Hexavalent chromium is an extremely carcinogenic chemical that has been widely produced in the United States. This has led to major waste contamination and pollution throughout the country. According to the Environmental Working Group Hexavalent chromium has been found in 89% of city tap water. Most people believe they are safe using regular home filter systems however that is not true. A more expensive ion exchange water treatment unit is required. Therefore to protect yourselves from this carcinogenic metal a reliable test is required. In this study we have developed a Zinc Sulfide Manganese doped Quantum Rod technology to detect for presence of chromate and other harmful transitional metals in drinking water. Quantum Rods were synthesized using a hydrothermal reaction method. They were fully characterized using UV-visible absorption spectroscopy, fluorescence emission spectroscopy, X-ray Photoelectric Spectroscopy (XPS) and High Resolution Transmission Electron Microscopy (HRTEM). Quantum Rod metal detection studies were done with 28 different ions in a 96-well fluorescent plate reader. Results show that highest sensitivity to 8 ions including the toxic ions of chromate and mercury allowing us to create a sensor to detect these items.

Chronic respiratory diseases such as asthma and chronic bronchitis have significantly increased in prevalence in Africa over the past 10 years. Recent studies have demonstrated that exposure to air pollution may be associated with an increased risk of chronic respiratory diseases. However, such studies have predominantly been conducted in western societies or often used urbanicity as a proxy for exposure to air pollution. Therefore, we evaluated the association between PM2.5 exposure and asthma/chronic bronchitis in Senegal. A cross-sectional study was conducted for the time period of 3 October 2010 to 28 April 2011 using annual concentrations of PM2.5 measured via multiple satellite instruments, and asthma/chronic bronchitis, which was self-reported at baseline via a health survey questionnaire. We used mixed model logistic regression to evaluate the relationship between PM2.5 exposure and asthma/chronic bronchitis risk while adjusting for lifestyle factors, location, and other air pollutants. Sex was evaluated as an effect modifier. The adjusted association between PM2.5 and asthma/chronic bronchitis was 1.03 (95%CI: 0.99 – 1.06). In males the adjusted odds ratio was 1.09 (95%CI: 1.03-1.15), compared to females (aOR 1.01 (95%CI: 0.97 – 1.05). Our results suggest that increasing levels of exposure to PM2.5 puts individuals at a higher risk for chronic respiratory diseases, especially men. These findings have significant policy implications and should be built upon in future research.

Master of Public Health / Department of Diagnostic Medicine/Pathobiology / Deon Van der Merwe / Cyanobacteria are Gram-negative photosynthetic bacteria capable of producing toxins responsible for morbidity and mortality in humans and domestic animals. Many are capable of forming concentrated blooms that impact the environment by limiting the growth of sub-surface plants and phytoplankton. Harmful algal blooms (HABs) are also capable of producing multiple types of toxins, creating a potential hazard to recreational water users and animals drinking water from or near a bloom. Characterization of HABs is necessary to prevent these human and animal exposures and includes classifying of the type of cyanobacteria present and whether or not they are capable of toxin production, and the exact type of cyanotoxin that is actually present in bloom. Current methods used to classify cyanobacteria and cyanotoxins include microscopy, bioassays, ELISA, PCR, HPLC, and LC/MS. All of these methods, however, have limitations that include time, labor intensity, or cost. Fourier-Transform Infrared Spectroscopy (FTIR) is another potential tool for cyanobacterial classification that is not limited by these factors. To examine the practicality of this method, library screening with default software algorithms was performed on diagnostic samples received at the Kansas State University Veterinary Diagnostic Lab, followed by PCA of samples meeting minimum quality requirements to produce cluster analyses and dendrograms. Both spectrometers and software packages used were successful at distinguishing cyanobacteria from green algae in clean samples with 89.13% agreement. PCA resulted in clear classification of cyanobacteria or green algae demonstrated by a large order of magnitude difference produced by average Euclidian distance dendrograms. While this method is only capable of differentiating cyanobacteria from green algae or other aquatic environmental constituents, its simple, rapid use and low cost make it a beneficial screening tool when coupled with toxin-detection methods to characterize HABs.

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Background: Prenatal exposure to perfluoroalkyl substances (PFAS) has been associated with lower birth weight in epidemiologic studies. This association could be attributable to glomerular filtration rate (GFR), which is related to PFAS concentration and birth weight. Objectives: We used a physiologically based pharmacokinetic (PBPK) model of pregnancy to assess how much of the PFAS–birth weight association observed in epidemiologic studies might be attributable to GFR. Methods: We modified a PBPK model to reflect the association of GFR with birth weight (estimated from three studies of GFR and birth weight) and used it to simulate PFAS concentrations in maternal and cord plasma. The model was run 250,000 times, with variation in parameters, to simulate a population. Simulated data were analyzed to evaluate the association between PFAS levels and birth weight due to GFR. We compared simulated estimates with those from a meta-analysis of epidemiologic data. Results: The reduction in birth weight for each 1-ng/mL increase in simulated cord plasma for perfluorooctane sulfonate (PFOS) was 2.72 g (95% CI: –3.40, –2.04), and for perfluorooctanoic acid (PFOA) was 7.13 g (95% CI: –8.46, –5.80); results based on maternal plasma at term were similar. Results were sensitive to variations in PFAS level distributions and the strength of the GFR–birth weight association. In comparison, our meta-analysis of epidemiologic studies suggested that each 1-ng/mL increase in prenatal PFOS and PFOA levels was associated with 5.00 g (95% CI: –21.66, –7.78) and 14.72 g (95% CI: –8.92, –1.09) reductions in birth weight, respectively. Conclusion: Results of our simulations suggest that a substantial proportion of the association between prenatal PFAS and birth weight may be attributable to confounding by GFR and that confounding by GFR may be more important in studies with sample collection later in pregnancy.

Introduction Epidemiologic data supporting the role of organochlorine pesticides in pubertal development are limited. Methods Using a nested case-control design, serum collected during pregnancy from mothers of 218 girls who reported menarche before 11.5 years of age (cases) and 230 girls who reported menarche at or after 11.5 years of age (controls) was analyzed for 9 organochlorines and metabolites. We analyzed the association between in utero organochlorine concentrations and early menarche using multivariate logistic regression controlling for mother's age at menarche, or mother's prenatal BMI. Results We did not observe an association between in utero exposure to HCB, β-HCH, ϒ-HCH, p,p′-DDT, p,p′-DDE, oxychlordane or trans-nonachlor and early menarche. Conclusions This study is the first to examine the association between in utero exposure to HCB, β-HCH, ϒ-HCH, oxychlordane or trans-nonachlor and early menarche. In utero exposure to organochlorine pesticides does not appear to have a role in the timing of menarche in this study.

Globally, coal-fired power plant (CFPP) is a major source of mercury. China is developing its first National Implementation Plan on Mercury Control, which priorities the control of emissions from CFPPs. While social benefits play an important role in designing environmental policies in China, the benefits associated with mercury control are not yet understood, mainly due to the scientific challenges to trace mercury's emissions-to-impacts path. This study evaluates the benefits of mercury reductions in China's CFPPs for China and its three neighboring countries in East Asia. Four policy scenarios are analyzed following the policies-to-impacts path, which links a global atmospheric model to health benefit analysis models to estimate the economic gains from avoided mercury-related adverse health outcomes under each scenario, and take into account key uncertainties in the path. Under the most stringent scenario, the benefits of mercury reduction by 2030 are projected to be $432 billion (95% CI: $166–941 billion), with the benefits for China and the neighboring countries accounting for 96% and 4% of the total benefits, respectively. Policy scenario analysis indicates that coal washing generates the greatest benefits in the near term, whereas upgrading air pollution control devices maximizes health benefits in the long term.

“Supply-side structural reform” (SSSR) has been the most important ongoing economic reform in China since 2015, but its important environmental health effects have not been properly assessed. The present study addresses that gap by focusing on reduction of overcapacity in the coal, steel, and iron sectors, combined with reduction of emissions of sulfur dioxide (SO2), nitrogen oxide (NOx), and fine particulate matter (PM2.5), and projecting resultant effects on air quality and public health across cities and regions in China. Modeling results indicate that effects on air quality and public health are visible and distributed unevenly across the country. This assessment provides quantitative evidence supporting projections of the transregional distribution of such effects. Such uneven transregional distribution complicates management of air quality and health risks in China. The results challenge approaches that rely solely on cities to improve air quality. The article concludes with suggestions on how to integrate SSSR measures with cities' air quality improvement attainment planning and management performance evaluation

The health of the northern hardwood forest in the southern Appalachian Mountains of Tennessee, North Carolina, and Virginia has gained attention from the media and environmental stakeholders due to a purported decline in forest health at higher elevations. This project examined lead (Pb) and calcium (Ca) concentrations in growth rings of an important northern hardwood species, American beech (Fagus grandifolia Ehrh.) at Mount Rogers and Whitetop Mountain, Virginia and attempted to examine concentration relationships with stem growth patterns. Dominant and codominant trees were sampled from 16 research plots at two elevations. Tree cores were crossdated, divided into sections of 10-yr periods, and analyzed using atomic absorption spectroscopy. Lead concentrations correlated negatively with ring width. Elevation and aspect were significantly associated with the Pb concentration, while Ca concentrations were only associated with aspect. Tree core samples taken from higher elevation plots contained higher Pb concentrations than samples collected from lower elevation plots, while the northwest and southwest aspects contained significantly higher amounts of Pb and Ca. Both Pb and Ca concentrations increased during the 1860s and again during the mid-1900s.

Background: The leading cause of surface water impairment in United States’ rivers and streams is pathogen contamination. Although use of fecal indicators has reduced human health risk, current approaches to identify and reduce exposure can be improved. One important knowledge gap within exposure assessment is characterization of complex fate and transport processes of fecal pollution. Novel modeling processes can inform watershed decision-making to improve exposure assessment. Methods: We used the ecological model, Maxent, and the fecal indicator bacterium Escherichia coli to identify environmental factors associated with surface water impairment. Samples were collected August, November, February, and May for 8 years on Sinking Creek in Northeast Tennessee and analyzed for 10 water quality parameters and E. coli concentrations. Univariate and multivariate models estimated probability of impairment given the water quality parameters. Model performance was assessed using area under the receiving operating characteristic (AUC) and prediction accuracy, defined as the model’s ability to predict both true positives (impairment) and true negatives (compliance). Univariate models generated action values, or environmental thresholds, to indicate potential E. coli impairment based on a single parameter. Multivariate models predicted probability of impairment given a suite of environmental variables, and jack-knife sensitivity analysis removed unresponsive variables to elicit a set of the most responsive parameters. Results: Water temperature univariate models performed best as indicated by AUC, but alkalinity models were the most accurate at correctly classifying impairment. Sensitivity analysis revealed that models were most sensitive to removal of specific conductance. Other sensitive variables included water temperature, dissolved oxygen, discharge, and NO3. The removal of dissolved oxygen improved model performance based on testing AUC, justifying development of two optimized multivariate models; a 5-variable model including all sensitive parameters, and a 4-variable model that excluded dissolved oxygen. Discussion: Results suggest that E. coli impairment in Sinking Creek is influenced by seasonality and agricultural run-off, stressing the need for multi-month sampling along a stream continuum. Although discharge was not predictive of E. coli impairment alone, its interactive effect stresses the importance of both flow dependent and independent processes associated with E. coli impairment. This research also highlights the interactions between nutrient and fecal pollution, a key consideration for watersheds with multiple synergistic impairments. Although one indicator cannot mimic the plethora of existing pathogens in water, incorporating modeling can fine tune an indicator’s utility, providing information concerning fate, transport, and source of fecal pollution while prioritizing resources and increasing confidence in decision making. Methods We used the ecological model, Maxent, and the fecal indicator bacterium Escherichia coli to identify environmental factors associated with surface water impairment. Samples were collected August, November, February, and May for 8 years on Sinking Creek in Northeast Tennessee and analyzed for 10 water quality parameters and E. coli concentrations. Univariate and multivariate models estimated probability of impairment given the water quality parameters. Model performance was assessed using area under the receiving operating characteristic (AUC) and prediction accuracy, defined as the model’s ability to predict both true positives (impairment) and true negatives (compliance). Univariate models generated action values, or environmental thresholds, to indicate potential E. coli impairment based on a single parameter. Multivariate models predicted probability of impairment given a suite of environmental variables, and jack-knife sensitivity analysis removed unresponsive variables to elicit a set of the most responsive parameters. Results Water temperature univariate models performed best as indicated by AUC, but alkalinity models were the most accurate at correctly classifying impairment. Sensitivity analysis revealed that models were most sensitive to removal of specific conductance. Other sensitive variables included water temperature, dissolved oxygen, discharge, and NO3. The removal of dissolved oxygen improved model performance based on testing AUC, justifying development of two optimized multivariate models; a 5-variable model including all sensitive parameters, and a 4-variable model that excluded dissolved oxygen. Discussion Results suggest that E. coli impairment in Sinking Creek is influenced by seasonality and agricultural run-off, stressing the need for multi-month sampling along a stream continuum. Although discharge was not predictive of E. coli impairment alone, its interactive effect stresses the importance of both flow dependent and independent processes associated with E. coli impairment. This research also highlights the interactions between nutrient and fecal pollution, a key consideration for watersheds with multiple synergistic impairments. Although one indicator cannot mimic theplethora of existing pathogens in water, incorporating modeling can fine tune an indicator’s utility, providing information concerning fate, transport, and source of fecal pollution while prioritizing resources and increasing confidence in decision making.