用多变量模型处理车载传感器网络观测到的城市空气污染数据

Israel L. C. Vasconcelos, Andre L. L. Aquino
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引用次数: 0

摘要

这项工作提出了一项跨学科的评估,深入研究了城市环境中空气质量的跟踪。该应用程序利用车辆传感器网络(VSN)的优势,将传感器节点嵌入公共交通中,将采样活动分散到路线中访问的不同地点。我们基于从圣保罗市收集的真实数据进行了环境建模,同时考虑了来自化石燃料汽车的五种不同空气污染物(CO, O3, PM10, NO2和SO2)的多元空间行为,并且它也随时间变化。最后,我们基于vsn的方法显示,与传统的空气质量监测站相比,误差降低了126倍,覆盖率提高了11倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multivariate Modeling to handle Urban Air Pollution Data observed trough Vehicular Sensor Networks
This work presents an interdisciplinary assessment that looks in-depth at the tracking of air quality in urban environments. The proposed application takes advantage of Vehicle Sensor Networks (VSN) by embedding sensor nodes to public transportation, spreading the sampling activity through different places visited during the route. We perform environmental modeling based on real data collected from the city of São Paulo, considering the multivariate spatial behavior of five different air pollutants from fossil-fueled vehicles (CO, O3, PM10, NO2 and SO2) simultaneously while it also varies in time. Finally, our VSN-based approach showed an improvement of 126 times lower error and 11 times higher coverage about conventional monitoring with air quality stations.
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