2007-2008
Chacharee Therapong
PhD Thesis Title: Arsenic Stress
Response And Detoxification Mechanism In Arsenic Hyperaccumulating And
Non-Accumulating Plants
Supervisor: Dr. Sarkar and Dr. Rupali Datta
Abstract: Remediation of arsenic-contaminated soil and water is necessary for protecting both human life and agricultural production. Phytoremediation becomes an option to remediate arsenic-contaminated soils, mainly because it is cost-effective and environmentally friendly. The research purpose was to investigate arsenic stress response and detoxification mechanisms in arsenic hyper-accumulating and non-accumulating plants. The results showed the characteristics and detoxification pathway of Oryza sativa, Zea mays, and Pteris vittata on their growth behavior and antioxidative production that were important to consider in phytoremediation factors. These characteristics might be of direct benefit in remediating As in contaminated soils.
PhD Thesis Title: Determination of Seismic Efficiency and Storage Constants of the Edwards Aquifer: Balcones Fault Zone
Supervisor: Alan R. Dutton Ph.D.
Abstract: Specific storage is a constant that has become more important today as confined aquifers are modeled for management decisions. Historically, distance drawdown tests have been used to measure storativity in aquifers, and if the saturated thickness is known, specific storage can then be calculated. However, since distance drawdown tests are not always possible, estimates of specific storage have been obtained using barometric and tidal efficiencies, where the calculation of storage parameters for an aquifer has long been achieved by extracting data about barometric or tidal forces, and reviewing the response in hydraulic head. A seismic efficiency, similar to the concept of a barometric or tidal-efficiency, has been developed using Rayleigh waves from earthquakes. A sensitivity analysis shows that although water compressibility and porosity are the controlling variables in the specific storage calculation, the aquifer deformation and the change in hydraulic head are important in producing variable results in specific storage within the same aquifer. With precise values for these parameters, an estimate for specific storage can be calculated using the seismic efficiency.
Specific storage is calculated for the Edwards aquifer of Central Texas, using seismic efficiencies with results ranging from 8.5e-7 to 2.3e-6 m-1 (2.6e-7 to 7.3e-7 ft-1). Two earthquakes on the North American continent were used to calculate values for the study wells, and comparison of the data from the two events shows that specific storage calculations using seismic efficiency are repeatable. Barometric efficiency-based specific-storage calculations were also performed for the study wells, and statistical analysis showed that there was no significant difference between the calculation results. Spatial mapping of the results from each calculation set showed some trends in the maps, but since there is not complete agreement between data sets, a larger data set is needed to define the specific storage trends in the Edwards aquifer.
Sheeba M. Thomas
PhD Thesis Title: Fate of Emerging Contaminants in a Simulated Wastewater Treatment Plant
Supervisors: Kyle E. Murray, Ph.D., Adria A.Bodour, Ph.D.
Abstract: This thesis presents three studies related to removal of emerging contaminants (ECs) from water. The first study summarizes literature on EC research including classifications of ECs, sources of ECs, effects of exposure to ECs, and treatment methods for ECs in water. The second and the third studies focused on the fate of three ECs (3-tert-Butyl-4-hydroxyanisole (BHA), chlorpyrifos, 1,3,4,6,7,8-hexahydro-4,6,6,7,8,8-hexamethylcyclopenta(g)-2-benzopyrane (HHCB)) by simulating conditions of a conventional wastewater treatment plant (WWTP) in San Antonio, Texas on a laboratory scale. The second study estimated partition coefficients of ECs, each individually and in comination, to activated sludge at 22 °C. Results showed that the sorption isotherms followed a linear trend (r2>0.9) when studied each individually generating sorption coefficients to sludge (Kd) values of 2689 L Kg-1, 27786 L Kg-1 and 31402 L Kg-1 for BHA, chlorpyrifos and HHCB respectively. The combined study isotherm followed a linear trend and generated a Kd of 1654 L Kg-1 which is 38% less when compared to individual study isotherm for BHA suggesting the presence of significant competing effects for sorption sites. The combined sorption study data for both HHCB and chlorpyrifos were analysed using Freundlich’s and Langmuir’s isotherm models. Synergistic effects on the concentration of the ECs in the liquid phase as well as competition for sorption sites were observed with spike concentration ≥ 25 mg L-1 and ≥ 20 mg L-1 for HHCB and chlorpyrifos respectively. The Kd values varied between 6,000,000-16,984 and 3000,000 – 19,536 L kg-1and L kg-1 for HHCB and chlorpyrifos, The third study concentrated on the degradation of the ECs in aerobic and anaerobic treatments with conditions emulated from the WWTP. The ECs mostly degraded in aerobic conditions that were acclimated with the ECs. Important phenomena such as sorption-desorption dynamics, response of microbial organisms and the complexity involved to degrade multiple ECs explains the data obtained for aerobic degradation study. Little or no degradation of ECs were observed when exposed to simulated anaerobic conditions.
Xianwei Wang
PhD Thesis Title: Applications of Remote Sensing and GIS in Surface Hydrology: Snow Cover, Soil Moisture, and Precipitation.
Supervisor: Hongjie Xie, Ph.D.
Abstract:
Studies on surface hydrology can generally be classified into two categories, observation for different components of surface water, and modeling their dynamic movements. This study only focuses on observation part of surface water components: snow cover, soil moisture, and precipitation. Moreover, instead of discussion on the detailed algorithm and instrument technique behind each component, this dissertation pours efforts on analysis of the standard remotely sensed products and their applications under different settings. First in Chapter 2, validation of MODIS Terra 8-day maximum snow cover composite (MOD10A2) in the Northern Xinjiang, China, from 2000-2006, shows that the 8-day MODIS/Terra product has high agreements with in situ measurements as the in situ snow depth is larger or equal to 4 cm, but the agreement is low for the patchy snow as the in situ snow depth less than 4 cm. However, the cloud blockage in the 8-day products is still high in most winter time although the clouds are much lower than that in the daily snow cover product. According to the in situ observation, this chapter develops an empirical algorithm to separate the cloud-covered pixels into snow and no snow. This separation generates a new snow cover time series that corrects the dramatic decrease of raw snow cover area caused by MODIS cloud mask, thus yielding a better estimation of the actual snow coverage in a watershed. This solves the problem of cloud blockage frustrating hydrologic modelers. This new cloud free snow cover time series is further used to study the seasonal and inter-annual variation of snow cover at this region. Variation of snow area extent (SAE) at Northern Xinjiang is closely associated with air temperature. The increase of elevation generally accompanies the decrease of air temperature, resulting in more snow cover extent and longer snow cover duration. During the six hydrologic years from 2000-2001 to 2005-2006, the SAE has a similar pattern, although there is variation at the beginning of snow accumulation and at the end of snow melting in different years. Because of the short duration of MODIS data, the change trend of snow cover is not obvious. Therefore, the continued long-term production of MODIS-type snow cover product is critical to assess water resources of the study area, as well as other larger scale global environment monitoring, offering critical inputs for hydrologic and climatic modeling, forecasting, and climate change analyses.Terra and Aqua satellites carry the same MODIS instrument and provide two parallel MODIS daily snow cover products at different time (local time 10:30 am and 1:30 pm, respectively). Chapter 3 develops an algorithm and automated scripts to combine the daily MODIS Terra (MOD10A1) and Aqua (MYD10A1) snow cover products, and to automatically generate multi-day Terra-Aqua snow cover image composites, with flexible starting and ending dates and a user-defined cloud cover threshold. The daily combination (MODMYD10DC) offers much more open space than the daily MODIS Terra or Aqua only since cloud keeps moving in most times. The multi-day Terra-Aqua composites (MODMYD10MC) provide about 3 times of images as and similar accuracy with the standard 8-day composite products with the 2003-2004 hydrologic year at test sites of Northern Xinjiang, China, and at the Colorado Plateau, USA. The new multi-day composites are significant contributions and complement to the standard MODIS snow cover product series. This approach can also be extended to other optical remote sensing images, e.g., to combine other Terra and Aqua MODIS products, like vegetation indices, etc. Chapter 4 systematically compares the difference between MODIS Terra and Aqua snow cover products within a hydrologic year of 2003-2004, validates the MODIS Terra and Aqua snow cover products using in situ measurements in Northern Xinjiang, and compares the accuracy among the standard MODIS Terra and Aqua snow cover products, and the new combined daily and multi-day composite from both MODIS Terra and Aqua daily products. Taken the MODIS/Terra daily and 8-day products as references when being compared with MODIS/Aqua daily and 8-day products (MYD10A2), respectively, the agreement of land classification from MODIS Terra and Aqua daily and 8-day snow cover products is close to 100% in the entire year under clear sky in both morning and afternoon. In contrast, the agreement of snow classification from MODIS Terra and Aqua is high only in the winter months, decreasing in the rest of the year. The snow cover agreement in the daily products is higher than that in the 8-day products. When being compared with the in situ snow depth observations, under the so-called clear sky conditions after removing the cloud data pairs, three daily products (MOD10A1, MYD10A1, and MODMYD10DC) have similar accuracy of snow and land classification. Moreover, in the actual weather/cloud conditions, the daily combination of MODIS Terra and Aqua reduces the cloud blockage and improves the land and snow classification accuracy, although it still has high cloud percentage. Three composite products (MOD10A2, MYD10A2, and MODMYD10MC) also have similar accuracy of snow and land classification after removing the cloud data pairs. Similarly, in the actual weather/cloud conditions, the new multi-day composite product reduces the cloud blockage and improves the snow classification accuracy against either the 8-day MODIS Terra or Aqua snow cover product. In Chapter 5, utilizing the new cloud-low multi-day composite of MODIS Terra and Aqua snow cover products, several new methods are developed to study the spatiotemporal variation of snow cover conditions from different aspects at the Northern Xinjiang and on the central Tianshan Mountains, mainly in China, partly covering Kazakhstan and Kyrgyzstan. MODIS-derived snow covered days (SCD) has good agreement of 90% with in situ SCD from hydrologic years of 2001-2005. Overall, SCD increases as elevation increases when elevation is less than 4 500 m. The R2 value between SCD and DEM is 0.62 on the entire Central Tianshan Mountains. Snow cover index (SCI) value contains both duration and extent information and is easily used to quantify the variation of the overall snow cover situation. MODIS-derived snow cover onset dates (SCOD) and snow cover melting dates (SCMD) also have good agreement with the major snow cover duration from in situ observation by one week forward and backward shift because of transient snowfall events in the early and at the end of the snow season, respectively. In the six years, the perennial snow in 2005 August has the least spatial extent (2 386 km2), which is believed to be a most close map of the glacier distribution at this peak area. More accurate map of glacier distribution can be obtained using Landsat or higher resolution images. Secondly, Chapter 6 investigates the feasibility to indirectly map root-zone soil moisture using optical remote sensing techniques and in situ measurements. Specifically, covariation of root-zone soil moisture with the normalized difference of vegetation index (NDVI) from MODIS observation is studied at three sites (New Mexico, Arizona, and Texas). The three sites represent two types of vegetation (shrub and grass) and two types of climate conditions: arid/semi-arid (New Mexico and Arizona) and humid (Texas). Results show that the root-zone soil moisture has significant linear correlation with vegetation (NDVI). Then, a linear regression model is developed to estimate the root-zone soil moisture based on NDVI and in situ soil moisture. The time-series of root-zone soil moisture estimated by NDVI using the linear regression model accounts for 42 - 71% of the observed soil moisture variations for the 3 sites. Thus the point ground measurement of soil moisture can be interpolated to areal soil moisture using NDVI as a proxy of soil moisture variation.Finally, Chapter 7 validates and compares the NEXRAD Stage III and MPE precipitation products using a high density rain gauge network on the Upper Guadalupe River Basin of the Texas Hill Country in 2001 and 2004. Because of the point-area representativeness error between rain gauge and radar rainfall estimation, this chapter develops a new method to automatically select uniform rainfall events based on coefficient of variation criteria of 3 by 3 radar cells. Only gauge observations of those uniform rainfall events are used as ground truth to evaluate radar rainfall estimation. Results show that rain gauge observations of uniform rainfall better represent ground truth of a 4 x 4 km2 radar cell than non-uniform rainfall; MPE has better performance than Stage III; Stage III tends to overestimate precipitation (19.5%), but MPE tends to underestimate (-7.2%).