Efficient design of electromagnetic field exposure maps with multi-method evolutionary ensembles

Abstract

Category: Environmental Research, Electromagnetic Exposure Mapping Institution: Not specified Tags: electromagnetic fields, exposure maps, evolutionary algorithms, public health, radio-frequency, spatial analysis, optimization DOI: 10.1016/j.envres.2025.121636 URL: sciencedirect.com Overview Radio-frequency electromagnetic field (EMF) exposure is a growing concern among the population. This concern has led to a need for practical tools to contribute to an adequate risk perception. Representing spatial variations after measurements from fixed sites and interpolating using different techniques is the most suitable method for obtaining EMF high-quality exposure maps. This paper uses evolutionary computation to obtain the optimal set of points to construct high-quality electromagnetic field exposure maps, minimizing an error measure with respect to a reference exposure map. A multi-method ensemble evolutionary approach, able to combine different search operators in a single population (PCRO-SL), is introduced for this particular problem, and it has been tested over actual measurements at Meco town, Madrid, Spain, obtaining good quality electromagnetic field exposure map reconstructions in terms of the differences with a reference EMF exposure map. Findings - The results show that it is possible to reduce the number of measurement points necessary to obtain significant exposure maps while still maintaining their representativeness. - The density of points required was reduced from the initial 8-10 points/km2 to just over 5 points/km2. - Division of the territory into 250-meter squares can be used for preliminary analysis, but it is necessary to optimize point selection for effective sampling. - At least one point in direct line-of-sight (LOS) within a first radius of 250 meters should be chosen. - Within a 500-meter radius, points should be chosen in different radial directions regardless of LOS/NLOS status to determine exposure variation. - If not included above, points should be placed on the perimeter to avoid interpolation artifacts at the edges. - The criteria established maintain the statistical properties of the measurement set (mean, variance, lognormality). Conclusion The evolutionary-type algorithm reduced the effort required to produce EMF exposure maps and has enabled more accurate perception of exposure risk among the general public. The study highlights the necessity of accurately representing exposure to ensure proper risk assessment relating to health impacts of electromagnetic fields. Future work will address different mobile technologies and measurement techniques for exposure mapping.

AI evidence extraction

Main findings

An ensemble evolutionary computation approach (PCRO-SL) was used to optimize measurement point selection for RF-EMF exposure mapping using actual measurements in Meco (Madrid, Spain). The approach reduced required sampling density from 8–10 points/km² to just over 5 points/km² while maintaining representativeness and producing exposure map reconstructions with good agreement to a reference map.

Outcomes measured

• Exposure mapping accuracy/error versus reference exposure map
• Required measurement point density (points/km2)
• Representativeness/statistical properties of measurement set (mean, variance, lognormality)

{
    "study_type": "engineering",
    "exposure": {
        "band": "RF",
        "source": null,
        "frequency_mhz": null,
        "sar_wkg": null,
        "duration": null
    },
    "population": null,
    "sample_size": null,
    "outcomes": [
        "Exposure mapping accuracy/error versus reference exposure map",
        "Required measurement point density (points/km2)",
        "Representativeness/statistical properties of measurement set (mean, variance, lognormality)"
    ],
    "main_findings": "An ensemble evolutionary computation approach (PCRO-SL) was used to optimize measurement point selection for RF-EMF exposure mapping using actual measurements in Meco (Madrid, Spain). The approach reduced required sampling density from 8–10 points/km² to just over 5 points/km² while maintaining representativeness and producing exposure map reconstructions with good agreement to a reference map.",
    "effect_direction": "unclear",
    "limitations": [],
    "evidence_strength": "insufficient",
    "confidence": 0.7399999999999999911182158029987476766109466552734375,
    "peer_reviewed_likely": "yes",
    "keywords": [
        "electromagnetic fields",
        "radio-frequency",
        "exposure maps",
        "exposure mapping",
        "evolutionary algorithms",
        "evolutionary computation",
        "ensemble methods",
        "spatial analysis",
        "interpolation",
        "optimization",
        "sampling design",
        "line-of-sight",
        "NLOS",
        "public health",
        "risk perception",
        "Madrid",
        "Meco"
    ],
    "suggested_hubs": []
}

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