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While Quality of Life (QOL) has long been an explicit or implicit policy goal, adequate definition and measurement have been elusive. Diverse "objective" and "subjective" indicators across a range of disci- plines and scales, and recent work on subjective well-being (SWB) surveys and the psychology of happiness have spurred renewed interest. Drawing from multiple disciplines, we present an integrative definition of QOL that combines measures of human needs with subjective well-being or happiness. QOL is proposed as a multi- scale, multi-dimensional concept that contains interacting objective and subjective elements. We relate QOL to the opportunities that are provided to meet human needs in the forms of built, human, social and natural capital (in addition to time) and the policy options that are available to enhance these opportunities. Issues related to defining, measuring, and scaling these concepts are discussed, and a research agenda is elaborated. Policy implications include strategies for investing in opportunities to maximize QOL enhancement at the individual, community, and national scales.

Enhancing Quality of Life (QOL) has long been an explicit or implicit goal for individuals, communities, nations, and the world. But defining QOL and measuring progress toward meeting this goal have been elusive. Diverse "objective" and "subjective" indicators across a range of disciplines and scales, and recent work on subjective well-being (SWB) surveys and the psychology of happiness have spurred interest. Drawing from multiple disciplines, we present an integrative definition of QOL that combines measures of human needs with subjective well-being or happiness. QOL is proposed as a multi-scale, multi-dimensional concept that contains interacting objective and subjective elements. We relate QOL to the opportunities that are provided to meet human needs in the forms of built, human, social and natural capital (in addition to time) and the policy options that are available to enhance these opportunities. Issues related to defining, measuring, and scaling these concepts are discussed, and a research agenda is elaborated. Policy implications include strategies for investing in opportunities to maximize QOL enhancement at the individual, community, and national scales.

Measurements from a fixed-bed, Froude-scaled hydraulic model of a stream in northeastern Vermont demonstrate the importance of forested riparian vegetation effects on near-bank turbulence during overbank flows. Sections of the prototype stream, a tributary to Sleepers River, have increased in channel width within the last 40 years in response to passive reforestation of its riparian zone. Previous research found that reaches of small streams with forested riparian zones are commonly wider than adjacent reaches with non-forested, or grassy, vegetation; however, driving mechanisms for this morphologic difference are not fully explained. Flume experiments were performed with a 1:5 scale, simplified model of half a channel and its floodplain, mimicking the typical non-forested channel size. Two types of riparian vegetation were placed on the constructed floodplain: non-forested, with synthetic grass carpeting; and forested, where rigid, randomly distributed, wooden dowels were added. Three-dimensional velocities were measured with an acoustic Doppler velocimeter at 41 locations within the channel and floodplain at near-bed and 0·6-depth elevations. Observations of velocity components and calculations of turbulent kinetic energy (TKE), Reynolds shear stress and boundary shear stress showed significant differences between forested and non-forested runs. Generally, forested runs exhibited a narrow band of high turbulence between the floodplain and main channel, where TKE was roughly two times greater than TKE in non-forested runs. Compared to non-forested runs, the hydraulic characteristics of forested runs appear to create an environment with higher erosion potential. Given that sediment entrainment and transport can be amplified in flows with high turbulence intensity and given that mature forested stream reaches are wider than comparable non-forested reaches, our results demonstrated a possible driving mechanism for channel widening during overbank flow events in stream reaches with recently reforested riparian zones. Copyright © 2007 John Wiley & Sons, Ltd.

We introduce a new method for exploratory analysis of large data sets with time-varying features, where the aim is to automatically discover novel relationships between features (over some time period) that are predictive of any of a number of time-varying outcomes (over some other time period). Using a genetic algorithm, we co-evolve (i) a subset of predictive features, (ii) which attribute will be predicted (iii) the time period over which to assess the predictive features, and (iv) the time period over which to assess the predicted attribute. After validating the method on 15 synthetic test problems, we used the approach for exploratory analysis of a large healthcare network data set. We discovered a strong association, with 100% sensitivity, between hospital participation in multi-institutional quality improvement collaboratives during or before 2002, and changes in the risk-adjusted rates of mortality and morbidity observed after a 1–2 year lag. The proposed approach is a potentially powerful and general tool for exploratory analysis of a wide range of time-series data sets.

Forty-six independent stream reaches in southeastern Pennsylvania were surveyed to assess the relationships between geomorphic and habitat variables and watershed total impervious area (TIA) and to test the ability of the impervious cover model (ICM) to predict the impervious category based on stream reach variables. Ten variables were analyzed using simple and multivariate statistical techniques including scatter-plots, Spearman's Rank correlations, principal components analysis (PCA), and discriminant analysis (DA). Graphical analysis suggested differences in the response to TIA between the stream reaches with less than 13 percent TIA and those with greater than 24 percent TIA. Spearman's Rank correlations showed significant relationships for large woody debris and sinuosity when analyzing the entire dataset and for depth diversity and the standard deviation of maximum pool depths when analyzing stream reaches with greater than 24 percent TIA. Classification into the ICM using DA was 49 percent accurate; however, the stream reaches did support the ICM in other ways. These results indicate that stream reach response to urbanization may not be consistent across geographical regions and that local conditions (specifically riparian buffer vegetation) may significantly affect channel response; and the ICM, used in the appropriate context, can aid in the management of stream reaches and watersheds.

Data from a pilot study, in which renewable energy was used for low-temperature subsurface heating in a northern climate, suggests that such an approach may be useful for remediating low permeable soils. Low-temperature soil heating is expected to enhance remediation effectiveness by increasing contaminant volatility, diffusion, desorption, and microbiological activity. Direct and indirect solar energy was harvested with a hybrid photovoltaic/wind electric system. The electrical energy generated by the hybrid renewable energy system was distributed to the subsurface using a control system and wire, then converted to heat energy using a resistive element emplaced in an unsaturated silty layer 2.3 m below grade. Renewable energy system performance, soil temperature, and environmental data were collected. Ambient soil temperatures fluctuated seasonally within the silt layer from 4 to 15°C. The small renewable energy system performed as predicted and injected 441 kWh of energy into the soil over the eight-month study. This energy input translated to increased soil temperatures ranging from 7.7 to 19.4°C and from 3.3 to 4.3°C above ambient at distances 0.3 and 0.9 m from the heating well, respectively. The system supplied sufficient heat to maintain soil temperatures above ambient even in winter in Vermont, where low direct solar energy was available and sustained low ambient temperatures prevail.

The ability to predict the response of peatland ecosystems to hydrologic changes is imperative for successful conservation and remediation efforts. We studied a 1.25-ha Vermont kettlehole bog for one year (September 2001–October 2002) to identify hydrologic controls, temporal and spatial variability in flow regimes, and to link hydrologic processes to density of the carnivorous plant (Sarracenia purpurea), an ombrotrophic bog specialist. Using a spatial array of nested piezometers, we measured surface and subsurface flow in shallow peat and surrounding mineral soil. Our unique sampling array was based on a repeated measures factorial design with: (1) incremental distances from a central kettlehole pond; (2) equal distances between piezometers; and (3) at three depths from the peat surface. Local flow patterns in the peat were controlled by snowpack storage during winter and spring months and by evapotranspiration and pond water elevation during summer and fall months. Hydraulic head values showed a local reversal within the peat during spring months which was reflected in higher chemical constituent concentrations in these wells. On a regional scale, higher permeable soils diverted groundwater beneath the peatland to a nearby wetland complex. Horizontal water gradient magnitudes were larger in zones where the peatland was perched above regional groundwater and smaller in zones where a hydraulic connection existed between the peatland and the regional groundwater. The density of pitcher plants (S. purpurea) is strongly correlated to the distance from a central pond, [Fe 3C ], [Na C ], [Cl K ], and ½SO 2K

Managers of landfill sites are faced with enormous challenges when attempting to detect and delineate leachate plumes with a limited number of monitoring wells, assess spatial and temporal trends for hundreds of contaminants, and design long-term monitoring (LTM) strategies. Subsurface microbial ecology is a unique source of data that has been historically underutilized in LTM groundwater designs. This paper provides a methodology for utilizing qualitative and quantitative information (specifically, multiple water quality measurements and genome-based data) from a landfill leachate contaminated aquifer in Banisveld, The Netherlands, to improve the estimation of parameters of concern. We used a principal component analysis (PCA) to reduce nonindependent hydrochemistry data, Bacteria and Archaea community profiles from 16SrDNA denaturing gradient gel electrophoresis (DGGE), into six statistically independent variables, representing the majority of the original dataset variances. The PCA scores grouped samples based on the degree or class of contamination and were similar over considerable horizontal distances. Incorporation of the principal component scores with traditional subsurface information using cokriging improved the understanding of the contaminated area by reducing error variances and increasing detection efficiency. Combining these multiple types of data (e.g., genome-based information, hydrochemistry, borings) may be extremely useful at landfill or other LTM sites for designing cost-effective strategies to detect and monitor contaminants. © 2005 American Chemical Society.

Roads in rural, upland landscapes are important sources of runoff and sediment to waterways. The downstream effects of these sources should be related to the connectivity of roads to receiving waters. Recent studies have explored this idea, but only simple metrics have been used to characterize connectivity and few studies have quantified the downstream effects of road–stream connectivity on sediment or solute budgets and channel morphology. In this study, we evaluated traditional and newly developed connectivity metrics that utilized features of landscape position and delivery pathway to characterize road–stream connectivity in upland settings. Using data on stream geomorphic conditions developed by the Vermont Agency of Natural Resources (Montpelier, VT), we related road connectivity metrics to channel condition on a set of 101 forested, upland streams with minimal development other than predominantly gravel road networks. Logistic regression indicated that measures of road density, proximity and orientation successfully distinguished among categories of stream geomorphic condition at multiple geographic scales. Discriminant function analysis using a set of inherent channel characteristics combined with road connectivity metrics derived at the reach corridor scale successfully distinguished channel condition for over 70% of the channels evaluated. This research contributes to efforts to evaluate the cumulative downstream effects of roads on stream channels and aquatic resources and provides a new means of watershed assessment to derive metrics that can be used to predict channel condition. Copyright © 2014 John Wiley & Sons, Ltd.

A self-organizing map (SOM) is a self-organized projection of high-dimensional data onto a typically 2-dimensional (2-D) feature map, wherein vector similarity is implicitly translated into topological closeness in the 2-D projection. However, when there are more neurons than input patterns, it can be challenging to interpret the results, due to diffuse cluster boundaries and limitations of current methods for displaying interneuron distances. In this brief, we introduce a new cluster reinforcement (CR) phase for sparsely-matched SOMs. The CR phase amplifies within-cluster similarity in an unsupervised, data-driven manner. Discontinuities in the resulting map correspond to between-cluster distances and are stored in a boundary (B) matrix. We describe a new hierarchical visualization of cluster boundaries displayed directly on feature maps, which requires no further clustering beyond what was implicitly accomplished during self-organization in SOM training. We use a synthetic benchmark problem and previously published microbial community profile data to demonstrate the benefits of the proposed methods.

Tropical Storm Irene swept through Vermont with 100-200 mm (4-8 inches) of rain, causing intensive flooding in the Southern and Central regions. Over 300 bridges suffered damage or failed as a result of heavy rains. Resources from the Vermont Agency of Transportation, Vermont Agency of Natural Resources, and Vermont Regional Planning Commission were joined to create a comprehensive bridge database. The database combines the stream characteristics and Irene damage along with the bridge attributes into a common resource that can be used for analysis. The examination of affected bridges showed scour as the dominant mode of damage from Irene. Bridges damaged tended to have floodplain contraction, and spans incapable of passing flood flows. Flow constriction and floodplain blockage were key indications of poor performance.

This paper presents a methodology on prioritizing deployment of scour sensors at high-risk bridges using a holistic approach. Specifically, stream geomorphic data, bridge design information, and bridge scour ratings are used to intelligently identify and prioritize bridge sites for remote scour monitoring. Scour is the leading cause of bridge failure, with 309 bridges in the State of Vermont rated as scour critical. Using Vermont as a case study, this work looks to correlate bridge structural design data with hydraulic and stream geomorphic information to identify bridges at high risk of scour processes, and specifically for bridges more structurally susceptible to scour damage. Including additional stream and hydraulic indicators in the analysis, rather than relying on structural evaluations alone, enables areas of geomorphic instability to be identified; and bridges in these areas can be outfitted with scour monitoring devices. Lowcost monitoring devices are proposed to monitor at-risk bridges and to provide additional information in scour-prone areas. A sensor under development would allow for direct installment into stream beds at existing bridges, and incorporate accelerometer-based monitoring, with wireless data transmission. The device would allow for real-time measurement of streambed degradation and aggradation during high flow events, thereby providing timely information regarding critical scour events. This work aims to aid State transportation engineers in identifying, which bridges in the transportation network are at risk, of scour processes, provides useful insight into scour rating systems, and assesses the value of the geomorphic assessments to improve our existing bridge rating system. The compilation of results from geomorphic assessments, scour ratings, and bridge design information will be presented.

Background In this study we compared the utility of two molecular biology techniques, cloning of the mitochondrial 12S ribosomal RNA gene and hydrolysis probe-based qPCR, to identify blood meal sources of sylvatic Chagas disease insect vectors collected with live-bait mouse traps (also known as Noireau traps). Fourteen T. guasayana were collected from six georeferenced trap locations in the Andean highlands of the department of Chuquisaca, Bolivia. Methodology/Principal Findings We detected four blood meals sources with the cloning assay: seven samples were positive for human (Homo sapiens), five for chicken (Gallus gallus) and unicolored blackbird (Agelasticus cyanopus), and one for opossum (Monodelphis domestica). Using the qPCR assay we detected chicken (13 vectors), and human (14 vectors) blood meals as well as an additional blood meal source, Canis sp. (4 vectors). Conclusions/Significance We show that cloning of 12S PCR products, which avoids bias associated with developing primers based on a priori knowledge, detected blood meal sources not previously considered and that species-specific qPCR is more sensitive. All samples identified as positive for a specific blood meal source by the cloning assay were also positive by qPCR. However, not all samples positive by qPCR were positive by cloning. We show the power of combining the cloning assay with the highly sensitive hydrolysis probe-based qPCR assay provides a more complete picture of blood meal sources for insect disease vectors., Author Summary The World Health Organization (WHO) estimates that 7 to 8 million people are currently infected with Trypanosoma cruzi, the parasite that causes Chagas disease. The WHO recommends insect vector control as the primary prevention method; and insecticide spraying is the most commonly used intervention technique. Sylvatic insect vectors are a special concern because they are a source of reinfestation after insecticides have been applied to living quarters (domestic) and immediate surroundings (peridomestic). To better understand sylvatic insect vector movement, we used two molecular biology techniques to detect the blood meal sources of sylvatic insect vectors. The first technique, cloning of 12S PCR products, allows us to cast a wide net and detect blood meal sources with no previous knowledge of vertebrates or mammals in the study site. After acquiring knowledge of vertebrates in the study site (either through the aforementioned cloning technique, literature review or survey of the area), the second technique, the species-specific hydrolysis probe-based qPCR provides a highly sensitive assay for particular taxa.

Diffuse nutrient loads are a common problem in developed and agricultural watersheds. While there has been substantial investment in best management practices (BMPs) to reduce diffuse pollution, there remains a need to better prioritize controls at the watershed scale as reflected in recent US-EPA guidance for watershed planning and Total Maximum Daily Load development. We implemented spatial optimization techniques among four diffuse source pathways in a mixed-use watershed in Northern Vermont to maximize total reduction of phosphorus loading to streams while minimizing associated costs. We found that within a capital cost range of 138 to 321 USD ha-1 a phosphorus reduction of 0.29 to 0.38 kg ha−1 year−1, is attainable. Optimization results are substantially more cost-effective than most scenarios identified by stakeholders. The maximum diffuse phosphorus load reduction equates to 1.25 t year−1using the most cost-effective technologies for each diffuse source at a cost of $3,464,260. However, 1.13 t year−1 could be reduced at a much lower cost of $976,417. This is the practical upper limit of achievable diffuse phosphorus reduction, above which additional spending would not result in substantially more phosphorus reduction. Watershed managers could use solutions along the resulting Pareto optimal curve to select optimal combinations of BMPs based on a water quality target or available funds. The results demonstrate the power of using spatial optimization methods to arrive at a cost-effective selection of BMPs and their distribution across a landscape.

The abundance of cosmogenic 36 Cl, measured in 41 limestone samples from a 9 m high bedrock fault scarp, allows us to construct the 14 kyr fault displacement history of the Nahef East normal fault, northern Israel (300 m above sea level, N33° latitude). The Nahef East fault is one of a series of fault scarps located along the 700 m high Zurim Escarpment, a major geomorphic feature. Samples at the top of the scarp have the highest nuclide concentrations (79 x 10 4 atoms (g rock) -1 ); samples at the base have the lowest (11 x 10 4 atoms (g rock) -1 ), Using chemical data from the samples, Nahef East fault scarp geometry, and surface and subsurface production rates for the 36 Cl-producing reactions, we have constructed a numerical model that calculates 36 Cl accumulation on a scarp through time, given a series of unique displacement scenarios. The resulting model 36 Cl concentrations are compared to those measured in the scarp samples. Faulting histories that result in a good match between measured and modeled 36 Cl abundances show three distinct periods of fault activity during the past 14 kyr with over 6 vertical meters of motion occurring during a 3 kyr time period in the middle Holocene. Smaller amounts of displacement occurred before and after the period of most rapid motion. The episodic behavior of the Nahef East fault indicates that the average displacement rate of this fault system has varied through time.

Exploratory data analysis on physical, chemical, and biological data from sediments and water in Lake Champlain reveals a strong relationship between cyanobacteria, sediment anoxia, and the ratio of dissolved nitrogen to soluble reactive phosphorus. Physical, chemical, and biological parameters of lake sediment and water were measured between 2007 and 2009. Cluster analysis using a self-organizing artificial neural network, expert opinion, and discriminant analysis separated the data set into no-bloom and bloom groups. Clustering was based on similarities in water and sediment chemistry and non-cyanobacteria phytoplankton abundance. Our analysis focused on the contribution of individual parameters to discriminate between no-bloom and bloom groupings. Application to a second, more spatially diverse data set, revealed similar no-bloom and bloom discrimination, yet a few samples possess all the physicochemical characteristics of a bloom without the high cyanobacteria cell counts, suggesting that while specific environmental conditions can support a bloom, another environmental trigger may be required to initiate the bloom. Results highlight the conditions coincident with cyanobacteria blooms in Missisquoi Bay of Lake Champlain and indicate additional data are needed to identify possible ecological contributors to bloom initiation.

In this study, we evaluate the effect of participatory Ecohealth interventions on domestic reinfestation of the Chagas disease vector Triatoma dimidiata after village-wide suppression of the vector population using a residual insecticide. The study was conducted in the rural community of La Brea, Guatemala between 2002 and 2009 where vector infestation was analyzed within a spatial data framework based on entomological and socio-economic surveys of home- steads within the village. Participatory interventions focused on community awareness and low-cost home improvements using local materials to limit areas of refuge and alternative blood meals for the vector within the home, and potential shelter for the vector outside the home. As a result, domestic infestation was maintained at £ 3% and peridomestic infestation at £ 2% for 5 years beyond the last insecticide spraying, in sharp contrast to the rapid reinfestation experi- enced in earlier insecticide only interventions.

The practice of engineering, especially the design process, involves many aspects beyond just the technical and includes such critical components as engineering ethics, sustainability and transferable skills such as communication, leadership and mentoring. Engineering educators often struggle with how to best incorporate these nontechnical aspects within their curricula. Service learning offers an opportunity to do this. The disconnect is that students often view engineering as only the technical number crunching and these other nontechnical components as less important. We report on the assessment of student written reflections across two very different service-learning engineering design projects for the purpose of evaluating student attitudes about these service-learning experiences and to assess their awareness and appreciation of transferable-skills development. In the spirit of service-learning pedagogy, we divided the contents of the written reflections into three categories – academic enhancement, civic engagement and personal growth skills. The commonality across both courses centered on academic enhancements and the value of transferable skills (i.e., leadership, teamwork, negotiation skills, mentoring, scheduling, verbal and written communication skills). Assessments show our current service-learning pedagogy improves students’ understanding of the importance of written and oral presentation skills. However, as of yet, many students do not consider leadership, negotiation skills, design setbacks, scheduling and mentoring skills to be part of “real” engineering.

A technique for obtaining a (nearly) optimal scheme using multiple management periods has been developed. The method has been developed for very large scale combinatorial optimization problems. Simulated annealing has been extended to this problem. An importance function is developed to accelerate the search for good solutions. These tools have been applied to groundwater remediation problems at Lawrence Livermore National Laboratory (LLNL). A deterministic site-specific engineering-type flow and transport model (based on the public domain code SUTRA) is combined with the heuristic optimization technique. The objective is to obtain the time-varying optimal locations of the remediation wells that will reduce concentration levels of volatile organic chemicals in groundwater below a given threshold at specified areas on the LLNL site within a certain time frame and subject to a variety of realistic complicating factors. The cost function incorporates construction costs, operation and maintenance costs for injection and extraction wells, costs associated with piping and treatment facilities, and a performance penalty for well configurations that generate flow and transport simulations that exceed maximum concentration levels at specified locations. The resulting application reported here comprises a huge optimization problem. The importance function detailed in this paper has led to rapid convergence to solutions. The performance penalty allows different goals to be imposed on different geographical regions of the site; in this example, short-term off-site plume containment and long-term on-site cleanup are imposed. The performance of the optimization scheme and the effects of various trade-offs in management objectives are explored through examples using the LLNL site.

Aquatic oligochaetes have long been appreciated for their value in assessing habitat quality because they are ubiquitous sediment-dwelling filter feeders. Many oligochaete taxa are also important in the transmission of fish diseases. Distinguishing resistant and susceptible taxa is important for managing fish disease, yet challenging in practice. Tubifex tubifex (Oligochaeta: Tubificidae) is the definitive host for the complex life-cycle parasite, Myxobolus cerebralis, the causative agent of salmonid whirling disease. We developed two hydrolysis probe-based qualitative real-time PCR (qPCR) multiplex assays that distinguish among tubificid taxa collected from the Madison River, Montana, USA. The first assay distinguishes T. tubifex from Rhyacodrilus spp.; while the second classifies T. tubifex identified by the first assay into two genetic lineages (I and III). Specificity and sensitivity were optimized for each assay; the two assays showed specificity of 94.3% and 98.6% for the target oligochaetes, respectively. DNA sequencing verified the results. The development of these assays allowed us to more fully describe tubificid community composition (the taxa and their abundance at a site) and estimate the relative abundances of host taxa. To relate tubificid relative abundance to fish disease risk, we determined M. cerebralis infection prevalence in samples identified as T. tubifex using similar molecular techniques. Given prior information (i.e., morphological identification of sexually mature worms), Bayesian analysis inferred that the first qPCR assay improved taxonomic identification. Bayesian inference of the relative abundance of T. tubifex, combined with infection assay results, identified sites with a high prevalence of infected T. tubifex. To our knowledge, this study represents both the first assessment of oligochaete community composition using a qPCR assay based on fluorescent probes and the first use of Bayesian analysis to fully characterize the dominant infected taxa in streams where whirling disease is observed.

We present a real-world application utilizing a Genetic Algorithm (GA) for exploratory multivariate association analysis of a large consumer survey designed to assess potential consumer adoption of Plug-in Hybrid Electric Vehicles (PHEVs). The GA utilizes an intersection/union crossover operator, in conjunction with high background mutation rates, to achieve rapid multivariate feature selection. We experimented with two alternative fitness measures based on classification results of a naive Bayes quadratic discriminant analysis; one fitness function rewarded only for correct classifications, and the other penalized for the degree of misclassification using a quadratic penalty function. We achieved high classification accuracy for three different survey outcome questions (with 3-, 5-, and 7- outcome classes, respectively). The quadratic penalty function yielded better overall results, returning smaller feature sets and overall more accurate contingency tables of predicted classes. Our results help to identify what consumer attributes best predict their likelihood of purchasing a PHEV. These findings will be used to better inform an existing agent-based model of PHEV market penetration, with the ultimate aim of helping auto manufacturers and policy makers identify leverage points in the system that will encourage PHEV market adoption.

We introduce a new method for exploratory analysis of large data sets with time-varying features, where the aim is to automatically discover novel relationships between features (over some time period) that are predictive of any of a number of time-varying outcomes (over some other time period). Using a genetic algorithm, we co-evolve (i) a subset of predictive features, (ii) which attribute will be predicted (iii) the time period over which to assess the predictive features, and (iv) the time period over which to assess the predicted attribute. After validating the method on 15 synthetic test problems, we used the approach for exploratory analysis of a large healthcare network data set. We discovered a strong association, with 100% sensitivity, between hospital participation in multi-institutional quality improvement collaboratives during or before 2002, and changes in the risk-adjusted rates of mortality and morbidity observed after a 1-2 year lag. The results provide indirect evidence that these quality improvement collaboratives may have had the desired effect of improving health care practices at participating hospitals. The proposed approach is a potentially powerful and general tool for exploratory analysis of a wide range of time-series data sets.

(1) Microbial biodiversity in groundwater and soil presents a unique opportunity for improving characterization and monitoring at sites with multiple contaminants, yet few computational methods use or incorporate these data because of their high dimensionality and variability. We present a systematic, nonparametric decision-making methodology to help characterize a water quality gradient in leachate-contaminated groundwater using only microbiological data for input. The data-driven methodology is based on clustering a set of molecular genetic-based microbial community proles. Microbes were sampled from groundwater monitoring wells located within and around an aquifer contaminated with landll leachate. We modied a self-organizing map (SOM) to weight the input variables by their relative importance and provide statistical guidance for classifying sample similarities. The methodology includes the following steps: (1) preprocessing the microbial data into a smaller number of independent variables using principal component analysis, (2) clustering the resulting principal component (PC) scores using a modied SOM capable of weighting the input PC scores by the percent variance explained by each score, and (3) using a nonparametric statistic to guide selection of appropriate groupings for management purposes. In this landll leachate application, the weighted SOM assembles the microbial community data from monitoring wells into groupings believed to represent a gradient of site contamination that could aid in characterization and long-term monitoring decisions. Groupings based solely on microbial classications are consistent with classications of water quality from hydrochemical information. These microbial community prole data and improved decision-making strategy compliment traditional chemical groundwater analyses for delineating spatial zones of groundwater contamination.

[1] Groundwater biogeochemistry is adversely impacted when municipal solid waste leachate, rich in nutrients and anthropogenic compounds, percolates into the subsurface from leaking landfills. Detecting leachate contamination using statistical techniques is challenging because well strategies or analytical techniques may be insufficient for detecting low levels of groundwater contamination. We sampled profiles of the microbial community from monitoring wells surrounding a leaking landfill using terminal restriction fragment length polymorphism (T-RFLP) targeting the 16S rRNA gene. Results show in situ monitoring of bacteria, archaea, and the family Geobacteraceae improves characterization of groundwater quality. Bacterial T-RFLP profiles showed shifts correlated to known gradients of leachate and effectively detected changes along plume fringes that were not detected using hydrochemical data. Experimental sediment microcosms exposed to leachate-contaminated groundwater revealed a shift from a β-Proteobacteria and Actinobacteria dominated community to one dominated by Firmicutes and δ-Proteobacteria. This shift is consistent with the transition from oxic conditions to an anoxic, iron-reducing environment as a result of landfill leachate-derived contaminants and associated redox conditions. We suggest microbial communities are more sensitive than hydrochemistry data for characterizing low levels of groundwater contamination and thus provide a novel source of information for optimizing detection and long-term monitoring strategies at landfill sites.

[1] This work contributes a combination of laboratory-based aquifer tracer experimentation and bias-aware Ensemble Kalman Filtering (EnKF) to demonstrate that systematic modeling errors (or bias) in source loading dynamics and the spatial distribution of hydraulic conductivity pose severe challenges for groundwater transport forecasting under uncertainty. The impacts of model bias were evaluated using an ammonium chloride tracer experiment conducted in a three dimensional laboratory tank aquifer with 105 near real-time sampling locations. This study contributes a bias-aware EnKF framework that (i) dramatically enhances the accuracy of concentration breakthrough forecasts in the presence of systematic, spatio-temporally correlated modeling errors, (ii) clarifies in space and time where transport gradients are maximally impacted by model bias, and (iii) expands the size and scope of flow-and-transport problems that can be considered in the future.

[1] A novel data-driven artificial neural network (ANN) that quantitatively combines large numbers of multiple types of soft data is presented for performing stochastic simulation and/or spatial estimation. A counterpropagation ANN is extended with a radial basis function to estimate parameter fields that reproduce the spatial structure exhibited in autocorrelated parameters. Applications involve using three geophysical properties measured on a slab of Berea sandstone and the delineation of landfill leachate at a site in the Netherlands using electrical formation conductivity as our primary variable and six types of secondary data (e.g., hydrochemistry, archaea, and bacteria). The ANN estimation fields are statistically similar to geostatistical methods (indicator simulation and cokriging) and reference fields (when available). The method is a nonparametric clustering/classification algorithm that can assimilate significant amounts of disparate data types with both continuous and categorical responses without the computational burden associated with the construction of positive definite covariance and cross-covariance matrices. The combination of simplicity and computational speed makes the method ideally suited for environmental subsurface characterization and other Earth science applications with spatially autocorrelated variables.

Background In humans, connective tissue forms a complex, interconnected network throughout the body that may have mechanosensory, regulatory and signaling functions. Understanding these potentially important phenomena requires non-invasive measurements of collagen network structure that can be performed in live animals or humans. The goal of this study was to show that ultrasound can be used to quantify dynamic changes in local connective tissue structure in vivo. We first performed combined ultrasound and histology examinations of the same tissue in two subjects undergoing surgery: in one subject, we examined the relationship of ultrasound to histological images in three dimensions; in the other, we examined the effect of a localized tissue perturbation using a previously developed robotic acupuncture needling technique. In ten additional non-surgical subjects, we quantified changes in tissue spatial organization over time during needle rotation vs. no rotation using ultrasound and semi-variogram analyses. Results 3-D renditions of ultrasound images showed longitudinal echogenic sheets that matched with collagenous sheets seen in histological preparations. Rank correlations between serial 2-D ultrasound and corresponding histology images resulted in high positive correlations for semi-variogram ranges computed parallel (r = 0.79, p < 0.001) and perpendicular (r = 0.63, p < 0.001) to the surface of the skin, indicating concordance in spatial structure between the two data sets. Needle rotation caused tissue displacement in the area surrounding the needle that was mapped spatially with ultrasound elastography and corresponded to collagen bundles winding around the needle on histological sections. In semi-variograms computed for each ultrasound frame, there was a greater change in the area under the semi-variogram curve across successive frames during needle rotation compared with no rotation. The direction of this change was heterogeneous across subjects. The frame-to-frame variability was 10-fold (p < 0.001) greater with rotation than with no rotation indicating changes in tissue structure during rotation. Conclusion The combination of ultrasound and semi-variogram analyses allows quantitative assessment of dynamic changes in the structure of human connective tissue in vivo.

Stream channel and watershed management requires an interdisciplinary approach by multiple stakeholders with often disparate goals and objectives. Even single management goals ( e.g. mitigation of property loss due to stream bank erosion and flooding, restoring stream channels and aquatic species habitat) require solutions that span multiple spatial and temporal scales. To deal with the complexity associated with goals such as reducing fluvial erosion hazards, sediment and nutrient loading and threats to aquatic habitat associated with geomorphic instability, the Vermont Agency of Natural Resources' River Management Program has been developing and testing 1) protocols for conducting field-based and remote sensing data and 2) a geographical information system (GIS)-based tool since 1999. These protocols and tools facilitate an understanding of channel instability and assist in developing strategies at appropriate scales to restore channel equilibrium. A hierarchical system of data-driven artificial neural networks (ANNs) has been developed to enhance these existing GIS-based watershed management tools for predicting channel geomorphic condition and inherent sensitivity at the reach scale. We research and develop these ANNs as an alternative model that can incorporate large amounts of data for use in the operational management of watersheds. The ANNs have been trained and tested on remotely sensed and in-field data along with corresponding expert opinions of geomorphic condition and sensitivity. The focus is performing sensitivity analyses and addressing inadequacies associated with assimilation activities that are closely tied to large amounts of hydrological-relevant remote sensing and field data.

We apply a modified counterpropagation artificial neural network (ANN) that uses multivariate data to several parameter estimation problems: (1) estimation of small scale Berea sandstone geophysical properties, (2) estimation of apparent conductivity at a leaking landfill using electromagnetic data and (3) estimation of hydraulic conductivity field at a landfill in New York State using pumping test and well log data. The counterpropagation algorithm has been enhanced in this research to allow for spatial interpolation that is comparable to traditional kriging methods. This enhanced ANN is data-driven, can incorporate large amounts of multiple data types to produce parameter estimates in real-time and does not require the computation of large covariance matrices associated with traditional geostatistical methods (kriging).

Accurate detection of a contaminant source is vital to the management of groundwater restoration projects. We developed a technique for identifying the contaminant source areas that combines site data, geostatistics and artificial neural networks (ANNs). The methodology involves replicating the kriging methods with ANNs to provide estimates of spatially dependent concentration fields, and for quantifying the uncertainty associated with the estimates. Once trained the ANNs approximate the results of ordinary kriging in a more computationally efficient manner. This provides a bi-directional prediction (i.e. forecast the future plume, as well as, the original source), which is impossible for many physics-based models.

The risk of fungal and bacterial crop disease can be predicted using risk models with specific environmental parameters such as temperature, relative humidity, solar radiation, wind speed, and leaf wetness duration (LWD). LWD has long been recognized as key in the management of crop disease. Air temperature and wetness influence the majority of fungal plant diseases. Wetness also impacts insect populations, as well as pollution deposits. Many parameters are well understood, readily defined, and easily measured. Unfortunately, LWD is a complex phenomenon, due to its spatial and temporal variability within a crop canopy. The inconvenience and uncertainty associated with monitoring LWD at the local leaf scale and the complexity of upscaling to the crop level prevent existing disease risk models from being used with reliability. In spite of their imprecision, LW projections are already included in a number of online weather products. One non- parametric statistical approach receiving scant attention for the modeling of LWD is that of artificial neural networks (ANNs). In this work, two previously untried ANNs estimate this key environmental variable at local crop scales, using local and regional weather station data and site-specific sensing data. The first ANN combines two statistical methods to accomplish this spatial mapping (a K-nearest means classifier and a Bayesian classifier), while the recurrent nature of the second ANN provides a means of leveraging the temporal property of the data. The ultimate goal is to embed the ANN into a highly-portable tool, designed to predict leaf wetness duration as an SOC (system on a chip) in conjunction with local weather stations, and as input to real-time decision support systems.

Groundwater remediation management does not end with the installation of the cleanup system and flipping the switch. Water quality samples from groundwater monitoring wells are used to verify that the remediation plan is working as planned, and to ensure that there are no unexpected impacts or risks. An important component of the adaptive long-term monitoring and operation system package aLTMOs TM (Rizzo et al., 2000) is a data-driven Bayesian filter, a data assimilation technique that integrates monitoring data and phenomenological modeling results to provide insightful analyses on plume dynamics in both spatial and temporal dimensions. In this process, a model prediction is calculated using the flow and transport simulation. When new measurements are made, they are used systematically to adjust the model prediction. Irregular spatial and temporal data collection can be incorporated. Concentration estimates and their uncertainties are updated as monitoring data are collected. Based on uncertainty analysis in spatial and temporal dimensions and cleanup goals, an optimal monitoring plan is selected. This paper uses a real superfund site problem to demonstrate the application of the data-driven Bayesian modeling technique in uncertainty analysis and redundant sampling reduction optimization in groundwater long term monitoring design.

Successful management of subsurface environmental resources is highly dependent on the quality of relevant characterization and monitoring. In particular, the ability to identify patterns in raw data measurements and to extract valuable spatial information from different measurement sources is critical to answering questions posed by management. These questions range from “what are the pathways by which receptors may be exposed to (human or ecological) health risks” to “can I determine from existing measurement data whether a buried facility is failing”.