Human exposure to air pollution is associated with increased risk of morbidity and mortality. However, personal air pollution exposures can vary substantially depending on an individual’s daily activity patterns and air quality within their residence and workplace. This work developed and validated an adaptive buffer size (ABS) algorithm capable of dynamically classifying an individual’s time spent in predefined microenvironments using data from global positioning systems (GPS), motion sensors, temperature sensors, and light sensors. Twenty-two participants in Fort Collins, CO were recruited to carry a personal air sampler for a 48-h period. The personal sampler was retrofitted with a GPS and a pushbutton to complement the existing sensor measurements (temperature, motion, light). The pushbutton was used in conjunction with a traditional time-activity diary to note when the participant was located at “home”, “work”, or within an “other” microenvironment. The ABS algorithm predicted the amount of time spent in each microenvironment with a median accuracy of 99.1%, 98.9%, and 97.5% for the “home”, “work”, and “other” microenvironments. The ability to classify microenvironments dynamically in real time can enable the development of new sampling and measurement technologies that classify personal exposure by microenvironment.
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This work was supported by grant ES24719 from the National Institute of Environmental Health Sciences and by grant OH010662 from the National Institute for Occupational Safety and Health. The authors wish to thank all of the participants who volunteered to help collect the samples. Finally, the authors would like to acknowledge those who helped with the development of the UPAS and AMAS: Daniel David Miller-Lionberg, Eric Wendt, Joshua Smith, Nathan Henry, and Nicholas Good.
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The authors declare that they have no conflict of interest.
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Quinn, C., Anderson, G.B., Magzamen, S. et al. Dynamic classification of personal microenvironments using a suite of wearable, low-cost sensors. J Expo Sci Environ Epidemiol 30, 962–970 (2020). https://doi.org/10.1038/s41370-019-0198-2