Previous studies identified the train compartment as the place where people can experience the highest exposure levels (still below the international guideline limits) to electromagnetic fields in the radiofrequency range. Here a possible scenario of a train compartment has been reproduced and characterized, both numerically and experimentally. A good agreement between the simulated electric field distributions and measurements has been found. Results indicate that the higher values of exposure in specific positions inside the train compartment depend on the number of active cell phones, the bad coverage condition, the cell orientation, and the presence of metallic walls. This study shows that the proposed approach, based on the scenarios characterization, may efficiently support the assessment of the individual electromagnetic exposure.
The huge diffusion of communication technologies based on radiofrequency (RF) electromagnetic (EM) fields, such as mobile communications (GSM, UMTS) and wireless data transfer (Wi-Fi, Wi-Max, Bluetooth, ZigBee, etc.), and their massive use in crowded environments, where people last for long time periods, as schools, hospitals, offices, and transportation means, have led to concern on possible health effects of this kind of low-level multiple exposure.
As a consequence, a lot of
A possible cause of these experimental discrepancies is an inadequate dosimetry, so clear guidelines for achieving accurate exposure conditions were proposed for both
For what concerns the epidemiological studies, a recent paper on a possible positive correlation between some kinds of brain cancer and cell phone exposure [
An accurate individual exposure assessment is of fundamental importance not only for quantifying the exposure during the epidemiological studies, but also for choosing the dose levels when designing
A common methodology, mostly used in the exposure assessment for the epidemiological studies, is based on the employment of the exposimeters [
A complementary approach has been proposed in [
In particular, in this paper, a typical train compartment has been chosen as an interesting case study; it has been reproduced and the
In this paper, the specific goal of the train compartment characterization is to test the aforementioned hypotheses underlying the higher exposure levels and to identify possible worst-case exposure conditions.
A more general aim is to suggest an approach based on scenarios identification, to support the assessment of individual EM exposure. The approach is based on the following steps: (i) the identification of a proper scenario on the basis of
This kind of approach can usefully complement other methodologies for the individual exposure assessment, such as the use of exposimeters.
Before moving towards the experimental and numerical characterization, the scenario has to be chosen and reproduced both in laboratory and in computer models. This preliminary activity requires a sequence of steps: identification of the scenario,
choice of typical configurations (mean and/or worst case), reproduction of the scenario.
To reproduce the chosen scenario, in terms of size, materials, and possible positions of the EM sources, a typical compartment of high-speed Italian trains was considered, and main geometrical dimensions and arrangements have been reproduced following the description in [
The experimental scenario was reproduced in the Laboratories of ENEA Casaccia, Technical Unit of Radiation Biology and Human Health (UT-BIORAD).
To reproduce worst-case exposure conditions, the GSM technology was chosen, transmitting higher power levels with respect to the UMTS [
Positioning of the four cell phones (#1–#4) and of the seven measurement points (A–G) inside the train compartment scenario.
The stability of phones emission was previously measured in a fixed point by means of a miniaturized isotropic
The wide band sensor Wandel & Goltermann EMR-300 was employed to measure the root mean square (RMS) of the
The sensors were mounted on a dielectric support, in the seven points (labeled from A to G) of Figure
With the aid of polystyrene supports, each phone was placed in different orientations (horizontal and vertical) leading to four configurations: horizontal: all cell phones in horizontal orientation (see Figure vertical: all cell phones in vertical orientation; mixed1: two facing cell phones in vertical orientation and the other two horizontal; mixed2: two cell phones lying on one diagonal in vertical orientation and the other two horizontal.
Measurements were carried out in the absence and in the presence of metallic panels placed on the floor and laterally at a distance of 65 cm from the cell phones.
All measured values were reported in the text with the associated standard uncertainty coming from the measurement instrumentation specifications and the measured phones power stability. The
The same train scenario was simulated using CST Microwave Studio 2010.
According to [
Numerical model of the cell phone (a) with a detail of the helix antenna (b).
The four cell phones were placed either vertically or horizontally inside an air box 1.4 × 1.7 × 1.6 m3. The total model was solved using the frequency solver at 900 MHz, a mesh of 50 lines per wavelength, and radiation boundary conditions.
To simulate the metallic panels, PEC walls were added at some boundaries of the simulation box, as shown in Figure
Numerical scenario with four cell phones placed in vertical orientation inside a box closed by four PEC walls: on the floor and on three sides. The wall on one side was 105 or 80 cm high to simulate the presence of a window starting at different plausible quotes.
The total
Accounting for the integration volume of the
As a preliminary step, both
Ratio between the
Measurement points |
|
|
---|---|---|
80 cm | 120 cm | |
A | 369 ± 79 | 389 ± 83 |
B | 387 ± 83 | 386 ± 83 |
C | 398 ± 85 | 309 ± 66 |
D | 375 ± 80 | 385 ± 82 |
E | 372 ± 80 | 389 ± 83 |
F | 366 ± 78 | 387 ± 83 |
G | 384 ± 82 | 380 ± 81 |
As already shown in [
In order to find the worst case, the
The
Table
Measurement points |
|
||
---|---|---|---|
#1 or #2 | #3 or #4 | All | |
B | 0.96 ± 0.39 | 1.44 ± 0.21 | 3.05 ± 0.45 |
C | 0.67 ± 0.50 | 1.26 ± 0.19 | 3.43 ± 0.50 |
D | 1.06 ± 0.42 | 1.59 ± 0.23 | 4.54 ± 0.67 |
In the following, all cell phones contemporarily transmitting at the maximum power have been considered as the worst case.
The other condition possibly affecting the individual exposure is the cell phones orientation. Figure
Measured
It is also possible to note that, at 80 cm of quote, the
This behavior is even more evident considering the
Simulated
However, it should be noticed that the numerical values refer to the
To roughly compare numerical and experimental results, the first ones have been rescaled by a factor 4 (measured values are root mean squared and mean power of a GSM signal is 1/8 of the corresponding CW); then the obtained values have been averaged over the integration volume, as described in Section
Comparison between measured and scaled simulated
From simulation results of Figure
Despite these differences among antennas, not accounted for in simulations, experimental and numerical results are almost always inside the error bars and show the same behavior in the explored domain. Such agreement allows us to use simulations to easily and quickly obtain useful support in identifying worst-case conditions.
As a consequence of what is discussed at the end of Section
Figure
Simulated
By lowering the window’s quote of 25 cm, the
The simulated scenario was then reproduced in the laboratory using movable metallic panels. Experimental results confirm the numerical prediction. The comparison between measured values in the presence and in the absence (free space) of metallic walls (Figure
Comparison between the measured
Such results confirm the hypothesis that the presence of metallic walls is one of the causes of the high individual exposure levels inside the train together with the bad coverage conditions that maximize the power emitted.
The numerical model can be further complicated, by changing the positions of the metallic walls in the train and accounting for the presence of passengers and other metallic and dielectric objects, in order to obtain more realistic results.
In this work, a scenario representing a train compartment has been numerically modeled, reproduced in laboratory, and completely characterized.
Results of this study show a good agreement between simulated and measured
Numerical and experimental results confirm that while remaining below the limits imposed by the international regulations [
The effect of the cell phone orientation is also important (vertical cell phones generate higher exposure levels) but only in the points closer to the sources, that is, on the 80 cm plane.
The case study of the train compartment, independently of the particular transmitting technology chosen for the EM sources, confirms the possibility of using the scenario characterization to integrate other methodologies in the individual exposure assessment.
The approach we want to suggest is completely described in the block diagram of Figure
Block diagram describing the proposed approach for the assessment of the individual EM exposure inside a scenario.
The blocks contoured by solid lines summarize all the steps described in this paper, where measurement and simulations are integrated to choose the most interesting scenario and exposure conditions. The subsequent steps are represented in Figure
The authors declare that there is no conflict of interests regarding the publication of this paper.
The authors want to thank G. A. Lovisolo for his support and fruitful discussion on future challenges of scenarios approach.