Drinking water sources, quality, and associated health outcomes in Appalachian Virginia: A risk characterization study in two counties.

OBJECTIVES: In the US, violations of drinking water regulations are highest in lower-income rural areas overall, and particularly in Central Appalachia. However, data on drinking water use, quality, and associated health outcomes in rural Appalachia are limited. We sought to assess public and private drinking water sources and associated risk factors for waterborne pathogen exposures for individuals living in rural regions of Appalachian Virginia. METHODS: We administered surveys and collected tap water, bottled water, and saliva samples in lower-income households in two adjacent rural counties in southwest Virginia (bordering Kentucky and Tennessee). Water samples were tested for pH, temperature, conductivity, total coliforms, E. coli, free chlorine, nitrate, fluoride, heavy metals, and specific pathogen targets. Saliva samples were analyzed for antibody responses to potentially waterborne infections. We also shared water analysis results with households. RESULTS: We enrolled 33 households (83 individuals), 82% (n = 27) with utility-supplied water and 18% with private wells (n = 3) or springs (n = 3). 58% (n = 19) reported household incomes of <$20,000/year. Total coliforms were detected in water samples from 33% (n = 11) of homes, E. coli in 12%, all with wells or springs (n = 4), and Aeromonas, Campylobacter, and Enterobacter in 9%, all spring water (n = 3). Diarrhea was reported for 10% of individuals (n = 8), but was not associated with E. coli detection. 34% (n = 15) of saliva samples had detectable antibody responses for Cryptosporidium spp., C. jejuni, and Hepatitis E. After controlling for covariates and clustering, individuals in households with septic systems and straight pipes had significantly higher likelihoods of antibody detection (risk ratios = 3.28, 95%CI = 1.01-10.65). CONCLUSIONS: To our knowledge, this is the first study to collect and analyze drinking water samples, saliva samples, and reported health outcome data from low-income households in Central Appalachia. Our findings indicate that utility-supplied water in this region was generally safe, and individuals in low-income households without utility-supplied water or sewerage have higher exposures to waterborne pathogens.

Emerging environmental contaminants (silver nanoparticles) altered the catabolic capability and metabolic fingerprinting of microbial communities.

Microbial community functional diversity enhances the degradation of organic matter and pollutants in the environment, but there is a growing concern that these ecosystem services may be altered by the introduction of emerging environmental contaminants including silver nanoparticles (AgNPs) into aquatic systems. We added 0, 25, 50, 75, 100, and 125 mg L-1 (nominal concentrations) of citrate-AgNP and polyvinylpyrrolidone-AgNP (PVP-AgNP) each to freshwater sediment and examined their antimicrobial effects on microbial communities using community-level physiological profiling. The results showed that citrate-AgNP decreased the overall microbial catabolic activity by 80% from 1.16 ± 0.02 to 0.23 ± 08 while PVP-AgNP decreased the catabolic activity by 51% from 1.25 ± 0.07 to 0.61 ± 0.19 at 125 mg L-1. Citrate-AgNP and PVP-AgNP caused a statistically significant reduction in substrate richness and substrate diversity that decreased microbial functional diversity. AgNPs decreased microbial catabolic capability and functional diversity at concentrations ranging from 0.12 ± 0.04 to 0.43 ± 0.07 mg Ag kg-1 which are lower than the predicted concentrations in freshwater sediment. To our knowledge, this is the first study to demonstrate inhibition of microbial functional diversity by citrate-AgNP and PVP-AgNP in a pathogen impaired stream. Citrate-AgNP caused greater inhibition of carbon substrate utilization but amino acids, carbohydrates, and carboxylic acids were the most affected carbon groups which led to a shift in the metabolic fingerprint pattern of the microbial community. AgNPs decreased the catabolic capability and the ability of the microbial community to degrade organic matter and a variety of pollutants in the environment.

Antimicrobial properties of silver nanoparticles may interfere with fecal indicator bacteria detection in pathogen impaired streams.

Silver nanoparticles (AgNPs) are expected to enter aquatic systems, but there are limited data on how they might affect microbial communities in pathogen impaired streams. We examined microbial community responses to citrate-AgNP (10.9 ± 0.7 nm) and polyvinylpyrrolidone (PVP)-AgNP (11.0 ± 0.7 nm) based on microbial concentration and enzyme activity in sediment from a pathogen impaired stream. Addition of each nanoparticle to sediment caused at least a 69% decrease in microbial concentration (1,264 ± 93.6 to 127 ± 29.5 CFU/g) and a 62% decrease in ß-glucosidase activity (11.7 ± 2.1 to 1.3 ± 0.3 µg/g/h). Each AgNP reduced alkaline phosphatase activity but their effects were not statistically significant. Sediment exposed to 0.108 mg Ag/kg of AgNO3 resulted in a 92% decrease in microbial concentration and a reduced enzyme activity which was not statistically significant. Measured total silver in sediments treated with AgNPs which exhibited significant inhibition effects on the microbial community ranged from 0.19 ± 0.02 to 0.39 ± 0.13 mg Ag/kg. These concentrations tested in this study are much lower than the expected concentrations (2-14 mg Ag/kg) in freshwater sediments. The results of this study demonstrate that AgNPs can alter microbial community activity and population size, which may lead to false negative fecal indicator bacteria detection and enumeration using methods that rely on ß-glucosidase activity. We conclude that the presence of AgNPs in impaired streams and recreational waters can influence pathogen detection methods, potentially affecting public health risk estimates.

Canonical Variable Selection for Ecological Modeling of Fecal Indicators.

More than 270,000 km of rivers and streams are impaired due to fecal pathogens, creating an economic and public health burden. Fecal indicator organisms such as are used to determine if surface waters are pathogen impaired, but they fail to identify human health risks, provide source information, or have unique fate and transport processes. Statistical and machine learning models can be used to overcome some of these weaknesses, including identifying ecological mechanisms influencing fecal pollution. In this study, canonical correlation analysis (CCorA) was performed to select parameters for the machine learning model, Maxent, to identify how chemical and microbial parameters can predict impairment and F-somatic bacteriophage detections. Models were validated using a bootstrapping cross-validation. Three suites of models were developed; initial models using all parameters, models using parameters identified in CCorA, and optimized models after further sensitivity analysis. Canonical correlation analysis reduced the number of parameters needed to achieve the same degree of accuracy in the initial model (84.7%), and sensitivity analysis improved accuracy to 86.1%. Bacteriophage model accuracies were 79.2, 70.8, and 69.4% for the initial, CCorA, and optimized models, respectively; this suggests complex ecological interactions of bacteriophages are not captured by CCorA. Results indicate distinct ecological drivers of impairment depending on the fecal indicator organism used. impairment is driven by increased hardness and microbial activity, whereas bacteriophage detection is inhibited by high levels of coliforms in sediment. Both indicators were influenced by organic pollution and phosphorus limitation.

Maxent estimation of aquatic Escherichia coli stream impairment.

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 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.

Development of Multiple Regression Models to Predict Sources of Fecal Pollution.

This study assessed the usefulness of multivariate statistical tools to characterize watershed dynamics and prioritize streams for remediation. Three multiple regression models were developed using water quality data collected from Sinking Creek in the Watauga River watershed in Northeast Tennessee. Model 1 included all water quality parameters, model 2 included parameters identified by stepwise regression, and model 3 was developed using canonical discriminant analysis. Models were evaluated in seven creeks to determine if they correctly classified land use and level of fecal pollution. At the watershed level, the models were statistically significant (p < 0.001) but with low r2 values (Model 1 r2 = 0.02, Model 2 r2 = 0.01, Model 3 r2 = 0.35). Model 3 correctly classified land use in five of seven creeks. These results suggest this approach can be used to set priorities and identify pollution sources, but may be limited when applied across entire watersheds.

Novel carbon nanotube (CNT)-based ultrasensitive sensors for trace mercury(II) detection in water: A review.

Infamous for "Mad hatter syndrome" and "Minamata disease", mercury (Hg) is ranked high on the Agency for Toxic Substances and Disease Registry's priority list of hazardous substances for its potent neurologic, renal, and developmental toxicities. Most typical exposures are via contaminated water and food. Although regulations and advisories are exercised at various levels, Hg pollution from both natural and anthropogenic sources has remained a major public health and safety concern. Rapid detection of solvated aqueous Hg2+ ions at low levels is critical for immediate response and protection of those who are vulnerable (young children, pregnant and breast-feeding women) to acute and chronic exposures to Hg2+. Various types of sensors capable of detecting Hg in water have been developed. In particular, the novel use of engineered carbon nanotubes (CNTs) has garnered attention due to their specificity and sensitivity towards Hg2+ detection in solution. In this focused review, we describe the sensitivity, selectivity and mechanisms of Hg2+ ion sensing at trace levels by employing CNT-based various sensor designs, and appraise the open literature on the currently applied and "proof-of-concept" methods. Five different types of CNT-based sensor systems are described: potentiometric, DNA-based fluorescence, surface plasmon resonance (SPR), colorimetric, and stripping voltammetric assays. In addition, the recognized merits and shortcomings for each type of electrochemical sensors are discussed. The knowledge from this succinct review shall guide the development of the next generation CNT-based biochemical sensors for rapid Hg2+ detection in the environment, which is a significant first step towards human health risk analysis of this legacy toxicant.

Application of multivariate statistical methodology to model factors influencing fate and transport of fecal pollution in surface waters.

The increasing number of polluted watersheds and water bodies with total maximum daily loads (TMDLs) has resulted in increased research to find methods that effectively and universally identify fecal pollution sources. A fundamental requirement to identify such methods is understanding the microbial and chemical processes that influence fate and transport of fecal indicators from various sources to receiving streams. Using the Watauga River watershed in northeast Tennessee as a model to better understand these processes, multivariate statistical analyses were conducted on data collected from four creeks that have or are expected to have pathogen TMDLs. The application of canonical correlation and discriminant analyses revealed spatial and temporal variability in the microbial and chemical parameters influencing water quality, suggesting that these creeks differ in terms of the nature and extent of fecal pollution. The identification of creeks within a watershed that have similar sources of fecal pollution using this data analysis approach could change prioritization of best management practices selection and placement. Furthermore, this suggests that TMDL development may require multiyear and multisite data using a targeted sampling approach instead of a 30-d geometric mean in large, complex watersheds. This technique may facilitate the choice between watershed TMDLs and single segment or stream TMDLs.

Impacts of select organic ligands on the colloidal stability, dissolution dynamics, and toxicity of silver nanoparticles.

Key understanding of potential transformations that may occur on silver nanoparticle (AgNP) surface upon interaction with naturally ubiquitous organic ligands (e.g., -SH (thoil), humic acid, or -COO (carboxylate)) is limited. Herein we investigated how dissolved organic carbon (DOC), -SH (in cysteine, a well-known Ag(+) chelating agent), and -COO (in trolox, a well-known antioxidant) could alter the colloidal stability, dissolution rate, and toxicity of citrate-functionalized AgNPs (citrate-AgNPs) against a keystone crustacean Daphnia magna. Cysteine, DOC, or trolox amendment of citrate-AgNPs differentially modified particle size, surface properties (charge, plasmonic spectra), and ion release dynamics, thereby attenuating (with cysteine or trolox) or promoting (with DOC) AgNP toxicity. Except with DOC amendment, the combined toxicity of AgNPs and released Ag under cysteine or trolox amendment was lower than of AgNO3 alone. The results of this study show that citrate-AgNP toxicity can be associated with oxidative stress, ion release, and the organism biology. Our evidence suggests that specific organic ligands available in the receiving waters can differentially surface modify AgNPs and alter their environmental persistence (changing dissolution dynamics) and subsequently the toxicity; hence, we caveat to generalize that surface modified nanoparticles upon environmental release may not be toxic to receptor organisms.

Rapid screening of aquatic toxicity of several metal-based nanoparticles using the MetPLATE™ bioassay.

Current understanding of potential toxicity of engineered nanomaterials to aquatic microorganisms is limited for risk assessment and management. Here we evaluate if the MetPLATE™ test can be used as an effective and rapid screening tool to test for potential aquatic toxicity of various metal-based nanoparticles (NPs). The MetPLATE bioassay is a heavy metal sensitive test based on ß-galactosidase activity in Escherichia coli. Five different types of metal-based NPs were screened for toxicity: (1) citrate coated nAg (Citrate-nanosilver), (2) polyvinylpyrrolidone coated nAg (PVP-nAg), (3) uncoated nZnO, (4) uncoated nTiO(2) and (5) 1-Octadecylamine coated CdSe Quantum Dots (CdSe QDs); and compared with their corresponding ionic salt toxicity. Citrate-nAg was further fractionated into clean Citrate-nAg, unclean Citrate-nAg and permeate using a tangential flow filtration (TFF) system to eliminate residual ions and impurities from the stock Citrate-nAg suspension and also to differentiate between ionic- versus nano-specific toxicity. Our results showed that nAg, nZnO and CdSe QDs were less toxic than their corresponding ionic salts tested, while nano- or ionic form of TiO(2) was not toxic as high as 2.5 g L(-1) to the MetPLATE™ bacteria. Although coating-dependent toxicity was noticeable between two types of Ag NPs evaluated, particle size and surface charge were not adequate to explain the observed toxicity; hence, the toxicity appeared to be material-specific. Overall, the toxicity followed the trend: CdCl(2)>AgNO(3)>PVP-nAg>unclean Citrate-nAg>clean Citrate-nAg>ZnSO(4)>nZnO>CdSe QDs>nTiO(2)/TiO(2). These results indicate that an evaluation of ß-galactosidase inhibition in MetPLATE™ E. coli can be an important consideration for rapid screening of metal-based NP toxicity, and should facilitate ecological risk assessment of these emerging contaminants.

Dendrochemical analysis of lead and calcium in southern Appalachian American beech.

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.