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

Area of Science:

• Environmental Science and Urban Planning
• Public Health
• Artificial Intelligence in Environmental Monitoring

Background:

• Effective air quality monitoring is crucial for urban management and public health.
• High implementation costs often hinder the deployment of comprehensive monitoring systems.
• Balancing cost-efficiency with citizen satisfaction is a key challenge in urban air quality management.

Purpose of the Study:

• To optimize air quality monitoring systems by identifying the optimal trade-off between cost-effectiveness and citizen satisfaction.
• To develop a framework for formulating performance requirements for AI algorithms used in air quality data analysis.
• To ensure timely air pollution warnings for public health protection while maintaining budgetary constraints.

Main Methods:

• Optimization of the number of clusters, where each cluster represents users served by the nearest air quality sensor.
• Development of a penalty function to prioritize prompt air pollution warnings.
• Formulation of performance requirements for AI-driven air quality data analysis algorithms.

Main Results:

• An optimal balance between user satisfaction and budgetary constraints for air quality monitoring systems was identified.
• The proposed penalty function effectively emphasizes prompt pollution warnings, enabling preventive actions.
• The methodology provides a basis for setting performance requirements for AI algorithms in this domain.

Conclusions:

• Efficient and user-centric air quality monitoring systems can be developed by balancing infrastructure investment and AI algorithm efficiency.
• The study highlights the importance of considering both cost and user satisfaction in designing urban environmental monitoring solutions.
• Findings support the development of AI-driven systems for improved public health outcomes through better air quality management.