In this study, the effects of the neonicotinoid pesticide, thiamethoxam, are examined through the Proboscis Extension Response (PER) in honey bees (Apis mellifera). PER is a form of classical conditioning applied to honey bees through scent and reward association which quantifies learning rates. Results between groups treated with thiamethoxam did not differ significantly from untreated control groups. Potential reasons for these results are discussed. The method and experimental apparatus for testing the PER assay are also discussed.

Exposure to mercury poses significant risks to the health of humans and wildlife. Globally, coal-fired power plant (CFPP) is a major source of mercury emissions, with China being the largest contributor to global atmospheric mercury. As a signatory country of the Minamata Convention on Mercury, China is developing its National Implementation Plan on Mercury Control, which gives priority to control of mercury emissions from CFPPs. While social benefits play an important role in designing environmental policies in China, the potential public health and economic benefits of mercury control in the nation are not yet understood, mainly due to the scientific challenges to trace mercury’s emissions-to-impacts path. Moreover, little is known about the potential benefits for the neighboring countries in East Asia resulted from China’s mercury control. This study evaluates the health and economic benefits for China and neighboring countries in East Asia from mercury reductions from China’s CFPPs. Four representative mercury control policy scenarios are analyzed, and the evaluation is explicitly conducted following the policies-to-impacts path under each policy scenario. We link a global atmospheric model to health impact assessment and economic valuation models to estimate economic gains for China and its three neighboring countries (Japan, South Korea and North Korea) from avoided mercury-related adverse health outcomes under the four emission control scenarios, and also take into account the key uncertainties in the policies-to-impacts path. Under the most stringent control scenario, the cumulative benefit of the mercury reduction by 2030 is projected to be $430 billion for the four countries together (the 95% confidence interval is $102-903 billion, in 2010 USD). Our findings suggest that although China is the biggest beneficiary of the mercury reduction in CFPPs, neighboring countries including Japan, South Korea and North Korea can also benefit (~7% of the total benefits) from China’s mercury reduction.

Introduction: The Aedes aegypti mosquito is responsible for the transmission of Yellow Fever, Dengue, Chikungunya and Zikavirus, making it a deadly vector and global public health threat. Zikavirus and Chikungunya, which were previously restricted to smaller geographic areas, have both appeared in the Western Hemisphere in the past three years and spread to areas where A. aegypti are present. This means that the pathogens have now entered areas in which the population has no previous immunity, which can lead to extensive outbreaks and epidemics. As the effects of global climate change become apparent, the areas of the globe that are suitable for inhabitance by A. aegypti may change. Additionally, this vector prefers human hosts for blood meals and requires standing water to breed, which is often created by water storage containers. This means that increasing urbanization and human population density are likely to put populations at higher risk of exposure to this vector. Methods: To create maps of the future risk of exposure to Aedes aegypti globally, species occurrence data for the vector and the Maxent modeling approach were used. Current and projected climate data were downloaded from WorldClim.org for the four representative concentration pathways (RCPs) used to model future climate change. Human population density, projected to 2050, the same timeframe as the future climate data, were used to model changes in human populations. To identify areas at high risk for future presence of A. aegypti populations, current and future models were compared across areas with at least a 50% probability of increased risk. These results where then used to create maps displaying high risk areas. Results: The AUC, an indicator of model fit, signaled that the models had high predictive power. However, high omission rates indicated that the trade-off of risk mapping may be a need to decrease probability thresholds below 50% to capture the full at-risk population. Future high-risk areas were most often those surrounding current cities, which supports the idea that the combination of urbanization and increasing human population density will work synergistically to increase the disease burden within and around urban centers. Additionally, expansion at the current geographic margins of this species shows that incursion into currently non-endemic areas is possible. Conclusions: Urban and peri-urban populations are likely to be at higher risk of exposure compared to rural areas due to global climate change and changes in population density. Attempts to model expansion of vector habitats should consider how these human population characteristics will change the risk to populations and how to best identify the areas at highest risk. Thresholds for the probability of a population being at risk of exposure to a vector may need to be different from those required to determine whether or not a habitat is suitable for a species. Appropriately determining which areas are high-risk results in maps and models can then be used to identify areas where climate change mitigation and vector control efforts are likely to have the highest impacts.

Background: Exposure to perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS) have been associated with higher circulating concentrations of total cholesterol (TC) and low density lipoprotein cholesterol (LDL-C). ApoB is the primary apolipoprotein component of LDL-C, and acts as a ligand for LDL-C receptors in various cells throughout the body. Circulating concentrations of ApoB are considered to be a better indicator of heart disease risk than TC or LDL-C. Objectives: Explore associations of concentrations of PFOA and PFOS with serum ApoB in adults. Methods: We analyzed data from 2744, 20-80 years old participants in the 2005–2006 National Health and Nutrition Examination Survey (NHANES). Linear regression models were used to estimate adjusted predicted means of serum ApoB (in g/L) for quartiles of PFOA and PFOS (in ng/mL) to describe patterns of associations. Results: Adjusted predicted mean concentrations of serum ApoB did not appear to vary meaningfully with increasing concentrations of PFOA (Q1 1.11, Q2 1.02, Q3 1.01, Q4 1.02) or increasing concentrations of PFOS (Q1 1.06, Q2 1.05, Q3 1.07, Q4 0.99) in study participants. Conclusions: Exposure to PFOA or PFOS does not appear to alter Apo B concentrations in adults.

We miss summer time during winter especially when it snows heavily resulting in cancelation of classes but we turn to ignore high temperature and its associated health impacts during summer. Several studies have shown that high temperatures during summer are associated with morbidity and mortality in many cities in the United States over the past decade. Gradual increase in temperature over the past years raises public health concerns about the impacts of heat on human health in future and the role of adaptation. Our study aimed at assessing future heat-related mortality due to climate change in the United States. We hypothesized that incidence of premature death will increase with future temperature rise and population adaptation will reduce the mortality rate. We reviewed research articles on temperature-related premature death. The literature search was limited to studies conducted in United States and seven studies which demonstrated positive association between temperature and premature death were selected for this study. We predicted future high temperature-related mortality using BenMap benefit model designed to estimate 2015 Appalachian Student Research Forum Page 111 air pollution impacts on public health. Based on the selected studies, BenMap model projected 2020-2050 temperature scenario using modeled daily mean apparent temperature to estimate future heat-related mortality. Our results showed that high temperatures would cause an increase in heat-related mortality and adaptation would minimize the effects of climate change as people get used to high temperatures. The outcome of our study confirms the positive association between high temperature and mortality which emphasizes the need for policy makers to take appropriate actions such as greenhouse gas emission reduction to protect public health.

The impacts of mining are easily observable in the way they alter the terrain of landscapes, displace animals, and increase waste accumulation in an area. An unobservable impact and one that lasts a long time is by radioactive exposure in the environment. Specifically, this is a risk at uranium (U) mine sites which are expanding in number to accommodate the world’s growing energy needs, and even to accommodate weapons manufacturing. This paper analyses the impacts of an abandoned uranium mine on the local environment through measurements of Uranium concentration in soil, plants, and rocks. Transect sampling was used to collect 22 soil samples and 17 plant samples between 5 and 100m of the mine shaft. Uranium concentrations in soil and plant samples, digested with nitric acid, were measured with an Inductively Coupled Plasma - Optical Emission Spectrometry (ICP-OES), and an X-ray Powder Diffraction (XRD) analysis was used to find the mineral contents of the rock samples. Satellite positions were associated with each sample, which allowed an effective spatial analysis of the Uranium concentration values. U values in soil ranged from 0 to 5.291ppm, with mean concentrations of 0.710 ppm, and U values in plants ranged from 0.0323 to 0.1121ppm with mean concentrations of 0.0558 ppm. A paired t-test determined that there was no spatial autocorrelation in U concentrations of plants and adjacent soils. The highest U concentration was found closest to the mine, peaking at ~7.3 meters from the mine, and low spatial variability occurs in U concentrations at greater than 10 meters from the mine. In comparison with other mines internationally, U concentrations at this study site were low, which may be indicative of a small operating mine, efficient clean-up, and transport mechanisms of U in desert environments.

Contaminants such as pharmaceuticals are of increasing concern due to their ubiquitous use and persistence in surface waters worldwide. Limited attention has been paid to the effects of pharmaceuticals on marine life, despite widespread detection of these contaminants in the marine environment. Of the existing studies, the majority assess the negative effects of pharmaceuticals over an exposure period of 30 days or less and focus on cellular and subcellular biomarkers. Longer studies are required to determine if chronic contaminant exposure poses risks to marine life at environmentally relevant concentrations. Also scarce in the literature is examination of whole organism effects to identify potential community-level consequences. Two long-term studies with the antidepressant pharmaceutical, fluoxetine (the active constituent in Prozac®) were conducted to determine whether nominal concentrations detected in estuarine and coastal environments affect organism health and interactions. First, we measured whole organism metrics in the California mussel, Mytilus californianus over a period of 107 days. Specifically, we measured algal clearance rates, growth, and condition indices for both reproductive and overall health. We found that fluoxetine negatively affects all measured characteristics, however many effects are mediated by length of exposure. Perhaps the most notable result was that mussels spiked with fluoxetine cleared less algae after 30 days of exposure. Reduced growth and condition indices likely are a consequence of improper nutrition among fluoxetine-treated mussels. Any level of fluoxetine significantly affected the gonadosomatic index after 47 days. The results from this study on mussels fill an important data gap, highlighting organism-level effects of chronic exposure periods; such data more explicitly identify the impacts of pharmaceuticals and other contaminants on marine communities and ecosystems. Fluoxetine has also been documented to affect the behavior of fish and invertebrates, including freshwater and marine bivalves, crustaceans, and fish. Given that other crustaceans exhibited increased activity levels under fluoxetine exposure, we hypothesized that this would subject them to greater predation risk. In our second exposure study, we assessed whether a similar range of fluoxetine concentrations used in the mussel study altered the risk behavior of the Oregon mud crab, Hemigrapsus oregonensis, in response to a common predator, the red rock crab, Cancer productus. We conducted this study for 60 days, conducting day and night behavioral trials (with and without predators) four times a week. We found that crabs exposed to any amount of fluoxetine (3 or 30 ng/L) had increased activity levels relative to controls; however behaviors of 3 ng/L-spiked crabs were not always significantly different from controls. Among control crabs, day and night trials yielded similar results, where a clear response to the addition of the predator was observed. Crabs dosed with fluoxetine exhibited more foraging and active behaviors in the presence of the predator. Additionally, crabs spiked with fluoxetine at 30 ng/L had the greatest risk of mortality either by predation by red rock crabs or due to more aggressive behaviors among conspecifics. The results of this study shed light on a particularly unexplored area of contaminants research: how do psychoactive pharmaceuticals affect animal behavior when exposed to the low concentrations persisting in the aquatic environment for a prolonged period of time?

Networks of distributed, remote sensors are providing ecological scientists with a view of our environment that is unprecedented in detail. However, these networks are subject to harsh conditions, which lead to malfunctions in individual sensors and failures in network communications. This behavior manifests as corrupt or missing measurements in the data. Consequently, before the data can be used in ecological models, future experiments, or even policy decisions, it must be quality controlled (QC'd) to flag affected measurements and impute corrected values. This dissertation describes a probabilistic modeling approach for real-time automated QC that exploits the spatial and temporal correlations in the data to distinguish sensor failures from valid observations. The model adapts to a site by learning a Bayesian network structure that captures spatial relationships among sensors, and then extends this structure to a dynamic Bayesian network to incorporate temporal correlations. The final QC model contains both discrete and continuous variables, which makes inference intractable for large sensor networks. Consequently, we examine the performance of three approximate methods for inference in this probabilistic framework. Two of these algorithms represent contemporary approaches to inference in hybrid models, while the third is a greedy search-based method of our own design. We demonstrate the results of these algorithms on synthetic datasets and real environmental sensor data gathered from an ecological sensor network located in western Oregon. Our results suggest that we can improve performance over networks with less sensors that use exhaustive asynchronic inference by including additional sensors and applying approximate algorithms. / Graduation date: 2013

This research was designed to provide an understanding of physical wind mechanisms within the complex terrain of the Great Valley of Eastern Tennessee to assess the impacts of regional air flow with regard to synoptic and mesoscale weather changes, wind direction shifts, and air quality. Meteorological data from 2008–2009 were analyzed from 13 meteorological sites along with associated upper level data. Up to 15 ancillary sites were used for reference. Two-step complete linkage and K-means cluster analyses, synoptic weather studies, and ambient meteorological comparisons were performed to generate hourly wind classifications. These wind regimes revealed seasonal variations of underlying physical wind mechanisms (forced channeled, vertically coupled, pressure-driven, and thermally-driven winds). Synoptic and ambient meteorological analysis (mixing depth, pressure gradient, pressure gradient ratio, atmospheric and surface stability) suggested up to 93% accuracy for the clustered results. Probabilistic prediction schemes of wind flow and wind class change were developed through characterization of flow change data and wind class succession. Data analysis revealed that wind flow in the Great Valley was dominated by forced channeled winds (45–67%) and vertically coupled flow (22–38%). Down-valley pressure-driven and thermally-driven winds also played significant roles (0–17% and 2–20%, respectively), usually accompanied by convergent wind patterns (15–20%) and large wind direction shifts, especially in the Central/Upper Great Valley. The behavior of most wind regimes was associated with detectable pressure differences between the Lower and Upper Great Valley. Mixing depth and synoptic pressure gradients were significant contributors to wind pattern behavior. Up to 15 wind classes and 10 sub-classes were identified in the Central Great Valley with 67 joined classes for the Great Valley at-large. Two-thirds of Great Valley at-large flow was defined by 12 classes. Winds flowed on-axis only 40% of the time. The Great Smoky Mountains helped create down-valley pressure-driven winds, downslope mountain breezes, and divergent air flow. The Cumberland Mountains and Plateau were associated with wind speed reductions in the Central Great Valley, Emory Gap Flow, weak thermally-driven winds, and northwesterly down sloping. Ridge-and-valley terrain enhanced wind direction reversals, pressure-driven winds, as well as locally and regionally produced thermally-driven flow.

The objective of this research is to apply micromachined silicon-based resonant gravimetric sensors to the detection of gas-phase volatile organic compounds (VOCs). This is done in two primary tasks: 1) the optimization and application of silicon disk resonators to the detection of gas-phase VOCs, and 2) the development and application of a novel gravimetric-capacitive multisensor platform for the detection of gas-phase VOCs. In the rst task, the design and fabrication of a silicon-based disk resonator structure utilizing an in-plane resonance mode is undertaken. The resonance characteristics of the disk resonator are characterized and optimized. The optimized characteristics include the resonator Q-factor as a function of geometric parameters, and the dynamic displacement of the in-plane resonance mode. The Q-factors of the disk resonators range from 2600 to 4360 at atmosphere for disk silicon thicknesses from 7 µm to 18 µm, respectively. The resonance frequency of the in-plane resonance mode ranges from 260 kHz up to 750 kHz. The disk resonators are applied to the sensing of gas-phase VOCs using (poly)isobutylene as a sensitive layer. Limits of detection for benzene, toluene and m-xylene vapors of 5.3 ppm, 1.2 ppm, and 0.6 ppm are respectively obtained. Finally, models for the limits of detection and chemical sensitivity of the resonator structures are developed for the case of the polymer layers used. In the second task, a silicon-based resonator is combined with a capacitive structure to produce a multisensor structure for the sensing of gas-phase VOCs. Fabrication of the multisensor structure is undertaken, and the sensor is theoretically modeled. The baseline capacitance of the capacitor component of the multisensor is estimated to be 170 fF. Finally, initial VOC detection results for the capacitive aspect of the sensor are obtained.