AAAR 37th Annual Conference October 14 - October 18, 2019 Oregon Convention Center Portland, Oregon, USA
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Estimating Personal Exposures from a Multi-Hazard Sensor Network
KIRSTEN KOEHLER, Christopher Zuidema, Larissa Stebounova, Sinan Sousan, Alyson Gray, Oliver Stroh, Geb Thomas, Thomas Peters, Johns Hopkins Bloomberg School of Public Health
Abstract Number: 574 Working Group: Air Quality Sensors: Low-cost != Low Complexity
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 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 deployed to mimic personal sampling but varied by hazard and type of employee. The RMSE between network-derived exposure estimates and reference measurements for mobile employees was 0.15 mg/m3, 1 ppm, 27 ppb, and 3 dBA for PM, CO, O3and noise, respectively. Pearson correlation varied by hazard in the combined time periods of the mobile routines, it was highest for CO (r = 0.66) and lowest for noise (r = 0.39). Correlation between network-derived exposure estimates and reference measurements ranged from 0.39 (noise for mobile employees) to 0.75 (noise for stationary employees). Despite the error observed, the use of sensor networks to estimate personal exposures easily, frequently, and on many people holds promise as a way to complement sparce data obtained with traditional personal sampling.