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KMID : 1156220200460050576
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Journal of Environmental Health Sciences
2020 Volume.46 No. 5 p.576 ~ p.587
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A Study on Analyzing the Validity between the Predicted and Measured Concentrations of VOCs in the Atmosphere Using the CalTOX Model
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Kim Ok
Lee Min-Woo
Park Sang-Hyun
Park Chang-Young
Song Young-Ho
Kim Byeong-Bin
Choi Jin-Ha
Lee Jin-Heon
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Abstract
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Objectives: This study calculated local residents exposures to VOCs (Volatile Organic Compounds) released into the atmosphere using the CalTOX model and carried out uncertainty analysis and sensitivity analysis. The model validity was analyzed by comparing the predicted and the actual atmospheric concentrations.
Methods: Uncertainty was parsed by conducting a Monte Carlo simulation. Sensitivity was dissected with the regression (coefficients) method. The model validity was analyzed by applying r2 (coefficient of determination), RMSE (root mean square error), and the Nash-Sutcliffe EI (efficiency index) formula.
Results: Among the concentrations in the atmosphere in this study, benzene was the highest and the lifetime average daily dose of benzene and the average daily dose of xylene were high. In terms of the sensitivity analysis outcome, the source term to air, exposure time, indoors resting (ETri), exposure time, outdoors at home (ETao), yearly average wind speed (v_w), contaminated area in m2 (Area), active breathing rate (BRa), resting breathing rate (BRr), exposure time, and active indoors (ETai) were elicited as input variables having great influence upon this model. In consequence of inspecting the validity of the model, r2 appeared to be a value close to 1 and RMSE appeared to be a value close to 0, but EI indicated unacceptable model efficiency. To supplement this value, the regression formula was derived for benzene with y=0.002+15.48x, ethylbenzene with y ≡ 0.001+57.240x, styrene with y=0.000+42.249x, toluene with y=0.004+91.588x, and xylene with y=0.000+ 0.007x.
Conclusions: In consequence of inspecting the validity of the model, r2 appeared to be a value close to 1 and RMSE appeared to be a value close to 0, but EI indicated unacceptable model efficiency. This will be able to be used as base data for securing the accuracy and reliability of the model.
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KEYWORD
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Uncertainty analysis, sensitivity analysis, efficiency verification, validity
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