Ex-ante quantification of nutrient, total solids, and water flows in sanitation systems
To prioritise sustainable sanitation systems in strategic sanitation planning, indicators such as local appropriateness or resource recovery have to be known at the pre-planning phase. The quantification of resource recovery remains a challenge because existing substance flow models require large amounts of input data and can therefore only be applied for a few options at a time for which implementation examples exist. This paper aims to answer two questions: How can we predict resource recovery and losses of sanitation systems ex-ante at the pre-planning phase? And how can we do this efficiently to consider the entire sanitation system option space? The approach builds on an existing model to create all valid sanitation systems from a set of conventional and emerging technologies and to evaluate their appropriateness for a given application case. It complements the previous model with a Substance Flow Model (SFM) and with transfer coefficients from a technology library to quantify nutrients (phosphorus and nitrogen), total solids (as an indicator for energy and organics), and water flows in sanitation systems ex ante. The transfer coefficients are based on literature data and expert judgement. Uncertainties resulting from the variability of literature data or ignorance of experts are explicitly considered, allowing to assess the robustness of the model output. Any (future) technologies or additional products can easily be added to the library. The model is illustrated with a small didactic example showing how 12 valid system configurations are generated from a few technologies, and how substance flows, recovery ratios, and losses to soil, air, and water are quantified considering uncertainties. The recovery ratios vary between 0 and 28% for phosphorus, 0–10% for nitrogen, 0–26% for total solids, and 0–12% for water. The uncertainties reflect the high variability of the literature data but are comparable to those obtained in studies using a conventional post-ante material flow analysis (generally about 30% variability at the scale of a an urban area). Because the model is fully automated and based on literature data, it can be applied ex-ante to a large and diverse set of possible sanitation systems as shown with a real application case. From the 41 technologies available in the library, 101,548 systems are generated and substance flows are modelled. The resulting recovery ratios range from nothing to almost 100%. The two examples also show that recovery depend on technology interactions and has therefore to be assessed for all possible system configurations and not at the single technology level only. The examples also show that there exist trade-offs among different types of reuse (e.g. energy versus nutrients) or different sustainability indicators (e.g. local appropriateness versus resource recovery). These results show that there is a need for such an automated and generic approach that provides recovery data for all system configurations already at the pre-planning phase. The approach presented enables to integrate transparently the best available knowledge for a growing number of sanitation technologies into a planning process. The resulting resource recovery and loss ratios can be used to prioritise resource efficient systems in sanitation planning, either for the pre-selection or the detailed evaluation of options using e.g. MCDA. The results can also be used to guide future development of technology and system innovations. As resource recovery becomes more relevant and novel sanitation technologies and system options emerge, the approach presents itself as a useful tool for strategic sanitation planning in line with the Sustainable Development Goals (SDGs).