Applying spatial regression to evaluate risk factors for microbiological contamination of urban groundwater sources in Juba, South Sudan

Engström, Emma; Mörtberg, Ulla; Karlström, Anders; Mangold, Mikael

Applying spatial regression to evaluate risk factors for microbiological contamination of urban groundwater sources in Juba, South Sudan | Application de la régression spatiale pour évaluer les facteurs de risque de contamination microbiologique des sources d’eau souterraine en milieu urbain à Juba, Sud Soudan Aplicación de la regresión espacial para evaluar los factores de riesgo de contaminación microbiológica de las fuentes de agua subterránea urbanas en Juba, Sudán del Sur 应用空间回归分析评价南苏丹朱巴地区城市水资源微生物污染的风险因素 Aplicando regressão espacial para avaliar fatores de risco para a contaminação microbiológica de fontes de água subterrânea urbana em Juba, Sudão do Sul

2017

Engström, Emma | Mörtberg, Ulla | Karlström, Anders | Mangold, Mikael

This study developed methodology for statistically assessing groundwater contamination mechanisms. It focused on microbial water pollution in low-income regions. Risk factors for faecal contamination of groundwater-fed drinking-water sources were evaluated in a case study in Juba, South Sudan. The study was based on counts of thermotolerant coliforms in water samples from 129 sources, collected by the humanitarian aid organisation Médecins Sans Frontières in 2010. The factors included hydrogeological settings, land use and socio-economic characteristics. The results showed that the residuals of a conventional probit regression model had a significant positive spatial autocorrelation (Moran’s I = 3.05, I-stat = 9.28); therefore, a spatial model was developed that had better goodness-of-fit to the observations. The most significant factor in this model (p-value 0.005) was the distance from a water source to the nearest Tukul area, an area with informal settlements that lack sanitation services. It is thus recommended that future remediation and monitoring efforts in the city be concentrated in such low-income regions. The spatial model differed from the conventional approach: in contrast with the latter case, lowland topography was not significant at the 5% level, as the p-value was 0.074 in the spatial model and 0.040 in the traditional model. This study showed that statistical risk-factor assessments of groundwater contamination need to consider spatial interactions when the water sources are located close to each other. Future studies might further investigate the cut-off distance that reflects spatial autocorrelation. Particularly, these results advise research on urban groundwater quality.

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