Abstract
Air quality monitoring plays a critical role in providing reliable information to the public and supporting the assessment of health impacts in urban environments. This challenge is particularly relevant in Astana, Kazakhstan, where only 9 fixed monitoring stations are available, which is not enough to reflect the spatial variation of air pollution across the city. This paper proposes a GIS-based multi-criteria optimization methodology for the optimal placement of IoT-based air quality sensors. The novelty of the approach lies in integrating GIS-based urban feature extraction, buffer-based urban masking, grid-based candidate generation, contextual suitability scoring, and wind-aware risk modeling within a unified optimization framework. The objective function maximizes two key criteria: weighted demand coverage and contextual suitability, based on spatial indicators reflecting the density of important urban elements within predefined radii. Three scenarios, baseline, coverage-priority, and risk-priority, are evaluated across deployment scales from 50 to 400 sensors. The results indicate that a configuration of approximately 100 sensors provides an optimal balance between spatial coverage and environmental relevance. The findings show that the proposed approach supports data-driven sensor placement by identifying priority locations and clarifying trade-offs between coverage and risk. The framework can help improve air quality monitoring in Astana and can also be applied in other rapidly growing cities.