A method of converting a road GIS file to an AERMOD input file

Abstract

The present application belongs to the field of road traffic pollution assessment, and relates to a method for converting a road GIS file into an AERMOD input file. The method comprises the following steps: S1. obtaining geographic coordinate data meeting the requirements of AERMOD based on a GIS line layer file; S2. generating the geometric shape of an AERMOD source, and selecting a line source, a surface source or a volume source; S3. generating a near-road side receiver and a grid receiver; S4. obtaining emission rate data matching the geometric shape of the AERMOD source; and S5. integrating the geometric shape information of the converted AERMOD source, the position information of the near-road side receiver and the grid receiver, and the emission rate information matching the geometric shape of the AERMOD source, to generate an AERMOD input file. The present application can improve work efficiency, reduce the risk of errors, and is more convenient to operate, and is suitable for multiple scenarios.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of road traffic pollution assessment, and relates to a method for converting a road GIS file into an AERMOD input file. BACKGROUND

[0002] Practical experience at home and abroad shows that even when the economy is changing from the industrialization stage to the post-industrialization stage, traffic energy consumption and emission pollution will continue to grow rapidly. As a large-scale emission source with strong space-time dynamics and closely related to human activities, road traffic has become the core field of urban ecological civilization and the air pollution prevention and control campaign.

[0003] Precise characterization of urban road traffic pollution fields is an important prerequisite for governments and environmental protection agencies to assess urban roadside air quality and develop effective traffic control policies. The characterization of urban traffic pollution diffusion is mainly achieved through air pollution concentration monitoring and pollution diffusion simulation. In terms of pollution diffusion simulation, the current models mainly include AERMOD, CALINE 4, and ADMS, etc. Among them, the open-source steady-state plume dispersion model AERMOD developed by the U.S. Environmental Protection Agency (EPA) has become the main tool for conducting fine traffic project hotspot analysis in recent years. However, most traffic projects usually involve many road elements with complex geometric designs, such as horizontal curves with different radii, which are affected by factors such as terrain, road grade, design speed, and sight distance. One of the key challenges in fine diffusion modeling is how to spatially encode the geometric shape of the road and the location of the concentration receptor in AERMOD, especially for the open-source, non-commercial AERMOD software, as there is no embedded geographic visualization function. This process requires a lot of manual operation and is likely to cause analysis errors (Wu, Y. & Niemeie, D., 2016. Strategy of AERMOD Configuration for Transportation Conformity Hot-spot Analysis.).

[0004] Therefore, in order to improve the preparation efficiency of the input file before diffusion modeling and reduce the risk of errors, especially when modeling roads with complex geometric designs, it is crucial to seek a processing method based on open-source road GIS layer files, which can quickly and accurately realize spatial encoding of the geometric shape of the road and the location of the concentration receptor, generate input files that meet the AERMOD specifications. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a method for converting a road GIS file into an AERMOD input file, which can rapidly and accurately automatically generate a standard input file directly used for AERMOD simulation based on an open-source available road GIS layer file.

[0006] The technical scheme of the present application is as follows:

[0007] A method for converting a road GIS file into an AERMOD input file comprises the following steps:

[0008] S1. Obtain geographic coordinate data meeting the requirements of AERMOD based on a GIS line layer file;

[0009] S2. Generate a geometric shape of an AERMOD source, and select a line source, a surface source or a volume source;

[0010] S3. Generate a near-roadside receptor and a grid receptor;

[0011] S4. Obtain emission rate data matching the geometric shape of the AERMOD source;

[0012] S5. Integrate the geometric shape information of the converted AERMOD source, the position information of the near-roadside receptor and the grid receptor, and the emission rate information matching the geometric shape of the AERMOD source, to generate an AERMOD input file.

[0013] The present application relies on a road GIS data file and realizes the automatic generation of an AERMOD input file through Python programming technology, thereby greatly simplifying the spatial coding process of road geometric shapes when using AERMOD for diffusion simulation, significantly improving the preparation efficiency of the AERMOD input file, and supporting the rapid testing of various modeling configurations, facilitating the analysis and evaluation of multiple traffic scenarios or parameter sensitivity. The present application makes road traffic pollution simulation more efficient and accurate, and provides strong technical support for traffic environment evaluation and management.

[0014] The present application has the following beneficial effects:

[0015] Improved work efficiency: the present application can significantly reduce the input file preparation time, reduce the frequency of manual operation, make the model setting more rapid, and thus accelerate the overall process of traffic pollution modeling;

[0016] Reduced error risk: the automatic conversion process reduces the probability of human error, ensures file consistency and accuracy, and thus improves model reliability;

[0017] Convenient to use: the application of the present application makes the model operation more simple, and even users who are not familiar with air quality modeling can easily perform simulation;

[0018] Multi-scenario quick test: automatically generated files enable users to easily perform multi-scenario or sensitivity analysis to better understand traffic pollution under different conditions.

[0019] Provide decision support: this technology provides an effective tool for urban planning and environmental protection, which helps to improve urban air quality and transportation planning by evaluating traffic pollution and formulating corresponding policies and management strategies. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 The main process of the present application is to convert road GIS files into AERMOD input files;

[0021] Figure 2 The geometric shape determination algorithm principle of the area source in step 2 of the present application is shown;

[0022] Figure 3 The geometric shape determination algorithm principle of the volume source in step 2 of the present application is shown;

[0023] Figure 4 In step 3.1 of the present application, the buffer area boundary is used as the positioning layer of the near-roadside receiver;

[0024] Figure 5 In step 3.2 of the present application, equidistant near-roadside receivers are generated;

[0025] Figure 6 In step 3.3 of the present application, the scene is shown;

[0026] Figure 7 In the embodiment, the study area projected into the local Cartesian coordinate system is shown;

[0027] Figure 8 In the embodiment, the AERMOD line source generated by the present application is shown;

[0028] Figure 9 In the embodiment, the AERMOD area source generated by the present application is shown;

[0029] Figure 10 In the embodiment, the AERMOD volume source generated by the present application is shown;

[0030] Figure 11 In the embodiment, the receiver position generated by the present application is shown. DETAILED DESCRIPTION

[0031] In order to have a clearer understanding of the technical features, objectives and effects of the present application, the specific embodiments of the present application will be described in detail with reference to the drawings.

[0032] As Figure 1As shown in the figure, a method for converting road GIS files into AERMOD input files includes the following steps:

[0033] S1. Obtain geographic coordinate data meeting AERMOD requirements based on GIS line layer files;

[0034] S2. Generate the geometric shape of the AERMOD source, select a line source, an area source, or a volume source;

[0035] S3. Generate near-roadside receivers and grid receivers;

[0036] S4. Obtain emission rate data matching the geometric shape of the AERMOD source;

[0037] S5. Integrate the geometric shape information of the converted AERMOD source, the position information of the near-roadside receivers and grid receivers, and the emission rate information matching the geometric shape of the AERMOD source to generate an AERMOD input file.

[0038] Further, step S1 takes publicly available GIS line layer files as input, projects the user-defined research boundary (spatial range) and reference position (0, 0) into the local Cartesian coordinate system by referring to the database of the European Petroleum Survey Group (EPSG) geographic spatial reference system and projection, and then determines the geographic coordinate data of the research area based on the reference position.

[0039] Further, step S2 takes the converted road GIS file containing Cartesian geographic coordinate data as input, and generates the geometric shape type of the pollution source meeting the modeling requirements of AERMOD, selecting a line source (LINE, RLINE, RLINEXT), an area source (AREA), or a volume source (VOLUME);

[0040] wherein,

[0041] The geometric shape of the line source is determined by the GIS road node coordinates and the road width (special, the road curve segment is approximated by combining adjacent line sources);

[0042] The geometric shape of the area source is determined by the vertex coordinates of the polygon;

[0043] The geometric shape of the volume source is determined by the source center coordinates and the initial lateral diffusion coefficient (syinit).

[0044] Further, in step S2, the geometric shape of the area source is determined by the vertex coordinates of the polygon, as shown in the figure, and the specific steps are as follows: Figure 2

[0045] ​(b2.1) Targeting the equal width of the area source (road width) all the time, taking the road GIS polyline as the road centerline, determining the position relationship of the two area source vertices associated with each road node, i.e. determining the unit vector α(x α ,y α ) of the connecting line of the two vertices associated with the node, the calculation formula is as follows:

[0046]

[0047]

[0048] Wherein, D(i-1,i) represents the distance between node i-1 and node i, and the value is equal to

[0049] Similarly, D(i,i+1) represents the distance between node i and node i+1, and the value is equal to

[0050]

[0051] M i represents a standardization factor, which ensures that x α 2 +y α 2 =1.

[0052] (b 2.2) Combining the node position, the position relationship of the two vertices associated with it, and the road width information, determining the coordinate information of the two vertices (Li and Ri), the calculation formula is as follows:

[0053]

[0054]

[0055] Wherein, W represents the road width; θ represents the road inflection angle.

[0056] In particular, when the number of generated area source vertices exceeds 20 vertices (i.e. the road GIS polyline has 11 or more nodes), the entire road area will be divided into multiple polygon area sources (each area source has no more than 20 vertices) to meet the input requirements of the AERMOD source.

[0057] Further, in step S2, the geometry of the volume source is determined by the source center coordinates and the initial lateral dispersion coefficient (syinit), as shown in Figure 3 , specifically as follows:

[0058] (c2.1) Determine the number of adjacent body source unit layers N required for simulating road sources according to road specific properties (width, number of lanes) and body source unit width setting restrictions (considering that the monitoring point should be located at a position 5 meters away from the source, and should not be set within the exclusion area, i.e. the body source unit width d should satisfy: d / 2+1<5→d<8m) lyr and the body source unit width d, the calculation formula is as follows:

[0059]

[0060]

[0061] wherein d max represents the upper limit of the body source unit width setting, which can be adjusted and set within the range of 8m according to actual needs; int represents the floor function.

[0062] (c2.2) Determine the number of body source unit layers n according to the length of the road j , the calculation formula is as follows:

[0063]

[0064] wherein S j represents the length of the jth layer of body source, which is approximately equal to the length of the road;

[0065] (c2.3) Determine the center position (distance from the starting node of the road) of each body source unit according to the length of the road and the number of body source unit layers, the calculation formula is as follows:

[0066]

[0067]

[0068] wherein l j represents the interval of the jth layer of body source unit; loc ij represents the position of the ith body source unit of the jth layer.

[0069] (c2.4) According to the center position information of each body source unit, generate the center coordinates of each body source unit based on the "GeoPandas" module in Python programming language;

[0070] The initial lateral diffusion coefficient (syinit) is calculated according to the body source unit width d, the formula is as follows:

[0071]

[0072] Further, the step S3 comprises the following steps:

[0073] (3.1) Preset the lateral distance d of the receiver away from the road edge nr Create the buffer zone width within the specified distance by means of the "buffer" function in Python programming language, buffer zone width = W + d nr Use the buffer zone boundary as the positioning layer of the near-roadside receiver (as shown in Figure 4 ); wherein the number of positioning layers of the near-roadside receiver depends on the road grade.

[0074] For example, for a highway, generally 4 receiver positioning layers are generated, which are located at a distance of 5 meters, 15 meters, 50 meters and 100 meters from the road edge, i.e. the lateral distance d nr = 5 meters, 15 meters, 50 meters and 100 meters.

[0075] (3.2) Determine the receiver spacing of each positioning layer according to the road grade, and generate equidistant near-roadside receivers (as shown in the red circles in Figure 5 ) by means of the "line interpolation" module in Python programming language: for example, for a highway, the spacing of the receivers in the 4 positioning layers is 20 meters, 30 meters, 50 meters and 100 meters, respectively.

[0076] (3.3) Generate grid receivers according to the preset grid spacing (as shown in Figure 6 ), while in order to avoid overlapping with the near-roadside receivers and to avoid the receiver position being too close to the emission source, remove the grid receivers located inside the buffer zone;

[0077] (3.4) Set the height of the receiver, which is usually set in the height range of 1.5 meters to 1.6 meters from the ground.

[0078] Further, in the step S4, the emission rate data in the GIS file is converted into emission rate data matching the AERMOD source geometry by combining the road properties (width, length, area, etc.) and the AERMOD source characteristics.

[0079] Further, in the step S5, the geometric shape information of the converted AERMOD source, the position information of the near-roadside receivers and the grid receivers, and the emission rate information matching the AERMOD source geometry are integrated and compiled by means of Python programming technology to form an input file containing all the parameters and data required for AERMOD operation, which can be directly executed by AERMOD.

[0080] Embodiment

[0081] Taking a GIS layer file of a city road area as an example, the AERMOD input file suitable for traffic pollution diffusion simulation of the area is generated by converting the road GIS file into the AERMOD input file according to the technology provided in the application, and the specific implementation steps are as follows:

[0082] S1. Taking the GIS line layer file as input, referring to the database of EPSG geospatial reference system and projection, projecting the user-defined research boundary (spatial range) and reference position (0, 0) into the local Cartesian coordinate system. Further, the geographical coordinate data of the research area with the reference position as the benchmark is determined.

[0083] S2. Taking the road GIS file containing the Cartesian geographical coordinate data after conversion as input, generating the pollution source geometric shape type meeting the modeling requirements of AERMOD, selecting the line source (LINE, RLINE, RLINEXT), the area source (AREA) or the volume source (VOLUME).

[0084] When the line source is selected as the AERMOD source, the geometric shape of the line source is determined by the GIS road node coordinates and the road width, as shown in the following formula: Figure 7 The road node coordinates and the road width information of the generated line source are shown in Table 1:

[0085] Table 1: Summary table of line source geometric shape information (only part is listed)

[0086]

[0087] When the area source is selected as the AERMOD source, the geometric shape of the area source is determined according to the following steps:

[0088] (1) Taking the road GIS polyline as the road center line and keeping the area source width (road width) always equal as the target, the position relationship of the two area source vertices associated with each road node is determined, that is, the unit vector α(x α ,y α ) of the connecting line of the two vertices associated with the node is determined, and the calculation formula is as follows:

[0089]

[0090]

[0091] wherein D(i-1, i) represents the distance between the node i-1 and the node i, and the value is equal to Similarly, D(i, i+1) represents the distance between the node i and the node i+1, and the value is equal to M i represents a standardization factor, which ensures that x α 2 +yα 2 = 1.

[0092] (2) Combining the node position, the position relationship of the two vertices associated therewith, and the road width information, the coordinate information of the two vertices (Li and Ri) is determined, and the calculation formula is as follows:

[0093]

[0094]

[0095] wherein, W represents the road width; and θ represents the road inflection point angle.

[0096] The geometric shape of the generated surface source is as shown in FIG. 2, and the vertex coordinate information of the surface source is shown in Table 2: Figure 8

[0097] Table 2 Summary of Surface Source Geometric Shape Information (Only a part is listed)

[0098]

[0099]

[0100] When a volume source is selected as the AERMOD source, the geometric shape of the volume source is determined according to the following steps:

[0101] (1) Set the upper limit d of the volume source unit width to 8 m, determine the number of adjacent volume source units N required for simulating the road source and the volume source unit width d, and the calculation formula is as follows: max = 8 m, and the calculation formula is as follows: lyr

[0102]

[0103]

[0104] (2) According to the road length, determine the number n of volume source units in each layer, and the calculation formula is as follows: j

[0105]

[0106] wherein, S j represents the length of the jth layer of volume source, which is approximately equal to the road length.

[0107] (3) According to the road length and the number of volume source units in each layer, the center position (distance from the starting node of the road) of each volume source unit is determined, and the calculation formula is as follows:

[0108]

[0109] ​​​

[0110] wherein, l j represents the interval of the jth layer body source unit; loc ij represents the position of the ith body source unit of the jth layer.

[0111] (4) According to the center position information of each body source unit, the center coordinates of each body source unit are sequentially generated based on the "GeoPandas" module in Python programming language.

[0112] (5) The initial transverse diffusion coefficient (syinit) is calculated according to the body source unit width d, and the formula is as follows:

[0113]

[0114] The generated body source geometry is shown in Figure 9 , and the corresponding body source unit position information is shown in Table 3:

[0115] Table 3 Summary of Body Source Geometry Information (Only a part is listed)

[0116]

[0117] S3. Generate near-roadside receivers and grid receivers positions, specifically including the following steps:

[0118] (1) According to the road grade, determine the lateral distance d nr , use the "buffer" function in Python programming language to create a buffer area within the specified distance (width = W+d nr ), and determine the positioning layer of the near-roadside receiver;

[0119] (2) According to the road grade, determine the receiver interval of each positioning layer, and generate equidistant near-roadside receivers by means of the "line interpolation" module in Python programming language;

[0120] (3) The grid interval is set to 200m, and the grid receivers are generated, while the receivers falling inside the buffer area are removed;

[0121] (4) Set the height of the receiver to 1.5m.

[0122] The generated receiver geometry is shown in Figure 10 . The corresponding receiver position information is shown in Table 4:

[0123] Table 4 Summary of Receiver Position Information (Only a part is listed)

[0124]

[0125] S4. With the road attributes (width, length, area, etc.) and the generated AERMOD source characteristics, the emission rate data in the GIS file (take the MOVES emission data as an example) is converted into the emission rate data matching the AERMOD source geometry, and the unit conversion method is shown in Table 5:

[0126] Table 5: Emission rate unit conversion formula

[0127]

[0128] According to the above formula, the converted emission rate data matching the AERMOD source geometry is shown in Table 6:

[0129] Table 6: Emission rate matching AERMOD source (take the line source RLINE as an example)

[0130]

[0131] S5. With the help of Python programming technology, the geometric shape information of the converted AERMOD source (select one of them), the location information of the near-road side receiver and the grid receiver, and the emission rate information matching the AERMOD source geometry are integrated and compiled to form an input file containing all the parameters and data required for AERMOD operation.

[0132] The above embodiments are intended to detail the present application, however the present application is not limited to this single example. Any modification, modification, replacement, combination, simplification, etc. of the described embodiments without deviating from the spirit and principles of the present application should be considered as equivalent replacement methods and included in the protection scope of the present application.

Claims

1. A method of converting a road GIS file to an AERMOD input file, characterized by, The method comprises the following steps: S1. Obtain geographic coordinate data meeting the requirements of AERMOD based on a GIS line layer file; S2. Generate the geometric shape of an AERMOD source, and select a line source, an area source or a volume source; S3. Generate a near-roadside receiver and a grid receiver; S4. Obtain emission rate data matching the geometric shape of the AERMOD source; S5. Integrate the geometric shape information of the converted AERMOD source, the position information of the near-roadside receiver and the grid receiver, and the emission rate information matching the geometric shape of the AERMOD source, to generate an AERMOD input file; Step S1 takes a publicly available GIS line layer file as input, projects a user-defined research boundary for determining a spatial range and a reference position (0, 0) into a local Cartesian coordinate system with reference to a database of EPSG geographic spatial reference systems and projections, and then determines geographic coordinate data of a research area with the reference position as a reference; Step S2 takes a road GIS file containing Cartesian geographic coordinate data after conversion as input, and generates a pollution source geometric shape type meeting the modeling requirements of AERMOD, selects a line source, an area source AREA or a volume source VOLUME, and selects a line source including LINE, RLINE and RLINEXT; wherein, the geometric shape of the line source is determined by GIS road node coordinates and road width; the geometric shape of the area source is determined by the vertex coordinates of a polygon; The geometry of the volume source is given by the source center coordinates and the initial transverse diffusion coefficient determined; Step S3 comprises the following steps: (3.1) a pre-set lateral distance of the receiver from the road edge a buffer zone width within the specified distance is created by means of the "buffer" function in the Python programming language, the buffer zone width = 2 * distance W represents the road width, and the buffer zone boundary is used as the positioning layer of the near-side receiver; wherein the number of positioning layers of the near-side receiver depends on the road grade; (3.2) Determine the receiver spacing of each positioning layer according to the road grade, and generate equidistant near-roadside receivers by means of the "lineinterpolation" module in the Python programming language; (3.3) Generate grid receivers according to a preset grid spacing, and remove the grid receivers located inside a buffer zone in order to avoid overlapping with the near-roadside receivers and to avoid the receiver position being too close to the emission source; (3.4) Set the height of the receiver to a height range of 1.5 meters to 1.6 meters from the ground.

2. The method of converting a road GIS file to an AERMOD input file of claim 1, wherein, In step S2, the geometric shape of the area source is determined by the vertex coordinates of a polygon, and the specific steps are as follows: (b2.1) The road width of the road is determined by the face source width, and the position relationship of the two face source vertices associated with each road node is determined, i.e., the unit vector of the connecting line of the two vertices associated with the node is determined, taking the road GIS broken line as the road center line, with the goal of keeping the face source width always equal , and the calculation formula is as follows: wherein, represents a node i -1 and a distance between nodes i , whose value is equal to ; By analogy, representing a node i with a node i +1 between the distance, the value is equal to ; represents a standardization factor, ensuring ; (b 2.2) Determine the coordinate information of the left and right two vertices Li and Ri in combination with the node position, the positional relationship of the two vertices associated therewith and the road width information, and the calculation formula is as follows: wherein ; represents a road width; represents a road inflection angle; In particular, when the number of generated area source vertices exceeds 20 vertices, i.e. the road GIS polyline has 11 or more nodes, the entire road area is divided into multiple polygon area sources, and the number of vertices of each area source does not exceed 20, so as to meet the input requirements of the AERMOD source.

3. The method of converting a road GIS file to an AERMOD input file of claim 1, wherein, In step S2, the geometry of the volume source is defined by a source center coordinate and an initial transverse diffusion coefficient is determined, in particular as follows: (c2.1) in combination with two specific properties of the road, width, number of lanes, and the set limit of the volume source unit width, the limit is set that the volume source unit width d satisfies: , the number of adjacent volume source unit layers required to determine the simulated road source and the volume source unit width d, the calculation formula is as follows: wherein, represents the upper limit of the body source unit width setting, which can be adjusted within the range of 8 m ;[ ] int represents a floor function; W represents a road width; (c2.2) Determining the number of body source units per layer according to road length The calculation formula is as follows: wherein, represents the length of the layer source, which approximates the road length; j represents the length of the layer source, which approximates the road length; (c2.3) Determine the center position of each volume source unit according to the distance between each volume source unit and the starting node of the road based on the road length and the number of volume source units per layer, and the calculation formula is as follows: wherein, represents the first j layer body source unit interval; represents the first j layer the i position of the individual source unit; (c2.4) Generate the center coordinates of each volume source unit in sequence based on the "GeoPandas" module in the Python programming language according to the center position information of each volume source unit; Initial lateral diffusion coefficient The calculation is made according to the body-source cell width d, with the following formula:

4. The method of converting a road GIS file to an AERMOD input file of claim 1, wherein, In the step S4, the emission rate data in the GIS file is converted into emission rate data matching the AERMOD source geometry by combining the road attribute and the AERMOD source characteristics.

5. The method of converting a roadway GIS file to an AERMOD input file of claim 1, wherein, In the step S5, the geometry information of the converted AERMOD source, the position information of the near-road side receiver and the grid receiver, and the emission rate information matching the AERMOD source geometry are integrated and compiled by means of the Python programming technology to form an input file containing all the parameters and data required for AERMOD operation, which can be directly executed by AERMOD.

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