Estimating Personal Exposures from a Multi-Hazard Sensor Network

Details

• Personal Author:
  Gray A ; Koehler K ; Peters T ; Sousan S ; Stebounova LV ; Stroh O ; Thomas G ; Zuidema C
• Description:
  Occupational exposure assessment is almost exclusively accomplished with personal sampling. However, personal sampling can be burdensome and suffers from low sample sizes, resulting in inadequately characterized workplace exposures. Sensor networks offer the opportunity to measure occupational hazards with a high degree of spatiotemporal resolution. Here, we demonstrate an approach to estimate personal exposure to respirable particulate matter (PM), carbon monoxide (CO), ozone (O3), and noise using hazard data from a sensor network. We simulated stationary and mobile employees that work at the study site, a heavy-vehicle manufacturing facility. Network-derived exposure estimates compared favorably to measurements taken with a suite of personal direct-reading instruments (DRIs) deployed to mimic personal sampling but varied by hazard and type of employee. The root mean square error (RMSE) between network-derived exposure estimates and personal DRI measurements for mobile employees was 0.15 mg/m3, 1 ppm, 82 ppb, and 3 dBA for PM, CO, O3, and noise, respectively. Pearson correlation between network-derived exposure estimates and DRI measurements ranged from 0.39 (noise for mobile employees) to 0.75 (noise for stationary employees). Despite the error observed estimating personal exposure to occupational hazards it holds promise as an additional tool to be used with traditional personal sampling due to the ability to frequently and easily collect exposure information on many employees. [Description provided by NIOSH]
• Subjects:
  Carbon Monoxide Noise Noise, Occupational Occupational Exposure Occupational Health Ozone Particulate Matter Safety
• Keywords:
  Area Sampling Author Keywords: Low-cost Sensors Carbon Monoxide Exposure Assessment Noise Exposure Occupational Exposure Particulates Particulate Sampling Methods Personal Sampling Sampling Sensor Networks Sensors

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• ISSN:
  1559-0631
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Iowa ; Maryland ; North Carolina ; OSHA Region 10 ; OSHA Region 3 ; OSHA Region 4 ; OSHA Region 7 ; Washington
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  30
• Issue:
  6
• NIOSHTIC Number:
  nn:20058265
• Citation:
  J Expo Sci Environ Epidemiol 2020 Nov; 30(6):1013-1022
• Contact Point Address:
  Kirsten Koehler, PhD, Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
• Email:
  kirsten.koehler@jhu.edu
• CAS Registry Number:
  Carbon monoxide (CAS RN 630-08-0) ; Ozone (CAS RN 10028-15-6)
• Federal Fiscal Year:
  2021
• Performing Organization:
  Johns Hopkins University, Baltimore, Maryland
• Peer Reviewed:
  True
• Start Date:
  20140901
• Source Full Name:
  Journal of Exposure Science and Environmental Epidemiology
• End Date:
  20180831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:c90278342d1b205203368f509c5c077aaf0c2b78a45ecd0bc58b4305765647743a29e4824743004ddc8eab7dbd456bccc81fac6f5df318d289f95e9ac212e0b9
• Download URL:
  https://stacks.cdc.gov/view/cdc/221395/cdc_221395_DS1.pdf
• File Type:
  [PDF - 1.21 MB ]