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Urine diversion systems, which include waterless urinals and urine-diverting flush toilets, offer opportunities to conserve water, recover nutrients, promote circular economies, and improve sustainability. While technical development of these systems is critical, understanding stakeholder perceptions is equally important for their successful innovation, implementation, and adoption. This study conducts an exploratory analysis of stakeholder perceptions at U.S. academic institutions regarding how urine diversion technologies fit within the broader array of water conservation technologies and factors that influence decision-making related to urine diversion in buildings. We surveyed 65 stakeholders, including executive leaders, administrators, facilities managers, building managers, and sustainability professionals—key groups responsible for the adoption and maintenance of such systems but underrepresented in prior research. Participants reported a high level of awareness of water conservation technologies and varying degrees of implementation. Across all technologies, low-flush fixtures had the highest rate of implementation and waterless urinals had the highest rate of implementation and subsequent removal due to maintenance and infrastructure challenges. Participants also indicated that compliance with plumbing codes was the most critical factor when considering the implementation of urine diversion systems, followed by efficacy and cost-savings, and water conservation and nutrient recovery were among the least important factors. Future studies should focus on aligning urine diversion systems with building codes and regulations, mitigating clogging and odors to improve acceptance and adoption, and estimating costs and benefits when deciding on their implementation.
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Sandtank-ML: An Educational Tool at the Interface of Hydrology and Machine Learning
Hydrologists and water managers increasingly face challenges associated with extreme climatic events. At the same time, historic datasets for modeling contemporary and future hydrologic conditions are increasingly inadequate. Machine learning is one promising technological tool for navigating the challenges of understanding and managing contemporary hydrological systems. However, in addition to the technical challenges associated with effectively leveraging ML for understanding subsurface hydrological processes, practitioner skepticism and hesitancy surrounding ML presents a significant barrier to adoption of ML technologies among practitioners. In this paper, we discuss an educational application we have developed—Sandtank-ML—to be used as a training and educational tool aimed at building user confidence and supporting adoption of ML technologies among water managers. We argue that supporting the adoption of ML methods and technologies for subsurface hydrological investigations and management requires not only the development of robust technologic tools and approaches, but educational strategies and tools capable of building confidence among diverse users.
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- PAR ID:
- 10336009
- Date Published:
- Journal Name:
- Water
- Volume:
- 13
- Issue:
- 23
- ISSN:
- 2073-4441
- Page Range / eLocation ID:
- 3328
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/w13233328
