Developing Criteria and an Algorithm for Low-Cost IoT-Based Air Quality Sensor Network for Near-Road Air Quality Monitoring

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 highg-exposure corridors and aligns with public-health priorities. The framework is reproducible for other cities to enhance near-road air quality monitoring and management.