Simulating dioxane transport in a heterogeneous glacial aquifer system (Washtenaw County, Michigan) using publicly available models and data

The primary challenge in groundwater and contaminant transport modeling is obtaining the data needed for constructing, calibrating and testing the models. Large amounts of data are necessary for describing the hydrostratigraphy in areas with complex geology. Increasingly states are making spatial data available that can be used for input to groundwater flow models. The appropriateness of this data for large-scale flow systems has not been tested. This study focuses on modeling a plume of 1,4-dioxane in a heterogeneous aquifer system in Scio Township, Washtenaw County, Michigan. The analysis consisted of: (1) characterization of hydrogeology of the area and construction of a conceptual model based on publicly available spatial data, (2) development and calibration of a regional flow model for the site, (3) conversion of the regional model to a more highly resolved local model, (4) simulation of the dioxane plume, and (5) evaluation of the model's ability to simulate field data and estimation of the possible dioxane sources and subsequent migration until maximum concentrations are at or below the Michigan Department of Environmental Quality's residential cleanup standard for groundwater (85 ppb). MODFLOW-2000 and MT3D programs were utilized to simulate the groundwater flow and the development and movement of the 1, 4-dioxane plume, respectively. MODFLOW simulates transient groundwater flow in a quasi-3-dimensional sense, subject to a variety of boundary conditions that can simulate recharge, pumping, and surface-/groundwater interactions. MT3D simulates solute advection with groundwater flow (using the flow solution from MODFLOW), dispersion, source/sink mixing, and chemical reaction of contaminants. This modeling approach was successful at simulating the groundwater flows by calibrating recharge and hydraulic conductivities. The plume transport was adequately simulated using literature dispersivity and sorption coefficients, although the plume geometries were not well constrained.

THE DISPERSION AND SELECTIVE FLOCCULATION OF HEMATITE ORE

Iron ore is one of the most important ores in the world. Over the past century, most mining of iron ore has been focused on magnetite (Fe3O4). As the name suggests, magnetite is magnetic in nature and is easily separated from gangue (unwanted) minerals through magnetic separation processes. Unfortunately, the magnetite ore bodies are diminishing. Because of this, there has been a recent drive to pursue technology that can economically separate hematite (Fe2O3) from its gangue minerals as hematite is a much more abundant source of iron. Most hematite ore has a very small liberation size that is frequently less than 25μm. Beneficiation of any ore with this fine of a liberation size requires advanced processing methods and is seldom pursued. A single process, known as selective flocculation and dispersion, has been successfully implemented at a plant scale for the beneficiation of fine liberation size hematite ore. Very little is known about this process as it was discovered by the U.S. Bureau of Mines by accident. The process is driven by water chemistry and surface chemistry modifications that enhance the separation of the hematite from its gangue minerals. This dissertation focuses on the role of water chemistry and process reagents in this hematite beneficiation process. It has been shown that certain ions, including calcium and magnesium, play a significant role in the process. These ions have a significant effect on the surface chemistry as reported by zeta potential studies. It was shown that magnesium ions within the process water have a more significant impact on surface chemistry than calcium ions due to steric hindrance effects at the hematite surface. It has also been shown that polyacrylic acid dispersants, if used in the process, can increase product quality (increase iron content, decrease phosphorus content, decrease silica content) substantially. Water, surface and reagent chemistry experiments were performed at a laboratory, pilot, and full plant scale during the course of this work. Many of the conclusions developed in the laboratory and pilot scale were found to be true at the full plant scale as well. These studies are the first published in history to develop theories of water chemistry and surface chemistry interactions at a full plant scale.

Plotting regression coefficients and other estimates in Stata

Graphical presentation of regression results has become increasingly popular in the scientific literature, as graphs are much easier to read than tables in many cases. In Stata such plots can be produced by the -marginsplot- command. However, while -marginsplot- is very versatile and flexible, it has two major limitations: it can only process results left behind by -margins- and it can only handle one set of results at the time. In this article I introduce a new command called -coefplot- that overcomes these limitations. It plots results from any estimation command and combines results from several models into a single graph. The default behavior of -coefplot- is to plot markers for coefficients and horizontal spikes for confidence intervals. However, -coefplot- can also produce various other types of graphs. The capabilities of -coefplot- are illustrated in this article using a series of examples.

COEFPLOT: Stata module to plot regression coefficients and other results

coefplot plots results from estimation commands or Stata matrices. Results from multiple models or matrices can be combined in a single graph. The default behavior of coefplot is to draw markers for coefficients and horizontal spikes for confidence intervals. However, coefplot can also produce various other types of graphs.

Les juifs en France, en Italie et en Espagne : recherches sur leur état depuis leur dispersion jusqu'à nos jours sous le rapport de la législation, de la littérature et du commerce

par J. Bédarride

Plotting regression coefficients and other estimates

Graphical display of regression results has become increasingly popular in presentations and in scientific literature because graphs are often much easier to read than tables. Such plots can be produced in Stata by the marginsplot command (see [R] marginsplot). However, while marginsplot is versatile and flexible, it has two major limitations: it can only process results left behind by margins (see [R] margins), and it can handle only one set of results at a time. In this article, I introduce a new command called coefplot that overcomes these limitations. It plots results from any estimation command and combines results from several models into one graph. The default behavior of coefplot is to plot markers for coefficients and horizontal spikes for confidence intervals. However, coefplot can also produce other types of graphs. I illustrate the capabilities of coefplot by using a series of examples.

Electronic band dispersion of graphene nanoribbons via Fourier-transformed scanning tunneling spectroscopy

The electronic structure of atomically precise armchair graphene nanoribbons of width N=7 (7-AGNRs) are investigated by scanning tunneling spectroscopy (STS) on Au(111). We record the standing waves in the local density of states of finite ribbons as a function of sample bias and extract the dispersion relation of frontier electronic states by Fourier transformation. The wave-vector-dependent contributions from these states agree with density functional theory calculations, thus enabling the unambiguous assignment of the states to the valence band, the conduction band, and the next empty band with effective masses of 0.41±0.08me,0.40±0.18me, and 0.20±0.03me, respectively. By comparing the extracted dispersion relation for the conduction band to corresponding height-dependent tunneling spectra, we find that the conduction band edge can be resolved only at small tip-sample separations and has not been observed before. As a result, we report a band gap of 2.37±0.06 eV for 7-AGNRs adsorbed on Au(111).