Cluster Analysis of RF-EMF Exposure to Detect Time Patterns in Urban Environment: A Model-Based Approach

Authors: Pasquino N, Solmonte N, Djuric N, Kljajic D, Djuric S

Year: 2025

Category: Epidemiology

Journal: IEEE Access

DOI: 10.1109/ACCESS.2025.3586905

URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11072665&isnumber=10820123

Abstract

Overview 📊

The increase in human exposure to electromagnetic fields (EMFs), driven by advancements in telecommunication systems like the 5G mobile system, highlights the need for continuous EMF monitoring. Advanced techniques for data analysis, based on machine learning like clustering, can decompose daily variations in EMF exposure into distinct patterns, providing a clearer understanding of how exposure fluctuates over time.

Issues in Existing Research 👁️

  • Several exposure monitoring systems exist in Europe.
  • Only a few studies have examined time variability.
  • Understanding temporal exposure patterns is crucial to EMF safety and public health, including possible health risks from long-term or fluctuating exposure.

Methodology 🛠️

This study addresses the gap by applying model-based clustering techniques to analyze the temporal patterns in EMF exposure. Specifically, it characterizes fluctuations in field strength during workdays and holidays.

  • Continuous monitoring data collected via the Serbian EMF RATEL network in Novi Sad.
  • Data analyzed using the Log-Normal Mixture Model (LNMM) clustering algorithm.
  • Mixture distributions used to segment and detect patterns.

Findings and Insights 🔍

  • LNMM can separate night and day exposure values.
  • Identification of periods when exposure values persist longer in the day.
  • Model-based clustering is found effective for understanding local, time-distributed exposure patterns.

Conclusion 📝

Model-based clustering aids in the nuanced analysis of EMF exposure, especially with the rapid adoption of telecommunications technology. Understanding temporal exposure dynamics supports efforts to improve EMF safety by identifying when and where elevated exposures occur, contributing valuable data to address associated health risks.

RF-EMF exposure time patterns clustering urban environment machine learning EMF monitoring public health
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