Park scale carbon emission multi-platform collaborative traceability method and system

Abstract

The invention belongs to the technical field of carbon emission traceability, and particularly relates to a park scale carbon emission multi-platform collaborative traceability method and system. Comprising the steps that a multi-platform cooperative monitoring mode is adopted, and the actual emission flux of CO2, CH4 and N2O in a park is obtained; dividing the park greenhouse gas emission sources into a plurality of core categories, and determining CH4 / CO2 and N2O / CO2 characteristic ratios of the emission sources of each core category; based on the actual emission flux of CO2, CH4 and N2O in the park, a flux balance simultaneous equation of three types of park greenhouse gases of CO2, CH4 and N2O is established, the flux contribution of each emission source is solved, and the carbon emission traceability of the park is realized. According to the method, the park high-resolution emission list and the improved linear hybrid model are combined, the multi-species flux ratio characteristics are utilized, the total carbon flux is attributed to different emission source categories in the park, and data support is provided for park carbon emission fine management and emission reduction decision making.

Description

Technical Field

[0001] This invention belongs to the field of carbon emission traceability technology, and in particular relates to a multi-platform collaborative traceability method and system for carbon emissions at the park scale. Background Technology

[0002] Industrial parks, as concentrated areas of industrial production activities, are a significant source of carbon emissions. Accurately understanding the spatiotemporal distribution characteristics of carbon emissions within the park and the contribution percentage of each emission source is a core prerequisite for developing targeted emission reduction strategies.

[0003] Currently, carbon emission monitoring and source tracing in the industrial park mainly rely on two types of methods: One approach is the "bottom-up" emissions inventory method, which calculates total emissions and source contributions by statistically analyzing energy consumption data, process parameters, and fuel consumption of enterprises within the industrial park, combined with emission factors. However, this method relies heavily on manually submitted data, resulting in issues such as data lag, large statistical errors, and low spatiotemporal resolution (mostly at annual / quarterly scales). Furthermore, it struggles to capture instantaneous emissions fluctuations (such as emission peaks caused by changes in production load).

[0004] The second method is the "top-down" single-point monitoring method, which involves setting up a small number of ground-based monitoring stations in the park and using techniques such as eddy covariance to measure carbon flux. However, single-point monitoring has a limited coverage area, making it difficult to reflect the spatial heterogeneity of different areas and emission sources in the park, and it cannot directly attribute the total flux to specific emission source categories.

[0005] In addition, existing eddy covariance monitoring focuses mainly on CO2 as a single species, lacking simultaneous monitoring of potent greenhouse gases such as CH4 and N2O. This makes it impossible to separate the source contributions of CH4 and N2O, resulting in biases in emission reduction decisions due to the omission of potent greenhouse gases. Summary of the Invention

[0006] To overcome the shortcomings of the existing technologies, this invention provides a multi-platform collaborative source tracing method and system for carbon emissions at the park scale. Through collaborative monitoring of airborne platforms, UAV platforms and ground-based platforms, it achieves high spatiotemporal resolution acquisition of carbon flux in the park through an integrated "air-ground" approach. Combining the park's high-resolution emission inventory with an improved linear hybrid model, and utilizing the flux ratio characteristics of multiple species (CO2, CH4 and N2O), the total carbon flux is attributed to different emission source categories within the park, providing data support for refined carbon emission management and emission reduction decisions in the park.

[0007] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: The first aspect of this invention provides a multi-platform collaborative source tracing method for carbon emissions at the park scale.

[0008] A multi-platform collaborative source tracing method for carbon emissions at the park scale includes the following steps: A multi-platform collaborative monitoring mode is adopted to monitor the fluxes of greenhouse gases in the park, including CO2, CH4 and N2O; The raw data obtained from monitoring are preprocessed to obtain the actual emission fluxes of CO2, CH4 and N2O in the park. Based on the emission characteristics of CO2, CH4 and N2O, the greenhouse gas emission sources in the park are divided into multiple core categories, and based on the actual emission flux, the characteristic ratios of CH4 / CO2 and N2O / CO2 for each core category of emission sources are determined. Based on the actual emission fluxes of CO2, CH4, and N2O in the park, a simultaneous equation for the flux balance of the three greenhouse gases in the park is established. The flux contribution of each emission source is obtained through algebraic solution, thus realizing the source tracing of carbon emissions in the park.

[0009] The second aspect of this invention provides a multi-platform collaborative traceability system for carbon emissions at the park scale.

[0010] A multi-platform collaborative traceability system for carbon emissions at the park scale includes: The data acquisition module is configured to monitor the fluxes of greenhouse gases in the park, including CO2, CH4 and N2O, using a multi-platform collaborative monitoring mode. The preprocessing module is configured to preprocess the raw data obtained from monitoring to obtain the actual emission fluxes of CO2, CH4 and N2O in the park. The emission source classification and characteristic ratio calculation module is configured to: classify the greenhouse gas emission sources in the park into multiple core categories based on the emission characteristics of CO2, CH4 and N2O, and determine the CH4 / CO2 and N2O / CO2 characteristic ratios for each core category of emission sources based on the actual emission flux; The simultaneous solution module is configured to: establish simultaneous equations for the flux balance of three greenhouse gases in the park, namely CO2, CH4 and N2O, based on the actual emission fluxes of CO2, CH4 and N2O in the park; obtain the flux contribution of each emission source through algebraic solution; and realize the source tracing of carbon emissions in the park.

[0011] The above one or more technical solutions have the following beneficial effects: This invention designs a three-level multi-platform collaborative monitoring architecture, which innovatively adopts a three-level architecture of "floating platform (continuous monitoring of the whole area) + UAV platform (mobile monitoring of key sources) + ground platform (long-term monitoring of fixed points)" to achieve 100% coverage of the park area and 100m (meter) level spatial resolution, solve the problem of blind spots in single-point monitoring, and the equipment parameters of each platform are clear and can be reproduced through the selection of general equipment.

[0012] This invention enables simultaneous monitoring of all species and application of characteristic ratios. It can simultaneously monitor three types of greenhouse gases: CO2, CH4, and N2O. By using the source-specific ratios of CH4 / CO2 and N2O / CO2 from different emission sources as the basis for source tracing, it significantly improves the source category differentiation and breaks through the accuracy limitations of existing single-species monitoring.

[0013] This invention designs a park-adaptive linear hybrid model, which constructs multi-species simultaneous equations for five core emission sources, including industrial process sources and fuel combustion sources in parks. It introduces simplified models such as the "non-biological source assumption" and the "limited source contribution assumption" while ensuring rationality, and is adaptable to different types of parks.

[0014] This invention features a scalable feature ratio calibration mechanism. It determines the source feature ratio through "on-site sampling + literature calibration", establishes an updatable "source category-feature ratio" database, and supports dynamic adjustment of parameters according to park type, thereby improving the model's universality.

[0015] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0016] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0017] Figure 1 This is a flowchart of the method in Example 1.

[0018] Figure 2 This is a flowchart illustrating the actual application process in the park, as shown in Example 1. Detailed Implementation

[0019] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0020] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0021] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0022] Example 1 like Figure 1 As shown, the multi-platform collaborative source tracing method for carbon emissions at the park scale includes the following steps: A multi-platform collaborative monitoring mode is adopted to monitor the fluxes of greenhouse gases in the park, including CO2, CH4 and N2O; The raw data obtained from monitoring are preprocessed to obtain the actual emission fluxes of CO2, CH4 and N2O in the park. Based on the emission characteristics of CO2, CH4 and N2O, the greenhouse gas emission sources in the park are divided into multiple core categories, and based on the actual emission flux, the characteristic ratios of CH4 / CO2 and N2O / CO2 for each core category of emission sources are determined. Based on the actual emission fluxes of CO2, CH4, and N2O in the park, a simultaneous equation for the flux balance of the three greenhouse gases in the park is established. The flux contribution of each emission source is obtained through algebraic solution, thus realizing the source tracing of carbon emissions in the park.

[0023] The multi-platform collaborative source tracing method for carbon emissions at the park scale disclosed in this embodiment provides the following key technologies: First, by coordinating the monitoring of the airborne monitoring platform, the drone monitoring platform and the ground-based monitoring platform, the dual monitoring needs of full coverage of the park and precise focus on key sources can be met, thereby eliminating monitoring blind spots and improving the spatial resolution to the level of hundreds of meters. Second, it enables simultaneous monitoring of all species of three greenhouse gases: CO2, CH4, and N2O. At the same time, it utilizes the source-specific flux ratios of CH4 / CO2 and N2O / CO2 from different emission sources to improve the source differentiation and solve the problem of insufficient source tracing accuracy caused by monitoring a single species. Third, we will construct a linear hybrid model that adapts to the core emission types of industrial process sources, fuel combustion sources, and waste treatment sources in the park, so as to achieve accurate decomposition of total flux into specific emission source categories.

[0024] This invention achieves integrated "air-ground" high spatiotemporal resolution acquisition of carbon flux in the park through collaborative monitoring of airborne platforms, UAV platforms, and ground-based platforms. By combining the park's high-resolution emission inventory with an improved linear hybrid model, and utilizing the flux ratio characteristics of multiple species (CO2, CH4, N2O), the total carbon flux is attributed to different emission source categories within the park, providing data support for refined carbon emission management and emission reduction decisions in the park.

[0025] It can be understood that the flux mentioned in this embodiment specifically means: the net vertical exchange of three greenhouse gases, CO2, CH4, and N2O, per unit time and per unit area within the park. Its physical meaning is: the intensity of gas migration between the underlying surface (such as industrial facilities, vegetation, and soil) and the atmosphere within the park; a positive value represents gas emissions from the underlying surface to the atmosphere (such as CO2 and CH4 emissions from industrial sources); a negative value represents gas absorption from the atmosphere by the underlying surface (such as CO2 absorption by vegetation photosynthesis).

[0026] The source characteristic ratio means that the ratio of the molar emissions of two target gases in the greenhouse gas emission process of a specific emission source category (such as industrial process source, fuel combustion source, waste treatment source, etc.) within the park is the molar ratio of CH4 to CO2 (CH4 / CO2) and the molar ratio of N2O to CO2 (N2O / CO2).

[0027] The technical solution of this embodiment will be explained in detail below.

[0028] The implementation of this technical solution includes four core steps: multi-platform and multi-species monitoring, data transmission and processing, construction of greenhouse gas emission inventory in the park, and attribution of multi-species linear mixture model.

[0029] (a) Multi-platform, multi-species monitoring.

[0030] The multi-platform, multi-species monitoring system is used to simultaneously measure carbon flux at the park scale. It includes an aerial monitoring platform, a drone monitoring platform, and a ground-based monitoring platform. The three types of platforms are equipped with eddy covariance equipment and high-precision gas analyzers adapted for CO2, CH4, and N2O monitoring, respectively, covering different heights and areas of the park.

[0031] (1) Aerial monitoring platform.

[0032] It employs a tethered helium aerostat (10kg payload, aloft time ≥7 days), equipped with a high-frequency open-circuit eddy covariance system, a quantum cascade laser multi-gas analyzer, and meteorological sensors. Deployed 60-120m above the core emission area of ​​the park, covering the entire park area (radius 1-3km), it enables continuous monitoring of three types of gases.

[0033] (2) Unmanned aerial vehicle (UAV) monitoring platform.

[0034] Employing an industrial-grade multi-rotor UAV (endurance ≥80 min, payload ≥3 kg), equipped with a miniaturized closed-circuit eddy covariance system and a portable CH4 / N2O analyzer, it is used for mobile monitoring of key sources, such as chemical workshops (N2O), wastewater treatment plants (CH4), and the vicinity of gas pipelines (CH4 leaks). A single flight completes flux collection at 4-6 fixed points (30 min monitoring per point).

[0035] (3) Foundation monitoring platform.

[0036] Set up 4-6 fixed stations to cover the source proximity stations (such as nitration process workshops, gas boilers, and sewage treatment plants, 50-100m away from the emission source boundary), park boundary stations (located 50-200m outside the park to monitor the outward diffusion flux of gas) and background stations (no industrial / agricultural activity areas outside the park, ≥2km away from the park boundary, used for background value subtraction).

[0037] Each station is equipped with a high-precision open-circuit eddy covariance system (for simultaneous monitoring of CO2 and CH4), a dedicated N2O analyzer, and a data storage and transmission module to achieve 24-hour continuous monitoring.

[0038] (ii) Data transmission and processing.

[0039] Each platform transmits real-time data to the cloud data center via a 5G wireless network. The data center performs a three-tiered processing procedure to ensure data validity and accuracy. (1) Quality control.

[0040] Outliers were removed using the "modified median absolute deviation method". These outliers specifically included: ①Instantaneous spikes caused by equipment interference (such as a sudden increase in CH4 concentration to more than 5 times the background value); ② Meteorological interference data (such as periods of rainfall, air pressure fluctuations >5 kPa); ③ Insufficient turbulence data (friction velocity u < 0.2 m / s, in which case the flux measurement error is ≥ 20%).

[0041] (2) Flux calculation.

[0042] Standardization processing is performed on the raw vorticity data, specifically including: ① Coordinate double rotation: Eliminate the influence of platform tilt on vertical wind speed measurement and ensure the accuracy of vertical flux calculation; ② High-pass filter correction (using Moncliffe method): Removes low-frequency trend term interference and retains the flux signal contributed by turbulence; ③ Low-pass attenuation correction (using the Frattini method): compensates for high-frequency signal loss caused by closed-loop system pipeline transmission.

[0043] Finally, the net CO2 flux at the 30-minute scale was calculated. ), net CH4 flux ( ), N2O net flux ( ).

[0044] (3) Spatiotemporal matching and background subtraction.

[0045] 1) Spatiotemporal matching.

[0046] All platform data are unified to UTC (Coordinated Universal Time), and monitoring areas are associated with GPS positioning information (such as drone coordinates and ground station latitude and longitude) to ensure that data from the same time period and the same area can be compared.

[0047] 2) Background subtraction.

[0048] The actual emission flux of the park is obtained by subtracting the concurrent flux value of the background station from the measured value of the park (actual flux = measured flux - background flux).

[0049] (III) Construction of greenhouse gas emission inventory in the park.

[0050] A hybrid approach combining bottom-up statistical analysis and field measurement calibration was adopted to construct a high-resolution emission inventory including CH4 and N2O, providing prior parameters for the model. (1) Source category segmentation.

[0051] Based on the emission characteristics of the three types of greenhouse gases, the emission sources in the park are divided into five core categories, and the emission species and process associations of each category are clearly defined: 1) Industrial process sources: refers to greenhouse gas emissions that accompany industrial production processes, such as chemical nitration / denitration processes (producing N2O), solvent evaporation (producing CH4), tail gas from synthetic ammonia processes (containing CH4), and carbonate decomposition processes (calcination of limestone to produce cement / lime, mainly producing CO2).

[0052] 2) Fuel combustion sources: refers to emissions generated during the fuel combustion process, such as gas / oil boilers (producing CO2, CH4, and N2O), coal-fired boilers (producing CO2, with additional N2O generated from nitrogen-containing fuels), and industrial furnaces (producing CO2, and small amounts of CH4 and N2O).

[0053] 3) Waste treatment sources: refers to the emissions from the waste treatment process in the park, such as anaerobic wastewater treatment (producing CH4 and a small amount of CO2), anaerobic digestion of biological sludge (producing CH4 and a small amount of CO2), and temporary garbage dumping (producing a small amount of CH4 and CO2).

[0054] 4) Mobile sources: refers to emissions from mobile equipment within the park, such as gas / diesel freight vehicles (producing CO2 and CH4) and forklifts (producing CO2 and CH4).

[0055] 5) Biosphere source: refers to carbon exchange between vegetation and soil in the park, such as CO2 absorption by green vegetation photosynthesis, CO2 emission by plant / soil respiration, and the emission of CH4 and N2O can be ignored (<1% of total emissions).

[0056] (2) Determination of source characteristic emission ratio.

[0057] The characteristic ratios of CH4 / CO2 and N2O / CO2 for each source type (unit: mmol / mol) were determined through "on-site sampling + literature calibration" and used as core parameters for the linear mixture model. 1) On-site sampling: Gas samples were collected from typical emission sources in the park (such as exhaust outlets of the nitration workshop, boiler chimneys, and anaerobic ponds of the sewage treatment plant). The concentrations of CO2, CH4, and N2O were measured using a high-precision gas analyzer, and the real-time emission ratio was calculated.

[0058] 2) Literature calibration: Refer to the emission ratio statistics of industrial parks in the same industry to correct the on-site sampling data and ensure the representativeness of the parameters.

[0059] Use the emission ratio statistics of industrial parks in the same industry to provide a range value, and then correct the on-site sampling data based on this range value.

[0060] 3) Parameter storage: Establish a "source category - feature ratio" database, which supports dynamic updates based on park type (chemical, steel, food).

[0061] (iv) Attribution of multi-species linear mixture model.

[0062] Based on the measured flux and source characteristic ratios of three gases—CO2, CH4, and N2O—an improved linear mixing model is constructed to achieve precise decomposition of total flux into specific emission source categories. (1) Core assumptions of the model.

[0063] Based on the actual characteristics of the emission sources in the industrial park, three key assumptions are set: 1) Flux conservation assumption.

[0064] Total CO2 flux in the park ( ) by industrial process source CO2 flux ( ), CO2 flux from fuel combustion sources ( ), CO2 flux from waste treatment sources ( ), mobile source CO2 flux ( Biosphere source CO2 flux ( )composition.

[0065] 2) Non-biological source hypothesis.

[0066] CH4 originates solely from industrial processes, fuel combustion, waste treatment, and mobile sources; contributions from biosphere sources are negligible.

[0067] 3) Finite source contribution assumption.

[0068] N2O mainly comes from industrial process sources (such as nitration and denitrification) and fuel combustion sources (such as high-temperature combustion in boilers), with waste treatment sources and mobile sources contributing less than 5%.

[0069] (2) Solve the simultaneous equations.

[0070] Based on the above assumptions, a simultaneous equation for flux balance of the three types of gases is established, and the flux contribution of each source is obtained by algebraic solution.

[0071] 1) CO2 total flux equation:

[0072] 2) CH4 total flux equation:

[0073] 3) N2O total flux equation:

[0074] in, The "species y / CO2 characteristic emission ratio" representing "source x"; This represents the characteristic CH4 / CO2 emission ratio from industrial process sources; This indicates the characteristic CH4 / CO2 emission ratio of a fuel combustion source; This indicates the characteristic CH4 / CO2 emission ratio of waste treatment sources; This represents the characteristic CH4 / CO2 emission ratio of a mobile source; This represents the characteristic N2O / CO2 emission ratio from industrial process sources; This represents the characteristic N2O / CO2 emission ratio of a fuel combustion source; This indicates the total CH4 flux in the park; This represents the total N2O flux in the park.

[0075] For example, =0.2 indicates that the ratio of N2O / CO2 from the industrial process source is 0.2. =6.5 indicates that the waste treatment source CH4 / CO2 = 6.5.

[0076] (3) Output the results.

[0077] The model outputs two core results after solving, supporting the park's emission reduction decisions: 1) Source contribution percentage: The percentage of each source that contributes to the flux of CO2, CH4, and N2O (e.g., industrial process sources account for 37.1% of total CO2 emissions and 100% of total N2O emissions). 2) Spatial distribution heat map: Combine GPS positioning data from various platforms to generate a carbon emission hotspot distribution map of the park (such as high N2O flux areas around the nitration workshop and high CH4 flux areas around the wastewater treatment plant).

[0078] Next, the implementation steps of this technical solution will be explained using a specific application scenario.

[0079] Taking a medium-sized chemical industrial park as an example, covering an area of ​​3 km², its core facilities include one nitration process workshop, two natural gas boilers, one anaerobic wastewater treatment plant, and 80 gas-powered freight trucks. Figure 2 The diagram illustrates the implementation steps of this embodiment's technology in the aforementioned park, specifically including: (a) Platform deployment and equipment calibration.

[0080] In this implementation step, multi-platform monitoring equipment is deployed, and CO2, CH4, N2O analyzers and eddy covariance systems are calibrated.

[0081] The aerobatic platform is a 100m³ tethered aerobatic vehicle with a 100m tether rope. It is equipped with a ground mooring device, an open-circuit eddy covariance system and a gas analyzer, and is deployed 80m above the geometric center of the park for aerial observation.

[0082] The UAV is equipped with a miniaturized closed-circuit eddy covariance system and is designed with 3 survey routes, covering the nitrification process workshop (3 fixed points), the anaerobic pool of the sewage treatment plant (2 fixed points), and the gas pipeline (2 fixed points), with a fixed point spacing of 300-500m.

[0083] The ground station is equipped with an open-circuit eddy covariance system and a gas analyzer, and is deployed at four stations: "100m east of the boiler plant" (combustion source monitoring), "50m north of the sewage treatment plant" (waste treatment source monitoring), "west boundary of the industrial park" (diffusion monitoring), and "2km outside the industrial park in farmland" (background monitoring).

[0084] Standard gas was introduced into the analyzer to calibrate the concentration measurement deviation; the sampling flow rate of the eddy covariance system was calibrated using a gas flow calibration device; and the clocks of all platforms were synchronized via an NTP server to avoid data misalignment caused by time deviation.

[0085] NTP servers are network protocols used to synchronize computer time by correcting time using Coordinated Universal Time (UTC).

[0086] (ii) Multi-platform monitoring and data collection.

[0087] In this implementation step, three types of gas flux and meteorological data are collected, transmitted to the cloud for processing, and background values ​​are subtracted.

[0088] The floating platform has started continuous monitoring mode and automatically stores one set of data every 30 minutes. , , The data also records meteorological parameters such as wind speed and wind direction. , , These represent net CO2 flux, net CH4 flux, and net N2O flux, respectively. The drone flies twice a day, at 8:00 AM and 2:00 PM, staying at each fixed point for 30 minutes. After completing the throughput collection, it automatically uploads the data to the cloud. The ground station operates continuously for 24 hours, summarizing data once every hour (including two 30-minute throughput values), and supports dual storage of local backup and cloud storage.

[0089] Invalid data is removed in real time. For example, if sampling is interrupted by strong winds (wind speed > 8 m / s) during drone flight, it is automatically marked as "invalid". Background stations are checked daily. If the fluctuation range is exceeded, check for instrument malfunction.

[0090] (III) List Construction.

[0091] In this implementation step, a high-resolution inventory of the park is constructed, and the characteristic ratios of CH4 / CO2 and N2O / CO2 for each source are determined.

[0092] Collect basic data. For example, the park's energy consumption over 12 months (natural gas 1.2 × 10⁻⁶). 5 m³), process output (nitration product 5×10 4 t (tons)), sewage treatment capacity (3×10 6 m³).

[0093] Measured characteristic ratio. For example, sampling the exhaust vent of the nitration workshop and measuring... =0.22; Samples were taken from the anaerobic tank of the wastewater treatment plant and measured... =6.5.

[0094] (iv) Model attribution.

[0095] In this implementation step, a multi-species linear mixture model is substituted to solve for the flux contribution of each source to CO2, CH4, and N2O.

[0096] Input the measured data. (The park data for a certain time period is missing.) =14μmol m - ²s - ¹(micromoles / square meter / second) =29 nmol m - ²s - ¹(nanomoles / square meter / second) =2.1 nmol m - ²s - ¹(nanomoles / square meter / second); Substitute the characteristic ratio parameter. =0.22、 =0.8、 =6.5、 =1.2; Solution results. =5.2μmol m - ²s - ¹(37.1%) =4.8μmol m - ²s - ¹(34.3%) =2.5μmol m- ²s - ¹(17.9%) =1.0 μmol m - ²s - ¹(7.1%) =0.5μmol m - ²s - ¹ (3.6%); CH4 mainly comes from waste treatment sources (62.3%), while N2O comes entirely from industrial process sources (100%).

[0097] (v) Output the results of greenhouse gas source tracing in the park.

[0098] This embodiment has the following technical advantages: (1) Better comprehensive coverage. Existing single-point monitoring can only cover an area of ​​≤1km² (square kilometers). This embodiment achieves full-area monitoring without blind spots in a 1-5km² park through multi-platform collaboration. At the same time, it takes into account "full-area continuous monitoring + key mobile monitoring". The spatial resolution is improved to 100m level, which can accurately locate hotspots such as CH4 leakage and N2O process emissions, solving the problems of "incomplete coverage and inaccurate positioning" in existing technologies.

[0099] (2) Higher source tracing accuracy. Existing technologies are based on single-species attribution of CO2. This embodiment combines the source-specific ratios of CH4 / CO2 and N2O / CO2 with an improved linear mixture model, which can separately separate the source contributions of CH4 and N2O, avoiding the omission of powerful greenhouse gases and resulting in emission reduction decision bias.

[0100] (3) Faster time response. The existing "bottom-up" list method has data lag (annual / quarterly level). This embodiment realizes throughput calculation and tracing with a time resolution of 30 minutes, which can capture dynamic emissions such as changes in production load and instantaneous leakage.

[0101] (4) Greater universality. Existing models are mostly adapted to urban traffic and civilian sources, but cannot cover industrial process sources and waste treatment sources in industrial parks; this embodiment is designed for core sources in industrial parks, and the characteristic ratio parameters can be flexibly adjusted through on-site sampling, making it suitable for different types of industrial parks such as chemical, steel, and food processing parks, and thus has wider applicability.

[0102] (5) Controllable implementation costs. All platforms use general-purpose equipment (no customization required), such as airships, industrial drones, and commercial gas analyzers, which are easy to procure and have low maintenance costs; the data processing flow is standardized, eliminating the need for complex algorithm development and lowering the threshold for implementation in the park.

[0103] Example 2 This embodiment discloses a multi-platform collaborative traceability system for carbon emissions at the park scale.

[0104] A multi-platform collaborative traceability system for carbon emissions at the park scale includes: The data acquisition module is configured to monitor the fluxes of greenhouse gases in the park, including CO2, CH4 and N2O, using a multi-platform collaborative monitoring mode. The preprocessing module is configured to preprocess the raw data obtained from monitoring to obtain the actual emission fluxes of CO2, CH4 and N2O in the park. The emission source classification and characteristic ratio calculation module is configured to: classify the greenhouse gas emission sources in the park into multiple core categories based on the emission characteristics of CO2, CH4 and N2O, and determine the CH4 / CO2 and N2O / CO2 characteristic ratios for each core category of emission sources based on the actual emission flux; The simultaneous solution module is configured to: establish simultaneous equations for the flux balance of three greenhouse gases in the park, namely CO2, CH4 and N2O, based on the actual emission fluxes of CO2, CH4 and N2O in the park; obtain the flux contribution of each emission source through algebraic solution; and realize the source tracing of carbon emissions in the park.

[0105] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0106] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A multi-platform collaborative source tracing method for carbon emissions at the industrial park scale, characterized in that, Includes the following steps: A multi-platform collaborative monitoring mode is adopted to monitor the fluxes of greenhouse gases in the park, including CO2, CH4 and N2O; The raw data obtained from monitoring are preprocessed to obtain the actual emission fluxes of CO2, CH4 and N2O in the park. Based on the emission characteristics of CO2, CH4 and N2O, the greenhouse gas emission sources in the park are divided into multiple core categories, and based on the actual emission flux, the characteristic ratios of CH4 / CO2 and N2O / CO2 for each core category of emission sources are determined. Based on the actual emission fluxes of CO2, CH4, and N2O in the park, a simultaneous equation for flux balance of the three greenhouse gases in the park is established. The flux contribution of each emission source is obtained through algebraic solution, thus realizing the source tracing of carbon emissions in the park.

2. The multi-platform collaborative source tracing method for carbon emissions at the park scale as described in claim 1, characterized in that, The multi-platform collaborative monitoring mode includes an airborne monitoring platform, an unmanned aerial vehicle (UAV) monitoring platform, and a ground-based monitoring platform, wherein: The floating monitoring platform is deployed above the core emission area of ​​the park, covering the entire park area, and realizes continuous monitoring of three types of gases: CO2, CH4 and N2O. The drone monitoring platform is used for mobile monitoring of key emission sources. It can collect the fluxes of three types of gases, CO2, CH4 and N2O, at multiple fixed points in a single flight. The ground-based monitoring platform uses multiple fixed stations to collect fluxes of three types of gases: CO2, CH4, and N2O. These fixed stations cover stations near emission sources, park boundaries, and background stations.

3. The multi-platform collaborative source tracing method for carbon emissions at the park scale as described in claim 1, characterized in that, The raw data obtained from monitoring undergoes preprocessing, specifically including: Outliers are removed, including instantaneous spikes caused by equipment interference, meteorological interference data, and insufficient turbulence data; Perform standardization processing on the raw data; Spatiotemporal matching and background subtraction were performed to obtain the actual emission fluxes of CO2, CH4 and N2O in the park.

4. The multi-platform collaborative source tracing method for carbon emissions at the park scale as described in claim 1, characterized in that, The park's greenhouse gas emission sources are categorized into several core types, specifically including industrial process sources, fuel combustion sources, waste treatment sources, mobile sources, and biosphere sources, among which: The industrial process source is an emission source that generates greenhouse gas emissions through industrial production processes; The fuel combustion source is an emission source that generates greenhouse gas emissions through the fuel combustion process; The waste treatment source is an emission source that generates greenhouse gas emissions through the waste treatment process in the park; The mobile source is an emission source that generates greenhouse gas emissions through mobile devices within the park; The biosphere source refers to the emission source that generates greenhouse gas emissions through carbon exchange between vegetation and soil within the park.

5. The multi-platform collaborative source tracing method for carbon emissions at the park scale as described in claim 1, characterized in that, The characteristic ratios of CH4 / CO2 and N2O / CO2 for each core category of emission sources were determined by combining on-site sampling with literature calibration. Specifically, these ratios included: On-site sampling was conducted on various core emission sources within the park to obtain sampled gas samples. The concentrations of CO2, CH4, and N2O in the sampled gas samples were measured, and the real-time emission ratio was calculated. The real-time emission ratio is corrected by referring to the statistical data of emission ratios in similar industrial parks to ensure the accuracy of the parameters; Establish a source category-feature ratio database that supports dynamic updates based on park type.

6. The multi-platform collaborative source tracing method for carbon emissions at the park scale as described in claim 1, characterized in that, Before establishing the simultaneous equations for the flux balance of three types of greenhouse gases in the park—CO2, CH4, and N2O—three key assumptions are also included: Flux conservation assumption: Total CO2 flux in the park CO2 flux from industrial process sources CO2 flux from fuel combustion sources CO2 flux from waste treatment sources Mobile source CO2 flux and biosphere source CO2 flux composition; Non-biological source hypothesis: CH4 originates only from industrial processes, fuel combustion, waste treatment, and mobile sources, ignoring contributions from biosphere sources; Limited source contribution assumption: N2O mainly comes from industrial process sources and fuel combustion sources.

7. The multi-platform collaborative source tracing method for carbon emissions at the park scale as described in claim 6, characterized in that, The established simultaneous equations for the flux balance of three types of greenhouse gases in the park—CO2, CH4, and N2O—specifically include: The equation for the total CO2 flux is as follows: ； The total flux equation for CH4 is as follows: ； The total flux equation for N2O is as follows: ； in, The species y representing source x has a CO2 characteristic emission ratio; This represents the characteristic CH4 / CO2 emission ratio from industrial process sources; This indicates the characteristic CH4 / CO2 emission ratio of a fuel combustion source; This indicates the characteristic CH4 / CO2 emission ratio of waste treatment sources; This represents the characteristic CH4 / CO2 emission ratio of a mobile source; This represents the characteristic N2O / CO2 emission ratio from industrial process sources; This represents the characteristic N2O / CO2 emission ratio of a fuel combustion source; This indicates the total CH4 flux in the park; This represents the total N2O flux in the park.

8. The multi-platform collaborative source tracing method for carbon emissions at the park scale as described in claim 1, characterized in that, The flux contribution of each emission source is obtained by algebraic solution, specifically including the source contribution ratio and spatial distribution heat map, where the source contribution ratio includes the percentage contribution of each source to the flux of CO2, CH4 and N2O.

9. The multi-platform collaborative source tracing method for carbon emissions at the park scale as described in claim 2, characterized in that, The locations of the emission source proximity stations, park boundary stations, and background stations are as follows: The emission source proximity stations are deployed at a first predetermined distance from the boundary of each emission source to monitor the emission flux of the emission sources. The park boundary station is deployed at a second predetermined distance outside the park to monitor the outward diffusion flux of gas; The background station is located in an area outside the park where there are no industrial or agricultural activities, at a third predetermined distance from the park boundary, and is used for background value subtraction.

10. A multi-platform collaborative traceability system for carbon emissions at the park scale, characterized in that: include: The data acquisition module is configured to monitor the fluxes of greenhouse gases in the park, including CO2, CH4 and N2O, using a multi-platform collaborative monitoring mode. The preprocessing module is configured to preprocess the raw data obtained from monitoring to obtain the actual emission fluxes of CO2, CH4 and N2O in the park. The emission source classification and characteristic ratio calculation module is configured to: classify the greenhouse gas emission sources in the park into multiple core categories based on the emission characteristics of CO2, CH4 and N2O, and determine the CH4 / CO2 and N2O / CO2 characteristic ratios for each core category of emission sources based on the actual emission flux; The simultaneous solution module is configured to: establish simultaneous equations for the flux balance of three greenhouse gases in the park, namely CO2, CH4 and N2O, based on the actual emission fluxes of CO2, CH4 and N2O in the park; obtain the flux contribution of each emission source through algebraic solution; and realize the source tracing of carbon emissions in the park.

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