Establishment method of urban atmospheric environmental pollution refined attribution model

Abstract

The invention relates to the technical field of urban environmental pollution, and particularly discloses a method for establishing a refined attribution model of urban atmospheric environmental pollution. The method comprises the following steps: S1, collecting and preprocessing multi-source data, analyzing a spatial and temporal change rule of air quality, and defining change characteristics of human activity intensity; s2, researching an influence factor parameterization method of the urban air quality, performing optimal variable screening, and selecting an optimal variable according to a contribution rate and incorporating the optimal variable into a model; s3, performing repeated sampling simulation on the original observation data to solve an average regression coefficient and a root-mean-square error of a simulation result so as to evaluate the uncertainty of model fitting, and comparing the difference between a predicted value and the concentration of the urban air quality observation station to perform precision verification; and S4, quantitatively analyzing the cause of urban air quality pollution according to the obtained quantitative attribution model. According to the technical scheme, the difference of urban environmental pollution levels can be quickly clarified, and a supporting effect on early warning and intervention of heavy pollution events is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of urban atmospheric environmental pollution, and in particular to a method for establishing a refined attribution model for urban atmospheric environmental pollution. Background Art

[0002] Research on the attribution of urban atmospheric pollution can help people better understand the main influencing factors of urban environmental pollution. Quantitative attribution of pollution has become the key to the formulation of air quality improvement policies, and is very important for reducing residents' health risks and improving their sense of happiness.

[0003] Urban air quality levels are jointly affected by geographical location, pollutant emissions, meteorological conditions, and regional transmission. The impact of geographical location is usually fixed; pollutant emissions are divided into natural sources and anthropogenic sources, among which changes in natural source emissions usually fluctuate less, while changes in anthropogenic emissions are the main driving factor for changes in atmospheric pollutant concentrations; meteorological conditions affect the diffusion, transmission and deposition of atmospheric pollutants and atmospheric processes related to meteorological conditions, such as: hygroscopic growth of particulate matter, photochemical reactions, etc.; air mass transmission, accompanied by changes in meteorological elements, affects air quality on a scale of thousands of kilometers.

[0004] Existing quantitative attribution assessment methods are mainly based on source-oriented models and receptor inverse models. The source-oriented model simulates the changes in pollutant concentrations based on the air quality model from the perspective of emission sources, but the air quality model is time-consuming to run and requires the preparation of source inventory information in advance. Therefore, it is very important to quantify the impact of meteorological and emission factors based on the observation perspective. It is mainly based on the observation information of the receptor point, and uses mathematical methods to quantitatively analyze the impact of various factors on the changes in the monitoring information of the receptor point.

[0005] Existing receptor inverse models often assume that there is a linear relationship between each factor and the pollutant concentration. However, in real life, the effect of variables is usually not linear, and the linear assumption may not meet actual needs or even directly violate the actual situation. In addition, the impact of regional transmission on air quality is very important, but the existing receptor inverse model does not consider the impact of air mass transmission.

[0006] The linear assumption of the existing attribution model and the neglect of regional transmission factors reduce the accuracy of the atmospheric pollution cause results. Therefore, how to establish a method of attributing urban atmospheric pollution with high accuracy and quantify the impact of pollution source emission changes, meteorological factors, and regional transmission on air quality is an urgent problem to be solved by technicians in this field. Summary of the invention

[0007] In view of the above-mentioned problems in the prior art, the present invention provides a method for establishing a refined attribution model for urban atmospheric environmental pollution, which fully considers the deficiencies of existing assessment models of the impact of anthropogenic sources and meteorological conditions, can quickly clarify the differences in the levels of urban atmospheric environmental pollution caused by anthropogenic sources, meteorological conditions and regional transport in the short and long term, can support the early warning and intervention of severe pollution events, and provide a reference for the formulation of pollution prevention and control policies.

[0008] To achieve the above object, the present invention proposes a method for establishing a refined attribution model for urban atmospheric environmental pollution, the steps of which are as follows:

[0009] S1. Collect and preprocess multi-source data, analyze the temporal and spatial variation of air quality, and clarify the characteristics of changes in human activity intensity caused by different population distribution and economic development in different regions;

[0010] S2. Study the parameterization method of factors affecting urban air quality, screen the optimal variables for multi-source data and multiple influencing factors, and select the variables with higher contribution to pollutant prediction ability as the best prediction factors of the model. Select the optimal variables according to the contribution rate and include them in the model to improve the accuracy of the model and ensure its robustness;

[0011] S3. Evaluate the uncertainty of model fitting by repeatedly sampling the original observation data to obtain the average regression coefficient and root mean square error of the simulation results, and compare the difference between its predicted value and the concentration of urban air quality observation sites to verify the accuracy and comprehensively evaluate the application ability of the model;

[0012] S4. Quantitatively analyze the causes of urban air quality pollution based on the quantitative attribution model finally obtained in step S3.

[0013] Preferably, in S1, the multi-source data and multiple influencing factors include but are not limited to measured atmospheric pollutant concentration data, meteorological station observation data and simulation data, pollution source emission data, and socio-economic data.

[0014] Preferably, in S2, factors affecting air quality include anthropogenic influence, meteorological elements and regional transmission; the anthropogenic influence includes interannual variation, seasonal variation, daily variation, weekend effect and diurnal variation; the fixed meteorological factors include boundary layer height, wind speed and wind direction; the other meteorological variable factors include but are not limited to vertical diffusion, hygroscopic deposition and local horizontal migration of the atmosphere, and show seasonal periodic changes and mutual correlation; the parameterized factor of regional transmission is the long-distance transmission diffusion trajectory of particles.

[0015] Preferably, the impact of anthropogenic sources is characterized by multi-time scale factors; fixed meteorological factors are derived from remote sensing and ground observation data, the influence of the mixing layer height is characterized by a nonlinear relationship, the influence of wind speed and wind direction is characterized by two-dimensional nonlinear interaction; the impact of regional transmission is characterized by the Lagrangian potential source contribution clustering results.

[0016] Preferably, in S2, the specific steps of studying the parameterization method of influencing factors of urban air quality are to analyze the main influencing factors of target pollutant concentrations based on urban environmental observation data, analyze their changing characteristics with annual, monthly, daily and hourly scales, as well as the changing characteristics of major meteorological elements at different time scales, and determine the fixed explanatory variables in the model and the corresponding nonlinear function types.

[0017] Preferably, in S2, the contribution rate of the area to the potential source of pollutants at the observation point is the ratio of the number of particles in a certain grid to the total number, and the acquisition process is to obtain the hourly backward simulation particle longitude and latitude information based on backward simulation, divide the simulation area into polar coordinate grids, and calculate the number of particles in each grid.

[0018] Preferably, in S2, the specific steps for screening the optimal variables for multi-source data and multiple influencing factors are to first start with an empty model, and then add variables to the model one by one, one variable at a time, and select the variable with the highest contribution to the fitting determination coefficient of the dependent variable y as the candidate variable in each cycle, and screen through the contribution threshold. Variables with a contribution rate greater than or equal to the threshold are included in the model, and variables with a contribution rate less than the threshold are eliminated.

[0019] Preferably, in S2, fuzzy clustering is also performed on all retention time analysis results to reflect the degree to which the corresponding time belongs to a certain main air mass, so as to determine the impact of parameterized air mass transmission.

[0020] Preferably, the expression of the established urban environmental pollution attribution model is:

[0021]

[0022] Where E(Y) is the expected value of pollutant concentration, g is log transformation, and β0 represents the average concentration after removing the influence of anthropogenic sources and meteorological factors. Characterize the impacts of anthropogenic changes at multiple time scales, Characterize the nonlinear effects of various meteorological factors, It represents the role of regional transfer and ∈ represents the model residual.

[0023] Preferably, the optimization and verification of the attribution model of steps S2-S3 are specifically to screen the influencing factors with higher contribution rates and the optimal data source through sensitivity analysis, eliminate redundant variables, and avoid collinearity problems; perform repeated sampling and simulation with replacement through sampling method, calculate statistics such as the mean and variance of the estimated quantity, and evaluate the uncertainty of the model.

[0024] Therefore, the present invention proposes a method for establishing a refined attribution model for urban atmospheric environmental pollution, and its beneficial effects are as follows:

[0025] (1) The method for establishing a refined attribution model for urban atmospheric environmental pollution in the present invention fully considers the deficiencies of existing assessment models of the influence of anthropogenic sources and meteorological conditions. By comprehensively considering the nonlinear effects of time factors, meteorological conditions and regional transmission, the Lagrangian particle diffusion model is combined with the nonlinear model to achieve parameterized characterization of each factor, thereby establishing a highly efficient and interpretable nonlinear attribution method.

[0026] (2) The method for establishing a refined attribution model for urban atmospheric environmental pollution of the present invention is driven by environmental observation data and fusion of multi-source data, optimizes the model through optimal variable screening, statistical inference and other methods, and performs uncertainty assessment through repeated sampling simulation methods, so as to achieve refined quantitative attribution of factors affecting urban atmospheric environmental pollution, timely discover and control pollution sources, and thus effectively improve the quality of urban environment. This not only helps to improve the quality of life of residents, but also creates favorable conditions for the sustainable development of cities.

[0027] (3) The present invention provides a method for establishing a refined attribution model for urban atmospheric environmental pollution. This attribution method can quickly clarify the differences in the levels of urban environmental pollution caused by anthropogenic sources, meteorological conditions, and air mass transport in the short and long term, can support the early warning and intervention of severe pollution events, and provide a reference for the formulation of pollution prevention and control policies.

[0028] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is an overall flow chart of a method for establishing an urban environmental pollution attribution model of the present invention. DETAILED DESCRIPTION

[0030] In order to make the technical solutions, advantages and purposes of the present invention clearer, the technical solutions of the embodiments of the present invention are clearly and completely described below. The described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the protection scope of this application.

[0031] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0032] like Figure 1 As shown, the present invention provides a method for establishing a refined attribution model for urban atmospheric environmental pollution, and the specific steps are as follows:

[0033] S1. Collect and preprocess multi-source data, analyze the temporal and spatial variation of air quality, and clarify the characteristics of changes in human activity intensity caused by different population distribution and economic development in different regions;

[0034] Among them, multi-source data and multiple influencing factors include but are not limited to measured atmospheric pollutant concentration data, meteorological station observation data and simulation data, pollution source emission data, and socio-economic data.

[0035] S2. Study the parameterization method of factors affecting urban air quality, screen the optimal variables for multi-source data and multiple influencing factors, and select the variables with higher contribution to pollutant prediction ability as the best prediction factors of the model. Select the optimal variables according to the contribution rate and include them in the model to improve the accuracy of the model and ensure its robustness;

[0036] Factors affecting air quality include anthropogenic sources, meteorological elements and regional transmission; anthropogenic sources include interannual variation, seasonal variation, daily variation, weekend effect and diurnal variation. Among them, interannual variation is used to reflect the impact of long-term air pollution control policies on air quality, seasonal variation is related to the periodic changes in atmospheric diffusion conditions, and is used to characterize the seasonal periodic variation characteristics of pollutant levels, and short-term effects represent the periodic changes in human activities on weekends, weekdays, holidays and day and night.

[0037] Fixed meteorological factors include boundary layer height, wind speed and wind direction. The influence of mixing layer height is characterized by nonlinear relationship, and the influence of wind speed and wind direction is characterized by two-dimensional nonlinear interaction. Other meteorological variable factors include but are not limited to vertical diffusion of the atmosphere, hygroscopic deposition and local horizontal migration. The parameterized factor of regional transport is the long-distance transport diffusion trajectory of particles.

[0038] The impact of anthropogenic sources is characterized by multi-time scale factors; the fixed meteorological factors are derived from remote sensing and ground observation data, and the impact of regional transmission is characterized by the Lagrangian potential source contribution clustering results.

[0039] For other meteorological variables, such as relative humidity, air temperature, dew point temperature, precipitation, etc., these factors often show seasonal cyclical changes, and these factors are interrelated. Therefore, only variables with higher contribution to pollutant prediction ability are screened as the best predictors of the model.

[0040] In S2, the specific steps of studying the parameterization method of influencing factors of urban air quality are to analyze the main influencing factors of target pollutant concentrations based on urban environmental observation data, analyze their changing characteristics at annual, monthly, daily and hourly scales, and the changing characteristics of major meteorological elements at different time scales, and determine the fixed explanatory variables in the model and the corresponding nonlinear function types.

[0041] In S2, the contribution rate of the region to the potential source of pollutants at the observation point is the ratio of the number of particles in a certain grid to the total number. The acquisition process is to obtain the hourly backward simulation particle longitude and latitude information based on backward simulation, divide the simulation area into polar coordinate grids, and calculate the number of particles in each grid. Fuzzy clustering is performed on all retention time analysis results to reflect the degree to which the corresponding time belongs to a major air mass, so as to judge the impact of parameterized air mass transmission.

[0042] In S2, the specific steps for optimal variable screening for multi-source data and multiple influencing factors are to start with an empty model, then add variables to the model one by one, one variable at a time, and select the variable with the highest contribution to the fitting determination coefficient (r-Square, r2) of the dependent variable y in each cycle as the candidate variable, and screen through the contribution threshold. Variables with a contribution rate greater than or equal to the threshold are included in the model, and variables with a contribution rate less than the threshold are eliminated.

[0043] The expression of the established urban environmental pollution attribution model is:

[0044]

[0045] Where E(Y) is the expected value of pollutant concentration, g is log transformation, and β0 represents the average concentration after removing the influence of anthropogenic sources and meteorological factors. Characterize the impacts of anthropogenic changes at multiple time scales, Characterize the nonlinear effects of various meteorological factors, It represents the role of regional transfer and ∈ represents the model residual.

[0046] S3. Evaluate the uncertainty of model fitting by repeatedly sampling the original observation data to obtain the average regression coefficient and root mean square error of the simulation results, and compare the difference between its predicted value and the concentration of urban air quality observation stations to verify the accuracy and comprehensively evaluate the application ability of the model;

[0047] The optimization and verification of the attribution model in steps S2-S3 is specifically to screen the influencing factors with higher contribution rates and the optimal data source through sensitivity analysis, eliminate redundant variables, and avoid collinearity problems; perform 100 repeated sampling and simulations with replacement through the sampling method, calculate the mean and variance of the estimated quantity and other statistics, and evaluate the uncertainty of the model.

[0048] S4. Quantitatively analyze the causes of urban air quality pollution based on the quantitative attribution model finally obtained in step S3.

[0049] Therefore, the method for establishing an urban environmental pollution attribution model proposed in the present invention fully considers the deficiencies of the existing assessment models of the impact of anthropogenic sources and meteorological conditions, and combines the Lagrangian particle diffusion model with the nonlinear model by comprehensively considering the nonlinear impact of the time factor, meteorological conditions and regional transmission, realizes the parameterized characterization of each factor, and establishes a highly effective and high-precision nonlinear attribution method. , and is driven by environmental observation data and fusion of multi-source data, and the model is optimized by methods such as optimal variable screening and statistical inference, and uncertainty assessment is performed by repeated sampling simulation methods to achieve refined quantitative attribution of factors affecting urban atmospheric environmental pollution. This attribution method can quickly clarify the differentiation of short-term and long-term scale anthropogenic sources, meteorological conditions and air mass transport on urban environmental pollution levels, can support the early warning and intervention of heavy pollution events, and provide a reference for the formulation of pollution prevention and control policies.

[0050] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for establishing a refined attribution model for urban atmospheric environmental pollution, characterized in that: Here are the steps: S1. Collect and preprocess multi-source data, analyze the temporal and spatial variation of air quality, and clarify the characteristics of changes in human activity intensity caused by different population distribution and economic development in different regions; S2. Study the parameterization method of factors affecting urban air quality, screen the optimal variables for multi-source data and multiple influencing factors, and select the variables with higher contribution to pollutant prediction ability as the best prediction factors of the model. Select the optimal variables according to the contribution rate and include them in the model to improve the accuracy of the model and ensure its robustness; S3. Evaluate the uncertainty of model fitting by repeatedly sampling the original observation data to obtain the average regression coefficient and root mean square error of the simulation results, and compare the difference between its predicted value and the concentration of urban air quality observation stations to verify the accuracy and comprehensively evaluate the application ability of the model; S4. Quantitatively analyze the causes of urban air quality pollution based on the quantitative attribution model finally obtained in step S3.

2. The method for establishing a refined attribution model for urban atmospheric environmental pollution according to claim 1 is characterized in that: In S1, multi-source data and multiple influencing factors include but are not limited to measured atmospheric pollutant concentration data, meteorological station observation data and simulation data, pollution source emission data, and socio-economic data.

3. The method for establishing a refined attribution model for urban atmospheric environmental pollution according to claim 1 is characterized in that: In S2, the factors affecting air quality include anthropogenic sources, meteorological elements and regional transmission; the anthropogenic sources include interannual variation, seasonal variation, daily variation, weekend effect and diurnal variation; the fixed meteorological factors include boundary layer height, wind speed and wind direction; the other meteorological variable factors include but are not limited to relative humidity, air temperature, dew point temperature, precipitation, vertical diffusion of the atmosphere, hygroscopic deposition and local horizontal migration, and show seasonal periodic changes and mutual correlation; the parameterized factor of regional transmission is the long-distance transmission diffusion trajectory of particles.

4. The method for establishing a refined attribution model for urban atmospheric environmental pollution according to claim 3 is characterized in that: The impact of anthropogenic sources is characterized by multi-time scale factors; fixed meteorological factors are derived from remote sensing and ground observation data, the impact of the mixing layer height is characterized by nonlinear relationships, and the impact of wind speed and direction is characterized by two-dimensional nonlinear interactions; The impact of regional transport is characterized using the Lagrangian potential source contribution clustering results.

5. The method for establishing a refined attribution model for urban atmospheric environmental pollution according to claim 1 is characterized in that: In S2, the specific steps of studying the parameterization method of influencing factors of urban air quality are to analyze the main influencing factors of target pollutant concentrations based on urban environmental observation data, analyze their changing characteristics at annual, monthly, daily and hourly scales, and the changing characteristics of major meteorological elements at different time scales, and determine the fixed explanatory variables in the model and the corresponding nonlinear function types.

6. The method for establishing a refined attribution model for urban atmospheric environmental pollution according to claim 1 is characterized in that: In S2, the contribution rate of the area to the potential source of pollutants at the observation point is the ratio of the number of particles in a certain grid to the total number. The acquisition process is to obtain the hourly backward simulation particle longitude and latitude information based on backward simulation, divide the simulation area into polar coordinate grids, and calculate the number of particles in each grid.

7. The method for establishing a refined attribution model for urban atmospheric environmental pollution according to claim 1 is characterized in that: In S2, the specific steps for optimal variable screening for multi-source data and multiple influencing factors are to start with an empty model, then add variables to the model one by one, one variable at a time, and select the variable with the highest contribution to the fitting determination coefficient of the dependent variable y as the candidate variable in each cycle. The variables are screened by the contribution threshold, and the variables with a contribution rate greater than or equal to the threshold are included in the model, and the variables with a contribution rate less than the threshold are eliminated.

8. The method for establishing a refined attribution model for urban atmospheric environmental pollution according to claim 1 is characterized in that: In S2, fuzzy clustering is also performed on all retention time analysis results to reflect the degree to which the corresponding time belongs to a major air mass, so as to determine the impact of parameterized air mass transport.

9. The method for establishing a refined attribution model for urban air pollution according to claim 1, characterized in that: The expression of the established urban environmental pollution attribution model is: Where E(Y) is the expected value of pollutant concentration, g is log transformation, and β0 represents the average concentration after removing the influence of anthropogenic sources and meteorological factors. Characterize the impacts of anthropogenic changes at multiple time scales, Characterize the nonlinear effects of various meteorological factors, It represents the role of regional transfer and ∈ represents the model residual.

10. The method for establishing a refined attribution model for urban air pollution according to claim 1, characterized in that: The optimization and verification of the attribution model in steps S2-S3 is specifically to screen the influencing factors with higher contribution rates and the optimal data source through sensitivity analysis, eliminate redundant variables, and avoid collinearity problems; perform repeated sampling and simulation with replacement through sampling method, calculate statistics such as the mean and variance of the estimated quantity, and evaluate the uncertainty of the model.

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