A new Bayesian method is developed to identify the spatial distribution of permeabilities. In addition to sparsely sampled permeability and pressure data, this approach incorporates densely sampled seismic velocity data along with semiempirical relationships between seismic and hydraulic soil properties. The procedure consists first of performing a hydrological inversion based solely on the permeability and pressure data. In light of the available seismic data, the velocity-permeabilitypressure relationships are then used to update, in a Bayesian sense, the image of the permeability field. To investigate the usefulness of this approach, synthetic case studies are performed. These studies demonstrate that, even when the seismic data are corrupted by a significant level of error, a joint geophysical-hydrological inversion can produce improved images of permeability. Moreover, this paper derives rigorously the bounds on the error that can be tolerated in seismic velocities such that they are still useful for hydrological purposes.

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[1] Hydrologists routinely analyze pumping test data using conventional interpretation methods that are based on the assumption of homogeneity and that, consequently, yield single estimates of representative flow parameters. However, natural subsurface formations are intrinsically heterogeneous, and hence, the flow parameters influencing the drawdown vary as the cone of depression expands in time. In this paper a novel procedure for the analysis of pumping tests in heterogeneous confined aquifers is developed. We assume that a given heterogeneous aquifer can be represented by a homogeneous system whose flow parameters evolve in time as the pumping test progresses. At any point in time, the interpreted flow parameters are estimated using the ratio of the drawdown and its derivative observed at that particular time. The procedure is repeated for all times, yielding timedependent estimates of transmissivity T i (t) and storativity, S i (t). Based on the analysis of the sensitivity of drawdown to inhomogeneities in the T field, the time-dependent interpreted transmissivity values are found to be a good estimate of T g (r), the geometric mean of the transmissivity values encompassed within a progressively increasing radius r from the well. The procedure is illustrated for Gaussian heterogeneous fields with ln(T) variances up to a value of 2. The impact of the separation distance between the pumping well and observation point on data interpretation is discussed. The results show that information about the spatial variability of the transmissivity field can be inferred from time-drawdown data collected at a single observation point.Citation: Copty, N. K., P. Trinchero, and X. Sanchez-Vila (2011), Inferring spatial distribution of the radially integrated transmissivity from pumping tests in heterogeneous confined aquifers, Water Resour. Res., 47, W05526,

In recent years, biosorption is being considered as an environmental friendly technology for the recovery of rare earth metals (REE). This study investigates the optimal conditions for the biosorption of neodymium (Nd) from an aqueous solution derived from hard drive disk magnets using green microalgae (Chlorella vulgaris). The parameters considered include solution pH, temperature and biosorbent dosage. Best-fit equilibrium as well as kinetic biosorption models were also developed. At the optimal pH of 5, the maximum experimental Nd uptakes at 21, 35 and 50°C and an initial Nd concentration of 250 mg/L were 126.13, 157.40 and 77.10 mg/g, respectively. Analysis of the optimal equilibrium sorption data showed that the data fitted well (R2 = 0.98) to the Langmuir isotherm model, with maximum monolayer coverage capacity (qmax) of 188.68 mg/g, and Langmuir isotherm constant (KL) of 0.029 L/mg. The corresponding separation factor (RL) is 0.12 indicating that the equilibrium sorption was favorable. The sorption kinetics of Nd ion follows well a pseudo-second order model (R2>0.99), even at low initial concentrations. These results show that Chlorella vulgaris has greater biosorption affinity for Nd than activated carbon and other algae types such as: A. Gracilis, Sargassum sp. and A. Densus.

The purpose of this study was to investigate the impact of overland traffic on the spatial distribution of heavy metals in urban soils (Istanbul, Turkey). Road dust, surface, and subsurface soil samples were collected from a total of 41 locations along highways with dense traffic and secondary roads with lower traffic and analyzed for lead (Pb), zinc (Zn), and copper (Cu) concentrations. Statistical evaluation of the heavy metal concentrations observed along highways and along the secondary roads showed that the data were bimodally distributed. The maximum observed Pb, Zn, and Cu concentrations were 1,573, 522 and 136 mg/kg, respectively, in surface soils along highways and 99.3, 156, and 38.1 mg/kg along secondary roads. Correlation analysis of the metal concentrations in road dust, surface and 20-cm depth soils suggests the presence of a common pollution source. However, metal concentrations in the deeper soils were substantially lower than those observed at the surface, indicating low mobility of heavy metals, especially for Pb and Zn. A modified kriging approach that honors the bimodality of the data was used to estimate the spatial distribution of the surface concentrations of metals, and to identify hotspots. Results indicate that despite the presence of some industrial zones within the study area, traffic is the main heavy metal pollution source.