Title: Estimating personal exposures from a multi-hazard sensor network.

Authors: Zuidema, Christopher; Stebounova, Larissa V; Sousan, Sinan; Gray, Alyson; Stroh, Oliver; Thomas, Geb; Peters, Thomas; Koehler, Kirsten

Published In J Expo Sci Environ Epidemiol, (2020 11)

Abstract: 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.

PubMed ID: 31164703

MeSH Terms: Air Pollutants*/analysis; Environmental Monitoring; Humans; Manufacturing and Industrial Facilities; Occupational Exposure*/analysis; Particulate Matter/analysis