High-emission road identification method and device based on heavy vehicle remote online monitoring data, equipment and medium

Abstract

Embodiments of the present application provide a high-emission road identification method and device based on heavy vehicle remote online monitoring data, equipment and medium, relating to the technical field of environmental monitoring and air pollution, wherein the method comprises: establishing a correspondence between heavy vehicles and road sections; determining the NOx instantaneous emission rate of each type of heavy vehicle on the road section at different time periods, and inputting the meteorological data into the corrected pollutant diffusion model to output the NOx concentration contribution of each type of heavy vehicle on the road section at different time periods to the environmental monitoring station, and then calculating the NOx concentration contribution of the road to the environmental monitoring station; according to the ratio of the NOx concentration of the environmental monitoring station and the NOx concentration contribution of each road to the environmental monitoring station, the NOx concentration contribution rate of each road to the environmental monitoring station is calculated, and the high-emission road is determined according to the size of the contribution rate. The scheme can effectively identify high-emission roads based on accurate contribution rates.

Description

Technical Field

[0001] This invention relates to the field of mobile source environmental monitoring and air pollution analysis technology, and in particular to a method, device, equipment and medium for identifying high-emission roads based on remote online monitoring data of heavy vehicles. Background Technology

[0002] With rapid urbanization and a dramatic increase in the number of motor vehicles, urban air pollution has become increasingly severe. Nitrogen oxides (NOx), as one of the major air pollutants, have a significant negative impact on air quality, human health, and the ecological environment. Among the many sources of NOx emissions, heavy-duty vehicle exhaust emissions play a significant role. Due to their high engine power, high fuel consumption, and relatively outdated exhaust treatment technologies (especially in older vehicles), heavy-duty vehicles often emit large amounts of NOx. Furthermore, since heavy-duty vehicles tend to travel primarily on major urban roads, the NOx concentrations monitored at environmental monitoring stations along these roads are likely to be significantly affected by heavy-duty vehicle emissions.

[0003] Accurately assessing the contribution of heavy-duty vehicle emissions from different roads surrounding monitoring stations to NOx concentrations at those stations is of irreplaceable importance for developing precise and effective air pollution control strategies. This assessment helps environmental management departments identify the main sources and pathways of pollution, enabling them to take targeted measures, such as implementing traffic control on specific roads, optimizing heavy-duty vehicle routes, or raising emission standards for heavy-duty vehicles.

[0004] However, existing assessment methods have several limitations. Firstly, they do not fully utilize the data. While the development of remote online monitoring technology for heavy-duty vehicles has enabled us to obtain a wealth of detailed data on their operating status (such as vehicle speed and engine condition) and exhaust emissions (such as real-time NOx emission concentration), most current assessment methods fail to fully leverage the value of this data. For example, some existing methods may simply use average emission factors to estimate the NOx emissions of heavy-duty vehicles without considering the dynamic changes in actual emissions under different driving conditions.

[0005] On the other hand, existing assessment methods are not precise enough in considering the complex atmospheric diffusion relationships between roads and monitoring stations. Atmospheric diffusion is influenced by a combination of factors, including meteorological conditions (such as wind speed, wind direction, temperature, humidity, and atmospheric stability), topography (such as different terrains like mountains, plains, and valleys, as well as the obstruction and influence of buildings like high-rises in cities), and underlying surface type (such as vegetation cover, water surfaces, and concrete pavements). Existing assessment methods, when constructing models to describe these complex atmospheric diffusion relationships, often use simplified assumptions or generic model parameters, failing to accurately reflect the true relationship between road emission sources and monitoring stations within a specific area. For example, in some urban centers with numerous high-rise buildings and variable wind directions, existing atmospheric diffusion models may not accurately simulate the diffusion process of NOx from heavy-duty vehicle exhaust to monitoring stations under such complex conditions, leading to significant biases and inaccuracies in the assessment of contribution rates. Summary of the Invention

[0006] In view of this, embodiments of the present invention provide a method for identifying high-emission roads based on remote online monitoring data of heavy vehicles, to solve the technical problem of inaccurate contribution rate assessment in the prior art. The method includes:

[0007] Based on spatial arrangement, the roads surrounding the environmental monitoring stations are divided into different road sections;

[0008] Based on the location information of different types of heavy vehicles and the geographical information of road sections, establish the correspondence between different types of heavy vehicles and the road sections where the heavy vehicles are located.

[0009] For each road segment, the instantaneous NOx emission rate of each type of heavy-duty vehicle at different times is determined. The instantaneous NOx emission rate of each type of heavy-duty vehicle is obtained by summing the instantaneous NOx emission rates of each heavy-duty vehicle. The instantaneous NOx emission rate of each heavy-duty vehicle is calculated based on the NOx emission concentration of each heavy-duty vehicle monitored online.

[0010] The instantaneous NOx emission rate of each type of heavy-duty vehicle on the road segment at different time periods and meteorological data are input into the modified pollutant dispersion model, which outputs the NOx concentration contribution of each type of heavy-duty vehicle on the road segment to the environmental monitoring station at different time periods. For each road, the NOx concentration contribution of the road to the environmental monitoring station is calculated based on the NOx concentration contribution of all types of heavy-duty vehicles on all road segments included in the road at all time periods. The modified pollutant dispersion model is a pollutant dispersion model that considers the influence of topographic factors on pollutant dispersion.

[0011] Based on the ratio of the NOx concentration at the environmental monitoring station to the NOx concentration contribution of each road to the environmental monitoring station, the NOx concentration contribution rate of each road to the environmental monitoring station is calculated. Based on the magnitude of the NOx concentration contribution rate of different roads to the environmental monitoring station, high-emission roads are determined.

[0012] This invention also provides a high-emission road identification device based on remote online monitoring data of heavy vehicles, to solve the technical problem of inaccurate contribution rate assessment in the prior art. The device includes:

[0013] The partitioning module is used to divide the roads around the environmental monitoring station into different road segments according to space.

[0014] The corresponding construction module is used to establish the correspondence between different types of heavy vehicles and the road segments based on the location information of different types of heavy vehicles and the geographical information of the road segments.

[0015] The instantaneous emission rate calculation module is used to determine the instantaneous NOx emission rate of each type of heavy-duty vehicle at different times for each road segment. Specifically, the instantaneous NOx emission rate of each heavy-duty vehicle of the same type is obtained by superimposing the instantaneous NOx emission rates of each heavy-duty vehicle. The instantaneous NOx emission rate of each heavy-duty vehicle is calculated based on the NOx emission concentration of each heavy-duty vehicle monitored online.

[0016] The concentration contribution calculation module is used to input the instantaneous NOx emission rate of each type of heavy-duty vehicle and meteorological data of the road segment at different time periods into the modified pollutant diffusion model, and output the NOx concentration contribution of each type of heavy-duty vehicle of the road segment to the environmental monitoring station at different time periods; for each road, based on the NOx concentration contribution of all types of heavy-duty vehicles of all road segments included in the road to the environmental monitoring station at all time periods, the NOx concentration contribution of the road to the environmental monitoring station is calculated. The modified pollutant diffusion model is a pollutant diffusion model that considers the influence of topographic factors on pollutant diffusion.

[0017] The high-emission road identification module is used to calculate the NOx concentration contribution rate of each road to the environmental monitoring station based on the ratio of the NOx concentration of the environmental monitoring station to the NOx concentration contribution of each road to the environmental monitoring station, and to determine the high-emission roads based on the magnitude of the NOx concentration contribution rate of different roads to the environmental monitoring station.

[0018] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned high-emission road identification methods based on remote online monitoring data of heavy vehicles, thereby solving the technical problem of inaccurate contribution rate assessment in the prior art.

[0019] This invention also provides a computer-readable storage medium storing a computer program that executes any of the above-described high-emission road identification methods based on remote online monitoring data of heavy vehicles, in order to solve the technical problem of inaccurate contribution rate assessment in the prior art.

[0020] Compared with the prior art, the beneficial effects that the above-mentioned at least one technical solution adopted in the embodiments of this specification can achieve include at least the following: calculating the instantaneous NOx emission rate of each heavy-duty vehicle based on the NOx emission concentration of each heavy-duty vehicle monitored online, then inputting the instantaneous NOx emission rate of each type of heavy-duty vehicle on the road segment at different time periods and meteorological data into the modified pollutant diffusion model, outputting the contribution of each type of heavy-duty vehicle on the road segment to the NOx concentration of the environmental monitoring station at different time periods, and then combining the NOx concentration data of the environmental monitoring station to calculate the contribution rate of each road to the NOx concentration of the environmental monitoring station. This realizes the integration of remote online monitoring data of heavy-duty vehicles and NOx concentration data of environmental monitoring stations, and uses the improved AERMOD model (i.e., the modified pollutant diffusion model) to accurately calculate the contribution rate of different roads around the monitoring station to the NOx concentration. Therefore, based on the accurate contribution rate, high-emission roads can be effectively identified, providing a scientific basis for accurately tracing the source of air pollution and formulating targeted pollution control strategies. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a high-emission road identification method based on remote online monitoring data of heavy vehicles provided in an embodiment of the present invention;

[0023] Figure 2 This is a structural block diagram of a computer device provided in an embodiment of the present invention;

[0024] Figure 3 This is a structural block diagram of a high-speed road identification device based on remote online monitoring data of heavy vehicles, provided in an embodiment of the present invention. Detailed Implementation

[0025] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0026] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] In this embodiment of the invention, a method for identifying high-emission roads based on remote online monitoring data of heavy vehicles is provided, such as... Figure 1 As shown, the method includes:

[0028] Step S101: Divide the roads surrounding the environmental monitoring station into different road segments according to spatial arrangement;

[0029] Step S102: Based on the location information of different types of heavy vehicles and the geographical information of road segments, establish the correspondence between different types of heavy vehicles and the road segments where the heavy vehicles are located.

[0030] Step S103: For each road segment, determine the instantaneous NOx emission rate of each type of heavy-duty vehicle at different time periods. The instantaneous NOx emission rate of each type of heavy-duty vehicle is obtained by superimposing the instantaneous NOx emission rates of each heavy-duty vehicle. The instantaneous NOx emission rate of each heavy-duty vehicle is calculated based on the NOx emission concentration of each heavy-duty vehicle monitored online.

[0031] Step S104: Input the instantaneous NOx emission rate of each type of heavy-duty vehicle for this road segment at different time periods and meteorological data into the modified pollutant diffusion model, and output the NOx concentration contribution of each type of heavy-duty vehicle for this road segment to the environmental monitoring station at different time periods; for each road, calculate the NOx concentration contribution of this road to the environmental monitoring station based on the NOx concentration contribution of all types of heavy-duty vehicles in all road segments included in this road at all time periods. The modified pollutant diffusion model is a pollutant diffusion model that considers the influence of topographic factors on pollutant diffusion.

[0032] Step S105: Calculate the NOx concentration contribution rate of each road to the environmental monitoring station based on the ratio of the NOx concentration at the environmental monitoring station to the contribution of each road to the NOx concentration at the environmental monitoring station. Determine the high-emission roads based on the magnitude of the NOx concentration contribution rates of different roads to the environmental monitoring station.

[0033] In practice, multi-dimensional online monitoring data can be obtained from a heavy-duty vehicle remote online monitoring platform. This data includes, but is not limited to, the vehicle's real-time location (i.e., positioning information), speed, engine operating conditions, NOx emission concentration, vehicle type, and age. For example, data can be collected on a heavy-duty diesel vehicle that is 5 years old, a van, with a speed of 40 km / h, an engine speed of 1500 r / min, a NOx emission concentration of 800 ppm, and a geographical location of (116.214565, 39.913081). Other data parameters are shown in Table 1. This data can be transmitted to the remote online monitoring platform at regular intervals (e.g., every 10 seconds) via the vehicle's communication module (such as a 4G / 5G network module).

[0034] Table 1

[0035]

[0036] The remote online monitoring platform cleans and filters the received data, removing outliers. It performs preliminary integrity and accuracy checks. If data loss or obvious errors are found (such as emission concentrations exceeding reasonable ranges, such as greater than 2000 ppm or less than 0 ppm, data that is obviously inconsistent with the actual situation is discarded), the data for that period is marked for subsequent processing.

[0037] In practice, each road around the environmental monitoring station is regarded as a line source, and the parameters of the road sources are refined based on the remote online monitoring data of heavy vehicles.

[0038] Based on emission characteristics, each road source can be further subdivided into multiple sub-sources (i.e., multiple road segments). For example, a long main road can be divided into multiple sub-sources based on areas with concentrated heavy traffic (such as near intersections or entrances / exits of large logistics parks), with each sub-source having different emission characteristics.

[0039] The concept of time period and heavy-duty vehicle type is also introduced. This means that the emission characteristics of heavy-duty vehicles may differ across different spatial segments, time periods, and vehicle types, and their contribution to the overall NOx concentration at environmental monitoring stations may also vary. Therefore, time periods can be divided according to specific needs and preset time intervals, such as 1 hour, 3 hours, or 5 hours. Heavy-duty vehicle type refers to further classifying heavy-duty vehicles based on their model, age, etc., such as trucks, engineering vehicles, etc.

[0040] In practice, based on the location information of heavy vehicles and the geographical information of road segments, a correspondence between different types of heavy vehicles and the road segments where the heavy vehicles are located can be established. Furthermore, a correspondence between heavy vehicles, road segments where heavy vehicles are located, and time periods can be established based on the time when heavy vehicles pass through the road segments.

[0041] In practice, to fully utilize remote online monitoring data of heavy-duty vehicles, such as vehicle speed and engine operating conditions, as well as exhaust emissions (such as real-time NOx emission concentration), a dynamic emission rate for heavy-duty vehicles is introduced. This dynamic emission rate is closely related to factors such as vehicle speed, engine operating conditions, and vehicle type. For example, for a diesel heavy-duty vehicle, when the vehicle speed is 20-30 km / h and the engine is under high load, its NOx emission rate will be higher than under normal driving conditions.

[0042] For example, the instantaneous NOx emission rate of each heavy-duty vehicle per second can be calculated based on the second-by-second remote online monitoring data of heavy-duty vehicles using the following formula. E i (g / s):

[0043]

[0044] in, E i The instantaneous NOx emission rate for each heavy-duty vehicle, in g / s;

[0045] e i NOx emission concentration per heavy-duty vehicle, in ppm;

[0046] ρ The density of diesel fuel is expressed in kg / L, and is typically taken as 0.85 kg / L.

[0047] f Fuel Fuel mass flow rate, L / h;

[0048] f Air The intake air mass flow rate is kg / h.

[0049] Obtain the instantaneous NOx emission rate for each heavy-duty vehicle per second. Then, the instantaneous NOx emission rates of each heavy-duty vehicle of the same type are summed to obtain the instantaneous NOx emission rate of each type of heavy-duty vehicle; the sum of the instantaneous NOx emission rates of all heavy-duty vehicles of the same type in each road segment at the same time period is taken as the instantaneous NOx emission rate of each type of heavy-duty vehicle in different time periods of that road segment.

[0050] When inputting parameters into the AERMOD model, considering that all parameters related to NOx emissions from heavy vehicles on the road are different, inputting the instantaneous NOx emission rate of heavy vehicles passing through a certain road segment during a certain time period (e.g., 1 hour) can directly reflect the emission scale and is closely related to traffic flow, which is of great significance for assessing the contribution of regional pollution.

[0051] In practice, different spatial segments and time periods may contribute differently to the overall average concentration. For example, some road segments may have higher traffic volumes (such as segments near commercial or industrial areas), or traffic may be busier during certain time periods (such as morning or evening rush hours). We can introduce a weight wij for each spatial-temporal unit (i.e., a specific road segment during a certain time period). The total NOx concentration M of the entire road segment over one hour can be calculated using the following formula:

[0052]

[0053] M Let g be the NOx emissions of a certain road in 1 hour;

[0054] m ij For roads j During the period i (Time period) i The number of vehicles passing by within a 10-minute timeframe (for example);

[0055] E ijk for k Car on the road j During the period i Average NOx emission rate within the region, g / s;

[0056] t ijk for k Car on the road j During the period i The passage time within the area, in seconds.

[0057] Average NOx emission rate of a certain road section within 1 hour R (g / s) is:

[0058] .

[0059] In practice, environmental monitoring stations are equipped with high-precision NOx concentration monitoring instruments, such as chemiluminescence NOx analyzers, to measure NOx concentration. These instruments measure the NOx concentration in the ambient air hourly. Assuming the measured NOx concentration at a certain moment is 50 μg / m³, and simultaneously, the monitoring station's meteorological instruments measure a wind speed of 2 m / s, a southeasterly wind direction, a temperature of 25°C, and a humidity of 60%, this data is transmitted to the data processing center via a wired network.

[0060] Data on the geographical relationship between roads and environmental monitoring stations is needed, including distance, wind direction, and topography. Wind direction has a crucial impact on the direction of pollutant transport, and topography (such as valleys and highlands) may affect the diffusion path of pollutants.

[0061] Quality control is performed on NOx concentration data from environmental monitoring stations. A rationality analysis is conducted based on simultaneously measured meteorological data. For example, if the wind speed is high (e.g., greater than 5 m / s) and the wind direction is stable, but the NOx concentration suddenly increases significantly (e.g., from 30 μg / m³ to 80 μg / m³), it is necessary to check whether the monitoring instrument is malfunctioning or whether there are other temporary pollution sources affecting the data. The quality-controlled NOx concentration data is then organized into a time series to ensure that the time resolution matches that of the heavy-duty vehicle remote online monitoring data; here, it is standardized to hourly data.

[0062] In practice, data such as the instantaneous NOx emission rate of each type of heavy-duty vehicle on the road segment at different times, meteorological data (meteorological data between the road segment and the environmental monitoring station), and NOx concentration at the environmental monitoring station are input into the modified pollutant diffusion model. The pollutant diffusion model is used to simulate the diffusion process of NOx pollutants from each type of heavy-duty vehicle on the road segment at different times, and to obtain the contribution of each type of heavy-duty vehicle on the road segment to the NOx concentration at the environmental monitoring station at different times.

[0063] In practical implementation, the traditional AERMOD model is insufficient in depicting the "thermal-dynamic dual effect" of terrain, requiring complex parameter settings to improve accuracy. To address this issue, this embodiment considers the interaction mechanism between meteorological and terrain factors on NOx diffusion. For example, wind direction and speed affect the direction and speed of NOx transport in undulating terrain areas, while terrain undulations change airflow patterns, thereby affecting the diffusion range and concentration distribution of NOx. It proposes to consider the influence of terrain slope on thermal turbulence, modify the diffusion term of the pollutant diffusion model, and obtain the first modified pollutant diffusion model, realizing the diffusion of pollutants through terrain-meteorological coupling.

[0064] For example, the formula for the first revised pollutant diffusion model is as follows:

[0065]

[0066] Where C represents the contribution of each type of heavy vehicle to the NOx concentration at the environmental monitoring station on this road section at different time periods. The percentage change over time in the NOx concentration contribution of each type of heavy vehicle at this environmental monitoring station for this road section at different time periods. For wind speed vectors, This is the convection term, used to describe the flow of NO carried by the airflow. x The transmission process, where α is the slope of the terrain and β is the solar azimuth angle. For coefficients, (i.e., the source term) is the NO of heavy vehicles of type k in segment j during time period i. x Instantaneous emission rate, unit: mg / (m³) s), For gradient (representing the concept of gradient). NO x Concentration gradient, t is time, and D is diffusion coefficient.

[0067] Specifically, It uses refined input data by vehicle type, road segment, and time period, rather than the comprehensive emission rate of a single road segment. This enables the fine-tuning of road source parameters in the pollutant diffusion model, which helps improve the simulation accuracy of the pollutant diffusion model.

[0068] Specifically, the instantaneous NOx emission rate of each type of heavy-duty vehicle at different times and meteorological data for this road section are input into the original AERMOD model, and the NOx diffusion equation is as follows:

[0069]

[0070] The NOx diffusion equation uses a fixed diffusion coefficient D, which does not consider the influence of topographic thermal effects on turbulence, thus affecting the accuracy of the NOx diffusion equation and failing to reflect the physical process of "uneven solar radiation due to slope aspect differences → differences in thermal turbulence → changes in diffusion capacity". This application proposes to replace D with a dynamically corrected diffusion coefficient, and the specific correction logic is as follows:

[0071] a) Constructing the diffusion coefficient correction formula

[0072] Based on slope aspect α (the angle between the terrain slope and true north, 0~360°) and solar azimuth β (the angle between the sun's rays and true north, varying with season / time of day), a "thermal turbulence correction coefficient" is first defined. :

[0073]

[0074] The value of this coefficient ranges from 0 to 1: when the slope aspect is consistent with the solar azimuth (sunny slope), f(α,β) → 1, thermal turbulence is enhanced, and the diffusion coefficient needs to be increased; when the slope aspect is opposite to the solar azimuth (shady slope), f(α,β) → 0, thermal turbulence is weakened, and the diffusion coefficient is close to its original value.

[0075] Then, combine the experimental fitting coefficient k1 (which needs to be localized NO) x The dynamic diffusion coefficient is obtained by regressing concentration monitoring data (e.g., k1=0.23 in a certain case). :

[0076]

[0077] b) Substitute the dynamic diffusion coefficient into the original NOx diffusion equation to complete the diffusion term correction.

[0078] Replace D in the original NOx diffusion equation with D dir The corrected diffusion term is This yields the formula for the first modified pollutant diffusion model (i.e., the first modified pollutant diffusion model equation).

[0079] In practice, to further improve the accuracy of the pollutant diffusion model, it is proposed to consider the influence of changes in terrain height on wind speed and modify the convection term of the pollutant diffusion model to obtain a second modified pollutant diffusion model.

[0080] The formula for the second revised pollutant diffusion model is as follows:

[0081]

[0082] in, For coefficients, The elevation difference between adjacent grids in the topographic grid between roads and environmental monitoring stations. The distance between adjacent grid cells. is the diffusion coefficient.

[0083] Specifically, the convection term is corrected for the "dynamic effect" (by introducing a high-resolution DEM wind speed correction term):

[0084] Wind speed vector in the original equation This embodiment does not consider wind speed attenuation caused by terrain obstructions (such as mountains and steep slopes). Replace with the terrain-corrected wind speed vector. The specific correction logic is as follows:

[0085] a) Constructing the wind speed vector correction formula

[0086] Based on 30m resolution DEM data, the "terrain height change rate" (reflecting the strength of terrain undulations in obstructing airflow) within the grid cells is first calculated:

[0087]

[0088] in, , These represent the elevations (in meters) of two adjacent grids within the terrain grid between the road and the environmental monitoring station. The elevation difference between two adjacent grid cells. The spacing between adjacent grids is fixed at 30m, consistent with the DEM resolution.

[0089] Redefine the "wind speed attenuation factor" (based on the exponential decay model, reflecting the physical law that the greater the rate of change of altitude, the more significant the wind speed attenuation):

[0090]

[0091] Where k3 is the wind speed attenuation coefficient (which needs to be obtained through regression analysis of local wind speed monitoring data; for example, k3=0.8, in this case, the wind speed attenuation coefficient is lower than that of the mountain area). Larger, f( →0.5, meaning the wind speed decreases to less than 50% of its original value.

[0092] Finally, the terrain-corrected wind speed vector is obtained. :

[0093]

[0094] b) The terrain-corrected wind speed vector Substituting into the first revised pollutant diffusion equation, the convection term correction is completed.

[0095] The first revised pollutant diffusion equation Replace with This yields the final revised formula for the second pollutant diffusion model (i.e., the second revised pollutant diffusion equation).

[0096] The second revised pollutant diffusion equation, through the dual correction of "diffusion term + convection term", simultaneously addresses the shortcomings of the original equation in ignoring the "difference in thermal turbulence" and "wind speed attenuation" of the terrain. For example, in sunny slope areas, the NOx diffusion range is wider due to the increase of Ddir; in mountainous areas, the NOx residence time is extended due to the decrease, which is highly consistent with actual monitoring patterns.

[0097] Application Notes: Fitting coefficients k1 and k3 need to be "localized calibration"—it is necessary to collect NOx concentration, wind speed, DEM, and solar azimuth data from at least 3 monitoring stations within the correction area and obtain them through least squares regression (such as the method used to obtain the slope influence coefficient k=0.1 in the original model) to avoid directly applying coefficients from other regions, which would lead to increased errors.

[0098] In practice, the second revised pollutant diffusion model was validated using data from known NOx emission sources and monitoring stations. The simulation results were compared with actual monitoring data from the stations, and the accuracy of the model was assessed using root mean square error (RMSE) and mean absolute error (MAE). In the case of an industrial cluster, the calculated RMSE = 5 μg / m³ and MAE = 3 μg / m³, indicating that the model has a certain degree of accuracy.

[0099] In practice, multiple simulation scenarios are set up based on different time periods (such as peak hours and off-peak hours) and different weather conditions (such as sunny, cloudy, and rainy days) in the remote online monitoring data of heavy vehicles. Under each scenario, the improved AERMOD model is used to simulate the contribution of different road segments to the NOx concentration of the environmental monitoring station. Then, based on the contribution of all types of heavy vehicles on all road segments included in the road to the NOx concentration of the environmental monitoring station at all time periods, the NOx concentration contribution of the road to the environmental monitoring station is calculated (for example, the sum of the NOx concentration contributions of all types of heavy vehicles on all road segments included in the road to the NOx concentration of the environmental monitoring station at all time periods is taken as the NOx concentration contribution of the road to the environmental monitoring station).

[0100] For example, during the morning rush hour, road A contributes C to the NOx concentration at the environmental monitoring station. A1 =10μg / m³, the frequency of occurrence of the scenario during the morning rush hour is f1=30%.

[0101] During off-peak hours, road A contributes C to the NOx concentration at the environmental monitoring station. A2 =5μg / m³, the frequency of occurrence of the off-peak period scenario is f2=50%.

[0102] During the nighttime off-peak hours, road A contributes C to the NOx concentration at the environmental monitoring station. A3 =2μg / m³, the frequency of the scenario during the nighttime off-peak period is f3=20%.

[0103] The actual NOx concentrations detected at environmental monitoring stations were: =30μg / m³.

[0104] The contribution rate P of road A to the NOx concentration at the environmental monitoring station was calculated using the weighted average method. A .

[0105] First, calculate the weighted contribution of road A. :

[0106]

[0107] Based on the weighted contribution W A Calculate the contribution rate PA of road A to the NOx concentration at the environmental monitoring station:

[0108]

[0109] Similarly, the contribution rates of Road B and other roads to the NOx concentration at environmental monitoring stations can be calculated.

[0110] In practice, the calculated contribution rates are analyzed. The contribution rates of different roads are compared; for example, road A has the highest contribution rate, indicating that heavy vehicle emissions on road A have the greatest impact on NOx concentrations at the monitoring stations. The variation of contribution rates over time is analyzed; for instance, the contribution rates of all roads are generally higher during the morning rush hour than during the nighttime off-peak hours, which may be related to changes in heavy vehicle traffic volume. The variation of contribution rates with weather conditions is also analyzed; for example, on cloudy days, due to changes in atmospheric stability, the contribution rates of all roads may increase.

[0111] In practice, when determining high-emission roads based on the contribution rate of different roads to the NOx concentration of the environmental monitoring station, roads with a contribution rate greater than a preset threshold can be identified as high-emission roads, or the top few roads with a relatively large contribution rate can be identified as high-emission roads.

[0112] In practice, after obtaining the contribution rate of different roads to the NOx concentration at environmental monitoring stations, the contribution rate of high-emission roads can be optimized to reduce the NOx concentration at environmental monitoring stations. For example,

[0113] For each of the aforementioned high-emission roads, the traffic flow adjustment coefficients for each road segment within each high-emission road are adjusted at different time periods to regulate the traffic flow on each high-emission road, thereby reducing the NOx concentration at environmental monitoring stations. The traffic flow adjustment coefficients for each road segment at different time periods are adjusted using the following methods:

[0114]

[0115] in, x ij For road section j During the period i Adjusted traffic flow coefficient (0 <x ij ≤1), to prevent over-adjustment, , For road segment j during time period i Initial traffic flow adjustment coefficient, , For road segment j during time period i The contribution rate of NOx concentration to this environmental monitoring station. For road segment j during time period i Pollution sensitivity weights (values ​​range from 1.0 to 2.0; the higher the contribution rate and the more sensitive the time period (e.g., morning rush hour), the greater the weight. For example, morning rush hour W=1.8, nighttime W=1.0). For road segment j during time period i Flow saturation ( (The higher the saturation, the greater the adjustment). This is a calibration constant (with a value of 1.2 to 1.5, which needs to be obtained through regression of localized traffic data to avoid excessive traffic restrictions due to an excessively small coefficient). The traffic flow adjustment coefficient is adjusted. The following constraints must be met: , For road segment j during time period i Minimum traffic flow, For road segment j during time period i Traffic flow after adjusting the traffic flow adjustment coefficient For road segment j during time period i The original traffic flow, Given the design traffic flow for road segment j, the overall traffic flow adjustment range is as follows: T is the preset flow threshold.

[0116] Specifically, if The above formula is used to adjust the traffic flow adjustment coefficient of each road segment at different times; otherwise, the traffic flow adjustment coefficient of each road segment at different times is finely adjusted according to the contribution rate from high to low until the constraint is met.

[0117] In practice, adjusting the traffic flow adjustment coefficients for each road segment at different times aims to optimize the traffic flow on each road segment and the contribution rate of each road segment to the NOx concentration at environmental monitoring stations. To reduce or minimize NOx concentrations at environmental monitoring stations, for example, taking road A as an example, including road sections 1A, 2A, and 3A, the adjusted contribution rate of each road section to the NOx concentration at the environmental monitoring stations is calculated. as follows:

[0118]

[0119]

[0120]

[0121] The adjustment coefficients and contribution rates for other roads are also calculated accordingly. The adjusted NOx concentrations at environmental monitoring stations can be calculated using the following formula:

[0122]

[0123] In practical implementation, to reduce and minimize NOx concentrations at environmental monitoring stations, it was also proposed to adjust the proportion of different types of heavy-duty vehicles on each high-emission road, thereby adjusting the traffic flow on each high-emission road and ultimately reducing NOx concentrations at the environmental monitoring stations. For example,

[0124] For each of the aforementioned high-emission roads, the adjustment coefficients for the proportion of heavy-duty vehicle types in each road segment at different times are adjusted to adjust the proportion of different types of heavy-duty vehicles on each high-emission road, thereby reducing the NOx concentration at environmental monitoring stations. The adjustment coefficients for the proportion of heavy-duty vehicle types in each road segment at different times are adjusted in the following manner:

[0125]

[0126] For road segment j during time period i The proportional adjustment coefficient for heavy vehicle type k, For road segment j during time period i The original proportion of heavy vehicle type k, For road segment j during time period i The minimum proportion of heavy vehicle type k. For road segment j during time period i The maximum value of the proportion of heavy vehicle type k, to avoid over-adjustment. The following constraints must be met: , This is a preset ratio threshold.

[0127] For example, setting an acceptable range for adjusting the overall vehicle type ratio. S =0.2. Taking road A as an example, the adjustment coefficient for the proportion of heavy vehicles on road A during the morning rush hour is obtained. , To adjust the contribution rate of heavy vehicles on road A to the NOx concentration at the environmental monitoring station, light vehicles... (This indicates an appropriate increase in the proportion of light vehicles.) To adjust the contribution rate of light vehicles on Road A to the NOx concentration at the environmental monitoring station, the contribution rate of Road A to the NOx concentration at the environmental monitoring station after adjusting the vehicle type ratio is calculated as follows:

[0128]

[0129]

[0130] Adjustment factors and contribution rates for other road and vehicle types are also calculated accordingly. Then, the NOx concentration at the adjusted environmental monitoring stations is calculated using the following formula:

[0131]

[0132] The final adjusted NOx concentration was lower than the initial concentration.

[0133] In this embodiment, a computer device is provided, such as... Figure 2 As shown, it includes a memory 201, a processor 202, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned high-emission road identification methods based on remote online monitoring data of heavy vehicles.

[0134] Specifically, the computer device can be a computer terminal, a server, or a similar computing device.

[0135] In this embodiment, a computer-readable storage medium is provided, which stores a computer program that executes any of the above-described high-altitude road identification methods based on remote online monitoring data of heavy vehicles.

[0136] Specifically, computer-readable storage media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer-readable storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable storage media does not include transient media, such as modulated data signals and carrier waves.

[0137] Based on the same inventive concept, this invention also provides a high-altitude road identification device based on remote online monitoring data of heavy vehicles, as described in the following embodiments. Since the principle of the high-altitude road identification device based on remote online monitoring data of heavy vehicles is similar to that of the high-altitude road identification method based on remote online monitoring data of heavy vehicles, the implementation of the high-altitude road identification device based on remote online monitoring data of heavy vehicles can refer to the implementation of the high-altitude road identification method based on remote online monitoring data of heavy vehicles, and repeated details will not be elaborated further. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0138] Figure 3 This is a structural block diagram of a high-speed road identification device based on remote online monitoring data of heavy vehicles according to an embodiment of the present invention, such as... Figure 3 As shown, it includes:

[0139] The partitioning module 301 is used to divide the roads around the environmental monitoring station into different road segments according to space.

[0140] The corresponding construction module 302 is used to establish the correspondence between different types of heavy vehicles and the road segments based on the location information of different types of heavy vehicles and the geographical information of the road segments.

[0141] The instantaneous emission rate calculation module 303 is used to determine the instantaneous NOx emission rate of each type of heavy-duty vehicle at different times for each road segment. The instantaneous NOx emission rate of each type of heavy-duty vehicle is obtained by superimposing the instantaneous NOx emission rates of each heavy-duty vehicle. The instantaneous NOx emission rate of each heavy-duty vehicle is calculated based on the NOx emission concentration of each heavy-duty vehicle monitored online.

[0142] The concentration contribution calculation module 304 is used to input the instantaneous NOx emission rate of each type of heavy-duty vehicle and meteorological data of the road segment at different time periods into the modified pollutant diffusion model, and output the NOx concentration contribution of each type of heavy-duty vehicle of the road segment to the environmental monitoring station at different time periods; for each road, based on the NOx concentration contribution of all types of heavy-duty vehicles of all road segments included in the road to the environmental monitoring station at all time periods, the NOx concentration contribution of the road to the environmental monitoring station is calculated. The modified pollutant diffusion model is a pollutant diffusion model that considers the influence of topographic factors on pollutant diffusion.

[0143] The high-emission road identification module 305 is used to calculate the NOx concentration contribution rate of each road to the environmental monitoring station based on the ratio of the NOx concentration of the environmental monitoring station to the NOx concentration contribution of each road to the environmental monitoring station, and to determine the high-emission roads based on the magnitude of the NOx concentration contribution rate of different roads to the environmental monitoring station.

[0144] In one embodiment, it also includes:

[0145] The first correction module is used to consider the influence of terrain slope on thermal turbulence, correct the diffusion term of the pollutant diffusion model, and obtain the first corrected pollutant diffusion model.

[0146] In one embodiment, the formula for the first modified pollutant diffusion model is as follows:

[0147]

[0148] Where C represents the contribution of each type of heavy vehicle to the NOx concentration at the environmental monitoring station on this road section at different time periods. The percentage change over time in the NOx concentration contribution of each type of heavy vehicle at this environmental monitoring station for this road section at different time periods. For wind speed vectors, This is the convection term, used to describe the flow of NO carried by the airflow. x The transmission process, where α is the slope of the terrain and β is the solar azimuth angle. For coefficients, For road segment j, the NO of heavy vehicles of type k in time period i x Instantaneous emission rate, NO x Concentration gradient, Let t be the gradient, t be time, and D be the diffusion coefficient.

[0149] In one embodiment, it also includes:

[0150] The second correction module is used to consider the impact of changes in terrain height on wind speed, correct the convection term of the pollutant diffusion model, and obtain the second corrected pollutant diffusion model.

[0151] In one embodiment, the formula for the second modified pollutant diffusion model is as follows:

[0152]

[0153] in, For coefficients, The elevation difference between adjacent grids in the topographic grid between roads and environmental monitoring stations. The distance between adjacent grid cells. is the diffusion coefficient.

[0154] In one embodiment, it also includes:

[0155] The traffic flow adjustment module is used to adjust the traffic flow adjustment coefficients of each road segment within each high-emission road at different time periods, thereby adjusting the traffic flow of each high-emission road to reduce the NOx concentration at environmental monitoring stations. The traffic flow adjustment coefficients of each road segment at different time periods are adjusted in the following manner:

[0156]

[0157] in, x ij For road section j During the period i The adjusted traffic flow coefficient is as follows. For road segment j during time period i Initial traffic flow adjustment coefficient, , For road segment j during time period i The contribution rate of NOx concentration to this environmental monitoring station. For road segment j during time period i Pollution sensitivity weights For road segment j during time period i Flow saturation For calibration constant, The traffic flow adjustment coefficient is adjusted. The following constraints must be met: , For road segment j during time period i Minimum traffic flow, For road segment j during time period i Traffic flow after adjusting the traffic flow adjustment coefficient For road segment j during time period i The original traffic flow, Given the design traffic flow for road segment j, the overall traffic flow adjustment range is as follows: T is the preset flow threshold.

[0158] In one embodiment, it also includes:

[0159] The vehicle proportion adjustment module is used to adjust the proportion adjustment coefficient of heavy-duty vehicle types in each segment of each high-emission road at different times, so as to adjust the proportion of different types of heavy-duty vehicles on each high-emission road and reduce the NOx concentration at the environmental monitoring station. The proportion adjustment coefficient of heavy-duty vehicle types in each segment at different times is adjusted in the following way:

[0160]

[0161] For road segment j during time period i The proportional adjustment coefficient for heavy vehicle type k, For road segment j during time period i The original proportion of heavy vehicle type k, For road segment j during time period i The minimum proportion of heavy vehicle type k. For road segment j during time period i The maximum value of the proportion of heavy vehicle type k. The following constraints must be met: , This is a preset ratio threshold.

[0162] The embodiments of this invention achieve the following technical effects: The instantaneous NOx emission rate of each heavy-duty vehicle is calculated based on the NOx emission concentration of each vehicle monitored online. Then, the instantaneous NOx emission rates of each type of heavy-duty vehicle on the road segment at different time periods, along with meteorological data, are input into a modified pollutant diffusion model. The model outputs the contribution of each type of heavy-duty vehicle on the NOx concentration of the environmental monitoring station at different time periods. Combined with the NOx concentration data from the environmental monitoring station, the contribution rate of each road to the NOx concentration of the environmental monitoring station is calculated. This integrates remote online monitoring data of heavy-duty vehicles and NOx concentration data from environmental monitoring stations. By using an improved AERMOD model (i.e., a modified pollutant diffusion model), the contribution rate of different roads around the monitoring station to the NOx concentration can be accurately calculated. Based on this accurate contribution rate, high-emission roads can be effectively identified, providing a scientific basis for accurately tracing the sources of air pollution and formulating targeted pollution control strategies.

[0163] Obviously, those skilled in the art should understand that the modules or steps of the above-described embodiments of the present invention can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the embodiments of the present invention are not limited to any particular hardware and software combination.

[0164] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, various modifications and variations can be made to the embodiments of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for identifying high-speed roads based on remote online monitoring data of heavy vehicles, characterized in that, include: Based on spatial arrangement, the roads surrounding the environmental monitoring stations are divided into different road sections; Based on the location information of different types of heavy vehicles and the geographical information of road sections, establish the correspondence between different types of heavy vehicles and the road sections where the heavy vehicles are located. For each road segment, the instantaneous NOx emission rate of each type of heavy-duty vehicle at different times is determined. The instantaneous NOx emission rate of each type of heavy-duty vehicle is obtained by superimposing the instantaneous NOx emission rates of each heavy-duty vehicle. The instantaneous NOx emission rate of each heavy-duty vehicle is calculated based on the NOx emission concentration of each heavy-duty vehicle monitored online. The instantaneous NOx emission rate of each type of heavy-duty vehicle on the road segment at different time periods and meteorological data are input into the modified pollutant dispersion model, which outputs the NOx concentration contribution of each type of heavy-duty vehicle on the road segment to the environmental monitoring station at different time periods. For each road, the NOx concentration contribution of the road to the environmental monitoring station is calculated based on the NOx concentration contribution of all types of heavy-duty vehicles on all road segments included in the road at all time periods. The modified pollutant dispersion model is a pollutant dispersion model that considers the influence of topographic factors on pollutant dispersion. Based on the ratio of the NOx concentration at the environmental monitoring station to the contribution of each road to the NOx concentration at the environmental monitoring station, the NOx concentration contribution rate of each road to the environmental monitoring station is calculated. Based on the magnitude of the contribution rate of different roads to the NOx concentration at the environmental monitoring station, high-emission roads are determined. Among them, considering the influence of terrain slope on thermal turbulence, the diffusion term of the pollutant diffusion model is modified to obtain the first modified pollutant diffusion model. The formula for the first revised pollutant diffusion model is as follows: Where C represents the contribution of each type of heavy vehicle to the NOx concentration at the environmental monitoring station on this road section at different time periods. The percentage change over time in the NOx concentration contribution of each type of heavy vehicle at this environmental monitoring station for this road section at different time periods. For wind speed vectors, This is the convection term, used to describe the flow of NO carried by the airflow. x The transmission process, where α is the slope of the terrain and β is the solar azimuth angle. For coefficients, For road segment j, the NO of heavy vehicles of type k in time period i x Instantaneous emission rate, For gradient, NO x Concentration gradient, t is time, and D is diffusion coefficient.

2. The method as described in claim 1, characterized in that, Also includes: By considering the impact of changes in terrain elevation on wind speed, the convection term of the pollutant diffusion model is modified to obtain the second modified pollutant diffusion model.

3. The method as described in claim 2, characterized in that, The formula for the second revised pollutant diffusion model is as follows: in, For coefficients, The elevation difference between adjacent grids in the topographic grid between roads and environmental monitoring stations. The distance between adjacent grid cells. is the diffusion coefficient.

4. The method according to any one of claims 1 to 3, characterized in that, Also includes: For each of the aforementioned high-emission roads, the traffic flow adjustment coefficients for each road segment within each high-emission road are adjusted at different time periods to regulate the traffic flow on each high-emission road, thereby reducing the NOx concentration at environmental monitoring stations. The traffic flow adjustment coefficients for each road segment at different time periods are adjusted using the following methods: in, x ij For road section j During the period i The adjusted traffic flow coefficient is as follows. For road segment j during time period i Initial traffic flow adjustment coefficient, , For road segment j during time period i The contribution rate of NOx concentration to this environmental monitoring station. For road segment j during time period i Pollution sensitivity weights For road segment j during time period i Flow saturation For calibration constant, The traffic flow adjustment coefficient is adjusted. The following constraints must be met: , For road segment j during time period i Minimum traffic flow, For road segment j during time period i Traffic flow after adjusting the traffic flow adjustment coefficient For road segment j during time period i The original traffic flow, Given the design traffic flow for road segment j, the overall traffic flow adjustment range is as follows: T is the preset flow threshold.

5. The method according to any one of claims 1 to 3, characterized in that, Also includes: For each of the aforementioned high-emission roads, the adjustment coefficients for the proportion of heavy-duty vehicle types in each road segment at different times are adjusted to adjust the proportion of different types of heavy-duty vehicles on each high-emission road, thereby reducing the NOx concentration at environmental monitoring stations. The adjustment coefficients for the proportion of heavy-duty vehicle types in each road segment at different times are adjusted in the following manner: For road segment j during time period i The proportional adjustment coefficient for heavy vehicle type k, For road segment j during time period i The original proportion of heavy vehicle type k, For road segment j during time period i The minimum proportion of heavy vehicle type k. For road segment j during time period i The maximum value of the proportion of heavy vehicle type k. The following constraints must be met: , This is a preset ratio threshold.

6. A high-speed road identification device based on remote online monitoring data of heavy vehicles, characterized in that, include: The partitioning module is used to divide the roads around the environmental monitoring station into different road segments according to space. The corresponding construction module is used to establish the correspondence between different types of heavy vehicles and the road segments based on the location information of different types of heavy vehicles and the geographical information of the road segments. The instantaneous emission rate calculation module is used to determine the instantaneous NOx emission rate of each type of heavy-duty vehicle at different times for each road segment. Specifically, the instantaneous NOx emission rate of each heavy-duty vehicle of the same type is obtained by superimposing the instantaneous NOx emission rates of each heavy-duty vehicle. The instantaneous NOx emission rate of each heavy-duty vehicle is calculated based on the NOx emission concentration of each heavy-duty vehicle monitored online. The concentration contribution calculation module is used to input the instantaneous NOx emission rate of each type of heavy-duty vehicle and meteorological data of the road segment at different time periods into the modified pollutant diffusion model, and output the NOx concentration contribution of each type of heavy-duty vehicle of the road segment to the environmental monitoring station at different time periods; for each road, based on the NOx concentration contribution of all types of heavy-duty vehicles of all road segments included in the road to the environmental monitoring station at all time periods, the NOx concentration contribution of the road to the environmental monitoring station is calculated. The modified pollutant diffusion model is a pollutant diffusion model that considers the influence of topographic factors on pollutant diffusion. The high-emission road identification module is used to calculate the NOx concentration contribution rate of each road to the environmental monitoring station based on the ratio of the NOx concentration of the environmental monitoring station to the contribution of each road to the NOx concentration of the environmental monitoring station, and to determine the high-emission roads based on the magnitude of the contribution rate of different roads to the NOx concentration of the environmental monitoring station. Also includes: The first correction module is used to consider the influence of terrain slope on thermal turbulence, correct the diffusion term of the pollutant diffusion model, and obtain the first corrected pollutant diffusion model. The formula for the first revised pollutant diffusion model is as follows: Where C represents the contribution of each type of heavy vehicle to the NOx concentration at the environmental monitoring station on this road section at different time periods. The percentage change over time in the NOx concentration contribution of each type of heavy vehicle at this environmental monitoring station for this road section at different time periods. For wind speed vectors, This is the convection term, used to describe the flow of NO carried by the airflow. x The transmission process, where α is the slope of the terrain and β is the solar azimuth angle. For coefficients, For road segment j, the NO of heavy vehicles of type k in time period i x Instantaneous emission rate, For gradient, NO x Concentration gradient, t is time, and D is diffusion coefficient.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the high-emission road identification method based on remote online monitoring data of heavy vehicles as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that executes the high-emission road identification method based on remote online monitoring data of heavy vehicles as described in any one of claims 1 to 5.

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