The Deployment Modeling of Low-Cost Sensors for Urban Particulate Matter Monitoring: A Case Study for PM_2.5 Monitoring in Tehran City

Urban air quality management is critically dependent on robust air quality monitoring systems. Conventional systems, utilizing fixed, complex, and costly equipment, encounter challenges in stable data collection, transmission, and accessibility. Recent scholarly reports have highlighted the potential of low-cost sensors as an alternative to these conventional systems. This study endeavors to ascertain the optimal placement of low-cost PM_2.5 monitoring sensors in Tehran. The methodology involved a two-step process. Initially, PM_2.5 pollution maps of Tehran were generated utilizing data from local air quality monitoring stations and remote sensing data. Owing to its superior accuracy and comprehensive coverage, remote sensing data was employed for determining the optimal sensor placement. Subsequently, the Gaussian process model was utilized to optimize sensor locations. The initial placements were based on a scenario where a 1 × 1 km grid was defined for the initial placement of sensors. This scenario involved dividing the city into equal grids and choosing the center of each grid as the point for placing the sensor. The preliminary sensor placement considered a uniform distribution across Tehran at regular one-kilometer intervals. Following this, the Gaussian process model identified 592 optimal locations for the installation of low-cost PM_2.5 monitoring sensors. The study concludes that the strategic deployment of low-cost sensors, guided by the Gaussian process model, can significantly enhance the effectiveness of urban air quality monitoring systems. It is strongly recommended that urban authorities consider the integration of low-cost sensors at the identified locations for more efficient and economical air quality monitoring.