Applying spatial regression to evaluate risk factors for microbiological contamination of urban groundwater sources in Juba, South Sudan
Journal article, 2017

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 M,decins Sans FrontiSres 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.

Microbial processes

Statistical modeling


Urban groundwater

Sub-Saharan Africa


E. Engstrom

Royal Institute of Technology (KTH)

U. Mortberg

Royal Institute of Technology (KTH)

A. Karlstrom

Royal Institute of Technology (KTH)

Mikael Mangold

Chalmers, Civil and Environmental Engineering, Water Environment Technology

Médecins Sans Frontières (MSF)

Hydrogeology Journal

1431-2174 (ISSN) 14350157 (eISSN)

Vol. 25 4 1077-1091

Driving Forces

Sustainable development

Subject Categories

Water Engineering


Oceanography, Hydrology, Water Resources

Areas of Advance

Life Science Engineering (2010-2018)



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