A type of global road dust PM 2.5 Emissions Inventory Construction Methods

Abstract

This invention relates to the field of dust emission inventory construction, and more particularly to a global road dust PM2.5 standard. 2.5 The emission inventory construction method collects and processes vehicle and road data to obtain the number N of the i-th vehicle type on type j roads. i,j And the mileage M of the i-th type of vehicle on type j road over a certain period of time. i,j The adjustment coefficient K is obtained based on the traffic volume and vehicle speed on different roads. v According to the adjustment factor K v Obtain the silt load sL; obtain the emission coefficient E based on the average vehicle weight W; calculate the road dust PM at each grid point using the formula. 2.5 Emissions, including road dust PM at several grid points 2.5 Emissions, obtained from global road dust PM 2.5 The emission inventory. This invention can assess road dust PM in different areas. 2.5 The potential risks of pollution to public health can help identify high-risk areas and populations, provide a basis for developing health protection measures and public health policies, and promote public awareness.

Description

Technical Field

[0001] This invention relates to the field of dust emission inventory construction, and more particularly to a global road dust PM2.5 standard. 2.5 Methods for constructing emission inventories. Background Technology

[0002] It is widely acknowledged that particulate matter pollution from vehicle exhaust poses a health threat to humans. While the total amount of particulate matter generated by traffic has decreased, particularly PM2.5, due to the implementation of increasingly stringent vehicle emission standards worldwide, this has been mitigated. 2.5 Fine particulate matter. However, emissions of non-exhaust particulate matter, including those from brake wear, tire wear, road surface wear, and road resuspended particulate matter, are increasing.

[0003] Currently, many countries have established national or local-level road resuspended particulate matter (PM2.5) monitoring systems. 2.5 Most emission inventories are constructed using the US AP-42 method. However, a comprehensive, global-scale emission inventory is still lacking. The US Environmental Protection Agency (EPA) has established its own national emission inventory, which includes road-resuspended emissions; however, its formula has not been improved in 20 years, and the emissions figures are many times higher than actual observations. The European Environment Agency (EEA) and the European Long-Range Air Pollutant Transport Monitoring and Assessment Project (EMEP) jointly developed guidelines for air pollutant emission inventories and published a global emission inventory. While this inventory is global in scale, it only includes brake wear, tire wear, and road surface wear, and does not include road-resuspended particles.

[0004] Constructing a global-scale road dust emissions inventory faces numerous difficulties and challenges. For example, complete global road and vehicle data are difficult to obtain, and the impact of different climatic factors, such as relative humidity, on emissions is unknown during inventory compilation. This presents a significant challenge to the inventory's creation. However, with the continuous development of global vehicle electrification, electric vehicles will occupy a dominant position in the future. Electric vehicles do not release fossil fuels, resulting in less exhaust emissions and secondary PM2.5. 2.5 It will decrease dramatically, and correspondingly, road dust PM2.5 will decrease. 2.5 It will grow rapidly and will inevitably become a new source of urban PM2.5. 2.5 The source poses a huge threat to global human health.

[0005] Road resuspension is the primary source of heavy metal pollution in cities. Road resuspension contains heavy metals such as Si, Ca, Fe, Cu, Cd (cadmium), Pb, and Zn, which originate from braking, tire wear, and road surface abrasion. Road resuspension PM2.5... 2.5More toxic than vehicle exhaust, road-resuspended pollutants are characterized by their small size and large surface area, making them easily resuspended and entering the human body through inhalation and skin contact, potentially threatening the health of urban residents. With the vast majority of roads globally located in cities, they represent the greatest source of direct exposure risk to outdoor air pollutants for humans. Summary of the Invention

[0006] To address the lack of a comprehensive, global-scale emissions inventory in existing technologies, this invention provides a global road dust PM2.5 inventory. 2.5 The main methods for constructing emission inventories include: S1: Collect and process vehicle and road data to obtain the number N of the i-th type of vehicle on type j road. i,j And the mileage M of the i-th type of vehicle on type j road over a certain period of time. i,j ; S2: Adjustment coefficient K is obtained based on different road traffic volumes and road speeds. v ; S3: Based on the adjustment coefficient K v Obtain the silt load sL; S4: Obtain the average vehicle weight W for different vehicle types, and combine it with the silt load sL to obtain the emission coefficient E; S5: Based on the precipitation data, obtain the number of days n in which the precipitation within a certain period is greater than or equal to a set value; S6: Obtain the global drought index AI; S7: Interpolate vehicle data, road data, silt load sL, precipitation data corresponding to the number of days n obtained in S5, global drought index AI, and relative humidity φindex to a spatial resolution of 0.5×0.5 to obtain several grid points; S8: According to the formula ∑∑N i,j ×M i,j ×E× ) ×(AI) -1 ×φindex ×10 -6 The PM2.5 concentration of road dust at each grid point was calculated. 2.5 Emissions, where ∑∑N i,j ×M i,j This represents the total mileage traveled by all vehicles of type i on road j during the specified time period. Road dust PM at several grid points 2.5 Emissions, obtained from global road dust PM 2.5 Emissions inventory.

[0007] A computer device includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-described method.

[0008] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above method.

[0009] A computer program product includes a computer program or instructions that, when executed by a processor, implement the steps of the above-described method.

[0010] The beneficial effects of the technical solution provided by this invention are as follows: To address the problem of not considering the impact of different vehicle speeds on silt load, this invention incorporates the influence of different road vehicle speeds. To solve the problem of a surge in road emissions caused by abnormal dust deposition disrupting the balance between road deposition and removal, especially in areas affected by dust storms, this invention introduces adjustment coefficients for different roads to correct for silt load, making it applicable globally. This invention measures road dust PM at each grid point. 2.5 The emissions inventory obtained from emissions data can be used to assess road dust PM in different areas. 2.5 The potential health risks posed by pollution help identify high-risk areas and populations, providing a basis for developing health protection measures and public health policies, and promoting public awareness. The emission inventory of this invention can also be used as input for air quality models to analyze continuously changing pollution characteristics in time and space, compensating for the limitations of monitoring and observation in terms of spatiotemporal resolution, providing data support for air quality management, and aiding in the study of PM2.5 in the atmosphere. 2.5 It provides basic data for the formation, transformation, and transmission of [data / materials]. Attached Figure Description

[0011] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a global road dust PM embodiment of the present invention. 2.5 A flowchart of the emission inventory construction method. Detailed Implementation

[0012] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0013] Example 1 Please refer to Figure 1 , Figure 1 This is a global road dust PM embodiment of the present invention. 2.5 The flowchart of the emission inventory construction method includes: S1: Collect and process vehicle and road data, interpolate the data to a spatial resolution of 0.5 × 0.5, and obtain the number N of the i-th type of vehicle on type j road. i,j And the mileage M of the i-th type of vehicle on type j road within one year. i,j In this embodiment, emission data is collected on an annual basis.

[0014] The paved road data uses the Global Roads Inventory Project (GRIP), which is two to three times the total length of the best available country-based global road datasets. It is the most comprehensive, longest-covering, and free vector dataset to date. Paved road data from 2013-2017 uses the OpenStreetMap dataset (link provided). Road types are defined using the UNDITransportation data model, a globally applicable transport network attribute description designed by the United Nations Logistics Cluster. It defines and classifies various relevant road attributes, such as road type, pavement type, and seasonality. According to this data model, road segments are classified into one of four different functional road types: highways, major roads, minor roads, and tertiary roads. Speed ​​limits are available at (https: / / wiki.openstreetmap.org / wiki / Key:maxspeed).

[0015] Vehicle data for different countries was obtained from international organizations such as the International Energy Agency (IEA) and the International Automobile Federation (OICA), supplemented by local statistics from major economies (Oak Ridge National Laboratory). Based on the functional relationship between GDP and local vehicle ownership, the number of vehicles at the national level was allocated to grid points with a resolution of 0.5 degrees. To accurately obtain the actual number of vehicles in use, vehicle end-of-life curves for different countries were used to constrain the vehicle count. Vehicle end-of-life curves for countries with updated data were obtained from the latest literature. For countries without updated data, the vehicle end-of-life curves recommended by the IPCC were used.

[0016] The highway transport activity rate, which includes activity data such as vehicle mileage, is obtained through a fleet-based approach. This approach is constrained by national-level fuel consumption data from the International Energy Agency's Energy Statistics and Balance of Data, and includes the mileage of different vehicles and their share of mileage on different roads.

[0017] S2: Adjustment coefficient K is obtained based on different road traffic volumes and road speeds. v K v It is an adjustment coefficient derived from the speed limit requirements of different roads, and K is different for different roads. v different.

[0018] S3: Based on the adjustment coefficient K v From the formula sL=L×K v The silt load sL was calculated and interpolated to a spatial resolution of 0.5 × 0.5. The silt load sL refers to the mass of particles with a diameter equal to or less than 75 micrometers (µm) on a unit area of ​​the driving surface, expressed in g / m². kv represents the adjustment factor for silt load based on vehicle speed for different types of roads. L is the basic silt load obtained according to the updated AP-42 method of the US EPA, which is the basis for developing emission factors for paved roads. Because the AP-42 method is outdated and has many shortcomings, such as not considering the impact of different vehicle speeds on silt load, this invention incorporates the influence of different road speeds to address this issue. Specifically:

[0019] Among them, K v1 K v2 K v3 K v4 These represent different adjustment factors. ADT represents the average daily traffic volume, which is jointly constrained by the EDGAR and MEIC traffic and highway emission inventories. It is calculated based on the daily NOx emissions within the grid point and the national mandatory emission standards for that point, which are mandatory emission standards for different countries and years.

[0020] S4: Obtain the average vehicle weight W (in tons) for different vehicle types, and combine it with the silt load sL, according to the formula E:=k×(sL) 0.91 × (W) 1.02 Calculate the emission factor E; where k represents PM. 2.5 The particle size multiplier is a constant, and according to the Guidelines for Emissions of Road Dust in China, its unit is g / km. In this embodiment, the value of k is 0.15.

[0021] S5: Based on precipitation data, obtain the number of days n in a year with precipitation greater than or equal to 0.25 mm, and calculate... ), and interpolate to a spatial resolution of 0.5 × 0.5; S6: Obtain the Global Aridity Index (AI) and interpolate it to a spatial resolution of 0.5×0.5. AI represents the ratio of annual average precipitation to evaporation, which can comprehensively reflect the soil moisture content and indirectly affect the surface water content of roads, thereby affecting the degree of dust suspension.

[0022] S7: The relative humidity φindex is interpolated to a spatial resolution of 0.5×0.5. Combined with vehicle data, road data, silt load sL, precipitation data corresponding to the number of days n obtained in S5, and the global drought index AI, interpolated to a spatial resolution of 0.5×0.5, several grid points are obtained. φindex refers to the ratio of the actual water vapor content in the air to the maximum water vapor content that the air can hold at the same temperature, usually expressed as a percentage. The relative humidity φindex differs from AI; relative humidity directly affects the resuspension of dust by influencing the wet binding force in the air and the electrostatic force on the particle surface.

[0023] S8: According to the formula ∑∑N i,j ×M i,j ×E× ) ×(AI) -1 ×φindex ×10 -6 The PM2.5 concentration of road dust at each grid point was calculated. 2.5 Emissions, where ∑∑N i,j ×M i,j This represents the total mileage traveled by all vehicles of type i on road j within one year; Road dust PM at several grid points 2.5 Emissions, obtained from global road dust PM 2.5 Emissions inventory.

[0024] To verify the accuracy of the list of pollutants in this invention, the GEOS-Chem global numerical model was used to simulate road dust PM. 2.5 Concentration emissions, combined with PM2.5 concentration data from ground-based monitoring stations. 2.5 Observational data confirms road dust PM 2.5 Emissions inventory. The specific process involves including road dust PM2.5. 2.5 Emission inventory is coupled into global air quality numerical models to simulate road PM2.5. 2.5 The diurnal variation characteristics of PM at ground stations 2.5 Under the constraints of observational data, this study assesses the temporal and spatial differences in road dust flux and verifies the effectiveness of road dust PM2.5 emissions. 2.5 Emissions inventory.

[0025] Example 2 A computer device includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-described method.

[0026] Example 3 A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above method.

[0027] Example 4 A computer program product includes a computer program or instructions that, when executed by a processor, implement the steps of the above-described method.

[0028] The above description is only a preferred embodiment of the present invention and is not intended to limit 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 global road dust PM 2.5 emission inventory construction method characterized by, include: S1: Collect and process vehicle and road data to obtain the number N of the i-th type of vehicle on type j road. i,j And the mileage M of the i-th type of vehicle on type j road over a certain period of time. i,j ; S2: Obtain the adjustment coefficient K according to the traffic flow of different roads and the speed of the road v ; S3: According to the adjustment coefficient K v , the silt load sL is obtained; S4: Obtain the average vehicle weight W for different vehicle types, and combine it with the silt load sL to obtain the emission coefficient E; S5: Based on the precipitation data, obtain the number of days n in which the precipitation within a certain period is greater than or equal to a set value; S6: Obtain the global drought index AI; S7: Interpolate vehicle data, road data, silt load sL, precipitation data corresponding to the number of days n obtained in S5, global drought index AI, and relative humidity φindex to a spatial resolution of 0.5×0.5 to obtain several grid points; S8: According to the formula ∑∑N i,j ×M i,j ×E× ) ×(AI) -1 ×φindex ×10 -6 The PM2.5 concentration of road dust at each grid point was calculated. 2.5 Emissions, where ∑∑N i,j ×M i,j This represents the total mileage traveled by all vehicles of type i on road j during the specified time period. Road dust PM by grid cell 2.5 Emissions, resulting in a global road dust PM 2.5 emissions inventory.

2. The global road dust PM as described in claim 1 2.5 The method for constructing emission inventories is characterized by, In S3, the calculation formula of the silt load sL is: sL=L×K v wherein L represents the basic silt load, and K v represents the adjustment coefficient of the vehicle speed of different kinds of roads to the silt load.

3. A global road dust PM 2.5 Method for building an emission inventory, characterized in that, In S4, the calculation formula of the emission factor E is: E := k x (sL) 0.91 x (W) 1.02 wherein k represents a particle size multiplier of PM 2.5 .

4. A global road dust PM 2.5 Method for building an emission inventory, characterized in that, In S7, the number of vehicles at the national level is allocated to grid points with a resolution of 0.5 degrees based on the functional relationship between GDP and local vehicle ownership.

5. A global road dust PM 2.5 A method of building an emissions inventory, characterized by, The method uses a GEOS-Chem global numerical model to simulate road dust PM 2.5 emissions, Combined with ground station PM 2.5 Observational data confirms road dust PM 2.5 Emissions inventory.

6. A computer apparatus comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program, when executed by the processor, causes the processor to perform the method of any one of claims 1 to 5. The processor executes a computer program to implement the global road dust PM as described in any one of claims 1-5. 2.5 The steps involved in constructing an emissions inventory.

7. A computer-readable storage medium, characterized in that, A computer program is stored, when the program is executed by a processor, a global road dust PM 2.5 Steps of the method of emissions inventory building.

8. A computer program product, characterised in that, A computer program or instructions including, when executed by a processor, implement the global road dust PM emission inventory method of any one of claims 1-5 2.5 Steps of the emission inventory construction method.

Images