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In order to minimize the impact of future heat waves, this study predicts the spatial distribution of heat vulnerable areas by three demographic groups: young population, working-age population, and old population, prior to establishing heat wave adaptation strategies. First, the future population values of each demographic group were estimated through machine learning and explainable AI, and the estimates were interpreted. The population estimates showed that all three demographic groups are expected to continue to decline until 2042. The future heat wave vulnerable areas, derived by overlaying population estimates and future heat wave days by demographic group, were predicted to be mainly inland for all three demographic groups. The spatial distribution of future heat vulnerable areas by demographic group showed that the western part of the country is more vulnerable than the eastern part, and that the heat vulnerable areas for the young population will be located in Seoul, the southern part of the Seoul metropolitan area, and metropolitan areas, while the heat vulnerable areas for the working-age population will be located in Seoul, the southern part of the Seoul metropolitan area, and the Chungcheongbuk-do region. Accurate population estimation analysis is an essential step in proactively and effectively dealing with the effects of future heat waves, and the results of this study can be used as a basis for heat adaptation policies.

In the current Total Pollution Load Management system(TPLMs), the achievement of target water quality is evaluated using the average water quality over the past three years. However, this method has limitations as it does not reflect changes in water quality due to variations in flow rates, and it is inconsistent with the fundamental objective of TPLMs, which aims to achieve target water quality through pollutant load management. Therefore, this study proposes a method that enables the evaluation of target water quality achievement under various flow conditions based on pollutant loads. Using water quality and flow data, which are collected at eight-day intervals through the TPLM monitoring network, load duration curves (LDC) can be created. By utilizing this method, target water quality achievement can be assessed based on pollutant loads. Comparing evaluations for 21 sub-watersheds within the Geum River basin, target water quality achievement in 2023 was assessed by both the LDC method and the current three-year average method. The proportion of sub-watersheds exceeding the target BOD levels was 47.6%, 23.8%, and 19.0%, and for T-P, it was 57.1%, 23.8%, and 14.3% for each respective method. These results indicate that the LDC method yields relatively higher excess rates, showing that it provides a stricter evaluation of target water quality achievement compared to the current method The LDC method not only allows for the evaluation of target water quality achievement by considering flow variations but also is expected to be very useful when time-series predictions are required for target water quality assessments using dynamic models like watershed models, which are applied to estimate allocated loads.

This study aims to quantitatively evaluate the impact of the Biotope Area Factor (BAF) as a tool for Nature-Based Solutions (NbS) on ecosystem services in the urban development process. The study sites selected were Gimpo Han River New City, Goyang Samsong District, and Anseong Ayang District. To analyze changes in major ecosystem services, the study utilized InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) models, including Carbon Storage, Sediment Delivery Ratio (SDR), Urban Cooling, Annual Water Yield, and Habitat Quality. The analysis results showed a decreasing trend in most ecosystem service indicators, except for water supply, with significant declines in carbon storage and habitat quality. These findings suggest the need for qualitative improvements in ecosystem services, highlighting the limitations of relying solely on quantitative measures to ensure the sustainability of urban ecosystems. Therefore, the effective application of the BFF requires qualitative improvements and a strategic approach to enhancing the ecological functions of urban green spaces. This study also emphasizes the importance of establishing ecological networks through green infrastructure and the need for a continuous monitoring system to evaluate changes in ecosystem services. The results demonstrate that the BFF can be an important tool for enhancing the effectiveness of NbS and provide policy implications for maximizing ecosystem services during urban development.