Method for establishing a fine urban atmospheric environment pollution attribution model

Abstract

The application relates to the technical field of urban environmental pollution, and particularly discloses a method for establishing a fine urban atmospheric environmental pollution attribution model. The method comprises the following steps: S1, collecting and preprocessing multi-source data, analyzing the temporal and spatial variation law of air quality, and determining the variation characteristics of human activity intensity; S2, researching a parameterization method of influencing factors of urban air quality, and performing optimal variable screening, and selecting optimal variables according to contribution rates to be included in the model; S3, evaluating the uncertainty of model fitting by repeatedly sampling and simulating the original observation data to obtain the average regression coefficient and the root mean square error of the simulation result, and comparing the difference between the prediction value and the concentration of the urban air quality observation station to verify the accuracy; and S4, quantitatively analyzing the causes of urban air quality pollution according to the obtained quantitative attribution model. The technical scheme can quickly clarify the differentiation of the urban environmental pollution level, and supports the early warning and intervention of heavy pollution events.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban atmospheric environmental pollution, in particular to a method for establishing a fine urban atmospheric environmental pollution attribution model. BACKGROUND

[0002] Urban atmospheric environmental pollution attribution research can help people better understand the main influencing factors of urban environmental pollution, and the quantitative attribution of pollution is a key to air quality improvement policy making, which is very important for reducing the health risks of residents and improving happiness.

[0003] The level of urban air quality is influenced by geographical location, pollutant emissions, meteorological conditions and regional transmission. The influence of geographical location is usually fixed; pollutant emissions are divided into natural and human sources, among which the change of natural source emission usually fluctuates less, and the change of human emission is the main driving factor of the change of atmospheric pollutant concentration; 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 reaction, etc.; air mass transmission affects air quality on a scale of thousands of kilometers with changes in meteorological elements.

[0004] Existing quantitative attribution evaluation methods are mainly based on source-oriented models and receptor reverse models. The source-oriented model is based on the simulation of pollutant concentration changes by air quality model from the perspective of emission source, but the air quality model requires a long running time and needs to prepare source list information in advance. Therefore, it is important to quantitatively analyze the influence of meteorological and emission factors from the perspective of observation. It is mainly based on the observation information of the receptor point, and uses mathematical methods to quantitatively analyze the influence of each factor on the change of the observation information of the receptor point.

[0005] Existing receptor reverse models often assume a linear relationship between each factor and pollutant concentration, but in real life, the role of variables is usually not linear, and the linear assumption may not meet the actual needs, or even directly violate the actual situation. In addition, regional transmission has a significant impact on air quality, but existing receptor reverse models do not consider the influence of air mass transmission.

[0006] The linear assumption of existing attribution models and the neglect of regional transmission factors reduce the accuracy of the results of atmospheric environmental pollution causes. Therefore, how to establish a method for urban atmospheric environmental pollution attribution with high accuracy to quantitatively analyze the influence of pollutant emission changes, meteorological factors and regional transmission on air quality is a problem to be solved by those skilled in the art. SUMMARY

[0007] The present application aims at the above problems of the prior art, and provides a method for establishing a fine attribution model of urban atmospheric environmental pollution, which can quickly clarify the differentiation of short-term and long-term scale human sources, meteorological conditions and regional transport on the urban atmospheric environmental pollution level, support the early warning and intervention of heavy pollution events, and provide a reference for the formulation of pollution prevention and control policies.

[0008] To achieve the above object, the present application provides a method for establishing a fine attribution model of urban atmospheric environmental pollution, comprising the following steps:

[0009] S1, collecting and preprocessing multi-source data, analyzing the spatio-temporal variation law of air quality, and determining the human activity intensity variation characteristics caused by the changes of population distribution and economic development in each region;

[0010] S2, studying the parameterization method of the influencing factors of urban air quality, performing optimal variable screening on multi-source data and multiple influencing factors, and screening variables with high contribution to the prediction ability of pollutants as the best prediction factors of the model, selecting the optimal variables according to the contribution rate to enter the model, improving the model precision and ensuring its robustness;

[0011] S3, evaluating the uncertainty of model fitting by repeatedly sampling and simulating the original observation data to obtain the average regression coefficient and root mean square error of the simulation results, and comparing the difference between the predicted value and the concentration of the urban air quality observation station to verify the precision, and comprehensively evaluating the application ability of the model;

[0012] S4, quantitatively analyzing the causes of urban air quality pollution according to 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, simulation data, pollution source emission data and social and economic data.

[0014] Preferably, in S2, the influencing factors of air quality include human source influence, meteorological elements and regional transport; the human source 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 atmospheric vertical diffusion, hygroscopic deposition and local horizontal migration, and show seasonal periodic variation and mutual correlation; the parameterization factor of regional transport is particle long-distance transport and diffusion trajectory.

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

[0016] Preferably, in S2, the specific steps of researching the parameterization method of the influencing factor of urban air quality are as follows: analyzing the main influencing factors of the target pollutant concentration based on urban environmental observation data, analyzing the variation characteristics of the target pollutant concentration and the main meteorological factors on the interannual, intermonthly, daily, and hourly scales, and determining the fixed explanatory variables in the model and the corresponding nonlinear function types.

[0017] Preferably, in S2, the contribution rate of the region to the potential source influence of the observation point pollutant is the ratio of the number of particles in a certain grid to the total number, and the obtaining process is as follows: obtaining the longitude and latitude information of the backward simulation particles based on backward simulation, dividing the simulation area into polar coordinate grids, and calculating the number of particles in each grid.

[0018] Preferably, in S2, the specific steps of performing optimal variable selection on the multi-source data and multiple influencing factors are as follows: starting with an empty model, adding variables to the model one by one, adding one variable at a time, selecting the variable with the highest contribution to the determination coefficient of the dependent variable y as the candidate variable in each loop, and selecting the variables with a contribution rate greater than or equal to the threshold value to be included in the model and the variables with a contribution rate less than the threshold value to be excluded.

[0019] Preferably, in S2, all the remaining time analysis results are also subjected to fuzzy clustering to reflect the degree of belonging to a certain main air mass at a corresponding time, so as to determine the influence of parameterized air mass transport.

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

[0021]

[0022] wherein E(Y) is the expected value of the pollutant concentration, g is the log transformation, β0 represents the average concentration after removing the influence of human sources and meteorology, characterizing the influence of multi-time scale human source variation, characterizing the nonlinear influence of multiple meteorological factors, characterizing the effect of regional transport, and ∈ represents the model residual.

[0023] Preferably, the optimization and verification of the attribution model of steps S2-S3 are specifically filtering the influence factors with higher contribution rate and the optimal data source by sensitivity analysis, eliminating redundant variables and avoiding collinearity problems; the statistical quantities such as the mean and variance of the estimated quantity are calculated by repeated sampling with replacement and simulation through the sampling method, and the uncertainty of the model is evaluated.

[0024] Therefore, the present application proposes a method for establishing a fine urban atmospheric environmental pollution attribution model, which has the following beneficial effects:

[0025] (1) The method for establishing a fine urban atmospheric environmental pollution attribution model fully considers the shortcomings of the existing evaluation model affected by human sources and meteorological conditions, combines the Lagrangian particle diffusion model with the nonlinear model by comprehensively considering the nonlinear effects of time factors, meteorological conditions and regional transmission, realizes the parameterization representation of each factor, and establishes a high-time-efficiency nonlinear attribution method with interpretability.

[0026] (2) The method for establishing a fine urban atmospheric environmental pollution attribution model is driven based on environmental observation data and fusion of multi-source data, and the model is optimized through optimal variable screening and statistical inference, and the uncertainty is evaluated through repeated sampling simulation method, so as to realize the fine quantitative attribution of urban atmospheric environmental pollution influencing factors, discover and manage pollution sources in time, and effectively improve the urban environmental quality, which not only helps to improve the quality of life of residents, but also creates favorable conditions for the sustainable development of the city.

[0027] (3) The method for establishing a fine urban atmospheric environmental pollution attribution model can quickly clarify the differentiation of short-term and long-term scale human sources, meteorological conditions and air mass transport on urban environmental pollution level through the attribution method, can support the early warning and intervention of heavy pollution events, and provide a reference for the formulation of pollution prevention and control policies.

[0028] The technical solutions of the present application will be further described in detail below with the aid of the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 is the overall flowchart of the method for establishing a fine urban atmospheric environmental pollution attribution model. DETAILED DESCRIPTION

[0030] In order to make the technical solutions, advantages and objectives of the present application clearer, the technical solutions of the embodiments of the present application will be clearly and completely described below. The described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the described embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without any creative effort fall within the protection scope of the present application.

[0031] Unless otherwise defined, technical terms or scientific terms used in the present application shall have the usual meaning understood by those of ordinary skill in the art to which the present application belongs.

[0032] As shown in Figure 1 The present application provides a method for establishing a fine attribution model of urban atmospheric environmental pollution, and the specific steps are as follows:

[0033] S1, collecting and preprocessing multi-source data, analyzing the spatio-temporal variation law of air quality, and determining the characteristics of human activity intensity change caused by changes in population distribution and economic development in different regions;

[0034] Among them, the multi-source data and the plurality of influence factors include but are not limited to measured atmospheric pollutant concentration data, meteorological station observation data and simulation data, pollution source emission data, and social and economic data.

[0035] S2, studying the parameterization method of the influencing factors of urban air quality, performing optimal variable screening on the multi-source data and the plurality of influence factors, and screening variables with high contribution to pollutant prediction ability as the best prediction factors of the model, selecting the optimal variables according to the contribution rate to enter the model, improving the model precision and ensuring its robustness;

[0036] The influencing factors of air quality include human source influence, meteorological elements and regional transmission; the human source influence includes interannual variation, seasonal variation, daily variation, weekend effect and diurnal variation. Among them, the interannual variation is used to reflect the influence of long-term control policy on air quality, the seasonal variation is related to the periodic change of atmospheric diffusion condition, and is used to represent the periodic change characteristics of pollutant level, and the short-term effect represents the periodic change of human activity mode such as weekend, weekday, holiday and day and night.

[0037] The fixed meteorological factors include boundary layer height, wind speed and wind direction, the influence of mixed layer height is characterized by a 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 atmospheric vertical diffusion, hygroscopic deposition and local horizontal migration; the parameterization factor of regional transmission is the long-distance transmission and diffusion trajectory of particles.

[0038] Anthropogenic source is characterized by multi-time scale factor; fixed meteorological factor is derived from remote sensing and ground observation data, and the influence of regional transport is characterized by 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 periodic changes, and these factors are interrelated, therefore, only the variables with high contribution to the prediction ability of pollutants are selected as the best prediction factors of the model.

[0040] In S2, the specific steps of the parameterization method of the influencing factor of urban air quality are as follows: based on the analysis of the main influencing factors of the target pollutant concentration of urban environmental observation data, the variation characteristics of the main meteorological factors on the interannual, monthly, daily and hourly scales are analyzed, and the fixed explanatory variables and the corresponding nonlinear function types in the model are determined.

[0041] In S2, the contribution rate of the region to the potential source of the observation point pollutant is the ratio of the number of particles in a certain grid to the total number, and the process of obtaining it is as follows: based on the backward simulation, the longitude and latitude information of the hourly backward simulation particles is obtained, the simulation area is divided into polar coordinate grids, and the number of particles in each grid is calculated. Fuzzy clustering is performed on the results of all retention time analysis to reflect the degree of belonging to a certain main air mass at the corresponding time, so as to judge the influence of parameterization air mass transport.

[0042] In S2, the specific steps of optimal variable selection for multi-source data and multiple influencing factors are as follows: first, start with an empty model, then add variables to the model one by one, add one variable at a time, select the variable with the highest contribution to the determination coefficient (r-Square, r2) of the dependent variable y in each loop as the candidate variable, and select the variable with a contribution rate greater than or equal to the threshold value as the candidate variable. The variable with a contribution rate greater than or equal to the threshold value is included in the model, and the variable with a contribution rate less than the threshold value is excluded.

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

[0044]

[0045] Wherein, E(Y) is the expected value of the pollutant concentration, g is the log transformation, β0 represents the average concentration after removing the influence of anthropogenic source and meteorology, characterizing the influence of multi-time scale anthropogenic source, characterizing the nonlinear influence of multiple meteorological factors, characterizing the effect of regional transport, and ∈ represents the model residual.

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

[0047] The optimization and verification of the attribution model of steps S2-S3 are specifically: through sensitivity analysis, screening the influence factors with higher contribution rate and the optimal data source, eliminating redundant variables and avoiding collinearity problems; through sampling method, 100 times of repeated sampling with replacement and simulation are carried out, the mean and variance of the estimated quantity and other statistical quantities are calculated, and the uncertainty of the model is evaluated.

[0048] S4, according to the quantitative attribution model finally obtained in step S3, the causes of urban air quality pollution are quantitatively analyzed.

[0049] Therefore, the method for establishing the urban environmental pollution attribution model fully considers the deficiencies of the existing evaluation model affected by human sources and meteorological conditions, combines the Lagrangian particle diffusion model with the nonlinear model by comprehensively considering the nonlinear effects of time factors, meteorological conditions and regional transmission, realizes the parameterization representation of each factor, establishes a high-time-efficiency and high-precision nonlinear attribution method, and drives based on environmental observation data and fusion of multiple source data. Through model optimization by optimal variable screening and statistical inference, the uncertainty is evaluated by repeated sampling simulation method, and the fine quantitative attribution of urban atmospheric environmental pollution influencing factors is realized. The attribution method can quickly clarify the differentiation of short-term and long-term scale human sources, meteorological conditions and air mass transport on urban environmental pollution level, can support the early warning and intervention of heavy pollution events, and can provide reference for the formulation of pollution prevention and control policy.

[0050] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application but not to limit it, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that: it can still modify or equivalently replace the technical solutions of the present application, and these modifications or equivalent replacements also cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for establishing a fine urban atmospheric environment pollution attribution model, characterized in that, The steps are as follows: S1, collecting and preprocessing multi-source data, analyzing the spatial and temporal variation of air quality, and determining the characteristics of human activity intensity changes caused by changes in population distribution and economic development in different regions; S2, studying the parameterization method of the influencing factors of urban air quality, selecting the optimal variables from multiple sources of data and multiple influencing factors, and selecting variables with high contribution to pollutant prediction ability as the best prediction factors of the model, selecting the optimal variables according to the contribution rate to enter the model, improving the accuracy of the model and ensuring its robustness; The multiple influencing factors include fixed meteorological factors and other meteorological variable factors; In S2, the influencing factors of air quality include human source influence, meteorological elements and regional transmission; the human source 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 relative humidity, air temperature, dew point temperature, precipitation, vertical diffusion of atmosphere, hygroscopic deposition and local horizontal migration, and show seasonal periodicity and mutual correlation; the parameterization factor of regional transmission is the long-distance transmission and diffusion trajectory of particles; The human source influence is characterized by multi-time scale factors; the 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, and the influence of wind speed and wind direction is characterized by two-dimensional nonlinear interaction; The influence of regional transmission is characterized by Lagrangian potential source contribution clustering results; S3, the uncertainty of model fitting is evaluated by repeatedly sampling and simulating the original observation data to obtain the average regression coefficient and root mean square error of the simulation results, and the difference between the predicted value and the concentration of the urban air quality observation site is compared to verify the accuracy, and the application ability of the model is comprehensively evaluated; S4, according to the quantitative attribution model finally obtained in step S3, the causes of urban air quality pollution are quantitatively analyzed; The expression of the established urban environmental pollution attribution model is: ； where, is the pollutant concentration expectation value, is the log transformation, denotes the average concentration after removal of anthropogenic sources and meteorological influences, characterizes the influence of multi-time scale anthropogenic source variations, characterizes the non-linear influence of multiple meteorological factors, characterizes the effect of regional transport, denotes the model residual. 2.The method of claim 1, wherein, 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, simulation data, pollution source emission data and social economic data.

3. The method of claim 1, wherein the method further comprises: In S2, the specific steps of the parameterization method of the influencing factors of urban air quality are as follows: analyzing the best prediction factors of the target pollutant concentration based on urban environmental observation data, analyzing the variation characteristics of the influencing factors of air quality and the variation characteristics of meteorological elements at different time scales, and determining the fixed explanatory variables in the model and the corresponding nonlinear function type.

4. The method of claim 1, wherein the method further comprises: In S2, the contribution rate of the region to the potential source influence of the observation point pollutant is the ratio of the number of particles in a certain grid to the total number, and the acquisition process is to obtain the longitude and latitude information of the backward simulation particles based on backward simulation, divide the simulation area into polar coordinate grids, and calculate the number of particles in each grid.

5. The method of claim 1, wherein the method further comprises: In S2, the specific steps of optimal variable screening for multi-source data and multiple impact factors are as follows: firstly, starting from an empty model, then adding variables to the model one by one, adding one variable at a time, selecting the variable with the highest contribution to the determination coefficient of the dependent variable y as the candidate variable in each loop, screening through the contribution threshold, and the variable with a contribution rate greater than or equal to the threshold is included in the model, and the variable with a contribution rate less than the threshold is excluded.

6. The method of claim 1, wherein the method further comprises: In S2, all retention time analysis results are also subjected to fuzzy clustering to reflect the degree to which the corresponding time belongs to a certain main air mass, so as to determine the influence of parameterized air mass transmission.

7. The method of claim 1, wherein the method further comprises: The optimization and verification of the attribution model of steps S2-S3 are as follows: through sensitivity analysis, the impact factors with higher contribution rates and the optimal data sources are screened, redundant variables are removed, and the problem of multicollinearity is avoided; through the sampling method, repeated sampling with replacement is carried out, the mean and variance statistics of the estimated quantity are calculated, and the uncertainty of the model is evaluated.

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