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Exposimeters are increasingly applied in bioelectromagnetic research to determine personal radiofrequency electromagnetic field (RF-EMF) exposure. The main advantages of exposimeter measurements are their convenient handling for study participants and the large amount of personal exposure data, which can be obtained for several RF-EMF sources. However, the large proportion of measurements below the detection limit is a challenge for data analysis. With the robust ROS (regression on order statistics) method, summary statistics can be calculated by fitting an assumed distribution to the observed data. We used a preliminary sample of 109 weekly exposimeter measurements from the QUALIFEX study to compare summary statistics computed by robust ROS with a naïve approach, where values below the detection limit were replaced by the value of the detection limit. For the total RF-EMF exposure, differences between the naïve approach and the robust ROS were moderate for the 90th percentile and the arithmetic mean. However, exposure contributions from minor RF-EMF sources were considerably overestimated with the naïve approach. This results in an underestimation of the exposure range in the population, which may bias the evaluation of potential exposure-response associations. We conclude from our analyses that summary statistics of exposimeter data calculated by robust ROS are more reliable and more informative than estimates based on a naïve approach. Nevertheless, estimates of source-specific medians or even lower percentiles depend on the assumed data distribution and should be considered with caution.

We present a geospatial model to predict the radiofrequency electromagnetic field from fixed site transmitters for use in epidemiological exposure assessment. The proposed model extends an existing model toward the prediction of indoor exposure, that is, at the homes of potential study participants. The model is based on accurate operation parameters of all stationary transmitters of mobile communication base stations, and radio broadcast and television transmitters for an extended urban and suburban region in the Basel area (Switzerland). The model was evaluated by calculating Spearman rank correlations and weighted Cohen's kappa (kappa) statistics between the model predictions and measurements obtained at street level, in the homes of volunteers, and in front of the windows of these homes. The correlation coefficients of the numerical predictions with street level measurements were 0.64, with indoor measurements 0.66, and with window measurements 0.67. The kappa coefficients were 0.48 (95%-confidence interval: 0.35-0.61) for street level measurements, 0.44 (95%-CI: 0.32-0.57) for indoor measurements, and 0.53 (95%-CI: 0.42-0.65) for window measurements. Although the modeling of shielding effects by walls and roofs requires considerable simplifications of a complex environment, we found a comparable accuracy of the model for indoor and outdoor points.

We developed a geospatial model that calculates ambient high-frequency electromagnetic field (HF-EMF) strengths of stationary transmission installations such as mobile phone base stations and broadcast transmitters with high spatial resolution in the order of 1 m. The model considers the location and transmission patterns of the transmitters, the three-dimensional topography, and shielding effects by buildings. The aim of the present study was to assess the suitability of the model for exposure monitoring and for epidemiological research. We modeled time-averaged HF-EMF strengths for an urban area in the city of Basel as well as for a rural area (Bubendorf). To compare modeling with measurements, we selected 20 outdoor measurement sites in Basel and 18 sites in Bubendorf. We calculated Pearson's correlation coefficients between modeling and measurements. Chance-corrected agreement was evaluated by weighted Cohen's kappa statistics for three exposure categories. Correlation between measurements and modeling of the total HF-EMF strength was 0.67 (95% confidence interval (CI): 0.33-0.86) in the city of Basel and 0.77 (95% CI: 0.46-0.91) in the rural area. In both regions, kappa coefficients between measurements and modeling were 0.63 and 0.77 for the total HF-EMF strengths and for all mobile phone frequency bands. First evaluation of our geospatial model yielded substantial agreement between modeling and measurements. However, before the model can be applied for future epidemiologic research, additional validation studies focusing on indoor values are needed to improve model validity.

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The spatial variability of different fractions of particulate matter (PM) was investigated in the city of Basel, Switzerland, based on measurements performed throughout 1997 with a mobile monitoring station at six sites and permanently recorded measurements from a fixed site. Additionally, PM 10 measurements from the following year, which were concurrently recorded at two urban and two rural sites, were compared.Generally, the spatial variability of PM 4 , PM 10 , and total suspended particulates (TSP) within this Swiss urban IMPLICATIONS It has become popular in recent years to use the concentration of PM as an indicator of air pollution exposure in epidemiologic studies. Since many of these studies assess the exposure of subjects based on one measurement per city, the accuracy of this technique will markedly affect the result of cross-sectional studies. High spatial variability of PM could result in a large non-differential misclassification of exposure that would lead to a smaller recognized health effect of air pollution.The remarkable spatial homogeneity of long-term mean PM levels clearly reduces the error of assigning data from one fixed monitoring site to all study subjects living in Basel, as was done in recent cross-sectional health studies in Switzerland (Swiss Study on Air Pollution and Lung Diseases in Adluts, Swiss Study on Childhood Allergy and Respiratory Systems). In fact, all participants lived in urban Basel, rendering PM 4 , PM 10 , and even TSP useful city-wide surrogates for long-term exposure to outdoor air pollution. environment (area = 36 km2 ) was rather limited. With the exception of one site in a street canyon next to a traffic light, traffic density had only a weak tendency to increase the levels of PM. Mean PM 10 concentration at six sites with different traffic densities was in the range of less than ±10% of the mean urban PM 10 level. However, comparing the mean PM levels on workdays to that on weekends indicated that the impact of human activities, including traffic, on ambient PM levels may be considerable.Differences in the daily PM 10 concentrations between urban and more elevated rural sites were strongly influenced by the stability of the atmosphere. In summer, when no persistent surface inversions exist, differences between urban and rural sites were rather small. It can therefore be concluded that spatial variability of annual mean PM concentration between urban and rural sites in the Basel area may more likely be caused by varying altitude than by distance to the city center.

Cancer risk as a result of air pollution may be quantified by different approaches. We compared the sum of unit risk based effects of single pollutants with an epidemiology-based method by using PM(10) as a surrogate of the total air pollution. The excess rate for lung cancer cases attributable to an increase of 10 microg/m3 in average PM(10) exposure was estimated from available cohort studies. Applying the epidemiology-based risk method to the air pollution situation in the Basel area (Switzerland) resulted in 13.3 (95% CI = 6.9-19.8) excess lung cancer cases per 100,000 person years. This estimate was considerably higher than the unit risk-based estimate yielding 1.1 (range, 0.45-2.8) cancer cases per 100,000 person years. We discuss these discrepancies in light of inherent differences between approaches in toxicology and epidemiology.