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Ecotoxicological Investigations in Effluent-Dominated Stream Mesocosms

Description: The University of North Texas Stream Research Facility (UNTSRF) was designed to examine contaminant impacts on effluent-dominated stream ecosystems. Stream mesocosms, fed municipal effluent from the City of Denton, TX, Pecan Creek Water Reclamation Plant (PCWRP), were treated with 0, 15 or 140 µg/L cadmium for a 10-day study in August 2000. Laboratory toxicity test and stream macroinvertebrate responses indicated that cadmium bioavailability was reduced by constituents of effluent-dominated streams. The Biotic Ligand Model (BLM) for Cd was used to predict a 48 hour Cd EC50 for Ceriodaphnia dubia of 280 µg/L in these effluent-dominated streams. This value is higher that an EC50 of 38.3 µg/L Cd and a 7-day reproduction effect level of 3.3 µg/L Cd generated for C. dubia in reconstituted laboratory hard water. These results support use of a cadmium BLM for establishing site-specific acute water quality criteria in effluent-dominated streams. Although not affected by 15 µg/L treatments, organisms accumulated Cd in 15 µg/L treated streams. Hence, over longer exposure periods, Cd accumulation may increase and a no effect level may be lower than the observed 10-day no effect level of 15 µg/L. A toxicity identification evaluation procedure was utilized with in vitro and in vivo bioassays to identify estrogenic compounds in PCWRP effluent, previously identified to seasonally induce vitellogenin (VTG) in male fathead minnows. Steroids, nonylphenol ethoxylate metabolites, and other unidentified compounds were identified as causative effluent estrogens. These findings suggest that in vivo VTG bioassays should be used to confirm in vitro Yeast Estrogen Screening assay activity when effluents are fractionated or screened for estrogenicity. A subsequent 90-day cadmium study was initiated to assess long-term effluent and cadmium effects on fish endocrine function. Juvenile fathead minnows were placed in UNTSRF pool sections of replicate streams treated with 0, 5, 20 or 80 µg/L Cd. Male ...
Date: December 2002
Creator: Brooks, Bryan W.

A geospatial tool for assessing potential wildland fire risk in central Texas.

Description: Wildland fires in the United States are not always confined to wilderness areas. The growth of population centers and housing developments in wilderness areas has blurred the boundaries between rural and urban. This merger of human development and natural landscape is known in the wildland fire community as the wildland urban interface or WUI, and it is within this interface that many wildland fires increasingly occur. As wildland fire intrusions in the WUI increase so too does the need for tools to assess potential impact to valuable assets contained within the interface. This study presents a methodology that combines real-time weather data, a wildland fire behavior model, satellite remote sensing and geospatial data in a geographic information system to assess potential risk to human developments and natural resources within the Austin metropolitan area and surrounding ten counties of central, Texas. The methodology uses readily available digital databases and satellite images within Texas, in combination with an industry standard fire behavior model to assist emergency and natural resource managers assess potential impacts from wildland fire. Results of the study will promote prevention of WUI fire disasters, facilitate watershed and habitat protection, and help direct efforts in post wildland fire mitigation and restoration.
Date: August 2005
Creator: Hunter, Bruce Allan

On-Road Remote Sensing of Motor Vehicle Emissions: Associations between Exhaust Pollutant Levels and Vehicle Parameters for Arizona, California, Colorado, Illinois, Texas, and Utah

Description: On-road remote sensing has the ability to operate in real-time, and under real world conditions, making it an ideal candidate for detecting gross polluters on major freeways and thoroughfares. In this study, remote sensing was employed to detect carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxide (NO). On-road remote sensing data taken from measurements performed in six states, (Arizona, California, Colorado, Illinois, Texas, and Utah) were cleaned and analyzed. Data mining and exploration were first undertaken in order to search for relationships among variables such as make, year, engine type, vehicle weight, and location. Descriptive statistics were obtained for the three pollutants of interest. The data were found to have non-normal distributions. Applied transformations were ineffective, and nonparametric tests were applied. Due to the extremely large sample size of the dataset (508,617 records), nonparametric tests resulted in "p" values that demonstrated "significance." The general linear model was selected due to its ability to handle data with non-normal distributions. The general linear model was run on each pollutant with output producing descriptive statistics, profile plots, between-subjects effects, and estimated marginal means. Due to insufficient data within certain cells, results were not obtained for gross vehicle weight and engine type. The "year" variable was not directly analyzed in the GLM because "year" was employed in a weighted least squares transformation. "Year" was found to be a source of heteroscedasticity; and therefore, the basis of a least-squares transformation. Grouped-years were analyzed using medians, and the results were displayed graphically. Based on the GLM results and descriptives, Japanese vehicles typically had the lowest CO, HC, and NO emissions, while American vehicles ranked high for the three. Illinois, ranked lowest for CO, while Texas ranked highest. Illinois and Colorado were lowest for HC emissions, while Utah and California were highest. For NO, Colorado ranked highest ...
Access: This item is restricted to the UNT Community Members at a UNT Libraries Location.
Date: May 2003
Creator: Dohanich, Francis Albert

Phosphorus Retention and Fractionation in Masonry Sand and Light Weight Expanded Shale Used as Substrate in a Subsurface Flow Wetland

Description: Constructed wetlands are considered an inefficient technology for long-term phosphorus (P) removal. The P retention effectiveness of subsurface wetlands can be improved by using appropriate substrates. The objectives of this study were to: (i) use sorption isotherms to estimate the P sorption capacity of the two materials, masonry sand and light weight expanded shale; (ii) describe dissolved P removal in small (2.7 m3) subsurface flow wetlands; (iii) quantify the forms of P retained by the substrates in the pilot cells; and (iv) use resulting data to assess the technical and economic feasibility of the most promising system to remove P. The P sorption capacity of masonry sand and expanded shale, as determined with Langmuir isotherms, was 60 mg/kg and 971 mg/kg respectively. In the pilot cells receiving secondarily treated wastewater, cells containing expanded shale retained a greater proportion of the incoming P (50.8 percent) than cells containing masonry sand (14.5 percent). After a year of operation, samples were analyzed for total P (TP) and total inorganic P (TIP). Subsamples were fractionated into labile-P, Fe+Al-bound P, humic-P, Ca+Mg-bound P, and residual-P. Means and standard deviations of TP retained by the expanded shale and masonry sand were 349 + 169 and 11.9 + 18.6 mg/kg respectively. The largest forms of P retained by the expanded shale pilot cells were Fe+Al- bound P (108 mg/kg), followed by labile-P (46.7 mg/kg) and humic-P (39.8). Increases in the P forms of masonry sand were greatest in labile-P (7.5 mg/kg). The cost of an expanded shale wetland is within the range of costs conventional technologies for P removal. Accurate cost comparisons are dependent upon expansion capacity of the system under consideration. Materials with a high P sorption capacity also have potential for enhancing P removal in other constructed wetland applications such as stormwater wetlands and wetlands ...
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Date: August 2002
Creator: Forbes, Margaret G.

Spatially Explicit Modeling of West Nile Virus Risk Using Environmental Data

Description: West Nile virus (WNV) is an emerging infectious disease that has widespread implications for public health practitioners across the world. Within a few years of its arrival in the United States the virus had spread across the North American continent. This research focuses on the development of a spatially explicit GIS-based predictive epidemiological model based on suitable environmental factors. We examined eleven commonly mapped environmental factors using both ordinary least squares regression (OLS) and geographically weighted regression (GWR). The GWR model was utilized to ascertain the impact of environmental factors on WNV risk patterns without the confounding effects of spatial non-stationarity that exist between place and health. It identifies the important underlying environmental factors related to suitable mosquito habitat conditions to make meaningful and spatially explicit predictions. Our model represents a multi-criteria decision analysis approach to create disease risk maps under data sparse situations. The best fitting model with an adjusted R2 of 0.71 revealed a strong association between WNV infection risk and a subset of environmental risk factors including road density, stream density, and land surface temperature. This research also postulates that understanding the underlying place characteristics and population composition for the occurrence of WNV infection is important for mitigating future outbreaks. While many spatial and aspatial models have attempted to predict the risk of WNV transmission, efforts to link these factors within a GIS framework are limited. One of the major challenges for such integration is the high dimensionality and large volumes typically associated with such models and data. This research uses a spatially explicit, multivariate geovisualization framework to integrate an environmental model of mosquito habitat with human risk factors derived from socio-economic and demographic variables. Our results show that such an integrated approach facilitates the exploratory analysis of complex data and supports reasoning about the underlying spatial ...
Date: December 2015
Creator: Kala, Abhishek K.