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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Estimating personal exposures from a multi-hazard sensor network.

Christopher Zuidema1,2, Larissa V Stebounova3, Sinan Sousan3,4,5

  • 1Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

Journal of Exposure Science & Environmental Epidemiology
|June 6, 2019
PubMed
Summary

Sensor networks can estimate worker exposure to hazards like particulate matter (PM), carbon monoxide (CO), ozone (O3), and noise. This approach offers a promising alternative to traditional personal sampling for better workplace exposure assessment.

Keywords:
area samplingexposure assessmentlow-cost sensorspersonal samplingsensor networks

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Area of Science:

  • Occupational Health and Safety
  • Environmental Monitoring
  • Industrial Hygiene

Background:

  • Personal sampling is the standard for occupational exposure assessment but is often burdensome and yields small sample sizes.
  • This limits the thorough characterization of workplace exposures.
  • Sensor networks offer high spatiotemporal resolution for measuring occupational hazards.

Purpose of the Study:

  • To evaluate an approach for estimating personal exposure to respirable particulate matter (PM), carbon monoxide (CO), ozone (O3), and noise using sensor network data.
  • To compare network-derived exposure estimates with personal direct-reading instrument (DRI) measurements in a simulated manufacturing environment.

Main Methods:

  • A sensor network was deployed in a heavy-vehicle manufacturing facility.
  • Simulated stationary and mobile employees were used to collect exposure data.
  • Network-derived estimates were compared to measurements from personal DRIs.

Main Results:

  • Network-derived exposure estimates showed favorable comparison to personal DRI measurements, with variations by hazard and employee type.
  • Root mean square errors for mobile employees were 0.15 mg/m³ (PM), 1 ppm (CO), 82 ppb (O3), and 3 dBA (noise).
  • Pearson correlations ranged from 0.39 (noise, mobile) to 0.75 (noise, stationary).

Conclusions:

  • Estimating personal exposure to occupational hazards using sensor networks shows promise.
  • This method can complement traditional personal sampling by enabling frequent and easy collection of exposure data for numerous employees.