## 4 Discussion  
In this study, we compared radiofrequency electromagnetic field (RF-EMF) exposure measurements obtained using a broadband meter (BBM) and a personal exposimeter (PEM). The primary objective was to evaluate the necessary adjustments for making personal exposimeter measurements comparable to those from broadband meters when developing exposure maps. We analyzed the data collected and studied two correction approaches: a single correction factor and a double correction factor, depending on the existence of direct line of sight (LOS) to base stations. Additionally, we implemented a genetic algorithm to enhance the accuracy of exposure maps generated from PEM measurements and obtaining a range of values of the tested proportionality factors. Measurements made with the PEM tend to underestimate the value corresponding to the measurement site. This is due to the shielding of the body over the meter [34], which implies a lower final value. In the work of [PERSON] et al., a single correction factor is proposed over the whole set of measurements. Due to this shielding of the body, especially in the direct vision measurements, we proposed different correction depending on whether the measurement is made under LOS conditions to the BTS. If so, there is a predominant contribution in the received electric field due to this component, and its possible shielding significantly reduces the value measured by the PEM. When there is NLOS, the measured field value is due to several secondary contributions coming from the multipath of the signal, so that the possible shielding of some of them has less influence on the measured value. According to this hypothesis, we calculated correction factors for both cases. Considering a single factor gives a weighting factor for the measures of 1.557. This factor is consistent with that obtained by [PERSON] et al. (1.06 to 1.57) [32]. On the other hand, if the dataset is divided, a correction factor of 1.650 is obtained for LOS measurements and 1.380 for the rest. This second correction (PEM-2) achieves a better fit of the statistical values of the dataset between the BBM and the PEM. However, when it comes to representing these values on an exposure map, significant differences are observed both in the maximum exposure values obtained on the map and in their spatial variations when moving away from them. These types of corrections are based on equalizing the mean values of datasets or subsets of data. Since the final objective is the representation of exposure maps, an optimization was tested using a genetic algorithm that tries to minimize the differences between the exposure maps to be generated. The results obtained indicate that the differences between the proportionality constants for both subsets of data are more extreme than in the case of the calculation of the mean, even exceeding the limits determined by [PERSON] for a single correction. The largest differences in the exposure maps are obtained around the maxima, especially in the cases of the Bolte correction (PEM-1). For this reason, a different strategy was tested, which focuses on matching the spatial representations of exposure rather than just equalizing the average values of the data. A genetic algorithm was applied to optimize the proportionality constants between the LOS and NLOS measurement subsets. The goal was to minimize the differences between the exposure maps generated with BBM and the corrected PEM measurements. This approach allowed for more precise corrections, particularly in urban environments where the exposure can vary significantly depending on the presence of direct line of sight to base stations. By adjusting these factors through an evolutionary process, we aimed to produce exposure maps that were more consistent and reflective of the actual RF-EMF distribution. Results from the genetic algorithm show a promising improvement in the spatial accuracy of the exposure maps.