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安装在公车上的低成本感测器系统,用以监测城市中的空气质量。

A Low-Cost Sensor System Installed in Buses to Monitor Air Quality in Cities.

机构信息

Centro de Ciências e Tecnologias Nucleares, Instituto Superior Técnico, Universidade de Lisboa, Estrada Nacional 10, 2695-066 Bobadela, Portugal.

ISCTE-Instituto Universitário de Lisboa (ISCTE-IUL), Av. das Forças Armadas, 1649-026 Lisboa, Portugal.

出版信息

Int J Environ Res Public Health. 2023 Feb 24;20(5):4073. doi: 10.3390/ijerph20054073.

Abstract

Air pollution is an important source of morbidity and mortality. It is essential to understand to what levels of air pollution citizens are exposed, especially in urban areas. Low-cost sensors are an easy-to-use option to obtain real-time air quality (AQ) data, provided that they go through specific quality control procedures. This paper evaluates the reliability of the ExpoLIS system. This system is composed of sensor nodes installed in buses, and a Health Optimal Routing Service App to inform the commuters about their exposure, dose, and the transport's emissions. A sensor node, including a particulate matter (PM) sensor (Alphasense OPC-N3), was evaluated in laboratory conditions and at an AQ monitoring station. In laboratory conditions (approximately constant temperature and humidity conditions), the PM sensor obtained excellent correlations (R≈1) against the reference equipment. At the monitoring station, the OPC-N3 showed considerable data dispersion. After several corrections based on the k-Köhler theory and Multiple Regression Analysis, the deviation was reduced and the correlation with the reference improved. Finally, the ExpoLIS system was installed, leading to the production of AQ maps with high spatial and temporal resolution, and to the demonstration of the Health Optimal Routing Service App as a valuable tool.

摘要

空气污染是导致发病率和死亡率的一个重要因素。了解公民在何种程度上暴露于空气污染之下是非常重要的,尤其是在城市地区。低成本传感器是获取实时空气质量 (AQ) 数据的一种简单易用的选择,但前提是它们要经过特定的质量控制程序。本文评估了 ExpoLIS 系统的可靠性。该系统由安装在公共汽车上的传感器节点和一个健康最佳路由服务应用程序组成,用于告知通勤者他们的暴露程度、剂量和交通工具的排放量。一个传感器节点,包括一个颗粒物 (PM) 传感器 (Alphasense OPC-N3),在实验室条件和空气质量监测站进行了评估。在实验室条件下(温度和湿度条件大致恒定),PM 传感器与参考设备获得了极好的相关性(R≈1)。在监测站,OPC-N3 显示出相当大的数据分散。经过基于 k-Köhler 理论和多元回归分析的几次修正后,偏差得以减少,与参考值的相关性得到了提高。最后,安装了 ExpoLIS 系统,生成了具有高时空分辨率的空气质量地图,并展示了健康最佳路由服务应用程序作为一种有价值的工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6900/10002067/b3efca9e9867/ijerph-20-04073-g001.jpg

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