Designing sensor networks to resolve spatio-temporal urban temperature variations: fixed, mobile or hybrid?
Designing sensor networks to resolve spatio-temporal urban temperature variations: fixed, mobile or hybrid?
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DOI：
10.1088/1748-9326/ab25f8
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发表时间：
2019-07
影响因子：
6.7
通讯作者：
Jiachuan Yang;E. Bou‐Zeid
中科院分区：
文献类型：
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作者：
Jiachuan Yang;E. Bou‐Zeid
城市温度的时空变化影响着人类的福祉，特别是在大城市。低成本传感器现在可以以更精细的分辨率观测城市温度，近年来，固定和移动监测网络不断涌现。然而，如何设计这样的网络以最大化收集数据的信息内容仍然是一个悬而未决的挑战。在本研究中，我们通过部署虚拟传感器对美国四个城市的高分辨率天气模拟中的温度数据集进行采样，研究不同测量网络和策略的性能。结果表明，通过适当的设计和足够数量的传感器，固定网络可以很好地捕获城市内温度的时空变化。根据模拟研究，优化固定传感器位置的关键是捕获整个范围的不渗透部分。随机移动的移动系统在测量月平均温度趋势方面始终优于优化的固定系统，但在检测平均日最高温度方面表现不佳，误差高达 5 °C。对于这两个网络来说，最大的挑战是捕获短时间极端事件（例如热浪）下的异常温度。在这里，我们证明混合网络是极端事件下更鲁棒的系统，可以减少超过 50% 的错误，因为固定传感器检测到的极端事件的时间跨度和移动传感器测量的空间信息可以相互补充。本研究的主要结论涉及优化网络设计对于提高城市测量有效性的重要性。
The spatio-temporal variability of temperatures in cities impacts human well-being, particularly in a large metropolis. Low-cost sensors now allow the observation of urban temperatures at a much finer resolution, and, in recent years, there has been a proliferation of fixed and mobile monitoring networks. However, how to design such networks to maximize the information content of collected data remains an open challenge. In this study, we investigate the performance of different measurement networks and strategies by deploying virtual sensors to sample the temperature data set in high-resolution weather simulations in four American cities. Results show that, with proper designs and a sufficient number of sensors, fixed networks can capture the spatio-temporal variations of temperatures within the cities reasonably well. Based on the simulation study, the key to optimizing fixed sensor location is to capture the whole range of impervious fractions. Randomly moving mobile systems consistently outperform optimized fixed systems in measuring the trend of monthly mean temperatures, but they underperform in detecting mean daily maximum temperatures with errors up to 5 °C. For both networks, the grand challenge is to capture anomalous temperatures under extreme events of short duration, such as heat waves. Here, we show that hybrid networks are more robust systems under extreme events, reducing errors by more than 50%, because the time span of extreme events detected by fixed sensors and the spatial information measured by mobile sensors can complement each other. The main conclusion of this study concerns the importance of optimizing network design for enhancing the effectiveness of urban measurements.