Mapping Occupational Hazards with a Multi-sensor Network in a Heavy-Vehicle Manufacturing Facility. Issue 3 (22nd January 2019)

• Record Type: Journal Article
• Authors: Zuidema, Christopher; Sousan, Sinan; Stebounova, Larissa V; Gray, Alyson; Liu, Xiaoxing; Tatum, Marcus; Stroh, Oliver; Thomas, Geb; Peters, Thomas; Koehler, Kirsten
• Is Part Of: Annals of work exposures and health. Volume 63:Issue 3(2019)
• Page Start: 280
• Page End: 293
• Publication Date: 2019-01-22
• Subjects: air quality monitoring network -- carbon monoxide -- hazard mapping -- internet of things -- nitrogen dioxide -- noise -- ozone -- particulate matter; Medicine, Industrial -- Periodicals; Industrial hygiene -- Periodicals; 613.6205
• DOI: 10.1093/annweh/wxy111
• Languages: English
• ISSNs: 2398-7316

Abstract

Abstract: 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.