ABSTRACT Infographic showing methodology flow from two case studies through technology design, Monte Carlo simulation, multi-criteria analysis, and life-cycle costing to key findings, with regional scalability metrics for 280+ communities. Small coastal and insular communities in Latin America face critical sanitation challenges driven by seasonal flow variability, high-strength industrial discharges, limited budgets and sensitive marine ecosystems. This study develops a replicable multi-criteria decision framework integrating the Analytical Hierarchy Process and TOPSIS with Monte Carlo uncertainty analysis to compare four decentralized treatment configurations across two Ecuadorian scenarios: an island slaughterhouse generating high-strength wastewater and a coastal tourist town with sevenfold seasonal flow variation. For the slaughterhouse, a UASB reactor coupled with subsurface-flow constructed wetlands and UV disinfection ranked first, achieving 94.2% regulatory compliance probability and the lowest 20-year net present value. For the tourist town, stabilization ponds with polishing wetlands ranked first owing to passive hydraulic buffering, with 98.9% compliance probability during peak season. Probabilistic ranking confirmed that top-ranked options retained first position in 92% and 78% of iterations, respectively. Passive treatment trains reduced life-cycle costs by 44–63% relative to mechanized alternatives. Because performance estimates rely on literature-based design models rather than site-specific monitoring, results represent feasibility-level decision-support evidence. The framework offers a transparent, uncertainty-aware methodology for screening decentralized sanitation technologies in data-limited coastal settings.
Mendoza et al. (Fri,) studied this question.
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