Russo P, Cerri G, Vespasiani V. A numerical coefficient for evaluation of the environmental impact of electromagnetic fields radiated by base stations for mobile communications. Bioelectromagnetics. Aug 5, 2010. Ahead of print.

Wireless communication technologies have grown because of the customer demand. This prompted service providers to install more base station (BS) antennas to improve quality of their service. But general public concern about potential adverse health effects is growing in the last several years. A network optimization algorithm used to better position base stations was recently developed by the authors. They propose in this study, a simple technique to evaluate environmental impact of a base station. The numerical coefficient calculated is hoped to assist the regulator and service providers in decision-making policies for new BS sites.

The objective of the analysis is to develop an Electromagnetic Environmental Impact Factor (EEIF) and to provide a global numeric coefficient to determine electromagnetic pollution in an urban area. Also, the model was tested in two case studies representing situations chosen from a real urban area.

The EEIF was developed to easily insert values in a simple algorithm. The EEIF number is determined from electric field intensity values and it can quantify the environmental impact of existing and new BSs using measured data. Some guidelines were followed to develop the EEIF:
(a) The model refers to the Italian law that fixes, for the electric field strength, the value of 6 V/m as a warning level, and a quality task for long-term exposure in the frequency range 0.1MHz to 300 GHz;
(b)  Power of the antenna = maximum power radiated by the antenna;
(c) Most of the assumptions are arbitrary, but are always suggested as precautionary measures;
(d) The EEIF should be a tool for comparing the environmental electromagnetic impact of different BS locations;
(e) The electromagnetic simulations have been carried out using software.

Two case studies using the newly developed application were analyzed. In case study #1, the antenna is mounted on a pole. In case study #2, the antenna is located on a building rooftop.

The first case study analyzes a BS with the following characteristics: radiated power (P), 100W; antenna height (h), 25 m; tilt (y), 38; frequency (f), 900 MHz; antenna gain (G), 14.5 dBi, metallic building walls. The simulation showed that about 10% of the field samples in the radiated accessible area exceeded the legal limit of 6 V/m, so the algorithm rejected the configuration. A second simulation with the antenna height increased to a height of 30 metres, with all other parameters unchanged showed that all field values were within the allowable limit. The calculated EEIF was 56.

The second case study analyzes a BS with the following characteristics: radiated power (P) 100W; antenna height (h) 7m on the building rooftop; tilt (y), 38; frequency (f), 900 MHz; antenna gain (G), 14.5 dBi, metallic ground and building walls. All field values were within the allowable range. The calculated EEIF was 60.

The model simulation indicates that case study #1 has a better configuration with respect to the safety for the exposed population to EMF. Also, the results of the simulations indicate the capability of the algorithm to precisely assess two very similar scenarios. The EEIF is also robust enough to be quite independent of the accuracy of the methodology used to determine the electric field. The random error was between -20% and +20% to the field samples of the two case studies. The results in all trials for case study #1 are always 56, and case study #2 remains 60 for the allowable configurations. It was concluded that results of case study #1 are always better than case study #2.

The EEIF is a method to characterize the electromagnetic pollution level in an urban area. Its numerical value depends only on EMF, a physical quantity which can be easily obtained. The authors concluded that this characteristic makes the EEIF appropriate to assess the electromagnetic impact of existing BSs, and to determine in the design stage, the best location for a new BS.


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