Elsevier

Applied Radiation and Isotopes

Volume 115, September 2016, Pages 267-273
Applied Radiation and Isotopes

Using smartphone as a motion detector to collect time-microenvironment data for estimating the inhalation dose

https://doi.org/10.1016/j.apradiso.2016.06.024Get rights and content

Highlights

  • We constructed the time-microenvironment patterns with 1-min resolution by using a smartphone application.

  • Exposure to 131I at the dry distillation areas may lead to an acute inhalation dose significantly.

  • Using smartphone as a motion detector in indoor exposure monitoring is a reliable method.

Abstract

During the production of iodine-131 from neutron irradiated tellurium dioxide by the dry distillation, a considerable amount of 131I vapor is dispersed to the indoor air. People who routinely work at the production area may result in a significant risk of exposure to chronic intake by inhaled 131I. This study aims to estimate the inhalation dose for individuals manipulating the 131I at a radioisotope production. By using an application installed on smartphones, we collected the time-microenvironment data spent by a radiation group during work days in 2015. Simultaneously, we used a portable air sampler combined with radioiodine cartridges for grabbing the indoor air samples and then the daily averaged 131I concentration was calculated. Finally, the time-microenvironment data jointed with the concentration to estimate the inhalation dose for the workers. The result showed that most of the workers had the annual internal dose in 1÷6 mSv. We concluded that using smartphone as a motion detector is a possible and reliable way instead of the questionnaires, diary or GPS-based method. It is, however, only suitable for monitoring on fixed indoor environments and limited the targeted people.

Introduction

Radioiodine-131 is most commonly used for diagnostics and therapy in medicine. People who are occupationally exposed to an internal uptake of iodine includes medical staffs working at nuclear medicine departments and radiation workers of radioisotopes productions. Routine handling of solutions containing radioiodine may result in a significant risk of exposure of the subjects to chronic intake by inhalation of aerosols (International Atomic Energy Agency IAEA, 1999, Bitar et al., 2013, Carneiro et al., 2015, Vidal et al., 2007, Krajewska and Pachocki, 2013). A published paper showed that the annual internal effective dose for some workers were above 1 mSv and a worker reached 7.66 mSv with high-risk classification; and this worker must be monitored individually (Bitar et al., 2013). One of the situations needed to be monitored for internal exposure is handling of large quantities of radiopharmaceuticals, such as 131I for therapy (IAEA, 1999).

The choice of monitoring method depends on factors such as the availability of instrumentation, the analyzes costs, as well as on the sensitivity that is needed. Two in vitro methods were usually used aiming to assess the internal doses due to inhalation intake of 131I: first way was based on the measurement of 131I activity in 24-h urine samples, and the second one was based on workplace monitoring the 131I aerosol concentration of the indoor air (IAEA, 1999). To model exposure to airborne elements, one uses the conceptually simple approach of matching the locations that each exposed person visits with the time-averaged or dynamic air pollutant concentrations that are thought to exist in each visited location (Klepeis, 2006, Ott, 1982). For obtaining the time people spent in indoor visited locations, researchers used some approaches such as Questionnaire, diaries or GPS-based method (Goldin et al., 2014, Steinle et al., 2013, Carneiro et al., 2015, Glasgow et al., 2014).

Diary was usually used as a conventional tool for construct the time-activity pattern. A limiting factor, however, lay in the diaries that only provided restricted information about indoor environments and personal behavior, so the model produces uncertain values in consequence. The complexity and accuracy of the indoor model are limited by the diary accuracy (Gerharz et al., 2009).

Using GPS-based method may give an undesirable location precision. GPS-generated time-activity has been well tested and provides an average precision of 7 m in typical urban conditions (Nethery et al., 2014). The precision of 7 m may not be suitable for individual exposure monitoring in normal dimension rooms. In addition, the dependence of GPS-accuracy on many factors such as unfavorable or clustered satellite positions, or ionospheric disturbances in a particular local area is the obstacle must be considered. Another major cause of error is obstruction of the satellite signal. Therefore, GPS-errors will place a participant at the wrong location (Beekhuizen et al., 2013).

All the models mentioned above are not including the exact geographical position of the individuals. Researchers who used these methods have to minimize the inconvenience to routine work as well as would get the low accuracy of time-microenvironment (time-ME) data in case of indoor environment monitoring. To avoid these disadvantages, some authors advised that using mobile phone with the help of its application software in tracking environment. Smartphone with inbuilt camera can be used for monitoring fixed places and could lead to more detailed dataset (Broich et al., 2012, Gerharz et al., 2009). Alongside, static air samplers may be used to determine the concentration of airborne radioactive material, which can be combined with site specific assumptions about the physicochemical form of the material and the breathing rate and exposure time of the worker to estimate inhalation intakes (IAEA, 1999).

In this research, we set the Symbian-based smartphone as a motion detector by installing a commercial application named MotionRecorder which is developed by Ton Nam Software (Ton Nam, 2015). This monitoring system uses the built-in camera to detect movements in the surrounding area using an advanced motion detection algorithm without activating the built-in GPS. The application also indicates real-time on the phones display where the movement is detected. The aim of this paper is using low-cost and unwearable devices including a mobile phone's motion detection application and portable air sampler for estimating individual inhalation dose of workers processing 131I production.

Section snippets

Materials and methods

The inhalation dose of workers during the 131I processing period was estimated by using the methodology described in Fig. 1.

Results and discussion

The profiles shown in Fig. 4 are an example from the dataset collected by the motion detectors on July 18th, 2015 illustrating how time-ME distribute in a work day for each individual exposure.

There are eight workers, coded from W1 to W8, participated the radioiodine production in July 18th, 2015. Each individual of this group has a particular work, but it can be divided into three main tasks including distilling, dividing or packing product and supervising. Their work time usually starts at

Conclusions

Studying the human exposure is a laborious work. In this paper, by using an application installed on smartphones, we collected the time–ME data spent by a radiation group during 2015. This also used the portable air sampler which was combined with time data to estimate the individual indoor exposure due to inhalation of 131I vapor for eight workers working at DNRI. Based on the results and experience, we concluded that using a smartphone as a motion detector in collecting time-ME is a feasible

Acknowledgements

We would like to thank the staffs working at the Radioisotope Production Center and the Radiation Protection Center of Da Lat Nuclear Research Institute for their support to this work.

References (19)

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    Carneiro et al. (2015) recently used this type of sampler at a flow rate of 0.06 m3/h to collect air samples from above a hand-made AC sandwich filter in a radiopharmacy laboratory which was capable of assessing the internal dose received due to the radionuclide inhalation mentioned earlier. In contrast, static air samplers (Fig. 1, b) working at flow rates of 2.0 m3/h and 4.2 m3/h are commonly used to monitor air conditions in the workplace (Damien et al., 2015; Hoi et al., 2017, 2016; Schomäcker et al., 2017). For example, LVS have been used by Schomäcker et al. (2017) as an effective tool for evaluating the 131I retention capability of fume hoods in NM facilities and for determining and distinguishing the 131I compounds present in air.

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