Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item Monitoring and Predicting the Microbial Water Quality in Irrigation Ponds(2022) Stocker, Matthew Daniel; Hill, Robert L; Environmental Science and Technology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Small- to medium-sized farm ponds are a popular source of irrigation water and provide a substantial volume of water for crop growth in the United States. The microbial quality of irrigation waters is assessed by measuring concentrations of the fecal indicator bacteria Escherichia coli (E. coli). Minimal guidance currently exists on the use of surface irrigation waters to minimize consumer health risks. The overall objective of this work was to provide science-based guidance for microbial water quality monitoring of irrigation ponds. Spatial and temporal patterns of E. coli were evaluated in two Maryland irrigation ponds over three years of observations. Patterns of E. coli were stable over the three years and found to be significantly correlated to patterns of water parameters such as temperature, dissolved oxygen, turbidity, and pH. The EPA Environmental Fluid Dynamics Code model was used to evaluate the spatial 3D heterogeneity of E. coli concentrations within the ponds. Significant differences in E. coli concentrations by sampling depth were found. Spatial heterogeneity of E. coli within the pond also resulted in substantial temporal variation at the irrigation pump, which was dependent on the intake location. Diurnal variation of E. coli concentrations was assessed for three farm ponds. E. coli concentrations declined from 9:00 to 15:00 for each pond, but statistically significant declines were only observed in two of the three ponds. Dissolved oxygen, pH, and electrical conductance were found to be the most influential environmental variables affecting E. coli concentrations. To better describe the relationships between E. coli and the environmental variables, four machine learning algorithms were used to estimate E. coli concentrations using water quality parameters as predictors. The random forest algorithm provided the highest predictive accuracy with R2 = 0.750 and R2 = 0.745 for Ponds 1 and 2, respectively, in the multi-year dataset containing 12 predictors. Temperature, electrical conductance, and organic matter content were identified as the most influential predictors. It is anticipated that the recommendations contained in this dissertation will be used to improve microbial monitoring strategies and protect public health.Item Using citizen science to collaboratively research and manage Chesapeake Bay(2021) Webster, Suzanne E; Dennison, William C; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Chesapeake Bay is a complex socio-ecological system with an equally complex adaptive management program. The environmental management community has expressed a need for more local-scale environmental data and increased stakeholder engagement in Bay restoration efforts. Although citizen science has the capacity to meet both of these needs, participatory research is currently underused and undervalued. Additional research is needed to help Chesapeake Bay environmental stakeholders develop and leverage citizen science partnerships to accomplish diverse research and management goals. This dissertation explored various challenges that limit the use and potential impact of citizen science in Chesapeake Bay. Three distinct studies were conducted to gain a more complete understanding of stakeholders’ perceptions and experiences concerning public engagement in scientific research. These studies employed several qualitative and quantitative approaches, including interviews, participant observation, surveys, and cultural consensus analysis. This research provided evidence of widespread agreement that diverse stakeholder concerns should be more prominent in management decisions. Research also found shared feelings of disempowerment across the Chesapeake environmental community. Environmental stakeholders appreciated that science plays a central role in informing environmental policy, but they had mixed perspectives on the utility of citizen science. This research found an underlying cultural understanding of environmental monitoring that provides a foundation for collaboration among stakeholders with different priorities. These findings indicate that citizen science programs can a) serve as boundary spanning organizations that help stakeholders foster a more cooperative mentality, b) allow diverse groups to strategically work together to accomplish goals, and c) increase the impact of volunteer-collected data on Chesapeake science and management. This research also showed that using a transdisciplinary approach to citizen science can increase stakeholders’ feelings of engagement, improve perceptions of a program’s overall credibility, and increase the program’s overall likelihood for impact. The results of this place-based study in the Chesapeake region are also broadly applicable to other socio-environmental systems. This dissertation provides evidence-based support for continued and expanded stakeholder engagement in environmental science and management and offers specific recommendations to support more collaborative, productive, and empowering citizen science partnerships that inform holistic and innovative environmental management decisions.