Design and testing of a personalized noise monitoring system.

Agricultural workers are more prone to noise-induced hearing loss than are many other workers. Hearing protection device use among agricultural workers is low, but training can increase hearing protection device use. This work proposes a system designed to automatically inform agricultural workers when they were exposed to noises that exceed the National Institute for Occupational Safety and Health (NIOSH) recommended exposure level. The smartphone-based system worn on the arm uses a noise dosimeter to measure noise exposures throughout the day to within ±2 A-weighted decibels of a Class 2 sound level meter. The device collects location and audio data, which are transferred to a server and presented to the worker on a locally hosted website. The website details noise exposure and helps the worker identify where exposure occurred and what specific tasks exceed NIOSH's recommended exposure limit, putting them at higher risk of noise-induced hearing loss. With this understanding, the worker is expected to adopt behavior changes and better hearing protection use at critical places and times. This pilot study evaluates the accuracy of the noise dosimeter and GPS relative to gold-standard instruments. The system was tested on a farm with outputs compared with gold-standard instruments. A-weighted, 1-sec averaged sound pressure levels and position data were collected while users were performing a variety of tasks indoors and outdoors. The smartphone's external noise dosimeter read within ±2 A-weighted decibels of the Class 2 reference dosimeter 59% of the time. The positioning devices had an average error of sub-4 m. While not perfectly matching gold-standard instruments, the device is capable of identifying and collecting information relative to loud noise events that promote noise-induced hearing loss.

Estimating personal exposures from a multi-hazard sensor network.

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.

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

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.