This paper describes planned experimental measurements of electric and magnetic fields generated by electric vehicles during charging and driving. The abstract emphasizes that occupants can experience notable EMF exposure due to proximity to vehicle electrical systems, while stating that specific health risks in the EV context remain uncertain. It also notes that manufacturers implement technological design solutions intended to reduce exposure.

This cross-sectional study compared 183 higher-exposed residents with 126 matched reference residents and assessed symptoms via questionnaire alongside in-home RF-EMF power density measurements from mobile phone base stations. Higher exposure (including proximity within 50 m and power densities of 5–8 mW/m2) was reported to be associated with increased symptom prevalence across mood-energy, cognitive-sensory, inflammatory, and anatomical categories. The authors conclude that current public exposure limits may be inadequate for long-term, non-thermal biological impacts and call for precautionary policy updates.

This study tested magnetic fields from tablets, laptops, smartphones, and household/leisure magnets against 13 cardiac implantable electronic device (CIED) models to assess magnet mode activation. It reports that these consumer devices can trigger magnet mode when in close proximity, with median activation distances of 5–6.5 mm for phones/tablets/laptops and mainly contact-level activation for household/leisure magnets. None of the tested devices activated magnet mode at distances of 20 mm or more, and the authors emphasize patient awareness of proximity-related risk.

This multi-hospital case-control study (n=316) examined associations between use of common household electrical appliances emitting ELF EMF and tumour risk. Most devices showed below-unity odds ratios, while computer screen and microwave oven use showed slightly increased odds with wide confidence intervals. Proximity to electricity transformer stations was associated with higher tumour odds, whereas proximity to powerlines was not.

This analytical study evaluated machine learning models (SVM and Random Forest) to predict health symptoms in adults living near mobile phone base stations. The SVM model reportedly achieved high predictive performance for headache, sleep disturbance, dizziness, vertigo, and fatigue, and outperformed Random Forest and prior models. The abstract concludes that proximity to base stations is connected with increased prevalence of several symptoms and emphasizes distance, age, and duration of residence as key predictors.