Modeling method for distributed watershed antibiotic transport fate process model

Abstract

The present application relates to the field of antibiotic migration, in particular to a distributed watershed antibiotic migration and fate process modeling method, which comprises the following steps: step S100: the watershed is nestedly divided, the time and space range of the calculation domain is determined, the grid data and attribute data for model running are collected, and the initial state and physicochemical properties of each generalized object are set; step S200: the migration and fate process of antibiotics is simulated using the distributed watershed antibiotic migration and fate process modeling method according to the data set in step S100, and the migration amount and concentration data of antibiotics in each grid region of the divided watershed are analyzed and predicted; step S300: the antibiotic concentration data in the actual region are compared with the antibiotic concentration data simulated by the distributed watershed antibiotic migration and fate process modeling method, and the model reliability is verified by using the root mean square error and the bias percentage.

Description

Technical Field

[0001] This invention relates to the field of watershed water environment processes and simulation technology, specifically to a modeling method for distributed watershed antibiotic migration and fate processes. Background Technology

[0002] Due to the widespread use of antibiotics over the past few decades, large amounts of antibiotics remain in soil, sediments, groundwater, and surface water, which has had adverse effects on the ecological environment and human health. Therefore, quantitatively assessing the dynamic changes of antibiotics at the watershed scale with high spatiotemporal resolution is crucial for the prevention and control of antibiotic pollution and risk management. However, the relevant research models are insufficient in characterizing the dynamic processes and mechanisms of antibiotics in large-scale heterogeneous landscape units.

[0003] On the one hand, among the two commonly used models, the multi-media fugacity model, as a mass balance model, can describe the concentration and mass distribution of antibiotics in different environmental media and the transport flux between media. However, the model treats each medium as a homogenized gray box and lacks an accurate description of the heterogeneous structure of watershed units. The process model has been used in a few studies to simulate the migration process of antibiotics in surface water (rivers and bays), but there is still a lack of a model that can characterize the source-flow-sink dynamic process of antibiotics on land.

[0004] On the other hand, in agricultural watersheds, non-point source emissions of antibiotics account for the vast majority of total emissions. However, existing simulation technologies use emission inventory estimation methods to calculate emission loads from non-point sources and then use these loads as boundary conditions input to rivers after spatial and temporal equalization. This fails to clearly characterize the unique dynamic emission patterns and pollution output characteristics of different non-point source pollution sources, hindering a systematic understanding of antibiotic migration and fate processes at the watershed scale. This represents a significant technical gap in the simulation of antibiotic dynamic processes. Summary of the Invention

[0005] The purpose of this invention is to provide a modeling method for the migration and fate process of antibiotics in a distributed watershed, so as to solve the problems mentioned in the background art.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] A modeling method for the migration and fate process of antibiotics in a distributed watershed includes the following steps:

[0008] Step S100: Divide the watershed into nested sections, determine the spatiotemporal range of the computational domain, collect raster data and attribute data for model operation, and set the initial state and physicochemical properties of each generalized object.

[0009] Step S200: Based on the data set in Step S100, the distributed watershed antibiotic migration and fate process model is used to simulate the antibiotic migration and fate process, and to analyze and predict the migration amount and concentration data of antibiotics in the divided watershed grid. The antibiotic migration and fate process simulation includes watershed hydrological process simulation, watershed sediment transport process simulation, spatiotemporal dynamic simulation of antibiotic emission source intensity, vadose zone antibiotic migration and fate process simulation, shallow groundwater antibiotic migration and fate process simulation, and river channel antibiotic migration and fate process simulation.

[0010] Step S300: Collect antibiotic concentration data in the actual area and compare it with antibiotic concentration data simulated by the distributed watershed antibiotic migration and fate process model, and verify the reliability of the model using root mean square error and percentage deviation.

[0011] According to the above technical solution, in step S100, the watershed is divided into nested subdivisions. On the plane, the watershed is divided into rectangular grids, sub-watersheds and river networks. On the vertical plane, the grid is divided into canopy, mixed layer, vadose zone and shallow groundwater. Among them, the vadose zone is divided into three soil layers, and the mixed layer is used to characterize the area where exogenous antibiotics directly interact with the land.

[0012] By dividing geographical regions into nested grids, distributed simulations are performed on each sub-stage of the antibiotic migration and destination process. The simulation processes of each distributed computation are then summarized to obtain the overall migration data of antibiotics within the land area. While refining the analysis of each region within each spatial range, the overall analysis and simulation of the antibiotic migration process between different regions are realized, which can completely depict the dynamic migration process of antibiotic source-flow-sink within the land area.

[0013] According to the above technical solution, in step S200, the watershed hydrological process simulation includes the following: land surface hydrological simulation includes vertical simulation of groundfall rainfall, canopy interception, evapotranspiration, infiltration, leakage, and recharge processes; and vertical simulation of surface runoff, interflow, and groundwater runoff generation and confluence processes, and calculation of soil and river water temperatures. The river channel portion uses the Muskingan-Congee algorithm for river hydrological simulation.

[0014] In the simulation of watershed sediment transport processes, the land surface soil erosion simulation includes the combined effects of rainfall splash erosion, runoff erosion, gravity settling, and the sediment-carrying capacity of water flow to calculate the amount of soil erosion; the river channel sediment transport simulation refers to simulating the advection, settling, and resuspension processes of suspended sediment.

[0015] In the spatiotemporal dynamic simulation of antibiotic emission sources, point source emission load is directly used as the boundary condition input for the river channel; for area source emission sources, atmospheric dry and wet deposition and agricultural sources are considered. The influence range of agricultural sources is delineated, and the area source antibiotic flux input to the grid is simulated according to the emission pattern and dynamic characteristics of the source to obtain the migration amount of area source into the grid area.

[0016] We adopt a point-area differentiation simulation method for antibiotic pollution sources, optimize the calculation of area source emission load using the existing emission inventory estimation method for area source emission simulation, conduct detailed analysis of different area sources, accurately characterize emission patterns and antibiotic output characteristics, and then conduct a refined simulation of the spatiotemporal dynamics of antibiotic emission sources at the watershed scale.

[0017] In the simulation of antibiotic migration and fate in the vadose zone, stable and active antibiotic libraries are defined for the mixed layer and each soil layer. Each library consists of a dissolved phase and an adsorbed phase. The concentration and content of each phase are calculated based on the partition coefficient, according to the formula:

[0018]

[0019]

[0020] Among them, Conc lab,dis and Conc sta,dis M represents the concentration of antibiotics in the dissolved phase of the active and stable libraries, respectively. lab and M sta The values ​​of antibiotics in the active and stable libraries are respectively θ. sat K represents the soil saturation water content. p,soil ρ is the partition coefficient of antibiotics in soil between soil water and particles. b For soil bulk density, Thick soil Soil layer thickness;

[0021] The mass exchange between the active and stable libraries is calculated using the diffusion coefficient, according to the formula:

[0022]

[0023] Among them, F ex M represents the antibiotic exchange flux between the active and stable libraries. lab,dis and M sta,dis K represents the mass of the dissolved phase antibiotics in the active and stable libraries, respectively. ex For the exchange rate, φ sol Soil porosity;

[0024] The degradation rate of antibiotics in the vadose zone was calculated based on half-life and soil temperature, and then the migration rate of antibiotics in the vadose zone was analyzed according to the formula:

[0025]

[0026]

[0027] Among them, F deg,lab and F deg,sta M represents the degradation amount of antibiotics in the active and stable libraries, respectively. lab and M sta The quality of antibiotics in the active and stable libraries, respectively, DT 50,soil K is the half-life of the antibiotic. T Here is the temperature correction factor, where T is the soil temperature;

[0028] The concentration of migratable antibiotics is calculated using the formula:

[0029]

[0030] Among them, Conc mob M represents the concentration of migratable antibiotics in the vadose zone. lab To improve the quality of active antibiotics in the soil, w mob θ represents the amount of mobile water in the soil. sat K represents the saturated soil moisture content. p,soil ρ is the partition coefficient of antibiotics in soil between soil water and particles. b For soil bulk density, Thick soil It refers to the thickness of the soil layer to which it belongs;

[0031] Based on this concentration and the vertical leaching volume of antibiotics due to gravity drainage, the formula is calculated as follows:

[0032] F lch,ly =Conc mob,ly *w perc,ly ;

[0033] Where ly is the soil layer number, F lch,ly The amount of antibiotic leached from layer ly, Conc mob,ly For the migratable antibiotic concentration in layer ly, w perc,ly The leakage rate of the ly layer;

[0034] Then, based on antibiotic concentration, runoff depth, and soil erosion rate, the amount of antibiotics entering the river is calculated vertically to obtain the amount of antibiotic migration from the vadose zone into the river, according to the formula:

[0035] F surf | ly＝0 =Conc mob,ly *R surf *β surf ;

[0036] Fintf | ly>0 =Conc mob,ly *R intf ;

[0037] F ero | ly＝0 =Conc lab,dis | ly＝0 *K p,soil *Ero sed *ε;

[0038] Where ly = 0 represents the mixed layer, ly > 0 represents the soil layer, and F surf | ly＝0 For the amount of antibiotics entering the river in the mixed layer, F intf | ly>0 F represents the amount of antibiotics entering the river from the soil layer. ero | ly＝0 To account for the amount of antibiotics entering the river due to soil erosion, Conc lab,dis | ly＝0 R represents the antibiotic concentration in the dissolved phase of the mixed-layer active library. surf β represents surface runoff. surf R is the surface runoff dilution factor. intf Ero is the amount of water flowing through the soil. sed ε represents the soil erosion rate, and ε is the enrichment coefficient.

[0039] In the simulation of the migration and fate of antibiotics in shallow groundwater, a shallow groundwater antibiotic reservoir is defined. Its mass balance is calculated based on the shallow groundwater recharge and discharge rates. The antibiotic concentration in the reservoir is calculated based on the mixing coefficient, according to the formula:

[0040]

[0041] Among them, Conc gw M represents the concentration of antibiotics in shallow groundwater reservoirs. shallow For warehouse quality, M rchrg θ represents the shallow groundwater recharge. gw R represents the shallow groundwater content, α is the mixing coefficient, and R gwro This refers to the discharge of shallow groundwater.

[0042] In the simulation of the river channel antibiotic migration and fate process, the proportions of antibiotics in the dissolved and suspended sediment phases in the overlying water and sediment are calculated based on the distribution coefficient, according to the formula:

[0043]

[0044]

[0045] Among them, Fr dis,wat and Frdis,sed K represents the percentage of dissolved antibiotics in the overlying water and sediment, respectively. p,wat and K p,sed These are the antibiotic partition coefficients in overlying water and sediment, respectively. SPS φ represents the concentration of suspended sediment in the overlying water. sed ρ represents the porosity of the sediment. sed The bulk density of the sediment;

[0046] The diffusion flux between overlying water and sediment is calculated based on the antibiotic mass gradient of the overlying water and sediment. Then, the sedimentation and resuspension processes of antibiotics carried by suspended sediment are simulated, and the antibiotic degradation is calculated. This yields migration data for antibiotics during their migration and descent in the river channel, based on the formula:

[0047] F dif =K dif *(Fr dis,sed *M pool,sed -Fr dis,wat *M pool,wat )*v;

[0048]

[0049]

[0050] Among them, F dif For diffusion flux, F deg,wat and F deg,sed These represent antibiotic degradation fluxes in overlying water and sediment, respectively, K. dif DT is the diffusion coefficient. 50,wat and DT 50,sed M represents the antibiotic half-life in overlying water and sediment, respectively. pool,sed and M pool,wat denoted as the mass of antibiotics in sediment and overlying water, respectively, and τ as the water transport time.

[0051] According to the above technical solution, in step S300, the migration and fate process of antibiotics is simulated by the distributed watershed antibiotic migration and fate process model, the concentration of antibiotics in the river is simulated and analyzed, and then compared with the measured antibiotic concentration data in the river to verify the reliability of the distributed watershed antibiotic migration and fate process model.

[0052] According to the formula Calculate the root mean square error of the simulation results data; where S is the root mean square error of the simulation results data, x1, x2, ..., x n The simulation result data is x′1,x′2,…,x′ n The data are measured, and N is the number of data samples; the root mean square error threshold is set to T. sWhen the root mean square error of the simulation results is less than or equal to the threshold T s Output reliable signal of model data; when the root mean square error of the simulation result data is greater than the threshold T s The output model data is an unreliable signal;

[0053] According to the formula Calculate the percentage deviation of the simulation results data; where P is the percentage deviation between the simulation output value and the actual observed value. For simulation results data, These are actual measured data; the deviation percentage threshold range is set to... When the percentage deviation of the simulation result data is within the threshold range, the model data is output as a reliable signal; when the percentage deviation of the simulation result data is outside the threshold range, the model data is output as an unreliable signal.

[0054] When reliable signals are obtained from the results of two verification methods, the results of the distributed watershed antibiotic migration and homing process model are determined to be reliable; when unreliable signals are obtained from the results of one verification method, the results of the distributed watershed antibiotic migration and homing process model are determined to be reliable but contain errors; when unreliable signals are obtained from the results of two verification methods, the results of the distributed watershed antibiotic migration and homing process model are determined to be unreliable.

[0055] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0056] Previous studies on antibiotic simulation in watersheds lacked characterization of the complex multi-stage process of antibiotic emission, accumulation, reaction, and entry into rivers from non-point sources. Compared with existing technologies, this invention provides an effective distributed watershed antibiotic migration and fate simulation modeling method. It mathematically generalizes the multiphase antibiotic reservoirs and fluxes of various typical environmental media in the land-water system, and couples the effects of watershed hydrological cycle and sediment transport on antibiotic transport. It quantifies the dynamic response characteristics of antibiotic source-flow-sink under the superimposed natural and human driving forces, and simulates the high spatiotemporal accuracy of antibiotic migration and fate at the watershed scale. Attached Figure Description

[0057] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0058] Figure 1 This is a flowchart of the modeling method for the distributed watershed antibiotic migration and fate process of the present invention;

[0059] Figure 2 This invention describes the location of livestock farms and natural villages within the case study area of ​​the distributed watershed antibiotic migration and homing process modeling method.

[0060] Figure 3 This is the simulation result of the daily-scale dynamic change process of tetracycline antibiotic concentration in river channels using the distributed watershed antibiotic migration and fate process modeling method of this invention. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Please see Figures 1-3 The present invention provides the following technical solution:

[0063] A modeling method for the migration and fate of antibiotics in a distributed watershed includes the following steps:

[0064] Step S100: Divide the watershed into nested sections, determine the spatiotemporal range of the computational domain, collect raster data and attribute data for model operation, and set the initial state and physicochemical properties of each generalized object.

[0065] Step S200: Based on the data set in Step S100, the distributed watershed antibiotic migration and fate process model is used to simulate the antibiotic migration and fate process, and to analyze and predict the migration amount and concentration data of antibiotics in the divided watershed grid. The antibiotic migration and fate process simulation includes watershed hydrological process simulation, watershed sediment transport process simulation, spatiotemporal dynamic simulation of antibiotic emission source intensity, vadose zone antibiotic migration and fate process simulation, shallow groundwater antibiotic migration and fate process simulation, and river channel antibiotic migration and fate process simulation.

[0066] Step S300: Collect antibiotic concentration data in the actual area and compare it with antibiotic concentration data simulated by the distributed watershed antibiotic migration and fate process model, and verify the reliability of the model using root mean square error and percentage deviation.

[0067] In step S100, the watershed is divided into nested subdivisions. On the plane, the watershed is divided into rectangular grids, sub-watersheds, and river networks. On the vertical plane, the grid is divided into canopy, mixed layer, vadose zone, and shallow groundwater. The vadose zone is divided into three soil layers, and the mixed layer is used to characterize the area where exogenous antibiotics directly interact with the land.

[0068] By dividing geographical regions into nested grids, distributed simulations are performed on each sub-stage of the antibiotic migration and destination process. The simulation processes of each distributed computation are then summarized to obtain the overall migration data of antibiotics within the land area. While refining the analysis of each region within each spatial range, the overall analysis and simulation of the antibiotic migration process between different regions are realized, which can completely depict the dynamic migration process of antibiotic source-flow-sink within the land area.

[0069] In step S200, the watershed hydrological process simulation includes the following: the land surface hydrological simulation includes vertical simulation of the processes of groundfall rainfall, canopy interception, evapotranspiration, infiltration, leakage, and recharge, and vertical simulation of the generation and confluence processes of surface runoff, interflow, and groundwater runoff, and calculation of soil and river water temperatures; the river channel part uses the Muskingan-Congee algorithm for river channel hydrological simulation.

[0070] In the simulation of watershed sediment transport processes, the land surface soil erosion simulation includes the combined effects of rainfall splash erosion, runoff erosion, gravity settling, and the sediment-carrying capacity of water flow to calculate the amount of soil erosion; the river channel sediment transport simulation refers to simulating the advection, settling, and resuspension processes of suspended sediment.

[0071] In the spatiotemporal dynamic simulation of antibiotic emission sources, point source emission load is directly used as the boundary condition input for the river channel; for area source emission sources, atmospheric dry and wet deposition and agricultural sources are considered. The influence range of agricultural sources is delineated, and the area source antibiotic flux input to the grid is simulated according to the emission pattern and dynamic characteristics of the source to obtain the migration amount of area source into the grid area.

[0072] For livestock and poultry farming, a typical non-point source of antibiotic pollution, the influence range is defined by taking the geographical location of the farm as the center and using the radius. The antibiotic flux entering the target grid within the influence range is calculated based on the type and quantity of farming and the unit emission coefficient. The proportion of antibiotic flux entering the mixed layer and the top soil layer is allocated by the proportional coefficient.

[0073] According to formula F src,anm =K anm *N anm Calculate the antibiotic flux F entering the target grid via livestock farming within the influence area. src,anm Among them, K anm N represents the emission coefficient per unit of current aquaculture type. anm For the number of animals raised;

[0074] For the typical non-point source antibiotic pollution source of scattered rural domestic sewage, the influence range is defined by taking the geographical location of the natural village as the center and delineating it by radius. The dynamic process of antibiotics in septic tanks being collected into the tank by human excrement, degrading, periodically emptied and fertilized is simulated. The antibiotic flux entering the target grid through septic tank fertilization is calculated, and the proportion of antibiotic flux entering the mixed layer and the top soil layer is allocated by the proportional coefficient.

[0075] According to formula F src,hum =K hum *N hum *R deg Calculate the antibiotic flux F entering the target grid through septic tank fertilization. src,hum Among them, K hum N is the emission factor per unit population. hum R represents the number of permanent residents. deg This refers to the degradation rate in the septic tank.

[0076] We adopt a point-area differentiation simulation method for antibiotic pollution sources, optimize the calculation of area source emission load using the existing emission inventory estimation method for area source emission simulation, conduct detailed analysis of different area sources, accurately characterize emission patterns and antibiotic output characteristics, and obtain more accurate and detailed analysis results for the strong spatiotemporal dynamic simulation of antibiotic emission sources at the watershed scale.

[0077] In the simulation of antibiotic migration and fate in the vadose zone, stable and active antibiotic libraries are defined for the mixed layer and each soil layer. Each library consists of a dissolved phase and an adsorbed phase. The concentration and content of each phase are calculated based on the partition coefficient, according to the formula:

[0078]

[0079]

[0080] Among them, Conc lab,dis and Conc sta,dis M represents the concentration of antibiotics in the dissolved phase of the active and stable libraries, respectively. lab and M sta The values ​​of antibiotics in the active and stable libraries are respectively θ. sat K represents the soil saturation water content. p,soil ρ is the partition coefficient of antibiotics in soil between soil water and particles. b For soil bulk density, Thick soil Soil layer thickness;

[0081] The mass exchange between the active and stable libraries is calculated using the diffusion coefficient, according to the formula:

[0082]

[0083] Among them, F ex M represents the antibiotic exchange flux between the active and stable libraries. lab,dis and M sta,dis K represents the mass of the dissolved phase antibiotics in the active and stable libraries, respectively. ex For the exchange rate, φ sol Soil porosity;

[0084] The degradation rate of antibiotics in the vadose zone is calculated based on half-life and soil temperature, and then the migration rate of antibiotics in the vadose zone is analyzed according to the formula:

[0085]

[0086]

[0087] Among them, F deg,lab and F deg,sta M represents the degradation amount of antibiotics in the active and stable libraries, respectively. lab and M sta The quality of antibiotics in the active and stable libraries, respectively, DT 50,soil K is the half-life of the antibiotic. T Here is a temperature correction factor, where T is the soil temperature; the concentration of migratable antibiotics is calculated using the formula:

[0088]

[0089] Among them, Conc mob M represents the concentration of migratable antibiotics in the vadose zone. lab To improve the quality of active antibiotics in the soil, w mob θ represents the amount of mobile water in the soil. sat K represents the saturated soil moisture content. p,soil ρ is the partition coefficient of antibiotics in soil between soil water and particles. b For soil bulk density, Thick soil It refers to the thickness of the soil layer to which it belongs;

[0090] Based on this concentration and the vertical leaching volume of antibiotics due to gravity drainage, the following formula is used:

[0091] F lch,ly =Conc mob,ly *w perc,ly ;

[0092] Where ly is the soil layer number, F lch,ly The amount of antibiotic leached from layer ly, Conc mob,ly For the migratable antibiotic concentration in layer ly, w perc,ly The leakage rate of the ly layer;

[0093] Then, based on antibiotic concentration, runoff depth, and soil erosion rate, the amount of antibiotics entering the river is calculated vertically to obtain the amount of antibiotic migration from the vadose zone into the river, according to the formula:

[0094] F surf | ly＝0 =Conc mob,ly*R surf *β surf ;

[0095] F intf | ly>0 =Conc mob,ly *R intf ;

[0096] F ero | ly＝0 =Conc lab,dis | ly＝0 *K p,soil *Ero sed *ε;

[0097] Where ly = 0 represents the mixed layer, ly > 0 represents the soil layer, and F surf | ly＝0 For the amount of antibiotics entering the river in the mixed layer, F intf | ly>0 F represents the amount of antibiotics entering the river from the soil layer. ero | ly＝0 To account for the amount of antibiotics entering the river due to soil erosion, Conc lab,dis | ly＝0 R represents the antibiotic concentration in the dissolved phase of the mixed-layer active library. surf β represents surface runoff. surf R is the surface runoff dilution factor. intf Ero is the amount of water flowing through the soil. sed ε represents the soil erosion rate, and ε is the enrichment coefficient.

[0098] In the simulation of the migration and fate process of antibiotics in shallow groundwater, a shallow groundwater antibiotic reservoir is defined. Its mass balance is calculated based on the shallow groundwater recharge and discharge rates. The antibiotic concentration in the reservoir is calculated based on the mixing coefficient, according to the formula:

[0099]

[0100] Among them, Conc gw M represents the concentration of antibiotics in shallow groundwater reservoirs. shallow For warehouse quality, M rchrg θ represents the shallow groundwater recharge. gw R represents the shallow groundwater content, α is the mixing coefficient, and R gwro This refers to the discharge of shallow groundwater.

[0101] In the simulation of the river channel antibiotic migration and fate process, the proportions of antibiotics in the dissolved and suspended sediment phases in the overlying water and sediment are calculated based on the distribution coefficient, according to the formula:

[0102]

[0103]

[0104] Among them, Fr dis,wat and Fr dis,sed K represents the percentage of dissolved antibiotics in the overlying water and sediment, respectively. p,wat and K p,sed These are the antibiotic partition coefficients in overlying water and sediment, respectively. SPS φ represents the concentration of suspended sediment in the overlying water. sed ρ represents the porosity of the sediment. sed The bulk density of the sediment;

[0105] The diffusion flux between overlying water and sediment is calculated based on the antibiotic mass gradient of the overlying water and sediment. Then, the sedimentation and resuspension processes of antibiotics carried by suspended sediment are simulated, and the antibiotic degradation is calculated. This yields migration data for antibiotics during their migration and descent in the river channel, based on the formula:

[0106] F dif =K dif *(Fr dis,sed *M pool,sed -Fr dis,wat *M pool,wat )*τ;

[0107]

[0108]

[0109] Among them, F dif For diffusion flux, F deg,wat and F deg,sed These represent antibiotic degradation fluxes in overlying water and sediment, respectively, K. dif DT is the diffusion coefficient. 50,wat and DT 50,sed M represents the antibiotic half-life in overlying water and sediment, respectively. pool,sed and M pool,wat τ represents the mass of antibiotics in the sediment and the overlying water, respectively, and τ is the water transport time.

[0110] In step S300, the migration and fate process of antibiotics is simulated using the distributed watershed antibiotic migration and fate process model. The concentration of antibiotics in the river is simulated and analyzed, and then compared with the measured antibiotic concentration data in the river to verify the reliability of the distributed watershed antibiotic migration and fate process model.

[0111] According to the formula Calculate the root mean square error of the simulation results data; where S is the root mean square error of the simulation results data, x1, x2, ..., x n The simulation result data is x′1,x′2,…,x′n The data are measured, and N is the number of data samples; the root mean square error threshold is set to T. s When the root mean square error of the simulation results is less than or equal to the threshold T s Output reliable signal of model data; when the root mean square error of the simulation result data is greater than the threshold T s The output model data is an unreliable signal;

[0112] According to the formula Calculate the percentage deviation of the simulation results data; where P is the percentage deviation between the simulation output value and the actual observed value. For simulation results data, These are actual measured data; the deviation percentage threshold range is set to... When the percentage deviation of the simulation result data is within the threshold range, the model data is output as a reliable signal; when the percentage deviation of the simulation result data is outside the threshold range, the model data is output as an unreliable signal.

[0113] When reliable signals are obtained from the results of two verification methods, the results of the distributed watershed antibiotic migration and homing process model are determined to be reliable; when unreliable signals are obtained from the results of one verification method, the results of the distributed watershed antibiotic migration and homing process model are determined to be reliable but contain errors; when unreliable signals are obtained from the results of two verification methods, the results of the distributed watershed antibiotic migration and homing process model are determined to be unreliable.

[0114] The following content provides specific implementation examples of the modeling method for the distributed watershed antibiotic migration and fate process of the present invention;

[0115] Using four tetracycline antibiotics (tetracycline, oxytetracycline, chlortetracycline, and doxycycline) with high detection rates in agricultural watersheds as simulation subjects, and taking an agricultural watershed on the north bank of Chaohu Lake as a typical case area, the specific implementation example is as follows:

[0116] Collect and prepare the raster data and attribute data required for running the model. The raster data includes: land use type, soil type, digital elevation model, slope, flow direction, sub-basin zoning, and river network; the attribute data includes: daily cumulative rainfall, daily average temperature, soil physicochemical properties, river channel attributes, river network topology, and agricultural management information.

[0117] The simulation period was set from January 1, 2016 to December 31, 2021, with the model using daily calculation steps. Based on the acquired data, the experimental watershed was divided into rectangular grids, with the computational region discretized into 248*258 square grids, each 50m*50m in size. Each grid was vertically divided into a canopy, a mixed layer, a vadose zone, and shallow groundwater, with the mixed layer thickness at 0.01m. Sub-watersheds were delineated based on the digital elevation model and the actual river network, resulting in 24 sub-watershed zones and corresponding 24 river segments. The entire computational region included four land use types and four soil types. For grids with agricultural land use, a rice-oilseed rotation planting pattern was established based on local conditions. The physicochemical properties of the grid soils were defined using HWSD v1.2 soil data, including the proportions of sand, silt, and clay, the number of soil layers, soil layer thickness, soil bulk density, saturated hydraulic conductivity, wilting coefficient, and available water holding capacity. Initial soil moisture content was set as field capacity, river suspended sediment concentration was given based on observed averages, and initial antibiotic concentrations in soil and rivers were given based on observed averages. The case study area involved four rain gauges and one weather station providing rainfall and temperature data as meteorological drivers.

[0118] There is only one source of antibiotic discharge within the watershed, a wastewater treatment plant in a town located in the middle and lower reaches. According to a literature review, the concentration of tetracycline antibiotics in the atmosphere is extremely low, and all four tetracycline antibiotics were detected in rainwater; therefore, the amount of antibiotics migrating from atmospheric sources was not considered in this case study.

[0119] The case study area considers antibiotic non-source pollution from livestock and poultry farming and decentralized rural domestic sewage. For example... Figure 2 As shown, there are 12 livestock and poultry farms in the basin; among them, the types of livestock and poultry are sheep and chickens, which are free-range. The antibiotic flux of tetracycline antibiotics entering the target grid of each farm is calculated according to the unit emission coefficient. A circle with a radius of 2km is drawn with the farm as the center, and the total emission is evenly distributed to the agricultural land and forest land grids within the circle.

[0120] The location of local natural villages was determined based on statistical data and field surveys, such as Figure 2 As shown in the figure; based on the dynamic process of antibiotics in septic tanks collected by human excrement, degraded, periodically emptied and fertilized, the amount of antibiotics entering the target grid through septic tank fertilization is calculated. The distribution area is a circle with the septic tank as the center and a radius of 800m. The dynamic discharge is evenly distributed to the agricultural land grid within the area.

[0121] For sewage from livestock and poultry farming and decentralized rural domestic sewage, the proportion of antibiotics entering the mixed layer and the top soil layer is set at 70% and 30%, respectively.

[0122] like Figure 1The flowchart shown is a modeling method for the distributed watershed antibiotic migration and homing process of this invention. It describes how raster and attribute data are obtained by reading raster and attribute files, where t represents the simulation time and max represents the end time of the simulation. In the simulation of the watershed water-sediment-antibiotic coupling process, the land area simulation adopts a raster parallel computing method. Each raster is calculated sequentially in time, and the calculation of a single raster terminates when the end time is reached. When the number of completed raster calculations reaches the total number of raster calculations, the raster simulation terminates and automatically enters the land surface runoff calculation. The river segment simulation is calculated sequentially in time, and the river segment simulation terminates when the end time is reached.

[0123] At six sampling sections (S5, S7, S15, S17, S20, and S23), 1L of overlying water approximately 20cm below the water surface was collected. The water samples were filtered through a 0.75μm filter membrane, and the concentrations of four tetracycline antibiotics were measured in the filtered water samples. The reliability of the model was verified using the above monitoring data. In the actual verification, a root mean square error threshold T for the deviation simulation results was set. s =5ng*L -1 The deviation percentage threshold range is [-30%, 30%];

[0124] like Figure 3 As shown, in the distributed watershed antibiotic migration and homing process modeling method, for four tetracycline antibiotics, the percentage deviation between the simulation results and the actual observation data at all stations was within ±30%, with a mean deviation percentage of -5.32% and a mean root mean square error of 3.33 ng·L⁻¹. -1 The simulated trend can capture the response process of non-point source antibiotics to changes in rainfall, fertilization and temperature, and the model has passed the reliability test;

[0125] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0126] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A modeling method for the migration and fate process of antibiotics in a distributed watershed, characterized in that, The method includes the following steps: Step S100: Divide the watershed into nested sections, determine the spatiotemporal range of the computational domain, collect the raster data and attribute data for model operation, and set the initial state and physicochemical properties of each generalized object. Step S200: Based on the data set in Step S100, the distributed watershed antibiotic migration and fate process model is used to simulate the antibiotic migration and fate process, and to analyze and predict the migration amount and concentration data of antibiotics in the divided watershed grid. The antibiotic migration and fate process simulation includes watershed hydrological process simulation, watershed sediment transport process simulation, spatiotemporal dynamic simulation of antibiotic emission source intensity, vadose zone antibiotic migration and fate process simulation, shallow groundwater antibiotic migration and fate process simulation, and river channel antibiotic migration and fate process simulation. In the watershed hydrological process simulation, the land surface hydrological simulation includes vertical simulation of groundfall rainfall, canopy interception, evapotranspiration, infiltration, leakage and recharge processes, and vertical simulation of surface runoff, interflow and groundwater runoff generation and confluence processes, and calculation of soil and river water temperature; the river channel part uses the Muskingen-Congee algorithm for river channel hydrological simulation. In the simulation of watershed sediment transport processes, the land surface soil erosion simulation includes analyzing the combined effects of rainfall splash erosion, runoff erosion, gravity settling, and the sediment-carrying capacity of water flow to calculate the amount of soil erosion; the river channel sediment transport simulation refers to simulating the advection, settling, and resuspension processes of suspended sediment. In the spatiotemporal dynamic simulation of antibiotic emission sources, point source emission load is directly used as the boundary condition input for the river channel; for area source emission sources, atmospheric dry and wet deposition and agricultural sources are considered. The influence range of agricultural sources is defined, and the area source antibiotic flux input to the grid is simulated according to the emission pattern and dynamic characteristics of the source to obtain the migration amount of area source into the grid area. In the simulation of antibiotic migration and fate in the vadose zone, stable and active pools of antibiotics are defined for the mixed layer and each soil layer. Each pool consists of a dissolved phase and an adsorbed phase. The concentration and content of each phase are calculated based on the partition coefficient. The mass exchange between the active and stable pools is calculated using the diffusion coefficient. The degradation of antibiotics in the vadose zone is calculated based on the half-life and soil temperature, and then the migration of antibiotics in the vadose zone is analyzed. The concentration of migratable antibiotics is calculated. Then, based on the antibiotic concentration, runoff depth and soil erosion rate, the amount of antibiotics entering the river is calculated vertically to obtain the amount of antibiotics migrating from the vadose zone into the river. In the simulation of the migration and fate process of antibiotics in shallow groundwater, a shallow groundwater antibiotic reservoir is defined, its mass balance is calculated based on the shallow groundwater recharge and discharge, and the antibiotic concentration in the reservoir is calculated based on the mixing coefficient. In the simulation of the river antibiotic migration and destination process, the proportion of antibiotics in the dissolved phase and suspended sediment phase in the overlying water and sediment is calculated based on the distribution coefficient. The diffusion flux between the overlying water and sediment is calculated based on the antibiotic mass gradient in the overlying water and sediment. Then, by simulating the sedimentation and resuspension process of antibiotics with the movement of suspended sediment, and calculating the amount of antibiotic degradation, the migration data of antibiotics in the river migration and destination process is obtained. Step S300: Collect antibiotic concentration data in the actual area and compare it with antibiotic concentration data simulated by the distributed watershed antibiotic migration and fate process model, and verify the reliability of the model using root mean square error and percentage deviation.

2. The modeling method for the distributed watershed antibiotic migration and fate process according to claim 1, characterized in that: In step S100, the watershed is divided into nested subdivisions. On the plane, the watershed is divided into rectangular grids, sub-watersheds, and river networks. On the vertical plane, the grid is divided into canopy, mixed layer, vadose zone, and shallow groundwater. The vadose zone is divided into three soil layers, and the mixed layer is used to characterize the area where exogenous antibiotics directly interact with the land.

3. The modeling method for the distributed watershed antibiotic migration and fate process according to claim 1, characterized in that: The migration and fate process of antibiotics in the vadose zone is based on the formula: ； ； in, and M represents the concentration of antibiotics in the dissolved phase of the active and stable libraries, respectively. lab and M sta The values ​​of antibiotics in the active and stable libraries are respectively θ. sat K represents the soil saturation water content. p,soil This represents the partition coefficient of antibiotics in soil between soil water and particles. For soil bulk density, Soil layer thickness; The mass exchange between the active and stable libraries is calculated using the diffusion coefficient, according to the formula: ； Among them, F ex This represents the antibiotic exchange flux between the active and stable libraries. and K represents the mass of the dissolved phase antibiotics in the active and stable libraries, respectively. ex For the exchange rate, Soil porosity; The degradation rate of antibiotics in the vadose zone was calculated based on half-life and soil temperature, and then the migration rate of antibiotics in the vadose zone was analyzed according to the formula: ； ； Among them, F deg,lab and F deg,sta M represents the degradation amount of antibiotics in the active and stable libraries, respectively. lab and M sta The quality of antibiotics in the active and stable libraries, respectively, DT 50,soil K is the half-life of the antibiotic. T Here is the temperature correction factor, where T is the soil temperature; The concentration of migratable antibiotics is calculated using the formula: ； Among them, Conc mob M represents the concentration of migratable antibiotics in the vadose zone. lab To improve the quality of active antibiotics in the soil, w mob θ represents the amount of mobile water in the soil. sat K represents the saturated soil moisture content. p,soil ρ is the partition coefficient of antibiotics in soil between soil water and particles. b For soil bulk density, Thick soil It refers to the thickness of the soil layer to which it belongs; Based on this concentration and the vertical leaching volume of antibiotics due to gravity drainage, the formula is calculated as follows: ； Where ly is the soil layer number, F lch,ly For from the Antibiotic leaching amount in the layer, Conc mob,ly For the migratable antibiotic concentration in layer ly, w perc,ly The leakage rate of the ly layer; Then, based on antibiotic concentration, runoff depth, and soil erosion rate, the amount of antibiotics entering the river is calculated vertically to obtain the amount of antibiotic migration from the vadose zone into the river, according to the formula: ； ； ； Where ly=0 represents the mixed layer, and ly>0 represents the soil layer. This refers to the amount of antibiotics introduced into the mixed-layer river. This refers to the amount of antibiotics entering the river from the soil layer. The amount of antibiotics entering the river due to soil erosion, and the activity of the mixed layer. Antibiotic concentration in the soluble phase of the sex library, R surf β represents surface runoff. surf R is the surface runoff dilution factor. intf Ero is the amount of water flowing through the soil. sed ε represents the soil erosion rate, and ε is the enrichment coefficient.

4. The modeling method for the distributed watershed antibiotic migration and fate process according to claim 1, characterized in that: In the simulation of the migration and fate process of antibiotics in shallow groundwater, according to the formula: ； Among them, Conc gw M represents the concentration of antibiotics in shallow groundwater reservoirs. shallow For warehouse quality, M rchrg θ represents the shallow groundwater recharge. This refers to the content of shallow groundwater. The mixing coefficient, This represents the amount of shallow groundwater discharged.

5. The modeling method for the distributed watershed antibiotic migration and fate process according to claim 1, characterized in that: In the simulation of the river channel antibiotic migration and fate process, according to the formula: ； ； Among them, Fr dis,wat and Fr dis,sed K represents the percentage of dissolved antibiotics in the overlying water and sediment, respectively. p,wat and K p,sed These are the antibiotic partition coefficients in overlying water and sediment, respectively. SPS The concentration of suspended sediment in the overlying water. ρ represents the porosity of the sediment. sed The bulk density of the sediment; The diffusion flux between overlying water and sediment is calculated based on the antibiotic mass gradient of the overlying water and sediment. Then, the sedimentation and resuspension processes of antibiotics carried by suspended sediment are simulated, and the antibiotic degradation is calculated. This yields migration data for antibiotics during their migration and descent in the river channel, based on the formula: ； ； ； Among them, F dif For diffusion flux, F deg,wat and F deg,sed These represent antibiotic degradation fluxes in overlying water and sediment, respectively, K. dif DT is the diffusion coefficient. 50,wat and DT 50,sed M represents the antibiotic half-life in overlying water and sediment, respectively. pool,sed and M pool,wat denoted as the mass of antibiotics in sediment and overlying water, respectively, and τ as the water transport time.

6. The modeling method for the distributed watershed antibiotic migration and fate process according to claim 1, characterized in that: In step S300, the migration and fate process of antibiotics is simulated using the distributed watershed antibiotic migration and fate process model. The concentration of antibiotics in the river is simulated and analyzed, and then compared with the measured antibiotic concentration data in the river to verify the reliability of the distributed watershed antibiotic migration and fate process model. According to the formula Calculate the root mean square error of the simulation results data; where, The root mean square error of the simulation results data For simulation results data, The data are measured, and N is the number of data samples; the root mean square error threshold is set to T. s When the root mean square error of the simulation results is less than or equal to the threshold T s Output reliable signal of model data; when the root mean square error of the simulation result data is greater than the threshold T s The output model data is an unreliable signal; According to the formula Calculate the percentage deviation of the simulation results data; where P is the percentage deviation between the simulation output value and the actual observed value. For simulation results data, These are actual measured data; the deviation percentage threshold range is set to... When the percentage deviation of the simulation result data is within the threshold range, a reliable model data signal is output; when the percentage deviation of the simulation result data is outside the threshold range, an unreliable model data signal is output. When reliable signals are obtained from the results of two verification methods, the results of the distributed watershed antibiotic migration and homing process model are determined to be reliable; when unreliable signals are obtained from the results of one verification method, the results of the distributed watershed antibiotic migration and homing process model are determined to be reliable but contain errors; when unreliable signals are obtained from the results of two verification methods, the results of the distributed watershed antibiotic migration and homing process model are determined to be unreliable.

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