IoT- and AI-informed urban air quality models for vehicle pollution monitoring

• URL: http://arxiv.org/abs/2511.00187v1
• Date: Fri, 31 Oct 2025 18:47:16 GMT
• Title: IoT- and AI-informed urban air quality models for vehicle pollution monitoring
• Authors: Jan M. Armengol, Vicente Masip, Ada Barrantes, Gabriel M. Beltrami, Sergi Albiach, Daniel Rodriguez-Rey, Marc Guevara, Albert Soret, Eduardo Quiñones, Elli Kartsakli,
• Abstract summary: We present a real-world pilot deployment at a road intersection in Barcelona's Eixample district.<n>The system captures dynamic traffic conditions and environmental variables, processes them at the edge, and feeds real-time data into a high-performance computing (HPC) simulation pipeline.<n>Results are validated against official air quality measurements of nitrogen dioxide (NO2)<n>This work demonstrates a scalable, adaptive, and privacy-conscious solution for urban pollution monitoring and establishes a foundation for next-generation IoT-driven environmental intelligence.
• Score: 0.4893529843295925
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: With the rise of intelligent Internet of Things (IoT) systems in urban environments, new opportunities are emerging to enhance real-time environmental monitoring. While most studies focus either on IoT-based air quality sensing or physics-based modeling in isolation, this work bridges that gap by integrating low-cost sensors and AI-powered video-based traffic analysis with high-resolution urban air quality models. We present a real-world pilot deployment at a road intersection in Barcelona's Eixample district, where the system captures dynamic traffic conditions and environmental variables, processes them at the edge, and feeds real-time data into a high-performance computing (HPC) simulation pipeline. Results are validated against official air quality measurements of nitrogen dioxide (NO2). Compared to traditional models that rely on static emission inventories, the IoT-assisted approach enhances the temporal granularity of urban air quality predictions of traffic-related pollutants. Using the full capabilities of an IoT-edge-cloud-HPC architecture, this work demonstrates a scalable, adaptive, and privacy-conscious solution for urban pollution monitoring and establishes a foundation for next-generation IoT-driven environmental intelligence.

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