Dissolved oxygen (DO) distribution at the sediment-water interface of a flow over a smooth bed is investigated for Reynolds numbers Ͼ360 and Ͻ4,090. These conditions are commonly encountered in streams, wetlands, and lakes. A power-law scaling of DO distribution is derived and compared with experimental data. The scaling analysis is based on DO flux at the sediment-water interface in a turbulent flow. The power-law model with diffusive sublayer thickness (DSLT) as a fitting parameter agrees well with the data over the investigated range of Reynolds numbers. Using the proposed power-law model with a limited number of DO and flow properties away from the sediment-water interface provides the distribution of DO concentrations and corresponding DSLT at a submillimeter resolution. The estimate of DSLT is, on average, 30% lower than the traditional estimate, defined as a thin fluid layer bounded at the lower boundary by a sediment bed and extended upward in the main water column to where a bulk DO concentration intersects with a linear DO gradient at the bed.The transport processes across the sediment-water interface are of fundamental importance to biological and chemical processes in streams, rivers, and lakes (Boudreau and Jørgensen 2001). In most cases, researchers are concerned about quantifying the distribution and corresponding flux of a particular substance at the sediment-water interface. The dissolved oxygen (DO) concentration in water has been considered one of the most important ecological parameters determining the water quality and associated biological composition of aquatic environments. The sediments, being a repository for decaying biological material with a large organic content, are a major contributor to the DO reduction in the water column.Significant laboratory and field measurements have been devoted to addressing the DO transport process at the sediment-water interface (e.g., Jørgensen and Des Marais 1990; Mackenthun and Stefan 1998;Røy et al. 2002). Microstructure DO measurements with microsensors (e.g., Jørgensen and Revsbech 1985;Lorke et al. 2003;Røy et al. 2004) have been very instrumental in the advancement of theories and models fundamental to the DO transport at the sedimentwater interface. Knowledge of the characteristics of the diffusive sublayer thickness (DSLT) for the DO transport and corresponding DO distribution at the sediment-water interface is crucial in benthic ecology. The difficulty in the characterization of the diffusive sublayer lies in its thinness, usually Ͻ5 mm, and the proximity of a solid boundary.

Life-cycle assessment (LCA) has been applied to many biofuel and bioenergy systems to determine potential environmental impacts, but the conclusions have varied. Different methodologies and processes for conducting LCA of biofuels make the results difficult to compare, in-turn making it difficult to make the best possible and informed decision. Of particular importance are the wide variability in country-specific conditions, modeling assumptions, data quality, chosen impact categories and indicators, scale of production, system boundaries, and co-product allocation. This study has a double purpose: conducting a critical evaluation comparing environmental LCA of biofuels from several conversion pathways and in several countries in the Pan American region using both qualitative and quantitative analyses, and making recommendations for harmonization with respect to biofuel LCA study features, such as study assumptions, inventory data, impact indicators, and reporting practices. The environmental management implications are discussed within the context of different national and international regulatory environments using a case study. The results from this study highlight LCA methodology choices that cause high variability in results and limit comparability among different studies, even among the same biofuel pathway, and recommendations are provided for improvement.

ABSTRACT. For coupled human and natural systems (CHANS), sustainability can be defined operationally as a feasible, desirable set of flows (material, currency, information, energy, individuals, etc.) that can be maintained despite internal changes and changes in the environment. Sustainable development can be defined as the process by which CHANS can be moved toward sustainability. Specific indicators that give insight into the structure and behavior of feedbacks in CHANS are of particular interest because they would aid in the sustainable management of these systems through an understanding of the structures that govern system behavior. However, the use of specific feedbacks as monitoring tools is rare, possibly because of uncertainties regarding the nature of their dynamics and the diversity of types of feedbacks encountered in these systems. An information theory perspective may help to rectify this situation, as evidenced by recent research in sustainability science that supports the use of unit-free measures such as Shannon entropy and Fisher information to aggregate disparate indicators. These measures have been used for spatial and temporal datasets to monitor progress toward sustainability targets. Here, we provide a review of information theory and a theoretical framework for studying the dynamics of feedbacks in CHANS. We propose a combination of information-based indices that might productively inform our sustainability goals, particularly when related to key feedbacks in CHANS.

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The challenge of sustainably producing goods and services for healthy living on a healthy planet requires simultaneous consideration of economic, societal, and environmental dimensions in manufacturing. Enabling technology for data driven manufacturing paradigms like Smart Manufacturing (a.k.a. Industry 4.0) serve as the technological backbone from which sustainable approaches to manufacturing can be implemented. Unfortunately, these technologies are typically associated with broader and deeper factory automation that is often too expensive and complex for the small and medium sized manufacturers (SMMs) that comprise the majority of manufacturing business in the USA and for whom their most valuable asset are the people whose jobs automation while replace. This paper describes an edge intelligent platform to integrate internet-of-things technologies with computing hardware, software, computational workflows for machine learning, and data ingestion, enabling SMMs to transition into smart manufacturing paradigms by leveraging the intelligence of their people. The platform leverages consumer grade electronics and sensors (affordable and portable), customized software with open source software packages (accessible), and existing communication network infrastructures (scalable). The software systems are implemented via Kubernetes orchestration of Docker containerization to ensure scalability and programmability. The platform is adaptive via computational workflow engines that produce information from data by processing with low-cost edge computing devices while efficiently accessing resources of cloud servers as needed. The proposed edge platform connects workers to technological resources that provide computational intelligence (i.e., silicon-based sensing and computation for data collection and contextualization) to enable decision making at the edge of advanced manufacturing.

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