Kwanchul Kim

Transboundary wildfire smoke is difficult to attribute because satellite-detected emissions, atmospheric transport, and receptor-level air quality responses are typically evaluated separately. This study developed an Earth Observation (EO)-centered framework that integrates satellite fire and aerosol observations, Lagrangian trajectory analysis, surface measurements, and explainable machine learning to characterize source-to-receptor smoke transport from Northeast China to the Korean Peninsula. The framework was applied to seven burning episodes from March to April 2026. Moderate Resolution Imaging Spectroradiometer (MODIS) detected 39,750 active fire pixels. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) identified a dominant high-fire radiative power cluster along the Changchun–Harbin corridor. Satellite aerosol observations showed enhanced fine-mode aerosol loading over the source region, followed by elevated aerosol optical depth at Gangneung after approximately 1–3 days. Complementary forward and backward Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) analyses converged on northern Jilin Province as a major source region associated with transport toward the east coast of Korea. Fifteen of the 62 observation days were classified as fire-influenced, during which mean PM₂.₅ was approximately three times higher than on non-influenced days. Extreme Gradient Boosting (XGBoost) provided the lowest root mean square error among the evaluated models, whereas SHapley Additive exPlanations (SHAP) analysis showed positive contributions from the lagged fire radiative power and CO under high-fire conditions. Agreement among the independent evidence streams demonstrates the value of combining complementary EO information for transboundary smoke assessment. The modular framework is scalable to other regions and emerging satellite platforms where direct source–receptor observations are limited.

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.AcknowledgementsThis 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.

This study introduces the development and application of the advanced scanning LiDAR system, SMART LiDAR MK-II(Samwoo TCS co., Ltd), designed for the early detection of wildfires and industrial fires. Traditional fire detection methods face limitations due to diverse atmospheric conditions, topographical factors, and variability in fire and smoke characteristics. To address these challenges, monitoring systems with spatial resolutions below 30 meters are essential. The SMART LiDAR MK-II employs dual wavelengths (532 nm and 1064 nm) and provides 360° observations with an angular resolution of approximately 3° within a 30-minute interval, enabling the real-time detection of smoke and particulate matter under various environmental conditions. The system was validated through field deployment in the Sihwa Industrial Complex, South Korea, during a fire at an automotive painting factory on July 22, 2024. Positioned at a monitoring height of 55 meters and approximately 20 meters from the fire source, the SMART LiDAR MK-II detected smoke with peak PM10 and PM2.5 concentrations of 724 µg/m³ and 334 µg/m³, respectively. The smoke plume was observed dispersing over 5 km northward, influenced by prevailing winds. Furthermore, the system successfully captured the temporal reduction in particulate matter concentrations following fire suppression, demonstrating its capability to monitor emission dynamics and dispersion patterns. Currently, SMART LiDAR MK-II is undergoing rigorous waterproof and dustproof testing to ensure operational reliability under diverse conditions, with commercialization in progress. This cutting-edge technology represents a significant advancement in LiDAR-based fire detection, offering high spatial resolution, sensitivity, and reliability for real-time monitoring of smoke emissions and atmospheric impacts. The results highlight the transformative potential of SMART LiDAR MK-II to enhance global fire detection and environmental monitoring capabilities. Acknowledgment: This research was supported by a grant (2023-MOIS-20024324) from the Ministry-Cooperation R&D Program of Disaster-Safety funded by the Ministry of Interior and Safety (MOIS, Korea).

Wildfires are increasing globally due to climate change. Wildfires can spread rapidly in a short period of time, early detection is important. A CCTV and thermal imaging camera are used for early detection and prevention of wildfires and improved using camera analysis method and AI technology. However, the wildfire detection distance is still shortened depending on weather conditions and air quality, and image processing performance deteriorates due to low light at night. A satellite remote sensing technology is difficult to monitor wild fires in real time and affected by cloud mask, low spatial and temporal resolution. A Drone is also limited in flight time by communication, weather conditions, battery capacity, and payload. In the case of lidar-based long-distanced wildfire monitoring, advanced remote sensing monitoring that can monitor wildfires is possible by classifying the type of aerosol particles and the amount of light backscattered by smoke particles. Our recently developed wildfire scanning lidar technology uses light sources of two wavelengths (532 nm and 1064 nm) and developed a system capable of 360° observation within 30 minutes with an angular resolution of less than 1° in the horizontal direction. In addition, it is the wildfire scanning lidar capable of detectiong a wildfire in the atmosphere using the backscattering coefficient and aerosol optical properties calculated at two wavelengths. A depolarization of smoke aerosol in the air can be used to improve the accuracy of wildfire smoke detection using characterization of particles. Presently wildfire monitoring lidar technology under development is producing commercial products that protect eyesight and monitor forest fire smoke within a radius of 10 km through long-wavelength laser and object analysis. Acknowledgement: This research was supported by a grant (2023-MOIS-20024324) of Ministry-Cooperation R&D Program of Disaster-Safety funded by Ministry of Interior and Safety (MOIS, Korea).