UNIST Unveils AI-Driven Framework for Real-Time Multi-Pollutant Air Quality Monitoring

Abstract Simultaneous prediction of multiple air pollutants is essential for quantifying human co-exposure and evaluating the health impacts of pollutant mixtures. However, spatial and temporal gaps in geostationary satellite observations, chemical transport models, and ground-based monitoring networks hinder accurate hourly assessments of multi-pollutant dynamics. Here, we present Deep Learning for Multiple Air Pollutant analysis (DeepMAP), a deep learning framework that simultaneously predicts six major air pollutants─PM10, PM2.5, O3, NO2, CO, and SO2─at hourly resolution. DeepMAP demonstrated robust performance across multiple pollutants and generalized well to unseen regions. The framework accurately captured dynamic high-concentration co-pollution episodes during March 2021, with normalized RMSE values below 0.36 for all pollutants. DeepMAP revealed that PM10-PM2.5 co-exceedance was the most frequent across East Asia (91 days/year), followed by PM10-PM2.5-NO2 (42), PM2.5-O3 (18), and PM10-PM2.5-O3 (12). Hotspots for PM10-PM2.5-NO2–O3 co-exceedance were identified over the North China Plain, East China, and South Korea, where the regional annual totals reached 24, 19, and 15 days, respectively. A novel co-exposure index further identified three distinct hotspot regions where the contribution of NO2 was approximately twice that observed elsewhere. Our findings provide a high-resolution, data-driven framework for characterizing multi-pollutant co-exposure and identifying regional priorities for air quality management and public health protection. A research team, led by Professor Jungho Im from the Department of Civil, Urban, Earth, and Environmental Engineering at UNIST, has introduced DeepMAP, a cutting-edge artificial intelligence model that accurately estimates hourly levels of six key air pollutants across East Asia. The technology, developed from 2021 to 2023, provides new insights into the widespread and simultaneous exceedance of air quality standards, with significant implications for health policies and environmental management. DeepMAP integrates diverse data sources—including geostationary satellite imagery, atmospheric chemical transport models, meteorological data, and ground observations—to produce real-time, high-resolution maps of PM10, PM2.5, O₃, NO₂, SO₂, and CO. Operating at a 10 km spatial resolution and providing hourly predictions, the model captures dynamic pollution patterns and hotspots, revealing that Korea experiences about 15 days per year with four pollutants exceeding WHO safety thresholds simultaneously. Unlike traditional methods that estimate pollutants individually, DeepMAP’s multi-task learning approach models interactions among pollutants, significantly enhancing estimation accuracy. “By accurately capturing the complex interplay of multiple pollutants in real time, our model offers a powerful tool for assessing exposure risks and guiding effective policy decisions,” said Professor Jungho Im, lead researcher. “This represents a major step toward more realistic and comprehensive air quality management.” The study underscores the health risks posed by combined pollutant exposure, which can worsen respiratory and cardiovascular diseases. Traditional monitoring methods often fall short in providing detailed, regional, and real-time data. DeepMAP’s capabilities open new avenues for environmental monitoring, public health research, and proactive policymaking, especially during pollution episodes driven by seasonal phenomena like dust storms and high-pressure systems. The findings of this research have been published in Environmental Science & Technology on March 20, 2026. The study has been supported by the National Institute of Environmental Research (NIER) under the Ministry of Environment (ME), and by the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (MSIT). Journal Reference Eunjin Kang, Sihun Jung, Jungho Im, et al., "Quantifying Multi-pollutant Co-exposure via Deep Learning-Based Simultaneous Prediction Using Geostationary Satellite Data," Environ. Sci. Technol., (2026).

2026.04.10

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 Satyabrat Behera, Jong Sung Moon, Kirlie Iulius Figuera Michal, et al., "Electrical Control of Single Photon Emitters in WSe2 on a Si Nanopyramid Array with a Negligible Stark Effect," Nano Lett., (2026).

2026.04.10

New Study Unveils Mussel-Inspired Coating for Rapid Radioactive Surface Decontamination

Abstract Efficient and rapid decontamination of radioactive elements is important to prevent radioactive exposure. Herein, we develop a strippable catechol-terminated polyurethane (CPU) coating for effective surface decontamination. Our polyurethane coating can be directly applied to contaminated surfaces via simple spraying or solution casting, followed by rapid drying at room temperature. The resulting coating is easily stripped off with sufficient toughness and adhesion strength, showing superior 137Cs removal efficiency on stainless steel (∼94.9%) and rough cement (∼59.1%), in a much shorter time (<3 h) compared to commercial decontamination coatings (∼93.8% on stainless steel and ∼8.4% on cement after 24 h). This performance can be attributed to the strong adhesion and cohesion mediated by catechol moieties. Furthermore, after use, the coating waste is readily dissolvable in acetone, suggesting potential for reducing radioactive waste with the aid of an appropriate separation process, thereby preventing secondary contamination. These results establish CPU as a promising radioactive decontamination strategy. Professor Dong Woog Lee from the School of Energy and Chemical Engineering at UNIST, in collaboration with the Korea Atomic Energy Research Institute (KAERI) have unveiled a novel, mussel-inspired polyurethane coating capable of removing over 95% of radioactive cesium from surfaces in just three hours—far faster than existing methods. This breakthrough promises to enhance safety and efficiency in nuclear decontamination efforts. The innovative coating utilizes catechol groups—derived from mussel adhesion proteins—attached to polyurethane polymer chains, creating a highly adhesive surface. Applied onto contaminated surfaces, the coating dries within hours and can be peeled off like tape, effectively removing radioactive particles. Laboratory tests demonstrated a cesium removal efficiency of 94.9% on stainless steel and 13.1% on porous cement surfaces after two applications, outperforming commercial products that typically require 24 hours. Additionally, the coating can be dissolved in acetone post-use, enabling waste separation and reducing secondary contamination. Dr. Heeman Yang, lead researcher from KAERI, stated, “This technology offers a faster, more effective, and environmentally conscious approach to decontamination. Its ability to rapidly remove radioactive materials while facilitating waste management marks a significant advancement in nuclear safety.” This development addresses critical needs in nuclear facility decontamination, emergency response, and waste reduction. By combining speed, efficiency, and eco-friendliness, the coating holds promise for broad application in nuclear safety and environmental management, contributing to safer decommissioning and accident mitigation in the future. The findings of this research have been published online in the March 2026 issue of Materials Horizons. The study has been supported by the Institute of Civil Military Technology cooperation, funded by the Defense Acquisition Program Administration and the Ministry of Trade, Industry and Energy (MOTIE). Journal Reference Jae Seung Lee, Ye-won Jeong, Donghyun Kim, et al., "A strippable catechol-terminated polyurethane coating for large-area radioactive cesium decontamination," Mater. Horiz., (2026).

2026.04.09

New Study Unveils High-Purity, Electrically Controlled Single-Photon Source for Quantum Technologies

Abstract Single-photon emitters (SPEs) based on two-dimensional (2D) materials have attracted a lot of attention due to their unique benefits such as mechanical flexibility, high quantum yield, and easy integration on a chip. However, the electrical modulation of such emitters at a single-photon level still remains a challenge. Herein, we provide a new route to engineer electrically controllable purified SPEs in a monolayer (1L) WSe2 using a Si nanopyramid structure. The Si nanopyramid structures allow not only the generation of high strain to localize the SPEs but also the application of voltage for electrical modulation. Of particular interest, while our structure modulates the single-photon emission with an applied gate voltage, it does not exhibit unwanted spectral shift for the applied voltage, i.e., a negligible Stark effect, due to the existence of an air gap between the 1L-WSe2 and the nanopyramid. The single photon purity is improved by electrical modulation down to g(2)(0) = 0.06 ± 0.03. Researchers at UNIST have developed a novel single-photon emitter (SPE) that can be electrically switched on and off with high purity and stability. This breakthrough addresses key challenges in quantum device engineering, paving the way for scalable quantum computing and secure quantum communication. Based on a 2D semiconductor integrated with a silicon nanopyramid structure, the device generates single photons with minimal spectral shift during electrical modulation. By engineering a nanoscale air gap, the team effectively suppresses the Stark effect—preventing energy shifts in emitted photons—while maintaining a high level of single-photon purity (g(2)(0) ≈ 0.06). This design also reduces background emissions from defects, enhancing overall performance. “This work represents a significant step toward integrated quantum photonics,” said the research team. “Controlling single photons electrically without spectral degradation opens new possibilities for scalable quantum devices.” This study has been participated by Satyabrat Behera and Jong Sung Moon, as first authors of the study. Published in the March 2026 issue of Nano Letters, the research was supported by the IITP (Institute of Information & Communications Technology Planning & Evaluation)-ITRC (Information Technology Research Center) program by the Ministry of Science and ICT (MSIT), the National Research Foundation of Korea (NRF), and UNIST. Journal Reference Satyabrat Behera, Jong Sung Moon, Kirlie Iulius Figuera Michal, et al., "Electrical Control of Single Photon Emitters in WSe2 on a Si Nanopyramid Array with a Negligible Stark Effect," Nano Lett., (2026).

2026.04.08

When Chemical Intuition Meets Neural Networks: Hybrid AI Algorithm Predicts Metal–Ligand Coordination Structures

Hemoglobin, which gives blood its red color, the light-emitting materials in smartphone OLED displays, and catalysts used in manufacturing plastics and pharmaceuticals—all share a common feature that is they all involve metal–ligand coordination complexes. In these structures, a central metal ion is surrounded by organic molecules, known as ligands. Now, a novel artificial intelligence (AI) algorithm offers the promise of designing these complex materials more swiftly and accurately, significantly reducing trial-and-error efforts. Predicting how a metal ion will 'shake hands' with an organic molecule is an inherently complex task, often challenging even for seasoned scientists. This difficulty arises because organic ligands contain multiple potential binding sites, and the number and mode of coordination can vary widely depending on the specific metal and its oxidation state. As a result, dozens or even hundreds of potential structural configurations can exist. Led by Distinguished Professor Bartosz A. Grzybowski of the Department of Chemistry and Director of the Center for Algorithmic and Robotized Synthesis (CARS) within the Institute for Basic Science (IBS) at UNIST, the research team has addressed this challenge not through increased computational power, but by integrating 'chemical intuition' directly into AI. Published in Angewandte Chemie, their hybrid AI approach moves beyond simple pattern recognition by encoding fundamental chemical principles into neural networks. Figure 1. An AI-powered hybrid model predicts metal–ligand coordination modes by integrating chemical rules from crystal structures with machine learning, enabling accurate design of complex organometallic structures. "We have developed a 'hybrid' strategy that combines classical chemical knowledge with machine learning to accurately predict how metals and organic ligands bind," explains Professor Grzybowski. "By analyzing over 100,000 crystal structures from the Cambridge Structural Database, we trained a model that 'understands' the specific behavior of different metals and their oxidation states—something previous models could not achieve." This innovative technology is poised to become a key component in large-scale computational pipelines. Professor Grzybowski notes, “We expect it to be highly valuable in designing transition metal catalysts used across industries—from pharmaceuticals to advanced functional materials.” To make this cutting-edge tool accessible worldwide, the team has released RDMetallics, an open-source, Python-based software package, along with a dedicated web portal (coordinate.rdmetallics.net). Researchers anywhere in the world can now predict complex metal–ligand coordination modes directly through their web browsers—no coding skills or expensive hardware required. Supported by the Institute for Basic Science (IBS), this research was published online on Angewandte Chemie International Edition on February 21, 2026. Bartosz A. Grzybowski Distinguished Professor, Department of Chemistry, UNIST Director, IBS Center for Robotized and Algorithmic Synthesis (CARS) E: grzybor72@unist.ac.kr William I. Suh IBS Public Relations Team E: willisuh@ibs.re.kr Story Source Materials provided by the Institute of Basic Science. Journal Reference Galymzhan Moldagulov, Kisung Lee, Sanzhar Nurgaliyev, et al., "Hybrid Computational Strategy for Predicting Complex Ligand–Metal Architectures," Angew. Chem. Int. Ed. (2026). https://doi.org/10.1002/anie.202524655

2026.04.03

Targeting COL6A3-C5 with Nigericin to Reduce Endotrophin and Enhance Metabolic Health in Obesity

Abstract Endotrophin, a cleavage product of collagen VIα3 (COL6A3), contributes to fibroinflammation in adipose tissue and exacerbates systemic insulin resistance in obesity. Previously, we demonstrated that various hypoxia-induced matrix metalloproteinases (MMPs) are directly involved in the cleavage of COL6A3 to generate endotrophin in obese adipose tissue; thus, inhibition of endotrophin generation by blocking MMP access could be beneficial for treating obesity-related metabolic disease. Here we identified nigericin as an inhibitor of endotrophin generation, which improves fibroinflammation and insulin sensitivity in both hypoxic adipocytes in vitro and diet-induced obese mice in vivo. Mechanistically, nigericin directly binds to the COL6A3-C5 domain, competing with MMPs and thereby disrupting the interactions between the COL6A3-C5 domain and MMPs. This interference prevents the cleavage of endotrophin from the COL6A3 by MMPs, ultimately inhibiting its generation. Taken together, these results strongly suggest that pharmacological blockade of endotrophin cleavage, by using nigericin, effectively decreases endotrophin levels and improves endotrophin-mediated fibroinflammation and insulin resistance in obesity. Furthermore, this new therapeutic strategy could be applied to various metabolic diseases and solid tumors where endotrophin levels are pathologically elevated. Obesity is known to be a major risk factor that exacerbates metabolic disorders, such as type 2 diabetes. A key molecule involved in this process is endotrophin, a signaling protein that links excess fat accumulation to metabolic decline. Recent research from UNIST has identified a natural compound, capable of directly inhibiting endotrophin production, paving the way for innovative therapeutic interventions. Led by Professor Jiyoung Park of the Department of Biological Sciences, the research team demonstrated that nigericin, a natural substance derived from microorganisms, effectively suppresses endotrophin synthesis in obese adipose tissue. Their findings reveal that nigericin reduces fibrosis and inflammation within fat tissue while significantly improving insulin sensitivity. Obese adipose tissue often undergoes hypoxia, which triggers chronic inflammation and pathological extracellular matrix remodeling. This process leads to increased production of endotrophin, a fragment of collagen VIα3 (COL6A3), known to impair adipose function and worsen metabolic health. The team uncovered that nigericin binds specifically to a region of COL6A3, preventing matrix metalloproteinases (MMPs)—enzymes responsible for collagen cleavage—from accessing their substrate. By occupying this site, nigericin blocks endotrophin formation at the molecular level. "This strategy is particularly noteworthy because it directly targets the initial step in endotrophin production, unlike traditional therapies that indirectly suppress inflammation or alter gene expression," explained the research team. "It offers a new paradigm for disrupting the pathological signaling pathways underlying metabolic diseases." In preclinical models, administration of nigericin to high-fat diet-fed mice resulted in marked reductions in adipose tissue fibrosis and inflammation. These effects occurred without adverse effects on liver or kidney function. Notably, fasting blood glucose levels decreased by approximately 30%, and insulin sensitivity was significantly improved. The researchers screened over 1,000 natural compounds and identified nigericin—produced by actinomycetes—as a promising candidate, owing to its ability to stably inhibit endotrophin synthesis without directly affecting proteolytic enzymes. Professor Park concluded, "This discovery reveals a novel molecular mechanism for controlling endotrophin levels and highlights the potential for developing targeted therapies for obesity, diabetes, and other conditions characterized by fibroinflammation, including certain cancers." Supported by the Ministry of Science and ICT (MSIT) and the National Research Foundation of Korea (NRF), the study was published online in Experimental & Molecular Medicine, on March 5, 2026. Journal Reference Chu-Sook Kim, Woobeen Jo, Jungsun Yoo, et al., "Targeting COL6A3-C5 with nigericin suppresses endotrophin formation and enhances insulin sensitivity in obesity," Exp. Mol. Med., (2026).

2026.04.01