Air pollution tracing method and system based on air quality model

Abstract

The invention provides an air pollution tracing method and system based on an air quality model, and relates to the technical field of air pollution tracing, the air pollution tracing method based on the air quality model adopts CMAQ as the air quality model, and the model is preset based on the obtained data; the presetting comprises grid setting, simulation time range and simulation area range, running a CMAQ model based on preset input data, carrying out transmission and conversion simulation of atmospheric pollutants, simulating the transmission process of the pollutants in the atmosphere, generating a simulation result file, and carrying out analysis based on the simulation result file. The concentration distribution, the spatial distribution and the time change trend of the pollutants are specifically analyzed, so that the technical problems that a conventional method is difficult to comprehensively reflect the spatial distribution and the transmission path of the pollutants and the limitation is relatively large are solved, and the subsequent problem that the transmission and conversion processes of the pollutants are difficult to comprehensively analyze is avoided; and the use problem that the occurrence and propagation process of pollution events are difficult to reflect in time is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of atmospheric pollution source tracing, and more particularly to an atmospheric pollution source tracing method and system based on an air quality model. Background Art

[0002] Air pollution has serious impacts on human health and the environment. Therefore, tracing the sources of air pollutants is crucial for taking effective pollution control measures. However, conventional methods usually rely on field sampling, monitoring and statistical analysis. Due to the number and distribution of monitoring points, it is difficult to fully reflect the spatial distribution and transmission paths of pollutants. They have great limitations. Conventional methods also require a lot of time and human resources for field sampling and monitoring. The data acquisition and analysis cycle is long, making it difficult to timely reflect the occurrence and propagation process of pollution events. At the same time, conventional methods mainly rely on measured data for analysis, lacking comprehensive consideration of multiple factors such as atmospheric movement and chemical reactions, making it difficult to fully analyze the transmission and transformation process of pollutants.

[0003] Therefore, it is urgent to propose an atmospheric pollution source tracing method and system based on air quality models. Summary of the Invention

[0004] The purpose of the present invention is to address the shortcomings of the existing technology and provide an atmospheric pollution tracing method and system based on an air quality model. The method aims to run the CMAQ model through input data to simulate the transmission and transformation of atmospheric pollutants, simulate the transmission process of pollutants in the atmosphere, and generate a simulation result file. The simulation result file is analyzed based on the simulation result file to analyze the concentration distribution, spatial distribution and time variation trend of pollutants, understand the transmission path and diffusion range of pollutants in the atmosphere, solve the technical problems that conventional methods are difficult to fully reflect the spatial distribution and transmission path of pollutants and have large limitations, avoid the subsequent problems of difficulty in comprehensively analyzing the transmission and transformation process of pollutants, and avoid the use problem of difficulty in timely reflecting the occurrence and propagation process of pollution events.

[0005] To this end, this application provides an atmospheric pollution source tracing method based on an air quality model, comprising the following steps:

[0006] Step 100: Establish pollutant targets and scopes, confirm the scope and time scale of the study area based on the confirmed pollutant targets, collect corresponding data based on the confirmed pollutant targets, and pre-process the collected data;

[0007] Step 200: Use CMAQ as the air quality model and preset the model based on the obtained data, including grid settings, simulation time range, and simulation area range;

[0008] Step 300: Run the CMAQ model based on preset input data to simulate the transmission and transformation of atmospheric pollutants, simulate the transmission process of pollutants in the atmosphere, and generate a simulation result file;

[0009] Step 400: Analyze the simulation result file to specifically analyze the concentration distribution, spatial distribution, and temporal variation trend of the pollutants to understand the transmission path and diffusion range of the pollutants in the atmosphere;

[0010] Step 500: Based on the simulation results, the pollution source is tracked and identified. By analyzing the concentration distribution and transmission path of the pollutants in the simulation results, the location and impact range of the pollution source are determined;

[0011] Step 600: Verify the simulation results, compare them with the measured data, evaluate the accuracy and credibility of the simulation results, and modify and optimize the model based on the verification results.

[0012] Step 700: Based on the optimized model, the pollution source tracing results are analyzed and interpreted, and based on the analysis results, a pollution source tracing summary report is output.

[0013] In some specific embodiments, pollutant targets and scopes are established, the scope and time scale of the study area are confirmed based on the confirmed pollutant targets, corresponding data are collected based on the confirmed pollutant targets, and preprocessing is performed based on the collected data, specifically including:

[0014] Step 100.1. Establish pollutant targets and scopes. For the purpose of tracing the source of air pollution, identify the main pollutants, including PM2.5, PM10, O3, SO2, and NO2. Set PM2.5 as the pollutant target.

[0015] Step 100.2: Confirm the scope and time scale of the study area based on the confirmed pollutant targets;

[0016] Determine the scope of the study area and confirm the impact range for PM2.5. First, determine the spatial scope of the study area. The spatial scope is the area where PM2.5 concentration has a significant impact;

[0017] Confirm the time scale, based on the seasonal changes, diurnal changes and the impact of specific events on PM2.5 concentrations, and conduct research using seasonal changes, diurnal changes and specific events simultaneously to confirm the spatiotemporal distribution characteristics of PM2.5;

[0018] Step 100.3: Collect corresponding data based on the confirmed pollutant targets;

[0019] When collecting meteorological data, pay special attention to meteorological parameters related to PM2.5 concentrations, including wind speed, wind direction, and humidity;

[0020] Collect emission source data. In addition to general emission source data, special attention should be paid to PM2.5 emission source data, including industrial emissions, transportation emissions, and biomass combustion emissions;

[0021] Acquire topographic and geomorphological data, and based on the acquired topographic and geomorphological data, simulate the impact of the physical processes of the atmospheric boundary layer on PM2.5 concentration;

[0022] Step 100.4: Preprocess the collected data. The preprocessing includes data cleaning and processing, data interpolation and spatial matching, and data synchronization and format conversion.

[0023] In some specific embodiments, preprocessing is performed based on the collected data. The preprocessing includes data cleaning and processing, data interpolation and spatial matching, and data synchronization and format conversion, specifically including:

[0024] Step 100.4.1, data cleaning and processing;

[0025] Data quality check: Check the completeness and accuracy of the collected data. Use standard deviation to check data quality. Standard deviation is a measure used to describe the degree of dispersion of a data set. Specifically:

[0026]

[0027] In the formula, σ represents the standard deviation, x i represents the i-th observation in the data set, μ represents the mean of the data set, and n represents the number of observations in the data set. Missing values ​​and outliers are identified and processed based on the standard deviation calculation results.

[0028] Data screening: based on research objectives, screen data related to PM2.5;

[0029] Data normalization, converting data into uniform units and formats;

[0030] Data smoothing is performed using the moving average smoothing method, specifically:

[0031]

[0032] Where, MA t represents the moving average at time t, Y t-i+1 represents k consecutive observations starting from time t-i+1, where k represents the size of the moving average window, which is based on smoothing to reduce noise and fluctuations in the data;

[0033] Step 100.4.2, data interpolation and spatial matching;

[0034] The Kriging interpolation method is used to determine the weights of the interpolation points through the semivariogram function, specifically:

[0035] γ(h)=c0+c1(1-e -h / l )

[0036] Where γ(h) represents the semivariogram function, c0 and c1 are the parameters of the semivariogram function, l is the spatial correlation length, and h is the distance between interpolation points. Spatial interpolation is performed on meteorological data and emission source data to fill in data gaps.

[0037] Grid matching: Match the interpolated data to the CMAQ model grid to ensure that the spatial resolution of the data and the model grid are consistent;

[0038] Spatial smoothing processing is performed on the interpolated data to ensure that the data verifies the actual situation;

[0039] Step 100.4.3, data synchronization and format conversion;

[0040] Time synchronization: synchronize the collected data using the time difference analysis method, specifically:

[0041] Time difference analysis is used to analyze the characteristics of time series data. Time difference analysis includes first-order difference and second-order difference. The first-order difference represents the difference between the observation value at the current moment and the observation value at the previous moment, and the second-order difference represents the difference between the first-order difference sequence:

[0042] ΔY t =Y t -Y t-1

[0043] Δ 2 Y t =ΔY t -ΔY t-1

[0044] Where ΔY t represents the first-order difference sequence, Δ 2 Y t represents the second-order difference sequence, Y t represents the observation value at time t, and the time difference analysis method is used to ensure that the time resolution of the data is consistent and matches the simulation time range of the model;

[0045] Format conversion: converting data into the input format required by the CMAQ model, including meteorological data and emission source data, to ensure that the data can be correctly read and processed by the model;

[0046] Data scaling: Based on model requirements, data is scaled to fit the required range of the model.

[0047] In some embodiments, CMAQ is used as the air quality model, and the model is preset based on the obtained data. The preset includes grid settings, simulation time range, and simulation area range, specifically including:

[0048] Step 200.1: Confirm that the CMAQ model is suitable for simulating and predicting PM2.5, and confirm that the collected data is compatible with the model;

[0049] Step 200.2: Grid setup: determine the spatial resolution and range of the model grid and use the UTM coordinate system for grid division to ensure that the model can accurately simulate the transport and diffusion of atmospheric pollutants.

[0050] Step 200.3: Simulate the time range. The time range includes the start time, the end time, and the time step of the simulation to ensure that the simulation results can cover all important events and changes during the study period.

[0051] Step 200.4: The simulation area is determined using the boundary range in the UTM coordinate system to ensure that the model covers all important pollution sources and transmission paths;

[0052] Step 200.5: Model parameter setting: Based on the model requirements, set the model parameters, including atmospheric boundary layer parameters and chemical reaction parameters;

[0053] Step 200.6: Based on the model presets, prepare the input data required for the model, which includes meteorological data, emission data, and terrain data.

[0054] In some specific embodiments, the CMAQ model is run based on preset input data to simulate the transport and transformation of atmospheric pollutants, simulate the transport process of pollutants in the atmosphere, and generate a simulation result file, specifically including:

[0055] Step 300.1: Input data and run the CMAQ model. When the model is running, the model equations are discretized using the Euler method and solved by time integration, gradually advancing the simulation time.

[0056] Step 300.2: Within each time step, the model equation is spatially discretized and solved by spatial integration to calculate the concentration distribution of the pollutant within the model grid;

[0057] Step 300.3: Simulate the physical and chemical processes in the atmosphere at each time step.

[0058] Physical processes include turbulent diffusion simulation, wet deposition and dry deposition simulation;

[0059] Turbulent diffusion simulation: The CMAQ model simulates the horizontal and vertical diffusion processes of pollutants in the atmosphere through a turbulent diffusion parameterization scheme;

[0060] Wet and dry deposition simulation: The CMAQ model simulates the wet and dry deposition processes of pollutants in the atmosphere, including the exchange process between rainwater and aerosols, and the deposition rate of particulate matter in the atmosphere;

[0061] Chemical processes include photochemical reaction simulation. The CMAQ model uses a chemical kinetic model to simulate the photochemical reaction processes of pollutants in the atmosphere, including chemical reactions of photolysis, oxidation, and photooxidation.

[0062] Step 300.4: After the model is completed, the simulation results are output as a result file. The result file includes the simulation time, model grid, atmospheric pollutant concentration and particulate matter distribution simulation results.

[0063] In some specific embodiments, analysis is performed based on the simulation result file to specifically analyze the concentration distribution, spatial distribution, and temporal variation trend of pollutants to understand the transmission path and diffusion range of pollutants in the atmosphere, specifically including:

[0064] Step 400.1: Data reading: pollutant concentration data is read from the CMAQ simulation result file. The data is stored in NetCDF format and is read using xarray.

[0065] Step 400.2: Statistically analyze the read data and calculate the concentration distribution of pollutants;

[0066] Indicator calculations include mean, standard deviation, maximum, minimum, median and quartile calculations;

[0067] Average calculation, the average is the sum of all values ​​in the data set divided by the number of data, specifically:

[0068]

[0069] Where n is the number of data, x i is the value of the i-th data point;

[0070] Standard deviation calculation, the standard deviation is the square root of the average of the sum of the squares of the deviations of each data point from the mean in the data set, specifically:

[0071]

[0072] Maximum value calculation, the maximum value is the largest value in the data set, specifically:

[0073] Maximum=max(x1,x2,...,x n)

[0074] Minimum value calculation, the minimum value is the smallest value in the data set, specifically:

[0075] Minimum=min(x1,x2,...,x n )

[0076] Median calculation: The median is the middle value after all the values ​​in the data set are arranged in order of size. If the number of data is odd, the median is the middle value; if the number of data is even, the median is the average of the two middle values. Taking an even number of data as an example, the specific calculation is:

[0077]

[0078] Quartile calculation, quartiles are the values ​​that divide the data set into four equal parts, namely the first quartile, the second quartile, and the third quartile;

[0079] The first quartile is calculated as follows:

[0080]

[0081] The second quartile is calculated in the same way as the median formula;

[0082] The third quartile is calculated as follows:

[0083]

[0084] The numerical calculation of indicators provides a description of the basic statistical characteristics of the pollutant concentration dataset;

[0085] Step 400.3: Analyze the spatial distribution of pollutants, perform spatial analysis on the simulation result data, and analyze and visualize the spatial data based on GIS software;

[0086] Step 400.4: Analyze the temporal variation trend of pollutants, perform time series analysis on the simulation result data, and draw a trend chart of pollutant concentration variation over time;

[0087] Trend analysis, based on the linear regression model, assuming that the time series data is y t , where t represents the time point, and the linear regression model is used to fit the trend of the time series, specifically:

[0088] y t =β0+β1t+∈ t

[0089] Where β0 is the intercept term, β1 is the slope, ∈ tis the error term, slope test, by performing hypothesis test on the slope β1 of the linear regression model to determine whether the trend is significant;

[0090] Significance judgment: if the daily slope β1 is statistically significantly different from zero, that is, the p-value of the hypothesis test of β1 is less than the significance level, it is considered that there is a significant trend;

[0091] Periodic analysis uses the Fourier transform method to convert signals from the time domain to the frequency domain. The Fourier transform method converts time series data into a spectrum diagram, showing the amplitude and phase information of different frequency components in the signal. For a continuous signal f(t), its Fourier transform F(ω) is specifically:

[0092]

[0093] Where ω is the frequency and i is the imaginary unit;

[0094] For a discrete signal x n , whose discrete Fourier transform X k , specifically:

[0095]

[0096] Where N is the length of the signal and k is the discrete value of the frequency. Prepare the time series data and ensure that the data points are evenly distributed and that the signal contains periodic components. Perform a Fourier transform on the time series data to obtain a spectrogram. Perform spectrum analysis on the spectrogram to identify the main frequency components, that is, the frequencies corresponding to high amplitudes. These frequencies correspond to periodic changes in the time series data. Perform period identification. Based on the peak position and amplitude of the spectrogram, identify the main periods in the time series.

[0097] Step 400.5: Analyze the transmission path and diffusion range. Combined with the analysis results of spatial distribution and temporal variation, infer the transmission path and diffusion range of pollutants in the atmosphere and analyze the transmission patterns of pollutants in space and time.

[0098] Spatial autocorrelation analysis is used to identify spatial correlation in spatial data sets. Moran's I index is calculated by first calculating the correlation between the value of each location and the values ​​of its surrounding locations, then performing weighted summation, and finally obtaining the Moran's I index through standardization.

[0099] Suppose there are n locations, and the values ​​of each location are x1, x2, ..., x n , and the values ​​of its adjacent locations are x i1 ,x i2 ,…,x im, and there is a weight matrix W that represents the weights between adjacent locations. The spatial autocorrelation index is calculated as follows:

[0100]

[0101] Where, is the average value of all locations, w ij is the weight between location i and location j.

[0102] In some embodiments, based on the simulation results, the pollution source is tracked and identified. By analyzing the concentration distribution and transmission path of the pollutants in the simulation results, the location and impact range of the pollution source are determined, specifically including:

[0103] Step 500.1, determine the transmission path, analyze the transmission path of the pollutants in the simulation results, and obtain it by observing the calculated results of the pollutant concentration distribution;

[0104] Trajectory analysis: Based on the pollutant concentration distribution in the simulation results, the transmission trajectory of pollutants is tracked to determine the path of pollutants from the source to the target area;

[0105] Path identification: identifying the transmission path and determining the main transmission channels and diffusion paths based on the calculated results of pollutant concentrations;

[0106] Step 500.2: Determine the impact range, analyze the concentration distribution of pollutants in the simulation results, and identify high concentration areas and diffusion ranges;

[0107] Spatial diffusion analysis: Based on the transport of pollutants in the simulation results, the spatial diffusion degree and range of pollutants are analyzed;

[0108] Delineation of the impact scope: Based on the concentration distribution and transmission path, determine the scope of the pollution source's impact, including the main affected areas and the expected diffusion direction;

[0109] Scope demarcation, analyze the results, determine the boundaries and key areas of the impact range, and demarcate them;

[0110] Step 500.3: Locate the pollution source, analyzing the high-concentration areas in the simulation results and identifying the location of the pollution source;

[0111] Transmission path analysis: combining the transmission path and concentration distribution to confirm the location of the pollution source;

[0112] Geographic information comparison: compare the pollution source locations in the simulation results with the actual geographic information to identify similar pollution source locations;

[0113] Source tracing: Based on the analysis results, the location of the pollution source is tracked and verified to ultimately determine the location and impact range of the pollution source.

[0114] In some specific embodiments, verification is performed based on the simulation results, compared with the measured data, the accuracy and credibility of the simulation results are evaluated, and based on the verification results, the model is modified and optimized, specifically including:

[0115] Step 600.1. Collect measured data corresponding to the simulation results, including pollutant concentrations and meteorological parameters. Match the measured data with the simulation results to ensure consistency in temporal and spatial ranges. Perform comparative analysis of the simulation results and measured data, including statistical comparison and spatial distribution comparison.

[0116] Step 600.2: Evaluate the accuracy, compare the analysis results, and evaluate the accuracy and credibility of the simulation results, including error analysis and correlation analysis;

[0117] Step 600.3: Tuning process: Based on the verification results, adjust and optimize the model parameters and input data;

[0118] Step 600.4: Verification processing: perform simulation again based on the revised model and compare and verify with the measured data again to confirm the improvement effect of the model.

[0119] This application also provides an atmospheric pollution source tracing system based on an air quality model, including:

[0120] The pre-processing unit is used to establish pollutant targets and scopes, confirm the scope and time scale of the study area based on the confirmed pollutant targets, collect corresponding data based on the confirmed pollutant targets, and pre-process the collected data;

[0121] The pre-processing unit is used to use CMAQ as the air quality model and preset the model based on the obtained data. The preset includes grid settings, simulation time range and simulation area range;

[0122] The simulation unit is used to run the CMAQ model based on preset input data to simulate the transmission and transformation of atmospheric pollutants, simulate the transmission process of pollutants in the atmosphere, and generate simulation result files;

[0123] The analysis unit is used to analyze the simulation result files, specifically analyzing the concentration distribution, spatial distribution and temporal variation trend of pollutants, and understanding the transmission path and diffusion range of pollutants in the atmosphere;

[0124] The identification unit is used to track and identify pollution sources based on the simulation results. By analyzing the concentration distribution and transmission path of pollutants in the simulation results, the location and impact range of the pollution source are determined;

[0125] The verification unit is used to verify the simulation results, compare them with the measured data, evaluate the accuracy and credibility of the simulation results, and modify and optimize the model based on the verification results;

[0126] The summary unit is used to analyze and interpret the results of pollution source tracing based on the optimized model, and output a pollution source tracing summary report based on the analysis results.

[0127] The atmospheric pollution source tracing method based on the air quality model provided in this application establishes pollutant targets and ranges, confirms the scope and time scale of the research area based on the confirmed pollutant targets, collects corresponding data based on the confirmed pollutant targets, and preprocesses the collected data. CMAQ is used as the air quality model, and the model is preset based on the obtained data. The preset includes grid settings, simulation time range and simulation area range. The CMAQ model is run based on the preset input data to simulate the transmission and transformation of atmospheric pollutants, simulate the transmission process of pollutants in the atmosphere, and generate simulation result files. Analysis is performed based on the simulation result files, specifically analyzing the concentration distribution, spatial distribution and time change trend of pollutants to understand the pollutants in the atmosphere. Transmission path and diffusion range, based on the simulation results, the pollution source is tracked and identified, and the location and impact range of the pollution source are determined by analyzing the concentration distribution and transmission path of the pollutants in the simulation results. The simulation results are verified and compared with the measured data to evaluate the accuracy and credibility of the simulation results. Based on the verification results, the model is corrected and optimized. Based on the optimized model, the results of pollution source tracking are analyzed and interpreted. Based on the analysis results, a pollution source tracing summary report is output to solve the technical problems that conventional methods are difficult to fully reflect the spatial distribution and transmission path of pollutants and have large limitations, avoid the subsequent problems of difficulty in comprehensively analyzing the transmission and transformation process of pollutants, and avoid the use problem of difficulty in timely reflecting the occurrence and propagation process of pollution incidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0128] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0129] Figure 1 A flowchart of an atmospheric pollution source tracing method based on an air quality model provided in an embodiment of the present application. DETAILED DESCRIPTION

[0130] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0131] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0132] As used herein, the term "including" and its variations are open-ended, i.e., "including but not limited to." The term "based on" means "based, at least in part, on." The term "one embodiment" means "at least one embodiment," the term "another embodiment" means "at least one additional embodiment," and the term "some embodiments" means "at least some embodiments." Other terms are defined in the following description.

[0133] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0134] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0135] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0136] Please refer to Figure 1 , which shows the process of an embodiment of the atmospheric pollution source tracing method based on the air quality model according to the present disclosure.

[0137] like Figure 1 As shown, the atmospheric pollution source tracing method based on the air quality model includes the following steps:

[0138] Step 100: Establish pollutant targets and scopes, confirm the scope and time scale of the study area based on the confirmed pollutant targets, collect corresponding data based on the confirmed pollutant targets, and pre-process the collected data;

[0139] Here, specifically:

[0140] Step 100.1. Establish pollutant targets and scopes. For the purpose of tracing the source of air pollution, identify the main pollutants, including PM2.5, PM10, O3, SO2, and NO2. Set PM2.5 as the pollutant target.

[0141] Step 100.2: Confirm the scope and time scale of the study area based on the confirmed pollutant targets;

[0142] Determine the scope of the study area and confirm the impact range for PM2.5. First, determine the spatial scope of the study area. The spatial scope is the area where PM2.5 concentration has a significant impact;

[0143] Confirm the time scale, based on the seasonal changes, diurnal changes and the impact of specific events on PM2.5 concentrations, and conduct research using seasonal changes, diurnal changes and specific events simultaneously to confirm the spatiotemporal distribution characteristics of PM2.5;

[0144] Step 100.3: Collect corresponding data based on the confirmed pollutant targets;

[0145] When collecting meteorological data, pay special attention to meteorological parameters related to PM2.5 concentrations, including wind speed, wind direction, and humidity;

[0146] Collect emission source data. In addition to general emission source data, special attention should be paid to PM2.5 emission source data, including industrial emissions, transportation emissions, and biomass combustion emissions;

[0147] Acquire topographic and geomorphological data, and based on the acquired topographic and geomorphological data, simulate the impact of the physical processes of the atmospheric boundary layer on PM2.5 concentration;

[0148] Step 100.4: Preprocess the collected data. The preprocessing includes data cleaning and processing, data interpolation and spatial matching, and data synchronization and format conversion.

[0149] Here, specifically:

[0150] Step 100.4.1, data cleaning and processing;

[0151] Data quality check: Check the completeness and accuracy of the collected data. Use standard deviation to check data quality. Standard deviation is a measure used to describe the degree of dispersion of a data set. Specifically:

[0152]

[0153] In the formula, σ represents the standard deviation, x irepresents the i-th observation in the data set, μ represents the mean of the data set, and n represents the number of observations in the data set. Missing values ​​and outliers are identified and processed based on the standard deviation calculation results.

[0154] Data screening: based on research objectives, screen data related to PM2.5;

[0155] Data normalization, converting data into uniform units and formats;

[0156] Data smoothing is performed using the moving average smoothing method, specifically:

[0157]

[0158] Where, MA t represents the moving average at time t, Y t-i+1 represents k consecutive observations starting from time t-i+1, where k represents the size of the moving average window, which is based on smoothing to reduce noise and fluctuations in the data;

[0159] Step 100.4.2, data interpolation and spatial matching;

[0160] The Kriging interpolation method is used to determine the weights of the interpolation points through the semivariogram function, specifically:

[0161] γ(h)=c0+c1(1-e -h / l )

[0162] Where γ(h) represents the semivariogram function, c0 and c1 are the parameters of the semivariogram function, l is the spatial correlation length, and h is the distance between interpolation points. Spatial interpolation is performed on meteorological data and emission source data to fill in data gaps.

[0163] Grid matching: Match the interpolated data to the CMAQ model grid to ensure that the spatial resolution of the data and the model grid are consistent;

[0164] Spatial smoothing processing is performed on the interpolated data to ensure that the data verifies the actual situation;

[0165] Step 100.4.3, data synchronization and format conversion;

[0166] Time synchronization: synchronize the collected data using the time difference analysis method, specifically:

[0167] Time difference analysis is used to analyze the characteristics of time series data. Time difference analysis includes first-order difference and second-order difference. The first-order difference represents the difference between the observation value at the current moment and the observation value at the previous moment, and the second-order difference represents the difference between the first-order difference sequence:

[0168] ΔY t =Y t -Y t-1

[0169]

[0170] Where ΔY t represents the first-order difference sequence, Δ 2 Y t represents the second-order difference sequence, Y t represents the observation value at time t, and the time difference analysis method is used to ensure that the time resolution of the data is consistent and matches the simulation time range of the model;

[0171] Format conversion: converting data into the input format required by the CMAQ model, including meteorological data and emission source data, to ensure that the data can be correctly read and processed by the model;

[0172] Data scaling: Based on model requirements, data is scaled to fit the required range of the model.

[0173] Step 200: Use CMAQ as the air quality model and preset the model based on the obtained data, including grid settings, simulation time range, and simulation area range;

[0174] Here, specifically:

[0175] Step 200.1: Confirm that the CMAQ model is suitable for simulating and predicting PM2.5, and confirm that the collected data is compatible with the model;

[0176] Step 200.2: Grid setup: determine the spatial resolution and range of the model grid and use the UTM coordinate system for grid division to ensure that the model can accurately simulate the transport and diffusion of atmospheric pollutants.

[0177] Step 200.3: Simulate the time range. The time range includes the start time, the end time, and the time step of the simulation to ensure that the simulation results can cover all important events and changes during the study period.

[0178] Step 200.4: The simulation area is determined using the boundary range in the UTM coordinate system to ensure that the model covers all important pollution sources and transmission paths;

[0179] Step 200.5: Model parameter setting: Based on the model requirements, set the model parameters, including atmospheric boundary layer parameters and chemical reaction parameters;

[0180] Step 200.6: Based on the model presets, prepare the input data required for the model, which includes meteorological data, emission data, and terrain data.

[0181] Step 300: Run the CMAQ model based on preset input data to simulate the transmission and transformation of atmospheric pollutants, simulate the transmission process of pollutants in the atmosphere, and generate a simulation result file;

[0182] Here, specifically:

[0183] Step 300.1: Input data and run the CMAQ model. When the model is running, the model equations are discretized using the Euler method and solved by time integration, gradually advancing the simulation time.

[0184] Step 300.2: Within each time step, the model equation is spatially discretized and solved by spatial integration to calculate the concentration distribution of the pollutant within the model grid;

[0185] Step 300.3: Simulate the physical and chemical processes in the atmosphere at each time step.

[0186] Physical processes include turbulent diffusion simulation, wet deposition and dry deposition simulation;

[0187] Turbulent diffusion simulation: The CMAQ model simulates the horizontal and vertical diffusion processes of pollutants in the atmosphere through a turbulent diffusion parameterization scheme;

[0188] Wet and dry deposition simulation: The CMAQ model simulates the wet and dry deposition processes of pollutants in the atmosphere, including the exchange process between rainwater and aerosols, and the deposition rate of particulate matter in the atmosphere;

[0189] Chemical processes include photochemical reaction simulation. The CMAQ model uses a chemical kinetic model to simulate the photochemical reaction processes of pollutants in the atmosphere, including chemical reactions of photolysis, oxidation, and photooxidation.

[0190] Step 300.4: After the model is completed, the simulation results are output as a result file. The result file includes the simulation time, model grid, atmospheric pollutant concentration and particulate matter distribution simulation results.

[0191] Step 400: Analyze the simulation result file to specifically analyze the concentration distribution, spatial distribution, and temporal variation trend of the pollutants to understand the transmission path and diffusion range of the pollutants in the atmosphere;

[0192] Here, specifically:

[0193] Step 400.1: Data reading: pollutant concentration data is read from the CMAQ simulation result file. The data is stored in NetCDF format and is read using xarray.

[0194] Step 400.2: Statistically analyze the read data and calculate the concentration distribution of pollutants;

[0195] Indicator calculations include mean, standard deviation, maximum, minimum, median and quartile calculations;

[0196] Average calculation, the average is the sum of all values ​​in the data set divided by the number of data, specifically:

[0197]

[0198] Where n is the number of data, x i is the value of the i-th data point;

[0199] Standard deviation calculation, the standard deviation is the square root of the average of the sum of the squares of the deviations of each data point from the mean in the data set, specifically:

[0200]

[0201] Maximum value calculation, the maximum value is the largest value in the data set, specifically:

[0202] Maximum=max(x1,x2,...,x n )

[0203] Minimum value calculation, the minimum value is the smallest value in the data set, specifically:

[0204] Minimum=min(x1,x2,...,x n )

[0205] Median calculation: The median is the middle value after all the values ​​in the data set are arranged in order of size. If the number of data is odd, the median is the middle value; if the number of data is even, the median is the average of the two middle values. Taking an even number of data as an example, the specific calculation is:

[0206]

[0207] Quartile calculation, quartiles are the values ​​that divide the data set into four equal parts, namely the first quartile, the second quartile, and the third quartile;

[0208] The first quartile is calculated as follows:

[0209]

[0210] The second quartile is calculated in the same way as the median formula;

[0211] The third quartile is calculated as follows:

[0212]

[0213] The numerical calculation of indicators provides a description of the basic statistical characteristics of the pollutant concentration dataset;

[0214] Step 400.3: Analyze the spatial distribution of pollutants, perform spatial analysis on the simulation result data, and analyze and visualize the spatial data based on GIS software;

[0215] Step 400.4: Analyze the temporal variation trend of pollutants, perform time series analysis on the simulation result data, and draw a trend chart of pollutant concentration variation over time;

[0216] Trend analysis, based on the linear regression model, assuming that the time series data is y t , where t represents the time point, and the linear regression model is used to fit the trend of the time series, specifically:

[0217] y t =β0+β1t+∈ t

[0218] Where β0 is the intercept term, β1 is the slope, ∈ t is the error term, slope test, by performing hypothesis test on the slope β1 of the linear regression model to determine whether the trend is significant;

[0219] Significance judgment: if the daily slope β1 is statistically significantly different from zero, that is, the p-value of the hypothesis test of β1 is less than the significance level, it is considered that there is a significant trend;

[0220] Periodic analysis uses the Fourier transform method to convert signals from the time domain to the frequency domain. The Fourier transform method converts time series data into a spectrum diagram, showing the amplitude and phase information of different frequency components in the signal. For a continuous signal f(t), its Fourier transform F(ω) is specifically:

[0221]

[0222] Where ω is the frequency and i is the imaginary unit;

[0223] For a discrete signal x n , whose discrete Fourier transform X k , specifically:

[0224]

[0225] Where N is the length of the signal and k is the discrete value of the frequency. Prepare the time series data and ensure that the data points are evenly distributed and that the signal contains periodic components. Perform a Fourier transform on the time series data to obtain a spectrogram. Perform spectrum analysis on the spectrogram to identify the main frequency components, that is, the frequencies corresponding to high amplitudes. These frequencies correspond to periodic changes in the time series data. Perform period identification. Based on the peak position and amplitude of the spectrogram, identify the main periods in the time series.

[0226] Step 400.5: Analyze the transmission path and diffusion range. Combined with the analysis results of spatial distribution and temporal variation, infer the transmission path and diffusion range of pollutants in the atmosphere and analyze the transmission patterns of pollutants in space and time.

[0227] Spatial autocorrelation analysis is used to identify spatial correlation in spatial data sets. Moran's I index is calculated by first calculating the correlation between the value of each location and the values ​​of its surrounding locations, then performing weighted summation, and finally obtaining the Moran's I index through standardization.

[0228] Suppose there are n locations, and the values ​​of each location are x1, x2, ..., x n , and the values ​​of its adjacent locations are x i1 ,x i2 ,…,x im , and there is a weight matrix W that represents the weights between adjacent locations. The spatial autocorrelation index is calculated as follows:

[0229]

[0230] Where, is the average value of all locations, w ij is the weight between location i and location j.

[0231] Step 500: Based on the simulation results, the pollution source is tracked and identified. By analyzing the concentration distribution and transmission path of the pollutants in the simulation results, the location and impact range of the pollution source are determined;

[0232] Here, specifically:

[0233] Step 500.1, determine the transmission path, analyze the transmission path of the pollutants in the simulation results, and obtain it by observing the calculated results of the pollutant concentration distribution;

[0234] Trajectory analysis: Based on the pollutant concentration distribution in the simulation results, the transmission trajectory of pollutants is tracked to determine the path of pollutants from the source to the target area;

[0235] Path identification: identifying the transmission path and determining the main transmission channels and diffusion paths based on the calculated results of pollutant concentrations;

[0236] Step 500.2: Determine the impact range, analyze the concentration distribution of pollutants in the simulation results, and identify high concentration areas and diffusion ranges;

[0237] Spatial diffusion analysis: Based on the transport of pollutants in the simulation results, the spatial diffusion degree and range of pollutants are analyzed;

[0238] Delineation of the impact scope: Based on the concentration distribution and transmission path, determine the scope of the pollution source's impact, including the main affected areas and the expected diffusion direction;

[0239] Scope demarcation, analyze the results, determine the boundaries and key areas of the impact range, and demarcate them;

[0240] Step 500.3: Locate the pollution source, analyzing the high-concentration areas in the simulation results and identifying the location of the pollution source;

[0241] Transmission path analysis: combining the transmission path and concentration distribution to confirm the location of the pollution source;

[0242] Geographic information comparison: compare the pollution source locations in the simulation results with the actual geographic information to identify similar pollution source locations;

[0243] Source tracing: Based on the analysis results, the location of the pollution source is tracked and verified to ultimately determine the location and impact range of the pollution source.

[0244] Step 600: Verify the simulation results, compare them with the measured data, evaluate the accuracy and credibility of the simulation results, and modify and optimize the model based on the verification results.

[0245] Here, specifically:

[0246] Step 600.1. Collect measured data corresponding to the simulation results, including pollutant concentrations and meteorological parameters. Match the measured data with the simulation results to ensure consistency in temporal and spatial ranges. Perform comparative analysis of the simulation results and measured data, including statistical comparison and spatial distribution comparison.

[0247] Step 600.2: Evaluate the accuracy, compare the analysis results, and evaluate the accuracy and credibility of the simulation results, including error analysis and correlation analysis;

[0248] Step 600.3: Tuning process: Based on the verification results, adjust and optimize the model parameters and input data;

[0249] Step 600.4: Verification processing: perform simulation again based on the revised model and compare and verify with the measured data again to confirm the improvement effect of the model.

[0250] Step 700: Analyze and interpret the pollution source tracing results based on the optimized model, and output a pollution source tracing summary report based on the analysis results.

[0251] Here, specifically:

[0252] Step 700.1: Prepare the optimized model output results, including pollution source location, transmission path, and impact range information, and interpret and analyze the results of pollution source tracing, including the location of the pollution source, the determination of the transmission path, and the delineation of the impact range;

[0253] Step 700.2: Data visualization: Visualize the pollution source tracing results in the form of charts;

[0254] Step 700.3: Verify the results of the pollution source tracing to see if they are consistent with the actual situation and check the accuracy and credibility of the model output results.

[0255] Step 700.4: Output a summary report. Write a pollution source tracing summary report that summarizes the results and conclusions of the model analysis, including the tracking of pollution sources, the scope of impact, and recommended measures.

[0256] This application also provides an atmospheric pollution source tracing system based on an air quality model, including:

[0257] The pre-processing unit is used to establish pollutant targets and scopes, confirm the scope and time scale of the study area based on the confirmed pollutant targets, collect corresponding data based on the confirmed pollutant targets, and pre-process the collected data;

[0258] The pre-processing unit is used to use CMAQ as the air quality model and preset the model based on the obtained data. The preset includes grid settings, simulation time range and simulation area range;

[0259] The simulation unit is used to run the CMAQ model based on preset input data to simulate the transmission and transformation of atmospheric pollutants, simulate the transmission process of pollutants in the atmosphere, and generate simulation result files;

[0260] The analysis unit is used to analyze the simulation result files, specifically analyzing the concentration distribution, spatial distribution and temporal variation trend of pollutants, and understanding the transmission path and diffusion range of pollutants in the atmosphere;

[0261] The identification unit is used to track and identify pollution sources based on the simulation results. By analyzing the concentration distribution and transmission path of pollutants in the simulation results, the location and impact range of the pollution source are determined;

[0262] The verification unit is used to verify the simulation results, compare them with the measured data, evaluate the accuracy and credibility of the simulation results, and modify and optimize the model based on the verification results;

[0263] The summary unit is used to analyze and interpret the results of pollution source tracing based on the optimized model, and output a pollution source tracing summary report based on the analysis results.

[0264] The atmospheric pollution source tracing method based on the air quality model provided in this application establishes pollutant targets and ranges, confirms the scope and time scale of the research area based on the confirmed pollutant targets, collects corresponding data based on the confirmed pollutant targets, and preprocesses the collected data. CMAQ is used as the air quality model, and the model is preset based on the obtained data. The preset includes grid settings, simulation time range and simulation area range. The CMAQ model is run based on the preset input data to simulate the transmission and transformation of atmospheric pollutants, simulate the transmission process of pollutants in the atmosphere, and generate simulation result files. Analysis is performed based on the simulation result files, specifically analyzing the concentration distribution, spatial distribution and time change trend of pollutants to understand the pollutants in the atmosphere. Transmission path and diffusion range, based on the simulation results, the pollution source is tracked and identified, and the location and impact range of the pollution source are determined by analyzing the concentration distribution and transmission path of the pollutants in the simulation results. The simulation results are verified and compared with the measured data to evaluate the accuracy and credibility of the simulation results. Based on the verification results, the model is corrected and optimized. Based on the optimized model, the results of pollution source tracking are analyzed and interpreted. Based on the analysis results, a pollution source tracing summary report is output to solve the technical problems that conventional methods are difficult to fully reflect the spatial distribution and transmission path of pollutants and have large limitations, avoid the subsequent problems of difficulty in comprehensively analyzing the transmission and transformation process of pollutants, and avoid the use problem of difficulty in timely reflecting the occurrence and propagation process of pollution incidents.

[0265] The above description is merely a preferred embodiment of the present disclosure and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also includes other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this disclosure.

[0266] Although the subject matter has been described in language specific to structural features and / or methodological logical acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are merely example forms of implementing the claims.

Claims

1. The atmospheric pollution source tracing method based on the air quality model is characterized by: The steps include: S100. Establish pollutant targets and scopes, confirm the scope and time scale of the study area based on the confirmed pollutant targets, collect corresponding data based on the confirmed pollutant targets, and pre-process the collected data; S200, using CMAQ as the air quality model, and presetting the model based on the obtained data, including the grid setting, simulation time range, and simulation area range; S300, running the CMAQ model based on preset input data to simulate the transmission and transformation of atmospheric pollutants, simulate the transmission process of pollutants in the atmosphere, and generate a simulation result file; S400: Analyze the concentration distribution, spatial distribution, and temporal variation trends of pollutants based on the simulation result files to understand the transmission path and diffusion range of pollutants in the atmosphere; S500, based on the simulation results, tracking and identifying the pollution source, and determining the location and impact range of the pollution source by analyzing the concentration distribution and transmission path of the pollutants in the simulation results; S600: Verify the simulation results and compare them with the measured data to evaluate the accuracy and credibility of the simulation results. Based on the verification results, revise and optimize the model. S700: Based on the optimized model, the pollution source tracing results are analyzed and interpreted, and based on the analysis results, a pollution source tracing summary report is output.

2. The air pollution source tracing method based on the air quality model according to claim 1, characterized in that: Establish pollutant targets and scopes, confirm the scope and time scale of the study area based on the confirmed pollutant targets, collect corresponding data based on the confirmed pollutant targets, and pre-process the collected data, including: S100.

1. Establish pollutant targets and scopes. For the purpose of tracing the source of air pollution, identify the main pollutants, including PM2.5, PM10, O3, SO2, and NO2. Set PM2.5 as the pollutant target; S100.

2. Confirm the scope and time scale of the study area based on the confirmed pollutant targets; Determine the scope of the study area and confirm the impact range for PM2.

5. First, determine the spatial scope of the study area. The spatial scope is the area where PM2.5 concentration has a significant impact; Confirm the time scale, based on the seasonal changes, diurnal changes and the impact of specific events on PM2.5 concentrations, and conduct research using seasonal changes, diurnal changes and specific events simultaneously to confirm the spatiotemporal distribution characteristics of PM2.5; S100.

3. Collect corresponding data based on the confirmed pollutant targets; When collecting meteorological data, pay special attention to meteorological parameters related to PM2.5 concentrations, including wind speed, wind direction, and humidity; Collect emission source data. In addition to general emission source data, special attention should be paid to PM2.5 emission source data, including industrial emissions, transportation emissions, and biomass combustion emissions; Acquire topographic and geomorphological data, and based on the acquired topographic and geomorphological data, simulate the impact of the physical processes of the atmospheric boundary layer on PM2.5 concentration; S100.

4. Preprocess the collected data, including data cleaning and processing, data interpolation and spatial matching, and data synchronization and format conversion.

3. The method for tracing the source of atmospheric pollution based on an air quality model according to claim 2, wherein: Preprocessing is performed based on the collected data. Preprocessing includes data cleaning and processing, data interpolation and spatial matching, data synchronization and format conversion, specifically including: S100.4.

1. Data cleaning and processing; Data quality check: Check the completeness and accuracy of the collected data. Use standard deviation to check data quality. Standard deviation is a measure used to describe the degree of dispersion of a data set. Specifically: ； Where, represents the standard deviation, Indicates the first observations, represents the mean of the data set, Indicates the number of observations in a data set, identifies and handles missing values ​​and outliers based on the standard deviation calculation results; Data screening: based on research objectives, screen data related to PM2.5; Data normalization, converting data into uniform units and formats; Data smoothing is performed using the moving average smoothing method, specifically: ； Where, Indicates time The moving average of Indicates from The moment begins Continuous observations, Indicates the size of the moving average window, which is used to reduce noise and fluctuations in the data based on smoothing; S100.4.2, data interpolation and spatial matching; The Kriging interpolation method is used to determine the weights of the interpolation points through the semivariogram function, specifically: ; Where, represents the semivariogram function, and is the parameter of the semivariogram function, is the spatial correlation length, is the distance between interpolation points, which is used to spatially interpolate meteorological data and emission source data to fill in data gaps; Grid matching: Match the interpolated data to the CMAQ model grid to ensure that the spatial resolution of the data and the model grid are consistent; Spatial smoothing processing is performed on the interpolated data to ensure that the data verifies the actual situation; S100.4.3, data synchronization and format conversion; Time synchronization: synchronize the collected data using the time difference analysis method, specifically: Time difference analysis is used to analyze the characteristics of time series data. Time difference analysis includes first-order difference and second-order difference. The first-order difference represents the difference between the observation value at the current moment and the observation value at the previous moment, and the second-order difference represents the difference between the first-order difference sequence: ； ； Where, represents the first-order difference sequence, represents the second-order difference sequence, Indicates time The time difference analysis method is used to ensure that the time resolution of the data is consistent and matches the simulation time range of the model; Format conversion: converting data into the input format required by the CMAQ model, including meteorological data and emission source data, to ensure that the data can be correctly read and processed by the model; Data scaling: Based on model requirements, data is scaled to fit the required range of the model.

4. The method for tracing the source of atmospheric pollution based on an air quality model according to claim 3, wherein: CMAQ is used as the air quality model, and the model is preset based on the obtained data. The presets include grid settings, simulation time range, and simulation area range, specifically: S200.

1. Confirm that the CMAQ model is suitable for simulating and predicting PM2.5, and confirm that the collected data are compatible with the model; S200.

2. Grid setup: Determine the spatial resolution and range of the model grid and use the UTM coordinate system for grid division to ensure that the model can accurately simulate the transport and diffusion of atmospheric pollutants. S200.

3. Simulation time range, which includes the start and end time, as well as the simulation time step, to ensure that the simulation results can cover all important events and changes during the study period; S200.

4. The simulation area is determined by the boundary range in the UTM coordinate system to ensure that the model covers all important pollution sources and transmission paths; S200.5, Model parameter setting, based on model requirements, set model parameters, including atmospheric boundary layer parameters and chemical reaction parameters; S200.

6. Based on the model presets, prepare the input data required for the model, including meteorological data, emission data and terrain data.

5. The method for tracing the source of atmospheric pollution based on an air quality model according to claim 4, wherein: Run the CMAQ model based on preset input data to simulate the transport and transformation of atmospheric pollutants, simulate the transport process of pollutants in the atmosphere, and generate simulation result files, specifically: S300.

1. Input data and run the CMAQ model. When the model is running, the model equations are discretized using the Euler method and solved through time integration, gradually advancing the simulation time. S300.

2. Within each time step, the model equation is spatially discretized and solved by spatial integration to calculate the concentration distribution of pollutants within the model grid; S300.

3. Simulate the physical and chemical processes in the atmosphere at each time step; Physical processes include turbulent diffusion simulation, wet deposition and dry deposition simulation; Turbulent diffusion simulation: The CMAQ model simulates the horizontal and vertical diffusion processes of pollutants in the atmosphere through a turbulent diffusion parameterization scheme; Wet and dry deposition simulation: The CMAQ model simulates the wet and dry deposition processes of pollutants in the atmosphere, including the exchange process between rainwater and aerosols, and the deposition rate of particulate matter in the atmosphere; Chemical processes include photochemical reaction simulation. The CMAQ model uses a chemical kinetic model to simulate the photochemical reaction processes of pollutants in the atmosphere, including chemical reactions of photolysis, oxidation, and photooxidation. S300.

4. After the model is completed, the simulation results are output as a result file, which includes the simulation time, model grid, atmospheric pollutant concentration and particulate matter distribution simulation results.

6. The method for tracing the source of atmospheric pollution based on an air quality model according to claim 5, wherein: Analyze the simulation results file to analyze the concentration distribution, spatial distribution, and temporal variation trend of pollutants, and understand the transmission path and diffusion range of pollutants in the atmosphere, including: S400.1, data reading is based on reading pollutant concentration data from the CMAQ simulation result file. The data is Format storage, using Read; S400.

2. Perform statistical analysis on the read data and calculate the concentration distribution of pollutants; Indicator calculations include mean, standard deviation, maximum, minimum, median and quartile calculations; Average calculation, the average is the sum of all values ​​in the data set divided by the number of data, specifically: ； Where, is the number of data, It is The value of the data point; Standard deviation calculation, the standard deviation is the square root of the average of the sum of the squares of the deviations of each data point from the mean in the data set, specifically: ; Maximum value calculation, the maximum value is the largest value in the data set, specifically: ； Minimum value calculation, the minimum value is the smallest value in the data set, specifically: ； Median calculation: The median is the middle value after all the values ​​in the data set are arranged in order of size. If the number of data is odd, the median is the middle value; if the number of data is even, the median is the average of the two middle values. Taking an even number of data as an example, the specific calculation is: ； Quartile calculation, quartiles are the values ​​that divide the data set into four equal parts, namely the first quartile, the second quartile, and the third quartile; The first quartile is calculated as follows: ; The second quartile is calculated in the same way as the median formula; The third quartile is calculated as follows: ; The numerical calculation of indicators provides a description of the basic statistical characteristics of the pollutant concentration dataset; S400.

3. Analyze the spatial distribution of pollutants, perform spatial analysis on the simulation results data, and analyze and visualize the spatial data based on GIS software; S400.

4. Analyze the temporal variation trend of pollutants, conduct time series analysis on the simulation result data, and draw a trend chart of pollutant concentration variation over time; Trend analysis, based on the linear regression model, assumes that the time series data is ,in Represents a time point. The linear regression model is used to fit the trend of the time series, specifically: ； Where, is the intercept term, is the slope, is the error term, slope test, by the slope of the linear regression model Conduct hypothesis testing to determine whether the trend is significant; Significance judgment, daily slope is statistically significantly different from zero, that is, Hypothesis testing If the value is less than the significance level, it is considered that there is a significant trend; Periodic analysis uses the Fourier transform method to convert the signal from the time domain to the frequency domain. The Fourier transform method converts time series data into a spectrum diagram to display the amplitude and phase information of different frequency components in the signal. For continuous signals , whose Fourier transform , specifically: ； Where, is the frequency, is an imaginary unit; For discrete signals , whose discrete Fourier transform , specifically: ； Where, is the length of the signal, It is a discrete value of frequency. Prepare time series data and ensure that the data points are evenly distributed and the signal contains periodic components. Perform Fourier transform on the time series data to obtain a spectrogram. Perform spectrum analysis. Analyze the spectrogram to identify the main frequency components, that is, the frequencies corresponding to high amplitudes. These frequencies correspond to periodic changes in the time series data. Perform period identification. Based on the peak position and amplitude of the spectrogram, identify the main periods in the time series. S400.

5. Analyze the transmission path and diffusion range. Combined with the analysis results of spatial distribution and temporal changes, infer the transmission path and diffusion range of pollutants in the atmosphere and analyze the transmission patterns of pollutants in space and time. Use spatial autocorrelation analysis to identify the spatial correlation in spatial data sets and calculate The index first calculates the correlation between the value of each location and the values ​​of its surrounding locations, then performs weighted summation, and finally obtains it through normalization. index; With locations, and the values ​​for each location are , and the values ​​of its adjacent locations are , and there exists a weight matrix Represents the weight between adjacent locations and the spatial autocorrelation index is calculated as follows: ； Where, is the average value for all locations, It's the location and location The weight between .

7. The method for tracing the source of atmospheric pollution based on an air quality model according to claim 6, wherein: Based on the simulation results, the pollution sources are tracked and identified. By analyzing the concentration distribution and transmission path of pollutants in the simulation results, the location and impact range of the pollution sources are determined. Specifically: S500.

1. Determine the transmission path. Analyze the transmission path of pollutants in the simulation results and obtain the calculated results by observing the pollutant concentration distribution; Trajectory analysis: Based on the pollutant concentration distribution in the simulation results, the transmission trajectory of pollutants is tracked to determine the path of pollutants from the source to the target area; Path identification: identifying the transmission path and determining the main transmission channels and diffusion paths based on the calculated results of pollutant concentrations; S500.

2. Determine the impact range, analyze the concentration distribution of pollutants in the simulation results, and identify high concentration areas and diffusion ranges; Spatial diffusion analysis: Based on the transport of pollutants in the simulation results, the spatial diffusion degree and range of pollutants are analyzed; Delineation of the impact scope: Based on the concentration distribution and transmission path, determine the scope of the pollution source's impact, including the main affected areas and the expected diffusion direction; Scope demarcation, analyze the results, determine the boundaries and key areas of the impact range, and demarcate them; S500.

3. Pollution source location: Analyze high-concentration areas in the simulation results and identify the location of pollution sources; Transmission path analysis: combining the transmission path and concentration distribution to confirm the location of the pollution source; Geographic information comparison: compare the pollution source locations in the simulation results with the actual geographic information to identify similar pollution source locations; Source tracing: Based on the analysis results, the location of the pollution source is tracked and verified to ultimately determine the location and impact range of the pollution source.

8. The method for tracing the source of atmospheric pollution based on an air quality model according to claim 7, wherein: Verify the simulation results and compare them with the measured data to evaluate the accuracy and credibility of the simulation results. Based on the verification results, modify and optimize the model, including: S600.

1. Collect measured data corresponding to the simulation results, including pollutant concentrations and meteorological parameters. Match the measured data with the simulation results to ensure consistency in temporal and spatial ranges. Conduct comparative analysis of the simulation results and measured data, including statistical comparisons and spatial distribution comparisons. S600.

2. Evaluate accuracy, compare and analyze the results, and evaluate the accuracy and credibility of simulation results, including error analysis and correlation analysis; S600.3, Tuning process, based on the verification results, adjust and optimize the model parameters and input data; S600.

4. Verification: Perform simulation again based on the revised model and compare it with the measured data to confirm the improvement effect of the model.

9. The atmospheric pollution source tracing system based on the air quality model is characterized by: include: The pre-processing unit is used to establish pollutant targets and scopes, confirm the scope and time scale of the study area based on the confirmed pollutant targets, collect corresponding data based on the confirmed pollutant targets, and pre-process the collected data; The pre-processing unit is used to use CMAQ as the air quality model and preset the model based on the obtained data. The preset includes grid settings, simulation time range and simulation area range; The simulation unit is used to run the CMAQ model based on preset input data to simulate the transmission and transformation of atmospheric pollutants, simulate the transmission process of pollutants in the atmosphere, and generate simulation result files; The analysis unit is used to analyze the simulation result files, specifically analyzing the concentration distribution, spatial distribution and temporal variation trend of pollutants, and understanding the transmission path and diffusion range of pollutants in the atmosphere; The identification unit is used to track and identify pollution sources based on the simulation results. By analyzing the concentration distribution and transmission path of pollutants in the simulation results, the location and impact range of the pollution source are determined; The verification unit is used to verify the simulation results, compare them with the measured data, evaluate the accuracy and credibility of the simulation results, and modify and optimize the model based on the verification results; The summary unit is used to analyze and interpret the results of pollution source tracing based on the optimized model, and output a pollution source tracing summary report based on the analysis results.

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