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DMsan: A Multi-Criteria Decision Analysis Framework and Package to Characterize Contextualized Sustainability of Sanitation and Resource Recovery Technologies
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  • Hannah A. C. Lohman
    Hannah A. C. Lohman
    Department of Civil and Environmental Engineering, 3221 Newmark Civil Engineering Laboratory, University of Illinois Urbana-Champaign, 205 N. Mathews Avenue, Urbana, Illinois 61801, United States
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    Orcidhttps://orcid.org/0000-0001-8600-7235
  • Victoria L. Morgan
    Victoria L. Morgan
    Institute for Sustainability, Energy, and Environment, University of Illinois Urbana-Champaign, 1101 W. Peabody Drive, Urbana, Illinois 61801, United States
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    Orcidhttps://orcid.org/0000-0002-0350-0751
  • Yalin Li
    Yalin Li
    Institute for Sustainability, Energy, and Environment, University of Illinois Urbana-Champaign, 1101 W. Peabody Drive, Urbana, Illinois 61801, United States
    DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, 1206 W. Gregory Drive, Urbana, Illinois 61801, United States
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    Orcidhttps://orcid.org/0000-0002-8863-4758
  • Xinyi Zhang
    Xinyi Zhang
    Department of Civil and Environmental Engineering, 3221 Newmark Civil Engineering Laboratory, University of Illinois Urbana-Champaign, 205 N. Mathews Avenue, Urbana, Illinois 61801, United States
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    Orcidhttps://orcid.org/0000-0002-5858-9511
  • Lewis S. Rowles
    Lewis S. Rowles
    Institute for Sustainability, Energy, and Environment, University of Illinois Urbana-Champaign, 1101 W. Peabody Drive, Urbana, Illinois 61801, United States
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    Orcidhttps://orcid.org/0000-0002-3489-9179
  • Sherri M. Cook
    Sherri M. Cook
    Department of Civil, Environmental, and Architectural Engineering, University of Colorado Boulder, 1111 Engineering Drive, Boulder, Colorado 80309, United States
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    Orcidhttps://orcid.org/0000-0002-7648-4596
  • Jeremy S. Guest*
    Jeremy S. Guest
    Department of Civil and Environmental Engineering, 3221 Newmark Civil Engineering Laboratory, University of Illinois Urbana-Champaign, 205 N. Mathews Avenue, Urbana, Illinois 61801, United States
    Institute for Sustainability, Energy, and Environment, University of Illinois Urbana-Champaign, 1101 W. Peabody Drive, Urbana, Illinois 61801, United States
    DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, 1206 W. Gregory Drive, Urbana, Illinois 61801, United States
    *Email: [email protected]. Phone: 217-244-9247.
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    Orcidhttps://orcid.org/0000-0003-2489-2579
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ACS Environmental Au

Cite this: ACS Environ. Au 2023, 3, 3, 179–192
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https://doi.org/10.1021/acsenvironau.2c00067
Published March 27, 2023

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Abstract

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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.

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1. Introduction

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In a global push to achieve access to adequate and equitable sanitation and hygiene for all by 2030 (Sustainable Development Goal 6.2), we are only at one-fourth the necessary rate of progress to achieve this goal. (1) Over 50% of the global population lives without safely managed sanitation systems, and 25% lives without basic sanitation. (1) Despite efforts to improve sanitation, resource-limited settings experience failure rates up to 70% within the first 2 years of installing sanitation technologies. (2) Even with adequate planning and funding, failures arise from critical factors not assessed during technology evaluation, such as stakeholder preferences, (3,4) climate change and population growth, (5,6) system operational needs, (7,8) and user understanding and support of the system. (9,10) Successful long-term adoption of sanitation technologies is influenced by multiple factors related to technological, environmental, economic, and social dimensions of sustainability. (11)
Decision makers, such as engineers or community planners, commonly use a suite of techniques to assess the sustainability of sanitation and resource recovery systems, including life cycle assessment (LCA) to assess environmental impacts (12−14) and techno-economic analysis (TEA) to estimate system costs. (15,16) However, these techniques evaluate technologies in one pillar of sustainability at a time (i.e., environment or economic) and often fail to include the social dimensions of decision making that can be a core driver of successful long-term adoption. (8) Multi-criteria decision analysis (MCDA) supports the assessment of tradeoffs among conflicting criteria across dimensions of sustainability for decision making. (17,18) While MCDA can be a robust approach, the criteria and indicators currently used to assess alternatives in the sanitation and resource recovery field are ambiguous and at times have conflicting definitions. (8,9) For instance, Davis et a (l). evaluated six decision-making frameworks (13,19−23) intended for resource-limited settings that integrate an extensive list of 111 indicators, but reliability and social acceptability were the only technology indicators shared among all six frameworks. (8) Furthermore, the authors evaluated 12 sanitation systems for a specific location in each framework and obtained highly varied results, where one framework ranked an option best (1st out of 12) and the other framework ranked it as second to last (11th out of 12). These studies demonstrate that MCDA can be an informative yet subjective approach, the latter of which undermines its utility when for decision makers interested in generalizable insight.
In addition to informing the locality-specific evaluation of alternatives with clearly defined stakeholder-informed criteria and indicator weights, this approach can also be used when the decision-making process, implementation context, and stakeholder priorities are still uncertain. In particular, developers of novel decentralized sanitation and resource recovery systems can benefit from identifying and expanding the opportunity space for their new technology─the contexts in which it outcompetes alternative technologies. (24) Existing MCDA evaluation tools (e.g., TechSelect 1.0 (13) and Technology Applicability Framework (19)) are limited in their ability to evaluate the opportunity space of new technologies as a result of the (ir)relevance of weighting scenarios included (arbitrary or literature-informed weights vs context-specific stakeholder-informed weights) and the lack of consideration for uncertainty in evaluating indicators. Stakeholder engagement during early-stage technology development can help inform implementation contexts; however, limitations in time and financial resources often lead to technology evaluation with a limited number of weighting scenarios based on researcher judgment or literature. (25−27) Additionally, existing MCDA evaluation tools often expect fixed inputs for quantitative indicators with limited flexibility for incorporating uncertainty of inputs. (28) Fixed inputs can be problematic for early-stage technologies with high levels of uncertainty (e.g., ranges of possible cost per capita and environmental impacts), (29) and technology evaluation with fixed inputs may not capture the entire opportunity space for new technologies.
The objectives of this work are (i) to synthesize a MCDA framework with well-defined, comprehensive indicators and context-specific weighting scenarios, (ii) to develop an open-source MCDA Python package (DMsan) that enables users (e.g., implementation decision makers and technology developers) to use this tool to transparently compare alternatives and characterize the opportunity space for technologies, and (iii) to illustrate the use of DMsan through decision making among three sanitation design configurations in a specific context. Articles centered on sanitation decision making and the assessment of two or more sanitation systems were reviewed to develop a MCDA framework with criteria and comprehensive indicators (that evaluate those criteria) commonly used in sanitation and resource recovery studies. The MCDA framework was used to develop the core structure of DMsan, which includes five criteria (technical, resource recovery, economic, environmental, and social), 28 indicators, criteria weight scenarios, and indicator weight scenarios tailored to 250 countries/territories, informed by contextual drivers. DMsan integrates with the open-source Python package QSDsan (29,30) (quantitative sustainable design for sanitation and resource recovery systems) for the quantification of resource recovery, economic (via TEA), and environmental (via LCA) indicators under uncertainty. The DMsan package can be modified to include or exclude features as needed for an analysis, enabling users to evaluate their technologies in any country/territory and with every criteria weight combination under uncertainty. Users can characterize the opportunity space for new technologies while also charting pathways to increase sustainability, the likelihood of the technology being selected, and how best to expand the contexts in which it could be appropriate. The core capabilities of DMsan are illustrated using an existing sanitation system and two proposed alternative systems for Bwaise, an informal settlement in Kampala, Uganda. (4) Two example use cases highlight the robustness of DMsan for sanitation research, development, and deployment (RD&D): (i) use by decision makers interested in enhancing decision-making transparency and understanding the robustness of sanitation choices with 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. These examples highlight novel capacity of DMsan to provide insight for sanitation decision making in individual contexts and to increase transparency in technology evaluations, RD&D, and deployment decision making.

2. Methods

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2.1. Formulation of Decision-Making Framework

2.1.1. Identification of Articles Focused on Decision Making in Resource-Limited Settings

To better understand the existing literature and techniques related to sanitation decision making, articles were gathered through Scopus using title, abstract, and keyword search terms that accounted for a variety of terms related to sanitation technologies (e.g., sanitation), resource-limited settings (e.g., low-income and marginalized), and decision making (e.g., decision making and multi-criteria; Table S2). The search was limited to papers published from 1960 through September 2020. The 1385 papers resulting from the initial search were screened for the inclusion criteria requiring that (i) they evaluated two or more sanitation technologies/systems, and (ii) the implementation context was in a resource-limited setting (Table S2). After screening, the remaining 35 papers were analyzed to summarize the decision-support techniques, criteria, and assumptions used in each paper, including the decision-support technique(s), stakeholder(s), criteria, sub-criteria, and indicators included in the analysis (Tables S3–S6, S19, S21, and S23). While we recognize that this focus on academic literature may not capture the full diversity of processes employed by decision makers overseeing the implementation of projects, it does serve as a starting point to ensure that the DMsan package has adequate flexibility to adapt to a wide range of end-user preferences.

2.1.2. Justification of Decision-Making Methodologies, Criteria, and Indicators Included in the Decision-Making Framework

The results of the literature review were used to inform the methodologies, criteria, and indicators used within the DMsan package. Decision-support techniques were identified for each paper, with some papers using multiple techniques. (13,27,28,31) Out of the 35 papers, MCDA was the most common decision-support technique (22 papers) (3,10,13,25−28,31−45) followed by LCA (seven papers). (12−14,27,28,31,46) Other techniques included cost–benefit analysis (CBA; four papers); (47−50) appropriateness assessment (AA; two papers); (51,52) strengths, weaknesses, opportunities, and threats (SWOT) analysis (one paper); (53) agent-based modeling (ABM; one paper); (54) probabilistic model (one paper); (55) and decision-support systems (one paper; (56) Figure 1A). Within MCDA, the most widely used ranking methodologies were the technique for order of preference by similarity (TOPSIS; nine papers) (13,25,27,28,31,32,38,40,41) and analytical hierarchy process (AHP; nine papers; (10,26,28,33,34,37,38,43,44) Figure 1B). Other MCDA methods observed outside of TOPSIS and AHP included elimination of choice translating reality (ELECTRE; three papers), (26,35,36) multi-attribute utility theory (one paper), (39) qualitative MCDA (one paper), (42) and choosing by advantage (one paper). (3) MCDA methods were often combined to increase the robustness of the decision making framework by leveraging the advantages of multiple approaches, such as AHP paired with TOPSIS (28,38) or AHP paired with ELECTRE. (26) Some researchers argue that TOPSIS is a superior method because it yields multiple quantitative outputs, (9,11,12) whereas AHP may be preferred because it generates criteria and indicator weights with consistent pair-wise comparisons (while TOPSIS requires subjective expert judgment for inputting weights). (10,33,34)

Figure 1

Figure 1. Literature review results used to develop the decision-making sanitation framework. (A) Summary of decision-support techniques in the peer-reviewed literature. The decision-support techniques most commonly used in sanitation decision making were multi-criteria decision analysis (MCDA) (22 of 35 total articles) and life cycle assessment (LCA) (7 of the 35 total articles). Other techniques included cost–benefit analysis (CBA); appropriateness assessment (AA); agent-based modeling; decision-support systems; probabilistic modeling; and strengths, weaknesses, opportunities, and threats (SWOT) analysis. (B) Breakdown of MCDA methods. The MCDA methods most used were the technique for order of preference by similarity to ideal solution (TOPSIS; 9 of 22 MCDA articles) and analytical hierarchy process (AHP; 9 of 22 MCDA articles).

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Through the literature review of commonly used criteria in sanitation and resource recovery decision making, four main criteria were identified: (i) technical or functional, relating to the system ability and function; (ii) environmental or ecological, representing the impacts a system inflicts on the environment; (iii) economic, associated with the economic costs of the system; and (iv) social or institutional, relating to technology adoption and social impacts (Table S3). Criteria and indicator definitions vary in literature, but for this work, criteria are defined as the main or principal decision-making categories and indicators are the specific aspects in which the technology alternative is assessed (Table S1). Environmental was the most widely used criterion, represented in 32 of the 35 papers. Within this criterion, common indicators used to assess the environmental impacts included energy consumption, air contamination, sludge production, water contamination, water consumption, odor, and eutrophication. The second most used criterion was economic (29 of 35 papers) that included operation and maintenance (O&M) costs, capital costs, and potential profit from resource recovery. The technical category was represented in 28 of 35 papers and included reliability, robustness, resiliency, complexity, resource recovery efficiency, land use, and flexibility of a system. Finally, the social and institutional criterion accounted for 26 of the 35 papers and included socio-cultural acceptability, job creation, and compatibility with local policy. Throughout the studies, resource recovery indicators were used to characterize technical, economic, environmental, and social criteria (e.g., quantity of resources, value of resources, and willingness to use resources). When developing the MCDA framework, resource recovery cost and environmental impact offsets (due to fertilizer and biogas production) were incorporated into the economic and environmental indicator calculations. However, the quantity of recoverable resources is important for contexts with limited access to resources or high resource users (e.g., significant agriculture producers); therefore, resource recovery was incorporated as a fifth criterion to capture quantity of resources recovered.
Although stakeholder engagement is important for successful deployment and sustainability of sanitation systems, stakeholder engagement with multiple stakeholder types was rare in the existing sanitation decision-making studies. Across the 35 publications examined in the literature review, only 22 publications mentioned stakeholder input. Stakeholder types included in the decision-making process were water and sanitation professionals (12 papers), end-users (10 papers), government agencies (six papers), researchers (six papers), farmers (four papers), pit latrine emptiers (one paper), and landlords (one paper), along with five papers that used non-specific “experts” to define a stakeholder (undefined; Figure 2). Papers varied from including one to four types of stakeholders. Incorporating feedback from multiple stakeholder types, depending on the decision being made, can greatly benefit decision making; however, time and resources often prevent significant stakeholder involvement. In particular, active stakeholder involvement in scenario modeling can support the setting of criterion and indicator weights based on community preferences. Decision makers and technology developers with access to stakeholder feedback can input stakeholder-informed criterion and indicator weights. Additionally, to enable decision makers and technology developers to generate insight despite limited access to stakeholder groups, DMsan was designed to run simulations across a complete spectrum of criteria and indicator weightings to identify contexts and stakeholder preferences that lead to the selection of a given technology to advance sustainable sanitation locally and globally.

Figure 2

Figure 2. Summary of literature review of articles focused on decision making in resource-limited settings highlighting stakeholders involved in sanitation decision making. Each publication was characterized based on stakeholder engagement and the types of stakeholders engaged. Community stakeholder types include end-users (i.e., toilet users), farmers, and landlords. Expert stakeholder types include pit latrine emptiers, water and sanitation employees, government agencies, and researchers. The 14 unique stakeholder combinations were identified in literature (columns labeled A–N): A, (52) B, (36) C, (28,35,42,50) D, (56) E, (57) F, (53) G, (39) H, (51) I, (38) J, (45) K, (10,37,44) L, (13,33,43) M, (26,27) and N. (46) For example, one paper included stakeholders defined in combination A (end-users, farmers, pit latrine emptiers, and water and sanitation employees), and four papers included stakeholders defined in combination C (water and sanitation employees, government agencies, and researchers).

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2.2. DMsan MCDA Framework and Package Structure

2.2.1. DMsan Overview

Ultimately, the results of the literature review were used to inform the default MCDA methodologies and criteria included within the DMsan package: AHP paired with TOPSIS to evaluate the tradeoffs of five criteria (technical, environmental, resource recovery, economic, and social) characterized with 28 indicators (Table 1). DMsan was structured following the four key steps in MCDA: (i) selecting criteria and indicators, (ii) assigning weights to the criteria and indicators, (iii) determining indicator scores, and (iv) calculating performance scores of each alternative (Figure 3). To facilitate the execution of these steps, a Python (3.8+) package was developed, which contains generic algorithms and a built-in contextual driver database for rapid and flexible evaluation of the alternatives under uncertainty. Users can select criteria and indicators to include in an analysis from among the criteria and indicators included in the core structure or add new criteria and indicators as necessary. Criteria and indicators can be weighted using stakeholder informed weights, built-in criteria weight scenarios and a country/territory-specific contextual drivers database, or assumed weights. DMsan was constructed to enable integration with existing Python packages (e.g., QSDsan (29,30)) to simulate the quantitative indicator scores (for economics, environmental impacts, and resource recovery) of the system configurations with manual scoring for qualitative indicators (Section S5). QSDsan is an integrative platform for sanitation and resource recovery system design, simulation, techno-economic analysis, and life cycle assessment. QSDsan specifically can be used to calculate quantitative indicators, while DMsan is used to evaluate conflicting tradeoffs related to the alternatives’ indicator scores using MCDA. Sanitation decision makers and technology developers can access existing sanitation unit process models available on the public GitHub repository, and they can follow published tutorials to build new sanitation and resource recovery unit processes for use in DMsan. Alternatively, DMsan users can also manually input quantitative indicator score data without simulation in QSDsan if process modeling is unnecessary or data availability limits modeling capabilities. Overall performance of each alternative can be calculated via TOPSIS simulation default to DMsan. Source codes of the DMsan package are available as a public repository on GitHub, (58) and the package has been released on the Python package index (PyPI) repository. (59)

Figure 3

Figure 3. Overview of the MCDA methodology implemented in DMsan. The first step requires the user to select the relevant criteria and indicators to evaluate the sanitation alternatives for a specific context. Next, indicator weights are calculated using indicator contextual drivers for the country/territory through the AHP method, and criteria weights are either determined by community stakeholder preferences or generated by DMsan for the desired number of weight scenarios using Latin hypercube sampling. After the weights are established, indicator scores are assigned for each alternative: qualitative indicators are scored using predefined ranges (Section S2) and quantitative indicators are calculated through system simulation, TEA, and LCA in QSDsan. Finally, TOPSIS is used to calculate alternative rankings and performance scores to determine the best performing alternative for each criteria weight scenario.

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Table 1. Summary of Criteria, Sub-Criteria, Indicators, and Indicator Contextual Drivers Included in the DMsan Packageb
criteriasub-criteriaindicatorindicator score type used in illustrative exampleacontextual driver to determine indicator weight
technicalresiliency and robustnessuser interface robustnessqualitativeextent of training (60)
resiliency of treatment typequalitativepopulation without at least basic sanitation (1)
feasibilityaccessibility to partsqualitativetechnology absorption (60)
transportation feasibilityqualitativequality of roads (60)
construction skills requiredqualitativeconstruction skills available (61)
operation and maintenance skills requiredqualitativeprofessional skills available (60)
flexibilitypopulation flexibilityqualitativepopulation growth rate (62)
power outage flexibilityqualitativeelectricity coverage (62)
drought flexibilityqualitativebaseline water stress (63)
resource recoveryresource recovery feasibilitywater recovery*no indicator scorebaseline water stress (63)
nitrogen recoveryquantitativenitrogen fertilizer fulfillment (64,65)
phosphorous recoveryquantitativephosphorous fertilizer fulfillment (64,65)
potassium recoveryquantitativepotassium fertilizer fulfillment (64,65)
energy recoveryquantitativerenewable energy consumption (62)
supply chain feasibilityqualitativeinfrastructure quality (60)
environmentallife cycle environmental impactsdamage to ecosystemsquantitativeindicator weights are distributed equally across LCA categories (66)
damage to human healthquantitative
damage to resourcesquantitative
economicnet life cycle costsannual cost per capitaquantitativeno indicator weight
socialjob creationtotal jobs createdquantitativeunemployment rate (62)
high-paying jobs createdquantitativeinternational poverty line (61)
end-user acceptabilitydisposal frequencyquantitativedetermined by end-user community survey (4)
cleaning requirementqualitative
privacyquantitative
odor and fliesqualitative
securityquantitative
property manager acceptabilitydisposal frequency*no indicator scoredetermined by property manager community survey (4)
cleaning requirement*no indicator score
a

The indicator types shown in the table describe the designations used in the illustrative example of this manuscript. It should be noted that decision makers and technology developers using DMsan have complete flexibility to choose how to quantify indicators (quantitative vs qualitative) to best suit the goals and data availability for their analysis.

b

Sub-criteria were developed for the conceptual organization of criteria into indicators and are not assigned scores or weights. Qualitative and quantitative indicator scores (highlighted in the column “indicator”) are assigned and calculated for each sanitation and resource recovery system alternative (Section S2). Indicator contextual drivers are used to calculate indicator weights within a criterion (highlighted in the column “contextual driver to determine indicator weight”; Section S3). Indicators with an asterisk (*) were excluded in the illustrative example because property managers were not responsible for system disposal and cleaning efforts, and none of the systems incorporated water recovery.

2.2.2. Criteria and Indicator Selection

Informed by the literature review, DMsan includes five criteria commonly used in sanitation decision making to evaluate the capability and sustainability of sanitation and resource recovery systems. The criteria include technical (i.e., the engineering design requirements), resource recovery (i.e., the ability of the system to recover nutrient, energy, and water resources for reuse), environmental (i.e., the life cycle environmental impacts), economic (i.e., the life cycle costs per capita), and social (i.e., the acceptability for the system users and operators; Table 1). Each criterion is divided into one or more sub-criteria to further describe its contribution to decision making: resiliency and robustness, feasibility, and flexibility (technical); resource recovery feasibility (resource recovery); life cycle environmental impacts (environmental); net life cycle costs (economic); and job creation, end-user acceptability, and property manager acceptability (social). It should be noted that sub-criteria were developed for conceptual organization of the criteria and indicators and do not have sub-criteria scores or weights assigned. The sub-criteria are matched with 28 qualitative and quantitative indicators to represent the capability of each system by rating on a predefined scale or quantitative calculations (Section S2). In evaluation, DMsan users can choose to include all or any combination of the five criteria and 28 indicators available in the package or to add and/or modify criteria or indicators as desired. This flexibility is important because it can accommodate decision-making methods and indicators that were not presented in peer-reviewed papers (e.g., institutional reports) or that are specific to a certain community.

2.2.3. Criteria and Indicator Weight Assignment

The second step is to assign criteria and indicator weights (Figure 3). Weighting within DMsan is conducted in two phases. Criterion weight (26) is assigned to each criterion and represents the relative importance of that criterion in the decision-making process, and indicator weight (26) is assigned to each indicator and represents the importance of that indicator, within its criterion, in a given context. Criterion weights are used to reflect stakeholder preference, and users of DMsan can manually input a weight for each criterion (technical, resource recovery, environmental, economic, and social) when community preference data is available or if they wish to evaluate a specific combination of criterion weights. In lieu of such data, users can specify the desired number of criteria weight scenarios (e.g., 1000) and DMsan will generate a set of criterion weights using Latin hypercube sampling (Figure S1) to evaluate the entire spectrum of weight options for each criterion (criterion weight ranges from 0 to 1).
Indicator weights are used to rate the relative importance of each indicator’s contribution to a system’s overall criterion score for each of the five criteria (Section S3). Weights can be determined by using (i) relevant stakeholder surveys employing AHP, (ii) an embedded database that estimates the relative importance of each indicator in a given country/territory, or (iii) assumptions when data is unavailable for the basis of indicator weights (e.g., assumed equal distribution of weights for environmental indicators).
AHP was selected to calculate indicator weights, informed by community and stakeholder surveys, due to its use in multiple fields of environmental science (18) and its ability to calculate weights based on simple pair-wise comparisons. Community and stakeholder surveys can be used to collect data on pair-wise preference between indicators (e.g., indicator A is moderately more important than indicator B). In the absence of data on stakeholder’s pair-wise comparisons between indicators, DMsan users can utilize a database of country/territory-specific preferences (indicator contextual drivers) to inform pair-wise preferences. It is assumed that the relative importance of a contextual driver (on a scale of 0–100) for a country/territory is equivalent to the relative importance of its indicator. A technical indicator example was technology absorption (i.e., a country’s absorption of the latest, most novel technology), which was selected as the basis for the indicator weight for accessibility to parts. Countries with higher levels of advanced technology could have better access to custom parts required for novel and advanced systems─resulting in a low importance of accessibility to parts because all parts are viewed as accessible. As a resource recovery indicator example, the indicator weight for nitrogen recovery is calculated using the need for additional nitrogen fertilizer in the country calculated as the ratio between the mass of nitrogen fertilizer used by the country and the recommended mass of nitrogen fertilizers for a country’s crop production. Countries with ratios closer to 0 indicate a clear need for nitrogen-based fertilizers (resulting in a higher indicator weight for nitrogen recovery), which could be met through production of human excreta-derived fertilizers. The database consists of compiled data produced by the World Economic Forum, (60) the World Health Organization and United Nations Children’s Fund, (1) the International Labour Organization, (61) the World Bank, (62) the World Resources Institute, (63) and the Food and Agriculture Organization of the United Nations. (65) The database does not contain contextual driver data for environmental, economic, or end-user/property manager acceptability indicators. Users can conduct community surveys or make assumptions when data is unavailable.

2.2.4. Indicator Score Determination

Quantitative resource recovery, environmental, and economic indicator scores can be determined by conducting system simulations, TEA, and LCA in QSDsan (29,30) or by manually entering scores calculated outside of QSDsan. When using QSDsan, the quantities of recoverable nitrogen, phosphorus, potassium, and energy are calculated based on country-specific dietary intake parameters, expected nutrient and carbon excreted, (64,67,68) and technology-specific nutrient and energy recovery efficiencies. (64,67,68) The quantity of recoverable water is calculated as the volume of treated water that can be used for potable or non-potable uses. TEA is used to calculate the annual cost per capita for each sanitation system alternative. LCA calculations use the life cycle impact assessment method ReCiPe to calculate three endpoint environmental indicators (damage to human health, damage to ecosystems, and damage to resource availability) with options to select individualist, hierarchist (default method), and egalitarian cultural perspectives for the calculation. (66) Both TEA and LCA calculations incorporate context-specific input parameters (e.g., material costs, electricity prices, and electricity source) to quantify location-specific costs and environmental impacts. Monte Carlo analysis with Latin hypercube sampling (69) is used to evaluate the impact of uncertainty in modeling inputs on quantitative indicator scores (i.e., annual cost per capita, quantity of recovered resources, environmental impact indicators, and job creation; Section S5). Qualitative indicator scores related to the technical, resource recovery (i.e., supply chain feasibility), and social criteria (i.e., end-user and property manager acceptability indicators) are assigned using predefined score ranges (e.g., user interface robustness score ranges from 1 to 5 depending on the complexity of the toilet; Table S7). Because systems are a combination of several sanitation unit processes, the system scores─using predefined score ranges─are assumed to be the score of the worst performing unit process within the system (e.g., a system composed of both anaerobic and chemical/thermal treatment would receive a resiliency of treatment type score that of anaerobic treatment, which had higher maintenance needs; Table S8).

2.2.5. Performance Score Calculation

Sanitation system performance scores indicate the ability of a specific sanitation system to outrank the other alternatives (i.e., a higher score indicates better system performance). Because TOPSIS has a stronger mathematical foundation for quantitatively viewing tradeoffs among criteria compared to other ranking methodologies, (27,31,70) it was selected to calculate performance scores and ranks in DMsan using the criteria weights, indicator weights, and indicator scores (Section S4).

2.3. Illustrative Applications of DMsan for Decision Makers and Technology Developers

2.3.1. Bwaise, Uganda Context, and Sanitation System Alternatives

To illustrate the utility of DMsan, we evaluated sanitation and resource recovery systems for Bwaise, an informal settlement in Kampala, Uganda, described by Trimmer et al. (4) Bwaise is located in northern Kampala and is rapidly growing with over 100,000 people. Although sanitation is reported as a high development priority among residents, systems typically fail due to limited stakeholder participation. (9)
Three sanitation system alternatives were evaluated based on the systems described by Trimmer et al. (4) All alternatives incorporated a user interface, storage, conveyance, centralized treatment, and recovery of nutrients and/or biogas (Figure 4). The first alternative (Alternative A) is the existing sanitation system incorporating pit latrines, vacuum collection trucks, centralized treatment (sedimentation, solids drying beds, and lagoons), and recovery of nutrients for fertilizer (dried solids and nutrient-rich liquid effluent). Alternative B replaces the existing centralized treatment with an anaerobic baffled reactor, solids drying beds, and an additional planted bed for liquid treatment with solid and liquid nutrients recovered for land application and biogas for cooking fuel. Alternative C replaces the existing pit latrines with container-based urine-diverting dry toilets that use urine handcart and solids truck transportation to bring resources to the centralized treatment facility described in Alternative A, excluding sedimentation, as liquids and solids are already separated. Alternative C increases the nutrient recovery potential (relative to Alternatives A and B) through source separation.

Figure 4

Figure 4. Overview of the Bwaise, Uganda sanitation system alternatives as described by Trimmer et al. (4) Each alternative incorporates a user interface with onsite storage, conveyance, centralized treatment, and recovery and reuse. Alternative A is the existing system, whereas Alternative B replaces the centralized treatment system, and Alternative C leverages source separation and alternative conveyance. All three alternatives are described in more detail in the text.

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All five criteria and 26 of the 28 indicators were used in this illustrative analysis. The two excluded indicators were related to property manager acceptability (disposal frequency and cleaning requirement); they were excluded because they were not applicable since property managers were not responsible for system disposal and cleaning efforts. The two indicators related to disposal frequency and cleaning requirement by end-users were maintained to account for end-user responsibilities. Each alternative was simulated using the module developed in QSDsan (29,30) to calculate quantitative scores for resource recovery (quantity of recovered nitrogen, phosphorus, potassium, energy, and water), environmental (damage to human health, damage to ecosystems, and damage to resource availability), and economic (annual cost per capita) indicators (Section S5). Annual user cost and life cycle environmental impact estimates incorporated construction and operation of onsite and centralized facilities, conveyance, direct emissions from excreta degradation, and income and environmental impact offsets from recovered products. (4) Monte Carlo analysis with Latin hypercube sampling (1000 simulations) was used to account for possible variations in the QSDsan input parameters. Scores for qualitative technical, resource recovery, and social indicators were assigned based on each alternative’s capability on a predefined scale (Section S2).
To avoid constraining insight to a limited set of stakeholder-informed criteria weights, 1000 criteria weight scenarios were generated using DMsan’s embedded algorithms to represent the entire landscape of stakeholder preferences (Figure S1). Indicator weighting incorporated end-user community surveys, Uganda-specific contextual drivers found in the country/territory-specific database, and assumptions. Technical, resource recovery, and job creation social indicators were weighted using the values for Uganda in the country/territory-specific contextual driver database. The end-user community survey results presented by Trimmer et al. (4) were used to calculate the indicator weights related to social end-user acceptability (Section S2.5.2). Uniform weights were used for environmental indicators (i.e., 1/3 weight for each of the three indicators), and economic does not need an indicator weight because it only has one indicator (annual cost per capita). All codes used in the illustration are publicly available on GitHub. (59)

2.3.2. Use of DMsan by Decision Makers: Navigating the Choice among Alternatives

DMsan can be used by a wide range of stakeholders interested in assessing the sustainability and performance of sanitation and resource recovery system alternatives. The first illustration of the use of DMsan focuses on the implementation decision makers (e.g., sanitation engineers, urban planners, and utility staff) selecting a sanitation system among alternatives. The illustration highlights the utility of DMsan using a built-in indicator and criteria weight scenarios in the absence of community-informed preferences. MCDA results are presented for each alternative as the “probability of having the highest performance score (i.e., winning)” and the “scenarios in which the alternative has the highest winning probability”. For each criteria weight scenario, the probability of an alternative winning was calculated (the probability was based on the uncertainty in QSDsan inputs) as the ratio between the total number of times it has the highest performance score among all alternatives and the total number of indicator uncertainty simulations (1000). If an alternative has a higher winning probability than other alternatives (even if it is only by 1%) in a specific criteria weight scenario, then the criteria weight scenario will be included in an alternative’s opportunity space. Decision makers can use these results to navigate the choice among alternatives under varied stakeholder priorities and understand how criteria importance influences the selection.

2.3.3. Use of DMsan by Technology Developers: Expanding the Opportunity Space of Select Alternatives

The second illustration of the use of DMsan focuses on the technology developers interested in expanding the opportunity space of their technology. Technology developers can use DMsan to explore how improvements to specific indicators (e.g., decrease annual cost per capita and increase energy recovery) can impact a system’s sustainability and opportunity space. Technology developers can identify which indicators can be reasonably improved. In this illustration, indicator scores were modified from the baseline value to the theoretical best score, and technology developers can observe how specific indicator improvements impact the percent of criteria weight scenarios that their technology has the highest performance score compared to the baseline opportunity space. This illustration also provides a means for the technology developers to identify a path forward for technology development by evaluating the effect of simultaneous indicator improvements.

3. Results of the Illustrative Examples and Discussion

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3.1. Insight for Decision Makers: Understanding Stakeholder Influence on MCDA Outcomes

From the sanitation decision maker perspective, the results of DMsan can be used to identify the best alternative for implementation contexts with specific community preferences (i.e., criteria weight scenarios). For example, Alternative A (existing system) would be selected in 77 of the 1000 criteria weight scenarios, Alternative B (biogas recovery system) would be selected in 922 out of 1000 scenarios, and Alternative C (source separation system) would be selected in 1 of the 1000 scenarios (Figure 5). Although Alternative B outperforms Alternatives A and C in 92.2% of the criteria weight scenarios, each alternative still has an opportunity to be selected for implementation in the community, depending on the relative importance of the five criteria as perceived by community members and stakeholders during decision making. Decision makers can use these results to understand what community preferences lead to a particular technology’s selection and identify communities best suited for implementation based on the importance placed on decision-making criteria. For example, Alternative A is likely to be selected in communities that place the highest importance on the technical criterion (with technical criterion weights between 0.6 and 1.0; Figure 5A,D), while Alternative B is likely to be selected almost all criteria weight scenarios when the technical criterion is weighted less than 0.6. This finding stems from the fact that Alternative A outperforms or matches the capability of Alternative B in all technical indicators. It should be noted that Alternative C outperforms or meets the performance of Alternative A in nine of the nine technical indicators, but the relative importance of each indicator weight led to Alternative A being the best within the technical criterion. For example, Alternative C outperforms Alternative A in flexibility to power outages, but the indicator only contributes 2% to the overall technical criterion for decision making due to the infrequent nature of power outages in Uganda compared to the global maximum. Sanitation decision makers may find Alternative A sustainable when technical factors (i.e., simple user interface, minimal construction, and maintenance skills required) are highly favored within the community.

Figure 5

Figure 5. Evaluation of alternatives under varied criteria weight scenarios for decision making. (A–C) Probability of having the highest performance score among alternatives (0–100%). Each line represents a single criteria weight scenario, with darker lines indicating higher probabilities. (D–F) Criteria weight scenarios in which an alternative has the highest winning probability across the three alternatives. A line for a particular criteria weight scenario is present if the alternative is the best performing alternative. Among 1000 criteria weight scenarios, Alternative A outperforms B and C in 77 scenarios (shown in panel D), Alternative B outperforms A and C in 922 scenarios (shown in panel E), and Alternative C outperforms A and B in one scenario (shown in panel F). Alternative A is the best performing system when stakeholders place high importance on technical ability (criterion weight ∼0.6 to 1.0). The probability of the highest performance score increases for Alternative A as the technical criterion weight approaches 1. Alternative B has the highest performance score in criteria weight scenarios with high criterion weights for resource recovery, environmental, economic, or social or when criteria weights are evenly distributed. Alternative C is unlikely to be selected as the best performing alternative without improvements across indicators.

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The opportunity space for Alternative B is expansive as it scores well in recovering energy and nutrients, produces lower environmental impacts, and generates high economic returns from biogas recovery (Figure 5E). It also fosters end-user acceptability with the simple pour-flush toilet design to minimize odors, cleaning, and maintenance while also creating jobs for the community. However, Alternative B does not outrank other alternatives when technical criteria are favored with weights higher than 0.6. As the technology criterion weight approaches 0.6, the probability of Alternative B having the highest performance score decreases and eventually approaches zero because it is outperformed across technical indicators (Figure 5B). This system may not be an appropriate alternative for communities that value accessible parts, simple construction skills, and minimum operation and maintenance skills. Sanitation decision makers should target implementation of Alternative B in communities that value resource recovery (i.e., phosphorus, potassium, and energy), environmental, economic, and/or social factors.
Alternative C has the highest likelihood of outperforming Alternatives A and B in one criteria weight scenario where community members have some preference toward technical and resource recovery capabilities (Figure 5F). When considering the uncertainty in indicator scores, Alternative C can outperform Alternative B when environmental indicators are valued as Alternative C has lower damage to ecosystems and human health than Alternative B (Figure 5C). In the case of selecting one technology over another, sanitation decision makers should also consider the magnitude of differences in the simulated performance scores among the alternatives to determine if the differences are enough to choose one alternative over the others. Overall, these results can guide decision makers in selecting the most appropriate alternative for communities with varied decision-making preferences.

3.2. Insight for Technology Developers: Expanding the Opportunity Space through Indicator Improvements

DMsan can also be used by technology developers to identify indicator improvement opportunities that lead to the technology’s selection in the criteria weight scenarios in which it was initially outperformed by other alternatives. Since Alternative B outperforms the others in 92.2% of the 1000 criteria weight scenarios, Alternatives A and C were considered in the indicator improvement analysis. Indicators were included in the analysis if they reasonably could be improved without requiring a significant technology redesign. For example, the indicator resiliency of treatment type was excluded from the analysis because improvements to that indicator require a completely different treatment type category for the alternative (e.g., moving from an anaerobic to a chemical treatment system); however, the indicator annual cost per capita was included because technology developers could investigate ways to reduce costs without significantly changing the design of the system. As a result, of the 26 indicators analyzed, 13 indicators were selected for the indicator improvement analysis (i.e., three technical, three resource recovery, three environmental, one economic, and three social; Figure 6). Each indicator score was modified from the baseline value (i.e., median value in the indicator score uncertainty analysis) to the theoretical best score. For quantitative indicators, the theoretical best score was set to 10% better than the median indicator score of the best scoring alternative for the indicator (e.g., the theoretical best score for annual cost per capita was set to 6.60 USD·capita–1·year–1, which was 10% lower than Alternative B’s median of 7.34 USD·capita–1·year–1 annual cost per capita). This ensured that the improved alternative could outperform the other alternatives for each quantitative indicator. For qualitative indicators, the theoretical best score was set to be the top score identified in the predefined score ranges (e.g., if scored on a 1–5 scale, then the top score is 5; Section S2). Although some indicators may be more difficult to improve than others, understanding which indicators drive increased selection of an alternative and how far an indicator must be improved to reach a desired percent of criteria weight scenarios can aid technology development.

Figure 6

Figure 6. Indicator improvements to increase opportunities for selection of (A, C) Alternative A and (B, D) Alternative C. (A, B) Individual impact of indicator improvements on the percent of criteria weight scenarios for which Alternatives A and C have the highest performance score. Overall, 13 indicators were individually improved to the best indicator score to evaluate its individual impact on the alternative’s performance. Economic and environmental indicators were set to 10%, better than the best indicator score among the three alternatives (e.g., the improved economic score was 6.60, which is 10% lower than the least expensive alternative, Alternative B). Indicators were included in the analysis if they could reasonably be improved without requiring a complete design change (e.g., adding jobs does not require inherent changes to the technology, while energy recovery requires significant design changes to Alternatives A and C). (C, D) Combined impact of the top two indicator improvements on the percent of criteria weight scenarios for which Alternatives A and C have the highest performance score. Altogether, technology developers can use these results to prioritize research and development to achieve indicator improvements that would expand their technology’s opportunity space.

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At its baseline, Alternative A outperformed Alternatives B and C in 7.3% of the criteria weight scenarios. Increasing the quantity of high-paying jobs from 0 to 12 and decreasing the annual cost per capita from 14.23 to 6.60 USD·capita–1·year–1 resulted in the greatest impact on performance across criteria weight scenarios, which increased Alternative A’s opportunity space from 7.3% of criteria weight scenarios to 17.7 and 12.6%, respectively (Figure 6A). The remaining 11 indicator improvements lead to a marginal impact on the percent of criteria weight scenarios where Alternative A has the highest performance score (7.3–7.9% criteria weight scenarios). For Alternative C, indicator improvements with the most impact on its baseline performance were decreasing the damage to resources from 0.50 to −0.52 points capita–1·year–1 and decreasing the annual cost per capita from 14.23 to 6.60 USD·capita–1·year–1 (Figure 6B). These two improvements resulted in a notable increase in performance of Alternative C across the criteria weight scenarios (from 0.1 to 10.8 and 6.9%, respectively). Of the remaining 11 indicators, improving population flexibility resulted in increased performance for Alternative C (0.2%), while changes to the other 10 did not affect performance. Technology developers can use these results to develop a path forward for improving indicators that limit the implementation of their system. It is important to note that technology developers will need to know which changes are realistic for the alternative to maximize the value of the test to inform research and development.
While analyzing individual impacts can help to understand how indicators can improve an alternative’s performance, the effect could be more significant if multiple indicators are improved at the same time. The effect of changing a given alternative’s top two indicators simultaneously was evaluated for both Alternative A (high-paying jobs and annual cost per capita) and Alternative C (damage to resources and annual cost per capita). Annual cost per capita was varied from 0 to 24.65 USD·capita–1·year–1 (10% higher than the highest annual cost per capita among the three alternatives), high-paying jobs was varied from 0 to 14, and damage to resources was varied from −0.52 to 0.56 points·capita–1·year–1 (+/–10% of the best and worst scores among the three alternatives). When evaluating the spectrum of indicator improvements, technology developers can see how far an indicator must be improved to reach a desired percent of criteria weight scenarios that the alternative has the highest performance score. For example, in the case of Alternative A, decreasing the annual cost per capita to 14.23 USD·capita–1·year–1 and increasing the number of high-paying jobs to 12 can increase the opportunity space of Alternative A from 7.3 to 29% of criteria weight scenarios (Figure 6C). It should be noted that increasing the number of high-paying jobs will increase the annual cost per capita, but these indicators were varied independently for this illustrative analysis in Figure 6C. In reality, however, under the study’s set of assumptions, each additional high-paying job increases the annual cost per capita by 0.03 USD·capita–1·year–1 and by 0.41 USD·capita–1·year–1 for 12 additional jobs. Likewise, the impact of combining reductions in annual cost per capita with reductions in damage to resources was evaluated for Alternative C (Figure 6D). At its baseline, Alternative C outperforms Alternatives A and B in one criteria weight scenario (0.1% of scenarios), but with combined indicator scores identified in the indicator improvement analysis, it can increase performance to be selected in 31.1% of the criteria weight scenarios. To reduce the damage to resources (calculated via LCA), technology developers can identify the features of the technology driving environmental damage (e.g., electricity requirements and materials) and evaluate strategies to mitigate impacts such as transitioning to renewable energy sources, using more sustainable materials, or by increasing quantity of resources recovered (thereby increasing recovered resource offsets). Although not included in DMsan outputs, individual impact indicator values for a given LCIA method as well as the complete breakdown of LCA impacts (and costs and treatment performance) by the sanitation unit process or life cycle stage can be observed via QSDsan simulations of the alternatives. If technology developers are unable to reduce damage to resources, then cost reductions could still be a pathway to increase the selection of Alternative C. Technology developers could seek out grants and subsidies to offset the annual cost per capita further. Identification of indicator improvements to increase an alternative’s selection is only the first step toward implementing improvements. Although outside of the scope of the results presented in this study, technology developers should conduct further analyses to identify the pathway to implement the desired improvement (e.g., from minor technology design modifications up to significant policy changes). Overall, technology developers can use DMsan to identify the indicators driving their system’s performance and investigate how any changes in indicator scores may affect their technology’s opportunity space for implementation.

3.3. Implications of MCDA Framework and DMsan for RD&D of Sanitation and Resource Recovery Technologies

In this work, an MCDA framework was synthesized with well-defined indicators and criteria commonly used by sanitation and resource recovery decision makers. The framework incorporates robust decision-support techniques (e.g., LCA and TEA) to evaluate the performance of a portfolio of technologies and defines contextual drivers that can be used to calculate indicator weights (as a starting point) in the absence of stakeholder input. Incorporating the MCDA framework, DMsan was developed as an open-source package that enables users to transparently compare sanitation and resource recovery alternatives and evaluate the opportunity space for technologies. This package can help expedite the research and development of technologies by incorporating a comprehensive assessment of their performance related to technical, resource recovery, environmental, economic, and social drivers of decision making. DMsan is the first step in assisting technology developers that lack the resources (e.g., time, access to diverse groups of stakeholders, availability of high-resolution temporal and spatial data, and travel to deployment sites) during the technology’s research and development stage. DMsan is designed to easily integrate with other open-source packages (e.g., QSDsan (30)) and allows developers to mix and match technologies or unit operations in the sanitation resource chain (i.e., user interface, storage, conveyance, treatment, and distribution of resources) and seamlessly add or eliminate decision-making criteria and indicators as needed. Ultimately, technology developers can use the package to model stakeholder preferences and target deployment in contexts with similar stakeholder preferences and contextual drivers that lead to their technology outperforming other alternatives. DMsan can be used to assess preliminary sustainability for alternatives with built-in criteria weight scenarios.
For sanitation decision making focused on deployment, localized stakeholder preferences should be included in the package from multiple community and expert groups relevant to the decision (water and sanitation professionals, end-users, government agencies, farmers, etc.). Because the indicators included in the package were selected based on published peer-reviewed literature, DMsan users should consider surveying sanitation and resource recovery decision makers and practitioners in settings relevant to their work, which could help identify and incorporate useful indicators that were not included in traditional academic distribution channels (e.g., community-specific indicators related to governance and institutions).
In its current state, DMsan is ready to be used by decision makers and technology developers familiar with quantitative sustainable design (24) and the programming language Python. However, future work is needed to develop a graphical user interface that eliminates the need for end-users to develop codes; this additional feature could expand the user base by making it more accessible to non-technical community decision makers. Future DMsan users should be engaged during the user interface development process to ensure successful, sustained use. Overall, DMsan overcomes the challenges presented in existing MCDA tools that prevent the evaluation of the opportunity space of new technologies. DMsan accommodates multiple criterion and indicator weighting methods (e.g., leveraging a database for contextual drivers) compared to existing tools that include limited weighting scenarios that constrain insight (e.g., customary or literature-informed weights vs context-specific stakeholder-informed weights). Additionally, instead of using fixed inputs for quantitative indicators, DMsan incorporates a robust uncertainty analysis workflow that allows all feasible indicator values to be used in decision making to develop an opportunity space of potential decisions instead of a single result. Ultimately, DMsan provides the field of sanitation and resource recovery a valuable decision-making tool to evaluate a technology’s opportunity space with variable stakeholder input to guide deployment and increase access to and the sustainability of sanitation.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsenvironau.2c00067.

  • Terminology definitions; literature review of decision-making methods; determination of indicator weights; contextual drivers; description and scoring of indicators; multi-criteria decision analysis methods for ranking alternatives; and system simulation parameter values, ranges, and distributions (PDF)

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Author Information

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  • Corresponding Author
    • Jeremy S. Guest - Department of Civil and Environmental Engineering, 3221 Newmark Civil Engineering Laboratory, University of Illinois Urbana-Champaign, 205 N. Mathews Avenue, Urbana, Illinois 61801, United States;  Institute for Sustainability, Energy, and Environment, University of Illinois Urbana-Champaign, 1101 W. Peabody Drive, Urbana, Illinois 61801, United States;  DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, 1206 W. Gregory Drive, Urbana, Illinois 61801, United States;  Orcidhttps://orcid.org/0000-0003-2489-2579;  Email: [email protected]
  • Authors
    • Hannah A. C. Lohman - Department of Civil and Environmental Engineering, 3221 Newmark Civil Engineering Laboratory, University of Illinois Urbana-Champaign, 205 N. Mathews Avenue, Urbana, Illinois 61801, United States;  Orcidhttps://orcid.org/0000-0001-8600-7235
    • Victoria L. Morgan - Institute for Sustainability, Energy, and Environment, University of Illinois Urbana-Champaign, 1101 W. Peabody Drive, Urbana, Illinois 61801, United States;  Present Address: Hazen and Sawyer, 2420 Lakemont Avenue, Suite 325, Orlando, Florida 32814, United States (V.L.M.);  Orcidhttps://orcid.org/0000-0002-0350-0751
    • Yalin Li - Institute for Sustainability, Energy, and Environment, University of Illinois Urbana-Champaign, 1101 W. Peabody Drive, Urbana, Illinois 61801, United States;  DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, 1206 W. Gregory Drive, Urbana, Illinois 61801, United States;  Orcidhttps://orcid.org/0000-0002-8863-4758
    • Xinyi Zhang - Department of Civil and Environmental Engineering, 3221 Newmark Civil Engineering Laboratory, University of Illinois Urbana-Champaign, 205 N. Mathews Avenue, Urbana, Illinois 61801, United States;  Orcidhttps://orcid.org/0000-0002-5858-9511
    • Lewis S. Rowles - Institute for Sustainability, Energy, and Environment, University of Illinois Urbana-Champaign, 1101 W. Peabody Drive, Urbana, Illinois 61801, United States;  Present Address: Department of Civil Engineering and Construction, Georgia Southern University, 201 COBA Drive, BLDG 232, Statesboro, Georgia 30458, United States (L.S.R.);  Orcidhttps://orcid.org/0000-0002-3489-9179
    • Sherri M. Cook - Department of Civil, Environmental, and Architectural Engineering, University of Colorado Boulder, 1111 Engineering Drive, Boulder, Colorado 80309, United States;  Orcidhttps://orcid.org/0000-0002-7648-4596
  • Author Contributions

    H.A.C.L. and V.L.M. contributed equally to this work.

  • Notes
    The authors declare no competing financial interest.

Acknowledgments

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This publication is based on research funded in part by the Bill & Melinda Gates Foundation. The findings and conclusions contained within are those of the authors and do not necessarily reflect positions or policies of the Bill & Melinda Gates Foundation. We would like to thank Lara Iriarte, Emily Lin, and Katy Solak (University of Illinois Urbana-Champaign) for their contributions to the decision-making literature review.

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    The use of qualitative comparative analysis to identify pathways to successful and failed sanitation systems
    Davis, Allie; Javernick-Will, Amy; Cook, Sherri M.
    Science of the Total Environment (2019), 663 (), 507-517CODEN: STENDL; ISSN:0048-9697. (Elsevier B.V.)
    Sanitation systems globally fail at high rates. Researchers and practitioners attribute the causes of both sanitation success and failure to numerous factors that include tech. and non-tech. issues. A comprehensive understanding of what leads to sanitation failure and how to achieve sanitation success is imperative to prioritize the use of limited resources. To det. which combinations of causal conditions led to successful and failed sanitation systems, we applied fuzzy-set qual. comparative anal. to 20 cases in Karnataka and Tamil Nadu, India with small-scale sanitation systems. Two pathways led to successful sanitation systems, and four pathways led to failed sanitation systems. All successful systems required Sufficient O&M Funds, a Clear O&M Plan, and Tech. Support in addn. to either Addressed Sanitation Priorities and Community Participation in Planning or Behavior Change Education and Municipality Involved in Planning. All failed systems had Lack of Municipality in Planning, Unaddressed Sanitation Priorities, and No Tech. Support. Most failed systems also had No Clear O&M Plan, Poor Construction Quality, Lack of Community Participation in Planning, and Insufficient O&M Funds. Two failed cases had unique pathways because Government Barriers permanently disrupted use and maintenance. Overall, implementing organizations who initiate sanitation projects in resource-limited communities should ensure that (1) communities have adequate tech. and financial resources for maintenance; (2) community and municipality stakeholders are engaged in planning and know their maintenance responsibilities; and (3) appropriate technologies are selected that meet community needs and achieve community buy-in.
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  10. 10
    Katukiza, A. Y.; Ronteltap, M.; Oleja, A.; Niwagaba, C. B.; Kansiime, F.; Lens, P. N. L. Selection of Sustainable Sanitation Technologies for Urban Slums ─ A Case of Bwaise III in Kampala, Uganda. Sci. Total Environ. 2010, 409, 52– 62,  DOI: 10.1016/j.scitotenv.2010.09.032
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    Selection of sustainable sanitation technologies for urban slums - A case of Bwaise III in Kampala, Uganda
    Katukiza, A. Y.; Ronteltap, M.; Oleja, A.; Niwagaba, C. B.; Kansiime, F.; Lens, P. N. L.
    Science of the Total Environment (2010), 409 (1), 52-62CODEN: STENDL; ISSN:0048-9697. (Elsevier B.V.)
    Provision of sanitation solns. in the world's urban slums is extremely challenging due to lack of money, space, access and sense of ownership. This paper presents a technol. selection method that was used for the selection of appropriate sanitation solns. for urban slums. The method used in this paper takes into account sustainability criteria, including social acceptance, technol. and phys. applicability, economical and institutional aspects, and the need to protect and promote human health and the environment. The study was carried out in Bwaise III; a slum area in Kampala (Uganda). This was through administering of questionnaires and focus group discussions to obtain baseline data, developing a database to compare different sanitation options using technol. selection criteria and then performing a multi-criteria anal. of the technol. options. It was found that 15% of the population uses a public pit latrine; 75% uses a shared toilet; and 10% has private, non-shared sanitation facilities. Using the selection method, technologies such as Urine Diversion Dry Toilet (UDDT) and biogas latrines were identified to be potentially feasible sanitation solns. for Bwaise III. Sanitation challenges for further research are also presented.
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  11. 11
    Chambers, K. G.; Sheridan, P. M.; Cook, S. M. Sanitation Criteria: A Comprehensive Review of Existing Sustainability and Resilience Evaluation Criteria for Sanitation Systems. Environ. Sci. Technol. Lett. 2022, 9, 583– 591,  DOI: 10.1021/acs.estlett.2c00267
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    11
    Sanitation Criteria: A Comprehensive Review of Existing Sustainability and Resilience Evaluation Criteria for Sanitation Systems
    Chambers, Katherine G.; Sheridan, Patrick M.; Cook, Sherri M.
    Environmental Science & Technology Letters (2022), 9 (7), 583-591CODEN: ESTLCU; ISSN:2328-8930. (American Chemical Society)
    Despite the existence of numerous sanitation assessment frameworks, it remains unclear how to provide and measure sustainable access to sanitation. A systematic literature review was conducted to evaluate and collate sanitation criteria (i.e., indicators) for evaluating the sustainability or resilience of sanitation systems. The identified indicators represented 30 evaluation themes that included triple bottom line sustainability and tech. and resilience considerations, but none of the individual articles had included all themes. In particular, resilience themes were not commonly integrated with other themes. Further, many indicators were not measurable, even though most literature articles (85%) applied their indicators; over a third of the identified indicators had vague or absent metrics. These findings highlight several future research needs, such as developing validation processes to help ensure a set of indicators is sufficiently measuring progress toward or away from sanitation sustainability; expanding guidance on, and options for, data collection and anal. procedures, esp. for resilience; and supporting future indicator selection processes, for which a database of existing sanitation criteria was created. These all provide opportunities for improving the measurement of environmental and human well-being assocd. with sanitation access.
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    Awad, H.; Gar Alalm, M.; El-Etriby, H. K. Environmental and Cost Life Cycle Assessment of Different Alternatives for Improvement of Wastewater Treatment Plants in Developing Countries. Sci. Total Environ. 2019, 660, 57– 68,  DOI: 10.1016/j.scitotenv.2018.12.386
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    Environmental and cost life cycle assessment of different alternatives for improvement of wastewater treatment plants in developing countries
    Awad, Hamdy; Gar Alalm, Mohamed; El-Etriby, Hisham Kh.
    Science of the Total Environment (2019), 660 (), 57-68CODEN: STENDL; ISSN:0048-9697. (Elsevier B.V.)
    Most of wastewater treatment plants (WWTPs) in developing countries comprised primary and secondary treatment without including any tertiary treatment or sludge processing. Decision makers think that addnl. treatment is costly and the gained environmental benefits are limited. This study aims to investigate the environmental and economic benefits of improving current conventional WWTPs in developing countries by adding tertiary treatment and/or anaerobic digestion of sludge. For this purpose, life cycle assessment (LCA) for four different scenarios was studied for a wastewater plant located in Gamasa, Egypt. The 1st scenario is the plant in its current state. The 2nd scenario is the addn. of anaerobic digestion of sludge. The 3rd scenario is the addn. of a tertiary treatment stage. The 4th scenario is the addn. of anaerobic digestion of sludge and tertiary treatment stage. CML 2000 method was used for assessing the environmental impacts of the four scenarios. The 4th scenario attained max. environmental benefits for all categories due to the energy saving and the prospect of water reuse. The application of the 4th scenario achieved environmental benefits in some important categories such as ozone layer depletion. According to the economic evaluation, the addn. of tertiary treatment leads to gain financial profits due to the value of the reusable produced water. This study underlines the importance of considering LCA in development of WWTPs in developing countries.
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    Kalbar, P. P.; Karmakar, S.; Asolekar, S. R. Life Cycle-Based Decision Support Tool for Selection of Wastewater Treatment Alternatives. J. Cleaner Prod. 2016, 117, 64– 72,  DOI: 10.1016/j.jclepro.2016.01.036
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    Kalbar, P. P.; Karmakar, S.; Asolekar, S. R. Assessment of Wastewater Treatment Technologies: Life Cycle Approach. Water Environ. J. 2013, 27, 261– 268,  DOI: 10.1111/wej.12006
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    Assessment of wastewater treatment technologies: life cycle approach
    Kalbar, Pradip P.; Karmakar, Subhankar; Asolekar, Shyam R.
    Water and Environment Journal (2013), 27 (2), 261-268CODEN: WEJAAB; ISSN:1747-6585. (Wiley-Blackwell)
    Four municipal wastewater treatment plants (WWTPs) in India based on different technologies are compared by conducting Life Cycle Assessment (LCA) using field data. CML 2 baseline 2000 methodol. is adopted in which eight impact categories are considered. SBRs ranked highest in energy consumption and global warming potential (GWP) but also produced the best effluent with respect to orgs. and nutrients. Constructed wetlands have negligible energy consumption and neg. GWP because of carbon sequestration in the macrophytes. Emissions assocd. with electricity prodn. required to operate the WWTPs, emissions to water from treated effluent and heavy metal emissions from waste sludge applied to land are identified as main contributors for overall environmental impacts of WWTPs. This comparison of technologies suggests that results from LCA can be used as indicators in a multicriteria decision-making framework along with other sustainability indicators.
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  15. 15
    Lohman, H. A. C.; Trimmer, J. T.; Katende, D.; Mubasira, M.; Nagirinya, M.; Nsereko, F.; Banadda, N.; Cusick, R. D.; Guest, J. S. Advancing Sustainable Sanitation and Agriculture through Investments in Human-Derived Nutrient Systems. Environ. Sci. Technol. 2020, 54, 9217– 9227,  DOI: 10.1021/acs.est.0c03764
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    Advancing Sustainable Sanitation and Agriculture through Investments in Human-Derived Nutrient Systems
    Lohman, Hannah A. C.; Trimmer, John T.; Katende, David; Mubasira, Muwonge; Nagirinya, Maria; Nsereko, Fred; Banadda, Noble; Cusick, Roland D.; Guest, Jeremy S.
    Environmental Science & Technology (2020), 54 (15), 9217-9227CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    The sixth Sustainable Development Goal seeks to achieve universal sanitation, but a lack of progress due to inhibiting factors (e.g., limitations in financial resources, sociocultural conditions, household decision-making) demands innovative approaches to meet this ambitious goal. Resource recovery may generate income to offset sanitation costs while also enhancing agriculture through increased access to agricultural nutrients. The objective of this work was to det. if resource recovery sanitation can be a profitable business model in a specific context (Kampala, Uganda) and to explore the potential for this approach to translate to other Sub-Saharan African contexts. A techno-economic anal. was performed to evaluate the financial viability of two nutrient recovery systems and business models in urban communities in Kampala under two financing scenarios: (1) Startup relying on partial sanitation aid, and (2) Self-sustaining without philanthropic financing. Results show profitability can be achieved at a nutrient selling price at or below fertilizer market value in Uganda. Recoverable nutrients from the total population without at least basic sanitation services, in 10 Sub-Saharan African countries, are the same magnitude as nutrients distributed in subsidy programs (30-450% of distributed nutrients), indicating a potential to offset inorg. fertilizer consumption or increase nutrient availability. This research makes a case to support innovative sanitation strategies and the development and financial support of c fertilizer markets in areas with poor fertilizer and sanitation access.
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    Xu, M.; Zhang, Y.; Li, Y.; Lv, M.; Zhu, S.; Qian, T.; Fan, B. Energy Recovery Potential in Coupling of Sanitation and Agriculture: Techno-Economic Analysis on Resource-Oriented Sanitation. Waste Manage. 2021, 126, 141– 151,  DOI: 10.1016/j.wasman.2021.03.003
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    Energy recovery potential in coupling of sanitation and agriculture: Techno-economic analysis on resource-oriented sanitation
    Xu Mingjie; Zhang Yu; Li Yahui; Lv Minghuan; Zhu Shikun; Qian Tingting; Fan Bin
    Waste management (New York, N.Y.) (2021), 126 (), 141-151 ISSN:.
    The coupling of sanitation system and agriculture production is essential to mitigate the environmental burden and offset unsustainable fertilizer utilization by employing resource-oriented sanitation. Yet, the economic feasibility and energy recovery potential from domestic waste have rarely been investigated. To assess four scenarios (whether with kitchen waste separation; whether with energy recovery) in the resource-oriented sanitation system, an integrated assessment framework based on energy analysis and techno-economic analysis is employed to investigate the comprehensive sanitation system including both wastewater treatment and solid waste disposal. The results show that energy recovery from human excreta and kitchen waste can offset the energy consumption of the sanitation system and the energy surplus can even be 1067.70 kJ·PE(-1)·day(-1). The optimum covering range of the regional recovery center was quantified from the balance between scale effect and spatial distribution, and the serving inhabitants need to be more than 2800.
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    Kiker, G. A.; Bridges, T. S.; Varghese, A.; Seager, T. P.; Linkov, I. Application of Multicriteria Decision Analysis in Environmental Decision Making. Integr. Environ. Assess. Manage. 2005, 1, 95– 108,  DOI: 10.1897/IEAM_2004a-015.1
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    Application of multicriteria decision analysis in environmental decision making
    Kiker Gregory A; Bridges Todd S; Varghese Arun; Seager P Thomas P; Linkov Igor
    Integrated environmental assessment and management (2005), 1 (2), 95-108 ISSN:1551-3777.
    Decision making in environmental projects can be complex and seemingly intractable, principally because of the inherent trade-offs between sociopolitical, environmental, ecological, and economic factors. The selection of appropriate remedial and abatement strategies for contaminated sites, land use planning, and regulatory processes often involves multiple additional criteria such as the distribution of costs and benefits, environmental impacts for different populations, safety, ecological risk, or human values. Some of these criteria cannot be easily condensed into a monetary value, partly because environmental concerns often involve ethical and moral principles that may not be related to any economic use or value. Furthermore, even if it were possible to aggregate multiple criteria rankings into a common unit, this approach would not always be desirable because the ability to track conflicting stakeholder preferences may be lost in the process. Consequently, selecting from among many different alternatives often involves making trade-offs that fail to satisfy 1 or more stakeholder groups. Nevertheless, considerable research in the area of multicriteria decision analysis (MCDA) has made available practical methods for applying scientific decision theoretical approaches to complex multicriteria problems. This paper presents a review of the available literature and provides recommendations for applying MCDA techniques in environmental projects. A generalized framework for decision analysis is proposed to highlight the fundamental ingredients for more structured and tractable environmental decision making.
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    Cegan, J. C.; Filion, A. M.; Keisler, J. M.; Linkov, I. Trends and Applications of Multi-Criteria Decision Analysis in Environmental Sciences: Literature Review. Environ. Syst. Decis. 2017, 37, 123– 133,  DOI: 10.1007/s10669-017-9642-9
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    Oschewski, A.; Casey, V. The Technology Applicability Framework. A Participatory Tool to Validate Water, Sanitation, and Hygiene Technologies for Low-Income Urban Areas. In Technologies for Development; Springer International Publishing, 2015.
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    Lennartsson, M.; Kvarnström, E.; Lundberg, T.; Buenfil, J.; Sawyer, R. Comparing Sanitation Systems Using Sustainability Criteria; EcoSanRes Programme: Stockholm, 2009.
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    Molinos-Senante, M.; Gómez, T.; Caballero, R.; Hernández-Sancho, F.; Sala-Garrido, R. Assessment of Wastewater Treatment Alternatives for Small Communities: An Analytic Network Process Approach. Sci. Total Environ. 2015, 532, 676– 687,  DOI: 10.1016/j.scitotenv.2015.06.059
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    Assessment of wastewater treatment alternatives for small communities: An analytic network process approach
    Molinos-Senante, Maria; Gomez, Trinidad; Caballero, Rafael; Hernandez-Sancho, Francesc; Sala-Garrido, Ramon
    Science of the Total Environment (2015), 532 (), 676-687CODEN: STENDL; ISSN:0048-9697. (Elsevier B.V.)
    The selection of the most appropriate wastewater treatment (WWT) technol. is a complex problem since many alternatives are available and many criteria are involved in the decision-making process. The analytic network process (ANP) is applied for the 1st time to rank a set of seven WWT technol. set-ups for secondary treatment in small communities. A major advantage of ANP is that it incorporates interdependent relations between elements. Results illustrated that extensive technologies, constructed wetlands and pond systems are the most preferred alternatives by WWT experts. The sensitivity anal. performed verified that the ranking of WWT alternatives is very stable since constructed wetlands are almost always placed in the first position. This paper showed that ANP anal. is suitable to deal with complex decision-making problems, such as the selection of the most appropriate WWT system contributing to better understand the multiple interdependences among elements involved in the assessment.
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    Centers for Disease Control and Prevention. Evaluation of the Sustainability of Water and Sanitation Interventions in Central America after Hurricane Mitch February 12–27, 2006; U.S. Department of Health and Human Services: Atlanta, 2008.
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    UNICEF. Sustainability Checks: Guidance to Design and Implement Sustainability Monitoring in WASH; UNICEF: New York, 2017.
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    Li, Y.; Trimmer, J. T.; Hand, S.; Zhang, X.; Chambers, K. G.; Lohman, H. A. C.; Shi, R.; Byrne, D. M.; Cook, S. M.; Guest, J. S. Quantitative Sustainable Design (QSD) for the Prioritization of Research, Development, and Deployment of Technologies: A Tutorial and Review. Environ. Sci.: Water Res. Technol. 2022, 8, 2439– 2465,  DOI: 10.1039/D2EW00431C
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    Khattiyavong, C.; Lee, H. S. Performance Simulation and Assessment of an Appropriate Wastewater Treatment Technology in a Densely Populated Growing City in a Developing Country: A Case Study in Vientiane, Laos. Water 2019, 11, 1012,  DOI: 10.3390/w11051012
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    Performance simulation and assessment of an appropriate wastewater treatment technology in a densely populated growing city in a developing country: a case study in Vientiane, Laos
    Khattiyavong, Chanthephar; Lee, Han Soo
    Water (Basel, Switzerland) (2019), 11 (5), 1012CODEN: WATEGH; ISSN:2073-4441. (MDPI AG)
    The fast-growing population in Vientiane, the capital of Laos, has resulted in increasing domestic wastewater generation, which directly impacts the urban water environment due to the lack of a suitable wastewater treatment system. This study aims to assess six wastewater treatment alternatives based on two technologies-trickling filter and activated sludge-used for on-site, decentralized, and centralized wastewater treatment systems to support decision-making for selecting the most suitable and practical alternative for wastewater treatment in Vientiane. To det. the most appropriate treatment system, the wastewater treatment process simulation with BioWin and the technique for order preference by similarity to ideal soln. (TOPSIS) method are applied to assess the removal efficiencies for BOD (BOD), COD (COD), and total suspended solids (TSS), as well as to rank the six wastewater treatment technologies based on the following four environmental criteria: (1) land requirement, (2) electricity use, (3) sludge prodn., and (4) CO2 emissions. The BioWin results illustrate that the capacity of each alternative is similar in terms of domestic wastewater treatment efficiency, while differing in terms of environmental impacts. In addn., the alternative ranking shows that a centralized wastewater treatment system with a trickling-filter process is more suitable than on-site and decentralized wastewater treatment systems based on their environmental impacts. This finding provides evidence for decision-makers to select a suitable alternative for wastewater treatment in order to promote access to safe sanitation and sustainable urban wastewater management in Vientiane, Laos.
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    Lizot, M.; Goffi, A. S.; Thesari, S. S.; Trojan, F.; Afonso, P. S. L. P.; Ferreira, P. F. V. Multi-Criteria Methodology for Selection of Wastewater Treatment Systems with Economic, Social, Technical and Environmental Aspects. Environ. Dev. Sustainability 2021, 23, 9827,  DOI: 10.1007/s10668-020-00906-8
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    Kalbar, P. P.; Karmakar, S.; Asolekar, S. R. Selection of an Appropriate Wastewater Treatment Technology: A Scenario-Based Multiple-Attribute Decision-Making Approach. J. Environ. Manage. 2012, 113, 158– 169,  DOI: 10.1016/j.jenvman.2012.08.025
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    Selection of an appropriate wastewater treatment technology: a scenario-based multiple-attribute decision-making approach
    Kalbar Pradip P; Karmakar Subhankar; Asolekar Shyam R
    Journal of environmental management (2012), 113 (), 158-69 ISSN:.
    Many technological alternatives for wastewater treatment are available, ranging from advanced technologies to conventional treatment options. It is difficult to select the most appropriate technology from among a set of available alternatives to treat wastewater at a particular location. Many factors, such as capital costs, operation and maintenance costs and land requirement, are involved in the decision-making process. Sustainability criteria must also be incorporated into the decision-making process such that appropriate technologies are selected for developing economies such as that of India. A scenario-based multiple-attribute decision-making (MADM) methodology has been developed and applied to the selection of wastewater treatment alternative. The four most commonly used wastewater treatment technologies for treatment of municipal wastewater in India are ranked for various scenarios. Six scenarios are developed that capture the regional and local societal priorities of urban, suburban and rural areas and translate them into the mathematical algorithm of the MADM methodology. The articulated scenarios depict the most commonly encountered decision-making situations in addressing technology selection for wastewater treatment in India. A widely used compensatory MADM technique, TOPSIS, has been selected to rank the alternatives. Seven criteria with twelve indicators are formulated to evaluate the alternatives. Different weight matrices are used for each scenario, depending on the priorities of the scenario. This study shows that it is difficult to select the most appropriate wastewater treatment alternative under the "no scenario" condition (equal weights given to each attribute), and the decision-making methodology presented in this paper effectively identifies the most appropriate wastewater treatment alternative for each of the scenarios.
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    Kamble, S. J.; Singh, A.; Kharat, M. G. A Hybrid Life Cycle Assessment Based Fuzzy Multi-Criteria Decision Making Approach for Evaluation and Selection of an Appropriate Municipal Wastewater Treatment Technology. Euro-Mediterr. J. Environ. Integr. 2017, 2, 9,  DOI: 10.1007/s41207-017-0019-8
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    Li, Y.; Zhang, X.; Morgan, V. L.; Lohman, H. A. C.; Rowles, L. S.; Mittal, S.; Kogler, A.; Cusick, R. D.; Tarpeh, W. A.; Guest, J. S. QSDsan: An Integrated Platform for Quantitative Sustainable Design of Sanitation and Resource Recovery Systems. Environ. Sci.: Water Res. Technol. 2022, 2289,  DOI: 10.1039/D2EW00455K
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    Quantitative Sustainable Design Group. QSDsan: Quantitative Sustainable Design for Sanitation and Resource Recovery Systems; (accessed 2021-08-15).
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    Kalbar, P. P.; Karmakar, S.; Asolekar, S. R. Technology Assessment for Wastewater Treatment Using Multiple-Attribute Decision-Making. Technol. Soc. 2012, 34, 295– 302,  DOI: 10.1016/j.techsoc.2012.10.001
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    Padrón-Páez, J. I.; Almaraz, S. D.-L.; Román-Martínez, A. Sustainable Wastewater Treatment Plants Design through Multiobjective Optimization. Comput. Chem. Eng. 2020, 140, 106850,  DOI: 10.1016/j.compchemeng.2020.106850
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    Sustainable wastewater treatment plants design through multiobjective optimization
    Padron-Paez, Juan I.; Almaraz, Sofia De-Leon; Roman-Martinez, Alicia
    Computers & Chemical Engineering (2020), 140 (), 106850CODEN: CCENDW; ISSN:0098-1354. (Elsevier B.V.)
    Nowadays, an adequate design of wastewater treatment plants taking into consideration all sustainability dimensions- economic, environmental and social- is fundamental. This can be achieved by implementing systematic methodologies where conceptual and math. tools can be used together. This contribution proposes a framework that uses total cost, consumed energy, and reclaimed wastewater as sustainability metrics. A mixed-integer nonlinear programming problem arises from a general superstructure for wastewater treatment plants. A case study from Mexico City is solved by a hybrid multiobjective optimization approach that combines lexicog. and ε-constraint methods. Solns. are provided in the form of a Pareto front. A modified technique for order of preference by similarity to ideal soln. (M-TOPSIS) anal. is used as a multiple criteria decision-making tool to find the best trade-off soln. The optimal sustainable configuration resulted consists of three levels of treatment and 100% of treated water reuse.
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    Zhang, F.; Ju, Y.; Dong, P.; Wang, A.; Santibanez Gonzalez, E. D. R. Multi-Period Evaluation and Selection of Rural Wastewater Treatment Technologies: A Case Study. Environ. Sci. Pollut. Res. 2020, 27, 45897– 45910,  DOI: 10.1007/s11356-020-10307-z
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    Multi-period evaluation and selection of rural wastewater treatment technologies: a case study
    Zhang Fan; Ju Yanbing; Dong Peiwu; Zhang Fan; Wang Aihua; Santibanez Gonzalez Ernesto D R
    Environmental science and pollution research international (2020), 27 (36), 45897-45910 ISSN:.
    Rapid population growth and agricultural development are generating a considerable amount of effluents, which poses threats to the quality of rural water resources as well as sanitary conditions. However, with a range of rural wastewater treatment (WT) technologies available, one major problem facing the practitioners is which to choose as the most favorable option suited to specific areas. In this study, a novel decision-making framework is proposed to evaluate and select the optimal alternative in rural areas of Xi'an within multiple consecutive time periods. Firstly, an evaluation index system is constructed and picture fuzzy numbers (PFNs) are used to represent both evaluation levels and experts' refusal due to limitation of knowledge. Secondly, fuzzy analytical hierarchy process (FAHP) is applied to derive weights of criteria, which enables experts to assign fuzzy numbers to express their preferences for comparison judgments. Thirdly, evidence theory is utilized to obtain the aggregated values from multiple time periods. Finally, based on the belief intervals obtained, sequencing batch reactor (A4) is determined as the optimal rural WT technology in Xi'an from 2006 to 2020, whereas the membrane bio-reactor (A2) is the last option. The effectiveness of the proposed framework is further validated by comparative analysis. This research can hopefully serve as useful guidance for the assessment of rural WT technologies in various regions.
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    Promentilla, M. A. B.; Janairo, J. I. B.; Yu, D. E. C.; Pausta, C. M. J.; Beltran, A. B.; Huelgas-Orbecido, A. P.; Tapia, J. F. D.; Aviso, K. B.; Tan, R. R. A Stochastic Fuzzy Multi-Criteria Decision-Making Model for Optimal Selection of Clean Technologies. J. Cleaner Prod. 2018, 183, 1289– 1299,  DOI: 10.1016/j.jclepro.2018.02.183
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  • Abstract

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    Figure 1

    Figure 1. Literature review results used to develop the decision-making sanitation framework. (A) Summary of decision-support techniques in the peer-reviewed literature. The decision-support techniques most commonly used in sanitation decision making were multi-criteria decision analysis (MCDA) (22 of 35 total articles) and life cycle assessment (LCA) (7 of the 35 total articles). Other techniques included cost–benefit analysis (CBA); appropriateness assessment (AA); agent-based modeling; decision-support systems; probabilistic modeling; and strengths, weaknesses, opportunities, and threats (SWOT) analysis. (B) Breakdown of MCDA methods. The MCDA methods most used were the technique for order of preference by similarity to ideal solution (TOPSIS; 9 of 22 MCDA articles) and analytical hierarchy process (AHP; 9 of 22 MCDA articles).

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    Figure 2

    Figure 2. Summary of literature review of articles focused on decision making in resource-limited settings highlighting stakeholders involved in sanitation decision making. Each publication was characterized based on stakeholder engagement and the types of stakeholders engaged. Community stakeholder types include end-users (i.e., toilet users), farmers, and landlords. Expert stakeholder types include pit latrine emptiers, water and sanitation employees, government agencies, and researchers. The 14 unique stakeholder combinations were identified in literature (columns labeled A–N): A, (52) B, (36) C, (28,35,42,50) D, (56) E, (57) F, (53) G, (39) H, (51) I, (38) J, (45) K, (10,37,44) L, (13,33,43) M, (26,27) and N. (46) For example, one paper included stakeholders defined in combination A (end-users, farmers, pit latrine emptiers, and water and sanitation employees), and four papers included stakeholders defined in combination C (water and sanitation employees, government agencies, and researchers).

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    Figure 3

    Figure 3. Overview of the MCDA methodology implemented in DMsan. The first step requires the user to select the relevant criteria and indicators to evaluate the sanitation alternatives for a specific context. Next, indicator weights are calculated using indicator contextual drivers for the country/territory through the AHP method, and criteria weights are either determined by community stakeholder preferences or generated by DMsan for the desired number of weight scenarios using Latin hypercube sampling. After the weights are established, indicator scores are assigned for each alternative: qualitative indicators are scored using predefined ranges (Section S2) and quantitative indicators are calculated through system simulation, TEA, and LCA in QSDsan. Finally, TOPSIS is used to calculate alternative rankings and performance scores to determine the best performing alternative for each criteria weight scenario.

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    Figure 4

    Figure 4. Overview of the Bwaise, Uganda sanitation system alternatives as described by Trimmer et al. (4) Each alternative incorporates a user interface with onsite storage, conveyance, centralized treatment, and recovery and reuse. Alternative A is the existing system, whereas Alternative B replaces the centralized treatment system, and Alternative C leverages source separation and alternative conveyance. All three alternatives are described in more detail in the text.

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    Figure 5

    Figure 5. Evaluation of alternatives under varied criteria weight scenarios for decision making. (A–C) Probability of having the highest performance score among alternatives (0–100%). Each line represents a single criteria weight scenario, with darker lines indicating higher probabilities. (D–F) Criteria weight scenarios in which an alternative has the highest winning probability across the three alternatives. A line for a particular criteria weight scenario is present if the alternative is the best performing alternative. Among 1000 criteria weight scenarios, Alternative A outperforms B and C in 77 scenarios (shown in panel D), Alternative B outperforms A and C in 922 scenarios (shown in panel E), and Alternative C outperforms A and B in one scenario (shown in panel F). Alternative A is the best performing system when stakeholders place high importance on technical ability (criterion weight ∼0.6 to 1.0). The probability of the highest performance score increases for Alternative A as the technical criterion weight approaches 1. Alternative B has the highest performance score in criteria weight scenarios with high criterion weights for resource recovery, environmental, economic, or social or when criteria weights are evenly distributed. Alternative C is unlikely to be selected as the best performing alternative without improvements across indicators.

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    Figure 6

    Figure 6. Indicator improvements to increase opportunities for selection of (A, C) Alternative A and (B, D) Alternative C. (A, B) Individual impact of indicator improvements on the percent of criteria weight scenarios for which Alternatives A and C have the highest performance score. Overall, 13 indicators were individually improved to the best indicator score to evaluate its individual impact on the alternative’s performance. Economic and environmental indicators were set to 10%, better than the best indicator score among the three alternatives (e.g., the improved economic score was 6.60, which is 10% lower than the least expensive alternative, Alternative B). Indicators were included in the analysis if they could reasonably be improved without requiring a complete design change (e.g., adding jobs does not require inherent changes to the technology, while energy recovery requires significant design changes to Alternatives A and C). (C, D) Combined impact of the top two indicator improvements on the percent of criteria weight scenarios for which Alternatives A and C have the highest performance score. Altogether, technology developers can use these results to prioritize research and development to achieve indicator improvements that would expand their technology’s opportunity space.

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      Most of wastewater treatment plants (WWTPs) in developing countries comprised primary and secondary treatment without including any tertiary treatment or sludge processing. Decision makers think that addnl. treatment is costly and the gained environmental benefits are limited. This study aims to investigate the environmental and economic benefits of improving current conventional WWTPs in developing countries by adding tertiary treatment and/or anaerobic digestion of sludge. For this purpose, life cycle assessment (LCA) for four different scenarios was studied for a wastewater plant located in Gamasa, Egypt. The 1st scenario is the plant in its current state. The 2nd scenario is the addn. of anaerobic digestion of sludge. The 3rd scenario is the addn. of a tertiary treatment stage. The 4th scenario is the addn. of anaerobic digestion of sludge and tertiary treatment stage. CML 2000 method was used for assessing the environmental impacts of the four scenarios. The 4th scenario attained max. environmental benefits for all categories due to the energy saving and the prospect of water reuse. The application of the 4th scenario achieved environmental benefits in some important categories such as ozone layer depletion. According to the economic evaluation, the addn. of tertiary treatment leads to gain financial profits due to the value of the reusable produced water. This study underlines the importance of considering LCA in development of WWTPs in developing countries.
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      Advancing Sustainable Sanitation and Agriculture through Investments in Human-Derived Nutrient Systems
      Lohman, Hannah A. C.; Trimmer, John T.; Katende, David; Mubasira, Muwonge; Nagirinya, Maria; Nsereko, Fred; Banadda, Noble; Cusick, Roland D.; Guest, Jeremy S.
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      The sixth Sustainable Development Goal seeks to achieve universal sanitation, but a lack of progress due to inhibiting factors (e.g., limitations in financial resources, sociocultural conditions, household decision-making) demands innovative approaches to meet this ambitious goal. Resource recovery may generate income to offset sanitation costs while also enhancing agriculture through increased access to agricultural nutrients. The objective of this work was to det. if resource recovery sanitation can be a profitable business model in a specific context (Kampala, Uganda) and to explore the potential for this approach to translate to other Sub-Saharan African contexts. A techno-economic anal. was performed to evaluate the financial viability of two nutrient recovery systems and business models in urban communities in Kampala under two financing scenarios: (1) Startup relying on partial sanitation aid, and (2) Self-sustaining without philanthropic financing. Results show profitability can be achieved at a nutrient selling price at or below fertilizer market value in Uganda. Recoverable nutrients from the total population without at least basic sanitation services, in 10 Sub-Saharan African countries, are the same magnitude as nutrients distributed in subsidy programs (30-450% of distributed nutrients), indicating a potential to offset inorg. fertilizer consumption or increase nutrient availability. This research makes a case to support innovative sanitation strategies and the development and financial support of c fertilizer markets in areas with poor fertilizer and sanitation access.
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      Assessment of wastewater treatment alternatives for small communities: An analytic network process approach
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      The selection of the most appropriate wastewater treatment (WWT) technol. is a complex problem since many alternatives are available and many criteria are involved in the decision-making process. The analytic network process (ANP) is applied for the 1st time to rank a set of seven WWT technol. set-ups for secondary treatment in small communities. A major advantage of ANP is that it incorporates interdependent relations between elements. Results illustrated that extensive technologies, constructed wetlands and pond systems are the most preferred alternatives by WWT experts. The sensitivity anal. performed verified that the ranking of WWT alternatives is very stable since constructed wetlands are almost always placed in the first position. This paper showed that ANP anal. is suitable to deal with complex decision-making problems, such as the selection of the most appropriate WWT system contributing to better understand the multiple interdependences among elements involved in the assessment.
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      Performance simulation and assessment of an appropriate wastewater treatment technology in a densely populated growing city in a developing country: a case study in Vientiane, Laos
      Khattiyavong, Chanthephar; Lee, Han Soo
      Water (Basel, Switzerland) (2019), 11 (5), 1012CODEN: WATEGH; ISSN:2073-4441. (MDPI AG)
      The fast-growing population in Vientiane, the capital of Laos, has resulted in increasing domestic wastewater generation, which directly impacts the urban water environment due to the lack of a suitable wastewater treatment system. This study aims to assess six wastewater treatment alternatives based on two technologies-trickling filter and activated sludge-used for on-site, decentralized, and centralized wastewater treatment systems to support decision-making for selecting the most suitable and practical alternative for wastewater treatment in Vientiane. To det. the most appropriate treatment system, the wastewater treatment process simulation with BioWin and the technique for order preference by similarity to ideal soln. (TOPSIS) method are applied to assess the removal efficiencies for BOD (BOD), COD (COD), and total suspended solids (TSS), as well as to rank the six wastewater treatment technologies based on the following four environmental criteria: (1) land requirement, (2) electricity use, (3) sludge prodn., and (4) CO2 emissions. The BioWin results illustrate that the capacity of each alternative is similar in terms of domestic wastewater treatment efficiency, while differing in terms of environmental impacts. In addn., the alternative ranking shows that a centralized wastewater treatment system with a trickling-filter process is more suitable than on-site and decentralized wastewater treatment systems based on their environmental impacts. This finding provides evidence for decision-makers to select a suitable alternative for wastewater treatment in order to promote access to safe sanitation and sustainable urban wastewater management in Vientiane, Laos.
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    26. 26
      Lizot, M.; Goffi, A. S.; Thesari, S. S.; Trojan, F.; Afonso, P. S. L. P.; Ferreira, P. F. V. Multi-Criteria Methodology for Selection of Wastewater Treatment Systems with Economic, Social, Technical and Environmental Aspects. Environ. Dev. Sustainability 2021, 23, 9827,  DOI: 10.1007/s10668-020-00906-8
      There is no corresponding record for this reference.
    27. 27
      Kalbar, P. P.; Karmakar, S.; Asolekar, S. R. Selection of an Appropriate Wastewater Treatment Technology: A Scenario-Based Multiple-Attribute Decision-Making Approach. J. Environ. Manage. 2012, 113, 158– 169,  DOI: 10.1016/j.jenvman.2012.08.025
      27
      Selection of an appropriate wastewater treatment technology: a scenario-based multiple-attribute decision-making approach
      Kalbar Pradip P; Karmakar Subhankar; Asolekar Shyam R
      Journal of environmental management (2012), 113 (), 158-69 ISSN:.
      Many technological alternatives for wastewater treatment are available, ranging from advanced technologies to conventional treatment options. It is difficult to select the most appropriate technology from among a set of available alternatives to treat wastewater at a particular location. Many factors, such as capital costs, operation and maintenance costs and land requirement, are involved in the decision-making process. Sustainability criteria must also be incorporated into the decision-making process such that appropriate technologies are selected for developing economies such as that of India. A scenario-based multiple-attribute decision-making (MADM) methodology has been developed and applied to the selection of wastewater treatment alternative. The four most commonly used wastewater treatment technologies for treatment of municipal wastewater in India are ranked for various scenarios. Six scenarios are developed that capture the regional and local societal priorities of urban, suburban and rural areas and translate them into the mathematical algorithm of the MADM methodology. The articulated scenarios depict the most commonly encountered decision-making situations in addressing technology selection for wastewater treatment in India. A widely used compensatory MADM technique, TOPSIS, has been selected to rank the alternatives. Seven criteria with twelve indicators are formulated to evaluate the alternatives. Different weight matrices are used for each scenario, depending on the priorities of the scenario. This study shows that it is difficult to select the most appropriate wastewater treatment alternative under the "no scenario" condition (equal weights given to each attribute), and the decision-making methodology presented in this paper effectively identifies the most appropriate wastewater treatment alternative for each of the scenarios.
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    28. 28
      Kamble, S. J.; Singh, A.; Kharat, M. G. A Hybrid Life Cycle Assessment Based Fuzzy Multi-Criteria Decision Making Approach for Evaluation and Selection of an Appropriate Municipal Wastewater Treatment Technology. Euro-Mediterr. J. Environ. Integr. 2017, 2, 9,  DOI: 10.1007/s41207-017-0019-8
      There is no corresponding record for this reference.
    29. 29
      Li, Y.; Zhang, X.; Morgan, V. L.; Lohman, H. A. C.; Rowles, L. S.; Mittal, S.; Kogler, A.; Cusick, R. D.; Tarpeh, W. A.; Guest, J. S. QSDsan: An Integrated Platform for Quantitative Sustainable Design of Sanitation and Resource Recovery Systems. Environ. Sci.: Water Res. Technol. 2022, 2289,  DOI: 10.1039/D2EW00455K
      There is no corresponding record for this reference.
    30. 30
      Quantitative Sustainable Design Group. QSDsan: Quantitative Sustainable Design for Sanitation and Resource Recovery Systems; (accessed 2021-08-15).
      There is no corresponding record for this reference.
    31. 31
      Kalbar, P. P.; Karmakar, S.; Asolekar, S. R. Technology Assessment for Wastewater Treatment Using Multiple-Attribute Decision-Making. Technol. Soc. 2012, 34, 295– 302,  DOI: 10.1016/j.techsoc.2012.10.001
      There is no corresponding record for this reference.
    32. 32
      Padrón-Páez, J. I.; Almaraz, S. D.-L.; Román-Martínez, A. Sustainable Wastewater Treatment Plants Design through Multiobjective Optimization. Comput. Chem. Eng. 2020, 140, 106850,  DOI: 10.1016/j.compchemeng.2020.106850
      32
      Sustainable wastewater treatment plants design through multiobjective optimization
      Padron-Paez, Juan I.; Almaraz, Sofia De-Leon; Roman-Martinez, Alicia
      Computers & Chemical Engineering (2020), 140 (), 106850CODEN: CCENDW; ISSN:0098-1354. (Elsevier B.V.)
      Nowadays, an adequate design of wastewater treatment plants taking into consideration all sustainability dimensions- economic, environmental and social- is fundamental. This can be achieved by implementing systematic methodologies where conceptual and math. tools can be used together. This contribution proposes a framework that uses total cost, consumed energy, and reclaimed wastewater as sustainability metrics. A mixed-integer nonlinear programming problem arises from a general superstructure for wastewater treatment plants. A case study from Mexico City is solved by a hybrid multiobjective optimization approach that combines lexicog. and ε-constraint methods. Solns. are provided in the form of a Pareto front. A modified technique for order of preference by similarity to ideal soln. (M-TOPSIS) anal. is used as a multiple criteria decision-making tool to find the best trade-off soln. The optimal sustainable configuration resulted consists of three levels of treatment and 100% of treated water reuse.
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    33. 33
      Zhang, F.; Ju, Y.; Dong, P.; Wang, A.; Santibanez Gonzalez, E. D. R. Multi-Period Evaluation and Selection of Rural Wastewater Treatment Technologies: A Case Study. Environ. Sci. Pollut. Res. 2020, 27, 45897– 45910,  DOI: 10.1007/s11356-020-10307-z
      33
      Multi-period evaluation and selection of rural wastewater treatment technologies: a case study
      Zhang Fan; Ju Yanbing; Dong Peiwu; Zhang Fan; Wang Aihua; Santibanez Gonzalez Ernesto D R
      Environmental science and pollution research international (2020), 27 (36), 45897-45910 ISSN:.
      Rapid population growth and agricultural development are generating a considerable amount of effluents, which poses threats to the quality of rural water resources as well as sanitary conditions. However, with a range of rural wastewater treatment (WT) technologies available, one major problem facing the practitioners is which to choose as the most favorable option suited to specific areas. In this study, a novel decision-making framework is proposed to evaluate and select the optimal alternative in rural areas of Xi'an within multiple consecutive time periods. Firstly, an evaluation index system is constructed and picture fuzzy numbers (PFNs) are used to represent both evaluation levels and experts' refusal due to limitation of knowledge. Secondly, fuzzy analytical hierarchy process (FAHP) is applied to derive weights of criteria, which enables experts to assign fuzzy numbers to express their preferences for comparison judgments. Thirdly, evidence theory is utilized to obtain the aggregated values from multiple time periods. Finally, based on the belief intervals obtained, sequencing batch reactor (A4) is determined as the optimal rural WT technology in Xi'an from 2006 to 2020, whereas the membrane bio-reactor (A2) is the last option. The effectiveness of the proposed framework is further validated by comparative analysis. This research can hopefully serve as useful guidance for the assessment of rural WT technologies in various regions.
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    34. 34
      Promentilla, M. A. B.; Janairo, J. I. B.; Yu, D. E. C.; Pausta, C. M. J.; Beltran, A. B.; Huelgas-Orbecido, A. P.; Tapia, J. F. D.; Aviso, K. B.; Tan, R. R. A Stochastic Fuzzy Multi-Criteria Decision-Making Model for Optimal Selection of Clean Technologies. J. Cleaner Prod. 2018, 183, 1289– 1299,  DOI: 10.1016/j.jclepro.2018.02.183
      There is no corresponding record for this reference.
    35. 35
      Singhirunnusorn, W.; Stenstrom, M. K. Appropriate Wastewater Treatment Systems for Developing Countries: Criteria and Indictor Assessment in Thailand. Water Sci. Technol. 2009, 59, 1873– 1884,  DOI: 10.2166/wst.2009.215
      35
      Appropriate wastewater treatment systems for developing countries: criteria and indictor assessment in Thailand
      Singhirunnusorn W; Stenstrom M K
      Water science and technology : a journal of the International Association on Water Pollution Research (2009), 59 (9), 1873-84 ISSN:0273-1223.
      This paper presents a comprehensive approach with factors to select appropriate wastewater treatment systems in developing countries in general and Thailand in particular. Instead of focusing merely on the technical dimensions, the study integrates the social, economic, and environmental concerns to develop a set of criteria and indicators (C&I) useful for evaluating appropriate system alternatives. The paper identifies seven elements crucial for technical selection: reliability, simplicity, efficiency, land requirement, affordability, social acceptability, and sustainability. Variables are organized into three hierarchical elements, namely: principles, criteria, and indicators. The study utilizes a mail survey to obtain information from Thai experts-academicians, practitioners, and government officials-to evaluate the C&I list. Responses were received from 33 experts on two multi-criteria analysis inquiries-ranking and rating-to obtain evaluative judgments. Results show that reliability, affordability, and efficiency are among the most important elements, followed by sustainability and social acceptability. Land requirement and simplicity are low in priority with relatively inferior weighting. A number of criteria are then developed to match the contextual environment of each particular condition. A total of 14 criteria are identified which comprised 64 indicators. Unimportant criteria and indicators are discarded after careful consideration, since some of the indicators are local or site specific.
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    36. 36
      Vidal, B.; Hedström, A.; Barraud, S.; Körrman, E.; Herrmann, I. Assessing the Sustainability of On-Site Sanitation Systems Using Multi-Criteria Analysis. Environ. Sci.: Water Res. Technol. 2019, 5, 1599– 1615,  DOI: 10.1039/C9EW00425D
      36
      Assessing the sustainability of on-site sanitation systems using multi-criteria analysis
      Vidal, Brenda; Hedstroem, Annelie; Barraud, Sylvie; Kaerrman, Erik; Herrmann, Inga
      Environmental Science: Water Research & Technology (2019), 5 (9), 1599-1615CODEN: ESWRAR; ISSN:2053-1419. (Royal Society of Chemistry)
      Small on-site sanitation systems are widely present in suburban and rural areas in many countries. As these systems often underperform and have an impact on receiving waters, understanding their overall sustainability is of interest for policy and decision-makers. However, the definition and estn. of indicators defining sustainability are challenging, as it is finding the methodol. approach to combine qual. and quant. indicators into one comprehensive assessment. In this study, twelve indicators defined by environmental, economic, social, tech. and health-related criteria were used to compare nine alternatives of on-site sanitation for single households. A non-compensatory method for multi-criteria decision anal., ELECTRE III, was used for the assessment together with wts. assigned to each indicator by a ref. group. Several scenarios were developed to reflect different goals and a sensitivity anal. was conducted. Overall, the graywater-blackwater sepn. system resulted as the most sustainable option and, in terms of polishing steps for phosphorus removal, chem. treatment was preferred over the phosphorus filter, both options being implemented together with sand filters. Assessing the robustness of the systems was a crucial step in the anal. given the high importance assigned to the aforementioned indicator by the stakeholders, thus the assessment method must be justified. The proposed multi-criteria approach contributes to aid the assessment of complex information needed in the selection of sustainable sanitation systems and in the provision of informed preferences.
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    37. 37
      Livia, S.; María, M.-S.; Marco, B.; Marco, R. Assessment of Wastewater Reuse Potential for Irrigation in Rural Semi-Arid Areas: The Case Study of Punitaqui, Chile. Clean Technol. Environ. Policy 2020, 22, 1325– 1338,  DOI: 10.1007/s10098-020-01874-3
      37
      Assessment of wastewater reuse potential for irrigation in rural semi-arid areas: the case study of Punitaqui, Chile
      Livia, Serrao; Maria, Molinos-Senante; Marco, Bezzi; Marco, Ragazzi
      Clean Technologies and Environmental Policy (2020), 22 (6), 1325-1338CODEN: CTEPAI; ISSN:1618-954X. (Springer)
      Abstr.: Wastewater reuse in agriculture has been identified as an adaptive soln. to climate change. This practice copes with two of the most limiting factors that are affecting sustainable development in rural areas: the lack of sanitation services and water shortages. This paper proposes a multi-criteria methodol. approach to evaluate wastewater reuse potential for irrigation in rural areas and to select the most sustainable and context-aware wastewater treatment technol. In order to validate the procedure, an empirical application for the Coquimbo region (Chile) was developed. The water balance conducted illustrated that the potential wastewater source can only partially substitute for the water canonical sources. According to local citizen preferences, among the three evaluated, the most sustainable wastewater treatment technol. option consists of septic tank and constructed wetland. This study provides evidence for the relevance of integrating social and environmental criteria, in addn. to tech. and economic ones, for supporting decision-making processes related to wastewater reuse. Graphic abstr.: [graphic not available: see fulltext].
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    38. 38
      Sadr, S. M. K.; Saroj, D. P.; Mierzwa, J. C.; McGrane, S. J.; Skouteris, G.; Farmani, R.; Kazos, X.; Aumeier, B.; Kouchaki, S.; Ouki, S. K. A Multi Expert Decision Support Tool for the Evaluation of Advanced Wastewater Treatment Trains: A Novel Approach to Improve Urban Sustainability. Environ. Sci. Policy 2018, 90, 1– 10,  DOI: 10.1016/j.envsci.2018.09.006
      There is no corresponding record for this reference.
    39. 39
      Salisbury, F.; Brouckaert, C.; Still, D.; Buckley, C. Multiple Criteria Decision Analysis for Sanitation Selection in South African Municipalities. Water SA 2018, 44, 448– 458,  DOI: 10.4314/wsa.v44i3.12
      There is no corresponding record for this reference.
    40. 40
      Anaokar, G.; Khambete, A.; Christian, R. Evaluation of a Performance Index for Municipal Wastewater Treatment Plants Using MCDM – TOPSIS. IJTech – Int. J. Technol. 2018, 9, 715,  DOI: 10.14716/ijtech.v9i4.102
      There is no corresponding record for this reference.
    41. 41
      Mucha, Z.; Generowicz, A.; Wójcik, W.; Jóźwiakowski, K.; Baran, S. Application of Multi-Criterial Analysis to Evaluate the Method of Utilization of Sludge from Small Wastewater Treatment Plants with Sustainable Development of Rural Areas. Environ. Prot. Eng. 2016, 42, 97,  DOI: 10.5277/epe160408
      There is no corresponding record for this reference.
    42. 42
      McConville, J. R.; Kvarnström, E.; Nordin, A. C.; Jönsson, H.; Niwagaba, C. B. Structured Approach for Comparison of Treatment Options for Nutrient-Recovery From Fecal Sludge. Front. Environ. Sci. 2020, 8, 36,  DOI: 10.3389/fenvs.2020.00036
      There is no corresponding record for this reference.
    43. 43
      Perez, A.; Mena, M.; Oddershede, A. Wastewater Treatment System Selection Using the Analytical Hierarchy Process. OR52 Keynotes and Extended Abstracts - 52nd Conference of the Operational Research Society; José-Rodrigo Córdoba School of Management, Royal Holloway University of London, 2010, 130– 137.
      There is no corresponding record for this reference.
    44. 44
      Seleman, A.; Bhat, M. G. Multi-Criteria Assessment of Sanitation Technologies in Rural Tanzania: Implications for Program Implementation, Health and Socio-Economic Improvements. Technol. Soc. 2016, 46, 70– 79,  DOI: 10.1016/j.techsoc.2016.04.003
      There is no corresponding record for this reference.
    45. 45
      Willetts, J.; Paddon, M.; Nam, N. D. G.; Trung, N. H.; Carrard, N. Sustainability Assessment of Sanitation Options in Vietnam: Planning with the Future in Mind. J. Water, Sanit. Hyg. Dev. 2013, 262– 268,  DOI: 10.2166/washdev.2013.045
      There is no corresponding record for this reference.
    46. 46
      Gao, H.; Zhou, C.; Li, F.; Han, B.; Li, X. Economic and Environmental Analysis of Five Chinese Rural Toilet Technologies Based on the Economic Input–Output Life Cycle Assessment. J. Cleaner Prod. 2017, 163, S379– S391,  DOI: 10.1016/j.jclepro.2015.12.089
      There is no corresponding record for this reference.
    47. 47
      Singh, S.; Mohan, R. R.; Rathi, S.; Raju, N. J. Technology Options for Faecal Sludge Management in Developing Countries: Benefits and Revenue from Reuse. Environ. Technol. Innovation 2017, 7, 203– 218,  DOI: 10.1016/j.eti.2017.02.004
      There is no corresponding record for this reference.
    48. 48
      Hashemi, S.; Boudaghpour, S. Economic Analysis and Probability of Benefit of Implementing Onsite Septic Tank and Resource-Oriented Sanitation Systems in Seoul, South Korea. Environ. Technol. Innovation 2020, 18, 100762,  DOI: 10.1016/j.eti.2020.100762
      There is no corresponding record for this reference.
    49. 49
      Kerstens, S. M.; Leusbrock, I.; Zeeman, G. Feasibility Analysis of Wastewater and Solid Waste Systems for Application in Indonesia. Sci. Total Environ. 2015, 530-531, 53– 65,  DOI: 10.1016/j.scitotenv.2015.05.077
      49
      Feasibility analysis of wastewater and solid waste systems for application in Indonesia
      Kerstens, S. M.; Leusbrock, I.; Zeeman, G.
      Science of the Total Environment (2015), 530-531 (), 53-65CODEN: STENDL; ISSN:0048-9697. (Elsevier B.V.)
      Indonesia is one of many developing countries with a backlog in achieving targets for the implementation of wastewater and solid waste collection, treatment and recovery systems. Therefore a tech. and financial feasibility anal. of these systems was performed using Indonesia as an example. COD, BOD, N, P and pathogen removal efficiencies, energy requirements, sludge prodn., land use and resource recovery potential (P, energy, duckweed, compost, water) for on-site, community based and off-site wastewater systems were detd. Solid waste systems (conventional, centralized and decentralized resource recovery) were analyzed according to land requirement, compost and energy prodn. and recovery of plastic and paper. In the financial anal., investments, operational costs & benefits and total lifecycle costs (TLC) of all studied options were compared. Tech. performance and TLC were used to guide system selection for implementation in different residential settings. An anal. was undertaken to det. the effect of price variations of recoverable resources and land prices on TLC. A 10-fold increase in land prices for land intensive wastewater systems resulted in a 5 times higher TLC, whereas a 4-fold increase in the recovered resource selling price resulted in max. 1.3 times higher TLC. For solid waste, these impacts were reversed, land price and resource selling price variations resulted in a max. difference in TLC of 1.8 and 4 resp. Tech. and financial performance anal. can support decision makers in system selection and anticipate the impact of price variations on long-term operation. The tech. anal. was based on published results of international research and the approach can be applied for other tropical, developing countries. All costs were converted to per capita unit costs and can be updated to assess other countries' estd. costs and benefits. Consequently, the approach can be used to guide wastewater and solid waste system planning in developing countries.
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    50. 50
      Retamal, M.; Willetts, J.; Mitchell, C.; Carrard, N. Modelling Costs for Water and Sanitation Infrastructure: Comparing Sanitation Options for Can Tho, Vietnam. In The future of water, sanitation and hygiene in low-income countries: Innovation, adaptation and engagement in a changing world: Proceedings of the 35th WEDC International Conference ; Loughborough, UK, 2011; p 8.
      There is no corresponding record for this reference.
    51. 51
      Spuhler, D.; Scheidegger, A.; Maurer, M. Generation of Sanitation System Options for Urban Planning Considering Novel Technologies. Water Res. 2018, 145, 259– 278,  DOI: 10.1016/j.watres.2018.08.021
      51
      Generation of sanitation system options for urban planning considering novel technologies
      Spuhler, Dorothee; Scheidegger, Andreas; Maurer, Max
      Water Research (2018), 145 (), 259-278CODEN: WATRAG; ISSN:0043-1354. (Elsevier Ltd.)
      The identification of appropriate sanitation systems is particularly challenging in developing urban areas where local needs are not met by conventional solns. While structured decision-making frameworks such as Community-Led Urban Environmental Sanitation (CLUES) can help facilitate this process, they require a set of sanitation system options as input. Given the large no. of possible combinations of sanitation technologies, the generation of a good set of sanitation system options is far from trivial. This paper presents a procedure for generating a set of locally appropriate sanitation system options, which can then be used in a structured decision-making process. The systematic and partly automated procedure was designed (i) to enhance the reproducibility of option generation; (ii) to consider all types of conventional and novel technologies; (iii) to provide a set of sanitation systems that is technol. diverse; and (iv) to formally account for uncertainties linked to technol. specifications and local conditions. We applied the procedure to an emerging small town in Nepal. We assessed the appropriateness of 40 technologies and generated 17,955 appropriate system options. These were classified into 16 system templates including on-site, urine-diverting, biogas, and blackwater templates. From these, a subset of 36 most appropriate sanitation system options were selected, which included both conventional and novel options. We performed a sensitivity anal. to evaluate the impact of different elements on the diversity and appropriateness of the set of selected sanitation system options. We found that the use of system templates is most important, followed by the use of a weighted multiplicative aggregation function to quantify local appropriateness. We also show that the optimal size of the set of selected sanitation system options is equal to or slightly greater than the no. of system templates. As novel technologies are developed and added to the already large portfolio technol. options, the procedure presented in this work may become an essential tool for generating and exploring appropriate sanitation system options.
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    52. 52
      Spuhler, D.; Germann, V.; Kassa, K.; Ketema, A. A.; Sherpa, A. M.; Sherpa, M. G.; Maurer, M.; Lüthi, C.; Langergraber, G. Developing Sanitation Planning Options: A Tool for Systematic Consideration of Novel Technologies and Systems. J. Environ. Manage. 2020, 271, 111004,  DOI: 10.1016/j.jenvman.2020.111004
      52
      Developing sanitation planning options: A tool for systematic consideration of novel technologies and systems
      Spuhler Dorothee; Germann Verena; Langergraber Guenter; Kassa Kinfe; Ketema Atekelt Abebe; Sherpa Anjali Manandhar; Sherpa Mingma Gyalzen; Maurer Max; Luthi Christoph
      Journal of environmental management (2020), 271 (), 111004 ISSN:.
      To provide access to sustainable sanitation for the entire world population, novel technologies and systems have been developed. These options are often independent of sewers, water, and energy and therefore promise to be more appropriate for fast-growing urban areas. They also allow for resource recovery and and are adaptable to changing environmental and demographic conditions what makes them more sustainable. More options, however, also enhance planning complexity. Structured decision making (SDM) can help balance opposing interests. Yet, most of the current research focuses on the selection of a preferred option, assuming that a set of appropriate options is available. There is a lack of reproducible methods for the identification of sanitation system planning options that can consider the growing number of available technology and the many possible system configurations. Additionally, there is a lack of data, particularly for novel options, to evaluate the various sustainability criteria for sanitation.To overcome this limitation, we present a novel software supported approach: the SANitation sysTem Alternative GeneratOr (Santiago). To be optimally effective, Santiago is required to be integrated into an SDM approach. In this paper, we present all the elements that such an integration requires and illustrate these methods at the case of Arba Minch, a fast growing town in Ethiopia. Based on this example and experiences from other cases, we discuss the lessons learnt and present the advantages potentially brought by Santiago for sanitation planning The integration requires four elements: a set of technologies to be looked at, decision objectives for sustainable sanitation, screening criteria to evalute technology appropriateness, and about the technologies and the casea. The main output is a set of sanitation system options that is locally appropriate, diverse in order to reveal trade-offs, and of a manageable size. To support the definition of decision objectives, we developed a generic objective hierarchy for sustainable sanitation. Because one of the main challenges lies in the quantification of screening criteria, we established the data for 27 criteria and 41 technologies in a library.The case studies showed, that if the integration is successful, then Santiago can provide substantial benefits: (i) it is systematic and reproducible; (ii) it opens up the decision space with novel and potentially more appropriate solutions; (iii) it makes international data accessible for more empirical decision making; (iv) it enables decisions based on strategic objectives in line with the sustainable development goals; (v) it allows to prioritise appropriate and resource efficient systems right from the beginning (vi) and it contributes to a more citywide inclusive approach by birding strategic objectives with an area-based appropriateness assessment. The here presented approach enables the prioritisation of appropriate and resource efficient sanitation technologies and systems in strategic planning. Thereby this approach contributes to SDG 6.2, 6.3, and 11, sustainable sanitation for all.
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    53. 53
      Simiyu, S. Preference for and Characteristics of an Appropriate Sanitation Technology for the Slums of Kisumu, Kenya. Int. J. Urban Sustainable Dev. 2017, 9, 300– 312,  DOI: 10.1080/19463138.2017.1325366
      There is no corresponding record for this reference.
    54. 54
      Triantafyllidis, C. P.; Koppelaar, R. H. E. M.; Wang, X.; van Dam, K. H.; Shah, N. An Integrated Optimisation Platform for Sustainable Resource and Infrastructure Planning. Environ. Modell. Software 2018, 101, 146– 168,  DOI: 10.1016/j.envsoft.2017.11.034
      There is no corresponding record for this reference.
    55. 55
      Malekpour, S.; Langeveld, J.; Letema, S.; Clemens, F. Judgment under Uncertainty; a Probabilistic Evaluation Framework for Decision-Making about Sanitation Systems in Low-Income Countries. J. Environ. Manage. 2013, 118C, 106– 114,  DOI: 10.1016/j.jenvman.2013.01.007
      There is no corresponding record for this reference.
    56. 56
      Wahaab, R. A.; Mahmoud, M.; van Lier, J. B. Toward Achieving Sustainable Management of Municipal Wastewater Sludge in Egypt: The Current Status and Future Prospective. Renewable Sustainable Energy Rev. 2020, 127, 109880,  DOI: 10.1016/j.rser.2020.109880
      There is no corresponding record for this reference.
    57. 57
      Magalhães Filho, F. J. C.; de Queiroz, A. A. F. S. L.; Machado, B. S.; Paulo, P. L. Sustainable Sanitation Management Tool for Decision Making in Isolated Areas in Brazil. Int. J. Environ. Res. Public Health 2019, 16, 1118,  DOI: 10.3390/ijerph16071118
      There is no corresponding record for this reference.
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  • Supporting Information

    Supporting Information

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    • Terminology definitions; literature review of decision-making methods; determination of indicator weights; contextual drivers; description and scoring of indicators; multi-criteria decision analysis methods for ranking alternatives; and system simulation parameter values, ranges, and distributions (PDF)


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