The irresponsible disposal of mass-manufactured plastic has caused widespread environmental damage, including soil degradation, marine life toxicity, and contamination of groundwater. While recycling efforts are essential, they remain insufficient as costly plastic processing plants are inaccessible to many developing countries. Tenebrio Molitor larvae have demonstrated the ability to degrade polystyrene, presenting a potential unique solution to plastic waste. This study investigates how varying levels of water availability influence the rate of polystyrene consumption and degradation in T. Molitor larvae. Six groups of larvae were provided with different amounts of water (0, 0.5, 1, 1.5, 2, and 2.5 mL) along with 15 grams of polystyrene foam. Water was added every four days, and polystyrene consumption and degradation were monitored for 12 days. The final masses of remaining polystyrene were compared to assess the effect of moisture levels. Experimental data suggests that moisture levels significantly impact the degradation efficiency of T. Molitor as results show that larvae with moderate water levels (1-2.5 mL) consumed more polystyrene than those in the 0 mL group, which also had significant larval mortality. Specifically, the group given 2.5 mL of water consumed 3 times more polystyrene than the group given 0 mL.

Synthetic turf fields are made using rubber that is commonly found in tires. These rubber fields cannot be disposed of in just any manner due to the materials they contain. The known toxins present in this rubber include arsenic, cadmium, lead, nickel, polycyclic aromatic hydrocarbons (PAHs), and volatile organic compounds (VOCs). These chemicals found in the turf have been suspected of causing various health effects. The most common chemicals that are typically found in rubber include fillers, plasticizers, antioxidants, polyaromatic hydrocarbons, and phthalates. This research is focused on determining whether or not turf fields are responsible for causing water pollution.

Climate change is a major challenge that affects Connecticut in various ways, such as rising temperatures, extreme weather events, rising sea levels, and greenhouse gas emissions. This project aims to create a predictive model that can forecast the changes in temperature and emissions in Connecticut up to 2050 under different scenarios and assumptions. The project will use historical and projected data from various sources and apply regression analysis and scenario analysis techniques using Jupyter Notebook and Python libraries. The project will also analyze the results and provide recommendations for policymakers, stakeholders, and the public on how to adapt to the impacts of climate change and enhance the resilience and sustainability of Connecticut. The project will contribute to the scientific understanding and practical application of predictive analytics for climate change research and decision making.