Investigating dominant factors of coliform contamination in shallow groundwater: A logistic regression and AHP approach

Abstract

Shallow groundwater is a crucial source of clean water in developing countries like Indonesia. However, population growth has led to declining water quality due to inadequate infrastructure. This study aimed to identify dominant factors contributing to coliform contamination in shallow groundwater, addressing the lack of detailed statistical and multicriteria analyses in previous large-scale studies. The study was conducted in a densely populated urban area in Yogyakarta, Indonesia, and total coliform measurements from 42 groundwater samples were used as the independent variable. There are 14 dependent variables, categorized into three aspects: infrastructure (e.g., well type, well depth, wall of well, DEWATS type, well distance to the septic tank, latrine, and other contaminant sources); specific (e.g., population density and land use); and intrinsic (e.g., groundwater level, rainfall, soil type, and slope). Those parameters were statistically analyzed using logistic regression with IBM SPSS 26 software, and supported by the Analytical Hierarchy Process (AHP) that was employed using Expert Choice software to prioritize the factors. The results showed that 83.3% of the shallow groundwater samples exceeded the acceptable limits for total coliform, with key factors including well's proximity to other contamination sources, slope, distance from septic tanks, groundwater level, and population density. These findings highlight the importance of considering complex environmental factors in managing groundwater quality, particularly in developing countries. It is recommended that local authorities implement stricter regulations and infrastructure improvements to mitigate contamination risks.