Mijatovic Gorana, Kljajic Dragan, Kasas-Lazetic Karolina, Milutinov Miodrag, Stivala Salvatore, Busacca Alessandro, Cino Alfonso Carmelo, Stramaglia Sebastiano, Faes Luca
Faculty of Technical Sciences, University of Novi Sad, 21102 Novi Sad, Serbia.
Department of Engineering, University of Palermo, 90128 Palermo, Italy.
Entropy (Basel). 2022 May 20;24(5):726. doi: 10.3390/e24050726.
This work investigates the temporal statistical structure of time series of electric field (EF) intensity recorded with the aim of exploring the dynamical patterns associated with periods with different human activity in urban areas. The analyzed time series were obtained from a sensor of the EMF RATEL monitoring system installed in the campus area of the University of Novi Sad, Serbia. The sensor performs wideband cumulative EF intensity monitoring of all active commercial EF sources, thus including those linked to human utilization of wireless communication systems. Monitoring was performed continuously during the years 2019 and 2020, allowing us to investigate the effects on the patterns of EF intensity of varying conditions of human mobility, including regular teaching and exam activity within the campus, as well as limitations to mobility related to the COVID-19 pandemic. Time series analysis was performed using both simple statistics (mean and variance) and combining the information-theoretic measure of information storage (IS) with the method of surrogate data to quantify the regularity of EF dynamic patterns and detect the presence of nonlinear dynamics. Moreover, to assess the possible coexistence of dynamic behaviors across multiple temporal scales, IS analysis was performed over consecutive observation windows lasting one day, week, month, and year, respectively coarse grained at time scales of 6 min, 30 min, 2 h, and 1 day. Our results document that the EF intensity patterns of variability are modulated by the movement of people at daily, weekly, and monthly scales, and are blunted during periods of restricted mobility related to the COVID-19 pandemic. Mobility restrictions also affected significantly the regularity of the EF intensity time series, resulting in lower values of IS observed simultaneously with a loss of nonlinear dynamics. Thus, our analysis can be useful to investigate changes in the global patterns of human mobility both during pandemics or other types of events, and from this perspective may serve to implement strategies for safety assessment and for optimizing the design of networks of EF sensors.
这项工作研究了电场（EF）强度时间序列的时间统计结构，旨在探索与城市地区不同人类活动时期相关的动态模式。分析的时间序列来自安装在塞尔维亚诺维萨德大学校园区域的EMF RATEL监测系统的一个传感器。该传感器对所有有源商业EF源进行宽带累积EF强度监测，因此包括与人类使用无线通信系统相关的那些源。在2019年和2020年期间进行了连续监测，使我们能够研究人类移动性不同条件对EF强度模式的影响，包括校园内的常规教学和考试活动，以及与COVID-19大流行相关的移动性限制。使用简单统计量（均值和方差）以及将信息存储的信息论度量（IS）与替代数据方法相结合进行时间序列分析，以量化EF动态模式的规律性并检测非线性动力学的存在。此外，为了评估跨多个时间尺度的动态行为的可能共存，分别在持续一天、一周、一个月和一年的连续观测窗口上进行IS分析，在6分钟、30分钟、2小时和1天的时间尺度上进行粗粒度分析。我们的结果表明，EF强度变化模式在每日、每周和每月尺度上受到人员移动的调制，并且在与COVID-19大流行相关的移动受限时期变得平缓。移动限制也显著影响了EF强度时间序列的规律性，导致IS值较低，同时非线性动力学丧失。因此，我们的分析对于研究大流行或其他类型事件期间人类移动的全球模式变化可能有用，并从这个角度可用于实施安全评估策略和优化EF传感器网络的设计。
