Centralized or Onsite Testing? Examining the Costs of Water Quality Monitoring in Rural Africa

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 (1−2 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.