Need of accurate model prediction of variability of the concentration ratio for testing coherence among environmental quality objectives: a case study of polycyclic aromatic hydrocarbons.

Maintaining coherence among environmental quality objectives (EQOs) should be an important consideration for the EQOs to be met simultaneously. The objectives of the present work were to demonstrate the need of accurate variability prediction by models and to present considerations in selecting models for testing coherence of the EQOs. SimpleBox and POPsME were chosen as the two different types of models to compare the prediction variability and its influence on the results of coherence test among the maximum permissible concentrations (MPCs) of polycyclic aromatic hydrocarbons (PAHs) in South Korea. False calls by these models on coherence were found to occur often due to inaccurate prediction of variability in the concentration ratio at steady state, strongly suggesting that models for coherence test should be accurate in predicting not only the point value representing the concentration ratio but the variability of the value. It was demonstrated that spatially resolved dynamic models would have an intrinsic advantage over one box steady state models in reducing the rate of false negative call.