Performance of exposure models in experimental rom for various thermal conditions, airflow and workerr locations.
Authors
Lee E; Feigley J; Khan J
Source
American Industrial Hygiene Conference and Exposition, June 1-6, 2002, San Diego, California. Fairfax, VA: American Industrial Hygiene Association, 2002 Jun; :53-54
Link
NIOSHTIC No.
20045437
Abstract
Most simple mathematical models now in use for estimating exposure do not account for variation in airflow patterns and thermal conditions. Here measured breathing zone concentrations were compared with model estimates for various physical factors to explore model applicability. Contaminant distribution in the breathing zone (elevation=1.5m) in a 2.86(L)x2.35m(H)x 2.86m(W) room with a contaminant source (99.5% propylene) on a 1-m high pedestal was monitored using photoionization detector for steady conditions. Factorial combinations of two Reynolds number (Re=2870 and 2070), two Archimedes number (Ar=14700 and 3(000) and three worker locations (absent, north of source, east of source) were studied. A mannequin located at 0.5m from the source was used to represent a worker. Concentration estimated from the completely-mixed models for one-zone (CM- 1) and two-zones (CM-2), and the uniform turbulent diffusivity model (UD) were compared with experimental results. Model performance was evaluated in the "near-field" and "farfield" of the source. In the near field for the CM-l, CM-2, and UD models, the mean errors (+/- SD) were- 44(+/-17)%, 28(+/-39)% and 74(+/-106)%. The farfield mean errors for the CM-l and CM-2 were both -17(+/-14.9)% because these models are mathematically identical for the far field. The far-field mean error for the UD model was -20(+/-21)%. Although the mean error in the near field for CM-2 was lower than that for CM-l, the CM-2 estimates showed more variability than the CM-I estimates. In the near field, CM-2 was least biased. Also it was the best model for exposure asssment for protection of workers' health because CM-l tended to underestimate the near-field concentration. UD demonstrated the worst performance in the near field. All three models performed better in the far field. Wlth similar error means and standard deviations.
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