Site selection method and system for atmospheric greenhouse gas monitoring station based on multi-technology fusion and storage medium

Abstract

The present application relates to greenhouse gas monitoring technical field, more specifically, a kind of atmospheric greenhouse gas monitoring site selection method and system based on multi-technology fusion and storage medium.The site selection method, preliminary processing is carried out to multiple-source data by data research module, and basic database is formed;Through point preliminary screening module, accurate screening module, field reconnaissance module, scientific demonstration module and various different technical means, the site selection of greenhouse gas monitoring station is implemented, and the site selection step is clear, and the site selection process is standardized.The corresponding storage medium is convenient for storing the running program of the above method, and is applied to related equipment.In prior art, site selection work is mostly realized by artificial research, data arrangement, point inspection and subjective decision, and the whole decision-making process is inefficient, and the site selection may be poor due to insufficient information acquisition or the knowledge level of decision maker, and the present application can overcome the defect, reduce the artificial process and improve the site selection accuracy.

Description

Technical Field

[0001] This invention relates to the field of greenhouse gas monitoring technology, and more specifically to a method, system, and storage medium for selecting atmospheric greenhouse gas monitoring sites based on the integration of multiple technologies. Background Technology

[0002] Urban atmospheric greenhouse gas monitoring primarily reflects anthropogenic greenhouse gas emissions in cities by monitoring their concentrations. This differs from the air quality monitoring commonly known to the public. Air quality monitoring targets six air pollutants: PM10, PM2.5, NO2, SO2, CO, and O3. It reflects the city's air pollution level through pollutant concentration monitoring data, and the main function of air quality monitoring stations is to guide urban air pollution prevention and control. Urban atmospheric greenhouse gas monitoring, on the other hand, targets the concentrations of greenhouse gases such as CO2 and CH4. The purpose of this monitoring is to calculate and assess the city's greenhouse gas emissions based on the observed concentration data, obtaining accurate emission data for the city and providing data support for my country's international compliance negotiations.

[0003] The principles for the deployment of urban atmospheric greenhouse gas monitoring and air quality monitoring stations also differ. Air quality monitoring station construction primarily considers the concentration distribution of pollutants and aims to cover the entire built-up area of ​​the city. The deployment of urban atmospheric greenhouse gas monitoring stations, on the other hand, must be based on obtaining accurate emission data, comprehensively considering factors such as station spatial coverage and the contribution of urban carbon emissions to the monitoring stations. This ensures that the selected locations can effectively reflect the current status and transmission characteristics of greenhouse gas emissions in the urban area, maximizing the monitoring role of each station, avoiding redundant station construction, and reducing construction costs.

[0004] The location of urban atmospheric greenhouse gas monitoring stations involves numerous considerations and requirements. For example, the "Technical Guidelines for the Location of Urban Atmospheric Greenhouse Gas Monitoring Stations (First Edition)" outlines principles for station placement, including representativeness, comparability, and comprehensiveness. Representativeness requires that monitoring stations objectively reflect the levels and spatiotemporal variations of atmospheric greenhouse gases within a specific spatial range, meeting the needs of assessing urban greenhouse gas emissions. Comparability requires that monitoring stations of the same type be set up under as consistent conditions as possible, including placement principles, monitoring methods, quality control, and quality assurance, ensuring data comparability between different stations. Comprehensiveness requires consideration of comprehensive environmental factors such as urban topography and meteorology, as well as socio-economic characteristics such as energy structure and industrial layout, reflecting the city's main greenhouse gas emissions.

[0005] Furthermore, while existing technologies offer some site deployment guidelines, such as the "Technical Guidelines for the Deployment of Urban Atmospheric Greenhouse Gas Monitoring Sites (First Edition)," which includes schemes for urban atmospheric greenhouse gas monitoring, aiming to obtain greenhouse gas emission fluxes for the entire urban area through model analysis and inversion methods, obtaining accurate emission figures presupposes obtaining an accurate urban basic carbon emission inventory. This requires monitoring key pollution sources in key industries, such as power, steel, and coal mining, to identify the emission data of major urban emitters, which can significantly improve the quality of the initial urban emission inventory. However, the aforementioned methods rely on manual data collection and analysis for site selection, greatly increasing labor and time costs. Moreover, the uncertainty of data and results increases the risk of site selection errors, failing to meet the efficient decision-making needs of modern society.

[0006] Existing technologies have also proposed optimization methods for monitoring point deployment to address the aforementioned shortcomings. For example, Chinese patent CN201911063520.6 discloses a method for optimizing the layout of online detectors for atmospheric particulate matter concentration in ports. This patent discloses an online, real-time, and continuous detection method for PM2.5, PM10, and TSP concentrations in the port's atmospheric environment. The method includes the following steps: dividing the port into planar grids; detecting atmospheric particulate matter concentrations in the vertical space of the port grids; constructing the worst-affected surface with the most severe particulate matter pollution in the port space; spatial aggregation optimization of the port grids; and selecting the worst-affected surface of one grid in each aggregated grid set as the optimal layout scheme for deploying online detectors. This technical solution can aggregate and optimize the port grids based on the correlation between the spatial particulate matter concentrations of adjacent grids, and determine the optimal deployment location of online detectors by comprehensively considering the spatial relationships of different grids on the worst-affected surface with the most severe particulate matter pollution. This can reduce the number of monitoring points while ensuring the effectiveness of the detection results, thus improving the input-output efficiency of port pollution monitoring.

[0007] However, this patented technology is only a solution proposed for a small, localized area like a port. In such a specific area, the geographical environment, atmospheric conditions, and regional differences in air quality are relatively simple factors to consider, and the locations of monitoring points are also relatively ideal. Therefore, the main consideration is only the degree of air pollution under these conditions. However, if the monitoring area is expanded to the entire city or region, or even a larger area, the aforementioned geographical environment, atmospheric conditions, and regional differences in air quality become much more complex. The patented solution clearly does not consider the impact of these differences and cannot be applied to the requirements of monitoring point deployment over a large area.

[0008] In summary, the site selection of greenhouse gas monitoring stations is an important prerequisite for the construction of urban carbon monitoring networks. The design of greenhouse gas monitoring site selection schemes will directly affect the performance of urban carbon monitoring networks, and thus affect the construction of urban carbon monitoring assessment technology and methodology systems. Moreover, the corresponding site selection methods and systems are still relatively lacking in the current technology.

[0009] Furthermore, although the Ministry of Ecology and Environment's "Pilot Program for Carbon Monitoring and Assessment," and the China National Environmental Monitoring Centre's "Technical Reference Program for Pilot Monitoring of Urban Atmospheric Greenhouse Gases and Marine Carbon Sequestrations," "Guidelines for Quality Management and Quality Control of Pilot Monitoring of Urban Ambient Air Greenhouse Gases (Trial)," and "Technical Guidelines for the Layout of Urban Atmospheric Greenhouse Gas Monitoring Sites (First Edition)" all provide important guidance on the selection of greenhouse gas monitoring site locations and clarify five principles for monitoring site layout, the existing technologies still have the following shortcomings:

[0010] 1. Currently, there is no detailed explanation on how to implement the site selection of greenhouse gas monitoring stations through various technical means such as satellite remote sensing, field monitoring, numerical simulation, and on-site surveys; and different technical means with different focuses may lead to different site selection results, which may pose a risk of site selection errors.

[0011] 2. Currently, the selection of sites for urban atmospheric greenhouse gas monitoring stations mainly relies on the collection and analysis of data by humans. This involves a large workload, complex tasks, and significant human and time costs.

[0012] With the increasing sophistication of big data technology, there is an urgent need for a method that can comprehensively utilize big data technology to select sites for atmospheric greenhouse gas monitoring stations, in order to make up for the deficiencies in existing technologies. Summary of the Invention

[0013] This solution aims to overcome the shortcomings and defects of the existing technologies mentioned above, and to provide a site selection method and storage medium for atmospheric greenhouse gas monitoring stations based on multi-technology integration that can meet the requirements of large areas such as cities.

[0014] This solution achieves the above objectives through the following technical means:

[0015] This scheme is a site selection method for atmospheric greenhouse gas monitoring stations based on multi-technology integration, which mainly includes the following steps:

[0016] S1 data survey: collect basic information about the area where the site is located, and obtain a database that best reflects local characteristics;

[0017] S2 Initial Screening: Based on data from the database and carbon emission data obtained from short-term field monitoring, a sufficient number of sites that meet the monitoring requirements are initially screened. In one or more embodiments of the present invention, the sites are also described as stations; the initial screening is also described as preliminary selection. In one or more embodiments of the present invention, the sites obtained from the initial screening in step S2 are described as preliminary selected sites in some embodiments of the present invention.

[0018] S3 involves precise screening, which uses model analysis technology to precisely screen out the locations most sensitive to the main greenhouse gas emission sources in the selected area from the initial screening locations. In some embodiments of the present invention, the locations screened in step S3 are described as selected locations.

[0019] S4 conducted on-site surveys to ensure that the site conditions of the selected locations met the requirements for site construction and layout; and selected qualified locations as alternative sites.

[0020] S5 scientific verification involves numerically simulating greenhouse gas concentrations at candidate sites and comparing them with satellite remote sensing data to identify suitable candidate sites.

[0021] First, we conduct research to collect basic information about the region, including its geographical location, administrative divisions, topography, population distribution, industrial and energy structure, current carbon emissions, and future urban development plans. This comprehensive analysis of the region's natural and social characteristics aims to create a database that best reflects its unique features.

[0022] The database in step S1 specifically includes a basic overview database of the site selection area and a key information survey and analysis database. The key information survey and analysis database includes at least five dimensions: meteorological conditions, land use, spatiotemporal distribution of greenhouse gas emission sources, greenhouse gas concentration distribution characteristics, and site information.

[0023] The next step is to conduct an initial screening of sites, further investigate important data closely related to the selection of carbon monitoring sites, including meteorological conditions, land use, emission source data, greenhouse gas satellite remote sensing monitoring data, greenhouse gas ground monitoring data, etc., and combine the carbon emission data obtained from short-term field monitoring to supplement the spatiotemporal characteristics of carbon emissions, and initially screen out a sufficient number of sites that meet the monitoring requirements, thus completing the initial screening of sites.

[0024] The initial screening of points in step S2 includes at least the following parallel conditions:

[0025] Based on the survey results of step S1, S21, by analyzing meteorological conditions such as wind speed and wind direction, the prevailing wind direction of the region and the upwind and downwind directions of the prevailing wind direction are determined.

[0026] S22 uses geographic information system software to divide the selected site area into grids on the map software, imports the spatial distribution data of greenhouse gas emission sources obtained through greenhouse gas emission inventories into the geographic information system software, presents the distribution of emission sources on the grid map, and thus identifies the areas that are more affected by each emission source.

[0027] S23 uses satellite remote sensing and ground monitoring to analyze the concentration of greenhouse gases such as CO2, and identifies high-, medium-, and low-value areas of CO2 and other greenhouse gas emissions in the region.

[0028] Based on the above conditions, map software was used to select a certain range of sampling points in the urban area and the areas upwind and downwind of the prevailing wind direction, which are significantly affected by emission sources. Based on the distribution of emission source points, all points that meet the conditions were selected within the grid of the sampling area.

[0029] Next, a precise screening process is conducted. Based on the initial screening results, model analysis techniques are used to precisely screen the selected sites. The model analysis includes sensitivity analysis, flux footprint simulation, cluster analysis, and observation system simulation experiments. From the initial candidate sites, the sites most sensitive to the city's main greenhouse gas emission sources are precisely selected, and the site layout is optimized. This aims to significantly reduce the uncertainty in the assimilation and inversion of urban greenhouse gas emissions and clarify the precise screening results.

[0030] The model analysis techniques specifically include:

[0031] S31 Sensitivity Analysis:

[0032] S311 performs trajectory clustering analysis on the initially selected points by coupling the meteorological field with the backward trajectory model;

[0033] S312 performs footprint contribution analysis on the initially selected locations by coupling the meteorological field with the footprint contribution model;

[0034] S313 integrates trajectory clustering analysis and footprint contribution analysis to identify whether the initially selected locations can capture the main carbon emission information;

[0035] S32 Assimilation and Inversion Simulation Analysis:

[0036] S321 Based on the survey results of step S1, identify the sources of pollutant emissions, including greenhouse gases, within the area where the site is located;

[0037] S322 simulates the concentration distribution of greenhouse gases in the selected site area by coupling meteorological fields with regional air quality models, and by coupling regional air quality models with photosynthesis and respiration models.

[0038] Based on the survey results of step S1, S323 constructs a column concentration dataset of greenhouse gases in the selected site area, and combines it with emission information of pollutants other than greenhouse gases to establish an input model adapted to the atmospheric chemistry model.

[0039] Based on the survey results of step S1, and combined with the greenhouse gas concentrations monitored on the ground and the input model described in S323, S324 performs assimilation and inversion on the basis of existing emission sources to obtain dynamic high-resolution greenhouse gas emission sources.

[0040] S325 updates the dynamic high-resolution greenhouse gas emission sources to step S321 and re-simulates the concentration distribution of greenhouse gases in the site selection area through step S322;

[0041] S326 compares the greenhouse gas concentration distribution in the selected area obtained in step S325 with the greenhouse gas concentration distribution characteristics obtained in step S1 to evaluate the impact of the site selection scheme on greenhouse gas emissions in the assimilation and inversion process.

[0042] By combining steps S31 and S32, the sites most sensitive to the main greenhouse gas emission sources in the selected area are precisely screened.

[0043] The specific process of the sensitivity analysis in step S31 is as follows:

[0044] First, high-precision meteorological data constructed by the mesoscale weather model WRF is coupled with the backward trajectory model HYSPLIT. Cluster analysis is performed on the backward air mass trajectories at different heights in different seasons at the initial selection points. The percentage of airflow direction at different heights at the initial selection points is calculated to identify whether the initial selection points can capture the main carbon emission information and to determine the representativeness and sensitivity of the initial selection points and the height of the sampling port.

[0045] Then, the high-precision meteorological data constructed by the mesoscale weather model WRF is coupled with the STILT footprint contribution model to simulate and calculate the footprint weight of the influence of all areas passed by the airflow on the concentration of preset monitoring points at different seasons and sampling heights before the atmosphere finally moves to the initial site under the drive of the meteorological field. This identifies whether the initial site can capture the main carbon emission information and determines the representativeness and sensitivity of the initial site and the sampling port layout height.

[0046] By analyzing the trajectory clustering and footprint contribution of the initial selection sites, we can identify whether the initial selection sites can capture the main carbon emission information, clarify whether different initial selection sites and sampling port placement heights are representative and sensitive, and further screen the initial selection sites.

[0047] The specific process of the assimilation and inversion simulation analysis in step S32 is as follows:

[0048] <1> We collected and organized pollutant emission sources within the study area, coupled high-precision meteorological data constructed using the mesoscale weather model WRF with the regional air quality model CMAQ, and constructed a terrestrial ecosystem photosynthesis and respiration model VPRM based on hourly simulated meteorological parameters. This model was then coupled with the regional air quality model CMAQ to improve its description of CO2 source emissions, atmospheric transport and diffusion processes, and the impact of vegetation photosynthesis and respiration. Greenhouse gas concentrations were simulated, and the results were compared with the greenhouse gas concentration distribution characteristics in the data survey database.

[0049] <2> CO2 concentration data from GOSAT and OCO-2 satellites were collected and compared to construct a CO2 column concentration dataset based on satellite remote sensing. Combining satellite pixel-scale CO2 column concentration with monitoring data from a ground-based validation system, the impact of non-uniform land surface was simulated and analyzed. Using pollutant concentration data other than CO2, spatial scale effects and temporal matching relationships were established between multi-source satellite land environmental parameter data and atmospheric chemical transport model computational grids, creating an input model adapted to atmospheric chemical models. Addressing the spatiotemporal discontinuities between conventionally observed ground-based CO2 concentrations and satellite-retrieved CO2 column total data, a CO2 atmospheric assimilation inversion system was established based on the influence of factors such as optimal ensemble number, spurious long-range correlations, model errors, and observation errors on sample divergence. Correction inversion was performed on existing emission sources to obtain an optimized dynamic high-resolution CO2 emission source inventory. The corrected emission sources were then input into the system. <1> In the model, greenhouse gas concentrations are simulated, and the results are compared with the greenhouse gas concentration distribution characteristics in the data survey module database;

[0050] based on <1> and <2> The model simulation results were compared with observations to assess the impact of the site selection scheme on the assimilation and inversion of greenhouse gas emissions. It was found that the uncertainty of carbon emission assimilation and inversion was reduced by 30-60%, and the site selection scheme was further optimized to obtain the best sites.

[0051] Secondly, on-site surveys are conducted to assess the surrounding environment, sampling port location requirements, and tower platform conditions to ensure that the selected site conditions meet the requirements for station construction. At the same time, on-site monitoring is carried out based on technologies such as UAV remote sensing monitoring and mobile monitoring to evaluate the effectiveness of the selected site.

[0052] The S4 field survey includes S41 field investigation information and S42 on-site monitoring data;

[0053] The S41 field investigation information specifically includes: emission source information, capture space information, geological safety information, electromagnetic interference information, site topography information, on-site construction information, operation and maintenance management information, and emergency management information.

[0054] The S42 field monitoring data specifically includes: mobile monitoring and UAV remote sensing monitoring.

[0055] Finally, scientific verification is required, including numerical simulation of greenhouse gas concentrations at candidate sites and comparison with satellite remote sensing data; comparative verification of the differences in setting conditions for different candidate sites; and comparative verification of the urban and rural spatial pattern changes of candidate sites over the next five years. This will assess whether the candidate sites are representative, comprehensive, comparable, and forward-looking.

[0056] The scientific argumentation includes S51 representativeness and holistic analysis, which are detailed below:

[0057] S511 Based on the emission source information in step S1, emission simulations of candidate sites are performed at intervals by coupling meteorological fields and chemical models, and the greenhouse gas distribution and concentration characteristics in the qualitative / quantitative analysis database are compared.

[0058] S512 Based on the dynamic high-resolution greenhouse gas emission sources in step S3, emission simulations of candidate sites are performed at intervals by coupling meteorological fields and chemical models, and the greenhouse gas distribution and concentration characteristics in the qualitative / quantitative analysis database are compared.

[0059] By comparing the simulation results of S513 with those of S511 and S512 before and after the emission source replacement, it is determined whether the sampling points can objectively reflect the atmospheric greenhouse gas levels and spatiotemporal variation patterns within a certain spatial range, whether they can reflect the main greenhouse gas emission status, and whether they meet the needs of assessing greenhouse gas emissions.

[0060] The S5 scientific argumentation also includes S52 comparability analysis and S53 prospective analysis;

[0061] The comparability analysis of S52 is as follows:

[0062] Compare the setting conditions of different candidate sites, such as the surrounding area and the height of the sampling port from the ground, including whether the horizontal plane around the sampling port of the site meets the requirement of open collection space, whether the height of the sampling port from the ground is between 50 and 100m, and whether the difference in sampling height among candidate sites does not exceed 10m.

[0063] The specific forward-looking analysis of S53 is as follows:

[0064] Based on the urban planning information in step S1, clarify the future spatial pattern change trend of the candidate sites and examine whether the candidate sites can take into account the future spatial pattern change trend.

[0065] Based on the above method, the present invention also provides a multi-technology fusion-based atmospheric greenhouse gas monitoring station site selection system, comprising:

[0066] The data research module is used to collect basic information about the area where the site is located and to obtain a database that best reflects local characteristics.

[0067] The site screening module, based on data from the database and combined with carbon emission data obtained from short-term field monitoring, initially screens out a sufficient number of sites that meet the monitoring requirements; in one or more embodiments of the present invention, the sites are also described as stations; the initial screening is also described as preliminary selection; in one or more embodiments of the present invention, the sites obtained from the initial screening are described as preliminary selected sites in some embodiments of the present invention.

[0068] The precise screening module uses model analysis technology to precisely screen out the locations most sensitive to the main greenhouse gas emission sources in the selected area from the initial screening locations; in some embodiments of the present invention, the locations screened in step S3 are described as selected locations.

[0069] The field survey module is used to collect survey information, determine whether the site conditions of the selected location meet the requirements for site construction and layout, and screen out the qualified locations as candidate locations.

[0070] The scientific verification module compares and verifies the greenhouse gas concentrations at candidate sites with satellite remote sensing data through numerical simulation, and outputs candidate sites that meet the criteria.

[0071] The visualization module is used to output the content of the data research module, the initial site screening module, the precise screening module, the field survey module, and the scientific demonstration module, and to express some of the content through map display.

[0072] This solution further provides a storage medium that stores a computer program for implementing the aforementioned method for selecting atmospheric greenhouse gas monitoring sites based on multi-technology fusion.

[0073] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0074] This technical solution can effectively screen urban greenhouse gas monitoring sites, background monitoring sites, and boundary monitoring sites that reflect urban anthropogenic greenhouse gas emissions. Furthermore, this solution includes a self-verification process; in addition to the site selection steps provided in the guidelines, a scientific demonstration module is added to increase the accuracy of site selection. It solves the problems of time-consuming and labor-intensive processes and site selection bias in greenhouse gas monitoring site selection decisions.

[0075] Specifically, this application discloses a method for selecting greenhouse gas monitoring sites. It involves acquiring multi-source data through a data survey module, performing preliminary data processing to form a basic database, and then employing various technical means such as a site screening module, a precise screening module, a field survey module, and a scientific verification module to implement the site selection process. The method clarifies the selection steps and standardizes the process. It also includes a self-verification process (scientific verification module) to improve the accuracy of site selection.

[0076] This technical solution overcomes the shortcomings of traditional methods: site selection is often carried out manually, involving surveys, data processing, site visits, and subjective decisions. This process is inefficient and prone to errors due to insufficient information or the decision-maker's limited knowledge. However, using the computer service platform described above, data processing and integration are primarily handled by computers. Humans can then make decisions based on the computer's output, improving site selection efficiency, mitigating risks, providing more valuable site selection options, and increasing the success rate of site selection. Attached Figure Description

[0077] Figure 1 This is a block diagram of the computer system constructed using the method described in this scheme.

[0078] Figure 2 The data research module is designed to implement the data research process.

[0079] Figure 3 To implement the initial screening of locations, a location screening module is required.

[0080] Figure 4 A module for accurately selecting monitoring points is provided to facilitate the process of accurately selecting monitoring points.

[0081] Figure 5 This module enables on-site investigation of monitoring points.

[0082] Figure 6 The scientific argumentation module is designed to implement the scientific argumentation steps.

[0083] Figure 7 This is a flowchart of the method proposed in this scheme.

[0084] Figure 8 This is a visualization module for an example.

[0085] Figure 9 The computer equipment used to run this solution system. Detailed Implementation

[0086] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the scope of this patent. To better illustrate the following embodiments, some components in the drawings may be omitted, enlarged, or reduced, and do not represent the actual dimensions of the product; it is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0087] like Figure 7 As shown, the main workflow for selecting greenhouse gas monitoring sites is as follows: First, multi-source data is obtained through a data survey module for preliminary data processing to form a basic database. Then, various technical means, including site screening, precise screening, on-site investigation, and scientific demonstration, are used to select the sites for greenhouse gas monitoring.

[0088] The system comprises several modules: a data collection module to gather data and create a database; a basic overview database (110) and a key information research and analysis database (120); a preliminary screening module to select locations; a precise screening module to further screen the selected locations, returning those that do not meet the requirements to the previous module without further screening, and those that do meet the requirements as preferred locations for the next screening step; a field survey module to further screen the preferred locations, returning those that do not meet the requirements to the previous module without further screening, and those that do meet the requirements as candidate locations for the next screening step; and a scientific evaluation module to conduct scientific evaluation of the candidate locations, returning those that do not meet the requirements to the previous module, and those that do meet the requirements as the final selected candidate locations.

[0089] System modules using this method include Figure 1 As shown in the figure, the overall module diagram of the atmospheric greenhouse gas monitoring station site selection system based on multi-technology integration described in this embodiment is as follows: the data survey module 100 acquires multi-source data and performs preliminary data processing to form a basic database; the site selection of greenhouse gas monitoring stations is implemented through various technical means such as the site screening module 200, the precise screening module 300, the field survey module 400, and the scientific demonstration module 500.

[0090] Figure 1 This is a block diagram of the computer system constructed according to the above method. The following section further explains in detail the working process of each step or module of the above method and system.

[0091] 1. Data Research Module 100

[0092] 1.1 Basic Information

[0093] By utilizing data such as the "Statistical Yearbook," "Environmental Statistics Bulletin," and "Information Statistics Handbook" published on the official website, comprehensive data on regional geomorphology, population distribution, industrial layout, energy structure, current carbon emissions, and future urban development can be obtained. Figure 2 As shown, a database capable of grasping the basic overview of a region is established to provide basic support for site selection services.

[0094] (1) Geographical location and administrative divisions.

[0095] (2) Geomorphological features: The geomorphological features of the region are obtained based on satellite remote sensing.

[0096] (3) Population distribution: Data on the permanent resident population of the region are obtained based on the regional statistical yearbook and population size and distribution.

[0097] (4) Industrial structure: Data on the region’s industrial structure over the past five years were obtained from the Statistical Information Handbook and other sources.

[0098] (5) Energy structure: obtain information on the energy consumption structure of industrial enterprises in the region over the past three years through statistical yearbooks and other sources.

[0099] (6) Current status of carbon emissions: Based on the carbon emission inventory, obtain the carbon emission data of different regions and sectors.

[0100] (7) Urban development planning, clarifying the future development plan of the region.

[0101] 1.2 Research on Important Data

[0102] (1) Meteorological conditions

[0103] Meteorological data from various surface monitoring stations in the region over the past three years were collected, including elements such as wind speed, wind direction, temperature, humidity, and air pressure, and the overall trend of surface meteorological changes in the region over different time dimensions was analyzed.

[0104] Based on the fifth-generation atmospheric reanalysis (ERA5) data of global climate from the European Centre for Medium-Range Weather Forecasts (ECMWF), data such as the 100-meter u-wind component, 100-meter v-wind component, boundary layer height, 2-meter temperature, humidity, and mean sea level pressure for the past three years were obtained for the region. The ERA5 data were then processed using MATLAB programming software to present the spatiotemporal variation patterns of meteorological conditions in the region and its surrounding environment from multiple dimensions, and to clarify important meteorological conditions such as the prevailing wind direction in the region.

[0105] (2) Current land use

[0106] Based on the official website of the regional planning and natural resources bureau, the latest national land survey results are obtained, and the area of ​​cultivated land, orchards, forest land, grassland, wetlands, urban and rural land, industrial and mining land, transportation land, and water area and water conservancy facilities land in the region are statistically analyzed to comprehensively and objectively reflect the land use situation in the region.

[0107] Based on satellite remote sensing data, we can further obtain the overall distribution of major land types and obtain a regional land use cover map.

[0108] (3) Analysis of greenhouse gas emission sources

[0109] According to the "Guidelines for the Compilation of Provincial Greenhouse Gas Inventories (Trial)" and the "Guidelines for the Compilation of Provincial, Municipal, and County (District) Level Greenhouse Gas Inventories" issued by various provinces and cities, greenhouse gas emission sources / sinks mainly involve five major areas: energy activities, industrial production processes, agricultural activities, land use change and forestry, and waste treatment. By utilizing the historical atmospheric greenhouse gas emission source inventories and conventional atmospheric pollutant emission source inventories of the region, along with the latest environmental statistics and pollution census data, and employing mapping software such as ArcGIS and Aovi Maps, a comprehensive analysis is conducted to understand the spatiotemporal distribution characteristics, emission intensity, and emission outlet height of regional atmospheric greenhouse gas emission sources and sinks. This provides data support for selecting and deploying greenhouse gas monitoring sites.

[0110] Based on the shared origins and processes of CO2 and conventional air pollutants, and using a regional gridded inventory of conventional air pollutant emissions, a preliminary assessment of the spatial distribution characteristics of CO2 sources such as chemical fuel combustion, road sources, and non-road sources in the region is conducted.

[0111] (4) Analysis of location information

[0112] Comprehensive collection and analysis of information on proposed greenhouse gas monitoring sites, assessment of whether they meet the principles and requirements for site selection, and provision of data reference for site selection.

[0113] Based on big data from the internet, information on buildings in the area where the proposed site is located that are 50 to 100 meters high is obtained.

[0114] Using Aowei Maps mapping software (which includes Baidu Maps, Bing satellite imagery, and OpenCycle contour maps), a preliminary understanding of the surrounding environment of the site can be obtained. For example, Baidu Maps can be used in conjunction with emission source inventory results and environmental statistics to view the distribution of enterprises, roads, etc., within 1km or 10km of the proposed site, and to determine whether there are emission sources in the vicinity. Bing satellite imagery can be used to understand the land type, vegetation distribution, and building distribution of the proposed site, and to see if the surrounding environment is open and convenient for site construction. OpenCycle contour maps can be used to clarify the topography of the area where the proposed site is located, and whether there is a certain relative altitude, etc.

[0115] Collect latitude, longitude, and altitude information of existing tower base platforms at the proposed locations through meteorological bureaus, communications departments, and tower construction companies. These include public facilities that do not affect urban atmospheric circulation, such as meteorological towers and communication towers, or public facilities that are not affected by human activities, such as radar stations and water towers.

[0116] (5) Ground-based monitoring and analysis of greenhouse gases

[0117] Based on greenhouse gas data from existing greenhouse gas monitoring stations over the past 1 to 3 years, the temporal variation patterns of greenhouse gases in the region are obtained.

[0118] (6) Satellite remote sensing monitoring and analysis

[0119] Based on data from the Atmospheric Infrared Sounder (AIRS) instrument aboard NASA's Aqua satellite, this study uses a locally optimized CO2 and CH4 retrieval method (based on a CO2 and CH4 column concentration product retrieval model using TANSO-FTS data) to retrieve the spatial distribution of atmospheric greenhouse gases (CO2 and CH4) under clear-sky conditions for the past three years and four quarters in the region. The spatial resolution of both CO2 and CH4 satellite remote sensing retrieval images is 3 km × 3 km. Based on the CO2 and CH4 satellite remote sensing retrieval images for each quarter of the past three years, a comprehensive analysis of the spatial distribution characteristics of major greenhouse gas concentrations in different years and seasons is conducted. This analysis clarifies the distribution of major atmospheric greenhouse gases in suburban and urban areas, as well as their spatial distribution characteristics upwind and downwind, providing support for the selection of urban carbon monitoring station sites.

[0120] The inversion algorithm for satellite remote sensing data images consists of two parts: First, constructing prior profiles of CO2 and CH4, that is, determining the near-surface CO2 and CH4 concentrations based on observation data, constructing prior profiles of CO2 and CH4 for different regions and seasons, and integrating them into a nonlinear optimization algorithm; Second, under the premise of knowing the prior profiles of CO2 and CH4, the surface temperature, atmospheric temperature and humidity profiles monitored by satellite remote sensing, and reflectivity, etc., selecting the corresponding channels based on the absorption characteristics of each gas, using a nonlinear optimization algorithm to invert the scalar scaling factors of the target gas and interfering gas profiles, and then obtaining the total amount of the target gas through scaling and integration.

[0121] The data quality control method for satellite remote sensing inversion data images mainly involves comparing the total CO2 and CH4 amounts retrieved from ground-based FTS detection data with those retrieved from satellite monitoring data to obtain the accuracy of the data product. Other profile verification methods include using weather balloons released during satellite transit to verify atmospheric temperature and humidity profiles.

[0122] The collected and processed data is stored in a database.

[0123] 2. Initial screening module for sampling points (200)

[0124] Figure 3 This indicates that the initial screening module 200 mainly comprises two parts: initial screening based on the overall regional situation and screening based on the location of the sites. Specifically, it is divided into five parts: spatiotemporal analysis of meteorological conditions, spatiotemporal distribution analysis of greenhouse gas emission sources, spatiotemporal distribution analysis of greenhouse gas concentrations, basic condition analysis, and distribution of emission source points.

[0125] (1) Based on the data survey results, the prevailing wind direction and the upwind and downwind directions of the prevailing wind direction in the region are determined by analyzing meteorological conditions such as wind speed and wind direction;

[0126] (2) Using ArcGIS software, the regional map is divided into 1km×1km grids. The spatial distribution data of greenhouse gas emission sources obtained through the greenhouse gas emission inventory is imported into ArcGIS software to present the distribution of emission sources in the grid map, thereby clarifying the areas that are more affected by each emission source.

[0127] (3) By analyzing the concentration of greenhouse gases such as CO2 through satellite remote sensing and ground monitoring, the high-value, medium-value, and low-value areas of CO2 and other greenhouse gas emissions in the region can be identified;

[0128] (4) Using the Ovi Map software, taking into account the topographic features, land use type, future development of the city, and basic conditions such as tower base and high-rise buildings, a certain range (such as a 10km×10km grid) of sampling area is selected in the urban area and the upwind and downwind directions of the prevailing wind direction, in areas that are greatly affected by emission sources (taking into account areas with high, medium and low greenhouse gas concentrations).

[0129] (5) Based on the distribution of emission source points (such as power plants, key enterprises, etc.), select all points in the grid area that meet the conditions (such as having a certain relative height, being at least 1km away from the emission source, etc.).

[0130] A sufficient number of preliminary monitoring points that meet the monitoring requirements were initially selected for subsequent precise point selection.

[0131] 3. Monitoring point precise screening module 300

[0132] The precise filtering module mainly consists of two parts, such as... Figure 4 The figures shown represent sensitivity analysis and observation system simulation experiments, respectively.

[0133] Based on the initial site screening results, model analysis techniques were employed to conduct a precise selection of candidate sites. The model analysis included sensitivity analysis, flux footprint simulation, cluster analysis, and observation system simulation experiments. From the initial candidate sites, the most sensitive sites to the city's major greenhouse gas emission sources were precisely selected, optimizing site layout. This aimed to significantly reduce the uncertainty in the assimilation and inversion of urban greenhouse gas emissions and clarify the precise site selection results.

[0134] (1) Sensitivity analysis 310

[0135] First, high-precision meteorological data constructed by the mesoscale weather model WRF was coupled with the backward trajectory model HYSPLIT. Cluster analysis was performed on the backward air mass trajectories of the preliminary site at different altitudes in different seasons. The percentage of airflow direction at different altitudes of the preliminary site was calculated to identify whether the preliminary site could capture the main carbon emission information. The representativeness and sensitivity of the preliminary site and sampling port placement altitude were studied.

[0136] Then, the high-precision meteorological data constructed by the mesoscale weather model WRF was coupled with the STILT (Stochastic Time-Inverted Lagrangian Transport) footprint contribution model to simulate and calculate the influence of all areas passed by the airflow under the drive of the meteorological field on the concentration of preset monitoring points at different seasons and sampling heights before the final movement of the atmosphere reaches the initial site. This identified whether the initial site could capture the main carbon emission information and studied the representativeness and sensitivity of the initial site and the sampling port placement height.

[0137] By performing trajectory clustering and footprint contribution analysis on the initial selection sites, we can identify whether the initial sites can capture key carbon emission information, and clarify the representativeness and sensitivity of different initial selection sites and sampling port placement heights, thereby further screening the initial selection sites.

[0138] (2) Assimilation and Inversion Simulation Analysis 320 (Observation System Simulation Experiment)

[0139] <1> Pollutant emission sources within the study area were collected and analyzed. High-precision meteorological data from the mesoscale weather model WRF was coupled with the regional air quality model CMAQ. A terrestrial ecosystem photosynthesis and respiration model VPRM, based on hourly simulated meteorological parameters, was constructed and coupled with the regional air quality model CMAQ to improve its description of CO2 source emissions, atmospheric transport and diffusion processes, and the impacts of vegetation photosynthesis and respiration. Greenhouse gas concentrations were simulated, and the results were compared with the greenhouse gas concentration distribution characteristics in the data survey module database.

[0140] <2> CO2 concentration data from GOSAT and OCO-2 satellites were collected and compared to construct a CO2 column concentration dataset based on satellite remote sensing. Combining satellite pixel-scale CO2 column concentration with monitoring data from a ground-based validation system, the impact of non-uniform surface conditions was simulated and analyzed. Using pollutant concentration data other than CO2, the spatial scale effect and temporal matching relationship between multi-source satellite land environmental parameter data and atmospheric chemical transport model computational grids were studied from multiple perspectives, establishing an input model adapted to atmospheric chemical models. Addressing the spatiotemporal discontinuity of CO2 concentration data from conventional ground observations and total CO2 column data retrieved from satellites, the effects of optimal ensemble number, spurious long-range correlations, model errors, and observation errors on sample divergence were investigated. A CO2 atmospheric assimilation inversion system was established, and corrected inversion was performed based on existing emission sources to obtain an optimized dynamic high-resolution CO2 source-sink inventory. The corrected emission sources were input into the model in section ① to simulate greenhouse gas concentrations, and the results were compared with the greenhouse gas concentration distribution characteristics in the data survey module 100 database.

[0141] based on <1> and <2> The simulation results were compared with observations to assess the impact of the site selection scheme on the assimilation and inversion of greenhouse gas emissions, clarify the degree of uncertainty reduction in carbon emission assimilation and inversion (30-60%), and further optimize the site selection scheme to obtain the best sites.

[0142] 4. Monitoring point on-site survey module 400

[0143] The field survey module 400 includes two parts, such as... Figure 5 As shown, the main focus is on field investigation and on-site monitoring. For all the precisely selected locations, a field investigation plan is developed, and field investigation work is carried out to ensure that the site conditions of the selected locations meet the requirements for station construction and layout. At the same time, on-site monitoring is carried out to evaluate the effectiveness of the selected locations.

[0144] (1) The field investigation mainly includes the following:

[0145] 1) Emission source situation: Conduct on-site investigation to determine whether there are emission sources within 1 kilometer of the monitoring point (at least 10 kilometers for background points);

[0146] 2) Collection space conditions: On-site inspection to determine whether the horizontal plane around the sampling port can guarantee a 360-degree open collection space;

[0147] 3) Geological safety: Take photos of the eight-directional map of the monitoring point, check on-site whether the surrounding environment of the monitoring point is relatively stable, assess whether the geological conditions of the location are stable and solid in the long term, and whether the location can avoid the impact of local disasters such as flash floods, forest fires, and mudslides, to ensure that safety and fire prevention measures are adequately guaranteed.

[0148] 4) Electromagnetic interference situation: Conduct on-site inspections to check for strong electromagnetic interference near the monitoring point, and whether there is a stable and reliable power supply and lightning protection equipment in the surrounding area to ensure that communication lines can be easily installed and maintained;

[0149] 5) Site topography: Conduct on-site investigation to determine if the selected site has a certain relative height, and whether the relative height between the outlet and the tower base is within the range of 50-100 meters, in order to ensure that the sample gas is fully mixed, etc.

[0150] 6) On-site construction status: Conduct an on-site inspection to determine whether the area meets the conditions for construction personnel and whether it meets the infrastructure requirements;

[0151] 7) Operation and maintenance management: Conduct on-site inspections to determine whether the location of the monitoring points is convenient for management and whether equipment maintenance personnel are allowed to enter and exit to maintain the monitoring equipment, etc.

[0152] 8) Emergency Management: Conduct on-site inspections to determine if the locations of monitoring points have unobstructed and convenient access routes and conditions, so that personnel can arrive at the scene promptly to handle any emergencies.

[0153] (2) On-site monitoring mainly includes the following:

[0154] 1) Mobile monitoring

[0155] It is planned to conduct mobile monitoring of atmospheric carbon dioxide (CO2) / methane (CH4) (column) concentrations within a 1-kilometer radius of selected sites using non-dispersive infrared absorption spectrometry equipment that meets the requirements of the "Technical Reference Scheme for Pilot Monitoring of Urban Atmospheric Greenhouse Gases and Marine Carbon Sequestrations" issued by the China National Environmental Monitoring Centre. This will comprehensively understand the distribution of carbon dioxide (CO2) / methane (CH4) (column) concentrations within a 1-kilometer radius of the selected sites, in order to assess the effectiveness of the selected sites.

[0156] 2) Unmanned Aerial Vehicle (UAV) Remote Sensing Monitoring

[0157] We plan to use non-dispersive infrared absorption spectrometry equipment that meets the requirements of the "Technical Reference Scheme for Pilot Monitoring of Urban Atmospheric Greenhouse Gases and Marine Carbon Sequestrations" issued by the China National Environmental Monitoring Centre to conduct UAV remote sensing monitoring of atmospheric carbon dioxide (CO2) / methane (CH4) concentrations at selected sites. This will allow us to obtain the concentration characteristics of CO2 and CH4 at different heights in the vertical direction in order to evaluate the effectiveness of the selected sites.

[0158] Based on the results of the evaluation of the selected locations in 1) and 2), and combined with the situation of the site examined in (1), alternative locations are derived.

[0159] 5. Scientific Argumentation Module (500 words)

[0160] The scientific argumentation module 500 mainly includes three aspects, such as... Figure 6 As shown.

[0161] First, using the collected emission sources, four-month WRF-Chem simulations were conducted on the candidate sites for January, April, July, and October, comparing the results with the greenhouse gas distribution and concentration characteristics in the qualitative / quantitative analysis data module database. Second, using emission sources corrected by the observation system simulation experiments in the precise screening module, four-month WRF-Chem simulations were conducted on the candidate sites, again comparing the results with the greenhouse gas distribution and concentration characteristics in the qualitative / quantitative analysis data module database. The simulation results before and after the emission source change were also compared to further demonstrate whether the sites can objectively reflect the atmospheric greenhouse gas levels and spatiotemporal variations within a certain spatial range, whether they can reflect the main greenhouse gas emissions in the city, and whether they meet the needs of assessing urban greenhouse gas emissions.

[0162] Secondly, a comparison of the surrounding conditions and sampling port height of different candidate sites was conducted. The surrounding area should be as open as possible to avoid proximity to anthropogenic and natural greenhouse gas emission sources and the influence of local circulation. A 360° open capture space should be ensured around the sampling port. The sampling port height should be 50-100m above the ground to ensure fully mixed atmospheric samples, and the difference in sampling height between different candidate sites should not be too large. If the setup conditions of a candidate site differ significantly from other candidate sites, it should be considered for rejection.

[0163] Finally, based on the urban planning in the data survey module 100 database, we clarified the trend of urban and rural spatial pattern changes in the candidate sites over the next 5 years and examined whether the candidate sites could take into account the future trend of urban and rural spatial pattern changes.

[0164] In summary, output the candidate locations that meet the conditions.

[0165] 6. Visualization module (600)

[0166] The five modules mentioned above (data research module 100, initial site screening module 200, precise screening module 300, field investigation module 400, and scientific verification module 500) can effectively screen sites, while the visualization module 600 visualizes the content (or data) output from the previous five modules, such as... Figure 8As shown. For the data survey module 100, the output includes a geographical location and administrative division map, an elevation map, the resident population for the past 5 years and the urbanization rate for the latest year (table), a resident population size and population density distribution map, the industrial structure for the past 5 years, the industrial structure of each administrative region, the distribution of industrial output value, energy consumption of industrial enterprises above a certain scale, carbon emissions by sector, wind field map of the re-series data, boundary layer height map, land use cover map, pollutant emission distribution map, different map interfaces (such as Baidu Map, Bing satellite image, OpenCycle contour map), annual, seasonal, and monthly changes in greenhouse gas concentration, and greenhouse gas satellite remote sensing inversion data images; for the point screening module 200, the output includes a meteorological spatiotemporal distribution map, a spatiotemporal distribution map of emissions, and a spatiotemporal distribution map of greenhouse gas concentration (elevation, elevation, and height). The system outputs the following data: (1) Medium and low value areas; (2) Surrounding topography; (3) Land use type; (4) Tower base location; (5) Distribution of surrounding emission sources (e.g., whether there are point sources within 1km); (6) Spatial distribution map of preliminary selected sites; (7) Basic information table of preliminary selected sites. For the precise screening module 300, the system outputs the sensitivity analysis results of preliminary selected sites, simulation experiment results of the observation system, spatial distribution map of selected sites, and basic information table of selected sites. For the field survey module 400, the system outputs a table of field survey results, animated graphs of mobile monitoring results and UAV monitoring results, spatial distribution map of candidate sites, and basic information table of candidate sites. For the scientific demonstration module 500, the system outputs a comparison of the spatiotemporal distribution maps of greenhouse gases at candidate sites, a table of candidate site setting conditions, urban and rural planning maps of candidate sites for the next five years, and a basic information table of qualified candidate sites.

[0167] Another embodiment provides a computer-readable storage medium, such as Figure 9 As shown, the computer-readable storage medium stores computer instructions, which, when invoked, participate in the execution of the addressing method described above. Correspondingly, a computer service platform is provided for storage, computation, and display purposes.

[0168] Multi-source data can be acquired through an input device, processed to form a basic database, and stored in the database. Based on the five modules described above, the address selection module operates through an operating system and related programs. The content of the visualization module 600 is displayed in an interactive visual manner.

[0169] The following are supplementary descriptions regarding the above embodiments:

[0170] For the data research module, the specific data and types listed in this embodiment are only some examples. Since there may be differences in different cities, this type of data can be obtained from various channels according to actual needs.

[0171] In the initial site screening module, UAV remote sensing monitoring and mobile monitoring vehicle technology can be used in the final step to further evaluate the selected sites and preliminarily screen out a sufficient number of representative sites for subsequent precise site selection. However, considering costs, the further evaluation process of UAV remote sensing monitoring and mobile monitoring vehicle can be implemented or omitted according to actual needs.

[0172] Urban monitoring points: These are monitoring points set up within a city to monitor the concentration levels and trends of atmospheric greenhouse gases and to reflect the local anthropogenic greenhouse gas emissions.

[0173] Background points: Monitoring points set up in areas far from greenhouse gas emission sources in order to monitor the level and trend of urban background atmospheric greenhouse gas concentrations and reflect the impact of urban background greenhouse gas sources and sinks.

[0174] Boundary points: monitoring points set up in areas far from greenhouse gas emission sources on the edge of cities to monitor the concentration levels and trends of atmospheric greenhouse gases in urban boundary areas and to reflect the impact of external transport.

[0175] ERA5 is the fifth-generation atmospheric reanalysis dataset for global climate from the ECMWF. Reanalysis data combines model data with observational data from around the world to form a complete and consistent global dataset.

[0176] Remote sensing inversion: a technique that infers the electromagnetic wave conditions during the formation process of remote sensing images based on the electromagnetic wave characteristics of ground objects. Remote sensing image characteristics are formed by processes such as ground reflectivity and atmospheric effects. If we use remote sensing images as known quantities to calculate unknown parameters in the atmosphere that affect remote sensing imaging, we can transform remote sensing data into various surface characteristic parameters that people actually need.

[0177] Atmospheric inversion: By using atmospheric concentration observation data, combined with atmospheric chemical transport models, and employing optimized algorithms, the accuracy of global and regional surface carbon flux estimation can be improved.

[0178] WRF stands for The Weather Research and Forecasting Model, a mesoscale weather forecasting model. It is a next-generation mesoscale numerical model and data assimilation system following the PSU / NCAR Mesoscale Model (MM5), and was jointly developed by the National Center for Atmospheric Research (NCAR) and researchers from multiple research institutions and universities. The WRF model can be used for weather research and forecasting, with simulation scales ranging from a few meters to tens of kilometers.

[0179] HYSPLIT: Backtracking Trajectory Model. Developed jointly over the past 20 years by the Air Resources Laboratory of the National Oceanic and Atmospheric Administration (NOAA) and the Australian Bureau of Meteorology, this specialized model is used to calculate and analyze the transport and diffusion trajectories of atmospheric pollutants. This model possesses a relatively complete transport, diffusion, and deposition model capable of handling various meteorological input fields, multiple physical processes, and different types of pollutant emission sources. It has been widely applied in studies of the transport and diffusion of various pollutants in various regions. HYSPLIT calculates trajectories using the Lagrange method, assuming that air masses move with the wind field; therefore, the trajectory of an air mass is its spatial and temporal integral. The vector velocity at the location of the air mass is obtained spatially and temporally through linear interpolation.

[0180] STILT (Stochastic Time-Inverted Lagrangian Transport) is a footprint contribution model. It's a transport model based on Lagrange random walk theory that links the source (sink) flux upstream of an observation point to the concentration change at that point using footprint weights. Specifically, it simulates the backward motion trajectory of gas driven by turbulence and average wind direction by releasing a large number of air particles backward. The footprint weight is quantitatively calculated by determining the number of particles at a certain height within the boundary layer of an upstream region and the residence time of each particle.

[0181] The Observation System Simulation Experiment (OSSE) is a sensitivity experiment designed to answer questions about the impact and value of new observation systems, typically referring to their impact on numerical weather prediction. The "real atmosphere" in OSSE is the free atmosphere simulated using high spatiotemporal resolution models. All existing and future observations are based on free atmosphere simulations. By assimilating data and conducting numerical prediction, the study investigates the impact of future observation systems on numerical prediction errors, building upon existing systems.

[0182] WRF-Chem: Weather Research and Forecasting model coupled to Chemistry. Developed by NOAA Forecasting Systems Laboratory, WRF-Chem is a regional air quality model formed by coupling the meteorological model WRF and the chemical model chem. Released as part of the WRF model, its reliance on WRF necessitates experience with WRF installation, configuration, and operation. Compared to previous atmospheric chemical models, WRF-Chem better reflects the feedback between atmospheric aerosols and meteorological conditions, simulating a more realistic atmospheric environment.

[0183] CMAQ: Air Quality Forecasting and Assessment System. The third-generation air quality forecasting and assessment system (Models-3) developed by the U.S. Environmental Protection Agency. Models-3 is a common name for the third-generation air quality modeling system, and its core is the Community Multiscale Air Quality (CMAQ) model system; therefore, it can also be commonly referred to as the Models-3 / CMAQ model.

[0184] Data assimilation refers to the method of fusing new observational data during the dynamic operation of a numerical model, taking into account the spatiotemporal distribution of data and errors in the observation and background fields. Within the dynamic framework of the process model, it automatically adjusts the model trajectory by continuously fusing direct or indirect observational information from different sources and at different resolutions in a spatiotemporally discrete distribution through data assimilation algorithms. This improves the estimation accuracy of the dynamic model state and enhances the model's predictive ability.

[0185] VPRM (Vegetation Photosynthesis and Respiration Model) is a diagnostic model for studying the carbon balance of terrestrial ecosystems, based on vegetation light use efficiency. Building upon the VPM model, Mahadevan, a foreign scholar, comprehensively considered processes such as the half-saturation parameter PAR0 (reflecting the relationship between photosynthetically active radiation and photosynthesis) and ecosystem respiration (Reco), ultimately establishing the VPRM model in 2008 to simulate ecosystem NEE. Like other light use efficiency models, the VPRM model requires fewer parameters that are readily available, supporting continuous long-term CO2 flux simulation studies. The VPRM model's NEE simulation mainly consists of two parts: temperature-driven calculations and light-driven calculations. Temperature-driven calculations yield the ecosystem respiration term (Reco), while light-driven calculations yield GPP (Gross Power Product).

[0186] En4DVar: 4D variational data assimilation, a four-dimensional variational data assimilation scheme based on ensembles. A non-sequence data assimilation technique that fits observations within the entire assimilation window (optimal trajectory).

[0187] GOSAT Satellite: Greenhouse Gas Observatory. On January 23, 2009, Japan launched the world's first satellite dedicated to monitoring the distribution of greenhouse gas concentrations from space, IBUKI (also known as the Greenhouse Gas Observatory), using an H-2A rocket in a "one rocket, eight satellites" configuration. Over the next five years, this satellite will collect global carbon dioxide and methane concentration data from space, providing a basis for formulating emission reduction policies and helping researchers further understand how much carbon dioxide ecosystems can absorb and release.

[0188] The Orbiting Carbon Observatory 2 (OCO-2) satellite is NASA's first satellite dedicated to studying carbon dioxide emissions. NASA aims to understand the uneven distribution of CO2 in the global atmosphere beyond land and ocean absorption, to precisely measure carbon emissions and the carbon cycle, to improve understanding of the natural and anthropogenic sources of greenhouse gases, to refine global carbon cycle models, to better characterize atmospheric CO2 changes, and ultimately to more accurately predict global climate change. OCO-2 will uniformly sample the atmosphere over Earth's land and oceans, taking 500,000 samples daily over a two-year period across half of the Earth's sun-exposed region to provide a complete picture of regional geographic distribution and seasonal variations with defined accuracy, resolution, and coverage. OCO-2's three high-resolution spectrometers will monitor the solar optical spectrum, focusing on different color bands to analyze the absorption of specific colors by CO2 and oxygen molecules. The amount of light absorbed by these specific colors is proportional to the atmospheric CO2 concentration. Researchers will incorporate this new data into computational models to quantify global carbon sources and sinks. The OCO-2 spectrometer is designed to measure how sunlight passes through the Earth's atmosphere twice after being reflected from the surface. CO2 and O2 molecules in the atmosphere have very specific spectral characteristics. Therefore, when sunlight reaches the OCO-2 satellite's payload, it loses energy in these special spectral bands. The OCO-2 grating spectrometer scatters the sunlight, allowing it to capture the absorbed energy of CO2 and O2 in the corresponding spectral bands, thus measuring the local atmospheric CO2 and O2 concentrations.

[0189] Obviously, the above embodiments are merely examples to clearly illustrate the technical solution of this patent, and are not intended to limit the specific implementation of this patent. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the claims of this patent should be included within the protection scope of the claims of this patent.

Claims

1. A method for site selection of atmospheric greenhouse gas monitoring stations based on multi-technology fusion, characterized in that, Includes the following steps: S1 data survey: collect basic information about the area where the site is located, and obtain a database that best reflects local characteristics; S2 site initial screening: Based on the data in the database and the carbon emission data obtained from short-term field monitoring, sites that meet the monitoring requirements are initially screened. S3 Precision Screening uses model analysis technology to precisely screen out the locations most sensitive to greenhouse gas emission sources in the area from the initial screening. S4 On-site survey to confirm whether the site conditions of the selected location in step S3 meet the requirements for site construction; S5 scientific verification involves comparing and verifying the greenhouse gas concentration at the location obtained through numerical simulation step S4 with satellite remote sensing observation data, and outputting candidate locations that meet the conditions. The model analysis techniques in step S3 specifically include: S31 Sensitivity Analysis: S311 performs trajectory clustering analysis on the initially selected points by coupling the meteorological field with the backward trajectory model; S312 performs footprint contribution analysis on the initially selected locations by coupling the meteorological field with the footprint contribution model; S313 integrates trajectory clustering analysis and footprint contribution analysis to identify whether the initially selected locations can capture carbon emission information; S32 Assimilation and Inversion Simulation Analysis: S321 Based on the survey results of step S1, identify the sources of pollutant emissions, including greenhouse gases, within the area where the site is located; S322 simulates the concentration distribution of greenhouse gases in the selected site area by coupling meteorological fields with regional air quality models, and by coupling regional air quality models with photosynthesis and respiration models. Based on the survey results of step S1, S323 constructs a column concentration dataset of greenhouse gases in the selected site area, and combines it with emission information of pollutants other than greenhouse gases to establish an input model adapted to the atmospheric chemistry model. Based on the survey results of step S1, and combined with the greenhouse gas concentrations monitored on the ground and the input model described in S323, S324 performs assimilation and inversion on the basis of existing emission sources to obtain dynamic high-resolution greenhouse gas emission sources. S325 updates the dynamic high-resolution greenhouse gas emission sources to step S321 and re-simulates the concentration distribution of greenhouse gases in the site selection area through step S322; S326 compares the greenhouse gas concentration distribution in the selected area obtained in step S325 with the greenhouse gas concentration distribution characteristics obtained in step S1 to evaluate the impact of the site selection scheme on greenhouse gas emissions in the assimilation and inversion process. By combining steps S31 and S32, the sites most sensitive to greenhouse gas emission sources in the selected area are precisely screened. The database in step S1 specifically includes a basic overview database of the site selection area and a key information survey and analysis database. The key information survey and analysis database includes at least five dimensions: meteorological conditions, land use, spatiotemporal distribution of greenhouse gas emission sources, greenhouse gas concentration distribution characteristics, and site information.

2. The method for site selection of atmospheric greenhouse gas monitoring stations based on multi-technology fusion according to claim 1, characterized in that, The specific process of the sensitivity analysis in step S31 is as follows: First, high-precision meteorological data constructed by the mesoscale weather model WRF is coupled with the backward trajectory model HYSPLIT. Cluster analysis is performed on the backward air mass trajectories at different heights in different seasons at the initial selection points. The percentage of airflow direction at different heights at the initial selection points is calculated to identify whether the initial selection points can capture carbon emission information and to determine the representativeness and sensitivity of the initial selection points and the height of the sampling port. Then, the high-precision meteorological data constructed by the mesoscale weather model WRF is coupled with the STILT footprint contribution model to simulate and calculate the footprint weight of the influence of all areas passed by the airflow under the drive of the meteorological field on the concentration of the preset monitoring points at different seasons and sampling heights before the final movement of the atmosphere reaches the initial points. This identifies whether the initial points can capture carbon emission information and determines the representativeness and sensitivity of the initial points and the sampling port placement height. By analyzing the trajectory clustering and footprint contribution of the initial selection sites, we can identify whether the initial selection sites can capture carbon emission information, clarify whether different initial selection sites and sampling port placement heights are representative and sensitive, and further screen the initial selection sites.

3. The method for site selection of atmospheric greenhouse gas monitoring stations based on multi-technology fusion according to claim 1, characterized in that, The specific process of the assimilation and inversion simulation analysis in step S32 is as follows: <1> We collected and organized pollutant emission sources within the study area, coupled high-precision meteorological data constructed using the mesoscale weather model WRF with the regional air quality model CMAQ; we also constructed a terrestrial ecosystem photosynthesis and respiration model VPRM based on hourly simulated meteorological parameters, coupled with the regional air quality model CMAQ, to improve its description of CO2 source emissions, atmospheric transport and diffusion processes, and the impact of vegetation photosynthesis and respiration; we simulated greenhouse gas concentrations, and compared the results with the greenhouse gas concentration distribution characteristics in the data survey database. <2> We collected and compared CO2 concentration data from GOSAT and OCO-2 satellites to construct a CO2 column concentration dataset based on satellite remote sensing. Combining satellite pixel-scale CO2 column concentration with monitoring data from a ground-based validation system, we simulated and analyzed the impact of non-uniform land surface. Using pollutant concentration data other than CO2, we established the spatial scale effect and temporal matching relationship between multi-source satellite land environmental parameter data and atmospheric chemical transport model computational grids, and built an input model adapted to the atmospheric chemical model. Addressing the spatiotemporal discontinuity between conventionally observed ground-based CO2 concentrations and satellite-retrieved CO2 column total data, we established a CO2 atmospheric assimilation inversion system based on the impact of multiple factors, including optimal ensemble number, spurious long-range correlations, model errors, and observation errors, on sample divergence. This system performs correction inversion based on existing emission sources to obtain an optimized dynamic high-resolution CO2 emission source inventory. Input the corrected emission sources <1> In the simulation, greenhouse gas concentrations were compared with the greenhouse gas concentration distribution characteristics in the data survey module database; based on <1> and <2> The simulation results were compared with observations to assess the impact of the site selection scheme on the assimilation and inversion of greenhouse gas emissions. It was found that the uncertainty of carbon emission assimilation and inversion was reduced by 30-60%, and the site selection scheme was further optimized to obtain the best sites.

4. The method for site selection of atmospheric greenhouse gas monitoring stations based on multi-technology fusion according to claim 1, characterized in that, The S5 scientific argumentation includes the S51 representativeness and holistic analysis, which are detailed below: S511 Based on the emission source information in step S1, emission simulations of the points are performed at intervals by coupling meteorological fields and chemical models, and the greenhouse gas distribution and concentration characteristics in the qualitative / quantitative analysis database are compared. S512 Based on the dynamic high-resolution greenhouse gas emission sources in step S3, emission simulations of the points are performed at intervals by coupling meteorological fields and chemical models, and the greenhouse gas distribution and concentration characteristics in the qualitative / quantitative analysis database are compared. By comparing the simulation results of S513 with those of S511 and S512 before and after the emission source replacement, it is determined whether the sampling points can objectively reflect the atmospheric greenhouse gas level and spatiotemporal variation patterns within a certain spatial range, whether they can reflect the greenhouse gas emission status, and whether they meet the needs of assessing greenhouse gas emissions.

5. The method for site selection of atmospheric greenhouse gas monitoring stations based on multi-technology fusion according to claim 4, characterized in that, The S5 scientific argumentation also includes S52 comparability analysis and S53 prospective analysis; The comparability analysis of S52 is as follows: The conditions for setting up different sampling points and the height of the sampling port from the ground were compared. The conditions included whether the horizontal plane around the sampling port met the requirements for open collection space, whether the height of the sampling port from the ground was between 50 and 100m, and whether the difference in sampling height between the sampling points did not exceed 10m. The specific forward-looking analysis of S53 is as follows: Based on the urban planning information in step S1, clarify the future spatial pattern change trend of the location and examine whether the location can take into account the future spatial pattern change trend.

6. The method for site selection of atmospheric greenhouse gas monitoring stations based on multi-technology fusion according to any one of claims 1-5, characterized in that, The initial screening of points in step S2 includes at least the following parallel conditions: Based on the survey results of step S1, S21, by analyzing the meteorological conditions of wind speed and direction, the prevailing wind direction of the region and the upwind and downwind directions of the prevailing wind direction are determined. S22 uses geographic information system software to divide the selected site area into grids on the map software, imports the spatial distribution data of greenhouse gas emission sources obtained through greenhouse gas emission inventories into the geographic information system software, presents the distribution of emission sources on the grid map, and thus identifies the areas that are more affected by each emission source. S23 uses satellite remote sensing and ground monitoring to analyze CO2 greenhouse gas concentrations and identify high-, medium-, and low-value areas of regional CO2 greenhouse gas emissions. Based on the above conditions, map software was used to select a certain range of sampling areas in the urban area and in the upwind and downwind directions of the prevailing wind direction, in areas that are significantly affected by emission sources. Based on the distribution of emission sources, all points that meet the conditions are selected within the grid of the sampling area.

7. A multi-technology fusion-based atmospheric greenhouse gas monitoring station site selection system, used to implement the multi-technology fusion-based atmospheric greenhouse gas monitoring station site selection method according to any one of claims 1-6, characterized in that, include: The data survey module (100) is used to collect basic information about the area where the site is located and to obtain a database that best reflects local characteristics. The site screening module (200) uses data from the database and carbon emission data obtained from short-term field monitoring to initially screen sites that meet the monitoring requirements. The precision screening module (300) uses model analysis technology to precisely screen out the locations most sensitive to greenhouse gas emission sources in the selected area from the initial screening locations. The field survey module (400) is used to collect survey information and determine whether the site conditions of the selected points by the precision screening module (300) meet the requirements for site construction. The scientific demonstration module (500) compares and verifies the greenhouse gas concentration at the point obtained by the field survey module (400) through numerical simulation with the observation data of satellite remote sensing, and outputs the candidate points that meet the conditions. The visualization module (600) is used to output the content of the data survey module (100), the initial screening module (200), the precise screening module (300), the field survey module (400), and the scientific demonstration module (500), and express some of the content through map display.

8. A storage medium, characterized in that, The storage medium stores a computer program for implementing the multi-technology fusion-based atmospheric greenhouse gas monitoring site selection method as described in any one of claims 1-6.

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