Construction method of water pollution source emission list

Abstract

The invention relates to the technical field of drainage basin water environment management, in particular to a construction method of a water pollution source emission list. The method comprises the following steps: performing classification and account subdivision on different types of pollution sources based on pollution source sub-categories, pollution production modes, treatment technologies and pollution discharge paths to obtain a pollution source classification and classification system; a multi-source data fusion strategy and a sparse data processing strategy are utilized to carry out parameter fine value processing on data in the pollution source grading and classification system, and calculation parameters of different types of pollution sources are determined; performing modeling characterization on the calculation parameters of the different types of pollution sources according to a pollutant generation and emission mechanism to obtain the river entering quantity of different types of pollutants of the different types of pollution sources, and constructing a water pollution source emission list based on the river entering quantity. Therefore, the problems of difficulty in full-aperture refined estimation of the pollution source emission list, relatively low space-time precision of the list and the like are solved, and refined calculation of pollution source emission is realized.

Description

Technical Field

[0001] The present application relates to the technical field of river basin water environment management, and in particular to a method for constructing an emission inventory of water pollution sources. Background Art

[0002] The water pollution source emission inventory is one of the most commonly used pollution emission characterization methods. It can estimate the changes in pollutant emissions input to rivers, lakes and other water bodies by pollution sources from various departments within a certain time span and spatial area, with a temporal and spatial accuracy of up to monthly and 1km×1km grids. Although different researchers use different inventory compilation techniques, they all use pollution source census data and environmental statistical data as an important basis to make refined estimates of pollutant emissions from different departments, industries and spatial units.

[0003] In the related technology, (1) a method for compiling a surface water basin pollution source emission inventory (dynamic inventory), including pollution source classification, pollutant identification and emission calculation, and the establishment of the relationship between emission and water quality. The inventory covers five types of pollution sources, including industry, urban runoff, planting, livestock and poultry breeding, and rural life, as well as four pollutants, chemical oxygen demand, ammonia nitrogen, total nitrogen, and total phosphorus. The temporal accuracy is undetermined, the spatial accuracy is 1km×1km or 30m×30m, and the relationship between emission and water quality is established through a water quality model. (2) A basin hotspot identification method based on a high-resolution pollution source emission inventory, including three modules: establishing a county pollution source emission inventory, administrative and basin scale conversion, and hot and cold spot analysis of pollution source emissions. The inventory covers nine types of pollution sources, including rural life, free-range livestock and poultry, farmland, soil erosion, atmospheric deposition, urban sewage treatment, industrial emissions, large-scale livestock and poultry breeding, and freshwater aquaculture. The temporal accuracy of the inventory is on an annual scale, and the spatial accuracy is 1km×1km.

[0004] However, the above method has problems such as difficulty in making detailed estimates of the full scope of pollution source emission inventories and low temporal and spatial accuracy of the inventories, which need to be urgently addressed. Summary of the invention

[0005] The present application provides a method for constructing a water pollution source emission inventory to solve the problems of difficulty in making a full-caliber refined estimate of the pollution source emission inventory and low temporal and spatial accuracy of the inventory, and to achieve refined calculation of pollution source emissions.

[0006] To achieve the above objectives, the first embodiment of the present application proposes a method for constructing a water pollution source emission inventory, comprising the following steps:

[0007] Obtain pollution source subcategories, pollution generation methods, treatment technologies, and pollution discharge paths, and classify and subdivide different types of pollution sources based on the pollution source subcategories, pollution generation methods, treatment technologies, and pollution discharge paths to obtain a pollution source classification and grading system;

[0008] Using multi-source data fusion strategy and sparse data processing strategy, the data in the pollution source classification system is processed with refined parameters to determine the calculation parameters of the different types of pollution sources;

[0009] According to the pollutant generation and emission mechanism, the calculation parameters of the different types of pollution sources are modeled and characterized to obtain the amount of different types of pollutants entering the river from the different types of pollution sources. Based on the amount of different types of pollutants entering the river from the different types of pollution sources, an emission inventory of water pollution sources is constructed.

[0010] According to one embodiment of the present application, the amount of different types of pollutants entering the river from different types of pollution sources is:

[0011] E i，j =A i，j ×EF i，j ×λ i ×k×α i，j ×β i，j ;

[0012] Among them, E i，j A is the amount of the jth pollutant entering the river from the i-th pollution source, i，j is the activity level of the i-th pollution source when it produces the j-th pollutant, EF i，j is the emission intensity of the jth pollutant produced by the i-th pollution source, λ i is the comprehensive river inflow coefficient of the i-th type of pollution source, k is the coefficient used for dimension conversion, α i，j is the time distribution coefficient of the jth pollutant from the i-th pollution source, β i，j is the spatial distribution coefficient of the jth pollutant of the i-th pollution source, where i and j are natural numbers.

[0013] According to an embodiment of the present application, the multi-source data fusion strategy and the sparse data processing strategy are used to perform parameter refinement value processing on the data in the pollution source classification system to determine the calculation parameters of the different types of pollution sources, including:

[0014] Access different types of databases;

[0015] Based on the data characteristics of the different types of databases, determining the data connection link of each type of pollution source;

[0016] Based on the data connection link of each type of pollution source, a complete mapping relationship between the different types of databases and the calculation parameters of the different types of pollution sources is determined.

[0017] According to an embodiment of the present application, when determining the complete mapping relationship between the different types of databases and the calculation parameters of the different types of pollution sources, it also includes:

[0018] Based on the sparse data processing strategy, the calculation parameters of the different types of pollution sources are calculated with high spatiotemporal accuracy, wherein the sparse data processing strategy includes at least one of a downscaling strategy, a data mining strategy, an empirical formula strategy, a parameter fitting strategy, a Kriging interpolation strategy and a site-laboratory experiment strategy.

[0019] According to one embodiment of the present application, after constructing the water pollution source emission list, it also includes:

[0020] Using the source list classification assessment strategy and the preset water quality model, the accuracy of the point source and non-point source simulation of the river water quality in the water pollution source emission list is evaluated respectively;

[0021] and / or, based on a preset global sensitivity analysis strategy, using the preset water quality model, taking the calculation parameters of the different types of pollution sources as input, and evaluating the sensitivity of the water pollution source emission inventory to the river water quality simulation according to the degree of change of the output result;

[0022] And / or, using a preset control variable strategy, the uncertainty of the water pollution source emission inventory, the uncertainty of the preset water quality model, and the combined uncertainty of the water pollution source emission inventory and the preset water quality model are evaluated respectively.

[0023] According to the method for constructing a water pollution source emission inventory proposed in the embodiment of the present application, different types of pollution sources are classified and account-divided based on pollution source subcategories, pollution generation methods, treatment technologies, and pollution discharge paths, to obtain a pollution source classification and grading system; the data in the pollution source classification and grading system is processed with refined parameters using a multi-source data fusion strategy and a sparse data processing strategy to determine the calculation parameters of different types of pollution sources; the calculation parameters of different types of pollution sources are modeled and characterized according to the pollutant generation and emission mechanisms, and the amount of different types of pollutants entering the river from different types of pollution sources is obtained, and a water pollution source emission inventory is constructed based on the amount of pollutants entering the river. In this way, the problems of the difficulty of fine-tuning the full-caliber estimation of the pollution source emission inventory and the low spatiotemporal accuracy of the inventory are solved, and the refined calculation of pollution source emissions is realized.

[0024] To achieve the above-mentioned purpose, the second embodiment of the present application proposes a device for constructing a water pollution source emission inventory, comprising:

[0025] A classification module is used to obtain pollution source subcategories, pollution generation methods, treatment technologies, and pollution discharge paths, and to classify and subdivide different types of pollution sources based on the pollution source subcategories, pollution generation methods, treatment technologies, and pollution discharge paths, so as to obtain a pollution source classification and grading system;

[0026] A determination module is used to perform parameter refinement value processing on the data in the pollution source classification system by using a multi-source data fusion strategy and a sparse data processing strategy, and determine the calculation parameters of the different types of pollution sources;

[0027] A construction module is used to model and characterize the calculation parameters of different types of pollution sources according to the pollutant generation and emission mechanism, obtain the amount of different types of pollutants entering the river from the different types of pollution sources, and construct a water pollution source emission inventory based on the amount of different types of pollutants entering the river from the different types of pollution sources.

[0028] According to one embodiment of the present application, the amount of different types of pollutants entering the river from different types of pollution sources is:

[0029] E i，j =A i，j ×EF i，j ×λ i ×k×α i，j ×β i，j ;

[0030] Among them, E i，j A is the amount of the jth pollutant entering the river from the i-th pollution source, i，j is the activity level of the i-th pollution source when it produces the j-th pollutant, EF i，j is the emission intensity of the jth pollutant produced by the i-th pollution source, λ i is the comprehensive river inflow coefficient of the i-th type of pollution source, k is the coefficient used for dimension conversion, α i，j is the time distribution coefficient of the jth pollutant from the i-th pollution source, β i，j is the spatial distribution coefficient of the jth pollutant of the i-th pollution source, where i and j are natural numbers.

[0031] According to one embodiment of the present application, the determining module further includes:

[0032] An acquisition unit, used for acquiring different types of databases;

[0033] A first determining unit, configured to determine a data connection link of each type of pollution source based on data features of the different types of databases;

[0034] The second determining unit is used to determine the complete mapping relationship between the different types of databases and the calculation parameters of the different types of pollution sources based on the data connection link of each type of pollution source.

[0035] According to an embodiment of the present application, when determining the complete mapping relationship between the different types of databases and the calculation parameters of the different types of pollution sources, the second determination unit is further used to:

[0036] Based on the preset sparse data processing strategy, the calculation parameters of the different types of pollution sources are calculated with high spatiotemporal accuracy, wherein the sparse data processing strategy includes at least one of a downscaling strategy, a data mining strategy, an empirical formula strategy, a parameter fitting strategy, a Kriging interpolation strategy and a site-laboratory experiment strategy.

[0037] According to one embodiment of the present application, after constructing the water pollution source emission inventory, the construction module is further used to:

[0038] Using the source list classification assessment strategy and the preset water quality model, the accuracy of the point source and non-point source simulation of the river water quality in the water pollution source emission list is evaluated respectively;

[0039] and / or, based on a preset global sensitivity analysis strategy, using the preset water quality model, taking the calculation parameters of the different types of pollution sources as input, and evaluating the sensitivity of the water pollution source emission inventory to the river water quality simulation according to the degree of change of the output result;

[0040] And / or, using a preset control variable strategy, the uncertainty of the water pollution source emission inventory, the uncertainty of the preset water quality model, and the combined uncertainty of the water pollution source emission inventory and the preset water quality model are evaluated respectively.

[0041] According to the device for constructing the water pollution source emission inventory proposed in the embodiment of the present application, by grading and subdividing the different types of pollution sources based on the pollution source sub-category, pollution generation mode, treatment technology and pollution discharge path, a pollution source classification system is obtained; the data in the pollution source classification system is processed by parameter refinement and value taking using the multi-source data fusion strategy and sparse data processing strategy to determine the calculation parameters of different types of pollution sources; the calculation parameters of different types of pollution sources are modeled and characterized according to the pollutant generation and emission mechanism, and the amount of different types of pollutants entering the river from different types of pollution sources is obtained, and a water pollution source emission inventory is constructed based on the amount of water pollution source emission. In this way, the problems of the difficulty of fine estimation of the full-caliber pollution source emission inventory and the low spatiotemporal accuracy of the inventory are solved, and the fine calculation of pollution source emissions is realized.

[0042] To achieve the above-mentioned objectives, the third aspect of the present application proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a method for constructing a water pollution source emission inventory as in the above-mentioned embodiment.

[0043] To achieve the above objectives, the fourth aspect of the present application proposes a computer-readable storage medium on which a computer program is stored, and the program is executed by a processor to implement a method for constructing a water pollution source emission inventory as in the above embodiment.

[0044] To achieve the above objectives, the fifth aspect of the present application proposes a computer program product, which includes a computer program. When the computer program is executed by a processor, it is used to implement the method for constructing a water pollution source emission inventory as in the above embodiment.

[0045] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0047] Figure 1 A flowchart of a method for constructing a water pollution source emission inventory according to an embodiment of the present application;

[0048] Figure 2 A flowchart of a method for constructing a water pollution source emission inventory according to another embodiment of the present application;

[0049] Figure 3 A schematic diagram of a combined coding system for a water pollution source emission inventory according to an embodiment of the present application;

[0050] Figure 4 A schematic diagram of a breeding source account coding system according to an embodiment of the present application;

[0051] Figure 5 A schematic diagram of an analysis process for constructing a refined time profile of an industrial source according to an embodiment of the present application;

[0052] Figure 6 A generalized schematic diagram of a river section according to an embodiment of the present application;

[0053] Figure 7 A schematic diagram of a method for analyzing uncertainty in a water pollution source emission inventory according to an embodiment of the present application;

[0054] Figure 8 A schematic diagram of observation data coverage based on inventory uncertainty analysis according to an embodiment of the present application;

[0055] Fig. 9 A schematic diagram of the spatiotemporal distribution effect of a watershed water pollution source emission inventory according to an embodiment of the present application;

[0056] Fig.10 A schematic diagram of a reliability assessment result of a watershed water pollution source emission inventory according to an embodiment of the present application;

[0057] Fig.11A block diagram of a device for constructing a water pollution source emission inventory according to an embodiment of the present application;

[0058] Fig.12 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0059] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0060] The following describes a method for constructing a water pollution source emission inventory according to an embodiment of the present application with reference to the accompanying drawings.

[0061] Figure 1 It is a flow chart of a method for constructing a water pollution source emission inventory according to an embodiment of the present application.

[0062] Before introducing the method for constructing a water pollution source emission inventory proposed in the embodiment of the present application, the relevant technical background is first introduced.

[0063] The following difficulties are common in the compilation of existing water pollution source emission inventories: First, there are many departments and industries that generate pollution in the basin, with different characteristics. The production scale and process, sewage treatment process, and the destination of pollutant emissions will affect the generation or emission of pollutants. Therefore, it is necessary to build a systematic and comprehensive classification accounting system to accurately describe the impact of pollution-generating departments, industries, and technologies on pollutant emissions. Second, only important point sources of pollution in the basin have emission monitoring data. Therefore, the reliability of the inventory depends on the accuracy of the estimation of emissions from unmonitored pollution sources. Third, pollutant emissions are random and volatile, and the migration process of pollutants in the basin and water bodies is highly complex, which poses a great challenge to the accuracy verification of the emission inventory.

[0064] The existing technology for compiling water pollution source emission inventories has the following defects:

[0065] (1) The classification of pollution sources is not comprehensive, systematic, and standardized. Urban domestic sources (parts that are not collected and treated) and water body sources are not included in the research scope, and there is no clear classification and classification definition of pollution sources, which may lead to duplication, omission, and miscalculation of pollution source emissions.

[0066] (2) The temporal and spatial precision of the inventory is insufficient. For example, the nutrient emission inventory of sewage treatment plants still uses annual treatment volume and annual average emission concentration. Although the spatial precision is 1 km × 1 km, it is difficult to accurately describe the localized characteristics of pollutant emissions when using unified parameters for county-watershed-1 km × 1 km downscaling.

[0067] (3) The verification method of the inventory is insufficient.

[0068] (4) The application method of the inventory is not clear, especially how to use the refined emission inventory to evaluate the actual effectiveness of different pollution source control strategies.

[0069] Based on the above problems, the present application embodiment proposes a method for constructing a water pollution source emission inventory (Water Pollution Emission Inventory with High Spatio-Temporal Resolution, WEIHR for short), such as Figure 2 As shown, including pollution source classification and grading, emission inventory calculation, refined parameter selection, multi-dimensional inventory reliability assessment and multi-indicator evaluation of control strategies, it can realize the refined calculation of pollution source emissions, quantitative evaluation of emission inventory reliability and accurate identification of pollution source control strategies, thereby solving the problems of difficulty in full-caliber refined estimation of pollution source emission inventories and lack of emission inventory verification methods.

[0070] The following is a detailed description of the method for constructing a water pollution source emission inventory proposed in the embodiment of the present application.

[0071] For example, Figure 1 As shown, the method for constructing the water pollution source emission inventory includes the following steps:

[0072] In step S101, the pollution source subcategories, pollution generation methods, treatment technologies and pollution discharge paths are obtained, and based on the pollution source subcategories, pollution generation methods, treatment technologies and pollution discharge paths, different types of pollution sources are graded and account subdivided to obtain a pollution source classification and grading system.

[0073] It is understandable that pollution sources refer to the sources of pollution, including various human activities and natural processes, such as industrial production, transportation, agricultural activities, etc. Subcategories refer to the further subdivision of large-scale pollution sources into more specific types. For example, aquaculture pollution sources can be subdivided into large-scale livestock and poultry farming, free-range livestock and poultry, and aquaculture subcategories. Pollution generation methods refer to the specific ways or means of pollution generation. For example, in livestock and poultry farming, different breeding stages (such as nursery, fattening, and pregnancy) will have different feed feeding amounts, water intake, and excrement types, resulting in different pollutant emission characteristics and methods. Treatment technology refers to the technical means used to reduce or eliminate pollution, such as dry manure cleaning, water-flushing manure cleaning, and straw bedding. The discharge path refers to the way or channel for pollutants to be discharged into the environment, such as entering sewage treatment plants, returning to the fields, and as biogas fuel.

[0074] Specifically, the present embodiment of the application divides pollution sources into ten categories, including industrial sources, sewage treatment plants, and aquaculture sources, by referring to national economic industry classification standards, environmental statistics, pollution source census coefficient manuals, literature surveys, and expert interviews, as shown in Table 1. Different types of pollution sources are further classified and accounted for in terms of pollution source subcategories, pollution generation methods, treatment technologies, and pollution discharge paths, so as to establish a complete pollution source classification and grading system. In addition, Figure 3 The coding rules shown in the figure uniformly encode all types of accounts at all levels. Figure 4 This is an example of aquaculture source account coding. In actual application, it can be adjusted and modified based on the research basis of watershed pollution sources and data quality.

[0075] Table 1

[0076]

[0077]

[0078] In step S102, a multi-source data fusion strategy and a sparse data processing strategy are used to perform parameter refinement processing on the data in the pollution source classification system to determine the calculation parameters of different types of pollution sources.

[0079] It is understandable that in order to further improve the reliability of the water pollution source emission inventory, before building the inventory, the multi-source data fusion strategy and sparse data processing strategy can be used to refine the values ​​of the calculation parameters involved, that is, by integrating and optimizing data from different sources using the multi-source data fusion strategy, the integrity and accuracy of the data can be ensured. At the same time, through the sparse data processing strategy, scarce and difficult-to-obtain data points can be effectively processed, thereby reducing the impact of missing or incomplete data. In this way, the calculation parameters of different types of pollution sources can be determined more accurately, providing a solid data foundation for the subsequent construction of the water pollution source emission inventory.

[0080] As a possible implementation method, in some embodiments, a multi-source data fusion strategy and a sparse data processing strategy are used to perform parameter refinement processing on the data in the pollution source classification system to determine the calculation parameters of different types of pollution sources, including: obtaining different types of databases; determining the data connection links of each type of pollution source based on the data characteristics of different types of databases; and determining the complete mapping relationship between different types of databases and the calculation parameters of different types of pollution sources based on the data connection links of each type of pollution source.

[0081] Specifically, different databases can be divided into macro-statistical data, online monitoring data, spatial remote sensing data, field monitoring data, and literature research data from the perspective of data type. Different types of data have differentiated information characteristics such as frequency, spatiotemporal accuracy, and types of pollution sources covered. In order to achieve high spatiotemporal accuracy characterization of pollution emission characteristics from multiple pollution sources, the embodiments of the present application can analyze the data characteristics of each database, set matching quality inspection rules, and carry out multi-source data fusion.

[0082] In the connection and matching stage, the data connection links are determined for each type of pollution source, as shown in Table 2, in order to establish a complete mapping relationship between the database and the calculation parameters, clarify the data sources such as the activity level, emission parameters, and emission destination of each type of pollution source, and obtain the inventory calculation parameters, which include activity level parameters, emission intensity parameters, river discharge parameters, time allocation parameters, and space allocation parameters.

[0083] In the quality inspection phase, the key data such as the activity level and emission parameters of the database after connection and matching are checked for quality, and the integrity and quality of the database data are guaranteed through data cleaning, outlier removal, and missing and filling methods. In the data cleaning and outlier removal phase, the mean is used as the center and three times the standard deviation is used as the boundary to clean and remove outliers outside the boundary; in the missing and filling phase, for descriptive fields, they can be supplemented according to the main technical types of the same region and industry, and for numerical fields, they can be filled according to the mean of the parameters of departments with similar scale types in the same region, industry, and type.

[0084] Table 2

[0085]

[0086]

[0087] Furthermore, in some embodiments, when determining the complete mapping relationship between the calculation parameters of different types of databases and different types of pollution sources, it also includes: based on a sparse data processing strategy, calculating the calculation parameters of different types of pollution sources at high temporal and spatial precision, wherein the sparse data processing strategy includes at least one of a downscaling strategy, a data mining strategy, an empirical formula strategy, a parameter fitting strategy, a Kriging interpolation strategy, and a site-laboratory experiment strategy.

[0088] Specifically, due to the scarcity of high-precision data, the difficulty in obtaining data, the breakpoints or spatiotemporal discontinuity of online monitoring data, etc., a large number of activity levels and emission parameters are scarce. In the construction of the water pollution emission inventory, according to the emission patterns and characteristics of different pollution sources, a variety of data processing methods such as downscaling, data mining, and parameter fitting are used to establish a variety of scarce data processing methods to calculate the activity levels, emission parameters or emissions of various pollution sources at high spatiotemporal precision, and then construct a refined water pollution source emission inventory.

[0089] (1) Downscaling strategy: A commonly used refined inventory processing technology. The rationality of the time allocation coefficient and the space allocation coefficient is crucial to the reliability of the water pollution source emission inventory. This embodiment of the application is based on the activity level, emission characteristics and parameter information of each pollution source, combined with literature research to identify sensitive factors affecting emissions, and determines the key parameters for downscaling of each pollution source for inventory emission allocation, as shown in Table 3. Among them, due to the limited number of monitoring sites and monitoring frequency, inland waterway sources and water body sources can be constructed by evenly distributing the time and space dimensions.

[0090] Table 3

[0091]

[0092] In Table 3, “-” indicates that the inventory does not need to be downscaled and can be processed by other methods such as high-precision input data.

[0093] (2) Data mining strategy, that is, the process of mining useful but inaccessible information and patterns from massive, noisy actual monitoring data. In the process of constructing the water pollution source emission inventory, data mining methods are mainly used to process high-frequency online monitoring data of pollution source emissions. Currently, key industrial sources, sewage treatment plants and other pollution sources have successively installed high-frequency online emission monitoring equipment, with fast monitoring frequency and large amount of data. However, some enterprises still have not installed online monitoring equipment, and online monitoring equipment also has problems such as data discontinuity. Therefore, by analyzing and processing massive data, the pollution emission patterns of pollution sources with daily accuracy can be mined and the time profile of pollutant emissions of enterprises (i.e., the set of time distribution coefficients) can be constructed.

[0094] The data mining process includes data cleaning, data fusion, data pattern mining, pattern evaluation and other links. The construction and analysis of the time profile of pollutant emissions from industrial sources, sewage treatment plants, etc. in the water pollution emission inventory is as follows: Figure 5 As shown, the high-frequency emission data are extracted, the industry is classified and the data is cleaned, the pollution emission load and time-varying coefficient of each industry and enterprise are calculated (as shown in formula (1)), and the time series correlation between industries and between enterprises in the same industry is explored (as shown in formula (2)). If the correlation is strong, the constructed pollutant emission time series of each industry is mapped to low-frequency statistical data, and an emission list of water pollution sources from industrial sources, sewage treatment plants and domestic sources is established.

[0095] When determining the time series, the time variation coefficient α (referred to as the time variation coefficient) is used to characterize it, that is, the daily pollutant discharge or runoff A and the annual daily average Ratio:

[0096]

[0097] Calculation of cross-correlation of pollutant emission time series:

[0098]

[0099] Among them, x and y are different industries or different enterprises in the same industry, x(t) and y(t) are the time-varying coefficients of pollutant emissions at time t, σ x With σ y are the mean square errors of x(t) and y(t), and are the means of x(t) and y(t), respectively, C xy is the covariance of the two time series, r xy is the mutual correlation coefficient of the two time series, and n is the length of the time series.

[0100] (3) The empirical formula strategy is mainly used to calculate the emission time series of rainfall-driven pollution sources such as planting sources and surface runoff sources. In the process of constructing the water pollution source emission inventory, the rainfall-runoff time series of different land use types is a key parameter. When calculating the rainfall runoff generated within the grid, the SCS (Soil Conservation Service) runoff model developed by the Soil Conservation Service of the United States Department of Agriculture can be used, as shown in formula (3):

[0101]

[0102] Where Q is the runoff depth in mm; k rainfali is the rainfall, in mm; runoffis the initial loss coefficient, dimensionless; S is the potential water storage capacity, unit is mm; CN is the runoff curve number, dimensionless.

[0103] The CN value can be found in the U.S. National Engineering Manual, which is closely related to land use type and hydrological soil group. The U.S. Department of Agriculture Soil Conservation Service divides soil into four categories: A, B, C, and D, and their corresponding hydraulic conductivity coefficients K s The hydraulic conductivity coefficient K is respectively >180, 18-180, 1.8-18, and <1.8 mm / min. s The empirical relationship with soil composition is shown in formula (4):

[0104] K s =0.056×C+0.016×S+0.231×O m -0.693; (4)

[0105] Among them, K s is the hydraulic conductivity coefficient, in mm / min; C is the percentage of clay in the soil, in %; S is the percentage of sand in the soil, in %; O m It is the organic matter content in the soil, in %.

[0106] Based on the grid hydraulic conductivity K s Combined with the CN of different land use types and daily rainfall data, the daily rainfall-runoff time series of the grid under different land use types can be determined, which can be used to fill in the refined time series of the aquaculture source, planting source, forest and grass source, and urban surface runoff source list with grid accuracy.

[0107] (4) Parameter fitting strategy: by linking the pollutant emission data obtained from field experiments and on-site monitoring with multiple attribute information such as activity levels, meteorology and geography, the pollutant emission patterns can be simulated. This can be used to calculate gridded pollution emission parameters and river entry coefficients, thus filling the gap in scarce data in the spatial dimension.

[0108] Taking the construction of the planting source list as an example, the embodiment of this application combed through the planting source emission parameters in the "First National Pollution Source Census-Handbook of Fertilizer Loss Coefficients of Agricultural Pollution Sources" and the planting source spatial attributes of the region, and found that variables such as land use type and crop type have a strong correlation with the pollutant generation coefficients such as total nitrogen and total phosphorus. The distribution of pollutant generation coefficients corresponding to different attributes of the variables has significant differences after statistical tests. Therefore, by collecting the basic situation of planting sources and field monitoring data on pollutant emissions, land use type, crop type, and crop maturity are used as categorical variables, fertilizer application amount is used as a continuous variable, and pollutant emission coefficients are used as dependent variables to establish a multivariate linear regression model, which can be used to calculate the pollution emission parameters of planting sources under grid accuracy.

[0109] In the process of pollutant migration into the river, the migration loss rate is mostly an empirical coefficient, and its numerical value has a great influence on the calculation of the amount of pollution entering the river. Although existing studies have characterized the relationship, it is difficult to directly apply the relationship due to the large geographical and meteorological differences in different regions. The embodiment of the present application draws on the exponential relationship structure between the pollutant entry rate and the distance into the river in the basin buffer zone-distance related research, and through meta-analysis, collects multiple groups of site runoff simulation experimental data with soil types similar to the case area, constructs a relationship between the pollutant entry rate and the distance into the river, and uses integral calculation to solve the proportion of pollutant outflow, and determines the impact of distance on the spatial correction factor of pollutant emissions, as shown in formulas (5)-(12):

[0110]

[0111] in, is the spatial correction factor for rainfall at grid i; is the annual rainfall of grid i, in mm / a; It is the average annual rainfall in the basin, in mm / a.

[0112]

[0113] in, is the spatial correction factor of the terrain of grid i; is the slope of grid i; is the average slope of the basin; 0.61 is the empirical coefficient.

[0114]

[0115] in, is the spatial correction factor of vegetation coverage of grid i; is the normalized vegetation index of grid i; and are the maximum and minimum indices in the basin, respectively.

[0116]

[0117] Among them, y, is the total nitrogen emission rate, y2 is the total phosphorus emission rate, y, and y2 respectively describe the relationship between the emission rates of total nitrogen and total phosphorus pollutants and the change of distance, the unit is %; k distance is the distance between the grid and the river channel, in m; is the spatial correction factor for total nitrogen from plantation source pollutants due to the distance into the river, dimensionless; It is the spatial correction factor of total phosphorus of plant source pollutants due to the distance of entering the river, dimensionless.

[0118]

[0119] Among them, λagr.i It is an indicator of the comprehensive migration coefficient of pollutants from non-point sources into rivers.

[0120] (5) Kriging interpolation strategy is a regression algorithm with covariance function as the core, which can be used for spatial modeling and interpolation. Since it takes into account the spatial correlation of independent variables in the process of data rasterization, the interpolation result is close to the actual situation and is widely used in the spatial interpolation of various observations, such as single-point monitoring of temperature and precipitation in atmospheric science. In the process of constructing the water pollution source emission inventory, Kriging interpolation method is mainly used in the grid space interpolation of rainfall data at meteorological stations or the grid space interpolation of pollutant wet deposition flux at atmospheric wet deposition monitoring stations. The Kriging interpolation calculation formula is shown in formula (13):

[0121]

[0122] in, is the estimated value at the point (x0, y0), IDW i is the weight coefficient, and the value of the predicted point is calculated by weighted summation of the data of all monitoring points in space, which satisfies a set of optimal parameters that minimizes the difference between the estimated value and the true value at the point (x0, y0).

[0123] In the sparse data spatial interpolation stage of inventory construction, ArcGIS software is mostly used for processing. By loading site data and watershed grid maps, setting the processing range and performing raster analysis, monitoring data with grid accuracy can be obtained.

[0124] (6) Site / laboratory experimental strategy, that is, by conducting site monitoring or pollution source emission monitoring experiments on site or in the laboratory, the pollutant emission patterns of the pollution source are obtained. This strategy is suitable for the construction of pollution emission sources with limited emission information, missing or sparse emission parameters, etc. Taking the internal source of water as an example, the release rate of pollutants from the internal source of water is the key to calculating the amount of pollutant emissions. In order to better understand the dynamic release rate of pollutants in sediment under hydrodynamic conditions, the experimental method can be used to simulate the release process of sediment in the basin and explore the pollutant emission patterns of sediment under dynamic conditions.

[0125] In summary, the sparse data processing methods corresponding to various pollution sources in the construction of water pollution source emission inventory are shown in Table 4.

[0126] Table 4

[0127] Pollution Sources Treatment Industrial Source Data mining, downscaling Sewage Plant Data mining, downscaling Planting source Downscaling, empirical formula method, parameter fitting method, Kriging interpolation method Farming source Downscaling, empirical formula method, parameter fitting method, Kriging interpolation method Life Source Data mining, downscaling Forest and Grass Source Downscaling, empirical formula method, Kriging interpolation method Urban surface runoff sources Downscaling, empirical formula method, Kriging interpolation method Atmospheric deposition sources On-site monitoring method, Kriging interpolation method Inland waterway transport Downscaling Water body source Downscaling method, field / laboratory experiment method

[0128] In step S103, according to the pollutant generation and emission mechanism, the calculation parameters of different types of pollution sources are modeled and characterized to obtain the amount of different types of pollutants entering the river from different types of pollution sources, and based on the amount of different types of pollutants entering the river from different types of pollution sources, a water pollution source emission inventory is constructed.

[0129] It can be understood that the basic principle of establishing a water pollution source emission inventory is to model and characterize the activity level, emission parameters and calculation parameters such as the inflow coefficient of various sources (the specific parameter value selection method is shown in Table 5) at the pollution source and detailed classification level according to the pollutant generation and emission mechanism, and obtain the inflow of different types of pollutants from different types of pollution sources, as shown in formula (14):

[0130] E i，j =A i，j ×EF i，j ×λ i ×k×α i，j ×β i，j ; (14)

[0131] Among them, E i，j A is the amount of the jth pollutant from the i-th pollution source entering the river, in kg / d; i，j EF is the activity level of the i-th pollution source when it produces the j-th pollutant, and the unit depends on the specific activity; i，j is the emission intensity of the jth pollutant produced by the i-th pollution source, and the unit is determined according to the specific i and j; i is the comprehensive river entry coefficient of the i-th type of pollution source, dimensionless, where the river entry coefficient of the point source is calculated with reference to the comprehensive pollutant transfer coefficient, as shown in formula (2), and the river entry coefficient of the surface source is determined according to its discharge direction; k is the coefficient used for dimension conversion; α i，j is the time distribution coefficient of the jth pollutant from the i-th pollution source; β i，j is the spatial distribution coefficient of the jth pollutant of the i-th pollution source, that is, the downscaling parameter, dimensionless; i and j are natural numbers.

[0132] λ i =λ 1i ×λ 2i ×λ 3i ; (15)

[0133] Among them, λ 1i λ is the river inflow coefficient, which is determined by the distance (L) from the enterprise discharge outlet and the urban sewage treatment facility discharge outlet to the river discharge outlet. When L≤1km, the river inflow coefficient is 1; when 1km<L≤10km, the river inflow coefficient is 0.9; when 10km<L≤20km, the river inflow coefficient is 0.8; when 20km<L≤40km, the river inflow coefficient is 0.7; when L>40km, the river inflow coefficient is 0.6. 2i is the channel correction coefficient. When entering the river through an unlined open channel, the channel correction coefficient is 0.6 to 0.9; when entering the river through a lined concealed pipe, the channel correction coefficient is 0.9 to 1.0. 3iis the temperature correction coefficient. When the temperature is ≤10℃, the temperature correction coefficient is 0.95~1.0; when 10℃<temperature≤30℃, the temperature correction coefficient is 0.8~0.95; when the temperature is>30℃, the temperature correction coefficient is 0.7~0.8.

[0134] Table 5

[0135]

[0136]

[0137]

[0138] Furthermore, in some embodiments, after constructing the water pollution source emission inventory, it also includes: using the source inventory classification evaluation strategy and the preset water quality model to respectively evaluate the accuracy of the point source and non-point source simulation of the water pollution source emission inventory on the river water quality; and / or, based on a preset global sensitivity analysis strategy, using a preset water quality model, with the calculation parameters of different types of pollution sources as input, and according to the degree of change in the output results, evaluating the sensitivity of the water pollution source emission inventory to the river water quality simulation; and / or, using a preset control variable strategy, respectively evaluating the uncertainty of the water pollution source emission inventory, the uncertainty of the preset water quality model, and the combined uncertainty of the water pollution source emission inventory and the preset water quality model.

[0139] That is to say, after obtaining the water pollution source emission inventory, the embodiment of the present application also proposes a multi-dimensional inventory evaluation method system including accuracy-sensitivity-uncertainty to evaluate the reliability of the water pollution source emission inventory.

[0140] Specifically, the accuracy of the water pollution source emission inventory, that is, the degree to which the inventory reflects the actual changes in the emission of pollutants from the pollution source. Due to the mixed emissions of multiple pollution sources in the water pollution source emission inventory system, the difficulty of evaluating the accuracy of a single pollution source inventory is increased. The embodiment of the present application can adopt a method of joint analysis of source inventory classification evaluation and water quality simulation evaluation. On the one hand, from the perspective of pollution sources, the simulation accuracy of point source and non-point source inventories in the water pollution source emission inventory is evaluated separately; on the other hand, starting from the water quality of the water body observation section, with the water quality model as the connecting link, based on the input-response relationship, the overall simulation effect of the water pollution source emission inventory on river water quality is evaluated.

[0141] Since the accuracy assessment of the water pollution source emission inventory requires the use of a surface water quality model, in order to reduce the impact of model structure and parameter uncertainty on the results, the embodiment of the present application can use a relatively simple one-dimensional steady-state water quality model (i.e., a preset water quality model) to simulate the changes in surface water quality. Its basic hydrological and water quality equations are shown in equations (16)-(18). The generalization of the water quality model input includes: non-point source sewage outlets are generalized into linear emission sources along the river section, and the amount of non-point source pollutants entering the river is evenly distributed according to the distance along the way; point sources are determined according to their actual discharge outlet locations, where the generalized schematic diagram can be as follows: Figure 6 shown.

[0142] q i =q i-1 +q in，i -q out,i ; (16)

[0143]

[0144] Among them, q i is the outflow of the ith river unit, in m 3 / s;q i-1 is the outflow of the i-1th river unit, in m 3 / s;q in,i is the inflow of external pollution source to the i-th river unit, in m 3 / s;q out，i is the outflow of external pollution source of the i-th river unit, in m 3 / s;q i′ is the tributary flow, in m 3 / s; is the concentration of pollutants j after the mixture of river channel i and river channel i′, in mg / L; c i，j is the concentration of pollutant j in section i′ of the river, in mg / L; c i，j is the concentration of pollutant j in section i of the river, in mg / L; c i-1，j is the concentration of pollutant j at section i-1 upstream of river channel i, in mg / L; k 0，j is the temperature correction factor for the reaction rate of the j-type pollutant, dimensionless; θ is the reference value of the comprehensive degradation coefficient of the j-type pollutant at 20°C, in s -1 ; T is temperature, unit is ℃; X is the distance between section i and section i-1, unit is m; U i is the flow velocity of river channel i, in m / s.

[0145] When comparing the accuracy of the results, R 2, Nash-Sutcliffe efficiency coefficient (NSE) and root mean square error (RMSE). The calculation methods of different indicators are shown in formulas (19)-(21):

[0146]

[0147] Among them, X i is the simulated value, Y i is the monitoring value, is the simulated mean, is the monitoring mean.

[0148] The sensitivity of the water pollution source emission inventory refers to the degree to which the model output changes when a given input calculation parameter changes. High parameter sensitivity means that even a small parameter uncertainty will have a large impact on the output, and special attention should be paid when selecting parameters; for parameters with lower sensitivity, it can be considered to a certain extent that changes in their parameters will not significantly affect the output results, and no special attention is required during the simulation process. The embodiment of the present application combines the water pollution source emission inventory with a preset water quality model, and calculates the global sensitivity of the inventory parameters and the water quality model parameters based on the Sobol global sensitivity analysis, identifying key parameter information from upstream source emissions to downstream section water quality simulation.

[0149] Based on the accuracy and sensitivity evaluation of the inventory, the embodiment of the present application constructs an uncertainty assessment framework for the water pollution source emission inventory. By adopting the control variable method, the uncertainty under three situations is calculated (changing the inventory parameters, water quality model parameters, and changing the inventory and water quality model parameters at the same time), and the differences in the calculation results are compared, the inventory uncertainty, water quality model uncertainty, and inventory-water quality model joint uncertainty are evaluated, and the impact of the internal uncertainty of the water pollution source emission inventory on the cross-section water quality simulation after being transmitted through the water quality model is analyzed. The uncertainty analysis method is as follows: Figure 7 shown.

[0150] It is understandable that the difficulty of quantitatively evaluating the internal uncertainty of the water pollution source emission inventory lies in determining the distribution range and distribution shape of many parameters. First, based on the results of the above parameter sensitivity analysis, the parameters, distribution range and form of the parameters that need to be considered in the uncertainty analysis are determined, and used as the input of the inventory uncertainty analysis. According to the different sources of parameter data, they can be divided into measured statistical data and literature survey data. For measured statistical data such as online monitoring data and site monitoring data, their parameter distribution range can be analyzed and the parameter distribution type can be verified; for literature survey data, the corresponding parameters of the case area can be collected and sorted, and their distribution form and distribution range can be summarized and analyzed; for parameters without prior distribution, it can be studied to initially set them to uniform distribution. After determining the distribution form and range of each parameter, the Monte Carlo algorithm (Monte Carlo analysis method) is used to calculate each parameter after passing it through the inventory calculation model, and the uncertainty range and uncertainty of the emission of different types of pollution source inventories are output. Uncertainty is expressed as the ratio of standard deviation to mean. In the Monte Carlo simulation, each parameter is independently sampled according to its interval and the distribution characteristics within the interval, and then input into the model to obtain the model output under this set of sampling. The parameter value and the corresponding model output are recorded, and then the next set of sampling is performed until the specified number of times.

[0151] It should be noted that the reliability of the water quality results of the simulated section of the water pollution source emission inventory is affected not only by the water pollution source emission inventory but also by the water quality model. For the water quality model parameters, the embodiment of the present application can comprehensively consider the uncertainty of hydrology, water quality and pollutant degradation, and determine the distribution range and uncertainty of hydrology and water quality parameters through literature research and field monitoring. The inventory parameters, water quality model parameters and inventory and water quality model parameters are changed respectively to check the distribution of the simulation results. The results are respectively called water quality simulation results considering only inventory uncertainty, water quality simulation results considering only water quality model uncertainty and simulation results considering inventory-water quality model uncertainty jointly. By analyzing the three result intervals, we can obtain the impact of the uncertainty of the water pollution source emission inventory on the uncertainty of water quality output after being transmitted through the water quality model, that is, the transmission characteristics of uncertainty; at the same time, by comparing the coverage of the simulation results on the measured water quality data of the section, we can obtain the simulation effect of the inventory on the water environment of the basin after considering the uncertainty, where the coverage is expressed as the proportion of the number of measured data in the distribution interval to all measured data, such as Figure 8 shown.

[0152] Furthermore, in order to explore the effect of pollution source control strategies on improving the water environment quality of the basin, the embodiments of the present application selected emission reduction measures such as single pollution source control strategies and combined pollution source control strategies, and applied the water pollution source emission inventory and input-response relationship to evaluate the reduction effects of different control strategies on emissions in high-value areas and peak periods, as well as the impact on improving the water quality of the basin.

[0153] When selecting a single pollution source control strategy, it can be formulated based on the contribution of pollution source emissions in the study area, the results of inventory sensitivity and uncertainty analysis, and the relevant action plans or policy requirements of local ecological and environmental departments. The combined pollution source control strategy is a combination of single control strategies, which can be used to explore the environmental quality improvement effect under the coordinated control of multiple control strategies, and can further identify effective pollution source control strategies in different periods and regions by analyzing the contribution ratio of single control strategies to pollutant emission reduction of combined control strategies, and formulate a feasible strategy set for refined emission control.

[0154] The embodiment of the present application defines the coefficient of variation of the temporal and spatial distribution of the inventory (Emission ’ s Coefficient Indicators such as time-variety variation (ECV), emission peaks and high-value area intensity characterize the identification effect of water pollution source emission inventories on pollution reduction of various control strategies. The larger the value of the spatiotemporal distribution coefficient of variation (ECV), the greater the unevenness of pollution emissions at daily / grid accuracy, and the more drastic the emission fluctuations. Under the current situation and different pollution source control strategy scenarios, the spatiotemporal distribution coefficient of variation (ECV) can be used to measure the spatiotemporal distribution of pollutant emission reduction in each pollution source control strategy scenario, and compare the results of a single control strategy or a combination of strategies to evaluate the effect of pollution source control strategies on pollution reduction in the watershed. The calculation formulas for the spatiotemporal distribution coefficient of variation (ECV) of the inventory are shown in equations (22)-(23). ECV can be further divided into the time coefficient of variation (ECV) t ECV s .

[0155]

[0156] Among them, E ti is the inventory pollutant emission on day i, is the average daily pollution emission in the inventory, N t is the total number of days, E si is the pollutant emission of the ith grid inventory, is the average grid-by-grid pollution emission in the inventory, N s is the total number of grids.

[0157] The embodiment of the present application further utilizes the pollutant input-water quality response relationship. By adjusting the parameters corresponding to different pollution source control strategy scenarios, the corresponding water pollution source emission list is input into a preset water quality model. Combined with uncertainty analysis, the water quality changes of river sections corresponding to different pollution source control strategies are output, the daily average emission concentration of pollutants in the corresponding river sections is analyzed, and the water quality of river sections exceeding the standard is identified with daily accuracy.

[0158] In order to better explain the improvement effect of different pollution source control strategies on the water environment of the river basin, the embodiment of the present application takes into account three dimensions: total emission, spatiotemporal distribution of emission, and river water quality response, and sets seven indicators: annual pollutant emission load (I1), inventory time distribution variation coefficient (I2), inventory spatial distribution variation coefficient (I3), emission high value area intensity (I4), emission peak intensity (I5), section water quality exceeded the standard number of days (I6), and the cumulative load of all exceeded days accounted for the proportion of the total annual load (I7). The specific explanation of each indicator is shown in Table 6.

[0159] Table 6

[0160]

[0161]

[0162] In practical applications, the embodiments of the present application are applied to the study of pollution emission control strategies in the Fuhe River Basin upstream of Baiyangdian Lake. The water pollution source emission inventory has a significant effect on characterizing the differences in the temporal and spatial distribution of different pollutants from various pollution sources in the basin. Fig. 9 As shown, Fig. 9 (a) is a comprehensive time series diagram. Fig. 9 (b) is the grid distribution map for the whole year. In terms of time dimension, the emissions of different pollutants reach their annual peaks from June to August. The time-varying coefficients of ammonia nitrogen and total phosphorus pollutants are large, and the daily emission intensity varies by 18.2-31.9 times throughout the year. The spatial high-value areas of pollutant emissions are prominent. The high-value emission areas only account for 7.8% of the area of ​​the basin, and contribute 23%-46% of the total load of various pollutants in the basin. The water pollution source emission inventory is generally reliable after accuracy, sensitivity and uncertainty analysis. The daily average relative error between the simulated water quality and the observed water quality in the water pollution source emission inventory is within 30%, and the accuracy is good. The water quality model is used to quantify the impact of inventory uncertainty on water quality. After the inventory uncertainty is transmitted through the water quality model, the joint uncertainty increases by 2%-3%, and the annual joint uncertainty is about 13%. When considering uncertainty together, the coverage of the posterior distribution for observed water quality reaches above 0.85 (95% confidence interval), such as Fig.10As shown, the decision-making risk based on the simulation results is reduced to a certain extent. The study selected planting sources, domestic sources and surface runoff sources for management and control analysis, set control strategies for each pollution source, as shown in Table 7, and calculated the impact of the separate and combined implementation of different pollution source control strategies on pollutant emission reduction, spatial and temporal distribution of pollutant emissions and cross-section water quality improvement. According to the evaluation of the water pollution source emission inventory, domestic sources reduced the total amount of pollution emissions by 20%-56.7%. Although the surface runoff source strategy contributed only 0.6%-5% to the reduction of the total amount of pollution emissions, it contributed 5-10% to the reduction of the peak intensity of pollutant emissions. It is encouraged to carry out coordinated control of multiple pollution source strategies in different regions and water periods, accurately reduce pollution emission hotspots and peaks, and improve the water quality of the basin.

[0163] Compared with the traditional inventory compilation method, the water pollution source emission inventory proposed in the embodiment of the present application has an emission peak and high-value area intensity identification accuracy of 12 times, a reliability of 27.5%, and an overall reliable inventory methodology. The coefficient of variation of the time distribution of different pollution sources has increased by 0.2-2.9; in the spatial dimension, the spatial distribution of different pollutant emissions is extremely uneven, and the grid strength difference is 25.3-44.1 times. Compared with the traditional inventory, the coefficient of variation of the spatial distribution of a single pollution source has increased by 0.2-3.1. In the application of inventory control strategies, the identification effect of the peak shaving intensity of pollution source control in the water pollution source emission inventory can be enhanced by 0.3%-8.9%, effectively reducing the misjudgment rate of the implementation effect of the control strategy.

[0164] Table 7

[0165]

[0166] In summary, the method for constructing a water pollution source emission inventory in the embodiment of the present application has the following beneficial effects:

[0167] (1) The method for constructing the water pollution source emission inventory in the embodiment of this application overcomes the data challenges of the scarcity of existing water environment management data and heterogeneous data sources, and solves the technical difficulties of decoupling the relationship between pollution source management and water quality response. This application proposes a pollution source classification and grading system based on the characteristics of the production, discharge and sink of all-caliber pollutants in the basin, and establishes a parameter refinement method based on scarce data filling and multi-source data fusion, which can realize the pollution emission estimation of all-caliber pollution sources in the basin with daily step length and 1km×1km grid accuracy.

[0168] (2) The reliability assessment framework of the water pollution source emission inventory proposed in the embodiment of the present application can realize the accuracy and parameter sensitivity analysis of the refined water pollution source emission inventory results, quantify the inventory uncertainty and its superimposed impact on water quality simulation, and improve the scientificity and reliability of the water pollution source emission inventory.

[0169] (3) The method for screening watershed emission control strategies based on a refined water pollution source emission inventory and input-response relationship proposed in the embodiment of the present application evaluates the effect of different emission reduction measures on improving water quality from the spatial and temporal dimensions of reducing emissions in high-value areas and emissions during peak periods, and identifies a feasible strategy set for refined control of watershed pollution emissions, providing scientific support for the rationality and systematicness of refined water environment management decisions.

[0170] According to the method for constructing a water pollution source emission inventory proposed in the embodiment of the present application, different types of pollution sources are classified and account-divided based on pollution source subcategories, pollution generation methods, treatment technologies, and pollution discharge paths, to obtain a pollution source classification and grading system; the data in the pollution source classification and grading system is processed with refined parameters using a multi-source data fusion strategy and a sparse data processing strategy to determine the calculation parameters of different types of pollution sources; the calculation parameters of different types of pollution sources are modeled and characterized according to the pollutant generation and emission mechanisms, and the amount of different types of pollutants entering the river from different types of pollution sources is obtained, and a water pollution source emission inventory is constructed based on the amount of pollutants entering the river. As a result, the problems of the difficulty of refining the full-caliber estimation of the pollution source emission inventory and the low spatiotemporal accuracy of the inventory are solved, and the refined calculation of pollution source emissions is realized.

[0171] Next, a device for constructing a water pollution source emission inventory according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0172] Fig.11 It is a block diagram of a device for constructing a water pollution source emission inventory according to an embodiment of the present application.

[0173] like Fig.11 As shown, the device 10 for constructing the water pollution source emission inventory includes: a division module 100, a determination module 200 and a construction module 300.

[0174] The classification module 100 is used to obtain pollution source subcategories, pollution generation methods, treatment technologies and pollution discharge paths, and classify and subdivide different types of pollution sources based on the pollution source subcategories, pollution generation methods, treatment technologies and pollution discharge paths, so as to obtain a pollution source classification and grading system;

[0175] The determination module 200 is used to perform parameter refinement processing on the data in the pollution source classification system by using a multi-source data fusion strategy and a sparse data processing strategy, and determine the calculation parameters of different types of pollution sources;

[0176] Construction module 300 is used to model and characterize the calculation parameters of different types of pollution sources according to the pollutant generation and emission mechanism, obtain the amount of different types of pollutants entering the river from different types of pollution sources, and construct a water pollution source emission inventory based on the amount of different types of pollutants entering the river from different types of pollution sources.

[0177] Furthermore, in some embodiments, the amount of different types of pollutants entering the river from different types of pollution sources is:

[0178] E i，j =A i，j ×EF i，j ×λ i ×k×α i，j ×β i，j ;

[0179] Among them, E i，j A is the amount of the jth pollutant entering the river from the i-th pollution source, i，j is the activity level of the i-th pollution source when it produces the j-th pollutant, EF i，j is the emission intensity of the jth pollutant produced by the i-th pollution source, λ i is the comprehensive river inflow coefficient of the i-th type of pollution source, k is the coefficient used for dimension conversion, α i，j is the time distribution coefficient of the jth pollutant from the i-th pollution source, β i，j is the spatial distribution coefficient of the jth pollutant of the i-th pollution source, where i and j are natural numbers.

[0180] Furthermore, in some embodiments, the determination module 200 further includes:

[0181] An acquisition unit, used for acquiring different types of databases;

[0182] A first determining unit, used to determine the data connection link of each type of pollution source based on data features of different types of databases;

[0183] The second determining unit is used to determine the complete mapping relationship between the calculation parameters of different types of databases and different types of pollution sources based on the data connection link of each type of pollution source.

[0184] Furthermore, in some embodiments, when determining the mapping relationship between the calculation parameters of different types of databases and different types of pollution sources, the second determining unit is further configured to:

[0185] Based on the sparse data processing strategy, the calculation parameters of different types of pollution sources are calculated with high temporal and spatial accuracy, wherein the sparse data processing strategy includes at least one of a downscaling strategy, a data mining strategy, an empirical formula strategy, a parameter fitting strategy, a Kriging interpolation strategy and a site-laboratory experiment strategy.

[0186] Furthermore, in some embodiments, after constructing the water pollution source emission inventory, the construction module 300 is further used to:

[0187] Using the source list classification assessment strategy and the preset water quality model, the accuracy of the point source and non-point source simulation of the water pollution source emission inventory on river water quality was evaluated respectively;

[0188] and / or, based on a preset global sensitivity analysis strategy, using a preset water quality model, taking the calculation parameters of different types of pollution sources as input, and evaluating the sensitivity of the water pollution source emission inventory to the river water quality simulation according to the degree of change in the output results;

[0189] And / or, using a preset control variable strategy, the uncertainty of the water pollution source emission inventory, the uncertainty of the preset water quality model, and the uncertainty of the water pollution source emission inventory and the preset water quality model are evaluated separately.

[0190] It should be noted that the above explanation of the embodiment of the method for constructing a water pollution source emission inventory is also applicable to the device for constructing a water pollution source emission inventory of this embodiment, and will not be repeated here.

[0191] According to the device for constructing the water pollution source emission inventory proposed in the embodiment of the present application, by grading and subdividing the different types of pollution sources based on the pollution source sub-category, pollution generation mode, treatment technology and pollution discharge path, a pollution source classification system is obtained; the data in the pollution source classification system is processed by parameter refinement and value taking using the multi-source data fusion strategy and sparse data processing strategy to determine the calculation parameters of different types of pollution sources; the calculation parameters of different types of pollution sources are modeled and characterized according to the pollutant generation and emission mechanism, and the amount of different types of pollutants entering the river from different types of pollution sources is obtained, and a water pollution source emission inventory is constructed based on the amount of water pollution source emission. In this way, the problems of the difficulty of fine estimation of the full-caliber pollution source emission inventory and the low spatiotemporal accuracy of the inventory are solved, and the fine calculation of pollution source emissions is realized.

[0192] Fig.12 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0193] A memory 1201 , a processor 1202 , and a computer program stored in the memory 1201 and executable on the processor 1202 .

[0194] When the processor 1202 executes the program, the method for constructing the water pollution source emission inventory provided in the above embodiment is implemented.

[0195] Furthermore, the electronic device further comprises:

[0196] The communication interface 1203 is used for communication between the memory 1201 and the processor 1202 .

[0197] The memory 1201 is used to store computer programs that can be executed on the processor 1202 .

[0198] The memory 1201 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0199] If the memory 1201, the processor 1202 and the communication interface 1203 are implemented independently, the communication interface 1203, the memory 1201 and the processor 1202 can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.12 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0200] Optionally, in a specific implementation, if the memory 1201, the processor 1202 and the communication interface 1203 are integrated on a chip, the memory 1201, the processor 1202 and the communication interface 1203 can communicate with each other through an internal interface.

[0201] The processor 1202 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0202] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for constructing a water pollution source emission inventory as described above.

[0203] An embodiment of the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the above method for constructing a water pollution source emission inventory.

[0204] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0205] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0206] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for constructing a water pollution source emission inventory, characterized in that: The following steps are involved: Obtain pollution source subcategories, pollution generation methods, treatment technologies, and pollution discharge paths, and classify and subdivide different types of pollution sources based on the pollution source subcategories, pollution generation methods, treatment technologies, and pollution discharge paths to obtain a pollution source classification and grading system; Using multi-source data fusion strategy and sparse data processing strategy, the data in the pollution source classification system is processed with refined parameters to determine the calculation parameters of the different types of pollution sources; According to the pollutant generation and emission mechanism, the calculation parameters of the different types of pollution sources are modeled and characterized to obtain the amount of different types of pollutants entering the river from the different types of pollution sources. Based on the amount of different types of pollutants entering the river from the different types of pollution sources, an emission inventory of water pollution sources is constructed.

2. The method according to claim 1, characterized in that The amount of different types of pollutants entering the river from different types of pollution sources is: AND i,j =A i,j ×EF i,j ×λ i ×k×α i,j ×β i,j ; Among them, E i,j A is the amount of the jth pollutant entering the river from the i-th pollution source, i,j is the activity level of the i-th pollution source when it produces the j-th pollutant, EF i,j is the emission intensity of the jth pollutant produced by the i-th pollution source, λ i is the comprehensive river inflow coefficient of the i-th type of pollution source, k is the coefficient used for dimension conversion, α i,j is the time distribution coefficient of the jth pollutant from the i-th pollution source, β i,j is the spatial distribution coefficient of the jth pollutant of the i-th pollution source, where i and j are natural numbers.

3. The method according to claim 1, characterized in that The multi-source data fusion strategy and sparse data processing strategy are used to perform parameter refinement value processing on the data in the pollution source classification system to determine the calculation parameters of the different types of pollution sources, including: Access different types of databases; Based on the data characteristics of the different types of databases, determining the data connection link of each type of pollution source; Based on the data connection link of each type of pollution source, a complete mapping relationship between the different types of databases and the calculation parameters of the different types of pollution sources is determined.

4. The method according to claim 3, characterized in that When determining the complete mapping relationship between the different types of databases and the calculation parameters of the different types of pollution sources, it also includes: Based on the sparse data processing strategy, the calculation parameters of the different types of pollution sources are calculated with high spatiotemporal accuracy, wherein the sparse data processing strategy includes at least one of a downscaling strategy, a data mining strategy, an empirical formula strategy, a parameter fitting strategy, a Kriging interpolation strategy and a site-laboratory experiment strategy.

5. The method according to claim 1, characterized in that After constructing the water pollution source emission inventory, it also includes: Using the source list classification assessment strategy and the preset water quality model, the accuracy of the point source and non-point source simulation of the river water quality in the water pollution source emission list is evaluated respectively; and / or, based on a preset global sensitivity analysis strategy, using the preset water quality model, taking the calculation parameters of the different types of pollution sources as input, and evaluating the sensitivity of the water pollution source emission inventory to the river water quality simulation according to the degree of change of the output result; And / or, using a preset control variable strategy, the uncertainty of the water pollution source emission inventory, the uncertainty of the preset water quality model, and the combined uncertainty of the water pollution source emission inventory and the preset water quality model are evaluated respectively.

6. A device for constructing a water pollution source emission inventory, characterized in that: include: A classification module is used to obtain pollution source subcategories, pollution generation methods, treatment technologies, and pollution discharge paths, and to classify and subdivide different types of pollution sources based on the pollution source subcategories, pollution generation methods, treatment technologies, and pollution discharge paths, so as to obtain a pollution source classification and grading system; A determination module is used to perform parameter refinement value processing on the data in the pollution source classification system by using a multi-source data fusion strategy and a sparse data processing strategy, and determine the calculation parameters of the different types of pollution sources; A construction module is used to model and characterize the calculation parameters of the different types of pollution sources according to the pollutant generation and emission mechanism, obtain the amount of different types of pollutants entering the river from the different types of pollution sources, and construct a water pollution source emission inventory based on the amount of different types of pollutants entering the river from the different types of pollution sources.

7. The device according to claim 6, characterized in that The amount of different types of pollutants entering the river from different types of pollution sources is: AND i,j =A i,j ×EF i,j ×λ i ×k×α i,j ×β i,j ; Among them, E i,j A is the amount of the jth pollutant entering the river from the i-th pollution source, i,j is the activity level of the i-th pollution source when it produces the j-th pollutant, EF i,j is the emission intensity of the jth pollutant produced by the i-th pollution source, λ i is the comprehensive river inflow coefficient of the i-th type of pollution source, k is the coefficient used for dimension conversion, α i,j is the time distribution coefficient of the jth pollutant from the i-th pollution source, β i,j is the spatial distribution coefficient of the jth pollutant of the i-th pollution source, where i and j are natural numbers.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a method for constructing a water pollution source emission inventory as claimed in any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement a method for constructing a water pollution source emission inventory as described in any one of claims 1 to 5.

10. A computer program product, characterized in that It includes a computer program, which, when executed by a processor, is used to implement the method for constructing a water pollution source emission inventory according to any one of claims 1 to 5.

Images