Firefighters encounter serious occupational risks from burns, smoke inhalation, heat exhaustion, physical and mental stress, and higher incidence of cancer. The National Institute for Occupational Safety and Health (NIOSH) recently released two studies finding that firefighters’ cancer diagnoses are 9% above those of the general public and their cancer-related deaths are 14% greater than the general public (National Fire Protection Association, 2022). Many fire stations are located in populated central areas with close proximity to cell towers, which are a source of radio frequency-electromagnetic field (RF-EMF) exposure. Moreover, firefighters often are equipped with two-way communication radios and these devices are an additional source of RF exposure. A typical frequency used by fire department radio systems is 154 MHz (IAFF, 2008) Currently, the FCC has limits on RF exposure, and the International Commission on Non-Ionizing Radiation Protection has also published recommended exposure limits known as the ICNIRP (ICNIRP, 2010) limits. These limits are based on levels of radio frequency exposure found to cause tissue heating. It is possible that RF exposure below levels that cause tissue heating and tissue damage may cause detrimental health outcomes outside of tissue heating. In 2011, the International Agency for Research on Cancer (IARC) categorized RF as a possible human carcinogen (group 2B) (IARC, 2011). This was based on an increased risk for glioma, a malignant type of brain cancer associated with wireless phone use. Studies have also suggested that RF exposure may affect heart rate variability and sleep quality. (Misek et al., 2018) With the support of the IAFF (International Association of Fire Fighters), this project will measure RF exposure among firefighters in the Puget Sound area and will also analyze their sleep quality data through a validated survey distributed to fire stations in this geographical sampling. The goal of this pilot project is to test the feasibility of a larger scale research project.
Participating fire services included the Seattle Fire Department, Central Pierce Fire and Rescue, Puget Sound Regional Fire Authority, and the Renton Regional Fire Authority. Three of these fire districts were participating from the beginning, and one joined the study partway through. The stations sampled went through a rigorous selection process that was conducted in the programming language “R,” which weighed both their EPA land use classification, as well as their reported number of proximal cell towers. These fire stations had a range of urbanicity as well as a range of proximal cell towers. Once selected, GPS-logged outdoor radio frequency samples were taken. These samples were taken with an omnidirectional antenna and with a directional antenna. Indoor RF samples were taken at 10 of the stations that were monitored for outdoor RF exposure. Because of Covid-19 health concerns, indoor samples were not collected from fire service B. These samples were taken overnight to capture a larger data set. After walking through the fire station, the sampling equipment was placed in an area with measurable RF exposure to collect RF data for either 24 or 72 hours. To analyze these RF monitoring samples, the programming language “R” was again used. The data files were initially imported with a custom “R” function that did various data cleaning processes described in this paper (Grolemund & Wickham, 2011; Wickham, 2011, 2011, 2016; Wickham et al., 2022). The samples were run through another custom function that subset the data to the appropriate frequency range and output: 1. A plot showing the 95th quantile of RF intensity at 0.5mHz intervals 2. An Excel file with the 20 frequencies with highest RF intensities 3. An Excel file detailing the percentage of the INCIRP occupational limit the 95th quantile RF intensities are at After this, a sleep survey was sent to the initial three participating fire services as well as the fourth fire service that joined the study at this point through Redcap. This survey was a modified Pittsburgh Sleep Quality Index survey. The Pittsburgh Sleep Quality Index (PSQI) is an effective instrument used to measure the quality and patterns of sleep in adults.. Our survey was modified to adjust for shift work. We then scored the survey responses based on the PSQI scoring system.
We were able to identify the largest contributing frequencies to RF exposure. These frequencies were around 480 MHz, and 5.8 GHz for the outdoor omnidirectional antenna samples. The highest contributing frequencies for the outdoor directional antenna samples were around 750 MHz, 1.95 GHz, 5.8 GHz, and 8.5 GHz-9 GHz. The highest contributing frequencies for the indoor omnidirectional antenna samples were from 320 MHz-400 MHZ, and 5.8 GHz-5.81 GHz. Radio frequency exposures at all stations surveyed were below 1% of the ICNIRP occupational guideline for both outdoor and indoor samples. Our outdoor omnidirectional RF samples ranged from 0.05% to 0.37% of the ICNIRP occupational guideline. Our outdoor directional RF samples ranged from 0.05% to 0.31% of the ICNIRP occupational guideline. Our indoor overnight omnidirectional antenna samples ranged from 0.10% to 0.35% % of the ICNIRP occupational guideline. Results from our PSQI survey responses indicated that 102 (71%) of our respondents were classified as poor sleepers (PSQI score >= 5). In addition, another metric used to evaluate sleep, short sleep duration, revealed that 53 (37%) of our respondents reported that on average while not on shift, they have short sleep duration.
Results from the outdoor and indoor fire station RF sampling provided a varied range of exposures both between fire stations and within indoor and outdoor samples at any given fire station. This indicates that we were able to collect very low intensity RF exposures at the fire stations. This represents a novel method of sampling for RF exposure that could be used for a larger scale study on RF exposure and potential health outcomes. Results from the outdoor and indoor fire station RF sampling indicate that firefighters employed at the sampled stations are not at risk of tissue heating from RF exposure. We theorize that among the frequencies contributing the most to RF exposure, frequencies between 320 MHZ and 750 MHz correspond to emergency response communication systems, frequencies around 5.8 GHz are attributable to Wi-fi, and frequencies from 8-9 GHz are attributable to cellular phone service providers. (US Department of Transportation, 2017) Participating firefighters who answered the PSQI sleep survey do not have a significantly different proportion of individuals with short sleep duration (37%) than other protective service occupations in the U.S. (38.2%) (Luckhaupt et al., 2010a). We did not find a significant relationship between RF exposure at a fire station and increased PSQI scores in respondents from that fire station. This might be because a relationship between the two variables does not exist, or it might be because our sample size was too small.