Environmental Studies

The Seasonality of Infectious Diseases Across the Globe

Dysentery is an infection causing inflammation of the intestines and bloody diarrhea. It is often spread via contaminated food and water and was a prevalent public health concern throughout the twentieth century and still today to an extent. Data provided by the World Health Organization from 1944-1977 categorizes dysentery as either amoebic or bacillary. Amoebic dysentery is caused by the parasite, amoeba Entamoeba histolytica, and bacillary dysentery is caused by the Shigella bacteria. Dysentery cases were examined in a variety of countries around the world from 1944-1977. This data was first scanned from World Health Organization record books, and PDFs were converted into Excel files using optical character recognition (OCR) software. The cumulative data over the entire timespan was then graphed and visualized using the R programming language using time series to determine if any trends in seasonality existed for either or both forms of dysentery. These visualizations will help doctors and public health professionals better understand the seasonality of dysentery as well as where and when it is most prevalent. This will provide vital information that can improve timing and delivery of treatment in parts of the world with high incidences of dysentery.

The Historical Seasonality of Paratyphoid Across the Globe

Paratyphoid fever is one of many bacterial infectious diseases shown to exhibit seasonal dynamics, likely influenced by environmental conditions. However, a complete understanding of the seasonal patterns and identification of environmental factors which drive disease transmission have not been fully studied. We compiled and analyzed global data from the World Health Organization spanning from 1932-1957 and provincial level Thailand data from 1980-2020. Both of these datasets report monthly clinical cases and deaths of paratyphoid that occur in the given area. We used optical character recognition software to transform the data from pdf format to a format conducive to statistical analysis with R. Based on previous studies and initial findings, it appears that paratyphoid cases spike during hot weather months which vary depending on hemisphere. Other environmental factors, such as rainfall and variation in temperature have also been shown to influence paratyphoid spread and seasonality. Understanding the transmission dynamics of infectious diseases such as paratyphoid fever is imperative for public health officials to develop comprehensive guidelines and policies. The nuances of infectious disease transmission are also important to understand to reduce the size of epidemics and prevent the inundation of healthcare systems that would stop an otherwise treatable condition from being treated.

Civic Resilience and Neighborhood Resilience Projects and Hubs

As the climate crisis accelerates, emergency and civil services increasingly are overstretched. Resilience-based programming provides relief by empowering individual citizens, neighborhood organizations, and local institutions to share the planning and preparation on behalf of their communities. Settlements become more resilient to the impacts of climate change when they develop the capacity of neighborhoods to endure, quickly recover from, and renew after extreme events. To aid these processes, we developed “A Pattern Language for Neighborhood Resilience” as a starting point. This book contains a number of guides – or patterns – on how to invest in skills, small-scale infrastructure, relationships, and supplies that increase a neighborhood’s resilience. The book is currently in draft form and soon will be pre-tested before being released. As a living document, it will undoubtedly be modified and expanded throughout its adoption.

Progress Towards Developing a Quantitative Definition for Atmospheric Rivers

Atmospheric Rivers (ARs) are channels of water vapor in the atmosphere that result in a large amount of precipitation. They cause over 1.1 billion dollars of damage annually in the United States. As a result, accurately interpreting the presence of an AR has become crucial to ensuring the safety of citizens, especially those who live in regions frequently impacted by ARs. Progress is difficult, however, because the accepted AR definition is largely qualitative, and researchers who have tracked ARs use different identification methods. Therefore, an analysis of the strengths and weaknesses of various detection algorithms is of great interest. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is a multicentered, collaborative effort to quantify the differences among AR detection techniques. This study focuses on eight AR detection algorithms run on ECMWF’s ERA5 reanalysis product. The time intervals among the ERA5 datasets range in hours and the time frame generally covers the years 1980-2019. To facilitate comparison among detection approaches, we compared transects of AR landfall from East Asian countries using the Python Programming language. Assuming that each algorithm identifies the presence of an AR perfectly, the transect plots should be identical among the eight datasets. However, some plots differed significantly from the others. For future studies, one should develop a standardized approach to detecting and interpreting ARs. An accurate interpretation of these weather systems will not only save lives but help countries economically.

Investigation of South America Atmospheric Rivers

Previous studies have identified that atmospheric features with low-level moisture transport, known as atmospheric rivers (ARs), are connected to extreme precipitation events around the globe. While these ARs provide vital water resources to communities, they are also known to cause fatalities resulting from flooding and landslides. The severity of their impacts is expected to increase with climate change due to increased atmospheric moisture. Our understanding of what drives changes in AR behavior is still incomplete. While there has been a wide breadth of research conducted on ARs in the North Pacific region, much work has yet to be done in order to fill gaps in knowledge about ARs across the globe. The investigation of South America ARs aims to quantify how our understanding of ARs depends on algorithm choice. This specific study focuses on detection algorithms run on JRA-55 reanalysis (55 km resolution). The dataset consists of six different algorithms, namely the ARConnect, GuanWaliser, IDL, Mundhenk, Payne and Reid algorithms. Python is used for intercomparison and visual representation of the differences and strength of various detection algorithms, focusing on the understudied region of southern South America (15°N-60°S, 110°W-16°W). This project aims to identify algorithms that perform poorly and common AR characteristics where there is agreement. In doing so, it is the hope that affected communities across this region will better be able to respond to incoming extreme weather associated with AR events.

Investigating the Identity of an Ancient Fish Fossil Using CT Scanning

This research project examines Wardichthys cyclosoma, an extinct species of fish that lived during the Carboniferous period (around 350 million years ago) from Scotland. In the past, Wardichthys cyclosoma has been classified as a platysomid, a type of Paleozoic (540-252 million years ago) fish known for their deep, flat bodies. However, this classification is based on limited evidence since there is little detail visible on the fossil. This project applies X-ray computed tomography to examine the anatomy of a fossil of Wardichthys that is still enclosed in rock. Using past scientific literature, we found several points of similarity between Wardichthys cyclosoma and confirmed members of platysomid group. Major observations that are consistent with this placement within this platysomid group include the shape and arragement of the postclethrium, clavicle, cleithrum, and sclerotic ossicle. By corroborating past hypotheses, this research project helps refine the understanding of the early evolution of these ancient fishes.

Reporting the Lakes

Great Lakes Now is a news source and division of Detroit Public Television. It covers issues that affect the Great Lakes and the 40 million people that live within the watershed. GLN produces a monthly show in addition to articles covering topics like water quality, public policy, environmental justice, economic development, and resource conservation. We focused specifically on drinking water quality and infrastructure in the Great Lakes region, tracking water advisories in Indigenous communities and water infrastructure projects in cities from Detroit to Cleveland. Stories like these support GLN aims by increasing public knowledge of the lakes and furthering interest in protecting them.

Computed tomography reconstruction of fossil vertebrates

In our research project we are studying CT examination of ancient vertebrates. Understanding morphology can illuminate the possible relationships between ancient fish vertebrates and the niches they inhabited. The fish species that will be examined for this are lungfishes, which are often described as “living fossils.” A “living fossil” is a broadly applied term but can be generally understood as an organism exhibiting a close resemblance to its older relatives found in the fossil record. This project utilizes 3D reconstructions of fossil sarcopterygian fishes to enhance our understanding of their place in the phylogenetic tree. We acquired fossils from various universities and museums across the country, and segmented out their lower jaws using the program Materialize by Mimics. By doing this our work will be turned into 3D models in the program Blender. These 3D models will then be used to “landmark” or flag important differences between the jaw structure in both the same and different species. Based on Landmark data we can better understand their place in the phylogenetic tree and the relationship between morphology, diversity, and the environmental niches of these fishes.