Abteilung Wasserressourcen und Trinkwasser

The commonly observed heterogeneous and erratic emissions of methane from lacustrine sediments are difficult to quantify. The combined analysis of methane and noble gas concentrations in the pore water opens a novel route to analyse and quantify such emissions and was used in this work to explore the gas dynamics in the sediments of two lakes with different hydro-geochemical settings. In the hypertrophic Lake Rot the microbial activity in the sediment pore water results in a virtually continuous gas bubble formation and emission. By determining noble gas concentration gradients and the maximum methane supersaturation, the minimum methane emission from the sediment was quantified. The gas evolution in the pore water of the sediments of artificial Lake Lungern is strongly affected by annual lake level variations of up to 20 meters. Noble gas concentrations allow past gas dynamics at different lake levels to be traced back. At "hot spots" of bubble emission microbial methane from deeper sediments was released in response to the decreasing hydrostatic pressure, i.e. man-induced lake level drop.
Within zones of the sediment that seasonally fall dry, the oxygen supply to the sediment was inferred from the noble gas excess observed in the pore water, which is due to dissolving air bubbles which entered the sediment during the dry period.
These case studies demonstrate the potential of the combined analysis of noble gas and CH₄ concentrations for exploring the gas dynamics in lacustrine sediments. The feasibility of this method under different conditions calls for an application in other aquatic environments, such as wetlands and oceans.

This study aimed to compare three approaches for predicting the service life of full-scale GAC adsorbers for the removal of micropollutants. The approaches included (i) rapid small-scale column tests (RSSCTs), (ii) two pilot-scale sampling approaches, and (iii) predictive correlations that consider micropollutant properties and background water matrix characteristics. The RSSCT could predict full-scale performance only if a micropollutant-specific fouling index was applied. At the pilot-scale, water samples were collected (1) over time at the top sampling point only (empty bed contact time (EBCT) of 1 minute) to minimize time to breakthrough (method 1) and (2) at different column depths at a single time point (method 2). Breakthrough curves obtained with method 2 more closely matched those obtained at the full-scale. In addition, method 2 is more convenient since it requires only one sampling campaign. Method 2 was used as a prognostic tool to predict breakthrough curves for micropollutants without full-scale data and a comparison with an existing prediction model gave satisfactory results for 6 out of 13 compounds.

Groundwater is a critical resource in India for the supply of drinking water and for irrigation. Its usage is limited not only by its quantity but also by its quality. Among the most important contaminants of groundwater in India is arsenic, which naturally accumulates in some aquifers. In this study we create a random forest model with over 145,000 arsenic concentration measurements and over two dozen predictor variables of surface environmental parameters to produce hazard and exposure maps of the areas and populations potentially exposed to high arsenic concentrations (>10 µg/L) in groundwater. Statistical relationships found between the predictor variables and arsenic measurements are broadly consistent with major geochemical processes known to mobilize arsenic in aquifers. In addition to known high arsenic areas, such as along the Ganges and Brahmaputra rivers, we have identified several other areas around the country that have hitherto not been identified as potential arsenic hotspots. Based on recent reported rates of household groundwater use for rural and urban areas, we estimate that between about 18-30 million people in India are currently at risk of high exposure to arsenic through their drinking water supply. The hazard models here can be used to inform prioritization of groundwater quality testing and environmental public health tracking programs.