Wireless sensor network based system for underground chemical plume tracking, A

A real-time subsurface chemical plume monitoring and tracking system is being developed that uses wireless-sensor networking to automatically extract data from underground chemical sensors. This system is aimed at tracking plumes caused by the release of toxic chemicals and biological agents into the environment as a result of accidental spills and improper disposal. Current practice involves manual collection of samples from monitoring wells followed by laboratory analysis, an expensive process taking days to weeks; such a delay reduces the effectiveness of mitigation techniques as well. Virtual Sensor Networks (VSN), a novel resource efficient approach for sensor networking being developed to track the migrating underground plumes, will be applicable to a broad class of problems. Laboratory based experiments and simulations are in progress to demonstrate the feasibility of the approach for large-scale plume tracking.This research is supported in part by Army Research Office and the National Science Foundation.1st place, ISTeC Student Research Poster Contest (April 7, 2008)

A New Scope and Aims for Perspectives of Earth and Space Scientists

The journal Perspectives of Earth and Space Scientists has expanded both its aims and its scope to better serve the community of Earth and space scientists and represent its diverse range. Perspectives is now adding several new article formats to better meet the needs of the Earth and space science community. These include memorials, commentaries, debates, opinion pieces, and news updates. The journal remains fully open access with publication costs borne by the American Geophysical Union, but is no longer by-invitation-only and welcomes submissions from all segments of the geophysical community to better represent the diversity in nationality, ethnicity, culture, gender, and career stage of Earth and space scientists

Parameterization and prediction of nanoparticle transport in porous media : a reanalysis using artificial neural network

The continuing rapid expansion of industrial and consumer processes based on nanoparticles (NP) necessitates a robust model for delineating their fate and transport in groundwater. An ability to reliably specify the full parameter set for prediction of NP transport using continuum models is crucial. In this paper we report the reanalysis of a data set of 493 published column experiment outcomes together with their continuum modeling results. Experimental properties were parameterized into 20 factors which are commonly available. They were then used to predict five key continuum model parameters as well as the effluent concentration via artificial neural network (ANN)-based correlations. The Partial Derivatives (PaD) technique and Monte Carlo method were used for the analysis of sensitivities and model-produced uncertainties, respectively. The outcomes shed light on several controversial relationships between the parameters, e.g., it was revealed that the trend of math formula with average pore water velocity was positive. The resulting correlations, despite being developed based on a “black-box” technique (ANN), were able to explain the effects of theoretical parameters such as critical deposition concentration (CDC), even though these parameters were not explicitly considered in the model. Porous media heterogeneity was considered as a parameter for the first time and showed sensitivities higher than those of dispersivity. The model performance was validated well against subsets of the experimental data and was compared with current models. The robustness of the correlation matrices was not completely satisfactory, since they failed to predict the experimental breakthrough curves (BTCs) at extreme values of ionic strengths

Thank You to Our 2021 Peer Reviewers

The editorial board of AGU Advances thanks the individuals who reviewed for the journal in 2021. © 2022. The Authors. AGU Advances published by Wiley Periodicals LLC on behalf of American Geophysical Union

Confronting Racism to Advance Our Science

As individuals serving on the AGU Advances editorial board, we condemn racism, affirm that Black Lives Matter, and recognize that inequality is built into the systems that have allowed us to prosper. We aim to persistently foster discussion about racism, inequity, and the need to make our community more diverse and inclusive. This will help AGU Advances do a better job in publishing important science that inclusively reflects the ideas and contributions of all in our community

Thank You to Our 2020 Peer Reviewers

Thank you to the reviewers of AGU Advances. In 2020, we all faced the enormous and unexpected challenges of the Covid‐19 pandemic, with its host of new and competing demands on our time. Thus, we are especially grateful to the 154 people who provided reviews for AGU Advances and helped our fledgling journal complete its first year. Peer‐review is essential to the process of doing and publishing science, and our reviewers have helped define our new journal by indicating papers expected to have broad impact that advance a discipline, have broad impact across disciplines, or have policy relevance. All papers submitted to AGU Advances first go through an editorial consultation. We are committed to respecting reviewers’ time and only send papers for review that the consulting editors agree meet our criteria. Sometimes this means we send papers back to the authors with suggestions how to improve the fit to our journal. Another way we try to streamline the review process is by giving the authors the option to transfer reviews if after review we decide the paper is better suited to another AGU journal. As a relatively new journal, we still have few enough reviewers that we do not want to identify them by name. Nonetheless, you know who you are. Please accept our sincere thanks for generously sharing your expertise and working to improve AGU Advances

Arginine Cofactors on the Polymerase Ribozyme

The RNA world hypothesis states that the early evolution of life went through a stage in which RNA served both as genome and as catalyst. The central catalyst in an RNA world organism would have been a ribozyme that catalyzed RNA polymerization to facilitate self-replication. An RNA polymerase ribozyme was developed previously in the lab but it is not efficient enough for self-replication. The factor that limits its polymerization efficiency is its weak sequence-independent binding of the primer/template substrate. Here we tested whether RNA polymerization could be improved by a cationic arginine cofactor, to improve the interaction with the substrate. In an RNA world, amino acid-nucleic acid conjugates could have facilitated the emergence of the translation apparatus and the transition to an RNP world. We chose the amino acid arginine for our study because this is the amino acid most adept to interact with RNA. An arginine cofactor was positioned at ten different sites on the ribozyme, using conjugates of arginine with short DNA or RNA oligonucleotides. However, polymerization efficiency was not increased in any of the ten positions. In five of the ten positions the arginine reduced or modulated polymerization efficiency, which gives insight into the substrate-binding site on the ribozyme. These results suggest that the existing polymerase ribozyme is not well suited to using an arginine cofactor