Jeanne M. VanBriesen

Life cycle greenhouse gas emissions of Marcellus shale gas

This study estimates the life cycle greenhouse gas (GHG) emissions from the production of Marcellus shale natural gas and compares its emissions with national average US natural gas emissions produced in the year 2008, prior to any significant Marcellus shale development. We estimate that the development and completion of a typical Marcellus shale well results in roughly 5500 t of carbon dioxide equivalent emissions or about 1.8 g CO2e/MJ of gas produced, assuming conservative estimates of the production lifetime of a typical well. This represents an 11% increase in GHG emissions relative to average domestic gas (excluding combustion) and a 3% increase relative to the life cycle emissions when combustion is included. The life cycle GHG emissions of Marcellus shale natural gas are estimated to be 63‐75 g CO2e/MJ of gas produced with an average of 68 g CO2e/MJ of gas produced. Marcellus shale natural gas GHG emissions are comparable to those of imported liquefied natural gas. Natural gas from the Marcellus shale has generally lower life cycle GHG emissions than coal for production of electricity in the absence of any effective carbon capture and storage processes, by 20‐50% depending upon plant efficiencies and natural gas emissions variability. There is significant uncertainty in our Marcellus shale GHG emission estimates due to eventual production volumes and variability in flaring, construction and transportation.

Life Cycle Water Consumption and Wastewater Generation Impacts of a Marcellus Shale Gas Well

This study estimates the life cycle water consumption and wastewater generation impacts of a Marcellus shale gas well from its construction to end of life. Direct water consumption at the well site was assessed by analysis of data from approximately 500 individual well completion reports collected in 2010 by the Pennsylvania Department of Conservation and Natural Resources. Indirect water consumption for supply chain production at each life cycle stage of the well was estimated using the economic input–output life cycle assessment (EIO-LCA) method. Life cycle direct and indirect water quality pollution impacts were assessed and compared using the tool for the reduction and assessment of chemical and other environmental impacts (TRACI). Wastewater treatment cost was proposed as an additional indicator for water quality pollution impacts from shale gas well wastewater. Four water management scenarios for Marcellus shale well wastewater were assessed: current conditions in Pennsylvania; complete discharge; direct reuse and desalination; and complete desalination. The results show that under the current conditions, an average Marcellus shale gas well consumes 20 000 m3 (with a range from 6700 to 33 000 m3) of freshwater per well over its life cycle excluding final gas utilization, with 65% direct water consumption at the well site and 35% indirect water consumption across the supply chain production. If all flowback and produced water is released into the environment without treatment, direct wastewater from a Marcellus shale gas well is estimated to have 300–3000 kg N-eq eutrophication potential, 900–23 000 kg 2,4D-eq freshwater ecotoxicity potential, 0–370 kg benzene-eq carcinogenic potential, and 2800–71 000 MT toluene-eq noncarcinogenic potential. The potential toxicity of the chemicals in the wastewater from the well site exceeds those associated with supply chain production, except for carcinogenic effects. If all the Marcellus shale well wastewater is treated to surface discharge standards by desalination,

RESEARCH ARTICLE: Oil and Gas Produced Water Management and Surface Drinking Water Sources in Pennsylvania

59 000–270 000 per well would be required. The life cycle study results indicate that when gas end use is not considered hydraulic fracturing is the largest contributor to the life cycle water impacts of a Marcellus shale gas well.

An environmental sensor network to determine drinking water quality and security

Produced water from oil and gas development requires management to avoid negative public health effects, particularly those associated with dissolved solids and bromide in drinking water. Rapidly expanding drilling in the Marcellus Shale in Pennsylvania has significantly increased the volume of produced water that must be managed. Produced water management may include treatment followed by surface water discharge, such as at publically owned wastewater treatment plants (POTWs) or centralized brine treatment plants (CWTs). The use of POTWs and CWTs that discharge partially treated produced water has the potential to increase salt loads to surface waters significantly. These loads may cause unacceptably high concentrations of dissolved solids or bromide in source waters, particularly when rivers are at low-flow conditions. The present study evaluates produced water management in Pennsylvania from 2006 through 2011 to determine whether surface water discharges were sufficient to cause salt or bromide loads that would negatively affect drinking water sources. The increase in produced water that occurred in 2008 in Pennsylvania was accompanied by an increase in use of CWTs and POTWs that were exempt from discharge limits on dissolved solids. Estimates of salt loads associated with produced water and with discharges from CWTs and POTWs in 2008 and 2009 indicate that more than 50% of the total dissolved solids in the produced water generated in those years were released to surface water systems. Especially during the low-flow conditions of 2008 and 2009, these loads would be expected to affect drinking water.

Evaluation of methods to predict bacterialyield using thermodynamics

Finding patterns in large, real, spatio/temporal data continues to attract high interest (e.g., sales of products over space and time, patterns in mobile phone users; sensor networks collecting operational data from automobiles, or even from humans with wearable computers). In this paper, we describe an interdisciplinary research effort to couple knowledge discovery in large environmental databases with biological and chemical sensor networks, in order to revolutionize drinking water quality and security decision making. We describe a distribution and operation protocol for the placement and utilization of in situ environmental sensors by combining (1) new algorithms for spatialtemporal data mining, (2) new methods to model water quality and security dynamics, and (3) a sophisticated decision-analysis framework. The project was recently funded by NSF and represents application of these research areas to the critical current issue of ensuring safe and secure drinking water to the population of the United States.

Continuous monitoring of residual chlorine concentrations in response to controlled microbial intrusions in a laboratory-scale distribution system

Thermodynamic models can be used to predict bacterial yields and develop stoichiometric representation of biological reactions in the absence of empirical data. Several methods have been used by microbiologists, biotechnologists, and environmental engineers. This manuscript illustrates that these formulations are related. Yields predicted by estimation of Gibbs energy of dissipation and yields predicted by assumed efficiency of energy capture are comparable. Direct comparison of yield predictions from different methods shows the effects of assumptions inherent in the methodologies. Mathematical relationships between estimated values from the different methods help identify the best predictionsfrom each method to bound the estimate of bacterial yield.

OPTIMIZING SENSOR PLACEMENTS IN WATER DISTRIBUTION SYSTEMS USING SUBMODULAR FUNCTION MAXIMIZATION

The objective of this work was to evaluate the efficacy of deploying free chlorine sensors as surrogate monitors for bacterial contamination events in drinking water distribution systems. An on-line sensor integral with a laboratory-scale distribution system (LDS) was shown to respond rapidly to changes in residual free chlorine concentrations induced by injected loads of Escherichia coli suspended in a chlorine demand free buffer. The magnitude of the residual response was proportional to the injected cell concentration, the background free chlorine concentration in the LDS, and the contact time between the chlorine residual and the injected suspension, consistent with previous results in batch reactors. The magnitude of the residual response was predicted when kinetic models developed from reaction kinetics between free chlorine and E. coli determined in batch systems were evaluated at contact times determined from LDS hydraulics. This result highlights the suitability of using batch kinetics when modeling contaminant-induced chlorine decay in the distribution system. Modeling the propagation of chlorine demand signals generated by specific pathogens could aid in the assessment of distribution system vulnerability.

Studying Bacterial Metabolic States Using Raman Spectroscopy

Drinking water distribution networks represent complex systems. Water flow rates in a water distribution system vary with time, with periodic features that reflect temporal variations in water demand by consumers. The intentional introduction of a contaminant disrupts the system and could theoretically be detected by a sensor or network of sensors placed at nodes (pipe junctions, reservoirs, storage tanks, or even individual consumer taps) in the system. Determining the best locations for placement of these sensors represents a significant research question, because the system has multiple states, the number of possible intrusion points is large, and the likely high cost of these sensors limits the number that can realistically be deployed. The optimal placement of these sensors to minimize the effect of an introduced contaminant on the population is a critical issue. Sensor placement for intrusion detection exhibits an important diminishing returns property: adding a sensor to a sensor network improves the detection ability less than adding it to a subset of the sensor network. We prove that this submodularity property holds for the objective functions that we consider for placing sensors, and exploit it by applying algorithms for maximizing monotonic submodular functions. Unlike existing optimization algorithms for selecting sensor placements, our efficient optimization procedure has strong theoretical performance guarantees. In spite of the problem’s complexity, our algorithm is guaranteed to always find a solution that is at least within 63% of the optimum, and will often find a (near-)optimal solution. This method is applied to two hypothetical distribution systems (129 nodes and 12,527 nodes) to determine optimal sensor placements for a sensor network of 5 or 20 sensors. Optimization was based on multiple criteria including: (1) minimizing time to detection, (2) minimizing population affected prior to detection, (3) minimizing expected demand for contaminated water prior to detection, and (4) maximizing detection likelihood. A base scenario and three derivative scenarios were used to test the sensor location optimization for the hypothetical systems. In order to compute accurately the objective criteria, we exhaustively simulated all possible attack scenarios, using distributed computation. Five optimization objective functions were considered (i.e., optimization on each of the four objectives independently and then an equally weighted multi-objective optimization). The two networks analyzed in this project illustrate how a sensor network of 20 sensors is more than “adequate” for the example distribution system of 129 nodes, while a much larger sensor network would be needed for “adequate” detection in the example large network of 12,527 nodes. The developed algorithms generalize to networks of arbitrary size and can be constrained by expert knowledge or rankings of scenario likelihood. Further, the optimization algorithms have potential applications for placement of sensors in other complex, dynamic systems.

Multivariate distributions of disinfection by-products in chlorinated drinking water

Natural metabolic variability expected during characteristic growth phases in batch cultures of Escherichia coli and Staphylococcus epidermidis were studied by Raman spectroscopy. Spectral changes induced by metabolic changes found in the growth phases (i.e., lag, exponential, stationary, and decay) were identified. Maximum intensity of bands assigned to DNA and RNA bases are seen at the beginning of the exponential phase, when cells are metabolically active, and minimum intensities are seen when cells are decaying. High agreement in spectral variation due to growth phases was seen for all the trials that were performed, four growth cycles for E. coli and two for S. epidermidis. Batch cultures were monitored by standard plate counts to identify all growth phases, including decay. Spectral data were analyzed by principal component analysis (PCA) and discriminant analysis to identify similarities and differences and to estimate a classification performance based on growth phases. For the species evaluated, spectra during decay are grouped closer to each other and separated from lag, exponential, and stationary cells. These results suggest that Raman spectroscopy can be used to study metabolic states in bacteria and in particular cell viability.

Raman Spectroscopic Discrimination of Cell Response to Chemical and Physical Inactivation

Drinking water disinfection by-product (DBP) occurrence research is important in supporting risk assessment and regulatory performance assessment. Recent DBP occurrence surveys have expanded their scope to include non-regulated priority DBPs as well as regulated DBPs. This study applies a Box-Cox transformed multivariate normal model and data augmentation methods for left-censored and missing observations to US EPA Information Collection Rule (ICR) drinking water data to describe the variability in the trihalomethane (THM4), trihaloacetic acid (THAA), dihaloacetic acid (DHAA), and dihaloacetonitrile (DHAN) DBP classes, the relationship between class-sum and the occurrence of individual DBPs within these classes. Inferences about bromine incorporation in these classes are then compared to those made by Obolensky and Singer (2005). Results reported herein show that class-based and individual DBP concentrations are strongly related to bromine substitution, and that speciation and bromine substitution patterns are consistent across DBP classes. In addition, the multiple imputation approach employed reveals that uncertainties related to missing and left-censored DBPs have important implications for understanding bromine substitution in the THAA class. These concerns should be considered through alternative approaches to DBP regulation in subsequent Stage II D/DBP assessment and revisions, where appropriate.