A method and system for quantitatively analyzing air pollution for a valley-type city

Abstract

The application discloses a kind of atmospheric pollution quantification analysis method and system for valley type city, method includes: obtaining the historical pollution data of target valley city, constructs and includes four groups of scene data input queue of real benchmark, no terrain, no city and no two thereof;Drive the meteorology-chemical numerical simulation model coupled with city canopy model to execute each scene simulation, obtain corresponding pollution concentration result queue;Three independent component queues of city influence, terrain influence and the synergistic effect of both are calculated and generated using contribution decomposition algorithm;Through iteration judgment and optimization, make synergistic effect meet preset requirements;Finally, generate spatial quantification atlas based on each component queue.The application realizes the accurate decoupling and quantitative separation of terrain and urbanization synergistic pollution effect, solves the problem that traditional method is difficult to quantify complex interaction, provides direct quantitative decision basis for precise planning and pollution prevention and control of valley type city.

Description

Technical Field

[0001] This invention belongs to the field of atmospheric environmental analysis technology, and in particular relates to a method and system for quantitative analysis of atmospheric pollution in river valley cities. Background Technology

[0002] Valley-type cities often have limited atmospheric diffusion capacity due to their unique topography. Meanwhile, rapid urbanization alters land cover and local meteorological conditions. Existing research largely focuses on analyzing the impact of topography or urbanization on pollution in isolation, commonly employing comparative simulations with controlled variables (e.g., comparisons with / without urban scenarios). However, complex nonlinear interactions (synergistic effects) exist between topography and urbanization. For example, urban building clusters may further impede airflow in valleys, or the coupling of urban heat islands and valley inversions may exacerbate pollution retention. These synergistic effects are difficult to effectively isolate and quantify through simple comparative simulations.

[0003] Traditional assessment methods typically provide only qualitative or semi-quantitative conclusions, failing to accurately answer the crucial question: "In a specific river valley city, what are the respective contributions of topography, urbanization, and their synergistic effects to pollution concentration?" This results in a lack of precise, quantitative scientific basis for urban planning and pollution control strategies, making it difficult to implement targeted interventions in areas with significant synergistic effects. Summary of the Invention

[0004] This invention aims to overcome the shortcomings of existing technologies and provide a quantitative analysis method and system for air pollution in valley-type cities, so as to solve the problem that existing technologies are unable to accurately quantify the synergistic effect of topography and urbanization.

[0005] In a first aspect, the present invention provides a method for quantitative analysis of air pollution in valley-type cities, comprising:

[0006] Obtain the first dataset of the target river valley city during the historical pollution period. The first dataset includes at least meteorological observation data, air pollutant concentration data, land use type data, and digital elevation data.

[0007] Based on the first dataset, a set of data input queues is generated using preset data generation rules. The set of data input queues includes a baseline data input queue, a terrain modification data input queue, a city modification data input queue, and a composite modification data input queue.

[0008] Each data input queue in the data input queue set is input into the pre-constructed meteorological-chemical numerical simulation model of the coupled urban canopy model, and simulation calculations are performed to obtain the corresponding pollution concentration simulation result queue.

[0009] Based on the simulation results of each pollution concentration, a predefined contribution decomposition algorithm is used to calculate and generate a queue of urbanization-only influence components, a queue of topography-only influence components, and a queue of the combined influence components of urbanization and topography.

[0010] Determine whether the statistical characteristics of the synergistic effect component queue exceed a preset synergistic effect threshold;

[0011] If the synergistic effect threshold is exceeded, an update scheme for the land use type data and / or digital elevation data in the first dataset is generated based on the spatial distribution of the synergistic effect component queue, and the first dataset is updated based on the update scheme. In addition, the data input queue set and the pollution concentration simulation result queue are updated according to the updated first dataset until the updated synergistic effect component queue meets the preset requirements.

[0012] Based on the final cohorts of urbanization-only influence components, topography-only influence components, and synergistic influence components that meet the preset requirements, a spatial quantitative map of the contribution intensity of each influence factor to air pollution is determined.

[0013] Secondly, the present invention provides a quantitative analysis system for air pollution in river valley cities, comprising:

[0014] The acquisition module is configured to acquire the first dataset of the target river valley city during the historical pollution period. The first dataset includes at least meteorological observation data, air pollutant concentration data, land use type data, and digital elevation data.

[0015] The first generation module is configured to generate a set of data input queues based on the first dataset using preset data generation rules. The set of data input queues includes a baseline data input queue, a terrain modification data input queue, a city modification data input queue, and a composite modification data input queue.

[0016] The output module is configured to input each data input queue in the data input queue set into the pre-constructed meteorological-chemical numerical simulation model of the coupled urban canopy model, perform simulation calculations, and obtain the corresponding pollution concentration simulation result queue.

[0017] The second generation module is configured to calculate, based on each pollution concentration simulation result queue, using a predefined contribution decomposition algorithm, to generate a queue of urbanization-only influence components, a queue of topography-only influence components, and a queue of the combined influence components of urbanization and topography.

[0018] The judgment module is configured to determine whether the statistical characteristics of the collaborative influence component queue exceed a preset collaborative effect threshold.

[0019] The update module is configured to, if the synergistic effect threshold is exceeded, generate an update scheme for the land use type data and / or digital elevation data in the first dataset based on the spatial distribution of the synergistic effect component queue, update the first dataset based on the update scheme, and update the data input queue set and pollution concentration simulation result queue according to the updated first dataset, until the updated synergistic effect component queue meets the preset requirements.

[0020] The module is configured to determine the spatial quantitative map of the contribution intensity of each influencing factor to air pollution based on the final urbanization-only influence component queue, topography-only influence component queue, and synergistic influence component queue that meet the preset requirements.

[0021] Thirdly, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the method for quantitative analysis of air pollution in valley-type cities according to any embodiment of the present invention.

[0022] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the steps of the method for quantitative analysis of air pollution in valley-type cities according to any embodiment of the present invention.

[0023] This application presents a method and system for quantitative analysis of air pollution in valley-type cities. It acquires historical pollution data of the target valley city and constructs four data input queues: a real baseline, no topography, no city, and neither. A meteorological-chemical numerical simulation model coupled with a city canopy model is driven to simulate each scenario, yielding corresponding pollution concentration result queues. A contribution decomposition algorithm is used to calculate and generate three independent component queues: the individual impact of urbanization, the individual impact of topography, and the synergistic impact of both. Through iterative judgment and optimization, the synergistic impact is made to meet preset requirements. Finally, a spatial quantitative map is generated based on each component queue, achieving precise decoupling and quantitative separation of the synergistic pollution effects of topography and urbanization. This solves the problem of traditional methods struggling to quantify complex interactive effects, providing a direct quantitative decision-making basis for precise planning and pollution control in valley-type cities. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart illustrating a method for quantitative analysis of air pollution in valley-type cities, as provided in an embodiment of the present invention;

[0026] Figure 2 This is a structural block diagram of an air pollution quantitative analysis system for valley-type cities provided in an embodiment of the present invention;

[0027] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0029] Please see Figure 1 The diagram shows a flowchart of a method for quantitative analysis of air pollution in valley-type cities according to this application.

[0030] like Figure 1 As shown, the quantitative analysis method for air pollution in valley-type cities specifically includes the following steps:

[0031] Step S101: Obtain the first dataset of the target river valley city during the historical pollution period. The first dataset includes at least meteorological observation data, air pollutant concentration data, land use type data, and digital elevation data.

[0032] Step S102: Based on the first dataset, a set of data input queues is generated using preset data generation rules. The set of data input queues includes a baseline data input queue, a terrain modification data input queue, a city modification data input queue, and a composite modification data input queue.

[0033] In this step, the meteorological observation data, air pollutant concentration data, land use type data and digital elevation data in the first dataset are spatiotemporally matched and format unified to generate a first standardized data queue, and the first standardized data queue is directly used as the reference data input queue.

[0034] Extract the digital elevation data sub-queue from the first standardized data queue, calculate the average elevation value of the target valley area, replace the elevation values ​​of all grids in the valley area in the digital elevation data sub-queue with the average elevation value, generate the second digital elevation data sub-queue, merge the second digital elevation data sub-queue with the first other data sub-queue in the first standardized data queue to form the terrain modification data input queue, wherein the first other data sub-queue is the data sub-queue in the first standardized data queue excluding the digital elevation data sub-queue;

[0035] Extract the land use type data sub-queue from the first standardized data queue, identify all urban built-up area categories of the grid, uniformly modify the category attributes of the grid to the preset natural surface category attributes, generate the second land use type data sub-queue, merge the second land use type data sub-queue with the second other data sub-queue in the first standardized data queue to form the urban modified data input queue, wherein the second other data sub-queue is the data sub-queue in the first standardized data queue excluding the land use type data sub-queue;

[0036] The obtained terrain modification data input queue and the city modification data input queue are merged with the third other data sub-queue in the first standardized data queue to form the composite modification data input queue. The third other data sub-queue is the data sub-queue in the first standardized data queue excluding the digital elevation data sub-queue and the land use type data sub-queue. In one specific embodiment, data standardization and baseline queue generation are as follows:

[0037] First, the acquired first dataset (meteorology, pollution, land use, and elevation) underwent rigorous spatiotemporal alignment and format standardization. This process utilized data assimilation and gridding techniques to uniformly interpolate all data onto the pre-defined three-dimensional spatial grid and simulation time step of the meteorological-chemical numerical simulation model, forming a first standardized data queue with completely consistent spatiotemporal dimensions. Each grid point in this queue contains the corresponding meteorological conditions, background pollutant concentrations, land surface type attributes, and altitude. This standardized data queue, which has not been modified by any human intervention and reflects the real world, was directly defined as the "baseline data input queue," serving as the reference benchmark for all subsequent comparative analyses.

[0038] Generation of the terrain modification queue:

[0039] To quantify the pure impact of topography, a hypothetical scenario of "valleyless terrain" needs to be constructed. A digital elevation data sub-cohort is extracted from the first standardized data cohort. Through geostatistical analysis, the average elevation value H_avg (arithmetic mean) of all grid points in the target valley region (e.g., the entire basin) is calculated. Subsequently, for each grid within the valley area (which can be automatically identified by setting an elevation abrupt change gradient or terrain contour), its elevation value is forcibly replaced with H_avg, thus generating a second digital elevation data sub-cohort that has been "flattened." This modified elevation sub-cohort is then recombine with the unmodified meteorological, pollutant, and land use data sub-cohorts (i.e., the first other data sub-cohort) from the original standardized data cohort to form the "topography modification data input cohort." This cohort simulates an idealized scenario with the current urban layout and meteorological conditions, but with the terrain flattened.

[0040] Generation of city modification queues:

[0041] To quantify the pure impact of urbanization, a hypothetical scenario of "no urban underlying surface" needs to be constructed. A land use type data sub-cohort is extracted from the first standardized data cohort. Using remote sensing image interpretation labels or land use classification codes, all grids representing densely populated human activities such as "urban built-up areas" and "industrial, mining, and transportation land" are identified. The land use category attributes of these grids are uniformly modified to preset category attributes that represent the region's natural background (e.g., "cultivated land" or "forest land"), generating a second land use type data sub-cohort. This modified land use sub-cohort is then recombine with the unmodified meteorological, pollutant, and elevation data sub-cohorts from the original standardized data cohort (i.e., the second other data sub-cohort) to form the "urban modified data input cohort." This cohort simulates a scenario where the surface reverts to a near-natural state under real river valley topography.

[0042] Generation of composite modification queues:

[0043] To ultimately decouple the synergistic effect, a "dual-background" scenario free from both topographic and urban influences needs to be constructed. This step involves simultaneously performing the elevation replacement and land use category replacement operations described above. The resulting second digital elevation data sub-cohort ("flattened") and the second land use type data sub-cohort ("de-urbanized") are combined with the remaining meteorological and pollutant background data sub-cohort (i.e., the third other data sub-cohort) from the original standardized data cohort to generate a "composite modified data input cohort." This cohort represents an idealized, naturally flat background state and serves as the key calculation benchmark for separating the various influencing factors.

[0044] In summary, by generating four logically rigorous and physically meaningful driving data queues, the intertwined factors of terrain and urbanization in reality were effectively "experimentally isolated," creating pure "control groups" and "experimental groups" for subsequent quantitative attribution analysis. Secondly, this standardized queue generation method ensures high comparability between simulations of different scenarios, with a single variable strictly locked, fundamentally avoiding error confounding caused by data inconsistencies. Finally, this method provides a reliable data foundation for accurately calculating the independent contributions of urbanization, terrain, and their nonlinear synergistic effects, resulting in quantitative results with clear physical interpretation and high credibility. It provides crucial technical support for breaking through traditional qualitative or semi-quantitative analysis at the mechanistic level, directly serving high-precision environmental assessment and spatial planning decisions.

[0045] Step S103: Input each data input queue in the data input queue set into the pre-constructed meteorological-chemical numerical simulation model of the coupled urban canopy model, perform simulation calculations, and obtain the corresponding pollution concentration simulation result queue.

[0046] In this step, the baseline data input queue is input into the meteorological-chemical numerical simulation model, simulation calculation is performed, and the baseline pollution concentration simulation result queue is obtained.

[0047] The terrain modification data is input into the meteorological-chemical numerical simulation model, the simulation calculation is performed, and the simulation results of the terrain modification pollution concentration are obtained.

[0048] The modified city data input queue is input into the meteorological-chemical numerical simulation model, simulation calculation is performed, and the simulation result queue of the modified city pollution concentration is obtained.

[0049] The composite modified data is input into the meteorological-chemical numerical simulation model, and the simulation calculation is performed to obtain the simulation results queue of the composite modified pollution concentration.

[0050] In one specific embodiment, the four data input queues generated in step S102 are used as the sole source of variables for the model's initial field and boundary conditions, and the numerical model is run independently four times in the same high-performance computing environment.

[0051] The first round of simulation involves inputting baseline data into the model via an input queue. This queue contains real land use and topographic data. The model runs based on this data, comprehensively simulating the entire process of pollutant emission, transport, diffusion, chemical transformation, and deposition under the combined effects of a real city and real topography. After the simulation, the concentration data of target pollutants (such as NO2 and PM2.5) at each grid point are extracted throughout the simulation period. After time averaging, the baseline pollution concentration simulation results queue (C) is formed. base).

[0052] The second round of simulation involves inputting the terrain modification data into the model in a queue. In this queue, the terrain has been "filled in," but urban land use remains accurate. The model is then run again under this "no valley, but city" assumption. After the run, pollutant concentration data is extracted and processed in the same way to create a queue of terrain-modified pollution concentration simulation results.

[0053] The third round of simulation: Modified city data is input into the model via a queue. In this queue, urban built-up areas have been replaced with natural terrain, but the topography remains realistic. The model is then run again under this assumption of "river valleys but no cities." After the run, the data is extracted and processed to form a queue of simulation results for city-modified pollution concentrations.

[0054] Fourth round of simulation: The composite modified data is input into the model in the input queue. In this queue, both the terrain and the city have been changed. The model runs under the background assumption of "no valleys and no cities". After the run, the data is extracted and processed to form the simulation results queue of composite modified pollution concentration.

[0055] Result post-processing and queue generation:

[0056] To ensure a one-to-one correspondence between the spatial locations and grids of the four result queues, all raw concentration field data from the simulation outputs underwent the same post-processing, including: uniform spatial grid remapping (if necessary), uniform time-period averaging calculation, and uniform data format conversion. Ultimately, this generated four spatial distribution queues of pollution concentrations with completely consistent dimensions, structure, and spatiotemporal range, laying a rigorous mathematical foundation for the subsequent quantitative difference calculation.

[0057] Step S104: Based on the simulation results of each pollution concentration, a predefined contribution decomposition algorithm is used to calculate and generate a queue of urbanization-only influence components, a queue of topography-only influence components, and a queue of the combined influence components of urbanization and topography.

[0058] In this step, the average concentration values ​​of each spatial grid point during the simulation period are extracted from the baseline pollution concentration simulation result queue to form the first concentration field. ;

[0059] The average concentration values ​​of each spatial grid point during the simulation period are extracted from the terrain-modified pollution concentration simulation results queue to form a second concentration field. ;

[0060] The average concentration values ​​of each spatial grid point during the simulation period are extracted from the simulated pollution concentration results queue of the city to form a third concentration field. ;

[0061] The average concentration values ​​of each spatial grid point during the simulation period are extracted from the composite modified pollution concentration simulation result queue to form a fourth concentration field. ;

[0062] The first concentration field The values ​​of each grid point in the field and the third concentration field The values ​​at corresponding grid points are subtracted to obtain a queue of individual urbanization impact components. This queue contains the quantized values ​​of the individual urbanization impact components at the i-th spatial grid point. , Let be the average pollutant concentration value of the i-th grid point in the first concentration field during the simulation period. This represents the average pollutant concentration value of the i-th grid point in the third concentration field during the simulation period;

[0063] The first concentration field The values ​​of each grid point in the second concentration field The values ​​at corresponding grid points are subtracted to obtain the terrain-independent influence component queue, where the quantized value of the terrain-independent influence component at the i-th spatial grid point is... , This represents the average pollutant concentration value of the i-th grid point in the second concentration field during the simulation period;

[0064] The third concentration field The values ​​of each grid point in the second concentration field The values ​​of corresponding grid points in the target concentration field are summed to obtain the target concentration field. The values ​​of each grid point in the target concentration field are then sequentially compared with the values ​​of the first concentration field. The values ​​of the corresponding grid points, the fourth concentration field The values ​​at corresponding grid points are subtracted to obtain the co-influence component queue, where the quantized value of the co-influence component at the i-th spatial grid point is... , is the average pollutant concentration value of the i-th grid point in the fourth concentration field during the simulation period.

[0065] In summary, quantitatively separating the traditionally inseparable "topography-city" coupling effect not only yields the independent contributions of urbanization and topography, but more importantly, extracts the synergistic contributions generated by their interaction. This elevates the analysis of pollution causes from "qualitative attribution" to "quantitative decoupling," clearly answering the core question of "where and how significant are the synergistic effects?" This technology provides unprecedented scientific insights for accurately identifying key areas and core contradictions in pollution control. For example, if pollution in a region is primarily driven by synergistic effects, single emission reduction or ventilation measures may have limited effectiveness; a comprehensive governance strategy that considers both urban form and ecological pattern is necessary, thereby greatly improving the targeting and effectiveness of environmental decision-making.

[0066] Step S105: Determine whether the statistical characteristics of the synergistic influence component queue exceed a preset synergistic effect threshold.

[0067] In this step, the mean μ and standard deviation σ of all grid point values ​​in the co-influence component queue are calculated;

[0068] Set the synergistic effect threshold θ = μ + n * σ, where n is a preset positive integer used to control the threshold sensitivity;

[0069] Count the number of grid points with values ​​greater than θ in the queue of the collaborative influence components. and calculate Total number of grid points in the co-influence component queue proportion ;

[0070] judge Is it greater than the preset ratio threshold? That is, to determine whether the statistical characteristics of the synergistic influence component queue exceed the preset synergistic effect threshold.

[0071] In one specific embodiment, after determining whether the statistical characteristics of the synergistic influence component queue exceed a preset synergistic effect threshold, if they are not greater than the synergistic effect threshold, the spatial quantitative map of the contribution intensity of each influencing factor to air pollution is directly determined based on the current urbanization-only influence component queue, topography-only influence component queue, and synergistic influence component queue.

[0072] Step S106: If the synergistic effect threshold is exceeded, an update scheme for the land use type data and / or digital elevation data in the first dataset is generated based on the spatial distribution of the synergistic effect component queue. The first dataset is updated based on the update scheme. The data input queue set and the pollution concentration simulation result queue are updated according to the updated first dataset until the updated synergistic effect component queue meets the preset requirements.

[0073] In this step, the values ​​of collaborative influence components in the collaborative influence component queue are identified as being greater than a preset threshold. The grid regions constitute a set R of regions with high synergistic effects;

[0074] For each region r in the set of high synergy effect regions R, extract the land use type data L corresponding to each region r in the first dataset. r and digital elevation data E r ;

[0075] According to land use type data L r The category attribute is used to select the corresponding land use adjustment scheme from the preset urban planning adjustment strategy library. The adjustment scheme includes changing the building density, adjusting the green space ratio, or changing the functional area layout.

[0076] According to digital elevation data E r Based on the terrain features, a corresponding terrain adjustment scheme is selected from a preset terrain modification strategy library. The terrain adjustment scheme includes virtual fill and cut to change the local slope or elevation.

[0077] The selected land use adjustment scheme and terrain adjustment scheme are combined to generate update instructions for each region r. The update instructions for each region r are then summarized to form a global update scheme for the land use type data and / or digital elevation data in the first dataset.

[0078] In one specific embodiment, for each region r, the following operations are performed sequentially:

[0079] Regional grid extraction: Based on the spatial extent of region r, obtain a list of indices for all grids and their land use type data L from the first dataset. r and digital elevation data E r .

[0080] Scheme Analysis and Quantification: The selected land use adjustment scheme and topography adjustment scheme are converted into specific quantitative target values. For example:

[0081] Land use: If the plan is to "convert to green space", then determine the specific vegetation type code (e.g., based on local ecological zoning).

[0082] Terrain: If the plan is "raise 5 meters", then it is determined. , This is the modified digital elevation data.

[0083] Generate instruction entry: Create an instruction entry containing the above fields. If both land use and terrain adjustment are involved, generate two separate instructions or one combined instruction.

[0084] Store instructions: The generated instructions are temporarily stored in a structured data format (such as JSON, XML, or database records) and summarized after all regions have been processed.

[0085] Step S107: Based on the final urbanization-only influence component queue, topography-only influence component queue, and synergistic influence component queue that meet the preset requirements, determine the spatial quantitative map of the contribution intensity of each influence factor to air pollution.

[0086] In this step, the values ​​of the urbanization-influenced component queue U are mapped to a color space, and a corresponding color is assigned according to the U value of each grid point to generate a color patch map of the urbanization contribution intensity.

[0087] The values ​​of the terrain-influenced component queue T are mapped to a color space, and a corresponding color is assigned according to the T value of each grid point to generate a color patch map of terrain contribution intensity.

[0088] The values ​​of the cooperative influence component queue S are mapped to the color space, and the corresponding color is assigned according to the S value of each grid point to generate a color patch map of cooperative contribution intensity.

[0089] The urbanization contribution intensity color map, topography contribution intensity color map, and collaborative contribution intensity color map are overlaid on the base geographic map, and legends, scale bars, and directional labels are added to form three independent spatial quantification sub-maps.

[0090] The three independent spatial quantization sub-maps are spatially aligned and laid out to form the spatial quantization map.

[0091] In one specific embodiment, after the iterative optimization is completed, three independent component queues that ultimately meet the preset requirements are obtained:

[0092] Urbanization-only impact component cohort U: Represents the net contribution of urbanization factors (such as urban underlying surface, anthropogenic heat emissions, etc.) to pollutant concentration (unit: μg / m³ or ppm).

[0093] The topography-only influence component cohort T represents the net contribution of the actual topography (relative to flat topography) to the pollutant concentration.

[0094] Cooperative influence component queue S: represents the additional contribution value generated by the nonlinear coupling of urbanization and terrain.

[0095] These three queues share the same spatial grid system (identical number of rows and columns, projection, and resolution), with each grid point corresponding to a numerical value. The goal of this step is to transform this numerical data into intuitive and readable spatial maps, facilitating interpretation and application by urban planners, environmental managers, and researchers. The specific implementation process is as follows:

[0096] 1. Data Preprocessing and Statistical Analysis

[0097] Before visualization, basic statistical analysis is first performed on the three component queues to determine the range and grading strategy of the color mapping:

[0098] Calculate the global minimum, maximum, mean, standard deviation, and percentiles (e.g., 5%, 25%, 50%, 75%, 95%) for each queue.

[0099] Based on the data distribution characteristics, set a uniform color scale range (e.g., use the global minimum and maximum values ​​for all queues, or set a separate range for each queue to highlight its internal differences). It is generally recommended to assign a separate color to each queue, as the magnitude and physical meaning of the three may differ.

[0100] 2. Mapping numerical values ​​to colors (generating color patches)

[0101] The value of each grid point is converted into the corresponding color value (such as RGB triplet) through a color mapping function, generating three independent raster images (color patch images).

[0102] 2.1 Selecting a color scheme

[0103] Choose an appropriate color gradient scheme based on the physical meaning of the components:

[0104] Urbanization Individual Impact (UU): A red color scheme can be used (positive values ​​indicate increased pollution, negative values ​​indicate improvement), ranging from light red (low contribution) to dark red (high contribution).

[0105] Terrain-specific effects (TT): can be represented by a blue color scheme (cool tones), ranging from light blue (low contribution) to dark blue (high contribution), or by using yellow-orange-brown to represent terrain-blocking effects.

[0106] Synergistic effects (SS): Green or purple tones can be used to emphasize its non-linear characteristics, such as from light green (weak synergy) to dark green (strong synergy), or yellow-orange-red can be used.

[0107] If the contribution value can be positive or negative (e.g., some areas may have a negative contribution, i.e., an improvement effect), then a two-color gradient (e.g., blue-white-red, where blue represents a negative contribution, white represents zero, and red represents a positive contribution) should be used.

[0108] 2.2 Determine the grading method

[0109] Continuous Gradient (Stretch): This linearly maps a numerical range to a color gradient, suitable for situations where the data is continuously distributed and there are no significant breakpoints. Percentile stretching (e.g., 2%-98%) can be used to eliminate the interference of extreme outliers on color contrast.

[0110] Hierarchical color scheme: Divide the numerical range into several levels (e.g., 5-10 levels), and assign a color to each level. Hierarchical methods can include equal spacing, quantiles (each level contains the same number of grids), and the Jenks method, to highlight the natural clustering characteristics of the data.

[0111] 2.3 Generating Raster Images

[0112] Perform the following operations using Geographic Information System (GIS) software (such as ArcGIS, QGIS) or programming languages ​​(such as Python's matplotlib, rasterio libraries):

[0113] Load queue data (usually in NetCDF or GeoTIFF format) as a raster layer.

[0114] The selected color scheme and grading method are applied to generate three independent color patch raster files.

[0115] Ensure the projection of the output image matches the underlying geographic surface. Figure 1 The study area is consistent with and its geographical scope fully covers the research area.

[0116] 3. Overlay the base geographic map

[0117] To enhance spatial readability, the generated color patch map needs to be overlaid with a base map containing geographic features.

[0118] 3.1 Prepare the base map

[0119] The base map should include the following elements:

[0120] Administrative boundaries (provincial, municipal, and county boundaries).

[0121] Major rivers and lakes.

[0122] Major road and railway transportation networks.

[0123] The names of important landmarks (such as mountains, airports, and industrial areas) are marked.

[0124] Base map data can be obtained from public geographic databases (such as OSM, National Geomatics Center of China), ensuring that its coordinate system and color patches are consistent. Figure 1 To (usually a projected coordinate system such as UTM, or a geographic coordinate system such as WGS84).

[0125] 3.2 Overlay and Registration

[0126] In GIS software, the color patch map can be overlaid as a semi-transparent layer on top of the base map, or a "base map + layered coloring" method can be used (i.e., the base map is grayscale or light-colored, and the color patch map is in color). Key steps:

[0127] Adjust the transparency: Set the transparency of the color patch layer to 30%-50% to reveal the underlying background elements.

[0128] Adjust the order: place the base map at the bottom, place the color patch map on top of it, and place the labeled elements at the top.

[0129] Cropping: Cropping the color patch image to the boundary of the study area to avoid interference from unnecessary data.

[0130] 4. Add cartographic elements (legend, scale bar, direction labels)

[0131] Each sub-map must contain complete cartographic elements to ensure it can be read independently.

[0132] 4.1 Legend

[0133] Title: Clearly label the component names, such as "Urbanization Individual Contribution (μg / m³)", "Topography Individual Contribution (μg / m³)", and "Collaborative Contribution (μg / m³)".

[0134] Color bars: Display the correspondence between colors and values ​​in the form of color bars, marking key value points (such as minimum value, maximum value, median or grade breakpoints).

[0135] Unit: Indicate the concentration unit next to the color scale.

[0136] 4.2 Scale

[0137] Insert an appropriate scale bar according to the map scale and mark the actual distances (e.g., 0, 5, 10 km).

[0138] Ensure that the map scale matches the map projection (for large areas, distortion needs to be considered, usually by using the ratio of distance on the map to distance on the ground).

[0139] 4.3 Direction Labeling

[0140] Add a North Arrow to indicate the direction of the map.

[0141] 4.4 Other elements

[0142] Grid coordinates or latitude and longitude scale (optional).

[0143] Map title (e.g., "Spatial distribution of the contribution of urbanization to PM2.5 in XX City").

[0144] Metadata such as data source and mapping date.

[0145] 5. Generate three independent spatial quantization sub-maps

[0146] Following the steps above, generate three complete, independently publishable thematic map files:

[0147] Sub-map A: Color patch map of urbanization contribution intensity.

[0148] Sub-map B: Color patch map of topographic contribution intensity.

[0149] Sub-map C: Collaborative contribution intensity color patch map.

[0150] Each image is output in a high-resolution format (such as 300 dpi TIFF or PDF) for easy printing or embedding in reports.

[0151] 6. Spatial alignment and layout are integrated into the final spatial quantification map.

[0152] The three sub-maps are integrated into a unified map to facilitate comparative analysis.

[0153] 6.1 Spatial Alignment

[0154] Ensure that the three sub-maps use identical spatial extents, scales, and projections during typesetting so that readers can visually compare different contribution values ​​at the same location. This can be achieved in the following ways:

[0155] Set the same display extent in the GIS.

[0156] Maintain the same pixel size and geographic range when outputting images.

[0157] 6.2 Layout and Typesetting

[0158] Use image processing software (such as Adobe Illustrator, Inkscape) or GIS layout functions (such as ArcGIS Layout View) for typesetting:

[0159] Arrangement: Place the three sub-graphs side by side (e.g., left, center, right) or vertically, and keep appropriate white space between each graph.

[0160] Unified title: Add a general title at the top, such as "Spatial Quantitative Map of the Contribution of Air Pollution to Cities in XX River Valley".

[0161] Alignment: Ensure that the boundaries of the three maps are strictly aligned and that the positions of elements such as legends and scale bars are consistent.

[0162] Add supplementary explanations: Brief text descriptions can be added at the bottom or side to explain the definition and interpretation method of each component.

[0163] 6.3 Output the final map

[0164] Exporting the final, formatted map into a universal format, the resulting spatial quantitative map can intuitively reveal the intensity and spatial distribution of the contribution of urbanization, topography, and their synergistic effects to air pollution in river valley cities, providing direct scientific basis for precise pollution control and urban planning optimization.

[0165] In summary, the method presented in this application constructs four sets of sensitive scenario simulations with clear scientific assumptions and rigorous logical connections: "real baseline, no topography, no city, and no other factors." It also designs a contribution separation algorithm based on linear algebraic decomposition, successfully achieving complete decoupling and precise quantification of the "single impact of urbanization," "single impact of topography," and "nonlinear synergistic impact of both" in complex air pollution in valley-type cities. This method overcomes the limitations of traditional studies that can only qualitatively or semi-quantitatively describe composite impacts, advancing pollution attribution from fuzzy attribution to grid-level numerical source tracing. By introducing an iterative feedback mechanism based on statistical significance judgment, the robustness and reliability of the quantitative results are ensured, ultimately generating an intuitive and operable spatial quantitative map. Its significant technical effect lies not only in its profound revelation of the deep physical mechanisms of pollution formation under the intertwined effects of topographic constraints and urbanization, but more importantly, in providing direct and precise decision-making basis for urban planning and environmental management. For example, it can accurately identify high-risk areas of synergistic effects to optimize the layout of ventilation corridors and formulate differentiated emission reduction and spatial control strategies for high-contribution factors. This achieves an effective connection between scientific understanding and engineering practice, and has important theoretical value and application prospects for improving the air pollution control efficiency of cities with similar topographic constraints.

[0166] Please see Figure 2 The diagram shows a structural block diagram of an atmospheric pollution quantitative analysis system for valley-type cities according to this application.

[0167] like Figure 2 As shown, the atmospheric pollution quantitative analysis system 200 for valley-type cities includes an acquisition module 210, a first generation module 220, an output module 230, a second generation module 240, a judgment module 250, an update module 260, and a determination module 270.

[0168] The acquisition module 210 is configured to acquire a first dataset of the target river valley city during historical pollution periods. This first dataset includes at least meteorological observation data, air pollutant concentration data, land use type data, and digital elevation data. The first generation module 220 is configured to generate a data input queue set based on the first dataset using preset data generation rules. This data input queue set includes a baseline data input queue, a terrain modification data input queue, a city modification data input queue, and a composite modification data input queue. The output module 230 is configured to input each data input queue from the data input queue set into a pre-constructed meteorological-chemical numerical simulation model coupled with a city canopy model, perform simulation calculations, and obtain corresponding pollution concentration simulation result queues. The second generation module 240 is configured to perform calculations based on each pollution concentration simulation result queue using a predefined contribution decomposition algorithm. The system calculates and generates a queue of urbanization-only impact components, a queue of topography-only impact components, and a queue of synergistic impact components of urbanization and topography. A judgment module 250 is configured to determine whether the statistical characteristics of the synergistic impact component queues exceed a preset synergistic effect threshold. An update module 260 is configured to, if the synergistic effect threshold is exceeded, generate an update scheme for land use type data and / or digital elevation data in the first dataset based on the spatial distribution of the synergistic impact component queues, update the first dataset based on the update scheme, and update the data input queue set and pollution concentration simulation result queue based on the updated first dataset, until the updated synergistic impact component queues meet preset requirements. A determination module 270 is configured to, based on the final urbanization-only impact component queue, topography-only impact component queue, and synergistic impact component queue that meet the preset requirements, determine a spatial quantitative map of the contribution intensity of each impact factor to air pollution.

[0169] It should be understood that Figure 2 The modules and references described in the document Figure 1 The steps described in the text correspond to those in the method described above. Therefore, the operations, features, and corresponding technical effects described above also apply to the method described in the text. Figure 2 The various modules in the document will not be described in detail here.

[0170] In other embodiments, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the air pollution quantitative analysis method for valley-type cities in any of the above method embodiments.

[0171] In one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions, which are configured as follows:

[0172] Obtain the first dataset of the target river valley city during the historical pollution period. The first dataset includes at least meteorological observation data, air pollutant concentration data, land use type data, and digital elevation data.

[0173] Based on the first dataset, a set of data input queues is generated using preset data generation rules. The set of data input queues includes a baseline data input queue, a terrain modification data input queue, a city modification data input queue, and a composite modification data input queue.

[0174] Each data input queue in the data input queue set is input into the pre-constructed meteorological-chemical numerical simulation model of the coupled urban canopy model, and simulation calculations are performed to obtain the corresponding pollution concentration simulation result queue.

[0175] Based on the simulation results of each pollution concentration, a predefined contribution decomposition algorithm is used to calculate and generate a queue of urbanization-only influence components, a queue of topography-only influence components, and a queue of the combined influence components of urbanization and topography.

[0176] Determine whether the statistical characteristics of the synergistic effect component queue exceed a preset synergistic effect threshold;

[0177] If the synergistic effect threshold is exceeded, an update scheme for the land use type data and / or digital elevation data in the first dataset is generated based on the spatial distribution of the synergistic effect component queue, and the first dataset is updated based on the update scheme. In addition, the data input queue set and the pollution concentration simulation result queue are updated according to the updated first dataset until the updated synergistic effect component queue meets the preset requirements.

[0178] Based on the final cohorts of urbanization-only influence components, topography-only influence components, and synergistic influence components that meet the preset requirements, a spatial quantitative map of the contribution intensity of each influence factor to air pollution is determined.

[0179] Computer-readable storage media may include a stored program area and a stored data area, wherein the stored program area may store an operating system and an application program required for at least one function; the stored data area may store data created based on the use of the air pollution quantitative analysis system for valley-type cities, etc. Furthermore, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include memory remotely located relative to a processor, which can be connected to the air pollution quantitative analysis system for valley-type cities via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0180] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 3 As shown, the device includes a processor 310 and a memory 320. The electronic device may also include an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 3 Taking a bus connection as an example, the memory 320 is the computer-readable storage medium described above. The processor 310 executes various server functions and data processing by running non-volatile software programs, instructions, and modules stored in the memory 320, thereby implementing the air pollution quantitative analysis method for valley-type cities described in the above-described method embodiment. The input device 330 can receive input digital or character information and generate key signal inputs related to user settings and function control of the air pollution quantitative analysis system for valley-type cities. The output device 340 may include a display screen or other display device.

[0181] The aforementioned electronic device can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in the embodiments of the present invention.

[0182] In one implementation, the aforementioned electronic device is applied to an air pollution quantitative analysis system for valley-type cities, serving as a client, and includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to:

[0183] Obtain the first dataset of the target river valley city during the historical pollution period. The first dataset includes at least meteorological observation data, air pollutant concentration data, land use type data, and digital elevation data.

[0184] Based on the first dataset, a set of data input queues is generated using preset data generation rules. The set of data input queues includes a baseline data input queue, a terrain modification data input queue, a city modification data input queue, and a composite modification data input queue.

[0185] Each data input queue in the data input queue set is input into the pre-constructed meteorological-chemical numerical simulation model of the coupled urban canopy model, and simulation calculations are performed to obtain the corresponding pollution concentration simulation result queue.

[0186] Based on the simulation results of each pollution concentration, a predefined contribution decomposition algorithm is used to calculate and generate a queue of urbanization-only influence components, a queue of topography-only influence components, and a queue of the combined influence components of urbanization and topography.

[0187] Determine whether the statistical characteristics of the synergistic effect component queue exceed a preset synergistic effect threshold;

[0188] If the synergistic effect threshold is exceeded, an update scheme for the land use type data and / or digital elevation data in the first dataset is generated based on the spatial distribution of the synergistic effect component queue, and the first dataset is updated based on the update scheme. In addition, the data input queue set and the pollution concentration simulation result queue are updated according to the updated first dataset until the updated synergistic effect component queue meets the preset requirements.

[0189] Based on the final cohorts of urbanization-only influence components, topography-only influence components, and synergistic influence components that meet the preset requirements, a spatial quantitative map of the contribution intensity of each influence factor to air pollution is determined.

[0190] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0191] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A quantitative analysis method for air pollution in valley-type cities, characterized in that, include: Obtain the first dataset of the target river valley city during the historical pollution period. The first dataset includes at least meteorological observation data, air pollutant concentration data, land use type data, and digital elevation data. Based on the first dataset, a set of data input queues is generated using preset data generation rules. The set of data input queues includes a baseline data input queue, a terrain modification data input queue, a city modification data input queue, and a composite modification data input queue. The step of generating the set of data input queues based on the first dataset using preset data generation rules includes: Spatiotemporal matching and format unification are performed on the meteorological observation data, air pollutant concentration data, land use type data and digital elevation data in the first dataset to generate a first standardized data queue, and the first standardized data queue is directly used as the reference data input queue. Extract the digital elevation data sub-queue from the first standardized data queue, calculate the average elevation value of the target valley area, replace the elevation values ​​of all grids in the valley area in the digital elevation data sub-queue with the average elevation value, generate the second digital elevation data sub-queue, merge the second digital elevation data sub-queue with the first other data sub-queue in the first standardized data queue to form the terrain modification data input queue, wherein the first other data sub-queue is the data sub-queue in the first standardized data queue excluding the digital elevation data sub-queue; Extract the land use type data sub-queue from the first standardized data queue, identify all urban built-up area categories of the grid, uniformly modify the category attributes of the grid to the preset natural surface category attributes, generate the second land use type data sub-queue, merge the second land use type data sub-queue with the second other data sub-queue in the first standardized data queue to form the urban modified data input queue, wherein the second other data sub-queue is the data sub-queue in the first standardized data queue excluding the land use type data sub-queue; The obtained terrain modification data input queue and the city modification data input queue are merged with the third other data sub-queue in the first standardized data queue to form the composite modification data input queue, wherein the third other data sub-queue is the data sub-queue in the first standardized data queue excluding the digital elevation data sub-queue and the land use type data sub-queue; Each data input queue in the data input queue set is input into the pre-constructed meteorological-chemical numerical simulation model of the coupled urban canopy model, and simulation calculations are performed to obtain the corresponding pollution concentration simulation result queue. Based on the simulation results of each pollution concentration, a predefined contribution decomposition algorithm is used to calculate and generate a queue of urbanization-only influence components, a queue of topography-only influence components, and a queue of the combined influence components of urbanization and topography. Determine whether the statistical characteristics of the synergistic effect component queue exceed a preset synergistic effect threshold; If the synergistic effect threshold is exceeded, an update scheme for the land use type data and / or digital elevation data in the first dataset is generated based on the spatial distribution of the synergistic effect component queue, and the first dataset is updated based on the update scheme. In addition, the data input queue set and the pollution concentration simulation result queue are updated according to the updated first dataset until the updated synergistic effect component queue meets the preset requirements. Based on the final cohorts of urbanization-only influence components, topography-only influence components, and synergistic influence components that meet the preset requirements, a spatial quantitative map of the contribution intensity of each influence factor to air pollution is determined.

2. The method for quantitative analysis of air pollution in valley-type cities according to claim 1, characterized in that, The step of inputting each data input queue in the data input queue set into the pre-constructed meteorological-chemical numerical simulation model of the coupled urban canopy model, performing simulation calculations, and obtaining the corresponding pollution concentration simulation result queue includes: The baseline data is input into the meteorological-chemical numerical simulation model, simulation calculations are performed, and the baseline pollution concentration simulation results are obtained. The terrain modification data is input into the meteorological-chemical numerical simulation model, the simulation calculation is performed, and the simulation results of the terrain modification pollution concentration are obtained. The modified city data input queue is input into the meteorological-chemical numerical simulation model, simulation calculation is performed, and the simulation result queue of the modified city pollution concentration is obtained. The composite modified data is input into the meteorological-chemical numerical simulation model, and the simulation calculation is performed to obtain the simulation results queue of the composite modified pollution concentration.

3. The method for quantitative analysis of air pollution in valley-type cities according to claim 2, characterized in that, The steps of generating queues of urbanization-only impact components, topography-only impact components, and the combined impact components of urbanization and topography based on the simulation results of each pollution concentration queue using a predefined contribution decomposition algorithm include: The average concentration values ​​of each spatial grid point during the simulation period are extracted from the baseline pollution concentration simulation result queue to form the first concentration field. ; The average concentration values ​​of each spatial grid point during the simulation period are extracted from the terrain-modified pollution concentration simulation results queue to form a second concentration field. ; The average concentration values ​​of each spatial grid point during the simulation period are extracted from the simulated pollution concentration results queue of the city to form a third concentration field. ; The average concentration values ​​of each spatial grid point during the simulation period are extracted from the composite modified pollution concentration simulation result queue to form a fourth concentration field. ; The first concentration field The values ​​of each grid point in the field and the third concentration field The values ​​at corresponding grid points are subtracted to obtain a queue of individual urbanization impact components. This queue contains the quantized values ​​of the individual urbanization impact components at the i-th spatial grid point. , Let be the average pollutant concentration value of the i-th grid point in the first concentration field during the simulation period. This represents the average pollutant concentration value of the i-th grid point in the third concentration field during the simulation period; The first concentration field The values ​​of each grid point in the second concentration field The values ​​at corresponding grid points are subtracted to obtain the terrain-independent influence component queue, where the quantized value of the terrain-independent influence component at the i-th spatial grid point is... , This represents the average pollutant concentration value of the i-th grid point in the second concentration field during the simulation period; The third concentration field The values ​​of each grid point in the second concentration field The values ​​of corresponding grid points in the target concentration field are summed to obtain the target concentration field. The values ​​of each grid point in the target concentration field are then sequentially compared with the values ​​of the first concentration field. The values ​​of the corresponding grid points, the fourth concentration field The values ​​at corresponding grid points are subtracted to obtain the co-influence component queue, where the quantized value of the co-influence component at the i-th spatial grid point is... , is the average pollutant concentration value of the i-th grid point in the fourth concentration field during the simulation period.

4. The method for quantitative analysis of air pollution in valley-type cities according to claim 1, characterized in that, The step of determining whether the statistical characteristics of the synergistic effect component queue exceed a preset synergistic effect threshold includes: Calculate the mean μ and standard deviation σ of all grid point values ​​in the co-influence component queue; Set the synergistic effect threshold θ = μ + n * σ, where n is a preset positive integer used to control the threshold sensitivity; Count the number of grid points in the synergistic effect component queue whose values ​​are greater than the set synergistic effect threshold θ. And calculate the number of grid points. Total number of grid points in the co-influence component queue proportion ; Determine the ratio Is it greater than the preset ratio threshold? The ratio judgment result is used as the judgment result of whether the statistical characteristics of the synergistic influence component queue exceed the preset synergistic effect threshold.

5. The method for quantitative analysis of air pollution in valley-type cities according to claim 1, characterized in that, After determining whether the statistical characteristics of the synergistic effect component queue exceed a preset synergistic effect threshold, the method further includes: If the value is not greater than the aforementioned synergistic effect threshold, then the spatial quantitative map of the contribution intensity of each influencing factor to air pollution is directly determined based on the current urbanization-only influence component queue, topography-only influence component queue, and synergistic influence component queue.

6. The method for quantitative analysis of air pollution in valley-type cities according to claim 1, characterized in that, The step of generating an update scheme for land use type data and / or digital elevation data in the first dataset based on the spatial distribution of the co-influence component queue includes: Identify and extract collaborative influence components whose values ​​are greater than a preset threshold from the collaborative influence component queue. The grid regions are used to form a set R of regions with high synergistic effects; For each region r in the set of high synergy effect regions R, extract the land use type data L corresponding to each region r in the first dataset. r and digital elevation data E r ; According to land use type data L r The category attribute is used to select the corresponding land use adjustment scheme from the preset urban planning adjustment strategy library. The adjustment scheme includes changing the building density, adjusting the green space ratio, or changing the functional area layout. According to digital elevation data E r Based on the terrain features, a corresponding terrain adjustment scheme is selected from a preset terrain modification strategy library. The terrain adjustment scheme includes virtual fill and cut to change the local slope or elevation. The selected land use adjustment scheme and terrain adjustment scheme are combined to generate update instructions for each region r. The update instructions for each region r are then summarized to form a global update scheme for the land use type data and / or digital elevation data in the first dataset.

7. The method for quantitative analysis of air pollution in valley-type cities according to claim 1, characterized in that, The spatial quantitative map of the contribution intensity of each influencing factor to air pollution, based on the final urbanization-only influence component queue, topography-only influence component queue, and synergistic influence component queue that meet the preset requirements, includes: The values ​​of the urbanization-influenced component queue U are mapped to a color space, and a corresponding color is assigned to each grid point based on its U value to generate a color patch map of the urbanization contribution intensity. The values ​​of the terrain-influenced component queue T are mapped to a color space, and a corresponding color is assigned according to the T value of each grid point to generate a color patch map of terrain contribution intensity. The values ​​of the cooperative influence component queue S are mapped to the color space, and the corresponding color is assigned according to the S value of each grid point to generate a color patch map of cooperative contribution intensity. The urbanization contribution intensity color map, topography contribution intensity color map, and collaborative contribution intensity color map are overlaid on the base geographic map, and legends, scale bars, and direction labels are added to form three independent spatial quantification sub-maps. The three independent spatial quantization sub-maps are spatially aligned and laid out to form the spatial quantization map.

8. A quantitative analysis system for air pollution in valley-type cities, characterized in that, include: The acquisition module is configured to acquire the first dataset of the target river valley city during the historical pollution period. The first dataset includes at least meteorological observation data, air pollutant concentration data, land use type data, and digital elevation data. The first generation module is configured to generate a set of data input queues based on the first dataset using preset data generation rules. The set of data input queues includes a baseline data input queue, a terrain modification data input queue, a city modification data input queue, and a composite modification data input queue. The first other data sub-queue is a data sub-queue in the first standardized data queue excluding the digital elevation data sub-queue. Extract the land use type data sub-queue from the first standardized data queue, identify all urban built-up area categories of the grid, uniformly modify the category attributes of the grid to the preset natural surface category attributes, generate the second land use type data sub-queue, merge the second land use type data sub-queue with the second other data sub-queue in the first standardized data queue to form the urban modified data input queue, wherein the second other data sub-queue is the data sub-queue in the first standardized data queue excluding the land use type data sub-queue; The obtained terrain modification data input queue and the city modification data input queue are merged with the third other data sub-queue in the first standardized data queue to form the composite modification data input queue, wherein the third other data sub-queue is the data sub-queue in the first standardized data queue excluding the digital elevation data sub-queue and the land use type data sub-queue; The output module is configured to input each data input queue in the data input queue set into the pre-constructed meteorological-chemical numerical simulation model of the coupled urban canopy model, perform simulation calculations, and obtain the corresponding pollution concentration simulation result queue. The second generation module is configured to calculate, based on each pollution concentration simulation result queue, using a predefined contribution decomposition algorithm, to generate a queue of urbanization-only influence components, a queue of topography-only influence components, and a queue of the combined influence components of urbanization and topography. The judgment module is configured to determine whether the statistical characteristics of the collaborative influence component queue exceed a preset collaborative effect threshold. The update module is configured to, if the synergistic effect threshold is exceeded, generate an update scheme for the land use type data and / or digital elevation data in the first dataset based on the spatial distribution of the synergistic effect component queue, update the first dataset based on the update scheme, and update the data input queue set and pollution concentration simulation result queue according to the updated first dataset, until the updated synergistic effect component queue meets the preset requirements. The module is configured to determine the spatial quantitative map of the contribution intensity of each influencing factor to air pollution based on the final urbanization-only influence component queue, topography-only influence component queue, and synergistic influence component queue that meet the preset requirements.

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