Organic matter dynamics in the Florida coastal Everglades: A molecular marker and isotopic approach

Everglades National Park (ENP) is about to undergo the world's largest wetland restoration with the aim of improving the quality, timing and distribution of water flow. The changes in water flow are hypothesized to alter the nutrient fluxes and organic matter (OM) dynamics within ENP, especially in the estuarine areas. This study used a multi-proxy approach of molecular markers and stable δ 13C isotope measurements, to determine the present day OM dynamics in ENP. ^ OM dynamics in wetland soils/sediments have proved to be difficult to understand using traditional geochemical approaches. These are often inadequate to describe the multitude of OM sources (e.g. higher land plant, emergent vegetation, submerged vegetation) to the soils/sediments and the complex diagenetic processes that can alter the OM characteristics. A multi-proxy approach, however, that incorporates both molecular level and bulk parameter information is ideal to comprehend complex OM dynamics in aquatic environments. Therefore, biomass-specific molecular markers or proxies can be useful in tracing the sources and processing of OM. This approach was used to examine the OM dynamics in the two major drainage basins, Shark River Slough and Taylor River Slough, of ENP. Freshwater to marine transects were sampled in both systems for soils/sediments and suspended particulate organic matter (SPOM) to be characterized through bulk OM analyses, lipid biomarker determinations (e.g. sterols, fatty acids, hydrocarbons and triterpenoids) and compound-specific stable carbon isotope (δ 13C) determinations. ^ One key accomplishment of the research was the assessment of a molecular marker proxy (Paq) to distinguish between emergent/higher plant vegetation from submerged vegetation within ENP. This proxy proved to be quite useful at tracing OM inputs to the soils/sediments of ENP. A second key accomplishment was the development of a 3-way model using vegetation specific molecular markers. This novel, descriptive model was successfully applied to the estuarine areas of Taylor and Shark River sloughs, providing clear evidence of mixing of freshwater, estuarine and marine derived OM in these areas. In addition, diagenetic transformations of OM in these estuaries were found to be quite different between Taylor and Shark Rivers, and are likely a result of OM quality and hydrological differences. ^

Chlorine contribution to quantitative structure and activity relationship models of disinfection by -products' quantum chemical descriptors and toxicities

Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: (1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (E LUMO) via QSAR modelling and analysis; (2) to validate the models by using internal and external cross-validation techniques; (3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl ) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: (1) Linear or Multi-linear Regression (MLR); (2) Partial Least Squares (PLS); and (3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: (1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; (2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; (3) E LUMO are shown to correlate highly with the NCl for several classes of DBPs; and (4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.

Chlorine Contribution to Quantitative Structure and Activity Relationship Models of Disinfection By-Products' Quantum Chemical Descriptors and Toxicities

Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: 1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (ELUMO) via QSAR modelling and analysis; 2) to validate the models by using internal and external cross-validation techniques; 3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: 1) Linear or Multi-linear Regression (MLR); 2) Partial Least Squares (PLS); and 3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: 1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; 2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; 3) ELUMO are shown to correlate highly with the NCl for several classes of DBPs; and 4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.