Dual-Wavelength Scanning LiDAR for Fire Smoke and Aerosol Monitoring in Industrial Areas

Early identification of fires and reliable monitoring of particulate matter in industrial areas are challenging due to complex emission sources and the limitations of passive optical sensors. We present a dual-wavelength (532/1064 nm) scanning LiDAR system designed for simultaneous fire smoke detection and particulate monitoring in industrial environments. The system operates horizontally at approximately 55 m above ground level, with full azimuthal scanning and kilometer-scale range coverage, enabling near-source observation of aerosol plumes at stack height. Elastic backscatter signals at both wavelengths are used to retrieve aerosol extinction coefficients, from which the Ångström exponent (AE) is derived to characterize particle size. In parallel, polarization-resolved measurements provide the linear depolarization ratio (δ), indicating particle shape and non-sphericity. By jointly analyzing extinction (α), AE, and δ, the system discriminates between fine soot-dominated combustion aerosols, ash-laden near-source smoke, and non-combustion industrial particulates in real time. Field deployment in the Siheung Industrial Complex (Republic of Korea) captured an actual fire event on 22 July 2024. During the early combustion phase, the smoke plume exhibited moderate AE and elevated depolarization, consistent with coarse, irregular ash particles. As the fire stabilized, the aerosol signature transitioned to higher AE and low depolarization, indicating fine soot-dominated smoke. Additional observations revealed clear contrasts between daytime and after-hours particulate regimes, with nighttime conditions showing expanded hotspots associated with higher extinction and coarser particle characteristics. These results demonstrate that horizontally scanning, dual-wavelength polarization LiDAR provides a robust and practical approach for integrated fire detection and particulate monitoring in complex industrial environments, offering enhanced situational awareness for air-quality management and early fire warning.

Acknowledgements

This work was supported by the Ministry of the Interior and Safety (MOIS), Republic of Korea, through the Joint Cooperation R&D Program (Project No. 2023-MOIS-20024324), and by the Advanced Institute of Convergence Technology (AICT), Seoul National University.