The COVID-19 shutdown in the states of Washington and New York significantly reduced transportation and cut normal daily activities due to constraints issued by governments. To understand how air pollution was affected during the shutdown, this research studied various air pollutants at two different locations in each state; Seattle and Olympia/Tacoma in Washington state, and New York City and Rochester in the state of New York. Daily averages of carbon monoxide, carbon dioxide, sulfur dioxide, nitrogen dioxide, PM2.5, and tropospheric ozone were collected for each location from 2016-2020, including the months from January through August. A linear regression model with a 95% confidence interval, was built using the 2016 to 2019 data to estimate the monthly averages for 2020 to determine if there was a change in any of the air pollution levels due to the COVID-19 shutdown. While there was no notable difference in most of the air pollution levels during the COVID-19 shutdown, there was a significant drop in nitrogen dioxide levels at all four locations. More surprisingly, carbon dioxide was showing an increase during the shutdown. It is speculated that there are two reasons behind the increase in carbon dioxide. First, carbon dioxide is showing an overall yearly increase during our selected research time interval. Secondly, the biggest carbon dioxide producers are industry and power plants. Due to said constraints and confinements, it is concluded that households' electricity consumption went up. This could be explained by the fact that schools and businesses moved entirely online requiring everyone to participate via video conferencing systems and to operate daily tasks via online platforms. As a whole, this research is significant to the study of climate change and its effects, and mitigation of said effects of climate change.

Astronomy of the 21st century is driven by large data sets collected by large automated sky surveys. The largest survey project currently being built is the Legacy Survey of Space and Time (LSST), which will be a 10-year survey of the southern sky, expected to discover 5.5 million small bodies in our Solar System. The greater scientific community needs to know what the research potential and scope of the data LSST will collect. I am building a database -- accessible at http://ls.st/ssdb -- of simulated LSST observations of asteroids in our Solar System. My work delves into simulation accuracy, big data analysis, and database management. This dataset consists of individual observations, an orbit catalog, and a catalog of physical and observational characteristics. Using simulations from the University of Washington’s Data Intensive Research in Astrophysics and Cosmology Institute (DiRAC) and scripting in Python, I am attempting to accurately present this data and areas of possible research before LSST becomes operational. This project will be integral to preparing for research projects that will analyze actual LSST data.

Soil pedogenic carbonates are precipitates of carbonate minerals, majorly calcite, that accumulate in soils in arid, temperate, and subtropical areas. They retain clues to their environment in their isotopic signature that make them important for paleoclimate reconstructions across subtropical to subpolar regions. However, we lack an exact understanding of pedogenic carbonate accumulation in subtropical and monsoonal regions, where heavy rainfall encourages leaching in the soil profile, yet soil carbonates are still observed. A clearer understanding of the process by which carbonates accumulate in the subtropics will aid the interpretation of isotopic data from past warmer and wetter periods. To better understand their formation in these soils, I used HYDRUS-1D, a numerical model that simulates soil processes, to track calcite accumulation over a five-year period under conditions resembling the environment of Myanmar, which today lies in the monsoonal domain. I tested various scenarios of rainfall, vegetation, soil type, and temperature to determine the control of these environmental factors on the depth, timing, and amount of calcite accumulation. Results show that as precipitation increases, the major effect on accumulation results from varying rainfall distribution and rooting depth. Most carbonate precipitation occurs during the dry season in winter and spring, indicating a clear seasonal bias in their isotopic record. This seasonal bias – commonly ignored in paleoenvironmental studies – should be considered in future work.

Modern astronomy predominantly consists of analyzing large data sets from automated sky surveys. The largest survey project, the Legacy Survey of Space and Time (LSST), is currently under construction at the Vera C. Rubin Observatory. One of its goals is to create a catalog of smaller objects such as asteroids and comets in the Solar System. The LSST Solar System Object catalog and other LSST-sourced data critically rely on the ability to predict object positions, as well as to recognize and to link previously unknown ones. Object positions are calculated with software known as integrators. There are several well-known integrators in the solar system dynamics community: JPL Horizons, OpenOrb, and OrbFit. They are credited as acceptable for calculating positions, however, they have not been rigorously compared to one another. This project addresses this issue. For my research, I built an automated system that compares the most popular integrators by testing them on a set of known objects. These objects are picked to explore both usual and unusual regions in space. The system compares the object positions by evaluating a number of metrics (e.g., on-sky distance, position vector 3D distance, and others), and will visualize the results in form of a dashboard. This allows for the assessment of various integration package suitability as a function of population to be integrated, as well as tracking their performance in an automated fashion as improvements and changes are made. The most important product of my work is the clear definition of each integrator's bounds of applications. Currently, this is the only comprehensive comparison of its kind. Using my comparison, other scientists will be able to decide what integrator to use for their specific use case. Given the broad implications, this work will prove to be invaluable to the astronomical community as a whole.

X-ray absorption spectroscopy (XAS) is used in the physical and biological sciences as well as in materials research to infer the chemical properties of compounds by characterizing their electronic structures. However, current methods of XAS spectra analysis rely heavily on prior knowledge of a variety of factors including possible molecular structures, chemical environment, etc. We use unsupervised machine learning (ML) techniques to classify spectra and define the classes of information present independently in XAS spectra. Building on our group’s prior work on XAS spectra of sulforganic molecules, we seek to understand the encoding of oxidation state, bonding geometry, and other chemical descriptors in XAS spectra of phosphorganic molecules. To do this, we have defined and implemented a data pipeline that starts with the PubChem database, uses appropriate filtering for metadata such as oxidation state and ligand group identity, and then employs the NWChem software package to calculate simulated XAS spectra for each molecule. The resulting training data set is then investigated with several unsupervised ML methods, including a Variational Autoencoder, to obtain dimension reduction. Clusters of similar spectra in the dimensionally reduced data spaces are then analyzed for their connections to underlying atomic and chemical properties. We demonstrate that unsupervised ML is a powerful tool for the extraction of chemically-relevant information from XAS spectra.

Extrasolar planets (planets orbiting another star) were discovered in the early 1990’s and since then over 4,350 exoplanets were confirmed to exist as of February 2021, according to the NASA Exoplanet Archive. Our research focuses on looking for trends between the physical/orbital properties and the atmospheric properties of exoplanets. To search for these trends, we use Python and the Habitable Zone Gallery, which is a website that is dedicated to tracking the orbits of exoplanets in relation to their habitable zones, to select exoplanets that are within our desired scope. We have written software that extracts particular physical and orbital data on planets from the Habitable Zone Gallery, but currently, both the download and the transfer to our Google Drive are done manually. These manual tasks are inefficient, among other things, due to the fact that they do not account easily for updates. I will present the software we have developed and our latest developments to resolve these issues, such as modifications to automatically download and upload all of the necessary data. This increases efficiency and in turn, ensures everyone on our team is using the same set of data. Additionally, we are designing a PostgreSQL database that would hold all of our collected data. To increase accessibility of the database, we would utilize interfaces that allow individuals from various academic backgrounds to perform searches on our data. This helps guarantee that all of our data is in one place, accessible, and up to date. In the future, we intend for our software and results to be published for the scientific community. This allows for all of our research to be accessible to individuals who wish to learn more about the relation between the physical/orbital and atmospheric properties of exoplanets.

Two-dimensional (2D) materials are a class of layered crystals which can be isolated down to an effectively 2D film. These materials offer a swath of interesting properties, such as magnetism and superconductivity, which are uniquely tied to crystal structure at the nanometer scale. Previous studies have predicted exotic behavior as a result of straining 2D materials, but few studies have explored high compressive and tensile strain at cryogenic temperatures. Yet, such extreme temperatures are required for these unique quantum phenomena to manifest. Here, we report a novel technique for applying in-situ strain at cryogenic temperatures. This technique, which is compatible with all standard 2D fabrication techniques, consists of constructing a 2D heterostructure on a narrow, thin silicon pillar. The silicon pillar is strained, and the strain is transmitted to the sample sitting on top. We also discuss Finite Element Analysis of the mechanical properties of our strain apparatus. This modeling explores the spatial distribution of strain applied to samples under various experimental conditions and compares the theoretical results to experimental measurements. The outlook of our modeling suggests that the technique can apply large strains with high spatial uniformity. Finally, we report the preliminary effects of applying uniaxial strain to a hexagonal boron nitride / graphene heterostructure. Our results thus establish a powerful new tuning method for exploring unique strain behavior in 2D materials and heterostructures.