Lessons learnt on biases and uncertainties in personal exposure measurement surveys of radiofrequency electromagnetic...

Personal exposure measurements of radio frequency electromagnetic fields are important for epidemiological studies and developing prediction models. Minimizing biases and uncertainties and handling spatial and temporal variability are important aspects of these measurements. This paper reviews the lessons learnt from testing the different types of exposimeters and from personal exposure measurement surveys performed between 2005 and 2015. Applying them will improve the comparability and ranking of exposure levels for different microenvironments, activities or (groups of) people, such that epidemiological studies are better capable of finding potential weak correlations with health effects. Over 20 papers have been published on how to prevent biases and minimize uncertainties due to: mechanical errors; design of hardware and software filters; anisotropy; and influence of the body. A number of biases can be corrected for by determining multiplicative correction factors. In addition a good protocol on how to wear the exposimeter, a sufficiently small sampling interval and sufficiently long measurement duration will minimize biases. Corrections to biases are possible for: non-detects through detection limit, erroneous manufacturer calibration and temporal drift. Corrections not deemed necessary, because no significant biases have been observed, are: linearity in response and resolution. Corrections difficult to perform after measurements are for: modulation/duty cycle sensitivity; out of band response aka cross talk; temperature and humidity sensitivity. Corrections not possible to perform after measurements are for: multiple signals detection in one band; flatness of response within a frequency band; anisotropy to waves of different elevation angle. An analysis of 20 microenvironmental surveys showed that early studies using exposimeters with logarithmic detectors, overestimated exposure to signals with bursts, such as in uplink signals from mobile phones and WiFi appliances. Further, the possible corrections for biases have not been fully applied. The main findings are that if the biases are not corrected for, the actual exposure will on average be underestimated.