Basin antibiotic ecological risk prevention and control strategy simulation method based on environmental fate

Abstract

The invention relates to the technical field of chemical informatics, in particular to a watershed antibiotic ecological risk prevention and control strategy simulation method based on environmental fate, which comprises the following steps: collecting a plurality of samples along a river watershed, the samples including water samples and sediments, and pre-treating the samples; constructing a fugacity model framework; setting fugacity model parameters; solving the fugacity model; the fugacity capacity and the migration coefficient are solved; sensitivity analysis and uncertainty analysis are carried out; evaluating the ecological risk of antibiotics; different risk prevention and control management scenes are simulated based on fugacity fate simulation and antibiotic ecological risk evaluation; and adjusting input parameters of the fugacity model, and drawing up an antibiotic risk prevention and control strategy according to a result. According to the method, on the basis of establishing the fugacity model, the antibiotic risk prevention and control strategy is generated by adjusting the input parameters for multiple times and calculating the ecological risks of the watershed antibiotics in different scenes, and strategy support is provided for prevention and control of the antibiotic risk of the watershed.

Description

Technical Field

[0001] This invention relates to the technical field of cheminformatics, and in particular to a simulation method for watershed antibiotic ecological risk prevention and control strategies based on environmental fate. Background Technology

[0002] When antibiotics are used by humans and animals, their excrement, containing antibiotic residues, is released into the environment through wastewater and feces, resulting in antibiotic residues in the environment. Although the concentration of antibiotics in the environment is usually low (ng / L or ng / g), it has already produced significant toxic effects on some antibiotic-sensitive organisms. In addition, long-term exposure to antibiotics can lead to antibiotic resistance in bacteria. These resistance genes are heritable and can enter the human body through various routes, such as direct contact or contamination of the food chain, thus enhancing human resistance to antibiotics.

[0003] The average annual precipitation in a certain river basin is 660 mm, and it belongs to the temperate semi-humid monsoon climate zone. The basin area is 5688 km². 2 The average annual natural runoff is 10.22 × 10⁸ m³. 3 However, studies have shown that some antibiotic residues already exist in the watershed. Although the current concentration of antibiotics remaining in the environment is low, their harm to the environment cannot be ignored.

[0004] Currently, there are many studies on the ecological risk assessment of individual antibiotics in aquatic environments, all using ecological risk entropy (i.e., the ratio of the detected or predicted concentration to the predicted ineffective concentration) to assess their ecological risk. Research on the ecological risk of individual antibiotics in aquatic environments is relatively mature. However, in real-world environments, multiple antibiotics often coexist, and their ecological risk can vary due to synergistic or antagonistic effects. Therefore, assessing the combined ecological risk of antibiotics is more important. Simple superposition models are widely used due to their simplicity and abundant toxicological databases. There are also methods based on toxicological data of mixtures of multiple antibiotics to assess the combined ecological risk of multiple antibiotics. This method is closer to reality than simple superposition models, but due to a lack of basic toxicological data, there is currently limited research on this approach.

[0005] Antibiotics remaining in the environment pose certain risks to some microorganisms, some even posing a high ecological risk. Therefore, the prevention and control of the ecological risks of antibiotics in the environment is very important, but relevant research is currently limited. Existing studies on antibiotic risk prevention and control only qualitatively propose certain control measures, with a lack of quantitative research on control measures. Summary of the Invention

[0006] To predict the environmental fate of antibiotics and simulate risk control strategies, and to provide strategic support for reducing antibiotic risks in watersheds, this invention provides a watershed antibiotic ecological risk control strategy simulation method based on environmental fate.

[0007] This invention provides a simulation method for watershed antibiotic ecological risk prevention and control strategies based on environmental fate, employing the following technical solution: A simulation method for watershed antibiotic ecological risk prevention and control strategies based on environmental fate includes the following steps: Multiple samples were collected along the river basin, including water samples and sediments, and the samples were pretreated. Construct a framework for the fugacity model; Set the fugacity model parameters; Solve the fugacity model; Solve to calculate the predicted concentration and migration flux; Sensitivity analysis and uncertainty analysis are performed to calculate the sensitivity coefficient of the input parameters. If the sensitivity coefficient is greater than or equal to 0.5, it is considered a sensitive parameter, and control parameters are selected based on the sensitive parameters. Environmental risk assessment of antibiotics; Based on fugacity regression simulation and antibiotic ecological risk assessment, this study uses prevention and control parameters as parameter variables in scenario simulation. By adjusting the driving quantities of different prevention and control parameters, the study identifies the characteristics of changes in the ecological risk of antibiotics in the water phase of the basin under different risk prevention and control management scenarios, and proposes possible optimal prevention and control strategies.

[0008] In a specific feasible implementation, the parameters of the fugacity model include: the physicochemical properties of the target antibiotic, environmental properties, and emission load parameters; The emission load is estimated based on consumption, and the calculation formula is as follows:

[0009]

[0010]

[0011]

[0012] In the above formula, Antibiotic emissions per person, in tons per year (t / a); Antibiotic emissions from animals, expressed in tons per year (t / a), subscript These correspond to pigs, poultry, and other animals, respectively. This represents the discharge load entering the aqueous phase, expressed in t / a. This represents the emission load entering the soil phase, expressed in t / a. Antibiotic consumption per person, in t / a; Antibiotic consumption in animals, expressed in tons per year (t / a). Representatives' excretion ratios of the parent form and glucuronide-bound form of various antibiotic classes, in percentages (%). The proportion of parental and glucuronide-bound forms of antibiotics excreted by animals for each class, in percentages (%). Represents the proportion of urban population, in percentages: % Represents the proportion of the rural population, in percentages: % This represents the city's wastewater treatment rate, expressed in % %. This represents the rural sewage treatment rate, expressed in % %. This represents the removal rate of the target antibiotic by the wastewater treatment plant; The excretion rate of pig urine is expressed in % (%). This represents the flushing rate of pig manure, expressed in percent (%).

[0013] In a specific feasible implementation, solving the fugacity model includes the following steps: The environment was divided into four main phases: air, water, soil, and sediment, and the fugacity capacity of each environmental medium was defined. Calculate the migration coefficients for each environmental process; Interphase migration flux is calculated by multiplying the migration coefficient by the corresponding fugacity of the main environment phase, and then a mass balance equation based on migration flux is established: Inflow = Outflow; After calculating the fugacity in each main environmental phase, the distribution of the target antibiotic in each phase is calculated.

[0014] In a specific feasible implementation plan, the mass balance equations for the four main environmental phases—air, water, soil, and sediment—are as follows: Air phase:

[0015] Aqueous phase:

[0016] Soil phase:

[0017] Sedimentary facies:

[0018] In the above formula, For the first Phase fugacity, These represent the air phase, water phase, soil phase, and sediment phase, respectively. For the first The emission rate of the phase, in units of mol / h; For the first The advection inflow velocity of the phase, in meters per second (m). 3 / h; For the first The advection concentration of the phase, in units of mol / m³ 3 ; For the first The reaction rate of the phase, referring here to the degradation reaction, is expressed in mol / (Pa·h). For the first Advection of phase, unit: mol / (Pa·h); The migration coefficient is expressed in mol / (Pa·h).

[0019] In a specific feasible implementation, the formula for calculating the distribution of the target antibiotic in each phase is:

[0020] in, For the first Distribution of the target antibiotic in each phase; For the first Fugacity capacity of a phase.

[0021] In a specific feasible implementation, sensitivity analysis and uncertainty analysis include the following steps: By setting the model parameter variation range to ±10%, the calculation formula is as follows:

[0022] In the above formula, For the first The sensitivity coefficient of the phase; For the output result; For input parameters; Sensitive parameters in the input parameters are identified. Assuming that all sensitive parameters conform to a log-normal distribution, Monte Carlo simulation is used to perform uncertainty analysis to evaluate the impact of the uncertainty of the input parameters on the uncertainty of the model output variables.

[0023] In a specific feasible implementation, the antibiotic ecological risk assessment includes the following steps: Use a grading method to select toxicity data and assessment factors. The ratio determines the predicted no-effect concentration. : Select the concentration for long-term ineffective observation ( )and The ratio is calculated, where The value can be 10, 50, or 100; when there is no chronic toxicity data, the half-maximal effect concentration is used. Or half maximum death concentration and Calculation of the ratio; Based on the predicted no-effect concentration Using ecological risk entropy Assess the ecological risks of antibiotics in aquatic environments; Based on the fact that multiple antibiotics coexist in the environment, the classic concentration superposition model is used to calculate the combined ecological risk of antibiotics. The ecological risk of antibiotic mixtures is calculated based on a cumulative concentration prediction equation.

[0024] In a specific feasible implementation, the formula for calculating the combined ecological risk of antibiotics using the classical concentration superposition model is as follows:

[0025] In the formula, For joint ecological risk entropy; The number of components in the mixture; It is the sum of the toxicity unit values; The value here is 1000.

[0026] In a specific feasible implementation, the formula for calculating the ecological risk of antibiotic mixtures is:

[0027] In the formula, The effect concentration of the mixture that elicits the x% effect; For the first The concentration at which a component causes an x% effect when used alone; For the first The molar ratio of the components in the mixture.

[0028] S3.2, Health risk assessment of antibiotics.

[0029] In a specific feasible implementation, antibiotic health risk assessment includes the following steps: The health risks of antibiotics in the aquatic environment are assessed using a risk entropy calculation model based on the acceptable daily intake. The calculation formula is as follows:

[0030]

[0031] In the formula, For human health risk entropy, when When the value is zero, it indicates that there is a risk; the larger the value, the greater the risk. Exposure dose, in µg / (person·d); Exposure frequency, in days per year (d / a); The duration of exposure is expressed in years (a). The acceptable daily intake is expressed in µg / (Kg·d). The total average contact time is expressed in days (d). Average body weight, in kg; The concentration of the target antibiotic in water is expressed in µg / L. The percentage of antibiotics remaining after treatment at the water treatment plant is expressed as % (%). This refers to the average daily water intake for humans, expressed in L / d.

[0032] In summary, the present invention has the following beneficial effects: 1. By calculating the environmental risk assessment of antibiotics and modifying parameters, and by repeatedly adjusting the input parameters to simulate different scenarios, the risks brought about by the uncertain simulation are transformed into data support for prevention and control strategies, and provide data support for the subsequent formulation of strategies to reduce the risk of antibiotics in the watershed.

[0033] 2. By analyzing the environmental impact of single antibiotics, we can also analyze the synergistic effects of multiple antibiotics on the environment, making the risk analysis of antibiotics more comprehensive. Attached Figure Description

[0034] Figure 1This is a technical roadmap for a watershed antibiotic ecological risk prevention and control strategy simulation method based on environmental fate.

[0035] Figure 2 This is a flowchart simulating the prevention and control strategy.

[0036] Figure 3 This is a basic framework diagram of the fugacity model.

[0037] Figure 4 It is a sampled image.

[0038] Figure 5 This is a statistical chart showing the percentage of antibiotic consumption in the study area.

[0039] Figure 6 This is a statistical chart of the total annual excretion of antibiotics.

[0040] Figure 7 This is a logarithmic distribution map of the differences in target antibiotics between the aqueous phase and the sediment phase.

[0041] Figure 8 This is a distribution diagram of the target antibiotic in different environmental phases.

[0042] Figure 9 This is a schematic diagram of the interface flux of EFX between various environmental media.

[0043] Figure 10 This is the result of the model sensitivity analysis (SCi>0.1).

[0044] Figure 11 It refers to the ecological risk level of a single antibiotic in the aqueous phase. Detailed Implementation

[0045] The following combination Figures 1-11 The present invention will be described in further detail below.

[0046] Reference Figure 1 A simulation method for watershed antibiotic ecological risk prevention and control strategies based on environmental fate includes the following steps: S1, Sample collection and sample processing.

[0047] Multiple samples were collected along the river basin, including water samples and sediments. The samples were stored in brown wide-mouthed glass bottles at -4°C in a light-proof and sealed manner. Water samples needed to be pretreated within 24 hours, and sediment samples needed to be dried and pretreated within one week.

[0048] The pretreatment of water samples for antibiotic detection employs solid-phase extraction, including steps such as filtration, activation, column chromatography, elution, nitrogen blowing, and resolution. Sediment pretreatment first requires the extraction of antibiotics from the sediments; the other steps are the same as for water samples.

[0049] Four groups of spiked experiments were conducted on water and sediment samples at concentrations of 10, 50, 100, and 500 ng / L (g) to validate the experimental method. The recoveries of the spiked experiments on water and sediment samples ranged from 62% to 115%. The instrument detection limit for the target antibiotic ranged from 0.003 ng / L to 0.77 ng / L, and the quantitation limit ranged from 0.01 ng / L to 2.56 ng / L.

[0050] S2, Fugacity Model Construction.

[0051] The specific steps involved in constructing the fugacity model are as follows: S2.1, Construction of the Fugacity Model Framework.

[0052] Reference Figure 3 A Level III fugacity model was selected to generalize the study area, identify the main environmental phases, and then further subdivide each main environmental phase into several sub-environmental phases. Based on the migration and transformation processes between environmental media, the fate of the target antibiotic across multiple media phases was determined. The migration and transformation processes of the target antibiotic across multiple media phases in the study area mainly include diffusion and non-diffusion processes, as well as inflow / outflow and advection inflow / outflow from the external environment.

[0053] S2.2, Fugacity model parameter settings.

[0054] The parameters of the fugacity model include: physicochemical properties of the target antibiotic, environmental attributes, and emission load parameters. Among these, the particulate organic carbon (Toc) content in the aqueous phase, soil phase, and sediment of the target antibiotic was measured in the laboratory using a TOC analyzer. Other parameters, such as the water-side mass transfer coefficient in the sediment and the diffusion path length in the soil, can be obtained from relevant literature. Environmental attributes are derived from Donald Mackay's typical recommendations and literature values.

[0055] The target compound emission load serves as a key input parameter for the fugacity model, acting as its driving force and a crucial parameter for the model's predictive success. Antibiotics discharged into the study area have three potential pollution sources: humans (urban and rural populations), pigs, and poultry and other animals (i.e., cattle and sheep). Since most livestock farms have limited wastewater treatment facilities, it is generally assumed that all animal excrement enters the environment directly. In this embodiment, the antibiotic emission load in the study area is estimated based on consumption, using the following formula:

[0056]

[0057]

[0058]

[0059] In the above formula, Antibiotic emissions per person, in tons per year (t / a); Antibiotic emissions from animals, expressed in tons per year (t / a), subscript These correspond to pigs, poultry, and other animals (cows and sheep), respectively. This represents the discharge load entering the aqueous phase, expressed in t / a. This represents the emission load entering the soil phase, expressed in t / a. Antibiotic consumption per person, in t / a; Antibiotic consumption in animals, expressed in tons per year (t / a). Representatives' excretion ratios of the parent form and glucuronide-bound form of various antibiotic classes, in percentages (%). The proportion of parental and glucuronide-bound forms of antibiotics excreted by animals for each class, in percentages (%). Represents the proportion of urban population, in percentages: % Represents the proportion of the rural population, in percentages: % This represents the city's wastewater treatment rate, expressed in % %. This represents the rural sewage treatment rate, expressed in % %. This represents the removal rate of the target antibiotic by the wastewater treatment plant; The excretion rate of pig urine is expressed in % (%). This represents the flushing rate of pig manure, expressed in percent (%).

[0060] S2.3 Solving the fugacity model The study area was divided into four main environmental phases: air, water, soil, and sediment. Representation. Then define the fugacity capacity of each environmental medium ( ), calculate the migration coefficients of each environmental process ( ). and then utilize Fugacity with the corresponding main environment ( The interphase migration flux is calculated by multiplying the inflow and outflow, and then a mass balance equation based on the migration flux is established: Inflow = Outflow, with the corresponding calculation formula as follows: Air phase:

[0061] Aqueous phase:

[0062] Soil phase:

[0063] Sedimentary facies:

[0064] In the above formula, For the first Phase fugacity; For the first The emission rate of the phase, in units of mol / h; For the first The advection inflow velocity of the phase, in meters per second (m). 3 / h; For the first The advection concentration of the phase, in units of mol / m³ 3 ; For the first The reaction rate of the phase, referring here to the degradation reaction, is expressed in mol / (Pa·h). For the first Advection of phase, unit: mol / (Pa·h); The migration coefficient is expressed in mol / (Pa·h), and its subscript indicates the migration direction between different phases, such as... This represents the migration coefficient from the air phase to the water phase. The code for a Level III fugacity model was written using Matlab R2010b to solve the model.

[0065] The fugacity in each primary environmental phase is calculated using the above formula. Then, we can further apply the formula:

[0066] Calculate the distribution of the target antibiotic in each phase. .

[0067] S2.4, solve for fugacity capacity and migration coefficient.

[0068] The four main environmental phases of the study area were further divided into several sub-environmental phases based on their properties. The total number of the four main environmental phases... It is calculated by summing the product of its corresponding sub-environment phase and its volume fraction.

[0069] migration coefficient ( The fugacity parameter is a key parameter in the fugacity model, which can summarize and process the complex and intricate migration and transformation between phases. The main environmental processes in the study area include advection, reaction (degradation), diffusion, and other interfacial processes such as sedimentation.

[0070] 2.5 Sensitivity analysis and uncertainty analysis.

[0071] Model sensitivity analysis is used to evaluate the impact of changes in input parameters on model results, in order to identify key parameters affecting the model output. The range of model parameter variation is set to ±10%, and the resulting calculation formula is as follows:

[0072] In the above formula, For the first The sensitivity coefficient of the phase; For the output result; For input parameters.

[0073] Based on the ratio of the absolute sensitivity coefficient to 0.5, if | A value ≥ 0.5 is considered a sensitive parameter; otherwise, it is not taken into consideration. Sensitive parameters are parameters that affect the concentration of antibiotics in the watershed, specifically referring to input parameters such as antibiotic usage, wastewater treatment plant treatment rate, and antibiotic removal rate.

[0074] After identifying the sensitive parameters, assuming all sensitive parameters follow a log-normal distribution, the Monte Carlo simulation was encoded and computed using Matlab R2012b, and repeated 10,000 times to obtain the probability distribution of antibiotic concentrations. The difference between the third and first quartiles (SQR) of the simulation results was used to quantify the uncertainty in order to assess the impact of the uncertainty of the input parameters on the uncertainty of the model output variables.

[0075] Based on the results of uncertainty analysis, control parameters are selected from sensitive parameters. The selection principles for control parameters are: parameters that have a certain impact on the concentration predicted by the fugacity model; parameters that are positively or negatively correlated with the predicted concentration should be included in the scope of consideration; and parameters that can be controlled by human and technical means.

[0076] S3, Environmental Risk Assessment of Antibiotics.

[0077] Based on the concentrations of antibiotics in the aquatic phase predicted by the fugacity model, the ecological risks of individual antibiotics and the combined ecological risks of antibiotics were assessed using the risk entropy method and the classical concentration superposition model. Furthermore, a concentration-cumulative prediction equation was used to assess the synergistic / antagonistic effects between antibiotics and to determine the impact of antibiotic mixtures on the ecosystem. A preliminary assessment of health risks in the study area was conducted based on a risk entropy calculation model using acceptable daily intake. The specific steps include: S3.1, Ecological risk assessment of antibiotics.

[0078] Using ecological risk entropy ( The ecological risk of antibiotics in the aquatic environment is assessed using the following formula:

[0079] In the above formula, To define ecological risk entropy, and to refine ecological risks, [the following will be used]. Divided into three levels: high risk ( >1), medium risk (0.1 < <1), low risk (0.01 < <0.1); The predicted concentration of antibiotics is given in ng / L. The concentration for predicting no-effect is given in ng / L.

[0080] Through acute toxicity data or chronic toxicity data and assessment factors ( The ratio of ) is determined as follows:

[0081]

[0082] In the formula, The concentration for long-term observation of no effect is expressed in mg / L. The half-maximum death concentration is expressed in mg / L. The acute half-maximal effect concentration is expressed in mg / L.

[0083] A grading method is typically used to select toxicity data, with concentrations observed for long-term no effect being preferred. )and The ratio is calculated, where Values ​​were taken as 10 (first trophic level), 50 (second trophic level), and 100 (third trophic level). When chronic toxicity data were unavailable, the half-maximal effect concentration / half-maximal mortality concentration was used. / )and ( Calculate the ratio of ).

[0084] Since multiple antibiotics coexist in the environment, this embodiment uses the classic concentration superposition model to calculate the combined ecological risk of antibiotics. The calculation formula is as follows:

[0085] In the formula, For joint ecological risk entropy; The number of components in the mixture; It is the sum of the toxicity unit values; The value here is 1000.

[0086] Furthermore, to investigate whether there are synergistic effects among antibiotic mixtures, this paper calculates the ecological risk of antibiotic mixtures based on a cumulative concentration prediction equation. The calculation formula is as follows:

[0087] In the formula, The effect concentration of the mixture that elicits the x% effect; For the first The concentration at which a component causes an x% effect when used alone; For the first The molar ratio of the components in the mixture.

[0088] S3.2, Health risk assessment of antibiotics.

[0089] The health risks of antibiotics in the aquatic environment were assessed using a risk entropy calculation model based on the acceptable daily intake, as shown in the following formula:

[0090]

[0091] In the formula, For human health risk entropy, when When the value is zero, it indicates that there is a risk; the larger the value, the greater the risk. Exposure dose, in µg / (person·d); Exposure frequency, in days per year (d / a); The duration of exposure is expressed in years (a). The acceptable daily intake is expressed in µg / (Kg·d). The total average contact time is expressed in days (d). Average body weight, in kg; The concentration of the target antibiotic in water is expressed in µg / L. The percentage of antibiotics remaining after treatment at the water treatment plant is expressed as % (%). This refers to the average daily water intake for humans, expressed in L / d.

[0092] S4, Simulation of Antibiotic Risk Control Strategies.

[0093] Based on the fugacity regression simulation in step S2 and the antibiotic ecological risk assessment in step S3, different risk prevention and control management scenarios are simulated. The prevention and control parameters are used as parameter variables in the scenario simulation. By adjusting the driving quantities of different prevention and control parameters, the characteristics of changes in the water phase antibiotic ecological risk in the watershed under different risk prevention and control management scenarios are identified, and an achievable optimal prevention and control strategy is proposed.

[0094] Specifically, by adjusting the control parameters to design different scenario models, the changes in antibiotic ecological risk are calculated. After repeating each control parameter multiple times, all control parameters are adjusted to calculate the characteristics of changes in antibiotic ecological risk. The antibiotic risk in the watershed under different scenarios is assessed and calculated to provide support for control strategies.

[0095] To facilitate understanding, the following explanation will use the watershed in the background technology as the research object.

[0096] S1, Sample collection and sample processing.

[0097] Twenty-two water samples and 19 sediment samples were collected along the river basin. The water samples were stored in 1L brown wide-mouth glass bottles and the sediment samples were stored in 125ml brown wide-mouth glass bottles at -4℃, protected from light. The water samples underwent pretreatment within 24 hours, and the sediment samples underwent drying and pretreatment within one week.

[0098] Ultra-high performance liquid chromatography-tandem mass spectrometry (Waters, USA) was used in the sample pretreatment process.

[0099] Four groups of spiked experiments were conducted on water and sediment samples at concentrations of 10, 50, 100, and 500 ng / L (g) to validate the experimental method. The recoveries of the spiked experiments in water and sediment samples ranged from 62% to 115%. The instrument detection limits for the target antibiotics ranged from 0.003 ng / L to 0.77 ng / L, and the quantitation limits ranged from 0.01 ng / L to 2.56 ng / L. The detection results of 16 antibiotics in water and sediment are shown in Table 1.

[0100] Table 1

[0101] Note: NH indicates that it is a veterinary antibiotic.

[0102] S2, Fugacity Model Construction.

[0103] The specific steps involved in constructing the fugacity model are as follows: S2.1, Construction of the Fugacity Model Framework.

[0104] The study area was generalized using a Level III fugacity model to identify the main environmental phases, which were then further subdivided into several sub-environmental phases. Based on the migration and transformation processes between environmental media, the fate of the target antibiotic across these multiple media phases was determined. The migration and transformation processes of the target antibiotic across these multiple media phases in the study area mainly include diffusion and non-diffusion processes, as well as inflow / outflow and advection inflow / outflow from the external environment.

[0105] S2.2, Fugacity model parameter settings.

[0106] The parameters of the fugacity model include: physicochemical properties of the target antibiotic, environmental properties, and emission load. The particulate organic carbon (Toc) content in the aqueous phase, soil phase, and sediment of the target antibiotic was measured in the laboratory using a TOC analyzer (Elementar vario, Germany). Other parameters were derived from reference values.

[0107] The values ​​of emission load-related parameters were obtained from literature and statistical yearbooks. Due to data scarcity, the population and livestock farming data for the watershed were calculated by multiplying the total population and livestock farming volume in different areas of the watershed in 2016 by a proportionality coefficient. Study area and The value was obtained by multiplying the average usage of each antibiotic by different individuals nationwide in 2013 with the population / livestock farming population in the watershed. , and , The average values ​​for different regions in 2016 were 74.23%, 33.95%, 60.50%, and 39.50%, respectively. Statistical tables of relevant parameters for different regions are shown in Table 2. , , , and The values ​​of relevant parameters for estimating emission loads are shown in Table 3.

[0108] Table 2

[0109] Table 3

[0110] S2.3 Solving the fugacity model The study area was divided into four main environmental phases: air, water, soil, and sediment. Representation. Then define the fugacity capacity of each environmental medium ( ), calculate the migration coefficients of each environmental process ( ). and then utilize Fugacity with the corresponding main environment ( The interphase migration flux is calculated by multiplying the product of the two phases, and then a mass balance equation based on the flux is established: inflow = outflow.

[0111] Finally, the distribution of the target antibiotic in each phase was calculated. The specific calculation formula will not be elaborated here.

[0112] S2.4, solve for fugacity capacity and migration coefficient.

[0113] The four main environmental phases of the study area were further divided into several sub-environmental phases based on their properties. The total number of the four main environmental phases... The sub-environmental phases are calculated by summing the products of their corresponding sub-environmental phases and their volume fractions. The sub-environmental phases are divided as shown in Table 4.

[0114] migration coefficient ( It can group together the complex and intricate migration and transformation processes between phases. The main environmental processes in the study area are: advection, reaction (degradation), diffusion, and other interfacial processes such as sedimentation, which are specifically classified as shown in Table 5.

[0115] Table 4

[0116] Table 5

[0117] 2.5 Sensitivity analysis and uncertainty analysis.

[0118] Model sensitivity analysis is used to evaluate the impact of changes in input parameters on model results in order to identify key parameters that affect the model output. The range of model parameter variation is set to ±10%.

[0119] After identifying the sensitive parameters, Monte Carlo simulations were used to perform uncertainty analysis to assess the impact of input parameter uncertainty on the uncertainty of the model output variables. Assuming all sensitive parameters follow a log-normal distribution, the Monte Carlo simulations were encoded and computed using Matlab R2012b, and repeated 10,000 times to obtain the probability distribution of antibiotic concentrations. The uncertainty was quantified using the difference between the third and first quartiles of the simulation results (SQR).

[0120] Calculations showed that the total annual consumption of the 16 target antibiotics in the study area was approximately 60.32 tons, with human antibiotics accounting for 25% of the total consumption. Furthermore, QNs and MLs had the highest consumption, especially the veterinary antibiotic NFX, with an annual consumption of 8.72 tons. The antibiotic consumption distribution in the study area is as follows: Figure 5 As shown.

[0121] The source emissions of antibiotics fundamentally determine the level of antibiotic pollution in the environment. The total annual excretion of 16 target antibiotics in the study area is approximately 32.29 tons, accounting for about half of the antibiotic consumption. Calculations of antibiotic excretion in the study area show that QNs (Quick Neck Staining Antibiotics) have the highest annual excretion, followed by MLs (Multi-Low Acids), TCs (Total Cholesterols), and SAs (Saturated Acids). The total annual excretion of the four major classes of target antibiotics is as follows: Figure 6 As shown.

[0122] Most antibiotics excreted in urine and feces are treated at wastewater treatment plants before being released into the environment, while a small portion may be directly released (e.g., through scattered livestock farming in rural areas). Ultimately, antibiotics consumed by humans and animals are partially released into water bodies and partially into the soil environment. Antibiotics released into water and soil then migrate and transform, with some entering sediments. Table 6 shows the statistical table of the emission load of target antibiotics in the study area.

[0123] Table 6

[0124] Table 7 shows the statistical results of the measured antibiotic concentrations in the aqueous and sedimentary phases of the study area.

[0125] Table 7

[0126] Note: nd indicates not detected.

[0127] The average concentrations of 16 antibiotics in the aqueous phase ranged from 2.29 to 347.32 ng / L. The total concentrations of the four antibiotic classes, ranked from highest to lowest, were: TCs > MLs > SAs > QNs. The average concentration of TCs was higher than the other three classes, with OTCs having the highest concentration. For MLs, RTM and TYL had lower average concentrations, while ETM-H2O was abundant in the aqueous phase.

[0128] TYL was detected in sediments from all samples within the sedimentary facies. Overall, SA concentrations were lower in the sedimentary facies than in the aqueous facies. TCs and QNs were more abundant in the sedimentary facies.

[0129] Based on the aforementioned emission loads and the physicochemical properties of the target antibiotics, a Level III fugacity model was used to simulate the fate of the target antibiotics in the study area. The predicted concentration distribution of the target antibiotics in the environmental phases (air, water, sediment, and soil) is shown in Table 8. For the air phase, because the target antibiotics are not easily volatilized, the amount of antibiotics entering the air phase through water and soil is very limited; therefore, the antibiotic concentration in the air phase is basically close to 0 ng / L. The concentration range of antibiotics in the water phase is 12.69-223.01 ng / L, the maximum concentration of target antibiotics in the soil is 1.25 ng / L, and the maximum concentration of target antibiotics in the sediment is 130.10 ng / g. Furthermore, the concentration difference of target antibiotics in the soil phase is relatively small, followed by the water phase, and the concentration difference is the largest in the sediment phase. The antibiotics in the sediment mainly originate from the water phase, and the main reason for this difference is the difference in partition coefficients.

[0130] Table 8

[0131] Since only water and sediment samples were collected, the PEC and MEC of the target antibiotic were compared and analyzed only in these two environmental media. The reliability of the model results was verified by calculating the logarithm (LTDs) of the MEC and the corresponding PEC entropy. Figure 7 As shown in the figure. Overall, the average values ​​of PEC and MEC in the aqueous phase agree well. Except for SCP and CFX, the LTDs of other antibiotics are generally controlled within one order of magnitude, with 50% of the LTDs remaining within 0.5 orders of magnitude. In the sedimentary phase, the predicted and measured median values ​​agree well. It can be seen that the LTDs of all antibiotics remain within two orders of magnitude, with more than 43% of antibiotics having LTDs controlled within one order of magnitude. Except for SAs, >80% of other antibiotics have LTDs less than 1.5. Compared with other antibiotics, the predicted values ​​of SAs differ significantly from the measured values. The possible reason for this is that SAs have been used for the longest time, and the cumulative effect makes their concentration prediction difficult.

[0132] Based on the PEC values ​​estimated using the fugacity model and combined with the volumes of each phase in the study area, the antibiotic reserves in the environmental media of each phase in the study area were estimated, such as... Figure 8 As shown (excluding the air phase), the soil phase contains a relatively high amount of antibiotics, mainly because antibiotics in livestock urine and feces enter the soil environment directly without treatment during livestock farming. Antibiotics (SAs) are more abundant in the aqueous phase but less abundant in sediments, while the other three types of antibiotics are more abundant in sediments than in the aqueous phase. The main reason for this phenomenon is that SAs have lower KOC values, making them more likely to accumulate in the aqueous phase, while the other three types of antibiotics have higher KOC values ​​and are more likely to accumulate in the sediment phase.

[0133] Based on the fugacity model, the migration flux between different phases was estimated. Source emissions contributed the most to the input flux of antibiotics, with 56.84% of antibiotics entering the aquatic phase and 43.16% entering the soil phase. Antibiotics entering the environmental phase undergo degradation through a series of physicochemical reactions. Advection migration and degradation in the aquatic phase are the main elimination pathways for antibiotics in the study area. In the aquatic, soil, and sediment phases, antibiotic degradation accounted for 24.05%, 65.05%, and 10.90% of the total degradation, respectively. Interphase migration was mainly from the aquatic phase to the sedimentary phase and from the soil phase to the aquatic phase, accounting for 87.63% and 10.32% of the total migration, respectively. Since the source of antibiotics does not originate from the air phase and antibiotics are not easily volatile, the antibiotic migration flux between the air phase and other phases is 0. Figure 9 Taking EFX as an example, a schematic diagram of the interface flux between various environmental media is given.

[0134] The 28 model input parameters were analyzed, including the absolute sensitivity coefficient (| There are 14 parameters where |) > 0.1. Statistical charts as follows Figure 10 As shown. The external discharges of the soil phase (1), KOC (0.93), external discharges of the water phase (0.91), soil half-life (0.73), and sediment half-life (0.54) are expressed as follows: The value >0.5 and is positively correlated with antibiotic concentration. The five parameters—soil phase area (0.91), water flow velocity (0.78), soil phase thickness (0.69), water volume fraction in sediment phase (0.57), and sediment phase thickness (0.53)—show a significant correlation. The absolute sensitivity coefficient of the KOC (Knowledge, Oxygen, and Carbon) value is greater than 0.5 and negatively correlated with antibiotic concentration. Other parameters have absolute sensitivity coefficients between 0.1 and 0.25, having a relatively small impact on antibiotic concentration. Generally, the source emission rate fundamentally determines the antibiotic concentration, while the KOC value determines the antibiotic concentration in sediments; a higher KOC value results in a higher antibiotic concentration in the sedimentary phase. Furthermore, a longer half-life leads to slower antibiotic decomposition, resulting in a higher concentration of antibiotics remaining in the environmental medium. However, given a fixed total amount of antibiotics, a larger volume results in a lower concentration. Therefore, the area and thickness of the soil phase, the volume fraction of water in the sediment, and the thickness of the sedimentary phase are negatively correlated with antibiotic concentration. Larger water flows make it less likely for antibiotics to accumulate and, to some extent, accelerate antibiotic degradation reactions; therefore, larger water flows result in lower antibiotic concentrations.

[0135] Based on the sensitivity analysis results above, assuming these parameters follow a log-normal distribution, Monte Carlo simulations were performed using Matalab2012b to conduct uncertainty analysis and determine the impact of parameter variations on the results. Overall, for most target antibiotics, the SQR of 90.62% of the uncertainty analysis results remained within one order of magnitude across the four environmental phases.

[0136] Figure 10 In the equation, τ2, τ3, and τ4 represent the half-lives of the antibiotic in water, soil, and sediment, respectively, in hours (h); N4 is the molecular diffusion coefficient in the sediment; R... w The velocity of the water flow is expressed in meters (m). 3 / h;E w E s These represent the external influx of antibiotics into the water and soil phases, respectively, in tons per year (t / a); KOC is the organic carbon-water partition coefficient, in L / kg; A 1, A1 and A2 represent the areas of the air and water phases, respectively, in m². 2 h2, h3, and h4 represent the thicknesses of the aqueous phase, soil phase, and sediment phase, respectively, in meters (m). 42 This represents the volume fraction of water in the sediment phase.

[0137] Based on the sensitivity analysis results of the model, parameters that have a significant impact on the model were identified. Then, with the aim of reducing the concentration level of antibiotics in the study area, key control parameters for the study area were selected. The selection of control parameters follows two principles: (1) They have a certain impact on the concentration predicted by the model: parameters that are positively or negatively correlated with the predicted concentration should be included in the consideration. (2) They can be controlled by human and technical means.

[0138] The sensitivity parameter analysis of the fugacity model indicates that the external discharge of antibiotics into the aquatic and soil phases is positively correlated with the model-predicted concentrations, and has the greatest impact on the prediction results. The fundamental issue affecting external discharges into the aquatic and soil phases is antibiotic consumption, which is controllable. Therefore, based on the target users of antibiotics in the study area, the consumption of human and veterinary antibiotics was selected as control parameters. Besides the external discharges of antibiotics into the aquatic and soil phases, other parameters, although having some influence on the model results, cannot be selected as control parameters because they are not controllable.

[0139] Furthermore, the wastewater treatment rate and the antibiotic removal rate of the wastewater treatment plant determine the antibiotic content in the wastewater discharged into the environment, therefore these are selected as control parameters. In summary, human antibiotic consumption (Uh) and livestock antibiotic consumption (Uh) are selected as control parameters. ), sewage treatment rate ( —Urban sewage treatment rate and —Rural sewage treatment rate) and antibiotic removal rate of sewage treatment plants ( These four parameters are used as prevention and control parameters.

[0140] S3, Environmental Risk Assessment of Antibiotics.

[0141] S3.1, Ecological risk assessment of antibiotics.

[0142] Using ecological risk entropy ( Assess the ecological risks of antibiotics in the aquatic environment. The calculation process is described above and will not be repeated here.

[0143] S3.2, Health risk assessment of antibiotics.

[0144] The health risks of antibiotics in the aquatic environment were assessed using a risk entropy calculation model based on the acceptable daily intake.

[0145] The PNEC values ​​in this embodiment were calculated based on relevant literature, and the specific values ​​are shown in Table 9. Considering the worst-case scenario, the most sensitive species were selected as the test organisms to estimate the ecological risk level of antibiotics in the aquatic phase. The calculation results are as follows: Figure 11 As shown.

[0146] Table 9

[0147] The ecological risk assessment results of target antibiotics in the aquatic environment indicate that, overall, the ecological risk of various antibiotics in the aquatic phase is high, with >80% of antibiotics posing at least a moderate risk. Among antibiotics (SAs), SDZ and SMX showed moderate risk. SDZ can damage hepatocytes by interfering with the activity of rainbow trout hepatocytes, thereby affecting the metabolic processes of rainbow trout, and its potential risk should be given sufficient attention. TCs showed moderate risk, TYL among MLs showed high risk, and ETM-H2O and RTM showed moderate risk. QNs, due to their high biosensitivity and high concentration in the aquatic environment, had generally high RQs values, all indicating high risk, with CFX even reaching an RQs value of 9.26. Therefore, QNs in the study area should be a key focus for control, and other types of antibiotics should also be given sufficient attention.

[0148] However, antibiotics do not exist in isolation in the environment, and the synergistic effects between antibiotics can cause more serious harm than individual antibiotics. Therefore, the ecological risks posed by antibiotic mixtures deserve greater attention. Toxicological data for antibiotics are from literature sources, as shown in Table 10. This paper selects only nine antibiotics to study their combined ecological risks, and the calculation results are shown in Table 11.

[0149] Table 10

[0150] Table 11

[0151] The criteria for evaluating MRQs are consistent with those for RQs. Taking the aquatic environment as an example, the MRQs value in the study area was as high as 8.12, indicating that the ecological risk posed by these nine mixed antibiotics was high. Furthermore, as shown in Table 11, algae are generally the most sensitive to antibiotics, followed by invertebrates, while fish have the lowest sensitivity. Algae are one of the primary producers in the aquatic environment, and antibiotics can alter the natural algal population structure by affecting the synthesis of algal proteins and cell walls, thereby affecting the food web structure of the entire ecosystem.

[0152] The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A simulation method for watershed antibiotic ecological risk prevention and control strategy based on environmental fate, characterized in that: The method comprises the following steps: Collecting a plurality of samples along a river basin, the samples comprising water samples and sediments, and pre-treating the samples; Building a fugacity model framework; Setting fugacity model parameters; Solving the fugacity model; Solving and calculating predicted concentrations and migration fluxes; Sensitivity analysis and uncertainty analysis, calculating sensitivity coefficients of input parameters, if the sensitivity coefficient is greater than or equal to 0.5, the parameter is identified as a sensitive parameter, and the sensitive parameter is used to screen control parameters; Antibiotic environmental risk assessment; Based on the fugacity simulation and the antibiotic ecological risk assessment, the control parameters are used as parameter variables in scenario simulation, the driving amount of different control parameters is adjusted, the characteristics of the water phase antibiotic ecological risk in the basin under different risk control management scenarios are identified, and an optimal control strategy is proposed.

2. The environmental-fate-based watershed antibiotic ecological risk prevention and control strategy simulation method according to claim 1, characterized in that: The parameters of the fugacity model include: physicochemical property parameters, environmental attribute parameters and emission load parameters of the target antibiotic; The emission load is estimated based on consumption, and the calculation formula is: In the above formula, Representative antibiotic emissions, in t / a; representative of the antibiotic emissions of animals, in t / a, subscript , corresponding to pigs, poultry and other animals, respectively; This represents the discharge load entering the aqueous phase, expressed in t / a. represents the emission load into the soil phase, in t / a; Representative antibiotic consumption in tons per year; Antibiotic consumption, representing the amount of antibiotics consumed by animals, in t / a; Representative human excretion of each class of antibiotic in both parent and glucuronide forms, in %; Representative animal excretion ratio of each category of antibiotics in the parent form and glucuronide conjugate form, in %; Representative town population ratio, in %; Representative of the proportion of the rural population, in %; Representative of the urban wastewater treatment rate, in %; Representing the rural sewage treatment rate, unit: %; representative of the removal of the target antibiotic by the wastewater treatment plant; represents the rate of excretion of pig urine, in %; represents the flushing rate of the pig manure, in %.

3. The environmental-fate-based watershed antibiotic ecological risk prevention and control strategy simulation method according to claim 1, characterized in that: Solving the fugacity model comprises the following steps: Dividing four main environmental phases of air phase, water phase, soil phase and sediment phase, and defining the fugacity capacity of each environmental medium; Calculating the migration coefficient of each environmental process; Using the product of the migration coefficient and the corresponding main environmental phase fugacity to calculate the inter-phase migration flux, and then establishing a mass balance equation based on the migration flux: inflow = outflow; After calculating the fugacity in each main environmental phase, the distribution of the target antibiotic in each phase is calculated.

4. The environmental-fate-based watershed antibiotic ecological risk prevention and control strategy simulation method according to claim 3, characterized in that: The mass balance equations of the four main environmental phases of air phase, water phase, soil phase and sediment phase are as follows: Air phase: Water phase: Soil phase: Sediment phase: In the above formulae, For the The fugacity of the phase, respectively represent the air phase, the water phase, the soil phase and the sediment phase; For the first Phase discharge rate in mol / h; For the first The rate of advection into the phase, in m 3 / h; For the first The concentration of the advective inflow of the phase, in units of: mol / m 3 ; For the first The degradation reaction rate, referred to here as the reaction rate of the phase, is given in units of mol / (Pa h). For the first Phase of the advection, in units of: mol / (Pa h); The migration coefficient is in mol / (Pa.h).

5. The environmental-fate-based watershed antibiotic ecological risk prevention and control strategy simulation method according to claim 3, characterized in that: The formula for calculating the distribution of the target antibiotic in each phase is: wherein, is the the distribution of the target antibiotic in each phase; For the first Phase fugacity capacity.

6. The environmental-fate-based watershed antibiotic ecological risk prevention and control strategy simulation method according to claim 1, characterized in that: Sensitivity analysis and uncertainty analysis comprise the following steps: The variation range of the model parameters is set to ±10%, and the calculation formula is as follows: In the above formula, is the first the sensitivity coefficient of the phase; OutputResult is the output result; is an input parameter; Identify the sensitive parameters in the input parameters, assume that all sensitive parameters conform to the lognormal distribution, and use Monte Carlo simulation to perform uncertainty analysis to evaluate the influence of the uncertainty of the input parameters on the uncertainty of the model output variables.

7. The environmental-fate-based watershed antibiotic ecological risk prevention and control strategy simulation method according to claim 1, characterized in that: The antibiotic ecological risk assessment comprises the following steps: Toxicity data selected using tiered approach to assessment factors Predicted no effect concentration determined as the ratio of : long term no observed effect concentration selected and calculated, where, values of 10, 50 or 100 are used; when no chronic toxicity data is available, the half maximal effect concentration or half maximal lethal concentration and are calculated; Based on the predicted no-effect concentration Using ecological risk entropy Assess the ecological risks of antibiotics in aquatic environments; Based on the fact that multiple antibiotics coexist in the environment, a classic concentration superposition model is used to calculate the combined ecological risk of antibiotics. According to the prediction equation of concentration accumulation, the ecological risk of the antibiotic mixture is calculated.

8. The environmental-fate-based watershed antibiotic ecological risk prevention and control strategy simulation method according to claim 7, characterized in that: The formula for calculating the combined ecological risk of antibiotics by the classic concentration superposition model is: In the formula, is the joint ecological risk entropy; n is the number of components in the mixture; is the sum of the toxicity unit values; Here the value is 1000.

9. The environmental-fate-based watershed antibiotic ecological risk prevention and control strategy simulation method according to claim 7, characterized in that: The formula for calculating the ecological risk of the antibiotic mixture is: wherein the effect concentration of the mixture to elicit the x% effect; For the first Corresponding concentration of the component that causes the x% effect when used alone; For the first Molar ratio of components in the mixture.

10. The environmental-fate-based watershed antibiotic ecological risk prevention and control strategy simulation method according to claim 1, characterized in that: The antibiotic health risk assessment comprises the following steps: The health risk of antibiotics in the water environment is evaluated by using a risk entropy calculation model of daily average acceptable intake, and the calculation formula is: wherein is the human health risk entropy, when is at risk, the greater the value the greater the risk; For exposure dose, the unit is: pg / (person-d); for exposure frequency, units: d / a; for the exposure duration, in units of a; Daily Acceptable Intake, in µg / (Kg.d); is the total average contact time in d; For the per capita weight, the unit is: Kg; Ctarget is the concentration of the target antibiotic in water, in µg / L; Residual antibiotic concentration after treatment in the waterworks, in %; The daily water consumption of the human body is L / d.

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