Developing early warning systems to predict water lead levels in tap water for private systems.

Lead is a chemical contaminant that threatens public health, and high levels of lead have been identified in drinking water at locations across the globe. Under-served populations that use private systems for drinking water supplies may be at an elevated level of risk because utilities and governing agencies are not responsible for ensuring that lead levels meet the Lead and Copper Rule at these systems. Predictive models that can be used by residents to assess water quality threats in their households can create awareness of water lead levels (WLLs). This research explores and compares the use of statistical models (i.e., Bayesian Belief classifiers) and machine learning models (i.e., ensemble of decision trees) for predicting WLLs. Models are developed using a dataset collected by the Virginia Household Water Quality Program (VAHWQP) at approximately 8000 households in Virginia during 2012-2017. The dataset reports laboratory-tested water quality parameters at households, location information, and household and plumbing characteristics, including observations of water odor, taste, discoloration. Some water quality parameters, such as pH, iron, and copper, can be measured at low resolution by residents using at-home water test kits and can be used to predict risk of WLLs. The use of at-home water quality test kits was simulated through the discretization of water quality parameter measurements to match the resolution of at-home water quality test kits and the introduction of error in water quality readings. Using this approach, this research demonstrates that low-resolution data collected by residents can be used as input for models to estimate WLLs. Model predictability was explored for a set of at-home water quality test kits that observe a variety of water quality parameters and report parameters at a range of resolutions. The effects of the timing of water sampling (e.g., first-draw vs. flushed samples) and error in kits on model error were tested through simulations. The prediction models developed through this research provide a set of tools for private well users to assess the risk of lead contamination. Models can be implemented as early warning systems in citizen science and online platforms to improve awareness of drinking water threats.

Compatibility of the Drawover Vaporizer With Military Field Ventilators.

For far-forward resuscitative care for combat trauma casualties, the field anesthesia machine best suited is the Universal Portable Anesthesia Complete (UPAC, Datex Ohmeda, GE Healthcare). However, no provision was made for mechanical ventilation. Historically the Impact Uni-Vent 754 ventilator (Impact Instrumentation, Zoll Medical Corp) was used to provide hands-free ventilation but has limitations. There are 3 other field-capable ventilators that could fill the gap, but no known published research studies exist that examine the compatibility of these ventilators with the UPAC. The research question of this experimental, crossover design study was: Are there operational differences in the Impact 754, Impact Uni-Vent 731, Hamilton-T1 (Hamilton Medical Inc), and second-generation Simplified Automated Ventilator (SAVe II, AutoMedx) ventilators' performance relative to tidal volumes, rates, and inhaled anesthetic concentrations when used with the UPAC? The Impact 754 was most accurate (P<.05), followed by the Impact 731, and Hamilton-T1, and the SAVe II was the least accurate related to minute ventilation comparisons. Each ventilator demonstrated effective inhaled anesthetic delivery (r=0.97-0.99). Data analysis results demonstrated statistical and clinical significance in the reliability of the tested ventilators to deliver set volumes and rates while delivering predictable and accurate amounts of volatile anesthetic.

Developing a framework for classifying water lead levels at private drinking water systems: A Bayesian Belief Network approach.

The presence of lead in drinking water creates a public health crisis, as lead causes neurological damage at low levels of exposure. The objective of this research is to explore modeling approaches to predict the risk of lead at private drinking water systems. This research uses Bayesian Network approaches to explore interactions among household characteristics, geological parameters, observations of tap water, and laboratory tests of water quality parameters. A knowledge discovery framework is developed by integrating methods for data discretization, feature selection, and Bayes classifiers. Forward selection and backward selection are explored for feature selection. Discretization approaches, including domain-knowledge, statistical, and information-based approaches, are tested to discretize continuous features. Bayes classifiers that are tested include General Bayesian Network, Naive Bayes, and Tree-Augmented Naive Bayes, which are applied to identify Directed Acyclic Graphs (DAGs). Bayesian inference is used to fit conditional probability tables for each DAG. The Bayesian framework is applied to fit models for a dataset collected by the Virginia Household Water Quality Program (VAHWQP), which collected water samples and conducted household surveys at 2,146 households that use private water systems, including wells and springs, in Virginia during 2012 and 2013. Relationships among laboratory-tested water quality parameters, observations of tap water, and household characteristics, including plumbing type, source water, household location, and on-site water treatment are explored to develop features for predicting water lead levels. Results demonstrate that Naive Bayes classifiers perform best based on recall and precision, when compared with other classifiers. Copper is the most significant predictor of lead, and other important predictors include county, pH, and on-site water treatment. Feature selection methods have a marginal effect on performance, and discretization methods can greatly affect model performance when paired with classifiers. Owners of private wells remain disadvantaged and may be at an elevated level of risk, because utilities and governing agencies are not responsible for ensuring that lead levels meet the Lead and Copper Rule for private wells. Insight gained from models can be used to identify water quality parameters, plumbing characteristics, and household variables that increase the likelihood of high water lead levels to inform decisions about lead testing and treatment.

Effectiveness of transcutaneous electrical nerve stimulation for postoperative pain control in adult surgical patients: an umbrella review protocol.

OBJECTIVE: The objective of this review is to evaluate the effectiveness of transcutaneous electrical nerve stimulators for postoperative pain in comparison to current opioid-centric standard of care in adult surgical patients. INTRODUCTION: In recent years, opioid pain medications have presented two major problems: national shortages triggering a pain management crisis and an increase in opioid related deaths. Opioids also have a variety of untoward side effects, complicating pain management in postoperative individuals. Due to these serious issues, this review will investigate the use of transcutaneous electrical nerve stimulation as a non-pharmacological means to control postoperative pain. INCLUSION CRITERIA: The review will consider for inclusion all systematic reviews, meta-analyses and research syntheses utilizing transcutaneous electrical nerve stimulation as a primary means of postoperative pain treatment, or those comparing transcutaneous electrical nerve stimulation and the current opioid-centric standard of pain management in the treatment of postoperative pain in adult surgical patients. METHODS: This umbrella review will consider all published and unpublished literature, limited to the English language only. Sources of data will be Cochrane Library, JBI Evidence Synthesis, CINAHL and PubMed. Unpublished meta-analyses and systematic reviews will be obtained through ProQuest Dissertations and Theses, the United States National Library of Medicine, and Open Grey: System for Information on Grey Literature in Europe. JBI methodology for study selection, critical appraisal, data extraction and summarizing the data will be utilized to reduce bias and minimize the risk of error in extracting relevant data in the accepted reviews. SYSTEMATIC REVIEW REGISTRATION NUMBER: Pending.

Risk-based decision making to evaluate pollutant reduction scenarios.

A total maximum daily load (TMDL) is required for water bodies in the U.S. that do not meet applicable water quality standards. Computational watershed models are often used to develop TMDL pollutant reduction scenarios. Uncertainty is inherent in the modeling process. An explicit uncertainty analysis would improve model performance and result in more robust decision making when comparing alternative pollutant reduction scenarios. This paper presents a risk-based framework for evaluating alternative pollutant allocation scenarios considering reliability in achieving water quality goals. We demonstrate a generic routine for the application of Generalized Likelihood Uncertainty Estimation (GLUE) to Hydrological Simulation Program-FORTRAN (HSPF) using existing softwares to evaluate two bacteria reduction scenarios from a recently developed TMDL that addressed a bacterial impairment in a mixed land use watershed in Virginia, U.S. Our probabilistic analysis showed that for reliability levels <25%, the recommended TMDL bacterial load reduction scenario had the same exceedance rate as the full reduction scenario (fully reducing all bacterial loads except wildlife), while for reliability levels between 25% and 50%, the exceedance rates for the two pollutant reduction scenarios were similar, with the TMDL recommended scenario violating the water quality criteria only slightly more often. The full reduction scenario performed better in higher reliability levels, although it could not meet the water quality criteria. Our results indicated that, in this case, achieving water quality goals with very high reliability was not possible, even with extreme levels of pollutant reduction. The risk-based framework presented here illustrates a method to propagate watershed model uncertainty and assess performance of alternative pollutant reduction scenarios using existing tools, thereby enabling decision makers to understand the reliability of a given scenario in achieving water quality goals.

Effectiveness of non-opioid pharmacological adjuncts for adult surgical patients: an umbrella review protocol.

OBJECTIVE: The objective of this review is to determine the effectiveness of non-opioid pharmacological adjuncts for decreasing perioperative morphine equivalents and acute postoperative pain scores in adult surgical patients. INTRODUCTION: Opioids are commonly administered during anesthesia to dull the senses, relieve pain and induce sleep. However, there are significant adverse effects associated with intraoperative opioid use. Anesthesia providers can impact the current opioid epidemic by administering non-opioid-centric anesthetic medications. A large-scale evidence-based review is needed to inform a standardized non-opioid pain treatment strategy in the perioperative period. INCLUSION CRITERIA: This review will consider studies of adults 19 years or older who are undergoing surgical procedures and receiving non-opioid oral or intravenous perioperative analgesic medications administered by the anesthesia team. Studies that include patients who receive non-opioid medication as a local infiltrate by the surgical team will be excluded, as will studies with patients who receive regional or neuraxial opioid-sparing techniques. Only systematic reviews and meta-analyses published in English after 2007 will be considered. METHODS: MEDLINE, CINAHL and Embase will be searched, as well as two trial registers and two sources of unpublished reviews. Titles and abstracts will be screened to identify potentially relevant papers. Retrieval of full-text studies, assessment of methodological quality and data extraction will be performed independently by two reviewers. Meta-analyses will be performed if possible, and a Grading of Recommendations, Assessment, Development and Evaluation (GRADE) Summary of Findings presented. SYSTEMATIC REVIEW REGISTRATION NUMBER: PROSPERO CRD42019135852.

Scale-dependent impacts of urban and agricultural land use on nutrients, sediment, and runoff.

We coupled a spatially-explicit land use/land cover (LULC) change model, Dinamica EGO, (Environment for Geoprocessing Objects), with the Chesapeake Bay Watershed Model (CBWM) to project the impact of future LULC change on loading of total nitrogen (TN), total phosphorous (TP) and total suspended solids (TSS) as well as runoff volume in the watersheds surrounding Virginia's Shenandoah National Park in the eastern United States. We allowed for the dynamic transition of four LULC classes, Developed, Forest, Grasses (including both pasture and hayfields) and Crops. Using 2011 as a baseline scenario and observed differences in LULC between 2001 and 2011, we estimated the temporal and spatial patterns of LULC change as influenced by physiographic and socio-economic drivers 50 years in the future (2061). Between transitions of the four LULC classes, the greatest absolute change occurred between the gain in total Developed land and loss in total Forest. New Developed land was driven primarily by distance to existing Developed land and population density. Major findings on the effect of LULC change on watershed model outputs were that: the impact of LULC change on pollutant loading and runoff volume is more pronounced at finer spatial scales; increases in the area of Grasses produced the greatest increase in TP loading, while loss of Forest increased TN, TSS, and runoff volume the most; and land-river segments with a greater proportion of Developed or a smaller proportion of Forest in the 2011 scenario experienced a greater change in runoff than other land-river segments. Results of this study illustrate the potential impact of projected LULC change on nutrient and sediment loads which can adversely impact water quality. Studies like this contribute to a broader understanding of how ecosystem services such as fresh water respond to LULC change, information relevant to those in planning and watershed management.

Evaluating Geologic Sources of Arsenic in Well Water in Virginia (USA).

We investigated if geologic factors are linked to elevated arsenic (As) concentrations above 5 μg/L in well water in the state of Virginia, USA. Using geologic unit data mapped within GIS and two datasets of measured As concentrations in well water (one from public wells, the other from private wells), we evaluated occurrences of elevated As (above 5 μg/L) based on geologic unit. We also constructed a logistic regression model to examine statistical relationships between elevated As and geologic units. Two geologic units, including Triassic-aged sedimentary rocks and Triassic-Jurassic intrusives of the Culpeper Basin in north-central Virginia, had higher occurrences of elevated As in well water than other geologic units in Virginia. Model results support these patterns, showing a higher probability for As occurrence above 5 μg/L in well water in these two units. Due to the lack of observations (<5%) having elevated As concentrations in our data set, our model cannot be used to predict As concentrations in other parts of the state. However, our results are useful for identifying areas of Virginia, defined by underlying geology, that are more likely to have elevated As concentrations in well water. Due to the ease of obtaining publicly available data and the accessibility of GIS, this study approach can be applied to other areas with existing datasets of As concentrations in well water and accessible data on geology.

Tattoos and administration of regional anesthesia: a comprehensive systematic review protocol.

REVIEW QUESTION/OBJECTIVE: The review question is what is the impact of tattoos on the administration of regional anesthesia?The quantitative objective is to identify and quantify the risks to a patient when advancing a needle through tattooed skin for the purpose of administering a regional anesthetic.The qualitative objective is to investigate anesthesia providers' perceptions and experiences when presented with a patient and/or a surgeon requests for a regional anesthetic that would require needle trespass through tattooed skin. An additional qualitative objective is to identify the thoughts, opinions and biases related to the administration of a regional anesthetic through tattooed skin from the perspective of the patient, anesthesia provider, surgeon or other affected parties (e.g. patient families, hospital or clinic administrators or insurance providers).

Effects of humerus intraosseous versus intravenous amiodarone administration in a hypovolemic porcine model.

OBJECTIVE: To compare the effects of amiodarone administration by humerus intraosseous (HIO) and intravenous (IV) routes on return of spontaneous circulation (ROSC), time to maximum concentration (Tmax), maximum plasma drug concentration (Cmax), time to ROSC, and mean concentrations over time in a hypovolemic cardiac arrest model. DESIGN: Prospective, between subjects, randomized experimental design. SETTING: TriService Research Facility. SUBJECTS: Yorkshire-cross swine (n = 28). INTERVENTION: Swine were anesthetized and placed into cardiac arrest. After 2 minutes, cardiopulmonary resuscitation was initiated. After an additional 2 minutes, amiodarone 300 mg was administered via the HIO or the IV route. Blood samples were collected over 5 minutes. The samples were analyzed using high-performance liquid chromatography tandem mass spectrometry. MAIN OUTCOME MEASUREMENTS: ROSC, Tmax, Cmax, time to ROSC, and mean concentrations over time. RESULTS: There was no difference in ROSC between the HIO and IV groups; each had five achieve ROSC and two that did not (p = 1). There was no difference in Tmax (p = 0.501) or in Cmax between HIO and IV groups (p = 0.232). Means ± standard deviations in seconds were 94.3 ± 78.3 compared to 115.7 ± 87.3 in the IV versus HIO groups, respectively. The mean ± standard deviation in nanograms per milliliter for the HIO was 49,041 ± 21,107 and 74,258 ± 33,176 for the IV group. There were no significant differences between the HIO and IV groups relative to time to ROSC (p = 0.220). A repeated analysis of variance indicated that there were no significant differences between the groups relative to concentrations over time (p > 0.05). CONCLUSION: The humerus intraosseous provides rapid and reliable access to administer life-saving medications during cardiac arrest.

Profiling Private Water Systems to Identify Patterns of Waterborne Lead Exposure.

Although extensive literature documents corrosion in municipal water systems, only minimal data is available describing corrosion in private water systems (e.g., wells), which serve as a primary source of drinking water for approximately 47 million Americans. This study developed a profiling technique specifically tailored to evaluate lead release in these systems. When applied in an intensive field study of 15 private systems, three patterns of lead release were documented: no elevated lead or lead elevated in the first draw only (Type I), erratic spikes of particulate lead (Type II), and sustained detectable lead concentrations (Type III). While flushing protocols as short as 15-30 s may be sufficient to reduce lead concentrations below 15 µg/L for Types I and III exposure, flushing may not be an appropriate remediation strategy for Type II exposure. In addition, the sustained detectable lead concentrations observed with Type III exposure likely result from corrosion of components within the well and therefore cannot be reduced with increased flushing. As profiling techniques are labor- and sample-intensive, we discuss recommendations for simpler sampling schemes for initial private system surveys aimed at quantifying lead and protecting public health.

The effectiveness of tools used to evaluate successful critical decision making skills for applicants to healthcare graduate educational programs: a systematic review.

BACKGROUND: Students leave healthcare academic programs for a variety of reasons. When they attrite, it is disappointing for the student as well as their faculty. Advanced practice nursing and other healthcare professions require not only extensive academic preparation, but also the ability to critically evaluate patient care situations. The ability to critically evaluate a situation is not innate. Critical decision making skills are high level skills that are difficult to assess. For the purpose of this review, critical decision making and critical thinking skills refer to the same constructs and will be referred to globally as critical decision making skills. OBJECTIVE: The objective of this review was to identify the effectiveness of tools used to evaluate critical decision making skills for applicants to healthcare graduate educational programs. TYPES OF PARTICIPANTS: Adult (18 years of age or older) applicants, students enrolled and/or recent graduates (within one year from completion) of healthcare graduate educational programs. Types of interventions: This review considered studies that evaluated the utilization of unique tools as well as standard tools, such as the Graduate Record Exam or grade point average, to evaluate critical decision making skills in graduate healthcare program applicants. Types of studies: Experimental and non-experimental studies were considered for inclusion. Types of outcomes: Successful quantitative evaluations based on specific field of study standards. SEARCH STRATEGY: The search strategy aimed to find both published and unpublished studies. Studies published in English after 1969 were considered for inclusion in this review. Databases that included both published and unpublished (grey) literature were searched. Additionally, reference lists from all articles retrieved were examined for articles for inclusion. METHODOLOGICAL QUALITY: Selected papers were assessed by two independent reviewers using standardized critical appraisal instruments from Joanna Briggs Institute. Any disagreement between reviewers was resolved through discussion or with a third reviewer. DATA COLLECTION: Data was extracted independently by each reviewer from papers included in the review using a Microsoft Excel spreadsheet. Included data included study type, 'r' values, number of subjects and reported 'p' values. These were indexed by author, year and study title. DATA SYNTHESIS: The meta-analysis was performed using the method for effect size analysis from Hunter and Schmidt. The syntax for equations was transposed into a Microsoft Excel spreadsheet for data entry, analysis and graph creation. RESULTS: No articles or paper addressing unique tools for ascertaining critical decision making skills met the inclusion criteria. Standard tools, which were represented in the literature, assess critical decision making skills via prediction of academic and clinical success, which indicates the presence of critical decision making skills in graduate healthcare students. A total of 16 studies addressing standard tools were included in this review. All were retrospective case series studies. The date range for the included studies was 1970 to 2009. The strongest relationship was undergraduate grade point average's correlation to graduate grade point average (small effect size with an 'r' value of 0.27, credibility interval of 0.18-0.37). The second strongest relationship was between Graduate Record Examination's verbal section and graduate grade point average (small effect size with an r value of 0.24, CrI of 0.11-0.37). CONCLUSIONS: An applicant's undergraduate GPA has the strongest correlation with graduate healthcare program success of the indicators analyzed (r = 0.27, small effect size). The next best predictor of graduate healthcare program success was the GRE Verbal score (r = 0.24, small effect size). However, all of the variables carried positive correlations with graduate success, just of lesser effect size strength. This review supports the continued use of traditional indicators of graduate school potential in the undergraduate grade point average and the various sections of the Graduate Record Examination for the selection of graduate healthcare applicants. IMPLICATIONS FOR RESEARCH: Primary studies should be funded and performed to assess the use of unique tools in assessing critical thinking in graduate healthcare students.

Incidence of waterborne lead in private drinking water systems in Virginia.

Although recent studies suggest contamination by bacteria and nitrate in private drinking water systems is of increasing concern, data describing contaminants associated with the corrosion of onsite plumbing are scarce. This study reports on the analysis of 2,146 samples submitted by private system homeowners. Almost 20% of first draw samples submitted contained lead concentrations above the United States Environmental Protection Agency action level of 15 µg/L, suggesting that corrosion may be a significant public health problem. Correlations between lead, copper, and zinc suggested brass components as a likely lead source, and dug/bored wells had significantly higher lead concentrations as compared to drilled wells. A random subset of samples selected to quantify particulate lead indicated that, on average, 47% of lead in the first draws was in the particulate form, although the occurrence was highly variable. While flushing the tap reduced lead below 15 µg/L for most systems, some systems experienced an increase, perhaps attributable to particulate lead or lead-bearing components upstream of the faucet (e.g., valves, pumps). Results suggest that without including a focus on private as well as municipal systems it will be very difficult to meet the existing national public health goal to eliminate elevated blood lead levels in children.

Enhanced nitrate and phosphate removal in a denitrifying bioreactor with biochar.

Denitrifying bioreactors (DNBRs) are an emerging technology used to remove nitrate-nitrogen (NO) from enriched waters by supporting denitrifying microorganisms with organic carbon in an anaerobic environment. Field-scale investigations have established successful removal of NO from agricultural drainage, but the potential for DNBRs to remediate excess phosphorus (P) exported from agricultural systems has not been addressed. We hypothesized that biochar addition to traditional woodchip DNBRs would enhance NO and P removal and reduce nitrous oxide (NO) emissions based on previous research demonstrating reduced leaching of NO and P and lower greenhouse gas production associated with biochar amendment of agricultural soils. Nine laboratory-scale DNBRs, a woodchip control, and eight different woodchip-biochar treatments were used to test the effect of biochar on nutrient removal. The biochar treatments constituted a full factorial design of three factors (biochar source material [feedstock], particle size, and application rate), each with two levels. Statistical analysis by repeated measures ANOVA showed a significant effect of biochar, time, and their interaction on NO and dissolved P removal. Average P removal of 65% was observed in the biochar treatments by 18 h, after which the concentrations remained stable, compared with an 8% increase in the control after 72 h. Biochar addition resulted in average NO removal of 86% after 18 h and 97% after 72 h, compared with only 13% at 18 h and 75% at 72 h in the control. Biochar addition also resulted in significantly lower NO production. These results suggest that biochar can reduce the design residence time by enhancing nutrient removal rates.

Associations between fecal indicator bacteria prevalence and demographic data in private water supplies in Virginia.

Over 1.7 million Virginians rely on private water sources to provide household water. The heaviest reliance on these systems occurs in rural areas, which are often underserved with respect to available financial resources and access to environmental health education. This study aimed to identify potential associations between concentrations of fecal indicator bacteria (FIB) (coliforms, Escherichia coli) in over 800 samples collected at the point-of-use from homes with private water supply systems and homeowner-provided demographic data (household income and education). Of the 828 samples tested, 349 (42%) of samples tested positive for total coliform and 55 (6.6%) tested positive for E. coli. Source tracking efforts targeting optical brightener concentrations via fluorometry and the presence of a human-specific Bacteroides marker via quantitative real-time polymerase chain reaction (qPCR) suggest possible contamination from human septage in over 20 samples. Statistical methods implied that household income has an association with the proportion of samples positive for total coliform, though the relationship between education level and FIB is less clear. Further exploration of links between demographic data and private water quality will be helpful in building effective strategies to improve rural drinking water quality.

Development of total maximum daily loads for bacteria impaired watershed using the comprehensive hydrology and water quality simulation model.

The objective of this study was to develop bacteria total maximum daily loads (TMDLs) for the Hardware River watershed in the Commonwealth of Virginia, USA. The TMDL program is an integrated watershed management approach required by the Clean Water Act. The TMDLs were developed to meet Virginia's water quality standard for bacteria at the time, which stated that the calendar-month geometric mean concentration of Escherichia coli should not exceed 126 cfu/100 mL, and that no single sample should exceed a concentration of 235 cfu/100 mL. The bacteria impairment TMDLs were developed using the Hydrological Simulation Program-FORTRAN (HSPF). The hydrology and water quality components of HSPF were calibrated and validated using data from the Hardware River watershed to ensure that the model adequately simulated runoff and bacteria concentrations. The calibrated and validated HSPF model was used to estimate the contributions from the various bacteria sources in the Hardware River watershed to the in-stream concentration. Bacteria loads were estimated through an extensive source characterization process. Simulation results for existing conditions indicated that the majority of the bacteria came from livestock and wildlife direct deposits and pervious lands. Different source reduction scenarios were evaluated to identify scenarios that meet both the geometric mean and single sample maximum E. coli criteria with zero violations. The resulting scenarios required extreme and impractical reductions from livestock and wildlife sources. Results from studies similar to this across Virginia partially contributed to a reconsideration of the standard's applicability to TMDL development.

Quantitative analysis of microbial contamination in private drinking water supply systems.

Over one million households rely on private water supplies (e.g. well, spring, cistern) in the Commonwealth of Virginia, USA. The present study tested 538 private wells and springs in 20 Virginia counties for total coliforms (TCs) and Escherichia coli along with a suite of chemical contaminants. A logistic regression analysis was used to investigate potential correlations between TC contamination and chemical parameters (e.g. NO3(-), turbidity), as well as homeowner-provided survey data describing system characteristics and perceived water quality. Of the 538 samples collected, 41% (n = 221) were positive for TCs and 10% (n = 53) for E. coli. Chemical parameters were not statistically predictive of microbial contamination. Well depth, water treatment, and farm location proximate to the water supply were factors in a regression model that predicted presence/absence of TCs with 74% accuracy. Microbial and chemical source tracking techniques (Bacteroides gene Bac32F and HF183 detection via polymerase chain reaction and optical brightener detection via fluorometry) identified four samples as likely contaminated with human wastewater.

Modeling and assessing the impact of reclaimed wastewater irrigation on the nutrient loads from an agricultural watershed containing rice paddy fields.

Two models were used in concert to predict nutrient loads in a waterbody receiving irrigation return flows from a rice paddy production system. Two irrigation scenarios were simulated, one using reclaimed wastewater as the irrigation water source, the other using water from a surface reservoir designed to supply irrigation water. Total nitrogen (TN) and total phosphorus (TP) loads in irrigation return flows from the rice paddy fields were simulated using the field-scale water quality model Chemical, Runoff and Erosion from Agricultural Management System model for rice paddy fields (CREAMS-PADDY). The output from CREAMS-PADDY was then used as input data for Hydrological Simulation Program-FORTRAN (HSPF) model. HSPF was used to evaluate TN and TP loads in the receiving waterbody at the watershed-scale. CREAMS-PADDY and HSPF were calibrated for both hydrology and water quality using observed data. Both CREAMS-PADDY and HSPF showed good agreement between the observed and simulated data during the calibration and validation periods. Simulation indicated that TN and TP loads from the study paddy fields increased by 207% and 1022% when reclaimed wastewater was used for irrigation compared to conventional irrigation. Irrigating paddy fields (18.8% of the 385 ha study watershed) with reclaimed wastewater increased the TN load at the watershed outlet by 10.3% and TP by 14.0%. The increase in nutrient loads was the result of the high nutrient concentration in the reclaimed wastewater. The procedures used in this research can be used to develop wastewater reuse strategies that minimize environmental impacts on watershed water quality.

Development of bacteria and benthic total maximum daily loads: a case study, Linville Creek, Virginia.

Two total maximum daily load (TMDL) studies were performed for Linville Creek in Rockingham County, Virginia, to address bacterial and benthic impairments. The TMDL program is an integrated watershed management approach required by the Clean Water Act. This paper describes the procedures used by the Center for TMDL and Watershed Studies at Virginia Tech to develop the Linville Creek TMDLs and discusses the key lessons learned from and the ramifications of the procedures used in these and other similar TMDL studies. The bacterial impairment TMDL was developed using the Hydrological Simulation Program-Fortran (HSPF). Fecal coliform loads were estimated through an intensive source characterization process. The benthic impairment TMDL was developed using the Generalized Watershed Loading Function (GWLF) model and the reference watershed approach. The bacterial TMDL allocation scenario requires a 100% reduction in cattle manure direct-deposits to the stream, a 96% reduction in nonpoint-source loadings to the land surface, and a 95% reduction in wildlife direct-deposits to the stream. Sediment was identified as the primary benthic stressor. The TMDL allocation scenario for the benthic impairment requires an overall reduction of 12.3% of the existing sediment loads. Despite the many drawbacks associated with using watershed-scale models like HSPF and GWLF to develop TMDLs, the detailed watershed and pollutant-source characterization required to use these and similar models creates information that stakeholders need to select appropriate corrective measures to address the cause of the water quality impairment when implementing the TMDL.