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

TL;DR

This paper presents an integrated IoT and AI-based system for real-time urban air quality monitoring that combines low-cost sensors, traffic analysis, and high-resolution modeling, validated through a pilot in Barcelona.

Contribution

It introduces a scalable, adaptive framework that combines IoT sensors, AI analysis, and high-performance computing for enhanced real-time pollution monitoring in cities.

Findings

Improved temporal resolution of air quality predictions.

Validated system accuracy against official NO2 measurements.

Demonstrated scalability and privacy-conscious design.

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.