Urban street walking space pollen sensitization risk evaluation method and system

Abstract

The invention relates to an urban street walking space pollen sensitization risk evaluation method and system, and belongs to the technical field of pollen sensitization risk evaluation. In order to solve the problem that the existing sensitization evaluation tool has single evaluation on pollen sensitization influence factors, the method comprises the following steps: acquiring multi-source data information of urban street walking space, and extracting street space features, meteorological features and street vegetation risk source features from the multi-source data information; the method comprises the steps of determining a walking space sensitization risk dispersity index according to street space features and meteorological features, calculating a walking space sensitization risk index according to the walking space sensitization risk dispersity index and street vegetation risk source features, dividing grids, calculating risk index scores of intersection positions of the grids, and drawing a street sensitization risk distribution map. And obtaining an evaluation result of the walking space sensitization risk. According to the method, the influence of wind and air quality on the pollen sensitization risk is fully considered, the vegetation evaluation mode is improved, real-time walking space sensitization risk distribution is generated, and allergic people are helped to know the sensitization risk.

Description

Technical Field

[0001] This invention relates to the field of pollen allergy risk assessment technology, and in particular to a method and system for assessing pollen allergy risk in urban street pedestrian spaces. Background Technology

[0002] According to incomplete statistics, over 25% of the world's population is affected by pollen allergies. Studies have found that with the acceleration of urbanization, allergies are becoming increasingly prevalent. A significant environmental trigger is the increased allergenic pollen brought in by urban green spaces. Furthermore, climate change and urban air pollutants such as NO2 and SO2 further exacerbate the release and allergenicity of pollen particles. Through multi-point field measurements of airborne pollen counts, it was confirmed that urban green spaces are the primary source of surrounding airborne pollen. Moreover, pollen concentrations in urban spaces are not uniform; the quantity and types of pollen vary significantly across different locations. The allergenic risk in pedestrian areas along urban streets, which urban residents traverse daily, is not consistent. Understanding the distribution of allergenic risk along different routes is crucial for risk prevention for sensitive populations.

[0003] Current methods for assessing pollen allergy risk can be summarized into three main technical directions: pollen monitoring results, airborne allergen molecules, and vegetation information. The first two can quantify pollen concentration and pollen allergen molecule concentration, directly quantifying the pollen allergy risk at monitoring points. However, due to high costs, it is difficult to densely deploy monitoring points, making it impossible to conduct multi-scale, multi-point, and all-time risk monitoring and assessment in cities. Allergy assessment based on vegetation information, depending on the evaluation object, can be summarized into three categories of evaluation indicators and methods in international research: plant species (AI, PAV), urban green space plant communities (SAI, IUGZA), and urban space (AIROT, AIROTm, tree pollen dispersal simulation).

[0004] Urban spatial characteristics and microclimate features can influence pollen dispersal, including building and street layout, meteorological factors, and vegetation. Buildings, especially high-rise buildings, can have a "washout" effect on pollen dispersal. Wide streets and squares are conducive to pollen dispersal. Light breezes (wind speed 1.5–3 m / s) are even more conducive to pollen dispersal. In windless environments, pollen is dispersed within a short distance of plants. When the wind speed exceeds a certain range, the pollen is quickly dispersed and does not easily accumulate. The height-to-width ratio of streets, the proximity / closeness to the road, and the location of the street are the main factors affecting pedestrian space. Wind environment factors generally have a greater height-to-width ratio (h / d) than other factors, leading to greater interference with the wind environment and making it harder for pollutants to be dispersed. Buildings closer to the road boundary line experience a more pronounced scouring effect, and street intersections typically experience higher wind speeds. Furthermore, meteorological factors such as temperature, sunshine, and rainfall can influence pollen formation and release, and pollutants can further promote the release of pollen cytoplasm. Vegetation surrounding urban street spaces is a major source of airborne pollen. Different plants exhibit varying pollen allergenicity, pollen blooming period, pollen release volume, and pollination methods. Tools for evaluating vegetation allergenicity include... The primary indicator tool uses a value of 0 to 1 to comprehensively reflect the allergenicity of green spaces. However, this indicator does not reflect temporal factors; plants do not cause pollen allergies in humans outside of their flowering season. The Plant Allergenicity Value (PAV) score is a constant, calculated based on pollination method, pollen season duration, and allergenicity potential classification. However, plant flowering periods are seasonal; most pollen-allergenic trees in my country flower in spring, while herbaceous pollen-allergenic trees flower in autumn. The peak pollen count for each species exhibits a parabolic variation over time. Tree pollen dispersal simulations are based on tree pollen release models and aerodynamic models, lacking pollen release models for specific allergenic tree species in my country. Furthermore, their basic information relies on detailed vegetation location and specifications, limiting their widespread application.

[0005] Currently, the most commonly used tool for allergen assessment is the optimized atmospheric biological index of tree allergen risk, namely... Based on AIROT, it considers the influence of wind to calculate the allergen risk index of a certain location in the urban space. However, it does not consider the influence of other meteorological factors such as sunshine and precipitation on pollen release, nor does it consider the influence of air quality on the release of pollen allergens. At the same time, its data base relies on the location information of plants and lidar, and it cannot generate direct interaction with users.

[0006] Therefore, for pedestrian spaces in urban streets, constructing an urban environmental allergy risk assessment index system that has practical application value and is easily accessible, and achieving quantitative evaluation, will help promote the progress of allergy assessment and research on urban green spaces. Summary of the Invention

[0007] In view of the various deficiencies of the existing technologies analyzed above, the purpose of this invention is to provide a method and system for assessing pollen allergy risk in urban street pedestrian spaces. From the two aspects of the dispersion of pollen concentration due to street spatial morphology characteristics and information acquisition methods, this invention innovatively constructs an allergy risk assessment system based on street spatial characteristics and open source data, thereby effectively solving the problems existing in the background technology.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] In a first aspect, the present invention discloses a method for assessing the risk of pollen allergy in urban street pedestrian spaces, comprising the following steps:

[0010] S1: Obtain multi-source data information on pedestrian spaces in urban streets;

[0011] S2: Extract street spatial features, meteorological features, and street vegetation risk source features from multi-source data;

[0012] S3: Determine the allergenic risk dispersion index of pedestrian space based on street space characteristics and meteorological characteristics;

[0013] S4: Calculate the pedestrian space sensitization risk index based on the pedestrian space sensitization risk dispersion index and the street vegetation risk source characteristics;

[0014] S5: Divide the risk grid according to the data accuracy of the pedestrian space sensitization risk index, and use the inverse distance weighting method to calculate the risk index score of other areas within the risk assessment range;

[0015] S6: Draw a street sensitization risk distribution map using risk index scores to obtain the evaluation results of sensitization risk in urban street pedestrian spaces.

[0016] A further preferred implementation of the above technical solution is as follows: In step S3, air quality and wind potential dispersion are extracted from meteorological characteristics, and street profile morphology index is extracted from street spatial characteristics. Based on air quality, wind potential dispersion, and street profile morphology index, a pedestrian space sensitization risk dispersion index is established, the expression of which is:

[0017] RDIPSA = Ai × PWi × SPPi

[0018] In the formula: RDIPSA is the pedestrian space sensitization risk dispersion index, Ai is the air quality at point i, PWi is the potential dispersion of wind at point i, and SPPi is the street profile morphology index at point i.

[0019] Based on the above scheme, further, among the influencing factors of the pedestrian space sensitization risk dispersion index, Ai is based on the assignment of NO2 and SO2 concentrations, and the assignment conditions for Ai are as follows:

[0020] Ai = ifs(NO2≤10μg / m 3 1,10μg / m 3 <NO2≤25μg / m 3 ,2,NO2>25μg / m 3 ,3)×ifs

[0021] (SO2≤5μg / m 3 1.5 μg / m 3 <SO2≤10μg / m 3 ,2,SO2>10μg / m 3 ,3).

[0022] Based on the above scheme, further, among the influencing factors of the pedestrian space sensitization risk dispersion index, the assignment conditions for PWi are as follows:

[0023] PWi=ifs(vi×|cosθ|≤1.5m / s,2,1.5m / s<vi×|cosθ|≤3m / s,3,vi×|cosθ|>3m / s,1)

[0024] In the formula: vi is the potential dispersion of the wind at point i, and θ is the angle between the wind direction and the street direction.

[0025] Based on the above scheme, the assignment conditions for SPPi, among the influencing factors of the pedestrian space sensitization risk dispersion index, are as follows:

[0026] SPPi=ifs(Hr / W≤0.1,1,0.1<Hr / W≤0.5,1.5,0.5<Hr / W≤1,2,Hr / W>1,3)

[0027] In the formula: Hr is the height of the building closest to point i on the street, and W is the width of the street.

[0028] A further preferred embodiment of the above technical solution is as follows: In step S4, the expression for the sensitization risk index of the pedestrian space is:

[0029] RIPSA = RVA × RDI × U

[0030] In the formula: RIPSA is the pedestrian space allergy risk index, RVA is the road vegetation allergy index, RVA is extracted from the risk source characteristics of street vegetation, RDI is the pedestrian space allergy risk dispersion index of the target location, and U is the urban base, i.e. the local pollen seasonality index.

[0031] Based on the above scheme, the expression for RVA among the influencing factors of the pedestrian space sensitization risk index is as follows:

[0032]

[0033] In the formula: APj is the pollen allergenic potential of species j, Hj is the high influence of species j, Tj is the proximity of the current pollen peak of species j, and CR is the cross-reactivity.

[0034] Secondly, this invention discloses a pollen allergy risk assessment system for urban street pedestrian spaces. This system is used in any of the aforementioned methods for assessing pollen allergy risks in urban street pedestrian spaces. The system includes:

[0035] The data acquisition module is used to acquire multi-source data information of pedestrian spaces in urban streets;

[0036] The feature extraction module is used to extract street spatial features, meteorological features, and street vegetation risk source features from multi-source data.

[0037] The calculation module includes a risk dispersion index calculation submodule and a risk index calculation submodule. The street spatial characteristics and meteorological characteristics are input into the risk dispersion index to obtain the pedestrian space sensitization risk dispersion index. The street vegetation risk source characteristics and the obtained pedestrian space sensitization risk dispersion index are input into the risk index calculation submodule to obtain the pedestrian space sensitization risk index.

[0038] The mapping module combines street remote sensing imagery to generate a risk grid, obtains the risk index score at the intersection of the risk grid, and draws a sensitization risk distribution map based on the risk index score.

[0039] Thirdly, the present invention also discloses an electronic device, the electronic device comprising at least one processor and a memory communicatively connected to the processor; wherein the memory stores instructions executable by the processor, the instructions being executed by the processor to enable the processor to perform the pollen allergy risk assessment method for urban street pedestrian spaces as described in any of the preceding claims.

[0040] By adopting the above-described solution, the present invention can produce the following beneficial effects:

[0041] 1. This invention provides a tool for assessing the risk of allergens in urban street spaces over a large area. It quantifies the distribution of allergen risks in various urban streets and integrates map services to help allergy sufferers obtain information on pollen allergen risks in the urban environment in a timely manner, and adjust their travel methods or travel plans accordingly. This effectively fills the current gap in the assessment of allergen risks in street spaces.

[0042] 2. The evaluation method of this invention, based on fundamental factors such as wind and street height-to-width ratio, adds meteorological indicators, including air quality factors and wind factors, to construct a calculation method for the pedestrian space allergy risk dispersion index. Furthermore, by combining the pedestrian space allergy risk dispersion index with the characteristics of street vegetation risk sources, the calculation method for the pedestrian space allergy risk index is determined. This improves the evaluation methods for vegetation and street factors, and can generate real-time street pedestrian space allergy risk distribution at specific spatial locations. It is a novel risk assessment tool that can reflect the seasonal characteristics of pollen allergy and the influence of meteorological factors at specific spatial locations on the street.

[0043] 3. The evaluation method of this invention can be calculated based on on-site surveys or open-source urban big data (such as street view images, remote sensing data building element sets, etc.). The data sources are diverse and easy to obtain, and the calculation accuracy is continuously optimized based on the quality of sample data. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0045] Figure 1 This is an overall flowchart of a pollen allergy risk assessment method for urban street pedestrian spaces according to the present invention;

[0046] Figure 2 This invention relates to a remote sensing image and sample point location map of a university campus.

[0047] Figure 3 This diagram illustrates the process of adding measured latitude and longitude coordinates and calculated RIPSA values ​​using ArcGIS Pro in this invention (i.e., adding data and setting parameters); where: a represents a partial screenshot of the ArcGIS Pro page; b represents opening and adding data; c represents the parameter settings for converting points in the xy table;

[0048] Figure 4 This diagram illustrates the process by which the present invention uses the inverse distance weighting method to extrapolate the sensitization risk of any other point within a region from measured point data; where: a represents the page for opening the inverse distance weighting method; b represents the parameter settings for the inverse distance weighting method; and c represents the range and environment settings for the inverse distance weighting method.

[0049] Figure 5 This is the evaluation result of the sensitization risk of urban street pedestrian spaces obtained by the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0051] Example:

[0052] Reference Figures 1-3 This invention proposes a method for assessing the risk of pollen allergy in urban street pedestrian spaces, comprising the following steps:

[0053] S1: Obtain multi-source data information on pedestrian spaces in urban streets;

[0054] In this step, data from multiple sources can be obtained through open-source methods, or precise data can be obtained through on-site investigations, as detailed below:

[0055] (1) Street view image: Baidu Maps street view;

[0056] (2) Meteorological data: National Meteorological Information Center;

[0057] (3) Multi-source data on urban construction:

[0058] ① Road data: Open Street Map, an open-source map data community

[0059] ② Building height: The following are the data on buildings in my country, including their height:

[0060] Che,Y.,Li,X.,Liu,X.,Wang,Y.,Liao,W.,Zheng,X.,Zhang,X.,Xu,X.,Shi,Q.,Zhu,J.,Yuan,H.,&Dai,Y.(2024).Building height of Asia in 3D-GloBFP[Data set].Zenodo.https: / / doi.org / 10.5281 / zenodo.11397015;

[0061] 2020 Global Urban Building Height Dataset at 150m Resolution: Ma, X., Zheng, G., Xu, C. et al. A global product of 150-m urban building height based on spaceborne lidar. SciData 11, 1387 (2024). https: / / doi.org / 10.1038 / s41597-024-04237-5;

[0062] 2024 Multi-Attribute Building Vector Data of my country: Zhang,Y.,Zhao,H.&Long,Y.CMAB:A Multi-Attribute Building Dataset of China.Sci Data 12,430(2025).https: / / doi.org / 10.1038 / s41597-025-04730-5.

[0063] S2: Extract street spatial features, meteorological features, and street vegetation risk source features from multi-source data;

[0064] In this step, the extraction of street spatial features includes the street's height-to-width ratio; the extraction of meteorological features includes wind direction, wind speed, and the concentrations of air pollutants NO2 and SO2; and the extraction of street vegetation risk source features includes the species and size of the vegetation. To reduce computational burden and thus improve computational speed, a sampling method is used for data extraction, covering key feature locations of the streets, as shown in Table 1, with 7 sample points covering the main feature streets of the target site. The following uses meteorological data from midday in mid-April to demonstrate the process of extracting sensitization risk features and assessing spatiotemporal risks.

[0065] Table 1. Coordinates of sample points at key feature locations of the street.

[0066]

[0067] The street height-to-width ratio is obtained from building vector datasets (such as OpenStreetMap, OSM) and the building height directly recorded by the height label; or from the building:levels label, the number of floors in the building, multiplied by an estimated floor height (such as 3 meters / floor) to obtain the approximate height. Based on field surveys, the building height and street width information on the right side of the sample street in this embodiment are shown in Table 2 below:

[0068] Table 2. Height of buildings on the right side of the street and width of the street in the sample area

[0069] Points Building height Hr Street width W 1 16.59 35.15 2 19.65 31.81 3 14.72 38.55 4 12.31 123.98 5 17.79 119.29 6 14.81 62.88 7 20.00 19.70

[0070] Air pollutant data for this region, released by the National Meteorological Center on that day, are shown in Table 3 below:

[0071] Table 3 Air Pollutants in Sample Streets

[0072] Meteorological data NO2 SO2 wind speed wind direction 12 o'clock on a certain day in mid-April <![CDATA[28.4μg / m 3 ]]> <![CDATA[14.0μg / m 3 ]]> 9.1 Northeast wind (45°)

[0073] Vegetation information was automatically or visually interpreted using street view services such as Google StreetView, Baidu StreetView, and Mapillary, or high-resolution aerial / drone imagery. Based on the field survey, the vegetation information within the field of view of the sample points in this embodiment is shown in Table 4 below:

[0074] Table 4. Vegetation information within the field of view of the sample points.

[0075]

[0076]

[0077] S3: Determine the allergenic risk dispersion index of pedestrian space based on street space characteristics and meteorological characteristics;

[0078] In this step, the expression for the pedestrian space allergenicity risk diversification index (RDIPSA) is constructed from the street profile morphology index extracted from street spatial features and the potential dispersion of wind and air quality extracted from meteorological features.

[0079] RDIPSA = Ai × PWi × SPPi

[0080] In the formula: Ai is the air quality at point i, PWi is the potential dispersion of wind at point i, and SPPi is the street profile morphology index at point i.

[0081] More specifically, among the factors influencing RDIPSA mentioned above, Ai is based on the assignment of NO2 and SO2 concentrations, and the conditions for assigning Ai are as follows:

[0082] Ai = ifs(NO2≤10μg / m 3 1,10μg / m 3 <NO2≤25μg / m 3 ,2,NO2>25μg / m 3 ,3)×ifs

[0083] (SO2≤5μg / m 3 1.5 μg / m 3 <SO2≤10μg / m 3 ,2,SO2>10μg / m 3 ,3).

[0084] In this embodiment, based on an NO2 concentration of 28.4 g / m³... 3 The corresponding value is 3, and the SO2 concentration is 14.0 μg / m³. 3The corresponding value is 3, and Ai = 9.

[0085] The assignment conditions for PWi are as follows:

[0086] PWi=ifs(vi×|cosθ|≤1.5m / s,2,1.5m / s<vi×|cosθ|≤3m / s,3,vi×|cosθ|>3m / s,1)

[0087] In the formula: vi is the potential dispersion of the wind at point i, and θ is the angle between the wind direction and the street direction.

[0088] The potential dispersion of street wind at the sample points in this embodiment is shown in Table 5:

[0089] Table 5 shows the potential wind dispersion at the sample street locations.

[0090]

[0091] The assignment conditions for SPPi are as follows:

[0092] SPPi=ifs(Hr / W≤0.1,1,0.1<Hr / W≤0.5,1.5,0.5<Hr / W≤1,2,Hr / W>1,3)

[0093] In the formula: Hr is the height of the building closest to point i on the street, and W is the width of the street.

[0094] The street profile morphology index of the sample streets in this embodiment is shown in Table 6:

[0095] Table 6 Street Profile Morphology Index

[0096] Points Hr / W SPPi 1 0.47 1.5 2 0.62 2 3 0.38 1.5 4 0.1 1 5 0.15 1.5 6 0.24 1.5 7 1.02 3

[0097] Therefore, based on open-source big data, the RDIPSA value of the spatial sensitization risk dispersion index of the sample points can be obtained, as shown in Table 7:

[0098] Table 7. RDISA values ​​of pedestrian space sensitization risk dispersion index for sample streets.

[0099] Points Ai PWi SPPi RDIPSA 1 9 1 1.5 13.5 2 9 1 2 18 3 9 1 1.5 13.5 4 9 1 1 9 5 9 1 1.5 13.5 6 9 1 1.5 13.5 7 9 1 3 27

[0100] In addition, when conditions permit, more accurate risk assessment results can be obtained by utilizing on-site monitoring information from environmental sensor networks.

[0101] S4: Calculate the pedestrian space sensitization risk index based on the pedestrian space sensitization risk dispersion index and the street vegetation risk source characteristics;

[0102] In this step, the expression for the constructed Pedestrian Space Allergenicity Risk Index (RIPSA) is as follows:

[0103] RIPSA = RVA × RDI × U

[0104] In the formula: RVA is the road vegetation allergen index, RDI is the pedestrian space allergen risk dispersion index at the target location, and U is the urban base, i.e., the local pollen seasonality index. Taking Beijing as an example, according to past phenological records, the peak of spring pollen occurs at the end of March or the beginning of April, and the peak of autumn pollen occurs at the end of August or the beginning of September. The impact of the urban base in different time periods can be set accordingly. In some implementations, the value of U for northern cities can refer to Beijing's proposed high pollen allergen risk from March 20 to April 9 and from August 15 to September 10, with a value of 3; from March 6 to March 19, April 11 to May 20, August 6 to August 15, and September 11 to September 30, the value is 2; and from March 1 to March 5, May 21 to August 5, and October 1 to October 25, the value is 1. In this embodiment, the time is mid-April, and the value of U is set to 2.

[0105] RVA represents the sensitization risk of vegetation within the field of view of a sampling point at the current moment, and its expression is as follows:

[0106]

[0107] In the formula: APj represents the pollen allergenic potential of species j, which is divided into severe allergy 4, high allergy 3, moderate allergy 2, mild allergy 1, and no allergy 0; Hj represents the height influence of species j, with a value of 2 above 10m and a value of 1 below 10m; Tj represents the proximity of species j to the current pollen peak, which is divided into three levels: 2, 1, and 0; CR represents cross-reactivity, with a value of 2 for pollen with cross-reactivity and a value of 1 for pollen without cross-reactivity in the plant composition at this point.

[0108] The plant composition of the sample sites and the RVA calculation process in this embodiment are shown in Table 8:

[0109] Table 8. Plant composition and RVA at the sampling sites

[0110]

[0111]

[0112] Therefore, based on street view images, the pedestrian spatial sensitization risk index (RIPSA) of the sample points can be obtained by manually identifying tree species through image viewing. The values ​​are shown in Table 9.

[0113] Table 9 shows the RIPSA (Risk Index of Allergens in Pedestrian Space) for the sample points.

[0114]

[0115]

[0116] S5: Based on the obtained RIPSA (Risk Index for Spatial Sensitivity) data of the sample points, calculate the risk index score for other areas within the risk assessment range using the inverse distance weighting method. In this step, the more RIPSA data obtained from the sample points and the more accurate the raw data, the more accurate the assessment result of the calculated risk index score will be.

[0117] Specifically, the process of calculating the risk index score for other areas within the risk assessment scope using the inverse distance weighting method is as follows:

[0118] (1) Using ArcGIS Pro for data and field settings: ArcGIS Pro is a professional Geographic Information System (GIS) software developed by Esri Corporation, used to create, manage, analyze, and visualize geospatial data; such as Figure 3 The image shows how to add x and y data and set the corresponding fields in ArcGIS Pro.

[0119] (2) Open the Inverse Distance Weighting (IDW) tool, input the point coordinates and fields, set the range environment, and then run the calculation. Figure 4 As shown;

[0120] (3) After generating the IDW risk map, set up a symbol system to show the different sensitization risks in different areas.

[0121] S6: A risk distribution map of street-level sensitization risk is plotted using the risk index scores to obtain the evaluation results of the sensitization risk in urban street pedestrian spaces. The evaluation results of the sensitization risk in urban street pedestrian spaces obtained in this embodiment are as follows: Figure 3 As shown.

[0122] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for assessing the risk of pollen allergy in urban street pedestrian spaces, characterized in that, Includes the following steps: S1: Obtain multi-source data information on pedestrian spaces in urban streets; S2: Extract street spatial features, meteorological features, and street vegetation risk source features from multi-source data; S3: Determine the allergenic risk dispersion index of pedestrian space based on street space characteristics and meteorological characteristics; S4: Calculate the pedestrian space sensitization risk index based on the pedestrian space sensitization risk dispersion index and the street vegetation risk source characteristics; S5: Divide the risk grid according to the data accuracy of the pedestrian space sensitization risk index, and use the inverse distance weighting method to calculate the risk index score within the assessment range; S6: Draw a street sensitization risk distribution map using risk index scores to obtain the evaluation results of sensitization risk in urban street pedestrian spaces.

2. The method for assessing the risk of pollen allergy in urban street pedestrian spaces according to claim 1, characterized in that, In step S3, air quality and wind potential dispersion are extracted from meteorological characteristics, and street profile morphology index is extracted from street spatial characteristics. Based on air quality, wind potential dispersion, and street profile morphology index, a pedestrian space sensitization risk dispersion index is established, the expression of which is: RDIPSA = Ai × PWi × SPPi In the formula: RDIPSA is the pedestrian space sensitization risk dispersion index, Ai is the air quality at point i, PWi is the potential dispersion of wind at point i, and SPPi is the street profile morphology index at point i.

3. The method for assessing the risk of pollen allergy in urban street pedestrian spaces according to claim 2, characterized in that, Among the factors influencing the dispersion index of sensitization risk in pedestrian spaces, Ai is based on the assignment of NO2 and SO2 concentrations. The assignment conditions for Ai are as follows: Ai=ifs(NO2≤10μg / m 3 ,1,10μg / m 3 <NO2≤25μg / m 3 ,2,NO2>25μg / m 3 ,3)×ifs (SO2≤5μg / m 3 ,1,5μg / m 3 <SO2≤10μg / m 3 ,2,SO2>10μg / m 3 ,3)。 4. The method for assessing the risk of pollen allergy in urban street pedestrian spaces according to claim 2, characterized in that, Among the factors influencing the dispersion index of sensitization risk in pedestrian spaces, the assignment conditions for PWi are as follows: PWi=ifs(vi×|cosθ|≤1.5m / s,2,1.5m / s<vi×|cosθ|≤3m / s,3,vi×|cosθ|>3m / s,1) where: vi is the potential dispersion of the wind at point i, and θ is the angle between the wind direction and the street direction.

5. The method for assessing the risk of pollen allergy in urban street pedestrian spaces according to claim 2, characterized in that, Among the factors influencing the dispersion index of sensitization risk in pedestrian spaces, the assignment conditions for SPPi are as follows: SPPi=ifs(Hr / W≤0.1,1,0.1<Hr / W≤0.5,1.5,0.5<Hr / W≤1,2,Hr / W>1,3) In the formula: Hr is the height of the building closest to point i on the street, and W is the width of the street.

6. A method for assessing the risk of pollen allergy in urban street pedestrian spaces according to any one of claims 5, characterized in that, In step S4, the expression for the sensitization risk index of the pedestrian space is: RIPSA = RVA × RDI × U In the formula: RIPSA is the pedestrian space allergy risk index, RVA is the road vegetation allergy index, RVA is extracted from the risk source characteristics of street vegetation, RDI is the pedestrian space allergy risk dispersion index of the target location, and U is the urban base, i.e. the local pollen seasonality index.

7. The method for assessing the risk of pollen allergy in urban street pedestrian spaces according to claim 6, characterized in that, Among the factors influencing the allergy risk index of pedestrian spaces, the expression for RVA is as follows: In the formula: APj is the pollen allergenic potential of species j, Hj is the high influence of species j, Tj is the proximity of the current pollen peak of species j, and CR is the cross-reactivity.

8. A pollen allergy risk assessment system for urban street pedestrian spaces, characterized in that, This system is used in any one of the pollen allergy risk assessment methods for urban street pedestrian spaces according to claims 1-7, and the system comprises: The data acquisition module is used to acquire multi-source data information of pedestrian spaces in urban streets; The feature extraction module is used to extract street spatial features, meteorological features, and street vegetation risk source features from multi-source data. The calculation module includes a risk dispersion index calculation submodule and a risk index calculation submodule. The street spatial characteristics and meteorological characteristics are input into the risk dispersion index to obtain the pedestrian space sensitization risk dispersion index. The street vegetation risk source characteristics and the obtained pedestrian space sensitization risk dispersion index are input into the risk index calculation submodule to obtain the pedestrian space sensitization risk index. The mapping module combines street remote sensing imagery to generate a risk grid, obtains the risk index score at the intersection of the risk grid, and draws a sensitization risk distribution map based on the risk index score.

9. An electronic device, characterized in that: The electronic device includes at least one processor and a memory communicatively connected to the processor; wherein the memory stores instructions executable by the processor to enable the processor to perform the method of any one of claims 1-7.

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