Type of Document Thesis Author Fernandes, Raoul I. URN etd-07152011-101611 Title Statistical Methods for Estimating the Denitrification Rate Degree Master of Science Department Earth, Ocean & Atmospheric Science, Department of Advisory Committee
Advisor Name Title Ming Ye Committee Chair William Parker Committee Co-Chair Stephen Kish Committee Member Yang Wang Committee Member Keywords
- Nitrate Loss
- Simplified denitrification models
Date of Defense 2011-06-15 Availability unrestricted AbstractNitrates (NO3- ) are one of the principal contaminants in ground water. Excess nitrate in ground water is known to cause serious illnesses such as methemoglobinemia, and cancer. In addition to the adverse impact on the health of humans, excess nitrate is known to have unfavorable effects on the ecosystem. One of the major contributors to nitrates in the system are septic tanks. Approximately one-third of Florida’s population uses Onsite Wastewater Treatment System (OWTS). In order to quantify the nitrate load to a water body several models have been developed, these models always ignore nitrate from normally working septic tanks and denitrification that occurs between the septic tank drain field and the water body. Additionally these models are often complex and developed specifically for a given site.
The aim of this project is to develop a simplified model that can estimate nitrate fate and transport from an On-site Wastewater Treatment System (OWST) to a targeted water body. The Simplified model is developed in two parts, the first to estimate the fate and transport of nitrate and the second the development of a denitrification rate (Rdn). This work focuses on the development of a model to estimate the rate of denitrification using easily available parameters.
To estimate the denitrification rate, data was first collated from existing literature values and data available from other researchers. The data collected included the main factors that controlled denitrification i.e. texture, temperature, water filled porosity (WFP), organic carbon, pH, bulk density, soil depth, nitrate concentration and the denitrification rate. A total of 1129 distinct set of parameters and denitrification rates were collected and then statistically analyzed to determine the relationships between the factors and the denitrification rate. The denitrification rates ranged from not detectable up to 157 .
Three statistical methods were used to estimate the denitrification rate, linear regressions with Monte Carlo simulation, Multi Regression analysis and the development of a neural network. Denitrification rates were found to be dependent on the WFP as well as organic carbon. For the linear regressions a predictive relationship could not be established between WFP and the denitrification rate. In addition, although an increase in organic carbon content is typically assumed to increase denitrification, a linear relationship between organic carbon and the denitrification rate could not be obtained unless the additional controlling parameters are fixed. Stable isotope data is used to predict the percent of nitrate removed due to denitrification. This method serves as an alternative to estimate the loss of nitrate due to denitrification, but is unable to estimate a rate of denitrification.
The developed methods are then applied to three study areas in Jacksonville and the estimated denitrification rates from the methods are compared. Overall the results from the each of the methods except for the multi-regression analysis are a reasonable estimate of the denitrification rate. Due to the complexity of denitrification it is the Neural Networks that are able to best estimate the denitrification rate. Thus by using easily available parameters and existing data the models are able to match or improve the accuracy in predicting the denitrification rate at a fraction of the cost without requiring site specific data.
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