Hydrogeology Journal 25(4) pp. 1077-1091, doi: 10.1007/s10040-016-1504-x Abstract This study developed methodology for statistically assessing groundwater contamination mechanisms. It focused on microbiahumanitarian 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 mostsignificant 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.

Emma Engström

I defended my PhD thesis on predictive modeling of groundwater contamination at the Department of Sustainable Development, Environmental Science and Engineering (SEED), KTH, Royal Institute of Technol

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Venue: Holländargatan 13, Stockholm Research seminar with Erez Maggor, Assistant Professor at Ben-Gurion University. He studies the political economy of industrial policy and innovation. Register here &

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