During the Pleistocene Epoch approximately 13,000 years ago, retreating glaciers deposited several stranded icebergs where the wetlands on the north end of the North Seattle College campus lie today. We collected and analyzed several borehole samples within the Meridian Avenue Basin to determine the stratigraphy and identify organic material found within the basin. The stratigraphy of the basin revealed that these features are a result of Vashon Glaciation during the end of the Pleistocene Epoch. Our subsurface core samples exposed a glacial till-lined basin ranging from 3-9 meters deep with overlying layers of gravelly clay, silt, sand and peat. A carbon date we retrieved from organic material within the peat indicates the sediment was deposited during the last glacial recession. The fill of the Meridian Avenue Basin and the carbon date retrieved indicate that it was created by an iceberg stranded in a shallow, post-glacial lake during the last ice age. Unlike typical kettle basins, load-induced subsidence of this iceberg into saturated lake-floor sediments created its shape and depth. The basin lacks the characteristic outwash depositions seen in kettle lakes and appears to be unique, suggesting the geologic processes which may have created the basin seldom occur and are poorly understood. Researching the origins and stratigraphy of the Meridian Ave basin will help advance knowledge of this possibly rare periglacial phenomenon.

Growing sustainable crops on Mars is an important aspect of developing a colony on the red planet. The goal of this research is to modify Martian regolith simulant to support plant growth. Results will be presented for the readjustment of the pH of Mars soil (pH 8) simulant to match that of typical fertile earth soil (pH 6) using nitric and phosphoric acid. The acids used were chosen based on their viability for transport to Mars and their ability to add crucial nutrients for plant growth in the unfertile soil. During the project, both acids effectively lowered the regolith pH, but in the hours and days following the pH increased significantly, which has motivated testing the buffer capacity of Mars soil simulant. The data collected was used to prepare three samples of Mars soil simulant; the first was modified with phosphoric acid, the second with nitric acid, and the third was also modified with nitric acid and had a buffer of dihydrogen phosphate added. The growth of kale was measured in the three modified soils, each mixed with equal parts potting soil. Kale growth was compared to trials performed without the acidification or buffering of Mars simulant soil.  Our research presents progress towards growing food in Mars regolith to sustain colonization efforts on the planet. This work can also be applied to the potential need to grow food in adverse conditions on Earth as the human population increases and the impacts of climate change advance.

In the study of plankton, it is common to count and identify them manually with the use of a microscope and sampling containers, which can be a tedious process. To address this problem, a Python-based neural network will be created to automatically identify common phytoplankton genera in Possession Sound. Since the most abundant phytoplankton in Possession Sound are diatoms, which include Thalassiosira, Coscinodiscus, and Chaetoceros, the network’s primary purpose will be to identify these genera. The neural network will be trained using approximately 1000 photos of each genus in varying orientations and lighting conditions, with the images being drawn from research trips aboard the Ocean Research College Academy vessel Phocoena beginning in 2007. After completing the training process, the network’s performance will be validated using samples taken at two sites around Possession sound, and it will be determined whether it meets a benchmark of 80% accuracy. It is expected that a number of challenges will be encountered with distinguishing between phytoplankton that are distorted or layered on top of one another, and these issues could be further addressed in the future. Despite these possible problems, the neural network shows promise as a low-cost alternative to current automated phytoplankton identification devices such as the FlowCAM, which can cost upwards of $100,000.