지구환경도시건설공학과

Research 2026년 07월 08일 수요일

New Deep Learning Model Extends Reliable Wildfire Forecasts Beyond Two Weeks

Abstract The 2025 Los Angeles wildfires highlighted growing risks from climate-driven extremes and the need for reliable wildfire forecasting beyond short lead times. Although the European Center for Medium-Range Weather Forecasts provides global fire weather index forecasts, these products are primarily designed for long-range climate outlooks, and their practical effectiveness remains uncertain in regions with limited local forecasting infrastructure. Here we show that a global deep learning framework, forecasting daily fire weather index values up to 31 days ahead, consistently improves forecast accuracy and reduces bias relative to operational numerical forecasts. The framework learns nonlinear and lagged fire–weather relationships by integrating past fire weather index dynamics and future meteorological conditions. Importantly, forecast bias is reduced in 85% of grid cells where high wildfire exposure coincides with high socioeconomic vulnerability. This demonstrates that data-driven forecasting can help bridge critical information gaps in underserved regions and support more equitable climate risk management. Reliable wildfire forecasts become increasingly difficult beyond the first few weeks, limiting their value for medium-range decision-making. A research team, led by Professor Jungho of the Department of Civil, Urban, Earth, and Environmental Engineering at UNIST, has shown that a global deep learning framework can extend reliable daily wildfire forecasts to 31 days while reducing forecast bias compared with existing operational forecasting systems. Known as FWI-Net, the framework predicts daily values of the Fire Weather Index (FWI), a widely used measure of wildfire danger. Unlike conventional forecasting methods, it combines historical fire-weather conditions with future meteorological forecasts, allowing it to capture the cumulative effects of prolonged heat and drought on future wildfire risk. Across the full 31-day forecasting period, FWI-Net reduced prediction error by 6.6% compared with the operational forecasting system of the European Center for Medium-Range Weather Forecasts (ECMWF). During the first week, prediction error decreased by 12.4%. The model also extended the period of meaningful forecasts under very high wildfire danger by five days. The model performed particularly well in regions where high wildfire exposure coincides with high socioeconomic vulnerability. Forecast bias was reduced across 85% of these areas, and in regions with limited forecasting infrastructure, FWI-Net maintained useful forecasting skill for an average of 22 days. “By combining historical fire-weather conditions with future weather forecasts, the model captures patterns that conventional forecasting systems often miss,” the research team said. “Most importantly, it provides more reliable forecasts for regions facing high wildfire risk despite limited forecasting infrastructure.” “As climate change increases wildfire risk around the world, reliable forecasting is becoming essential for disaster preparedness,” said Professor Im. “We expect this framework to support medium-range wildfire planning while helping reduce information gaps in regions with limited forecasting capacity.” The study was co-led by Professor Yoojin Kang of Kookmin University and Sihyun Lee of UNIST, who served as first authors. The findings were published online in Communications Earth & Environment on June 28, 2026. The research was supported by the Ministry of Environment (ME), the Korea Forest Service, and the National Research Foundation of Korea (NRF). Journal Reference Yoojin Kang, Sihyun Lee, Dongjin Cho, and Jungho Im, "Deep learning-based forecasting provides a pathway to closing wildfire information gaps in underserved regions," Commun. Earth Environment. , (2026).

Research 2026년 07월 01일 수요일

Beyond Algorithms

《Editor's Note: Scientific discovery has always depended on our ability to observe and understand the world. Increasingly, however, many of science's most pressing questions—from interpreting human behavior and dynamic environments to advancing healthcare and understanding Earth's changing climate—have grown too complex for conventional approaches alone. Across disciplines, UNIST researchers are using artificial intelligence to reveal patterns, improve prediction, and open new pathways for discovery. The latest work—from enabling machines to understand human emotion and perceive the physical world to revealing new insights into cities and climate—reflects a broader shift in science: AI is no longer simply a field of research. It is becoming part of how research itself is done.》 From Data to Insight AI is becoming increasingly capable of interpreting the world with greater nuance and context. At UNIST, researchers are advancing AI's ability to understand both people and the physical world through innovations in computer vision and multimodal learning. One research team developed an AI framework that translates emotional cues from speech into realistic facial expressions, enabling more natural human–AI interaction. Another explored how robots can better interpret social touch and express emotions through movement, making human–robot interaction feel more intuitive and lifelike. Researchers also introduced a new camera calibration method that significantly improves how machines perceive dynamic environments. Together, these studies show how AI is moving beyond recognizing data toward interpreting people, interaction, and the physical world. ■ AI Turns Speech into Natural Facial Expressions (CVPR 2026) ■ Making Human-Robot Interaction More Natural (ICRA 2026) ■ AI Advances Event Camera Calibration (CVPR Highlight 2026) From Insight to Discovery AI is emerging as an essential research tool, helping scientists uncover patterns and relationships that would be difficult to detect through conventional methods alone. Rather than replacing scientific expertise, AI is expanding how researchers investigate complex systems across disciplines. At UNIST, researchers developed LightSplat, enabling AI to recognize objects within complex 3D scenes from natural language descriptions in just five seconds. Another team combined deep learning with hyperspectral satellite imagery to detect methane emissions faster and more accurately, improving how researchers monitor one of the world's most potent greenhouse gases. Together, these studies illustrate how AI is accelerating scientific discovery across both digital and environmental systems. ■ AI Recognizes 3D Objects in Five Seconds (CVPR 2026) ■ AI Detects Methane from Space (npj Clim. Atmos. Sci. l Mar. 2026) Across disciplines, AI is becoming more than a field of study. It is emerging as a common language for discovery—expanding not only what researchers can discover, but the questions they are able to ask.

Community 2026년 06월 25일 목요일

New Publication by Professor Jaewon Cho Explores the Future of Higher Education in the Age of AI

“As artificial intelligence (AI) becomes increasingly capable of generating information and answering questions, what should students learn—and what role should universities play?” Those questions are at the center of a new book by Professor Jaewon Cho of Civil, Urban, Earth, and Environmental Engineering at UNIST. Published under his pen name, Kang Hadan, the book—whose Korean title references a future vision of higher education—examines how education might evolve in an era shaped by AI, large language models, and digital technologies. Professor Cho argues that many educational systems remain focused on transmitting established knowledge and rewarding students for finding correct answers. Yet today's learners face challenges—from climate change to economic uncertainty—that rarely come with predetermined solutions. In this environment, he suggests, education must place greater emphasis on inquiry, collaboration, and the creation of new knowledge. The book proposes a future learning ecosystem in which students, teachers, and AI work together to explore questions, test ideas, and develop new ways of understanding the world. Teachers serve less as providers of answers and more as guides who help students navigate different perspectives and connect learning to real-world challenges. A recurring theme throughout the book is the need to look beyond conventional measures of achievement. Professor Cho argues that curiosity, experimentation, collaboration, and creative problem-solving may be just as important as grades and test scores in preparing students for a rapidly changing world. The book also examines how emerging technologies could transform the way learning is documented and recognized. Rather than focusing solely on outcomes, future educational systems may place greater value on the process of learning itself—how students ask questions, develop ideas, respond to setbacks, and build knowledge with others. Another key theme is the role of play in education. Professor Cho suggests that meaningful learning often begins when students pursue questions out of genuine curiosity, collaborate with peers, and experiment without fear of failure. “The amount of knowledge we need to memorize is shrinking, while the amount of knowledge we need to create is growing,” Professor Cho writes. “Education should not be about finding predetermined answers, but about developing new ways to understand the world.” At UNIST, Professor Cho teaches and conducts research in environmental engineering, environmental ethics, and the humanities of science and technology. He previously served as director of the Science Walden, where he led interdisciplinary initiatives addressing issues such as climate change, social inequality, and the future of work.

Research 2026년 06월 22일 월요일

Satellite and Climate Models Help Explain the Variability of Urban Heat Islands Globally

Abstract Urban morphology and background climate are evolving drivers of the urban heat island (UHI) effect, yet the thermal influence of surrounding urban structure and its coupling with climate remain elusive. Here, we show that climate and morphology jointly shape global urban heat. Using a six-class urban typology, long-term climatology, and machine learning, we quantify the thermal influence of surrounding urban structure and project future urban heat across 2,213 cities. We define a city-level thermal impact of the surrounding built environment (TBE) as the area-weighting UHI change induced by specific built-up types. Climatically, cold regions most frequently exhibit high daytime TBE, while arid regions dominate high nighttime TBE. Structurally, a universal pattern persists; high TBE corresponds to denser and taller forms, whereas sparser and lower types dominate low TBE during day and night. Future projections indicate that climate change dominates TBE change in 69% of cities, whereas the Global South exhibits stronger tendencies toward morphology-driven and synergistic intensification than the Global North. Our results highlight the need for locally tailored adaptation strategies that target the dominant drivers—climatic, morphological, or both. Urban Heat Islands (UHIs) are more than just a temperature difference—they emerge from the complex interplay between local climate and how cities are built. Recognizing this connection highlights the need for tailored solutions that address both environmental and structural factors. Professor Jungho Im from the Department of Civil, Urban, Earth, and Environmental Engineering at UNIST, in collaboration with Professor Cheolhee Yoo from the Department of Urban Planning and Engineering at Pusan National Universit (PNU), Seoul National University (SNU), and Pacific Northwest National Laboratory (PNNL) analyzed data from 2,213 cities worldwide. Using satellite imagery, machine learning, and climate models, they revealed that the intensity and patterns of UHIs depend heavily on regional climate and urban morphology. The phenomenon occurs as buildings, roads, and pavement absorb heat, altering airflow and trapping warmth. The study shows that in colder regions, UHIs tend to be most prominent during the day, driven by building heat absorption. In dry climates, the effect shifts to nighttime, when heat stored during the day is released, amplifying nighttime heat. Within the same climate zones, urban density and building height also influence the strength of UHIs: dense, high-rise areas retain more heat, while sparser, lower structures tend to moderate it. Using AI, the researchers divided cities into 1-kilometer grid cells. They analyzed how surrounding urban features and climate conditions contribute to local temperatures. Each cell was categorized into one of six urban types based on building density and height, considering both the immediate area and its surroundings. Looking ahead, the team projected future UHI trends under different climate and development scenarios. Climate change emerges as the main driver, worsening heat in nearly 70% of cities. Meanwhile, rapid urban growth in developing nations is expected to intensify UHIs, especially where dense, tall buildings become more common. Professor Im stresses that understanding urban heat requires integrating large-scale spatial data with AI tools. “Our findings show that effective strategies must be tailored to each city's climate and layout,” he said. “One-size-fits-all solutions don't work. We need approaches that reflect local conditions.” He further added, “Advanced data analysis helps us grasp how cities generate heat. This research will support policies on heatwave response, urban redevelopment, and protecting vulnerable areas.” The study was led by Siwoo Lee from UNIST and Professor Cheolhee Yoo of PNU, as co-first authors. Supported by the Ministry of Science and ICT (MSIT) and the National Research Foundation of Korea (NRF), the work was published in Nature Communications on May 11, 2026. Journal Reference Siwoo Lee, Cheolhee Yoo, Bokyung Son, et al. , “Global patterns of urban heat shaped by climate and morphology,” Nat. Commun. , (2026).

Research 2026년 06월 01일 월요일

AI-Driven Satellite Detects Methane—84 Times More Potent Than CO2—Faster and More Precise

Abstract Methane (CH4) is a dominant driver of near-term warming, yet global emission monitoring remains constrained by slow processing and large uncertainties. Hyperspectral spectrometers enable sensitive detection of CH4 plumes, but the relative advantages of enhancement-based (ENH) and radiance-based (RAD) approaches have not been systematically evaluated. Here we introduce a dual-path deep-learning framework that systematically compares both approaches using globally distributed, expert-validated CH4 plume datasets from EMIT and Tanager-1. The ENH models exhibit higher segmentation accuracy across plume scales, whereas the RAD models, operating directly on 49 shortwave-infrared channels, avoid computationally expensive preprocessing (eg, matched filtering) and enable rapid screening. Both pathways markedly reduce labor-intensive workflows and latency relative to traditional processing while maintaining competitive performance by utilizing deep learning. Explainable AI analyzes demonstrate that the models learn spatial-spectral features consistent with CH4 absorption structure and plume morphology, providing evidence of scientific validity. Cross-sensor evaluation demonstrates architectural robustness across EMIT and Tanager-1, establishing a physics-grounded framework adaptable across hyperspectral sensors. Methane (CH4) is a powerful greenhouse gas—84 times more potent than carbon dioxide over 20 years. Quickly identifying leaks is crucial, but current methods rely on slow, manual analysis of satellite images. Researchers at UNIST have created an AI-based system that automates methane plume detection, making monitoring faster and more precise. Led by Professor Jungho Im from the Department of Civil, Urban, Earth, and Environmental Engineering, the team built a deep learning model to identify methane leaks in satellite imagery. They tested different data types and modeling techniques to determine the most effective approach for global deployment. Hyperspectral satellites capture reflected sunlight across hundreds of narrow wavelengths, revealing detailed surface information. The team trained their models on data from NASA's EMIT satellite, which orbits the International Space Station. The models detect methane by recognizing specific infrared wavelengths absorbed by the gas—clear indicators of leaks. The system successfully identified methane emissions from diverse sources, including oil and gas facilities, waste sites, and coal mines across regions such as Turkmenistan, Algeria, and the US The analysis revealed that the AI system does not merely recognize visual patterns; It interprets physical signals such as absorption features and plume shapes consistent with real-world physics. They tested three deep learning architectures—CNN-ASPP, Inception U-Net, and SegFormer—using two data types: raw radiance and processed data that highlights methane concentration. Models trained on the enhanced data performed better overall. But models trained on raw data responded faster, making them useful for quick scans. Applying these models to Tanager-1 satellite data—commercial hyperspectral imagery—yield similar results, demonstrating the approach's versatility across different sensors. First authors Seyoung Yang and Yejin Kim played key roles in this work. The models work across various resolutions and conditions, relying on physical principles to quickly flag large leaks for further investigation. Professor Im emphasized the importance, “Fast leak detection is essential for reducing methane emissions. Traditional methods are slow and require experts. By combining hyperspectral data with AI, we can identify potential trouble spots in real time and strengthen global monitoring efforts.” Supported by the Ministry of Environment and the Ministry of Education, this research was published in npj Climate and Atmospheric Science on March 25, 2026. Journal Reference Seyoung Yang, Yejin Kim, Minki Choo, et al ., “Beyond localized methane plume detection: a dual-path deep learning framework for sensor-agnostic global hyperspectral methane plume monitoring,” npj Clim. Atmos. Sci., (2026).

Research 2026년 04월 22일 수요일

New Study Uncovers Shift in Climate Drivers Intensifying Wildfires in Australia

Abstract Wildfire variability in Southeastern Australia (SEA) has intensified in recent decades, posing increasing risks to ecosystems and agriculture under a changing climate. However, the mechanisms driving the recent amplification of extreme fire weather remain unclear. Using austral-summer data from 1981–2022, we quantify interannual links between the Forest Fire Danger Index (FFDI) and land–atmosphere variables. Fire Weather Days (FWD) are defined as days exceeding an extreme FFDI threshold each fire season and are validated against satellite-based burned area and fire intensity across SEA. We show that recent fire risk in SEA is characterized not by a gradual increase but by a regime shift in extreme fire weather conditions. An early-2000s transition is marked by enhanced interannual variability and an approximately fivefold increase in FWD, linked to increased positive skewness in daily FFDI. Among FFDI components, the drought factor (DF), representing hydrological stress, exhibits the largest increase in extreme occurrences, especially when co-occurring with high temperature (T) and low relative humidity (RH). The contribution of compound DF & RH & T events to total FWD more than doubles between 1981–2001 (P1) and 2002–2022 (P2). Segmented regression further reveals strengthened interannual FWD sensitivity to DF in P2. In P1, variability reflected atmospheric warming and drying, whereas P2 is characterized by intensified land–atmosphere coupling that amplifies hydrological stress and compound extremes. This transition coincides with changes in large-scale circulation, with the Southern Annular Mode (SAM) emerging as the dominant driver of FWD variability in the recent period, while ENSO exerted a stronger influence earlier. Increased FWD variability is also closely linked to interannual maize yield fluctuations across SEA. These findings highlight a hydrologically-driven regime shift in extreme fire weather and underscore the need for integrated climate-fire-agriculture risk assessment. An international team of researchers, affiliated with UNIST, has identified a dramatic transformation in wildfire patterns across Southeastern Australia (SEA). Analyzing data from 1981 to 2022, the research shows that since the early 2000s, the region has experienced a fivefold increase in extreme fire weather days, driven increasingly by the Southern Annular Mode (SAM) rather than the traditionally dominant El Niño–Southern Oscillation (ENSO). This shift highlights new challenges in predicting and managing wildfires under a changing climate. Led by Professor Myong-In Lee from the Department of Civil, Urban, Earth and Environmental Engineering at UNIST, this study was conducted in collaboration with experts from the University of Hawaii and POSTECH. In this study, the team identified a regime shift beginning in the early 2000s, characterized by emphasized interannual variability and a sharp rise in extreme fire weather days. Over the past two decades, wildfire risk volatility has more than doubled. This change is primarily attributed to the strengthening of land-atmosphere coupling, where drought conditions intensify surface heating, creating a feedback loop that fuels more frequent and severe wildfires. beginning in the early 2000s, marked by heightened interannual variability and a sharp rise in extreme fire weather days. Over the past two decades, wildfire risk volatility has more than doubled. This change is primarily driven by strengthened land–atmosphere coupling: drought conditions dry out surface soils, creating a feedback loop that amplifies surface heating and fosters more frequent and severe wildfires. While ENSO has traditionally been the main climate driver influencing Australian wildfires, recent evidence indicates that the SAM’s influence has grown, now serving as the dominant factor regulating wildfire variability. Kiwook Kim, the main author of the study, comments, “Our findings emphasize the need for enhanced monitoring of atmospheric circulation patterns and soil moisture levels. This knowledge is vital for improving fire risk predictions and informing climate adaptation strategies to safeguard communities and ecosystems.” “Understanding how climate factors influence wildfires is more critical than ever,” says Professor Lee. “Recognizing the increasing role of the Southern Annular Mode and the complex land-atmosphere interactions enables us to develop more accurate prediction models and better prepare for future wildfire seasons.” The findings of this research were published in Agricultural and Forest Meteorology on April 11, 2026. This research was supported by the Korea Environment Industry & Technology Institute (KEITI) under the Climate Change R&D Project for New Climate Regime project, funded by the Ministry of Environment (MOE) of Korea. Journal Reference Kiwook Kim, Myong-In Lee, Seungseok Lee, et al. , “Local and remote drivers of increased variability in extreme wildfire conditions in Southeastern Australia,” Agric. For. Meteorol., (2026).

Research 2026년 04월 10일 금요일

Sea Surface Temperature Shifts in the Pacific Improve Year-Ahead Winter Climate Predictions

Abstract The winter North Atlantic Oscillation (NAO) is a dominant mode of climate variability affecting temperature and precipitation across the Northern Hemisphere, yet its prediction at seasonal-to-decadal (S2D) lead times remains challenging. Here, using multi-year hindcasts from a multi-model ensemble initialized on 1 November for 1962–2019, we show that NAO skill one year ahead improves significantly when the El Niño–Southern Oscillation (ENSO) undergoes a phase transition next year. This improvement is linked to the northward propagation of anomalous atmospheric angular momentum, which dynamically organizes the NAO and is captured in reanalysis and models. During ENSO transition years, prediction skill increases with ensemble size, and when more than 10 members are used, the forecasts display the signal-to-noise paradox. These findings highlight the potential for enhanced one-year NAO predictability when ENSO transitions are present and large ensemble sizes are used in S2D prediction systems, given the skillful prediction of ENSO phase transitions at one-year lead times by multi-model ensembles. A research team, affiliated with UNIST, in collaboration with the UK Met Office Hadley Centre, has identified that major changes in the equatorial Pacific’s sea surface temperatures—such as transitions between El Niño and La Niña—significantly boost the accuracy of winter weather forecasts in the Northern Hemisphere. Led by Professor Myong-In Lee in the Department of Civil, Urban, Earth, and Environmental Engineering, their findings mark a breakthrough in understanding how tropical Pacific variability influences mid-latitude climate prediction. The study reveals that during ENSO transition years, the correlation coefficient for NAO prediction improves markedly—up to 0.60—compared to near-zero correlations in stable ENSO years. This phenomenon occurs because the oceanic temperature changes trigger atmospheric angular momentum shifts that gradually propagate northward, affecting the NAO pattern approximately one year later. The process involves the interaction of delayed effects—initiated by sea surface temperature anomalies—and the rapid transfer of atmospheric signals via Rossby waves, large-scale planetary waves influenced by Earth's rotation. “During ENSO transition years, the tropical ocean changes significantly influence the atmospheric circulation, strengthening the signals used for long-term forecasting,” explained Professor Myong-In Lee. “This understanding can help improve climate prediction models and support strategic planning in sectors like agriculture and energy.” These insights provide a crucial step toward enhancing Korea’s climate prediction capabilities and developing more accurate regional models. By understanding the dynamical mechanisms linking tropical Pacific variability to Northern Hemisphere winter patterns, researchers can improve forecasts for long-term climate variability, aiding disaster preparedness and resource management. This research was supported by the Korea Meteorological Administration and the National Institute of Meteorological Sciences, through projects focused on operational climate prediction systems and climate crisis response strategies. The findings of this research have been published in Nature Communications on March 25, 2026. The study has been supported by the Korea Meteorological Administration (KMA) and the National Institute of Meteorological Sciences (NIMS), through projects focused on operational climate prediction systems and climate crisis response strategies. Journal Reference Kiwook Kim, Myong-In Lee, Adam A. Scaife et al., "ENSO phase transition enables prediction of winter North Atlantic Oscillation one year ahead," Nat. Commun., (2026).

Community 2026년 01월 14일 수요일

Four UNIST Professors Elected as New Member of National Academy of Engineering of Korea

Four faculty members from UNIST have been elected as new members of the National Academy of Engineering of Korea (NAEK), one of Korea's most prestigious honors related to engineering and technology. On January 11, NAEK announced that the election of 84 new general members and 49 regular members in recognition of their distinguished and sustained achievements in research and technological advancement. Election to NAEK membership is widely regarded as one of the highest professional distinctions accorded to engineers in Korea. NAEK members are selected through a rigorous, multi-stage evaluation process conducted over ten months, recognizing individuals who have contributed to national development through innovation, research excellence, and talent cultivation. The four UNIST faculty members elected as 2026 general members are Professor Myoungsu Shin (Department of Civil, Urban, Earth and Environmental Engineering) in the Architectural, Civil and Environmental Engineering Division, Professor Jin Young Kim (School of Energy and Chemical Engineering) in the Chemical and Biological Engineering Division, Professor Young-Choon Kim (Graduate School of Technology and Innovation Management) in the Technology, Management and Policy Division, and Professor Jae-Young Sim (Graduate School of Artificial Intelligence) in the Computer Science and Engineering Division. In addition to individual research excellence, institutional support also contributed to this achievement. UNIST Research Planning Team identified eligible faculty members and provided guidance throughout the nomination process, helping to ensure broad and informed participation. Professor Myoungsu Shin is recognized for his work in sustainable construction technologies and earthquake engineering. He has advanced AI-based seismic design and performance evaluation methods, led the development of low-carbon, high-performance construction materials, and recently expanded his research into smart construction using 3D printing technologies to enhance the resilience of urban infrastructure. Professor Jin Young Kim specializes in next-generation solar cells and optoelectronic devices. His research on perovskite solar cells has been widely noted for improving both efficiency and long-term stability, strengthening their prospects for commercialization. Through international collaborations, he has also contributed to renewable energy research and carbon-neutrality initiatives. Professor Young-Choon Kim conducts research in organizational theory and technology innovation strategy. His empirical studies examine innovation strategies in both established firms and startups, as well as the relationship between organizational learning and innovation performance. His work has appeared in leading international journals such as Organization Science and Research Policy. Professor Jae-Young Sim is a researcher in visual intelligence and serves as Dean of the UNIST Graduate School of Artificial Intelligence. His research on image and large-scale 3D data reconstruction supports applications in autonomous driving, robotics, drones, and digital twin technologies. He is also actively involved in educating students with the skills needed to apply AI to real-world industrial challenges. UNIST President Chong Rae Park said, "We will continue to strengthen institutional support so that world-class research can flourish at UNIST." He added, "At the same time, we will ensure that outstanding research achievements are appropriately recognized by leading academic and professional organizations, both in Korea and abroad."

Community 2026년 01월 13일 화요일

New Book Brings Together the Final Reflections of Two Environmental Engineers

A lifetime of shared thought and dialogue between two environmental engineers has been brought together in a single volume. 《A Final Dialogue on Environment and the Future of Possibility》 is a coauthored work by Professor Jaeweon Cho of the Department of Civil, Urban, Earth, and Environmental Engineering at UNIST and the late Professor Kyoung-Woong Kim of Gwangju Institute of Science and Technology (GIST), who passed away unexpectedly in the fall of 2025. The book presents the culmination of conversations the two environmental engineers shared over more than 30 years of research, teaching, and intellectual companionship devoted to the question of how environmental thinking can guide the future. Work on the book was interrupted by the sudden passing of Professor Kim before the manuscript was completed. However, with the support of GIST Press—where he spent 28 years of his academic career—and the collective commitment of colleagues who wished to honor his voice, the project was brought to completion. The book brings together his final reflections on environmental challenges and the future he continued to care deeply about. Environmental ethics is a form of scientific practice within society, aimed at proposing meaningful alternatives to the challenges we face. Cover of 《A Final Dialogue on Environment and the Future of Possibility》. l Image Credit: GIST Press Rather than treating the environment as a policy issue or a field of technical intervention alone, the book presents it as a guiding principle—one that cuts across technology, society, and ethics. Through dialogue and reflection, the authors argue that environmental thinking, long regarded as peripheral, has the potential to serve as a unifying framework for understanding the defining challenges of our time. The book addresses a wide range of contemporary issues, including the climate and energy crisis, demographic change, the ethics of artificial intelligence, and the values emerging in the era of quantum computing. These topics are examined through the lens of environmental engineering, not as isolated problems but as interconnected questions shaped by shared conditions and responsibilities. The authors' perspective is grounded in their lived experience as engineers who chose to step beyond disciplinary boundaries. Rather than offering prescriptive solutions, they focus on the assumptions, choices, and responsibilities that underlie technological progress. In doing so, they articulate a form of environmental philosophy shaped not by abstraction, but by long engagement with real systems and real consequences. Professor Kim was internationally recognized for his work in soil and groundwater remediation and for his commitment to translating research into practice. Alongside Professor Cho, he led initiatives, like the Ongdalsam Project, which developed non-powered membrane water purification systems distributed to more than 20 countries, and helped establish an environmental engineering degree program at the Royal University of Phnom Penh in Cambodia. These practical efforts resonated deeply with Professor Cho's own approach to environmental engineering, which treats the field not merely as a technical discipline, but as a way of thinking about what society chooses to value and protect. Their shared yet differing perspectives—what Professor Cho later described as "[T]he space between agreement and difference"—form the intellectual core of the book. Reflecting on his colleague and friend, Professor Cho writes, "The space left behind by my friend feels like a possibility—one that may yet help fill a world facing crisis. Though the seat beside me is empty, the time he left with us will remain." In closing, the authors acknowledge that their dialogue remains unfinished, and that its continuation now belongs to the students and researchers who will carry it forward.

Research 2025년 12월 31일 수요일

New Simulation Model Provides Accurate Assessment of Urban Spread and Residual Risks of CWAs

Abstract Chemical warfare agents (CWAs) are highly toxic and environmentally persistent, posing human health risks. For example, Venomous Agent X (VX) exhibits low volatility and water solubility, allowing persistence as liquid-phase droplets. To quantify liquid-phase CWA behavior, DREAM-CWA (Dynamic fugacity-based Regional Environmental model for Air-surface exchange and Multimedia fate of Chemical Warfare Agents) was developed as a GIS-based Level IV fugacity model incorporating a droplet compartment. The model estimates phase partitioning and intermedia transport of droplets. Soil, asphalt, coated concrete (non-porous), and water are represented as distinct surface compartments to reflect urban environmental characteristics. Validation against the fugacity model CoZMo-POP 2 confirmed the reliability of DREAM-CWA. A case study of a hypothetical VX chemical attack was conducted as a preliminary evaluation to optimize the multimedia framework, focusing on liquid-phase persistence and multimedia interactions. Initially, VX was retained in droplets and surfaces, with volatilization driving increases in air concentrations. Soil and sediment acted as a dominant short-term reservoir and long-term sink, respectively. Secondary emissions from droplets and surfaces sustained atmospheric exposure. By providing volatilization fluxes resolved in time and space, DREAM-CWA outputs can be coupled with computational fluid dynamics simulations to estimate near-surface dispersion and exposure. This integrated framework offers a robust tool for assessing the environmental risks of CWAs and supporting decision-making during chemical attack responses. A research team, affiliated with UNIST has reported a new simulation tool to better understand how liquid-phase chemical warfare agents (CWAs) disperse and persist in urban environments. Their findings demonstrate that certain highly toxic chemical agents can remain dangerous even after initial deployment, mainly because droplets that settle on surfaces can evaporate over time and cause secondary exposure. Professor Sung-Deuk Choi and his team at in the Department of Civil, Urban, Earth, and Environmental Engineering at UNIST, in collaboration with the Agency for Defense Development (ADD) announced the successful development of DREAM-CWA (Dynamic fugacity-based Regional Environmental model for Air-surface exchange and Multimedia fate of Chemical Warfare Agents)—a model designed to predict how liquid chemical agents move and linger after release. What sets DREAM-CWA apart from existing models is its ability to explicitly consider that chemical agents can stay in droplet form on surfaces, like soil, asphalt, or concrete. The model also divides urban surfaces into categories, allowing for more precise simulations of evaporation rates and how much toxin re-enters the air. Using DREAM-CWA, the team simulated a scenario where a persistent, highly toxic chemical agent is released in a liquid state at room temperature. The results showed that, 30 minutes after the release, the evaporation of droplets on the ground caused airborne toxin levels to spike 32 times higher than initially, with the amount of toxin released back into the air increasing by about 50%. This data can be fed into three-dimensional computational fluid dynamics (CFD) models to predict local concentrations of toxic gases at human breathing height—about two meters above ground. By calculating how much toxin is emitted from surface droplets, the CFD simulations can track how the gases spread through complex urban airflow patterns, such as those between buildings. Professor Choi explained, "Developing a multi-media environmental model that traces the entire process—from chemical release, to droplets, soil, and urban waterways—is a first of its kind, both in Korea and internationally.” Researchers from ADD see this model as a significant step forward. They believe it will enhance existing systems, like NBC_RAMS, enabling more accurate predictions of how liquid chemical agents spread, how people might be exposed, and how long they remain in the environment—crucial information for military responses to chemical threats or terrorist incidents. The findings of this research have been published in the Journal of Hazardous Materials on December 5, 2025. Supported by the ADD grant, this study was carried out as a commissioned research effort, with Professor Jaejin Kim from Pukyong National University, leading the CFD work. Journal Reference Ho-Young Lee, Jeong-Tae Ju, Jae-Jin Kim, "Development and application of a multimedia environmental model for assessing the behavior of chemical warfare agents," J. Hazard. Mater., (2025).

News 2025년 12월 14일 일요일

UNIST Featured in Nature Branded Content on AI-Driven Clean Energy Innovation

《Editor's Note: Portions of this article are adapted from the branded content article "How AI Is Speeding the Progress of Green Hydrogen," published on Nature in collaboration with Springer Nature on December 11, 2025.》 Explore the full branded content article on Nature to learn more about UNIST's work at the forefront of clean-energy innovation. UNIST is featured in a branded content article published on Nature , in collaboration with Springer Nature, highlighting its research strengths in green hydrogen, AI-driven energy modeling, and industry-based innovation. Titled " How AI Is Speeding the Process of Green Hydrogen ," the article explores how UNIST researchers are applying AI, economic modeling, and real-world data to accelerate the deployment of scalable clean-energy technologies. At the core of the article is the work of Professor Hankwon Lim from the UNIST Graduate School of Carbon Neutrality, whose team evaluates clean-energy pathways from a holistic perspective. Moving beyond traditional ' Technology Readiness Levels ,' which often overlook market, regulatory, and environmental dimensions, his group integrates AI simulations, process and energy modeling, and life-cycle analysis to assess which solutions—such as green hydrogen or green ammonia—are most viable for large-scale, region-specific adoption. The article also highlights UNIST's collaboration with industry to advance practical clean-energy solutions. Current initiatives include modeling supply chains for importing and converting green ammonia, developing mechanochemistry-based systems for CO₂ capture, and conducting joint research with partners such as Samsung and Hyundai. In addition, the feature introduces UNIST's tailored degree programs designed for industry professionals, supporting companies in applying advanced energy modeling to real-world R&D challenges. "Many company employees join UNIST classrooms as students and return to industry ready to lead innovation," said UNIST Vice President Sung Chul Bae, emphasizing UNIST's role in supporting Korea's industrial transition. This collaboration with Springer Nature reflects UNIST's broader commitment to driving a sustainable energy future through research excellence, technology validation, and strong industry partnerships.

Research 2025년 12월 09일 화요일

UNIST Unveils Integrated Framework to Better Assess Air Pollution Health Risks

Abstract In industrial cities, identifying sources and assessing the risks of hazardous air pollutants are critical for protecting public health. This study employed passive air samplers (PASs) to analyze 13 PAHs at 15 sites in Daesan, a petrochemical industrial city in South Korea, during the warm season. The mean Σ13 PAH concentration was 8.1 ± 6.2 ng/m3, comparable to levels observed in other industrial cities. Elevated PAH concentrations and cancer risks were observed in the industrial and central areas of Daesan, primarily influenced by emissions from industrial stacks, as confirmed by 3D air dispersion modeling. Although the lifetime cancer risk of PAHs (2.11 × 10−10–3.41 × 10−7) remained within the US EPA’s acceptable range, cancer risk maps highlighted a strong association between industrial emissions and cancer risk, especially for high-percentile exposure groups. This study highlights the combined effects of local industrial and vehicle emissions on air quality and cancer risk. Our findings demonstrate that integrating PASs with 3D dispersion modeling and percentile-based risk assessment provides a novel framework for identifying residential exposure and major emission sources. This approach effectively reveals elevated risks for vulnerable subpopulations, often hidden in conventional average-risk estimates, providing a valuable basis for future health impact assessments. A research team, affiliated with UNIST unveiled an integrated air pollution analysis framework that enables more precise assessment of exposure risks from carcinogenic air pollutants commonly emitted from industrial complexes. The approach is expected to help identify exposure “blind spots” often overlooked by conventional assessments and provide a scientific basis for strengthening environmental management policies in industrial areas. Led by Professor Sung-Deuk Choi of the Department of Civil, Urban, Earth, and Environmental Engineering at UNIST, the research team combined passive air sampling (PAS), three-dimensional air dispersion modeling, and probabilistic health risk assessment into a single analytical framework. Passive air samplers (PASs) collect airborne pollutants by allowing them to naturally adsorb onto porous, sponge-like media. Because the method is cost-effective and easy to deploy, samplers can be installed across wide areas to generate high-resolution spatial maps of air pollution. However, PAS data alone provide limited insight into where pollutants originate or how they travel through the atmosphere. Figure 1. Spatial distribution of the measured Σ13 PAH concentrations and the boundaries representing the top 25 % and 10 % of the modeled SOX contributions from point sources during (a) period 1 (May 26–June 23) and (b) period 2 (June 23–July 21). To address this limitation, the researchers integrated 3D dispersion modeling, which simulates how emissions released from industrial stacks spread and move under varying wind conditions. This modeling approach makes it possible to track how pollutants rise, disperse, and descend over distances of several kilometers, depending on factors such as stack height and prevailing wind direction—offering a clearer picture of how industrial emissions affect surrounding residential areas. The team further applied probabilistic risk assessment to better capture exposure risks among high-exposure groups that may be masked by average-based evaluations. Traditional risk assessments often rely on fixed assumptions—such as average outdoor activity time—whereas probabilistic methods treat behavioral and environmental variables as probability distributions. This allows researchers to estimate cancer risks for higher-percentile exposure groups, including individuals who may experience prolonged outdoor exposure on days with elevated pollution levels. "By moving beyond average values, this framework helps uncover hidden health risks in residential areas near industrial complexes," said Professor Choi. "Our findings provide scientific evidence that can inform policies such as optimizing stack heights, managing emission pathways, and establishing buffer zones to better protect public health." First author Dr. Sang-Jin Lee added that the framework is not limited to polycyclic aromatic hydrocarbons (PAHs). "This integrated approach can also be applied to other hazardous air pollutants, including volatile organic compounds (VOCs), persistent organic pollutants (POPs), and heavy metals, to better understand their transport patterns and exposure characteristics," he said. The findings of this research were published online in the Journal of Hazardous Materials on November 14, 2025. This study was supported by a grant from the National Institute of Environmental Research (NIER), funded by the Korean Ministry of Environment. Additional support was provided by the National Research Foundation, funded by the Korean Ministry of Education. Journal Reference Sang-Jin Lee, Ho-Young Lee, Seong-Joon Kim, et al., "Pollution characteristics and cancer risk of PAHs in a petrochemical industrial city: Insights from passive air sampling and three-dimensional dispersion modeling," J. Hazard. Mater., (2025).