Accurate pollutant prediction is essential in fields such as meteorology, meteorological disasters, and climate change studies. In this study, long short-term memory (LSTM) and deep neural network (DNN) models were applied to six pollutants and comprehensive air-quality index (CAI) predictions from 2015 to 2020 in Korea. In addition, we used the network method to find the best data sources that provide factors affecting comprehensive air-quality index behaviors. This study had two steps: (1) predicting the six pollutants, including fine dust (PM₁₀), fine particulate matter (PM_2.5), ozone (O₃), sulfurous acid gas (SO₂), nitrogen dioxide (NO₂), and carbon monoxide (CO) using the LSTM model; (2) forecasting the CAI using the six predicted pollutants in the first step as predictors of DNNs. The predictive ability of each model for the six pollutants and CAI prediction was evaluated by comparing it with the observed air-quality data. This study showed that combining a DNN model with the network method provided a high predictive power, and this combination could be a remarkable strength in CAI prediction. As the need for disaster management increases, it is anticipated that the LSTM and DNN models with the network method have ample potential to track the dynamics of air pollution behaviors.

Airlines provide one of the most popular and important transportation services for passengers. While the importance of the airline industry is rising, flight cancellations are also increasing due to abnormal weather factors, such as rainfall and wind speed. Although previous studies on cancellations due to weather factors considered both aircraft and weather factors concurrently, the complex network studies only treated the aircraft factor with a single-layer network. Therefore, the aim of this study was to apply a multilayer complex network (MCN) method that incorporated three different factors, namely, aircraft, rainfall, and wind speed, to investigate aircraft cancellations at 14 airports in the Republic of Korea. The results showed that rainfall had a greater impact on aircraft cancellations compared with wind speed. To find out the most important node in the cancellation, we applied centrality analysis based on information entropy. According to the centrality analysis, Jeju Airport was identified as the most influential node since it has a high demand for aircraft. Also, we showed that characteristics and factors of aircraft cancellation should be appropriately defined by links in the MCN. Furthermore, we verified the applicability of the MCN method in the fields of aviation and meteorology. It is expected that the suggested methodology in this study can help to understand aircraft cancellation due to weather factors.

Due to climate change, rainfall occurs at a higher frequency than the design frequency, and flood damage has occurred in excess of the river design standard. Currently, river management in general is gray infrastructure such as embankments and weirs for irrigation and flood control. However, the river management plan through the gray infrastructure emits carbon dioxide, increasing the occurrence of extreme weather due to climate change and intensifying flood damage, causing a vicious cycle to repeat. Therefore, since the river management method by gray infrastructure cannot be adopted as a sustainable solution, the concept of Nature-based Solutions(NbS), which seeks to solve environmental and social problems through ecosystem services, is attracting attention recently. Therefore, in this study, the flood reduction effect of river management using NbS was quantitatively analyzed for the Hwang River, which is directly downstream of Hapcheon Dam. In addition, using the climate change scenarios of the IPCC 6th Assessment Report, the study tried to confirm the ability to respond to climate change through NbS. We used SSP5-8.5(Shared Socioeconomic Pathways5-8.5) and SSP2-4.5 scenarios for future precipitation, and the design flood discharge was calculated through HEC-HMS. Floodplain excavation and dyke relocation, which are included in the NbS, were applied to the flood risk area of the Huang River. As a result of analyzing the flood level of the river through the unsteady flow analysis of HEC-RAS, it was possible to confirm the effect of reducing the flood level by 5 to 7 cm for each scenario at the confluence of the Nakdong River. The results of this study can be expected to be sufficiently utilized as a basis for use as a management plan through NbS rather than the river management with grey infrastructure.

Riverine wetlands are reservoirs of biodiversity and provide various ecological functions, including carbon absorption. However, they have been subjected to continuous degradation and loss due to river management practices focused on irrigation and flood control. This study aims to quantify the carbon absorption capacity of riverine wetlands and propose strategies for their restoration and management. To achieve this, a laboratory-scale wetland model was developed, and carbon absorption rates were analyzed under varying hydrological conditions. The results revealed that while methane emissions increased under inundation conditions, the absorption of carbon dioxide increased even more significantly. When assessed using the Global Warming Potential (GWP) metric, the overall carbon absorption capacity was found to improve. Wetlands were spatially categorized into waterside wetlands (outside the levee) and landside wetlands (inside the levee) to establish a carbon absorption assessment framework. This framework was used to evaluate restoration needs and propose tailored restoration scenarios for each wetland type. For waterside wetlands, strategies were suggested to regulate carbon absorption based on inundation zones and hydrological characteristics. For landside wetlands, a model was developed to enhance carbon absorption through the creation of carbon forests using Nature-based Solutions (NbS) and biochar application. Additionally, the carbon cycle was established as a closed system, termed the "Carbon-Closing System," to promote sustainability. This study provides standardized models and evaluation frameworks for carbon-neutral riverine wetlands, advancing technologies for wetland creation, restoration, and management while contributing to climate change mitigation and ecological value enhancement. Keywords: Carbon Absorption, Hydrological Conditions, Restoration Scenarios, Riverine Wetlands Acknowledgement: This work was supported by Korea Environmental Industry&Technology Institute through Wetland Ecosystem Value Evaluation and Carbon Absorption Value Promotion Technology Development Project, funded by Korea Ministry of Environment(MOE)(2022003630001)

Drought is a complex natural hazard that develops across different temporal scales and may propagate from meteorological drought to hydrological drought. In addition, the physical characteristics of drought do not necessarily correspond directly to how drought is represented in the news media. This study analyzed meteorological and hydrological drought characteristics at the level of the 17 first-level administrative divisions (provinces) of South Korea and compared them with the frequency of drought-related news articles. The Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Streamflow Drought Index (SDI) were calculated at 1-, 3-, 6-, and 12-month timescales using long-term meteorological and streamflow observations. Drought-related news articles published from 2021 to 2025 were retrieved from BIGKinds and processed using explicit cleaning rules, and regions with relatively high frequencies of drought-related news were identified. The spatial agreement between regions with high drought-news frequency and regions with high drought severity derived from each drought index was then evaluated using the top-10 overlap, the Jaccard index, a threshold-based comparison, rank correlation, and permutation tests, and their temporal correspondence was evaluated using monthly rank correlations. In the spatial comparison, the overlap between the top-10 provinces (at most 7 of 10) did not exceed that expected by chance, and this result did not depend on the ranking threshold, the annual aggregation method, the method of counting articles, or normalization by population. In contrast, the monthly number of drought-related articles increased with the number of provinces under drought conditions, most strongly for the 12-month indices (Spearman ρ = 0.42–0.57), and was significantly correlated with the provincial SPI-12 and SPEI-12 values in 8–11 of the 16 provinces. These exploratory results for 2021–2025 suggest that drought-related news coverage follows the temporal evolution of prolonged drought, but that its distribution among provinces cannot be represented by a single drought index or timescale and is only weakly related to the spatial pattern of physical drought severity. News frequency is therefore better interpreted as an indicator of when drought attracts media attention than of where drought is most severe, and its use as complementary information in drought monitoring requires validation against independent drought-impact data.