Solar energy is a significant contributor to the renewable energy mix. Many urban developments are making investments to install solar systems across feasible areas. The allocation of solar systems relies on the land’s geography and the amount of solar radiation received. The purpose of this study is to apply to determine the best sites for solar installations in urban areas. Using the TCU area of Fort Worth, Texas as a case study, this study will use ESRI’s ArcMap and ArcGIS Pro to estimate the solar power potential of different residential rooftops. The results will be useful in showing what households are most suitable for solar installation based on their expected energy yield.

Rapid industrialization and global population growth have increased the number of people living in urban areas worldwide. Developing countries, have seen tremendous increases in their industries over the past decades, which generated both positives and negative effects on their people, environment, and economy. One of the negative impacts of industrialization is industrial pollution and the increase in the number of pollutants released into the environment_ in this case, heavy metals. Heavy metal contamination is an alarming problem that many Developing countries are becoming aware of and trying to address. Heavy metal direct or indirect consumption may result in several health effects in the body, including damage and alteration of normal functioning of organs such as the brain, kidney, lungs, liver, and blood, which later result into acute or chronic diseases. This case study will look at heavy metal contamination cases in Rwanda in different drinking water sources. The focus of this case study will be on some common heavy metals released from industrial waste: Lead, Manganese, Iron, Cadmium, Zinc, and Chromium.

Teachers have experimented with the idea of virtual learning and its’ effects on student achievement. Due to the coronavirus pandemic, many schools and universities transitioned from traditional classroom-focused learning to asynchronous online learning. Asynchronous online learning is a type of instruction where online learning is not happening at the same time or place. Consequently, TCU made the abrupt transition in the Spring of 2021 to fully online asynchronous courses. To understand the magnitude of how remote learning can effect students’ academic success, my research project looks at what factors, including remote learning, can predict final grade utilizing GEOL 10113 student performance data and survey data from the spring semester of 2020 surveys. To investigate the impact of online learning, I tested several linear models to determine what confounders have a significant role in predicting students’ success in online and in remote learning. These models investigated which factors, ranging from demographic information to GPA, are significant predictors of both final grade and remote grade. I started the linear model selection process by testing a complex linear model, which had all the possible factors including interactions that can impact final grade or remote grade from the surveys. Once I knew which factors were significant from the complex model, I eliminated non-significant variables and created new models, comparing each model by their AIC values until I found the best-fit linear model for final grade and remote grade. AIC is a measurement of how well a linear model fits and the lower the AIC value the better fit the linear model has. After testing each linear model: GPA, students’ lecture section, remote grade, and exam average were significant to final grade. These models suggest that while remote grade is a significant predictor of final grade, no variable measured in this study is significant enough to impact remote grade. Differing from previous research, my results showed that there were no gaps in achievement amongst gender and underrepresented minority students. Although statistically no variable significantly impacted remote grade, there are trends amongst demographic variables and remote grade, suggesting some potential relationships that could be explored in future studies.

The reusing, recycling, and reduction of waste streams is seen as a viable sustainability strategy. One major waste stream is coffee grounds with about 11.5 million kilograms being generated per day in America of which 90% is landfilled. This waste stream can be repurposed into usable carbon-based materials to address issues of climate, pollution, or engineering applications. For my research, I am converting spent (used) coffee grounds into biochars, a type of carbon-based material, with different charring (burning) temperature to measure the removal of lead (Pb2+) from contaminated water. The charring temperature was changed in order to determine the optimal charring temperature for water treatment. This presentation will go into the maximum amount of lead the biochars can remove, how fast the biochars can remove the lead and the properties of biochars that allow for such removal. Further results, methodology, and modeling applications will be discussed in the presentation.

Anti-inflammatory drugs such as ibuprofen and triclosan are widely used and available in many pharmaceutical and personal care products (PPCP’s). The concentrations of these drugs are increasing in public surface and groundwaters and is often linked to negative impacts on aquatic life. These impacts are due to the fact that PPCP’s bypass water treatment facilities since they are not typically regulated and water treatment methods at the facilities are not designed to remove them. My research focuses on removing PPCP’s using reactive environmental sorbents like nanocrystalline ferrihydrite. Specifically, I examined the interaction of two widely used PPCP’s (Ibuprofen and Triclosan) with nanocrystalline ferrihydrite of varying particle size (<125, 125-250, >250). Results thus far show that when Ibuprofen interacts with nanocrystalline ferrihydrite at pH 4.3-4.8; 28.29% was removed when the particle size was less than 125 microns; 45.89% was removed when the particle size was 125-250 microns; and 49.92% was removed when the particle size was greater than 250. While for Triclosan 40.55%, 54.7%, 23.80% was removed by nanocrystalline ferrihydrite with size <125, 125-250, >250 respectively. My presentation will further cover surface properties of nanocrystalline ferrihydrite controlling the sorption of ibuprofen and triclosan.