Search for: All records

Quantifying river gains and losses is critical for effective water resource management, particularly in systems with streamflow and water quality regimes dictated by variable groundwater and surface water exchanges. Traditional methods for estimating riverine exchanges often require significant resources and can be limited in temporal or spatial scope. This study presents a practical methodology using inverse water temperature modeling and temperature data from inexpensive loggers to estimate gains and losses at different reach scales over time. A physically based, 1‐D water temperature model, was calibrated at many intermediate locations and where temperature observations were available by incorporating detailed gain and loss observations from a synoptic seepage study during an independent 2 day period. Assuming calibrated parameters held over time, gains and losses were then estimated during 18 additional 2‐day periods via inverse temperature modeling using three different spatial reach segmentations (high, medium, and low resolution). Gain and loss estimates, primarily attributed to groundwater exchanges, were then interpolated over a 5‐month study period to assess the effect of reach resolution on the method accuracy. Results show that a higher resolution segmentation best matched the pattern of both net and gross gains and losses observed during seepage study periods. The medium‐resolution segmentation most accurately represented the total volume of observed gain and loss. This methodology effectively estimates riverine gains and losses, and temperatures over space and time at a relatively low‐cost to inform hydrologic modeling and water management in systems where significant surface or subsurface exchanges alter system response. 

Per- and Polyfluoroalkyl Substances (PFAS) are an emerging class of persistent organic pollutants. Although their thermal/chemical stability and water/stain repellence enable their widespread use in various products, such as personal care products, food packaging and firefighting foams, these properties also make them particularly resistant to degradation. This unwelcome persistence, with their trace concentrations, environmental prevalence, bioaccumulation and probable toxicities, poses a potential threat to environmental and human health. As such, much work is directed to finding ways to efficiently abate PFAS in the environment. Per- and Polyfluoroalkyl Substance Treatment Technologies provides a thorough review of the current state of research in treatment technologies for removing PFAS from the environment, particularly water. Beginning with a brief introduction to PFAS challenges and research needs, it covers established and promising technologies for PFAS removal from drinking water, wastewater, and groundwater. This is a great book for environmental engineers, environmental chemists, and industrialists interested in pollution remediation. 

This Work in Progress paper describes the lessons learned from a new pathway for doctoral candidates in STEM programs allowing capstone degree requirements to be fulfilled by research culminating in a patent application. The Pathways to Entrepreneurship (PAtENT) model aims to bring greater alignment between doctoral degrees and the rapidly changing employment landscape. Given that seventy percent of PhDs exit academic careers within three years [1], creating doctoral pathways that align with multiple career options is an imperative. We describe the PAtENT model, rationale and goals. Components of the pilot program will be explained through a curriculum alignment describing key activities that respond to recommendation for STEM graduate programs identified by the National Academies of Sciences, Engineering and Medicine [2]: developing scientific and technological literacy and conducting original research; and developing leadership, communication, and professional competencies. After two years of development and implementation, we are also able to discuss lessons learned and strategies for scaling the model. We present findings from students in the program and a reflective interview of the project leadership team. In order to adopt this innovative education model, students, faculty, and universities need understanding of career pathways and opportunities beyond traditional academic pursuits. 

Label Propagation is not only a well-known machine learning algorithm for classification, but it is also an effective method for discovering communities and connected components in networks. We propose a new Direction-Optimizing Label Propagation Algorithm (DOLPA) framework that enhances the performance of the standard Label Propagation Algorithm (LPA), increases its scalability, and extends its versatility and application scope. As a central feature, the DOLPA framework relies on the use of frontiers and alternates between label push and label pull operations to attain high performance. It is formulated in such a way that the same basic algorithm can be used for finding communities or connected components in graphs by only changing the objective function used. Additionally, DOLPA has parameters for tuning the processing order of vertices in a graph to reduce the number of edges visited and improve the quality of solution obtained. We present the design and implementation of the enhanced algorithm as well as our shared-memory parallelization of it using OpenMP. We also present an extensive experimental evaluation of our implementations using the LFR benchmark and real-world networks drawn from various domains. Compared with an implementation of LPA for community detection available in a widely used network analysis software, we achieve at most five times the F-Score while maintaining similar runtime for graphs with overlapping communities. We also compare DOLPA against an implementation of the Louvain method for community detection using the same LFR-graphs and show that DOLPA achieves about three times the F-Score at just 10% of the runtime. For connected component decomposition, our algorithm achieves orders of magnitude speedups over the basic LP-based algorithm on large diameter graphs, up to 13.2 × speedup over the Shiloach-Vishkin algorithm, and up to 1.6 × speedup over Afforest on an Intel Xeon processor using 40 threads.