REU: Sustainability of Horizontal Civil Networks in Rural Areas

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.

Significance: The transportation sector is among the largest contributors of greenhouse gas emissions in the United States. A key pathway to decarbonization of the sector is electrification of the private vehicle stock. This pathway is particularly important in rural areas, where transit and land use planning are less feasible options. Rural residents generally drive further than their urban counterparts, while facing a relative deficit in infrastructure investments. As such, there is a need for an analysis of both the sufficiency of charging infrastructure in rural regions and the ability of electric vehicles to meet rural residents’ travel needs. The primary objective of this research is to examine the ability of electric vehicles (EVs) to satisfy the travel requirements of rural Nebraskans. In addition, the spatial allocation of charging stations relative to travel demand will be investigated to identify whether there is a rural deficit.

Significance: Current drinking water treatment facility capacity to treat emerging contaminants of concern in Nebraska is not well understood.  Rising levels of new contaminants such as PFAS result in strains on existing drinking water infrastructure, with many very small water systems (serving less than 500 people) needing immediate or long-term improvement or retrofitting in order to adequately provide safe drinking water to customers.   It is particularly challenging in small systems to implement new technologies in a timely manner due to financial, technical and managerial concerns.  The first step to addressing contaminants of concern such as PFAS is therefore to identify at-risk systems which will help prioritize resources (money, time, etc.) and provide small systems with a reliable improvement in a timely manner.

Significance: Agricultural nonpoint source pollution (NPS) is a significant contributor to the contamination of surface water and groundwater resources. With increasing demands on global agricultural production and the need to maintain sustainable water resources in the future, it is crucial to identify areas with high agricultural NPS potentials. Understanding the spatial distribution of NPS pollution is essential for the design of mitigation strategies. This project will quantify and predict the spatial distribution of agricultural NPS risks in the United States under historical and future climate scenarios. 

Significance: 

There has been considerable interest in electric vehicles (EVs) from consumers, government agencies, and manufacturers resulting in an exponential growth in new EV sales. EV-related run-off-road (ROR) crashes and interactions with roadside features can pose unique and significant concerns. It is imperative that infrastructure be compatible with all vehicles to avoid unnecessary loss of life and critical transportation infrastructure damage. However, little is known about EV crash performance with roadside features. The objective of this research is to review real-world crash data involving EVs with roadside features and guide transportation agencies to implement the optimal deployment of safe products, research, and interactions. This research aims to identify (1) what EV and roadside feature compatibility problems exist, (2) what are contributing factors to “bad outcomes” involving EVs and roadside features, and (3) provide recommendations to improve the universality of roadside feature design.

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.).

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?

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 design to consistent standards and have been observed to perform poorly in recent severe windstorms. This research aims to generate a fundamental understanding of the performance of steel grin bins during extreme windstorms to enhance rural resilience to natural hazards.