The article examines the cost-effectiveness of different water quality monitoring approaches in rural Africa, focusing on microbial contamination, particularly E. coli. It compares four testing methods: centralized, semi-centralized, decentralized, and mobile laboratory analysis. Using case studies from Ghana and Uganda, and a Monte Carlo simulation model, the study identifies the most cost-effective approach based on factors like distance, water system density, and sampling frequency. The findings suggest that centralized testing is generally the most affordable option, but semi-centralized or decentralized methods may be better for remote areas. The article also explores alternative low-cost testing methods to enhance decentralized testing.
Author(s): Trimmer, John T.; Delaire, Caroline; Marshall, Katherine; Khush, Ranjiv; Peletz, Rachel
Published: 2024
Language: English
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Additional Information
Rural water systems in Africa have room to improve
water quality monitoring. However, the most cost-effective approach for microbial water testing remains uncertain. This study compared the cost per E. coli test (membrane filtration) of four approaches representing different levels of centralization: (i) one centralized laboratory serving all water systems, (ii) a mobile laboratory serving all systems, (iii) multiple semi-centralized laboratories serving clusters of systems, and (iv) decentralized analysis at each system. We employed Monte Carlo analyses to model the costs of these approaches in three real-world contexts in Ghana and Uganda and in hypothetical simulations capturing various conditions across rural Africa. Centralized testing was the lowest cost in two real-world settings and the widest variety of simulations, especially those with water systems close to a central laboratory (<36 km). Semi-centralized testing was the lowest cost in one real-world setting and in simulations with clustered water systems and intermediate sampling frequencies (1โ2 monthly samples per system). The mobile lab was the lowest cost in the fewest simulations, requiring few systems and infrequent sampling. Decentralized testing was cost-effective for remote systems and frequent sampling, but only if sampling did not require a dedicated vehicle. Alternative low-cost testing methods could make decentralized testing more competitive.