ArticleACS ES&T water2024
Reinforcing Feedbacks for Sustainable Implementation of Rural Drinking-Water Treatment Technology.
Article in ACS ES&T water, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
2 authors.
Funding
Abstract
Progress toward universal access to safe drinking water depends on rural water service delivery models that incorporate water safety management. Water supplies of all types have high rates of fecal contamination unless water safety risks are actively managed through water source protection, treatment, distribution, and storage. Recognizing the role of treatment within this broader risk-based framework, this study focuses on the implementation of passive chlorination and ultraviolet (UV) disinfection technologies in rural settings. These technologies can reduce the health risk from microbiological contaminants in drinking water; however, technology-focused treatment interventions have had limited sustainability in rural settings. This study examines the requirements for sustainable implementation of rural water treatment through qualitative content analysis of 26 key informant interviews, representing passive chlorination and UV disinfection projects in rural areas in South America, Africa, and Asia. The analysis is aligned with the RE-AIM framework and delivers insight into 18 principal enablers and barriers to rural water treatment sustainability. Analysis of the interrelationships among these factors identifies leverage points and encourages fit-for-purpose intervention design reinforced by collaboration between facilitating actors through hybrid service delivery models. Further work should prioritize health impact evidence, water quality reporting guidance, and technological capabilities that optimize trade-offs in fit-for-purpose treatment design.
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Registered trials
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