Sewage pathogen propagation prediction method based on urban drainage pipe network hydraulic model

Abstract

The invention discloses a sewage pathogen propagation prediction method based on an urban drainage pipe network hydraulic model, which comprises the following steps: constructing a drainage pipe network hydraulic model, and simulating the concentration of dissolved oxygen in a drainage pipe network; counting a virus concentration attenuation rule; establishing a change relationship between the dissolved oxygen concentration and the virus concentration of the drainage pipe network; and judging a virus transmission risk level and predicting the number of infected people. According to the method, distribution of virus concentration in sewage of the whole pipe network is obtained on the basis of a small number of monitoring points, the defects that existing monitoring coverage is insufficient and monitoring cost is high are overcome, the hydraulic model can reconstruct the whole pipe network operation working condition state at any historical moment, the future change trend can be predicted, disease prevention and control can be better served, and the method has good application prospects. And the urban public health safety guarantee level is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pathogen prediction, and in particular to a sewage pathogen transmission prediction method based on a city drainage pipe network hydraulic model. BACKGROUND

[0002] By monitoring the concentrations of pathogens such as influenza and syncytial viruses, coronaviruses, noroviruses, monkeypox viruses, rotaviruses, and enteroviruses in sewage pipe networks, the virus infection status and development trend can be determined, which has attracted widespread attention from many countries and regions. However, the existing monitoring methods have the following shortcomings:

[0003] (1) Insufficient monitoring coverage. The existing technology only monitors the virus concentration in sewage at key nodes of the pipe network, and the number of monitoring points is small, the coverage is insufficient, the virus at the end of the branch pipe network may bypass the upstream monitoring point, the virus in the ring pipe network may spread along the unexpected path, there is a monitoring blind area, and only the virus concentration in the sewage at the monitoring point can be indicated, and the virus concentration in the area where the monitoring point is located cannot be indicated.

[0004] (2) High monitoring cost. The online monitoring equipment and daily maintenance cost are high, the battery life is short, the mechanical parts are large, the chemical reagent consumption is large, the communication cost in remote locations is high, a field operation team is needed, and the labor cost is high. SUMMARY

[0005] In order to solve the problems of the prior art, the present application provides a sewage pathogen transmission prediction method based on a city drainage pipe network hydraulic model, which simulates the real-time operating conditions of all nodes in the pipe network, establishes the corresponding change relationship between the dissolved oxygen value in the pipe network sewage and the virus concentration in the pipe network sewage, converts the dissolved oxygen value in the pipe network sewage into an indication of the virus concentration, and further obtains the distribution of the virus concentration in the entire pipe network sewage. The distribution of the virus concentration in the entire pipe network sewage is obtained based on a small number of monitoring points, the problems of insufficient monitoring coverage and high monitoring cost are solved, and the hydraulic model can reconstruct the operating condition state of the entire pipe network at any historical moment, predict the future trend, better serve the disease prevention work, and improve the level of urban public health and safety.

[0006] The embodiments of the present application provide the following solutions:

[0007] The embodiments of the present application provide a sewage pathogen transmission prediction method based on a city drainage pipe network hydraulic model, which comprises the following steps:

[0008] S1, constructing a drainage pipe network hydraulic model to simulate the dissolved oxygen concentration in the drainage pipe network;

[0009] S2, arranging monitoring points on the experimental sewage pipe section, collecting the virus concentration and dissolved oxygen concentration in the sewage in real time, and counting the virus concentration decay law;

[0010] S3, establishing a relationship between dissolved oxygen concentration and virus concentration in the drainage network using the virus concentration decay law;

[0011] S4, calculating the virus concentration index using the relationship between dissolved oxygen concentration and virus concentration in the drainage network, so as to judge the virus transmission risk level and predict the number of infected people.

[0012] In an alternative embodiment, step S1 includes the following processes: basic data sorting, preliminary construction of the drainage network hydraulic model, monitoring data collection and analysis, and model calibration.

[0013] In an alternative embodiment, step S2 also regularly monitors the virus concentration in the drainage network by laying monitoring points, so as to correct the virus concentration decay law.

[0014] In an alternative embodiment, the relationship between dissolved oxygen concentration and virus concentration in the drainage network described in step S3 is described by the following formula:

[0015] C t =C0·k total ,

[0016] Where C t represents the virus concentration after time t, C0represents the initial virus concentration, and k total represents the total decay coefficient:

[0017] k total =k bio +k chem +k phy +k temp +k pipe ,

[0018] k bio is the biological decay coefficient:

[0019]

[0020] Where DO is the dissolved oxygen concentration, and BOD5is the five-day biochemical oxygen demand;

[0021] k chem is the chemical decay coefficient:

[0022] k chem =k d [Dis] n ,

[0023] Where K d is the disinfection reaction rate constant, Dis is the disinfectant concentration, and n is the reaction order;

[0024] k phy is the physical sedimentation coefficient:

[0025]

[0026] Among them, V s The velocity of the virus-attached particles is h, which is the water depth, and η is the sedimentation velocity. attach The adhesion rate of the virus to particulate matter;

[0027] k temp Temperature correction factor:

[0028] k temp =k 20 θ (T-20) ,

[0029] Where, k 20 The attenuation rate is based on a 20℃ reference temperature, θ is the temperature coefficient, and T is the water temperature.

[0030] k pipe For the pipe wall effect coefficient:

[0031]

[0032] in, β is the ratio of pipe wall area to water volume. biofilm α is the biofilm adsorption enhancement factor, and α is the pipe material influence coefficient.

[0033] In an optional embodiment, the virus concentration index in step S4 is calculated through the following process: by collecting the virus concentration and dissolved oxygen value in the sewage in real time, the virus concentration at each node of the drainage network is calculated through the relationship between the dissolved oxygen concentration and virus concentration changes, and then the average regional virus concentration for the day is calculated as the virus concentration index.

[0034] In one optional embodiment, after calculating the average regional virus concentration for the day, the 5-day moving weighted average of the regional virus concentration is further calculated as a virus concentration indicator.

[0035] In one optional embodiment, low-risk, medium-risk, and high-risk thresholds for virus concentration are set, and a risk warning is issued when the virus concentration index reaches each threshold.

[0036] In one optional embodiment, for areas with a risk of virus transmission, the corresponding sewage treatment volume for the day is multiplied by a sewage volume reduction coefficient and divided by the residential sewage discharge quota to obtain the number of people in the area for the day, which is recorded as the first number of people; the area's water supply for the day is divided by the per capita water consumption quota to obtain the number of people in the area for the day, which is recorded as the second number of people; the weighted average of the first number of people and the second number of people is taken to obtain the final number of people in the area for the day; the final number of people in the area for the day is multiplied by the low-risk, medium-risk, and high-risk infection rates to obtain the predicted number of virus infections in the area for the day.

[0037] The present application has the beneficial effects of its technical solutions in that:

[0038] (1) The present application can break through the spatial limitations of physical monitoring. Traditional monitoring can only obtain real-time data of limited points, and it is difficult to fully reflect the overall operation state of the pipe network. However, the present application can dynamically simulate the virus concentration in sewage at any position of the entire pipe network through mathematical simulation, realize the overall grasp of the change law of virus concentration in complex pipe network sewage and the prediction of future transmission conditions, and provide a global perspective for disease prevention and control early warning operation.

[0039] (2) The present application has the advantages of high efficiency, low cost and flexibility. Traditional monitoring requires a large number of equipment, installation and maintenance costs, and it is time-consuming and laborious to adjust the monitoring scheme. The present application can quickly simulate multiple "assumption" scenarios in a computer environment, evaluate the effects of different disease prevention and control schemes, and greatly reduce the trial and error cost and risk, which is difficult to achieve by relying on physical point monitoring. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments will be briefly introduced as follows.

[0041] Figure 1 The flowchart of the embodiment of the present application.

[0042] Figure 2 The process diagram for constructing the drainage pipe network hydraulic model.

[0043] Figure 3 The calculation result diagram of dissolved oxygen in sewage of the drainage pipe network.

[0044] Figure 4 The calculation result diagram of dissolved oxygen in sewage of a single node of the drainage pipe network.

[0045] Figure 5 The process diagram for statistical virus concentration decay law.

[0046] Figure 6 The map visualization display of the prediction result. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art belong to the scope of protection of the embodiments of the present application.

[0048] Reference Figure 1The application provides a sewage pathogen transmission prediction method based on a city drainage pipe network hydraulic model, and the method comprises the following steps:

[0049] S1, a drainage pipe network hydraulic model is constructed to simulate the dissolved oxygen concentration in the drainage pipe network.

[0050] As shown in Figure 2 , the construction of the drainage pipe network hydraulic model comprises the following steps: basic data collation, preliminary construction of the drainage pipe network hydraulic model, monitoring data collection and analysis, and model checking. Thus, the actual pipe network can be digitally modeled, and appropriate monitoring points can be designed for subsequent sensor arrangement.

[0051] Figure 3 After the construction of the drainage pipe network hydraulic model for a certain region, the calculation results of the dissolved oxygen in the sewage in the drainage pipe network are shown in the figure, and different colors are used to distinguish the size of the dissolved oxygen value. Figure 4 The calculation results of the dissolved oxygen in the sewage in a single node in the drainage pipe network are shown in the figure.

[0052] S2, a straight sewage pipe with a length of 800 meters and without branch connection is selected as an experimental sewage pipe section, and the virus concentration and dissolved oxygen concentration in the upstream, middle and downstream sewage are monitored. The virus concentration is monitored twice each time, and the attenuation law of the virus concentration under different sewage dissolved oxygen conditions is statistically analyzed.

[0053] Referring to Figure 5 , 40 monitoring points can also be arranged in the drainage pipe network, and the virus concentration is monitored twice a week. The virus detection results of the sewage and the virus concentration of the monitoring points are recorded, and the 5-day moving weighted average of the virus concentration in the area where the monitoring points are located is calculated to correct the virus concentration attenuation law.

[0054] S3, the relationship between the dissolved oxygen concentration and the virus concentration in the drainage pipe network is established by using the virus concentration attenuation law, which is described by the following formula:

[0055] C t =C0·k total ,

[0056] wherein C t represents the virus concentration after time t (hours, h), C0 represents the initial virus concentration (gene copy number per liter, GC / L), k total represents the total attenuation coefficient (h-1):

[0057] k total =k bio +k chem +k phy +k temp +k pipe ,

[0058] k bioBio-decay coefficient:

[0059]

[0060] wherein DO is the dissolved oxygen concentration (mg / L) and BOD5 is the five-day biochemical oxygen demand (mg / L);

[0061] k chem Chemical decay coefficient:

[0062] k chem = k d [Dis] n ,

[0063] wherein K d is the disinfection reaction rate constant, Dis is the disinfectant concentration, and n is the reaction order;

[0064] k phy Physical sedimentation coefficient:

[0065]

[0066] wherein V s is the virus-attached particle sedimentation velocity (m / h), h is the water depth (m), and η attach is the virus attachment rate on particulate matter;

[0067] k temp Temperature correction coefficient:

[0068] k temp = k 20 θ (T-20) ,

[0069] wherein k 20 is the 20°C reference decay rate, θ is the temperature coefficient, and T is the water temperature (°C);

[0070] k pipe Pipe wall effect coefficient:

[0071]

[0072] wherein A is the pipe wall area-water volume ratio, β biofilm is the biofilm adsorption enhancement factor, and a is the pipe material influence coefficient.

[0073] S4, calculate the virus concentration index by using the relationship between the dissolved oxygen concentration and the virus concentration in the drainage pipe network, so as to judge the virus transmission risk level and predict the number of infected people: input the virus concentration and dissolved oxygen value in the sewage at the monitoring point of the pipe network, the system simulates and calculates the virus concentration of each node in the entire pipe network, the virus concentration of each node in the pipe network is related to the sewage treatment capacity of the sewage treatment plant in each region on the same day, the weighted average of the virus concentration in the region on the same day is calculated, the weighted average of the virus concentration in the region on the same day is obtained, considering the influence of the fluctuation of the sewage treatment capacity, the 5-day moving weighted average of the virus concentration in the region is calculated as the final virus concentration calculation index. Set the low risk threshold, medium risk threshold and high risk threshold of the virus concentration, compare the 5-day moving weighted average of the virus concentration in the region with the virus concentration threshold, and obtain the extremely low, low, medium and high virus transmission risk in the region, and use different colors to represent.

[0074] For the region with virus transmission risk, the corresponding sewage treatment capacity on the same day is multiplied by the sewage quantity reduction coefficient and divided by the per capita sewage discharge quota to obtain the number of people in the region on the same day, the water supply quantity in the region on the same day is divided by the per capita water consumption quota to obtain the number of people in the region on the same day, and the weighted average of the two region numbers is taken to obtain the final number of people in the region on the same day. The number of people in the region on the same day is multiplied by the low risk, medium risk and high risk infection rate to obtain the number of people infected by the virus in the region on the same day. The remote water meter is installed in the residential user in the demonstration area, and based on the water consumption data of each remote water meter, the number of infected people in a specific street, community or factory can be accurately predicted, which provides support for the scientific formulation of public safety management decisions by the disease control department.

[0075] In this embodiment, the following table is the monitoring data of different regional monitoring stations:

[0076]

[0077]

[0078] The following table is the risk level setting of this embodiment:

[0079] Virus transmission risk level Virus concentration threshold (Copies / L) Infection rate Very low risk <21000 <0.1％ Low risk 21000-210000 0.1％-1.0％ Medium risk 210000-1050000 1.0％-5.0％ High risk >1050000 >5.0％

[0080] The following table is the risk level judgment and the number of infected people predicted by the output of this embodiment:

[0081]

[0082]

[0083] The map visualization display of the prediction result is shown in Figure 6

[0084] ​While the preferred embodiments of the application have been described, additional variations and modifications can be made to these embodiments by those skilled in the art once they have the benefit of the present disclosure without departing from the spirit and scope of the application. Accordingly, it is intended that the appended claims include all such modifications and variations as fall within the scope of the present application.

[0085] It is apparent that those skilled in the art can make various changes and modifications to the application without departing from the spirit and scope of the application. It is therefore intended that the present application cover all such changes and modifications that are within its scope.

Claims

1. A method for predicting the spread of pathogens in wastewater based on a hydraulic model of urban drainage networks, characterized in that, The method includes: S1. Construct a hydraulic model of the drainage network to simulate the dissolved oxygen concentration in the drainage network; S2. Set up monitoring points in the experimental sewage pipe section to collect the virus concentration and dissolved oxygen concentration in the sewage in real time, and statistically analyze the virus concentration decay pattern. S3. Establish the relationship between dissolved oxygen concentration and virus concentration changes in drainage pipe network by utilizing the virus concentration decay law; S4. Calculate the virus concentration index by utilizing the relationship between dissolved oxygen concentration and virus concentration changes in the drainage network, thereby determining the level of virus transmission risk and predicting the number of infections.

2. The method for predicting the spread of pathogens in wastewater based on a hydraulic model of urban drainage pipe networks according to claim 1, characterized in that: Step S1 includes the following processes: basic data organization, preliminary construction of the hydraulic model of the drainage network, monitoring data collection and analysis, and model verification.

3. The method for predicting the spread of pathogens in wastewater based on a hydraulic model of urban drainage pipe networks according to claim 1, characterized in that: Step S2 also involves setting up monitoring points in the drainage network to regularly monitor the virus concentration in order to correct for the virus concentration decay pattern.

4. The method for predicting the spread of pathogens in wastewater based on a hydraulic model of urban drainage pipe networks according to claim 1, characterized in that: The relationship between the changes in dissolved oxygen concentration and virus concentration in the drainage network mentioned in step S3 is described by the following formula: C t =C0·k total , Among them, C t C represents the virus concentration after time t, C0 represents the virus concentration at the initial time, and k represents the virus concentration after time t. total Indicates the total attenuation coefficient: k total =k bio +k chem +k phy +k temp +k pipe , k bio Biological attenuation coefficient: Wherein, DO is the dissolved oxygen concentration, and BOD5 is the five-day biochemical oxygen demand; k chem Chemical decay coefficient: k chem =k d [Dis] n , Among them, K d denoted as the disinfection reaction rate constant, Dis is the disinfectant concentration, and n is the reaction order; k phy Physical settlement coefficient: Among them, V s The velocity of the virus-attached particles is h, which is the water depth, and η is the sedimentation velocity. attach The adhesion rate of the virus to particulate matter; k temp Temperature correction factor: k temp =k 20 i (T-20) , Where, k 20 The attenuation rate is based on a 20℃ reference temperature, θ is the temperature coefficient, and T is the water temperature. k pipe For the pipe wall effect coefficient: in, β is the ratio of pipe wall area to water volume. biofilm α is the biofilm adsorption enhancement factor, and α is the pipe material influence coefficient.

5. The method for predicting the spread of pathogens in wastewater based on a hydraulic model of urban drainage pipe networks according to claim 1, characterized in that: Step S4, the virus concentration index, is calculated through the following process: by collecting real-time data on the virus concentration and dissolved oxygen value in the sewage, the virus concentration at each node of the drainage network is calculated based on the relationship between the dissolved oxygen concentration and virus concentration changes, and then the average regional virus concentration for the day is calculated as the virus concentration index.

6. The method for predicting the spread of pathogens in wastewater based on a hydraulic model of an urban drainage network according to claim 5, characterized in that: After calculating the average regional virus concentration for the day, the 5-day moving weighted average of the regional virus concentration is further calculated as the virus concentration indicator.

7. The method for predicting the spread of pathogens in wastewater based on a hydraulic model of an urban drainage network according to claim 5 or 6, characterized in that: Set low-risk, medium-risk, and high-risk thresholds for virus concentration. When the virus concentration index reaches each threshold, a risk warning will be issued.

8. The method for predicting the spread of pathogens in wastewater based on a hydraulic model of urban drainage pipe networks according to claim 7, characterized in that: For areas with a risk of virus transmission, the corresponding sewage treatment volume for the day is multiplied by the sewage volume reduction coefficient and divided by the residential sewage discharge quota to obtain the number of people in the area that day, which is recorded as the first number. The area's water supply for the day is divided by the per capita water consumption quota to obtain the number of people in the area that day, which is recorded as the second number. The weighted average of the first and second numbers is taken to obtain the final number of people in the area that day. The final number of people in the area that day is multiplied by the low-risk, medium-risk, and high-risk infection rates to obtain the predicted number of virus infections in the area that day.

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