Subsurface characterization of groundwater contaminated by landfill leachate using microbial community profile data and a nonparametric decision-making process

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 profiles. Microbes were sampled from groundwater monitoring wells located within and around an aquifer contaminated with landfill leachate. We modified 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 modified 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 landfill 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 classifications are consistent with classifications of water quality from hydrochemical information. These microbial community profile data and improved decision-making strategy compliment traditional chemical groundwater analyses for delineating spatial zones of groundwater contamination. © 2011 by the American Geophysical Union

Modeling the influence of public risk perceptions on the adoption of green stormwater infrastructure: An application of bayesian belief networks versus logistic regressions on a statewide survey of households in vermont

There is growing environmental psychology and behavior literature with mixed empirical evidence about the influence of public risk perceptions on the adoption of environmentally friendly “green behaviors”. Adoption of stormwater green infrastructure on residential properties, while costlier in the short term compared to conventional greywater infrastructure, plays an important role in the reduction of nutrient loading from non-point sources into freshwater rivers and lakes. In this study, we use Bayesian Belief Networks (BBNs) to analyze a 2015 survey dataset (sample size = 472 respondents) about the adoption of green infrastructure (GSI) in Vermont’s residential areas, most of which are located in either the Lake Champlain Basin or Connecticut River Basin. Eight categories of GSI were investigated: roof diversion, permeable pavement, infiltration trenches, green roofs, rain gardens, constructed wetlands, tree boxes, and others. Using both unsupervised and supervised machine learning algorithms, we used Bayesian Belief Networks to quantify the influence of public risk perceptions on GSI adoption while accounting for a range of demographic and spatial variables. We also compare the effectiveness of the Bayesian Belief Network approach and logistic regression in predicting the pro-environmental behaviors (adoption of GSI). The results show that influencing factors for current adoption differ by the type of GSI. Increased perception of risk from stormwater issues is associated with the adoption of rain gardens and infiltration trenches. Runoff issues are more likely to be considered the governments’ (town, state, and federal agencies) responsibility, whereas lawn erosion is more likely to be considered the residents’ responsibility. When using the same set of variables to predict pro-environmental behaviors (adoption of GSI), the BBN approach produces more accurate predictions compared to logistic regression. The results provide insights for further research on how to encourage residents to take measures for mitigating stormwater issues and stormwater management

Incorporating systems thinking and sustainability within civil and environmental engineering curricula at UVM

As part of an NSF Department Level Reform (DLR) grant, the civil and environmental engineering programs at the University of Vermont (UVM) incorporated systems thinking and a systems approach to engineering problem solving within their programs. A systems thinking approach regards social, environmental and economic factors as necessary components of the problem solution. Because it is a whole systems approach it also encompasses sustainability. We have integrated systems thinking in the following ways; 1) new material has been included into key courses (e.g. the first-year introductory and senior design courses), 2) a sequence of three related environmental and transportation systems courses have been included within the curricula (i.e., Introduction to Systems, Decision Making, and Modeling), and 3) service-learning (S-L) projects have been integrated into key required courses as a way of practicing a systems approach. This culminates in the senior design course in which many of the projects specifically focus on sustainability. A variety of assessment methods have been implemented as part of our reform including student surveys, focus groups, faculty interviews, and assessment of student work. We specifically designed a survey tool that addressed sustainability understanding (both open ended and Likert scale). The survey was given to first-year first semester (FYFS) civil and environmental engineering students, FYFS environmental science students, and senior civil and environmental engineering students. Approximately 50% of the incoming civil and environmental engineering students could not define or give reasonable examples of what sustainability means, while their counterparts in environmental science showed that almost 100% could provide a good definition and provide reasonable examples of sustainability. However, by the end of the introductory course in engineering, the majority of the engineering students had a good working definition of sustainability and examples. Female students in both groups showed a statistically significantly higher interest in learning about sustainability than their male counterparts. © 2011 American Society for Engineering Education

Characterization of increased persistence and intensity of precipitation in the northeastern United States

We present evidence of increasing persistence in daily precipitation in the northeastern United States that suggests that global circulation changes are affecting regional precipitation patterns. Meteorological data from 222 stations in 10 northeastern states are analyzed using Markov chain parameter estimates to demonstrate that a significant mode of precipitation variability is the persistence of precipitation events. We find that the largest region‐wide trend in wet persistence (i.e., the probability of precipitation in 1 day and given precipitation in the preceding day) occurs in June (+0.9% probability per decade over all stations). We also find that the study region is experiencing an increase in the magnitude of high‐intensity precipitation events. The largest increases in the 95th percentile of daily precipitation occurred in April with a trend of +0.7 mm/d/decade. We discuss the implications of the observed precipitation signals for watershed hydrology and flood risk

A tandem evolutionary algorithm for identifying causal rules from complex data

We propose a new evolutionary approach for discovering causal rules in complex classification problems from batch data. Key aspects include (a) the use of a hypergeometric probability mass function as a principled statistic for assessing fitness that quantifies the probability that the observed association between a given clause and target class is due to chance, taking into account the size of the dataset, the amount of missing data, and the distribution of outcome categories, (b) tandem age-layered evolutionary algorithms for evolving parsimonious archives of conjunctive clauses, and disjunctions of these conjunctions, each of which have probabilistically significant associations with outcome classes, and (c) separate archive bins for clauses of different orders, with dynamically adjusted order-specific thresholds. The method is validated on majority-on and multiplexer benchmark problems exhibiting various combinations of heterogeneity, epistasis, overlap, noise in class associations, missing data, extraneous features, and imbalanced classes. We also validate on a more realistic synthetic genome dataset with heterogeneity, epistasis, extraneous features, and noise. In all synthetic epistatic benchmarks, we consistently recover the true causal rule sets used to generate the data. Finally, we discuss an application to a complex real-world survey dataset designed to inform possible ecohealth interventions for Chagas disease

Identifying the spatial pattern and importance of hydro-geomorphic drainage impairments on unpaved roads in the northeastern USA

Roads have been widely studied as sources of runoff and sediment and identified as pollutant production sources to receiving waters. Despite the wealth of research on logging roads in forested, upland settings, little work has been conducted to examine the role of extensive networks of rural, low-volume, unpaved roads on water quality degradation at the catchment scale. We studied a network of municipal unpaved roads in the northeastern US to identify the type and spatial extent of ‘hydro-geomorphic impairments’ to water quality. We mapped erosional and depositional features on roads to develop an estimate of pollutant production. We also mapped the type and location of design interventions or best management practices (BMPs) used to improve road drainage and mitigate water quality impairment. We used statistical analyses to identify key controls on the frequency and magnitude of erosional features on the road network, and GIS to scale up from the survey results to the catchment scale to identify the likely importance of unpaved roads as a pollutant source in this setting. An average of 21 hydro-geomorphic impairments were mapped per kilometer of road, averaging 0.3 m3 in volume. Road gradient and slope position were key controls on the occurrence of these features. The presence of BMPs effectively reduced erosion frequency. Scaled up to the watershed and using a conservative estimate of road–stream connectivity, our results for the Winooski River watershed in the northeastern US suggest that roughly 16% and 6% of the average annual sediment and phosphorus flux, respectively, of the Winooski River may be derived from unpaved roads. Our study identifies an under-appreciated source of water quality degradation in rural watersheds, provides insights into identifying ‘hot spots’ of pollutant production associated with these networks, and points to effectiveness of design interventions in mitigating these adverse impacts on water quality. Copyright © 2017 John Wiley & Sons, Ltd

Application of unmanned aircraft system (UAS) for monitoring bank erosion along river corridors

Excessive streambank erosion is a significant source of fine sediments and associated nutrients in many river systems as well as poses risk to infrastructure. Geomorphic change detection using high-resolution topographic data is a useful method for monitoring the extent of bank erosion along river corridors. Recent advances in an unmanned aircraft system (UAS) and structure from motion (SfM) photogrammetry techniques allow acquisition of high-resolution topographic data, which are the methods used in this study. To evaluate the effectiveness of UAS-based photogrammetry for monitoring bank erosion, a fixed-wing UAS was deployed to survey 20 km of river corridors in central Vermont, in the northeastern United States multiple times over a two-year period. Digital elevation models (DEMs) and DEMs of difference allowed quantification of volumetric changes along selected portions of the survey area where notable erosion occurred. Results showed that UAS was capable of collecting high-quality topographic data at fine resolutions even along vegetated river corridors provided that the surveys were conducted in early spring, after snowmelt but prior to summer vegetation growth. Longer term estimates of streambank movements using the UAS showed good comparison to previously collected airborne lidar surveys and allowed reliable quantification of significant geomorphic changes along rivers

A New Machine-Learning Approach for Classifying Hysteresis in Suspended-Sediment Discharge Relationships Using High-Frequency Monitoring Data

Studying the hysteretic relationships embedded in high-frequency suspended-sediment concentration and river discharge data over 600+ storm events provides insight into the drivers and sources of riverine sediment during storm events. However, the literature to date remains limited to a simple visual classification system (linear, clockwise, counter-clockwise, and figure-eight patterns) or the collapse of hysteresis patterns to an index. This study leverages 3 years of suspended-sediment and discharge data to show proof-of-concept for automating the classification and assessment of event sediment dynamics using machine learning. Across all catchment sites, 600+ storm events were captured and classified into 14 hysteresis patterns. Event classification was automated using a restricted Boltzmann machine (RBM), a type of artificial neural network, trained on 2-D images of the suspended-sediment discharge (hysteresis) plots. Expansion of the hysteresis patterns to 14 classes allowed for new insight into drivers of the sediment-discharge event dynamics including spatial scale, antecedent conditions, hydrology, and rainfall. The probabilistic RBM correctly classified hysteresis patterns (to the exact class or next most similar class) 70% of the time. With increased availability of high-frequency sensor data, this approach can be used to inform watershed management efforts to identify sediment sources and reduce fine sediment export