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Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

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UAV-Based Hyperspectral Imaging Bridges the Spatial Gap in Pollution Source Identification.

Chengzhi Xing¹, Haochen Peng^1,2, Tiliang Zou^1,3

¹Key Lab of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.

Analytical Chemistry

|January 23, 2026

Summary

This summary is machine-generated.

A new unmanned aerial vehicle (UAV) system uses hyperspectral imaging to precisely locate and measure industrial pollution, even under clouds. This technology bridges gaps in air quality monitoring, improving emission control and regional models.

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Area of Science:

Environmental Science
Remote Sensing
Atmospheric Chemistry

Background:

Accurate pollution source localization is vital for air quality management.
Current methods face limitations in resolving hotspots under clouds and bridging satellite-ground observation gaps.

Purpose of the Study:

To develop and validate an unmanned aerial vehicle (UAV)-based hyperspectral imaging system for pinpointing and quantifying industrial pollutant emissions.
To overcome challenges in UAV remote sensing for improved air quality monitoring.

Main Methods:

Developed a lightweight hyperspectral payload for UAV deployment.
Implemented an enhanced signal-to-noise ratio (SNR) module for ground-level pollutant detection.
Applied robust air mass factor correction and stabilized observation angles to address UAV sensing challenges.

Main Results:

The UAV system achieved meter-scale spatial and 20-30 min temporal resolution.
Demonstrated strong agreement (R = 0.94 for NO2) with mobile differential optical absorption spectroscopy (DOAS).
Successfully captured subcloud pollution plumes and identified high-emission facilities.

Conclusions:

The UAV hyperspectral system effectively bridges the observational gap between orbital and ground-based monitoring.
This technology offers scalability and precise source identification for refining emission models.
It promises a paradigm shift in pollution control strategies.