Multi-source constraint gridding atmospheric pollutant and greenhouse gas emission estimation method

Abstract

The invention provides a multi-source constraint gridding atmospheric pollutant and greenhouse gas emission estimation method, and relates to the technical field of environmental information processing, and the method comprises the steps: constructing a hierarchical grid structure of an urban core domain and a peripheral background domain based on a unified data basic set, forming a gridding baseline emission list through unified activity data and emission factors, establishing a multi-source scale coordination mechanism, fusing ground monitoring and satellite observation information, and generating an observation data set; according to the method, an SMOKE sparse matrix processing chain is embedded to construct a joint estimation model, parameter inversion and consistency constraint optimization are implemented, boundary flux coordination is carried out between a core domain and a background domain, and finally an emission list which has cross-atmospheric pollutant and greenhouse gas consistency and can be used for refined emission inversion is formed. According to the method, the problem of unstable data fusion caused by scale mismatching between the multi-source observation data and the emission model in the prior art is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of environmental information processing, in particular to a multi-source constraint gridding atmospheric pollutant and greenhouse gas emission estimation method. BACKGROUND

[0002] At present, the preparation of city-scale atmospheric pollutant and greenhouse gas emission inventory is mainly based on the SMOKE (Sparse Matrix Operator Kernel Emissions) model or similar sparse matrix emission processing system, which generates regional gridding emission inventory through energy, industry, transportation, building and other department activity data and emission factors. This kind of method has formed a standardized process at the regional scale, which can support air quality simulation and policy evaluation. However, with the increasing demand for fine management of urban air quality and carbon emission double control, the existing model shows the problems of rigid structure and poor adaptability in processing high spatio-temporal resolution data and multi-source observation fusion. Especially at the city-level grid precision, the observation data sources are diverse, and the resolution difference is significant. The original SMOKE operator chain is difficult to realize the integration and constraint balance of different types of data.

[0003] In recent years, the fusion of satellite inversion, ground monitoring and Internet of Things sensing data provides a new data source for city-level emission inventory, and the trend is to realize fine emission estimation with high resolution, high frequency and multi-pollutant integration. However, the existing technology generally uses offline weighting or external optimization means to combine multi-source observation and emission inventory, which not only breaks the SMOKE operator processing chain, but also cannot stably process the time and space scale difference of different observation data, which easily causes the discontinuity and deviation of inventory correction results. In addition, the data of cross-pollutant and greenhouse gas are estimated separately, and there is a lack of unified constraint mechanism at the inventory level, which limits the consistency and integrity of the emission inventory.

[0004] Comprehensive analysis shows that the current city-scale emission inventory processing has the following outstanding problems:

[0005] The spatial and temporal scales of multi-source observation data do not match, which is difficult to process uniformly within the SMOKE operator framework; the SMOKE model lacks an embedded joint estimation mechanism, and the observation constraint is separated from the inventory generation; the consistency control of cross-pollutant and greenhouse gas emission results is lacking, which leads to the imbalance of energy and carbon conservation relationship; the city main domain and the peripheral grid boundary conditions cannot be coordinated in the inventory stage, which affects the continuity of subsequent modeling. SUMMARY

[0006] In order to overcome the shortcomings of the prior art, the purpose of the present application is to provide a multi-source constrained gridded atmospheric pollutant and greenhouse gas emission estimation method, which solves the problem of unstable data fusion caused by scale mismatch between multi-source observation data and emission models in the prior art.

[0007] In order to achieve the above-mentioned purpose, the present application provides the following solutions.

[0008] A multi-source constrained gridded atmospheric pollutant and greenhouse gas emission estimation method comprises:

[0009] Multi-source urban activity data, ground monitoring data and satellite inversion data are acquired, and the multi-source urban activity data, ground monitoring data and satellite inversion data are uniformly processed in time, space and format to obtain a unified data base set;

[0010] A nested grid system of a city core domain and a peripheral background domain is constructed, and a boundary mapping relationship and a spatial resolution parameter of the nested grid system are set to obtain a hierarchical grid structure set;

[0011] Based on the unified data base set, baseline emissions of each department are calculated according to the activity level of each source and the emission factor, and an initial gridded baseline emission inventory is obtained on the hierarchical grid structure set by using spatial proxy distribution;

[0012] A multi-source scale coordination mechanism is established according to the unified data base set, and an observation data set is obtained by using the multi-source scale coordination mechanism and the gridded baseline emission inventory;

[0013] The observation data set is embedded into a SMOKE sparse matrix processing chain to construct a joint estimation model, and a joint estimation emission result set is obtained through the joint estimation model;

[0014] Cross-pollutant and greenhouse gas emission consistency constraints are established on the joint estimation emission result set to obtain a consistency adjusted emission result set;

[0015] Based on the consistency adjusted emission result set, boundary flux coordination processing is performed between the city core domain and the peripheral background domain to form a boundary coordinated emission inventory;

[0016] The boundary coordinated emission inventory is subjected to data consistency checking and error verification to generate a final city-scale multi-pollutant and greenhouse gas gridded emission inventory.

[0017] Preferably, the multi-source urban activity data, ground monitoring data and satellite inversion data are acquired, and the multi-source urban activity data, ground monitoring data and satellite inversion data are uniformly processed in time, space and format to obtain a unified data base set, which comprises:

[0018] Obtain the raw multi-source data set, wherein the raw multi-source data set includes: multi-source urban activity data, ground monitoring data and satellite inversion data;

[0019] The coordinate system, geographic projection, and spatial resolution of the multi-source urban activity data, ground monitoring data, and satellite inversion data in the original multi-source dataset are uniformly transformed to obtain a unified spatial dataset.

[0020] Interpolate, resample, and time-align the time frequency and observation period of the spatial unified data set to obtain a temporally unified data set.

[0021] The time-unified data set is subjected to format conversion, field naming, and unit standardization to obtain a standardized data set;

[0022] Based on the standardized dataset, outlier removal, missing test item imputation, and consistency checks are performed to obtain a unified data base set.

[0023] Preferably, a nested grid system of urban core domain and surrounding background domain is constructed, and the boundary mapping relationship and spatial resolution parameters of the nested grid system are set to obtain a hierarchical grid structure set, including:

[0024] Based on the unified data base, the spatial range of the urban core area and the surrounding background area is delineated to obtain the regional boundary dataset;

[0025] Based on the aforementioned regional boundary dataset, different grid resolutions and hierarchical structures are set according to the spatial differences between the urban core area and the surrounding background area to obtain a preliminary hierarchical grid scheme.

[0026] Based on the preliminary hierarchical grid scheme, the boundaries of adjacent grids between the urban core area and the outer background area are numbered and mapped to obtain a set of boundary mapping relationships;

[0027] Based on the boundary mapping relationship set, the resolution parameters, coordinate projection method and spatial indexing system of the grid are configured to obtain a spatial parameterized grid system;

[0028] The spatial parameterized grid system and the boundary mapping relationship set are integrated to generate a hierarchical grid structure set that supports joint calculation of the urban core domain and the surrounding background domain, thus obtaining the hierarchical grid structure set.

[0029] Preferably, the step of calculating baseline emissions for each sector based on a unified data base set, according to the activity levels and emission factors of various sources, and then using spatial proxy allocation on a hierarchical grid structure set to obtain an initial gridded baseline emission inventory includes:

[0030] Based on the unified data base set, activity level data for energy, industry, transportation, and construction are extracted, and corresponding emission factors are matched to the activity level data to obtain sectoral activity emission datasets.

[0031] Based on the aforementioned departmental activity emission dataset, the total emissions of each department are calculated according to source type and pollutant type, resulting in a baseline emission inventory data table;

[0032] Based on the hierarchical grid structure set, and combined with land use, population density, road distribution and industrial location, a spatial proxy factor set corresponding to each sector is constructed.

[0033] Using the aforementioned spatial proxy factor set, the baseline emissions of each sector in the baseline emission inventory data table are allocated to each grid cell of the hierarchical grid structure set according to the spatial proxy weight, resulting in a gridded emission allocation result set.

[0034] Based on the gridded emission allocation result set, consistency balancing and statistical verification are performed on the total emissions across sectors and pollutants to obtain an initial gridded baseline emission inventory.

[0035] Preferably, the step of establishing a multi-source scale coordination mechanism based on the unified data base set, and using the multi-source scale coordination mechanism and the gridded baseline emission inventory to obtain the observation dataset includes:

[0036] Based on the unified data base set, the scale feature differences of multi-source urban activity data, ground monitoring data and satellite inversion data in terms of temporal resolution, spatial resolution and data accuracy are identified to obtain a scale feature dataset.

[0037] Based on the scale feature dataset, spatial interpolation, temporal weighting, and resolution resampling algorithms that match various data sources are selected to construct a scale mapping model set;

[0038] Using the scale mapping model set, spatiotemporal coordination transformation is performed on multi-source urban activity data, ground monitoring data and satellite inversion data to generate unified scale weight coefficients, and a multi-source scale coordination mechanism is established based on the unified scale weight coefficients.

[0039] Based on the aforementioned multi-source scale coordination mechanism, the gridded baseline emission inventory is integrated and weighted with the ground monitoring data and satellite inversion data in the unified data base set to obtain a coordinated observation data sample set;

[0040] Anomaly detection and integrity verification are performed on the coordinated observation data sample set to obtain the observation dataset.

[0041] Preferably, the step of embedding the observation dataset into the SMOKE sparse matrix processing chain, constructing a joint estimation model, and obtaining a joint estimated emission result set through the joint estimation model includes:

[0042] The SMOKE sparse matrix processing chain is used to establish a set of sparse matrix operators by calling spatial allocation, temporal allocation, and component mapping.

[0043] Based on the observation dataset, the ground monitoring and satellite inversion observation values ​​corresponding to each grid cell are mapped to the corresponding link nodes in the sparse matrix operator set, and the input coupling relationship between the observation information and the emission inventory is established to obtain the coupled observation input matrix.

[0044] By combining the operator structure of the coupled observation input matrix and the SMOKE sparse matrix processing chain, a joint estimation model is constructed;

[0045] The joint estimation model is inverted and iteratively optimized using the least squares method, and the grid cell emissions are updated on the optimized joint estimation model to obtain the joint estimated emission parameter set.

[0046] Based on the jointly estimated emission parameter set, the output process of the SMOKE sparse matrix processing chain is executed to obtain the jointly estimated emission result set.

[0047] Preferably, the expression for the joint estimation model is:

[0048] ;

[0049] in, This is the optimal solution for jointly estimating the emission vector; This is the gridded estimation vector for the emissions to be determined; Baseline emission vector; This is the spatial structure weight matrix, used to constrain the spatial deviation between emissions and the baseline; This is the observation mapping matrix, used to map emission estimates to the corresponding observation dimensions; This is a vector of observation data obtained by a multi-source coordination mechanism; These are the observation constraint balance coefficients, used to adjust the degree of observation fit; This is the spatial gradient regularization coefficient, used to suppress unreasonable abrupt changes in the emission field; coordinates in two-dimensional space Emission gradient operator on; As a consistency constraint factor; This is an energy-matter constraint matrix among emission components, used to express the synergistic relationships between different pollutants and greenhouse gas emissions; This is the overall emission conservation constant term, used to maintain the mass balance constraint.

[0050] Preferably, the step of establishing consistency constraints across pollutant and greenhouse gas emissions on the joint estimated emission result set to obtain a consistent adjusted emission result set includes:

[0051] Based on the joint estimated emission result set, correlation parameters for consistency control are extracted to obtain the correlation feature set of pollutants and greenhouse gases;

[0052] Based on the pollutant greenhouse gas correlation feature set, a corresponding consistency constraint function set is constructed, and the consistency constraint function set is mapped to the grid dimension of the joint estimated emission result set. Weighting coefficients and adjustment thresholds are set for different pollutants and greenhouse gas emission items to obtain the gridded consistency constraint matrix.

[0053] Based on the gridded consistency constraint matrix, the joint estimated emission result set is constrained and proportionally adjusted to obtain the consistency optimization result set.

[0054] The consistency optimization result set and the joint estimated emission result set are differentially verified to obtain the consistency adjusted emission result set.

[0055] Preferably, the step of performing boundary flux coordination processing between the urban core area and the surrounding background area based on the consistency adjustment emission result set to form a boundary coordinated emission inventory includes:

[0056] Establish a boundary grid index set based on the hierarchical grid structure set;

[0057] Based on the aforementioned consistency adjustment emission result set, emission intensity, wind field direction, and meteorological diffusion parameters are calculated for each grid cell in the boundary grid index set to obtain the boundary flux dataset;

[0058] Based on the boundary flux dataset, a balance analysis and correction of the net fluxes of pollutants and greenhouse gases between the two domains are performed to obtain the boundary flux correction set.

[0059] Based on the boundary flux correction set, a boundary coordination parameter set reflecting the spatial gradient and flow direction characteristics is established, thus obtaining the boundary coordination parameter set;

[0060] Based on the boundary coordination parameter set, the consistency adjustment emission result set is redistributed and intensity corrected within the boundary region to obtain the boundary coordination emission inventory.

[0061] Preferably, the step of adjusting the emission result set based on the consistency, and calculating the emission intensity, wind direction, and meteorological diffusion parameters for each grid cell within the boundary grid index set to obtain the boundary flux dataset includes:

[0062] Based on the consistency adjustment of the emission result set, the emission flux data of each pollutant and greenhouse gas in the boundary grid index set are extracted to obtain the boundary emission intensity matrix;

[0063] Based on meteorological driving data from a unified data base, wind speed, wind direction, temperature, and atmospheric data of the corresponding boundary areas are extracted to obtain a boundary meteorological feature set.

[0064] Using the aforementioned boundary meteorological feature set and combined with the coordinate system of the hierarchical grid structure, the wind field direction and flow intensity of each unit within the boundary grid are interpolated and vectorized to obtain the boundary wind field direction array;

[0065] Based on the boundary meteorological feature set and the boundary wind field direction array, the diffusion coefficient, turbulence intensity and mixing layer height are calculated to obtain the meteorological diffusion parameter matrix;

[0066] Based on the boundary emission intensity matrix, the boundary wind field direction array, and the meteorological diffusion parameter matrix, the flux intensity and net exchange of the boundary cells are calculated using the principle of mass conservation, resulting in a boundary flux dataset.

[0067] The present invention discloses the following technical effects:

[0068] This invention provides a multi-source constrained gridded method for estimating atmospheric pollutant and greenhouse gas emissions, comprising: acquiring multi-source urban activity data, ground monitoring data, and satellite inversion data; performing time, spatial, and format-unified processing on the multi-source urban activity data, ground monitoring data, and satellite inversion data to obtain a unified data base set; constructing a nested grid system of urban core domain and peripheral background domain, and setting the boundary mapping relationship and spatial resolution parameters of the nested grid system to obtain a hierarchical grid structure set; based on the unified data base set, calculating the baseline emissions of each sector according to the activity levels and emission factors of various sources, and using spatial proxy allocation on the hierarchical grid structure set to obtain an initial gridded baseline emission inventory; and according to the unified data... A multi-source scale coordination mechanism is established based on the baseline set. Using this mechanism and a gridded baseline emission inventory, an observation dataset is obtained. This dataset is then embedded into a SMOKE sparse matrix processing chain to construct a joint estimation model. A joint estimation emission result set is obtained through this model. Consistency constraints across pollutants and greenhouse gas emissions are established on the joint estimation emission result set to obtain a consistency-adjusted emission result set. Based on this consistency-adjusted emission result set, boundary flux coordination processing is implemented between the urban core area and the surrounding background area to form a boundary-coordinated emission inventory. Data consistency verification and error validation are performed on the boundary-coordinated emission inventory to generate the final urban-scale multi-pollutant and greenhouse gas gridded emission inventory. This invention improves data fusion accuracy by establishing a unified data base set and a hierarchical grid structure to achieve temporal, spatial, and formatal coordination of multi-source data. It also enhances the observability constraints and spatial consistency of emission estimation by embedding observational data into a SMOKE sparse matrix processing chain and constructing a joint estimation model. Furthermore, it addresses the discontinuity of emission boundaries between the core and background domains and the bias in total emission conservation by using consistency constraints across pollutants and greenhouse gases and boundary flux coordination. Ultimately, it achieves the generation of a unified, detailed, and highly consistent urban-scale emission inventory for multiple pollutants and greenhouse gases, significantly improving the scientific rigor, comparability, and regional simulation accuracy of the emission inventory. Attached Figure Description

[0069] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0070] Figure 1 This is a flowchart of a multi-source constrained gridded method for estimating atmospheric pollutant and greenhouse gas emissions, provided as an embodiment of the present invention. Detailed Implementation

[0071] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0072] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0073] like Figure 1 As shown, this invention provides a multi-source constrained gridded method for estimating atmospheric pollutant and greenhouse gas emissions, comprising:

[0074] Step 100: Acquire multi-source urban activity data, ground monitoring data, and satellite inversion data, and perform time, space, and format unification processing on the multi-source urban activity data, ground monitoring data, and satellite inversion data to obtain a unified data base set;

[0075] Step 200: Construct a nested grid system of the urban core domain and the surrounding background domain, and set the boundary mapping relationship and spatial resolution parameters of the nested grid system to obtain a hierarchical grid structure set;

[0076] Step 300: Based on the unified data base set, calculate the baseline emissions of each sector according to the activity level and emission factors of various sources, and use spatial proxy allocation on the hierarchical grid structure set to obtain the initial gridded baseline emission inventory;

[0077] Step 400: Establish a multi-source scale coordination mechanism based on the unified data base set, and use the multi-source scale coordination mechanism and the gridded baseline emission inventory to obtain the observation dataset;

[0078] Step 500: Embed the observation dataset into the SMOKE sparse matrix processing chain, construct a joint estimation model, and obtain a joint estimated emission result set through the joint estimation model;

[0079] Step 600: Establish consistency constraints across pollutants and greenhouse gas emissions on the joint estimated emission result set to obtain a consistent adjusted emission result set;

[0080] Step 700: Based on the consistent adjustment emission result set, perform boundary flux coordination processing between the urban core area and the surrounding background area to form a boundary coordinated emission inventory;

[0081] Step 800: Perform data consistency verification and error validation on the boundary coordinated emission inventory to generate the final city-scale multi-pollutant and greenhouse gas gridded emission inventory.

[0082] Furthermore, the specific implementation process of step 100 is as follows:

[0083] This embodiment first addresses the issues of source differences and coordinate system mismatches among multi-source urban activity data, ground monitoring data, and satellite inversion data. After obtaining the original multi-source dataset, a unified geographic reference system is used for reprojection and spatial resolution normalization. By performing vector-raster integrated mapping on high-resolution urban activity data and allocating ground monitoring station data to grid cells using nearest neighbor or bilinear interpolation, while performing orbital projection correction and pixel refinement on satellite inversion data, it ensures that multi-source data achieves full-area matching and seamless boundary connection within the same spatial framework, forming a spatially unified dataset.

[0084] This embodiment further addresses the temporal mismatch problem caused by inconsistent time sampling from different data sources by performing temporal interpolation and resampling on a spatially unified dataset. A hierarchical temporal reconstruction method with a fixed time step is employed to update daily or monthly activity data to the time frequencies corresponding to ground monitoring and satellite inversion data. A time window fitting algorithm is then used to align the temporal sequences of various data types, ensuring temporal consistency across multiple sources within the same observation period, thereby generating a temporally unified dataset.

[0085] Finally, this embodiment addresses the differences in format and physical quantity units among multi-source data by standardizing the time-unified data set and unifying the physical quantities. The dataset is reconstructed according to a unified field structure and naming rules, converting the units of various emission-related variables to a unified international standard system, and implementing missing data imputation and outlier removal. Consistency checks ensure that attribute fields, statistical periods, and spatial indices match, ultimately resulting in a unified data base set that can directly support subsequent emission calculations and model inputs.

[0086] Furthermore, the specific implementation process of step 200 is as follows:

[0087] This embodiment first delineates the spatial range of the urban core area and the surrounding background area based on the spatial distribution data of urban boundaries, land use, and emission activities from a unified data foundation. By overlaying administrative division and remote sensing land cover data, core emission source areas such as high-emission dense areas, industrial clusters, and major transportation arteries are identified and defined as the urban core area. For areas extending a certain distance beyond the core area, the range of the surrounding background area is determined by combining meteorological diffusion field characteristics and flow direction distribution, forming a regional boundary dataset with clear boundary lines and coordinate frameworks, providing basic spatial constraints for subsequent nested grid construction.

[0088] This embodiment further constructs a nested mesh structure based on the regional boundary dataset, taking into account the resolution differences between the core and background domains. By analyzing the emission source density gradient and terrain undulation characteristics, a fine mesh layer with higher spatial resolution is set for the urban core domain, while a gradually expanding coarse-resolution outer mesh layer is set for the surrounding background domain, thus forming a multi-level, nestable preliminary hierarchical mesh scheme. To improve the model's computational efficiency and boundary continuity, this embodiment adopts a spatially adaptive meshing strategy during the mesh generation process, ensuring a smooth transition of the mesh scale in the core domain boundary region and eliminating computational instability caused by abrupt resolution changes.

[0089] After forming a preliminary hierarchical grid scheme, this embodiment configures boundary numbering and mapping relationships for grid cells at the boundary between the core domain and the background domain. By identifying the spatial matching relationships of adjacent grid nodes, a boundary mapping relationship set is established to achieve a one-to-one correspondence between grid cells at different resolution levels and a data interaction channel. Subsequently, based on this boundary mapping relationship set, this embodiment uniformly configures the resolution parameters, coordinate projection method, and spatial indexing system of the grid system to form a spatially parameterized grid system. Finally, the spatially parameterized grid system and the boundary mapping relationship set are integrated to construct a hierarchical grid structure set that supports joint computation and flux boundary processing of the urban core domain and the surrounding background domain.

[0090] Furthermore, the specific implementation process of step 300 is as follows:

[0091] This embodiment first extracts and organizes activity level data from major emitting sectors such as energy, industry, transportation, and construction, based on a unified data foundation set. For the energy sector, fuel consumption and energy consumption structure are used as activity level parameters; for the industrial sector, output value, raw material input, and process type are used as indicators; for the transportation sector, road traffic flow, vehicle type, and mileage intensity are used as main factors; and for the construction sector, building area and energy intensity data are used as the basis. This embodiment matches the activity level data with corresponding emission factors according to the pollutant emission pathways of different sectors, automatically generating sectoral activity emission datasets. The selection of emission factors is based on emission source type, energy combustion characteristics, or industrial process characteristics to ensure data comparability and regional adaptability, providing an accurate foundation for subsequent emission calculations.

[0092] This embodiment then calculates baseline emissions based on the sectoral activity emissions dataset. By multiplying the activity level parameters by the matching emission factors source-by-source and summing the results, the total sectoral emissions for different species are calculated, generating emissions including NO. x SO2, PM 2.5Baseline emission inventory data tables for typical pollutants such as VOCs and greenhouse gases such as CO2 are provided. To ensure data reliability, this embodiment performs multi-dimensional statistical comparisons of emission results for each sector, including emission intensity per unit area, inter-sectoral ratios, and historical comparisons, filtering out outlier calculated values ​​to ensure that the total emission results are statistically consistent with industry emission characteristics and actual activity scale.

[0093] This embodiment further utilizes a hierarchical grid structure set to spatially allocate the baseline emission inventory. For each sector's characteristics, this embodiment constructs different sets of spatial proxy factors: the industrial sector uses industrial land distribution and enterprise point source locations as proxies; the transportation sector uses road grade and traffic flow density as proxies; and the energy and building sector uses population density and building functional area distribution as proxies, forming a multi-dimensional spatial proxy factor matrix. This embodiment uses normalized weight calculations to allocate the total baseline emissions of each sector to hierarchical grid units according to the spatial proxy weights, and implements cross-sectoral emission consistency balancing and statistical verification to ensure emission conservation between sector boundaries and pollutant types. Finally, an initial gridded baseline emission inventory that reflects spatial distribution characteristics and total emission balance is generated.

[0094] Furthermore, the specific implementation process of step 400 is as follows:

[0095] This embodiment first identifies scale differences in multi-source urban activity data, ground monitoring data, and satellite inversion data based on a unified data foundation set. By calculating the time step, spatial resolution, and observation error range of each data source, it analyzes the unbalanced characteristics of urban activity data in terms of temporal frequency, the location limitations of ground monitoring data, and the high-resolution attributes of satellite inversion data in terms of spatial coverage, constructing cross-source difference description parameters. Combining these parameters, this embodiment forms a scale feature dataset with temporal, spatial, and precision dimensions, providing a quantitative basis for subsequent scale matching and coordination processing.

[0096] This embodiment establishes a set of scale mapping models between multi-source data based on a scale feature dataset. At the spatial level, geographic weighted regression interpolation and spatial variogram interpolation models are used to estimate the representative distribution of ground monitoring data in unobserved areas. At the temporal level, time-series weighted smoothing and piecewise resampling algorithms are employed, using satellite transit time as a reference to achieve multi-source time-series synchronization. To address resolution differences, a multi-level resolution resampling model is used to scale-match high-resolution satellite data with lower-resolution activity data, thereby obtaining a set of algorithms that can unify the scale of data from different sources. After the model runs, it outputs a unified scale weight coefficient, which reflects the relative contribution of each source data to the temporal and spatial fusion result.

[0097] This embodiment further utilizes a unified scale weighting coefficient to establish a multi-source scale coordination mechanism for integrated processing of multi-source data. Based on this mechanism, this embodiment fuses the gridded baseline emission inventory with ground monitoring data and satellite inversion data from a unified data base. Through weighted matching, spatial and temporal scale convergence between observational information and emission estimates is achieved, generating a coordinated observational data sample set. To ensure the reliability of the fusion results, this embodiment performs anomaly detection and integrity verification on the sample set, eliminating measurement biases or interference from missing measurement points. The final result is a structurally complete and temporally and spatially consistent observational dataset, used for subsequent joint estimation model construction and optimization.

[0098] "Weighted matching" refers to assigning different weights to different data sources based on their temporal, spatial, and accuracy characteristics during multi-source data fusion, ensuring that various data types contribute in a reasonable proportion to the fusion result. Specifically, when generating coordinated observation data, the system comprehensively considers the differences in spatial coverage, temporal synchronization, and observation reliability among the various data sources. For example, while ground monitoring data has limited coverage, it has the highest accuracy and therefore carries a larger weight; satellite inversion data possesses more comprehensive spatial information, but its weight is relatively smaller due to temporal transit and inversion errors; urban activity data provides detailed temporal variation information but has lower spatial resolution, thus its weight is moderate. During fusion, these data are matched and superimposed according to their weights to form a coordinated observation result that balances ground accuracy, spatial continuity, and temporal integrity. In simple terms, weighted matching balances and fuses data from different sources on a uniform scale using a "contribution based on reliability" approach, resulting in a reliable, complete, and usable coordinated observation data sample set for model inversion.

[0099] Furthermore, the specific implementation process of step 500 is as follows:

[0100] This embodiment first invokes the spatial allocation, temporal allocation, and component mapping modules in the SMOKE sparse matrix processing chain to establish a set of sparse matrix operators for emission data transformation and mapping. The SMOKE sparse matrix processing chain describes the multidimensional mapping relationship from emission sources to grid cells in sparse matrix form. This embodiment defines spatial allocation operators to control the geographical mapping of emission sources, temporal allocation operators to express diurnal and seasonal variation patterns, and component mapping operators to describe the transformation and derivation relationships between pollutant components. By combining these operators, a set of sparse matrix operators with a scalable node structure is formed, providing a mathematical foundation for observation data embedding and joint estimation model establishment.

[0101] This embodiment, based on the observation dataset, maps ground monitoring values ​​and satellite inversion values ​​in each grid cell to spatial and temporal link nodes in a sparse matrix operator set, thereby forming an input coupling mechanism between observation information and emission inventories. To achieve accurate mapping, this embodiment automatically matches various types of observation data to their respective matrix positions using grid spatial indexing and time step numbering rules, establishing a coupled observation input matrix. This matrix uses rows to identify the observation data type and columns to identify sparse matrix nodes, thus achieving integrated coupled input of multi-dimensional observation signals.

[0102] This embodiment further combines the coupled observation input matrix with a sparse matrix operator structure to construct a joint estimation model. The model uses the linear approximation relationship between the observed signal and emission intensity as its core framework, employs the least squares method for parameter inversion, and automatically adjusts the emission estimates of each grid cell through an iterative optimization process to minimize the sum of squared observation residuals. After optimization, this embodiment outputs a set of jointly estimated emission parameters and re-executes the output flow of the SMOKE sparse matrix processing chain to update the spatial allocation and component mapping results, ultimately generating a set of jointly estimated emission results that reflects the constraints of multi-source observations and the characteristics of dynamic equilibrium.

[0103] Specifically, this embodiment constructs a joint estimation model by establishing an emission estimation optimization framework based on observational data constraints, spatial structure constraints, and component consistency constraints. This framework enables refined inversion and dynamic correction of multiple pollutant and greenhouse gas emissions at the urban scale. The model uses gridded emission variables as the objective solver, taking baseline emission data, observational data, and a spatial relationship matrix as inputs. It comprehensively evaluates the consistency between the emission estimates and multiple observation sources. This embodiment first uses the baseline distribution of urban emissions as an initial reference. A spatial proximity deviation constraint between the emission estimation results and the baseline emission field is defined through a spatial structure weight matrix, ensuring that the estimation results retain the overall distribution characteristics of the original emission structure. Based on this, an observation mapping relationship matrix is ​​set to project the emission estimation vector onto the observation space of ground monitoring and satellite inversion, thereby establishing a numerical correspondence between the emission estimation results and actual observational information to ensure the verifiability and inversionability of the model parameters.

[0104] During the model solution process, an observation constraint balance coefficient and a spatial gradient regularization coefficient are introduced to achieve a balance between observation fitting accuracy and spatial smoothness. The observation constraint balance coefficient is determined by the temporal frequency and measurement uncertainty of various observation data. For example, when ground observations are dense and errors are small, this coefficient is set to a higher value to enhance the model's fit to the observation data. The spatial gradient regularization coefficient, by constraining the spatial gradient intensity of the emission field, suppresses non-physical emission abrupt changes. It is usually set according to the urban heterogeneity scale and grid resolution to ensure the spatial continuity of the emission field. The spatial gradient term is calculated using a two-dimensional finite difference approximation, where the difference in value between corresponding grid cells in the latitude and longitude directions is used to quantify the local gradient magnitude.

[0105] To ensure consistency between the constraints on individual components and the overall total emissions, this embodiment introduces a consistency constraint factor and a component coordination matrix. The consistency constraint factor reflects the degree of interaction and coupling between different pollutants and greenhouse gases in energy conservation and mass balance, and is used to maintain the stability of emission ratios. The component coordination matrix defines the interconversion, co-emission, and source coupling relationships among pollutants, such as the co-emission ratio of volatile organic compounds and nitrogen oxides in certain industrial processes, which can be determined through empirical statistics or energy consumption structure analysis. The overall conservation constant term represents the total emission boundary constraint for the entire city and its surrounding areas, and is used to maintain the mass balance during model optimization. This embodiment uses a method of minimizing the objective function during the solution process, updating the grid estimate through iterative optimization to ensure that all constraints are simultaneously satisfied within the error threshold. In actual operation, the parameters can be adaptively adjusted according to the characteristics of the data source and the spatial complexity of the city, thereby ensuring that the joint estimation results have higher physical reliability and application stability.

[0106] Furthermore, the specific implementation process of step 600 is as follows:

[0107] This embodiment first extracts and analyzes data on different pollutant and greenhouse gas emissions in each grid cell based on the joint estimated emission result set, constructing a correlation feature set for consistent control. By calculating the energy coupling relationship, chemical conversion coefficient, and common source ratio of emission activities among pollutants, characteristic parameters reflecting the interconnected changes of pollutants and greenhouse gases are extracted. For example, the emission ratios of carbon dioxide and carbon monoxide, the chemical response coefficients of nitrogen oxides and volatile organic compounds, and the synergistic relationship between particulate matter and combustion emission source intensity are extracted as key correlation elements to form a pollutant and greenhouse gas correlation feature set. This feature set can reflect the inherent interconnected characteristics of different emission components in multi-source emission areas such as energy utilization, industrial processes, and transportation activities, providing parameter support for subsequent establishment of cross-pollutant constraints.

[0108] This embodiment further constructs a set of consistency constraint functions based on the correlation feature set of pollutants and greenhouse gases, and maps this set of constraint functions to the spatial grid dimension of the joint estimated emission result set. By defining three types of functions—emission ratio constraint, synergy coefficient constraint, and mass conservation constraint—boundary control of the proportion relationship between different pollutants is achieved. The emission ratio constraint function is used to limit the deviation of the ratio between emissions from the same source; the synergy coefficient constraint is used to control the consistency of the spatial distribution of pollutants with chemical or process dependence; and the mass conservation constraint is used to maintain a stable relationship between the total amount of emissions of all types and the energy balance. In constructing the gridded consistency constraint matrix, this embodiment sets weighting coefficients for different pollutants and greenhouse gases, and sets corresponding adjustment thresholds according to their sensitivity in the emission source structure, so that the model can automatically control local deviations while maintaining physical consistency, forming a gridded consistency constraint matrix that can directly participate in the iterative solution.

[0109] This embodiment concludes by solving for constraints and adjusting the scale of the jointly estimated emission results set based on a gridded consistency constraint matrix. A step-by-step solution strategy is adopted. First, energy conservation and mass balance are used as hard constraints, and matrix weighting is used to correct cross-pollutant emission terms. Then, a cooperative constraint function is used as a soft constraint to smoothly adjust the scale of interrelated emission species, ensuring that the emission trends of different pollutants remain consistent within the same spatial unit. Through iterative calculation, this embodiment generates a consistency optimization result set, which is then differentially verified with the original jointly estimated emission result set to identify and eliminate abnormal adjustment units. Finally, a consistent adjusted emission result set is output. This result set features unified numerical coordination across pollutants, energy conservation, and spatial distribution continuity, and can serve as a stable input basis for subsequent boundary flux coordination and emission inventory integration.

[0110] The specific implementation process of step 700 is as follows:

[0111] This embodiment first identifies and extracts the boundary units between the urban core domain and the surrounding background domain based on the hierarchical grid structure set, establishing a boundary grid index set. By traversing the grid number and spatial coordinate association table, the set of continuous grid units at the boundary between the two domains is determined, and the geometric center point, area, and normal direction of each unit are recorded for spatial positioning in subsequent flux calculations. This embodiment ensures the spatial connectivity and coordinate consistency of boundary units during boundary information extraction to eliminate boundary breaks or overlaps, thereby forming a complete and indexable boundary grid index set, providing a spatial framework constraint for boundary flux quantification analysis.

[0112] This embodiment further calculates the emission intensity and meteorological dynamic characteristics of each grid cell within the boundary grid index set based on a consistent adjustment of the emission result set. First, gridded emission flux data of various pollutants and greenhouse gases within the boundary cells are extracted to establish a boundary emission intensity matrix. Then, based on meteorological driving data from a unified data base set, parameters such as wind speed, wind direction, temperature, and atmospheric stability in the boundary region are extracted to form a boundary meteorological feature set. To reflect spatial heterogeneity, this embodiment employs a hierarchical grid structure coordinate mapping system to interpolate and vectorize the wind field within the boundary cells, generating a boundary wind field direction array representing changes in wind direction and flow intensity. Combining the wind field direction array with the meteorological feature set, the diffusion coefficient, turbulence intensity, and mixing layer height of the boundary cells are calculated to form a meteorological diffusion parameter matrix, used to characterize the material transport and diffusion capacity of the boundary region.

[0113] More specifically, in this embodiment, the mixing layer height is calculated using wind speed and temperature profiles from the boundary meteorological feature set, and is used to characterize the effective height of vertical mixing within the boundary layer; the diffusion coefficient and turbulence intensity are calculated based on wind speed, wind direction, temperature, and atmospheric state parameters in the boundary region. Specifically, the diffusion coefficient is estimated based on the spatial distribution of the wind speed field and temperature gradient characteristics, combined with atmospheric stability parameters. When the temperature decreases with altitude and the wind speed is high, it indicates that the atmosphere is in an unstable or neutral stratification state, and the diffusion coefficient takes a larger value; when there is an inversion layer or a low wind speed area, the diffusion coefficient takes a smaller value to reflect the restricted diffusion characteristics of pollutants in a stable atmosphere. The turbulence intensity is calculated by analyzing the rate of change of the wind speed field in the time or spatial dimension; the ratio of the standard deviation of the wind speed vector to the average wind speed characterizes the degree of local pulsation. The more uneven the wind speed distribution and the more obvious the wind field shear in the boundary region, the higher the turbulence intensity. Through the above parameterized relationships, this embodiment can utilize existing wind speed, wind direction, temperature, and atmospheric driving data to completely obtain the diffusion coefficient and turbulence intensity, ensuring the physical consistency of the boundary layer meteorological and dynamic characteristics.

[0114] In this embodiment, the mixing layer height is calculated using wind speed and temperature profiles from the boundary meteorological feature set, and is used to characterize the effective vertical mixing height within the boundary layer. The diffusion coefficient and turbulence intensity are calculated based on wind speed, wind direction, temperature, and atmospheric state parameters in the boundary region. Specifically, the diffusion coefficient is estimated based on the spatial distribution of the wind speed field and temperature gradient characteristics, combined with atmospheric stability parameters. When the temperature decreases with altitude and the wind speed is high, it indicates that the atmosphere is in an unstable or neutral stratification state, and the diffusion coefficient takes a larger value. When there is an inversion layer or a low wind speed area, the diffusion coefficient takes a smaller value to reflect the restricted diffusion characteristics of pollutants in a stable atmosphere. The turbulence intensity is calculated by analyzing the rate of change of the wind speed field in the time or spatial dimension, and the ratio of the standard deviation of the wind speed vector to the average wind speed characterizes the degree of local pulsation. The more uneven the wind speed distribution and the more significant the wind field shear in the boundary region, the higher the turbulence intensity. Through the above parameterized relationships, this embodiment can utilize existing wind speed, wind direction, temperature, and atmospheric driving data to fully determine the diffusion coefficient and turbulence intensity, ensuring the physical consistency between the boundary layer meteorological dynamic characteristics and the pollutant transport process.

[0115] In this embodiment, "atmosphere" does not refer to the abstract whole of air, but rather to the set of atmospheric physical characteristics reflecting the meteorological state of the boundary layer in meteorological driving data, including but not limited to parameters such as atmospheric pressure, humidity, atmospheric stability, and gas density. This "atmosphere" information is used to calculate temperature gradients, airflow buoyancy, and stability indices, thereby participating in the calculation of diffusion coefficients, turbulence intensity, and mixing layer height. By introducing atmospheric state elements, this embodiment achieves a dynamic characterization of the meteorological background in emission flux calculations, making the boundary flux data more consistent with actual atmospheric transport and diffusion characteristics.

[0116] This embodiment calculates the boundary flux dataset based on the principle of mass conservation, coupling emission intensity, wind direction, and diffusion parameters. For each boundary grid cell, vector decomposition is used to calculate the flux components in both vertical and horizontal directions to determine the actual exchange direction and net flux intensity of pollutants and greenhouse gases. This calculation process comprehensively considers the nonlinear relationship between wind speed and meteorological diffusion coefficient, obtaining the time-averaged flux through numerical integration, thus forming the net exchange matrix of emission input and output for the boundary cell. After completing the flux calculation, this embodiment performs a balance analysis and correction on the pollutant flow between the core and background domains, further establishing a boundary coordination parameter set reflecting spatial gradient and flow direction characteristics. Based on this, emission results within the boundary region are redistributed and intensity corrected, ultimately obtaining a boundary coordinated emission inventory that satisfies spatial continuity and mass balance constraints.

[0117] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0118] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A multi-source constrained gridded method for estimating atmospheric pollutant and greenhouse gas emissions, characterized in that, include: Acquire multi-source urban activity data, ground monitoring data, and satellite inversion data, and perform time, space, and format unification processing on the multi-source urban activity data, ground monitoring data, and satellite inversion data to obtain a unified data base set; A nested grid system of urban core domain and surrounding background domain is constructed, and the boundary mapping relationship and spatial resolution parameters of the nested grid system are set to obtain a hierarchical grid structure set; Based on a unified data base set, baseline emissions for each sector are calculated according to the activity levels and emission factors of various sources, and an initial gridded baseline emission inventory is obtained by spatial proxy allocation on a hierarchical grid structure set. A multi-source scale coordination mechanism is established based on the unified data base set, and an observation dataset is obtained using the multi-source scale coordination mechanism and the gridded baseline emission inventory. The observation dataset is embedded into the SMOKE sparse matrix processing chain to construct a joint estimation model, and the joint estimation emission result set is obtained through the joint estimation model. Consistency constraints across pollutant and greenhouse gas emissions are established on the joint estimated emission result set to obtain a consistent adjusted emission result set. Based on the consistent adjustment emission result set, boundary flux coordination processing is carried out between the urban core area and the surrounding background area to form a boundary coordinated emission inventory. Data consistency checks and error verifications are performed on the boundary coordinated emission inventory to generate the final city-scale multi-pollutant and greenhouse gas gridded emission inventory.

2. The method for estimating multi-source constrained gridded atmospheric pollutant and greenhouse gas emissions according to claim 1, characterized in that, The process involves acquiring multi-source urban activity data, ground monitoring data, and satellite inversion data, and then performing time, spatial, and format-unified processing on these data to obtain a unified data base set, including: Obtain the raw multi-source data set, wherein the raw multi-source data set includes: multi-source urban activity data, ground monitoring data and satellite inversion data; The coordinate system, geographic projection, and spatial resolution of the multi-source urban activity data, ground monitoring data, and satellite inversion data in the original multi-source dataset are uniformly transformed to obtain a unified spatial dataset. Interpolate, resample, and time-align the time frequency and observation period of the spatial unified data set to obtain a temporally unified data set. The time-unified data set is subjected to format conversion, field naming, and unit standardization to obtain a standardized data set; Based on the standardized dataset, outlier removal, missing test item imputation, and consistency checks are performed to obtain a unified data base set.

3. The method for estimating multi-source constrained gridded atmospheric pollutant and greenhouse gas emissions according to claim 1, characterized in that, A nested grid system of urban core domain and surrounding background domain is constructed, and the boundary mapping relationship and spatial resolution parameters of the nested grid system are set to obtain a hierarchical grid structure set, including: Based on the unified data base, the spatial range of the urban core area and the surrounding background area is delineated to obtain the regional boundary dataset; Based on the aforementioned regional boundary dataset, different grid resolutions and hierarchical structures are set according to the spatial differences between the urban core area and the surrounding background area to obtain a preliminary hierarchical grid scheme. Based on the preliminary hierarchical grid scheme, the boundaries of adjacent grids between the urban core area and the outer background area are numbered and mapped to obtain a set of boundary mapping relationships; Based on the boundary mapping relationship set, the resolution parameters, coordinate projection method and spatial indexing system of the grid are configured to obtain a spatial parameterized grid system; The spatial parameterized grid system and the boundary mapping relationship set are integrated to generate a hierarchical grid structure set that supports joint calculation of the urban core domain and the surrounding background domain, thus obtaining the hierarchical grid structure set.

4. The method for estimating multi-source constrained gridded atmospheric pollutant and greenhouse gas emissions according to claim 1, characterized in that, The baseline emissions for each sector are calculated based on a unified data base set, according to the activity levels and emission factors of various sources, and an initial gridded baseline emissions inventory is obtained by spatial proxy allocation on a hierarchical grid structure set, including: Based on the unified data base set, activity level data for energy, industry, transportation, and construction are extracted, and corresponding emission factors are matched to the activity level data to obtain sectoral activity emission datasets. Based on the aforementioned departmental activity emission dataset, the total emissions of each department are calculated according to source type and pollutant type, resulting in a baseline emission inventory data table; Based on the hierarchical grid structure set, and combined with land use, population density, road distribution and industrial location, a spatial proxy factor set corresponding to each sector is constructed. Using the aforementioned spatial proxy factor set, the baseline emissions of each sector in the baseline emission inventory data table are allocated to each grid cell of the hierarchical grid structure set according to the spatial proxy weight, resulting in a gridded emission allocation result set. Based on the gridded emission allocation result set, consistency balancing and statistical verification are performed on the total emissions across sectors and pollutants to obtain an initial gridded baseline emission inventory.

5. The method for estimating multi-source constrained gridded atmospheric pollutant and greenhouse gas emissions according to claim 1, characterized in that, The process involves establishing a multi-source scale coordination mechanism based on the unified data base set, and using this mechanism and a gridded baseline emission inventory to obtain an observation dataset, including: Based on the unified data base set, the scale feature differences of multi-source urban activity data, ground monitoring data and satellite inversion data in terms of temporal resolution, spatial resolution and data accuracy are identified to obtain a scale feature dataset. Based on the scale feature dataset, spatial interpolation, temporal weighting, and resolution resampling algorithms that match various data sources are selected to construct a scale mapping model set; Using the scale mapping model set, spatiotemporal coordination transformation is performed on multi-source urban activity data, ground monitoring data and satellite inversion data to generate unified scale weight coefficients, and a multi-source scale coordination mechanism is established based on the unified scale weight coefficients. Based on the aforementioned multi-source scale coordination mechanism, the gridded baseline emission inventory is integrated and weighted with the ground monitoring data and satellite inversion data in the unified data base set to obtain a coordinated observation data sample set; Anomaly detection and integrity verification are performed on the coordinated observation data sample set to obtain the observation dataset.

6. The method for estimating multi-source constrained gridded atmospheric pollutant and greenhouse gas emissions according to claim 1, characterized in that, The process of embedding the observation dataset into the SMOKE sparse matrix processing chain to construct a joint estimation model, and obtaining a joint estimated emission result set through the joint estimation model, includes: The SMOKE sparse matrix processing chain is used to establish a set of sparse matrix operators by calling spatial allocation, temporal allocation, and component mapping. Based on the observation dataset, the ground monitoring and satellite inversion observation values ​​corresponding to each grid cell are mapped to the corresponding link nodes in the sparse matrix operator set, and the input coupling relationship between the observation information and the emission inventory is established to obtain the coupled observation input matrix. By combining the operator structure of the coupled observation input matrix and the SMOKE sparse matrix processing chain, a joint estimation model is constructed; The joint estimation model is inverted and iteratively optimized using the least squares method, and the grid cell emissions are updated on the optimized joint estimation model to obtain the joint estimated emission parameter set. Based on the jointly estimated emission parameter set, the output process of the SMOKE sparse matrix processing chain is executed to obtain the jointly estimated emission result set.

7. The method for estimating multi-source constrained gridded atmospheric pollutant and greenhouse gas emissions according to claim 6, characterized in that, The expression for the joint estimation model is: ； in, This is the optimal solution for jointly estimating the emission vector; This is the gridded estimation vector for the emissions to be determined; Baseline emission vector; This is the spatial structure weight matrix, used to constrain the spatial deviation between emissions and the baseline; This is the observation mapping matrix, used to map emission estimates to the corresponding observation dimensions; This is a vector of observation data obtained by a multi-source coordination mechanism; These are the observation constraint balance coefficients, used to adjust the degree of observation fit; This is the spatial gradient regularization coefficient, used to suppress unreasonable abrupt changes in the emission field; coordinates in two-dimensional space Emission gradient operator on; As a consistency constraint factor; This is an energy-matter constraint matrix among emission components, used to express the synergistic relationships between different pollutants and greenhouse gas emissions; This is the overall emission conservation constant term, used to maintain the mass balance constraint.

8. The method for estimating multi-source constrained gridded atmospheric pollutant and greenhouse gas emissions according to claim 1, characterized in that, The process of establishing consistency constraints across pollutant and greenhouse gas emissions on the joint estimated emission result set to obtain a consistent adjusted emission result set includes: Based on the joint estimated emission result set, correlation parameters for consistency control are extracted to obtain the correlation feature set of pollutants and greenhouse gases; Based on the pollutant greenhouse gas correlation feature set, a corresponding consistency constraint function set is constructed, and the consistency constraint function set is mapped to the grid dimension of the joint estimated emission result set. Weighting coefficients and adjustment thresholds are set for different pollutants and greenhouse gas emission items to obtain the gridded consistency constraint matrix. Based on the gridded consistency constraint matrix, the joint estimated emission result set is constrained and proportionally adjusted to obtain the consistency optimization result set. The consistency optimization result set and the joint estimated emission result set are differentially verified to obtain the consistency adjusted emission result set.

9. The method for estimating multi-source constrained gridded atmospheric pollutant and greenhouse gas emissions according to claim 1, characterized in that, The aforementioned emission result set, based on consistency adjustment, performs boundary flux coordination processing between the urban core area and the surrounding background area to form a boundary coordinated emission inventory, including: Establish a boundary grid index set based on the hierarchical grid structure set; Based on the aforementioned consistency adjustment emission result set, emission intensity, wind field direction, and meteorological diffusion parameters are calculated for each grid cell in the boundary grid index set to obtain the boundary flux dataset; Based on the boundary flux dataset, a balance analysis and correction of the net fluxes of pollutants and greenhouse gases between the two domains are performed to obtain the boundary flux correction set. Based on the boundary flux correction set, a boundary coordination parameter set reflecting the spatial gradient and flow direction characteristics is established, thus obtaining the boundary coordination parameter set; Based on the boundary coordination parameter set, the consistency adjustment emission result set is redistributed and intensity corrected within the boundary region to obtain the boundary coordination emission inventory.

10. The method for estimating multi-source constrained gridded atmospheric pollutant and greenhouse gas emissions according to claim 9, characterized in that, The emission intensity, wind direction, and meteorological diffusion parameters of each grid cell within the boundary grid index set are calculated based on the consistency adjustment emission result set to obtain the boundary flux dataset, including: Based on the consistency adjustment of the emission result set, the emission flux data of each pollutant and greenhouse gas in the boundary grid index set are extracted to obtain the boundary emission intensity matrix; Based on meteorological driving data from a unified data base, wind speed, wind direction, temperature, and atmospheric data of the corresponding boundary areas are extracted to obtain a boundary meteorological feature set. Using the aforementioned boundary meteorological feature set and combined with the coordinate system of the hierarchical grid structure, the wind field direction and flow intensity of each unit within the boundary grid are interpolated and vectorized to obtain the boundary wind field direction array; Based on the boundary meteorological feature set and the boundary wind field direction array, the diffusion coefficient, turbulence intensity and mixing layer height are calculated to obtain the meteorological diffusion parameter matrix; Based on the boundary emission intensity matrix, the boundary wind field direction array, and the meteorological diffusion parameter matrix, the flux intensity and net exchange of the boundary cells are calculated using the principle of mass conservation, resulting in a boundary flux dataset.

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