Development of Air Quality Monitoring Systems: Balancing Infrastructure Investment and User Satisfaction Policies

Air quality monitoring is a critical aspect of urban management. While poor air quality negatively impacts public health and well-being, implementing effective monitoring systems often involves significant costs. This paper addresses the optimization of air quality monitoring systems by balancing cost-effectiveness with citizen satisfaction. The core objective is to identify an optimal trade-off between user satisfaction and budgetary constraints. To achieve this, we optimize the number of clusters, where each cluster represents a group of users served by the nearest air quality sensor. Additionally, we present a penalty function that emphasizes prompt air pollution warnings, facilitating preventive actions to reduce exposure to polluted areas while ensuring a cost-effective solution. This approach enables the formulation of well-founded performance requirements for AI-driven algorithms tasked with analyzing air quality data. The findings contribute to the development of efficient, user-centric air quality monitoring systems, highlighting the delicate balance between infrastructure investment, AI algorithm efficiency, and user satisfaction.