Outdoor and indoor sources of residential radiofrequency electromagnetic fields, personal cell phone and cordless phone use, and cognitive function in 5-6 years old children.

BACKGROUND: Little is known about the exposure of young children to radiofrequency electromagnetic fields (RF-EMF) and potentially associated health effects. We assessed the relationship between residential RF-EMF exposure from mobile phone base stations, residential presence of indoor sources, personal cell phone and cordless phone use, and children's cognitive function at 5-6 years of age. METHODS: Cross-sectional study on children aged 5-6 years from the Amsterdam Born Children and their Development (ABCD) study, the Netherlands (n=2354). Residential RF-EMF exposure from mobile phone base stations was estimated with a 3D geospatial radio wave propagation model. Residential presence of indoor sources (cordless phone base stations and Wi-Fi) and children's cell phone and cordless phone use was reported by the mother. Speed of information processing, inhibitory control, cognitive flexibility, and visuomotor coordination was assessed using the Amsterdam Neuropsychological Tasks. RESULTS: Residential presence of RF-EMF indoor sources was associated with an improved speed of information processing. Higher residential RF-EMF exposure from mobile phone base stations and presence of indoor sources was associated with an improved inhibitory control and cognitive flexibility whereas we observed a reduced inhibitory control and cognitive flexibility with higher personal cordless phone use. Higher residential RF-EMF exposure from mobile phone base stations was associated with a reduced visuomotor coordination whereas we observed an improved visuomotor coordination with residential presence of RF-EMF indoor sources and higher personal cell phone use. CONCLUSIONS: We found inconsistent associations between different sources of RF-EMF exposure and cognitive function in children aged 5-6 years.

Validity of at home model predictions as a proxy for personal exposure to radiofrequency electromagnetic fields from mobile phone base stations.

BACKGROUND: Epidemiological studies on the potential health effects of RF-EMF from mobile phone base stations require efficient and accurate exposure assessment methods. Previous studies have demonstrated that the 3D geospatial model NISMap is able to rank locations by indoor and outdoor RF-EMF exposure levels. This study extends on previous work by evaluating the suitability of using NISMap to estimate indoor RF-EMF exposure levels at home as a proxy for personal exposure to RF-EMF from mobile phone base stations. METHODS: For 93 individuals in the Netherlands we measured personal exposure to RF-EMF from mobile phone base stations during a 24h period using an EME-SPY 121 exposimeter. Each individual kept a diary from which we extracted the time spent at home and in the bedroom. We used NISMap to model exposure at the home address of the participant (at bedroom height). We then compared model predictions with measurements for the 24h period, when at home, and in the bedroom by the Spearman correlation coefficient (rsp) and by calculating specificity and sensitivity using the 90th percentile of the exposure distribution as a cutpoint for high exposure. RESULTS: We found a low to moderate rsp of 0.36 for the 24h period, 0.51 for measurements at home, and 0.41 for measurements in the bedroom. The specificity was high (0.9) but with a low sensitivity (0.3). DISCUSSION: These results indicate that a meaningful ranking of personal RF-EMF can be achieved, even though the correlation between model predictions and 24h personal RF-EMF measurements is lower than with at home measurements. However, the use of at home RF-EMF field predictions from mobile phone base stations in epidemiological studies leads to significant exposure misclassification that will result in a loss of statistical power to detect health effects.

Air pollution from livestock farms, and asthma, allergic rhinitis and COPD among neighbouring residents.

OBJECTIVES: There is an ongoing debate regarding environmental health risks of exposures to dust and microbial agents from livestock farming in the Netherlands. The aims of the study were (1) to investigate associations between indicators of air pollution from livestock farms and asthma, allergic rhinitis and chronic obstructive pulmonary disease (COPD) among neighbouring residents; and (2) to assess associations between farm exposures and endotoxin levels in participants' homes. METHODS: Electronic medical records of all 92 548 patients of 27 general practices in a rural area with a high density of animal farms were analysed, followed up by a case-control component using a subsample of the full population. Distance between livestock farms and home address, presence of livestock within 500 m, and particulate matter (PM)10 emissions from farms within 500 m were computed as proxies for farm exposure. Potential confounding was investigated through a case-control questionnaire study in 269 adult patients with asthma and 546 controls. Endotoxin levels were assessed in 493 homes. RESULTS: Modelled PM10 emission was inversely associated with asthma, allergic rhinitis and COPD (p<0.05). A smaller distance to the nearest farm, and the presence of swine, goat and sheep farms were also inversely related to respiratory morbidity, whereas mink farms showed positive associations with asthma and allergic rhinitis. Adjustment for confounding in the case-control study did not change results. Farm exposures were not associated with endotoxin levels in neighbouring residents' homes. CONCLUSIONS: In conclusion, indicators of air pollution from livestock farms were inversely associated with respiratory morbidity among neighbouring residents.

Impact of input data uncertainty on environmental exposure assessment models: A case study for electromagnetic field modelling from mobile phone base stations.

BACKGROUND: With the increased availability of spatial data and computing power, spatial prediction approaches have become a standard tool for exposure assessment in environmental epidemiology. However, such models are largely dependent on accurate input data. Uncertainties in the input data can therefore have a large effect on model predictions, but are rarely quantified. METHODS: With Monte Carlo simulation we assessed the effect of input uncertainty on the prediction of radio-frequency electromagnetic fields (RF-EMF) from mobile phone base stations at 252 receptor sites in Amsterdam, The Netherlands. The impact on ranking and classification was determined by computing the Spearman correlations and weighted Cohen's Kappas (based on tertiles of the RF-EMF exposure distribution) between modelled values and RF-EMF measurements performed at the receptor sites. RESULTS: The uncertainty in modelled RF-EMF levels was large with a median coefficient of variation of 1.5. Uncertainty in receptor site height, building damping and building height contributed most to model output uncertainty. For exposure ranking and classification, the heights of buildings and receptor sites were the most important sources of uncertainty, followed by building damping, antenna- and site location. Uncertainty in antenna power, tilt, height and direction had a smaller impact on model performance. CONCLUSIONS: We quantified the effect of input data uncertainty on the prediction accuracy of an RF-EMF environmental exposure model, thereby identifying the most important sources of uncertainty and estimating the total uncertainty stemming from potential errors in the input data. This approach can be used to optimize the model and better interpret model output.

Using GPS-derived speed patterns for recognition of transport modes in adults.

BACKGROUND: Identification of active or sedentary modes of transport is of relevance for studies assessing physical activity or addressing exposure assessment. We assessed in a proof-of-principle study if speed as logged by GPSs could be used to identify modes of transport (walking, bicycling, and motorized transport: car, bus or train). METHODS: 12 persons commuting to work walking, bicycling or with motorized transport carried GPSs for two commutes and recorded their mode of transport. We evaluated seven speed metrics: mean, 95th percentile of speed, standard deviation of the mean, rate-of-change, standardized-rate-of-change, acceleration and deceleration. We assessed which speed metric would best identify the transport mode using discriminant analyses. We applied cross validation and calculated agreement (Cohen's Kappa) between actual and derived modes of transport. RESULTS: Mode of transport was reliably classified whenever a person used a mode of transport for longer than one minute. Best results were observed when using the 95th percentile of speed, acceleration and deceleration (kappa 0.73). When we combined all motorized traffic into one category, kappa increased to 0.95. CONCLUSIONS: GPS-measured speed enable the identification of modes of transport. Given the current low costs of GPS devices and the built-in capacity of GPS tracking in most smartphones, the use of such devices in large epidemiological studies may facilitate the assessment of physical activity related to transport modes, or improve exposure assessment using automated travel mode detection.

Radiofrequency electromagnetic fields, screen time, and emotional and behavioural problems in 5-year-old children.

BACKGROUND: Little is known about the exposure of young children to radiofrequency electromagnetic fields (RF-EMF) and potentially associated health effects. We assessed the relationship of RF-EMF exposure from different sources and screen time exposure with emotional and behavioural problems in 5-year-old children. METHODS: Cross-sectional study including 3102 children aged 5 years from the Amsterdam Born Children and their Development (ABCD) study, in the Netherlands. Residential RF-EMF exposure from mobile phone base stations was estimated with a 3D geospatial radio wave propagation model. Residential presence of RF-EMF indoor sources (cordless phone base stations and Wireless Fidelity (WiFi)), children's mobile phone and cordless phone calls and screen time exposure (computer/video game and television watching) was reported by the mother. Teachers (n = 2617) and mothers (n = 3019) independently reported child emotional and behavioural problems using the Strengths and Difficulties Questionnaire. RESULTS: No associations were found between mobile phone and cordless phone calls and emotional and behavioural problems. Children exposed to higher RF-EMF levels from mobile phone base stations showed higher odds of maternal-reported emotional symptoms (OR 1.82, 95%CI 1.07 to 3.09). Children with cordless phone at home had lower odds of teacher-reported problematic prosocial behaviour (OR 0.68, 95%CI 0.48 to 0.97) and of maternal-reported peer relationship problems (OR 0.61, 95% CI 0.39 to 0.96). Children who watched television ≥1.5 h/day had higher odds of maternal-reported hyperactivity/inattention (OR 3.13, 95%CI 1.43 to 6.82). CONCLUSION: Mobile phone and cordless phone calls, which lead to peak RF-EMF exposures to the head, were not associated with any emotional and behavioural problems in 5-year-old children. Environmental RF-EMF exposure from mobile phone base stations and from indoor sources and television watching, which both contribute very little to RF-EMF exposure, were associated with specific emotional and behavioural problems but mainly when reported by the mothers. We cannot, however, discard residual confounding or reverse causality. Further longitudinal research in particular as children will increase the use of telecommunication devices with the age may help to better understand the exact contribution of the different RF-EMF exposure sources if any. Moreover, a thorough control for confounding is essential for a correct interpretation of the studies on screen time and emotional and behavioural problems.

Residential exposure to RF-EMF from mobile phone base stations: Model predictions versus personal and home measurements.

INTRODUCTION: Geospatial models have been demonstrated to reliably and efficiently estimate RF-EMF exposure from mobile phone base stations (downlink) at stationary locations with the implicit assumption that this reflects personal exposure. In this study we evaluated whether RF-EMF model predictions at the home address are a good proxy of personal 48h exposure. We furthermore studied potential modification of this association by degree of urbanisation. METHOD: We first used an initial NISMap estimation (at an assumed height of 4.5m) for 9563 randomly selected addresses in order to oversample addresses with higher exposure levels and achieve exposure contrast. We included 47 individuals across the range of potential RF-EMF exposure and used NISMap to re-assess downlink exposure at the home address (at bedroom height). We computed several indicators to determine the accuracy of the NISMap model predictions. We compared residential RF-EMF model predictions with personal 48h, at home, and night-time (0:00-8:00AM) ExpoM3 measurements, and with EME-SPY 140 spot measurements in the bedroom. We obtained information about urbanisation degree and compared the accuracy of model predictions in high and low urbanised areas. RESULTS: We found a moderate Spearman correlation between model predictions and personal 48h (rSp=0.47), at home (rSp=0.49), at night (rSp=0.51) and spot measurements (rSp=0.54). We found no clear differences between high and low urbanised areas (48h: high rSp=0.38, low rSp=0.55, bedroom spot measurements: high rSp=0.55, low rSp=0.50). DISCUSSION: We achieved a meaningful ranking of personal downlink exposure irrespective of degree of urbanisation, indicating that these models can provide a good proxy of personal exposure in areas with varying build-up.

What input data are needed to accurately model electromagnetic fields from mobile phone base stations?

The increase in mobile communication technology has led to concern about potential health effects of radio frequency electromagnetic fields (RF-EMFs) from mobile phone base stations. Different RF-EMF prediction models have been applied to assess population exposure to RF-EMF. Our study examines what input data are needed to accurately model RF-EMF, as detailed data are not always available for epidemiological studies. We used NISMap, a 3D radio wave propagation model, to test models with various levels of detail in building and antenna input data. The model outcomes were compared with outdoor measurements taken in Amsterdam, the Netherlands. Results showed good agreement between modelled and measured RF-EMF when 3D building data and basic antenna information (location, height, frequency and direction) were used: Spearman correlations were >0.6. Model performance was not sensitive to changes in building damping parameters. Antenna-specific information about down-tilt, type and output power did not significantly improve model performance compared with using average down-tilt and power values, or assuming one standard antenna type. We conclude that 3D radio wave propagation modelling is a feasible approach to predict outdoor RF-EMF levels for ranking exposure levels in epidemiological studies, when 3D building data and information on the antenna height, frequency, location and direction are available.

Environmental Radiofrequency Electromagnetic Fields Exposure at Home, Mobile and Cordless Phone Use, and Sleep Problems in 7-Year-Old Children.

BACKGROUND: We evaluated if exposure to RF-EMF was associated with reported quality of sleep in 2,361 children, aged 7 years. METHODS: This study was embedded in the Amsterdam Born Children and their Development (ABCD) birth cohort study. When children were about five years old, school and residential exposure to RF-EMF from base stations was assessed with a geospatial model (NISMap) and from indoor sources (cordless phone/WiFi) using parental self-reports. Parents also reported their children's use of mobile or cordless phones. When children were seven years old, we evaluated sleep quality as measured with the Child Sleep Habits Questionnaire (CSHQ) filled in by parents. Of eight CSHQ subscales, we evaluated sleep onset delay, sleep duration, night wakenings, parasomnias and daytime sleepiness with logistic or negative binomial regression models, adjusting for child's age and sex and indicators of socio-economic position of the parents. We evaluated the remaining three subscales (bedtime resistance, sleep anxiety, sleep disordered breathing) as unrelated outcomes (negative control) because these were a priori hypothesised not to be associated with RF-EMF. RESULTS: Sleep onset delay, night wakenings, parasomnias and daytime sleepiness were not associated with residential exposure to RF-EMF from base stations. Sleep duration scores were associated with RF-EMF levels from base stations. Higher use mobile phones was associated with less favourable sleep duration, night wakenings and parasomnias, and also with bedtime resistance. Cordless phone use was not related to any of the sleeping scores. CONCLUSION: Given the different results across the evaluated RF-EMF exposure sources and the observed association between mobile phone use and the negative control sleep scale, our study does not support the hypothesis that it is the exposure to RF-EMF that is detrimental to sleep quality in 7-year old children, but potentially other factors that are related to mobile phone usage.

Use of portable exposure meters for comparing mobile phone base station radiation in different types of areas in the cities of Basel and Amsterdam.

BACKGROUND: Radiofrequency electromagnetic fields (RF-EMF) are highly variable and differ considerably within as well as between areas. Exposure assessment studies characterizing spatial and temporal variation are limited so far. Our objective was to evaluate sources of data variability and the repeatability of daily measurements using portable exposure meters (PEMs). METHODS: Data were collected at 12 days between November 2010 and January 2011 with PEMs in four different types of urban areas in the cities of Basel (BSL) and Amsterdam (AMS). RESULTS: Exposure from mobile phone base stations ranged from 0.30 to 0.53 V/m in downtown and business areas and in residential areas from 0.09 to 0.41 V/m. Analysis of variance (ANOVA) demonstrated that measurements from various days were highly reproducible (measurement duration of approximately 30 min) with only 0.6% of the variance of all measurements from mobile phone base station radiation being explained by the measurement day and only 0.2% by the measurement time (morning, noon, afternoon), whereas type of area (30%) and city (50%) explained most of the data variability. CONCLUSIONS: We conclude that mobile monitoring of exposure from mobile phone base station radiation with PEMs is useful due to the high repeatability of mobile phone base station exposure levels, despite the high spatial variation.

Radio-frequency electromagnetic field (RF-EMF) exposure levels in different European outdoor urban environments in comparison with regulatory limits.

BACKGROUND: Concerns of the general public about potential adverse health effects caused by radio-frequency electromagnetic fields (RF-EMFs) led authorities to introduce precautionary exposure limits, which vary considerably between regions. It may be speculated that precautionary limits affect the base station network in a manner that mean population exposure unintentionally increases. AIMS: The objectives of this multicentre study were to compare mean exposure levels in outdoor areas across four different European cities and to compare with regulatory RF-EMF exposure levels in the corresponding areas. METHODS: We performed measurements in the cities of Amsterdam (the Netherlands, regulatory limits for mobile phone base station frequency bands: 41-61 V/m), Basel (Switzerland, 4-6 V/m), Ghent (Belgium, 3-4.5 V/m) and Brussels (Belgium, 2.9-4.3 V/m) using a portable measurement device. Measurements were conducted in three different types of outdoor areas (central and non-central residential areas and downtown), between 2011 and 2012 at 12 different days. On each day, measurements were taken every 4s for approximately 15 to 30 min per area. Measurements per urban environment were repeated 12 times during 1 year. RESULTS: Arithmetic mean values for mobile phone base station exposure ranged between 0.22 V/m (Basel) and 0.41 V/m (Amsterdam) in all outdoor areas combined. The 95th percentile for total RF-EMF exposure varied between 0.46 V/m (Basel) and 0.82 V/m (Amsterdam) and the 99th percentile between 0.81 V/m (Basel) and 1.20 V/m (Brussels). CONCLUSIONS: All exposure levels were far below international reference levels proposed by ICNIRP (International Commission on Non-Ionizing Radiation Protection). Our study did not find indications that lowering the regulatory limit results in higher mobile phone base station exposure levels.

Performance of GPS-devices for environmental exposure assessment.

Integration of individual time-location patterns with spatially resolved exposure maps enables a more accurate estimation of personal exposures to environmental pollutants than using estimates at fixed locations. Current global positioning system (GPS) devices can be used to track an individual's location. However, information on GPS-performance in environmental exposure assessment is largely missing. We therefore performed two studies. First, a commute-study, where the commute of 12 individuals was tracked twice, testing GPS-performance for five transport modes and two wearing modes. Second, an urban-tracking study, where one individual was tracked repeatedly through different areas, focused on the effect of building obstruction on GPS-performance. The median error from the true path for walking was 3.7 m, biking 2.9 m, train 4.8 m, bus 4.9 m, and car 3.3 m. Errors were larger in a high-rise commercial area (median error=7.1 m) compared with a low-rise residential area (median error=2.2 m). Thus, GPS-performance largely depends on the transport mode and urban built-up. Although ~85% of all errors were <10 m, almost 1% of the errors were >50 m. Modern GPS-devices are useful tools for environmental exposure assessment, but large GPS-errors might affect estimates of exposures with high spatial variability.

Q fever and pneumonia in an area with a high livestock density: a large population-based study.

Concerns about public health risks of intensive animal production in The Netherlands continue to rise, in particular related to outbreaks of infectious diseases. The aim was to investigate associations between the presence of farm animals around the home address and Q fever and pneumonia.Electronic medical record data for the year 2009 of all patients of 27 general practitioners (GPs) in a region with a high density of animal farms were used. Density of farm animals around the home address was calculated using a Geographic Information System. During the study period, a large Q fever outbreak occurred in this region. Associations between farm exposure variables and pneumonia or 'other infectious disease', the diagnosis code used by GPs for registration of Q fever, were analyzed in 22,406 children (0-17 y) and 70,142 adults (18-70 y), and adjusted for age and sex. In adults, clear exposure-response relationships between the number of goats within 5 km of the home address and pneumonia and 'other infectious disease' were observed. The association with 'other infectious disease' was particularly strong, with an OR [95%CI] of 12.03 [8.79-16.46] for the fourth quartile (>17,190 goats) compared with the first quartile (<2,251 goats). The presence of poultry within 1 km was associated with an increased incidence of pneumonia among adults (OR [95%CI] 1.25 [1.06-1.47]).A high density of goats in a densely populated region was associated with human Q fever. The use of GP records combined with individual exposure estimates using a Geographic Information System is a powerful approach to assess environmental health risks.