Graduation Year

2017

Document Type

Thesis

Degree

M.S.C.E.

Degree Name

MS in Civil Engineering (M.S.C.E.)

Degree Granting Department

Civil and Environmental Engineering

Major Professor

Maya Trotz, Ph.D.

Committee Member

Shawn Landry, Ph.D.

Committee Member

Mahmood Nachabe, Ph.D.

Keywords

Framework, Spatial Analysis, Stormwater, Urban Ecosystems, Water Quality

Abstract

Nutrient inputs into the environment greatly impact urban ecosystems. Appropriate management strategies are needed to limit eutrophication of surface water bodies and contamination of groundwater. In many existing urban environments, retrofits or complete upgrades are needed for stormwater and/or wastewater infrastructure to manage nutrients. However, sustainable urban nutrient management requires comprehensive baseline data that is often not available. A Framework for Urban Nutrient (FUN) Management for Geographic Information Systems (GIS) was developed to specifically address those areas with limited data access. Using spatial analysis in GIS, it links water quality, land use, and socio-demographics, thereby reducing data collection and field-based surveying efforts. It also presents preliminary results in a visually accessible format, potentially improving how data is shared and discussed amongst diverse stakeholders. This framework was applied to two case studies, one in Orange County Florida and one in Placencia, Belize.

A stormwater pond index (SPI) was developed to evaluate 961 residential wet ponds in Orange County, Florida where data was available for land use and socio-demographic parameters, but limited for water quality. The SPI consisted of three categories (recreation, aesthetics, education) with a total of 13 indicators and provided a way to score the cultural and ecosystem services of 41 ponds based on available data. Using only three indicators (presence of a fence, Dissolved Oxygen (DO) < 4 mg/l, and water depth < 3 ft), 371 out of 961 stormwater ponds were assessed. Additional criteria based on socio-demographic information (distance to a school, population density, median household income under $50,000, percentage of population below the poverty line, and distance to parks) identified seven wet ponds as optimum for potential intervention to benefit residents and urban nutrient management purposes.

For the second case study, a water quality analysis and impact assessment was performed for the Placencia peninsula and lagoon in Belize. This study had access to water quality data, but limited land use data and very limited socio-demographic data. Since May 2014, water quality samples have been taken from 56 locations and analyzed monthly. For this study, Dissolved Oxygen (DO), Nitrate (NO3--N), Ammonia (NH3), Chemical Oxygen Demand (COD), and 5-Day Biochemical Oxygen Demand (BOD5), Escherichia coli (E. coli), and Enterococci were selected to assess spatial and temporal variation of water quality in the groundwater on the peninsula as well as the surface water in lagoon, estuaries and along the coast. A spline interpolation of DO, Nitrate, BOD5, and COD for June 2016 indicated the concentration distribution of those parameters and areas of special concern. A spatial analysis was conducted that showed that Nitrate and Enterococci exceeded the effluent limits of Belize very frequently in the complete study area while the other parameters contributed to the identification of key areas of concern. As a high variability of concentrations over time was observed, a temporal analysis was conducted identifying a link between the water quality data and two temporal impact factors, rainfall and tourism. The two case studies showed the broad and flexible application of the FUN management for GIS and the great advantages the use of GIS offers to reduce costs and resources use.

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