Carbon and methane cycling in arsenic-contaminated aquifers

Geogenic arsenic (As) contamination of groundwater is a health threat to millions of people worldwide, particularly in alluvial regions of South and Southeast Asia. Mitigation measures are often hindered by high heterogeneities in As concentrations, the cause(s) of which are elusive. Here we used a comprehensive suite of stable isotope analyses and hydrogeochemical parameters to shed light on the mechanisms in a typical high-As Holocene aquifer near Hanoi where groundwater is advected to a low-As Pleistocene aquifer. Carbon isotope signatures (δ$^{13}$C-CH$_{4}$, δ$^{13}$C-DOC, δ$^{13}$C-DIC) provided evidence that fermentation, methanogenesis and methanotrophy are actively contributing to the As heterogeneity. Methanogenesis occurred concurrently where As levels are high (>200 µg/L) and DOC-enriched aquitard pore water infiltrates into the aquifer. Along the flowpath to the Holocene/Pleistocene aquifer transition, methane oxidation causes a strong shift in δ$^{13}$C-CH$_{4}$ from -87‰ to +47‰, indicating high reactivity. These findings demonstrate a previously overlooked role of methane cycling and DOC infiltration in high-As aquifers

A Robust Self-Calibrating Data Fusion Architecture

We present a general mathematical framework for the fusion of noisy sensor data. On the basis of the mathematical theory of dynamical systems we couple the outputs of the sensors to obtain a nonlinearly averaged overall estimate of the physical quantity to measure which automatically discards outliers from the averaging process. Drifts within the time series of single sensors can be compensated through a recalibration by the method of time scale inversion. By means of a unified way of representing information as stable states of a dynamical system it is possible to integrate di#erent sorts of information such as expert knowledge and sensor information smoothly within the data fusion system. We verify the feasibility of our approach on the basis of simulated stochastic data sets and on the basis of data from a study in which the brightness temperature of oil films on sea water was measured. The proposed self-calibrating sensor fusion architecture extends the work we presented at IGARSS ..

Attractor Dynamics to Fuse Strongly Perturbed Sensor Data

We present a new approach to multi sensor fusion which is based on coupled nonlinear attractor dynamics. The state of the dynamics represents the fused estimate of a physical entity measured by multiple sensors. Each sensorreading but also general expert knowledge about the measured system specifies a local stable fixed point (attractor) with a limited basin of attraction of the dynamics. The dynamic state variable converges to a global stable state which is the system's fused estimate. For the example of measuring the oil film thickness on seawater by means of multispectral radiometer measurements gathered during flights across a polluted area, we show that our approach is particularly useful for fusing multimodal strongly perturbed sensor data. I. Introduction The aim of every fusion technique is to achieve improved accuracies and more specific inferences caused by the inherent redundancy provided by multiple sensors. A general overview about theoretical and application--oriented pa..

Innovation: Under Cover: Synthetic-Aperture GNSS Signal Processing

Researchers are developing new GNSS receivers and antennas based on an innovative signal-processing scheme to significantly improve GNSS tracking reliability and accuracy under degraded signal conditions. It is based on the principles of synthetic-aperture radar. Like in a multi-antenna phased array receiver, GNSS signals from different spatial locations are combined coherently forming an optimized synthetic antenna-gain pattern. The method is implemented in a real-time PC-based software receiver and works with GPS, GLONASS, and Galileo signals. Multiple frequencies are generally supported. The idea of synthetic-aperture processing is realized as a coherent summation of correlation values of each satellite over the so-called beamforming interval. Each correlation value is multiplied with a phase factor. For example, the phase factor can be chosen to compensate for the relative antenna motion over the beam-forming interval and the resulting sum of the scaled correlation values represents a coherent correlation value maximizing the line of sight signal power

Long-term preservation of biomolecules in lake sediments: potential importance of physical shielding by recalcitrant cell walls

Even though lake sediments are globally important organic carbon (OC) sinks, the controls on long-term OC storage in these sediments are unclear. Using a multiproxy approach, we investigate changes in diatom, green algae, and vascular plant biomolecules in sedimentary records from the past centuries across five temperate lakes with different trophic histories. Despite past increases in the input and burial of OC in sediments of eutrophic lakes, biomolecule quantities in sediments of all lakes are primarily controlled by postburial microbial degradation over the time scales studied. We, moreover, observe major differences in biomolecule degradation patterns across diatoms, green algae, and vascular plants. Degradation rates of labile diatom DNA exceed those of chemically more resistant diatom lipids, suggesting that chemical reactivity mainly controls diatom biomolecule degradation rates in the lakes studied. By contrast, degradation rates of green algal and vascular plant DNA are significantly lower than those of diatom DNA, and in a similar range as corresponding, much less reactive lipid biomarkers and structural macromolecules, including lignin. We propose that physical shielding by degradation-resistant cell wall components, such as algaenan in green algae and lignin in vascular plants, contributes to the long-term preservation of labile biomolecules in both groups and significantly influences the long-term burial of OC in lake sediments.ISSN:2752-654

Bedarfsanalyse Energiespeicher 2 - Auswirkungen der räumlichen Verteilung von Anlagen zur Stromerzeugung und Bewertung von Energieausgleichstechnologien. Abschlussbericht

Im Rahmen des Vorhabens wurden eine Methodik und die dafür notwendigen Modelle entwickelt, um die Aufstellungsorte von Stromerzeugern hinsichtlich der Auswirkungen auf das Stromversorgungssystem bewertet werden zu können. Weiterhin wurde der Energieausgleichsbedarf sowohl in deutschlandweiten Szenarien als auch innerhalb von Regionen untersucht. Ein weiteres Ziel des Projektes war die Entwicklung eines Modells, mit dem verschiedene Energieausgleichsoptionen und ihre Kombinationen in ihrer zeitlichen und räumlichen Dynamik abgebildet und bewertet werden können. Dabei wurden die individuellen Charakteristiken sowie Vor- und Nachteile der verschiedenen Energieausgleichsoptionen berücksichtigt. Es wurden Technologien zur Stromerzeugung und zum Energieausgleich eruiert und charakterisiert. Anschließend wurden zwei Referenzregionen ausgewählt und Optimierungsziele definiert. Im nächsten Schritt wurde mit Hilfe von Simulationsrechnungen eine optimale Verteilung der betrachteten Stromerzeuger entwickelt und der deutschlandweite Energieausgleichsbedarf in den Regionen berechnet. Es wurde die Datenbasis für die Simulationen in den Referenzregionen erarbeitet sowie Szenarien definiert. Anschließend wurden die Ausgleichsoptionen und die relevante Netzebenen in den Referenzregionen modelliert. Abschließend erfolgte eine zusammenfassende Bewertung der Simulationsergebnisse sowie des Einflusses der Aufstellungsorte von Stromerzeugern und der Energieausgleichsoptionen auf den Energieausgleichsbedarf