Mitchell J. Small

Risks and Risk Governance in Unconventional Shale Gas Development

A broad assessment is provided of the current state of knowledge regarding the risks associated with shale gas development and their governance. For the principal domains of risk, we identify observed and potential hazards and promising mitigation options to address them, characterizing current knowledge and research needs. Important unresolved research questions are identified for each area of risk; however, certain domains exhibit especially acute deficits of knowledge and attention, including integrated studies of public health, ecosystems, air quality, socioeconomic impacts on communities, and climate change. For these, current research and analysis are insufficient to either confirm or preclude important impacts. The rapidly evolving landscape of shale gas governance in the U.S. is also assessed, noting challenges and opportunities associated with the current decentralized (state-focused) system of regulation. We briefly review emerging approaches to shale gas governance in other nations, and consider new governance initiatives and options in the U.S. involving voluntary industry certification, comprehensive development plans, financial instruments, and possible future federal roles. In order to encompass the multiple relevant disciplines, address the complexities of the evolving shale gas system and reduce the many key uncertainties needed for improved management, a coordinated multiagency federal research effort will need to be implemented.

Extending the Boundaries of Life‐Cycle Assessment through Environmental Economic Input‐Output Models

Life-cycle assessment (LCA) is a cornerstone of current practice in industrial ecology, linking the product life cycle, from design to disposition, with the environmental impacts generated at each stage. It is one of the ways that industrial ecology brings a systems approach to environmental analysis. LCA aids environmental improvement by revealing the complete impact of a product, rather than just the emissions generated in the usual course of production by the manufacturer. Manufacturers, service providers, and government agencies can use these methods to consider the total impact of their procurement and business activities, and to tailor them to be more environmentally friendly. A number of different approaches to LCA have been developed, with different focus, time, and resource requirements. Key among these are:

Seasonal variations in sulfate, nitrate and chloride in the greenland ice sheet: Relation to atmospheric concentrations

Samples from three snowpits near Dye 3 in South Greenland have been used to study seasonal variations in contaminant transport from the atmosphere to the Ice Sheet. The snowpits cover the years 1982–1987. The samples have been dated by comparing δ18O values with meteorological data from Dye 3. Airborne concentrations of SO2−4 over the Ice Sheet have been estimated for the dates corresponding to each snowpit sample by statistically analyzing data from several air monitoring stations throughout the Arctic, and computing average values from the appropriate stations. Seasonal variations in concentrations in air, concentrations in snow, and mass-basis scavenging ratios (concentration in snow divided by concentration in air) have been identified. Results indicate that concentrations of SO2−4in the air show a strong peak in late February, resulting from long-range transport of mid-latitude anthropogenic emissions, while those in the snow show a broad peak in January, February and March with smaller seasonal variation overall. The smaller variation in the snow is attributed in part to the effect of riming, which results in more efficient scavenging during warm weather when airborne concentrations are low. The importance of riming is also supported by the annual cycle in scavenging ratio which peaks in mid-summer coincident with maximum temperatures. In agreement with previous estimates, dry deposition appears to account for 10–30% of the total SO2−4 in the snow. Concentrations of NO−3 in the snow show a strong peak in summer; natural material from the stratosphere as well as anthropogenic emissions transported from the mid-latitudes may be responsible. Concentrations of Cl− in the snow are maximum in January, with relatively high concentrations during October through March and a smaller peak in July. The winter peak is believed to reflect long-range transport (LRT) of marine aerosol from north Atlantic storms, while the summer peak is attributed to seaspray from nearby coastal Greenland. Riming also may influence the seasonal variations in NO−3 and Cl− in the snow.

An environmental sensor network to determine drinking water quality and security

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.

Bayesian Environmental Policy Decisions: Two Case Studies

Statistical decision theory can be a valuable tool for policy-making decisions. In particular, environmental problems often benefit from the application of Bayesian and decision-theoretic techniques that address the uncertain nature of problems in the environmental and ecological sciences. This paper discusses aspects of implementing statistical decision-making tools in situations where uncertainty is present, looking at issues such as elicitation of prior distributions, covariate allocation, formulation of loss functions, and minimization of expected losses subject to cooperation constraints. These ideas are illustrated through two case studies in environmental remediation. See full-text article at JSTOR

Trajectory analysis of source regions influencing the south Greenland ice sheet during the Dye 3 Gas and Aerosol Sampling Program

Abstract Backward air mass trajectories for Dye 3, Greenland (elevation 2.5 km) show source regions that vary with season: the direction of greatest transport distance is from the southwest in fall, west in winter, and northwest in spring; the trajectories in summer do not show a strong preferred direction. Based on 5 d transit times, the trajectories in fall suggest the importance of North America as a potential source region, with occasional trajectories from western Europe. The trajectories in spring, especially in April, suggest Eurasia (transport over the Pole), eastern North America, and Western Europe as potential source regions. Less transport of chemical constituents to Dye 3 is expected in summer when transport distances are shorter. Although some long-range transport to Greenland occurs in winter, the stability of the atmosphere over the ice sheet at this time of year is likely to limit the delivery of chemical constituents to the surface. Sources outside of these regions can also influence Dye 3 if transit times longer than 5 d are considered. These results are in contrast to trajectories reported by others for sea-level arctic locations such as Barrow, Alaska and Mould Bay, Canada, where transport over the Pole from Eurasia is responsible for high chemical species concentrations over much of the winter and early spring. Overall, the trajectories are consistent with aerosol chemical data for this time period at Dye 3 reported by several investigators, showing peak concentrations in spring and fall.

Correlation of the Physicochemical Properties of Natural Organic Matter Samples from Different Sources to Their Effects on Gold Nanoparticle Aggregation in Monovalent Electrolyte

Engineered nanoparticles (NPs) released into natural environments will interact with natural organic matter (NOM) or humic substances, which will change their fate and transport behavior. Quantitative predictions of the effects of NOM are difficult because of its heterogeneity and variability. Here, the effects of six types of NOM and molecular weight fractions of each on the aggregation of citrate-stabilized gold NPs are investigated. Correlations of NP aggregation rates with electrophoretic mobility and the molecular weight distribution and chemical attributes of NOM (including UV absorptivity or aromaticity, functional group content, and fluorescence) are assessed. In general, the >100 kg/mol components provide better stability than lower molecular weight components for each type of NOM, and they contribute to the stabilizing effect of the unfractionated NOM even in small proportions. In many cases, unfractionated NOM provided better stability than its separated components, indicating a synergistic effect between the high and low molecular weight fractions for NP stabilization. Weight-averaged molecular weight was the best single explanatory variable for NP aggregation rates across all NOM types and molecular weight fractions. NP aggregation showed poorer correlation with UV absorptivity, but the exponential slope of the UV-vis absorbance spectrum was a better surrogate for molecular weight. Functional group data (including reduced sulfur and total nitrogen content) were explored as possible secondary parameters to explain the strong stabilizing effect of a low molecular weight Pony Lake fulvic acid sample to the gold NPs. These results can inform future correlations and measurement requirements to predict NP attachment in the presence of NOM.

Storms, Investor Decisions, and the Economic Impacts of Sea Level Rise

Past research on the economic impacts of aclimate-induced sea level rise has been based on thegradual erosion of the shoreline, and humanadaptation. Erosion which is accelerated by sea levelrise may also increase the vulnerability to stormdamage by decreasing the distance between the shoreand structures, and by eroding protective coastalfeatures (dunes). We present methods of assessingthis storm damage in coastal regions where structuralprotection is not pursued. Starting from the boundingcases of no foresight and perfectforesight of Yohe et al. (1996), we use adisaggregated analysis which models the random natureof storms, and models market valuation and privateinvestor decisions dynamically. Using data from theNational Flood Insurance Program and a hypotheticalcommunity, we estimate that although the total stormdamage can be large, the increase in storm damageattributable to sea level rise is small (<5% oftotal sea level rise damages). These damages,however, could become more significant under otherreasonable assumptions or where dune erosion increasesstorm damage.

Modeling lake-chemistry distributions: approximate Bayesian methods for estimating a finite-mixture model

A modification of the Laplace method is presented and applied to estimation of posterior functions in a Bayesian analysis of finite-mixture distributions. The technique is nonsequential yet relatively fast and provides estimates of mixture-model parameters and classification probabilities. The method is applied to a regional distribution of lake-chemistry data for north central Wisconsin. A mixture density of two lognormal populations is estimated for the acid-neutralizing capacity of lakes in the region, using several other lake characteristics as explanatory variables for classification into lake subpopulations. The fitted mixture model provides a good representation of the observed distribution. Separation into subpopulations based solely on the other lake characteristics matches the mixture-model classification relatively well.

An Integrated Risk Model of a Drinking-Water-Borne Cryptosporidiosis Outbreak

A dynamic risk model is developed to track the occurrence and evolution of a drinking-water-borne cryptosporidiosis outbreak. The model characterizes and integrates the various environmental, medical, institutional, and behavioral factors that determine outbreak development and outcome. These include contaminant delivery and detection, water treatment efficiency, the timing of interventions, and the choices that people make when confronted with a known or suspected risk. The model is used to evaluate the efficacy of alternative strategies for improving risk management during an outbreak, and to identify priorities for improvements in the public health system. Modeling results indicate that the greatest opportunity for curtailing a large outbreak is realized by minimizing delays in identifying and correcting a drinking-water problem. If these delays cannot be reduced, then the effectiveness of risk communication in preemptively reaching and persuading target populations to avoid exposure becomes important.