A screening and identification method and system for key road dust control sections

Abstract

The present invention discloses a screening and identification method and system for key road dust control sections. The method includes: obtaining road network data, traffic data, and meteorological data within a preset range around a monitoring site during a target period; calculating the dust emission factor and dust emission amount of each road and the contribution of dust emission to the pollutant concentration of each monitoring site to obtain the dust emission contribution concentration; calculating the ratio of the dust emission contribution concentration to the corresponding dust emission amount to obtain the environmental impact sensitivity coefficient; performing dimensionless normalization on the dust emission amount and the environmental impact sensitivity coefficient, assigning weights and performing weighted summation calculation to obtain a comprehensive control evaluation index; determining that a road with a comprehensive road evaluation index greater than or equal to a key control index threshold is a key control section. Through the technical solution of the present invention, it is possible to screen and identify key controlled roads with large emissions and sensitive impacts on the air quality of the monitoring site, providing support for refined control of atmospheric pollution.

Description

Technical Field

[0001] The present invention relates to the technical field of air pollution prevention and control, and in particular to a screening and identification method for key road dust control sections and a screening and identification system for key road dust control sections. Background Art

[0002] With the continuous increase in the number of motor vehicles and the expansion of road scale in my country's cities, road dust has gradually become a key source of emissions. Road dust directly contributes to ambient particulate matter concentrations. Especially when meteorological conditions are unfavorable for pollutant diffusion, the dust surface can become a site for rapid growth of particulate matter, exacerbating pollution levels and affecting residents' health. Different roads generate different dust emissions and their contribution to particulate matter concentrations at assessment sites. Therefore, clarifying which roads are more sensitive to emissions at the site's particulate matter concentrations can help managers develop optimized control plans and improve control efficiency. Therefore, screening and identifying key sections for road dust control is of great significance for achieving refined road dust control.

[0003] Dust loads, vehicle types, and traffic volumes vary significantly across different roads. For example, heavy trucks predominate near construction sites, resulting in high dust loads. In contrast, urban arterial roads are primarily used by small passenger cars, resulting in lower dust loads. This leads to significant differences in dust emissions across different roads. However, current approaches to controlling road dust primarily focus on the selection and frequency of cleaning measures, lacking research on differentiated control measures for different roads. In practice, all roads are often controlled with the same intensity, ignoring the differences in dust emissions from roads used for different purposes and their contribution to particulate matter concentrations at assessment sites, resulting in significant expenditures of manpower, material, and financial resources. Summary of the Invention

[0004] To address the above issues, the present invention provides a method and system for screening and identifying key road sections for dust control. Based on road network, traffic, and meteorological data within the target period surrounding the monitoring station, the system calculates the dust emissions of each road and the contribution of road dust emissions to the pollutant concentration at the monitoring station. The environmental impact sensitivity coefficient of unit dust emissions for each road is then obtained. Normalization is used to derive a comprehensive road evaluation index, determine the threshold for the key control index, and screen for key dust control sections. This invention can screen and identify key roads with high emissions and a sensitive impact on the air quality of the monitoring station, supporting refined control of road dust.

[0005] To achieve the above objectives, the present invention provides a method for screening and identifying key road sections for dust control, comprising:

[0006] Obtain road network data, traffic data, and meteorological data for the target period within a preset range around the monitoring site;

[0007] Calculating dust emission factors and dust emission amounts for each road based on the road network data and the traffic data;

[0008] Calculate the contribution of dust emissions from each road to the pollutant concentration at each monitoring station to obtain the dust emission contribution concentration of each road;

[0009] Through sensitivity analysis, the ratio of dust emission contribution concentration of each road to the corresponding dust emission amount is calculated to obtain the environmental impact sensitivity coefficient of the unit dust emission amount of each road to the pollutant concentration contribution;

[0010] The dust emission and environmental impact sensitivity coefficient of each road are dimensionlessly normalized, and weighted and summed to obtain the comprehensive evaluation index of the corresponding road.

[0011] A key control index threshold is determined based on the comprehensive control evaluation index, and a road whose comprehensive road evaluation index is greater than or equal to the key control index threshold is determined as a key control section.

[0012] In the above technical solution, preferably, the road network data includes road name, road length and road dust load, the road dust load is measured by combined AP-42 and TRAKER methods using cruise mobile monitoring after correction, and the road length is obtained by ArcGIS and cruise mobile monitoring;

[0013] The traffic data includes vehicle flow data and vehicle weight data. The vehicle flow data is obtained using a speed-flow model through online maps, public information from traffic management departments, video recognition technology, and speed based on cruise monitoring;

[0014] The meteorological data includes wind direction, wind speed, temperature, humidity and air pressure data within the target period of the monitoring site.

[0015] In the above technical solution, preferably, the specific formula for calculating the dust emission factor and dust emission amount for each road is:

[0016]

[0017] E m,t =EF m,t ×L m ×V m,t

[0018] Among them, EF m,t is the road dust emission factor of road m at time t, g / (km·vehicle); k is the particle size correction factor; sL m is the road dust load of road m, g / m 2 ; is the average vehicle weight at time t, in tons; E m,t is the road dust emission of road m at time t, g; L m is the length of the road m, km; V m,t is the traffic flow of road m at time t, vehicles / h.

[0019] In the above technical solution, preferably, the specific method for calculating the contribution of dust emissions from each road to the pollutant concentration at each monitoring site is:

[0020] Based on numerical simulation or machine learning methods, the contribution of dust emissions from each road to the concentration of pollutants monitored at each monitoring station is calculated.

[0021] In the above technical solution, preferably, the specific method for calculating the contribution concentration of dust emissions of each road to the pollutants monitored by the monitoring station based on the numerical simulation method includes:

[0022] Use WRF, MM5 meteorological models and CALMET modules to pre-generate meteorological data for the target area;

[0023] The dust emissions from the corresponding roads were input into the CALPUFF, CMAQ, and CAMx models, and the models were used to simulate the contribution of each road to the particulate matter concentration at each monitoring station.

[0024] Combined with the post-processing module, the pollutant contribution concentration of each road to the monitoring site is obtained.

[0025] In the above technical solution, preferably, the method for screening and identifying key road sections for dust control is characterized in that the specific method for calculating the contribution concentration of dust emissions of each road to the pollutants monitored at the monitoring station based on the machine learning method includes:

[0026] Based on the correlation between contribution concentration and meteorological data from target monitoring stations, pollutant concentration data, road-by-road emissions, and historical contribution concentration data, factors influencing contribution concentration were selected as characteristic variables for the input model. RNN and LSTM algorithms were used to train the characteristic variables, ultimately establishing a relationship model between meteorological conditions, emissions, and contribution concentration.

[0027] The trained relationship model is used to predict the pollutant concentration contributed by each road to the monitoring station.

[0028] In the above technical solution, preferably, the sensitivity analysis is performed to calculate the ratio of the dust emission contribution concentration of each road to the corresponding dust emission amount, so as to obtain the environmental impact sensitivity coefficient of the unit dust emission amount of each road to the pollutant concentration, and the calculation formula is:

[0029]

[0030] Among them, S m,t is the sensitivity coefficient of road m at time t; C m,t is the contribution concentration of road dust emissions from road m at time t to the pollutants at the monitoring site, μg / m 3 ;E m,t is the road dust emission on road m at time t, g.

[0031] In the above technical solution, preferably, the specific method of performing dimensionless normalization processing on the dust emission and environmental impact sensitivity coefficient of each road is:

[0032] The dust emission of each road and the environmental impact sensitivity coefficient are placed in the same evaluation range. The Min-Max normalization is used to process the hourly road dust emission. The normalization specified interval is 0 to 1. The specific calculation formula is as follows:

[0033]

[0034]

[0035] Among them, i is the dust emission E or environmental impact sensitivity coefficient S, m is the road, t is the time, is the normalized median value of road i at time t, X i,m,t is the original value of i of road m at time t, X i,m,t min is the minimum value of the original value of i of road m at time t, X i,m,t max is the maximum value of the original value of i of road m at time t, Y i,m,t is the final result of normalization of road m at time t, mx is the maximum value of the normalized specified interval, and mi is the minimum value of the normalized specified interval;

[0036] According to the preset actual control focus, weights are assigned and weighted sum calculation is performed. The calculation formula is:

[0037] P m,t =α×Y E,m,t +β×Y S,m,t

[0038] Among them, P m,t is the comprehensive evaluation index of road m at time t; α is the weight coefficient of dust emission; β is the weight coefficient of environmental impact sensitivity coefficient; Y E,m,t is the final result after normalization of dust emissions; Y S,m,t It is the final result after normalization of the environmental impact sensitivity coefficient.

[0039] In the above technical solution, preferably, the specific method for determining the key control index threshold based on the comprehensive control evaluation index includes:

[0040] Based on the comprehensive evaluation index of management and control of each road, determine the key control index threshold value for dividing the key control sections based on the comprehensive evaluation index of management and control.

[0041] The present invention further proposes a screening and identification system for key road dust control sections, which uses the screening and identification method for key road dust control sections disclosed in any of the above technical solutions, including:

[0042] A monitoring data acquisition module is used to obtain road network data, traffic data and meteorological data within a preset range around the monitoring site during a target period;

[0043] An emission factor calculation module, configured to calculate the dust emission factor and dust emission amount for each road based on the road network data and the traffic data;

[0044] The contribution concentration calculation module is used to calculate the contribution of dust emissions from each road to the pollutant concentration of each monitoring station, and obtain the dust emission contribution concentration of each road;

[0045] The sensitivity coefficient calculation module is used to calculate the ratio of the dust emission contribution concentration of each road to the corresponding dust emission amount through sensitivity analysis, and obtain the environmental impact sensitivity coefficient of the unit dust emission amount of each road to the pollutant concentration contribution;

[0046] The evaluation index calculation module is used to perform dimensionless normalization on the dust emission and environmental impact sensitivity coefficient of each road, assign weights to them respectively, and perform weighted summation calculation to obtain the comprehensive evaluation index of the corresponding road management;

[0047] The controlled road section determination module is used to determine the key control index threshold based on the control comprehensive evaluation index, and determine that the road whose comprehensive evaluation index is greater than or equal to the key control index threshold is the key control section.

[0048] Compared with existing technologies, the present invention has the following beneficial effects: by using road network, traffic, and meteorological data within the target time period surrounding the monitoring station, the dust emissions of each road and the contribution of road dust emissions to the pollutant concentration at the monitoring station are calculated. The environmental impact sensitivity coefficient of dust emissions per unit of road is then obtained. Normalization is used to derive a comprehensive road evaluation index, determine the threshold for the key control index, and screen for key dust control sections. This invention can identify key control roads with high emissions and sensitive impacts on the air quality of the monitoring station, achieving refined control of road dust. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic flow chart of a method for screening and identifying key road dust control sections disclosed in one embodiment of the present invention;

[0050] Figure 2 A schematic diagram of the perimeter of a monitoring site disclosed in one embodiment of the present invention;

[0051] Figure 3 A schematic diagram of road emissions and environmental impact sensitivity coefficients disclosed in one embodiment of the present invention;

[0052] Figure 4 A schematic diagram of the screening results of key controlled road sections disclosed in an embodiment of the present invention;

[0053] Figure 5 This is a module schematic diagram of a screening and identification system for key road dust control sections disclosed in an embodiment of the present invention.

[0054] In the figure, the corresponding relationship between each component and the reference numeral is as follows:

[0055] 1. Monitoring data acquisition module, 2. Emission factor calculation module, 3. Contribution concentration calculation module, 4. Sensitivity coefficient calculation module, 5. Evaluation index calculation module, 6. Controlled road section determination module. DETAILED DESCRIPTION

[0056] 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 in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0057] The present invention is described in further detail below with reference to the accompanying drawings:

[0058] like Figure 1 As shown, a method for screening and identifying key road sections for dust control provided by the present invention includes:

[0059] Obtain road network data, traffic data, and meteorological data for the target period within a preset range around the monitoring site;

[0060] Calculate dust emission factors and dust emissions for each road based on road network data and traffic data;

[0061] Calculate the contribution of dust emissions from each road to the pollutant concentration at each monitoring station to obtain the dust emission contribution concentration of each road;

[0062] Through sensitivity analysis, the ratio of dust emission contribution concentration of each road to the corresponding dust emission amount is calculated to obtain the environmental impact sensitivity coefficient of the unit dust emission amount of each road to the pollutant concentration contribution;

[0063] The dust emission and environmental impact sensitivity coefficient of each road are dimensionlessly normalized, and weighted and summed to obtain the comprehensive evaluation index of the corresponding road.

[0064] The key control index threshold is determined based on the comprehensive evaluation index of control, and roads with a comprehensive evaluation index greater than or equal to the key control index threshold are identified as key control sections.

[0065] In this implementation, based on road network, traffic, and meteorological data surrounding the monitoring station during the target period, the dust emissions and their contribution to the pollutant concentration at the monitoring station are calculated for each road. The environmental impact sensitivity coefficient for each road's unit dust emissions is then derived. Normalization is then used to derive a comprehensive road evaluation index, determine the threshold for the key control index, and select key dust control sections. This invention enables the identification of key control roads with high emissions and a sensitive impact on the air quality of the monitoring station, achieving refined control of road dust.

[0066] Specifically, in the above embodiment, preferably, the road network data includes road name, road length and road dust load, the road dust load is measured by combined AP-42 and TRAKER methods using cruise mobile monitoring after correction, and the road length is obtained by ArcGIS and cruise mobile monitoring;

[0067] Traffic data includes traffic flow data and vehicle weight data. Traffic flow data is obtained through online maps, public information from traffic management departments, video recognition technology, and speed based on cruise monitoring, using a speed-flow model.

[0068] Meteorological data include wind direction, wind speed, temperature, humidity and air pressure during the target period (year, season, month, hour) of the monitoring station.

[0069] In the above embodiment, preferably, the specific formula for calculating the dust emission factor and dust emission amount of each road is:

[0070]

[0071] E m,t =EF m,t ×L m ×V m,t

[0072] Among them, EF m,tis the road dust emission factor of road m at time t, in g / (km·vehicle), k is the particle size correction factor, sL m is the road dust load of road m, in g / m 2 , is the average vehicle weight at time t, in tons; E m,t is the road dust emission of road m at time t, in grams (g), L m is the length of the road m, in km, V m,t is the traffic volume of road m at time t, in vehicles / hour.

[0073] In the above embodiment, preferably, the specific method for calculating the contribution of dust emissions from each road to the pollutant concentration at each monitoring site is:

[0074] Based on numerical simulation (such as CALPUFF, WRF-CAMx, etc.) or machine learning (such as LSTM, RNN, etc.) methods, the contribution concentration of dust emissions from each road to the pollutants monitored at each monitoring station is calculated, and the contribution concentration is recorded as C.

[0075] In the above embodiment, preferably, the specific method for calculating the contribution concentration of dust emissions of each road to the pollutants monitored at each monitoring station based on the numerical simulation method includes:

[0076] Use meteorological models such as WRF and MM5 and modules such as CALMET to pre-generate meteorological data for the target area;

[0077] The dust emissions from the corresponding roads were input into the CALPUFF, CMAQ, CAMx and other models, and the models were used to simulate the contribution of each road to the particulate matter concentration at each monitoring station.

[0078] Combined with the post-processing module, the pollutant contribution concentration of each road to the monitoring site is obtained.

[0079] In the above embodiment, preferably, the specific method for calculating the contribution concentration of dust emissions of each road to the pollutants monitored by the monitoring station based on the machine learning method includes:

[0080] Based on the correlation between contribution concentration and meteorological data from target monitoring sites, pollutant concentration data, road-by-road emissions, historical contribution concentrations, and other data, factors influencing contribution concentration are selected as characteristic variables for the input model. Algorithms such as RNN and LSTM are used to train the characteristic variables, ultimately establishing a relationship model between meteorology, emissions, and contribution concentration.

[0081] The trained relationship model is used to predict the pollutant concentration contributed by each road to the monitoring station.

[0082] In the above embodiment, preferably, the ratio of the dust emission contribution concentration of each road to the corresponding dust emission amount is calculated through sensitivity analysis to obtain the environmental impact sensitivity coefficient of the unit dust emission amount of each road to the pollutant concentration. The calculation formula is:

[0083]

[0084] Among them, S m,t is the environmental impact sensitivity coefficient of road m at time t, C m,t is the contribution concentration of road dust emissions from road m at time t to the dust emissions of pollutants at the monitoring site, in μg / m 3 , E m,t is the road dust emission of road m at time t, in g.

[0085] In the above embodiment, preferably, the specific method of dimensionlessly normalizing the dust emission and environmental impact sensitivity coefficient of each road is as follows:

[0086] The dust emission of each road and the environmental impact sensitivity coefficient are placed in the same evaluation range. The Min-Max normalization is used to process the hourly road dust emission. The normalization specified interval is 0 to 1. The specific calculation formula is as follows:

[0087]

[0088]

[0089] Among them, i is the dust emission E or environmental impact sensitivity coefficient S, m is the road, and t is the time, from 0 to 23 o'clock. is the normalized median value of road i at time t, X i,m,t is the original value of i of road m at time t, X i,m,t min is the minimum value of the original value of i of road m at time t, X i,m,t max is the maximum value of the original value of i of road m at time t, Y i,m,t is the final result of normalization of road i at time t, mx is the maximum value of the normalized specified interval, and mi is the minimum value of the normalized specified interval.

[0090] In the above embodiment, preferably, according to the preset actual control focus, weights are assigned respectively and weighted sum calculation is performed to obtain the calculation formula of the road control comprehensive evaluation index (P):

[0091] P m,t =α×Y E,m,t +β×Y S,m,t

[0092] Among them, P m,t is the comprehensive evaluation index of road m at time t; α is the weight coefficient of dust emission; β is the weight coefficient of environmental impact sensitivity coefficient; Y E,m,t is the final result after normalization of dust emissions; Y S,m,t It is the final result after normalization of the environmental impact sensitivity coefficient.

[0093] In the above embodiment, preferably, the specific method for determining the key control index threshold based on the comprehensive control evaluation index includes:

[0094] Based on the comprehensive evaluation index of each road, the key control index threshold L is determined, which is used to divide the key control sections based on the comprehensive evaluation index. Roads with a comprehensive evaluation index greater than or equal to the key control index threshold L are screened, that is, roads with P ≥ L (0-2) are determined as key control sections for road dust.

[0095] During implementation, the screening and identification method for key road dust control sections disclosed in the above embodiment is described through the following examples.

[0096] Example 1:

[0097] Based on this method, monitoring site A is selected as an example application site (e.g. Figure 2 As shown in the figure, based on the dust emissions from different roads and the differences in the sensitivity of the stations to dust emissions from different roads, the road sections of monitoring station A that are key to controlling dust emissions are selected. The specific process is as follows:

[0098] Step (1): Obtain hourly meteorological data (wind direction, wind speed, temperature, humidity, and air pressure) during the target period (e.g., January) at monitoring station A and all roads within a 3 km radius around monitoring station A: NS1, NS2, NS3, NS4, NS5, NS6, WE1, WE2, WE3, WE4, WE5, WE6, and WE7. Use ArcGIS to obtain the road lengths of each of the above roads, and use mobile cruise monitoring to obtain the dust load of each road. Process the monitoring data, screen the valid data, and calculate the average value as the dust load of the road. Combine the public information of the transportation department and artificial intelligence visual recognition (e.g., Yolov5+DeepSort) to obtain the hourly traffic flow information of each vehicle type, and then calculate the average vehicle weight of the road section based on the traffic flow of different vehicle types.

[0099] Step (2): Calculate the emission factor of each road using the road dust load and average vehicle weight. Calculate the hourly emission of road dust based on the dust emission factor of each road, combined with the road length and vehicle flow data, to obtain the dust source emission of each road (E) (e.g. Figure 3 The specific calculation formula is as follows:

[0100]

[0101] Among them, EF m,t is the road dust emission factor of road m at time t, in g / (km·vehicle), k is the particle size correction factor, sL m is the road dust load of road m, in g / m 2 , is the average vehicle weight at time t, in tons;

[0102] E m,t =EF m,t ×L m ×V m,t

[0103] Among them, E m,t is the road dust emission of road m at time t, in grams (g), L m is the length of the road m, in km, V m,t is the traffic volume of road m at time t, in vehicles / hour.

[0104] Step (3): Calculate the contribution of dust emissions from different roads to the pollutant concentration of the monitoring site based on the CALPUFF model: Based on the CALMET module, pre-generate the meteorological field data of the target area, input the dust emissions of NS1-6 and WE1-7 into the CALPUFF model respectively, use the CALPUFF module to simulate the contribution concentration of each road to the particulate matter at the monitoring site A, and combine the CALPOST post-processing module to obtain the contribution concentration (C) of each road to the particulate matter at the monitoring site A;

[0105] Based on the above-mentioned sensitivity analysis of the contribution concentration of particulate matter to monitoring station A, the ratio of the contribution concentration of NS1-6 and WE1-7 to their corresponding emissions was calculated to obtain the contribution coefficient of the unit dust emission of each road to the pollutant concentration, that is, the environmental impact sensitivity coefficient (S) (such as Figure 3 The specific calculation formula is as follows:

[0106]

[0107] Among them, S m,t is the environmental impact sensitivity coefficient of road m at time t, C m,t is the contribution concentration of road dust emissions from road m at time t to the dust emissions of pollutants at the monitoring site, in μg / m 3 , E m,t is the road dust emission of road m at time t, in g.

[0108] Step (4): Normalize the dust emission and sensitivity coefficient of NS1~6 and WE1~7 respectively, use Min-Max normalization to process the hourly road dust emission, and the normalization specified interval is 0~1. According to the preset actual control requirements, assign weights (1:1) and perform weighted summation to obtain the comprehensive evaluation index of the road (P) (such as Figure 4 The specific calculation formula is as follows:

[0109]

[0110]

[0111] Among them, i is the dust emission E or environmental impact sensitivity coefficient S, m is the road, and t is the time, from 0 to 23 o'clock. is the normalized median value of road i at time t, X i,m,t is the original value of i of road m at time t, X i,m,t min is the minimum value of the original value of i of road m at time t, X i,m,t max is the maximum value of the original value of i of road m at time t, Y i,m,t is the final result of normalization of road i at time t, mx is the maximum value of the normalized specified interval, and mi is the minimum value of the normalized specified interval.

[0112] P m,t =α×Y E,m,t +β×Y S,m,t

[0113] Among them, P m,t is the comprehensive evaluation index of road m at time t; α is the weight coefficient of dust emission; β is the weight coefficient of environmental impact sensitivity coefficient; Y E,m,t is the final result after normalization of dust emissions; Y S,m,t It is the final result after normalization of the environmental impact sensitivity coefficient.

[0114] Step (6): Based on the comprehensive evaluation index (P) of roads NS1-6 and WE1-7, the threshold value (L) of the key control index is determined to be 0.5, and the roads with a comprehensive evaluation index greater than or equal to the threshold (P ≥ 0.5) are determined as the key control sections of road dust, that is, EW1, EW4, NS1, NS3, and NS6 are determined as the key control roads of monitoring station A (such as Figure 4 shown).

[0115] like Figure 5 As shown, the present invention also proposes a screening and identification system for key road dust control sections, which applies the screening and identification method for key road dust control sections disclosed in any of the above embodiments, including:

[0116] Monitoring data acquisition module 1, used to obtain road network data, traffic data and meteorological data within a preset range around the monitoring site during a target period;

[0117] Emission factor calculation module 2, used to calculate the dust emission factor and dust emission amount of each road based on road network data and traffic data;

[0118] Contribution concentration calculation module 3 is used to calculate the contribution of dust emissions from each road to the pollutant concentration of each monitoring station, and obtain the dust emission contribution concentration of each road;

[0119] Sensitivity coefficient calculation module 4 is used to calculate the ratio of the dust emission contribution concentration of each road to the corresponding dust emission amount through sensitivity analysis, and obtain the environmental impact sensitivity coefficient of the unit dust emission amount of each road to the pollutant concentration contribution;

[0120] Evaluation index calculation module 5 is used to perform dimensionless normalization processing on the dust emission and environmental impact sensitivity coefficient of each road, assign weights to them respectively, and perform weighted summation calculation to obtain the comprehensive evaluation index of the corresponding road control;

[0121] The controlled section determination module 6 is used to determine the key control index threshold based on the comprehensive control evaluation index, and determine that the road with a comprehensive road evaluation index greater than or equal to the key control index threshold is a key control section.

[0122] According to the screening and identification system for key road dust control sections disclosed in the above-mentioned embodiment, the functions to be implemented by each module correspond to the steps in the screening and identification method for key road dust control sections disclosed in the above-mentioned embodiment. During implementation, operations are performed with reference to the above-mentioned embodiment, which will not be repeated here.

[0123] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for screening and identifying key road sections for dust control, characterized in that: include: Obtain road network data, traffic data, and meteorological data for the target period within a preset range around the monitoring site; Calculating dust emission factors and dust emission amounts for each road based on the road network data and the traffic data; Based on numerical simulation or machine learning methods, the contribution of dust emissions on each road to the pollutant concentration monitored at each monitoring station is calculated to obtain the dust emission contribution concentration of each road. The specific method for calculating the contribution concentration of dust emissions on each road to the pollutant concentration monitored at the monitoring station based on the machine learning method includes: Based on the correlation between contribution concentration and meteorological data from target monitoring stations, pollutant concentration data, road-by-road emissions, and historical contribution concentration data, factors influencing contribution concentration were selected as characteristic variables for the input model. RNN and LSTM algorithms were used to train the characteristic variables, ultimately establishing a relationship model between meteorological conditions, emissions, and contribution concentration. Use the trained relationship model to predict the pollutant concentration contributed by each road to the monitoring station; Through sensitivity analysis, the ratio of dust emission contribution concentration of each road to the corresponding dust emission amount was calculated to obtain the environmental impact sensitivity coefficient of the unit dust emission amount of each road to the pollutant concentration contribution; The dust emission and environmental impact sensitivity coefficient of each road are dimensionlessly normalized, and weighted and summed to obtain the comprehensive evaluation index of the corresponding road. Determine a key control index threshold based on the comprehensive control evaluation index, and determine a road whose comprehensive road evaluation index is greater than or equal to the key control index threshold as a key control section; The sensitivity analysis is performed to calculate the ratio of the dust emission contribution concentration of each road to the corresponding dust emission amount, and the environmental impact sensitivity coefficient of the unit dust emission amount of each road to the pollutant concentration is obtained. The calculation formula is: Among them, S m,t is the sensitivity coefficient of road m at time t; C m,t is the contribution concentration of road dust emissions from road m at time t to the pollutants at the monitoring site, μg / m 3 ;E m,t is the road dust emission on road m at time t, g; The specific method for dimensionless normalization of the dust emission and environmental impact sensitivity coefficient of each road is as follows: The dust emission of each road and the environmental impact sensitivity coefficient are placed in the same evaluation range. The Min-Max normalization is used to process the hourly road dust emission. The normalization specified interval is 0 to 1. The specific calculation formula is as follows: Among them, i is the dust emission E or environmental impact sensitivity coefficient S, m is the road, t is the time, is the normalized median value of road i at time t, X i,m,t is the original value of i of road m at time t, X i,m,tmin is the minimum value of the original value of i of road m at time t, X i,m,tmax is the maximum value of the original value of i of road m at time t, Y i,m,t is the final result of normalization of road m at time t, mx is the maximum value of the normalized specified interval, and mi is the minimum value of the normalized specified interval; According to the preset actual control focus, weights are assigned and weighted sum calculation is performed. The calculation formula is: P m,t =α×Y E,m,t +β×Y S,m,t Among them, P m,t is the comprehensive evaluation index of road m at time t; α is the weight coefficient of dust emission; β is the weight coefficient of environmental impact sensitivity coefficient; Y E,m,t is the final result after normalization of dust emissions; Y S,m,t It is the final result after normalization of the environmental impact sensitivity coefficient; The specific method for determining the key control index threshold based on the comprehensive control evaluation index includes: Based on the comprehensive evaluation index of management and control of each road, determine the key control index threshold value for dividing the key control sections based on the comprehensive evaluation index of management and control.

2. The method for screening and identifying key road dust control sections according to claim 1, characterized in that: The road network data includes road names, road lengths, and road dust loads. The road dust loads are measured using a combination of AP-42 and TRAKER methods using modified cruise-based mobile monitoring. The road lengths are obtained using ArcGIS and cruise-based mobile monitoring. The traffic data includes vehicle flow data and vehicle weight data. The vehicle flow data is obtained using a speed-flow model through online maps, public information from traffic management departments, video recognition technology, and speed obtained based on cruise monitoring; The meteorological data includes wind direction, wind speed, temperature, humidity and air pressure data within the target period of the monitoring site.

3. The method for screening and identifying key road sections for dust control according to claim 1, characterized in that: The specific formula for calculating the dust emission factor and dust emission amount for each road is: E m，t =EF m，t ×L m ×V m,t Among them, EF m,t is the road dust emission factor of road m at time t, g / (km·vehicle); k is the particle size correction factor; sL m is the road dust load of road m, g / m 2 ; is the average vehicle weight at time t, in tons; E m,t is the road dust emission of road m at time t, g; L m is the length of the road m, km; V m,t is the traffic flow of road m at time t, vehicles / h.

4. The method for screening and identifying key road sections for dust control according to claim 1, characterized in that: The specific method for calculating the contribution concentration of dust emissions of each road to the pollutants monitored at the monitoring station based on the numerical simulation method includes: Use WRF, MM5 meteorological models and CALMET modules to pre-generate meteorological data for the target area; The dust emissions from the corresponding roads were input into the CALPUFF, CMAQ, and CAMx models, and the models were used to simulate the contribution of each road to the particulate matter concentration at each monitoring station. Combined with the post-processing module, the pollutant contribution concentration of each road to the monitoring site is obtained.

5. A screening and identification system for key road dust control sections, characterized by: The method for screening and identifying key road dust control sections according to any one of claims 1 to 4 comprises: A monitoring data acquisition module is used to obtain road network data, traffic data and meteorological data within a preset range around the monitoring site during a target period; An emission factor calculation module, configured to calculate the dust emission factor and dust emission amount for each road based on the road network data and the traffic data; The contribution concentration calculation module is used to calculate the contribution of dust emissions from each road to the pollutant concentration of each monitoring station, and obtain the dust emission contribution concentration of each road; The sensitivity coefficient calculation module is used to calculate the ratio of the dust emission contribution concentration of each road to the corresponding dust emission amount through sensitivity analysis, and obtain the environmental impact sensitivity coefficient of the unit dust emission amount of each road to the pollutant concentration contribution; The evaluation index calculation module is used to perform dimensionless normalization on the dust emission and environmental impact sensitivity coefficient of each road, assign weights to them respectively, and perform weighted summation calculation to obtain the comprehensive evaluation index of the corresponding road management; The controlled road section determination module is used to determine the key control index threshold based on the control comprehensive evaluation index, and determine that the road whose comprehensive evaluation index is greater than or equal to the key control index threshold is the key control section.

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