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Tools for predicting microbial water...

Gonzalez, Raul Alexander.

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Tools for predicting microbial water quality in estuaries used for recreation and shellfish harvesting.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Tools for predicting microbial water quality in estuaries used for recreation and shellfish harvesting./
Author:	Gonzalez, Raul Alexander.
Description:	158 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-01(E), Section: B.
Contained By:	Dissertation Abstracts International75-01B(E).
Subject:	Environmental Sciences. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3594164
ISBN:	9781303378249

Tools for predicting microbial water quality in estuaries used for recreation and shellfish harvesting.
Gonzalez, Raul Alexander.

Tools for predicting microbial water quality in estuaries used for recreation and shellfish harvesting. - 158 p.

Source: Dissertation Abstracts International, Volume: 75-01(E), Section: B.

Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2013.

To reduce public health risks and associated economic costs, legislation has been passed to ensure that surface waters meet standards necessary for human contact. These guidelines recommend that states routinely monitor water quality and notify the public when waters are unsafe for recreational contact or shellfish harvesting. Traditional, culture-based methods require 18-24 hours for incubation. This long processing time causes delays in the time between sample collection and public notification, which is typically on a time scale longer than that of fecal contamination events themselves. To reduce this time lag, recent national and international recommendations have placed an emphasis on the use of rapid molecular and predictive methods as tools to improve public protection. In this dissertation, I developed and applied newly approved rapid methods to predict fecal indicator bacteria (FIB) in an eastern North Carolina (NC) estuary. E. coli and enterococci concentrations can be predicted using multiple linear regression (MLR) models and a combination of antecedent rainfall, climate, and environmental variables. E. coli and enterococci models accurately predicted a high percentage (>87%) of management decisions based on regulatory thresholds. The combined assessment of quantitative PCR (qPCR) and MLR models showed both methods can be used in tandem to provide rapid estimates of water quality in estuaries. Model equivalency was established for enterococci and E. coli MLR models using culture- and qPCR-based data. Using time-frequency analysis, I determined that there is currently no optimal length of data needed for MLR model creation in eastern NC. Rather, managers can initiate their models with several weeks of data and then continually update models as new data become available. Lastly, I sought to understand the microbial dynamics of water quality across a range of hydrodynamic and meteorological conditions. Work here detailed a descriptive characterization of creeks to aid in variable selection during MLR development. Throughout the work, qPCR inhibition was the major complication. Therefore, I developed an approach to predict inhibition prior to sample processing. By using the tools outlined in this dissertation, managers in the region should be able to efficiently apply rapid methods and prediction tools in mid-Atlantic estuaries.

ISBN: 9781303378249Subjects--Topical Terms:

676987
Environmental Sciences.

Tools for predicting microbial water quality in estuaries used for recreation and shellfish harvesting.
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245 1 0 $a Tools for predicting microbial water quality in estuaries used for recreation and shellfish harvesting.
300 $a 158 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-01(E), Section: B.
500 $a Adviser: Rachel T. Noble.
502 $a Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2013.
520 $a To reduce public health risks and associated economic costs, legislation has been passed to ensure that surface waters meet standards necessary for human contact. These guidelines recommend that states routinely monitor water quality and notify the public when waters are unsafe for recreational contact or shellfish harvesting. Traditional, culture-based methods require 18-24 hours for incubation. This long processing time causes delays in the time between sample collection and public notification, which is typically on a time scale longer than that of fecal contamination events themselves. To reduce this time lag, recent national and international recommendations have placed an emphasis on the use of rapid molecular and predictive methods as tools to improve public protection. In this dissertation, I developed and applied newly approved rapid methods to predict fecal indicator bacteria (FIB) in an eastern North Carolina (NC) estuary. E. coli and enterococci concentrations can be predicted using multiple linear regression (MLR) models and a combination of antecedent rainfall, climate, and environmental variables. E. coli and enterococci models accurately predicted a high percentage (>87%) of management decisions based on regulatory thresholds. The combined assessment of quantitative PCR (qPCR) and MLR models showed both methods can be used in tandem to provide rapid estimates of water quality in estuaries. Model equivalency was established for enterococci and E. coli MLR models using culture- and qPCR-based data. Using time-frequency analysis, I determined that there is currently no optimal length of data needed for MLR model creation in eastern NC. Rather, managers can initiate their models with several weeks of data and then continually update models as new data become available. Lastly, I sought to understand the microbial dynamics of water quality across a range of hydrodynamic and meteorological conditions. Work here detailed a descriptive characterization of creeks to aid in variable selection during MLR development. Throughout the work, qPCR inhibition was the major complication. Therefore, I developed an approach to predict inhibition prior to sample processing. By using the tools outlined in this dissertation, managers in the region should be able to efficiently apply rapid methods and prediction tools in mid-Atlantic estuaries.
590 $a School code: 0153.
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710 2 $a The University of North Carolina at Chapel Hill. $b Environmental Sciences and Engineering. $3 2104684
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