Soil is extremely important for providing food, biomass and raw materials, water and nutrient storage; supporting biodiversity and providing foundations for man-made structures. However, its health is threatened by human activities, which can greatly affect the potential of soils to fulfil their functions and, consequently, result in environmental, economic and social damage. These issues require the characterisation of the impact and spatial extent of the problems. This can be achieved through the creation of detailed and comprehensive soil maps that describe both the spatial and vertical variability of key soil properties. Detailed three-dimensional (3D) digital soil maps can be readily used and embedded into environmental models. Three-dimensional soil mapping is not a new concept. However, only with the recent development of more powerful computers has it become feasible to undertake such data processing. Common techniques to estimate soil properties in the three-dimensional space include geostatistical interpolation, or a combination of depth functions and geostatistics. However, these two methods are both partially flawed. Geostatistical interpolation and kriging in particular, estimate soil properties in unsampled locations using a weighted average of the nearby observations. In order to produce the best possible estimate, this form of interpolation minimises the variance of each weighted average, thus decreasing the standard deviation of the estimates, compared to the soil observations. This appears as a smoothing effect on the data and, as a consequence, kriging interpolation is not reliable when the dataset is not sampled with a sampling designs optimised for geostatistics. Depth function approaches, as they are generally applied in literature, implement a spline regression of the soil profile data that aims to better describe the changes of the soil properties with depth. Subsequently, the spline is resampled at determined depths and, for each of these depths, a bi-dimensional (2D) geostatistical interpolation is performed. Consequently, the 3D soil model is a combination of a series of bi-dimensional slices. This approach can effectively decrease or eliminate any smoothing issues, but the way in which the model is created, by combining several 2D horizontal slices, can potentially lead to erroneous estimations. The fact that the geostatistical interpolation is performed in 2D implies that an unsampled location is estimated only by considering values at the same depth, thus excluding the vertical variability from the mapping, and potentially undermining the accuracy of the method. For these reasons, the literature review identified a clear need for developing, a new method for accurately estimating soil properties in 3D – the target of this research, The method studied in this thesis explores the concept of soil specific depth functions, which are simple mathematical equations, chosen for their ability to describe the general profile pattern of a soil dataset. This way, fitting the depth function to a particular sample becomes a diagnostic tool. If the pattern shown in a particular soil profile is dissimilar to the average pattern described by the depth function, it means that in that region there are localised changes in the soil profiles, and these can be identified from the goodness of fit of the function. This way, areas where soil properties have a homogeneous profile pattern can be easily identified and the depth function can be changed accordingly. The application of this new mapping technique is based on the geostatistical interpolation of the depth function coefficients across the study area. Subsequently, the equation is solved for each interpolated location to create a 3D lattice of soil properties estimations. For this way of mapping, this new methodology was denoted as top-down mapping method. The methodology was assessed through three case studies, where the top-down mapping method was developed, tested, and validated. Three datasets of diverse soil properties and at different spatial extents were selected. The results were validated primarily using cross-validation and, when possible, by comparing the estimates with independently sampled datasets (independent validation). In addition, the results were compared with estimates obtained using established literature methods, such as 3D kriging interpolation and the spline approach, in order to define some basic rule of application. The results indicate that the top-down mapping method can be used in circumstances where the soil profiles present a pattern that can be described by a function with maximum three coefficients. If this condition is met, as it was with key soil properties during the research, the top-down mapping method can be used for obtaining reliable estimates at different spatial extents.

Thesis (M. Phil.)--University of Hong Kong, 1993.

Thesis (M.Phil.)--University of Hong Kong, 1993. / Also available in print.

Master of Science / Department of Chemical Engineering / Larry Erickson / Urban soils contaminated with lead can pose a health risk if vegetables and fruits from the garden are consumed. In general, we don’t think our gardens as dangerous or toxic, but unfortunately some garden soils do contain toxic levels of lead. Chipping paint around older structures will raise the lead level in the soils directly adjacent to the building. Restrictions to lead paint started in the 1950’s. Today lead paint content has been reduced; however paint companies are allowed to mix up to 0.05% lead in paints. Lead use has been reduced significantly, but not entirely eliminated. Soil can be contaminated with lead from other sources such as industrial sites, industrial sludge with heavy metals, auto emissions, old lead plumbing pipes or even old orchard sites in production when lead arsenate was in use. The main concern with lead in firing ranges is the fate and transport of heavy metals from bullets fragments accumulating in soil. Of these metals, lead is the predominant contaminant. Lead is considered the top environmental threat to children’s health. The U.S. military alone has cleaned up more than 700 firing ranges across the country over the past several years. The U.S. Air Force conducted a study at Shaw Air Force base to determine the lead concentrations in ground water and soil collected from the Small Arms Firing Range in 1992. The purpose of this study was to determine the levels of contamination in the soil in order to develop a restoration plan. The goal of the restoration plan was to clean up the land for future use. The Defense Environmental Restoration Program (DERP) conducted a project at Beale Air Force Base to clean up contaminated lead soil and to prevent any future fine and environmental expenses for the base. The main goal was to protect the base population from the lead and other contaminants hazards. In 1992 the Air Force conducted an investigation that included environmental sampling of soil and lead of the Tyndall Elementary School grounds. The Air Force collected lead samples in areas where children play on the school ground. Because lead concentrations results were below the toxic levels for lead, the Air Force concluded that no further action was needed. Further investigation for soil removal took part in 1992 and 2009. Under the Critical Removal Action field activities included site preparation, waste characterization, investigative sample chemical analysis, contaminated soil excavation, dust control, disposal, backfill and grading, and site restoration. Over the years the Air Force has been able to educate the military community on health hazards in the base facilities especially lead exposure and have been able to implement programs dedicated to prevent any lead overexposure.

Digitized by Kansas Correctional Industries

Call number: LD2668 .T4 1944 F61 / Master of Science

Call number: LD2668 .T4 1958 T43

Call number: LD2668 .T4 1959 E93

Call number: LD2668 .T4 1961 F87

Mana computer programs have been developed for solving slope stability problems. Since slope stability problems can be characterized as optimization problems, many optimization techniques can be used for searching for the lowest safety factor for a given problem and the corresponding critical slip surface. Most of the slope stability programs use the direct search method which requires only the function value (i.e., safety factor value). In this thesis, a new optimization technique, the Broyden (1970), Fletcher (1970), Goldfarb (1970), and Shanno (1970) (BFGS) quasi-Newton optimization method, is used in conjunction with the STABR program of Lefebvre (1971) to solve slope stability problems. This method of optimization requires the function value and the first derivative value, which can be found by the finite difference method. A new program CSLIP3, incorporating the BFGS technique, is used to solve a variety of realistic slope stability problems. It is determined that CSLIP3 is reliable and efficient.