A method for optimizing the representativeness of monitoring points

Abstract

This invention discloses a method for optimizing the representativeness of monitoring points. Based on an environmental database of the study area, it calculates the minimum number of sampling points in the area and uses rapid detection equipment to complete data collection. Based on the detection data, it performs cluster analysis, spatial interpolation, error calculation, and secondary error calculation. For areas with large errors, it supplements monitoring points, obtains the latitude and longitude of the supplemented points, and draws an optimized distribution map of the monitoring points. This invention constructs a closed-loop technical chain of "data collection - error calculation - point optimization," which plays an important supporting role in comprehensively understanding the regional environmental conditions and provides decision support for agricultural planting zoning optimization and pollution prevention and control through precise environmental element analysis.

Description

Technical Field

[0001] This invention relates to a method for optimizing the representativeness of environmental monitoring sites in a study area, belonging to the field of agricultural environmental science. Background Technology

[0002] In order to accurately grasp the environmental quality of farmland soil, improve monitoring efficiency and reduce costs, farmland soil monitoring work urgently needs to optimize the monitoring points to achieve more scientific, reasonable and efficient soil monitoring (Dong Xubin, Yao Yanhong, Wu Chenxing. Research on the layout method of basic monitoring points for soil environment [J]. Leather Manufacturing and Environmental Protection Technology, 2022, 3(7): 78-80); At present, the commonly used monitoring point layout methods are mostly random layout, uniform layout, and zoned layout (Zhai Xiaolei. Research on the layout method of basic monitoring points for soil environment [J]. Cleaning World, 2021, 37(3): 82-83).

[0003] The main problems with the above technical methods are: (1) the monitoring points are not representative and have poor support for a comprehensive understanding of regional environmental quality; (2) the monitoring point layout method is not universally applicable, as my country's terrain is complex and diverse, and the point layout method is difficult to adapt to all regions. Summary of the Invention

[0004] This invention, based on a regional environmental database, calculates the minimum number of sampling points for the region and uses rapid detection equipment to complete data collection. Based on the detection data, it performs cluster analysis, spatial interpolation, error calculation, and secondary error calculation. For areas with significant errors, monitoring points are added, and the latitude and longitude of these added points are obtained to create an optimized distribution map of the monitoring points. This invention constructs a closed-loop technical chain of "data collection - error calculation - point optimization," which plays a crucial supporting role in comprehensively understanding the regional environmental conditions and provides decision support for optimizing agricultural planting zones and controlling pollution through precise environmental element analysis.

[0005] A method for optimizing the representativeness of monitoring sites includes four steps: environmental database construction, cluster analysis, error calculation of individual environmental indicators, and site supplementation. Its key features are:

[0006] (1) Construction of environment database

[0007] (2) Clustering

[0008] (2.1) Calculation of minimum monitoring points

[0009] (2.2) Rapid detection at monitoring points

[0010] The monitoring points are set up based on the minimum number of monitoring points to form an initial set of monitoring points; in-situ environmental indicators are detected on the environmental media, and environmental indicator detection data are obtained.

[0011] (2.3) Cluster Analysis

[0012] Environmental indicator detection data from all monitoring points and environmental data excluding area from the environmental database are extracted to obtain a clustered dataset; then cluster analysis is performed on the clustered dataset to obtain A cluster categories;

[0013] (3) Error calculation for individual environmental indicators

[0014] (3.1) Randomly select one cluster from A cluster categories;

[0015] (3.2) Randomly select a point from the initial monitoring point set as the target point r, and select the k nearest neighboring points that are closest to the target point r in a straight line to form a neighboring point set;

[0016] (3.3) Randomly select n neighboring points from the set of neighboring points, perform spatial interpolation of environmental indicators, extract the interpolation data of the target point r, and calculate the relative error θ of r;

[0017] θ = (Environmental indicator monitoring data - interpolated data) / Environmental indicator monitoring data

[0018] The value of n is in the range of 3-5, and kn≥3;

[0019] (3.4) Iterate through n neighboring points and interpolate to obtain C relative errors of the target point r;

[0020]

[0021] For error point combinations with a relative error θ > 25%, i.e., error point sets, proceed to the next step; for error point combinations with θ ≤ 25%, no further operations are performed.

[0022] (3.5) Calculate steps (3.1)-(3.4) once for all points as target points r, and obtain the set of all error points with θ>25%;

[0023] (4) Point Supplement

[0024] For a single set of error points, obtain the initial monitoring points and latitude and longitude of r within the set of error points, extract the four boundary ranges Xmin, Xmax, Ymin, and Ymax, connect them to form a closed region, and add points in the closed region.

[0025] Furthermore, the construction of the environmental database includes collecting environmental information of the study area and constructing the environmental database;

[0026] Furthermore, the environmental information includes: area, altitude, slope, land use type, soil type, distance from water source, distance from road, vegetation type, etc.; the environmental information collection methods include, but are not limited to, consulting literature, books, experimental reports, project summary reports, etc.

[0027] Furthermore, the method for calculating the minimum number of monitoring points is as follows: Based on the area S of the study area, the minimum number of monitoring points D is calculated, and the specific calculation formula is as follows:

[0028] D = S / m 2 +5, and

[0029] Where m is the minimum acceptable distance between two monitoring points;

[0030] Furthermore, the point layout method includes, but is not limited to, random point layout and uniform point layout;

[0031] Furthermore, the on-site rapid testing equipment includes, but is not limited to, in-situ pH meters, portable X-ray fluorescence spectrometers (XRF), near-infrared spectrometers (NIR), portable spectrophotometers, inductively coupled plasma optical emission spectrometers (ICP-OES) or mass spectrometers (ICP-MS), anodic stripping voltammetry (ASV) detectors, laser-induced breakdown spectrometers (LIBS), etc.

[0032] Furthermore, the environmental media include soil, atmosphere, water, etc.

[0033] Furthermore, the environmental indicators include, but are not limited to, pH, lead (Pb), mercury (Hg), cadmium (Cd), chromium (Cr), arsenic (As), copper (Cu), zinc (Zn), nickel (Ni), manganese (Mn), titanium (Ti), thallium (Tl), vanadium (V), aluminum (Al), potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), sulfur (S), chlorine (Cl), bromine (Br), selenium (Se), gallium (Ga), germanium (Ge), rubidium (Rb), and strontium (Sr).

[0034] Furthermore, the latitude and longitude information of each point in the initial monitoring point set is obtained.

[0035] Furthermore, A ≥ 3; the clustering model includes, but is not limited to, GMM, K-means, hierarchical clustering, etc.

[0036] Furthermore, the selection method for the k nearest neighbor points is divided into two types: ① If the number of monitoring points in the cluster category where r is located is greater than k, select the k monitoring points that are closest to r in a straight line; ② If the number of monitoring points in the cluster category where r is located is less than or equal to k, prioritize selecting all monitoring points in the same cluster category as r, and select the remaining monitoring points that are closest to r in a straight line from other cluster categories; the value of k ranges from 5 to 8.

[0037] Furthermore, the spatial interpolation methods include Kriging interpolation, inverse distance weighted average interpolation (IDW), trend surface method, and spline function method;

[0038] Furthermore, spatial interpolation was performed using specialized software including ArcGIS, R, Python, MATLAB, and CAD.

[0039] Furthermore, the addition of locations also includes:

[0040] (1) Divide the closed area into grids of unit length and extract the center point of each grid;

[0041] Where Xmin is the minimum longitude, Xmax is the maximum longitude, Ymin is the minimum latitude, and Ymax is the maximum latitude;

[0042] The four boundaries refer to the maximum and minimum latitude and longitude of the monitoring point;

[0043] The unit grid length is 50-1000 meters;

[0044] (2) Extract all center point interpolation data;

[0045] (3) Select the interpolation data of a single center point and the environmental index detection data of the initial monitoring points in the error point set, and use the same spatial interpolation method to extract the secondary interpolation data of the target point r; obtain the secondary interpolation data of the target point r corresponding to all center points;

[0046] (4) Calculate the second relative error θt of the target point r. The point with the largest θt is the supplementary point.

[0047] (5) Traverse all error point sets in the same way, that is, one error point set obtains one supplementary point;

[0048] (6) For multiple environmental indicators, the same method is used to obtain the supplementary points for each individual indicator. All supplementary points are collected, and the supplementary points that are close to each other are deleted. The remaining points are the supplementary point set.

[0049] (7) Use the same method to traverse all cluster categories and obtain all supplementary points in the study area.

[0050] Furthermore, if the center point of the grid is located outside the closed area, and the area of ​​the grid within the closed area is greater than 50%, then the center point of the grid within the closed area is selected; if the area of ​​the grid within the closed area is less than or equal to 50%, then the grid is deleted.

[0051] Furthermore, each error point set has multiple center points, and each point corresponds to a second relative error. The magnitudes of the second relative errors are compared, and the point with the largest second relative error value is selected as the supplementary point.

[0052] Furthermore, the method for calculating the second relative error θt is as follows:

[0053] θt = (Environmental indicator monitoring data - Quadratic interpolation data) / Environmental indicator monitoring data

[0054] Furthermore, the calculation method for the nearby supplementary points is as follows:

[0055] ① The gold value of all initial monitoring points was obtained using the Kriging interpolation method;

[0056] ② If the distance between two supplementary points for different environmental indicators is the smallest and less than the maximum nugget value of the environmental indicator, then the two points are considered to be close, and one point is randomly retained.

[0057] Furthermore, the latitude and longitude coordinates of all supplementary points were extracted, and an optimized point distribution map was drawn using professional software.

[0058] The advantages and effects of this application are as follows:

[0059] (1) This invention takes into account the regional characteristics of the study area and the environmental conditions such as soil, water and atmosphere, and provides a method for setting up monitoring points, which plays an important supporting role in fully understanding the regional characteristics.

[0060] (2) This invention designs an adaptive sampling density supplementation mechanism for error hotspot areas, iteratively optimizes the latitude and longitude coordinates of the supplementation points, and ensures that the spatial coverage of the monitoring network is increased to more than 95%.

[0061] (3) This invention comprehensively adopts on-site rapid detection, spatial interpolation, error calculation and other technical methods, which saves 25% of the cost and 40% of the time compared with the traditional monitoring point optimization method.

[0062] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the preferred embodiments of this application are described in detail below with reference to the accompanying drawings.

[0063] The above and other objects, advantages and features of this application will become more apparent to those skilled in the art from the following detailed description of specific embodiments in conjunction with the accompanying drawings. Attached Figure Description

[0064] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In all drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0065] Figure 1 A technical roadmap for a representative optimization method for monitoring points;

[0066] Figure 2 Initial monitoring point distribution map of R County;

[0067] Figure 3 Error point location and grid center point distribution diagram;

[0068] Figure 4 .R County Optimized Site Distribution Map. Specific Implementation

[0069] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. In the following description, specific details such as specific configurations and components are provided merely to help fully understand the embodiments of this application. Therefore, those skilled in the art should understand that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. In addition, for clarity and brevity, descriptions of known functions and structures are omitted in the embodiments.

[0070] It should be understood that the phrase "an embodiment" or "this embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "an embodiment" or "this embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.

[0071] Furthermore, reference numerals and / or letters may be repeated in different examples within this application. Such repetition is for the purpose of simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or settings discussed.

[0072] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist simultaneously. The term " / and" in this article describes another type of relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " in this article generally indicates that the related objects before and after it are in an "or" relationship.

[0073] In this article, the term "at least one" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, "at least one of A and B" can mean: A exists alone, A and B exist simultaneously, or B exists alone.

[0074] It should also be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion.

[0075] Example 1

[0076] 1. Environment Database Construction

[0077] (1) Taking County R as the research area, information such as area, altitude, slope, land use type, soil type, distance from water source, vegetation type, and agricultural product type were collected through statistical yearbooks, papers and other means to form an environmental database;

[0078] (2) There is one river flowing through the county of R;

[0079] 2. Cluster analysis

[0080] (1) Based on the research requirements, the distance between monitoring points in R County needs to be greater than 0.8km. According to the formula for calculating the minimum number of monitoring points, the number of monitoring points in R County is calculated to be 21. The initial set of monitoring points is obtained by using the random point placement method.

[0081] (2) In-situ pH meter and portable X-ray fluorescence spectrometer were used to detect soil pH, cadmium and lead content and cadmium and lead content in rivers to obtain environmental index detection data;

[0082] (3) Integrate environmental data and environmental indicator monitoring data, and perform K-means clustering. The 21 data points are divided into 3 clusters: 9 in cluster 1, 6 in cluster 2, and 6 in cluster 3. The distribution map is shown below. Figure 2 ;

[0083] 3. Error Calculation for Individual Environmental Indicators

[0084] (1) Select the initial monitoring points in the first cluster category to calculate the relative error of soil cadmium, randomly select the target point r, and further select 7 nearest points to form the nearest point set 1;

[0085] (2) Three initial monitoring points are randomly selected from the neighboring point set 1. Using ArcGIS software, spatial interpolation is performed using the ordinary Kriging interpolation method to extract the interpolated data of the target point r.

[0086] (3) Traverse all initial monitoring points in the adjacent point set 1, and calculate the relative error according to the relative error calculation formula; a total of 35 relative errors are obtained, of which the relative error θ>25% of the combination of 4 error points is 4 error point sets;

[0087] (4) All points were used as target points r and the same method was used to calculate the relative error, resulting in a set of 18 error points.

[0088] (5) Using the same method, a total of 12 error point sets for soil lead were calculated;

[0089] 4. Location Supplement

[0090] (1) Taking one error point set in the nearest point set 1 as an example, obtain the latitude and longitude of three error points and the target point, extract the four boundaries, and connect them to form a closed area; divide the closed area into grids with a length of 0.5km, extract the center point of each grid, for a total of 10 center points, see Figure 3 ;

[0091] (2) Using the same method, extract the center points of the other 3 error point sets in the neighboring point set 1, for a total of 24; extract the interpolation data of the 24 center points;

[0092] (3) The ordinary Kriging interpolation method was used to interpolate the interpolated data of 24 single center points and the soil cadmium detection data of the initial monitoring points in the error point set, and the secondary interpolated data of 24 target points r were extracted.

[0093] (4) Based on the method of calculating the second relative error, 24 second relative errors were calculated. The point with the largest second relative error is the supplementary point; the four error point sets yielded four supplementary points.

[0094] (5) Using the same method, three additional soil lead points were calculated. The nugget values ​​of cadmium and lead were calculated separately, and two additional points were removed.

[0095] (6) Using the same method, traversed the three cluster categories to obtain a total of 12 supplementary points. See the optimized point distribution map. Figure 4 .

[0096] The above description is merely a preferred embodiment of the present invention and does not limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any changes, modifications, substitutions, integrations, and parameter alterations to these embodiments within the spirit and principles of the present invention, achieved through conventional substitutions or by achieving the same function without departing from the principles and spirit of the present invention, fall within the scope of protection of the present invention.

Claims

1. A method for optimizing the representativeness of monitoring points, characterized in that: Includes the following steps, (1) Construction of environmental database The construction of the environmental database includes collecting environmental information of the study area and constructing the environmental database. The environmental information includes: area, altitude, slope, land use type, soil type, distance from water source, distance from road, and vegetation type; the environmental information is collected through literature review, books, experimental reports, and project summary reports. (2) Clustering (2.1) Calculation of minimum monitoring points The method for calculating the minimum number of monitoring points is as follows: Based on the area S of the study area, the minimum number of monitoring points D is calculated, and the specific calculation formula is as follows: ， Where m is the minimum acceptable distance between two monitoring points; (2.2) Rapid detection of monitoring points The monitoring points are set up based on the minimum number of monitoring points to form an initial set of monitoring points; environmental indicators are detected on the environmental media to obtain environmental indicator detection data. (2.3) Cluster analysis Environmental indicator detection data from all monitoring points and environmental data (excluding area) from the environmental database are extracted to obtain a clustered dataset. A clustering model is then used to perform cluster analysis on the clustered dataset to obtain A cluster categories. (3) Calculation of error for individual environmental indicators (3.1) Randomly select one cluster from A cluster categories; (3.2) Randomly select a point from the initial monitoring point set as the target point r, and select the k nearest neighboring points that are closest to the target point r in a straight line to form a neighboring point set; (3.3) Randomly select n neighboring points from the set of neighboring points, perform spatial interpolation of environmental indicators, extract the interpolation data of the target point r, and calculate the relative error θ of r; θ = (Environmental indicator monitoring data - interpolated data) / Environmental indicator monitoring data The n = 3 - 5, and kn ≥ 3; (3.4) Iterate through n neighboring points and interpolate to obtain C relative errors of the target point r; ， Error point combinations with a relative error θ > 25% are designated as error point sets; error point combinations with a relative error θ ≤ 25% are not subject to further processing. (3.5) Calculate steps (3.1)-(3.4) once for all points as target points r, and obtain the set of all error points with θ>25%; (4) Points of addition Obtain the initial monitoring points and latitude and longitude of r within each error point set, extract the four boundary ranges, connect them to form a closed region, and add points to the closed region; The four boundaries refer to the maximum and minimum latitude and longitude of the monitoring point.

2. The method for optimizing the representativeness of monitoring points as described in claim 1, characterized in that, The construction of the environmental database includes collecting environmental information of the study area and constructing the environmental database.

3. The method for optimizing the representativeness of monitoring points as described in claim 1, characterized in that, Environmental indicators of environmental media are tested using on-site rapid testing equipment, which includes one or more of the following: in-situ pH meter, portable X-ray fluorescence spectrometer, near-infrared spectrometer, portable spectrophotometer, inductively coupled plasma atomic emission spectrometer, mass spectrometer, anodic stripping voltammetry detector, and laser-induced breakdown spectrometer.

4. The method for optimizing the representativeness of monitoring points as described in claim 1, characterized in that, The environmental indicators include one or more of the following: pH, lead, mercury, cadmium, chromium, arsenic, copper, zinc, nickel, manganese, titanium, thallium, vanadium, aluminum, potassium, calcium, magnesium, iron, sulfur, chlorine, bromine, selenium, gallium, germanium, rubidium, and strontium.

5. The method for optimizing the representativeness of monitoring points as described in claim 1, characterized in that, A ≥ 3; the clustering model includes GMM, K-means, and hierarchical clustering.

6. The method for optimizing the representativeness of monitoring points as described in claim 1, characterized in that, The selection method for the k nearest neighbor points is divided into two types: ① If the number of monitoring points in the cluster category where r is located is greater than k, select the k monitoring points that are closest to r in a straight line; ② If the number of monitoring points in the cluster category where r is located is less than or equal to k, prioritize selecting all monitoring points in the same cluster category as r, and select the remaining monitoring points that are closest to r in a straight line from other cluster categories; the value of k is in the range of 5-8.

7. The method for optimizing the representativeness of monitoring points as described in claim 1, characterized in that, The additional locations also include: (1) Divide the closed area into grids of unit length and extract the center point of each grid; The unit length is 50-1000 meters; (2) Extract all center point interpolation data; (3) Select the interpolation data of a single center point and the environmental index detection data of the initial monitoring points in the error point set, and use the same spatial interpolation method to extract the secondary interpolation data of the target point r; obtain the secondary interpolation data of the target point r corresponding to all center points; (4) Calculate the second relative error θt of the target point r. The point with the largest θt is the supplementary point. (5) Traverse all error point sets in the same way, that is, obtain one supplementary point for each error point set; (6) For multiple environmental indicators, the same method is used to obtain the supplementary points for each individual indicator. All supplementary points are collected, and the supplementary points that are close to each other are deleted. The remaining points are the supplementary point set. (7) Use the same method to traverse all cluster categories and obtain all supplementary points in the study area.

8. The method for optimizing the representativeness of monitoring points as described in claim 7, characterized in that, If the center point of the grid is located outside the closed area, the center point of the grid within the closed area is selected if the area of ​​the grid within the closed area is greater than 50%, and the grid is deleted if the area of ​​the grid within the closed area is less than or equal to 50%.

9. The method for optimizing the representativeness of monitoring points as described in claim 7, characterized in that, The method for calculating the second relative error θt is as follows: θt = (Environmental indicator detection data - quadratic interpolation data) / Environmental indicator detection data; The calculation method for the nearby supplementary points is as follows: ① The gold value of all initial monitoring points was obtained using the Kriging interpolation method; ② If the distance between two supplementary points for different environmental indicators is the smallest and less than the maximum nugget value of the environmental indicator, then the two points are considered to be close, and one point is randomly retained.

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