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In an increasingly wireless world, spatiotemporal monitoring of the exposure to environmental radiofrequency (RF) electromagnetic fields (EMF) is crucial to appease public uncertainty and anxiety about RF-EMF. However, although the advent of smart city infrastructures allows for dense networks of distributed sensors, the costs of accurate RF sensors remain high, and dedicated RF monitoring networks remain rare. This paper describes a comprehensive study comprising the design of a low-cost RF-EMF sensor node capable of monitoring four frequency bands used by wireless telecommunications with an unparalleled temporal resolution, its application in a small-scale distributed sensor network consisting of both fixed (on building façades) and mobile sensor nodes (on postal vans), and the subsequent analysis of over a year of data between January 2019 and May 2020, during which slightly less than 10 million samples were collected. From the fixed nodes' results, the potential errors were determined that are induced when sampling at lower speeds (e.g., one sample per 15 min) and measuring for shorter periods of time (e.g., a few weeks), as well as an adequate resolution (30 min) for diurnal and weekly temporal profiles which sufficiently preserves short-term variations. Furthermore, based on the correlation between the sensors, an adequate density of 100 sensor nodes per km 2 was deduced for future networks. Finally, the mobile sensor nodes were used to identify potential RF-EMF exposure hotspots in a previously unattainable area of more than 60 km 2 . In summary, through the analysis of a small number of RF-EMF sensor nodes (both fixed and mobile) in an urban area, this study offers invaluable insights applicable to future designs and deployments of distributed RF-EMF sensor networks.

Personal exposure to Radio-Frequency Electromagnetic Fields (RF-EMFs) was studied using personal measurements in five different microenvironments in each of five cities (Brussels, Antwerp, Ghent, Bruges and Hasselt) in Flanders, Belgium. These measurements were carried out by two researchers using on-body calibrated personal exposimeters. In three out of the five studied cities (Brussels, Ghent and Bruges), temporal aspects of personal exposure to RF-EMFs were studied as well. Measurements during and outside of rush hours (7:00-9:15 and 16:30-19:00) were compared. Likewise, measurements were executed during night time and compared to the ones measured during working hours. Representativeness and repeatability of the measurement method was studied as well. The highest mean total exposure was found in Brussels (2.63 mW/m 2 ), the most densely populated city in this study. However, we measured higher downlink exposure in Antwerp than in Brussels, which might be an effect of the stronger legislation on base stations in Brussels. The measurements and used protocol were found to be both repeatable over time (r = 0.95 for median total exposure) and representative for the studied microenvironments in terms of path selection (r = 0.88 for median total exposure). Finally, in 10 out of the 13 on-body calibrated frequency bands we found that the measurement devices underestimate the intensity of the incident RF-EMFs with median underestimations up to 68%., (Copyright © 2019 Elsevier Inc. All rights reserved.)

In this study, a novel methodology is proposed to create heat maps that accurately pinpoint the outdoor locations with elevated exposure to radiofrequency electromagnetic fields (RF-EMF) in an extensive urban region (or, hotspots), and that would allow local authorities and epidemiologists to efficiently assess the locations and spectral composition of these hotspots, while at the same time developing a global picture of the exposure in the area. Moreover, no prior knowledge about the presence of radiofrequency radiation sources (e.g., base station parameters) is required. After building a surrogate model from the available data using kriging, the proposed method makes use of an iterative sampling strategy that selects new measurement locations at spots which are deemed to contain the most valuable information-inside hotspots or in search of them-based on the prediction uncertainty of the model. The method was tested and validated in an urban subarea of Ghent, Belgium with a size of approximately 1 km2. In total, 600 input and 50 validation measurements were performed using a broadband probe. Five hotspots were discovered and assessed, with maximum total electric-field strengths ranging from 1.3 to 3.1 V/m, satisfying the reference levels issued by the International Commission on Non-Ionizing Radiation Protection for exposure of the general public to RF-EMF. Spectrum analyzer measurements in these hotspots revealed five radiofrequency signals with a relevant contribution to the exposure. The radiofrequency radiation emitted by 900 MHz Global System for Mobile Communications (GSM) base stations was always dominant, with contributions ranging from 45% to 100%. Finally, validation of the subsequent surrogate models shows high prediction accuracy, with the final model featuring an average relative error of less than 2dB (factor 1.26 in electric-field strength), a correlation coefficient of 0.7, and a specificity of 0.96., (Copyright © 2013 Elsevier Inc. All rights reserved.)