Numerical estimation of nitrogen load from septic systems to surface water bodies in St. Lucie River and Estuary Basin, Florida View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2016-12-28

AUTHORS

Ming Ye, Huaiwei Sun, Katie Hallas

ABSTRACT

Nitrogen pollution is one of the most prevalent and challenging environmental problems worldwide. Wastewater treatment using onsite sewage treatment and disposal systems (aka septic systems) is one source of nitrogen sources in the environment. This study presents a numerical study for estimating nitrogen load from septic systems to surface water bodies in the St. Lucie River and Estuary Basin, Florida, USA. The load estimation is conducted using an ArcGIS-based nitrogen load estimation toolkit, a screening-level modeling software that considers key mechanisms (i.e., advection, dispersion, and denitrification) controlling nitrogen transport as well as spatial variability of model parameters (e.g., hydraulic conductivity, porosity, septic system locations, and surface water bodies). The simulated nitrogen plumes and load estimates demonstrate the importance of considering spatial variability in the load estimation for nitrogen pollution management. The load estimates are strongly correlated with nitrogen concentrations in surface water quality data, suggesting that septic systems are an important factor for water quality deterioration in the St. Lucie River. The estimates can be used directly to support Basin Management Action Plan (BMAP). Evaluating the load estimates in the BMAP context indicates that nitrogen load from septic systems is a significant nitrogen source in the St. Lucie River and Estuary Basin. It is found in this study that the load estimates depend on lengths of flow paths, groundwater flow velocities, and soil drainage conditions. These findings are useful for nitrogen pollution management in similar areas facing nitrogen pollution problems. More... »

PAGES

32

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12665-016-6358-y

DOI

http://dx.doi.org/10.1007/s12665-016-6358-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1031405887


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