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评估密集传感器网络在学术校园区域 PM 监测中的有用性。

Assessing the usefulness of dense sensor network for PM monitoring on an academic campus area.

机构信息

Wrocław University of Science and Technology, Faculty of Environmental Engineering, Wyb. Wyspiańskiego 27, 50-370 Wrocław, Poland.

Wrocław University of Science and Technology, Faculty of Environmental Engineering, Wyb. Wyspiańskiego 27, 50-370 Wrocław, Poland.

出版信息

Sci Total Environ. 2020 Jun 20;722:137867. doi: 10.1016/j.scitotenv.2020.137867. Epub 2020 Mar 18.

Abstract

Low-cost sensors provide an opportunity to improve the spatial and temporal resolution of air quality measurements. Networks of such devices may complement the traditional air quality monitoring and provide some useful information about pollutants and their impact on health. This paper describes the network of 20 nodes for ambient PM monitoring on a campus area of Wrocław University of Science and Technology (Wrocław, Poland). Sensor nodes were equipped with optical sensors PMS A003 (Plantower), which showed high reproducibility between units. The distribution of the sensor nodes was characterised by both high density (14 devices on the main campus area) and wide spread across the city (6 devices on peripheral campuses). During the measurement campaign, signals from sensor nodes were consistent with results from regulatory monitoring stations and sensor devices were capable of indicating elevated levels of PM concentrations. A great advantage of this system was the ability to provide up-to-date air quality information to the public. Furthermore, air quality messaging was site-specific because of the observed differences in PM concentrations. Data analysis was aimed at assessing variability between locations using Kendall's τ metric and assessing the statistical significance of the differences in measurement results from neighbouring sensor nodes using the Kolmogorov-Smirnov test. The analysis showed high importance of the nodes in the middle of the main campus and variations of signals from nodes on the peripheries. Differences in signals from sensors located in close proximity to each other were in some cases significant, but only for short-term averaged data. Nevertheless, highly visible variation in PM signals was observed in the case of nodes arranged vertically on two buildings. PM concentrations were even 2-4 times greater near the top parts of the buildings than near the ground. The effect of stratification of PM levels was observed under conditions of temperature inversion.

摘要

低成本传感器为提高空气质量测量的时空分辨率提供了机会。此类设备网络可以补充传统的空气质量监测,并提供有关污染物及其对健康影响的一些有用信息。本文介绍了在弗罗茨瓦夫科技大学(波兰弗罗茨瓦夫)校园区域设置的 20 个节点的环境 PM 监测网络。传感器节点配备了光学传感器 PMS A003(Plantower),这些传感器在各单元之间具有很高的重现性。传感器节点的分布具有高密度(主校区有 14 个设备)和广泛分布在整个城市(6 个设备分布在周边校区)的特点。在测量活动期间,传感器节点的信号与监管监测站的结果一致,并且传感器设备能够指示 PM 浓度升高。该系统的一个很大优势是能够向公众提供最新的空气质量信息。此外,由于观察到 PM 浓度存在差异,空气质量消息是特定于地点的。数据分析旨在使用 Kendall's τ 度量评估位置之间的可变性,并使用 Kolmogorov-Smirnov 检验评估来自相邻传感器节点的测量结果之间的差异的统计显着性。分析表明,主校区中部节点的重要性很高,并且边缘节点的信号变化很大。彼此靠近的传感器的信号差异在某些情况下是显着的,但仅适用于短期平均数据。然而,在两个建筑物上垂直排列的节点的情况下,观察到 PM 信号的高度可见变化。建筑物顶部附近的 PM 浓度比地面附近高 2-4 倍。在温度逆温条件下观察到 PM 水平分层的影响。

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