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

CIVIL & ENVIRONMENTAL ENGINEERING: TRANSPORTATION ENGINEERING

AI-Powered Approaches for Equitable Facility Accessibility Improvement in Rural Infrastructure Systems

Significance: This project develops AI-powered approaches to improve equitable access to critical public facilities (e.g., hospitals, schools, transit stations) through structural infrastructure modifications such as adding transportation links, transmission lines, or multimodal pathways. National and international initiatives—including the U.S. Bipartisan Infrastructure Law and the United Nations Sustainable Development Goals—emphasize infrastructure expansion to improve access to essential services for underserved communities. However, existing facility accessibility improvement problem (FAIP) models cannot adequately address multiple facilities, agent preferences, or infrastructure disruptions. This project establishes scalable and efficient algorithmic foundations to support large-scale, equity-focused infrastructure planning.

Student Participation: The student will formulate a simplified rural case study of the facility accessibility improvement problem using publicly available transportation or infrastructure network data. The student will implement and test AI-based optimization algorithms (e.g., preference-based accessibility models or network modification heuristics) to evaluate infrastructure improvement scenarios. The student will conduct a parametric study to examine how accessibility outcomes change under varying disruption levels and population priority weights. The student will also assist in organizing and analyzing results from tabletop exercises involving planners and community stakeholders to incorporate preference data into the model. Student Outcomes: The student will gain experience in infrastructure modeling, AI-based optimization, and resilience analysis while developing skills in data-driven decision-making for equitable infrastructure planning. The project will position the student to contribute to a conference paper or poster submission. Prerequisite Knowledge and Training: Basic programming experience (e.g., Python) is preferred. No prior knowledge of advanced optimization or AI is required; the student will receive structured onboarding through guided tutorials and supervised coding exercises during the first phase of the project.

Dr. Wissam Kontar

CIVIL & ENVIRONMENTAL ENGINEERING: TRANSPORTATION ENGINEERING

HEARTLand Link: Addressing Pharmaceutical Deserts via Autonomous Logistics 

Significance: Many rural communities face “pharmaceutical deserts,” where residents experience long travel times, limited pharmacy availability, and reduced access to essential medications. This challenge is fundamentally geographical and transportation-driven: distance, sparse networks, and disruption sensitivity combine to reduce reliability of access. HEARTLand Link is a software-based research project that integrates geospatial analysis with transportation systems modeling to identify access gaps and evaluate scalable solutions. The project will develop and test hub-and-spoke service designs that combine conventional and autonomous ground mobility with aerial delivery as a contingency or augmentation layer to improve coverage and reliability.

Student Participation: Students will first conduct a geospatial and transportation accessibility analysis to identify and characterize pharmaceutical deserts (e.g., travel-time burden, population served, spatial inequities, sensitivity to network disruption). Next, students will build a network-based simulation environment representing candidate hubs (e.g., pharmacies/clinics/depots) and demand nodes, and implement hub-and-spoke operational policies. Students will run scenario experiments comparing baseline autonomous ground-only service to hybrid designs where aerial mobility provides surge capacity or disruption-resilient service, and quantify outcomes (coverage, time-to-access, reliability, equity-oriented metrics). The project is primarily software and modeling, no hardware deployment or field testing is expected in the initial phase. Student Outcomes: Students will produce a reproducible analysis pipeline (data + code), a set of simulation results with clear figures/maps, and a final research paper to be submitted to Transportation Research Board Annual Meeting. Prerequisite Knowledge and Training: Introductory programming experience (Python preferred) and comfort working with data. Geospatial analysis, network modeling, and simulation methods will be taught through structured onboarding materials and weekly milestones; no prior experience in drones, robotics, or optimization is required, yet they are preferred.

Dr. Kaycie Lane

CIVIL & ENVIRONMENTAL ENGINEERING: ENVIRONMENTAL ENGINEERING

Examining the association between nitrate and manganese in drinking water sources

Significance: Small drinking water systems often blend multiple wells to decrease elevated levels of common contaminants in drinking water, reducing downstream treatment costs and increasing efficiency.  Recent anecdotal evidence from small water systems in Nebraska indicate there is an inverse trend between the presence of nitrate and manganese in groundwater sources; currently the UNL Citizen Science program is collecting data for both manganese and nitrate but historical trends are unknown.  This project aims to investigate existing water quality data trends between nitrate and manganese.  Nitrate is a well documented concern in Nebraska and manganese is an evolving concern; the relationship between these two contaminants in water therefore represents an important consideration for small groundwater systems when choosing sustainable water sources and treatment technologies.

SStudent Participation: Students will work with the mentor to complete the following tasks over the course of the summer: Gather and clean historical data about nitrate and manganese in Nebraska; Merge data sets to allow for comparisons between nitrate and manganese in specific drinking water systems; and Statistical analyses to determine a relationship in historical and Citizen Science data. Student Outcomes: Students will learn how to: Use the R software language to clean, organize, summarize, analyze and plot historical data; Map data as necessary to determine where future sampling effort need to occur; Construct publication quality plots in the R software language; and Outline and draft a research publication. Prerequisite Knowledge and Training: No prior knowledge is required to complete this project. Students will learn how to use the coding language R over the Summer 2027 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

Remote Sensing for Rail Safety and Track Condition Assessment

Significance: Remote sensing using uncrewed aerial systems (UASs or drones) provides an efficient and repeatable means to monitor the safety of rail infrastructure. Railway corridors, particularly those in rural regions, are critical transportation lifelines for agricultural, freight, and other goods, yet remain difficult to inspect due to their length and accessibility. While traditional inspection approaches employ specialized vehicles and sensor platforms operating directly on the track, these methods can be costly, limited in spatial coverage, and may create traffic bottlenecks. High-resolution aerial imagery and 3D point clouds derived through structure-from-motion (SfM) photogrammetry provide complementary perspectives that capture detailed information on track geometry, ballast condition, and structural integrity. This research develops workflows for analyzing UAS-derived data using computer vision and artificial intelligence to characterize track health and damage, thereby advancing safety and reliability across rural rail corridors. The broader project also includes the integration of these digital datasets into augmented and virtual reality (AR/VR) environments for immersive digital inspection and workforce training applications, allowing users to explore reconstructed rail environments in realistic, interactive settings. 

Student Participation: The student will assist in the collection and analysis of rail inspection data using drones equipped with high-resolution RGB cameras. They will curate and annotate datasets from newly collected and existing imagery to support the development and testing of machine-learning models for rail and tie defect detection. The student will also contribute to digital reconstructions of track sections from orthmosaic imagery and 3D SfM point clouds, which will later support immersive AR/VR simulations for digital track assessment and educational outreach. Student Outcomes: The student will gain experience integrating geospacial data, machine learning, and remote sensing for civil infrastructure assessment. They will develop skills in image interpretation, data annotation, and digital reconstruction while learning how automation and artificial intelligence improve inspection safety, objectivity, and efficiency. Prerequisite Knowledge and Training: No formal coursework is required. Training in data collection, processing, and annotation will be provided by a graduate student and faculty mentor during the first two weeks.

Dr. Li Zhao

CIVIL & ENVIRONMENTAL ENGINEERING; TRANSPORTATION ENGINEERING

Human Driving Behavior and Interaction with Automated Driving Systems on Rural Roads

Significance: Automated driving systems have the potential to improve transportation safety and mobility, particularly in rural areas where transportation services are limited. However, automated vehicles that are not fully driverless may still require human operators to monitor the driving environment, interact with automation, and take over control when necessary. These interactions raise safety concerns related to driver attention, cognitive workload, situation awareness, and takeover performance. This research aims to investigate human driving behavior and interactions with automated driving systems, particularly shared control between professional operators of automated vehicles or shuttles and the vehicle automation systems. Using the MiniSim half-cab driving simulator, the research will examine how cognitive workload and different driving conditions affect driver behavior and performance during automated driving operations. The findings will support safer human–automation interactions and the implementation of automated shuttle and vehicle services in rural areas.

Student Participation: The student will assist in designing and conducting driving simulator experiments using the MiniSim half-cab simulator. The student will help develop rural driving scenarios involving automated driving, human–automation interactions, and driver takeover situations. The student will also participate in experimental testing, data collection, and analysis of driver behavior, cognitive workload, and takeover performance. Student Outcomes: The student will gain hands-on experience in driving simulation, human factors research, experimental design, and data analysis, as well as an understanding of automated driving technologies and transportation safety. Prerequisite Knowledge and Training: No formal prerequisite knowledge is required. A background in psychology, human factors, computer science, or related fields would be beneficial. Basic programming or data analysis skills are preferred but not required. Training will be provided during the research project.

Dr. Yumeng Zhao

CIVIL & ENVIRONMENTAL ENGINEERING: GEOTECHNICAL & MATERIALS ENGINEERING

Flow Behavior of Irregular-Shaped Granular Materials

Significance: Granular materials are ubiquitous in both natural and engineered systems, especially in rural settings where they play critical roles in agriculture, energy production, and hazard mitigation. The efficient storage, transport, and mechanical processing of irregular biomass particles, such as ground corn stalks, are essential for bioenergy utilization but are often hindered by complex flow behavior. Similarly, natural hazards like landslides and debris flows involve irregular grains whose interlocking and frictional characteristics govern large-scale mobility. Understanding the flow behavior of such irregular-shaped granular materials is thus vital for improving agricultural processing, renewable energy systems, and the resilience of rural infrastructure. The objective of this research is to quantify how particle shape influences the flow behavior of irregular-shaped granular materials using a vane shear rheometer. This fundamental investigation will provide critical insights into the mechanics of such materials, leading to a better understanding of their behavior in varied applications, especially for rural settings.

Student Participation: The REU student will conduct vane shearing testing using a commercial rheometer to investigate the flow behavior of irregular-shaped 3D-printed granular particles. The student will record shearing torque as a function of time, particle shape, loading condition and shearing rate. The student will also take images of the sheared sample and post-process the collected data. Student Outcomes: The REU student will gain fundamental knowledge on granular mechanics and scientific data processing. Prerequisite Knowledge and Training: Basic knowledge of using excel or MATLAB is recommended but not required. Basic knowledge in introductory level mechanics is preferred.