An urban atmospheric environmental pollution contribution index analysis method

Abstract

The application discloses a kind of urban atmospheric environmental pollution contribution index analytical method, comprising: by collecting the data set including turbulent flow observation data, pollutant concentration data and boundary layer height data, turbulent vertical diffusion flux is calculated, and then turbulent concentration change rate is obtained, observe hourly pollutant concentration change, obtain total concentration change rate, and calculate non-turbulent change rate according to this, then, using turbulent concentration change rate and total concentration change rate, turbulent contribution index and non-turbulent contribution index are obtained respectively, to quantitatively evaluate the relative contribution of turbulent flow and non-turbulent factor to pollutant concentration change, the method also calculates turbulent cumulative contribution index and non-turbulent cumulative contribution index, to eliminate the randomness of short-term meteorological disturbance, the method provides a kind of transparent, repeatable and physical meaning clear index, facilitate daily analysis pollution process turbulent diffusion capacity change to the influence of pollutant concentration, realize the quantitative analysis of urban atmospheric environmental pollution contribution.

Description

Technical Field

[0001] This invention relates to the field of environmental pollution technology, and in particular to a method for analyzing the contribution index of urban air pollution. Background Technology

[0002] With the acceleration of urbanization and rapid industrialization, urban air pollution has become an increasingly serious problem, posing a significant challenge to sustainable urban development and residents' health. Air pollutants, such as particulate matter (PM2.5, PM10), sulfur dioxide (SO2), nitrogen oxides (NOx), and ozone (O3), not only directly harm human health but also cause environmental problems like acid rain and photochemical smog, profoundly impacting ecosystems. Therefore, scientifically analyzing the sources and contributions of urban air pollution is crucial for developing effective pollution control strategies and improving air quality.

[0003] Traditional methods for apportioning air pollution sources primarily rely on emission inventories and atmospheric diffusion models. Emission inventories provide the foundational data for source apportionment by statistically analyzing the emissions from various pollution sources; while atmospheric diffusion models utilize meteorological data and emission inventories to simulate the transport and diffusion of pollutants in the atmosphere, assessing the contribution of different pollution sources to receiver sites (such as monitoring stations). However, these methods have several limitations in practical applications. Emission inventories often depend on a large amount of statistical data and assumptions, and their accuracy and timeliness are limited by the difficulty of data acquisition and the frequency of updates. Atmospheric diffusion models exhibit significant uncertainties when simulating pollutant transport under complex meteorological conditions and topographical features, making it difficult to accurately quantify the contributions of different pollution sources.

[0004] In recent years, with the continuous advancement of atmospheric observation technology and the innovation of data processing methods, pollution source apportionment methods based on observational data have gradually become a research hotspot. Among them, the method of using turbulence observation data to analyze the contribution of air pollution has attracted widespread attention due to its advantages such as clear physical meaning, high transparency, and strong repeatability. Turbulence is an important form of motion in the atmosphere, which affects the diffusion and dilution processes of pollutants through vertical mixing. By observing turbulence parameters (such as sensible heat flux and friction velocity) and pollutant concentration data, the vertical diffusion flux of turbulence can be calculated, thereby assessing the contribution of turbulence to changes in pollutant concentration. This method can not only reveal the impact of changes in turbulent diffusion capacity on changes in pollutant concentration, but also provide a new perspective and means for pollution source apportionment.

[0005] However, existing pollution source apportionment methods based on turbulence observations still have some shortcomings. They often only provide the contribution ratio of turbulent factors to pollutant concentration changes, but cannot comprehensively consider the influence of other non-turbulent factors (such as anthropogenic emissions, regional transport, chemical transformation, etc.). Existing methods, when apportioning pollution sources, typically only provide contribution information in the time dimension, and cannot determine the spatial location and vertical release height of the pollution source. To address these issues, this invention proposes a method for analyzing the contribution index of urban atmospheric environmental pollution. This method aims to quantitatively assess the magnitude and direction of the contribution of turbulent and non-turbulent factors to pollutant concentration changes by comprehensively utilizing turbulence observation data, pollutant concentration data, and boundary layer height data, achieving a comprehensive analysis and three-dimensional positioning of the contribution of urban atmospheric environmental pollution. This method not only contributes to a deeper understanding of the causes and mechanisms of urban air pollution, but also provides strong support for formulating scientific pollution control strategies. Summary of the Invention

[0006] This application provides a method for analyzing the contribution index of urban atmospheric environmental pollution, a method for calculating the contribution of turbulence from observational data, and an index that is transparent, repeatable, and has clear physical meaning. This index can be used for routine analysis of the impact of changes in turbulent diffusion capacity on the rise and fall of pollutant concentrations during pollution processes, thereby achieving quantitative analysis of the contribution of urban atmospheric environmental pollution.

[0007] This application provides a method for analyzing the contribution index of urban air pollution, including: S101, Collect a dataset and calculate the turbulent vertical diffusion flux based on the dataset; the dataset includes turbulent observation data, pollutant concentration data, and boundary layer height data; S102, calculate the turbulent concentration change rate based on the turbulent vertical diffusion flux and the boundary layer height, calculate the total concentration change rate by observing the hourly concentration changes, and obtain the non-turbulent change rate based on the total concentration change rate and the turbulent concentration change rate; S103, obtain the turbulent contribution index based on the turbulent concentration change rate and the total concentration change rate, and calculate the non-turbulent contribution index based on the non-turbulent change rate and the total concentration change rate; S104. Obtain the cumulative contribution index of turbulence based on the rate of change of turbulent concentration and the rate of change of total concentration; calculate the cumulative contribution index of non-turbulent concentration based on the rate of change of non-turbulent concentration and the rate of change of total concentration.

[0008] Preferably, the formula for calculating the vertical diffusion flux of turbulence is: ,in, For turbulent vertical diffusion flux, Let be the friction velocity at time t. It is a constant, dimensionless. For observation altitude, The aerodynamic roughness length, This is a correction function for thermal stability. Let Z be the pollutant concentration measured at height z at time t. Let be the reference concentration at time t.

[0009] Preferably, the formula for calculating the rate of change of turbulent concentration is: ,in, The turbulent concentration change rate, For turbulent vertical diffusion flux, Let be the height of the mixing layer at time t. Unit conversion factor.

[0010] Preferably, the formulas for calculating the turbulence contribution index and the non-turbulence contribution index are as follows: ,in, Contribution index to turbulence The turbulent concentration change rate, The total concentration change rate at time t; ,in, The non-turbulent contribution index is TCI(t) + NTCI(t) = 100%; The non-turbulent contribution rate of concentration at time t Let t be the total concentration change rate at time t, where t is the time index.

[0011] Preferably, the contribution index analysis method further includes: S201, divide the height interval, obtain the turbulent concentration change rate at different heights, construct a low-level turbulent concentration change rate sequence and a high-level turbulent concentration change rate sequence according to the timestamp based on the turbulent concentration change rate at different heights, calculate the vertical phase difference between the low-level turbulent concentration change rate sequence and the high-level turbulent concentration change rate sequence, and determine the height interval of the emission source based on the vertical phase difference using a judgment rule; the height interval includes the low-level height interval, the middle-level height interval, and the high-level height interval. S202, based on the division of height intervals, the flux footprint is obtained using a footprint model; the flux footprint is the distribution area of ​​the contribution weights of each spatial location in the horizontal direction of the ground to the turbulent flux at the observation point. S203, construct a three-dimensional joint probability field of emission sources based on the height range and horizontal footprint distribution area, and calculate the horizontal grid contribution index and vertical layer contribution index based on the three-dimensional joint probability field of emission sources.

[0012] Preferably, the vertical phase difference is obtained by cross-correlation analysis: taking the low-level turbulent concentration change rate sequence as a reference, the high-level turbulent concentration change rate sequence is shifted on the time axis, the Pearson correlation coefficient between the shifted high-level sequence and the low-level sequence is calculated, and the shift corresponding to the maximum correlation coefficient is taken as the vertical phase difference, where a shift greater than 0 indicates that the high-level sequence is lagging, and a shift less than 0 indicates that the high-level sequence is leading.

[0013] Preferably, the construction of the three-dimensional joint probability field of the emission sources includes: allocating vertical weight coefficients according to the emission source height interval determined by the vertical phase difference, calculating the horizontal grid comprehensive probability, and multiplying the horizontal grid comprehensive probability with the vertical distribution function to obtain the three-dimensional joint probability field of the emission sources.

[0014] Preferably, the contribution index analysis method further includes: S301, the offset state is identified based on the sign of the turbulent concentration change rate and the non-turbulent concentration change rate, and the range of values ​​for the turbulent contribution index and the non-turbulent contribution index is expanded; the offset state includes the dilution-dominated state, the emission reduction-dominated state, the pseudo-decline state, and the pseudo-increase state. S302 identifies over-dilution states based on turbulent concentration change rate, non-turbulent concentration change rate, and total concentration change rate, and draws an emission-purge offset map based on the offset state and the over-dilution state.

[0015] Preferably, when simultaneously satisfying <0 and | |>| When | is in an over-dilution state, it means that even without turbulent dilution, the actual emission is in the opposite direction to turbulent dilution, and its absolute value is greater than the absolute value of turbulent dilution.

[0016] Preferably, the method for drawing an emissions-cleanup offset map is as follows: using GIS technology to divide the urban area into grids, and marking the corresponding time period status in each grid based on the offset status and over-dilution status, thereby drawing an emissions-cleanup offset map.

[0017] One or more technical solutions provided in this application have at least the following technical effects or advantages: the method for calculating turbulence contribution from observation data provides a transparent, repeatable, and physically meaningful index for routine analysis of the impact of changes in turbulence diffusion capacity on pollutant concentrations during pollution processes, thereby achieving quantitative analysis of the contribution to urban atmospheric pollution. Through vertical phase difference analysis and footprint-constrained spatial inversion, the three-dimensional location of emission sources was achieved, extending the contribution index from the time dimension to the spatial dimension, thus realizing the three-dimensional location of emission sources and clarifying the horizontal position and vertical release height of pollution sources. Introducing the concept of negative contribution and redefining the contribution index range to -100% to +100% allows for a more comprehensive and accurate reflection of the impact of various factors on pollutant concentration changes. By analyzing the sign and magnitude relationship between turbulent and non-turbulent contributions, multiple "offset states" can be identified, enabling accurate differentiation between different situations. This allows for a more accurate analysis of the net contribution of urban air pollution, distinguishing the impact of different factors on pollutant concentration changes, and providing a more scientific basis for urban air pollution control, including the rational control of emissions and improvement of diffusion conditions. Attached Figure Description

[0018] Figure 1 This is a block diagram of a method for analyzing the contribution index of urban air pollution according to the present invention. Figure 2 A schematic diagram illustrating the process of constructing a three-dimensional joint probability field for this invention; Figure 3 A schematic diagram illustrating the process of drawing an emission-removal offset map for this invention. Detailed Implementation

[0019] To facilitate understanding of the present invention, a more complete description of this application will be given below with reference to the accompanying drawings, which illustrate preferred embodiments of the invention. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to enable a more thorough and complete understanding of the disclosure of the present invention.

[0020] It should be noted that the terms "vertical," "horizontal," "up," "down," "left," "right," and similar expressions used in this article are for illustrative purposes only and do not represent the only possible implementation.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention; the term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0022] Example 1: Figure 1 This is a flowchart illustrating a method for analyzing the contribution index of urban air pollution according to an embodiment of the present invention, including: S101, Collect a dataset and calculate the turbulent vertical diffusion flux based on the dataset; the dataset includes turbulent observation data, pollutant concentration data, and boundary layer height data; Specifically, the dataset includes turbulence observation data, pollutant concentration data, and boundary layer height data. Sensible heat flux and friction velocity are acquired using an ultrasonic anemometer. Sensible heat flux reflects the heat exchange between the atmosphere and the underlying surface, while friction velocity is closely related to the intensity of near-surface turbulence. These two data constitute the observation data, recorded hourly. Continuous data recording captures the changing characteristics of turbulence at different time scales. Pollutant concentration monitoring equipment is used to acquire pollutant concentration information at different heights. Differences in pollutant concentration at different heights reflect the vertical diffusion of pollutants. The mixing layer height is obtained using weather radar, which detects the boundary layer structure in the atmosphere by emitting electromagnetic waves and receiving echoes. The collected turbulence observation data, pollutant concentration data, and boundary layer height data are preprocessed, including data cleaning and removal of outliers and missing values. The turbulent vertical diffusion flux is calculated based on the collected dataset using the following formula: ,in, This represents the vertical diffusion flux during turbulence; negative values ​​indicate the flux direction is downwards, and positive values ​​indicate upwards. The friction velocity at time t, obtained from turbulence observation data, reflects the intensity of near-surface turbulence. It is a constant, dimensionless. The observation height refers to the installation height of the pollutant concentration monitoring equipment. The aerodynamic roughness length characterizes the degree of resistance of the ground surface to airflow, and depends on the type of underlying surface (0.5~2 m for urban areas, and about 0.01~0.05 m for grasslands). This is a dimensionless thermal stability correction function used to correct for the effects of non-neutral atmospheric stratification on turbulent exchange. =0 (neutral condition) =0; <0 (unstable); >0 (stable) =5 , Let Z be the pollutant concentration measured at height z at time t. The reference concentration at time t is the background concentration (the concentration at the upwind station of the city). and The difference in concentration forms a vertical concentration gradient, which drives turbulent diffusion.

[0023] S102, calculate the turbulent concentration change rate based on the turbulent vertical diffusion flux and the boundary layer height, calculate the total concentration change rate by observing the hourly concentration changes, and obtain the non-turbulent change rate based on the total concentration change rate and the turbulent concentration change rate; Furthermore, according to the law of conservation of mass, the vertical divergence of the turbulent vertical flux leads to local concentration changes. The formula for calculating the rate of change of turbulent concentration based on the turbulent vertical diffusion flux and the boundary layer height is as follows: ,in, This represents the rate of change of turbulent concentration. A negative value indicates that turbulence reduces the concentration (diffusion and dilution), while a positive value indicates that turbulence increases the concentration (nighttime inversion layer collapse leads to the washing of pollutants from upper levels). For turbulent vertical diffusion flux, Let be the mixing layer height at time t, i.e., the boundary layer height, representing the effective thickness of vertical atmospheric mixing. For unit conversion factors, since The unit includes seconds, while The unit includes hours, so multiplying by 3600 converts seconds to hours. When >0 (upward conveying), A negative value indicates that turbulence reduces the concentration; when When <0 (downward conveying), A positive value indicates that turbulence increases the concentration (typically occurring during nighttime collapse of the residual layer); the total concentration change rate is calculated based on the actual observed hourly pollutant concentration change rate, using the following formula: ,in, The total concentration change rate at time t, in μg·m³. -3 ·h -1 This represents the net change in pollutant concentration per hour; a positive value indicates an increase in concentration, and a negative value indicates a decrease in concentration. The pollutant concentration at time t, in μg / m³ 3 , which is the hourly average. This represents the pollutant concentration at the previous time point (t-1 hour). The time interval represents the time difference corresponding to the concentration change. The total concentration change rate consists of four parts: turbulent concentration change rate, horizontal advection change rate, local emission change rate, and other physicochemical processes. The horizontal advection change rate, local emission change rate, and other physicochemical processes are combined into a non-turbulent change rate. The non-turbulent change rate is obtained based on the difference between the calculated total concentration change rate and the turbulent concentration change rate. The non-turbulent change rate represents the net contribution of all processes (including anthropogenic emission changes, regional transport, horizontal wind convergence and divergence, chemical transformation, deposition, etc.) to the concentration change, except for local turbulent vertical mixing.

[0024] S103, obtain the turbulent contribution index based on the turbulent concentration change rate and the total concentration change rate, and calculate the non-turbulent contribution index based on the non-turbulent change rate and the total concentration change rate; Specifically, to quantitatively assess the relative contributions and directions of turbulent and non-turbulent factors to the change in total pollutant concentration, we calculated the turbulence contribution index and the non-turbulence contribution index separately. The turbulence contribution index was obtained based on the rate of change of turbulent concentration and the rate of change of total concentration, as shown in the formula: ,in, The turbulence contribution index represents the relative contribution and direction of turbulent factors to the change in total concentration. This index is undefined when the rate of change in total concentration is zero, because there is no net change to decompose at this point. The turbulent concentration change rate, Let be the rate of change of total concentration at time t. A value >0 indicates that turbulence factors have led to a net increase in concentration. When <0, it indicates that turbulent factors cause a net decrease in concentration; the non-turbulent contribution index is calculated based on the non-turbulent rate of change and the total concentration rate of change, using the following formula: ,in, The non-turbulent contribution index represents the relative contribution and direction of non-turbulent factors to the total concentration change. A positive value indicates that non-turbulent factors such as anthropogenic sources and transport generally increase the concentration; a negative value indicates that these factors decrease the concentration, and TCI(t) + NTCI(t) = 100%. The non-turbulent contribution rate of concentration at time t The total concentration change rate is denoted by t. When the total change rate is zero, the exponent is invalid. t is the time index, usually representing the t-th hour. All variables are the values ​​corresponding to that hour.

[0025] S104. Obtain the cumulative contribution index of turbulence based on the rate of change of turbulent concentration and the rate of change of total concentration; calculate the cumulative contribution index of non-turbulent concentration based on the rate of change of non-turbulent concentration and the rate of change of total concentration.

[0026] Furthermore, the cumulative contribution index of turbulence is obtained based on the rate of change of turbulent concentration and the rate of change of total concentration, using the following formula: ,in, The cumulative contribution index to turbulence, The turbulent concentration change rate, Let be the total concentration change rate at time t, and T be the total time length (in hours), representing the time window for cumulative calculation. The formula for calculating the non-turbulent cumulative contribution index based on the non-turbulent change rate and the total concentration change rate is: ,in, The cumulative contribution index for non-turbulent flow.

[0027] The cumulative contribution index of turbulence and the cumulative contribution index of non-turbulence can eliminate the randomness of short-term meteorological disturbances. If the cumulative contribution index of non-turbulence continues to decline, it indicates that the emission control policy has achieved substantial results.

[0028] The technical solutions described in the above embodiments of this application have at least the following technical effects or advantages: the method of calculating turbulence contribution using observation data provides a transparent, repeatable, and physically meaningful index for daily analysis of the impact of changes in turbulence diffusion capacity on pollutant concentrations, thereby achieving quantitative analysis of the contribution to urban atmospheric pollution.

[0029] Example 2: The pollutant contribution index analysis method in Example 1 can only provide the contribution ratio of different factors (turbulence, anthropogenic sources, regional transport) to concentration changes, but cannot explain where the pollution comes from or from which it is released. This example determines the effective release height range of the emission source by calculating the lag correlation between the time series of turbulence contribution indices at different heights; then, combined with the horizontal source region probability distribution given by the flux footprint model, a three-dimensional joint probability field is constructed to output the location of the emission source, such as... Figure 2 As shown.

[0030] S201, divide the height interval, obtain the turbulent concentration change rate at different heights, construct a low-level turbulent concentration change rate sequence and a high-level turbulent concentration change rate sequence according to the timestamp based on the turbulent concentration change rate at different heights, calculate the vertical phase difference between the low-level turbulent concentration change rate sequence and the high-level turbulent concentration change rate sequence, and determine the height interval of the emission source based on the vertical phase difference using a judgment rule; the height interval includes the low-level height interval, the middle-level height interval, and the high-level height interval. Furthermore, based on the actual conditions of the urban observation tower and the characteristics of the surrounding underlying surface, the vertical observation space is divided into three height intervals, each corresponding to the influence range of different emission sources: the three height intervals include a low-level height interval, a mid-level height interval, and a high-level height interval. The low-level height interval is selected at a height near the urban street valley layer, ranging from 0m to 30m. This low-level height interval is close to the ground emission sources (vehicle exhaust, dust, low chimneys), enabling it to respond most quickly to changes in near-ground emissions and having minimal impact from vertical mixing delays. The urban street valley layer refers to the air layer between the ground and the average height of buildings (i.e., roof height). The mid-level height interval is selected at an intermediate height between the top of the urban rough sub-layer and the bottom of the mixing layer, ranging from 30m. The upper-level height range, up to 80m, is used to distinguish between mid-level sources (industrial chimneys) and pure near-surface or elevated sources. The urban rough sub-pause refers to a transition layer located above the street valley layer and near the bottom of the atmospheric boundary layer. The mixing layer (also known as the convective boundary layer) refers to the air layer in the atmospheric boundary layer from the ground upwards to the bottom of the inversion layer, where vertical turbulence is intensely mixed and the properties of potential temperature and water vapor are basically uniform. The upper-level height range is selected at the lower part of the mixing layer, ranging from 80m to 120m. This upper-level height range represents a relatively uniform concentration area after vertical mixing and is more sensitive to high-altitude emission sources (elevated chimneys) or long-distance transported air masses, while showing a significant lag in response to near-surface sources. Based on the above-defined height range, the turbulent concentration change rate at different heights is calculated using the formula in step S102, i.e.: Based on the above formulas, the turbulent concentration change rate in the lower and upper altitude ranges is calculated respectively. For both the lower and upper altitude ranges, the turbulent contribution concentration change rate of the continuous time series is extracted. and Where t=1,2,...; t is the hourly indexed timestamp to ensure time alignment of the two sequences. The vertical phase difference is calculated using cross-correlation analysis, specifically: taking the turbulent contribution concentration change rate sequence in the lower altitude range as a reference, the turbulent contribution concentration change rate sequence in the upper altitude range is shifted on the time axis. The shift range is set to -3 hours to +3 hours, with a step size of 1 hour. The Pearson correlation coefficient between the shifted upper altitude range turbulent contribution concentration change rate sequence and the lower altitude range turbulent contribution concentration change rate sequence is calculated. The shift amount that maximizes the Pearson correlation coefficient is then determined. For each shift amount... When the translation amount When the value is greater than zero, it indicates that the turbulent contribution concentration change rate sequence in the upper altitude range has shifted to the right. After hours, the Pearson correlation coefficient with the turbulent contribution concentration change rate sequence in the lower altitude range is the largest, indicating that the turbulent contribution concentration change rate sequence in the upper altitude range lags behind the turbulent contribution concentration change rate sequence in the lower altitude range. At this time, the sign of the vertical phase difference is positive; when the translation amount When the value is less than zero, it indicates that the turbulent contribution concentration change rate sequence in the upper altitude range has shifted to the left. After hours, the Pearson correlation coefficient with the turbulent contribution concentration change rate sequence in the lower altitude range is the largest, indicating that the turbulent contribution concentration change rate sequence in the upper altitude range leads the turbulent contribution concentration change rate sequence in the lower altitude range. At this time, the sign of the vertical phase difference is negative. When the value is zero, it means that the maximum correlation can be achieved without translation, indicating that the high and low layer responses are synchronized. This translation amount is the vertical phase difference. If the maximum Pearson correlation coefficient is less than the preset coefficient threshold, it means that the vertical phase difference is not significant.

[0031] The height range of the emission source is determined based on the magnitude and direction of the vertical phase difference using a judgment rule. Specifically, the judgment rule involves setting a vertical phase difference threshold range, which includes a maximum phase difference threshold and a minimum phase difference threshold. If the vertical phase difference... The maximum phase difference threshold, and the peak value of the Pearson correlation coefficient being greater than the coefficient threshold, indicates that the lower-level sequence responds before the upper-level sequence (upper-level lag). Pollutants are first detected in the lower levels and then transported upwards to the upper levels via turbulent mixing. This typically corresponds to emission sources located in the near-surface layer (higher than the observation height in the lower levels, i.e., 0–30 m). In other words, near-surface sources (0–30 m) correspond to the lower-level height range, such as vehicle road emissions, construction dust, and low-lying industrial chimneys. At the minimum phase difference threshold, i.e., the upper-level response and the lower-level response are almost simultaneous, it indicates that the pollutants are already uniformly distributed in the vertical direction. This suggests that the emission source may be an elevated source (release height higher than the lower-level observation height, but lower than or close to the upper-level observation height, i.e., above 80m), or it may be an air mass transported from a long distance into the mixing layer. In this case, the emission source height range is defined as an elevated source / long-distance transport source (above 80m), corresponding to the upper-level height range; if The negative number of the maximum phase difference threshold, that is, the response of the upper level before the lower level, and the peak value of the correlation coefficient is greater than the coefficient threshold, usually occurs when the residual layer collapses at night or when the systematic downdraft brings the upper-level pollutants to the ground. This is a relatively special meteorological process. At this time, the emission source height range is determined to be an upper-level source (above 150m).

[0032] S202, based on the division of height intervals, the flux footprint is obtained using a footprint model; the flux footprint is the distribution area of ​​the contribution weights of each spatial location in the horizontal direction of the ground to the turbulent flux at the observation point. Furthermore, the flux footprint refers to the weighted distribution area of ​​the contribution of various spatial locations on the ground in the horizontal direction to the turbulent flux at the observation point under given observation height and atmospheric turbulence conditions. It is used to establish the correlation between turbulence observation and horizontal spatial location. Based on the height layers within the divided height range, the rate of change of turbulent contribution concentration was calculated independently at the lower and upper levels, and the corresponding turbulent flux source areas are also different. Therefore, it is necessary to calculate the flux footprint corresponding to each height layer to obtain the spatial source area information of that layer. Based on the observation height, friction velocity, boundary layer height, and wind direction obtained in step S101, the Kljun model is used as the footprint model. The observation height, friction velocity, boundary layer height, and wind direction are input into the Kljun model. The Kljun model uses its internal functions to perform simulation calculations and output a weighting function in a two-dimensional horizontal coordinate system. A horizontal two-dimensional grid coordinate system is established with the tower base center as the origin: the X-axis is eastward, the Y-axis is northward, and the range covers -3 km to +3 km. For each horizontal grid cell, the footprint value is calculated using the following formula: ,in, The horizontal footprint value represents the contribution weight of this grid to the current height turbulence observation. The larger the weight, the greater the influence of surface emissions in this grid area on the measured turbulence signal. The weight function output by the footprint model is used to calculate the horizontal footprints at the low and high levels, respectively. For the low-level horizontal footprint, the observation height of the low level is set as z=H. low (20-30 m), friction velocity, boundary layer height, and wind direction are input into the footprint model. The output horizontal source region is small, concentrated within 200-500 m around the tower base, and has a long and narrow shape, which is the distribution area of ​​the horizontal footprint. For high-level horizontal footprints, the observation height z=H of the high-level area is used. high (80-120 m), friction velocity, boundary layer height and wind direction are input into the footprint model. The output horizontal source area is larger, reaching 1-2 km, and has a wider shape. The low-level horizontal footprint and high-level horizontal footprint calculated above are purely horizontally distributed. Each weight only represents the contribution ratio of a certain grid on the ground to the observed flux and does not contain any vertical height information.

[0033] S203, construct a three-dimensional joint probability field of emission sources based on the height range and horizontal footprint distribution area, and calculate the horizontal grid contribution index and vertical layer contribution index based on the three-dimensional joint probability field of emission sources.

[0034] Specifically, based on the height range division, the distribution of low-level and high-level footprints is integrated through weight allocation. When it is a near-ground source (low-level height range), the weight of the low-level source is set to 0.7, and the weight of the high-level source is set to 0.3. When it is an elevated source (level range), the weight of the high-level source (vertical weight coefficient) is set to 0.7, and the weight of the low-level source is set to 0.3. For high-altitude sources, since the height is much higher than the top of the observation tower, the horizontal footprint cannot be constrained and does not participate in the horizontal grid positioning. Based on the above footprint distribution, the comprehensive probability of the horizontal grid is calculated using the following formula: ,in, The overall probability (horizontal probability field) of the horizontal grid represents the relative probability that an emission source is located within that horizontal grid, given the vertical phase difference (source height range) and footprint information of two height layers. This is the vertical weighting coefficient. The low-level footprint value represents the proportion of the grid's contribution to the low-level turbulent flux observations. For the high-level horizontal footprint value, the horizontal probability field is multiplied by the vertical height interval to construct a three-dimensional joint probability field for the emission source. For the vertical height interval, the maximum and minimum values ​​of the height interval divided in step S201 are used. The calculation formula for the three-dimensional joint probability field is as follows: ,in, This represents the relative probability density (three-dimensional joint probability field) of the emission source located at spatial coordinates (x, y, z), integrating horizontal footprint information and vertical height interval information. It indicates the likelihood of the pollution source appearing at that location under given observation conditions. The overall probability of the horizontal grid. The vertical distribution function is determined based on the maximum and minimum values ​​of the height intervals divided in step S201.

[0035] The horizontal grid contribution index is calculated based on the three-dimensional joint probability field of the emission sources, using the following formula: ,in, The contribution index of a horizontal grid represents the proportion of the total pollution emissions contributed by that grid area. A higher value indicates that the grid is more likely to be a major emission source location. Given a three-dimensional joint probability field, the formula for calculating the vertical layer contribution index based on the three-dimensional joint probability field is as follows: ,in, The vertical layer contribution index represents the proportion of total pollution emissions contributed by a height range. For the k-th vertical height interval, It is a three-dimensional joint probability field.

[0036] The technical solutions described in the above embodiments of this application have at least the following technical effects or advantages: by using vertical phase difference analysis and footprint constraint spatial inversion, the three-dimensional positioning of the emission source is realized, the contribution index is extended from the time dimension to the spatial dimension, the three-dimensional positioning of the emission source is realized, and the horizontal position and vertical release height of the pollution source are clearly defined.

[0037] Example 3: Examples 1 and 2 above can only express positive contributions, losing the physical information of the dilution effect. This example introduces the concept of negative contribution. By analyzing the sign and magnitude relationship between turbulent and non-turbulent contributions, three states are identified: net contribution (dominated by a single factor), offsetting state (both are opposite), and dilution-dominated state, such as... Figure 3 As shown.

[0038] S301, the offset state is identified based on the sign of the turbulent concentration change rate and the non-turbulent concentration change rate, and the range of values ​​for the turbulent contribution index and the non-turbulent contribution index is expanded. The offset state includes the dilution-dominated state, the emission reduction-dominated state, the pseudo-decline state, and the pseudo-increase state. Specifically, a negative contribution is introduced, expanding the range of the turbulence contribution index and the non-turbulence contribution index to -100% to +100%. Specifically: the original numerator (turbulence contribution rate or non-turbulence contribution rate, which can be positive or negative) is retained, while the denominator is uniformly taken as the absolute value of the total concentration change rate. Thus, when a factor causes an increase in concentration, its index is positive; when it causes a decrease in concentration, the index is negative. The sum of the two signed indices must equal ±100%, with the sign consistent with the actual direction of concentration change. For example, if the turbulence contribution index is -40% and the non-turbulence contribution index is +140%, then the total change is +100%, indicating an increase in concentration, where turbulence partially offsets the increase. The expression for the turbulence contribution index (with a sign) is: The expression for the non-turbulent contribution index (with coincidence) is: ,and = (when ).

[0039] The cancellation state is identified based on the signs of the turbulent and non-turbulent concentration change rates. When the signs of the turbulent and non-turbulent concentration change rates are opposite, they are in a state of mutual cancellation, which is further divided into dilution-dominated state, emission reduction-dominated state, pseudo-decline state, and pseudo-increase state. <0, >0, and | |>| When | is in a dilution-dominated state, emissions increase (positive contribution), but turbulent dilution is stronger (negative contribution in absolute value is larger), ultimately leading to a decrease in concentration. This is defined as true dilution. True dilution refers to an actual increase in emissions, but due to favorable diffusion conditions, the concentration decreases. The output is labeled as true dilution, where the increase in emissions is masked by turbulence. >0, <0, and | |>| When | is the dominant state for emission reduction, emission reduction (negative contribution) dominates. Although turbulence is unfavorable to diffusion (positive contribution), the concentration still decreases, defined as a true improvement. The output shows a true improvement, and the emission reduction effect outweighs the diffusion-unfavorable effect. When <0, <0, both are negative, indicating a false decrease. Emission reduction and turbulent dilution both contribute to the concentration decrease, but their individual contributions cannot be distinguished. If | |>| |, primarily emission reduction, marked as emission-driven decline, if | |>| |, dilution is dominant, marked as dilution-driven decline; if >0, >0, both are positive, indicating that both increased emissions and unfavorable turbulence lead to an increase in concentration, but it is impossible to distinguish their respective contributions.

[0040] S302 identifies over-dilution states based on turbulent concentration change rate, non-turbulent concentration change rate, and total concentration change rate, and draws an emission-purge offset map based on the offset state and the over-dilution state.

[0041] Furthermore, when simultaneously satisfying <0 and | |>| When | is in a state of excessive dilution, it means that even without turbulent dilution, the rate of concentration change will be | Including the masked portion, the net contribution of actual emissions or transmission ( The concentration rises faster (or falls slower) in the opposite direction to turbulent dilution, and its absolute value is greater than that of turbulent dilution. Emissions or transport would normally cause the concentration to rise faster (or fall slower), but strong turbulence completely masks this trend.

[0042] Using GIS (Geographic Information System) technology, the urban area is divided into appropriately sized grids. The grid size should comprehensively consider factors such as the city's geographical characteristics, the distribution density of monitoring stations, and the research objectives. Based on the dilution-dominant state, emission reduction-dominant state, pseudo-decline state, pseudo-increase state, and excessive dilution state, the state of each period is marked within each grid. Different colors, symbols, or patterns can be used to distinguish different states, forming an emission-removal offset map. The emission-removal offset map is used to identify the areas most sensitive to changes in meteorological conditions, protected areas, and investigation areas. The emission-removal offset map identifies areas whose states change frequently or easily transition from one state to another under various meteorological conditions, which are the areas most sensitive to changes in meteorological conditions. Protected areas are ventilation corridor protection areas. In the emission-removal offset map, areas that can form good air circulation channels under natural conditions (such as without strong anthropogenic emission interference) are identified as protected areas. For investigation areas, areas in emission-increase-related states (such as hidden emission increases in excessive dilution states) or areas that frequently show abnormal pollution changes should be the focus of law enforcement investigations.

[0043] The technical solutions described in the embodiments of this application have at least the following technical effects or advantages: the introduction of the concept of negative contribution and the redefinition of the contribution index range from -100% to +100% can more comprehensively and accurately reflect the influence of various factors on the change of pollutant concentration. By analyzing the sign and magnitude relationship between turbulent and non-turbulent contributions, multiple "offset states" can be identified, which can accurately distinguish different situations and more accurately analyze the net contribution of urban air pollution. The influence of different factors on the change of pollutant concentration can be distinguished, providing a more scientific basis for the governance of urban air pollution, including reasonable emission control and improvement of diffusion conditions.

[0044] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for analyzing the contribution index of urban air pollution, characterized in that, include: S101: Collect datasets and calculate turbulent vertical diffusion flux based on the datasets; The dataset includes turbulence observation data, pollutant concentration data, and boundary layer height data; S102, calculate the turbulent concentration change rate based on the turbulent vertical diffusion flux and the boundary layer height, calculate the total concentration change rate by observing the hourly concentration changes, and obtain the non-turbulent change rate based on the total concentration change rate and the turbulent concentration change rate; S103, obtain the turbulent contribution index based on the turbulent concentration change rate and the total concentration change rate, and calculate the non-turbulent contribution index based on the non-turbulent change rate and the total concentration change rate; The formulas for calculating the turbulence contribution index and the non-turbulence contribution index are as follows: ,in, Contribution index to turbulence The turbulent concentration change rate, The total concentration change rate at time t; ,in, The non-turbulent contribution index is TCI(t) + NTCI(t) = 100%; The non-turbulent contribution rate of concentration at time t The total concentration change rate at time t is the time index; S104, obtain the cumulative contribution index of turbulence based on the rate of change of turbulent concentration and the rate of change of total concentration; calculate the cumulative contribution index of non-turbulent concentration based on the rate of change of non-turbulent concentration and the rate of change of total concentration; Contribution index analysis methods also include: S201, divide the height interval, obtain the turbulent concentration change rate at different heights, construct a low-level turbulent concentration change rate sequence and a high-level turbulent concentration change rate sequence according to the timestamp based on the turbulent concentration change rate at different heights, calculate the vertical phase difference between the low-level turbulent concentration change rate sequence and the high-level turbulent concentration change rate sequence, and determine the height interval of the emission source based on the vertical phase difference using a judgment rule; the height interval includes the low-level height interval, the middle-level height interval, and the high-level height interval. S202, based on the division of height intervals, the flux footprint is obtained using a footprint model; the flux footprint is the distribution area of ​​the contribution weights of each spatial location in the horizontal direction of the ground to the turbulent flux at the observation point. S203, construct a three-dimensional joint probability field of emission sources based on the height range and horizontal footprint distribution area, and calculate the horizontal grid contribution index and vertical layer contribution index based on the three-dimensional joint probability field of emission sources; The vertical phase difference is obtained by cross-correlation analysis: taking the low-level turbulent concentration change rate sequence as a reference, the high-level turbulent concentration change rate sequence is shifted on the time axis, the Pearson correlation coefficient between the shifted high-level sequence and the low-level sequence is calculated, and the shift corresponding to the maximum correlation coefficient is taken as the vertical phase difference. A shift greater than 0 indicates that the high-level sequence is lagging, and a shift less than 0 indicates that the high-level sequence is leading.

2. The method for analyzing the contribution index of urban air pollution according to claim 1, characterized in that, The formula for calculating the vertical diffusion flux of turbulence is: ,in, For turbulent vertical diffusion flux, Let be the friction velocity at time t. It is a constant, dimensionless. For observation altitude, The aerodynamic roughness length, This is a correction function for thermal stability. Let Z be the pollutant concentration measured at height z at time t. Let be the reference concentration at time t.

3. The method for analyzing the contribution index of urban air pollution according to claim 2, characterized in that, The formula for calculating the rate of change of turbulent concentration is: ,in, The turbulent concentration change rate, For turbulent vertical diffusion flux, Let be the height of the mixing layer at time t. Unit conversion factor.

4. The method for analyzing the contribution index of urban air pollution according to claim 1, characterized in that, The construction of the three-dimensional joint probability field of the emission sources includes: allocating vertical weight coefficients according to the emission source height interval determined by the vertical phase difference, calculating the horizontal grid comprehensive probability, and multiplying the horizontal grid comprehensive probability with the vertical distribution function to obtain the three-dimensional joint probability field of the emission sources.

5. The method for analyzing the contribution index of urban air pollution according to claim 1, characterized in that, The contribution index analysis method also includes: S301, the offset state is identified based on the sign of the turbulent concentration change rate and the non-turbulent concentration change rate, and the range of values ​​for the turbulent contribution index and the non-turbulent contribution index is expanded. The offset state includes the dilution-dominated state, the emission reduction-dominated state, the pseudo-decline state, and the pseudo-increase state. S302 identifies over-dilution states based on turbulent concentration change rate, non-turbulent concentration change rate, and total concentration change rate, and draws an emission-purge offset map based on the offset state and the over-dilution state.

6. The method for analyzing the contribution index of urban air pollution according to claim 5, characterized in that, When both conditions are met <0 and | |>| When | is in an over-dilution state, it means that even without turbulent dilution, the actual emission is in the opposite direction to turbulent dilution, and its absolute value is greater than the absolute value of turbulent dilution.

7. The method for analyzing the contribution index of urban air pollution according to claim 6, characterized in that, The method for drawing an emissions-cleanup offset map is as follows: using GIS technology, the urban area is divided into grids, and based on the offset state and the over-dilution state, the state of the corresponding time period is marked in each grid to draw an emissions-cleanup offset map.

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