Method and system for evaluating and predicting pollution reduction and carbon reduction synergistic efficiency of bioretention facility

Abstract

The invention discloses a method and system for evaluating and predicting the pollution reduction and carbon reduction synergistic effect of a bioretention facility, and belongs to the technical field of pollution reduction and carbon reduction.The pollution reduction and carbon reduction synergistic effect of the bioretention facility can be evaluated according to the environmental conditions of the bioretention facility such as interference of different types of antibiotics by establishing a model and a database; and design parameters and application scenes can be selected according to the expected pollutant removal effect or greenhouse gas emission reduction effect. Meanwhile, the device is easy to operate, convenient to operate and suitable for wide popularization. And support is provided for promoting wide application of the bioretention facility in the aspects of urban rainwater management and greenhouse gas emission reduction.

Description

Technical Field

[0001] The present invention relates to the technical field of pollution reduction and carbon reduction, and more particularly to a method and system for evaluating and predicting the synergistic effectiveness of pollution reduction and carbon reduction in bioretention facilities. Background Art

[0002] Human activities are currently impacting climate change, necessitating the reduction of greenhouse gas emissions. Bioretention facilities play a crucial role in pollution and carbon reduction, purifying runoff and sequestering carbon within a small area. However, research on traditional bioretention facilities has focused on their effectiveness in controlling runoff pollution, with less attention paid to their greenhouse gas reduction capabilities. This has limited their comprehensive benefits in urban stormwater management.

[0003] Therefore, in order to improve the pollution reduction and carbon reduction efficiency of bioretention facilities, researchers in this field have adopted various methods such as improving fillers and changing plant species to optimize the design of bioretention facilities, but the pollutant removal and greenhouse gas emission reduction effects vary greatly; at the same time, the existing performance evaluation models also focus on a single pollution control indicator, and are still insufficient in predicting the influence of multiple factors and the coordinated control effect of pollution reduction and carbon reduction. In addition, the parameter optimization of bioretention facilities relies on empirical trial and error methods, and an adaptive optimization system under multi-objective constraints has not been developed.

[0004] Therefore, the present invention considers predicting the pollution reduction and carbon reduction efficiency of the bioretention facility based on its own structural parameters and the environmental conditions in which it is located by establishing a model and a database, and selecting the optimal design parameters based on the desired pollution reduction and carbon reduction efficiency, to provide support for promoting the widespread application of bioretention facilities in urban rainwater management and greenhouse gas emission reduction. Summary of the Invention

[0005] In response to the aforementioned technical issues, the present invention provides a method and system for evaluating and predicting the synergistic pollution and carbon reduction effectiveness of bioretention facilities. This method can assess the pollution and carbon reduction effectiveness of bioretention facilities based on parameters such as environmental conditions, such as interference from different types of antibiotics. Furthermore, design parameters and application scenarios can be selected based on the desired pollutant removal or greenhouse gas reduction effects. Furthermore, the present invention is simple to operate and convenient to run, making it suitable for widespread adoption.

[0006] To achieve the above object, the technical solution adopted by the present invention is:

[0007] In a first aspect, the present invention provides a method for evaluating and predicting the synergistic effectiveness of pollution reduction and carbon reduction in a bioretention facility, the method comprising the following steps:

[0008] S1: For bioretention facilities with known design parameters and environmental conditions, collect data on pollutant removal efficiency, greenhouse gas emissions, and GWP;

[0009] S2: preprocessing the collected data;

[0010] S3: Using the preprocessed data, the pollutant removal rate prediction model and GWP prediction model of the bioretention facility are obtained based on the robust regression analysis method; the design parameters and environmental factors of the target bioretention facility are input into the prediction model to obtain the pollutant removal rate prediction value and GWP prediction value of the target bioretention facility, and evaluate its synergistic effect of pollution reduction and carbon reduction; and establish a database.

[0011] Furthermore, in S1, the design parameters include: planting four plants, namely, Canna, Vetiver, Iris and Sedum multiflorum, and no plants; and the environmental conditions include: using three different antibiotics, namely, sulfamethoxazole, tetracycline and ciprofloxacin, for interference.

[0012] Furthermore, in S2, the collected data are preprocessed using the Huber loss function and the t-test.

[0013] Furthermore, in S3, the calculation formula of the robust regression analysis method is:

[0014] Y=Σβ i x i +∈

[0015] Where Y is the dependent variable; x i is the value of the independent variable; β i is the regression coefficient of each variable, ∈ is a constant; β i The method to obtain ∈ is:

[0016]

[0017] Where, X T is the transpose of the design matrix X; (X T X) -1 is the matrix X T The inverse matrix of X; the resulting matrix It is a matrix with one column and n rows, where the number in the first row is a constant ∈, and the remaining numbers are the regression coefficients β corresponding to the respective variables i .

[0018] Furthermore, in S3, different plant types and types of antibiotics added are used as independent variables to perform stable regression on pollutant removal rate and GWP value, where the pollutant removal rate is the COD removal rate, and the following is obtained:

[0019] The COD removal rate prediction model is:

[0020] P COD=75.593+0.214×Canna indica+2.201×Vetiver+0.302×Iris ternata-0.720×Sedum truncatum-4.684×No plant+6.796×Sulfamethoxazole+7.634×Tetracycline+3.942×Ciprofloxacin;

[0021] Where, P COD is the predicted value of COD removal rate; if the bioretention facility contains plants or antibiotics in the model, the corresponding plant or antibiotic value is recorded as 1, otherwise it is recorded as 0;

[0022] The GWP prediction model is:

[0023] P GWP =506.499+6.771×Canna indica-0.647×Vetiver-0.949×Iris sibiricum+0.110×Sedum truncatum+8.544×No plant-136.491×Sulfamethoxazole-120.319×Tetracycline-133.533×Ciprofloxacin;

[0024] Where, P GWP is the predicted value of GWP; if the bioretention facility contains the plants or antibiotics in the model, the value of the corresponding plants or antibiotics is recorded as 1, otherwise it is recorded as 0.

[0025] Furthermore, the method further comprises:

[0026] S4: Determine the positive and negative impacts and their proportions of each influencing factor of the target bioretention facility through standardization and normalization, and identify the key influencing factors.

[0027] Furthermore, the method further comprises:

[0028] S5: Based on the database parameters and key influencing factors, and based on the target pollutant removal rate or GWP value, the random forest algorithm is used to select the optimal parameters for the design and construction of the target bioretention facility and its most suitable environmental conditions.

[0029] In a second aspect, the present invention further provides a system for evaluating and predicting the synergistic effectiveness of pollution reduction and carbon reduction in bioretention facilities, applying the above-mentioned method for evaluating and predicting the synergistic effectiveness of pollution reduction and carbon reduction in bioretention facilities, the system comprising:

[0030] The data acquisition module is used to collect data on pollutant removal rate, greenhouse gas emissions and GWP of bioretention facilities with known design parameters and environmental conditions;

[0031] A data processing module is used to pre-process the collected data;

[0032] The model establishment and application module is used to use the preprocessed data to obtain the pollutant removal rate prediction model and GWP prediction model of the biological retention facility based on the robust regression analysis method; the design parameters and environmental factors of the target biological retention facility are input into the prediction model to obtain the pollutant removal rate prediction value and GWP prediction value of the target biological retention facility, and evaluate its synergistic effect of pollution reduction and carbon reduction; and establish a database.

[0033] Furthermore, the system also includes:

[0034] The key influencing factor determination module is used to determine the positive and negative impacts and their proportions of various influencing factors of the target bioretention facility through standardization and normalization processing, and to determine the key influencing factors.

[0035] Furthermore, the system also includes:

[0036] The design parameter and environmental condition optimization selection module is used to select the optimal parameters for the design and construction of the target bioretention facility and its most suitable environmental conditions based on the database parameters and key influencing factors, the target pollutant removal rate or GWP, and the random forest algorithm.

[0037] In a third aspect, the present invention also provides an electronic device comprising a processor and a memory, wherein the memory stores machine executable instructions that can be executed by the processor, and the processor executes the machine executable instructions to implement the above-mentioned method for evaluating and predicting the synergistic effectiveness of pollution reduction and carbon reduction of biological retention facilities.

[0038] Compared with the existing technology, the present invention provides a method for evaluating and predicting the synergistic efficiency of pollution reduction and carbon reduction of bioretention facilities, which has at least the following beneficial technical effects:

[0039] 1. The present invention establishes a model and database to predict the pollution reduction and carbon reduction efficiency of the bioretention facility based on its own structural parameters and the environmental conditions in which it is located. The prediction effect is better and more comprehensive, which is conducive to promoting the widespread application of bioretention facilities in urban rainwater management and greenhouse gas emission reduction.

[0040] 2. The present invention can select the optimal design parameters and application scenarios according to the desired pollutant removal effect or greenhouse gas emission reduction effect.

[0041] 3. The present invention is simple to operate, convenient to run, and suitable for wide promotion.

[0042] Other features and advantages of the present invention will be described in the following description, and part of them will become obvious from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0043] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0045] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0046] Figure 1 A flow chart of a method for evaluating and predicting the synergistic effectiveness of pollution reduction and carbon reduction in bioretention facilities provided in an embodiment of the present invention.

[0047] Figure 2 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0049] In describing the present invention, it should be noted that some processes described in this specification and accompanying drawings include multiple operations that appear in a specific order. However, it should be understood that these operations may be performed in a different order than the order in which they appear, or may be performed in parallel. Furthermore, the use of various sequence numbers is for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0050] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0051] See also Figure 1As shown, an embodiment of the present invention provides a method for evaluating and predicting the synergistic effectiveness of bioretention facilities in reducing pollution and carbon emissions. This method can evaluate the effectiveness of bioretention facilities based on parameters such as their filler material, structure, and environmental conditions, such as interference from different types of antibiotics. Design parameters and application scenarios can also be selected based on the desired pollutant removal or greenhouse gas emission reduction effects. Furthermore, the present invention is simple to operate and easy to run, making it suitable for widespread promotion. The method primarily includes the following steps:

[0052] In this embodiment, for a bioretention facility with known design parameters and environmental conditions, the Huber loss function and t-test are first used to pre-process data such as pollutant removal rate, greenhouse gas emissions, and global warming potential (GWP).

[0053] Furthermore, based on the robust regression analysis method, the prediction model formulas for the pollutant removal rate, greenhouse gas emissions and GWP value of the bioretention facility were obtained and a database was established;

[0054] Furthermore, by inputting the design parameters of the bioretention facility (such as plant species, etc.) and environmental factors (such as antibiotic type and concentration, etc.), the pollutant removal rate, greenhouse gas emissions and GWP value under specific conditions can be obtained;

[0055] Furthermore, through standardization and normalization, the positive and negative impacts of each influencing factor and their proportions are determined, and the main influencing factors (i.e., key influencing factors) are determined. Subsequently, the pollution reduction and carbon reduction efficiency of the bioretention facility can be improved by adjusting the parameters of the main influencing factors.

[0056] Furthermore, based on the database parameters, and with target pollutant removal rate or greenhouse gas emissions, the optimal parameters for the design and construction of bioretention facilities and the most suitable environmental conditions can be selected based on the random forest algorithm.

[0057] The working principle and specific implementation methods of the present invention are described in detail below:

[0058] The experiment involved setting up five bioretention columns (bioretention facilities) planted with four different plants—canna, vetiver, iris, and Sedum truncatum—as well as an unplanted column. Three parallel experiments were conducted in each column to account for the interference of three different antibiotics: sulfamethoxazole, tetracycline, and ciprofloxacin. Simulated rainfall was performed using laboratory-prepared rainwater. The effluent COD concentration was measured using rapid digestion spectrophotometry, and the removal efficiency was calculated. Gas chromatography was used to measure CO2, CH4, and N2O emissions, and the GWP was calculated.

[0059] Based on the experimental data, the types of antibiotics and plants were used as independent variables, and COD removal rate and GWP value were used as dependent variables. The Huber loss function was first used to reduce the influence of outliers, and the t-test (p value < 0.05) was used to screen significant variables and eliminate parameters with small correlation. The robust regression analysis method was used to establish a prediction model (1):

[0060] Y=∑β i x i +∈ (1)

[0061] Where Y is the dependent variable; x i is the value of the independent variable; i is the regression coefficient of each variable, ∈ is a constant, β i The sum ∈ is obtained by the least squares method, the specific method is as follows:

[0062] First, add a column of all 1s to the first column of the independent variable matrix to represent the intercept term, and obtain the design matrix X; the regression coefficient β i From formula (2), we can get:

[0063]

[0064] Where, X T is the transpose of the design matrix X; (X T X) -1 is the matrix X T The inverse matrix of X; Y is the observed value vector of the dependent variable. The resulting matrix It is a matrix with one column and n rows, where the number in the first row is the constant ∈ in the prediction model (1), and the remaining (n-1) numbers are the regression coefficients β corresponding to the respective variables i .

[0065] Furthermore, cross-validation was used to evaluate the stability of the model, and residual analysis was performed to optimize the model.

[0066] For example, in bioretention facilities planted with canna, vetiver, iris, and sedum truncatum under the interference of sulfamethoxazole, tetracycline, and ciprofloxacin, and without plants, the COD removal rate and GWP value of bioretention facilities planted with different plants under different antibiotic interference were obtained through repeated experiments. With the type of added antibiotics and different plant types as independent variables, the COD removal rate and GWP value were subjected to stable regression, and the following results were obtained:

[0067] (1) The prediction model of COD removal rate is:

[0068] P COD=75.593+0.214×Canna indica+2.201×Vetiver+0.302×Iris ternata-0.720×Sedum truncatum-4.684×No plant+6.796×Sulfamethoxazole+7.634×Tetracycline+3.942×Ciprofloxacin;

[0069] Where, P COD is the predicted value of COD removal rate (%), which is recorded as 1 if the plant or antibiotic is planted, and recorded as 0 if not. In the COD removal rate prediction model, the values of 75.593, 0.214, 2.201, 0.302, 0.720, 4.684, 6.796, 7.634 and 3.942 are obtained from the above formula (2).

[0070] For example, the model can predict the COD removal rate of the bioretention facility planted with vetiver grass under the interference of sulfamethoxazole to be P COD =75.593+2.201+6.796=84.59%.

[0071] (2) The GWP emission prediction model is:

[0072] P GWP =506.499+6.771×Canna indica-0.647×Vetiver-0.949×Iris sibiricum+0.110×Sedum truncatum+8.544×No plant-136.491×Sulfamethoxazole-120.319×Tetracycline-133.533×Ciprofloxacin;

[0073] Where, P GWP is the predicted value of GWP (mg / (m 2 h), if such plants are grown or such antibiotics are added, it is recorded as 1, otherwise it is recorded as 0. In this prediction model, the values of 506.499, 6.771, 0.647, 0.949, 0.110, 8.544, 136.491, 120.319, and 133.533 are obtained from the above formula (2).

[0074] For example, the model can predict that the GWP of a bioretention facility planted with canna under the interference of sulfamethoxazole is P GWP =506.499+6.771-136.491=376.779mg / (m 2 ·h).

[0075] In a preferred embodiment, the regression coefficient β of the independent variable in the prediction formula is also calculated. i Standardization and normalization are performed. First, the data is standardized using formula (3) to eliminate the dimension effect.

[0076]

[0077] Where u i is the mean, σ i is the standard deviation.

[0078] When using formula (4) to transform the regression coefficient β in formula (1) i Standardization is Beta coefficient β j , which is used to express the impact of changes in the independent variable on the dependent variable:

[0079]

[0080] Where, β i is the standardized regression coefficient, σ x and σ y are the standard deviations of the independent and dependent variables, respectively.

[0081] In order to better compare the differences between different factors, the standardized Beta coefficient is normalized using formula (5) and the data is scaled to the [-1, 1] interval:

[0082]

[0083] Where, is the coefficient normalized to the interval [-1,1], β j is the Beta coefficient after standardization, and max(|β|) is the maximum absolute value of all standardized coefficients. When it is positive, it means that this influencing factor has a promoting effect on pollutant removal rate and greenhouse gas emissions. When it is negative, it means that this influencing factor will reduce the pollutant removal rate and greenhouse gas emissions. The larger the absolute value, the greater the impact of this influencing factor on the pollutant removal rate and greenhouse gas emissions; therefore, the main influencing factors can be determined by analyzing the normalized coefficients, and the design parameters can be adjusted according to the environmental conditions to improve the pollution reduction and carbon reduction efficiency of the bioretention facility.

[0084] For example, when predicting the COD removal efficiency of bioretention facilities planted with Canna, Vetiver, Iris and Sedum truncatum under the conditions of sulfamethoxazole, tetracycline and ciprofloxacin, the results were as follows: The COD removal rate of the bioretention facility is 0.156, while that of the planted Sedum truncatum is -0.051, indicating that the planted Vetiver grass in the bioretention facility can improve the COD removal rate, while the planted Sedum truncatum will reduce the COD removal rate of the bioretention facility. The value is only 0.021, indicating that planting vetiver grass has a better effect on COD removal efficiency in bioretention facilities than planting iris. Therefore, it is predicted that planting vetiver grass in bioretention facilities can achieve better COD removal efficiency.

[0085] Based on the random forest algorithm, data on pollutant removal rates and greenhouse gas emissions of existing bioretention facilities under different design parameters (such as biochar doping type and ratio, plant type, etc.) and environmental conditions (such as rainfall intensity, temperature, antibiotic interference type and concentration, etc.) were collected from platforms such as Web of Science and ScienceDirect, as well as experiments and projects. The data were converted into a format suitable for modeling using the reshape2 package. The data were subjected to regression analysis and standardization using formulas (3)(4)(5) to screen the main influencing factors. Multiple subsets were randomly selected from the data set, and decision trees were constructed using the gbm package. Then, a random forest model was constructed using the randomForest package. The dismo package was used to tune the model parameters and evaluate the model stability through cross-validation. The rpart.plot package was used to visualize the decision tree structure. Through the decision tree structure diagram formed by the database, the pollutant removal rate and greenhouse gas emissions can be inferred based on the planned design parameters and environmental conditions. The ggplot2 programming package can be used to visualize the comparison between the predicted results and the actual values. Based on the predicted results, it can be judged whether the design parameters and environmental conditions meet the target pollutant removal rate and greenhouse gas emissions requirements. If they do not meet the requirements, the design parameters can be adjusted and re-predicted.

[0086] From the description of the above embodiments, those skilled in the art can know that: the embodiment provided by the present invention exemplifies the prediction method and influencing factor weights of the COD removal rate and GWP value of the bioretention facility in which different types of plants are planted under the interference of different types of antibiotics. Based on this method, the structural parameters such as the type and amount of biochar added to the bioretention facility, the submergence depth, the thickness of the medium layer, and the environmental factors such as the initial concentration of pollutants, rainfall intensity, ambient temperature, and dry-off period can be introduced to establish a more comprehensive prediction model for the pollutant removal rate, greenhouse gas emissions, and synergistic effect of pollution reduction and carbon reduction of the bioretention facility. Based on this model, the pollutant removal effect and greenhouse gas emissions of the bioretention facility can be predicted according to its structural parameters and the environmental conditions before the construction of the bioretention facility. If it does not meet the expected requirements, timely adjustments can be made. It is conducive to providing support for promoting the widespread application of bioretention facilities in urban rainwater management and greenhouse gas emission reduction.

[0087] Furthermore, an embodiment of the present invention also provides a system for evaluating and predicting the synergistic effectiveness of pollution reduction and carbon reduction in a bioretention facility, which is applied to a method for evaluating and predicting the synergistic effectiveness of pollution reduction and carbon reduction in a bioretention facility in the above embodiment. The system includes:

[0088] The data acquisition module is used to collect data on pollutant removal rate, greenhouse gas emissions and GWP of bioretention facilities with known design parameters and environmental conditions;

[0089] A data processing module, used for pre-processing the collected data;

[0090] The model building and application module is used to use pre-processed data to develop a pollutant removal rate prediction model and a GWP prediction model for the bioretention facility based on robust regression analysis. The design parameters and environmental factors of the target bioretention facility are input into the prediction model to obtain the predicted pollutant removal rate and GWP values of the target bioretention facility, and to evaluate its synergistic effect in reducing pollution and carbon emissions. A database is also established.

[0091] The key influencing factor determination module is used to determine the positive and negative impacts and their proportions of each influencing factor of the target bioretention facility through standardization and normalization processing, and to determine the key influencing factors;

[0092] The design parameter and environmental condition optimization selection module is used to select the optimal parameters for the design and construction of the target bioretention facility and its most suitable environmental conditions based on the database parameters and key influencing factors, the target pollutant removal rate or GWP value, and the random forest algorithm.

[0093] An embodiment of the present invention provides a system for evaluating and predicting the synergistic efficiency of pollution reduction and carbon reduction in biological retention facilities. Its implementation principle and technical effects are the same as those of the aforementioned method embodiment. For the sake of brief description, for parts not mentioned in this embodiment, please refer to the corresponding content in the aforementioned method embodiment, and no further details will be given here.

[0094] In addition, refer to Figure 2 As shown, an embodiment of the present invention also provides an electronic device, which may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and run on the processor 10. The processor executes the computer program to implement a method for evaluating and predicting the synergistic efficiency of pollution reduction and carbon reduction of biological retention facilities in the above method embodiment.

[0095] In some embodiments, the processor 10 may be comprised of an integrated circuit, such as a single packaged integrated circuit or a plurality of packaged integrated circuits with the same or different functions, including a combination of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control core of the electronic device, connecting the various components of the entire electronic device using various interfaces and circuits. It executes or runs programs or modules stored in the memory 11 and accesses data stored in the memory 11 to perform various functions of the electronic device and process data.

[0096] The memory 11 may be, for example, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples of storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (RAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanical encoding device, and any suitable combination thereof.

[0097] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, electronic devices, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0098] It should be noted that the word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer.

[0099] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0100] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for evaluating and predicting the synergistic effectiveness of pollution reduction and carbon reduction in a bioretention facility, characterized in that: The method comprises the following steps: S1: For bioretention facilities with known design parameters and environmental conditions, collect data on pollutant removal efficiency, greenhouse gas emissions, and GWP; S2: preprocessing the collected data; S3: Using the preprocessed data, the pollutant removal rate prediction model and GWP prediction model of the bioretention facility are obtained based on the robust regression analysis method; the design parameters and environmental factors of the target bioretention facility are input into the prediction model to obtain the pollutant removal rate prediction value and GWP prediction value of the target bioretention facility, and evaluate its synergistic effect of pollution reduction and carbon reduction; and establish a database.

2. The method for evaluating and predicting the synergistic efficiency of pollution reduction and carbon reduction of bioretention facilities according to claim 1, characterized in that: In S1, the design parameters include: planting four plants, namely, Canna indica, Vetiver, Iris odorata and Sedum multiflorum, and no plants; and the environmental conditions include: using three different antibiotics, namely, sulfamethoxazole, tetracycline and ciprofloxacin, for interference.

3. The method for evaluating and predicting the synergistic efficiency of pollution reduction and carbon reduction of bioretention facilities according to claim 1, characterized in that: In S2, the collected data are preprocessed using the Huber loss function and the t-test.

4. The method for evaluating and predicting the synergistic efficiency of pollution reduction and carbon reduction of bioretention facilities according to claim 2, characterized in that: In S3, the calculation formula of the robust regression analysis method is: Y=Sv i x i +∈ Where Y is the dependent variable; x i is the value of the independent variable; i is the regression coefficient of each variable, ∈ is a constant; β i The method to obtain ∈ is: Where, X T is the transpose of the design matrix X; (X T X) -1 is the matrix X T The inverse matrix of X; the resulting matrix It is a matrix with one column and n rows, where the number in the first row is a constant ∈, and the remaining numbers are the regression coefficients β corresponding to the respective variables i .

5. The method for evaluating and predicting the synergistic efficiency of pollution reduction and carbon reduction of bioretention facilities according to claim 4, characterized in that: In S3, different plant types and antibiotic addition types are used as independent variables to perform stable regression on pollutant removal rate and GWP value, where the pollutant removal rate is the COD removal rate, and the results are: The COD removal rate prediction model is: P COD =75.593+0.214×Canna indica+2.201×Vetiver+0.302×Iris ternata-0.720×Sedum truncatum-4.684×No plant+6.796×Sulfamethoxazole+7.634×Tetracycline+3.942×Ciprofloxacin; Where, P COD is the predicted value of COD removal rate; if the bioretention facility contains plants or antibiotics in the model, the corresponding plant or antibiotic value is recorded as 1, otherwise it is recorded as 0; The GWP prediction model is: P GWP =506.499+6.771×Canna indica-0.647×Vetiver-0.949×Iris sibiricum+0.110×Sedum truncatum+8.544×No plant-136.491×Sulfamethoxazole-120.319×Tetracycline-133.533×Ciprofloxacin; Where, P GWP is the predicted value of GWP; if the bioretention facility contains the plants or antibiotics in the model, the value of the corresponding plants or antibiotics is recorded as 1, otherwise it is recorded as 0.

6. The method for evaluating and predicting the synergistic efficiency of pollution reduction and carbon reduction of bioretention facilities according to claim 1, characterized in that: The method further includes: S4: Determine the positive and negative impacts and their proportions of each influencing factor of the target bioretention facility through standardization and normalization, and identify the key influencing factors.

7. The method for evaluating and predicting the synergistic efficiency of pollution reduction and carbon reduction of bioretention facilities according to claim 6, characterized in that: The method further includes: S5: Based on the database parameters and key influencing factors, and based on the target pollutant removal rate or GWP value, the random forest algorithm is used to select the optimal parameters for the design and construction of the target bioretention facility and its most suitable environmental conditions.

8. A system for evaluating and predicting the synergistic effectiveness of pollution reduction and carbon reduction in bioretention facilities, characterized in that: The method for evaluating and predicting the synergistic efficiency of pollution reduction and carbon reduction of a bioretention facility according to any one of claims 1 to 7 is applied, the system comprising: The data acquisition module is used to collect data on pollutant removal rate, greenhouse gas emissions and GWP of bioretention facilities with known design parameters and environmental conditions; A data processing module is used to pre-process the collected data; The model establishment and application module is used to use the preprocessed data to obtain the pollutant removal rate prediction model and GWP prediction model of the biological retention facility based on the robust regression analysis method; the design parameters and environmental factors of the target biological retention facility are input into the prediction model to obtain the pollutant removal rate prediction value and GWP prediction value of the target biological retention facility, and evaluate its synergistic effect of pollution reduction and carbon reduction; and establish a database.

9. An electronic device, characterized in that: It includes a processor and a memory, the memory stores machine-executable instructions that can be executed by the processor, and the processor executes the machine-executable instructions to implement a method for evaluating and predicting the synergistic effectiveness of pollution reduction and carbon reduction of biological retention facilities as described in any one of claims 1 to 7.

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