Sustainability and Resilience in Rural Areas

Dr. Nirupam Aich

CIVIL & ENVIRONMENTAL ENGINEERING: ENVIRONMENTAL ENGINEERING

Advanced nanomaterials and manufacturing for PFAS remediation

Significance: Per- and polyfluoroalkyl substances (PFAS) is an emerging pollutant that has become a major threat to the environment and public health. PFAS compounds are difficult to degrade in the natural environment as well as conventional water treatment processes and can accumulate within human body leading to different diseases. Our group designs sustainable nanomaterials and nanotechnology with unique capabilities to treat and remove PFAS compounds from our drinking water, ground water or wastewater. We are also combining 3D printing approaches to design nanotechnology-based water filters that can simultaneously separate and degrade PFAS from water. The objective of this research is to design catalytic nano-filters using 3D printing. We will design novel filters, characterize their mechanical, physical, and chemical properties, and also, we will identify their capabilities to achieve PFAS adsorption and/or degradation.  

Student Participation: The REU student will synthesize new nanocomposite-based 3D printing ink, design the new filter via 3D printing the previously synthesized ink, perform PFAS adsorption experiments, and analyze the obtained data to conclude about the performance of the new filter for PFAS adsorption/degradation.  Student Outcomes: The REU student will learn about the impact of nanotechnology and 3D printing in designing novel water filter. The student will also learn about PFAS pollution problem and will be able to critically think about solutions to this problem. Prerequisite Knowledge and Training: No formal course prerequisites. 

Dr. Shannon Bartelt-Hunt

CIVIL & ENVIRONMENTAL ENGINEERING: ENVIRONMENTAL ENGINEERING

Microplastics Occurrence in Agricultural Streams

Significance: The occurrence of microplastics, an emerging contaminant in agricultural systems, is very poorly characterized. Plastics are a frequently observed component of marine debris and there is growing concern about microplastic ecotoxicity, and the impacts of sorbed hazardous organic contaminants, heavy metals and biofilms on microplastic surfaces. However, microplastics are increasingly being found in terrestrial freshwater environments in addition to marine systems. To date, there is little information about how surrounding land use affects the concentrations of microplastics in freshwater streams. The primary research question to be addressed in this project is how concentrations of microplastics in freshwater streams differ between agricultural and suburban land uses. 

Student Participation: The REU student will install nets to gather microplastics from streams in agricultural and suburban areas in Nebraska. The student will separate and characterize the microplastics using stereoscopy. Student Outcomes: The REU student will gain exposure to field research and laboratory training in microplastic characterization methods including density separation and oxidation. The student will learn to use visible light and UV microscopes. Prerequisite Knowledge and Training: No formal course prerequisites. Microscopy training to be conducted during the first week of the program by the graduate student and faculty mentor. 

Dr. Nathan Huynh and Dr. Li Zhao

CIVIL & ENVIRONMENTAL ENGINEERING: TRANSPORTATION ENGINEERING

Multi-Sensor Fusion for Proactive Commercial Motor Vehicle Safety at Work Zone

Significance: The overarching goal is this project is to support commercial motor vehicle (CMV) safety programs with the acquisition of enriched, high-quality data sets of CMV movement in work zone areas and to target unsafe driving of CMVs and non-CMVs with proactive safety-focused countermeasures; that is, real-time guidance to mitigate future crash/near-crash events for approaching vehicles.  The project will create co-simulation tools and augment them with a virtual collaborative environment to educate the public, motor carriers, and CMV drivers about these work zone safety enhancement solutions. Many studies have demonstrated that combining data from multiple sensors, such as cameras, LiDAR, and RADAR, has the potential to significantly reduce vehicle detection errors and improve the overall quality of traffic data. However, the effectiveness of fused data and Digital Twin technology for improving work zone safety has not been investigated. To bridge this gap, research is needed to investigate effective ways of multi-sensor data collection to develop work zone safety models, improve the accuracy and reliability of predictive models and ensure that these models can be used to mitigate potential safety issues. 

Student Participation: The student will first learn three well-known simulation software, VISSIM (microscopic traffic simulation software), TruckSim (heavy truck dynamics simulation software), and CARLA (autonomous driving simulator).  Then the student will develop a co-simulation environment that takes inputs from the three software and output sensor information, vehicle state, and performance metrics related to operational and safety conditions.  Lastly, the student will evaluate and design a proactive work zone safety warning system using the co-simulation environment. Student Outcomes: The student will learn about traffic simulation, truck dynamics simulation, and autonomous driving simulation.  The student will learn about systems integration.  Lastly, the student will learn about the process of designing and conducting simulation experiments, and subsequently, drawing conclusions from the experiments. Prerequisite Knowledge and Training: No formal prerequisite knowledge is required.  Training will be provided during the course of the research.

Dr. Kacie Lane

CIVIL & ENVIRONMENTAL ENGINEERING: ENVIRONMENTAL ENGINEERING

Characterizing long-term functionality and resiliency of small drinking water systems

Significance: This study focuses on helping researchers and water systems stakeholders such as regulator, town clerks, managers and operators to better characterize the technical capacity of small drinking water systems to meet the challenge of emerging contaminants of concern.  Specifically, this study seeks to generate a more versatile and accurate metric to determine how to characterize “long-term functionality” to replace the commonly used metric of age.  Current data collected at small water systems is often overlooked as a potential source of rich information that can inform stakeholders about the technical capacity of a small system to meet upcoming challenges.  This study seeks to understand what current data and what potential data needs to be collected to better inform how we as water industry engineers and researchers characterize the sustainability, longevity and functionality of drinking water systems. The study has the following objectives: 1) Pilot a survey of small drinking water systems in EPA Region 7 to characterize current data collection practices and needs; 2) Analyze the results of the survey and propose a set of in-depth qualitative research questions to be conducted with a select group of small drinking water systems based on the results of the summer’s activities.

Student Participation: The student will work with the mentor to complete the following tasks over the course of the summer: 1) Familiarize themselves with the technical capacity literature that has been used to develop the initial survey questions for this study; 2) Review a previously developed set of survey questions and help the mentor revise and iterate upon the survey questions; 3) Code the survey questions into a survey software such as Qualtrics; 4) Help the mentor disseminate the survey across various platforms such as LinkedIn and via email; 5) Work with the mentor to collect survey responses and format the data received for coding in the R programming language; 6) Analyze the initial results of the survey in the R programming language; 7) Propose a set of follow-up interview questions for a select group of stakeholders based on the results of the survey. Student Outcomes: Students will learn how to effectively conduct mixed methods research by (1) learning how to conduct qualitative surveys and interviews and (2) how to analyze the semi-quantitative data available from surveys and literature in the R coding language. Prerequisite Knowledge and Training: No prior knowledge is required to complete this project. Students will learn how to use survey tools such as Qualtrics and the coding language R over the Summer 2025 REU program.  Prior knowledge of any coding language for data analysis is a plus.

Dr. Xu Li

CIVIL & ENVIRONMENTAL ENGINEERING: ENVIRONMENTAL ENGINEERING

Using the One Health Approach to Address Antimicrobial Resistance

Significance: This project is at the interface of environmental engineering and public health. The dominant transmission routes for antimicrobial resistant pathogens are between humans, between animals, and between humans and animals.  The transmission between humans and animals can be direct or indirect via the environment, often through fecal contamination.  The importance of the environment as a medium for pathogen transmission receives increasing attention, particularly under the shifting environmental conditions caused by the climate change.  This is a collaborative project with the Nebraska Public Health Laboratory (NPHL) and the Nebraska Veterinary Diagnostic Center (NVDC). NPHL receives and analyzes clinical samples from human patients in urban and rural areas, while NVDC veterinary samples from livestock and companion animals.  The proposed One Health approach, which involves human, animal, and the environment, will lead to the development of a holistic pathogen surveillance and mitigation strategy for residents in Nebraska. The project has the following objectives: 1) track the transmission of clinically relevant antimicrobial resistant bacteria and genes of human- and animal-origin in the environment; and 2) investigate the evolution of antimicrobial resistance in fecal bacteria in the environment by assessing the acquisition of antimicrobial resistance genes from environmental microbiota.  

Student Participation: Task 1 (Field): The REU student will help with field sampling of antimicrobial resistant bacteria from the environment (e.g., surface water, soil, and wastewater). Task 2 (Lab): The REU student will help with isolating and characterizing antimicrobial resistant bacteria from environmental samples in the lab. Task 3 (Bioinformatics): The REU student will conduct bioinformatic analyses on the whole genome sequence of the bacteria from the environment as well as in clinical and veterinary samples. Student Outcomes: Student will gain first-hand experience in field sampling and sample processing and characterization of bacteria important to public health, and learn how to use state-of-the-art bioinformatic pipelines in analyzing the whole genome sequence of antimicrobial resistant bacteria. Prerequisite Knowledge and Training: Basic knowledge of environmental engineering. Basic knowledge of microbiology is desirable, but not required. 

Dr. Yusong Li

CIVIL & ENVIRONMENTAL ENGINEERING: WATER RESOURCES ENGINEERING

Micro- and Nanoplastics released from plastic food packaging

Significance: The widespread use of plastic products in food handling poses a direct risk of releasing tiny plastic particles, such as microplastics (less than 5 mm) and nanoplastics (less than 1 µm), into our food. Recent research, including our own, has uncovered alarming findings about these particles being released from plastic food containers, even during typical usage or when usage guidelines are ignored. Some containers can release as many as 4.27 billion microplastics and 2.29 trillion nanoplastics into a liter of water in just three minutes of microwave heating. Our preliminary toxicity study shows that exposure to these particles can result in the death of human embryonic kidney cells. This revelation has prompted pressing public health concerns, with unresolved questions about release circumstances, plastic types, ingestion levels, and toxicity. Moreover, it is vital to explore potential disparities based on demographics and access to alternative containers. 

Student Participation: The REU student will help conduct microplastics and nanoplastics release experiments. The student will measure the number of particles released into food. Student Outcomes: The REU student will gain exposure to scientific research, including detecting microplastics and nanoplastics using various instrument. Prerequisite Knowledge and Training: No formal course prerequisites. Students will be trained in the first week of the program by the graduate students and faculty mentor on how to use various lab equipment.

Dr. Tirthankar Roy

CIVIL & ENVIRONMENTAL ENGINEERING: WATER RESOURCES ENGINEERING

Machine learning approaches to address problems related to rural hydrology

Significance: Proper understanding of hydrology can help us better manage our water resources and build resilience to hydrologic extremes, such as floods and droughts. New datasets of different hydrologic variables are becoming more readily available with the advances in remote sensing technologies, in situ monitoring, and model-based assessments. Machine learning has great potential in effectively addressing a plethora of problems in the field of hydrology, leveraging these large datasets. Several problems are becoming increasingly more tractable, which was not the case before with limited data availability. This is also opening up several avenues for testing novel hypotheses related to hydrologic process-understanding. Students in this project will be working on machine learning algorithms to address hydrologic problems in the rural settings. The problems can be related to physical process-understanding where, among other things, we try to understand what factors influence different hydrologic processes and how. We study how these processes interact with each other and coevolve. The problem can also be related to hydrologic modeling, where we try to model different aspects of the physical system. Once we have a model of the system, it can be used for a wide range of problems (e.g., generation of forecasts, analysis of future scenarios, optimal water management, etc.). 

Student Participation: Students will run machine learning algorithms, for which some preliminary codes will be provided. Students will apply these algorithms to different hydrology problems and analyze the results. Student Outcomes: The projects will involve working with machine learning algorithms applied to a wide range of problems in hydrology. We can also discuss other potential research topics. Prerequisite Knowledge and Training: Introductory course on hydrology or water resources. Some prior experience with coding will be useful.

Dr. Joshua Steelman

CIVIL & ENVIRONMENTAL ENGINEERING: STRUCTURAL ENGINEERING

Revisiting Reliability for Rural Bridges

Significance: Rural bridges are crucial to agricultural economic activities, particularly during harvest seasons when crop yield transportation imposes heavy loads on bridges. Many bridges in rural areas are at or beyond their intended service life and were designed either for unknown or lower vehicle loading than required in modern codes. Unnecessarily imposing load restrictions on bridges leads to increased trip frequencies and lengths for freight vehicles, or demolishing and replacing safe bridges.  Therefore, it is desirable to maximize permitted vehicle loading and extend service lives of aging bridges. Reassessing the structural capacity and mechanical response to vehicular loads for rural bridges is critical to achieving this goal. The primary research question that this project addresses is: how does uncertainty in mechanical response to vehicular loads influence structural reliability for rural bridges? 

Student Participation: The REU student on this project will conduct analyses to investigate the relationships between uncertain structural characteristics (e.g. composite shear transfer on steel beams designed neglecting composite action) and risk-targeted safe load carrying capacity. The student will propose, conduct, and analyze results for a small experimental testing plan related to the analytical work. Student Outcomes: The student will gain experience performing structural experimental testing and an understanding of 3-dimensional structural system behavior.  The student will be introduced to probabilistic concepts for structural engineering evaluation and reliability assessment, advanced mechanical modeling, and machine learning techniques. Prerequisite Knowledge and Training: Mechanics, which is typically acquired by the second year in an engineering program.

Dr. Jamilla Teixeira

CIVIL & ENVIRONMENTAL ENGINEERING: GEOTECHNICAL & MATERIALS ENGINEERING

Use of Residues from Nebraska Agriculture Sites as Paving Material

Significance: The recycling of waste materials and reducing the carbon footprint of manufactured products through conserving energy and reducing the use of raw materials has become a primary focus. Pavement maintenance and new roadway construction in rural area require tons of new materials. Landfill, as a traditional residue waste disposal method, has a high demand for land resources, which has also become a key issue for solid waste disposal. Recycled asphalt pavement (RAP), asphalt shingles (RAS), waste plastic residues (WPR), agriculture wastes and/or filler by-products can be alternative sustainable materials for asphalt mixture production. The goal of this research project is, first, to determine what are the main types of residues from Nebraska agricultural areas. From this initial assessment, our goal is to identify potential residues that could be used for asphalt mixture production. Our ultimate goal is to determine the most appropriate addition method and percentage of selected residues in the mixture to obtain optimized and feasible asphalt mixtures with recycled material addition.   

Student Participation: The REU student will conduct a preliminary survey to obtain data related to residue generation in Nebraska rural areas. The student will collect samples from different residues and perform physical and mechanical characterization of the materials in the UNL/CEE Geomaterials Laboratory. The student will apply the balance mix design to determine optimum material blends (asphalt binder, aggregates, and selected residues) with satisfactory performance results (Superpave Volumetric Design combined with Cracking and Rutting Performance Tests). Student Outcomes: The REU student will be able to conduct primary characterization tests and to understand the material’s physical and mechanical characteristics effects on the asphalt mix design. The REU student will learn how to design an experimental plan to perform asphalt mix design based on the concept of balance mix design, i.e., combining volumetric assessment with performance evaluation. Prerequisite Knowledge and Training: Basic knowledge of excel is recommended but not required. Sampling will be conducted with a graduate student or faculty mentor during the first two weeks.

Dr. Christine Wittich

CIVIL & ENVIRONMENTAL ENGINEERING: STRUCTURAL ENGINEERING

Seismic Performance Assessment and Shake Table Testing of Steel Grain Bins 

Significance: Despite the criticality of the agricultural industry to both U.S. and global sustainable food production, the resulting lack of economic diversity in most rural areas is theorized to be a major contributor to the low resilience of rural communities to natural hazards, including earthquakes and windstorms. While resilience is a function of many socioeconomic and organizational factors, the disaster response of the built environment is a critical aspect that cannot be ignored. In many rural areas, critical infrastructure includes vital agricultural support and production systems, such steel grain bins. However, these structures are not typically designed to consistent standards and have been observed to perform poorly in recent events. This research aims to generate a fundamental understanding of the performance of steel grain bins during extreme windstorms to enhance rural resilience to natural hazards. 

Student Participation: The student will perform a parametric study using a pre-developed computational model to assess key design variables for steel grain bins. The student will work in the full-scale structures laboratory with a graduate student to perform destructive shake table tests for validation of the computational model. Student Outcomes: Student will gain a basic understanding of structural and earthquake engineering, agricultural infrastructure, and full-scale structural testing/shake table testing methods. Prerequisite Knowledge and Training: College-level mechanics/physics, which is typically covered during the first year. This project, in part, requires manual labor associated with full-scale testing and experience with construction or an ability to lift 50 lbs is a plus. Laboratory training to be provided. 

Dr. Richard Wood

CIVIL & ENVIRONMENTAL ENGINEERING: STRUCTURAL ENGINEERING

Remote Sensing for Wind Characterization in Rural Areas

Significance: Remote sensing data collection from unpiloted aerial systems (or drones) is an efficient and well-known approach to study the impact following extreme windstorms. Example windstorms include hurricanes, tornadoes, and straight-line winds; which result in damage to both the built and natural environment.  Post-event damage surveys typically utilize the Enhanced Fujita (EF) scale to relate structural damage to wind speeds; however, these are limited in application to rural areas.  Rural areas, which encompass a significant portion of the US with high windstorm risk, are typically sparsely populated with few structures and consequently, the relationship of natural and agricultural systems to wind speed is highly uncertain. Remote sensing data in terms of high-resolution imagery and point clouds can collect perishable data related to the distribution, orientation, and severity of damage for understanding windstorms. This research aims to develop workflows for analyzing remote sensing data through the application of computer vision and artificial intelligence techniques to understand the wind hazard and response of the built and natural environment with a particular focus on rural areas. 

Student Participation: The student will collect and process field remote sensing data related to recent windstorms, inclusive of recent tornadoes, thunderstorms, and the August 2020 Midwest Derecho. The student will also have the option to apply machine learning techniques to extract and characterize features of interest. Student Outcomes: The student will gain an understanding of machine learning techniques and geospatial data in terms of high-resolution orthomosaic images and point clouds as applied for civil engineering, which is typically not taught in undergraduate courses. Prerequisite Knowledge and Training: No formal coursework is necessary. Data collection and processing training will be conducted by a graduate student or faculty mentor during the first two weeks.