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 $R^2$ 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.

Multi-use of offshore wind farms with low-trophic aquaculture could provide sustainable energy, nutritious seafood, and restorative ecosystem services through nutrients and carbon capture and utilization. In a transition zone between marine and brackish seas, our model predicted that allocating 10% of projected wind farm areas to blue mussel and sugar kelp aquaculture in the North Sea - Baltic Sea transition zone could yield 18 t-fresh weight ha⁻¹ yr⁻¹. Total carbon captured and harvested from seaweed biomass and mussel shells would equal 40% of the carbon dioxide emissions from the Danish agricultural sector. Furthermore, global aquaculture production is projected to increase by 132% compared to current production. With technological and regulatory challenges still to be addressed, these findings demonstrate a vast potential of multi-use in offshore areas, which can generate blue biomass with fewer user conflicts, while mitigating eutrophication and climate change, thereby supporting multiple global sustainable development goals.

Harmful algal blooms (HABs) are a global problem, exacerbated by rising temperatures, cultural eutrophication, urbanization, and agricultural development. During these HABs, phytoplankton consumption of CO₂ may result in conditions of C limitation, where algal taxa best adapted for these conditions will be at a competitive advantage. Many cyanobacteria are capable of alleviating CO₂ limitation by a variety of strategies, including the active assimilation of . In this study, we utilized a high‐resolution, month‐long time series of stable C isotopes and high‐performance liquid chromatograph‐based algal taxonomy in the hypereutrophic Lake Taihu, China, to investigate whether cyanobacteria are indeed advantaged by CO₂ limiting conditions. We employed a model of phytoplankton C acquisition to support the inferences derived from direct measurements. Diurnal cycles of production and respiration caused δ¹³C_DIC to vary between −4‰ and −9‰, while δ¹³C_POC varied between −29.6‰ and −19.6‰. Measured and modeled phytoplankton fractionation of DIC were positively correlated with pCO₂ and negatively correlated with cyanobacterial abundance, suggesting that CO₂ limitation preferentially favored increased cyanobacterial biomass, relative to other taxa. We propose that the ability of many cyanobacteria to access otherwise limiting pools of inorganic C is intrinsically linked with their capacity to cope with CO₂ limiting conditions, and may be a key factor in their dominance during HABs.