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Characterizing percolation patterns in unsaturated zones has posed a greater challenge to numerical modeling investigations than comparable saturated zone studies, because of the heterogeneous nature of unsaturated media as well as the great number of variables impacting unsaturated zone flow. This paper presents an integrated modeling methodology for quantitatively characterizing percolation patterns in the unsaturated zone of Yucca Mountain, Nevada, a proposed underground repository site for storing high-level radioactive waste. It takes into account the multiple coupled processes of air, water, heat flow and chemical isotopic transport in Yucca Mountain s highly heterogeneous, unsaturated fractured tuffs. The modeling approach integrates a wide variety of moisture, pneumatic, thermal, and isotopic geochemical field data into a comprehensive three-dimensional numerical model for modeling analyses. Modeling results are examined against different types of field-measured data and then used to evaluate different hydrogeological conceptual models and their results of flow patterns in the unsaturated zone. In particular, this integration model provides a much clearer understanding of percolation patterns and flow behavior through the unsaturated zone, both crucial issues in assessing repository performance. The integrated approach for quantifying Yucca Mountain s flow system is also demonstrated to provide a comprehensive modeling tool for characterizing flow and transport processes in complex subsurface systems.

Two biodegradation models are developed to represent natural attenuation of fuel-hydrocarbon contaminants as observed in a comprehensive natural-gradient tracer test in a heterogeneous aquifer on the Columbus Air Force Base in Mississippi, USA. The first, a first-order mass loss model, describes the irreversible losses of BTEX and its individual components, i.e., benzene (B), toluene (T), ethyl benzene (E), and xylene (X). The second, a reactive pathway model, describes sequential degradation pathways for BTEX utilizing multiple electron acceptors, including oxygen, nitrate, iron and sulfate, and via methanogenesis. The heterogeneous aquifer is represented by multiple hydraulic conductivity (K) zones delineated on the basis of numerous flowmeter K measurements. A direct propagation artificial neural network (DPN) is used as an inverse modeling tool to estimate the biodegradation rate constants associated with each of the K zones. In both the mass loss model and the reactive pathway model, the biodegradation rate constants show an increasing trend with the hydraulic conductivity. The finding of correlation between biodegradation kinetics and hydraulic conductivity distributions is of general interest and relevance to characterization and modeling of natural attenuation of hydrocarbons in other petroleum-product contaminated sites.

This paper presents a large-scale modeling study characterizing fluid flow and tracer transport in the unsaturated zone of Yucca Mountain, Nevada, the proposed underground repository site for storing high-level radioactive waste. The modeling study is conducted using a three-dimensional numerical model, which incorporates a wide variety of field data and takes into account the coupled processes of flow and transport in Yucca Mountain's highly heterogeneous, unsaturated, fractured porous rock. The modeling approach is based on a dual-continuum formulation. Using different conceptual models of unsaturated flow, various scenarios of current and future climate conditions and their effects on the unsaturated zone are evaluated to aid in the assessment of the repository's system performance. These models are calibrated against field-measured data. Model-predicted flow and transport processes under current and future climates are discussed.

Polyamide (PA) and bisphenol A (BPA) are selected as typical microplastic and endocrine-disrupting chemicals in this study. The adsorption of BPA on the surface of PA and the effect of PA on the transport behavior of BPA in groundwater are systematically investigated using a combination of batch experiments, column experiments and numerical models. The results of scanning electron microscope (SEM) and Fourier transform infrared spectra (FTIR) show that the surface of PA particles is changed significantly after adsorption of BPA. The isothermal adsorption process of BPA can be simulated by the Langmuir model and the Freundlich model. Kinetic adsorption, on the other hand, can be fitted by a quasi-first-order adsorption model, and the adsorption results indicate that the maximum adsorption of BPA on PA reaches 13 mg·g−1. The results of the column experiments suggest that the mass recovery rate of BPA decreases with PA content, and increases with flow velocity, while initial concentration has no apparent influence on BPA transport. In addition, due to the hydrolysis of BPA, the mass recovery rate of BPA does not change with pH under conditions of pH < 10.2 and increases substantially to 94% when pH > 10.2. Moreover, Ca2+ has a significant inhibitory effect on the transport of BPA, while Na+ has no apparent influence on the transport of BPA. The transport process of BPA in porous media is simulated using a single-point kinetic model, and the fitted mathematical relationships for the variation of kinetic parameters with environmental factors are obtained by regression analysis. [ABSTRACT FROM AUTHOR]

This paper investigates the parameter estimation of errors-in-variables Wiener (EIV-W) nonlinear systems. In such nonlinear systems, both input and output contain interference noises, and some intermediate processes are also interfered by noises. The hierarchical technology is applied to decompose the whole system into two subsystems firstly. For the linear subsystem, in order to obtain unbiased estimates of model parameters, a bias compensation method is introduced. Then, the bias-compensated least squares (BLS) algorithm is proposed. For the nonlinear subsystem, on the basis of particle swarm optimisation (PSO), the fuzzy control technology is added to improve the ability of jumping out of the local optimum. Thus, a bias-compensated least squares and fuzzy PSO based hierarchical (BLS-FPSO-H) method is derived at last. In simulation, a numerical example and a case study about the carbon fibre stretching process are implemented. Results indicate that the BLS-FPSO-H algorithm can effectively identify EIV-W nonlinear systems, the convergence speed and identification accuracy are greatly improved than the basic PSO method and some other PSO variants. [ABSTRACT FROM AUTHOR]