at this depth, and multiply it by the toxicity weight of the pollutant. Then, sum the above calculation results for each type of pollutant and divide them by the total number of detected pollutant types to obtain the comprehensive score for chemical pollution. At the same time, calculate the absolute value of the relative deviation between the average temperature at this depth during the current monitoring period and the average temperature of the reference layer during the same period, as the temperature anomaly degree, and multiply it by the preset temperature adjustment coefficient to obtain the comprehensive score for temperature anomaly. The comprehensive score of chemical pollution and the comprehensive score of temperature anomaly are added together to obtain the water quality status index for the target sampling depth. The reference layer is determined based on long-term monitoring data from the monitoring well. The reference layer is selected from the depth layers where all water quality parameters have not exceeded the natural background value range and no target pollutants have been detected within the preset historical period.

7. The method for in-situ sampling and dynamic monitoring of shallow damaged groundwater stratification according to claim 6, characterized in that: The water quality risk level thresholds include low risk thresholds. Medium risk threshold and high risk threshold And satisfy ; The water quality status index at each target sampling depth is compared with the water quality risk level threshold, and the water quality risk level of the groundwater at that target sampling depth is classified according to the following rules: like If so, the sampling depth of the target is determined to be of low risk level; like If so, the sampling depth of the target is determined to be of medium risk level; like If so, the sampling depth of the target is determined to be of a high-risk level; like If the sampling depth of the target is determined to be at a severe risk level, then the target sampling depth is determined to be at a severe risk level.

8. The method for in-situ sampling and dynamic monitoring of shallow damaged groundwater stratification according to claim 7, characterized in that: Within the same detection period, the sampling depth for each target is unique; For each target sampling depth, the water quality parameter sequence corresponding to the three most recent samples within the current monitoring period is extracted. Based on the water quality parameter sequence, a comprehensive water quality index value is calculated. A comprehensive water quality index value sequence is constructed based on the three corresponding comprehensive water quality index values, denoted as […]. ,in, , , They represent the first The comprehensive water quality index values ​​at each sampling point, the first The comprehensive water quality index values ​​at each sampling point and the first Comprehensive water quality index values ​​at each sampling point; For each target sampling depth, the comprehensive water quality index value is calculated based on its water quality parameter sequence. Specifically, for each of the eight water quality parameters, namely pH, conductivity, dissolved oxygen, oxidation-reduction potential, temperature, nitrate concentration, ammonia nitrogen concentration, and heavy metal ion concentration, the deviation between the measured value and the corresponding water quality standard value is first calculated, and the deviation is squared to amplify the abnormal signal. Then, it is divided by the water quality standard value for dimensionless processing. The result is summed with the unit constant and the natural logarithm is taken. Finally, it is multiplied by the preset weight corresponding to the water quality parameter. The results of the eight water quality parameters after the above processing are summed, and the sum is the comprehensive water quality index value for the target sampling depth. A sequence is constructed based on the comprehensive water quality index values ​​calculated from the three most recent samples within the current monitoring period, and the trend slope of the sequence is calculated. ; like ,and If so, it is determined that the water quality risk at the target sampling depth is showing a continuously increasing trend; The highest water quality risk level at the current sampling time for all target sampling depths within the statistical target monitoring area is recorded as the current highest risk level of the area; the proportion of water quality risk showing a continuous upward trend among all target sampling depths within the statistical target monitoring area is recorded as the area risk increase proportion.

9. The method for in-situ sampling and dynamic monitoring of shallow damaged groundwater stratification according to claim 8, characterized in that: Based on the following combinations of conditions, generate and output different levels of early warning information: If the highest risk level in the target monitoring area is "severe risk level", a red alert will be generated, indicating that there is a severely polluted layer in the target monitoring area; If the highest risk level in the target monitoring area is "high risk level" and the risk increase rate in the area exceeds the preset threshold, an orange alert will be generated, indicating that the pollution risk in the target monitoring area is spreading rapidly. If the highest risk level in the target monitoring area is "medium risk" or below, and no red or orange alert has been generated, then no alert will be triggered.

10. A shallow damaged groundwater stratification in-situ sampling and dynamic monitoring device, characterized in that: The shallow damaged groundwater stratification in-situ sampling and dynamic monitoring device is used to perform the shallow damaged groundwater stratification in-situ sampling and dynamic monitoring method according to any one of claims 1-9, including: The well-layout module is used to lay out monitoring wells within the target monitoring area, and to set up multiple monitoring points with different water depths at intervals along the vertical direction within the monitoring wells; The sequence building module is used to collect water quality parameters in situ at each monitoring point during the monitoring period and to match the collected data with the water depth of the corresponding monitoring point to form a sequence of water quality parameters of each monitoring point over time. The analysis and calibration module is used to determine the adjacent monitoring points based on the location relationship of any monitoring point. It determines the dispersion and deviation of the water quality parameters at each monitoring point based on the water quality parameter sequence of each monitoring point and its adjacent monitoring points. Based on the dispersion and deviation, it evaluates the fluctuation stability of each monitoring point during the sampling process, selects the monitoring points whose fluctuation stability meets the calibration conditions, and determines the water immersion depth of the monitoring points that meet the calibration conditions as the target sampling depth. The sampling and quality assessment module is used to collect groundwater samples at the target sampling depth and detect the types and concentrations of pollutants to obtain laboratory analysis data. The water quality parameter sequence and laboratory analysis data are input into a preset water quality status assessment function to obtain the water quality status index for each target sampling depth. The laboratory analysis data includes the detected concentration of pollutants, the number of pollutant types, and the corresponding toxicity weights. The early warning analysis module is used to compare the water quality status index corresponding to each target sampling depth with the preset water quality risk level threshold, classify the water quality risk level of groundwater at each target sampling depth, and count the highest risk level and the regional risk increase ratio within the target monitoring area to determine the early warning information for the target monitoring area.

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