Assessing titanium dioxide nanoparticles transport models by Bayesian uncertainty analysis - Liu, Jin; Zeng, Xiankui; Wu, Jichun; Liang, Xiuyu; Sun, Yuanyuan; Zhan, Hongbin (Stochastic environmental research and risk assessment, 2018)
With the rapid growth of nanotechnology industry, nanomaterials as an emerging pollutant are gradually released into subsurface environments and become great concerns. Simulating the transport of nanomaterials in groundwater is an important approach to investigate and predict the impact of nanomaterials on subsurface environments. Currently, a number of transport models are used to simulate this process, and the outputs of these models could be inconsistent with each other due to conceptual model uncertainty. However, the performances of different models on simulating nanoparticles transport in groundwater are rarely assessed in Bayesian framework in previous researches, and these will be the primary objective of this study. A porous media column experiment is conducted to observe the transport of Titanium Dioxide Nanoparticles (nano-TiO 2 ). Ten typical transport models which consider different chemical reaction processes are used to simulate the transport of nano-TiO 2 , and the observed nano-TiO 2 breakthrough curves data are used to calibrate these models. For each transport model, the parameter uncertainty is evaluated using Markov Chain Monte Carlo, and the DREAM (ZS) algorithm is used to sample parameter probability space. Moreover, the Bayesian model averaging (BMA) method is used to incorporate the conceptual model uncertainty arising from different chemical reaction based transport models. The results indicate that both two-sites and nonequilibrium sorption models can well reproduce the retention of nano-TiO 2 transport in porous media. The linear equilibrium sorption isotherm, first-order degradation, and mobile-immobile models fail to describe the nano-TiO 2 retention and transport. The BMA method could instead provide more reliable estimations of the predictive uncertainty compared to that using a single model.