KSKY Hi-Tech Corp., Canada

With accelerated urbanization and increased vehicle ownership, air pollution—particularly fine particulate matter (PM2.5), ozone (O₃), and nitrogen dioxide (NO₂)—has become a growing threat to public health. While traditional ground-based monitoring stations offer high accuracy, their spatial coverage is limited and cannot meet the demand for high-resolution, dynamic, multi-scale, and multi-temporal air quality monitoring across urban areas. This project proposes the establishment of an “Integrated Smart Satellite-Low Altitude-Ground Urban Air Quality High-Precision Monitoring Station”, integrating high-resolution satellite remote sensing, low-altitude UAV sensing, ground-based sensor networks, and AI algorithms. This system aims to achieve comprehensive perception, automated early warning, and scientific decision support for urban air quality, and will be directly applicable to county-level environmental protection systems. High-precision and real-time three-dimensional monitoring enables targeted pollution control, achieving better results with greater efficiency and generating both economic and social benefits.

To develop a high spatiotemporal resolution, automated, intelligent, and scalable air quality monitoring system for urban areas, with specific objectives including:

·       Real-time, all-weather monitoring and retrieval of PM2.5, NO₂, O₃ across urban areas (>100 km²),

·       Development of remote sensing models based on the COOL algorithm (Clarity Optimization via Objective Light-haze removal) for haze removal and atmospheric parameter inversion.

·       Deployment of a UAV-based low-altitude intelligent sampling network to fill gaps between satellite and ground observation,

·       Integration of fixed and mobile ground monitoring station data into a unified system to form a robust feedback loop for model calibration and validation,

·       Construction of an AI-driven data fusion and early warning platform for predictive modeling and visualized air quality management.

III. Technical Framework

1. Satellite Remote Sensing Module (High-Altitude)

• Apply the      COOL algorithm for haze removal and image clarification of high-resolution      satellite imagery,

• Extract      PM2.5, inhalable particles, NO₂, and O₃ distribution using atmospheric      radiative transfer models and AI inversion,

• Satellite      data sources: Sentinel-2, SPOT, GF-series, ZY-3, and other commercial      high-resolution satellites.

2. Low-Altitude UAV Module (Near-Surface Layer)

• Deploy      intelligent drones equipped with sensors for VOCs, PM, O₃, CO₂, etc.,

• Perform      regular or on-demand aerial sampling to gather vertical atmospheric      profiles,

• Upload data      to the cloud to support 3D pollution tomography modeling.

3. Ground Monitoring Network (Baseline Layer)

• Integrate existing fixed air quality monitoring station data,

• Deploy multiple mobile micro air quality monitors,

• Provide ground truth for remote sensing model calibration,

• Support long-term operations, data return, remote control, and edge      AI processing.

4. AI Data Fusion and Platform Development

• Use deep      learning models (LSTM, GNN, Transformer) to fuse satellite, UAV, and      ground data,

• Build models      for spatiotemporal pollution evolution to predict trends 3–24 hours ahead,

• Enable      pollution source identification, impact range assessment, decision-making      support, and risk warning.

IV. Key Innovations

• First      application of the COOL satellite image haze removal algorithm in      air quality retrieval,

• Collaborative      modeling using UAV imagery, mobile sampling, and fixed sensor networks,

• AI-driven      fusion of heterogeneous data from three sources (satellite, UAV, ground),

• Fully      automated and mobile workstation design, allowing for city-specific      customization and replication.

V. Application Scenarios

• Visualization      of urban air pollution spatial distribution,

• Dynamic air      quality monitoring for key locations like schools, hospitals, industrial      zones, and construction sites,

• Quantitative      analysis of urban expansion and traffic management impacts on air quality,

• Rapid      response and source tracing for pollution emergencies,

• Supporting      environmental regulation, public health protection, and carbon neutrality      policy evaluation.

VI. Implementation Plan and Phase Objectives

VII. Expected Outcomes

• Establish      2–3 integrated urban air quality monitoring workstations,

• Release      real-time air quality heatmaps and dynamic pollution source tracking,

• Publish      high-level academic papers and obtain software copyrights/patents,

• Promote      large-scale adoption in smart cities, environmental agencies, and research      institutions,

• Provide      scientific support for urban carbon peak and carbon neutrality strategies.

VIII. Budget Estimate 

 IX. Conclusion and Invitation for Collaboration

This proposal aims to shift urban air quality monitoring from isolated points to full-area coverage, and from passive response to proactive early warning. We sincerely invite collaboration from environmental authorities, research institutions, remote sensing companies, and AI platform providers to jointly develop a new paradigm in smart urban air quality perception networks.

转载本文请联系原作者获取授权，同时请注明本文来自杜勇科学网博客。

链接地址：https://wap.sciencenet.cn/blog-1151100-1491629.html?mobile=1

收藏

分享