Suspended particulate grain size dynamics and their implications for trace metal sorption in the Don River

We examined particle size distributions of suspended particulate matter (SPM); physical and environmental influences on the observed distributions; and relationships between particle size and geochemical partitioning of metals, over the fall and winter period in a small urban river (Don River, Toronto, Ontario, Canada). For this dataset, the majority of particles (80%) in suspension were less than 10 µm in size. In addition, while total SPM concentrations showed a positive trend with increasing discharge (Q); the proportions of particles found within given size classes were independent of both SPM concentration and Q. Temperature was the only measured environmental variable related to the particle concentrations within size classes. As water temperature increased, the concentration of particles in the smallest size class (1–4 µm) decreased, while the concentration of silt and/or algae sized particles (10–50 µm) increased. Increasing water temperatures may promote bacterial attachment to particles and their subsequent flocculation into larger sized particles. Decreasing concentrations of leachable (most labile) Cd, Zn and Mn were associated with increasing concentrations of the largest particles (70–150 µm) in suspension. In contrast, higher reducible (oxides) associated concentrations of Cd, Zn and Mn occurred with increasing concentrations of smaller particles (1–10 µm) in suspension. Both of these trends are speculated to reflect the importance of particle surface area for metal sorption reactions.     

Spatial Variability of Rock Depth in Bangalore Using Geostatistical, Neural Network and Support Vector Machine Models

Geospatial technology is increasing in demand for many applications in geosciences. Spatial variability of the bed/hard rock is vital for many applications in geotechnical and earthquake engineering problems such as design of deep foundations, site amplification, ground response studies, liquefaction, microzonation etc. In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 km². In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, Geostatistical model based on Ordinary Kriging technique, Artificial Neural Network (ANN) and Support Vector Machine (SVM) models have been developed. In Ordinary Kriging, the knowledge of the semi-variogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of the Bangalore, where field measurements are not available. A new type of cross-validation analysis developed proves the robustness of the Ordinary Kriging model. ANN model based on multi layer perceptrons (MLPs) that are trained with Levenberg–Marquardt backpropagation algorithm has been adopted to train the model with 90% of the data available. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing loss function has been used to predict the reduced level of rock from a large set of data. In this study, a comparative study of three numerical models to predict reduced level of rock has been presented and discussed.     

Meshless BEM and overlapping Schwarz preconditioners for exterior problems on spheroids

We consider the exterior Neumann problem of the Laplacian with boundary condition on spheroids. We propose to use spherical radial basis functions in the solution of the boundary integral equation arising from the Dirichlet-to-Neumann map. Our meshless approach with radial basis functions is particularly suitable for handling scattered satellite data. We also propose a preconditioning technique based on an overlapping domain decomposition method to deal with ill-conditioned matrices arising from the approximation problem.