Methane formation and consumption by sediments in a cross-channel profile of a small river impoundment
Methane dynamics in aquatic sediments
Rivers are a natural source of methane (CH4) into the atmosphere and may contribute significantly to total CH4 emissions. Even though the details of sources of CH4 in rivers are not fully understood, weirs have been recognized as a hotspot of CH4 emissions. In this study, we investigated CH4 production and consumption in air-exposed river sediments along a cross-channel transect located upstream of a weir. Stable carbon isotopes were used for determination of individual methanogenic pathways. In order to understand the relationship between physicochemical and biological processes, additional parameters such as organic matter, grain median size, and carbon and nitrogen content were characterized as well. Generally, samples from the surface sediment layer (0-10 cm) had higher CH4 production than sediments from the deeper layer (10-20 cm) during the incubation experiments. Sediments near the bank zones and in the mid-channel were characterized by the highest organic carbon content (6.9 %) as well the highest methanogenic activity (2.5 mmol g-1 DW d-1). The CH4 production was predominated by H2/CO2 dependent methanogenesis in the surface sediment layer (0-10 cm), while the proportion of acetoclastic and hydrogenotrophic methanogenesis in the deeper sediment layer (10-20 cm) was balanced. The CH4 oxidation potential of sediments showed the same spatial pattern as observed for the CH4 production. Our results showed high spatial variability of sediment CH4 production and oxidation in the cross-channel profile upstream of the weir, whereas the highest CH4 dynamics were observed in the littoral zones. This variability was closely linked with the carbon and nitrogen content in the sediment samples.
PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.
Copyright (c) 2019 The Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.