A method and device for constructing a fine particulate matter spatiotemporal distribution map

Abstract

The application discloses a kind of fine particulate matter space-time distribution diagram construction method and device, including obtaining the background environment fine particulate matter concentration dataset and fine particulate matter concentration dataset corresponding to the preset research area;Based on the background environment fine particulate matter concentration dataset, concentration correction operation is carried out to fine particulate matter concentration dataset, to determine the background concentration correction dataset;Through social science statistical software, average fine particulate matter concentration value and each road corresponding vehicle data are subjected to pearson correlation detection operation, to generate correlation scatter diagram;In the background concentration correction dataset, the background concentration correction data corresponding to the road where all abnormal points in correlation scatter diagram are located is excluded, to generate traffic correction dataset;Through geographic information system software, a plurality of preset variation function models are used to carry out regional interpolation analysis on traffic correction dataset, to determine fine particulate matter space-time distribution diagram.The technical problem that the accuracy of the obtained PM 2.5 concentration spatial distribution is low is solved.

Description

Technical Field

[0001] This invention relates to the field of atmospheric pollutant spatiotemporal distribution calculation technology, and in particular to a method and apparatus for constructing a spatiotemporal distribution map of fine particulate matter. Background Technology

[0002] In recent years, the number of cars has increased dramatically, leading to problems such as traffic congestion and large-scale vehicle exhaust emissions, which have also significantly impacted indoor air quality in buildings. Among the pollutants emitted from traffic sources, PM2.5 is a major component. 2.5 (English name: particulate matter) 2.5 Fine particulate matter (PM2.5), also known as particulate matter in Chinese, is the most representative pollutant, with traffic exhaust emissions having the greatest impact on urban PM2.5. 2.5 The contribution rate to pollution exceeded 20%.

[0003] Because traffic flow and background weather conditions within a street are time-varying, traffic-generated PM2.5... 2.5 The emissions and transmission of PM2.5 exhibit dynamic and time-varying characteristics, while current methods for constructing PM2.5 systems... 2.5 Methods for spatial distribution of concentrations include traditional construction methods such as atmospheric stationary monitoring or numerical simulation.

[0004] However, these methods typically rely on relatively singular data sources and fail to reflect the time-varying characteristics of particulate matter emissions from traffic sources and the variability in urban street-level diffusion. This leads to the loss of valuable information from the original data, resulting in inaccurate estimates of traffic source PM2.5. 2.5 The accuracy of spatial distribution of concentration is low. Summary of the Invention

[0005] This invention provides a method and apparatus for constructing a spatiotemporal distribution map of fine particulate matter, solving the problem that existing technologies only estimate missing data trends from a single spatial or temporal perspective, resulting in a limited data source and loss of valuable information from the original data, thus leading to suboptimal PM data. 2.5 Technical problem of low accuracy in spatial distribution of concentration.

[0006] The present invention provides a method for constructing a spatiotemporal distribution map of fine particulate matter, comprising:

[0007] Obtain the background environmental fine particulate matter concentration dataset and the fine particulate matter concentration dataset corresponding to the preset study area;

[0008] Based on the background environment fine particulate matter concentration dataset, a concentration correction operation is performed on the fine particulate matter concentration dataset to determine the background concentration correction dataset;

[0009] The average fine particulate matter concentration value for each road in the study area was calculated using the aforementioned fine particulate matter concentration dataset.

[0010] Pearson correlation analysis was performed on the average fine particulate matter concentration value and the vehicle data corresponding to each road using social science statistical software to generate a correlation scatter plot.

[0011] Within the background concentration correction dataset, remove the background concentration correction data corresponding to the roads where all outliers in the correlation scatter plot are located to generate a traffic correction dataset.

[0012] The traffic correction dataset was analyzed by using multiple preset variogram models through geographic information system software to determine the spatiotemporal distribution map of fine particulate matter.

[0013] Optionally, the background fine particulate matter concentration dataset includes background fine particulate matter concentrations acquired in minutes; the fine particulate matter concentration dataset includes fine particulate matter concentrations acquired in seconds; the step of performing a concentration correction operation on the fine particulate matter concentration dataset based on the background fine particulate matter concentration dataset to determine the background concentration correction dataset includes:

[0014] Multiple background concentration correction data are determined by performing difference calculations on the fine particulate matter concentration and the corresponding background fine particulate matter concentration;

[0015] A background concentration correction dataset is constructed using all the aforementioned background concentration correction data;

[0016] The formula for calculating background concentration correction data is:

[0017] c tij =x tij -y ri ;

[0018] In the formula, c tij Define the movement rate r per minute of point t within the study area. i t per second ij Background concentration correction data; x tij r per minute for moving point t i t per second ij Fine particulate matter concentration data; y ri For a fixed measuring point r, the time per minute r i Background environmental fine particulate matter concentration data.

[0019] Optionally, the vehicle data includes traffic flow and vehicle type; the step of performing Pearson correlation analysis on the average fine particulate matter concentration value and the vehicle data corresponding to each road using social science statistical software to generate a correlation scatter plot includes:

[0020] The fine particulate matter emission factor corresponding to the vehicle type and the traffic flow are substituted into the traffic line source emission intensity formula to calculate and determine the traffic line source emission intensity.

[0021] The average vehicle weight is determined by averaging the traffic flow and the vehicle weight corresponding to the vehicle type.

[0022] The dust source emission intensity is determined by substituting the road dust load, average vehicle weight, and traffic flow corresponding to the study area into the road dust emission formula.

[0023] The combined emission source intensity is determined by summing the emission intensity of the traffic line source and the emission intensity of the dust source.

[0024] The average fine particulate matter concentration and the comprehensive emission source intensity were analyzed using social science statistical software, and a correlation scatter plot was generated.

[0025] Optionally, the formula for the emission intensity of traffic line sources is:

[0026] Q V =E·C;

[0027] The formula for road dust emissions is:

[0028]

[0029] In the formula, Q V For traffic line source emission intensity; Q Y E is the dust source emission intensity; C is the fine particulate matter emission factor; k is the hourly traffic flow; k is the dust emission coefficient, with a value of 0.66; sL is the road dust load; and W is the average vehicle weight.

[0030] Optionally, the step of using geographic information system software to perform regional interpolation analysis on the traffic correction dataset using multiple preset variogram models to determine the spatiotemporal distribution map of fine particulate matter includes:

[0031] The traffic correction dataset is subjected to regional interpolation analysis using multiple preset variogram models through geographic information system software, generating interpolation maps and standard root mean square values ​​corresponding to each variogram model;

[0032] Among all the standard root mean square values, the interpolation plot corresponding to the largest standard root mean square value is selected as the target interpolation plot;

[0033] An image masking operation is performed on the target interpolation map to generate a spatiotemporal distribution map of fine particulate matter.

[0034] A second aspect of the present invention provides an apparatus for constructing a spatiotemporal distribution map of fine particulate matter, comprising:

[0035] The dataset acquisition module is used to acquire the background environmental fine particulate matter concentration dataset and the fine particulate matter concentration dataset corresponding to the preset study area.

[0036] The background correction dataset determination module is used to perform a concentration correction operation on the fine particulate matter concentration dataset based on the background environment fine particulate matter concentration dataset, and determine the background concentration correction dataset;

[0037] The mean calculation module is used to calculate the average fine particulate matter concentration value for each road in the study area using the fine particulate matter concentration dataset.

[0038] The scatter plot generation module is used to perform Pearson correlation detection on the average fine particulate matter concentration value and the vehicle data corresponding to each road using social science statistical software, and generate a correlation scatter plot.

[0039] The traffic correction dataset generation module is used to remove the background concentration correction data corresponding to the roads where all outliers in the correlation scatter plot are located from the background concentration correction dataset to generate the traffic correction dataset.

[0040] The distribution map determination module is used to perform regional interpolation analysis on the traffic correction dataset using multiple preset variogram models through geographic information system software to determine the spatiotemporal distribution map of fine particulate matter.

[0041] Optionally, the background environment fine particulate matter concentration dataset includes background environment fine particulate matter concentrations acquired in minutes; the fine particulate matter concentration dataset includes fine particulate matter concentrations acquired in seconds; the background correction dataset determination module includes:

[0042] The background concentration correction data calculation submodule is used to perform difference calculations on the fine particulate matter concentration and the corresponding background environment fine particulate matter concentration to determine multiple background concentration correction data.

[0043] A submodule for constructing a concentration correction dataset is provided, which is used to construct a background concentration correction dataset using all the background concentration correction data described above.

[0044] The formula for calculating background concentration correction data is:

[0045] c tij =x tij -y ri ;

[0046] In the formula, c tij Define the movement rate r per minute of point t within the study area. i t per second ij Background concentration correction data; xtij r per minute for moving point t i t per second ij Fine particulate matter concentration data; y ri For a fixed measuring point r, the time per minute r i Background environmental fine particulate matter concentration data.

[0047] Optionally, the vehicle data includes traffic flow and vehicle type; the scatter plot generation module includes:

[0048] The traffic line source emission intensity calculation submodule is used to substitute the fine particulate matter emission factor corresponding to the vehicle type and the traffic flow into the traffic line source emission intensity formula to calculate and determine the traffic line source emission intensity.

[0049] The average vehicle weight calculation submodule is used to perform an average calculation using the traffic flow and the vehicle weight corresponding to the vehicle type to determine the average vehicle weight.

[0050] The dust source emission intensity calculation submodule is used to substitute the road dust load, the average vehicle weight and the traffic flow corresponding to the study area into the road dust emission formula to calculate and determine the dust source emission intensity.

[0051] The submodule for calculating the comprehensive emission source intensity is used to perform a summation operation on the emission intensity of the traffic line source and the emission intensity of the dust source to determine the comprehensive emission source intensity.

[0052] The execution detection operation submodule is used to perform Pearson correlation detection on the average fine particulate matter concentration value and the comprehensive emission source intensity using social science statistical software, and generate a correlation scatter plot.

[0053] Optionally, the formula for the emission intensity of traffic line sources is:

[0054] Q V =E·C;

[0055] The formula for road dust emissions is:

[0056]

[0057] In the formula, Q V For traffic line source emission intensity; Q Y E is the dust source emission intensity; C is the fine particulate matter emission factor; k is the hourly traffic flow; k is the dust emission coefficient, with a value of 0.66; sL is the road dust load; and W is the average vehicle weight.

[0058] Optionally, the distribution map determination module includes:

[0059] The regional interpolation analysis submodule is used to perform regional interpolation analysis on the traffic correction dataset using multiple preset variogram models through geographic information system software, and generate interpolation maps and standard root mean square values ​​corresponding to each variogram model.

[0060] The target interpolation plot determination submodule is used to select the interpolation plot corresponding to the largest standard root mean square value from all the standard root mean square values ​​as the target interpolation plot;

[0061] The image masking submodule is used to perform image masking operations on the target interpolation map to generate a spatiotemporal distribution map of fine particles.

[0062] As can be seen from the above technical solutions, the present invention has the following advantages:

[0063] This method involves acquiring background environmental fine particulate matter (FPM) concentration datasets and fine particulate matter concentration datasets corresponding to a predefined study area. Based on the background FPM concentration dataset, a concentration correction operation is performed on the fine particulate matter concentration dataset to determine the background concentration correction dataset. Pearson correlation detection is then performed on the average FPM concentration value and vehicle data corresponding to each road using social science statistical software to generate a correlation scatter plot. The background concentration correction dataset is then used to remove the background concentration correction data corresponding to all roads with outliers from the correlation scatter plot, generating a traffic correction dataset. Finally, geographic information system (GIS) software is used to perform regional interpolation analysis on the traffic correction dataset using multiple predefined variogram models to determine the spatiotemporal distribution map of FPM. This approach addresses the problem that existing technologies only estimate missing data trends from a single spatial or temporal perspective, resulting in a limited data source and the loss of valuable information from the original data, thus leading to inaccurate PM data. 2.5 The technical problem of low accuracy in the spatial distribution of PM concentration effectively improved PM2.5 concentration. 2.5 The accuracy of concentration spatial distribution enables multi-faceted optimization of traffic emissions and building layout, providing guidance for planning urban blocks with good air quality. Attached Figure Description

[0064] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1 A flowchart illustrating the steps of a method for constructing a spatiotemporal distribution map of fine particulate matter according to Embodiment 1 of the present invention;

[0066] Figure 2The flowchart illustrates the steps of a method for constructing a spatiotemporal distribution map of fine particulate matter according to Embodiment 2 of the present invention.

[0067] Figure 3 This is a structural block diagram of a device for constructing a spatiotemporal distribution map of fine particulate matter, provided in Embodiment 3 of the present invention. Detailed Implementation

[0068] This invention provides a method and apparatus for constructing a spatiotemporal distribution map of fine particulate matter, addressing the problem that existing technologies only estimate missing data trends from a single spatial or temporal perspective, resulting in a limited data source and the loss of valuable information from the original data, thus leading to suboptimal PM data quality. 2.5 Technical problem of low accuracy in spatial distribution of concentration.

[0069] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0070] Please see Figure 1 , Figure 1 This is a flowchart illustrating the steps of a method for constructing a spatiotemporal distribution map of fine particulate matter, as provided in Embodiment 1 of the present invention.

[0071] The present invention provides a method for constructing a spatiotemporal distribution map of fine particulate matter, comprising:

[0072] Step 101: Obtain the background environment fine particulate matter concentration dataset and the fine particulate matter concentration dataset corresponding to the preset study area.

[0073] First, based on the pre-defined study area and road grid red lines, a corresponding interconnected mobile observation route is designed for the study area. Then, at multiple times on typical meteorological days, the fine particulate matter concentration and corresponding latitude and longitude coordinates of each measuring point are recorded by walking or cycling. This yields the fine particulate matter concentration and latitude and longitude coordinates of each measuring point along the mobile route within each hour, i.e., the fine particulate matter concentration and corresponding latitude and longitude coordinates of each road within the pre-defined study area. These two are then paired according to the time series to construct a fine particulate matter concentration dataset. During the mobile observation process, fixed monitoring points are set up within the study area to record the background environmental fine particulate matter concentration. A background environmental fine particulate matter concentration dataset is constructed using all background environmental fine particulate matter concentrations.

[0074] For example, a specific area in the Zhujiang New Town CBD was selected as the research subject. This area encompasses densely packed office and residential buildings, with a diverse variety of street trees. Preliminary surveys revealed a large daily commuting population, resulting in high traffic volume and diverse vehicle types, reflecting its time-varying particulate matter emission characteristics. Based on the streetscape, traffic flow distribution, and street tree arrangement, two closed-loop fine particulate matter (FPM) mobile observation routes were designed. These routes aimed to cover diverse high-density building complexes and streets with varying traffic flow distributions and street tree arrangements. FPM mobile observations were conducted on typical days during summer, transitional seasons, and winter months. To obtain more stable test data, mobile observations were primarily conducted by pedestrians or cyclists equipped with a high-precision geolocation device (A8 Beidou terminal) and a continuous particulate matter recorder (handheld particulate matter detector). Data was collected at pedestrian height. Additionally, a fixed weather station was set up within the area to continuously monitor background FPM concentrations.

[0075] In this embodiment of the invention, a background environmental fine particulate matter concentration dataset and a fine particulate matter concentration dataset corresponding to a preset research area are obtained.

[0076] Step 102: Based on the background environment fine particulate matter concentration dataset, perform a concentration correction operation on the fine particulate matter concentration dataset to determine the background concentration correction dataset.

[0077] It should be noted that since the fine particulate matter concentration results from mobile observations, i.e., the fine particulate matter concentration data, include the fine particulate matter concentration in the background atmosphere, it is necessary to correct the fine particulate matter data from mobile observations to obtain traffic source fine particulate matter data, i.e., background concentration corrected data.

[0078] In this embodiment of the invention, a concentration correction operation is performed on the fine particulate matter concentration dataset based on the background environment fine particulate matter concentration dataset to determine the background concentration correction dataset.

[0079] Step 103: Calculate the average fine particulate matter concentration value for each road in the study area using the fine particulate matter concentration dataset.

[0080] It should be noted that by averaging the mobile observation fine particulate matter concentration data of each road at different times, the average fine particulate matter concentration value of each road in the study area at a certain time can be obtained.

[0081] In this embodiment of the invention, the average fine particulate matter concentration value corresponding to each road in the study area is calculated using a fine particulate matter concentration dataset.

[0082] Step 104: Perform Pearson correlation analysis on the average fine particulate matter concentration and the vehicle data corresponding to each road using social science statistical software to generate a correlation scatter plot.

[0083] It should be noted that the social science statistical software refers to SPSS software. The average fine particulate matter concentration value of each road is constructed into a comparative dataset with the road traffic source emission intensity calculated from the corresponding vehicle data at that time. Finally, it is imported into SPSS software for Pearson correlation detection, which can generate a correlation scatter plot. The correlation scatter plot also includes a linear trend line equation.

[0084] In this embodiment of the invention, Pearson correlation analysis is performed on the average fine particulate matter concentration and the vehicle data corresponding to each road using social science statistical software to generate a correlation scatter plot.

[0085] Step 105: Remove the background concentration correction data corresponding to all outliers in the correlation scatter plot from the background concentration correction dataset to generate the traffic correction dataset.

[0086] It should be noted that outliers refer to deviations from the linear trend line that deviate to a preset deviation level, or points outside the linear trend line can be considered deviation points.

[0087] In this embodiment of the invention, background concentration correction data corresponding to all outliers in the correlation scatter plot are removed from the background concentration correction dataset to generate a traffic correction dataset.

[0088] Step 106: Using geographic information system software, multiple preset variogram models are used to perform regional interpolation analysis on the traffic correction dataset to determine the spatiotemporal distribution map of fine particulate matter.

[0089] It should be noted that the geographic information system software refers to ArcGIS software, which uses the Kriging method in the Geostatical Analyst module to perform regional interpolation processing to determine the spatiotemporal distribution map of fine particulate matter.

[0090] In this embodiment of the invention, multiple preset variogram models are used in geographic information system software to perform regional interpolation analysis on the traffic correction dataset to determine the spatiotemporal distribution map of fine particulate matter.

[0091] In this embodiment of the invention, a background environmental fine particulate matter concentration dataset and a fine particulate matter concentration dataset corresponding to a preset research area are obtained. Based on the background environmental fine particulate matter concentration dataset, a concentration correction operation is performed on the fine particulate matter concentration dataset to determine the background concentration correction dataset. A Pearson correlation detection operation is performed on the average fine particulate matter concentration value and the vehicle data corresponding to each road to generate a correlation scatter plot. The background concentration correction data corresponding to the roads where all outliers in the correlation scatter plot are located are removed from the background concentration correction dataset to generate a traffic correction dataset. Multiple preset variogram models are used to perform regional interpolation analysis on the traffic correction dataset to determine the spatiotemporal distribution map of fine particulate matter. This solves the problem of obtaining PM 2.5 The technical problem of low accuracy in the spatial distribution of PM concentration effectively improved PM2.5 concentration. 2.5 Accuracy of spatial distribution of concentration.

[0092] Please see Figure 2 , Figure 2 This is a flowchart illustrating the steps of a method for constructing a spatiotemporal distribution map of fine particulate matter, as provided in Embodiment 2 of the present invention.

[0093] The present invention provides a method for constructing a spatiotemporal distribution map of fine particulate matter, comprising:

[0094] Step 201: Obtain the background environment fine particulate matter concentration dataset and the fine particulate matter concentration dataset corresponding to the preset study area.

[0095] It should be noted that multiple times on representative meteorological days in the target study area were selected. The study area was traversed by walking or cycling. At each time point, the participants started from the beginning of the mobile observation route, completed the entire route at a uniform speed, and returned to the end point. Then, the collected fine particulate matter concentrations and latitude and longitude coordinates were mapped according to the time series to construct a fine particulate matter concentration dataset.

[0096] In this embodiment of the invention, a background environmental fine particulate matter concentration dataset and a fine particulate matter concentration dataset corresponding to a preset research area are obtained.

[0097] Step 202: Based on the background environment fine particulate matter concentration dataset, perform a concentration correction operation on the fine particulate matter concentration dataset to determine the background concentration correction dataset.

[0098] The background fine particulate matter concentration dataset includes background fine particulate matter concentrations acquired in minutes, while the fine particulate matter concentration dataset includes fine particulate matter concentrations acquired in seconds.

[0099] It should be noted that the fine particulate matter concentration at each measuring point was obtained by intermittently recording data using a GT-1000 handheld particulate matter detector at a height equivalent to that of a pedestrian, with a recording interval of 1 second. The latitude and longitude coordinates were obtained by intermittently recording data using a high-precision GPS recorder, with a recording interval of 1 second. The high-precision geolocation device had a single-point accuracy (2m–5m), and the handheld PM... 2.5 The detector has a testing accuracy of ±3%. By placing fixed measuring points within the target study area, the GRIMM particulate matter detector was used to monitor the concentration of fine particulate matter in the background environment, with a recording interval of 1 minute.

[0100] Furthermore, step 202 may include the following sub-steps:

[0101] Step S21: Perform difference calculations on the fine particulate matter concentration and the corresponding background fine particulate matter concentration to determine multiple background concentration correction data.

[0102] It should be noted that multiple background concentration correction data are determined by performing difference calculations on the fine particulate matter concentration and the corresponding background fine particulate matter concentration. For example, when the time is 11:01, 60 fine particulate matter concentrations obtained in seconds are subtracted from the corresponding background fine particulate matter concentration obtained in minutes at 11:01, thereby obtaining 60 background concentration correction data.

[0103] The formula for calculating background concentration correction data is:

[0104] c tij =x tij -y ri ;

[0105] In the formula, c tij Define the movement rate r per minute of point t within the study area. i t per second ij Background concentration correction data; x tij r per minute for moving point t i t per second ij Fine particulate matter concentration data; y ri For a fixed measuring point r, the time per minute r i Background environmental fine particulate matter concentration data.

[0106] In this embodiment of the invention, the fine particulate matter concentration and the corresponding background fine particulate matter concentration are used to perform difference calculations to determine multiple background concentration correction data.

[0107] Step S22: Construct a background concentration correction dataset using all background concentration correction data.

[0108] In this embodiment of the invention, a background concentration correction dataset is constructed using all background concentration correction data.

[0109] Step 203: Calculate the average fine particulate matter concentration value for each road in the study area using the fine particulate matter concentration dataset.

[0110] In this embodiment of the invention, the average fine particulate matter concentration value corresponding to each road in the study area is calculated using a fine particulate matter concentration dataset.

[0111] Step 204: Perform Pearson correlation analysis on the average fine particulate matter concentration and the vehicle data corresponding to each road using social science statistical software to generate a correlation scatter plot.

[0112] Vehicle data includes traffic flow and vehicle type; vehicle type includes cars, buses, motorcycles, light trucks, and heavy trucks.

[0113] It should be noted that the hourly traffic flow and vehicle type of each road on the pre-set study area should be statistically analyzed. In order to obtain the hourly traffic volume of each road on the pre-set study area, the vehicles and vehicle types passing through each road within a continuous 5-minute period should be videotaped and recorded within each hour. The total traffic volume within that hour is obtained by multiplying the traffic flow within the 5 minutes by 12. The traffic volume data for the continuous 5 minutes should exclude the period when traffic accidents occur on the road segment.

[0114] Furthermore, step 204 may include the following sub-steps:

[0115] Step S41: Substitute the fine particulate matter emission factor corresponding to the vehicle type and the traffic flow into the traffic line source emission intensity formula to calculate and determine the traffic line source emission intensity.

[0116] It should be noted that different vehicle types have different particulate matter emission factors, and the particulate matter emission factor can be obtained according to the vehicle type.

[0117] The formula for traffic line source emission intensity is:

[0118] Q V =E·C;

[0119] In the formula, Q V E represents the emission intensity from traffic sources; E represents the particulate matter emission factor; and C represents the hourly traffic flow.

[0120] In this embodiment of the invention, the fine particulate matter emission factor corresponding to the vehicle type and the traffic flow are substituted into the traffic line source emission intensity formula for calculation to determine the traffic line source emission intensity.

[0121] Step S42: Calculate the average vehicle weight by averaging the traffic flow and vehicle weight corresponding to the vehicle type.

[0122] It should be noted that different vehicle types have different weights, and their weights can be obtained according to the vehicle type.

[0123] In this embodiment of the invention, the average vehicle weight is determined by averaging the traffic flow and the vehicle weight corresponding to the vehicle type.

[0124] Step S43: Substitute the road dust load, average vehicle weight, and traffic flow corresponding to the study area into the road dust emission formula to calculate and determine the dust source emission intensity.

[0125] It should be noted that the value of road dust load varies depending on the specifications of urban roads, i.e., different study areas, and can be obtained from the local traffic particulate matter emission inventory.

[0126] The formula for road dust emissions is:

[0127]

[0128] In the formula, Q Y denoted as _i_dust_source_emission_intensity; _C_ ...

[0129] In this embodiment of the invention, the road dust load, average vehicle weight, and traffic flow corresponding to the study area are substituted into the road dust emission formula for calculation to determine the dust source emission intensity.

[0130] Step S44: Perform a summation calculation on the emission intensity of traffic line sources and the emission intensity of dust sources to determine the comprehensive emission source intensity.

[0131] Based on the above, and using the collected traffic flow and vehicle type data, the traffic source emission intensity and dust source emission intensity of each road can be calculated. Then, the sum of these two emission intensities is used to determine the overall emission source intensity. For example, Table 1 shows the calculated overall emission source intensity of each road in a certain area of ​​the Zhujiang New Town CBD, which is used as the pre-set study area.

[0132] The formula for calculating the comprehensive emission source intensity is:

[0133] Q S =Q V +Q Y ;

[0134] In the formula, Q V For traffic line source emission intensity; Q Y The emission intensity of dust sources; QS To determine the intensity of comprehensive emission sources.

[0135] Table 1 Calculation results of comprehensive emission source intensity for each road

[0136]

[0137] In this embodiment of the invention, the emission intensity of traffic line sources and the emission intensity of dust sources are summed to determine the comprehensive emission source intensity.

[0138] Step S45: Perform Pearson correlation analysis on the average fine particulate matter concentration and the comprehensive emission source intensity using social science statistical software to generate a correlation scatter plot.

[0139] It should be noted that by averaging the mobile observation fine particulate matter concentration data of each road at different times, the average fine particulate matter concentration value of each road in the study area at a certain time can be obtained. This value is then compared with the comprehensive emission source intensity calculated at that time to form a comparative dataset. Finally, the dataset is imported into SPSS software for Pearson correlation detection, which can generate correlation scatter plots and linear trend line equations.

[0140] In this embodiment of the invention, Pearson correlation analysis is performed on the average fine particulate matter concentration and the comprehensive emission source intensity using social science statistical software to generate a correlation scatter plot.

[0141] Step 205: Remove the background concentration correction data corresponding to all outliers in the correlation scatter plot from the background concentration correction dataset to generate the traffic correction dataset.

[0142] It should be noted that outlier points in the scatter plot are manually removed. This involves eliminating the background concentration correction data corresponding to all roads where outliers in the correlation scatter plot are located from the background concentration correction dataset. The remaining points can be considered highly correlated with traffic emission sources. Therefore, the fine particulate matter concentration data of each moving observation on the road corresponding to that point at that moment can be used for subsequent PM2.5 analysis. 2.5 Construction of spatiotemporal distribution atlases.

[0143] In this embodiment of the invention, background concentration correction data corresponding to all outliers in the correlation scatter plot are removed from the background concentration correction dataset to generate a traffic correction dataset.

[0144] Step 206: Using geographic information system software, perform regional interpolation analysis on the traffic correction dataset using multiple preset variogram models to generate interpolation maps and standard root mean square values ​​corresponding to each variogram model.

[0145] It should be noted that after importing the traffic correction dataset into the geographic information system software, before interpolation, a variogram model is selected from multiple preset variogram models for the study area and input into ArcGIS software for internal verification. Common models include spherical functions, exponential functions, and Gaussian functions. After software fitting and calculation, interpolation maps and corresponding standard root mean square values ​​can be generated.

[0146] In this embodiment of the invention, geographic information system software is used to perform regional interpolation analysis on the traffic correction dataset using multiple preset variogram models, generating interpolation maps and standard root mean square values ​​corresponding to each variogram model.

[0147] Step 207: Select the interpolation plot corresponding to the largest standard root mean square value from all standard root mean square values ​​as the target interpolation plot.

[0148] It should be noted that different variogram models were selected and imported into the geographic information system software in sequence. The standard root mean square (SRM) values ​​of the different variogram models were repeatedly compared. Finally, the variogram model with the largest SRM was selected as the interpolation prediction model for the study area, and the interpolation map with the largest SRM was selected as the target interpolation map.

[0149] In this embodiment of the invention, the interpolation map corresponding to the largest standard root mean square value among all standard root mean square values ​​is selected as the target interpolation map.

[0150] Step 208: Perform an image masking operation on the target interpolation map to generate a spatiotemporal distribution map of fine particulate matter.

[0151] It should be noted that the interpolated images are uniformly masked, and the contour range and color band colors are changed to ultimately obtain the PM2.5 concentration from traffic sources in urban blocks of the target study area. 2.5 Spatiotemporal distribution map.

[0152] Furthermore, by performing the above processing on different weather days at various times, spatiotemporal distribution maps of fine particulate matter at each time were obtained. These maps were then used to construct a PM2.5 concentration map for traffic sources in urban blocks within the pre-defined study area. 2.5 Spatiotemporal distribution atlas.

[0153] In this embodiment of the invention, an image masking operation is performed on the target interpolation map to generate a spatiotemporal distribution map of fine particulate matter.

[0154] In this embodiment of the invention, a background fine particulate matter concentration dataset and a fine particulate matter concentration dataset corresponding to a preset study area are obtained. Based on the background fine particulate matter concentration dataset, a concentration correction operation is performed on the fine particulate matter concentration dataset to determine the background concentration correction dataset. The average fine particulate matter concentration value corresponding to each road in the study area is calculated using the fine particulate matter concentration dataset. Pearson correlation detection is performed on the average fine particulate matter concentration value and the vehicle data corresponding to each road using social science statistical software to generate a correlation scatter plot. The background concentration correction data corresponding to the roads where all outliers in the correlation scatter plot are removed from the background concentration correction dataset to generate a traffic correction dataset. The traffic correction dataset is then subjected to regional interpolation analysis using multiple preset variogram models using geographic information system software to determine the spatiotemporal distribution map of fine particulate matter. This solves the problem that existing technologies only estimate missing data trends from a single spatial or temporal perspective, resulting in a relatively singular data source and the loss of valuable information in the original data, which in turn leads to the obtained PM... 2.5 The technical problem of low accuracy in the spatial distribution of PM concentration effectively improved PM2.5 concentration. 2.5 The accuracy of concentration spatial distribution enables multi-faceted optimization of traffic emissions and building layout, providing guidance for planning urban blocks with good air quality.

[0155] Please see Figure 3 , Figure 3 This is a structural block diagram of a device for constructing a spatiotemporal distribution map of fine particulate matter, provided in Embodiment 3 of the present invention.

[0156] This invention provides an apparatus for constructing a spatiotemporal distribution map of fine particulate matter, comprising:

[0157] The dataset acquisition module 301 is used to acquire the background environment fine particulate matter concentration dataset and the fine particulate matter concentration dataset corresponding to the preset study area.

[0158] The background correction dataset module 302 is used to perform a concentration correction operation on the fine particulate matter concentration dataset based on the background environment fine particulate matter concentration dataset to determine the background concentration correction dataset.

[0159] The mean calculation module 303 is used to calculate the average fine particulate matter concentration value for each road in the study area using the fine particulate matter concentration dataset.

[0160] The scatter plot generation module 304 is used to perform Pearson correlation detection on the average fine particulate matter concentration value and the vehicle data corresponding to each road using social science statistical software, and generate a correlation scatter plot.

[0161] The traffic correction dataset generation module 305 is used to remove the background concentration correction data corresponding to all outliers in the correlation scatter plot within the background concentration correction dataset, thereby generating the traffic correction dataset.

[0162] The distribution map module 306 is used to perform regional interpolation analysis on the traffic correction dataset using multiple preset variogram models through geographic information system software to determine the spatiotemporal distribution map of fine particulate matter.

[0163] Furthermore, the background correction dataset determination module 302 includes:

[0164] The background concentration correction data calculation submodule is used to perform difference calculations on the fine particulate matter concentration and the corresponding background fine particulate matter concentration to determine multiple background concentration correction data.

[0165] The Concentration Correction Dataset Submodule is used to construct a background concentration correction dataset using all background concentration correction data.

[0166] The formula for calculating background concentration correction data is:

[0167] c tij =x tij -y ri ;

[0168] In the formula, c tij Define the movement rate r per minute of point t within the study area. i t per second ij Background concentration correction data; x tij r per minute for moving point t i t per second ij Fine particulate matter concentration data; y ri For a fixed measuring point r, the time per minute r i Background environmental fine particulate matter concentration data.

[0169] Furthermore, the scatter plot generation module 304 includes:

[0170] The traffic line source emission intensity calculation submodule is used to substitute the fine particulate matter emission factor corresponding to the vehicle type and the traffic flow into the traffic line source emission intensity formula to calculate and determine the traffic line source emission intensity.

[0171] The average vehicle weight calculation submodule is used to calculate the average vehicle weight by averaging the vehicle weights corresponding to traffic flow and vehicle type.

[0172] The submodule for calculating dust source emission intensity is used to substitute the road dust load, average vehicle weight, and traffic flow corresponding to the study area into the road dust emission formula to calculate and determine the dust source emission intensity.

[0173] The submodule for calculating the overall emission source intensity is used to perform a summation calculation on the emission intensity of traffic line sources and the emission intensity of dust sources to determine the overall emission source intensity.

[0174] The execution detection operation submodule is used to perform Pearson correlation detection on the average fine particulate matter concentration and the comprehensive emission source intensity using social science statistical software, and generate a correlation scatter plot.

[0175] The formula for traffic line source emission intensity is:

[0176] Q V =E·C;

[0177] The formula for road dust emissions is:

[0178]

[0179] In the formula, Q V For traffic line source emission intensity; Q Y E is the dust source emission intensity; C is the fine particulate matter emission factor; k is the hourly traffic flow; k is the dust emission coefficient, with a value of 0.66; sL is the road dust load; and W is the average vehicle weight.

[0180] Furthermore, the distribution map module 306 includes:

[0181] The regional interpolation analysis submodule is used to perform regional interpolation analysis on the traffic correction dataset using multiple preset variogram models through geographic information system software, and generate interpolation maps and standard root mean square values ​​corresponding to each variogram model.

[0182] The target interpolation plot submodule is used to select the interpolation plot corresponding to the largest standard root mean square value among all standard root mean square values ​​as the target interpolation plot.

[0183] The image masking submodule is used to perform image masking operations on the target interpolation map to generate a spatiotemporal distribution map of fine particles.

[0184] In this embodiment of the invention, a background fine particulate matter concentration dataset and a fine particulate matter concentration dataset corresponding to a preset study area are obtained. Based on the background fine particulate matter concentration dataset, a concentration correction operation is performed on the fine particulate matter concentration dataset to determine the background concentration correction dataset. The average fine particulate matter concentration value corresponding to each road in the study area is calculated using the fine particulate matter concentration dataset. Pearson correlation detection is performed on the average fine particulate matter concentration value and the vehicle data corresponding to each road using social science statistical software to generate a correlation scatter plot. The background concentration correction data corresponding to the roads where all outliers in the correlation scatter plot are removed from the background concentration correction dataset to generate a traffic correction dataset. The traffic correction dataset is then subjected to regional interpolation analysis using multiple preset variogram models using geographic information system software to determine the spatiotemporal distribution map of fine particulate matter. This solves the problem that existing technologies only estimate missing data trends from a single spatial or temporal perspective, resulting in a relatively singular data source and the loss of valuable information in the original data, which in turn leads to the obtained PM... 2.5 The technical problem of low accuracy in the spatial distribution of PM concentration effectively improved PM2.5 concentration. 2.5 The accuracy of concentration spatial distribution enables multi-faceted optimization of traffic emissions and building layout, providing guidance for planning urban blocks with good air quality.

[0185] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, modules, and sub-modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0186] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0187] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0188] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method of constructing a spatiotemporal distribution map of fine particulate matter, characterized by, The method comprises the following steps: obtaining a background environment fine particulate matter concentration dataset and a fine particulate matter concentration dataset corresponding to a preset study area; performing a concentration correction operation on the fine particulate matter concentration dataset based on the background environment fine particulate matter concentration dataset to determine a background concentration correction dataset; calculating average fine particulate matter concentration values corresponding to each road in the study area using the fine particulate matter concentration dataset; performing a Pearson correlation detection operation on the average fine particulate matter concentration values and vehicle data corresponding to each road using social science statistical software to generate a correlation scatter plot; removing background concentration correction data corresponding to roads where all abnormal points in the correlation scatter plot are located from the background concentration correction dataset to generate a traffic correction dataset; performing regional interpolation analysis on the traffic correction dataset using multiple preset variogram function models through geographic information system software to determine a fine particulate matter spatiotemporal distribution map; The background environment fine particulate matter concentration dataset comprises background environment fine particulate matter concentrations obtained in minutes; and the fine particulate matter concentration dataset comprises fine particulate matter concentrations obtained in seconds. The step of performing a concentration correction operation on the fine particulate matter concentration dataset based on the background environment fine particulate matter concentration dataset to determine a background concentration correction dataset comprises the following steps: performing difference operations on the fine particulate matter concentrations and the corresponding background environment fine particulate matter concentrations respectively to determine multiple background concentration correction data; constructing a background concentration correction dataset using all the background concentration correction data; The calculation formula of the background concentration correction data is: ； In the formula, Define the movement rate r per minute of point t within the study area. i t per second ij Background concentration correction data; r per minute for moving point t i t per second ij Fine particulate matter concentration data; For a fixed measuring point r, the time per minute r i Background environmental fine particulate matter concentration data.

2. The method of claim 1, wherein The vehicle data comprises vehicle flow and vehicle type; and the step of performing a Pearson correlation detection operation on the average fine particulate matter concentration values and the vehicle data corresponding to each road using social science statistical software to generate a correlation scatter plot comprises the following steps: calculating traffic line source emission intensity by substituting fine particulate matter emission factors corresponding to the vehicle type and the vehicle flow into a traffic line source emission intensity formula; performing mean value operations on the vehicle flow and the vehicle weight corresponding to the vehicle type to determine an average vehicle weight; calculating dust source emission intensity by substituting road dust load corresponding to the study area, the average vehicle weight, and the vehicle flow into a road dust emission formula; performing sum value operations on the traffic line source emission intensity and the dust source emission intensity to determine comprehensive emission source intensity; performing a Pearson correlation detection operation on the average fine particulate matter concentration values and the comprehensive emission source intensity using social science statistical software to generate a correlation scatter plot.

3. The method of claim 2, wherein The traffic line source emission intensity formula is: ； The road dust emission formula is: ； In the formula, Q V is the traffic line source emission intensity; Q Y is the dust source emission intensity; E is the fine particulate matter emission factor; C is the hourly vehicle flow; k is the dust emission coefficient, and the value is 0.66; sL is the road dust load; and W is the average vehicle weight of the driving vehicle.

4. The method of claim 1, wherein The step of performing regional interpolation analysis on the traffic correction dataset using multiple preset variogram function models through geographic information system software to determine a fine particulate matter spatiotemporal distribution map comprises the following steps: performing regional interpolation analysis on the traffic correction dataset using multiple preset variogram function models through geographic information system software to generate an interpolation map and a standard root mean square value corresponding to each variogram function model; Select the interpolation graph corresponding to the maximum standard root mean square value among all the standard root mean square values as a target interpolation graph; Perform an image mask operation on the target interpolation graph to generate a fine particulate matter space-time distribution graph.

5. A device for constructing a fine particulate matter spatiotemporal distribution map, applied to the method for constructing a fine particulate matter spatiotemporal distribution map according to claim 1, characterized in that, It comprises: An acquisition dataset module is configured to acquire a background environment fine particulate matter concentration dataset and a fine particulate matter concentration dataset corresponding to a preset research area; A background correction dataset determination module is configured to perform a concentration correction operation on the fine particulate matter concentration dataset based on the background environment fine particulate matter concentration dataset to determine a background concentration correction dataset; A mean value calculation module is configured to calculate average fine particulate matter concentration values corresponding to roads in the research area by using the fine particulate matter concentration dataset; A scatter plot generation module is configured to generate a correlation scatter plot by performing a Pearson correlation detection operation on the average fine particulate matter concentration values and vehicle data corresponding to the roads by using social science statistical software; A traffic correction dataset generation module is configured to exclude background concentration correction data corresponding to roads where all abnormal points in the correlation scatter plot are located from the background concentration correction dataset to generate a traffic correction dataset; A distribution graph determination module is configured to determine a fine particulate matter space-time distribution graph by performing regional interpolation analysis on the traffic correction dataset by using a plurality of preset variation function models through geographic information system software; The background environment fine particulate matter concentration dataset comprises background environment fine particulate matter concentrations acquired in minutes; and the fine particulate matter concentration dataset comprises fine particulate matter concentrations acquired in seconds; The background correction dataset determination module comprises: A background concentration correction data calculation sub-module is configured to determine a plurality of background concentration correction data by performing difference operations on the fine particulate matter concentrations and the corresponding background environment fine particulate matter concentrations, respectively; A concentration correction dataset construction sub-module is configured to construct a background concentration correction dataset by using all the background concentration correction data; The calculation formula of the background concentration correction data is: ； In the formula, Define the movement rate r per minute of point t within the study area. i t per second ij Background concentration correction data; r per minute for moving point t i t per second ij Fine particulate matter concentration data; For a fixed measuring point r, the time per minute r i Background environmental fine particulate matter concentration data.

6. The device for constructing a spatiotemporal distribution map of fine particulate matter according to claim 5, characterized in that, The vehicle data comprises vehicle flow and vehicle types; and the scatter plot generation module comprises: A traffic line source emission intensity calculation sub-module is configured to determine a traffic line source emission intensity by substituting fine particulate matter emission factors corresponding to the vehicle types and the vehicle flow into a traffic line source emission intensity formula for calculation; An average vehicle weight calculation sub-module is configured to determine an average vehicle weight by performing mean value operations on the vehicle flow and vehicle weights corresponding to the vehicle types; A dust source emission intensity calculation sub-module is configured to determine a dust source emission intensity by substituting a road dust load corresponding to the research area, the average vehicle weight, and the vehicle flow into a road dust emission formula for calculation; A comprehensive emission source intensity calculation sub-module is configured to determine a comprehensive emission source intensity by performing sum value operations on the traffic line source emission intensity and the dust source emission intensity; A detection operation execution sub-module is configured to generate a correlation scatter plot by performing a Pearson correlation detection operation on the average fine particulate matter concentration values and the comprehensive emission source intensity by using social science statistical software.

7. The device for constructing a spatiotemporal distribution map of fine particulate matter according to claim 6, characterized in that, The traffic line source emission intensity formula is: ； The road dust emission formula is: ； In the formula, Q V is the traffic line source emission intensity; Q Y is the fugitive dust source emission intensity; E is the fine particulate matter emission factor; C is the hourly vehicle flow; k is the fugitive dust emission coefficient, with a value of 0.66; sL is the road fugitive dust load; and W is the average vehicle weight of the driving vehicles.

8. The device for constructing a spatiotemporal distribution map of fine particulate matter according to claim 5, characterized in that, The distribution graph determination module comprises: The regional interpolation analysis submodule is configured to perform regional interpolation analysis on the traffic correction data set by using a plurality of preset variogram function models through geographic information system software, to generate an interpolation map corresponding to each variogram function model and a standard root mean square value; The target interpolation map submodule is configured to select an interpolation map corresponding to a maximum standard root mean square value from all the standard root mean square values as a target interpolation map; The image mask submodule is configured to perform an image mask operation on the target interpolation map to generate a particulate matter spatiotemporal distribution map.

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