Method and device for evaluating agricultural non-point source pollution load based on water quality monitoring and medium

Abstract

The application discloses a kind of agricultural non-point source pollution load evaluation method, device and medium based on water quality monitoring, and is related to agricultural non-point source detection field, wherein, method includes: according to hydrology water quality monitoring data, the sampling days of each target pollutant are estimated, the daily load data corresponding to the estimated minimum sampling days is posterior stratification, and the annual load of each target pollutant in each stratification is estimated by applying ratio method, and finally the annual total load of each target pollutant is obtained.In the application, the minimum sampling days is obtained by estimating the sampling days, which reduces the number of samples involved in pollution load calculation, improves the calculation efficiency, and uses ratio method to estimate pollution load, which ensures the accuracy of load estimation value under the premise of relatively high efficiency.

Description

Methods, devices, and media for assessing agricultural non-point source pollution load based on water quality monitoring Technical Field

[0001] This invention relates to the field of agricultural non-point source pollution detection, and in particular to a method, apparatus and medium for assessing agricultural non-point source pollution load based on water quality monitoring. Background Technology

[0002] Agricultural non-point source pollution refers to the pollution of water bodies, soil, air, and agricultural products caused by pollutants generated during agricultural production in rural areas that are not properly treated. Non-point source pollution, especially agricultural non-point source pollution, is gradually becoming a major source of surface water pollution. Therefore, effective prevention and control of agricultural non-point source pollution is increasingly important. Monitoring and estimating agricultural non-point source pollution load is a crucial part of agricultural non-point source risk assessment and is of great significance for carrying out agricultural non-point source pollution prevention and control and improving the ecological environment. However, current watershed monitoring in my country is mainly based on supervisory detection, and existing methods for estimating agricultural non-point source pollution load have drawbacks such as difficulty in obtaining parameters and inaccurate estimation results. Therefore, a more efficient and accurate method for estimating agricultural non-point source pollution load is lacking. Summary of the Invention

[0003] The purpose of this invention is to provide a method, device, and medium for assessing agricultural non-point source pollution load based on water quality monitoring. The minimum number of sampling days is estimated to reduce the amount of data involved in the calculation and improve the calculation efficiency. Furthermore, the ratio method is selected for pollution load estimation, which ensures the accuracy of the load estimation value while maintaining relatively high efficiency.

[0004] To achieve the above objectives, the present invention provides the following solution:

[0005] A method for assessing agricultural non-point source pollution load based on water quality monitoring, the method comprising:

[0006] The catchment area to be monitored is determined based on the water quality exceeding standards within the target area and the relationship between natural and artificial water catchment. The target monitoring area is then determined within the catchment area based on the distribution of agricultural pollution sources. Multiple hydrological and water quality monitoring points are set up within the target monitoring area.

[0007] The number of sampling days for each target pollutant within the target monitoring area is estimated to obtain the minimum number of sampling days for each target pollutant.

[0008] Obtain daily load sequence data of the target pollutants monitored at the hydrological and water quality monitoring points; the daily load sequence data is the daily load data corresponding to the minimum number of sampling days;

[0009] The daily load sequence data is stratified posteriorly, and the annual load of each target pollutant in each stratum is estimated using the ratio method. The annual loads of the same target pollutant in each stratum are summed to obtain the total annual load of the same target pollutant in the target monitoring area.

[0010] A computer device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for assessing agricultural non-point source pollution load based on water quality monitoring.

[0011] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method for assessing agricultural non-point source pollution load based on water quality monitoring.

[0012] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0013] This invention provides a method, device, and medium for assessing agricultural non-point source pollution load based on water quality monitoring. It estimates the number of sampling days, reduces the number of samples involved in the calculation, improves calculation efficiency, reduces load calculation costs, and uses a ratio method to estimate the pollution load, ensuring the accuracy of the estimated load value while maintaining relatively high efficiency. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 is a schematic flowchart of an agricultural non-point source pollution load assessment method based on water quality monitoring provided in Embodiment 1 of the present invention;

[0016] Figure 2 is a schematic diagram of a technology for assessing agricultural non-point source pollution load based on water quality monitoring;

[0017] Figure 3 is a diagram of the internal structure of a computer device. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] The purpose of this invention is to provide a method, device, and medium for assessing agricultural non-point source pollution load based on water quality monitoring. It estimates the minimum number of sampling days and selects the ratio method for pollution load estimation, which can effectively reduce the cost of load calculation while ensuring the accuracy of load calculation.

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] Example 1

[0022] As shown in Figures 1 and 2, this embodiment presents a method for assessing agricultural non-point source pollution load based on water quality monitoring. The method includes: 1. Watershed delineation: Based on basic non-point source pollution conditions and water quality exceedances, a general range is selected, further defining the defined area and catchment area. 2. Monitoring point deployment: Based on the watershed delineation, target areas are selected, and monitoring points are deployed according to relevant information. 3. Ratio method estimation: After preliminary preparation, sampling volume is estimated. Based on actual conditions, reasonable posterior stratification is performed, and the sampling volume for each stratum is calculated. Finally, the annual load of each stratum is estimated, and the results are summed to obtain the final total load. This invention can accurately estimate agricultural non-point source pollution load. The method specifically includes the following steps:

[0023] S1: The catchment area to be monitored is obtained based on the water quality exceeding the standard in the target area and the relationship between natural and artificial water flow. The target monitoring area is determined within the catchment area to be monitored based on the distribution of agricultural pollution sources. Multiple hydrological and water quality monitoring points are set up in the target monitoring area, and each monitoring point is equipped with a hydrological and water quality monitoring device or a manual monitoring point.

[0024] Basic information on townships in the study area was collected, such as crop planting area, fertilizer and pesticide usage, livestock farming intensity, and distribution of large-scale livestock farms. The supporting unit conducted a preliminary calculation of pollutant emissions based on this data, obtaining information on non-point source pollution (agricultural pollution sources and their spatial distribution). High-load areas for different pollutants were delineated based on the basic information on non-point source pollution. Water quality monitoring information, including data on sections exceeding standards and locations of water bodies exceeding standards, was provided by the ecological and environmental departments. The supporting unit processed and overlaid this data to determine the water quality exceedance situation in the study area. Different types of watersheds exceeding standards were delineated based on the water quality exceedance situation. For some watersheds that cross county-level administrative regions or have ambiguous catchment areas, approximate ranges were selected based on non-point source pollution and water quality exceedance situations to define the area and determine the catchment area to be monitored within the study area.

[0025] After delineating the catchment area, specific target monitoring areas are selected. The boundaries of the catchment area to be monitored are delineated. Then, data such as elevation data, surface water system data, and high-resolution satellite imagery are collected within the catchment area. This data is processed using ArcGIS hydrological analysis to determine the accurate surface water system, thereby defining the target monitoring area.

[0026] Data accuracy and source:

[0027] Elevation data: 30-meter precision DEM data (publicly available data)

[0028] Surface water systems: Water systems below level 5 (public data) can be generated using DEM.

[0029] High-resolution satellite imagery: Class 14, 17-meter to 16, 4-meter accuracy satellite data (publicly available data)

[0030] After the target monitoring area is selected, the key monitoring area can be further divided according to the regional data and specific regional conditions. The key monitoring area can be further divided into multiple non-point source pollution control demonstration areas. Each demonstration area may include a section of a river and multiple tributaries.

[0031] For the deployment of monitoring points, automatic and manual monitoring points can be set up at the boundaries or edges of the non-point source pollution control demonstration zones. The number of points depends on the number and location of the demonstration zones, and it should be ensured that monitoring points cover every section of the river and its main tributaries. The sampling river section should be determined centered on the monitoring point, with a length of approximately 100m for wading rivers and 1000m for non-wading rivers. After the monitoring points are deployed, the location of the monitoring stations, the detection indicators, and the monitoring duration can be determined for hydrological and water quality monitoring.

[0032] S2: Estimate the number of sampling days for each target pollutant within the target monitoring area to obtain the minimum number of sampling days for each target pollutant.

[0033] For estimating the minimum sampling days, this invention provides estimation methods for both scenarios with and without background monitoring data. The background monitoring data for the target monitoring area refers to historical hydrological and water quality monitoring data within the target area, or historical hydrological and water quality monitoring data from similar areas with a hydrological and water quality similarity to the target monitoring area exceeding a preset value.

[0034] (i) Estimation of the minimum sampling days when background monitoring data is available

[0035] When background monitoring data is available, the number of sampling days should be estimated, assuming the data is normally distributed and random sampling is guaranteed.

[0036] In one alternative implementation where background monitoring data is available, the flux value is obtained by multiplying the daily pollutant concentration and water flow rate using previously monitored flow data or stream flow and pollutant concentration data from nearby similar land use, and then calculating statistical data such as the average and variance of the flux.

[0037] This flux data can be substituted into formula (1) or (2) to estimate the overall variance.

[0038]

[0039]

[0040] In the formula: S 2 —Variance; RA—Difference between the maximum and minimum flux values; IQR—Difference between the 75th percentile and the 25th percentile after flux sorting. For example, after sorting 100 flux values, the difference between the 75th and 25th flux values ​​is used as the IQR value.

[0041] As an example: For the target pollutant—phosphorus, after flux calculation, the maximum flux of total phosphorus is 8.3, and the minimum is 0.07. Therefore, RA = 8.3 - 0.07 = 8.23;

[0042] To better detect load changes, it is recommended to set an expected error. For example, if the expected target is to control the average daily load within ±15%, then the expected error is 0.15. The flux variance S obtained above... 2 Substitute the proposed acceptable error (expected error) into formula (3) to calculate the estimated number of days for sampling.

[0043]

[0044] Where: n—number of days for sampling estimation; S 2 —Variance; t—Critical value at the α confidence level for a two-tailed test with n degrees of freedom; E—The deviation of the average daily load, calculated as the product of the acceptable error and the average daily flux.

[0045] Based on historical experience data, a sample size is estimated in advance, i.e., the estimated number of sampling days. If the estimated number of sampling days is greater than the preset value (e.g., 30), the t value at n=∞ (i.e., the t value at degrees of freedom >1000 in Table 1) can be used without inappropriate error (Sanders et al, 1983, p.158). Substitute this t value into formula (3) to calculate the estimated number of sampling days.

[0046] If the estimated number of sampling days is less than the preset value (e.g., 30), a reasonable guess is made about the number of samples (one sample is one daily load) based on the estimated number of sampling days. The t-value is determined based on Table 1 using the sample guess value, and then the t-value is substituted into formula (3) to calculate the number of samples. The correct value of the estimated number of sampling days will be between the guess value and the calculated value, but closer to the calculated value. Then another corresponding sample guess value is selected, and this process is repeated. The final calculated value of the number of samples will show increasingly smaller fluctuations, eventually falling within an integer, which is the final estimated number of sampling days; the t-value can be found in Table 1. In Table 1, when calculating the confidence interval, a confidence level must first be determined, commonly 90%, 95%, and 99%. This level reflects the degree of confidence that the confidence interval contains the true population parameter. For example, if the confidence interval is 95%, the t-value at n=∞ is 1.96, and substituted into formula (3), then

[0047]

[0048] Table 1 Critical values ​​at each confidence level

[0049]

[0050]

[0051] After obtaining the estimated number of sampling days, further correction is needed. If the obtained sample size exceeds 10% of the possible number of observations (365 daily loads per year), the estimated sample size should be corrected using the finite population correction (Formula 4).

[0052]

[0053] Where: n0—the corrected estimated number of sampling days; n—the original estimated number of sampling days; N—the total number of possible observations, i.e., the maximum number of observation days for daily load, 365 days;

[0054] For example:

[0055] Therefore, in this invention, step S2, "estimating the number of sampling days for each target pollutant within the target monitoring area to obtain the minimum number of sampling days for each target pollutant," specifically includes:

[0056] (201) Determine the daily flux value of each target pollutant based on the background monitoring data of the target monitoring area; the daily flux value is obtained by multiplying the concentration of the target pollutant and the water flow rate on a daily basis.

[0057] (202) Calculate the average flux and flux variance for each of the target pollutants.

[0058] (203) Set the expected error of the daily load, and calculate the deviation of the average daily load of each of the target pollutants based on the expected error and the average flux.

[0059] (204) The minimum number of sampling days for each target pollutant is obtained by applying the sampling quantity calculation formula based on the deviation of the daily average load and the flux variance; the sampling quantity calculation formula is: Where n represents the estimated minimum number of sampling days; S 2 denoted by ; E represents the deviation of the average daily load; t represents the value of the two-tailed T-statistic with probability α / 2 and n degrees of freedom; α represents the preset confidence level.

[0060] In this step (204), the minimum number of sampling days is calculated as follows:

[0061] (401) For each of the target pollutants, determine the estimated number of samples based on the sampling experience information of the target monitoring area or a nearby similar monitoring area.

[0062] (402) Determine whether the estimated number of samples is greater than the preset value, and obtain the first determination result.

[0063] (403) If the first judgment result is yes, then the t value is determined according to the estimated sampling quantity and the preset confidence level, and the t value, the deviation of the daily average load and the throughput variance are substituted into the sampling quantity calculation formula to obtain the minimum sampling days.

[0064] (404) If the first judgment result is negative, a sample quantity guess value is determined within the preset range of the sample quantity estimate; the t value is determined based on the current sample quantity guess value and the preset confidence level, and the t value, the deviation of the daily average load and the throughput variance are substituted into the sample quantity calculation formula to obtain the current sample quantity calculation value.

[0065] (405) Within the numerical range between the current sample quantity guess value and the current sample quantity calculated value, and close to the current sample quantity calculated value, randomly determine a new sample quantity guess value, set the new sample quantity guess value to the current sample quantity guess value, and return to the step "determine the t value based on the current sample quantity guess value and the preset confidence level" until multiple consecutive sample quantity calculated values ​​converge to the same integer, then the current integer is the minimum number of sampling days.

[0066] To ensure the accuracy of the estimated number of sampling days, after performing step (204) "applying the sampling quantity calculation formula based on the deviation of the daily average load and the flux variance to obtain the minimum number of sampling days for each target pollutant", the method further includes:

[0067] Determine whether the minimum number of sampling days for each target pollutant is greater than a preset number of days, and obtain a second determination result; the preset number of days is obtained by multiplying the maximum number of observation days of daily load by a preset percentage.

[0068] If the second judgment result is yes, then the minimum sampling days are corrected to obtain the corrected estimated sampling days; and the daily load data is extracted based on the corrected estimated sampling days.

[0069] If the second judgment result is negative, then there is no need to modify the minimum sampling days.

[0070] In another alternative implementation where background monitoring data is available, the present invention further employs a priori stratification to determine the minimum number of sampling days.

[0071] Stratification involves dividing a sampling operation or sample set into two or more distinct but relatively homogeneous parts. Stratifying a dataset can lead to more accurate load calculations. Stratification is typically performed based on runoff period, season, or water level rise and fall.

[0072] For tributaries where non-point source input (especially particulate input) is dominant, storm runoff is characterized by significantly increased flow and concentration. Therefore, a large portion of the total variance of a daily load sample is caused by runoff events.

[0073] Seasonal stratification can be useful in some tributaries. For example, it may be important to consider the spring snowmelt period as a separate stratum in a river, since most of the annual load is believed to form during this time.

[0074] In some tributaries, the concentration of particulate pollutants rises faster than the flow rate and reaches its peak value more quickly. Therefore, creating separate stratifications on the rising and falling surfaces of the waterline could be considered.

[0075] Other stratification schemes may be suitable for specific rivers and parameters. As a general rule, any appropriate stratification, if performed correctly, will result in a more accurate load estimate for a given sampling operation.

[0076] Prior stratification is performed before sampling and must be based on prior knowledge of the system. It involves determining the stratification method and number of strata before sampling to improve the accuracy of load calculation results. If prior stratification is used, the sample size must be recalculated.

[0077] Calculate the total number of samples using formula (5):

[0078]

[0079] Where: n—estimated minimum number of sampling days; t—critical value at the α confidence level for a two-tailed test with f degrees of freedom; N—total number of possible observations, i.e., the maximum number of observation days for the daily load (365 days); N i —The total number of possible observations in the i-th layer; S i —Standard deviation of flux at layer i; E—deviation of average daily load.

[0080] To calculate the number of samples required for stratified sampling, the effective degrees of freedom f for stratification must be determined to obtain an appropriate t value. Based on the effective degrees of freedom f and the preset confidence level, the value of t is determined in Table 1, and the t value is substituted into formula (5) to obtain the total number of samples calculated based on prior stratification. The effective degrees of freedom f is given by formula (6):

[0081]

[0082] In the formula: f eff —Effective degrees of freedom of the layer; f i —The degrees of freedom associated with the flux variance estimate of the i-th layer, numerically calculated as the number of samples in the i-th layer minus 1; N i —Number of observation days for the i-th layer; S i —Standard deviation of flux at layer i.

[0083] Therefore, step S2, "estimating the number of sampling days for each target pollutant within the target monitoring area to obtain the minimum number of sampling days for each target pollutant," specifically includes:

[0084] (211) For each target pollutant, the background monitoring data of the target monitoring area is divided into layers according to the first preset layering method and the number of layers.

[0085] (212) Determine the daily flux value of the target pollutant in each stratum and calculate the average flux value and flux variance of the target pollutant in each stratum; the daily flux value is obtained by multiplying the daily concentration of the target pollutant and the water flow rate.

[0086] (213) Set the expected error of the daily load, and calculate the deviation of the average daily load of the target pollutant based on the expected error and the average flux of each layer.

[0087] (214) Determine the minimum number of sampling days for the target pollutant based on the deviation of the average daily load, the maximum number of observation days of the daily load, and the average flux and flux variance of each stratum.

[0088] (ii) Estimation of the minimum sampling days in the absence of background monitoring data

[0089] The minimum statistical sample size can be estimated as the minimum sampling days. Specifically, the minimum sampling days are determined based on the maximum number of observation days of daily load and the expected error. The formula for calculating the minimum sampling days is as follows:

[0090]

[0091] In the formula, n represents the minimum number of sampling days, N is the maximum number of observation days for daily load; p is the expected error (if the data error can fluctuate within a range of 5%, then p = 5); x is the error parameter.

[0092] The error parameter is calculated using this formula:

[0093]

[0094] Zc is the critical value at the confidence level c. Generally, the confidence level c is set to 90%, 95%, or 99%. Using 365 degrees of freedom as the specific value, and combining this with the preset value of the confidence level c, an approximation of 500 degrees of freedom can be obtained with minimal error. The value of Zc is determined according to Table 1. r is the response score interval, generally taken as the maximum sample size, i.e., r = 50.

[0095] S3: Obtain the daily load sequence data of the target pollutants monitored at the hydrological and water quality monitoring points; the daily load sequence data is the daily load data corresponding to the minimum number of sampling days.

[0096] In this invention, the daily load sequence data can be extracted from all daily load data of the target monitoring area (e.g., load data for the entire year or a specific time period). The amount of data extracted should be equal to the amount of data corresponding to the minimum sampling days. To ensure more accurate calculation of subsequent pollutant load data, the extracted data should be normally distributed and random sampling should be ensured. Alternatively, the daily load data can be obtained based on the minimum sampling days at the monitoring point. To ensure more accurate calculation of subsequent pollutant load data, the sampled data should also be normally distributed. The daily load is derived from hydrological and water quality monitoring data within a single day.

[0097] S4: Perform posterior stratification on the daily load sequence data, and use the ratio method to estimate the annual load of each target pollutant in each stratum; sum the annual loads of each stratum of the same target pollutant to obtain the total annual load of the same target pollutant in the target monitoring area.

[0098] Posterior stratification is a stratification method performed after observation, based on the observation results themselves. It is a calculation of stratified loading. Specifically, after determining the stratification method and the number of stratification layers, the number of observations for each layer is determined to complete the stratification of the dataset, and then the loading is calculated.

[0099] After stratifying the data, the required sample size for each stratum needs to be calculated based on the estimated total number of samples. There are many possible methods for allocating the number of sample observations between strata. The simplest is proportional sampling, which, based on past experience, is proportional to the expected population of the stratum (see Equation (9)). The optimal allocation, also known as the Neiman allocation, minimizes the estimated variance of the average daily load, and is given by Equation (10).

[0100]

[0101]

[0102] Where: n i — The number of observations to be extracted from the i-th layer; n — The total number of observations in the sample; N i —The total number of possible observations in the i-th layer; N—The total number of possible observations; S i —Estimated standard deviation of the population in the i-th stratum.

[0103] After determining the number of sample observations for each stratum, load estimation is performed. Pollutant concentrations, annual average flow rates, and daily average flow rates are available from monitoring stations. The ratio method uses the data for the current year to calculate the average daily load, specifically the product of pollutant concentration and water flow rate. This average daily load is then adjusted by multiplying it by a flow ratio, derived by dividing the annual average flow rate by the average flow rate of the selected collection days. The annual load is obtained by multiplying the average daily load by 365 (366 in leap years).

[0104] The ratio method is based on the assumption that the ratio of load to flow rate throughout the year should be the same as the ratio of load to flow rate on the day the concentration is measured. Therefore:

[0105]

[0106] Assuming the daily traffic volume is known throughout the year, then:

[0107]

[0108]

[0109] In the formula: —Annual average load; —Average annual flow; —Daily average load; — Daily average flow; L — Total load of the i-th layer; The total annual load of the stream is obtained by adding up the total loads L calculated for each layer.

[0110]

[0111] In the formula: L 总 —Total annual load; Li —Total load of the i-th floor; m —Number of floors.

[0112] When two related parameters are involved, such as flow rate and load, the ratio method will have a bias, so a bias correction factor is required. The Beer ratio estimator, which is widely used in load calculations in the Great Lakes region, is discussed in detail in Baun (1982), specifically as shown in equations (15) and (16):

[0113]

[0114]

[0115] In the formula: — Deviation correction factor; s lq —The covariance of flux and flow rate in the i-th layer; s qq —Flow variance based on the number of daily load observation days in the i-th stratum; M—Expected total size, i.e., the expected number of observation days in the i-th stratum, equal to the proportion of the observation days of a certain stratum to the total number of observation days of the entire stratum multiplied by 365; n i —The number of observation days extracted from the i-th stratum.

[0116] In stratified sampling, the expected population size is the probability in a given stratum multiplied by the sampling frequency of that stratum (with a sampling frequency of 1). For example, if flow stratification is performed using the 20th percentile (by time) cutoff point, the expected population size for the high flow stratum is 365 * 0.2 = 292.

[0117] The expected error of the Beer ratio estimator varies with 1 / n 2The error varies with the value of n, and therefore quickly approaches zero as n increases (Tin, 1965). Both theory and simulation show that the error becomes insignificant when enough samples are collected to provide an acceptable level of accuracy.

[0118] Therefore, in step S4, the extracted daily load data for each target pollutant are stratified posteriorly, and the annual load of each target pollutant in each stratum is estimated using the ratio method. Specifically, this includes:

[0119] (1) For each target pollutant, the extracted daily load data is divided into data layers according to the second preset stratification method and the number of stratification layers; and the number of observation days for the daily load in each layer is determined.

[0120] (2) Calculate the annual average load of each layer based on the daily average load and daily average flow rate of each layer and the annual average flow rate using the ratio method.

[0121] (3) Obtain the annual load of each layer based on the annual average load of each layer.

[0122] This embodiment has the following effects:

[0123] 1. This invention involves investigating agricultural pollution sources, dividing the target area, and then setting up monitoring points accordingly, thereby ensuring the rationality of the monitoring point layout.

[0124] 2. This invention selects the ratio method for pollution load estimation, which ensures the accuracy of the load estimation value while being relatively efficient.

[0125] 3. This invention employs both posterior and prior stratification, and explains the corresponding adjustments to the number of samples under different conditions, enabling the method to adapt to a wider range of real-world situations while ensuring accuracy.

[0126] 4. This invention estimates the number of samples to be collected, which ensures accurate load assessment while reducing the number of samples involved in the calculation, thus reducing the amount of computation and improving computational efficiency.

[0127] Example 2

[0128] A computer device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for assessing agricultural non-point source pollution load based on water quality monitoring in Embodiment 1.

[0129] Example 3

[0130] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for assessing agricultural non-point source pollution load based on water quality monitoring in Embodiment 1.

[0131] Example 4

[0132] A computer program product includes a computer program that, when executed by a processor, implements the steps of an agricultural non-point source pollution load assessment method based on water quality monitoring as described in Example 1.

[0133] Example 5

[0134] A computer device, which may be a database, has an internal structure as shown in Figure 3. The computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores pending transactions. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the agricultural non-point source pollution load assessment method based on water quality monitoring as described in Example 1.

[0135] It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this invention are all information and data authorized by the object or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0136] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided by this invention may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided by this invention may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0137] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0138] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for assessing agricultural non-point source pollution load based on water quality monitoring, characterized in that, The method includes: determining the catchment area to be monitored based on the water quality exceedance situation and the relationship between natural and artificial water catchment within the target area; determining the target monitoring area within the catchment area based on the distribution of agricultural pollution sources; deploying multiple hydrological and water quality monitoring points within the target monitoring area; estimating the sampling days for each target pollutant within the target monitoring area to obtain the minimum sampling days for each target pollutant; acquiring daily load sequence data of the target pollutants monitored by the hydrological and water quality monitoring points; the daily load sequence data is the daily load data corresponding to the minimum sampling days; performing posterior stratification on the daily load sequence data, and applying the ratio method to estimate the annual load of each target pollutant in each stratum; summing the annual loads of the same target pollutant in each stratum to obtain the total annual load of the same target pollutant in the target monitoring area; the ratio method calculates the average daily load by multiplying the pollutant concentration and water flow by the flow ratio; the flow ratio is calculated by dividing the annual average flow by the selected sampling days. The average daily flow rate is obtained; wherein, the number of sampling days for each target pollutant within the target monitoring area is estimated to obtain the minimum number of sampling days for each target pollutant, specifically including: determining the daily flux value of each target pollutant based on the background monitoring data of the target monitoring area; the daily flux value is obtained by multiplying the daily concentration of the target pollutant by the water flow rate; the background monitoring data of the target monitoring area refers to historical hydrological and water quality monitoring data within the target area or historical hydrological and water quality monitoring data of similar areas with a hydrological and water quality similarity to the target monitoring area higher than a preset value; calculating the average flux value and flux variance of each target pollutant; setting the expected error of the daily load, and calculating the deviation of the average daily load of each target pollutant based on the expected error and the average flux value; applying the sampling quantity calculation formula based on the deviation of the average daily load and the flux variance to obtain the minimum number of sampling days for each target pollutant; the sampling quantity calculation formula is: n Where n represents the estimated number of days for sampling; S 2 denoted by ; E represents the deviation of the average daily load; t represents the critical value at the α confidence level for a two-tailed test with n degrees of freedom.

2. The method for assessing agricultural non-point source pollution load based on water quality monitoring according to claim 1, characterized in that, The minimum number of sampling days for each target pollutant is calculated using a sampling quantity calculation formula based on the deviation of the daily average load and the flux variance. Specifically, this includes: for each target pollutant, determining an estimated sampling quantity based on sampling experience information from the target monitoring area or nearby similar monitoring areas; determining whether the estimated sampling quantity is greater than a preset value to obtain a first judgment result; if the first judgment result is yes, determining a t-value based on the estimated sampling quantity and a preset confidence level, and substituting the t-value, the deviation of the daily average load, and the flux variance into the sampling quantity calculation formula to obtain the minimum number of sampling days; if the first judgment result is no, then within a preset range of the estimated sampling quantity... Determine a sample quantity estimate; determine a t-value based on the current sample quantity estimate and a preset confidence level; substitute the t-value, the deviation of the daily average load, and the flux variance into the sample quantity calculation formula to obtain the current sample quantity calculation value; within the numerical range between the current sample quantity estimate and the current sample quantity calculation value, and close to the current sample quantity calculation value, randomly determine a new sample quantity estimate, set the new sample quantity estimate to the current sample quantity estimate, and return to the step "determine the t-value based on the current sample quantity estimate and a preset confidence level" until multiple consecutive sample quantity calculation values ​​converge to the same integer, then the current integer is the minimum number of sampling days.

3. The method for assessing agricultural non-point source pollution load based on water quality monitoring according to claim 2, characterized in that, After performing the step "to calculate the minimum number of sampling days for each target pollutant by applying the sampling quantity calculation formula based on the deviation of the daily average load and the flux variance", the method further includes: determining whether the minimum number of sampling days for each target pollutant is greater than a preset number of days, and obtaining a second determination result; the preset number of days is obtained by multiplying the maximum number of observation days of the daily load by a preset percentage; if the second determination result is yes, then the minimum number of sampling days is corrected to obtain a corrected estimated number of sampling days; daily load data is extracted based on the corrected estimated number of sampling days; if the second determination result is no, then there is no need to correct the minimum number of sampling days.

4. The method for assessing agricultural non-point source pollution load based on water quality monitoring according to claim 1, characterized in that, The sampling days for each target pollutant within the target monitoring area are estimated to obtain the minimum sampling days for each target pollutant. Specifically, this includes: for each target pollutant, stratifying the background monitoring data of the target monitoring area according to a first preset stratification method and number of stratification layers; the background monitoring data of the target monitoring area refers to historical hydrological and water quality monitoring data within the target area or historical hydrological and water quality monitoring data of similar areas with a hydrological and water quality similarity to the target monitoring area higher than a preset value; determining the daily flux value of the target pollutant in each stratum, and calculating the average flux value and flux variance of the target pollutant in each stratum; the daily flux value is obtained by multiplying the daily concentration of the target pollutant by the water flow rate; setting the expected error of the daily load, and calculating the deviation of the average daily load of the target pollutant based on the expected error and the average flux value of each stratum; determining the minimum sampling days for the target pollutant based on the deviation of the average daily load, the maximum number of observation days of the daily load, and the average flux value and flux variance of each stratum.

5. The method for assessing agricultural non-point source pollution load based on water quality monitoring according to claim 1, characterized in that, The sampling days for each target pollutant within the target monitoring area are estimated to obtain the minimum sampling days for each target pollutant. Specifically, this includes determining the minimum sampling days based on the maximum daily load observation days and the expected error. The formula for calculating the minimum sampling days is as follows: In the formula, n represents the minimum number of sampling days, N is the maximum number of observation days for daily load; p is the expected error; and x is the error parameter.

6. The method for assessing agricultural non-point source pollution load based on water quality monitoring according to claim 1, characterized in that, The daily load sequence data is stratified posteriorly, and the annual load of each target pollutant in each stratum is estimated using the ratio method. Specifically, this includes: for each target pollutant, the extracted daily load data is stratified according to a second preset stratification method and number of stratification layers; the number of observation days for the daily load in each stratum is determined; the annual average load of each stratum is calculated based on the daily average load and daily average flow rate of each stratum, combined with the annual average flow rate, using the ratio method; and the annual load of each stratum is obtained based on the annual average load of each stratum.

7. The method for assessing agricultural non-point source pollution load based on water quality monitoring according to claim 6, characterized in that, The expression for the annual average load of each stratum is: ;in, Indicates the annual average load; Indicates the average annual flow; Indicates the average daily load; This represents the daily average flow rate; the expression for the annual average load of each stratum is: ;in, In the formula, This represents the annual average load of the i-th stratum; Indicates the average annual flow; Indicates the average daily load; Indicates the average daily flow; Indicates the deviation correction factor; n i s represents the number of samples drawn from the i-th stratum; lq s represents the covariance between flux and flow rate in the i-th layer; qq Let M represent the flow variance within the number of daily load observation days for the i-th stratum; M represents the expected number of observation days for the i-th stratum.

8. A computer device, comprising: The memory, the processor, and the computer program stored in the memory and executable on the processor are characterized in that the processor executes the computer program to implement the steps of the method for assessing agricultural non-point source pollution load based on water quality monitoring according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of any one of the methods for assessing agricultural non-point source pollution load based on water quality monitoring, as described in any one of claims 1-7.

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