Particulate matter concentration influence monitoring, simulation and evaluation system based on road dust accumulation sailing

Abstract

The invention discloses a particulate matter concentration influence monitoring simulation evaluation system based on road dust accumulation sailing, and belongs to the technical field of environmental science and engineering. The system comprises a data acquisition module, a data processing module, a data simulation module, an evaluation judgment module and a terminal interface module, wherein the data acquisition module acquires a data set comprising road dust retention, traffic flow and meteorological parameters; the data processing module cleans and calibrates the multivariate data set, and fuses the multivariate data set to obtain a road network dust accumulation load and traffic flow spatio-temporal data set; the model simulation module is used for acquiring a road raised dust emission list, a gridding particulate matter concentration data set and a road grading particulate matter concentration influence distribution data set according to the road raised dust emission list and the gridding particulate matter concentration data set; the evaluation judgment module performs grading early warning on the road grading particulate matter concentration data, generates a pollution map, contributes ranking and evaluates a report; and the terminal interface module displays the report and pushes the report to a third-party platform. The system can comprehensively monitor, simulate and evaluate the influence of road dust accumulation on the particulate matter concentration of a detection point, assists in mastering the road pollution condition, and provides data support for environmental protection decision making.

Description

Technical Field

[0001] The present invention relates to the field of environmental science and engineering technology, and in particular to a monitoring, simulation and evaluation system for the impact of particle concentration on road dust accumulation. Background Art

[0002] With the acceleration of urbanization, road traffic activities are becoming increasingly frequent, and road dust has become one of the important sources of urban particulate matter pollution. Road dust, raised by vehicle driving, will significantly increase the concentration of particulate matter in the air, which will have a serious impact on urban air quality and residents' health.

[0003] Traditional particulate matter concentration monitoring methods are often limited to fixed-point monitoring, which cannot fully and dynamically reflect the impact of road dust on particulate matter concentration. They have problems such as limited monitoring range and inability to track in real time. At the same time, there is a lack of in-depth analysis and simulation evaluation methods for the relationship between road dust and particulate matter concentration, making it difficult to accurately grasp the spatiotemporal distribution characteristics and pollution contribution of road pollution. This means that sanitation departments lack sufficient data support and scientific basis when formulating targeted pollution control measures, and are unable to effectively solve the particulate matter pollution problem caused by road dust. Therefore, how to comprehensively and accurately monitor and simulate the impact of road dust on particulate matter concentration has become an urgent problem to be solved. To this end, this paper proposes a particulate matter concentration impact monitoring, simulation and evaluation system based on road dust navigation. Summary of the Invention

[0004] The present invention aims to provide a system for monitoring and simulating the impact of particulate matter concentration on road dust accumulation, so as to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] The particulate matter concentration impact monitoring and simulation evaluation system based on road dust accumulation includes a data acquisition module, a processing module, a simulation module, an evaluation and judgment module, and a terminal interface module;

[0007] The data acquisition module is used to obtain data sets including road dust accumulation, traffic flow and meteorological parameters in real time;

[0008] The processing module is used to clean or calibrate the acquired road dust, traffic flow and meteorological parameter datasets, and fuse the cleaned or calibrated road dust, traffic flow and meteorological parameter datasets to obtain a road network dust load and traffic flow spatiotemporal dataset;

[0009] The simulation module is used to obtain a road dust emission inventory using a dust lift model for the spatiotemporal datasets of road network dust load and traffic flow, and input the obtained road dust emission inventory into a local-scale air quality model to simulate and obtain a gridded particulate matter concentration dataset. At the same time, the gridded particulate matter concentration dataset is used to obtain a road-level particulate matter concentration impact distribution dataset through a coupling model.

[0010] The assessment and judgment module is used to perform graded early warning processing on the road-level particle matter concentration impact distribution dataset to obtain the dust pollution level of each road. At the same time, based on the road-level particle matter concentration impact distribution dataset, a road pollution map and a road dust pollution contribution ranking are generated, and a road pollution assessment report is generated accordingly.

[0011] The terminal interface module is used to display the road pollution assessment report and push it to a third-party platform.

[0012] Preferably, in the data acquisition module, the process of acquiring road dust, traffic flow and meteorological parameter data sets in real time is as follows:

[0013] The laser-induced breakdown spectroscopy sensor mounted on the UAV uses high-energy laser pulses to focus on road dust, causing the road dust to instantly vaporize and excite plasma. The spectrum emitted by the plasma is analyzed to determine the type and content of elements in the road dust, thereby obtaining road dust data. The road dust data is collected at a collection frequency f. At the same time, combined with the on-board GPS system and the UAV's built-in clock, the on-board GPS system determines the road position information of the UAV, and the collected road dust data is mapped to a specific road position. The UAV's built-in clock records the time of each road dust data collection and generates a timestamp. A road dust table containing road dust data, timestamp, and road position information fields is created through the database. The road dust data, timestamp, and road position information collected each time are filled into the rows of the table, thereby constructing a road dust dataset.

[0014] The traffic flow sensor mounted on the moving vehicle collects traffic data on the road area, including traffic flow data, average speed, and proportion of vehicle types at a collection frequency of f. The traffic flow sensor counts the number of vehicles passing a certain road location in real time per unit time to obtain traffic flow data, monitors the speed and identifies the type of passing vehicles, records the speed and type of each vehicle, and calculates the average speed of the road area and the proportion of different vehicle types in the total number of vehicles. At the same time, the on-board GPS system and the built-in clock of the moving vehicle are combined to obtain the timestamp and road location information corresponding to the traffic data. A traffic flow data table containing traffic data, timestamp, and road location information fields is created through the database. The traffic data, timestamp, and road location information are filled into the traffic flow data table to construct a traffic flow dataset.

[0015] The meteorological sensors mounted on the traveling vehicle are used to obtain regional meteorological data on wind speed, wind direction, temperature, and humidity in the road area at a collection frequency of f. At the same time, the on-board GPS system and the built-in clock of the traveling vehicle are used to obtain the timestamp and road location information corresponding to the regional meteorological data. A meteorological parameter data table containing fields for regional meteorological data, timestamp, and road location information is created through the database. The regional meteorological data, timestamp, and road location information are filled into the meteorological parameter data table to construct a meteorological parameter dataset.

[0016] Preferably, in the processing module, the process of cleaning or calibrating the acquired road dust, traffic flow and meteorological parameter data sets is as follows:

[0017] The road dust dataset is cleaned. The 3σ principle is used to identify and delete the road dust data that exceeds ±3 times the standard deviation. The deleted road dust data set is filled with the deleted road dust data using linear interpolation between known data points to obtain the cleaned road dust dataset.

[0018] The road dust dataset is cleaned. The 3σ principle is used to identify and delete the road dust data that exceeds ±3 times the standard deviation. The deleted road dust data set is filled with the deleted road dust data using linear interpolation to obtain the cleaned road dust dataset.

[0019] The traffic flow dataset is cleaned and processed. The Poisson distribution model is used to test the traffic flow data in the traffic dataset, and outliers with a sudden increase of more than 50% are identified and removed. Based on this, a cleaned traffic flow dataset is obtained.

[0020] Calibrate the meteorological parameter data set. Compensate wind speed and humidity are obtained by using the logarithmic wind profile formula and the dry-bulb temperature formula, respectively, to replace the original wind speed and humidity, thereby obtaining a calibrated meteorological parameter data set.

[0021] The logarithmic wind profile formula is:

[0022]

[0023] Where u(z) is the wind speed at height z, u ref is the reference height z ref The wind speed at the location z0 is the surface roughness length, z is the height, z ref is the reference height;

[0024] The dry-bulb and wet-bulb temperature formulas are:

[0025] e=e s (T W )-γ×(TT W )×P;

[0026] Among them, e is the actual water vapor pressure, e s (T W ) is the wet bulb temperature T W The corresponding saturated water vapor pressure, γ is the hygrometer constant, T is the dry bulb temperature, P is the atmospheric pressure, T W is the wet bulb temperature.

[0027] Preferably, in the processing module, the cleaned or calibrated road dust, traffic flow and meteorological parameter datasets are fused to obtain the spatiotemporal dataset of road network dust load and traffic flow:

[0028] The cleaned or calibrated road dust, traffic flow, and meteorological parameter datasets were aligned in time. The timestamps of the cleaned or calibrated road dust, traffic flow, and meteorological parameter datasets were converted to the ISO 8601 format using UTC. The ISO 8601-formatted road dust, traffic flow, and meteorological parameter datasets were then aligned to a 15-minute time step using cubic spline interpolation. Missing time points in the unified time step road dust, traffic flow, and meteorological parameter datasets were filled with the first non-missing data preceding the missing value using the forward filling method. This resulted in the time-aligned road dust, traffic flow, and meteorological parameter datasets.

[0029] The cubic spline interpolation method is: given a time series t1, t2, ..., t n and the corresponding values ​​y1,y2,...,y n , construct a piecewise cubic polynomial s(t) that satisfies: s(t i )=y i ,s'(t i )=s' i-1 ,s”(t i )=s” i-1 ;

[0030] The road dust, traffic flow, and meteorological parameter datasets that were aligned in the time dimension were aligned in the spatial dimension. The road location information in the time-aligned road dust, traffic flow, and meteorological parameter datasets was mapped to the UTM projection coordinate system using the Python pyproj library. The road location information in the UTM projection coordinate system was associated with the road ID information using the Geographic Information System (GIS). At the same time, based on the location information of the road dust, traffic flow, and meteorological parameter datasets in the UTM projection coordinate system, the road dust data in the road dust dataset, the traffic data in the traffic flow dataset, and the regional meteorological data in the meteorological parameter dataset were associated with the road ID using the Kriging interpolation method. This resulted in a spatially and temporally aligned road dust, traffic data, and meteorological parameter dataset, namely, a spatiotemporal dataset of road network dust load and traffic flow.

[0031] The Kriging interpolation method is a spatial interpolation method widely used in geographic spatial analysis and geostatistics.

[0032] Preferably, in the simulation module, the process of obtaining the road dust emission inventory through the dust accumulation model is as follows:

[0033] Dust emission data, including road dust data, vehicle volume data, average vehicle speed, and vehicle type ratio, were extracted from the road network dust load and traffic flow spatiotemporal dataset. The dust emission data were input into the dust emission model to obtain the dust emission amount. The dust emission amount was then associated with the timestamp, road ID, and road location information corresponding to the road dust data in the road network dust load and traffic flow spatiotemporal dataset to generate a road dust emission inventory.

[0034] The dust raising model is:

[0035] D=α×C×V β ×(1+γ×P);

[0036] Among them, D is the amount of dust raised, α, β and γ are coefficients, C is the road dust data, V is the average vehicle speed, and P is the proportion of vehicle types.

[0037] Preferably, in the simulation module, the process of obtaining a spatiotemporal emission inventory and obtaining a gridded concentration dataset through a local-scale air quality model is as follows:

[0038] Extract dust accumulation and road location information for each road ID from the road dust emission inventory at a 15-minute time step. At the same time, obtain regional meteorological data corresponding to the road dust emission inventory through an API interface based on the timestamp, road ID, and road location information of the road dust emission inventory. Also, use a geographic information system (GIS) to obtain road environment data for the area containing the road location information, including road networks, terrain, and buildings.

[0039] Road environmental data, regional meteorological data corresponding to the road dust emission inventory, dust collection amounts for different road IDs, and road location information are input into the local-scale air quality model AERMOD. The local-scale air quality model AERMOD performs complex calculations based on the input data, taking into account the influence of meteorological conditions and topography on the diffusion of particulate matter in the atmosphere after dust collection. It ultimately outputs a gridded particle concentration dataset that includes grid locations and the particle concentrations corresponding to the grid locations. The gridded particle concentration dataset divides the study area into grids, each of which has a corresponding particle concentration value. The gridded particle concentration dataset can be used to intuitively understand the particle concentration distribution in different regions at different times.

[0040] The coupled model is a tool for accurately simulating the distribution of particulate matter concentration around roads.

[0041] Preferably, in the simulation module, the process of obtaining the road-level particulate matter concentration impact distribution dataset by coupling the gridded particulate matter concentration dataset is as follows:

[0042] At a 15-minute time step, the particle concentration information corresponding to each grid location at each time point was extracted from the gridded particle concentration dataset. Using the Geographic Information System (GIS), the grid locations in the gridded particle concentration dataset were associated with the road ID and road location information. The GIS determined the actual road information corresponding to the grid location based on the grid location, obtained the grid road ID and road location information, and extracted road dust data associated with the road location information at the grid location from the road network dust load and traffic flow spatiotemporal dataset.

[0043] The particulate matter concentration information corresponding to the extracted grid locations, the road ID and road location information associated with the grid locations, and the road dust accumulation data are input into the coupling model. The coupling model is a tool for accurately simulating the distribution of particulate matter concentration around roads. Based on the input data, the coupling model comprehensively considers the emission characteristics of the road itself, the particulate matter concentration in the surrounding atmospheric environment, and traffic conditions. The output is a road-level particulate matter concentration impact distribution dataset that includes road ID, timestamp, road location information, and particulate matter concentration. The road-level particulate matter concentration impact distribution dataset focuses on the road level, determines the particulate matter concentration of different roads at various time points, and intuitively reflects the pollution level of each road.

[0044] Preferably, in the evaluation and judgment module, the process of obtaining the dust pollution degree of each road and generating a road pollution evaluation report is as follows:

[0045] The particulate matter concentration in the road graded concentration distribution dataset is judged by the set graded warning threshold to obtain the pollution level corresponding to the particulate matter concentration. The pollution level corresponding to the particulate matter concentration is associated with the road ID corresponding to the particulate matter concentration to obtain the dust pollution degree of each road;

[0046] The process of obtaining the pollution level corresponding to the particulate matter concentration is as follows:

[0047] If the particle concentration is less than 100 μg / m 3 , then the pollution level corresponding to the particulate matter concentration is level one;

[0048] If the particle concentration is less than 200 μg / m 3 and greater than or equal to 100 μg / m 3 , then the pollution level corresponding to the particulate matter concentration is level 2;

[0049] If the particle concentration is greater than or equal to 300 μg / m 3 , then the pollution level corresponding to the particulate matter concentration is level three;

[0050] The road ID, particulate matter concentration, and timestamp were extracted from the road-graded concentration distribution dataset, and a road pollution map was generated using a geographic information system (GIS). Taking the particulate matter concentration of 0 as the baseline value, the particle matter concentration corresponding to the road location information and timestamp in the road-graded concentration distribution dataset was taken as the particulate matter concentration at the road monitoring point. The increase in particulate matter concentration at the road monitoring point compared to the baseline value was calculated, and the road monitoring points were arranged in descending order according to the increase, resulting in a ranking of the road dust pollution contributions of the road monitoring points. The ranking of road dust pollution contributions clearly identifies which roads are the main sources of pollution, providing data support for determining governance priorities.

[0051] Based on this, a road pollution assessment report is obtained, which includes the degree of road pollution, road pollution map and the ranking of road dust pollution contributions. The road pollution assessment report comprehensively presents the comprehensive situation of road pollution and provides a comprehensive and intuitive basis for the sanitation department to formulate targeted pollution control measures and environmental monitoring plans.

[0052] Preferably, in the terminal interface module, the process of pushing the road pollution assessment report to the third-party platform is as follows:

[0053] Construct a request URL according to the API specification, including the interface address and necessary request parameters. Add the road pollution assessment report as the request body to the request. Perform identity authentication according to the third-party platform's authentication method. If the authentication is successful, complete the request construction, send an HTTP request, and push the road pollution assessment report data to the third-party platform.

[0054] Preferably, the terminal interface module displays the process of the road pollution assessment report:

[0055] The road pollution map in the pollution assessment report is rendered using Leaflet's heat map plug-in. Different areas are assigned different colors based on the pollution level of the data. The ECharts JavaScript file is introduced in the HTML file. An ECharts instance is created in the JavaScript file's code, the chart container is specified, and the chart options are configured, including setting the chart type to a bar chart, the road ID on the X-axis, and the pollution contribution value on the Y-axis. The road dust pollution contribution ranking is bound to the chart to generate a road dust pollution contribution ranking bar chart. The road pollution level is annotated with a label. A dynamic web interface is built using Vue.js to display the road pollution map rendered using Leaflet, the road dust pollution contribution ranking bar chart, and the road pollution level annotated with a label.

[0056] The Leaflet, ECharts and Vue.js are important JavaScript technology tools for interactive map display, data visualization and building efficient and maintainable user interfaces.

[0057] Due to the adoption of the above technical solution, the present invention has the following technical advancements compared to the prior art:

[0058] 1. The present invention achieves comprehensive and accurate detection. Traditional monitoring is limited to fixed points and cannot fully reflect the impact of road dust accumulation on particulate matter concentration. The present invention uses laser-induced breakdown spectroscopy sensors, meteorological sensors, and vehicle flow sensors carried by a UAV to obtain multi-source data in real time, covering different road areas, realizing dynamic monitoring and comprehensively understanding road pollution conditions. This makes up for the shortcomings of traditional monitoring in scope and real-time performance and can accurately reflect pollution changes.

[0059] 2. The data processing of the present invention is scientific and efficient. The existing technology lacks in-depth simulation and evaluation methods for the relationship between road dust accumulation and particulate matter concentration. The multi-source data collected by the present invention are cleaned, calibrated and fused to generate a spatiotemporal dataset of road network dust load and traffic flow. Based on the spatiotemporal dataset of road network dust load and traffic flow, through the dust lift model, local-scale air quality model and coupling model, the road dust emission inventory, gridded particulate matter concentration dataset and road-graded particulate matter concentration impact distribution dataset are obtained, and the impact process of road dust on particulate matter concentration is accurately simulated. The spatiotemporal distribution characteristics and pollution contribution of road dust pollution are deeply analyzed, which can provide more targeted decision-making basis for sanitation departments. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0061] Figure 1 Schematic diagram of the system function modules of the present invention; DETAILED DESCRIPTION

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0063] Examples, such as Figure 1 The system for monitoring and simulating the impact of particle concentration on road dust accumulation and navigation is described, and includes a data acquisition module, a processing module, a simulation module, an evaluation and judgment module, and a terminal interface module, which work together to complete the task of monitoring and simulating the impact of particle concentration on road dust accumulation and navigation.

[0064] Data acquisition module, used to obtain real-time data sets including road dust accumulation, traffic flow and meteorological parameters;

[0065] A processing module is used to clean or calibrate the acquired road dust, traffic flow, and meteorological parameter datasets, and fuse the cleaned or calibrated road dust, traffic flow, and meteorological parameter datasets to obtain a spatiotemporal dataset of road network dust load and traffic flow;

[0066] The simulation module is used to obtain a road dust emission inventory using a dust lift model for the spatiotemporal datasets of road network dust load and traffic flow. The obtained road dust emission inventory is then input into a local-scale air quality model to simulate and obtain a gridded particulate matter concentration dataset. Simultaneously, a coupling model is used to obtain a road-level particulate matter concentration impact distribution dataset for the gridded particulate matter concentration dataset.

[0067] The assessment and judgment module is used to perform graded early warning processing on the road-level particle matter concentration impact distribution dataset to determine the degree of dust pollution on each road. At the same time, based on the road-level particle matter concentration impact distribution dataset, a road pollution map and a ranking of road dust pollution contributions are generated, and a road pollution assessment report is generated based on this data.

[0068] The terminal interface module is used to display the road pollution assessment report and push it to a third-party platform.

[0069] Furthermore, the working principle of the present invention is described below by way of examples:

[0070] Consider a densely populated medium-sized city named City A. With the growth of urban construction and traffic volume, particulate matter pollution caused by road dust has become increasingly prominent. To effectively monitor and address this issue, the city has introduced a particulate matter concentration impact monitoring simulation and assessment system based on the movement of road dust. The city has a complex road network consisting of main roads, secondary roads, and branch roads. The traffic volume, road conditions, and surrounding environment of different roads vary greatly.

[0071] The UAV is equipped with a laser-induced breakdown spectroscopy sensor, which collects road dust data at a frequency of 5 minutes. At the same time, the on-board GPS system and the UAV's built-in clock record the timestamp and road location information of each collection. For example, at 9 a.m., dust data is collected at (30.6521°N, 114.2315°E) on Main Road A. The data is then stored in the road dust table in the PostgreSQL database to construct a road dust dataset. The traffic flow sensor on the UAV collects traffic data at a frequency of 1 minute. Combined with the on-board GPS and clock, the data is recorded in the image. Obtain timestamp and road location information. For example, at 9:05, the traffic volume at a certain location on Main Road A was 50 vehicles per minute, with an average speed of 30 km / h and a truck ratio of 10%. This data is stored in a traffic flow data table to construct a traffic flow dataset. The meteorological sensor on the UAV collects meteorological data on wind speed, wind direction, temperature, and humidity in the road area at a frequency of 15 minutes. At 9:15, the wind speed at Main Road A was 3 m / s, the wind direction was southeast, the temperature was 25°C, and the humidity was 50%. Combined with the location and time information, this data is stored in a meteorological parameter data table to construct a meteorological parameter dataset.

[0072] When processing the road dust dataset, the 3σ principle is used to identify and delete abnormal data that exceeds ±3 times the standard deviation. Linear interpolation is used to fill in missing values. Assuming that a certain road dust data point deviates too much from the mean and is deleted, the linear interpolation method is used to estimate and fill in the data before and after to obtain a cleaned road dust dataset. The Poisson distribution model is used to test the traffic flow data in the traffic flow dataset, and abnormal values ​​that increase by more than 50% are removed to obtain a cleaned traffic flow dataset. The wind speed and humidity in the meteorological parameter dataset are calibrated using the logarithmic wind profile formula and the dry-bulb temperature formula respectively. For example, according to the logarithmic wind profile formula, the wind speed at a certain height is calibrated, and the actual water vapor pressure is calculated according to the dry-bulb temperature formula to replace the original humidity data to obtain a calibrated meteorological parameter dataset. With UTC as the benchmark, the timestamps of the three datasets are converted to ISO 8601 format, unified into a 15-minute time step through cubic spline interpolation, and processed with forward filling method for missing time points to achieve time dimension alignment. The road location information was mapped to the UTM projection coordinate system using Python's pyproj library, and the road ID information was associated with GIS. The data of each dataset was associated with the road ID through Kriging interpolation method to obtain the spatiotemporal dataset of road network dust load and traffic flow.

[0073] Road dust data, vehicle volume data, average vehicle speed, and vehicle type ratio are extracted from the road network dust load and traffic flow spatiotemporal dataset and input into the dust lift model D = 0.01 × C × V 0.5 ×(1+0.1×P), calculate the dust accumulation amount. For example, the dust accumulation data C of a certain road section is 5g / m 2 , the average vehicle speed V is 30 km / h, the vehicle type proportion P is 10%, and the calculated dust emission D is about 0.14 g / s. The timestamp, road ID and location information are associated to generate a road dust emission inventory. The dust emission and road location information are extracted from the emission inventory with a 15-minute time step. Regional meteorological data are obtained through the API interface, and road environment data are obtained using GIS. The data are input into the local-scale air quality model AERMOD to obtain a gridded particulate matter concentration dataset. For example, the particulate matter concentration at a certain grid location (100,200) at a certain time is 90 μg / m 3 , extract the particle concentration information of the grid location with a step size of 15 minutes from the gridded concentration dataset, use GIS to associate the road ID and location information, extract the relevant road dust data from the road network dust load and traffic flow spatiotemporal dataset, input the coupled model, and obtain the road-level particle concentration impact distribution dataset. For example, the particle concentration of a certain road at 10 o'clock is 95μg / m 3 .

[0074] The degree of road dust pollution is determined based on the set graded warning thresholds. If the concentration of particulate matter on a certain road at a certain time is 95 μg / m 3, the pollution level is level one. The road ID, particulate matter concentration and timestamp are extracted from the road graded concentration distribution dataset. A road pollution map is generated using GIS. Taking the particulate matter concentration of 0 as the baseline value, the increase in the particulate matter concentration of the road monitoring points compared with the baseline value is calculated and ranked to obtain the road dust pollution contribution ranking. A road pollution assessment report is generated that includes the pollution degree, pollution map and pollution contribution ranking.

[0075] In the terminal interface module, Leaflet's heat map plug-in is used to render the road pollution map, ECharts is used to build a bar chart ranking the contribution of road dust pollution, and the degree of road pollution is marked with labels. Vue.js is used to build a dynamic web interface display, and the request URL is constructed according to the API specification. The road pollution assessment report is added to the request as the request body. After authentication, an HTTP request is sent and the report is pushed to third-party platforms such as the environmental protection department and the traffic management department to provide data support for relevant departments to formulate pollution control measures and management policies.

[0076] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A system for monitoring and simulating the impact of particulate matter concentration on road dust accumulation, characterized by: include: Data acquisition module, used to obtain real-time data sets including road dust accumulation, traffic flow and meteorological parameters; A processing module is used to clean or calibrate the acquired road dust, traffic flow, and meteorological parameter datasets, and fuse the cleaned or calibrated road dust, traffic flow, and meteorological parameter datasets to obtain a spatiotemporal dataset of road network dust load and traffic flow; The simulation module is used to obtain a road dust emission inventory using a dust lift model for the spatiotemporal datasets of road network dust load and traffic flow. The obtained road dust emission inventory is then input into a local-scale air quality model to simulate and obtain a gridded particulate matter concentration dataset. Simultaneously, a coupling model is used to obtain a road-level particulate matter concentration impact distribution dataset for the gridded particulate matter concentration dataset. The assessment and judgment module is used to perform graded early warning processing on the road-level particle matter concentration impact distribution dataset to determine the degree of dust pollution on each road. At the same time, based on the road-level particle matter concentration impact distribution dataset, a road pollution map and a ranking of road dust pollution contributions are generated, and a road pollution assessment report is generated based on this data. The terminal interface module is used to display the road pollution assessment report and push it to a third-party platform.

2. The particulate matter concentration impact monitoring simulation and evaluation system based on road dust navigation according to claim 1 is characterized in that: In the data acquisition module, the process of obtaining road dust, traffic flow and meteorological parameter data sets in real time: Road dust data is collected using a laser-induced breakdown spectroscopy sensor mounted on a UAV. Combined with the vehicle's onboard GPS system and the vehicle's built-in clock, the timestamp and road location information corresponding to the road dust data are obtained. A road dust table containing road dust data, timestamp, and road location information fields is created through a database. The road dust data, timestamp, and road location information are then entered into the road dust table to construct a road dust dataset. Traffic flow data, average speed, and vehicle type ratios in the road area are collected using traffic flow sensors mounted on the vehicle. The onboard GPS system and the vehicle's built-in clock are used to obtain the timestamps and road location information corresponding to the traffic data. A traffic flow data table containing traffic data, timestamps, and road location information fields is created through the database. The traffic data, timestamps, and road location information are then entered into the traffic flow data table to construct a traffic flow dataset. The regional meteorological data of wind speed, wind direction, temperature and humidity in the road area are obtained through the meteorological sensors installed on the traveling vehicle. At the same time, the timestamp and road location information corresponding to the regional meteorological data are obtained by combining the on-board GPS system and the built-in clock of the traveling vehicle. A meteorological parameter data table containing regional meteorological data, timestamp and road location information fields is created through the database. The regional meteorological data, timestamp and road location information are filled into the meteorological parameter data table, and the meteorological parameter dataset is constructed accordingly.

3. The particulate matter concentration impact monitoring simulation and evaluation system based on road dust navigation according to claim 2 is characterized in that: In the processing module, the process of cleaning or calibrating the acquired road dust, traffic flow and meteorological parameter data sets: The road dust dataset is cleaned. The 3σ principle is used to identify and delete the road dust data that exceeds ±3 times the standard deviation. The deleted road dust data set is filled with the deleted road dust data using linear interpolation to obtain the cleaned road dust dataset. The traffic flow dataset is cleaned and processed. The Poisson distribution model is used to test the traffic flow data in the traffic dataset, and outliers with a sudden increase of more than 50% are identified and removed. Based on this, a cleaned traffic flow dataset is obtained. Calibrate the meteorological parameter data set. Compensate wind speed and humidity are obtained by using the logarithmic wind profile formula and the dry-bulb temperature formula, respectively, to replace the original wind speed and humidity, thereby obtaining a calibrated meteorological parameter data set. The linear interpolation method is a method for estimating data between known data points.

4. The particulate matter concentration impact monitoring simulation and evaluation system based on road dust navigation according to claim 3 is characterized in that: In the processing module, the cleaned or calibrated road dust, traffic flow and meteorological parameter datasets are fused to obtain the spatiotemporal datasets of road network dust load and traffic flow: The cleaned or calibrated road dust, traffic flow, and meteorological parameter datasets were aligned in time. Based on the Universal Time Coordinated (UTC) standard, the timestamps of the cleaned or calibrated road dust, traffic flow, and meteorological parameter datasets were converted to the ISO 8601 format. The ISO 8601 format road dust, traffic flow, and meteorological parameter datasets were then unified into a 15-minute time step using cubic spline interpolation. Missing time points in the unified time step road dust, traffic flow, and meteorological parameter datasets were processed using the forward filling method. This resulted in the time-aligned road dust, traffic flow, and meteorological parameter datasets. The road dust, traffic flow, and meteorological parameter datasets that were aligned in the time dimension were aligned in the spatial dimension. The road location information in the time-aligned road dust, traffic flow, and meteorological parameter datasets was mapped to the UTM projection coordinate system using the Python pyproj library. The road location information in the UTM projection coordinate system was associated with the road ID information using the Geographic Information System (GIS). At the same time, based on the location information of the road dust, traffic flow, and meteorological parameter datasets in the UTM projection coordinate system, the road dust data in the road dust dataset, the traffic data in the traffic flow dataset, and the regional meteorological data in the meteorological parameter dataset were associated with the road ID using the Kriging interpolation method. This resulted in a spatially and temporally aligned road dust, traffic data, and meteorological parameter dataset, namely, a spatiotemporal dataset of road network dust load and traffic flow. The time dimension alignment and space dimension alignment are two processes of fusion processing; The Kriging interpolation method is a spatial interpolation method; The time step is a time interval.

5. The particulate matter concentration impact monitoring simulation and evaluation system based on road dust navigation according to claim 4 is characterized in that: In the simulation module, the process of obtaining the road dust emission inventory through the dust accumulation model is as follows: Dust emission data, including road dust data, vehicle volume data, average vehicle speed, and vehicle type ratio, were extracted from the road network dust load and traffic flow spatiotemporal dataset. The dust emission data were input into the dust emission model to obtain the dust emission amount. The dust emission amount was then associated with the timestamp, road ID, and road location information corresponding to the road dust data in the road network dust load and traffic flow spatiotemporal dataset to generate a road dust emission inventory.

6. The system for monitoring and simulating the impact of particulate matter concentration on road dust accumulation according to claim 5 is characterized in that: In the simulation module, the road dust emission inventory is input into the local-scale air quality model to simulate the process of obtaining a gridded particulate matter concentration dataset: Extract dust accumulation and road location information for each road ID from the road dust emission inventory at a 15-minute time step. At the same time, obtain regional meteorological data corresponding to the road dust emission inventory through an API interface based on the timestamp, road ID, and road location information of the road dust emission inventory. Also, use a geographic information system (GIS) to obtain road environment data for the area containing the road location information, including road networks, terrain, and buildings. Road environmental data, regional meteorological data corresponding to the road dust emission inventory, dust collection amounts of different road IDs, and road location information are input into the local-scale air quality model AERMOD to obtain a gridded particulate matter concentration dataset including grid locations and the particle matter concentrations corresponding to the grid locations.

7. The particulate matter concentration impact monitoring simulation and evaluation system based on road dust navigation according to claim 6 is characterized in that: In the simulation module, the process of obtaining the road-level particle concentration impact distribution dataset through the coupling model for the gridded particle concentration dataset is as follows: Particulate matter concentration information corresponding to grid locations was extracted from the gridded particulate matter concentration dataset at a 15-minute time step. Using a geographic information system (GIS), the grid locations in the gridded particulate matter concentration dataset were associated with road IDs and road location information. Simultaneously, road dust accumulation data associated with the road locations at the grid locations were extracted from the road network dust load and traffic flow spatiotemporal dataset. The extracted particulate matter concentration information corresponding to the grid locations, the road IDs and road location information associated with the grid locations, and the road dust accumulation data were input into the coupled model to obtain a road-level particulate matter concentration impact distribution dataset containing road IDs, timestamps, road location information, and particulate matter concentrations. The coupled model is a tool for accurately simulating the distribution of particulate matter concentration around roads.

8. The particulate matter concentration impact monitoring simulation and evaluation system based on road dust navigation according to claim 7 is characterized in that: In the evaluation and judgment module, the process of obtaining the dust pollution degree of each road and generating a road pollution assessment report is as follows: The particulate matter concentration in the road graded concentration distribution dataset is judged by the set graded warning threshold to obtain the pollution level corresponding to the particulate matter concentration. The pollution level corresponding to the particulate matter concentration is associated with the road ID corresponding to the particulate matter concentration to obtain the dust pollution degree of each road; Road IDs, particulate matter concentrations, and timestamps were extracted from the road-graded concentration distribution dataset. A road pollution map was generated using a geographic information system (GIS). Using a particle concentration of 0 as the baseline value, the increase in particle matter concentration at each monitoring point compared to the baseline was calculated based on the road location information and timestamps in the road-graded concentration distribution dataset. The road monitoring points were ranked in descending order based on the increase in particle concentration to obtain a ranking of their contribution to road dust pollution. Based on this, a road pollution assessment report is obtained, which includes the road pollution degree, road pollution map and the ranking of road dust pollution contributions; The particulate matter concentration at the road monitoring point is the particulate matter concentration corresponding to the road position information in the nearest road graded concentration distribution data set.

9. The system for monitoring and simulating the impact of particulate matter concentration on road dust accumulation according to claim 8 is characterized in that: In the terminal interface module, the process of pushing the road pollution assessment report to the third-party platform: The road pollution assessment report is pushed to the third-party platform through the API interface.

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