Atmospheric environmental pollution science popularization propagation system based on virtual reality

Abstract

The invention relates to the technical field of atmospheric environmental pollution science popularization propagation, and particularly discloses an atmospheric environmental pollution science popularization propagation system based on virtual reality, which comprises a data acquisition and processing module used for acquiring atmospheric pollutant concentration data in real time, and performing standardization and time-space correlation processing on the data to obtain a data acquisition and processing module; generating a pollution data set with a geographic position and a timestamp; the three-dimensional scene construction module is used for constructing a three-dimensional virtual environment basic model of a target area based on geographic information system data and fusing the pollution data set with the three-dimensional virtual environment basic model; and the pollutant visualization module is used for visualizing pollutants according to the types and concentration grades of the pollutants. The atmospheric environmental pollution science popularization propagation system based on virtual reality not only breaks through the technical bottleneck of traditional environmental science popularization, but also provides an efficient innovative tool for promoting the public to participate in environmental protection.

Description

A Virtual Reality-Based Popular Science Communication System for Atmospheric Environmental Pollution Technical Field

[0001] This invention relates to the field of atmospheric environmental pollution science popularization and dissemination technology, specifically to an atmospheric environmental pollution science popularization and dissemination system based on virtual reality. Background Technology

[0002] Traditional methods of popularizing atmospheric pollution science mainly rely on print media, charts, 2D animations, or simple 3D models. While these methods play a basic role in information delivery, they generally suffer from inherent flaws such as insufficient intuitiveness, poor interactivity, and low public participation. In the field of environmental monitoring, various data visualization systems have been developed, but these systems are mostly geared towards professional analysts, using 2D charts or simple spatial distribution maps, failing to provide an intuitive and immersive pollution perception experience. In the field of virtual reality technology, although there are applications combining geographic information systems with 3D visualization, most remain at the level of static terrain display, lacking dynamic integration with real-time environmental data, and have not yet formed a complete science education system.

[0003] Existing solutions for 3D terrain modeling often employ uniform meshing or simple layering techniques, resulting in low rendering efficiency for large-scale terrain scenes and difficulty in achieving real-time interaction in virtual reality environments. In terms of pollution data visualization, existing methods often simplify complex 3D diffusion processes into 2D planar displays, failing to accurately reflect the transmission and accumulation effects of pollutants in three-dimensional space. In terms of science education design, existing systems often adopt a linear content push model, lacking dynamic assessment of users' cognitive states and personalized learning path planning. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides: a virtual reality-based system for popularizing science about atmospheric environmental pollution, comprising:

[0005] The data acquisition and processing module is used to acquire atmospheric pollutant concentration data in real time, and to perform standardization and spatiotemporal correlation processing on the data to generate a pollution dataset with geographical location and timestamp.

[0006] A 3D scene construction module is used to construct a 3D virtual environment base model of the target area based on geographic information system data, and to fuse the pollution dataset with the 3D virtual environment base model;

[0007] The pollutant visualization module is used to visualize and render pollutants in the three-dimensional virtual environment base model using predefined colors, densities, and particle effects, based on the type and concentration level of the pollutants, to generate a dynamic visual representation of pollution diffusion.

[0008] The interactive science popularization content module is used to provide an interactive interface with visualized pollutants and virtual environmental objects, respond to user interaction operations, and trigger and present preset science popularization knowledge content related to the pollutant.

[0009] The virtual reality presentation module is used to output the rendered 3D virtual environment, which integrates pollution visualization and popular science content, to the virtual reality display device to provide users with an immersive experience.

[0010] The user behavior tracking and feedback module is used to track users' behavior, gaze focus, and interaction choices in the virtual environment, and dynamically adjust the presentation method or difficulty of popular science content based on the tracking data.

[0011] Preferably, the data acquisition and processing module specifically includes:

[0012] A multi-source data access unit is used to access at least two data sources, including national monitoring stations, micro-sensor networks, satellite remote sensing inversion data, and meteorological data platforms.

[0013] The data fusion unit uses spatial interpolation algorithms and spatiotemporal kriging to fuse and extrapolate pollution data from different sources and at different resolutions, generating a continuous spatially distributed pollution field.

[0014] Preferably, the pollutant visualization module supports multiple visualization modes, including:

[0015] The spatiotemporal evolution mode dynamically displays the diffusion, accumulation, and dissipation of pollutants within a preset historical period using a timeline control method.

[0016] The comparison mode visualizes pollution conditions under different periods and policy scenarios on the same screen.

[0017] Profile analysis mode, responding to user actions, displays a profile of pollutant concentration along a specified vertical path.

[0018] Preferably, the popular science content provided by the interactive popular science content module includes:

[0019] Animated simulation of pollutant sources: The generation mechanism and transport pathway of specific pollutants such as PM2.5 and ozone are shown in dynamic 3D animation.

[0020] Health impact simulation: Through virtual human body models, the short-term and long-term effects of different concentrations of pollutants on the respiratory and cardiovascular systems are visually demonstrated.

[0021] Simulation of governance measures: Using an interactive sandbox format, the virtual effects of different governance measures such as afforestation, industrial emission reduction, and vehicle restrictions on improving air quality are simulated and displayed.

[0022] Preferably, the user behavior tracking and feedback module further includes:

[0023] The assessment unit is used to generate an assessment report on the user's pollution awareness level based on the interactive tasks completed by the user, the popular science questions answered, and the exploration path in the virtual environment.

[0024] The personalized recommendation unit, based on the evaluation report, recommends virtual areas or popular science learning content for users to focus on exploring next.

[0025] Preferably, the propagation method of the propagation system includes the following steps:

[0026] S1. Acquire multi-source air pollution monitoring data and geographic information data of the target area, and perform spatiotemporal fusion processing to generate a structured pollution dataset;

[0027] S2. Construct a basic three-dimensional virtual environment model of the target area;

[0028] S3. Map the structured pollution dataset to the three-dimensional virtual environment base model, and render it using the corresponding visualization scheme according to the pollutant type and concentration to generate an immersive pollution scene;

[0029] S4. Set up interactive hotspots in the immersive pollution scene, and associate the hotspots with detailed popular science media content;

[0030] S5. Present the immersive pollution scene to the user through a virtual reality device and capture the user's interaction commands in real time;

[0031] S6. Respond to user interaction with the interactive hotspots, seamlessly present corresponding popular science media content in the virtual environment, and record user interaction behavior data for feedback and evaluation.

[0032] Preferably, step S2, "constructing a basic model of the three-dimensional virtual environment of the target area," specifically includes the following steps:

[0033] S2.1 Data preparation and preprocessing: Acquire digital elevation models, satellite remote sensing images, vector map data, and 3D model data of landmark buildings for the target area, and perform preprocessing on the data, including coordinate system 1, scale normalization, and format conversion;

[0034] S2.2 Terrain and Geomorphological Modeling: Based on the digital elevation model data, a three-dimensional terrain grid reflecting the real terrain undulations is generated. The satellite remote sensing image is used as a texture map and mapped onto the three-dimensional terrain grid to form a basic geographic scene with real surface features.

[0035] S2.3, Artificial Feature Integration: The road, water system, green space and administrative division boundary information in the vector map data are superimposed on the basic geographic scene in the form of semi-transparent patches, and the three-dimensional model data of the landmark buildings are placed in the corresponding positions of the scene according to their real geographic coordinates and scale.

[0036] S2.4 Environmental Elements and Semantic Enhancement: Add a skybox, dynamic lighting, and weather system that conform to physical laws to the scene, and attach semantic tags to key objects in the scene. The semantic tags include object type, name, and associated popular science content index.

[0037] Preferably, step S2.2, "generating a three-dimensional terrain mesh reflecting the real terrain undulations based on the digital elevation model data," is specifically implemented through a multi-level adaptive terrain mesh generation method, including the following steps:

[0038] S2.2.1 Multi-resolution elevation data fusion: acquire digital elevation model data of two different spatial resolutions in the target area, use high-resolution data as the detail layer and low-resolution data as the base layer, and use a feature-preserving data fusion algorithm to adaptively fuse the high-frequency terrain features of the detail layer into the overall terrain framework of the base layer to generate an enhanced elevation matrix that integrates multi-scale details.

[0039] S2.2.2 Adaptive meshing based on terrain features: The enhanced elevation matrix is ​​subjected to terrain feature analysis, the terrain roughness factor and slope change rate of each region are calculated, and according to the preset mesh subdivision rules, regions with high terrain roughness or drastic slope changes are subjected to higher density triangular mesh subdivision, and regions with flat terrain are subjected to lower density mesh subdivision, generating an initial terrain mesh with non-uniform distribution of the number and size of triangles, but which can accurately express the terrain features.

[0040] S2.2.3 Mesh Optimization and Manifold Guarantee: The initial terrain mesh is smoothed and geometrically error-detected to eliminate possible narrow triangles and mesh cracks. Edge folding and vertex splitting algorithms are applied to lightweight the mesh while maintaining the terrain contour features, ensuring that the final generated 3D terrain mesh is geometrically manifold and meets the real-time requirements of virtual reality rendering.

[0041] S2.2.4 Multi-level detailed mesh generation: Based on the final terrain mesh, progressive meshing technology is used to automatically generate simplified mesh models of multiple different levels of detail in the same area, and establish mapping relationships between models of each level to support dynamic loading and switching of terrain at different levels of detail according to the user's viewpoint distance in the virtual environment, thereby achieving efficient rendering of large-scale terrain.

[0042] Preferably, in step S3, the visualization scheme includes:

[0043] For gaseous pollutants, a three-dimensional isosurface rendering with semi-transparency and color gradient is used for representation;

[0044] For particulate pollutants, a dynamic particle system in which density is proportional to concentration is used for simulation.

[0045] This invention provides a virtual reality-based system for popularizing science about atmospheric environmental pollution. It has the following beneficial effects:

[0046] This invention, through technological innovations such as multi-source environmental data fusion, adaptive 3D terrain modeling, dynamic pollutant visualization, interactive science popularization content design, and intelligent feedback on user behavior, transforms abstract pollutant concentration data into an immersive 3D dynamic experience. It effectively solves the core problems of traditional science popularization methods, including poor intuitiveness, weak interactivity, and low public participation. It achieves synergistic promotion of knowledge, attitudes, and behaviors, ensuring real-time performance and accuracy in large-scale scenarios, resulting in a better user experience. It can dynamically assess users' cognitive levels and generate personalized learning paths, significantly improving the accuracy of science education. This invention not only breaks through the technical bottlenecks of traditional environmental science popularization but also provides an efficient, intuitive, and quantifiable innovative tool for improving public environmental literacy and promoting public participation in environmental protection, demonstrating significant social benefits and broad application prospects. Detailed Implementation

[0047] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0048] In a first embodiment, the present invention provides a technical solution: a dissemination method for a virtual reality-based atmospheric environmental pollution science popularization system includes the following steps:

[0049] S1. Data Processing:

[0050] S1.1 Multi-source data acquisition and fusion:

[0051] The system periodically collects data from the following data sources through a standard data interface:

[0052] Real-time air quality data from national environmental monitoring stations, including concentrations of six major pollutants: PM2.5, PM10, SO2, NO2, O3, and CO.

[0053] Meteorological data including temperature, humidity, wind speed, wind direction, and air pressure;

[0054] Atmospheric composition data retrieved from satellite remote sensing;

[0055] Supplemental monitoring data from ground-based micro-sensor networks;

[0056] Geographic information data, including digital elevation models, high-resolution satellite imagery, and vector map data;

[0057] S1.2 Data Preprocessing and Quality Control:

[0058] The collected raw data underwent the following processing:

[0059] Coordinate system transformation 1: Unify all spatial data to the CGCS2000 National Geodetic Coordinate System;

[0060] Time alignment: Aligning data from different sampling frequencies to a standard timestamp;

[0061] Outlier detection and correction: Statistical analysis methods are used to identify and process outlier monitoring values;

[0062] Missing data imputation: Use spatiotemporal kriging interpolation to complete missing monitoring data;

[0063] S1.3 Spatiotemporal Data Fusion and Inference:

[0064] The system employs an improved spatiotemporal interpolation algorithm to transform discrete monitoring point data into a continuous three-dimensional pollution concentration field. Specific methods include:

[0065] Establish a pollutant diffusion model that takes into account topography, meteorological conditions, and the distribution of pollution sources;

[0066] Use data assimilation techniques to fuse monitoring data and model prediction results;

[0067] Generate gridded pollution concentration data with adjustable resolution;

[0068] S2, 3D terrain modeling:

[0069] Multi-level adaptive terrain mesh generation technology includes the following steps:

[0070] S2.1 Multi-resolution data fusion:

[0071] The system acquires digital elevation data of the target area at multiple resolutions:

[0072] Base layer: Full-area coverage data with 30-meter resolution;

[0073] Enhancement layer: Data for key areas with a resolution of 5-10 meters;

[0074] Detail layer: Key landmark area data at 1-meter resolution;

[0075] By using a feature-preserving fusion algorithm, terrain detail features from high-resolution data are adaptively fused into the basic terrain framework to generate an enhanced digital elevation model that retains the overall terrain features while containing rich details.

[0076] S2.2 Feature-driven mesh generation:

[0077] System analysis of terrain feature parameters:

[0078] Calculate the terrain roughness index for each grid cell;

[0079] Analyze the rate of change of slope and curvature characteristics;

[0080] Identify terrain feature lines;

[0081] Based on the terrain feature analysis results, an adaptive grid partitioning strategy is adopted:

[0082] High-density triangulation is performed on areas with complex terrain.

[0083] Low-density subdivision is used for flat areas;

[0084] Maintain high grid accuracy near terrain feature lines;

[0085] S2.3 Mesh Optimization and Manifold Guarantee:

[0086] The generated initial mesh underwent the following optimization:

[0087] Geometric optimization: Eliminate narrow triangles and optimize mesh quality;

[0088] Topology check: Ensure the mesh is a manifold structure with no isolated vertices or boundary errors;

[0089] Lightweighting: Reducing the number of mesh patches while preserving terrain features;

[0090] S2.4, Multi-level detail generation:

[0091] The system generates mesh models with multiple levels of detail for the same region:

[0092] LOD0: Original high-precision grid, used for close-up observation;

[0093] LOD1: Simplifies the mesh of 50% of the facets for use at medium distances;

[0094] LOD2: Simplifies the mesh of 80% of the facets, suitable for long distances;

[0095] LOD3: A minimalist mesh that simplifies 95% of the facets, suitable for extremely long distances;

[0096] Establish transition relationships between different layers to support smooth switching when the viewpoint distance changes;

[0097] S3. Visualization of pollutants:

[0098] Dynamic pollutant rendering technologies include:

[0099] Volume rendering of gaseous pollutants:

[0100] Use three-dimensional textures to store pollutant concentration field data;

[0101] Volume rendering is achieved using a ray casting algorithm;

[0102] Define color mapping relationships based on pollutant type:

[0103] PM2.5: Yellowish-brown gradient;

[0104] O3: Sky blue gradient;

[0105] SO2: Pale yellow gradient;

[0106] NO2: Reddish-brown gradient;

[0107] Achieve a non-linear mapping between transparency and concentration;

[0108] Particle system simulation of particulate matter:

[0109] Generate a corresponding number of particles based on the particulate matter concentration;

[0110] Particle size, color, and transparency are related to concentration;

[0111] Simulates the Brownian motion of particulate matter;

[0112] Considering the effects of gravity settlement and wind field;

[0113] Spatiotemporal evolution visualization mode:

[0114] Time axis control: Users can control the evolution of pollution through a time slider;

[0115] Historical Replay: Recreating Pollution Changes Over the Past 24 Hours and Week;

[0116] Future forecasts: Simulating future pollution trends based on weather forecasts and emission scenarios;

[0117] Comparison mode: Displays pollution levels from different periods side-by-side;

[0118] S4. Interactive Science Popularization Content Design:

[0119] The system offers a variety of interactive science popularization modules:

[0120] Pollution source identification module:

[0121] Industrial pollution sources: Virtual tour of factory emission processes;

[0122] Traffic pollution sources: Simulated emissions from road motor vehicles;

[0123] Sources of pollution from daily life: Displaying emissions from catering and heating.

[0124] Agricultural pollution sources: Demonstration of the impact of straw burning in agriculture;

[0125] Health Impact Simulation Module:

[0126] Respiratory system effects: Demonstrating the process of pollutant intrusion using a virtual lung model;

[0127] Cardiovascular effects: Simulating the effects of pollutants on the circulatory system;

[0128] Long-term exposure risk: Demonstrates changes in health risk over different exposure durations;

[0129] Impact on vulnerable populations: Special emphasis is placed on the impact on children, the elderly, and patients;

[0130] Governance Measures Experience Module:

[0131] Engineering measures: Virtual experience of the effects of installing dust removal, desulfurization and denitrification equipment;

[0132] Management measures: Simulate the impact of traffic control and industrial production restrictions;

[0133] Ecological measures: Experience the environmental benefits of afforestation and wetland protection;

[0134] Personal protection: Learn how to use protective equipment correctly;

[0135] Knowledge Q&A and Challenge Module:

[0136] Scenario-based multiple-choice questions: Setting up multiple-choice question scenarios in a virtual environment;

[0137] Ranking Challenge: Users are required to rank pollution control measures by their effectiveness;

[0138] Matching game: Match pollution phenomena with their causes;

[0139] Virtual experiment: Allows users to observe changes in pollution by adjusting parameters;

[0140] S5. User behavior tracking and personalized feedback:

[0141] User behavior data collection:

[0142] Eye tracking: Records the user's gaze points and duration in a virtual environment;

[0143] Interaction Log: Records user interactions with various popular science topics;

[0144] Movement trajectory: Records the user's exploration path in the virtual space;

[0145] Dwell time: Statistics on the duration users spend in the key knowledge point area;

[0146] Cognitive level assessment:

[0147] The system establishes an atmospheric pollution knowledge graph, including:

[0148] Basic conceptual layer: definition of pollutants, classification of sources;

[0149] Mechanism of influence: health impacts, environmental effects;

[0150] Governance measures level: technical measures and management policies;

[0151] Comprehensive application layer: case analysis, problem solving;

[0152] Based on user behavior data, the system assesses the user's mastery of each knowledge point and identifies areas of weakness in knowledge.

[0153] Personalized learning path recommendations:

[0154] Based on the evaluation results, the system dynamically adjusts the science popularization content:

[0155] For knowledge points that are well grasped, more in-depth extended content is provided;

[0156] Provide more explanatory content and practice opportunities for weak areas;

[0157] Adjust the way content is presented based on learning style preferences;

[0158] Generate personalized learning reports and improvement suggestions.

[0159] Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art and related fields based on the embodiments of the present invention without inventive effort should fall within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described and explained in the present invention, unless otherwise specified or limited, shall be implemented according to conventional means in the art.

Claims

1. A virtual reality-based science popularization and dissemination system for atmospheric environmental pollution, characterized in that, include: The system comprises the following modules: a data acquisition and processing module, which acquires atmospheric pollutant concentration data in real time, standardizes and performs spatiotemporal correlation processing on the data to generate a pollution dataset with geographical location and timestamps; a 3D scene construction module, which constructs a 3D virtual environment basic model of the target area based on geographic information system data and integrates the pollution dataset with the 3D virtual environment basic model; a pollutant visualization module, which performs visualization rendering in the 3D virtual environment basic model according to the type and concentration level of pollutants, using predefined colors, densities, and particle effects to generate a dynamic visual representation of pollution diffusion; an interactive science popularization content module, which provides an interactive interface with visualized pollutants and virtual environment objects, responds to user interaction operations, triggers and presents preset science popularization content related to the pollutant; a virtual reality presentation module, which outputs the rendered 3D virtual environment integrating pollution visualization and science popularization content to a virtual reality display device to provide users with an immersive experience; and a user behavior tracking and feedback module, which tracks user behavior, gaze focus, and interaction choices in the virtual environment, and dynamically adjusts the presentation method or difficulty of the science popularization content based on the tracking data.

2. The atmospheric environmental pollution popular science dissemination system based on virtual reality according to claim 1, characterized in that, The data acquisition and processing module specifically includes: a multi-source data access unit, used to access at least two data sources including national monitoring stations, micro-sensor networks, satellite remote sensing inversion data, and meteorological data platforms; and a data fusion unit, which uses spatial interpolation algorithms and spatiotemporal kriging to fuse and extrapolate pollution data from different sources and at different resolutions to generate a continuous spatially distributed pollution field.

3. The atmospheric environmental pollution popular science dissemination system based on virtual reality according to claim 1, characterized in that, The pollutant visualization module supports multiple visualization modes, including: a spatiotemporal evolution mode, which dynamically displays the diffusion, accumulation, and dissipation of pollutants within a preset historical period using a timeline control method; a comparison mode, which visualizes the pollution status under different periods and policy scenarios on the same screen; and a profile analysis mode, which displays a profile of pollutant concentration along a specified vertical path in response to user operations.

4. The atmospheric environmental pollution popular science dissemination system based on virtual reality according to claim 1, characterized in that, The interactive science popularization content module provides the following science popularization content: Pollutant source animation simulation: showing the generation mechanism and transmission path of specific pollutants such as PM2.5 and ozone in the form of dynamic 3D animation; Health impact simulation: intuitively showing the short-term and long-term effects of different concentrations of pollutants on the respiratory and cardiovascular systems through virtual human models; Governance measure simulation: simulating the virtual effects of different governance measures such as afforestation, industrial emission reduction, and vehicle restriction on improving air quality in the form of an interactive sandbox.

5. A virtual reality-based science popularization system for atmospheric environmental pollution according to claim 1, characterized in that, The user behavior tracking and feedback module further includes: an evaluation unit, used to generate an evaluation report on the user's pollution awareness level based on the interactive tasks completed by the user, the popular science questions answered, and the exploration path in the virtual environment; and a personalized recommendation unit, which recommends virtual areas or popular science learning content for the user to focus on exploring next based on the evaluation report.

6. The atmospheric environmental pollution popular science dissemination system based on virtual reality according to claim 1, characterized in that, The propagation method of the propagation system includes the following steps: S1. Acquire multi-source air pollution monitoring data and geographic information data of the target area, and perform spatiotemporal fusion processing to generate a structured pollution dataset; S2. Construct a three-dimensional virtual environment basic model of the target area; S3. Map the structured pollution dataset to the three-dimensional virtual environment basic model, and render it according to the pollutant type and concentration using a corresponding visualization scheme to generate an immersive pollution scene; S4. Set interactive hotspots in the immersive pollution scene, with each hotspot associated with detailed popular science media content; S5. Present the immersive pollution scene to the user through a virtual reality device and capture the user's interaction commands in real time; S6. Respond to the user's interaction with the interactive hotspots, seamlessly present the corresponding popular science media content in the virtual environment, and record user interaction behavior data for feedback and evaluation.

7. A virtual reality-based science popularization system for atmospheric environmental pollution according to claim 6, characterized in that, Step S2, "Constructing a basic 3D virtual environment model of the target area," specifically includes the following steps: S2.1, Data preparation and preprocessing: Acquiring the digital elevation model, satellite remote sensing imagery, vector map data, and 3D model data of landmark buildings for the target area; performing preprocessing on the data, including coordinate system unification, scale normalization, and format conversion; S2.2, Terrain and landform modeling: Based on the digital elevation model data, generating a 3D terrain mesh that reflects the real terrain undulations; mapping the satellite remote sensing imagery as texture maps onto the 3D terrain mesh to form a realistic terrain model. S2.3, Artificial Feature Integration: The road, water system, green space and administrative division boundary information in the vector map data are superimposed on the basic geographic scene in the form of semi-transparent patches. The three-dimensional model data of the landmark buildings are placed in the corresponding positions of the scene according to their real geographic coordinates and scale. S2.4, Environmental Elements and Semantic Enhancement: Skyboxes, dynamic lighting and weather systems that conform to physical laws are added to the scene. Semantic tags are attached to key geographic objects in the scene. The semantic tags include object type, name and associated popular science content index.

8. A virtual reality-based science popularization system for atmospheric environmental pollution according to claim 7, characterized in that, Step S2.2, "Generating a three-dimensional terrain mesh reflecting real terrain undulations based on the digital elevation model data," is specifically implemented through a multi-level adaptive terrain mesh generation method, including the following steps: S2.2.1, Multi-resolution elevation data fusion: Acquire digital elevation model data of two different spatial resolutions for the target area, using high-resolution data as the detail layer and low-resolution data as the base layer. Through a feature-preserving data fusion algorithm, the high-frequency terrain features of the detail layer are adaptively fused into the overall terrain framework of the base layer, generating an enhanced elevation matrix that integrates multi-scale details; S2.2.2, Adaptive mesh subdivision based on terrain features: Perform terrain feature analysis on the enhanced elevation matrix, calculate the terrain roughness factor and slope change rate for each region, and, according to preset mesh subdivision rules, perform higher-density triangular mesh subdivision for regions with high terrain roughness or drastic slope changes, thus generating an enhanced elevation matrix that integrates multi-scale details; S2.2.

3. Mesh Optimization and Manifold Guarantee: The initial terrain mesh is smoothed and geometrically error-detected to eliminate possible elongated triangles and mesh cracks. Edge folding and vertex splitting algorithms are applied to lightweight the mesh while maintaining the terrain contour features, ensuring that the final generated 3D terrain mesh is geometrically manifold and meets the real-time requirements of virtual reality rendering. S2.2.

4. Multi-level Detail Mesh Generation: Based on the final terrain mesh, progressive meshing technology is used to automatically generate simplified mesh models of multiple different levels of detail for the same area, and establish mapping relationships between models of each level to support dynamic loading and switching of different levels of detail based on the user's viewpoint distance in the virtual environment, achieving efficient rendering of large-scale terrain.

9. A virtual reality-based science popularization system for atmospheric environmental pollution according to claim 6, characterized in that, In step S3, the visualization scheme includes: for gaseous pollutants, a three-dimensional isosurface rendering with semi-transparency and color gradient is used for representation; for particulate pollutants, a dynamic particle system with density proportional to concentration is used for simulation.