Geospatial modelling of electromagnetic fields from mobile phone base stations.

The Science of the total environment · 2013 · Unclear · Moderate evidence

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UnclearModerate evidencePaper 4629

A 3D geospatial RF-EMF model (NISMap) for Amsterdam showed good agreement with mobile monitoring measurements across five urban areas. Spearman correlations between modelled and measured values were 0.86 (GSM900) and 0.85 (UMTS); the model underestimated GSM9…

Study snapshot

PublicationThe Science of the total environment · 2013

Study typeexposure_assessment

Reported directionUnclear

Evidence ratingModerate

Abstract

There is concern that exposure to radio frequency electromagnetic fields (RF-EMF) from mobile phone base stations might lead to adverse health effects. In order to assess potential health risks, reliable exposure assessment is necessary. Geospatial exposure modelling is a promising approach to quantify ambient exposure to RF-EMF for epidemiological studies involving large populations. We modelled RF-EMF for Amsterdam, The Netherlands by using a 3D RF-EMF model (NISMap). We subsequently compared modelled results to RF-EMF measurements in five areas with differing built-up characteristics (e.g., low-rise residential, high-rise commercial). We performed, in each area, repeated continuous measurements along a predefined ~2 km long path. This mobile monitoring approach captures the high spatial variability in electric field strengths. The modelled values were in good agreement with the measurements. We found a Spearman correlation of 0.86 for GSM900 and 0.85 for UMTS between modelled and measured values. The average measured GSM900 field strength was 0.21 V/m, and UMTS 0.09 V/m. The model underestimated the GSM900 field strengths by 0.07 V/m, and slightly overestimated the UMTS field strengths by 0.01 V/m. NISMap provides a reliable way of assessing environmental RF-EMF exposure for epidemiological studies of RF-EMF and health in urban areas.

Machine Enhanced Logic

AI evidence extraction

Structured fields below were generated from the paper’s abstract and metadata to make the evidence easier to inspect. Automated extraction may be incomplete or incorrect; verify important details against the original paper.

Study typeexposure_assessment

Effect directionunclear

PopulationNot supplied

Sample sizeNot supplied

ExposureRF

Evidence strengthModerateConfidence: 78% · Peer reviewed: yes

Main findings

Outcomes measured

Modelled vs measured RF-EMF electric field strength agreement (GSM900, UMTS)
Spearman correlation between modelled and measured values
Average measured field strengths (V/m)
Model bias (under/overestimation in V/m)

View raw extracted JSON

{
  "study_type": "exposure_assessment",
  "exposure": {
    "band": "RF",
    "source": "mobile phone base station",
    "frequency_mhz": null,
    "sar_wkg": null,
    "duration": null
  },
  "population": null,
  "sample_size": null,
  "outcomes": [
    "Modelled vs measured RF-EMF electric field strength agreement (GSM900, UMTS)",
    "Spearman correlation between modelled and measured values",
    "Average measured field strengths (V/m)",
    "Model bias (under/overestimation in V/m)"
  ],
  "main_findings": "A 3D geospatial RF-EMF model (NISMap) for Amsterdam showed good agreement with mobile monitoring measurements across five urban areas. Spearman correlations between modelled and measured values were 0.86 (GSM900) and 0.85 (UMTS); the model underestimated GSM900 by 0.07 V/m and slightly overestimated UMTS by 0.01 V/m.",
  "effect_direction": "unclear",
  "limitations": [],
  "evidence_strength": "moderate",
  "confidence": 0.78,
  "peer_reviewed_likely": "yes",
  "keywords": [
    "RF-EMF",
    "radiofrequency",
    "mobile phone base stations",
    "geospatial modelling",
    "3D model",
    "NISMap",
    "exposure assessment",
    "Amsterdam",
    "GSM900",
    "UMTS",
    "mobile monitoring",
    "electric field strength"
  ],
  "suggested_hubs": [
    {
      "slug": "who-icnirp",
      "weight": 0.2,
      "reason": "Focuses on environmental RF-EMF exposure assessment relevant to epidemiological risk assessment frameworks."
    }
  ]
}

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AI-extracted fields are generated from the abstract and metadata and may be incomplete or incorrect. This material is for informational purposes and is not medical advice.

Identifiers and source

DOI: 10.1016/j.scitotenv.2012.12.020
PubMed: 23333516

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