Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Applying spatial regression to evaluate risk factors for microbiological contamination of urban groundwater sources in Juba, South Sudan
Chalmers University of Technology, Sweden; Watsan MSF-OCB, Sudan.ORCID iD: 0000-0002-5044-6989
2017 (English)In: Hydrogeology Journal, ISSN 1431-2174, E-ISSN 1435-0157, Vol. 25, no 4, p. 1077-1091Article in journal (Refereed) Published
Abstract [en]

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. © 2016, The Author(s).

Place, publisher, year, edition, pages
Springer Verlag , 2017. Vol. 25, no 4, p. 1077-1091
Keywords [en]
Health, Microbial processes, Statistical modeling, Sub-Saharan Africa, Urban groundwater, coliform bacterium, feces, groundwater pollution, microbiology, numerical model, regression analysis, risk factor, Central Equatoria, Juba, South Sudan
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-60852DOI: 10.1007/s10040-016-1504-xScopus ID: 2-s2.0-85004072374OAI: oai:DiVA.org:ri-60852DiVA, id: diva2:1704377
Available from: 2022-10-18 Created: 2022-10-18 Last updated: 2023-05-25Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Mangold, Mikael

Search in DiVA

By author/editor
Mangold, Mikael
In the same journal
Hydrogeology Journal
Natural Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf