TY - CONF A1 - Ronen, A. A1 - Vogel, Christian A1 - Leube, Peter A1 - Ben Efraim, R. A1 - Nir, O. A1 - Chaudhary, M. A1 - Futterlieb, M. A1 - Panglisch, S. T1 - DEFEAT-PFAS: Detection, Quantification, and Treatment of Per- and Polyfluoroalkyl Substances (PFAS) in Groundwater N2 - The research project focuses on tackling the detection, measurement, and elimination of per- and polyfluoroalkyl substances (PFAS) from polluted groundwater, with a particular emphasis on addressing short (C4-C7) and ultrashort (C1-C3) chain PFAS. Given the widespread use of PFAS in various products, they are commonly found in groundwater near industrial and military sites in Germany and Israel. Moreover, recent regulations limiting the use of long chain PFAS have led industries to shift towards shorter chain alternatives. Hence, our efforts are geared towards refining detection, quantification, and removal methods for short and ultrashort chain PFAS. In terms of detection, ww are developing passive sampling devices capable of collecting and tracking the temporal distribution of PFAS species in groundwater. This will enable us to analyze contaminations in German and Israeli groundwater using cutting-edge analytical techniques. Additionally, contaminated groundwater will undergo a two-stage treatment process aimed at concentrating the relatively low PFAS concentrations using innovative membrane technologies such as closed-circuit reverse osmosis and mixed matrix composite nanofiltration membrane adsorbers. Subsequently, the streams containing higher PFAS concentrations will be treated through coagulation, with the remaining PFAS being adsorbed onto carbonaceous nanomaterials. The outcome of this research will include the creation of advanced tools for detecting, measuring, and eliminating PFAS from polluted groundwater, while also enhancing our understanding of the scope of these contaminations. T2 - German Israeli Water Technology Status Seminar CY - Koblenz, Germany DA - 18.06.2024 KW - Ground water KW - Per- and Polyfluoroalkyl substances (PFAS) KW - Remediation PY - 2024 UR - https://indico.scc.kit.edu/event/4029/attachments/7552/12045/Proceedings_Ger-Isr-Coop_Status-Seminar-2024.pdf SP - 33 EP - 36 AN - OPUS4-60331 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Vogel, Kristin A1 - Sieg, T. A1 - Veh, G. A1 - Fiedler, B. A1 - Moran, T. A1 - Peter, M. A1 - Rottler, E. A1 - Bronstert, A. T1 - Natural Hazards in a Changing World: Methods for Analyzing Trends and Non‐Linear Changes N2 - Estimating the frequency and magnitude of natural hazards largely hinges on stationary models, which do not account for changes in the climatological, hydrological, and geophysical baseline conditions. Using five diverse case studies encompassing various natural hazard types, we present advanced statistical and machine learning methods to analyze and model transient states from long‐term inventory data. A novel storminess metric reveals increasing European winter windstorm severity from 1950 to 2010. Non‐stationary extreme value models quantify trends, seasonal shifts, and regional differences in extreme precipitation for Germany between 1941 and 2021. Utilizing quantile sampling and empirical mode decomposition on 148 years of daily weather and discharge data in the European Alps, we assess the impacts of changing snow cover, precipitation, and anthropogenic river network modifications on river runoff. Moreover, a probabilistic framework estimates return periods of glacier lake outburst floods in the Himalayas, demonstrating large differences in 100‐year flood levels. Utilizing a Bayesian change point algorithm, we track the onset of increased seismicity in the southern central United States and find correlation with wastewater injections into deep wells. In conclusion, data science reveals transient states for very different natural hazard types, characterized by diverse forms of change, ranging from gradual trends to sudden change points and from altered seasonality to overall intensity variations. In synergy with the physical understanding of Earth science, we gain important new insights into the dynamics of the studied hazards and their possible mechanisms. KW - Natural hazards KW - Time dependent modeling KW - Transient state KW - Data science KW - Climate change PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-601540 DO - https://doi.org/10.1029/2023EF003553 VL - 12 IS - 5 SP - 1 EP - 29 PB - American Geophysical Union (AGU) AN - OPUS4-60154 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Schmidinger, J. A1 - Barkov, V. A1 - Tavakoli, H. A1 - Correa, J. A1 - Ostermann, Markus A1 - Atzmueller, M. A1 - Gebbers, R. A1 - Vogel, S. T1 - Which and how many soil sensors are ideal to predict key soil properties: A case study with seven sensors N2 - Soil sensing enables rapid and cost-effective soil analysis. However, a single sensor often does not generate enough information to reliably predict a wide range of soil properties. Within a case-study, our objective was to identify how many and which combinations of soil sensors prove to be suitable for high-resolution soil mapping. On a subplot of an agricultural field showing a high spatial soil variability, six in-situ proximal soil sensors (PSSs) next to remote sensing (RS) data from Sentinel-2 were evaluated based on their capabilities to predict a set of soil properties including: soil organic carbon, pH, moisture as well as plant-available phosphorus, magnesium and potassium. The set of PSSs consisted of ion-selective pH electrodes, a capacitive soil moisture sensor, an apparent soil electrical conductivity measuring system as well as passive gamma-ray-, X-ray fluorescence- and nearinfrared spectroscopy. All possible combinations of sensors were exhaustively evaluated and ranked based on their predict KW - XRF KW - Soil KW - Remote Sensing KW - Precision agriculture PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-613920 DO - https://doi.org/10.1016/j.geoderma.2024.117017 VL - 450 SP - 1 EP - 17 PB - Elsevier B.V. AN - OPUS4-61392 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Vo, P. H.N. A1 - Vogel, Christian A1 - Nguyen, H. T.M. A1 - Hamilton, B. R. A1 - Thai, P. K. A1 - Roesch, Philipp A1 - Simon, Franz-Georg A1 - Mueller, J. F. T1 - µ-X-ray fluorescence (XRF) and fluorine K-edge µ-X-ray absorption near-edge structure (XANES) spectroscopy for detection of PFAS distribution in the impacted concrete N2 - An improved understanding of the distribution of per- and polyfluoroalkyl substances (PFAS) in PFAS-impacted concrete is important for risk management and decontamination of PFAS. This study incorporates µ-X-ray fluorescence (µ-XRF) and fluorine K-edge µ-X-ray absorption near-edge structure (µ-XANES) spectroscopy to gain non-destructive insights into PFAS distribution in the impacted concrete. The μ-XRF and μ-XANES spectroscopy provided additional details on the detection of PFAS, which were not detected by the desorption electrospray ionization (DESI) imaging method conducted previously. The shorter chain PFAS were found on the top part of the concrete core (0.5 cm), and longer chain PFAS were mostly at the bottom part of the concrete core (5 cm). The inorganic fluorine fraction was also detected, and it likely hampered the detection of organic fluorine such as PFAS in the concrete. Thus, this non-destructive technique is an complementary approach to detect PFAS in contaminated concrete. KW - Beton KW - Per- and Polyfluoroalkyl substances (PFAS) KW - XANES spectroscopy PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-616711 DO - https://doi.org/10.1016/j.hazl.2024.100134 SN - 2666-9110 VL - 5 SP - 1 EP - 5 PB - Elsevier B.V. AN - OPUS4-61671 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -