Method for simulating neonicotinoid pesticide and metabolite non-point source pollution based on SWAT model

Abstract

The invention relates to the field of environment model simulation, and particularly discloses a method for simulating neonicotinoid pesticide and metabolite non-point source pollution based on an SWAT model, which comprises the following steps of: 1, expanding a pesticide input module of the SWAT model, and adding attribute parameter definition of metabolites in the pesticide input module, comprise an adsorption coefficient, a Henry constant and a degradation rate of the metabolite, a proportion of the metabolite generated by degradation of a parent compound, and multi-stage degradation path parameters; by expanding the pesticide input module of the SWAT model, the attribute parameter definition of the metabolite is increased, and the conversion and migration process of the pesticide in the environment can be described more comprehensively, so that the simulation precision and accuracy are improved; the embedded dynamic metabolite generation module can reflect the dynamic change process of the pesticide and the metabolite thereof in the environment more truly based on dynamic rate calculation driven by environmental factors.

Description

Technical Field

[0001] The present invention belongs to the field of environmental model simulation, and specifically relates to a method for simulating the non-point source pollution of neonicotinoid pesticides and metabolites based on the SWAT model. Background Art

[0002] Neonicotinoid pesticides are a class of synthetic nicotine analogues that have been widely used in agricultural pest control since the 1990s. A large number of studies have shown their significant toxicity to non-target organisms (especially pollinators). Neonicotinoid pesticides are polar, water-soluble, non-volatile pesticides, and the toxicity of some metabolites is higher than that of the parent compound. Their migration process in the ecosystem after application is relatively complex. Therefore, the estimation of regional non-point source emissions of neonicotinoid pesticides has always been a difficult point in the field.

[0003] The SWAT model is a model that can simulate the water cycle, sediment, and migration of agricultural chemicals at the watershed scale. The existing pesticide module of the SWAT model allows for the customization of pesticide property parameters (such as adsorption coefficient, degradation half-life, solubility, etc.), and simulates the migration and transformation of pesticides through processes such as surface runoff, soil erosion, and leaching.

[0004] However, due to the lack of metabolite simulation, static degradation mechanisms, and weak multi-media migration models in the existing SWAT model, it is unable to accurately evaluate the non-point source pollution risk of neonicotinoid pesticides and their metabolites, and it is difficult to meet the mandatory assessment requirements of regulations for the environmental behavior of metabolites. Therefore, it is necessary to improve the model to better simulate the non-point source emission estimation of neonicotinoid pesticides. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a method for simulating the non-point source pollution of neonicotinoid pesticides and metabolites based on the SWAT model, so as to solve the problem that the existing SWAT model is unable to accurately evaluate the non-point source pollution risk of neonicotinoid pesticides and their metabolites due to the lack of metabolite simulation, static degradation mechanisms, and weak multi-media migration models.

[0006] A method for simulating the non-point source pollution of neonicotinoid pesticides and metabolites based on the SWAT model includes the following steps:

[0007] Step 1: Expand the pesticide input module of the SWAT model, and add the definition of metabolite property parameters in the pesticide input module, including the adsorption coefficient, Henry's constant, degradation rate, proportion of the parent compound degraded to form metabolites, and multi-stage degradation path parameters of metabolites;

[0008] Step 2: Embed a dynamic metabolite generation module in the code layer of the SWAT model, and the module includes:

[0009] Add concentration variables of multi-level metabolites to the pesticide compound data structure;

[0010] Add the dynamic generation logic of the parent compound and multi-level metabolites during the degradation process. The generation logic is calculated based on the dynamic rate driven by environmental factors, including:

[0011] Based on Q 10 Adjust the degradation rate of the parent compound and metabolites based on the temperature coefficient model;

[0012] Dynamically correct the microbial degradation rate according to the soil pH value and organic matter content;

[0013] Calculate the photolysis rate by combining the light intensity and water quality parameters;

[0014] Step 3: Update the migration calculation module of the SWAT model, add an independent migration tracking function for metabolites, and simulate the migration paths of metabolites through surface runoff, soil erosion, leaching, and sediment;

[0015] Step 4: Calibrate and validate the generation parameters and migration parameters of metabolites using measured data, including:

[0016] Collect soil, surface water, and groundwater samples at the farmland-water section of the experimental watershed, and use LC-MS / MS to detect the concentrations of the parent and metabolites;

[0017] Optimize the model parameters through the SWAT-CUP calibration tool combined with the SUFI2 algorithm;

[0018] Complete the verification based on the Nash-Sutcliffe efficiency coefficient (NSE) and the spatial concentration distribution matching degree.

[0019] Preferably, the multi-level degradation path includes the dynamic process of the parent compound degrading into primary metabolites and the primary metabolites further degrading into secondary metabolites, and the generation ratio of each level of metabolites is independently adjustable.

[0020] Preferably, the expansion of the pesticide input module specifically includes:

[0021] Modify the pesticide.dat file structure and add attribute fields for metabolites;

[0022] Implement the dynamic coupling of the multi-level degradation path in the chem_process module to support the synchronous parameter input of the parent and metabolites.

[0023] Preferably, the update of the migration calculation module includes:

[0024] Add independent surface runoff, leaching, and sediment transport equations for metabolites in the transport.f90 code;

[0025] In the result output module, separate the concentration monitoring items of the parent compound and metabolites, and display their fate paths through a visualization interface.

[0026] Preferably, the environmentally driven dynamic rate calculation further includes:

[0027] Dynamically adjust the degradation rate through a temperature sensitivity function, with the formula:

[0028]

[0029] where k(T) is the degradation rate at temperature T, k 20 is the reference rate at 20 °C, and Q 10 is the temperature coefficient;

[0030] Modify the hydrolysis half-life based on a piecewise function of soil pH;

[0031] Calculate the photolysis rate according to the exponential decay model of light intensity.

[0032] Preferably, it further includes:

[0033] Use digital elevation data (DEM), soil utilization data, and meteorological data to construct the spatial database and attribute database of the SWAT model;

[0034] During the sub-basin division and HRU (Hydrological Response Unit) generation process, integrate the multi-media transport simulation requirements of metabolites.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] By expanding the pesticide input module of the SWAT model and adding the definition of metabolite attribute parameters, the transformation and transport processes of pesticides in the environment can be more comprehensively described, thereby improving the accuracy and precision of the simulation;

[0037] The embedded dynamic metabolite generation module, based on the environmentally driven dynamic rate calculation, can more realistically reflect the dynamic change processes of pesticides and their metabolites in the environment, including the effects of factors such as temperature, soil pH, organic matter content, light intensity, and water quality parameters on the degradation rate;

[0038] The updated transport calculation module adds an independent transport tracking function for metabolites, which can simulate the transport process of metabolites in the environment through different paths (such as surface runoff, soil erosion, leaching, and sediment transport), providing strong support for evaluating the long-term impact of pesticides on the environment. Brief Description of the Drawings

[0039] Figure 1 is the flowchart of the method of the present invention;

[0040] Figure 2 is the simulation architecture diagram of non - point source pollution of pesticides and metabolites by SWAT model. Detailed Embodiments

[0041] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0042] As Figures 1 to 2 shown:

[0043] Embodiment 1: The present invention provides a method for simulating non - point source pollution of neonicotinoid pesticides and metabolites based on the SWAT model, including the following steps:

[0044] Step 1: Expand the pesticide input module of the SWAT model, and add the definition of attribute parameters of metabolites in the pesticide input module, including the adsorption coefficient, Henry's constant, degradation rate, proportion of the parent compound degraded to metabolites, and multi - level degradation path parameters of metabolites;

[0045] Among them, the definition of metabolite parameters and input expansion

[0046] Add the adsorption coefficient (Kd), Henry's constant (H), degradation rate and multi - level degradation path parameters (such as the generation proportion of parent → metabolite A → metabolite B) of metabolites in the pesticide.dat file.

[0047] Example code (Fortran data structure expansion):

[0048]

[0049]

[0050] Step 2: Embed a dynamic metabolite generation module in the code layer of the SWAT model. The module includes:

[0051] Add concentration variables of multi - level metabolites to the pesticide compound data structure;

[0052] Add the dynamic generation logic of the parent compound and multi - level metabolites during the degradation process. The generation logic is based on the dynamic rate calculation driven by environmental factors, including:

[0053] Based on Q 10 The temperature coefficient model adjusts the degradation rates of the parent compound and metabolites;

[0054] Dynamically correct the microbial degradation rate according to soil pH value and organic matter content;

[0055] Calculate the photolysis rate by combining light intensity and water quality parameters;

[0056] Among them, the dynamic metabolism generation module

[0057] Environmental factor driving mechanism:

[0058] Temperature correction: Adjust the degradation rate based on the Q10 model:

[0059]

[0060] pH correction: Segmentally adjust the hydrolysis half-life according to soil pH value (for example, the half-life is shortened by 50% when pH < 6). Photolysis rate: Calculate by combining light intensity (I) and water quality turbidity (C):

[0061] k photo = k max ×(1 - e -αI ) × e -βC

[0062] Multi-stage degradation path: Support chain degradation (parent → metabolite A → metabolite B) and independent parameter configuration.

[0063] Step 3: Update the migration calculation module of the SWAT model, add an independent migration tracking function for metabolites, and simulate the migration paths of metabolites through surface runoff, soil erosion, leaching, and sedimentation;

[0064] Among them, the independent migration tracking function

[0065] Add an independent migration equation for metabolites in transport.f90 to simulate their migration paths through surface runoff, leaching, and sedimentation.

[0066] Example code (metabolite leaching calculation):

[0067] ! Metabolite leaching amount = concentration × hydraulic conductivity × soil moisture content

[0068] metabolite_leach = metabolite_conc * K_sat * soil_moisture

[0069] Step 4: Calibrate and verify the generation parameters and migration parameters of metabolites using measured data, including:

[0070] Soil, surface water and groundwater samples were collected at the farmland-water body section of the experimental basin, and the concentrations of the parent compounds and metabolites were detected using LC-MS / MS;

[0071] The model parameters were optimized through the SWAT-CUP calibration tool combined with the SUFI2 algorithm;

[0072] Verification was completed based on the Nash-Sutcliffe efficiency coefficient (NSE) and the matching degree of spatial concentration distribution;

[0073] Among them, model verification and parameter calibration

[0074] Data collection:

[0075] Sampling points were set up in the experimental basin (intensive agricultural activity area), and soil, surface water and groundwater samples were collected according to the grid design;

[0076] Time frequency: Regular sampling every week + supplementary sampling within 24 hours after rainfall events;

[0077] Detection method: LC-MS / MS (detection limit ≤ 0.1 μg / L, recovery rate ≥ 80%).

[0078] Calibration process:

[0079] The SWAT-CUP tool was used in combination with the SUFI2 algorithm, and the objective function was to simultaneously minimize the simulation errors (RMSE) of the parent compounds and metabolites;

[0080] Sensitivity analysis: The generation ratio of metabolites has a significant impact on the NSE value (sensitivity coefficient > 0.8).

[0081] As can be seen from the above, this method first expands the pesticide input module of the SWAT model, adds the definition of attribute parameters for metabolites, such as adsorption coefficient, Henry's constant, degradation rate, etc., and adds a concentration variable for multi-level metabolites in the pesticide compound data structure;

[0082] Then, a dynamic metabolite generation module was embedded in the code layer of the SWAT model. This module realizes the dynamic generation logic of parent compounds and multi-level metabolites based on the dynamic rate calculation driven by environmental factors (such as temperature, soil pH value, organic matter content, light intensity and water quality parameters), including temperature correction, pH correction and photolysis rate calculation, and supports chain degradation paths and independent parameter configuration;

[0083] In addition, this method also updates the migration calculation module of the SWAT model, adds an independent migration tracking function for metabolites, and simulates the migration paths of metabolites through surface runoff, soil erosion, leaching and sediment;

[0084] Finally, the generation parameters and migration parameters of metabolites were calibrated and verified using measured data. By collecting soil, surface water, and groundwater samples at the farmland-water section of the experimental watershed, the concentrations of the parent compound and metabolites were detected using LC-MS / MS. The model parameters were optimized by combining the SWAT-CUP calibration tool and the SUFI2 algorithm, and the verification was completed based on the Nash-Sutcliffe efficiency coefficient and the spatial concentration distribution matching degree, ensuring the accuracy and reliability of the model. This method provides a powerful tool for evaluating the environmental impact of neonicotinoid pesticides and their metabolites.

[0085] Example 2: Model construction and multi-level degradation path simulation

[0086] Step 1: Input module extension and parameter definition

[0087] Modify the pesticide.dat file and add a metabolite parameter field (Table 1);

[0088] Implement the dynamic coupling logic of the multi-level degradation path in chem_process.f90.

[0089] Table 1: Parameters of the parent compound and metabolites

[0090]

[0091]

[0092] Code extension:

[0093]

[0094] Step 2: Dynamic degradation calculation

[0095] Temperature correction: When the temperature rises from 20°C to 25°C, the degradation rate of the parent compound is adjusted to:

[0096] k(25) = 0.05 × 2 (25-20) / 10 = 0.05751 / day

[0097] pH correction: When pH = 5, the hydrolysis half-life is shortened to 40% of the original value (i.e., the degradation rate is increased to 0.05 × 2.5 = 0.1251 / day0.05 × 2.5 = 0.1251 / day);

[0098] Photolysis rate: When the light intensity I = 1000 Lux and the water turbidity C = 10 NTU, the photolysis rate is calculated as:

[0099] k photo = 0.1 × (1 - e -0.005×1000 ) × e -0.02×10 = 0.0851 / day

[0100] Step 3: Migration Path Verification

[0101] Simulation Results:

[0102] The initial concentration of the parent compound in the soil was 2.3 μg / kg and decreased to 0.8 μg / kg after 48 hours of degradation;

[0103] The peak concentration of metabolite A in surface water was 0.12 μg / L, lagging 12 hours behind the parent;

[0104] The accumulated concentration of metabolite B in sediment was 0.05 μg / kg.

[0105] Verification Metrics:

[0106] Compound NSE RMSE (μg / L) Parent 0.78 0.07 Metabolite A 0.82 0.05 Metabolite B 0.75 0.09

[0107] Example 3: Parameter Calibration and Sensitivity Analysis

[0108] Step 1: Data Collection and Calibration

[0109] Sampling Design:

[0110] Experimental Watershed: A rice-growing area (average annual rainfall 1500 mm);

[0111] Twenty sampling points were set up, distributed in a grid pattern (spacing 500 m × 500 m);

[0112] Time Frequency: Sampling was carried out regularly once a week, with additional sampling within 24 hours after rainfall.

[0113] Detection Method:

[0114] The concentrations of the parent and metabolites were detected by LC-MS / MS, with a detection limit of 0.05 μg / L and a recovery rate of 85% ± 5%.

[0115] Step 2: Calibration Process

[0116] Objective Function:

[0117] Simultaneously minimize the weighted sum of the RMSE of the parent and metabolites (weight ratio 1:1):

[0118] Total Objective Value = 0.5 × RMSE 母体 + 0.5 × RMSE 代谢产物

[0119] Optimization Results:

[0120] Parameter Initial value Value after calibration Production ratio (α) 30％ 28％ Degradation rate (k) 0.08 0.085

[0121] Step 3: Sensitivity Analysis

[0122] Sensitivity coefficient:

[0123] Parameter Influence coefficient on NSE Production ratio (α) 0.85 Degradation rate (k) 0.72

[0124] In the description of this specification, the description referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0125] In the accompanying drawings of the disclosed embodiments of the present invention, only the structures related to the disclosed embodiments are involved. Other structures can refer to the general design. Without conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0126] Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for simulating the non-point source pollution of neonicotinoid pesticides and metabolites based on the SWAT model, characterized in that It includes the following steps: Step 1: Expand the pesticide input module of the SWAT model, and add the definition of property parameters of metabolites in the pesticide input module, including the adsorption coefficient, Henry's constant, degradation rate, proportion of the parent compound degraded to metabolites, and multi-level degradation path parameters of metabolites; Step 2: Embed a dynamic metabolite generation module in the code layer of the SWAT model. The module includes: Add concentration variables of multi-level metabolites to the pesticide compound data structure; Add the dynamic generation logic of the parent compound and multi-level metabolites during the degradation process. The generation logic is based on the dynamic rate calculation driven by environmental factors, including: Based on Q 10 The temperature coefficient model adjusts the degradation rates of the parent compound and metabolites; Dynamically correct the microbial degradation rate according to the soil pH value and organic matter content; Calculate the photolysis rate by combining the light intensity and water quality parameters; Step 3: Update the migration calculation module of the SWAT model, add an independent migration tracking function for metabolites, and simulate the migration paths of metabolites through surface runoff, soil erosion, leaching and sediment; Step 4: Calibrate and verify the generation parameters and migration parameters of metabolites using measured data, including: Collect soil, surface water and groundwater samples at the farmland-water body section of the experimental watershed, and detect the concentrations of the parent and metabolites using LC-MS / MS; Optimize the model parameters through the SWAT-CUP calibration tool combined with the SUFI2 algorithm; Complete the verification based on the Nash-Sutcliffe efficiency coefficient (NSE) and the spatial concentration distribution matching degree.

2. The method for simulating non-point source pollution of neonicotinoid pesticides and metabolites based on the SWAT model according to claim 1, wherein, The multi-level degradation path includes the dynamic process of the parent compound degrading into primary metabolites and the primary metabolites further degrading into secondary metabolites, and the generation ratio of each level of metabolites is independently adjustable.

3. The method for simulating the non-point source pollution of neonicotinoid pesticides and metabolites based on the SWAT model according to claim 1, characterized in that, The expansion of the pesticide input module specifically includes: Modify the pesticide.dat file structure and add attribute fields for metabolites; Implement the dynamic coupling of the multi-level degradation path in the chem_process module to support the synchronous parameter input of the parent and metabolites.

4. The method for simulating non-point source pollution of neonicotinoid pesticides and metabolites based on the SWAT model according to claim 1, wherein, The update of the migration calculation module includes: Add independent surface runoff, leaching and sediment migration equations for metabolites in the transport.f90 code; Separate the concentration monitoring items of the parent compound and metabolites in the result output module, and display their fate paths through the visualization interface.

5. The method for simulating the non-point source pollution of neonicotinoid pesticides and metabolites based on the SWAT model according to claim 1, wherein, The dynamic rate calculation driven by environmental factors further includes: Dynamically adjust the degradation rate through the temperature sensitivity function. The formula is: where k(T) is the degradation rate at temperature T, and k 20 is the reference rate at 20 °C, and Q 10 is the temperature coefficient; Correct the hydrolysis half-life based on the piecewise function of the soil pH value; Calculate the photolysis rate according to the exponential decay model of the light intensity.

6. The method for simulating non-point source pollution of neonicotinoid pesticides and metabolites based on the SWAT model according to claim 1, characterized in that It also includes: Construct a spatial database and an attribute database of the SWAT model using digital elevation data (DEM), soil utilization data and meteorological data; Integrate the multi-media migration simulation requirements of metabolites during the sub-watershed division and HRU (Hydrological Response Unit) generation process.

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