Phytoremediation with black willow (Salix nigra) was conducted in 1996 by planting trees at a density of 2000 trees/hectare at BASF Corporation in a shallow groundwater plume containing low levels of bentazon. In this project, the roots, stems,and leaves of the black willow plants were extracted and analyzed to see if bentazon from the ground water was taken up by the plants. Different extraction procedures such as liquid-phase extraction and solid-phase extraction were compared. Final determination using different analytical techniques such as GC-MSD, GC-ECD, and HPLC with UV detection for the isolation and analysis of bentazon residues from the plant matrix were compared and the best clean-up and analytical procedure for its determination in plant samples was demonstrated. On analysis using the LLE and GC-ECD method, bentazon concentrations in the plant samples collected over a one-year period did not show any discernible pattern. Residues of the pesticide were found in 15% of all the plant samples analyzed at the method detection limit of 0.27 mg/kg. The average bentazon concentration found in the samples was 0.48mg/kg with a range of 0.7mg/kg and 0.32mg/kg. Comparison of the LLE method with the SPE method of sample extraction shows the advantages of the SPE method over LLE due to decrease in the extraction time, use of less quantity of organic solvents, better quality of final extracts, and better recoveries. The average recoveries for leaves using LLE and SPE procedures with GC-MSD were 39.1% and 49.9% respectively. Average recoveries for stems using LLE and SPE with GC-MSD were 56.3% and 63.9% respectively. Average recoveries for the LLE procedure for all the five sets of samples using GC-ECD were 51.7%, 58.1% and 59.6% respectively for leaves, stems, and roots matrices. Comparison of the HPLC method with GC demonstrates that GC is a preferable method for analysis of bentazon from plant matrices due to its sensitivity, reliability and higher separation efficiency. Positive identification of bentazon was difficult with HPLC because of its insufficient sensitivity for trace analysis and its low separation efficiency resulting in coelution and broad peaks.

Laboratory microcosm experiment and parallel ex situ bioremediation studies were conducted to study the effect of temperature on in situ bioremediation systems installed at Superfund site in southeast Georgia. Laboratory microcosms, inoculated with PAHs/phenols specific microorganisms, at high temperatures (42±2°C; 107±3°F) demonstrated a significant reduction of residual polycyclic aromatic hydrocarbons (PAHs) and selected phenols from 4927±1356ng/g dry weight soil to 1158±530ng/g with a kinetic rate of 76.1±16.8ng/g/day (84% reduction; p≤0.01) in 49days. High temperature non-inoculated microcosms were reduced of residual PAHs and phenols from 1117±436ng/g to 341±116ng/g with a kinetic rate of 15.8±6.5ng/g/day (65% reduction; p≤0.01) in 49days. Low temperature inoculated treatment (21±1°C; 69±2°F), was reduced from 3048±200ng/g to 1,094±240ng/g PAHs/phenols with kinetic rate of 39.8±1ng/g/day (66% reduction, p≤0.01) in 49days; A 50% reduction in low temperature non-inoculated treatment from 813±189ng/g to 367±79ng/g (p≤0.1794) was also observed with kinetic rate of 9.1±2.2ng/g/day. The laboratory study demonstrated that efficiency of bioremediation could be enhanced by maintaining an elevated temperature using amended and/or indigenous microflora. Amended populations were also proved to be more effective at lower temperatures. Field biopile data sets presented somewhat different findings. Minimal reduction at high ambient temperature range of 70-79°F from 9349±1420 to 9300±1017mg/kg (kinetic rate: 0.40±3.3ng/g/day) was seen in the first 120days of the field study. Evaluations of the pile indicated significant desiccation occurred due to black polypropylene cover used to entrain heat. By correcting moisture level using trickling spray under the plastic sheeting, significant reductions of ≥35% (13912±2054 -9021±1660mg/kg PAHs/phenols) with a kinetic rate of 40.7±3.3ng/g/day, despite lower air temperatures of 48-58°F in the last 120days. The results showed the efficacy of correctly using immobilized bed bioreactors (bioplugs) under plastic sheeting coupled with proper soil moisture management during colder winter months. These studies show that optimizing temperature for in situ bioremediation technologies at Superfund sites can reduce time required for treatment of hazardous wastes; hence reduction in operational time and effort to ensure smooth running of the remediation process year round (especially in cold regions and cold weather) can be realized.

Utilizing Bayesian hierarchical techniques, regional-scale eutrophication models were developed for use in the Total Maximum Daily Load (TMDL) process. The Bayesian tree-based (BTREED) approach allows association of multiple environmental stressors with biological responses, and quantification of uncertainty sources in the water quality model. Simple parametric models are often inadequate for describing complex datasets; the BTREED approach partitions the dataset, and describes the localized subsets of data with linear models, thereby providing a comprehensive representation of stressor and response interactions. Nutrient criteria data for lakes, ponds and reservoirs across the United States were obtained from the Environmental Protection Agency (U.S. EPA) National Nutrient Criteria Database. Model estimation was accomplished by randomly splitting the composite dataset into training and test sets, and using the training dataset in model estimation, and the test dataset to evaluate and validate the model. Mean squared error was reported for both training and test data of the highest log-likelihood models. The Bayesian approach to regional-scale eutrophication models is also beneficial from a decision-theoretic perspective. Predictions regarding the variable of interest are quantified by probability distributions, providing the decision maker with valuable information about the distribution of the biological response conditional on the stressors, and information about the model error.

The Baton Rouge Air Quality Control Region has come under fire due to difficulties in reaching attainment of one-hour daily ozone limitations and its general perception as being a polluted place to live. Ozone is a reactive oxygen species which has been shown to result in damage to biological molecules and is detrimental to human physiology, especially in regards to cardio-respiratory structure and function. In the last decade, fine particulate matter less than 2.5 microns in mean aerodynamic diameter have been identified as a possible pollutant that is affecting public health at levels lower than the EPAs established limits. PM2.5 has also been linked to adverse cardiovascular and respiratory health affects. The intent of this study was to examine the air quality of Baton Rouge and the surrounding five parishes and compare these concentrations to mortality cases for cardiovascular and respiratory causes of death. Poisson regression analysis found that PM2.5 had no significant effect on mortality frequency. Ozone was found to have a negative relationship; as ozone levels increased, mortality rates decreased. Multiple regression of both pollutants confirmed the results obtained individually, with no indications of synergistic or antagonistic effects.

Natural disasters devastate the United States through both economic loss and loss of life. The world wide economic damage that results from natural disasters has more than tripled in the last thirty years. Of these natural disasters, floods are the most chronic and costly disasters, comprising an average $5 billion dollars of damage each year. FEMA has released a new software program called HAZUS-MH, which attempts to capture economic losses caused by flooding before losses occur and predict losses from real-time events. This estimate is accomplished through the coupling of flood hazard modeling with local data. FEMAs goal is that the information constructed within the program will help planners to mitigate and capture flood related losses. This study provides a methodology for assessing the accuracy of HAZUS level one flood loss estimates by examining the extent to which HAZUS default building stock inventory data represents the built local environment. The study area is concentrated in the northwest corner of Livingston Parish, Louisiana. The area is comprised of 200 census blocks that were chosen due to their proximity to the Amite River. Thus it is an area prone to floods. Livingston Parish is located in the Mississippi River and Lake Maurepas Basin, which collectively cover approximately 236,000 acres. 70% of the Parishs land is located within FEMAs 100-year flood plain. Building count for structures was obtained using remote sensing technology, processed and used to populate HAZUS ® MH default databases. Flood loss estimations were run for all of the data sets and results were compared for a significant difference. Differences in flood loss between the two analyses were found in isolated areas. This demonstrated the need to incorporate growth and development information into flood loss estimation methodologies.

Research was conducted to identify the states that compiled Comparative Risk Reports and the year in which the study was completed. The information relating to the type of committee that compiled the risk report and the final risk rankings was examined. State Comparative Risk Reports were collected for twenty states and the risk rankings that each state compiled were reviewed to determine which states ranked third generation environmental problems and the ranking that those problems were given. The risk reports compiled by each state were also used to determine which states ranked natural resource based problems and the ranking that those problems were given. Dependent variables were constructed for the third-generation problems global warming, ozone depletion, acid rain as well as for natural resource-based problems. Independent variables were used to indicate the contextual setting of the responses of the individual states. The independent variables that were used are as follows: Total Green Index score, Green Policy score, Green Conditions score, air pollution score, natural resource GSP, per capita income, and bachelors degree. Two different methods were used to analyze the data compiled for this research, difference of means testing and Pearson bivariate correlation analyses. The difference of means test was used to determine if there were patterns in the relationship between the dependent variables and independent variables that would suggest reasons why states would rank third generation environmental problems and natural resource based problems differently. One-tailed Pearson bivariate correlation analyses were conducted to determine if there were significant statistical associations between the dependent and independent variables. In analyzing the data from the risk rankings of each state, significant associations were found between concern for global warming and the Green Policy score. Significant associations were also found between the combined rankings of third generation environmental problems and the Air Pollution score taken from the Green Index and between natural resource based problems and natural resource GSP.

At least one third of the insecticide used in agriculture has been used to control the boll weevil in cotton. Historically, these insecticides have been toxic to humans and harsh on the environment. In addition, the intensive use of chemical insecticides to control the boll weevil results in the disruption of naturally occurring biological control factors that regulate other insect pest populations causing a chain reaction of secondary pest populations that require treatment followed by resurgence and repeat treatment. This situation has resulted in the development of resistance to insecticides, high control costs and unacceptable levels of chemical insecticide contamination in the environment. The boll weevil eradication program was instituted, in part, in an attempt to curtail the adverse environmental effects of traditional boll weevil control practices. A statistical analysis of 23 restricted use pesticides use in Louisiana cotton from 1991 through 2003 revealed that there was a significant change in the use of seven of these insecticides from the time before eradication (1991-1998) and during eradication (1999-2003) in Louisiana. Of these seven, five showed a significant decrease in use and two showed a significant increase in use. Impact quotients developed to take into account the annual use of each insecticide plus the toxicological values and environmental influence of each insecticide indicate that the insecticides with a significant change in use had a smaller mammalian toxicological impact quotient (MTIQ) during eradication than before eradication. The ecotoxicological impact quotient (EcoTIQ) increased dramatically during eradication as compared to before eradication and the environmental impact quotient (EnvIQ) increased slightly over the same period. In all cases, the MTIQ remained substantially lower than the EcoTIQ and EnvIQ in any given year and as eradication progressed and malathion use declined, so did the impact quotients and insecticide use, especially methyl parathion. As eradication of the boll weevil in Louisiana nears completion, a continued reduction in all three quotients is expected.

Coastal Louisiana wetlands contain more than 30% of the U.S. coastal wetlands, but its wetland loss accounts for about 90% of the continental states. Although the effects of coastal wetlands preservation and restoration never stop since the enactment of CWPPRA in 1990, these regulation projects benefit only a small fraction of the degraded Louisiana coastal wetlands because of the limited budget. The general objective of this study is to provide an understanding of the economic factors which establish property values in coastal wetlands private market in order to devise and implement cost efficient economic incentive mechanisms for private landowners and then address the wetlands loss of coastal southwestern Louisiana. The research collects 59 useful private property samples from Southwest Louisiana and covered the 1990-2002 period. Four wetland types (fresh marsh, intermediate marsh, brackish and saline marsh), open water, property size, a discrete variable indicating whether a property is separated into two or more parcels, and distance variables (i.e., distance from the nearest coast and road) were the factors affecting property values. With the help of GIS data and tools, hedonic functions are established. Results indicate that open water percentage and percentages of all wetland types have negative effects on property prices. Furthermore, wetland types have different marginal implicit prices. Intermediate marsh has the largest effect on property values, followed by the brackish and saline marsh, and open water in descending order. All three types of wetlands are statistically significant at the level of above 99%; however, fresh marsh percentage is statistically insignificant even at the level of 85%, and has little coefficient effect on property price when considered in relation to the base (i.e., other category).

A new structure-activity relationship (SAR) approach to modeling was utilized to study mammary gland carcinogens. A set of chemicals tested for mammary tumorigenesis that have been analyzed in the Carcinogenic Potency Database (CPDB) were subjected to several computational analyses in an attempt to predict each chemicals carcinogenic potential. A total of six learning sets (rat and mouse mammary gland carcinogen, CPDB rat and mouse, and female-specific rodent models) were developed and validated using a SAR modeling algorithm called categorical-SAR (cat-SAR). The predictive cat-SAR program evaluates active and inactive compounds of known biological activity and predicts their biological activity from this categorical data. Overall, this study demonstrates the usefulness of cat-SAR and its successful application in developing structural alerts to breast carcinogenicity. The resulting rat and mouse mammary carcinogen models achieved an 82.0% (sensitivity 76.7%; specificity 87.5%) and 80.6% (sensitivity 80%; specificity 81.8%) concordance between experimental and predicted results, respectively. Likewise, the general CPDB mouse and rat models were both 70% predictive. Corresponding sensitivity and specificity values were 74.2 and 66.7% and 70.4 and 68.5%, respectively. The analyses indicate the capability of cat-SAR in identifying molecular fragments that potentially interact with cellular components present only in the targeted cell type (e.g., breast tissue cells). Moreover, this method is expected to help pre-determine structural alerts to carcinogen-induced mammary cancer. Identification of these structural alerts can assist in understanding mechanisms involved in making a normal breast cell cancerous. Using the results of these analyses, it is possible to classify and rank structurally diverse chemicals as to their potential to induce mammary gland cancer.

The number of environmental chemicals found to have some level of endocrine activity has led to concern about the possible effects these compounds could have on human health and the health of other species, populations, and possibly whole ecosystems. The United States Environmental Protection Agency has been charged with testing a large number of these compounds, called endocrine-disrupting chemicals or hormonally active agents for hormonal activity. Limited testing resources have led to a call for alternate methods of screening, possibly for use in prioritizing this list to assist in efficient allocation of resources for further testing. This study describes a computational method, the categorical structure activity relationship (cat-SAR) program, which has demonstrated high predictivity for the estrogen-like activity of a set of diverse chemical structures. The data set for this model was taken from a set of 122 compounds assayed for estrogenicity with the ESCREEN assay, an in vitro assay for estrogenicity. Two endpoints were modeled. The model for relative proliferative potency demonstrated an 82% correct prediction rate, while the relative proliferative effect achieved an 86% correct rate of prediction in model validation. Preliminary evaluation of fragments upon which the models were based suggested a sound mechanistic basis. The models also compared similarly to previous ESCREEN models developed using a different methodology. Based on the results described herein, the cat-SAR method would be a useful approach in screening compounds for estrogen activity as well as for investigating their mechanism of action.