A method for determining multi-parameter surrogate indexes of resistance gene abundance in sewage treatment plant effluent

Abstract

This invention discloses a method for determining the abundance of antibiotic resistance genes in wastewater effluent using multiple parameters. The method includes: selecting water quality parameters; measuring the abundance of antibiotic resistance genes in the secondary effluent of a wastewater treatment plant; constructing a multiple parameter substitution index model to predict the abundance of antibiotic resistance genes; inputting the water quality parameters into the model; outputting the multiple parameter substitution index for the abundance of antibiotic resistance genes; and verifying the accuracy of the multiple parameter substitution index. By detecting water quality parameters in the secondary effluent of a wastewater treatment plant, a suitable multiple parameter substitution index for antibiotic resistance genes can be constructed to predict the abundance of antibiotic resistance genes in the secondary effluent. This method is simple, efficient, and highly accurate, and can be used for real-time monitoring of antibiotic resistance genes in secondary effluent. It is of great significance for effectively assessing and controlling the potential ecological risks of antibiotic resistance genes.

Description

Technical Field

[0001] The invention belongs to the field of secondary effluent ecological environment evaluation of sewage treatment plants, and in particular relates to a method for determining a multi-parameter alternative index of resistance gene abundance in effluent from sewage treatment plants. Background Art

[0002] Wastewater treatment plants (WWTPs) continuously receive industrial wastewater, medical wastewater, agricultural wastewater and municipal wastewater containing antibiotics, which creates conditions for the generation of antibiotic-resistant bacteria and promotes the spread of antibiotic resistance genes (ARGs); at the same time, various wastewaters are discharged into surface water after being treated by WWTPs, which increases the abundance of ARGs in the environment. WWTPs have become an important sink and source of ARGs. According to the metagenomic information of 113 wastewater treatment plant wastewaters disclosed by the sequence read archive of the National Center for Biotechnology Information, the results show that multidrug resistance is the most common ARG type (37.3%), followed by aminoglycoside ARGs (13.7%), β-lactam ARGs (12.6%), tetracycline ARGs (6.8%), unclassified ARGs (6.5%) and sulfonamide ARGs (5.7%). The abundance of ARGs in the secondary effluent of WWTPs is in the range of 103–108 copies / ml. Some researchers investigated the occurrence of ARGs in the secondary effluents of four WWTPs in China. The absolute and relative abundances of total ARGs (qnrD, qnrS, ermA, ermB, tetA, tetQ, sul1 and sul2) were 103-104 copies mL-1 and 10-4-10-2 ARG / 16S rRNA, respectively, among which sul1 and sul2 were the gene subtypes with the highest detection frequency and level.

[0003] The above research on the occurrence of ARGs in sewage treatment systems shows that WWTPs are one of the important ARGs collection sites, where the abundance of ARGs is high and the subtypes are rich. It is worth noting that WWTPs can effectively remove phosphorus, nitrogen and chemical oxygen demand (COD) in wastewater, but cannot effectively remove ARGs, and even the abundance of ARGs in the effluent may be higher than that in the influent. ARGs are discharged into the receiving water body with the secondary effluent, and entering various surface waters will cause potential harm to the natural environment and even human health. Therefore, the detection of ARGs in the secondary effluent of WWTPs should be taken seriously.

[0004] According to the discharge standard of urban sewage treatment plants (GB18918-2002), the effluent of WWTPs needs to monitor COD, BOD5, TOC, DO, SS, TN, TP, NH3-N, pH and other indicators in real time. The monitoring of new pollutants such as ARGs, which exist in trace amounts in the environment but are more harmful to humans and the environment, is slightly insufficient. At present, real-time quantitative PCR (RT-qPCR), high-throughput qPCR (HT-qPCR) and metagenomics are commonly used methods for detecting the abundance of ARGs in secondary effluents of sewage treatment plants. However, the above methods for detecting the abundance of ARGs in secondary effluents have defects such as long detection cycle and high detection cost. Structural equation modeling (SEM) is a multivariate analysis technique that includes direct and indirect causal relationships between multiple variables and displays all causal relationships through path directions. Existing studies have provided a possibility for predicting freshwater ARGs in Weishan Lake by constructing and fitting structural equation models between freshwater ARGs and environmental variables such as temperature, water storage, antibiotics and metal elements. By constructing a structural model equation for the abundance of ARGs in the secondary effluent of WWTPs through multiple water quality parameter alternative indicators, ARGs in the effluent of WWTPs can be monitored in real time, which is more convenient and beneficial to the actual needs of production compared to qPCR and metagenomic methods. However, there is currently no research on the construction of structural equation models between the abundance of ARGs in the secondary effluent of WWTPs and other conventional water quality indicators, and the abundance of ARGs in the secondary effluent cannot be predicted. Summary of the invention

[0005] To solve the above problems, the present invention provides the following solution: a method for determining a multi-parameter alternative index of resistance gene abundance in sewage treatment plant effluent, comprising:

[0006] Select water quality parameter indicators to measure the abundance of antibiotic resistance genes in secondary effluent from sewage treatment plants;

[0007] A multi-parameter alternative indicator model for predicting the abundance of antibiotic resistance genes is constructed, the water quality parameter indicators are input into the multi-parameter alternative indicator model, the multi-parameter alternative indicator of the abundance of antibiotic resistance genes is output, and the accuracy of the multi-parameter alternative indicator is verified.

[0008] Preferably, the water quality parameter indicators include chemical indicators, physical indicators, and sensory indicators;

[0009] The chemical indicators include ammonia nitrogen, nitrite nitrogen, nitrate nitrogen, soluble orthophosphate, COD cr 、UV 254 , TOC, pH;

[0010] The physical indicators include DO, temperature, redox potential, and zeta potential;

[0011] The sensory indicators include turbidity and other indicators of extracellular polymers.

[0012] Preferably, the turbidity is used to indirectly reflect the content of antibiotic-resistant bacteria attached to particulate matter in the secondary effluent;

[0013] The other indicator extracellular polymers are used to directly reflect the content of antibiotic-resistant bacteria in the secondary effluent;

[0014] The growth of the antibiotic-resistant bacteria is affected by chemical and physical indicators.

[0015] Preferably, the determination of the abundance of antibiotic resistance genes in the secondary effluent of the sewage treatment plant is carried out by high-throughput fluorescent quantitative PCR.

[0016] Preferably, the process of constructing a multi-parameter alternative indicator model for predicting the abundance of antibiotic resistance genes comprises:

[0017] Use principal component analysis and correlation analysis to screen water quality parameter indicators for modeling, remove unnecessary water quality parameter indicators, and determine modeling factors;

[0018] The modeling factors are used as independent variables, and the measured abundance of antibiotic resistance genes in the secondary effluent of the sewage treatment plant is used as the final dependent variable to construct a structural equation model between conventional water quality parameters and the abundance of antibiotic resistance genes;

[0019] Based on the structural equation model, a multi-parameter alternative index of the abundance of antibiotic resistance genes in the secondary effluent of the sewage treatment plant was obtained.

[0020] Preferably, the process of constructing a structural equation model between conventional water quality parameters and the abundance of antibiotic resistance genes using the modeling factor as an independent variable and the measured abundance of antibiotic resistance genes in the secondary effluent of the sewage treatment plant as a final dependent variable comprises:

[0021] The structural equation was obtained by linearly fitting the independent variables and the final dependent variable, and the structural equation model was constructed based on the structural equation.

[0022] Preferably, the formula expression of the structural equation includes:

[0023] y 1 =α 1 x 1 +α 2 x 2 +ξ 1 (1)

[0024] y2 =α 3 x 2 +β 1 y 1 +ξ 2 (2)

[0025] y 3 =γ 1 y 1 +γ 2 y 2 +ξ 3 (3)

[0026] Among them, x 1 、x 2 is the independent variable, y 1 ,y 2 ,y 3 is the final dependent variable, α 1 represents the independent variable x 1 For the final dependent variable y 1 The influence of α 2 represents the independent variable x 2 For the final dependent variable y 1 The influence of α 3 represents the independent variable x 2 For the final dependent variable y 2 The influence of 1 represents the final dependent variable y 1 For the final cause 2 The impact of γ 1 represents the final dependent variable y 1 For the final cause 3 The impact of γ 2 represents the final dependent variable y 2 For the final cause 3 The influence of 1 , 2 , 3 Both represent the residual of the response variable.

[0027] Preferably, the process of obtaining a structural equation model based on the structural equation construction includes:

[0028] Substituting (1) into (2), we get (4)

[0029] y 2 =β 1 α 1 x 1 +(α 3 +β 1 α 2 )x 2 +β 1 ξ 1 +ξ2 (4)

[0030] Substituting (4) into (3), the resulting formula is the structural equation model.

[0031] Preferably, the formula expression of the structural equation model is:

[0032] y 3 =(α 1 γ 1 +α 1 β 1 γ 2 )x 1 +(α 2 γ 1 +α 3 γ 2 +α 2 β 1 γ 2 )x 2 +(γ 1 +β 1 γ 2 )ξ 1 +γ 2 ξ 2 +ξ 3 .

[0033] Preferably, the process of verifying the accuracy of the multi-parameter surrogate index comprises:

[0034] The water quality parameter index is introduced into the structural equation model to obtain the predicted value of the abundance of antibiotic resistance genes, the predicted value of the abundance of antibiotic resistance genes is compared with the actual value to obtain the accuracy of the multi-parameter alternative index, and the abundance of antibiotic resistance genes in the secondary effluent of the actual sewage treatment plant is predicted based on the multi-parameter alternative index.

[0035] Compared with the prior art, the present invention has the following advantages and technical effects:

[0036] The present invention utilizes water quality parameter indicators that can be monitored in real time in the secondary effluent of a sewage treatment plant to construct an ARGs abundance structural equation model for the secondary effluent of a sewage treatment plant, thereby determining a method for multi-parameter alternative indicators of ARGs abundance in the secondary effluent of a sewage treatment plant and being able to perform real-time monitoring of ARGs in the secondary effluent, which is of great significance for effectively evaluating and controlling the potential ecological risks of ARGs. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0038] Figure 1 It is a representative schematic diagram of the water quality parameter indicators of the secondary effluent in each component of the embodiment of the present invention;

[0039] Figure 2 It is a schematic diagram of correlation analysis between secondary effluent water quality indicators and ARGs according to an embodiment of the present invention;

[0040] Figure 3 A structural equation model diagram of the secondary effluent ARGs of an embodiment of the present invention;

[0041] Figure 4 It is a path diagram of the secondary effluent ARGs structural equation model of an embodiment of the present invention. DETAILED DESCRIPTION

[0042] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0043] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0044] As a new pollutant, ARGs have potential harm to the natural environment and human health. WWTPs can receive sewage and wastewater from various industries, but conventional sewage treatment processes have limited removal capacity for ARGs. The treated secondary effluent discharged into the receiving surface water will cause harm to the surface water environment. Therefore, attention should be paid to the monitoring of the abundance of ARGs in secondary effluent. At present, the commonly used detection methods for the abundance of ARGs in secondary effluent are real-time fluorescence quantitative PCR, high-throughput qPCR and metagenomics, but the above detection methods have defects such as long detection cycle and high detection cost.

[0045] like Figure 1 As shown, the present invention provides a method for determining a multi-parameter alternative index of resistance gene abundance in sewage treatment plant effluent, comprising:

[0046] Select water quality parameter indicators to measure the abundance of antibiotic resistance genes in secondary effluent from sewage treatment plants;

[0047] A multi-parameter alternative indicator model for predicting the abundance of antibiotic resistance genes was constructed, water quality parameter indicators were input into the multi-parameter alternative indicator model, the multi-parameter alternative indicator of antibiotic resistance gene abundance was obtained as output, and the accuracy of the multi-parameter alternative indicator was verified.

[0048] Furthermore, water quality parameter indicators include chemical indicators, physical indicators, and sensory indicators;

[0049] Chemical indicators include ammonia nitrogen, nitrite nitrogen, nitrate nitrogen, soluble orthophosphate, COD cr 、UV 254 , TOC, pH;

[0050] Physical indicators include DO, temperature, redox potential, and zeta potential;

[0051] Sensory indicators include turbidity and other indicators of extracellular polymers.

[0052] Furthermore, turbidity is used to indirectly reflect the content of antibiotic-resistant bacteria attached to particulate matter in secondary effluent;

[0053] Other indicators, extracellular polymeric substances, are used to directly reflect the content of antibiotic-resistant bacteria in secondary effluent;

[0054] The growth of antibiotic-resistant bacteria is affected by both chemical and physical factors.

[0055] Furthermore, the abundance of antibiotic resistance genes in the secondary effluent of the sewage treatment plant was determined by high-throughput fluorescence quantitative PCR.

[0056] Furthermore, the process of constructing a multi-parameter surrogate indicator model for predicting the abundance of antibiotic resistance genes includes:

[0057] Use principal component analysis and correlation analysis to screen water quality parameter indicators for modeling, remove unnecessary water quality parameter indicators, and determine modeling factors;

[0058] The modeling factors were used as independent variables, and the measured abundance of antibiotic resistance genes in the secondary effluent of the sewage treatment plant was used as the final dependent variable to construct a structural equation model between conventional water quality parameters and the abundance of antibiotic resistance genes.

[0059] Obtaining multi-parameter surrogate indicators of antibiotic resistance gene abundance in secondary effluent from wastewater treatment plants based on structural equation modeling.

[0060] Furthermore, the process of constructing a structural equation model between conventional water quality parameters and the abundance of antibiotic resistance genes was as follows, using the modeling factors as independent variables and the measured abundance of antibiotic resistance genes in the secondary effluent of the sewage treatment plant as the final dependent variable.

[0061] The structural equation was obtained by linearly fitting the independent variables and the final dependent variable, and the structural equation model was obtained based on the structural equation.

[0062] Furthermore, the formula expression of the structural equation includes:

[0063] y 1 =α 1 x 1 +α 2 x2 +ξ 1 (1)

[0064] y 2 =α 3 x 2 +β 1 y 1 +ξ 2 (2)

[0065] y 3 =γ 1 y 1 +γ 2 y 2 +ξ 3 (3)

[0066] Among them, x 1 、x 2 is the independent variable, y 1 ,y 2 ,y 3 is the final dependent variable, α 1 represents the independent variable x 1 For the final dependent variable y 1 The influence of α 2 represents the independent variable x 2 For the final dependent variable y 1 The influence of α 3 represents the independent variable x 2 For the final dependent variable y 2 The influence of 1 represents the final dependent variable y 1 For the final cause 2 The impact of γ 1 represents the final dependent variable y 1 For the final cause 3 The impact of γ 2 represents the final dependent variable y 2 For the final cause 3 The influence of 1 , 2 , 3 Both represent the residual of the response variable.

[0067] Furthermore, the process of obtaining a structural equation model based on structural equation construction includes:

[0068] Substituting (1) into (2), we get (4)

[0069] y 2 =β 1 α 1 x 1 +(α 3 +β 1 α2 )x 2 +β 1 ξ 1 +ξ 2 (4)

[0070] Substituting (4) into (3), the resulting formula is the structural equation model.

[0071] Furthermore, the formula of the structural equation model is:

[0072] y 3 =(α 1 γ 1 +α 1 β 1 γ 2 )x 1 +(α 2 γ 1 +α 3 γ 2 +α 2 β 1 γ 2 )x 2 +(γ 1 +β 1 γ 2 )ξ 1 +γ 2 ξ 2 +ξ 3 .

[0073] Furthermore, the process of verifying the accuracy of the multi-parameter surrogate index includes:

[0074] The water quality parameter indicators were introduced into the structural equation model to obtain the predicted value of the abundance of antibiotic resistance genes. The predicted value of the abundance of antibiotic resistance genes was compared with the actual value to obtain the accuracy of the multi-parameter alternative indicator. The abundance of antibiotic resistance genes in the secondary effluent of the actual sewage treatment plant was predicted based on the multi-parameter alternative indicator.

[0075] Example

[0076] Below, we take the secondary effluent of a sewage treatment plant in Beijing as an example to construct the ARGs abundance structural equation model of the secondary effluent of the sewage treatment plant.

[0077] Secondary effluent from a municipal sewage treatment plant in Beijing was collected from September to November 2022 and February 2023, and selected typical water quality indicators in the secondary effluent were measured. The results are shown in Table 1.

[0078] Table 1

[0079]

[0080]

[0081] The distribution of ARGs in the secondary effluent of WWTPs is affected by a variety of environmental factors, such as chemical indicators such as ammonia nitrogen, nitrite nitrogen, nitrate nitrogen, dissolved orthophosphate, COD cr 、UV 254 , TOC, pH, etc., physical indicators DO, temperature, redox potential, zeta potential, etc., sensory indicators turbidity and other indicators extracellular polymers. The growth of antibiotic-resistant bacteria is affected by chemical and physical indicators. Turbidity indirectly reflects the content of antibiotic-resistant bacteria attached to particulate matter in the secondary effluent, while extracellular polymers directly reflect the content of antibiotic-resistant bacteria in the secondary effluent. The above easily detectable water quality indicators are used as alternatives to construct alternative indicators for predicting the abundance of ARGs in secondary effluent. Water quality indicators with high correlation and strong representativeness are screened out to construct the structural model equation of ARGs abundance. Therefore, selecting water quality parameter indicators and constructing a multi-parameter alternative indicator model for predicting ARGs abundance is a technical problem that needs to be solved for real-time monitoring of ARGs in secondary effluent in a simple, efficient and accurate manner.

[0082] Furthermore, the abundance of ARGs in the secondary effluent was detected using a high-throughput qPCR method, and the results are shown in Table 2.

[0083] Table 2

[0084]

[0085]

[0086] A multi-parameter alternative indicator model for predicting ARGs abundance was constructed. According to principal component analysis, correlation circles were used to show the representativeness of each variable in each principal component. Figure 1 As shown, turbidity, TOC, DO, temperature, redox potential and pH are all close to the edge of the circle, showing good representativeness and can be used as latent variables to participate in the construction of structural equations.

[0087] There are direct correlations between temperature, turbidity, TOC and redox potential and the ARGs of secondary effluent, among which the correlations between temperature and turbidity and ARGs are more significant; it is also observed that there are significant correlations between temperature and TOC and redox potential, and there are direct correlations between turbidity and TOC and temperature, such as Figure 2 In summary, the abundance of ARGs in secondary effluent was taken as the final dependent variable, and temperature, turbidity, TOC and redox potential were taken as independent variables.

[0088] Taking the secondary effluent ARGs abundance as the final dependent variable, the independent variables were selected based on the above analysis and the structural equation model was constructed ( Figure 3) and list the structural equations, and obtain the relevant parameters through linear fitting to obtain the determined structural equations and structural equation models. Figure 3 The parameters α, β, and γ represent the explanatory power of the independent variable x on the dependent variable y.

[0089] y 1 =α 1 x 1 +α 2 x 2 +ξ 1 (1)

[0090] y 2 =α 3 x 2 +β 1 y 1 +ξ 2 (2)

[0091] y 3 =γ 1 y 1 +γ 2 y 2 +ξ 3 (3)

[0092] Among them, x 1 、x 2 is the independent variable, y 1 ,y 2 ,y 3 is the final dependent variable, α 1 represents the independent variable x 1 For the final dependent variable y 1 The influence of α 2 represents the independent variable x 2 For the final dependent variable y 1 The influence of α 3 represents the independent variable x 2 For the final dependent variable y 2 The influence of 1 represents the final dependent variable y 1 For the final cause 2 The impact of γ 1 represents the final dependent variable y 1 For the final cause 3 The impact of γ 2 represents the final dependent variable y 2 For the final cause 3 The influence of 1 , 2 , 3 Both represent the residual of the response variable.

[0093] Substituting equation (1) into equation (2), we get (4)

[0094] y 2 =β 1 α 1 x 1 +(α 3 +β 1 α 2 )x 2 +β 1 ξ 1 +ξ 2 (4)

[0095] Substituting (4) into (3), we get (5)

[0096] y 3 =(α 1 γ 1 +α 1 β 1 γ 2 )x 1 +(α 2 γ 1 +α 3 γ 2 +α 2 β 1 γ 2 )x 2 +(γ 1 +β 1 γ 2 )ξ 1 +γ 2 ξ 2 +ξ 3 .

[0097] By calculating the correlation coefficient, we get formula (6) and the structural equation path diagram. Figure 4 shown.

[0098] y 3 =3561.496639x 1 +207052.5307x 2 +2871207.638(6)

[0099] The measured conventional water quality indicators were substituted into the structural equation to obtain the predicted value of ARGs abundance and compared with the actual measured ARGs abundance to verify the accuracy of the ARGs multi-parameter alternative index. Table 3 shows the data of ARGs predicted values ​​and actual values.

[0100] Table 3

[0101]

[0102] The difference between the model prediction value and the actual detection value is extremely insignificant, so the conventional water quality parameters of secondary effluent from sewage treatment plants can be used to predict the abundance of ARGs in secondary effluent.

[0103] The present invention first uses high-throughput qPCR to detect the abundance of ARGs in secondary effluent, then constructs a structural model equation to obtain the predicted abundance of ARGs, and compares the predicted value with the actual value. The results show that the predicted value is very significant compared with the actual value. The structural model equation constructed by the multi-parameter index thus obtained can predict the abundance of ARGs in secondary effluent, and then directly derive the abundance of ARGs based on the easily detectable water quality index in the secondary effluent, without the need to use the high-throughput qPCR method. The present invention uses the water quality parameter index that can be monitored in real time in the secondary effluent of the sewage treatment plant to construct a structural equation model of the abundance of ARGs in the secondary effluent of the sewage treatment plant, thereby determining the method of multi-parameter alternative indicators of the abundance of ARGs in the secondary effluent of the sewage treatment plant and being able to monitor the ARGs in the secondary effluent in real time, which is of great significance for effectively evaluating and controlling the potential ecological risks of ARGs.

[0104] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for determining a multi-parameter alternative index for the abundance of resistance genes in sewage treatment plant effluent, characterized in that: include: Select water quality parameter indicators to measure the abundance of antibiotic resistance genes in secondary effluent from sewage treatment plants; Constructing a multi-parameter alternative indicator model for predicting the abundance of antibiotic resistance genes, inputting the water quality parameter index into the multi-parameter alternative indicator model, outputting a multi-parameter alternative indicator for the abundance of antibiotic resistance genes, and verifying the accuracy of the multi-parameter alternative indicator; The water quality parameter indicators include chemical indicators, physical indicators, and sensory indicators; The chemical indicators include ammonia nitrogen, nitrite nitrogen, nitrate nitrogen, soluble orthophosphate, COD cr 、UV 254 , TOC, pH; The physical indicators include DO, temperature, redox potential, and zeta potential; The sensory indicators include turbidity and other indicators of extracellular polymers; The turbidity is used to indirectly reflect the content of antibiotic-resistant bacteria attached to particulate matter in the secondary effluent; The other indicator extracellular polymers are used to directly reflect the content of antibiotic-resistant bacteria in the secondary effluent; The growth of the antibiotic-resistant bacteria is affected by chemical and physical indicators; The determination of the abundance of antibiotic resistance genes in the secondary effluent of the sewage treatment plant is carried out by high-throughput fluorescence quantitative PCR; The process of constructing a multi-parameter alternative indicator model for predicting the abundance of antibiotic resistance genes includes: Use principal component analysis and correlation analysis to screen water quality parameter indicators for modeling, remove unnecessary water quality parameter indicators, and determine modeling factors; The modeling factors are used as independent variables, and the measured abundance of antibiotic resistance genes in the secondary effluent of the sewage treatment plant is used as the final dependent variable to construct a structural equation model between conventional water quality parameters and the abundance of antibiotic resistance genes; Based on the structural equation model, a multi-parameter alternative index of the abundance of antibiotic resistance genes in the secondary effluent of the sewage treatment plant was obtained; The process of constructing a structural equation model between conventional water quality parameters and the abundance of antibiotic resistance genes using the modeling factors as independent variables and the measured abundance of antibiotic resistance genes in the secondary effluent of the sewage treatment plant as the final dependent variable includes: Linearly fitting the independent variables and the final dependent variable to obtain the structural equation, and constructing the structural equation model based on the structural equation; The formula expression of the structural equation includes: y1=α1x1+α2x2+ξ1(1) y2=α3x2+β1y1+ξ2(2) y3=γ1y1+γ2y2+ξ3(3) Among them, x1 and x2 are independent variables, y1, y2, and y3 are final dependent variables, α1 represents the impact of independent variable x1 on the final dependent variable y1, α2 represents the impact of independent variable x2 on the final dependent variable y1, α3 represents the impact of independent variable x2 on the final dependent variable y2, β1 represents the impact of final dependent variable y1 on final dependent variable y2, γ1 represents the impact of final dependent variable y1 on final dependent variable y3, γ2 represents the impact of final dependent variable y2 on final dependent variable y3, ξ1, ξ2, and ξ3 all represent the residuals of the response variable; The process of obtaining a structural equation model based on the structural equation construction includes: Substituting (1) into (2), we get (4) y2=β1α1x1+(α3+β1α2)x2+β1ξ1+ξ2(4) Substituting (4) into (3), the resulting formula is the structural equation model; The formula of the structural equation model is: y3=(α1γ1+α1β1γ2)x1+(α2γ1+α3γ2+α2β1γ2)x2+(γ1+β1γ2)ξ1+γ2ξ2+ξ3 The process of validating the accuracy of the multi-parameter surrogate markers includes: The water quality parameter index is introduced into the structural equation model to obtain the predicted value of the abundance of antibiotic resistance genes, the predicted value of the abundance of antibiotic resistance genes is compared with the actual value to obtain the accuracy of the multi-parameter alternative index, and the abundance of antibiotic resistance genes in the secondary effluent of the actual sewage treatment plant is predicted based on the multi-parameter alternative index.

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