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

2025-10-31

AI Summary

Traditional urban air quality models rely on static emission inventories and thus fail to capture dynamic traffic-related pollution. To address this limitation, this study proposes an IoT–AI–numerical simulation integrated framework for real-time air quality monitoring. A low-power edge sensor network collects multi-source environmental data; AI-driven traffic video analytics enable vehicle flow detection and dynamic emission inversion; and a high-resolution air quality model simulates and forecasts pollutant dispersion at minute-level temporal resolution within an edge–cloud collaborative architecture. The key innovation lies in transcending the static-inventory paradigm to establish a spatiotemporally adaptive, privacy-preserving system for dynamic pollution sensing and inference. Validated in Barcelona, the framework significantly reduces NO₂ concentration prediction error, achieving a correlation coefficient of 0.92 with official monitoring station data—demonstrating both efficacy and scalability.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Security and privacy of machine learning and AI applications

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.

Problem

• Integrating IoT sensors with AI traffic analysis
• Enhancing real-time urban air quality predictions
• Developing scalable IoT-edge-cloud-HPC pollution monitoring

Innovation

• Integrating IoT sensors with AI traffic analysis
• Combining edge computing and HPC simulation pipeline
• Using scalable IoT-edge-cloud-HPC architecture for monitoring