Abstract. Air pollution poses significant environmental and public health risks, particularly in urban areas of low and middle-income countries like the Philippines. Regulatory air quality monitoring stations, while accurate, are expensive and limited in spatial coverage, highlighting the need for low-cost IoT-based sensor networks to provide broader and real-time air quality data. This study establishes a methodology using Geographic Information Systems (GIS) and a heuristic algorithm to determine locations for deploying low-cost IoT-based air quality sensors in urban environments, focusing on near-road areas in Quezon City. Using multi-criteria analysis, Street Aspect Ratio (SAR), traffic emissions, Global Horizontal Irradiance (GHI), and road proximity were combined to produce a suitability map; scores ranged from 0 to 6. The algorithm then selected sensor locations by combining suitability and population rasters while enforcing a minimum spacing between nodes. In a 40‑sensor test, the resulting networks covered approximately 1.27 - 1.35 million residents (23.0%–24.4% of the city’s population) across weighting schemes while maintaining balanced spatial dispersion. These results indicate that the method achieves substantial population coverage in high‑exposure corridors and aligns with public‑health priorities. The framework is reproducible for other cities to enhance near‑road air quality monitoring and management.
Magdaong et al. (2026) studied this question.