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  1. Geochemical Assessment for Carbon Sequestration in the Conasauga Group, Northwest Georgia, USA

    Sedimentary geological formations are known to be great candidates for geological carbon sequestration. Published studies suggest the southeast of the United States contains many formations suitable for carbon storage. The Cassville 1 Stratigraphic Borehole well could act as a potential carbon reservoir for nearby energy resource facilities in Georgia, United States. Although studies have shown that porous formations are adequate for geological carbon sequestration, it is important to understand possible geochemical reactions between CO2 and the targeted geological formation before injecting any fluids. In this study, a sandstone sample from the Cassville 1 well is being considered for geological carbonmore » sequestration in the Conasauga Group in Northwest Georgia. Here, the collected sandstone sample, consisting of quartz, K-feldspar, micas, kaolinite, and carbonate minerals such as calcite and dolomite, has a 6% porosity. Leveraging the formation composition and porosity, a one-dimensional continuum reactive transport model was built using CrunchFlow to assess possible geochemical reactions between injected CO2 and the geological formation. Simulation results show that the carbonate minerals, calcite and dolomite, dissolve during the injection period of 10,000 days, increasing formation porosity from 6% to as much as 30%. The rate and extent of carbonate mineral dissolution and resulting porosity increase are highly sensitive to mineral reactive surface area values. No evidence of mineral precipitation was observed, suggesting that dissolution reactions will control porosity evolution during the CO2 injection period.« less
  2. Publishing Environmental Assessment and Management Science: Crossing the Hurdles

    Benefits accrue to scientists, resource managers, companies, and policymakers when environmental scientists publish in peer-reviewed journals. However, environmental scientists and practitioners face challenges, including the sometimes low value placed on journal articles, institutional vested interests in outcomes, and the changing priorities of employers and project sponsors. Confidentiality agreements can also lead scientists to assume publication is not an option. Case studies may be viewed by potential authors as too routine for peer-reviewed journals. On the basis of 30 years of experience, we suggest that publishing hurdles can be overcome and that environmental scientists have a range of options. The topicsmore » of manuscripts can include not only results from case studies and perspectives based on them but also byproducts of assessments, including definitions, plans, monitoring methods and models, and decision frameworks. Environmental scientists have unique opportunities to move science forward with their practical knowledge if they can move across the institutional, logistical, data-related, and content-related hurdles.« less
  3. Play Fairway analysis of geothermal resources across the State of Hawai‘i: 4. Updates with new groundwater chemistry, subsurface stress analysis, and focused geophysical surveys

    This paper is the fourth in a series on a play fairway analysis of geothermal resources across the State of Hawai‘i. Here we describe recent exploration activities that include groundwater sampling in ten locations statewide, as well as geophysical surveys on Lana‘i, across the SW rift of Haleakala Volcano (Maui), and surrounding Mauna Kea Volcano (Hawai‘i Island). We derive a first-order method for computing topographic stress using Green’s functions, finding that topography can induce appreciable crustal stress. We develop a new method for incorporating depth information about resistivity, density, and topographic stresses into our previously published equations to calculate resourcemore » probability and confidence. We incorporate newly collected and modeled data into our calculations to update statewide maps of probability and confidence. Lana‘i Island and southeast Mauna Kea are identified as the top targets for exploratory drilling. The east rift of Haleakala Volcano, the southern-most region of Mauna Loa’s SW rift (Hawai‘i Island), and central Kauai are identified as targets for geophysical surveying.« less
  4. Modeling assessment of storm surge in the Salish Sea

    Here we present a modeling assessment of storm surge in the Salish Sea—a large and complex estuarine system in the Pacific Northwest that is exposed to extratropical cyclones. A method was developed to systematically identify storm surge events in the Salish Sea based on water level records at four representative tidal gauges in the Salish Sea. The maximum storm surge distribution in the Salish Sea was calculated based on model simulations of 34 major historical storm events from 1980 to 2016. The model simulations were validated by field observations of water level and velocity using a suite of model performancemore » metrics. Model results suggest that the maximum storm surge in the Salish Sea is generally in the range of 0.8 m–1.03 m and is heterogeneous in space. The model results also show a strong north-south gradient with larger surge magnitudes occurring in the north that is primarily caused by the southerly wind in winter months. The sensitivity analysis of wind forcing suggested that storm surge in the Salish Sea is dominated by the remote surge from the Pacific Ocean, and the local wind contributes up to approximately 20% of the surge variability. This paper systematically elucidates storm surge characteristics and potential risk in the Salish Sea.« less
  5. Costs and consequences of wind turbine wake effects arising from uncoordinated wind energy development

    Optimal wind farm locations require a strong and reliable wind resource and access to transmission lines. As onshore and offshore wind energy grows, preferred locations become saturated with numerous wind farms. An upwind wind farm generates 'wake effects' (decreases in downwind wind speeds) that undermine a downwind wind farm's power generation and revenues. Here we use a diverse set of analysis tools from the atmospheric science, economic and legal communities to assess costs and consequences of these wake effects, focusing on a West Texas case study. We show that although wake effects vary with atmospheric conditions, they are discernible inmore » monthly power production. In stably stratified atmospheric conditions, wakes can extend 50+ km downwind, resulting in economic losses of several million dollars over six years for our case study. However, our investigation of the legal literature shows no legal guidance for protecting existing wind farms from such significant impacts.« less
  6. Assessing the costs and benefits of US renewable portfolio standards

    Abstract Renewable portfolio standards (RPS) exist in 29 US states and the District of Columbia. This article summarizes the first national-level, integrated assessment of the future costs and benefits of existing RPS policies; the same metrics are evaluated under a second scenario in which widespread expansion of these policies is assumed to occur. Depending on assumptions about renewable energy technology advancement and natural gas prices, existing RPS policies increase electric system costs by as much as $31 billion, on a present-value basis over 2015−2050. The expanded renewable deployment scenario yields incremental costs that range from $23 billion to $194 billion,more » depending on the assumptions employed. The monetized value of improved air quality and reduced climate damages exceed these costs. Using central assumptions, existing RPS policies yield $97 billion in air-pollution health benefits and $161 billion in climate damage reductions. Under the expanded RPS case, health benefits total $558 billion and climate benefits equal $599 billion. These scenarios also yield benefits in the form of reduced water use. RPS programs are not likely to represent the most cost effective path towards achieving air quality and climate benefits. Nonetheless, the findings suggest that US RPS programs are, on a national basis, cost effective when considering externalities.« less
  7. Imputation for multisource data with comparison and assessment techniques

    Missing data are prevalent issue in analyses involving data collection. The problem of missing data is exacerbated for multisource analysis, where data from multiple sensors are combined to arrive at a single conclusion. In this scenario, it is more likely to occur and can lead to discarding a large amount of data collected; however, the information from observed sensors can be leveraged to estimate those values not observed. We propose two methods for imputation of multisource data, both of which take advantage of potential correlation between data from different sensors, through ridge regression and a state-space model. These methods, asmore » well as the common median imputation, are applied to data collected from a variety of sensors monitoring an experimental facility. Performance of imputation methods is compared with the mean absolute deviation; however, rather than using this metric to solely rank themethods,we also propose an approach to identify significant differences. Imputation techniqueswill also be assessed by their ability to produce appropriate confidence intervals, through coverage and length, around the imputed values. Finally, performance of imputed datasets is compared with a marginalized dataset through a weighted k-means clustering. In general, we found that imputation through a dynamic linearmodel tended to be the most accurate and to produce the most precise confidence intervals, and that imputing the missing values and down weighting them with respect to observed values in the analysis led to the most accurate performance.« less
  8. The Emergence of Life as a First-Order Phase Transition

    It is well known that life on Earth alters its environment over evolutionary and geological timescales. An important open question is whether this is a result of evolutionary optimization or a universal feature of life. In the latter case, the origin of life would be coincident with a shift in environmental conditions. Here in this paper we present a model for the emergence of life in which replicators are explicitly coupled to their environment through the recycling of a finite supply of resources. The model exhibits a dynamic, first-order phase transition from nonlife to life, where the life phase ismore » distinguished by selection on replicators. We show that environmental coupling plays an important role in the dynamics of the transition. The transition corresponds to a redistribution of matter in replicators and their environment, driven by selection on replicators, exhibiting an explosive growth in diversity as replicators are selected. The transition is accurately tracked by the mutual information shared between replicators and their environment. In the absence of successfully repartitioning system resources, the transition fails to complete, leading to the possibility of many frustrated trials before life first emerges. Often, the replicators that initiate the transition are not those that are ultimately selected. The results are consistent with the view that life's propensity to shape its environment is indeed a universal feature of replicators, characteristic of the transition from nonlife to life. We discuss the implications of these results for understanding life's emergence and evolutionary transitions more broadly.« less
  9. Forecasting the northern African dust outbreak towards Europe in April 2011: A model intercomparison

    In the framework of the World Meteorological Organisation's Sand and Dust Storm Warning Advisory and Assessment System, we evaluated the predictions of five state-of-the-art dust forecast models during an intense Saharan dust outbreak affecting western and northern Europe in April 2011. We assessed the capacity of the models to predict the evolution of the dust cloud with lead times of up to 72 h using observations of aerosol optical depth (AOD) from the AErosol RObotic NETwork (AERONET) and the Moderate Resolution Imaging Spectroradiometer (MODIS) and dust surface concentrations from a ground-based measurement network. In addition, the predicted vertical dust distributionmore » was evaluated with vertical extinction profiles from the Cloud and Aerosol Lidar with Orthogonal Polarization (CALIOP). To assess the diversity in forecast capability among the models, the analysis was extended to wind field (both surface and profile), synoptic conditions, emissions and deposition fluxes. Models predict the onset and evolution of the AOD for all analysed lead times. On average, differences among the models are larger than differences among lead times for each individual model. In spite of large differences in emission and deposition, the models present comparable skill for AOD. In general, models are better in predicting AOD than near-surface dust concentration over the Iberian Peninsula. Models tend to underestimate the long-range transport towards northern Europe. In this paper, our analysis suggests that this is partly due to difficulties in simulating the vertical distribution dust and horizontal wind. Differences in the size distribution and wet scavenging efficiency may also account for model diversity in long-range transport.« less
  10. A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals

    Endocrine disruptors such as polychlorinated biphenyls (PCBs), diethylstilbestrol (DES) and dichlorodiphenyltrichloroethane (DDT) are agents that interfere with the endocrine system and cause adverse health effects. Huge public health concern about endocrine disruptors has arisen. One of the mechanisms of endocrine disruption is through binding of endocrine disruptors with the hormone receptors in the target cells. Entrance of endocrine disruptors into target cells is the precondition of endocrine disruption. The binding capability of a chemical with proteins in the blood affects its entrance into the target cells and, thus, is very informative for the assessment of potential endocrine disruption of chemicals.more » α-fetoprotein is one of the major serum proteins that binds to a variety of chemicals such as estrogens. To better facilitate assessment of endocrine disruption of environmental chemicals, we developed a model for α-fetoprotein binding activity prediction using the novel pattern recognition method (Decision Forest) and the molecular descriptors calculated from two-dimensional structures by Mold2 software. The predictive capability of the model has been evaluated through internal validation using 125 training chemicals (average balanced accuracy of 69%) and external validations using 22 chemicals (balanced accuracy of 71%). Prediction confidence analysis revealed the model performed much better at high prediction confidence. Our results indicate that the model is useful (when predictions are in high confidence) in endocrine disruption risk assessment of environmental chemicals though improvement by increasing number of training chemicals is needed.« less
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