## 5 Discussion  
This study aimed to enhance the quality of life for vulnerable populations in the Gangseo District by identifying optimal locations for air quality monitoring. Through comprehensive spatial analysis and accessibility assessments, we proposed strategic placements for these monitors to ensure the effective protection of at-risk groups. The key conclusions derived from our detailed methodology are as follows. Figure 11: Comprehensive index results for Gangseo-gu with varying weights of key factors. First, our analysis revealed significant disparities in the accessibility of essential facilities among vulnerable populations. By mapping the locations of hospitals, nursing homes, and other critical care centers, we identified areas with poor accessibility and high population densities of vulnerable groups. These findings are crucial for determining optimal sites for air quality monitoring. Second, using isochrone maps allowed us to visualize the accessibility of these facilities within specific timeframes, highlighting regions where vulnerable populations face significant barriers in accessing healthcare and other services. This step was instrumental in identifying areas requiring urgent intervention. Finally, integrating demographic data with GIS enabled us to create detailed density maps of vulnerable populations. These maps provide a clear picture of where high-risk groups reside, further supporting our recommendations for placement monitoring. Despite these significant findings, our study has several limitations. The accuracy of our results depended on the quality and completeness of the spatial and demographic data. Additionally, our study focused solely on the Gangeo District, limiting the generalizability of our findings to other regions. Future studies should consider the dynamic nature of urban environments. Continuous data updates and real-time monitoring are essential for adapting to changing conditions. Expanding this research to other districts and cities will provide a broader understanding of the air quality management needs in different urban settings. Another limitation is the lack of detailed air pollution data in this study. The primary objective was to enhance the ease and speed with which vulnerable populations could receive PM alerts. Given that air pollution trends, particularly PM concentrations, show similar patterns across local regions, the study did not focus on the distribution of PM levels. However, we acknowledge that the absence of detailed air pollution data could limit the precision of our proposed system. As a result, this limitation will be addressed in the conclusion to highlight the potential for future research to incorporate detailed PM concentration data for a more refined system design. In addition, the linear combination of variables used in determining optimal locations for PM alert systems may oversimplify the complexity of multi-criteria decision-making. We recognize that the method does not fully account for the varying importance of criteria under different stakeholder scenarios. A more sophisticated multi-criteria analysis could incorporate weight variations to assess how the top 20 proposed locations change under different assumptions. This would provide a more robust framework for evaluating the stability and sensitivity in the proposed locations. Future research should consider incorporating a sensitivity analysis or scenario-based approaches to better reflect the diverse priorities of stakeholders. Another important direction for future research is the application of this methodology to regions where air quality monitors are already installed. A comparative analysis between regions with existing monitors and proposed placements could provide valuable insights into the accuracy and effectiveness of our model. This would further enhance the robustness of the proposed placements and validate the method under different conditions. These findings have practical applications in urban planning, public health, and environmental management.