Method for estimating in-river flux of surface water background pollutants in area without data

Abstract

The invention provides a surface water background pollutant in-river flux estimation method for areas without data. A background pollutant output medium is collected, leaching, leaching and natural rainfall-runoff experiments are carried out, and the background pollutant concentration, the loss coefficient and the river entering coefficient are obtained. A soil loss general equation is introduced and improved, and a background pollutant loss general equation is established. And through verification of a small watershed experiment, determining a correlation coefficient, and further estimating the small-scale river entry flux of the watershed. And determining a large-watershed parameter by using a small-watershed result, and completing large-scale river entry flux estimation and verification. According to the method, dependence on a large amount of basic data is reduced, operation is easy and convenient, rapid estimation can be carried out with acceptable precision (the error is + / -20%) under the limited data condition, a scientific basis is provided for ecological compensation, water quality management and the like of areas without data, and the application problem of a traditional model in the areas is effectively solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the relevant field, specifically a kind of no data area surface water background pollutant river inflow estimation method. BACKGROUND

[0002] Background pollutants in surface water environment (such as humus-derived chemical oxygen demand, ammonia nitrogen, etc.) have important influence on water quality evaluation and management. For example, in the northeast forest region and other natural basins, soil humus is the main source of high-concentration organic matter in rivers, while the traditional pollution model (such as SWAT, HSPF) has relatively high accuracy, which needs to input parameters such as basin terrain, soil type, meteorological data, etc. However, a large number of data-poor areas in China (such as remote forest areas, grasslands, deserts and hills) lack basic data, resulting in that the contribution of background pollutants to water environment has been underestimated for a long time, affecting management decisions such as ecological compensation and water quality target setting.

[0003] With the development of fine environmental quality management, understanding and mastering the load of background pollutants are crucial to clarify the contribution of natural background and human pollution. In densely populated areas, mechanism models supported by long-term monitoring data (such as SWAT) can effectively analyze the migration law of pollutants, but in remote data-poor areas, traditional models are difficult to be directly applied due to the lack of basic hydrological, soil and water quality data. The load assessment of background pollutants (such as humus-derived chemical oxygen demand, carbonate weathering products in karst areas, etc.) in such areas has long relied on manual sampling monitoring, but due to the problems of complex terrain, high monitoring cost and seasonal traffic blockage, it is difficult to obtain continuous data, resulting in the long-term fuzzification of natural background value. In the existing alternative scheme, the empirical coefficient method can estimate the pollutant release potential through simple experiment, but it ignores the scale effect (such as nonlinear conversion of parameters between small experiment and large basin) and dynamic driving factors (such as the influence of rainfall intensity and slope on migration path), and the error is generally more than 30%, which is difficult to support management decisions. Environmental protection departments urgently need a lightweight method that balances the simplicity of operation and the reliability of results, which can quickly estimate the river inflow of background pollutants with acceptable accuracy (such as ± 20-30%) under limited data conditions, provide scientific basis for ecological compensation responsibility definition, dynamic adjustment of water quality criteria and priority division of management, and break the management deadlock in "data desert" areas. SUMMARY

[0004] The purpose of the present application is to provide a no data area surface water background pollutant river inflow estimation method to solve the problems raised in the background.

[0005] To achieve the above purpose, the present application provides the following technical scheme: a no data area surface water background pollutant river inflow estimation method, comprising the following steps:

[0006] Step 1, determine and collect the background pollutant output medium: collect the medium of the background pollutant in the surface water of the un-informed area;

[0007] Step 2, leaching experiment: the medium collected in step 1 is subjected to leaching experiment, and the concentration of the background pollutant is obtained, wherein the concentration obtained describes the maximum loss amount of the background pollutant in the medium;

[0008] Step 3, estimation of background pollutant load: the content of the background pollutant in the leaching solution of the medium in the leaching experiment in step 2 is taken as the maximum dissolution amount of the background pollutant in the medium, that is, the maximum loss amount t of the background pollutant per kilogram of the medium, which is estimated according to the product of the background pollutant concentration c1 and the background pollutant mass v in step 2; then the total amount W of the background pollutant medium stored in the un-informed area is determined, and the maximum storage amount T of the background pollutant in the medium of the watershed is estimated as T = W * t;

[0009] Step 4, estimation of background pollutant concentration leaching and loss coefficient: the medium collected in step 1 is placed in a leaching solution collection device, and a plurality of leaching solution collection devices are installed in the surface water basin of the un-informed area according to actual needs, that is, the medium stored in the leaching solution collection device is subjected to the natural rainfall process, and then the concentration c2 of the background pollutant in the medium after leaching under different rainfall events is obtained, and a weather recorder is arranged beside the leaching solution collection device to record rainfall information P; the loss coefficient of the medium is estimated according to the leaching experiment results c1 and the concentration c2 of the background pollutant in the leaching solution p1 is the ratio of the maximum loss concentration of the background pollutant in the medium to the actual leaching concentration, wherein 0 < p1 < 1;

[0010] Step 5, estimation of background pollutant concentration into river coefficient: natural rainfall-runoff experiment is carried out in the small watershed selected from the surface water basin of the un-informed area, that is, the background pollutant concentration and flow at the outlet section of the small watershed are monitored synchronously day by day, the background pollutant concentration c3 at the outlet of the small watershed is obtained, the flow of the small watershed is obtained by multiplying the rectangular cross-sectional area of the flow by the flow velocity Q, and the outdoor environmental weather station arranged at the outlet of the small watershed is used to record and obtain rainfall information P; the coefficient of the background pollutant from leaching to river is estimated according to the background pollutant concentration c3 at the outlet of the small watershed and the concentration c2 of the background pollutant in the leaching solution p2 is the concentration attenuation rate of the background pollutant leached out to the outlet of the small watershed after complex migration and transformation, that is, the concentration into river coefficient, 0 < p2 < 1;

[0011] Step 6, introduction of soil loss universal equation: the formula of the soil loss universal equation is:

[0012] A = R·K·LS·C·F

[0013]

[0014] D g (mm)=exp(0.01∑f i lnm i )

[0015] LS = (0.045L) a (65.41sinθ+4.56sinθ+0.065)

[0016] C = 0.6508 - 0.3436lgc

[0017] Where A is the soil erosion rate per square kilometer (t·km²) -2 ·a -1 R is the rainfall-runoff factor, and K is the organic pollutant erosibility factor (t·km). -2 ·a -1 ), LS represents slope length and slope factor, C represents vegetation and management factor, F represents soil and water conservation measures factor, P represents annual rainfall, P i D represents the average monthly rainfall. g f is the geometric mean particle size. i The percentage of particle size distribution in the original soil, in m i The arithmetic mean of particles smaller than this size is given, where θ is the slope. When θ > 2.86 degrees, a = 0.5; when 2.86 ≥ θ ≥ 1.72 degrees, a = 0.4; when 1.72 ≥ θ ≥ 0.57, a = 0.3; and when 0.57 ≥ θ, a = 0.2.

[0018] Step 7: Introduce Bi, λi, and c into the general equation to establish a general equation for background pollutant loss: Modify the general equation for soil loss in Step 6 into a general equation for background pollutant loss. A represents the annual inflow flux of background pollutants into the river (kg·km). -2 ·a -1 ), c is the average content of background pollutants in the medium, B i Let λ be the area of ​​the i-th sub-basin. i Let be the pollutant transport ratio of the i-th small watershed. Based on the formula for studying sediment transport ratio and watershed area, the pollutant transport ratios of n small watersheds are determined. Then, μ is introduced as a correction coefficient between sediment transport ratio and pollutant transport ratio, and μ is a constant. The general equation for background pollutant loss is improved as follows: The spatiotemporal variability of pollutants in the medium, further introduced When c and K are constants used for annual-scale estimation, the formula becomes:

[0019] Step 8, estimation of small-scale background pollutant flux into river in the basin: the experimental results of steps 2-5 are used to estimate the small-scale background pollutant flux into river in the basin;

[0020] Step 9, estimation of large-scale background pollutant flux into river in the basin: first, the constant a in the equation of step 7 is determined according to the estimation or measurement results of small-scale background pollutant flux into river in the basin in step 8, and then the parameters of the remaining small basins in step 7 are determined according to step 7 to estimate the large-scale background pollutant flux into river in the basin, and then verification is performed, the water quality and quantity are continuously monitored at the outlet of the large basin throughout the year, and the measured results are compared with the predicted results, and the error is within the preset range, that is, the estimation can be implemented. Step 9, estimation of large-scale background pollutant flux into river in the basin: first, the constant a in the equation of step 7 is determined according to the estimation or measurement results of small-scale background pollutant flux into river in the basin in step 8, and then the parameters of the remaining small basins in step 7 are determined according to step 7 to estimate the large-scale background pollutant flux into river in the basin, and then verification is performed, the water quality and quantity are continuously monitored at the outlet of the large basin throughout the year, and the measured results are compared with the predicted results, and the error is within the preset range, that is, the estimation can be implemented.

[0021] Preferably, the medium in step 1 includes but is not limited to soil and its cover.

[0022] Preferably, the leaching solution collecting device in step 4 includes an upper cylinder, a conical cylinder, a flow guide pipe and a collecting box, the flow guide pipe is welded on one side of the top of the collecting box, the conical cylinder is welded on the top of the flow guide pipe, the upper cylinder is welded on the top of the conical cylinder, the conical cylinder includes a funnel cylinder and a filter screen, the upper cylinder is welded on the top of the funnel cylinder, and the filter screen is laid on the inner surfaces of the upper cylinder and the funnel cylinder, wherein the filter screen and the inner surfaces of the upper cylinder and the funnel cylinder form a receiving cavity, and the medium collected in step 1 is placed in the receiving cavity.

[0023] Preferably, the parameters of the general equation for determining the background pollutant loss in step 6 are as follows: R is the rainfall runoff factor, On the scale of the basin, the slope length and slope factor LS terrain index is extracted through the DEM elevation data of the basin, and the vegetation and management factor C and the soil and water conservation measure factor F are queried according to the soil loss general equation table according to the actual situation of the basin.

[0024] Preferably, the estimation of small-scale background pollutant flux into river in the basin in step 8 is based on the leaching experiment of background pollutants in the medium, the leaching of the leaching solution collecting device and the concentration decay process of natural rainfall-runoff experiment to determine the concentration decay coefficient, that is, q1=p1*p2, the average runoff coefficient r1 is obtained from the measured rainfall-runoff process of the small basin, and the load output coefficient q1*r1 of the background pollutant load from the total source intensity output to the outlet of the small basin is estimated according to c*Q; the annual total load T of the basin background pollutant is obtained from the annual total load T of the basin background pollutant, and the measured data of the small basin natural rainfall-runoff experiment in step 5 is used for verification.

[0025] Preferably, the error in step 9 is ±20% of the preset threshold. ​

[0026] Compared with the prior art, the present application has the advantages of low data requirement and wide application range: traditional models (such as SWAT and HSPF) rely on a large number of parameters such as basin topography, soil type, and meteorological data, while there are many data-deficient areas in China (such as remote forest areas, grasslands, deserts, and hilly areas). Through innovative experiments and equation improvement, the present application can carry out estimation work by collecting background pollutant output medium, performing simple experiments, and obtaining a small amount of rainfall data, breaking through the data limitation and being applicable to various data-deficient areas, filling the technical gap in the estimation of background pollutant river inflow in such areas, and providing strong support for subsequent environmental management.

[0027] Combination of experiments and models improves estimation accuracy: The present application combines the results of leaching experiments, leaching experiments, and natural rainfall-runoff experiments to accurately determine the loss coefficient and river inflow coefficient of background pollutants. For example, the maximum loss amount of background pollutants in the medium is obtained through leaching experiments, the actual leaching concentration is analyzed through leaching experiments, and the change of river inflow concentration is mastered through natural rainfall-runoff experiments. These experimental data provide reliable parameters for the model. At the same time, the general equation for soil loss is introduced and improved in combination with the actual situation of the basin, fully considering the influence of rainfall runoff, topography, vegetation, and other factors, and a general equation for the loss of background pollutants is constructed. Through actual examples, the relative error between the estimation results and the measured data is mostly within the acceptable range of ±20%-30% at the small and large basin scales, significantly improving the estimation accuracy.

[0028] High cost-effectiveness and simple operation: Compared with traditional mechanism models that rely on long-term monitoring data and complex parameters, the present application does not require large-scale and high-cost long-term monitoring in data-deficient areas to obtain rich data. Its experimental operation process is simple and can be completed using common experimental devices (such as a specific structure of a leaching solution collection device), and the calculation process based on limited data is relatively simple. This not only greatly reduces the cost of manpower, material resources, and time, but also improves work efficiency, enabling environmental protection departments to complete the estimation of background pollutant river inflow in a short period of time, and quickly providing scientific basis for environmental management decisions such as ecological compensation responsibility definition, water quality benchmark dynamic adjustment, and treatment priority division.

[0029] Consider scale effect and dynamic factor: the existing empirical coefficient method ignores the scale effect (such as nonlinear conversion of parameters of small experiments and large basins) and dynamic driving factors (such as the influence of rainfall intensity and slope on migration path), resulting in an error generally exceeding 30%. When constructing the general equation of background pollutant loss, the present application fully considers the influence of the dynamic changes of factors such as basin topography and rainfall on the migration of pollutants. Through accurate calculation and analysis of parameters such as slope length and slope factor and rainfall runoff factor, the scale effect is effectively overcome, and the migration and transformation law of pollutants at different scales is comprehensively reflected, providing a more scientific and reasonable method for estimating the background pollutant inflow flux into rivers in data-poor areas. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 The present application is a method flowchart;

[0031] Figure 2 The present application is a leaching solution collection device structure schematic diagram;

[0032] Figure 3 The present application is a schematic diagram of the sampling point position of the Kamaran River Basin;

[0033] Figure 4 The present application is a water quality-water quantity response relationship diagram of the outlet section of No. 2 Qiaozu Basin;

[0034] Figure 5 The present application is a background pollutant output process line of the Kamaran River Basin from May 9, 2019 to August 9, 2019;

[0035] Figure 6 The present application is a multi-year average rainfall distribution diagram of the Kamaran River Basin. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0037] Embodiment 1

[0038] Please refer to Figures 1-2 The present application provides a technical solution: a method for estimating the inflow flux of background pollutants into rivers in data-poor areas, comprising the following steps:

[0039] Step 1, determine and collect the background pollutant output medium: collect the medium in the background pollutants of surface water in data-poor areas;

[0040] Step 2, leaching experiment: the medium collected in step 1 is subjected to a leaching experiment to obtain the concentration of background pollutants, which describes the maximum loss of background pollutants in the medium;

[0041] Step 3, estimation of background pollutant load: the content of background pollutants in the medium leaching solution in the leaching experiment in step 2 is taken as the maximum dissolution of background pollutants in the medium, i.e. the maximum loss of background pollutants per kilogram of medium t is estimated according to the product of the background pollutant concentration c1 and the background pollutant mass v in step 2; then the total amount of background pollutant medium W stored in the unrecorded area is determined, and the maximum storage amount of background pollutants in the medium T = W * t is estimated;

[0042] Step 4, estimation of background pollutant concentration leaching and loss coefficient: the medium collected in step 1 is placed in a leaching solution collection device, and several leaching solution collection devices are installed in the surface water basin in the unrecorded area according to actual needs, i.e. the medium stored in the leaching solution collection device is subjected to the natural rainfall process, and then the concentration c2 of the background pollutants in the medium after leaching under different rainfall events is obtained, and a weather recorder is arranged beside the leaching solution collection device to record the rainfall information P; the loss coefficient of the medium is estimated according to the leaching experiment results c1 and the concentration of background pollutants in the leaching solution c2 p1 is the ratio of the maximum loss concentration of background pollutants in the medium to the actual leaching concentration, wherein 0 < p1 < 1;

[0043] Step 5, estimation of background pollutant concentration into river coefficient: select the surface water basin in the unrecorded area to divide n small basins to carry out natural rainfall-runoff experiment, i.e. monitor the background pollutant concentration and flow of the small basin outlet section synchronously day by day, obtain the small basin outlet background pollutant concentration c3, and the small basin flow is obtained by multiplying the rectangular cross-sectional area by the flow velocity Q, and the outdoor environmental weather station arranged at the small basin outlet records and obtains the rainfall information P; the coefficient of the concentration of background pollutants from leaching to river is obtained according to the small basin outlet background pollutant concentration c3 and the concentration of background pollutants in the leaching solution c2 p2 is the concentration attenuation rate of the leached concentration of background pollutants to the outlet of the small basin after complex migration and transformation, i.e. the concentration into river coefficient, 0 < p2 < 1;

[0044] Step 6, introduction of soil loss universal equation: the formula of the soil loss universal equation is:

[0045] A = R·K·LS·C·F

[0046]

[0047] D g (mm) = exp(0.01Σf i lnm i )

[0048] LS = (0.045L) a (65.41sinθ+4.56sinθ+0.065)

[0049] C = 0.6508 - 0.3436lgc

[0050] Where A is the soil erosion rate per square kilometer (t·km²) -2 ·a -1 R is the rainfall-runoff factor, and K is the organic pollutant erosibility factor (t·km). -2 ·a -1 ), LS represents slope length and slope factor, C represents vegetation and management factor, F represents soil and water conservation measures factor, P represents annual rainfall, P i D represents the average monthly rainfall. g f is the geometric mean particle size. i The percentage of particle size distribution in the original soil, in m i The arithmetic mean of particles smaller than this size is given, where θ is the slope. When θ > 2.86 degrees, a = 0.5; when 2.86 ≥ θ ≥ 1.72 degrees, a = 0.4; when 1.72 ≥ θ ≥ 0.57, a = 0.3; and when 0.57 ≥ θ, a = 0.2.

[0051] Step 7: Introduce Bi, λi, and c into the general equation to establish a general equation for background pollutant loss: Modify the general equation for soil loss in Step 6 into a general equation for background pollutant loss. A represents the annual inflow flux of background pollutants into the river (kg·km). -2 ·a -1 ), c is the average content of background pollutants in the medium, B i Let λ be the area of ​​the i-th sub-basin. i Let be the pollutant transport ratio of the i-th small watershed. Based on the formula for studying sediment transport ratio and watershed area, the pollutant transport ratios of n small watersheds are determined. Then, μ is introduced as a correction coefficient between sediment transport ratio and pollutant transport ratio, and μ is a constant. The general equation for background pollutant loss is improved as follows: The spatiotemporal variability of pollutants in the medium, further introduced When c and K are constants used for annual-scale estimation, the formula becomes:

[0052] Step 8: Estimation of small-scale background pollutant fluxes into rivers in the watershed: The experimental results from steps 2-5 are used to estimate the small-scale background pollutant fluxes into rivers in the watershed.

[0053] Step 9, estimation of large-scale background pollutant flux into river in the basin: first, determine the constant in the equation in step 7 for estimating the large-scale background pollutant flux into river in the basin, according to the estimation or measured results of small-scale background pollutant flux into river in the basin in step 8, apply the equation in step 7 to the small basin to obtain the constant value ; then determine the parameters of the remaining small basins in the large basin according to step 7, estimate the large-scale background pollutant flux into river in the basin, and then verify, monitor the water quality and quantity continuously throughout the year at the outlet of the large basin, compare the measured results with the predicted results, and the error is within the preset range, then the estimation can be implemented.

[0054] Further, the medium in step 1 includes but is not limited to soil and its cover.

[0055] Further, the leaching solution collecting device in step 4 includes an upper cylinder 10, a conical cylinder 11, a flow guide pipe 20 and a collecting box 30, the flow guide pipe 20 is welded on one side of the top of the collecting box 30, the conical cylinder 11 is welded on the top of the flow guide pipe 20, the upper cylinder 10 is welded on the top of the funnel cylinder 112, and the filter screen 111 is laid on the inner surface of the upper cylinder 10 and the funnel cylinder 112, wherein the filter screen 111 and the inner surface of the upper cylinder 10 and the funnel cylinder 112 form a receiving cavity, and the medium collected in step 1 is placed in the receiving cavity.

[0056] Further, the parameters of the general equation for determining the background pollutant loss in step 6 are as follows: R is the rainfall runoff factor, On the scale of the basin, the slope length and slope factor LS terrain index is extracted through the DEM elevation data of the basin, and the vegetation and management factor C and the soil and water conservation measure factor F are queried according to the soil loss general equation table according to the actual situation of the basin.

[0057] Further, the estimation of small-scale background pollutant flux into river in the basin in step 8 is based on the leaching experiment of background pollutants in the medium, the leaching of the leaching solution collecting device and the concentration decay process of natural rainfall-runoff experiment to determine the concentration decay coefficient, i.e. q1=p1*p2, the average runoff coefficient r1 is obtained from the measured rainfall-runoff process of the small basin, and the load output coefficient q1*r1 of the background pollutant load from the total source intensity output to the outlet of the small basin is estimated according to c×Q; the annual total load T of the background pollutant in the basin is obtained from the annual total load T of the background pollutant in the basin, and the measured data of the small basin natural rainfall-runoff experiment in step 5 is used for verification.

[0058] Further, the error in step 9 is ±20% of the preset threshold.

[0059] Example 2, this embodiment is specifically illustrated according to example 1

[0060] The technology is described by taking the Karmalan River Basin in the northeast forest region as an example in the present application, and the details are as follows:

[0061] 1) As Figure 3 determined, the output medium of the background pollutants (chemical oxygen demand, permanganate index, ammonia nitrogen) of the basin surface water is the soil humus layer with high humus content: the Karmalan River is the second largest tributary in the Huma River headwater protection area, located in the hinterland of the Greater Hinggan Mountains, with a basin area of 1264.7 km 2 , a river length of about 94.3 km 2 , and flows into the Huma River from west to east. The forest and grassland accounts for 96.4% of the entire basin. The sampling points and experimental sites during the experiment are A1-A3, A1 is the outlet section of the Karmalan River Basin, A2 is the sampling point under the forest, and A3 is the outlet of the No. 2 bridge small watershed, with an area of 7 km 2 .

[0062] 2) Soil humus layer leaching experiment: collect forest soil humus layer for leaching liquid experiment, and describe the maximum possible loss of background pollutants in the forest soil humus layer according to the concentration of background pollutants in the experimental test results. After the sample is soaked with distilled water in a mass ratio of 1:4 of dry branches and leaves (soil humus layer) to distilled water for 48 hours, it is shaken for 3 minutes, filtered with two layers of filter paper using a Buchner funnel until the filtrate is clear and transparent, and the chemical oxygen demand, permanganate index and ammonia nitrogen are detected according to the “Surface Water and Sewage Monitoring Technical Specification” (HJ / T91-2002). The sample type, collection site, quantity and time are shown in Table 1;

[0063] Table 1. Information table of background value source leaching and leaching experiment analysis samples

[0064]

[0065]

[0066] The leaching experiment results are shown in Table 2. The COD concentration in the soil humus layer leaching liquid ranges from 217 to 284 mg / L, with an average of 247.5 mg / L, the permanganate index concentration ranges from 108.6 to 158 mg / L, with an average of 123.2 mg / L, and the ammonia nitrogen average is 2.1 mg / L;

[0067] Table 2. Characteristic values of background pollutants in natural source leaching experiment (mg / L)

[0068]

[0069] 3) Background pollutant load estimation:

[0070] The soil type in the basin is mainly brown coniferous forest soil, the thickness of the soil leaching layer (A layer) is about 0.1 m, the soil density is about 1.25 g / cm3, according to the test results of leaching experiment, it is considered that the background pollutant content in the soil humus layer leaching solution can be used as the maximum possible dissolution amount thereof from the soil, and the maximum loss amount of background pollutants per kilogram of soil is about COD: 0.99 g, permanganate index: 0.49 g, ammonia nitrogen: 0.008 g, and the maximum possible loss amount of background substances per square kilometer of soil humus layer is 123.7 t, 61.1 t, and 1.05 t;

[0071] Table 3. Maximum possible dissolution amount of water chemical indicators per square kilometer of branch and leaf litter / soil

[0072]

[0073] 4) Background pollutant concentration leaching experiment and loss coefficient estimation: The soil leaching solution collection device is as shown in Figure 2 , and the sample collection information is shown in Table 1, including an upper cylinder 10, a conical cylinder 11, a flow guide pipe 20, and a collection box 30, the flow guide pipe 20 is welded on one side of the top of the collection box 30, the conical cylinder 11 is welded on the top of the flow guide pipe 20, the upper cylinder 10 is welded on the top of the funnel cylinder 112, and the conical cylinder 11 includes a funnel cylinder 112 and a filter screen 111, the filter screen 111 is laid on the inner surfaces of the upper cylinder 10 and the funnel cylinder 112, and a receiving cavity is formed between the filter screen 111 and the inner surfaces of the upper cylinder 10 and the funnel cylinder 112, and the inside of the receiving cavity is laid with soil layers according to the original structure of the soil body. The device is uniformly arranged in A2, and the water sample in the water collector after collecting the natural secondary rainfall process represents the loss amount of background pollutants of the natural source intensity.

[0074] The rain intensity and rainfall information of the test site during the three sampling times in the leaching experiment are shown in Table 4. The maximum rainfall is 24.1 mm, and the COD, permanganate index, and ammonia nitrogen concentrations in the leaching solution after the rainfall are 224 mg / L, 124.8 mg / L, and 2.23 mg / L, respectively. The analysis of the data of the three leaching experiments shows that the rainfall intensity is not the main control factor affecting the leaching of background pollutants, and the main reason for the analysis is that the interception effect of the forest canopy weakens the splashing erosion effect of the rainfall intensity on the underlying surface, especially for forests with high coverage and canopy density, the reduction effect is more obvious, therefore, for the penetrating rain, the mechanism of its effect on the forest underlying surface is mainly erosion rather than splashing erosion.

[0075] According to the analysis results of the source intensity experiment and the average concentration of background pollutants in the leaching solution, the loss coefficients of COD, permanganate index, and ammonia nitrogen concentrations in the soil humus layer are 0.7, 0.72, and 0.68 (soil humus layer), respectively;

[0076] Table 4. Background pollutant concentration loss coefficient and river entry coefficient estimation table

[0077]

[0078]

[0079] 5) Background pollutant concentration river entry coefficient estimation: The average concentrations of COD, permanganate index and ammonia nitrogen at the outlet of No. 2 bridge small watershed in this small watershed experiment were 35.1 mg / L, 13.6 mg / L and 0.21 mg / L respectively (see Table 4), and the concentration change rates of background substances from loss to river were 0.2, 0.16 and 0.14 respectively (see Table 4) estimated from the pollutant concentrations in soil leaching solution;

[0080] 6) Introducing the universal equation of soil loss (USLE): A = R·K·LS·C·F, rainfall runoff factor (R): In this case, the rainfall data of 2004-2013 was used instead of the rainfall data of the study area (CFSR, National Centers for Environmental Prediction) to input the conditions, and the monthly average rainfall of each sub-basin and the annual average rainfall were obtained by spatial difference, and were extracted to the center of the sub-basin. According to the formula of rainfall runoff factor, R of each sub-basin was calculated; slope length and slope factor (LS): On the scale of watershed, slope and slope length and other terrain indexes were extracted through Kamaran River 30x30m DEM elevation data; vegetation and management factor (C): In this case, C takes the value of the biological measure factor B in the Chinese soil loss equation, and the vegetation coverage of forest land in this case is 75.2%, and the value of C is 0.02; soil and water conservation measure factor (F): The study area is the source area of the river, and there is no any soil and water conservation measure, and the value of F is 1; only the organic pollutant erosion factor (K, (t·km -2 ·a -1 ) is not determined in the formula;

[0081] 7) Introducing Bi, λi and c into the universal equation of soil loss to establish the universal equation of background pollutant loss: USLE is applied to estimate the pollution load into the river of Kamaran River basin. Considering that the parameters of different sub-basins are different, first, Kamaran River basin is divided into 33 sub-basins according to the characteristics of water collection, and the parameters are introduced to improve the model as follows: A is the annual river flux of background pollutants (kg·km -2 ·a -1 ), c is the average content of background pollutants in the medium (g / kg), B i is the area of the i iThe pollutant transport ratio of the ith small watershed is determined according to the formula of the sediment transport ratio and the watershed area. However, it is obviously not suitable to directly use the relationship between the sediment transport ratio and the watershed area to replace the pollutant transport ratio. Therefore, μ is introduced as a correction factor between the sediment transport ratio and the pollutant transport ratio, and μ is a constant. The equation is improved as follows: Here, the equation is before the introduction of parameters, A is the soil erosion per square kilometer, and B and c are introduced as the area and the background pollutant content, respectively. Then A is the background pollutant erosion of the whole watershed. Then the formula can be changed to

[0082] 8) Determine the parameters of the general equation of background pollutant loss: the parameters have been determined in 6).

[0083] 9) Estimation of the background pollutant flux into the river at a small scale in the watershed: The rainfall-runoff in the study area mainly replenishes the river in the form of soil runoff or underground runoff. In this process, the rainfall-runoff first leaches the background pollutants in the fallen branches and leaves, and then enters the soil layer, and the concentration increases sharply. Then it migrates to the outlet of the small watershed through soil filtration and adsorption. The concentration of the background pollutants into the river is about 0.14-0.2. Combined with the analysis of the source intensity detection experiment, it can be known that the concentration of the background pollutants from the source to the outlet of the small watershed is about 0.1-0.14, and the runoff coefficient of the sub-watershed during the experiment is 0.08-0.23, and the average runoff coefficient is 0.12. According to c x Q, the output coefficient of the background pollutant load is about 0.012-0.017. Combined with the background pollutant load per unit area in the study area, the annual COD, permanganate index and ammonia nitrogen fluxes into the river per unit area of the sub-watershed are estimated to be 2.1 t / (km 2 .a), 0.80 t / (km 2 .a), 0.013 t / (km 2 .a).

[0084] Verification: In June-August 2020, four natural rainfall-runoff processes were captured in the No. 2 Bridge watershed. The total rainfall of the four rainfall events was 3.3 mm, 8.6 mm, 11.7 mm, and 24 mm, respectively. According to the calculation of the runoff coefficient of the No. 2 Bridge sub-watershed outlet in the four rainfall-runoff processes, the runoff coefficient of the sub-watershed ranged from 0.08 to 0.23, with an average runoff coefficient of 0.12. The COD load in the four rainfall-runoff processes of the No. 2 Bridge sub-watershed was calculated to be 62.16 kg, 234.68 kg, 368.18 kg, and 1918.71 kg, respectively. The discharge time, peak time, and background pollutant concentration curve varied greatly under different rainfall conditions. The permanganate index and ammonia nitrogen load output are shown in Table 5. According to the table, the correlation coefficient between rainfall and background pollutant load output is above 0.9, and the background pollutant load output has a very obvious response to rainfall;

[0085] Table 5. Background pollutant load output (kg) in the four rainfall-flow processes of the No. 2 Bridge sub-watershed

[0086]

[0087] The background value of water quality and flow at the outlet of the No. 2 Bridge sub-watershed during the experimental monitoring period had a significant correlation Figure 4 ). Therefore, for a background area affected by a single surface pollution source, rainfall-runoff is the main driving factor for the output of background pollutant load, and the water quality and flow show a relatively obvious linear relationship. In the case of no significant change in source strength, the rainfall-runoff process determines the loss process of background pollutants;

[0088] Based on the experimental data of the No. 2 Bridge sub-watershed, the correlation between rainfall and background pollutant load output is above 0.9, which has a significant correlation. Based on this, this paper estimates the annual output of background pollutant load in the No. 2 Bridge sub-watershed in 2019 based on the measured data of the No. 2 Bridge and the regional rainfall data in 2019. The COD, permanganate index, and ammonia nitrogen load output of the No. 2 Bridge sub-watershed in 2019 were 18.2 t, 7.7 t, and 0.11 t, respectively. In this case, the estimated annual output of background pollutants per unit area is 2.1 t / (km 2 .a), 0.80 t / (km 2 .a), and 0.013 t / (km 2 .a), and the area of the No. 2 Bridge sub-watershed is 7 km 2 . Therefore, the estimated annual load output is 14.5 t, 5.6 t, and 0.089 t, with an error within an acceptable range;

[0089] 10) Estimation of the background pollutant flux into the river at a large scale: The COD, permanganate index and ammonia nitrogen load outputs of the No. 2 Bridge sub-basin in 2019 were 18.2 t, 7.7 t and 0.11 t, respectively, which were brought into 8) to determine the value of the study area. R was calculated according to the rainfall data in 2019, and the values of COD, permanganate index and ammonia nitrogen were 34.3 (t·km -2 ·a -1 ), 13.1 (t·km -2 ·a -1 ) and 0.19 (t·km -2 ·a -1 ), respectively, which were calculated according to the generalized pollutant loss equation. Then the equation for estimating the background pollutant flux into the river in the Kamalan River Basin was as follows. The annual average fluxes of COD, permanganate index and ammonia nitrogen into the river in the Kamalan River Basin were 2072.1 t, 789.9 t and 11.5 t, respectively, which were calculated by applying the pollutant loss equation;

[0090]

[0091] Verification: During the experiment, the water quality and quantity of the outlet control section of the Kamalan River Basin were monitored simultaneously. The monitoring period was from 5 / 9 / 2020 to 5 / 28 / 2020, 16 / 6 / 2020 to 1 / 7 / 2020 and 19 / 7 / 2020 to 9 / 8 / 2020, and the dates without simultaneous monitoring data of water quality and quantity were interpolated. The background pollutant output process of the Kamalan River Bridge control section from May 9, 2020 to August 9, 2020 is shown in Figure 5 . The total loads of COD, permanganate index and ammonia nitrogen at the Kamalan River Bridge control section from May 9, 2020 to August 9, 2020 were 1412.7 t, 421.5 t and 9.8 t, respectively, which were calculated from the measured data of water quality and quantity;

[0092] Due to the lack of water quality and quantity monitoring data of the Kamalan River control section in other periods of 2019, and the good correlation between the runoff and the non-point source pollution into the river in the study area, the background pollutant flux into the river in other periods of the year in the Kamalan River Basin was estimated in this section according to the annual distribution characteristics of the basin rainfall combined with the pollutant flux into the river in this period. Figure 6The annual distribution of the multi-year average rainfall of the reanalyzed rainfall data of the Kamalan River Basin is shown in the figure. The rainfall from May 9 to August 9 (three months) accounts for 49.9% of the annual rainfall. Assuming that the runoff coefficient of the entire Kamalan River Basin is unique, according to the relationship between runoff and non-point source pollution load, the total load output of COD, permanganate index and ammonia nitrogen in the basin in 2019 is estimated to be 2825.5 t, 842.9 t and 19.6 t respectively;

[0093] The estimated results of COD and ammonia nitrogen into the river in the Kamalan River Basin in 2019 are significantly higher than the predicted average annual inflow, and the estimated results of the average annual inflow of permanganate index are relatively close to the inflow in 2019. Based on the background pollutant inflow in 2019, the relative errors of COD, permanganate index and ammonia nitrogen inflow are 26.6%, 6.3% and 41.3% respectively. The estimated results are close to the calculated values of the measured data. Considering that the background areas in this study are mainly located in remote mountainous forest areas, the availability of data is poor, therefore, the method of estimating the inflow of large-scale background pollutants into the river based on the pollutant loss equation is feasible.

[0094] In summary, an estimation method for the inflow of background pollutants into the river in the Northeast forest region is provided, which is different from other mechanism models in that it does not require excessive data support and saves manpower and resources. The process of the background pollutant loss transformation and migration experiment data is generalized, and the small watershed export background pollutant load output is estimated by using the loss process. The results are verified by experimental data. By embedding the parameters into the general equation of soil loss, the equation is converted into the general equation of background pollutant loss, and the constant term in the general model is calibrated by the measured data of the small watershed, so as to realize the application of the general equation of background pollutant loss to the estimation of the inflow of large-scale background pollutants into the river.

[0095] Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for estimating river flux of background contaminants in surface water in an ungauged catchment, characterized in that, The method comprises the following steps: Step 1, determining and collecting background pollutant output medium: collecting medium in surface water background pollutants in the un-informed area; Step 2, leaching experiment: the medium collected in step 1 is subjected to leaching experiment, and the concentration of the background pollutants is obtained, wherein the concentration obtained describes the maximum loss of the background pollutants in the medium; Step 3, background pollutant load estimation: the content of the background pollutants in the medium leaching solution in the leaching experiment in step 2 is taken as the maximum dissolution of the background pollutants in the medium, that is, the maximum loss of the background pollutants per kilogram of medium t is estimated according to the product of the background pollutant concentration c1 and the background pollutant mass v; then the total amount of the medium storing the background pollutants W in the un-informed area is determined, and the maximum storage amount of the background pollutants in the medium T = W * t is estimated; Step 4, background pollutant concentration leaching and loss coefficient estimation: the medium collected in step 1 is placed in a leaching solution collection device, and a plurality of leaching solution collection devices are installed in the surface water basin in the area without data according to the actual needs, that is, the medium stored in the leaching solution collection device receives the natural rainfall process, and then the concentration c2 of the background pollutants in the medium after leaching under different rainfall events is obtained, and a meteorological recorder is arranged beside the leaching solution collection device to record the rainfall information P; according to the leaching experiment results c1 and the background pollutant concentration c2 in the leaching solution, the loss coefficient of the medium is estimated p1 is the ratio of the maximum loss concentration of the background pollutants in the medium to the actual leaching concentration, wherein 0 < p1 < 1; Step 5, estimation of background pollutant concentration into river coefficient: select no data area surface water basin to divide n small basins to carry out natural rainfall-runoff experiment, that is, to carry out synchronous daily monitoring on background pollutant concentration and flow of small basin export section, to obtain small basin export background pollutant concentration c3, small basin flow is obtained by multiplying rectangular overfall section area with flow velocity Q, at the same time, outdoor environmental meteorological station arranged at small basin export records and obtains rainfall information P; according to small basin export background pollutant concentration c3 and concentration background pollutant c2 in leaching solution from leaching to into river coefficient p2 is the concentration attenuation rate of background pollutant leached out from concentration after complex migration and transformation to small basin export, that is, concentration into river coefficient, 0 < p2 < 1; Step 6, Bi, λi, c are introduced into the general equation to establish the general equation of background pollutant loss: the general equation of soil loss is introduced, and the general equation of soil loss is changed to the general equation of background pollutant loss: A is the annual inflow of background pollutants into the river, c is the average content of background pollutants in the medium, B i is the area of the ith sub-basin, λ i is the pollutant transport ratio of the ith small watershed, and then the general equation of background pollutant loss is improved to obtain Step 7, estimation of small-scale background pollutant inflow into river in the basin: the experimental results of steps 2-5 are used to estimate the small-scale background pollutant inflow into river in the basin; Step 8, estimation of large-scale background pollutant inflow into river in the basin: the improved general equation for loss of background pollutants is determined according to step 6, the large-scale background pollutant inflow into river in the basin is estimated, and then verification is performed; the water quality and quantity are continuously monitored throughout the year at the outlet of the large basin, and the measured results are compared with the predicted results; if the error is within the preset range, the estimation can be implemented.

2. The method according to claim 1, wherein the method is characterized by: The medium in step 1 includes but is not limited to soil and its coverings.

3. The method according to claim 1, wherein the method is characterized by: The leaching solution collecting device in step 4 comprises an upper cylinder (10), a conical cylinder (11), a flow guide pipe (20) and a collecting box (30), one side of the top of the collecting box (30) is welded with the flow guide pipe (20), the top of the flow guide pipe (20) is welded with the conical cylinder (11), the top of the conical cylinder (11) is welded with the upper cylinder (10), the conical cylinder (11) comprises a funnel cylinder (112) and a filter screen (111), the upper cylinder (10) is welded at the top of the funnel cylinder (112), and the filter screen (111) is laid on the inner surfaces of the upper cylinder (10) and the funnel cylinder (112), wherein the filter screen (111) and the inner surfaces of the upper cylinder (10) and the funnel cylinder (112) form a containing cavity, and the medium collected in step 1 is placed in the containing cavity.

4. The method according to claim 1, wherein the method is characterized by: The parameters of the general equation for determining the background pollutant loss in step 6 are as follows: R is a rainfall runoff factor, At the basin scale, the slope length and slope factor LS terrain index is extracted through the DEM elevation data of the basin, and the vegetation and management factor C and the soil and water conservation measure factor F are queried according to the soil loss general equation table according to the actual situation of the basin.

5. The method according to claim 1, wherein the method is characterized by: In step 8, the estimation of small-scale background pollutant inflow into river in the basin is based on the leaching experiment of the background pollutants in the medium, the leaching of the leaching solution collecting device and the concentration decay process of the natural rainfall-runoff experiment to determine the concentration decay coefficient, that is, q1 = p1 * p2, the average runoff coefficient r1 is obtained from the measured rainfall-runoff process of the small basin, the load output coefficient q1 * r1 of the background pollutant load from the total source output to the outlet of the small basin is estimated according to c x Q; the annual load total amount T of the background pollutants in the basin is obtained from step 3, the total amount of the background pollutant load into the river in the basin per year T * q1 * r1 is obtained, and the measured data of the small basin natural rainfall-runoff experiment in step 5 is used for verification.

6. The method according to claim 1, wherein the method is characterized by: The error in step 9 is ±20% of the preset threshold.

7. The method according to claim 1, wherein: The universal equation of soil erosion is introduced: the formula of the universal equation of soil erosion is: A=R·K·LS·C·F D g (mm) = exp(0.01∑f i lnm i ) LS = (0.045L) a (65.41 sin θ + 4.56 sin θ + 0.065) C=0.6508-0.3436lgc wherein A is the amount of soil erosion per square kilometer, R is the rainfall runoff factor, K is the organic pollutant erodibility factor, LS is the slope length and slope factor, C is the vegetation and management factor, F is the soil and water conservation measure factor, P is the total annual rainfall, P i is the average monthly rainfall, D g is the geometric mean particle size, f i is the percentage of particle size composition in the original soil, m i is the arithmetic mean value less than the particle size, θ is the slope, wherein when θ > 2.86 degrees, a = 0.5, when 2.86 ≥ θ ≥ 1.72 degrees, a = 0.4, when 1.72 ≥ θ ≥ 0.57, a = 0.3, and when 0.57 ≥ θ, a = 0.

2.

8. The method according to claim 1, wherein the method is characterized by: The specific logic of the improved general equation of background pollutant loss in step 6 is as follows: according to the formula for determining the sediment transport ratio and the area of the watershed, the pollutant transport ratio of n small watersheds is determined, and μ is introduced as a correction coefficient between the sediment transport ratio and the pollutant transport ratio, and μ is a constant. The improved general equation of background pollutant loss is as follows: Wherein the spatiotemporal variability of pollutants in the medium, and μ is introduced c and K are constants, which are used for annual scale estimation, and the formula becomes Wherein is the estimated large-scale background pollutant inflow flux of the watershed, according to the estimated or measured results of the small watershed background pollutant inflow flux in step 7, is applied to the small watershed to obtain the constant value.

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