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Surface water mixed with wastewater creates serious environmental concerns, particularly in densely populated urban areas with inadequate infrastructure. Such contamination threatens to cause major public health crises in the Ganga Basin where monsoonal flooding converges with 6 billion liters of untreated sewage that is discharged daily into the basin by 650 million people. GIS-based analytic hierarchy process (AHP) with remote sensing data was conducted to highlight areas of vulnerability along a 20-km wide riverscape. Analytic network process (ANP), Nested AHP, fuzzy AHP, and 1-N AHP (novel variant of AHP) were used to constrain AHP model uncertainties, and composites of these analyses were utilized to define the vulnerability of the river Ganga to pollution. AHP categorized 83.7% of the area as having extremely low or low vulnerability and 3.5% of the area as having highly or extremely high vulnerability. ANP and Nested AHP produced focused, yet dampened, vulnerability-score maps compared to AHP. Fuzzy AHP and 1-N AHP detected sensitivities to factor variability and potential unknown acute and chronic factors. While fuzzy AHP identified quintile-level changes in vulnerability based on scenario parameters, vulnerability scores of 1-N AHP and AHP showed no major differences. Normalized composite vulnerability ≥ 2 standard deviations highlighted particularly vulnerable locations and identified instances where network effects were greater than factor class and vice versa. Together, these analyses located areas of extreme vulnerability at the nexus of river Ganga and urban landscapes as well as regions of low vulnerability potentially suitable for conservation efforts or sustainable development practices to prevent their degradation. This approach contributes to a more comprehensive understanding of remote sensing data applications in environmental assessment, and these decision-making variants can also have broader applications in other areas of environmental management and sustainability, facilitating more precise and adaptable decision support frameworks in densely populated watersheds.
Acciavatti et al. (Fri,) studied this question.