IoT- and AI-informed urban air quality models for vehicle pollution monitoring
Jan M. Armengol, Vicente Masip, Ada Barrantes, Gabriel M. Beltrami, Sergi Albiach, Daniel Rodriguez-Rey, Marc Guevara, Albert Soret, Eduardo Quiñones, Elli Kartsakli
随着智能物联网(IoT)系统在城市环境中的兴起,新的机会正在涌现,以增强实时环境监测。 虽然大多数研究都集中在基于物联网的空气质量传感或基于物理的建模上,但这项工作通过将低成本传感器和基于人工智能的视频交通分析与高分辨率城市空气质量模型相结合,弥合了差距。 我们在巴塞罗那Eixample区的一个公路交叉路口进行了真实的试点部署,系统可以捕获动态交通状况和环境变量,在边缘处理它们,并将实时数据输入高性能计算(HPC)模拟管道。 结果根据二氧化氮(NO2)的官方空气质量测量结果进行验证。 与依赖静态排放清单的传统模型相比,物联网辅助方法增强了城市空气质量预测对交通相关污染物的时间粒度。 利用物联网边缘云-HPC架构的全部功能,这项工作展示了一个可扩展的、适应性的、具有隐私意识的城市污染监测解决方案,并为下一代物联网驱动的环境智能奠定了基础。
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 Eix...
