Earth Sciences Department Theses and Dissertations
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The IUPUI graduate program in Geology leads to a Master of Science degree from Indiana University. Our terminal degree at the IUPUI campus is the Master of Science. As a result, our faculty are able to focus their attention on our Masters program student research. We offer a thesis and non-thesis option; however, typically only thesis-option students are considered for funding. Our thesis option requires 24 credit hours of graduate level courses and 6 credit hours of a research thesis. We have between 8-12 full-time graduate students per year.
Interested students should contact us prior to applying. If applicable, an appointment/visit can be set up for you to see our facilities and meet a few of our faculty. Students can apply with an interest in a specific faculty member or a group of faculty members. Admission decisions are decided by our graduate committee and not individual faculty members. Once you enter the program, you will take a majority of your courses in your first year. Also, you will choose your research advisor and submit your thesis (research) proposal. Your second year (including the summer) is focused on completing your research project and writing your thesis while finishing your course work.
For more information: http://www.geology.iupui.edu/Degree_Programs/Graduate_Studies/index.htm
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Browsing Earth Sciences Department Theses and Dissertations by Author "Babbar-Sebens, Meghna"
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Item Analysis of Mercury Concentrations in Indiana Soil to Evaluate Patterns of Long-Term Atmospheric Mercury Deposition(2013-01-09) Crewe, Julie R.; Filippelli, Gabriel M.; Babbar-Sebens, Meghna; Risch, Martin R.Mercury (Hg) has proven to be a risk to the public, mainly through the consumption of fish. Because of this, many fish consumption advisories have been issued in Indiana. Although much is known about the global cycle of mercury, little is known about how local and regional emission sources of mercury impact local and regional mercury cycling. This study’s objective was to determine the scope of mercury concentration in central Indiana by using a broad grid of soil mercury measurements. Sampling was designed to capture the net retained mercury content in soils, and to determine whether spatial patterns in exist in soil mercury contents that could be related to emission sources of mercury and post-emission transport patterns from wind. Results from this study revealed significant differences in mercury concentrations for soils in central Indiana. The core of the study area, concentrated in the urban area of Indianapolis, exhibited soil mercury contents that were 20 times higher than values in the outskirts of the study area. The spatial pattern resembled a bulls-eye shape centered on Indianapolis, and with comparison to the reported Hg emission from local sources, including a coal-fired power plant, indicates a strong regional deposition signal linked to those emission sources but marked by wind-driven transport to the northeast. This effect of local emission sources resulting in local deposition indicates that limiting mercury emissions will have a net beneficial impact on local environmental quality and human health.Item Combining Multivariate Statistical Methods and Spatial Analysis to Characterize Water Quality Conditions in the White River Basin, Indiana, U.S.A.(2011-02-25) Gamble, Andrew Stephan; Babbar-Sebens, Meghna; Tedesco, Lenore P.; Peng, HanxiangThis research performs a comparative study of techniques for combining spatial data and multivariate statistical methods for characterizing water quality conditions in a river basin. The study has been performed on the White River basin in central Indiana, and uses sixteen physical and chemical water quality parameters collected from 44 different monitoring sites, along with various spatial data related to land use – land cover, soil characteristics, terrain characteristics, eco-regions, etc. Various parameters related to the spatial data were analyzed using ArcHydro tools and were included in the multivariate analysis methods for the purpose of creating classification equations that relate spatial and spatio-temporal attributes of the watershed to water quality data at monitoring stations. The study compares the use of various statistical estimates (mean, geometric mean, trimmed mean, and median) of monitored water quality variables to represent annual and seasonal water quality conditions. The relationship between these estimates and the spatial data is then modeled via linear and non-linear multivariate methods. The linear statistical multivariate method uses a combination of principal component analysis, cluster analysis, and discriminant analysis, whereas the non-linear multivariate method uses a combination of Kohonen Self-Organizing Maps, Cluster Analysis, and Support Vector Machines. The final models were tested with recent and independent data collected from stations in the Eagle Creek watershed, within the White River basin. In 6 out of 20 models the Support Vector Machine more accurately classified the Eagle Creek stations, and in 2 out of 20 models the Linear Discriminant Analysis model achieved better results. Neither the linear or non-linear models had an apparent advantage for the remaining 12 models. This research provides an insight into the variability and uncertainty in the interpretation of the various statistical estimates and statistical models, when water quality monitoring data is combined with spatial data for characterizing general spatial and spatio-temporal trends.Item Coupled biogeochemical cycles in riparian zones with contrasting hydrogeomorphic characteristics in the US Midwest(2013-12-11) Liu, Xiaoqiang; Vidon, Philippe G.; Jacinthe, Pierre-Andre; Babbar-Sebens, MeghnaNumerous studies have investigated the fate of pollutants in riparian buffers, but few studies have focused on the control of multiple contaminants simultaneously in riparian zones. To better understand what drives the biogeochemical cycles of multiple contaminants in riparian zones, a 19-month study was conducted in riparian buffers across a range of hydrogeomorphic (HGM) settings in the White River watershed in Indiana. Three research sites [Leary Webber Ditch (LWD), Scott Starling (SS) and White River (WR)] with contrasting hydro-geomorphology were selected. We monitored groundwater table depth, oxidation reduction potential (ORP), dissolved oxygen (DO), dissolved organic carbon (DOC), NO3-, NH4+, soluble reactive phosphorus (SRP), SO42- , total Hg and methylmercury (MeHg). Our results revealed that differences in HGM conditions translated into distinctive site hydrology, but significant differences in site hydrology did not lead to different biogeochemical conditions. Nitrate reduction and sulfate re-oxidation were likely associated with major hydrological events, while sulfate reduction, ammonia and methylmercury production were likely associated with seasonal changes in biogeochemical conditions. Results also suggest that the LWD site was a small sink for nitrate but a source for sulfate and MeHg, the SS site was a small sink for MeHg but had little effect on NO3-, SO42- and SRP, and the WR was an intermediate to a large sink for nitrate, an intermediate sink for SRP, and a small source for MeHg. Land use and point source appears to have played an important role in regulating solute concentrations (NO3-, SRP and THg). Thermodynamic theories probably oversimplify the complex patterns of solute dynamics which, at the sites monitored in the present study, were more strongly impacted by HGM settings, land use, and proximity to a point source.Item Effect of Stakeholder Attitudes on the Optimization of Watershed Conservation Practices(2013-01-30) Piemonti, Adriana Debora; Babbar-Sebens, Meghna; Jacinthe, Pierre-Andre; Mukhopadhyay, Snehasis; Luzar, E. Jane, 1951-Land use alterations have been major drivers for modifying hydrologic cycles in many watersheds nationwide. Imbalances in this cycle have led to unexpected or extreme changes in flood and drought patterns and intensities, severe impairment of rivers and streams due to pollutants, and extensive economic losses to affected communities. Eagle Creek Watershed (ECW) is a typical Midwestern agricultural watershed with a growing urban land-use that has been affected by these problems. Structural solutions, such as ditches and tiles, have helped in the past to reduce the flooding problem in the upland agricultural area. But these structures have led to extensive flooding and water quality problems downstream and loss of moisture storage in the soil upstream. It has been suggested that re-naturalization of watershed hydrology via a spatially-distributed implementation of non-structural and structural conservation practices, such as cover crops, wetlands, riparian buffers, grassed waterways, etc. will help to reduce these problems by improving the upland runoff (storing water temporally as moisture in the soil or in depression storages). However, spatial implementation of these upland storage practices poses hurdles not only due to the large number of possible alternatives offered by physical models, but also by the effect of tenure, social attitudes, and behaviors of landowners that could further add complexities on whether and how these practices are adopted and effectively implemented for benefits. This study investigates (a) how landowner tenure and attitudes can be used to identify promising conservation practices in an agricultural watershed, (b) how the different attitudes and preferences of stakeholders can modify the effectiveness of solutions obtained via classic optimization approaches that do not include the influence of social attitudes in a watershed, and (c) how spatial distribution of landowner tenure affects the spatial optimization of conservation practices on a watershed scale. Results showed two main preferred practices, one for an economic evaluation (filter strips) and one for an environmental perspective (wetlands). A land tenure comparison showed differences in spatial distribution of systems considering all the conservation practices. It also was observed that cash renters selected practices will provide a better cost-revenue relation than the selected optimal solution.Item Lead Distribution in Urban Soils: Relationship Between Lead Sources and Children's Blood Lead Levels(2011-06-14) Morrison-Ibrahim, Deborah E.; Filippelli, Gabriel M.; Steele, Gregory; Babbar-Sebens, Meghna; Li, LinItem Nitrous oxide emission from riparian buffers in agricultural landscapes of Indiana(2014-02-25) Fisher, Katelin Rose; Babbar-Sebens, Meghna; Jacinthe, Pierre-André; Vidon, Philippe G.Riparian buffers have well documented capacity to remove nitrate (NO3-) from runoff and subsurface flow paths, but information on field-scale N2O emission from these buffers is lacking. This study monitored N2O fluxes at two agricultural riparian buffers in the White River watershed (Indiana) from December 2009 to May 2011 to assess the impact of landscape and hydrogeomorphologic factors on emission. Soil chemical and biochemical properties were measured and environmental variables (soil temperature and moisture) were monitored in an attempt to identify key drivers of N2O emission. The study sites included a mature riparian forest (WR) and a riparian grass buffer (LWD); adjacent corn fields were also monitored for land-use comparison. With the exception of net N mineralization, most soil properties (particle size, bulk density, pH, denitrification potential, organic carbon, C:N) showed little correlation with N2O emission. Analysis of variance (ANOVA) identified season, land-use (riparian buffer vs. crop field), and site geomorphology as major drivers of N2O emission. At both study sites, N2O emission showed strong seasonal variability; the largest emission peaks in the riparian buffers (up to 1,300 % increase) and crop fields (up to 3,500 % increase) occurred in late spring/early summer as a result of flooding, elevated soil moisture and N-fertilization. Nitrous oxide emission was found to be significantly higher in crop fields than in riparian buffers at both LWD (mean: 1.72 and 0.18 mg N2O-N m-2 d-1) and WR (mean: 0.72 and 1.26 mg N2O-N m-2 d-1, respectively). Significant difference (p=0.02) in N2O emission between the riparian buffers was detected, and this effect was attributed to site geomorphology and the greater potential for flooding at the WR site (no flooding occurred at LWD). More than previously expected, the study results demonstrate that N2O emission in riparian buffers is largely driven by landscape geomorphology and land-stream connection (flood potential).Item Prediction of Spatial-Temporal Distribution of Algal Metabolites in Eagle Creek Reservoir, Indianapolis, IN(2012-10-29) Bruder, Slawa Romana; Babbar-Sebens, Meghna; Jacinthe, Pierre-Andre; Tedesco, Lenore P.In this research, Environmental Fluid Dynamic Code (EFDC) and Adaptive- Networkbased Fuzzy Inference System Models (ANFIS) were developed and implemented to determine the spatial-temporal distribution of cyanobacterial metabolites: 2-MIB and geosmin, in Eagle Creek Reservoir, IN. The research is based on the current need for understanding algae dynamics and developing prediction methods for algal taste and odor release events. In this research the methodology for prediction of 2-MIB and geosmin production was explored. The approach incorporated a combination of numerical and heuristic modeling to show its capabilities in prediction of cyanobacteria metabolites. The reservoir’s variable data measured at monitoring stations and consisting of chemical/physical and biological parameters with the addition of calculated mixing conditions within the reservoir were used to train and validate the models. The Adaptive – Network based Fuzzy Inference System performed satisfactorily in predicting the metabolites, in spite of multiple model constraints. The predictions followed the generally observed trends of algal metabolites during the three seasons over three years (2008-2010). The randomly selected data pairs for geosmin for validation achieved coefficient of determination of 0.78, while 2-MIB validation was not accepted due to large differences between two observations and their model prediction. Although, these ANFIS results were accepted, the further application of the ANFIS model coupled with the numerical models to predict spatio-temporal distribution of metabolites showed serious limitations, due to numerical model calibration errors. The EFDC-ANFIS model over-predicted Pseudanabaena spp. biovolumes for selected stations. The predicted value was 18,386,540 mm3/m3, while observed values were 942,478 mm3/m3. The model simulating Planktothrix agardhii gave negative biovolumes, which were assumed to represent zero values observed at the station. The taste and odor metabolite, geosmin, was under-predicted as the predicted v concentration was 3.43 ng/L in comparison to observed value of 11.35 ng/l. The 2-MIB model did not validate during EFDC to ANFIS model evaluation. The proposed approach and developed methodology could be used for future applications if the limitations are appropriately addressed.Item REMOTE SENSING DATA ASSIMILATION IN WATER QUALITY NUMERICAL MODELS FOR SIMULATION OF WATER COLUMN TEMPERATURE(2012-03-16) Xie, Shuangshuang; Babbar-Sebens, Meghna; Li, Lin; Zhu, LuodingNumerical models are important tools for simulating processes within complex natural systems, such as hydrodynamics and water quality processes within a water body. From decision makers’ perspectives, such models also serve as useful tools for predicting the impacts of water quality problems or develop early warning systems. However, accuracy of a numerical model developed for a specific site is dependent on multiple model parameters and variables whose values are attained via calibration processes and/or expert knowledge. Real time variations in the actual aquatic system at a site necessitate continuous monitoring of the system so that model parameters and variables are regularly updated to reflect accurate conditions. Multiple sources of observations can help adjust the model better by providing benefits of individual monitoring technology within the model updating process. For example, remote sensing data provide a spatially dense dataset of model variables at the surface of a water body, while in-situ monitoring technologies can provide data at multiple depths and at more frequent time intervals than remote sensing technologies. This research aims to present an overview of an integrated modeling and data assimilation framework that combines three-dimensional numerical model with multiple sources of observations to simulate water column temperature in a eutrophic reservoir in central Indiana. A variational data assimilation approach is investigated for incorporating spatially continuous remote sensing observations and spatially discrete in-situ observations to change initial conditions of the numerical model. This research addresses the challenge of improving the model performance by combining water temperature from multi-spectral remote sensing analysis and in-situ measurements. Results of the approach on a eutrophic reservoir in Central Indiana show that with four images of multi-spectral remote sensing data assimilated, the model results oscillate more from the in-situ measurements during the data assimilation period. For validation, the data assimilation has negative impacts on the root mean square error. According to quantitative analysis, more significant water temperature stratification leads to larger deviations. Sampling depth differences for remote sensing technology, in-situ measurements and model output are considered as possible error source.Item REMOTE SENSING OF WATER COLOR: MODEL SENSITIVITY ANALYSIS AND ESTIMATION OF PHYTOPLANKTON SIZE FRACTIONS(2013-08-14) Li, Zuchuan; Li, Lin; Babbar-Sebens, Meghna; Wilson, Jeffrey S. (Jeffrey Scott), 1967-Phytoplankton size classes (pico-plankton, nano-plankton, and micro-plankton) provide information about pelagic ocean ecosystem structure, and their spatiotemporal variation is crucial in understanding ocean ecosystem structure and global carbon cycling. Remote sensing provides an efficient approach to estimate phytoplankton size compositions on global scale. In the first part of this thesis, a global sensitivity analysis method was used to determine factors mainly controlling the variations of remote sensing reflectance and inherent optical properties inverse algorithms. To achieve these purposes, average mass-specific coefficients of particles were first calculated through Mie theory, using particle size distributions and refractive indices as input; and then a synthesis remote sensing reflectance dataset was created using Hydrolight. Based on sensitivity analysis results, an algorithm for estimating phytoplankton size composition was proposed in the second part. This algorithm uses five types of spectral features: original and normalized remote sensing reflectance, two-band ratios, continuum removed spectra, and spectral curvatures. With the spectral features, phytoplankton size compositions were regressed using support vector machine. According to validation results, this algorithm performs well with simulated and satellite Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS), indicating that it is possible to estimate phytoplankton size compositions through satellite data on global scale.