IoT- and AI-informed urban air quality models for vehicle pollution monitoring
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917053529587712 |
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| author | Armengol, Jan M. Masip, Vicente Barrantes, Ada Beltrami, Gabriel M. Albiach, Sergi Rodriguez-Rey, Daniel Guevara, Marc Soret, Albert Quiñones, Eduardo Kartsakli, Elli |
| author_facet | Armengol, Jan M. Masip, Vicente Barrantes, Ada Beltrami, Gabriel M. Albiach, Sergi Rodriguez-Rey, Daniel Guevara, Marc Soret, Albert Quiñones, Eduardo Kartsakli, Elli |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_00187 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IoT- and AI-informed urban air quality models for vehicle pollution monitoring Armengol, Jan M. Masip, Vicente Barrantes, Ada Beltrami, Gabriel M. Albiach, Sergi Rodriguez-Rey, Daniel Guevara, Marc Soret, Albert Quiñones, Eduardo Kartsakli, Elli Atmospheric and Oceanic Physics Computers and Society 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. |
| title | IoT- and AI-informed urban air quality models for vehicle pollution monitoring |
| topic | Atmospheric and Oceanic Physics Computers and Society |
| url | https://arxiv.org/abs/2511.00187 |