Experimental study and modeling of the transfer of zinc in a low reactive sand column in the presence of acetate

Nowadays, it is necessary to understand and identify the reactions governing the fate of heavy metals introduced into the environment with low complexing organic compounds, particularly when they are transferred through soils in urban areas. In this work the concomitant influence of pH and acetate on the fate of zinc on siliceous sand was studied in batch and non-saturated column experiments. Total zinc concentrations varied between 2 and 20 mg/l, and total acetate concentrations were fixed at 22, 72, 132, and 223 mM to obtain solution pHs of 4, 5, 6 and 7, respectively. Natural sand (diameter, 0.3-2 mm), mainly constituted of silica, was used. In batch adsorption experiments, zinc adsorption is insignificant at pH 4, low and linear at pH 5, and increasingly nonlinear, of the Langmuir type, at pH 6 and 7 indicating near-saturation conditions of surface sites at these high pH values. In column experiments, Zn retardation increases and the maximum outlet concentration of Zn decreases with rising pH and acetate concentrations. Previous column tracer experiments revealed the occurrence of regionalized water transport in the column. Modeling these data was based on a non-electrostatic approach. Batch and column data modeling was based on the PHREEQC code that allows concomitant resolution of chemical speciation and regionalized water transport. The speciation calculation indicates that the ZnAcetate+ species is the dominant Zn species in the solutions used. Batch experimental curves are correctly modeled assuming the formation of the three surface species triple bond SiOZn+, triple bond SiOH-Zn Acetate+ and triple bond SiO-Zn(Acetate)2-. The column data could be adequately modeled assuming a two-region water transport and the formation of the same three species with the same thermodynamic constants determined in the batch experiments. The hypothesis of the modeling leads to a slight overestimation of the quantities of zinc eluted (10%) at pH 6 and 7, mostly in the desorption phase. These results show that the methodology used facilitates the correct modeling of both batch and transport experiments and formulation of the hypothesis on the interactions between the low reactive sand and a complex solution.     

Development of a fuzzy-stochastic nonlinear model to incorporate aleatoric and epistemic uncertainty

Site uncertainties significantly influence groundwater flow and contaminant transport predictions. Aleatoric and epistemic uncertainty are both identified in site characterization and represented using proper uncertainty theories. When one theory best represents one parameter whereas a different theory may be more suitable for another parameter, the hybrid propagation of aleatoric (random) and epistemic (nonrandom) uncertainties will occur. The computational challenges of joint propagation of aleatoric and epistemic uncertainty through groundwater flow and contaminant transport models are significant. A fuzzy-stochastic nonlinear model was developed in this paper to incorporate these two types of uncertain site information and reduce the computational cost. The results show that (1) the computational cost using the nonlinear model is reduced compared with that of using the sparse grid algorithm and Monte Carlo methods; (2) the uncertainty of hydraulic conductivity (K) significantly influences the water head and solute distribution at the observation wells compared to other uncertain parameters, such as the storage coefficient and the distribution coefficient (K_d); and (3) the combination of multiple uncertain parameters substantially affects the simulation results. Neglecting site uncertainties may lead to unrealistic predictions.     

Numerical modeling of humic colloid borne americium (III) migration in column experiments using the transport/speciation code K1D and the KICAM model

The humic colloid borne Am(III) transport was investigated in column experiments for Gorleben groundwater/sand systems. It was found that the interaction of Am with humic colloids is kinetically controlled, which strongly influences the migration behavior of Am(III). These kinetic effects have to be taken into account for transport/speciation modeling. The kinetically controlled availability model (KICAM) was developed to describe actinide sorption and transport in laboratory batch and column experiments. Application of the KICAM requires a chemical transport/speciation code, which simultaneously models both kinetically controlled processes and equilibrium reactions. Therefore, the code K1D was developed as a flexible research code that allows the inclusion of kinetic data in addition to transport features and chemical equilibrium. This paper presents the verification of K1D and its application to model column experiments investigating unimpeded humic colloid borne Am migration. Parmeters for reactive transport simulations were determined for a Gorleben groundwater system of high humic colloid concentration (GoHy 2227). A single set of parameters was used to model a series of column experiments. Model results correspond well to experimental data for the unretarded humic borne Am breakthrough.     

A comparison of numerical solutions of partial differential equations with probabilistic and possibilistic parameters for the quantification of uncertainty in subsurface solute transport

Traditionally, uncertainty in parameters are represented as probabilistic distributions and incorporated into groundwater flow and contaminant transport models. With the advent of newer uncertainty theories, it is now understood that stochastic methods cannot properly represent non random uncertainties. In the groundwater flow and contaminant transport equations, uncertainty in some parameters may be random, whereas those of others may be non random. The objective of this paper is to develop a fuzzy-stochastic partial differential equation (FSPDE) model to simulate conditions where both random and non random uncertainties are involved in groundwater flow and solute transport. Three potential solution techniques namely, (a) transforming a probability distribution to a possibility distribution (Method I) then a FSPDE becomes a fuzzy partial differential equation (FPDE), (b) transforming a possibility distribution to a probability distribution (Method II) and then a FSPDE becomes a stochastic partial differential equation (SPDE), and (c) the combination of Monte Carlo methods and FPDE solution techniques (Method III) are proposed and compared. The effects of these three methods on the predictive results are investigated by using two case studies. The results show that the predictions obtained from Method II is a specific case of that got from Method I. When an exact probabilistic result is needed, Method II is suggested. As the loss or gain of information during a probability–possibility (or vice versa) transformation cannot be quantified, their influences on the predictive results is not known. Thus, Method III should probably be preferred for risk assessments.     

A real-time,dynamic early-warning model based on uncertainty analysis and risk assessment for sudden water pollution accidents

A real-time, dynamic, early-warning model (EP-risk model) is proposed to cope with sudden water quality pollution accidents affecting downstream areas with raw-water intakes (denoted as EPs). The EP-risk model outputs the risk level of water pollution at the EP by calculating the likelihood of pollution and evaluating the impact of pollution. A generalized form of the EP-risk model for river pollution accidents based on Monte Carlo simulation, the analytic hierarchy process (AHP) method, and the risk matrix method is proposed. The likelihood of water pollution at the EP is calculated by the Monte Carlo method, which is used for uncertainty analysis of pollutants’ transport in rivers. The impact of water pollution at the EP is evaluated by expert knowledge and the results of Monte Carlo simulation based on the analytic hierarchy process. The final risk level of water pollution at the EP is determined by the risk matrix method. A case study of the proposed method is illustrated with a phenol spill accident in China.     

Source estimation methods for atmospheric dispersion

Both forward and backward transport modeling methods are being developed for characterization of sources in atmospheric releases of toxic agents. Forward modeling methods, which describe the atmospheric transport from sources to receptors, use forward-running transport and dispersion models or computational fluid dynamics models which are run many times, and the resulting dispersion field is compared to observations from multiple sensors. Forward modeling methods include Bayesian updating and inference schemes using stochastic Monte Carlo or Markov Chain Monte Carlo sampling techniques. Backward or inverse modeling methods use only one model run in the reverse direction from the receptors to estimate the upwind sources. Inverse modeling methods include adjoint and tangent linear models, Kalman filters, and variational data assimilation, among others.This survey paper discusses these source estimation methods and lists the key references. The need for assessing uncertainties in the characterization of sources using atmospheric transport and dispersion models is emphasized.     

Evaluating the sensitivity of a subsurface multicomponent reactive transport model with respect to transport and reaction parameters.

The input variables for a numerical model of reactive solute transport in groundwater include both transport parameters, such as hydraulic conductivity and infiltration, and reaction parameters that describe the important chemical and biological processes in the system. These parameters are subject to uncertainty due to measurement error and due to the spatial variability of properties in the subsurface environment. This paper compares the relative effects of uncertainty in the transport and reaction parameters on the results of a solute transport model. This question is addressed by comparing the magnitudes of the local sensitivity coefficients for transport and reaction parameters. General sensitivity equations are presented for transport parameters, reaction parameters, and the initial (background) concentrations in the problem domain. Parameter sensitivity coefficients are then calculated for an example problem in which uranium(VI) hydrolysis species are transported through a two-dimensional domain with a spatially variable pattern of surface complexation sites. In this example, the reaction model includes equilibrium speciation reactions and mass transfer-limited non-electrostatic surface complexation reactions. The set of parameters to which the model is most sensitive includes the initial concentration of one of the surface sites, the formation constant (Kf) of one of the surface complexes and the hydraulic conductivity within the reactive zone. For this example problem, the sensitivity analysis demonstrates that transport and reaction parameters are equally important in terms of how their variability affects the model results.     

Influence of indoor transport and mixing time scales on the performance of sensor systems for characterizing contaminant releases

Optimizing real-time sensor systems to detect and identify relevant characteristics of an indoor contaminant event is a challenging task. The interpretation of incoming sensor data is confounded by uncertainties in building operation, in the forces driving contaminant transport, and in the physical parameters governing transport. In addition, simulation tools used by the sensor interpretation algorithm introduce modeling uncertainties. This paper explores how the time scales inherent in contaminant transport influence the information that can be extracted from real-time sensor data. In particular, we identify three time scales (within room mixing, room-to-room transport, and removal from the building) and study how they affect the ability of a Bayesian Monte Carlo (BMC) sensor interpretation algorithm to identify the release location and release mass from a set of experimental data, recorded in a multi-floor building. The research shows that some limitations in the BMC approach do not depend on details of the models or the algorithmic implementation, but rather on the physics of contaminant transport. These inherent constraints have implications for the design of sensor systems.     

Application of first-order reliability method to modelling the fate and transport of benzene in groundwater

Parameter uncertainty plays a significant role in decision making regarding groundwater contamination and remediation, especially for non-conservative chemicals. This paper presents a probabilistic screening model to accommodate parameter uncertainty in the aquifer media, and physical–chemical parameters, using the first-order reliability method (FORM). The application of the developed model is illustrated on transport of benzene in groundwater. The results matched those obtained using the Monte Carlo simulation method, with a smaller number of functional evaluations. Parametric studies were conducted to estimate the effect of various parameters on the results.     

Data assimilation in the atmospheric dispersion model for nuclear accident assessments

Uncertainty factors in atmospheric dispersion models may influence the reliability of model prediction. The ability of a model in assimilating measurement data will be helpful to improve model prediction. In this paper, data assimilation based on ensemble Kalman filter (EnKF) is introduced to a Monte Carlo atmospheric dispersion model (MCADM) designed for assessment of consequences after an accident release of radionuclides. Twin experiment has been performed in which simulated ground-level dose rates have been assimilated. Uncertainties in the source term and turbulence intensity of wind field are considered, respectively. Methodologies and preliminary results of the application are described. It is shown that it is possible to reduce the discrepancy between the model forecast and the true situation by data assimilation. About 80% of error caused by the uncertainty in the source term is reduced, and the value for that caused by uncertainty in the turbulence intensity is about 50%.     

Priority assessment of toxic substances in life cycle assessment. Part II: assessing parameter uncertainty and human variability in the calculation of toxicity potentials

Toxicity potentials are standard values used in life cycle assessment (LCA) to enable a comparison of toxic impacts between substances. This paper presents the results of an uncertainty assessment of toxicity potentials that were calculated with the global nested multi-media fate, exposure and effects model USES-LCA. The variance in toxicity potentials resulting from input parameter uncertainties and human variability was quantified by means of Monte Carlo analysis with Latin Hypercube sampling (LHS). For Atrazine, 2,3,7,8-TCDD and Lead, variation, expressed by the ratio of the 97.5%-ile and the 2.5%-ile, ranges from about 1.5 to 6 orders of magnitude. The major part of this variation originates from a limited set of substance-specific input parameters, i.e. parameters that describe transport mechanisms, substance degradation, indirect exposure routes and no-effect concentrations. Considerable correlations were found between the toxicity potentials of one substance, in particular within one impact category. The uncertainties and correlations reported in the present study may have a significant impact on the outcome of LCA case studies.     

Multi-component reactive transport modeling of natural attenuation of an acid groundwater plume at a uranium mill tailings site

Natural attenuation of an acidic plume in the aquifer underneath a uranium mill tailings pond in Wyoming, USA was simulated using the multi-component reactive transport code PHREEQC. A one-dimensional model was constructed for the site and the model included advective-dispersive transport, aqueous speciation of 11 components, and precipitation-dissolution of six minerals. Transport simulation was performed for a reclamation scenario in which the source of acidic seepage will be terminated after 5 years and the plume will then be flushed by uncontaminated upgradient groundwater. Simulations show that successive pH buffer reactions with calcite, Al(OH)3(a), and Fe(OH)3(a) create distinct geochemical zones and most reactions occur at the boundaries of geochemical zones. The complex interplay of physical transport processes and chemical reactions produce multiple concentration waves. For SO4(2-) transport, the concentration waves are related to advection-dispersion, and gypsum precipitation and dissolution. Wave speeds from numerical simulations compare well to an analytical solution for wave propagation.     

Contaminant transport in a municipal drinking water supply: a steady-state approach to estimate rate and uncertainty

Contaminant transport is generally considered to be a key factor when assessing and classifying the environmental risk of polluted areas. In the study presented here, a steady-state approach was applied to obtain estimates of the transit time and concentration of the pesticide metabolite BAM (2,6-dichlorobenzoamide) at a site where it is contaminating a municipal drinking water supply. A Monte Carlo simulation technique was used to quantify the uncertainty of the results and to evaluate the sensitivity of the used parameters. The adopted approach yielded an estimated median transit time of 10 y for the BAM transport from the polluted site to the water supply. Soil organic carbon content in the unsaturated zone and the hydraulic conductivity in the saturated zone explained 44% and 23% of the uncertainty in the transit time estimate, respectively. The sensitivity analysis showed that the dilution factor due to regional groundwater flow and the soil organic carbon content at the polluted site explained 53% and 31% of the uncertainty of concentration estimates, respectively. In conclusion, the adopted steady-state approach can be used to obtain reliable first estimates of transit time and concentration, but to improve concentration predictions of degrading contaminants, a dynamic model is probably required.     

Estimating uncertainties and uncertainty contributors of CMB PM2.5 source apportionment results

The chemical mass balance (CMB) model was applied for source apportionment of PM_2.5 in Atlanta in order to explore levels and causes of uncertainties in source contributions. Monte Carlo analysis with Latin hypercube sampling (MC-LHS) was performed to evaluate the source impact uncertainties and quantify how uncertainties in ambient measurement and source profile data affect results. In general, uncertainties in the source profile data contribute more to the final uncertainties in source apportionment results than do those in ambient measurement data. Uncertainty contribution estimates suggest that non-linear interactions among source profiles also affect the final uncertainties although their influence is typically less than uncertainties in source profile data.     

Modelling the closure-related geochemical evolution of groundwater at a former uranium mine

A newly developed reactive transport model was used to evaluate the potential effects of mine closure on the geochemical evolution in the aquifer downgradient from a mine site. The simulations were conducted for the K?nigstein uranium mine located in Saxony, Germany. During decades of operation, uranium at the former mine site had been extracted by in situ acid leaching of the ore underground, while the mine was maintained in a dewatered condition. One option for decommissioning is to allow the groundwater level to rise to its natural level, flooding the mine workings. As a result, pore water containing high concentrations of dissolved metals, radionuclides, and sulfate may be released. Additional contamination may arise due to the dissolution of minerals contained in the aquifer downgradient of the mine. On the other hand, dissolved metals may be attenuated by reactions within the aquifer. The geochemical processes and interactions involved are highly non-linear and their impact on the quality of the groundwater and surface water downstream of the mine is not always intuitive. The multicomponent reactive transport model MIN3P, which can describe mineral dissolution-precipitation reactions, aqueous complexation, and oxidation-reduction reactions, is shown to be a powerful tool for investigating these processes. The predictive capabilities of the model are, however, limited by the availability of key geochemical parameters such as the presence and quantities of primary and secondary mineral phases. Under these conditions, the model can provide valuable insight by means of sensitivity analyses.     

Meteorological Considerations in Air Quality Planning

Results from dispersion models which are routinely used for regulatory purposes do not reflect the uncertainties which are Inherent In the input data. To remedy this, a formula for propagating measurement uncertainties of emission rate, wind speed, wind direction, horizontal dispersion parameter, vertical dispersion parameter, effective emission height, and mixing depth is derived for EPA’s Industrial Source Complex Short Term (ISCST) Gaussian dispersion model for the simple case of a single stack-type source and nonbuoyant plume. Values for the uncertainties of the input variables are chosen and used to calculate ambient concentration uncertainties. These calculated uncertainties are compared with the standard deviation of ambient concentrations calculated from 2500 input data sets for each of four stability classes and three downwind distances, which were randomly altered to simulate the effects of measurement uncertainty. The calculated uncertainties do not differ significantly from the standard deviations of the randomized calculations for input data uncertainties as high as 30 percent and Stability Classes A-C. The calculated uncertainties overestimate the actual uncertainty of model calculations for input data uncertainties greater than 20 percent for Stability Class D.     

Incorporating layer- and local-scale heterogeneities in numerical simulation of unsaturated flow and tracer transport

This study characterizes layer- and local-scale heterogeneities in hydraulic parameters (i.e., matrix permeability and porosity) and investigates the relative effect of layer- and local-scale heterogeneities on the uncertainty assessment of unsaturated flow and tracer transport in the unsaturated zone of Yucca Mountain, USA. The layer-scale heterogeneity is specific to hydrogeologic layers with layerwise properties, while the local-scale heterogeneity refers to the spatial variation of hydraulic properties within a layer. A Monte Carlo method is used to estimate mean, variance, and 5th, and 95th percentiles for the quantities of interest (e.g., matrix saturation and normalized cumulative mass arrival). Model simulations of unsaturated flow are evaluated by comparing the simulated and observed matrix saturations. Local-scale heterogeneity is examined by comparing the results of this study with those of the previous study that only considers layer-scale heterogeneity. We find that local-scale heterogeneity significantly increases predictive uncertainty in the percolation fluxes and tracer plumes, whereas the mean predictions are only slightly affected by the local-scale heterogeneity. The mean travel time of the conservative and reactive tracers to the water table in the early stage increases significantly due to the local-scale heterogeneity, while the influence of local-scale heterogeneity on travel time gradually decreases over time. Layer-scale heterogeneity is more important than local-scale heterogeneity for simulating overall tracer travel time, suggesting that it would be more cost-effective to reduce the layer-scale parameter uncertainty in order to reduce predictive uncertainty in tracer transport.     

Migration of colloids in discretely fractured porous media: effect of colloidal matrix diffusion

Numerical simulations of colloid transport in discretely fractured porous media were performed to investigate the importance of matrix diffusion of colloids as well as the filtration and remobilization of colloidal particles in both the fractures and porous matrix. To achieve this objective a finite element numerical code entitled COLDIFF was developed. The processes that COLDIFF takes into account include advective-dispersive transport of colloids, filtration and remobilization of colloidal particles in both fractures and porous matrix, and diffusive interactions of colloids between the fractures and porous matrix. Three sets of simulations were conducted to examine the importance of parameters and processes controlling colloid migration. First, a sensitivity analysis was performed using a porous block containing a single fracture to determine the relative importance of various phenomenological coefficients on colloid transport. The primary result of the analysis showed that the porosity of the matrix and the process of colloid filtration in fractures play important roles in controlling colloid migration. Second, simulations were performed to replicate and examine the results of a laboratory column study using a fractured shale saprolite. Results of this analysis showed that the filtration of colloidal particles in the porous matrix can greatly affect the tailing of colloid concentrations after the colloid source was removed. Finally, field-scale simulations were performed to examine the effect of matrix porosity, fracture filtration and fracture remobilization on long-term colloid concentration and migration distance. The field scale simulations indicated that matrix diffusion and fracture filtration can significantly reduce colloid migration distance. Results of all three analyses indicated that in environments where porosity is relatively high and colloidal particles are small enough to diffuse out of fractures, the characteristics of the porous matrix that affect colloid transport become more important than those of the fracture network. Because the properties of the fracture network tend to have greater uncertainty due to difficulties in their measurement relative to those of the porous matrix, prediction uncertainties associated with colloid transport in discretely fractured porous media may be reduced.     

Uncertainties in predicted ozone concentrations due to input uncertainties for the UAM-V photochemical grid model applied to the July 1995 OTAG domain

The photochemical grid model, UAM-V, has been used by regulatory agencies to make decisions concerning emissions controls, based on studies of the July 1995 ozone episode in the eastern US. The current research concerns the effect of the uncertainties in UAM-V input variables (emissions, initial and boundary conditions, meteorological variables, and chemical reactions) on the uncertainties in UAM-V ozone predictions. Uncertainties of 128 input variables have been estimated and most range from about 20% to a factor of two. 100 Monte Carlo runs, each with new resampled values of each of the 128 input variables, have been made for given sets of median emissions assumptions. Emphasis is on the maximum hourly-averaged ozone concentration during the 12–14 July 1995 period. The distribution function of the 100 Monte Carlo predicted domain-wide maximum ozone concentrations is consistently close to log-normal with a 95% uncertainty range extending over plus and minus a factor of about 1.6 from the median. Uncertainties in ozone predictions are found to be most strongly correlated with uncertainties in the NO₂ photolysis rate. Also important are wind speed and direction, relative humidity, cloud cover, and biogenic VOC emissions. Differences in median predicted maximum ozone concentrations for three alternate emissions control assumptions were investigated, with the result that (1) the suggested year-2007 emissions changes would likely be effective in reducing concentrations from those for the year-1995 actual emissions, that (2) an additional 50% NO_x emissions reductions would likely be effective in further reducing concentrations, and that (3) an additional 50% VOC emission reductions may not be effective in further reducing concentrations.