Mapping Occupational Hazards with a Multi-Sensor Network in a Heavy-Vehicle Manufacturing Facility

Details

• Personal Author:
  Gray A ; Koehler K ; Liu X ; Peters T ; Sousan S ; Stebounova LV ; Stroh O ; Tatum M ; Thomas G ; Zuidema C
• Description:
  Due to their small size, low-power demands, and customizability, low-cost sensors can be deployed in collections that are spatially distributed in the environment, known as sensor networks. The literature contains examples of such networks in the ambient environment; this article describes the development and deployment of a 40-node multi-hazard network, constructed with low-cost sensors for particulate matter (SHARP GP2Y1010AU0F), carbon monoxide (Alphasense CO-B4), oxidizing gases (Alphasense OX-B421), and noise (developed in-house) in a heavy-vehicle manufacturing facility. Network nodes communicated wirelessly with a central database in order to record hazard measurements at 5-min intervals. Here, we report on the temporal and spatial measurements from the network, precision of network measurements, and accuracy of network measurements with respect to field reference instruments through 8 months of continuous deployment. During typical production periods, 1-h mean hazard levels +/- standard deviation across all monitors for particulate matter (PM), carbon monoxide (CO), oxidizing gases (OX), and noise were 0.62 +/- 0.2 mg m-3, 7 +/- 2 ppm, 155 +/- 58 ppb, and 82 +/- 1 dBA, respectively. We observed clear diurnal and weekly temporal patterns for all hazards and daily, hazard-specific spatial patterns attributable to general manufacturing processes in the facility. Processes associated with the highest hazard levels were machining and welding (PM and noise), staging (CO), and manual and robotic welding (OX). Network sensors exhibited varying degrees of precision with 95% of measurements among three collocated nodes within 0.21 mg m-3 for PM, 0.4 ppm for CO, 9 ppb for OX, and 1 dBA for noise of each other. The median percent bias with reference to direct-reading instruments was 27%, 11%, 45%, and 1%, for PM, CO, OX, and noise, respectively. This study demonstrates the successful long-term deployment of a multi-hazard sensor network in an industrial manufacturing setting and illustrates the high temporal and spatial resolution of hazard data that sensor and monitor networks are capable of. We show that network-derived hazard measurements offer rich datasets to comprehensively assess occupational hazards. Our network sets the stage for the characterization of occupational exposures on the individual level with wireless sensor networks. [Description provided by NIOSH]
• Subjects:
  Automobiles Carbon Monoxide Manufacturing And Industrial Facilities Noise Noise, Occupational Occupational Health Particulate Matter Safety Welding
• Keywords:
  Author Keywords: Air Quality Monitoring Network Carbon Monoxide Gases Hazard Mapping Hazards Internet Of Things Manufacturing Measurement Equipment Motor Vehicles Nitrogen Dioxide Noise Exposure Noise Levels Noise Propagation Ozone Particulate Matter Particulates Sensors

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• ISSN:
  2398-7308
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Iowa ; Maryland ; New York ; North Carolina ; OSHA Region 2 ; OSHA Region 3 ; OSHA Region 4 ; OSHA Region 7
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  280-293
• Volume:
  63
• Issue:
  3
• NIOSHTIC Number:
  nn:20054528
• Citation:
  Ann Work Expo Health 2019 Apr; 63(3):280-293
• Contact Point Address:
  Kirsten Koehler, Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205
• Email:
  kirsten.koehler@jhu.edu
• CAS Registry Number:
  Carbon monoxide (CAS RN 630-08-0) ; Nitrogen dioxide (CAS RN 10102-44-0)
• Federal Fiscal Year:
  2019
• Performing Organization:
  Johns Hopkins University, Baltimore, Maryland
• Peer Reviewed:
  True
• Start Date:
  20140901
• Source Full Name:
  Annals of Work Exposures and Health
• End Date:
  20180831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:4cafa6097983d7f9c98cf87684221745e47f08438b953c3760e2117ca8f85c17a85f4173d99e7a57b09923e6a8d47ca5e33656518fb2b0b5b274375e9ad483ee
• Download URL:
  https://stacks.cdc.gov/view/cdc/217059/cdc_217059_DS1.pdf
• File Type:
  [PDF - 1.66 MB ]