Project leaders: Dr. Nora Sutton (Wageningen University), Professor HU Chengzhi (CAS - Research Center for Eco-Environmental Sciences) 

Consortium partners: Delft University of Technology, Waternet, Suzhou Water Supply Co. Ltd.  

Drinking water production from surface water is challenged by the presence of organic micropollutants (OMPs), including pharmaceuticals and industrial chemicals, such as PFAS. Existing OMP removal technologies which are part of the current drinking water production process are energy, chemical, and cost intensive. Water SMART co-creates a new toolbox of green approaches to sustainably remove OMPs from surface water. This toolbox includes nature-based technologies, optimized treatments to reduce energy and chemical use and costs, and efficient steering of OMP degradation by novel screening of OMP transformation products. Water SMART’s research outputs are integrated into decision support models developed in close collaboration with the drinking water sector, leading to direct uptake of knowledge, technologies and tools to sustainably and robustly remove OMPs during drinking water production. 

Project leaders: Dr. Boris van Breukelen (Delft University of Technology), Professor LIU Gang (CAS - Research Center for Eco-Environmental Sciences)  

Consortium partners: Wageningen University, KWR Water Research Institute, Oasen, CAS – Institute of Geochemistry, Shanghai National Engineering Research Center of Urban Water Resources, Technical Research Center of MaoTai Group, Harbin Water Supply Group Co. Ltd., Wuchang City Water Supply Center 

River bank filtration (RBF) is a widely used nature-based technique to produce drinking water from surface water. Energy intensive post-treatment is often required, because pollutants are not always reliably removed. AQUIPURA investigates pollutant removal in RBFs in its entirety ranging from organic micro-pollutants to viruses. AQUIPURA develops and employs novel mechanistic insights, monitoring, and modelling tools. AQUIPURA improves design and performance resulting in improved reliability of a contaminant-free, safe, green RBF system in times of urbanization and climate change. AQUIPURA shares outcomes and knowledge in a Chinese-Dutch transdisciplinary consortium, to assist in achieving a green nature-based, climate resilient drinking water supply. 

Project leaders: Professor Arnold Tukker (Leiden University), Professor Zheng Yuming (CAS - Institute of Urban Environment)  

Consortium partners: Wageningen University, Dutch-Sino Business Promotion B.V., CAS – Center for Agricultural Resources Research, Shengfa Environmental Protection Technology (Xiamen) Co. Ltd., Fujian Lanshen Environmental Corporation Ltd. Company, Zhongke Environment Research Institute (Jiaxing) Co. Ltd. 

Wastewater from human and livestock sources can harm health, ecosystems, and the economy. Current wastewater management (WWM) uses energy and chemicals inefficiently, is insufficiently flexible to adjust to uncertain climate change impacts, and can be too expensive for less affluent places. Furthermore, existing models used to assess WWM options tend to focus on specific areas and regulations, making it hard to unlock WWM’s full environmental and social benefits. The FOREWARD project will co-create with various stakeholders new models and technologies that are better for the water environment and climate, are more cost-effective, and work well in different places. 

Project leaders: Professor Alexander Lazovik (University of Groningen), Professor LIU Yuan (CAS - Chongqing Institute of Green and Intelligent Technology)  

Consortium partners: University of Amsterdam, diTTo Engineering, CAS – Research Center for Eco-Environmental Sciences, Chongqing Academy of Science and Technology, Chongqing Haoyang Water Construction Management Co. Ltd.  

DDTclean focuses on the development of future-facing wastewater treatment technologies. These novel technologies can provide intelligent wastewater governance, including collection and treatment, with the help of electrochemical-membrane technologies and scalable Artificial Intelligence enhanced distributed Digital Twin framework. This project addresses the existing dilemma of low treatment efficiency of decentralised wastewater, while facilitating the communication and coordination of multiple stakeholders, aiming to improve the environmental quality, especially in remote areas, and to contribute to system-wide optimal operations.