Inland waters are of great importance for scientists as well as authorities since they are essential ecosystems and well known for their biodiversity. When monitoring their respective water quality, in situ measurements of water quality parameters are spatially limited, costly and time-consuming. In this paper, we propose a combination of hyperspectral data and machine learning methods to estimate and therefore to monitor different parameters for water quality. In contrast to commonly-applied techniques such as band ratios, this approach is data-driven and does not rely on any domain knowledge. We focus on CDOM, chlorophyll a and turbidity as well as the concentrations of the two algae types, diatoms and green algae. In order to investigate the potential of our proposal, we rely on measured data, which we sampled with three different sensors on the river Elbe in Germany from 24 June–12 July 2017. The measurement setup with two probe sensors and a hyperspectral sensor is described in detail. To estimate the five mentioned variables, we present an appropriate regression framework involving ten machine learning models and two preprocessing methods. This allows the regression performance of each model and variable to be evaluated. The best performing model for each variable results in a coefficient of determination R2 in the range of 89.9% to 94.6%. That clearly reveals the potential of the machine learning approaches with hyperspectral data. In further investigations, we focus on the generalization of the regression framework to prepare its application to different types of inland waters.

The impoundment of the Three Gorges Reservoir (TGR) on the Yangtze River in China burdened its tributary backwaters with severe environmental problems. 1Confluence zones of reservoir tributaries with the Yangtze River main channel are main drivers of pollutant dynamics in the TGR 2 and are thus keys to develop mitigation measures. Here, we show a novel experimental approach of spatiotemporal water quality analysis to trace water mass movements and identify pollutant transport pathways in reservoir water bodies. Our results show the movements of density currents in a major tributary backwater of the TGR. A huge interflow density current from the Yangtze River main channel transported its heavy metal carriage to the upstream reaches of the tributary backwater. Water from the upstream backwater moved counterwise and carried less but pollutant-enriched suspended sediments. This scenario illustrates the importance of confluence zone hydrodynamics for fates and pathways of pollutants through the widely unknown hydrodynamics of new reservoirs.

Increasing eutrophication and algal bloom events in the Yangtze River Three Gorges Reservoir, China, are widely discussed with regard to changed hydrodynamics and nutrient transport and distribution processes. Insights into water exchange and interaction processes between water masses related to large-scale water level fluctuations in the reservoir are crucial to understand water quality and eutrophication dynamics. Therefore, confluence zones of tributaries with the Yangtze River main stream are dedicated key interfaces. In this study, water quality data were recorded in situ and on-line in varying depths with the MINIBAT towed underwater multi-sensor system in the confluence zone of the Daning River and the Yangtze River close to Wushan City during 1 week in August 2011. Geostatistical evaluation of the water quality data was performed, and results were compared to phosphorus contents of selective water samples. The strongly rising water level throughout the measurement period caused Yangtze River water masses to flow upstream into the tributary and supply their higher nutrient and particulate loads into the tributary water body. Rapid algal growth and sedimentation occurred immediately when hydrodynamic conditions in the confluence zone became more serene again. Consequently, water from the Yangtze River main stream can play a key role in providing nutrients to the algal bloom stricken water bodies of its tributaries.

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Several groups of bacteria such as Dehalococcoides spp., Dehalobacter spp., Desulfomonile spp., Desulfuromonas spp., or Desulfitobacterium spp. are able to dehalogenate chlorinated pollutants such as chloroethenes, chlorobenzenes, or polychlorinated biphenyls under anaerobic conditions. In order to assess the dechlorination potential in Yangtze sediment samples, the presence and activity of the reductively dechlorinating bacteria were studied in anaerobic batch tests. Eighteen sediment samples were taken in the Three Gorges Reservoir catchment area of the Yangtze River, including the tributaries Jialing River, Daning River, and Xiangxi River. Polymerase chain reaction analysis indicated the presence of dechlorinating bacteria in most samples, with varying dechlorinating microbial community compositions at different sampling locations. Subsequently, anaerobic reductive dechlorination of tetrachloroethene (PCE) was tested after the addition of electron donors. Most cultures dechlorinated PCE completely to ethene via cis-dichloroethene (cis-DCE) or trans-dichloroethene. Dehalogenating activity corresponded to increasing numbers of Dehalobacter spp., Desulfomonile spp., Desulfitobacterium spp., or Dehalococcoides spp. If no bacteria of the genus Dehalococcoides spp. were present in the sediment, reductive dechlorination stopped at cis-DCE. Our results demonstrate the presence of viable dechlorinating bacteria in Yangtze samples, indicating their relevance for pollutant turnover. Responsible editor: Robert DuranElectronic supplementary material The online version of this article