Estimating Personal Exposures from a Multi-Hazard Sensor Network

摘要

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 using hazard data from a sensor network. We developed a multi-hazard monitor, constructed with low-cost sensors for particulate matter (PM), carbon monoxide (CO), oxidizing gases (OX) and noise (using a sensor developed in-house), and deployed a 40-node network in a heavy-vehicle manufacturing facility. During typical production periods, one-hr mean hazard levels ± standard deviation across all monitors for PM, CO, OX and noise were 0.62 ± 0.2 mg/m3, 7 ± 2 ppm, 155 ± 58 ppb, and 82 ± 1 dBA respectively. Next, we simulated stationary and mobile employees that work at the study site. Network-derived exposure estimates compared favorably to measurements taken with a suite of reference direct-reading instruments (DRIs) deployed to mimic personal sampling but varied by hazard and type of employee. The median magnitude of the percent bias between network-derived exposure estimates and DRI measurements for mobile employees was 33% for PM, 18% for CO, 119% for 03, and 3% for noise. 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.