Abstract
Assessing water quality and identifying the potential source of contamination, by Sanitary inspections (SI), are essential to improve household drinking water quality. However, no study link the water quality at a point of use (POU), household level or point of collection (POC), and associated SI data in a medium resource setting using a Bayesian Belief Network (BBN) model. We collected water samples and applied an adapted SI at 328 POU and 265 related POC from a rural area in East Sumba, Indonesia. Fecal contamination was detected in 24.4 and 17.7% of 1 ml POC and POU samples, respectively. The BBN model showed that the effect of holistic—combined interventions to improve the water quality were larger compared to individual intervention. The water quality at the POU was strongly related to the water quality at the POC and the effect of household water treatment to improve the water quality was more prominent in the context of better sanitation and hygiene conditions. In addition, it was concluded that the inclusion of extra “external” variable (fullness level of water at storage), besides the standard SI variables, could improve the model’s performance in predicting the water quality at POU. Finally, the BBN approach proved to be able to illustrate the interdependencies between variables and to simulate the effect of the individual and combination of variables on the water quality.
Highlights
Water quality has a prominent place in the Sustainable Development Goal 6.11, because it has been recognised that unsafe drinking water is responsible for high numbers of diarrheal morbidity and mortality among children below the age of five[1]
This paper introduces an application of Bayesian Belief Network (BBN) to analyse how water quality at the point of use is related to the water quality at the point of collection and associated sanitary inspection data in the medium resource settings in low-middle income countries
The results demonstrate that water quality at the point of collection (POC) was, as expected, related to the water quality at the point of use (POU) and household water treatment had a larger effect of improving the storage water quality in the case of better sanitation and hygiene conditions
Summary
Water quality has a prominent place in the Sustainable Development Goal 6.11, because it has been recognised that unsafe drinking water is responsible for high numbers of diarrheal morbidity and mortality among children below the age of five[1]. In order to assess potential sources of contamination in a water supply system, systematic observation, called sanitary inspections (SI), are performed. SI are not a substitute for drinking water quality testing, but identify contamination source in the system, especially in the context of risk management, and can be used to design appropriate actions to change the situation[9]. Conducting drinking water quality testing in LMICs, can be challenging, especially because of limited resources such as laboratory facilities or infrastructure[11]. The most common approach has been to analyse the SI and drinking water quality by using statistical analyses, e.g., bivariate correlation or regression analyses, especially in high resource s ettings[6,10,13,14,15,16]
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