Underground water monitoring real-time early warning method

Abstract

The invention relates to the technical field of geological survey, and discloses an underground water monitoring real-time early warning method which comprises the following steps: collecting site automatic monitoring parameters, underground water quality data and long-term monitoring data of an automatic simple monitoring station, collecting a water sample to simulate concentration data when the site is polluted, and determining a threshold range in an ideal state; a response mechanism of automatic monitoring parameters in historical data to groundwater pollution is studied, and early warning indication indexes are screened out; determining a pollution early-warning threshold range by using the early-warning indication index; a threshold range in an ideal state is used for constraint, and threshold verification is carried out on long-term monitoring data and manual sampling data of a simple automatic monitoring station. According to the groundwater pollution real-time monitoring and early warning method, various kinds of information of a groundwater system are integrated, various kinds of data are fully utilized, deeper information contained in the information can be mined, and the early warning timeliness and quality are improved.

Description

Technical Field

[0001] The present invention relates to the field of geological survey technology, and particularly relates to a real-time early warning method for groundwater monitoring. Background Art

[0002] Groundwater is an important fresh water resource, which has the characteristics of stable water volume, clean water quality, wide distribution, etc. Approximately 400 cities in the country and more than 70% of the population use groundwater as a drinking water source. With the rapid development of industrialization and urbanization, the problem of groundwater pollution has become increasingly prominent. Pollution sources such as industrial wastewater, domestic sewage, pesticide and fertilizer residues, and mineral wastewater have caused a significant deterioration in the quality of the groundwater environment. Some water source protection and recharge areas have long faced a high pollution risk, seriously threatening the ecological environment and the safety of residents' drinking water sources. Therefore, in order to prevent groundwater from being polluted or the pollution from becoming more serious, it is crucial to conduct an investigation and evaluation of groundwater pollution and issue an early warning in advance. At present, there is still a lack of real-time automatic monitoring for groundwater pollution in China, and it is difficult to quickly and accurately issue early warning information in the face of sudden and long-term pollution events. Summary of the Invention

[0003] To solve the above problems, the present invention provides a real-time early warning system for groundwater monitoring and a method for real-time monitoring of groundwater using this system. The present invention provides a real-time early warning method for groundwater monitoring, which is characterized by using a real-time early warning system for groundwater monitoring, including an abnormal point identification module, a probability trend early warning module, a pollutant inversion module, and a color block warning level module; The abnormal point identification module is used to receive and process monitoring data in real time, quickly calculate the monthly moving average and standard deviation, and automatically judge abnormal points according to the method of adding and subtracting n standard deviation multiples from the monthly moving average and based on the preset value of n; The probability trend early warning module is used to calculate the pollution early warning threshold range that most conforms to the actual monitoring wells, providing a basis for subsequent real-time early warning; The function of the color block warning level module is to set the color block warning levels of different colors according to the warning levels delimited by the probability trend early warning; The function of the pollutant inversion module is to automatically detect parameters to invert pollutants and pollutant-inverting polluting enterprises. Automatically detecting parameters to invert pollutants is used to retrieve the associated database and pop up the indicated types of pollutants. Pollutant-inverting polluting enterprises are used to prompt the types of enterprises that produce pollution in the industrial agglomeration area according to the exceeded pollutants; The real-time early warning method for groundwater monitoring includes the following steps: S1: Collect the automatic monitoring parameters of the site, groundwater quality data, long-term monitoring data of automatic simple monitoring stations, collect water samples to simulate the concentration data when the site is polluted, and determine the threshold range under ideal conditions; S2: Study the response mechanism of automatic monitoring parameters to groundwater pollution in historical data, and screen out warning indicators that can be used for early warning. S3: Determine the pollution warning threshold range using the warning indicators that can be used for early warning. S4: Use the threshold range in the ideal state for constraint, and verify the threshold with the long-term monitoring data of the simple automatic monitoring station and the artificial sampling data.

[0004] Furthermore, in S2, the laboratory method, water quality standard analysis method, frequency analysis method, coupling model method, and probability analysis method are used to study the response mechanism of automatic monitoring parameters to groundwater pollution in historical data. Specifically, the laboratory method is based on data monitoring carried out under standard conditions. The data obtained is under the condition that there is no precipitation evaporation and other external interference factors, and the groundwater is in an ideal stable state. The monitoring data is obtained, and the threshold obtained under the standard state can be used to constrain the threshold range of other methods; the water quality standard analysis method is to use the water quality category limit values of the characteristic pollution components in the "Groundwater Quality Standard" (GB / T 14848—2017) to limit the level of pollutants; the frequency analysis method is based on the statistical characteristics and frequency distribution law of the automatic monitoring parameter data. By analyzing the change law of the data, the warning threshold is set and the dynamic change of groundwater pollution is predicted; the coupling model method is to accurately capture and describe the potential regularity or trend in the data by constructing a mathematical model. On the basis of frequency analysis, the coupling analysis is carried out between the groundwater quality evaluation results and the frequency method, which constrains the range of the frequency method, making its threshold range more contracted and more accurate than the threshold of the frequency method; the probability analysis method is to count the monitoring data in each interval corresponding to the concentration of a certain characteristic pollutant on the X-axis, and calculate the probability of the monitoring points in each interval accounting for the total monitoring data. Furthermore, in S3, the determination of the threshold range adopts the threshold verification of the simple automatic monitoring station and the threshold verification of artificial sampling. The threshold verification of the simple automatic monitoring station is to use the long-term monitoring data of the simple automatic monitoring station for verification, and randomly select n groups of data to verify its threshold range. The threshold verification of artificial sampling is to select historical data for verification.

[0005] Furthermore, for dynamic threshold early warning, in the modeling stage, according to the long-term time series monitoring data of groundwater, the monitoring data is statistically analyzed by month, and an empirical statistical threshold is formed according to the statistical results. When the real-time monitoring data exceeds the statistical threshold, early warning is carried out. The periodic statistical threshold can be determined in the way of adding and subtracting multiples of the standard deviation from the moving average of the monitoring data of different months. The set multiple of the standard deviation should be analyzed according to the groundwater quality characteristics. Dynamic threshold formula: UCL = x + nS, LCL = x - nS; In the formula, UCL is the upper limit of the dynamic threshold,LCL is the lower limit of the dynamic threshold, x is the moving average of the monitoring data for different months, S is the standard deviation, n The value size is determined according to the actual situation.

[0006] The real-time monitoring and early warning method for groundwater pollution of the present invention synthesizes various information of the groundwater system, makes full use of various data, can excavate deeper information contained in the information, improves the timeliness and quality of early warning, and constructs a visual information system for site groundwater pollution monitoring and early warning with online monitoring - automatic early warning functions, having broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 is a flowchart of a real-time monitoring and early warning system for groundwater monitoring provided by an embodiment of the present invention.

[0008] Figure 2 is a flowchart of a real-time monitoring and early warning method for groundwater monitoring provided by an embodiment of the present invention.

[0009] Figure 3 is a fitting function threshold verification table of a real-time monitoring and early warning method for groundwater monitoring provided by an embodiment of the present invention.

[0010] Figure 4 is a threshold range table of a real-time monitoring and early warning method for groundwater monitoring provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0011] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0012] Figure 1 is a flowchart of a real-time monitoring and early warning system for groundwater monitoring provided by the present invention, a real-time early warning system for groundwater environment, including: abnormal point identification, probability trend early warning, pollutant inversion and color block warning level.

[0013] Abnormal point identification is used to receive and process monitoring data in real time, quickly calculate the monthly moving average and standard deviation, and automatically judge abnormal points in the way of adding and subtracting n times the standard deviation from the monthly moving average according to the preset n value; specifically, the selection of the n value needs to be flexibly adjusted according to the unique properties of the monitoring data, the complex and changeable environmental conditions of the monitoring area, and the actual requirements of the early warning system for sensitivity and specificity. Its value generally ranges from 1.5 to 2. In the case of frequent abnormal points leading to an increase in false alarms, appropriately increasing the n value can reduce false alarms and improve the overall efficiency of the early warning system.

[0014] Probability trend warning is used to explore and study the pollution warning threshold range that best conforms to the actual monitoring wells, providing a basis for subsequent real-time warning.

[0015] The color block warning level is used to set the color block warning levels of different colors according to the warning levels delimited by the probability trend warning.

[0016] Pollutant inversion is used to prompt the possible pollutants corresponding to the warning when the warning information appears, and then prompt the types of polluting enterprises according to the exceeded pollutants.

[0017] In this embodiment, the probability trend warning includes: laboratory method, water quality standard analysis method, frequency analysis method, coupling model method, probability analysis method; the laboratory method is based on the data monitoring carried out under standard conditions, and the data obtained is under the condition that there is no precipitation evaporation and other external interference factors, and the groundwater is in an ideal stable state. The monitoring data is obtained, and the threshold obtained under the standard state can restrict the threshold range of other methods; the water quality standard analysis method is used to limit the level of pollutants by using the water quality category limits of the characteristic pollution components in the "Groundwater Quality Standard" (GB / T 14848—2017); the frequency analysis method is used to set the warning threshold and predict the dynamic changes of groundwater pollution based on the statistical characteristics and frequency distribution laws of the automatic monitoring parameter data by analyzing the change laws of the data.

[0018] Specifically, for dynamic threshold warning, in the modeling stage, according to the long-term time series monitoring data of groundwater, the monitoring data is statistically analyzed by month, and an empirical statistical threshold is formed according to the statistical results. When the real-time monitoring data exceeds the statistical threshold, a warning is issued. The periodic statistical threshold can be determined by adding and subtracting multiples of the standard deviation from the moving average of the monitoring data for different months, and the set multiple of the standard deviation should be analyzed according to the groundwater quality characteristics. Dynamic threshold formula: UCL = x + nS, LCL = x - nS; In the formula, UCL is the upper limit of the dynamic threshold, LCL is the lower limit of the dynamic threshold, x is the moving average of the monitoring data for different months, S is the standard deviation, n The value of is determined according to the actual situation.

[0019] The purpose of threshold accuracy analysis is to study the probability that the warning signal issued by the system is consistent with the actual groundwater situation when the automatic monitoring parameters exceed the upper and lower limits of the threshold. The formula is as follows: Warning success rate = number of accurate warnings / total number of water quality changes; Where: The number of accurate early warnings refers to the number of times when, under the condition that the automatic monitoring system issues an early warning (i.e., a certain parameter is detected to exceed the preset threshold range), the actual value of the corresponding monitoring index also exceeds the Class III water standard of the "Groundwater Quality Standard" (GB / T 14848—2017). The total number of water quality changes refers to the number of times when the monitoring index actually exceeds the underground Class III water standard.

[0020] The coupling model method is used to accurately capture and describe the potential regularity or trend in the data by constructing a mathematical model. On the basis of frequency analysis, the groundwater quality evaluation results are coupled with the frequency method for analysis to constrain the range of the frequency method, making its threshold range more contracted and more accurate than the threshold of the frequency method.

[0021] The probability analysis method is used to count the monitoring data corresponding to each interval of a certain characteristic pollutant concentration on the X-axis and calculate the probability of the monitoring points in each interval accounting for the total monitoring data.

[0022] Specifically, taking a certain automatic monitoring parameter in groundwater as the Y-axis and the corresponding concentration of a certain characteristic pollutant as the X-axis, the "Groundwater Quality Standard" (GB / T 14848—2017) corresponding to a certain characteristic pollutant concentration on the X-axis is divided into 5 intervals: the range of Class I water interval is: [0—maximum limit of Class I water], the range of Class II water interval: [maximum limit of Class I water—maximum limit of Class II water], the range of Class III water interval: [maximum limit of Class II water—maximum limit of Class III water], the range of Class IV water interval: [maximum limit of Class III water—maximum limit of Class IV water], the range of Class V water interval: [> minimum limit of Class V water]; Count the monitoring data corresponding to each interval of a certain characteristic pollutant concentration on the X-axis and calculate the probability of the monitoring points in each interval accounting for the total monitoring data; within the interval, initially determine the most significant part or the part containing important signals in the data with reference to the decision tree, and initially divide a range for the automatic monitoring parameter; within the circled ranges ①, ②, ③... determine the optimal threshold range through repeated calculations to maximize the probability and obtain the optimal value of the early warning value.

[0023] In this embodiment, pollutant inversion includes inverting pollutants from automatic detection parameters and inverting polluting enterprises from pollutants; inverting pollutants from automatic detection parameters is used to retrieve the associated database and pop up the indicated pollutant types; specifically, when the automatic monitoring parameter prompts an early warning, this module will automatically retrieve the associated database and pop up the indicated pollutant types.

[0024] Inverting polluting enterprises from pollutants indicates the types of polluting enterprises in the industrial agglomeration area according to the excessive pollutants; specifically, integrate a large amount of survey data and literature data to construct an associated database of most pollutant types and possible polluting enterprise types, and indicate the types of polluting enterprises in the industrial agglomeration area according to the excessive pollutants.

[0025] Figure 2 This is a flowchart of a real-time early warning method for groundwater monitoring provided by an embodiment of the present invention. As Figure 2 shown, the core of the method of the present invention is threshold early warning. The present invention verifies the threshold based on the online monitoring data of some points in the project. The coupled model method is used to obtain the value of EC from the fitting function of TDS and EC. The fitting function is y = 0.00165x - 0.1168, and R 2 = 0.981, Figure 3 which is a threshold verification table. The results show that: except for Well A-5, other data are within the threshold range, so the success rate is 95%.

[0026] Select the phreatic water pollution source monitoring well as the well for threshold verification, and verify the threshold according to the online monitoring data of some points in the project. The results show that: except for A-6, other data are within the threshold range, and those not within the threshold range did not alarm, so the success rate is 95%, as Figure 4 shown in the threshold range table of

[0027] The probability distribution method and the water quality standard method are more applicable to the indication research of pH. The early warning range shows that the water body is alkaline, indicating that the water quality is more likely to be polluted in alkaline water bodies. The early warning water quality indicators are ammonia nitrogen and Mn; when pH is in the range of 7.23 - 7.47, the highest early warning success rate for indicating that the water quality of the water body is Class IV water is 83.33%, and the early warning indicator is Mn; among them, when pH is in the range of 7.91 - 8.55, it indicates that the water quality of the water body is Class V water, and the highest early warning success rate is 94.44%, and the early warning indicator is ammonia nitrogen; according to the "Groundwater Quality Standard" (GB / T 14848—2017), when 6.5 ≤ pH ≤ 8.5, the water quality of the water body is Class I, Class II, and Class III water; when 5.5 ≤ pH < 6.5, 8.5 < pH ≤ 9, the water quality of the water body is Class IV water; when pH < 5.5, pH > 9, the water quality of the water body is Class V water.

[0028] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A groundwater monitoring real-time early warning method, characterized in that: Adopting a real-time warning system for groundwater monitoring, including anomaly identification module, probability trend warning module, pollutant inversion module and color block warning level module; The abnormal point identification module is used to receive and process monitoring data in real time, calculate the monthly sliding mean and standard deviation, and automatically determine the abnormal point according to the method of adding or subtracting n standard deviation multiples from the monthly sliding mean, and according to the preset n value; The probability trend warning module is used to calculate the pollution warning threshold range that best matches the actual monitoring wells, providing a basis for subsequent real-time warnings; The color block warning level module is used to set the color block warning levels of different colors according to the warning levels defined by the probability trend warning; The pollutant inversion module is used to automatically detect parameter inversion pollutants and pollutant inversion polluting enterprises. The automatic detection parameter inversion pollutants are used to retrieve the associated database and pop up the indicated pollutant types. The pollutant inversion polluting enterprises are used to indicate the types of enterprises that produce pollution in the industrial agglomeration area according to the pollutants exceeding the standard. The groundwater monitoring real-time early warning method comprises the following steps: S1: Collect site automatic monitoring parameters and groundwater quality data, long-term monitoring data of automatic simple monitoring stations, collect water samples to simulate the concentration data of the site when it is polluted and determine the threshold range under ideal conditions; S2: Study the response mechanism of automatic monitoring parameters in historical data to groundwater pollution and screen out early warning indicators; S3: Determine the pollution warning threshold range using the warning indicator; S4: The threshold range under ideal conditions is used as a constraint, and the long-term monitoring data of the simple automatic monitoring station and the manual sampling data are used to verify the threshold.

2. The groundwater monitoring real-time early warning method according to claim 1 is characterized in that: In S2, the laboratory method, water quality standard analysis method, frequency analysis method, coupling model method, and probability analysis method are used to study the response mechanism of automatic monitoring parameters in historical data to groundwater pollution.

3. The groundwater monitoring real-time early warning method according to claim 2 is characterized in that: The laboratory method is based on data monitoring carried out under standard conditions. The data obtained are not affected by precipitation evaporation and other external interference factors. The groundwater is in an ideal stable state to obtain monitoring data. The threshold obtained under the standard state can constrain the threshold range of other methods; the water quality standard analysis method uses the water quality category limit of the characteristic pollution components in the "Groundwater Quality Standard" (GB / T 14848-2017) to limit the level of pollutants; the frequency analysis method is based on the statistical characteristics and frequency distribution law of automatic monitoring parameter data. By analyzing the change law of data, the early warning threshold is set and the dynamic change of groundwater pollution is predicted; the coupling model method accurately captures and describes the potential regularity or trend in the data by constructing a mathematical model. On the basis of frequency analysis, the groundwater quality evaluation results are coupled with the frequency method for analysis, which constrains the range of the frequency method, making its threshold range more narrow and more accurate than the frequency method threshold; the probability analysis method is to statistically analyze the monitoring data of each interval corresponding to a characteristic pollutant concentration in the X-axis, and calculate the probability of each interval monitoring point accounting for the total monitoring data.

4. The groundwater monitoring real-time early warning method according to claim 3 is characterized in that: In S3, the threshold range is determined by using simple automatic monitoring station threshold verification and manual sampling threshold verification. The simple automatic monitoring station threshold verification is verified by using the long-term monitoring data of the simple automatic monitoring station, and n groups of data are randomly selected to verify the threshold range. The manual sampling threshold verification is to select historical data for verification.

5. The groundwater monitoring real-time early warning method according to claim 4 is characterized in that: Dynamic threshold warning is carried out. In the modeling stage, according to the long-term series monitoring data of groundwater, the monitoring data is statistically analyzed by month, and the empirical statistical threshold is formed according to the statistical results. When the real-time monitoring data exceeds the statistical threshold, an early warning is issued. The periodic statistical threshold can be determined by adding or subtracting the standard deviation multiple of the sliding average of the monitoring data in different months. The set standard deviation multiple should be analyzed according to the groundwater quality characteristics. Dynamic threshold formula: UCL=x+nS, LCL = x-nS; In the formula, UCL is the upper limit of the dynamic threshold, LCL is the lower limit of the dynamic threshold, x It is the sliding average of the monitoring data in different months. S is the standard deviation, n The value is determined according to the actual situation.

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