In resource-limited settings, conventional sanitation
systems often
fail to meet their goals—with system failures stemming from
a mismatch among community needs, constraints, and deployed technologies.
Although decision-making tools exist to help assess the appropriateness
of conventional sanitation systems in a specific context, there is
a lack of a holistic decision-making framework to guide sanitation
research, development, and deployment (RD&D) of technologies.
In this study, we introduce DMsan—an open-source multi-criteria
decision analysis Python package that enables users to transparently
compare sanitation and resource recovery alternatives and characterize
the opportunity space for early-stage technologies. Informed by the
methodological choices frequently used in literature, the core structure
of DMsan includes five criteria (technical, resource recovery, economic,
environmental, and social), 28 indicators, criteria weight scenarios,
and indicator weight scenarios tailored to 250 countries/territories,
all of which can be adapted by end-users. DMsan integrates with the
open-source Python package QSDsan (quantitative sustainable design
for sanitation and resource recovery systems) for system design and
simulation to calculate quantitative economic (via techno-economic
analysis), environmental (via life cycle assessment), and resource
recovery indicators under uncertainty. Here, we illustrate the core
capabilities of DMsan using an existing, conventional sanitation system
and two proposed alternative systems for Bwaise, an informal settlement
in Kampala, Uganda. The two example use cases are (i) use by implementation
decision makers to enhance decision-making transparency and understand
the robustness of sanitation choices given uncertain and/or varying
stakeholder input and technology ability and (ii) use by technology
developers seeking to identify and expand the opportunity space for
their technologies. Through these examples, we demonstrate the utility
of DMsan to evaluate sanitation and resource recovery systems tailored
to individual contexts and increase transparency in technology evaluations,
RD&D prioritization, and context-specific decision making.