Urban bus exhaust and non-exhaust emission estimation method and system and electronic equipment

Abstract

The invention provides a method and a system for estimating exhaust and non-exhaust emissions of urban buses and electronic equipment, and belongs to the field of traffic. The method comprises the following steps: determining bus routes and departure shifts of a city to be estimated, and estimating the number of buses running on a single route; an exhaust emission factor and a non-exhaust emission factor of the bus are calculated, and the exhaust emission factor and the non-exhaust emission factor of the bus are corrected in advance; and based on the bus route, the departure shift, the bus exhaust emission factor and the non-exhaust emission factor, estimating the bus exhaust and non-exhaust emission. By constructing a comprehensive bus atmospheric pollutant emission list more conforming to the actual situation, data support and theoretical reference can be provided for decision makers to formulate strategies and actions for atmospheric pollutant emission reduction and climate change coping, and the estimation efficiency is further improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of transportation, and particularly relates to a method, system and electronic device for estimating the exhaust and non-exhaust emissions of urban buses. Background Art

[0002] With the strong advocacy of public transportation, the number of buses in operation has shown a rapid growth trend in the past two decades. Taking a first- or second-tier city as an example, the number of buses in operation has increased from 1,150 in 2000 to 8,157 in 2023, with an average annual growth rate of 8.89%. Buses are one of the main means of urban travel, with most routes passing through the city center and intersecting with residential and commercial areas along the way.

[0003] In the context of bus electrification, compared with traditional internal combustion engine buses, due to the installation of power batteries, the average vehicle mass of electric buses has increased by 30%, resulting in an increase in non-exhaust atmospheric particulate emissions that are positively correlated with vehicle weight.

[0004] However, current research on exploring the characteristics of exhaust and non-exhaust atmospheric pollutant emissions under the background of bus electrification in typical transportation cities based on bus routes and departure frequencies is still very scarce, unable to provide data support and theoretical reference for decision-makers to formulate strategies and actions for reducing atmospheric pollutant emissions and addressing climate change, and the estimation efficiency is relatively low. Summary of the Invention

[0005] The purpose of the embodiments of the present invention is to provide a method, system and electronic device for estimating the exhaust and non-exhaust emissions of urban buses, which are used to fully or at least partially solve the technical problems existing in the above-mentioned prior art, such as being unable to provide data support and theoretical reference for decision-makers to formulate strategies and actions for reducing atmospheric pollutant emissions and addressing climate change, and having relatively low estimation efficiency.

[0006] In a first aspect, an embodiment of the present application provides a method for estimating the exhaust and non-exhaust emissions of urban buses, including: Determine the bus routes and departure frequencies of the city to be estimated, and estimate the number of buses operating on a one-way route; Calculate the bus exhaust emission factor and non-exhaust emission factor, wherein the bus exhaust emission factor and non-exhaust emission factor are pre-calibrated; Estimate the bus exhaust and non-exhaust emissions based on the bus routes, departure frequencies, bus exhaust emission factor and non-exhaust emission factor.

[0007] Optionally, the target number of buses running on one-way routes, the ratio of bus running numbers on routes based on different fuel types, and the number of bus running numbers on routes based on different exhaust emission standards are determined; wherein the target number of buses running on one-way routes meets the departure schedule requirements of the one-way routes.

[0008] Optionally, the target number of buses running on a single route is calculated according to the following formula:

[0009]

[0010] In the formula, BP l,p Indicates a one-way route l of p The minimum number of buses running during peak hours, peak transition hours and off-peak hours, N p,l Indicates a one-way route l exist p The number of departures during the time period, n l Indicates a one-way route l The maximum number of departures of a bicycle, t represents the daily operating hours of the bus line, ns l Indicates a one-way route l The number of sites, t 1 Indicates the bus's stop time at intermediate stops other than the starting and ending stops. t 2 Indicates the bus stop time at the terminal station. L l Indicates a one-way route l Length, v l Indicates line l The average speed of buses.

[0011] Optionally, calculate the number of bus routes running on different fuel types according to the following formula:

[0012] In the formula, in the formula, BP l,p,j Indicates a one-way route l middle p Time period Fuel type j The number of buses in use, x l,j Indicates a one-way route l Medium fuel type jProportion of bus ownership.

[0013] Optionally, calculating the non-exhaust emission factor of buses includes: Determine the weight and fuel type of the bus, where the weight of the bus is the corrected weight; Obtain the non-exhaust PM 10 and PM 2.5 emission factor data for buses of different fuel types, and combine with the corrected weight of the bus to construct a regression equation between the non-exhaust emission factor and the weight, and perform non-linear least squares fitting to determine the non-exhaust emission factor.

[0014] Optionally, perform non-linear least squares fitting according to the following formula to complete the correction of the non-exhaust emission factor based on the corrected weight of the bus:

[0015] In the formula, BEF l,j,a,b represents the non-exhaust emission factor of type l on the line j non-exhaust sources of buses a atmospheric particulate matter b ; W p,l,i represents the time period p on the line l the average corrected weight of buses i ; e l,i,a,b and q l,i,a,b represent the line l on the bus i non-exhaust source a particulate matter b of the least squares regression coefficient.

[0016] Optionally, calculate the non-exhaust emissions of buses according to the following formula:

[0017] In the formula, NEM a,b represents the total annual emissions of non-exhaust sources a particulate matter b of buses; BP l,j represents the ownership of buses of type l in the one-way line j ; n l represents the maximum departure frequency per vehicle of the one-way line l ; L lIndicates a one-way line l length of, BEF l,j,a,b Indicates the line l type on j Non-exhaust source of bus a Particulate matter b emission factor of.

[0018] Optionally, calculate the bus exhaust emissions according to the following formula:

[0019] In the formula, EM k Indicates the total emissions of bus exhaust emissions k of, BP l,j,d Indicates a one-way line l type j with emission standards d operating quantity of buses, L l Indicates a one-way line l length of, EF l,j,d,k Indicates the line l type on j emission standards d bus emissions k emission factor of.

[0020] Second aspect, the embodiment of the present application also provides an estimation system for urban bus exhaust and non-exhaust emissions, including: Determination unit, used to determine the bus lines and departure schedules of the city to be estimated, and estimate the operating quantity of one-way line buses; Calculation unit, used to calculate the bus exhaust emission factor and non-exhaust emission factor, wherein the bus exhaust emission factor and non-exhaust emission factor are pre-calibrated; Estimation unit, used to estimate the bus exhaust and non-exhaust emissions based on the bus lines, departure schedules, bus exhaust emission factor and non-exhaust emission factor.

[0021] Third aspect, the embodiment of the application also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, it implements the steps of the above-mentioned estimation method for bus exhaust and non-exhaust emissions.

[0022] From the above technical solutions, it can be seen that the present invention has the following advantages: In the method, system, and electronic device for estimating the exhaust and non-exhaust emissions of urban buses provided by this application, the method for estimating the exhaust emission factors and non-exhaust emissions under the background of electrification of typical urban buses based on bus routes and departure frequencies can provide data support and theoretical reference for decision-makers to formulate strategies and actions for reducing air pollutant emissions and addressing climate change, and further improve the estimation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] To more clearly illustrate the technical solutions of the present invention, the drawings required for description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0024] Figure 1 It is a flowchart of the implementation of a method for estimating the exhaust and non-exhaust emissions of urban buses provided by an embodiment of the present invention; Figure 2 It is a detailed flowchart of the implementation of a method for estimating the exhaust and non-exhaust emissions of urban buses provided by an embodiment of the present invention; Figure 3 It is a schematic structural diagram of a system for estimating the exhaust and non-exhaust emissions of urban buses provided by an embodiment of the present invention; Figure 4 It is a schematic hardware structure diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] In the following detailed description, various embodiments of the present disclosure will be described more fully. The present disclosure may have various embodiments and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of the present disclosure to the specific embodiments disclosed herein, but the present disclosure should be understood to cover all adjustments, equivalents, and / or alternative solutions falling within the spirit and scope of the various embodiments of the present disclosure.

[0026] In the following, the term "including" or "may include" that may be used in various embodiments of the present disclosure indicates the presence of the disclosed functions or operations, and does not limit the addition of one or more functions or operations. In addition, as used in various embodiments of the present disclosure, the terms "including", "having" and their cognates are only intended to indicate the presence of specific features, numbers, steps, operations, or combinations of the foregoing items, and should not be construed as precluding the presence or addition of one or more other features, numbers, steps, operations, or combinations of the foregoing items.

[0027] In various embodiments of the present disclosure, the expression "or" or "at least one of A or / and B" includes any combination or all combinations of the recited words. For example, the expression "A or B" or "at least one of A or / and B" may include A, may include B, or may include both A and B.

[0028] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0029] Refer to Figure 1 Shown is a flowchart of a method for estimating the exhaust and non-exhaust emissions of urban buses in a specific embodiment, including the following implementation steps: Step 100: Determine the bus routes and departure schedules of the city to be estimated, and estimate the number of buses operating on a one-way route.

[0030] In some embodiments, the determination of bus routes and departure schedules: Taking a second-tier city as an example, a total of 621 bus routes are counted, which can be divided into five types: main city regular lines (320), rapid bus lines (13), main city commuter lines (33), inter-district bus lines (24), and non-main city regular lines (231). The departure times and schedules of each bus route are obtained through the "Real-time Bus" function of the Gaode Map APP, and at the same time, the actual operating length of the route from the starting point to the ending point is recorded. The bus routes are divided into outbound and return routes. This application assumes that the outbound and return route names are the same and the passing mileage is equal. The total daily driving mileage of a single bus route (including outbound and return) is calculated by the following formula:

[0031] In the formula, represents the total daily driving mileage of the bus on route l , km; represents the number of departure schedules per day for a one-way route l ; L l represents the operating length of a one-way route l , km.

[0032] Specifically, since the urban statistical yearbook does not include the data on the number of buses running on each bus line, the process of determining the number of buses running on a one-way line includes: determining the target number of buses running on the one-way line, the proportion of the number of buses running on the bus line based on different fuel types, and the number of buses running on the bus line based on different exhaust emission standards; among them, the target number of buses running on the one-way line meets the departure frequency requirements of the one-way line.

[0033] More specifically, the target number of buses running on the one-way line is calculated according to the following formula:

[0034]

[0035] In the formula, BP l,p represents the minimum number of buses running on the one-way line (during peak hours, peak transition hours, and off-peak hours), l of p the one-way line, N p,l represents the number of departures of the one-way line l during p the time period, n l represents the maximum number of departures per vehicle of the one-way line, l of the one-way line, t represents the daily operating time of the bus line, ns l represents the number of stations of the one-way line l of the one-way line, t 1 represents the stopping time of the bus at the intermediate stations (other stations except the starting and ending stations), t 2 represents the stopping time of the bus at the ending station, L l represents the length of the one-way line l of the one-way line, v l represents the line l average driving speed of the bus.

[0036] Exemplarily, the peak hours are: 6:00 - 10:00, 16:00 - 20:00; the peak transition hours are: 10:00 - 11:00, 15:00 - 16:00; the off-peak hours are: 5:00 - 6:00, 11:00 - 15:00, and 20:00 - 23:00; based on the operating schedule of the bus lines in the surveyed city, the daily operating time t of the bus in this embodiment is set to 17 hours; the stopping time of the bus at the intermediate stations (other stations except the starting and ending stations)t 1 , which is set to 1 minute based on the on-site investigation results; the stopping time of the bus at the terminal station t 2 is set to 1 hour; the average running speed of the bus is cited from the data of the main urban traffic health list published by AutoNavi Map (https: / / report.amap.com / diagnosis / index.do), km / h.

[0037] More specifically, based on the current operating conditions of typical urban buses, the present invention classifies buses into five categories according to fuel type: diesel buses (DBs), natural gas buses (NGBs), gas-electric hybrid buses (G-EHBs), diesel-electric hybrid buses (D-EHBs), and electric buses (EBs). Combining the data of the urban bus ownership obtained from the urban statistical yearbook, verifying the accuracy of the minimum ownership of the vehicles operating on the bus lines obtained, and comprehensively using methods such as on-site investigation and data investigation to obtain the vehicle type ownership ratio of buses in second-tier cities based on fuel type. The number of buses of different fuel types operating on each bus line is determined by the following formula:

[0038] In the formula, BP l,p,j represents the bus ownership of fuel type l in p the time period of the one-way line j , x l,j represents the proportion of the bus ownership of fuel type l in j the one-way line

[0039] Exemplarily, in some embodiments, the determination of the number of buses operating on the bus lines based on different exhaust emission standards: Buses powered by diesel and natural gas will emit atmospheric pollutants and climate stress factors into the ambient air through the tailpipe in addition to non-exhaust atmospheric particulate matter. To calculate the emissions of bus exhaust pollutants and climate stress factors, it is first necessary to determine the number of buses of DBs, NGBs, G-EHBs, and D-EHBs models powered by diesel and natural gas operating on the bus lines based on different emission standards. In this embodiment, the relevant information of the in-service buses in the studied city (such as the bus ownership classified by fuel type, the vehicle models of in-service buses) is determined by consulting the statistical analysis of the bus archive station, and then the vehicle models are retrieved through the bus network, and parameters such as vehicle emission standards, vehicle seat numbers, and vehicle weights can be obtained.

[0040] Step 101: Calculate the bus tailpipe emission factors and non-tailpipe emission factors, where the bus tailpipe emission factors and non-tailpipe emission factors are pre-calibrated.

[0041] It should be understood that bus emission factors are divided into tailpipe conventional air pollutants (CO, VOCs, NO X , SO 2 , PM 10 , PM 2.5 ) and climate forcing factors (CO 2 , CH 4 , BC, N 2 O) emission factors and non-tailpipe (tire wear, brake wear, road wear, and road dust) atmospheric particulate matter (PM 10 and PM 2.5 ) emission factors. The acquisition of the atmospheric particulate matter emission factors from the bus road dust source adopts the recommended method in the "Technical Manual for Compiling Urban Air Pollutant Emission Inventories", and the bus tailpipe and other non-tailpipe source emission factors are obtained based on the COPERT model.

[0042] Specifically, the calculation process of the bus non-tailpipe emission factors is as follows: Determine the weight of the bus and the fuel type, where the weight of the bus is the corrected weight; obtain the non-tailpipe PM 10 and PM 2.5 emission factor data for buses of different fuel types, and combine the corrected weight of the bus to construct a regression equation between the non-tailpipe emission factor and the weight, and perform non-linear least squares fitting to determine the non-tailpipe emission factor.

[0043] It should be understood that the vehicle weight is a key parameter affecting the non-tailpipe atmospheric particulate matter emissions of buses. In this embodiment, the emission factor-vehicle weight function method is used to calibrate the non-tailpipe emission factors of buses. The vehicle weight of the bus will change due to different numbers of passengers. According to the passenger flow volume in different time periods obtained from the investigation, the present invention corrects the weight of the bus through the following formula:

[0044]

[0045] where, WF p,l,i represents the time period p route l bus i 's vehicle weight coefficient; w l,i represents the empty weight of the bus on route l bus i , kg; mRepresents the average weight of passengers. The average weight of adults released by the physical fitness monitoring center is 65 kg; η l,p Represents the line l Time period p Bus occupancy rate (peak hours: η = 1, indicating an occupancy rate of 100%; peak transition period: η =0.8; off-peak hours: η =0.5); s l,i Represents the line l Bus i The number of passenger seats; A l,i Represents the line l Bus i The effective standing area of the carriage, m 2 ; r Represents the number of people allowed to stand per square meter of the bus. According to the "Technical Conditions for Safe Operation of Motor Vehicles" (GB7258-2012), the number of people standing per square meter of a bus is 8; ps p Represents the interval p Standing number correction factor (peak hours: ps = 1; other periods: ps =0); L l,i Represents the line l Bus i The length, m; D l,i Represents the line l Bus i The width, m; Through the actual measurement of the area of typical buses and literature research, in this embodiment, it is assumed that the effective standing area of the bus accounts for 20% of the total area of the bus.

[0046]

[0047] Among them, W p,l,i Represents the time period p Line l Bus i The corrected vehicle weight, kg; w l,i Represents the line l Bus i The empty vehicle weight, kg.

[0048] More specifically, non-linear least squares fitting is performed according to the following formula to determine the non-exhaust emission factor:

[0049] In the formula, BEF l,j,a,b Indicates line l Upper type j Buses are not exhaust sources a Atmospheric particulate matter b The emission factor is W p,l,i Indicates time period p line l Get on the bus i The average corrected weight, e l,i,a,b and q l,i,a,b Indicates line l Get on the bus i Non-exhaust source a Particles b The least squares regression coefficients of .

[0050] In some embodiments, correction of bus exhaust emission factors: the emission factors of conventional pollutants and climate stress factors of bus exhaust are obtained by simulation calculation using the COPERT model, where the localized parameters that need to be input include the vehicle's mileage, the number of buses with different exhaust emission standards, the driving speed, the monthly maximum and minimum temperatures, the average humidity, the saturated vapor pressure, the sulfur content of the fuel, etc. For research on the use of the COPERT model to calculate the exhaust emission inventory of motor vehicles, reference can be made to the prior art. This embodiment does not describe the method of using the model. For the correction method of bus exhaust emission factors, reference can be made to the recommended method of the "Technical Manual for the Preparation of Urban Air Pollutant Emission Inventories".

[0051] Step 102: Estimate the bus exhaust and non-exhaust emissions based on the bus routes, departure frequencies, bus exhaust emission factors and non-exhaust emission factors.

[0052] Specifically, the non-exhaust particulate matter (PM) was calculated based on the bus operation frequency, number of buses, route length and other activity data based on the literature survey and COPERT model simulation. 10 and PM 2.5 ) emission factor data, using the "bottom-up" emission factor method to estimate bus non-exhaust particulate matter emissions:

[0053] In the formula, NEM a,b Indicates that buses are not exhaust sources a Particles b The total annual emissions, BP l,j Indicates a one-way route l Medium Typej The bus ownership n l Indicates a one-way line l The maximum departure frequency of bicycles L l Indicates a one-way line l Length BEF l,j,a,b Indicates the line l Type on j Non-exhaust source of buses a Particulate matter b Emission factor

[0054] Specifically, based on the bus fuel type, calculate the emissions of conventional pollutants and climate stress factors from the exhaust of buses powered by diesel and natural gas respectively. When calculating, consider localization parameters such as fuel type, emission standard, driving mileage, driving speed, and environmental temperature and humidity. The main calculation formulas for the emissions of conventional pollutants and climate stress factors from bus exhaust are as follows:

[0055] In the formula EM k Indicates the total emissions of bus exhaust emissions k (CO, VOCs, NO X , SO 2 , PM 10 , PM 2.5 , CO 2 , CH 4 , BC, N 2 O), BP l,j,d Indicates a one-way line l Type j With emission standards d (Pre-China I, China I, ChinaII, China III, China IV, China V and China VI) The operating quantity of buses L l Indicates a one-way line l Length EF l,j,d,k Indicates the line l Type on j Emission standard d Emissions of bus emissions k Emission factor

[0056] In this embodiment, based on the bus line and departure frequency, estimate the conventional atmospheric pollutants (CO, VOCs, NOX , SO 2 , PM 10 , PM 2.5 ), and climate stress factors (CO 2 , CH 4 , BC, N 2 O) emission factors and non-exhaust (tire wear, brake wear, road surface wear, and road dust) atmospheric particulate matter (PM 10 and PM 2.5 ) emission amounts. By constructing a bus atmospheric pollutant emission inventory that is more in line with the actual situation and comprehensive, it can provide data support and theoretical reference for decision-makers to formulate strategies and actions for reducing atmospheric pollutant emissions and addressing climate change.

[0057] In one embodiment, Figure 2 FIG. is a detailed flowchart of an estimation method for the exhaust and non-exhaust emissions of an urban bus according to an embodiment of the present invention. This embodiment is further optimized and extended on the basis of the above embodiments.

[0058] The following combines Figure 2 Taking a bus line of K202 in Jinan City as an example, the annual emission amount of non-exhaust particulate matter PM 2.5 caused by brake wear during the morning and evening peak hours of this line is calculated.

[0059] Step (1) Determination of the bus line and departure frequency: The departure frequency of the K202 line during the peak hours is obtained as 94 trips through the "Real-time Bus" function of the Gaode Map APP, and the line mileage is 27.4 km. Then the total daily driving mileage of the K202 bus line is calculated by the following formula:

[0060] Step (2) Estimation of the minimum number of buses running on a one-way K202 line: Step (2.1) Determination of the minimum number of buses running on a one-way K202 line: The number of buses running on a one-way K202 line is determined by the following formula, and its minimum number must meet the departure frequency requirements of the one-way line on the same day. The number of stations on the K202 line is 42, and the average speed of the K202 line is 30.5 km / h. The maximum departure frequency of a single bus on the one-way K202 line is calculated by the following formula, and then the minimum number of buses running on the one-way K202 line during the peak hours can be calculated.

[0061]

[0062]

[0063] Proportion of the operating quantities of buses with different fuel types on the K202 route in step (2.2): Based on the data statistics of the Jinan Statistical Yearbook and the bus archive station, on-site research by the Public Transport Group Co., Ltd., etc., the proportions of the operating quantities of DBs, NGBs, G-EHBs, D-EHBs, and EBs vehicles on the current K202 route in Jinan are 0%, 3%, 18%, 0%, and 79% respectively. Among them, the vehicle models corresponding to NGBs, G-EHBs, and EBs vehicles are JNP6180GVC, JK6126GPHEVN5Q2, and LCK5180A respectively. The operating quantities of buses with different fuel types on the K202 bus route are determined by the following formula:

[0064]

[0065]

[0066] Determination of the bus weight during the morning and evening rush hours in step (3): By retrieving the vehicle models through the bus network, information such as the drive mode, length, width, number of vehicle seats, and vehicle weight of the vehicle can be obtained. For example, the vehicle information corresponding to the JK6126GPHEVN5Q2 model is as follows: a national V bus with NG / electric hybrid drive, a vehicle length of 11990 mm, a vehicle width of 2500 mm, 21 seats, and a vehicle weight of 12400 kg. The effective standing area of the bus is determined by the following formula:

[0067]

[0068]

[0069] Bus occupancy rate during the morning and evening rush hours η = 1, standing passenger number correction coefficient ps = 1. The correction coefficient of the bus weight during the rush hour can be obtained by the following formula:

[0070]

[0071]

[0072] Combining the empty vehicle weight of the bus and the vehicle weight correction coefficient, the corrected vehicle weight of the bus during the morning and evening rush hours is calculated by the following formula:

[0073]

[0074]

[0075] Step (4) PM of bus brake wear during morning and evening rush hours 2.5 Correction of emission factors: In this embodiment, the regression equations and related results based on the weights of different vehicle models and non-exhaust particulate matter emission factors constructed by domestic and foreign research scholars are selected to determine the least squares regression coefficients of PM of bus brake wear 2.5 and e and q The values are 4.2 and 1.9 respectively. To explore the dependence of the emission factor on the vehicle weight, the PM emission factor data of bus brake wear during morning and evening rush hours are corrected based on the following formula: 2.5 Emission factor data correction:

[0076] Step (5) PM of bus brake wear during morning and evening rush hours 2.5 Annual emission calculation: Based on the operation quantities, departure frequencies, route lengths of different fuel buses during morning and evening rush hours and the brake wear PM emission factors corrected based on vehicle weight, the annual PM emissions caused by bus brake wear during morning and evening rush hours are calculated based on the following formula: 2.5 It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention. 2.5 Annual emissions:

[0077] As shown in , the following is an embodiment of the estimation system for urban bus exhaust and non-exhaust emissions provided by the embodiments of the present disclosure. It belongs to the same inventive concept as the estimation methods for urban bus exhaust and non-exhaust emissions in the above embodiments. For the details not described in detail in the embodiments of the estimation system for urban bus exhaust and non-exhaust emissions, reference can be made to the embodiments of the estimation methods for urban bus exhaust and non-exhaust emissions.

[0078] As Figure 3 shown, the following is an embodiment of the estimation system for urban bus exhaust and non-exhaust emissions provided by the embodiments of the present disclosure. It belongs to the same inventive concept as the estimation methods for urban bus exhaust and non-exhaust emissions in the above embodiments. For the details not described in detail in the embodiments of the estimation system for urban bus exhaust and non-exhaust emissions, reference can be made to the embodiments of the estimation methods for urban bus exhaust and non-exhaust emissions.

[0079] Determination unit 30, configured to determine the bus routes and departure schedules of the city to be estimated, and estimate the operation quantity of one-way route buses; Calculation unit 31, configured to calculate the bus exhaust emission factor and non-exhaust emission factor, wherein the bus exhaust emission factor and non-exhaust emission factor are pre-calibrated; An estimation unit 32 for estimating bus exhaust gas and non-exhaust emissions based on bus routes, departure schedules, bus exhaust emission factors, and non-exhaust emission factors.

[0080] Figure 4 It is a schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0081] The estimation method for urban bus exhaust gas and non-exhaust emissions provided by the embodiments of the present application can be applied to electronic devices. Those skilled in the art can understand that the structure of the electronic device involved in the embodiments of the present invention does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements. In the embodiments of the present invention, the electronic device includes, but is not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown in the figure, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the embodiments of the present application described and / or claimed herein.

[0082] The electronic device may include a processor, an external memory interface, an internal memory, a universal serial bus (USB) interface, a charging management module, a power management module, a battery, a wireless communication module, an audio module, a speaker, a microphone, a sensor module, keys, a camera, a display screen, and a SIM card interface, etc.

[0083] It can be understood that the structure schematically shown in the embodiments of the present application does not constitute a specific limitation on the electronic device. In other embodiments of the present application, the electronic device may include more or fewer components than those shown in the figure, or combine certain components, or split certain components, or have different component arrangements. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0084] The processor may include one or more processing units. For example, the processor may include a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Among them, different processing units may be independent devices or integrated in one or more processors.

[0085] Among them, the processor may be the nerve center and command center of the electronic device. The controller may generate operation control signals according to the instruction operation code and timing signals to complete the control of fetching and executing instructions.

[0086] A memory may also be provided in the processor for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can save the instructions or data that the processor has just used or recycled. If the processor needs to use the instruction or data again, it can directly call it from this memory. This avoids repeated accesses, reduces the waiting time of the processor, and thus improves the system efficiency.

[0087] The external memory interface can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the electronic device. The external memory card communicates with the processor through the external memory interface to achieve the data storage function. For example, files such as music and videos are saved in the external memory card.

[0088] The internal memory can be used to store computer-executable program code, and the computer-executable program code includes instructions. The processor executes various functional applications and data processing of the electronic device by running the instructions stored in the internal memory. The internal memory may include a program storage area and a data storage area. The internal memory may include a high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), etc.

[0089] The wireless communication function of the electronic device can be implemented through an antenna, a wireless communication module, a modem processor, a baseband processor, etc.

[0090] The wireless communication module can provide solutions for wireless communications applied to electronic devices, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite systems (GNSS), frequency modulation (FM), near field communication (NFC), infrared technology (IR), etc.

[0091] The electronic device can implement audio functions through an audio module, speakers, receivers, microphones, headphone jacks, application processors, etc.

[0092] The electronic device can implement a shooting function through an ISP, a camera, a video codec, a GPU, a display screen, an application processor, etc.

[0093] The electronic device can implement a display function through a GPU, a display screen, an application processor, etc.

[0094] The GPU is a microprocessor for image processing, connecting the display screen and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processor may include one or more GPUs that execute program instructions to generate or change display information.

[0095] The display screen is used to display images, videos, etc. The display screen includes a display panel.

[0096] The above-mentioned electronic device implements the technical solution of the method for estimating urban bus exhaust and non-exhaust emissions in the present application, which determines the bus routes and departure schedules of the city to be estimated, and estimates the number of buses running on a one-way route; calculates the bus exhaust emission factor and non-exhaust emission factor, where the bus exhaust emission factor and non-exhaust emission factor are pre-calibrated; and estimates the bus exhaust and non-exhaust emissions based on the bus routes, departure schedules, bus exhaust emission factor and non-exhaust emission factor, achieving the beneficial effect of providing data support and theoretical reference for decision-makers to formulate strategies and actions for reducing air pollutant emissions and addressing climate change by constructing a more realistic and comprehensive bus air pollutant emission inventory.

[0097] In the storage medium provided by the present application, there is a program product capable of implementing the method for estimating urban bus exhaust and non-exhaust emissions.

[0098] The estimation method for the exhaust gas and non-exhaust emissions of urban buses includes: determining the bus routes and departure schedules of the city to be estimated, and estimating the number of buses operating on a one-way route; calculating the exhaust gas emission factor and non-exhaust emission factor of buses, wherein the exhaust gas emission factor and non-exhaust emission factor of buses are pre-calibrated; and estimating the exhaust gas and non-exhaust emissions of buses based on the bus routes, departure schedules, exhaust gas emission factor and non-exhaust emission factor of buses.

[0099] In some possible implementation manners, the estimation method and system for the exhaust gas and non-exhaust emissions of urban buses of the present disclosure may be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of this specification.

[0100] The storage medium of the present disclosure may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0101] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for estimating exhaust and non-exhaust emissions of urban buses, characterized in that: include: Determine the bus routes and departure times in the city to be estimated, and estimate the number of buses running on one-way routes; Calculating the exhaust emission factor and non-exhaust emission factor of the bus, wherein the exhaust emission factor and non-exhaust emission factor of the bus are pre-calibrated; Estimate bus exhaust and non-exhaust emissions based on bus routes, departure frequencies, bus exhaust emission factors and non-exhaust emission factors.

2. The method for estimating exhaust and non-exhaust emissions of urban buses according to claim 1, characterized in that: Estimate the number of buses running on a single route, including: Determine the target number of buses in operation on one-way routes, the ratio of bus operation numbers based on different fuel types, and the number of buses in operation based on different exhaust emission standards; wherein the target number of buses in operation on one-way routes meets the departure schedule requirements of the one-way routes.

3. The method for estimating exhaust and non-exhaust emissions of urban buses according to claim 2, characterized in that: The target number of buses running on a single route is calculated using the following formula: ； ； In the formula, BP l,p Indicates a one-way route l of p The minimum number of buses running during peak hours, peak transition hours and off-peak hours, N p,l Indicates a one-way route l exist p The number of departures during the time period, n l Indicates a one-way route l The maximum number of departures of a bicycle, t represents the daily operating hours of the bus line, ns l Indicates a one-way route l The number of sites, t 1 means the bus stops at intermediate stations other than the starting and ending stations. t 2 represents the bus stop time at the terminal station, L l Indicates a one-way route l Length, v l Indicates line l The average speed of buses.

4. The method for estimating exhaust and non-exhaust emissions of urban buses according to claim 3, characterized in that: The number of bus routes running on different fuel types is calculated using the following formula: ； In the formula, BP l,p,j Indicates a one-way route l middle p Time period Fuel type j The number of buses in use, x l,j Indicates a one-way route l Medium fuel type j The proportion of buses in use.

5. The method for estimating exhaust and non-exhaust emissions of urban buses according to claim 1, characterized in that: Calculate non-tailpipe emission factors for buses, including: determining a weight of the bus and a fuel type, wherein the weight of the bus is a corrected weight; Obtaining non-exhaust PM of buses with different fuel types 10 and PM 2.5 The emission factor data are combined with the corrected weight of the bus to construct a regression equation between the non-exhaust emission factor and the weight, and a nonlinear least squares fit is performed to determine the non-exhaust emission factor.

6. The method for estimating exhaust and non-exhaust emissions of urban buses according to claim 5, characterized in that: The non-tail gas emission factor was determined by performing nonlinear least square fitting according to the following formula: ； In the formula, BEF l,j,a,b Indicates line l Upper type j Buses are not exhaust sources a Atmospheric particulate matter b The emission factor, W p,l,i Indicates time period p line l Get on the bus i The average corrected weight, e l,i,a,b and q l,i,a,b Indicates line l Get on the bus i Non-exhaust source a Particles b The least squares regression coefficients of .

7. The method for estimating exhaust and non-exhaust emissions of urban buses according to claim 6, characterized in that: The non-exhaust emissions of buses are calculated according to the following formula: ； In the formula, NEM a,b Indicates that buses are not exhaust sources a Particles b The total annual emissions, BP l,j Indicates a one-way route l Medium Type j The number of buses in use, n l Indicates a one-way route l The maximum number of departures of a bicycle, L l Indicates a one-way route l Length, BEF l,j,a,b Indicates line l Upper type j Buses are not exhaust sources a Particles b The emission factor.

8. The method for estimating exhaust and non-exhaust emissions of urban buses according to claim 1, characterized in that: The exhaust emissions of buses are calculated according to the following formula: ； In the formula, EM k Exhaust emissions from buses k The total amount of emissions, BP l,j,d Indicates a one-way route l type j With emission standards d The number of buses running, L l Indicates a one-way route l Length, EF l,j,d,k Indicates line l Upper type j Emission standards d Bus emissions k The emission factor.

9. A system for estimating exhaust and non-exhaust emissions of urban buses, characterized in that: include: A determination unit, used for determining bus routes and departure times of the city to be estimated, and estimating the number of buses running on one-way routes; A calculation unit, used for calculating the exhaust emission factor and non-exhaust emission factor of the bus, wherein the exhaust emission factor and non-exhaust emission factor of the bus are calibrated in advance; The estimation unit is used to estimate the exhaust and non-exhaust emissions of buses based on bus routes, departure frequencies, bus exhaust emission factors and non-exhaust emission factors.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method for estimating exhaust and non-exhaust emissions of a city bus as claimed in any one of claims 1 to 8 are implemented.

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