Permanently charged and ionizable organic compounds (IOC) are a large and diverse group of compounds belonging to many contaminant classes, including pharmaceuticals, pesticides, industrial chemicals, and natural toxins. Sorption and mobility of IOCs are distinctively different from those of neutral compounds. Due to electrostatic interactions with natural sorbents, existing concepts for describing neutral organic contaminant sorption, and by extension mobility, are inadequate for IOC. Predictive models developed for neutral compounds are based on octanol–water partitioning of compounds ( K ow ) and organic-carbon content of soil/sediment, which is used to normalize sorption measurements ( K OC ). We revisit those concepts and their translation to IOC ( D ow and D OC ) and discuss compound and soil properties determining sorption of IOC under water saturated conditions. Highlighting possible complementary and/or alternative approaches to better assess IOC mobility, we discuss implications on their regulation and risk assessment. The development of better models for IOC mobility needs consistent and reliable sorption measurements at well-defined chemical conditions in natural porewater, better IOC-, as well as sorbent characterization. Such models should be complemented by monitoring data from the natural environment. The state of knowledge presented here may guide urgently needed future investigations in this field for researchers, engineers, and regulators.

Economically viable water treatment process plants for drinking water purification are a prerequisite for sustainable supply of safe drinking water in the future. However, modern membrane process development experiences a disconnect in this domain: the synthesis of the membrane and the design of the process are decoupled. We propose an optimization strategy to simultaneously design the performance of layer-by-layer nanofiltration membrane modules and the separation process. This approach achieves overall optimal performance by extending the search space and thus exploiting synergies. Better separation performances at a lower cost as compared to conventional optimization strategies can be achieved. The key feature of this optimization framework is the integration of artificial neural networks. This machine-learning technique describes membrane performance as a function of its synthesis protocol. We optimize the design problem rigorously by a deterministic global nonlinear optimization method. Thus, this framework yields membrane synthesis protocols and membrane processes that are optimally tailored to the desired separation task. In a showcase, the simultaneous membrane synthesis and process optimization design achieve immediately favorable results with lower impurities at comparable costs. The process investment and operation costs are compared to a state of the art commercially available membrane for nanofiltration.

Wastewater Treatment (WWT) for water reuse applications has been accepted as a strategic solution in improving water supplies across the globe; however, there are still various challenges that should be overcome. Selection of practical solutions is then

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Protecting our water resources in terms of quality and quantity is considered one of the big challenges of the twenty-first century, which requires global and multidisciplinary solutions. A specific threat to water resources, in particular, is the increased occurrence and frequency of flood events due to climate change which has significant environmental and socioeconomic impacts. In addition to climate change, flooding (or subsequent erosion and run-off) may be exacerbated by, or result from, land use activities, obstruction of waterways, or urbanization of floodplains, as well as mining and other anthropogenic activities that alter natural flow regimes. Climate change and other anthropogenic induced flood events threaten the quantity of water as well as the quality of ecosystems and associated aquatic life. The quality of water can be significantly reduced through the unintentional distribution of pollutants, damage of infrastructure, and distribution of sediments and suspended materials during flood events. To understand and predict how flood events and associated distribution of pollutants may impact ecosystem and human health, as well as infrastructure, large-scale interdisciplinary collaborative efforts are required, which involve ecotoxicologists, hydrologists, chemists, geoscientists, water engineers, and socioeconomists. The research network “project house water” consists of a number of experts from a wide range of disciplines and was established to improve our current understanding of flood events and associated societal and environmental impacts. The concept of project house and similar seed fund and boost fund projects was established by the RWTH Aachen University within the framework of the German excellence initiative with support of the German research foundation (DFG) to promote and fund interdisciplinary research projects and provide a platform for scientists to collaborate on innovative, challenging research. Project house water consists of six proof-of-concept studies in very diverse and interdisciplinary areas of research (ecotoxicology, water, and chemical process engineering, geography, sociology, economy). The goal is to promote and foster high-quality research in the areas of water research and flood-risk assessments that combine and build off-laboratory experiments with modeling, monitoring, and surveys, as well as the use of applied methods and techniques across a variety of disciplines.