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Water 2019, 11(2), 249; https://doi.org/10.3390/w11020249

Article
Assessing Sustainability of Wastewater Management Systems in a Multi-Scalar, Transdisciplinary Manner in Latin America
1
Institute for Integrated Management of Material Fluxes and of Resources (UNU-FLORES), United Nations University, 01067 Dresden, Germany
2
Chair of Environmental Development and Risk Management, Technische Universität Dresden, 01217 Dresden, Germany
*
Authors to whom correspondence should be addressed.
Received: 30 December 2018 / Accepted: 24 January 2019 / Published: 31 January 2019

Abstract

:
Wastewater management in Latin America faces great challenges to reach a sustainable state. Although enough infrastructure has been built to treat around 40% of wastewater, only between 15–20% is effectively treated, and abandoned or defective infrastructure is a common sight. Data about current conditions at specific sites is quite fragmented, when existing. This leads to challenges in management, decision making and planning for sustainable options. We argue that a main obstacle is the lack of a regionally relevant sustainability assessment framework that allows for a holistic understanding of wastewater management as a nexus problem. We therefore developed a comprehensive framework to (1) understand current conditions (2) involve stakeholders and (3) point to pathways to improve wastewater management in the Americas. Building on literature review and stakeholder involvement, we constructed a multi-scalar extended dataset framework that is adaptable to different study sites using specific criteria. Sustainability was assessed through a “distance-to-target” approach. Social and economic variables were the lowest ranking in both cases, with technical variables generally performing better. Although some dimensions of sustainability are performing acceptably, others, such as social and economic, are general low to very low performing. This means, when looked at in an integrated manner, neither of the wastewater management systems analysed can be considered sustainable. Here we present the approach itself, the results of its application in two pilot sites in Latin America, and our recommendation to shift waste water management into sustainability.
Keywords:
assessment framework; sustainability assessment; baseline assessment; co-design; stakeholder involvement; wastewater management

1. Introduction

Wastewater and Its Management in Latin America

Wastewater management systems (WWMS) serve multiple functions within their cities. They channel and treat the wastewater produced by their customers, reduce the pollution load to the environment and the catchment they are embedded in and thus safeguard it and its inhabitants from detrimental health effects. Usually citizens only notice them when they do not provide those services. Wastewater treatment systems can, in addition, provide resources, such as bioenergy from biogas produced during the decomposition of organic matter, irrigation water or stabilized sludge to be used as fertilizer. Understanding the risks and benefits that a wastewater treatment system can offer to its community is not limited to the technical understanding of its components. It demands understanding the multiple dimensions of sustainability, understood as ‘the maintenance of economic well-being, protection of the environment and prudent use of natural resources, and equitable social progress which recognizes the just needs of all individuals, communities, and the environment’ [1].
In Latin America, 80% of the population lives in urban areas, with small cities (up to half a million inhabitants) growing the most rapidly [2]. Exact data on sanitation and treatment coverage are not readily available [3], but it is known that wastewater treatment is in general poor, with infrastructure to treat around 40% of municipal wastewater having been built, but less than 20% of that wastewater effectively being treated [4,5]. Commonly built solutions have been centralised wastewater treatment plants (WWTP), which may satisfy the demand of highly populated areas, but do not necessarily comply with the new expectations about water recycling and reuse, and of nutrient recovery [6], as requested by Sustainable Development Goal (SDG) 6.3 or the New Urban Agenda adopted at the latest Habitat III Conference [7].
Tackling the deficit of safely treated wastewater is an urgent matter: Clean water and access to safe sanitation for all is one of the targets decided by the global community within the Sustainable Development Goals (SDG 6.2) [8]. In Latin America large cities concentrate the largest shares of population, but when it comes to issues in the water management services, rural areas and small- and medium-sized cities are the most affected zones, especially regarding sanitation and wastewater treatment [4]. Small- and medium-sized cities are defined according to population, varying in proportion to each country’s size, with a maximum of 1 million inhabitants for Latin American cities [3]. These types of cities show high urbanization rates, being the fastest growing urban areas [9]. This means that the established urban management systems have to consider the growth projections and adapt to keep up with the growing water demand and wastewater generation. Therefore, sustainable options for wastewater management for small- to medium-sized cities are urgently needed.
The SludgeTec project, a multinational partnership (the United Nations University’s Institute for Integrated Management of Material Fluxes and of Resources—UNU FLORES, the Universidad de San Carlos de Guatemala—USAC, the Mexican Trust Fideicomiso de Infraestructura Ambiental de los Valles de Hidalgo in Tepeji, Mexico—FIAVHI, and the Technische Universität Dresden-TUD, aimed for international experts and local stakeholders to co-design a sustainable wastewater treatment and management options for two pilot areas in the Americas: Los Cebollales WWTP in Panajachel, Lake Atitlan, Guatemala and Tlaxinacalapan WWTP in Tepeji, State of Hidalgo, Mexico. Research was carried out between November 2017 and February 2019 by a multi-disciplinary and international team of researchers and practitioners.
To achieve the project’s objective (co-designing sustainable options), it was first necessary to accurately assess current sustainability, that is, to describe baseline conditions. Establishing baselines is crucial for scientifically sound sustainability interventions [10], and is a key practice in many environmental fields, as it allows to evaluate the change in time of given parameters and therefore to track project success, for example. Without a baseline, it is impossible to carry out “before and after” comparisons [11]. Furthermore, a baseline assessment can be very useful in informing and engaging stakeholders [9], and a powerful way to gather and centralize otherwise dispersed data, assess data availability for a given topic, and eventually, socialize knowledge. This is particularly relevant in a region where data scarcity is known to be an issue.
The importance of baseline setting being clear, we were confronted with the non-existence of a comprehensive guideline to describe baseline and assess the sustainability of WWMS. Guidelines exist on the broad and very general steps to be followed in establishing a baseline [12], and on the data items to be considered in the assessment of specific components of a WWMS, such as finance, technical issues, etc. [13,14]. There has also been some research to systematise the indicators and data items needed for technology options evaluation [15,16,17]. However, the guidelines analysed during our literature review focus mostly on single dimensions of sustainability (environmental, technical, social), and do not take into consideration broader scales of analysis beyond the WWTP itself (to include for example the impacts of the WWTP’s function on the watershed or the subcatchment). We posit that a sustainability assessment must be multi-scalar (considering several territorial scales or spatial boundaries in one same study) and multi-dimensional (considering the different dimensions of sustainability).
We therefore developed a method to describe baseline conditions of WWMS and determine the degree of sustainability by (1) constructing a comprehensive and adaptable dataset framework and (2) applying a “distance-to-target” approach (further described in the methods section).
The method is underpinned by an emphasis on participation and transdisciplinarity. Scientists in the field of Integrated Water Resources Management highlight that participation can have positive effects on finding integrated solutions, e.g., by gathering and exchanging knowledge between vital stakeholders [18,19]. In terms of specific WASH-related problems, participation can help identify acceptable solutions on the ground. Based on this knowledge, practitioners and especially international donor organisations, apply participatory approaches in various contexts [20,21].
A research approach in which scientific and non-scientific actors collaborate in a participatory manner with the aim of creating scientific knowledge meant to address practical problems is here understood as transdisciplinary research (e.g., Reference [22]). ‘Transdisciplinary’ generally refers to an intensive inclusion of practitioners in the research process. To conceptualize transdisciplinary research, research provides a set of design criteria that are likely to have an impact on addressing complex problems in practice. These design criteria refer to (i) the type of actors involved, (ii) the stage of the research process where these stakeholders are involved, (iii) the degree of their involved, and (iv) the respective methodology [23]. Hence, various actors have been involved at different stages of the research process, from the design of research projects, via the implementation of the research projects, up to the evaluation of research results. In doing so, research questions, methods, and results are possibly better adapted to local needs, accepted, and thus also implemented [22,24]. Transferred to the field of wastewater management, the involvement of different scientific disciplines and practitioners from different realms may enable an ecologically, economically, environmentally and socially sustainable treatment of wastewater.
Participation is however no panacea for successful solutions. To achieve the potential benefits of participation, the thoughtful design of participatory processes is essential, including the right mix of actors (e.g., households, farmers, public authorities), degrees of participation (e.g., information sharing or co-decision-making), at the right scale (e.g., local or basin scale) [22,25].
In brief, in order to codesign sustainable options for the WWMS at the pilot sites, we built a method to first assess baseline sustainability, considering different territorial scales and the environmental, technical, economic and social dimensions. To broaden the possibility of accurate understanding of the issue and successful outcomes of the project, we worked in a transdisciplinary manner, i.e., in a diverse scientific team which closely worked with stakeholders and local partners, in every stage of research.

2. Materials and Methods

The method consists of four ‘building blocks’: (1) A thorough understanding of baseline conditions, which are then assessed under three different but converging perspectives: (2) Sustainability Assessment (SA), (3) Stakeholder Analysis and (4) Wickedness Analysis (WA). Blocks 1 and 2 are consecutive, i.e., number one is needed to perform number two. Blocks 3 and 4 are carried out separately. The assessment is made more thorough and comprehensive by bringing in the specific knowledge of each building block. This facilitates the understanding of bottlenecks and pathways towards sustainability, and as a final outcome, makes it possible to envision and evaluate solution options (Figure 1).
This paper describes the first two building blocks in detail, while the remaining two are the object of future publications.

2.1. Pilot Sites

Pilot sites (Figure 2) were chosen by local project partners based on their knowledge of the reality on the ground.

2.1.1. Panajachel Site description

At Panajachel, Guatemala, the pilot site is the Cebollales WWTP, an extended aeration, activated sludge plant built in 2013. The plant is operated by the municipality, with its financing sources being 100% public. The design flow is 37 liters per second (lps), and the current average flow is ~25 lps. It discharges into the San Francisco River, which, 200 m further downstream, feeds the Atitlan Lake. In the lake’s endorheic basin, 55% of households are connected to a sewage system, while the remaining 45% use latrines, septic tanks, or soak latrines. 45,500 m3 of wastewater is generated every day in the basin, and only approximately 20% receives treatment. Moreover, in the existing WWTPs, poor removal of pathogens and nutrients is a crucial challenge. These WWTPs face, among others, operation and maintenance problems.

2.1.2. Tepeji Site Description

At Tepeji, Mexico, the pilot site is the Tlaxinacalapan WWTP (built in 2017, started operations in January 2018). The WWTP has two treatment steps: a train of plastic anaerobic digestors built on site, followed by constructed wetlands. The design flow is 1.5 lps, and the current average flow is ~0.4 lps. It discharges into a tank from where water is taken to irrigate a football field and agricultural plots.

2.2. Dataset Framework

2.2.1. Preliminary Step. System Model: Boundaries and Scales of Analysis

Wastewater management is a wicked problem: a complex network of components, often interlinked in non-linear relationship and expanding across different territories. In addressing sustainability problems, systems approaches have been widely recognized to enable researchers to describe and understand reality more accurately, shedding light on a phenomenon’s structure and function [26,27,28], helping reveal otherwise “hidden” flows [29] and promoting the integrative thinking and interdisciplinary knowledge synthesis needed for sustainability [27,30]. System models are a key tool of systems approaches [27] and are widely used in cybernetics, physics, ecology and other fields where it is necessary to visually represent the complexity of real life networks and processes, in order to grasp the performance and behaviour patterns of systems. Our approach builds on systems thinking by using a system model as a fundamental research tool.
In building a system model, it is important to remember that, although WWMS are bound to human settlements, the sourcing of their inputs and the effluent and other outputs may have consequences well beyond their immediate geographical setting. Therefore, defining relevant scales of analysis and tracing analytical boundaries of the system is crucial. The choice of scales can determine the accuracy of diagnosis, and the effectiveness of projects [10,31]. Spatial resolution determines the visibility of objects and relations. If a model’s boundaries are too small, important factors influencing the model may be missed, whereas if they are too large, detail on specific processes may be lost. Avellán et al. [32] postulate that ‘the boundaries of the [Water-Soil-Waste Nexus] systems need to be (a) wide enough (to avoid microanalyses of plot levels as in some cases of INRM [Integrated Natural Resource Management]), (b) clear (to avoid confusion as in the WEF [Water-Energy-Food] Nexus), and (c) flexible enough to accommodate varying needs (to avoid geographic constrictions as is the case of the basin discussions in IWRM [Integrated Water Resources Management])’. By mixing in and contrasting different perspectives, a multi-scalar approach provides for more comprehensive analyses, which can lead to reduce biases caused by the use of a single “viewing-point” [10,33].
Different types of boundaries were identified: administrative (municipality, department, state, etc.), biophysical (catchments, geological, soil, etc.), and technical (treatment system, canal network, etc.). The relevance of each of these different spatial definitions was evaluated (Figure 3a), and four working scales were decided upon: 01 WWTP, 02 Municipality, 03 Subcatchment, 04 Watershed (Figure 3b). We argue that these scales together exhibit the needed specificity of the actual problem of wastewater treatment on the one hand but also enough scope to determine the impact that the system has on its surroundings.
A system model for the WWMS was drafted for each study site, using these chosen boundaries. System components (stocks) are represented in boxes and relations between them (flows) with lines (Figure 3c). The first versions of the system model were refined with participation from stakeholders during an assessment workshop held in Panajachel, Guatemala, in March 2018 [34]. Figure 3c shows the final version of the system model for the Panajachel site, resulting from the participative work at the workshop.

2.2.2. Constructing the Dataset Framework

2.2.2.1. Extended Dataset Framework

We created the framework for a dataset that allows for a deep and holistic understanding of baseline conditions and sustainability performance, across scales and across the dimensions—environmental, and social, technical, economic—of the nexus problem of wastewater management, specifically in Latin America. To do so, we iterated between a top-down method (literature reviews) and a bottom-up method (working directly on the pilot sites, e.g., analysing the system model, asking stakeholders what sort of data is relevant to them) (Table 1). The result is an extended dataset framework (EF), which is described in more detail in the results section.

2.2.2.2. Site-Specific Dataset Framework

The EF was edited into a smaller, site-specific dataset framework for each site. This was necessary in order to respond to local data needs as expressed by stakeholders, as not all data items on the set were relevant for the specific sites. Additionally, to respond to the research priorities as established by the research team after assessing data availability and incorporating stakeholder input.
To edit the EF (with 492 variables) into the site-specific dataset frameworks (with 195 and 218 variables), we classified and prioritised each item on the EF according to the criteria in Table 2. Note that these criteria were chosen for these two project-specific pilot sites, but they could easily be applicable generically for other WWMS.
Priority 1 (P1) was given to an item if two conditions are met: (a) that stakeholders had chosen it during the Assessment Workshop held in March 2018 in Panajachel, and (b) that the item had been found in wastewater management guidelines or other relevant literature during our literature review. Priority 2 (P2) was given when the data item is included in relevant local regulation, e.g., monitoring standards. Priority 3 (P3) was given when a threshold to compare current values to could be identified. Thresholds were found looking in:
  • Local legislation (region, state, basin).
  • National legislation.
  • Legislation valid for the other case study of this project (in this case Mexico or Guatemala).
  • International organisations (not legally binding but accepted as guidelines or recommendations).
In some cases, a data item is a “yes or no” question, and a threshold can be established with relative ease; for example, the existence of an operation manual for the plant, for which the threshold is “yes”, since that would be the desirable situation.
After the data gathering phase (see Section 2.4), these dataset frameworks were “filled in” with data, allowing to understand baseline conditions and perform a sustainability assessment.

2.3. Data Gathering

2.3.1. Identifying Data Holders

Possible data sources were identified through (1) an Assessment Workshop and (2) deskwork. The Assessment Workshop took place in March 2018 in Panajachel, Guatemala. Stakeholders from the Mexican cases were also present. More than 80 local stakeholders were invited, of which a total of 39 participated. The represented stakeholder groups were coming from Academia (43%), Federal officials (21%), Non-Governmental Oirganisations (NGOs) (20%), Municipal officials (8%), and Private enterprise (8%). Through the participatory activities crucial input needed to refine both the technical and the social assessment components of the project framework was obtained. A thorough comprehension of the current problem structure, made possible by including the views of key stakeholders in a very interactive and participatory manner [34]. Participants drafted lists with institutions and experts who they thought could have the information needed (or access to it) for each data item. The data holder lists made by the participants were screened, refined and complemented through desk work. The final list of data holders consulted or interviewed can be seen in Appendix C.

2.3.2. Data Collection

Part of the data needed was collected on the field. Fieldwork was carried out for two weeks at each site in August 2018, and included meetings with experts, practitioners and local authorities identified as data holders, as well as sampling and laboratory analysis. During the meetings, the interviewer (L. Benavides) went through the dataset with the stakeholder, who provided the answers he or she had available. Data holders were always asked to provide supporting documentation, but this was rarely available. In some cases, stakeholders did not have information at hand, but committed to sending it via email after the meeting.
For water quality parameters, sampling and laboratory analysis were carried out (Appendix D). Sampling and analysis were done in accordance to the norms in each country, in collaboration with certified local laboratories. In both cases, a composite sample of both the plant’s inflow and outflow was taken during a 24-h period. At the Panajachel site, sludge was also sampled. The sludge available had been stabilized on a covered drying yard for 28 days and was then piled up outdoors (i.e., under sun and rain) for at least two months prior to our visit. In Tepeji it was not possible to sample sludge, as according to the managers, the plant had not produced any in the 8 months of operation.
Further data was obtained from the revision of literature and documents produced by local and national authorities, which were made available to the research team during field work.

2.4. Sustainability Assessment

Sustainability Assessment (SA) processes aim at guiding decision-making towards sustainability [35] using different evaluative techniques [36] and definitions. ‘Sustainability Assessment’ can be considered a broad name tag for a series of methods and approaches: e.g., Sustainability Appraisal, Integrated Assessment, Integrated Sustainability Assessment, Sustainability Impact Assessment, Triple Bottom-Line Assessment, 3-E Integrated Assessment and Extended Integrated Assessment [35,36,37]. Methodologies found for SA include multicriteria approaches, systems analysis, life cycle analysis, economic analysis (cost-benefit analysis, life-cycle costing, etc.), weighting methods (exergy analysis, entropic weighing method), distance-to-target approaches, among others [15,38].
To determine the level of sustainability, we used a “distance-to-target” approach, comparing the current value of a variable with the threshold previously identified (see Section 2.2.2.2). The availability of a threshold finally defines whether a data item could be used in the sustainability assessment or not. Even though data for an item is available, if there is no appropriate threshold to compare it with, it is impossible to profit from this already existing data. Appendix E lists the variables for which thresholds could be identified and the thresholds values used to evaluate each variable of the site-specific dataset framework.
The “distance-to-target” was evaluated by adopting the “traffic light” method [39], where a variable is coded with green if it meets the threshold (good performance), with yellow when its performance does not meet ideal standards but is not far away from doing so, and red when it is performing sub-optimally. Table 3 discloses the quantitative criteria for each colour. Each variable was evaluated following these criteria. The result is a colour-coding of the data set (Appendix F).
Once the colour ranking was calculated for each variable, a colour ranking was also calculated for each of the three dimensions into which the variables are grouped in the sets: technical-environmental, economic, and social. To do this, we also followed the method described in Bertanza et al. (2016) [39], where a numeric value is assigned to each colour:
  • Green = 1
  • Yellow = 0
  • Red = −1
  • The colour-values are added, and a simple average in each category is calculated.
  • The results are later presented again using the “traffic light” colour-coding for the performance of each dimension of sustainability, as follows: (see results section)
  • Green: >0.33
  • Yellow: between −0.33 and 0.33
  • Red: ≤−0.33.
Although as stated in the introduction we believe a multi-scalar approach is necessary for a wide-enough perspective and an accurate understanding of a WWMS, due to the limited time scope of the SludgeTec project and the prolonged waiting periods to obtain data from data holders, it was only possible to perform a sustainability assessment on the first scale (WWTP, grey shaded areas on, and to include a multi-scalar social assessment of participation and social acceptance in the region where the WWTP operates (dataset IIb on Table 4 and Table 5).

3. Results

3.1. Dataset Framework

3.1.1. Extended Dataset Framework

The iterative collection process of data items to describe the multi-scalar WWMS resulted in a large dataset framework with 492 data items (for an overview see Table 4, for the full content see SM 1). This comprehensive or “extended” dataset framework contains data items useful for the transdisciplinary study of WWMS (environmental and technical, economic, and social factors). It is organised into three datasets, namely: Dataset 0 which describes generic context data, Dataset I containing technical and environmental data, and Dataset II, containing socio-economic data. All datasets contain information across the four different spatial scales identified in Section 2.3.1. (WWTP, municipality, subcatchment, watershed) (see Table 4).

3.1.2. Site-Specific Dataset Framework

The EF proved too extensive to be used for the assessment of the sites, as time was a limiting factor, and also because not all variables on the set were necessarily a priority or the data for needed for all was not available at the different sites. Therefore, from the 492 data items in the EF, a site-specific dataset framework was created for the Panajachel pilot site with 218 data items, and for the Tepeji pilot site with 195 data items (Table 5). The full site-specific dataset frameworks can be found in Appendix A for Panajachel and in Appendix B for Tepeji.

3.2. Data Gathering

Figure 4a,b show the distribution of sources from which the data came from.
About 77% of all data items in Scale 01 could be gathered for the Panajachel site, and ~76% for Tepeji (Table 6 and Table 7). However, out of the data that was gathered, only a fraction was of use, as can be seen in the last column of Table 6 and Table 7. The reasons why some of the data had to be discarded were:
  • Data quality. Stakeholders sometimes provided no supporting facts or documentation for the data they provided, or there was a considerable difference between data found for the same item from various sources, with no straight-forward way to choose amongst them.
  • No existing threshold. The data could be obtained but no threshold was found, and therefore the data was not used further.
This filtering process removed ~32% of the data for Panajachel and ~43% of the data gathered for Tepeji. With the remaining variables (62 variables for Panajachel, 55 variables for Tepeji) the sustainability assessment was performed.

3.3. Sustainability Assessment

3.3.1. Panajachel

Sustainability at the Cebollales WWTP in Panajachel was assessed with 62 variables: 52 in the technical-environmental dimension, three in the economic and seven in the social. All dimensions show a medium performance (yellow) except for the economic dimension, where the assessment is “poor” (coded with red). Just about half of the variables are performing relatively well (23 variables coded with green) and about half are coded in red (27), with two variables coded in yellow. Therefore, overall sustainability performance can be classified as medium to low (See Table 8. To see performance per variable, see Appendix F).
In the technical environmental dimension variables that performed well included heavy metal concentrations in the plant’s water outflow and sludge which were all found to comply with norms at the moment of sampling (except for Arsenic in the sludge). Additionally, the Sampling frequency is complied with. The plant is sampled with regularity. However, the results do not make it to on-the-ground stakeholders (plant operator, for example).
In contrast, variables performing sub-optimally include nutrients and organics: variables such as Total Nitrogen (TN), Total Phosphorus (TP), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Total Coliforms. The plant is not able to meet treated water outflow standards. This situation is likely partially driven by the fact that the inflow to the plant (municipal sewage) is already non-compliant as per regulations on discharges into the public sewage system. In addition, strong odours were detected while visiting and were also reported by local stakeholders. Lastly, maintenance is very irregular to non-existing; salaries are irregularly paid; no operation manual exists on site, the operators lack training and equipment. The risks that the WWTP and the treated water discharge into the nearby San Francisco river poses to health or to environment are unknown, as no risk assessment has been carried out, either for health or for ecosystems.
In the economic dimension there is no compliance. For example, the per capita cost of treatment is higher than the WHO illustrative value for activated sludge plants (upper limit set by WHO is 8 USD per capita per year. Using data provided by the municipality, we calculated 9.7 USD). The budget deficit is constant, i.e., the operating entity practically never has access to enough resources to cover operating costs or deliver worker’s salaries on time. There is also no valorisation of by products (biogas, sludge), nor has a plan for this purpose been outlined by managers.
In the social dimension stakeholders are generally aware and interested in wastewater-related issues and see opportunities for their suggestions to be heard. They however do no not perceive the solution(s) currently in place as acceptable, nor do they perceive that others accept them.

3.3.2. Tepeji

Sustainability at the Tlaxinacalpan WWTP in Tepeji was assessed with 55 variables: 48 in the technical-environmental dimension and 7 in the social dimension. No economic data was available from the WWTP managers at the time of data gathering, and therefore this dimension could not be evaluated. In the two dimensions evaluated, it shows a moderate to good performance (Table 9).
The technical-environmental dimension performance’ falls just above the border between medium and good performance, with 33 out of 48 being coded with green. Variables that perform well include compliance with heavy metal concentrations in the outflow (as established in local regulations, i.e., Norma Official Mexicana (NOM) 001), except for Cadmium (0.02 mg/L, which is double the allowed value). Additionally, all physical parameters are complied with (Total Suspended Solids (TSS), conductivity, colour, floating matter, grease and oils).
Variables that perform sub-optimally include nutrients and organics: TN, Faecal coliforms, pH, are not performing satisfactorily, neither when compared with the local norm (NOM 001) or with WHO standards for the use of treated wastewater use in agriculture. Odours were detected while visiting and were also reported by local stakeholders. No operation manual is available to key stakeholders such as the operator himself. No regular sampling seems to be occurring on the plant’s outflow, as although some interviewed stakeholders assured sampling has been done, no results were provided to us. Operators and managers lack adequate training on anaerobic plant operation. Standard design and operation practices are not being followed (such as an initial inoculation of the system with appropriate bacteria at the start of operations, assurance condition of air-tight conditions within anaerobic digestion tanks). Finally, the risks that the WWTP poses to health or to environment have not been studied, either prior to construction or once in operation, by any of the possibly interested parties.
In the social dimension stakeholders indicated that they are interested in and aware of wastewater related problems. They however do not feel that there is enough information available or opportunities to participate in decision making or to give recommendations to decision makers and managers. The current wastewater management system is generally not accepted or perceived as being accepted by interviewed stakeholders.

4. Discussion

4.1. Dataset Framework for Describing Wastewater Management Systems

We designed a transdisciplinary approach to assess baseline conditions and sustainability performance of wastewater management systems in Latin America, building on methods from both the social and the natural sciences (Figure 1), and with a heavy emphasis on stakeholder involvement and the understanding of baseline conditions. The approach was designed along with the development of a research project, in an iterative process between academic knowledge and the real experiences of what was possible to achieve within the conditions on the field.
We created an Extended dataset framework (EF, see Section 3.1), which we propose to be useful as a general guidance for data item selection for WWMS. It can be used as a sort of repertoire that can be “curated” or edited, choosing the items that are relevant to a specific site or research question, and thus creating a site-specific data framework.

4.1.1. Methodological Issues

The approach calls for not only a transdisciplinary but also a multi-scalar assessment. We attempted to simultaneously look at local scales within technical and administrative boundaries (the WWTP and the municipality), and ecological scales within hydrological boundaries (subcatchment and watershed). However, gathering, evaluating and processing the data required for a multi-scalar assessment was impracticable within our time scope. We therefore implemented the approach only on the scale of the WWTP (Scale 01) and were able to gather enough data to describe baseline conditions and to assess sustainability across all dimensions of sustainability in Panajachel and two out of three in Tepeji.
The approach proved to be practicable at one scale, with the strength of being able to incorporate local needs and conditions through the site-specific editing of the extended dataset framework (Table S1, see Section 2.2.2.2). The resulting datasets are useful as snapshots of the current status quo, and the data items can be used as a guideline for future data generation and periodical evaluation.
Building on a systems perspective, this approach calls for the construction of a system model as a tool: to identify important data items or variables to be investigated, or to localise “invisible” parts of the system, such as stakeholders, boundaries or legal frameworks. The tool proved useful not only for our own research process, but was also helpful during stakeholder involvement activities, where it helped structure the discussion. One example is that when discussing key stakeholders and responsibilities (“who is involved and who is responsible for what?”), the system model clearly depicts that, because the WWTP’s outflow eventually reaches the lake at the bottom of the basin, basin authorities (federal), river authorities (provincial) and tourists visiting the lake are involved, albeit to different degrees, in the problem. In other words, by explicitly linking upstream sewage system users to downstream fishermen affected, for example, the visual representations appeal strongly to very different stakeholders sitting around a discussion table, promoting a holistic and inclusive understanding of issues.
The same can be said for the boundaries discussion, also guided by the system model. By clearly illustrating which components fall into which boundary (e.g., the whole of the WWTP falls within the municipal boundary, but its outflow, ten meters ahead, falls into the river and thus provincial jurisdiction, while some of its inputs, such as pump parts, come from a different continent) administrative responsibilities can be made clearer and better understood; conflicting interests or overlapping mandates are made visually explicit and can therefore be more easily comprehended, and complexity is more easily grasped.
Finally, the combination of a technical-environmental assessment (Dataset I) with an economic (Dataset IIa) and a social (Dataset IIb) assessment proved not only enriching but allowed for insight into the drivers of the technical and environmental results. The technical-environmental variables provide an answer to the question “How is the system behaving?” while the social and economic data provide perspective into “Why the system is behaving so?” making the method better poised to identify bottlenecks and point to solution pathways. We see a challenge but a promising opportunity to improve sustainability thought and its tools in a more thorough transdisciplinary integration in the future. Overall, the approach showed potential for investigating the sustainability of WWMS. We see areas of improvement in, for example, reducing data intensity, systematising thresholds, and operationalizing the multi-scalar approach.

4.1.2. Data Availability

As shown in Table 6 and Table 7, roughly over 50% of the data we originally set out to gather for the multi-scalar site-specific dataset framework(s) was available with certain ease of access. Once we decided to focus on a single scale (WWTP), this proportion grew to ~75%, of which around a third had to be discarded due to quality issues.
Basic information such as monthly or yearly budgets and expenditures records, technical drawings and plans of the WWTP were for example not available for the Mexican case. In the Guatemalan site, non-continuous time series for monthly expenditures, inflow and outflow measurements were finally obtained via email after a waiting period following a stakeholder interview. Although indeed useful, the time series were neither long, nor gap-free, and data was not easily made available. In general, we found that stakeholders who, in theory, should have information (operating facilities, government bodies, WWTP managers) may be able to provide verbal answers in an interview, because of their empirical knowledge. The however very often lack supporting documentation, written records and systematic registries. In other cases, they lack the willingness or permission to share information. This is true mostly at the municipal and state levels, while federal agencies, particularly in the Mexican case, usually have well integrated and functional databases. The scale of federal-level data is however often not fine-grained or detailed enough to study a single treatment plant or even a municipal-level WWMS.
It is clear that large efforts are needed in terms of data generation, systematisation, sharing, and transparency. Examples would be digitising written records, using same standards throughout the region, information sharing between institutions, researchers and stakeholders or placing documents and data on the internet. Good starting points already exist, such as the National System for Water Information, kept by the National Water Commission in Mexico (CONAGUA), where geo-referenced information on water quality and quantity, irrigation, and watersheds is disclosed. We suggest that an immediate area of work should be furthering the capacity of key stakeholders, such as municipal and state or provincial governments to generate data, and the integration of all data generators into more detailed and/or numerous data bases, or conversely the creation of citizen-led observatories that foster awareness raising and demand and contribute to regular environmental and economic monitoring.
A significant issue to meaningfully assess sustainability stems from the still incipient integration of social indicators into sustainability. In the extended dataset framework 10 indicators (versus 380 for the environmental-technical dimension) across all scales could be identified from the literature or the stakeholder discussions. They are often linked to information that is not readily available but has to be generated via questionnaires and on-site interviews, coded analysis and other qualitative research methods. In order to strengthen future sustainability assessments of WWMS it is imperative to continue work on the integration of social indicators and methods to streamline the collection and analysis of these.

4.2. Sustainability of the Pilot Wastewater Management Systems

Our analyses show that the wastewater treatment systems currently in place in Panajachel, Guatemala, and Tepeji, Mexico, although performing well in various selected parameters, cannot be considered sustainable when looked at in a multidimensional manner, i.e., in terms of technical, environmental, economic, and social factors.

4.2.1. Technical-Environmental Issues

Both plants treat a municipal wastewater flow of domestic origin, with low to negligible heavy metal and metalloids content. In both cases, the quality of the inflowing wastewater is already below locally applicable standards for discharges into the public sewage network (Total Nitrogen and Total Coliforms in both cases, and Total Phosphorus, Biological Oxygen Demand, Chemical Oxygen Demand, and Total Suspended Solids (TSS) in Guatemala as well) meaning the plants are receiving a low-quality inflow from the start (see results per variable in Appendix F).
Once within the WWTPs, processes show different efficiency levels. Omitting metals, in which both plants practically fully comply (arguably because of an original low-metal content), the WWTP in Panajachel does not comply with virtually any of the examined physical, chemical or biological parameters, while the Tepeji WWTP performs slightly better, complying with half of them. Both plants, however, are performing poorly in the treatment of faecal coliforms, a crucial variable in terms of human and ecosystem health. In the particular case of Tepeji, where the water is being used for irrigation and the de-centralised, small-scale technology is being introduced to the community, a low quality and potentially risky outflow is not only a health risk, but an important hinderance to the success of the de-centralised treatment project, which has the aim of fostering wastewater use for agricultural irrigation. Social acceptance is key to the success of such new technology, and thus trust among the community has to be gained by the promoting entities.
Technical efficiency and environmental compliance are a major issue in both plants. Although visibly more critical in the Mexican case, lack of training of the operating and management personnel is a shared issue that is contributing to the situation. Systematic data generation and environmental monitoring, particularly in the Mexican case, are a challenge. Guatemalan managers were keeping a more detailed track of the WWTP performance. This may have to do with the fact that the WWTP in Guatemala has been in operation for a longer period (5 years versus 8 months), and also that it is operated by the municipality (vs a Trust).

4.2.2. Management Issues

In management-related issues both WWTPs perform the same, with only 2 out 7 management variables evaluated as positive, even though the systems operate at different scales (design flows of 1.5 lps in Tepeji vs 37 lps in Panajachel on average). Both plants lack operation manuals accessible to the operator, personnel lack training and capacities and laboratory analyses are not accessible and hassle-free in any of the cases (although in Panajachel sampling is carried out with norm-compliant frequency by a federal authority, key stakeholders—such as the plant’s operator—do not have access to the results and have therefore no feedback on their work).
In terms of risk and safety, operators in both pilot sites lack appropriate working conditions, (clothing, equipment, adequate-hand-washing-facilities). In neither of the cases had the risks posed to the environment or surrounding populations by malfunctioning of the WWTP been studied. Panajachel stakeholders manifested that environmental risk assessment is relatively new in public administration, and that they hope it will be integrated along with, for example, Environmental Impact Assessment (EIA), soon.
In Latin America in general, investments are often made to build an infrastructure project, but the funding for its long-term operation and maintenance (including equipping operators, performing routine samplings, etc.) is not secured, and nor are income generating options (resource recovery, for instance) duly considered [40]. Although this is a known issue, new infrastructure is being built as we write in Panajachel, while in Tepeji funds are being sought for the building of a large scale WWTP, still without a clear idea of how current infrastructure will continue to be financed or maintenance challenges faced (e.g., equipment repairs, salaries). Without a change towards adequate financial planning it is likely that both existing and new WWTPs at the studied sites will continue to operate sub-optimally.

4.2.3. Social Issues

The overarching recommendation, applicable to both sites, is to facilitate stakeholders the access to the information about their own social network. A common understanding of the problem itself is lacking. Who should be contacted with which need, or as formulated by Reed et al. “who is in and why?” [23] is a key question with a high degree of influence on the social development in both pilot sites. A common understanding of the problem is the basis for facilitating social interaction among the involved stakeholders. Economic and human resources should be provided to conduct an in-depth Stakeholder and Social Network Analysis in both pilot sites.

5. Conclusions

To advance towards sustainability in the urgent topic of wastewater management in the Americas, data scarcity and scatteredness must be overcome to allow for precise understanding of current or baseline sustainability performance. From such an understanding, bottlenecks can be made visible, and pathways towards sustainability can be envisioned. To increase the accuracy of the assessment and the adequacy of proposed solutions, research should go beyond one single perspective. To this end, we have proposed a multi-scalar data framework that includes variables for four different territorial scales: the WWTP, the municipality, the subcatchment and the watershed. Other scales could be chosen in other projects, what we propose is the multiscalar approach, not necessarily the scales themselves. Additionally, we propose to assess sustainability across four dimensions (environmental, technical, economic and social), and to incorporate other strands of scientific practice into the assessment (stakeholder analysis and wickedness analysis).
Transdisciplinarity is also a tool for improved success of research projects in this topic (see introduction). Throughout this project, we worked closely with local stakeholders and non-scientific practitioners. Their input was crucial in tailoring the framework to be locally relevant (see Section 2.2.2.2), and in the process of envisioning and evaluating solution options.
In this paper we present the method itself (Section 2) and partial results of its application in two pilot sites (Section 3). We also discuss the benefits and limitations of the method, and point to ideas for its future improvement and further application (Section 4.1).
As to the method itself, we found the multiscalar approach to enrich assessment and to allow to make visible issues that are not shown by single scale analysis, namely the interconnections of the technical system (WWTP) with ecological systems (watershed, riparian areas) and social systems (government, public administration, community dynamics, social perception). Shedding light on these interconnections, bottlenecks and obstacles to achieve sustainability are understood in a deeper and more detailed way, as many of the bottlenecks would be invisible when looking only at one scale or one dimension. The main limitations of the method are data and time intensity. Good planning, working closely with engaged local partners and performing a preliminary screening of data availability and data holders is recommended.
As to the results of the assessment presented here, Sustainability Assessment showed that technical and environmental variables tend in general to perform medianly to well, with microbiological parameters performing below the norms in both cases. Social and economic variables are the weakest spot of both of the WWMS analysed (Section 3.3). The results of the other two components of the method (stakeholder analysis and wickedness analysis) will be the object of future publications.

Supplementary Materials

The following are available online at https://www.mdpi.com/2073-4441/11/2/249/s1, Table S1: Extended Framework.

Author Contributions

Conceptualization, L.B. and T.A.; Data curation, A.M.; Formal analysis, L.B. and A.M.; Funding acquisition, T.A. and S.C.; Investigation, L.B. and A.H.; Methodology, L.B., T.A., S.C., A.H., S.K. and A.M.; Project administration, T.A., S.C. and A.H.; Supervision, T.A.; Visualization, L.B.; Writing—original draft, L.B.; Writing—review & editing, L.B., T.A., A.H. and A.M. With the exception of the first author, alphabetical order was applied for the remaining authorship’s order.

Funding

This research was funded by the German Bundesministerium für Bildung und Forschung, (BMBF) under the grant number 01DF17001. The Guatemalan Consejo Nacional de Ciencia y Tecnología provided support to the researchers of the University of San Carlos de Guatemala and financed, in part, the Assessment Workshop in Panajachel in March 2018. The Fideicomiso de Infraestructura Ambiental para los Valles de Hidalgo (FIAVHI) provided support to the participation of Mexican stakeholders to the Assessment Workshop and access to their premises. The APC was funded by the German Bundesministerium für Bildung und Forschung, (BMBF) under the grant number 01DF17001.

Acknowledgments

The authors wish to acknowledge the project’s partners: Ing. Jorge Cifuentes (USAC), and his students, Ing. Carlos Paillés and staff at FIAVHI, and our partners from the Technische Universität Dresden. Many thanks to AMSCLAE, the municipal authorities in Panajachel and Tepeji who kindly supported our research, and all stakeholders who accepted to be interviewed. Special thanks to Enrique Cosenza and Thelma Lopez for making the field in Panajachel possible. Finally, we wish to thank Laura Ferrans, Leon Zimmermann and Nestor de la Paz for their preliminary work into pilot sites, as well as Guido Bartolini for his support in the preparation of the final manuscript.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Appendix A

Prioritised (Site-Specific) Dataset Framework—Panajachel
Total Data Items218
PS = Prioritised by stakeholdersLI = Data Item comes from the literature
RG = Included in Guatemala regulationRM = Included in Mexican regulation
The numbers in the ID column refer to those of the extended set.
DATASET 0—Context Data—WWTP Scale
CategoryIDPSLIRGRMData ItemItem DescriptionNotes
GEOGRAPHY AA0.003 1 MapCartography at the adequate scale to understand the location of the plant in relation to nearest population settlement, water resources and other relevant features.All non-domestic wastewater generators have to prepare a technical study including this item. Acuerdo Gubernativo 12-2011, article 5 and 6
DATASET I.01—Technical Environmental Data—WWTP Scale
CategoryIDPSLIRGRMData ItemItem DescriptionNotes
GENERAL AA0.001xx1 Technology usedTechnical procedure with which the plan treats wastewater. Note any relevant particularities. If needed, include a diagram of the process in an annex.All non-domestic wastewater generators have to prepare a technical study including this item. Acuerdo Gubernativo 12-2011, article 5 and 6
A0.005xx Number of people served
INPUTS BB0.001xx Design inflow Flow capacity that the plant was originally designed for.
68B0.002xx Volume wastewater inputTotal volume of water entering the plant in the reporting year
B0.005xx Average plant capacity utilizationPercent of design capacity being used, on average, during the reporting year
B0.006xx Volumetric Efficiency Total wastewater entering the plant/Treated Wastewater (100)
Inflow quality parametersB1.001xx Temperature
B1B1.002xx BODBiological Oxygen demand
B1.003xx CODChemical oxygen demand
Inflow NutrientsB1.004xx Total Nitrogen
B1.008xx Total Phosphorus
B1.015 x Faecal coliforms
Pathogens inflowB1.016 x E.Coli
B1.021 x TSSTotal suspended solids
B1.023 x pH
Other inputs B2B2.001xx Raw materials used Raw materials as inputs necessary for the plant to function (e.g., machine oils, fuel, chemicals for the flocculation phase or other stages of the process, etc.), as well as office supplies and such. When data available is in other units, make sure to note so in the units column. Tonnes per year is a recommended unit.
B2.003xx Total energy consumedEnergy consumed in the reporting year, all energy carriers together and all energy uses considered.
OUTPUTS CC0.001xx Total volume Treated Water producedTotal Outflow of wastewater from the plant, in yearly total average.
C1.001xx11Temperature
C1.002xx11BODBiological Oxygen demand
C1.003xx1 CODChemical oxygen demand
C1.004xx11Total Nitrogen
C1.008xx11Total Phosphorus
Pathogens in outflowC1.015xx1 Faecal coliforms
C1.016xx E.coli
C1.017xx Helminths
C1.019xx Organic Matter
C1.021 1Sedimentable solids
C1.022xx11TSS
C1.023xx Turbidity
C1.024xx11pH
Metals, metalloids and trace elements in outflowC1.025xx Al
C1.026xx11As
C1.027xx Cd
C1.028 11Cyanide (CN)
C1.029xx Co
C1.030xx11Cr
C1.031xx11Cu
C1.032xx Fe
C1.033xx Mn
C1.034xx11Ni
C1.035xx Ti
C1.036xx11Zn
C1.037xx11Hg
C1.038xx11Pb
C1.039xx Se
C1.040xx B
C1.041xx Mo
C1.043x 11Grease and oils
C1.044x 11Floating matter
C1.045 1 Colour
Wastewater Reuse C2C2.001xx Percentage of wastewater output being recycled or reused
Sludge C3C3.001xx Total Sludge produced yearlyTotal amount of sludge produced in the reporting year.
Sludge Quality parametersC3.002xx Al
Metals, metalloids and trace elements in sludgeC3.003xx11As
C3.004xx11Cd
C3.005xx Co
C3.006xx11Cr
C3.007xx11Cu
C3.008xx Fe
C3.009xx Mn
C3.010xx11Ni
C3.011xx Ti
C3.012xx11Zn
C3.013xx11Hg
C3.014xx11Pb
C3.015xx Se
C3.016xx B
C3.017xx Mo
C3.030xx Calorific value
Pathogens in sludgeC3.031xx11Helminths
C3.032xx11Total coliforms
C3.033xx E.coli
C3.034 1Salmonella sp.
OrganicsC3.035xx Organic Matter
Sludge use C4C4.001xx Scope of sludge management% of sludge that is managed, including treatment in different ways, such as use in agriculture, thermal disposal, landfills, etc. As proposed by Popovic & Kraslawski (2018).
C4.002xx Current use/management of sludgeWhat is done with sludge once it is dried at the plant?
C4.004xx Potential sludge users
Emissions C5C5.001xx Total Biogas productionHow much biogas was produced in the reporting year?
C5.005xx GHG emissionsCan be divided into GHG emissions linked to plant operation and maintenance, and emissions produced by the wastewater itself. Specify and disclose method for Calculations performed in an annex. The online tool ECAM (wacclim.org/ecam) is an option for estimation.
Management D2D0.001xx Number of operators
Staff D0D0.003xx Employee/inhabitant ratioNumber of employees per 1000 inhabitants served by the plant.
Management D1D1.001xx Existence Operation manualDoes a clear, up to date operations manual exist on site, and available to all people operating the plant?
D1.002xx Regularity of maintenance
Capacities D2D2.001xx Capacity sufficiencyDoes all the personnel involved have the knowledge and skills they need to have?
D2.003xx Accessible sampling and processing equipmentDoes the plant have its own equipment or easy and hassle-free access to sample and analyse incoming wastewater, treated water and by-products quality?
Compliance and certification D3D3.001 x Discharge standards compliance Percent of time that the plant’s outflow complies with applicable regulations. State the regulations are being considered.
D3.002 x 1Analysis frequency complianceRatio of number of effluent samplings per month to number of effluent sampling per month required by law of wastewater treatment policy (as proposed by Popovic & Kraslawski (2018).
D3.003x CertificationDoes the plant have some quality certification (ISO, or other national/international standards)?
RISK E1E0.001 x Has a health risk assessment related to wastewater been performed at the site?
E0.002xx Are health risks being managed?
Health E0E0.003xx Do the operators have the necessary health and safety equipment?
E1.001 Has a natural hazard risk assessment been performed at the facility?
E1.002 Are natural hazard risks being managed?
E1.003 Has an environmental impact study relating wastewater with ecosystem health been performed at the site?
Other hazards E1E1.004xx What efforts are being made to reduce or manage environmental impacts?
E1.005 Presence or risk of groundwater pollution
E1.006 Presence or risk of surface water pollution
DATASET IIA.01—Economic Data—WWTP Scale
CategoryIDPSLI Data ItemItem DescriptionNotes
Costs A0A0.002 x Cost per m3 of water treatedCost of producing one cubic meter of water
A0.003 x Cost per inhabitant served
A0.006xx Proportion of costs: maintenance and repairsWhat proportion of the total expenses corresponds to energy?
A0.009 Proportion of costs: training, capacity buildingWhat proportion of the total expenses corresponds to energy?
Income A1A1.001 x Total plant incomeTotal income of the plant yearly. Specify currency used under ‘units’
A1.002 x Real financial availability per inhabitant served
A1.003 Budget deficit
A1.006 x Valorisation of by productsAre products of the plant being valorised (sold, recycled, etc.)
DATASET IIB.01—Social Acceptance—Multi-Scalar
CategoryIDPSIL Data ItemItem DescriptionNotes
SOCIAL BB0.001 Personal interest in wastewater management problems
Inclusion/ParticipationB0.002 Personal awareness of wastewater management problems
B0.003 Willingness to be informed about the wastewater management problems
B0.004 Accessibility to information
B0.005 Possibilities for providing a recommendation
B0.006 Recommendations are considered?
B0.007 Willingness to participate in decision-making
B0.008 Participative decision-making
B0.009 Personal acceptance of the current wastewater management
B0.010 Perception of social acceptance of the current wastewater management

Appendix B

Prioritised (Site-Specific) Dataset Framework—Tepeji
Total Data Items195
PS = Prioritised by stakeholdersLI = Data Item comes from the literature
RG = Included in Guatemala regulationRM = Included in Mexican regulation
The numbers in the ID column refer to those of the extended set.
DATASET 0.1—Context Data—WWTP Scale
CategoryIDPSLIRGRMData ItemItem DescriptionNotes
GEOGRAPHY AA0.003 1 MapCartography at the adequate scale to understand the location of the plant in relation to nearest population settlement, water resources and other relevant features.All non-domestic wastewater generators have to prepare a technical study including this item. Acuerdo Gubernativo 12-2011, article 5 and 6.
A0.006 x Land uses in 1 km radius
A0.007 x Distance to nearest house
DATASET I.01—Technical Environmental Data—WWTP Scale
CategoryIDPSLIRGRMData ItemItem DescriptionNotes
GENERAL AA0.001 x Technology usedTechnical procedure with which the plan treats wastewater. Note any relevant particularities. If needed, include a diagram of the process in an annex.All non-domestic wastewater generators have to prepare a technical study including this item. Acuerdo Gubernativo 12-2011, article 5 and 6
A0.002 x Construction yearYear of construction. When construction lasted more than one year, state ending year.
A0.005 x Number of people served
INPUTS BB0.001 x Design inflow Flow capacity that the plant was originally designed for.
B0.002 x Volume wastewater inputTotal volume of water entering the plant in the reporting year
Inflow B0B0.003 Average inflow (AF) Average flow (in a year) of wastewater into WWTP.
B0.005 x Average plant capacity utilizationPercent of design capacity being used, on average, during the reporting year
B0.006 x Volumetric Efficiency Total incoming wastewater/total treated water
Inflow quality parametersB1.001 x Temperature
B1B1.002 x BODBiological oxygen demand
B1.003 x CODChemical oxygen demand
Inflow NutrientsB1.004 x Total Nitrogen
B1.008 x Total Phosphorus
Salts inflowB1.009 x K
B1.010 x Ca
B1.011 x Mg
B1.012 x Na
B1.014 x Electric conductivityUseful when data for Na and other related parameters is not available, as general guidance of salts contents.
B1.015 x Faecal coliforms
Pathogens inflowB1.016 x E.coli
B1.021 x TSSTotal suspended solids
B1.023 x pH
B1.025 x As
B1.026 x Cd
B1.028 x Cr
B1.029 x Cu
B1.030 x Fe
B1.031 x Mn
B1.032 x Ni
B1.033 x Ti
B1.034 x Zn
B1.035 x Hg
B1.036 x Pb
B1.037 x Se
B1.038 x B
B1.039 x Mo
OthersB1.040 x Residual chlorine
B1.041 Grease and oils
B1.042 Floating matter
B1.043 Colour
B2.003 x Total energy consumedEnergy consumed in the reporting year, all energy carriers together and all energy uses considered.
B2.004 x Energy/m3 treated water
OUTPUTS CC0.001 x Total volume Treated Water producedTotal Outflow of wastewater from the plant, in yearly total average.
C1.001 x Temperature
C1.002 x BODBiological oxygen demand
C1.003 x CODChemical oxygen demand
C1.004 x Total Nitrogen
Nutrients in outflowC1.006 x Nitrates
C1.007 x Nitrites
C1.008 x Total Phosphorus
Salts in outflowC1.009 x K
C1.010 x Ca
C1.011 x Mg
C1.012 x Na
C1.014 x Electric conductivityUseful when data for Na and other related parameters is not available, as general guidance of salts contents.
Pathogens in outflowC1.015 x Faecal coliforms
C1.016 x E.coli
C1.017 x Helminths
C1.021 Sedimentable solids
C1.022 x TSSTotal suspended solids
C1.024 x pH
C1.026 x As
C1.027 x Cd
C1.028 Cyanide (CN)
C1.030 x Cr
C1.031 x Cu
C1.034 x Ni
C1.036 x Zn
C1.037 x Hg
C1.038 x Pb
C1.043 Grease and oils
C1.044 Floating matter
C1.045 Colour
Wastewater Reuse C2C2.001 x Percentage of wastewater output being recycled or reused
Sludge C3C3.001 x Total Sludge produced yearlyTotal amount of sludge produced in the reporting year.
Metals, metalloids and trace elements in sludgeC3.003 x As
C3.004 x Cd
C3.006 x Cr
C3.007 x Cu
C3.010 x Ni
C3.012 x Zn
C3.013 x Hg
C3.014 x Pb
Pathogens in sludgeC3.031 x Helminths
C3.032 x Total coliforms
C3.034 Salmonella sp.
sludge use C4C4.001 x Scope of sludge management% of sludge that is managed, including treatment in different ways, such as use in agriculture, thermal disposal, landfills, etc. As proposed by Popovic & Kraslawski (2018)
GHG EmissionsC5.006 x Are there complaints regarding odours?E.g., neighbours
C5.007 x Strength of odour in the treated wastewaterhigh, medium, low
Solid WasteC6.002 x Solid waste sustainable management planIs there a waste management programme in place that considers reuse and/or recycling of solid waste, and/or plans to reduce waste or eliminate it, e.g. by changing inputs?
Staff D0D0.003 x Employee/inhabitant ratioNumber of employees per 1000 inhabitants served by the plant.
Management D1D1.001 x Existence Operation manualDoes a clear, up to date operations manual exist on site, and available to all people operating the plant?
D1.002 x Regularity of maintenance
Capacities D2D2.001 x Capacity sufficiencyDoes all the personnel involved have the knowledge and skills they need to have?
D2.003 x Accessible Sampling and processing equipmentDoes the plant have its own equipment or easy and hassle-free access to sampling and analysis to monitor wastewater, treated water and by-products quality?
Compliance and certification D3D3.001 x Discharge standards compliance Percent of time that the plant’s outflow complies with applicable regulations. State which regulations are being considered
D3.002 x Analysis frequency complianceRatio between the number of effluent samplings per month and number of effluent sampling per month required by law of wastewater treatment policy (as proposed by Popovic & Kraslawski (2018))
D3.003 CertificationDoes the plant have some quality certification (ISO, or other national/international standards)
RISK E1E0.001 x Has a health risk assessment related to wastewater been performed at the site?
E0.002 x Are health risks being managed?
Health E0E0.003 x Do the operators have the necessary health and safety equipment?
E1.001 Has a natural hazard risk assessment been performed at the facility?
E1.002 Are natural hazard risks being managed?
E1.003 Has an environmental impact study relating wastewater with ecosystem health been performed at the site?
Other hazards E1E1.004 x What efforts are being made to reduce or manage environmental impacts?
E1.005 Presence or risk of groundwater pollution
E1.006 Presence or risk of surface water pollution
DATASET IIA.01—Social Economic Data—WWTP Scale
CategoryIDPSLI Data ItemItem DescriptionNotes
Costs A0A0.002 x Cost per m3 of water treatedCost of producing one cubic meter of water
16A0.003 x Cost per inhabitant served
A0.009 Proportion of costs: training, capacity buildingWhat proportion of the total expenses corresponds to energy?
Income A1A1.001 x Total plant incomeTotal income of the plant yearly. Specify currency used under ‘units’
A1.002 x Real financial availability per inhabitant served
A1.003 Budget deficit
A1.006 x Valorisation of by productsAre products of the plant being valorised (sold, recycled, etc.)
DATASET IIB.01—Social Acceptance—Multi-Scalar
CategoryIDPSLI Data itemItem descriptionNotes
SOCIAL BB0.001 Personal interest in wastewater management problems
Inclusion/ParticipationB0.002 Personal awareness of wastewater management problems
B0.003 Willingness to be informed about the wastewater management problems
B0.004 Accessibility to information
B0.005 Possibilities for providing a recommendation
B0.006 Recommendations are considered?
B0.007 Willingness to participate in decision-making
B0.008 Participative decision-making
B0.009 Personal acceptance of the current wastewater management
B0.010 Perception of social acceptance of the current wastewater management

Appendix C

Dataholders for the Panajachel Study Site—Final List
1—Stakeholder Local/Municipality2—Stakeholder Provincial or National3—Own Calculations4—Scientist Interview or Scientific Literature5—NGO Interview or Report
1Plant operator Julio Pablo de León1AMSCLAE interviews1Sampling and analysis1UVG—CEA1Amigos del Lago
2Encargado de la planta Cebollales Ing. Genaro Umul2AMSCLAE reports2Calculations2Laura Ferrans2Mancomunidad (Mankatitlán). Delvín Rolón, gerente
3Environmental office (oficina municipal del medio ambiente)/DIGAM3NE 3ERIS3Proyecto ProAtitlán
4Reports, monographs, other documentation published by municipality4MARN—provincial delegation at Sololá 4Elisandra Hernandez USAC4ANACAFE
5DGP—Planning authority at the municipality. Oficina Municipal de Agua5Ministerio de Salud 5Puravida
6Agua6MAGA—Ministerio de agricultura y ganadería 6Vivamos mejor
7Instituto Nacional de Estadística
8Energuate
Dataholders for the Tepeji Study Site—Final List
1—Stakeholder Local/Municipality2—Stakeholder Provincial or National3—Own Calculations4—Scientist Interview or Scientific Literature5—NGO Interview or Report
1CAAMTROH director1CONAGUA at state capital Pachuca1Sampling and analysis1Research by UNAM
2CAAMTROH/Field personnel2CONAGUA central office Mexico City2Calculations
3Dirección de ecología municipal3INEGI
4FIAVHI director
5FIAVHI technical staff
6Plant operator
7Urban development office at the municipality
8Owner of agricultural field who will receive treated WW

Appendix D

Water Quality Parameters Analyzed in Panajachel—Field Campaign 08.2018
Raw (WW) and Treated Wastewater (TWW)Sludge
1Temperature1Fecal coliforms
2pH2Helminth eggs
3Grease and oils3Al
4Floating matter4As
5BOD5Ca
6COD6Cd
7TSS7Co
8Total Nitrogen8Cr
9Total Phosphorus9Cu
10Fecal coliforms10Fe
11Apparent Color11Hg
12Al12K
13As13Mn
14Ca14Na
15Cd15Ni
16Co16P
17Cr17Pb
18Cu18Se
19Fe19Zn
20Hg
21K
22Mn
23Na
24Ni
25P
26Pb
27Se
28Zn
Water Quality Parameters Analyzed in Tepeji—Field Campaign 08.2018
Raw and Treated Wastewater
1Grease and oils
2Floating matter
3BOD
4COD
5Suspended solids
6TN
7TP
8pH
9Fecal coliforms
10Apparent color
11Al
12As
13Ca
14Cd
15Co
16Cr
17Cu
18Fe
19Hg
20K
21Mn
22Na
23Ni
24P
25Pb
26Se
27Zn
27Cn
28Sedimentable solids
29Nitrites
30Nitrates

Appendix E

Variables and threshold values considered for the Sustainability Assessment at the two study sites (Panajachel, Guatemala and Tepeji, Mexico). This table discloses the values and sources of the thresholds used in the Sustainability Assessment.
Variables and Thresholds for SA in Panajachel
Gt: Guatemala Regulation   Mx: Mexican Regulation   ST-Team: SludgeTec Team   WHO (2006): Guidelines for SUWA—Vol 2
No.Code (ID)VariableUnitThreshold ValueSourceRedYellowGreen
1TE7BTemperature—WW°C40AG 12-2011 Art. 14 (p.10) Gt>44>40 and ≤44≤40
2TE8BBiological Oxygen Demand (BOD)—WWmg/L100AG 12-2011 Art. 14 (p.10) Gt>110>100 and ≤110≤100
3TE9BChemical Oxygen Demand (COD)—WWmg/L200AG 12-2011 Art. 14 (p.10) Gt>220>200 and ≤220≤200
4TE10BTotal Nitrogen—WWmg/L20AG 12-2011 Art. 14 (p.10) Gt>22>20 and ≤22≤20
5TE11BTotal Phosphorus—WWmg/L10AG 12-2011 Art. 14 (p.10) Gt>11>10 and ≤11≤10
6TE12BFaecal coliforms—WWMPN/100 mL100,000AG 12-2011 Art. 14 (p.10) Gt>110,000>100,000 and ≤110,000≤100,000
7TE14BTotal Suspended Solids (TSS)—WWmg/L125AG 12-2011 Art. 14 (p.10) Gt>137.5>125 and ≤137.5≤125
8TE15BpH—WWpH unitbetween 6–9AG 12-2011 Art. 14 (p.10) Gt<6 and >9-≥6 and ≤9
9TE19CTemperature—TWW°CTRWB ±3AG 12-2011 Art. 11 (p.7) Gt<20 and >26-≥20 and ≤26
10TE20CBiological Oxygen Demand (BOD)—TWWmg/L30AG 12-2011 Art. 11 (p.7) Gt>33>30 and ≤33≤30
11TE21CChemical Oxygen Demand (COD)—TWWmg/L60AG 12-2011 Art. 11 (p.7) Gt>66>60 and ≤66≤60
12TE22CTotal Nitrogen—TWWmg/L5AG 12-2011 Art. 11 (p.7) Gt>5.5>5 and ≤5.5≤5
13TE23CTotal Phosphorus—TWWmg/L3AG 12-2011 Art. 11 (p.7) Gt>3.3>3 and ≤3.3≤3
14TE24CFaecal coliforms—TWWMPN/100 mL500AG 12-2011 Art. 11 (p.7) Gt>550>500 and ≤550≤500
15TE26CHelminths—TWW-5NOM-003-SEMARNAT-1997 Mx>5.5>5 and ≤5.5≤5
16TE29CTotal Suspended Solids (TSS)—TWWmg/L40AG 12-2011 Art. 11 (p.7) Gt>44>40 and ≤44≤40
17TE31CpH—TWWpH unitsbetween 6–9AG 12-2011 Art. 11 (p.7) Gt<6 and >9-≥6 and ≤9
18TE33CArsenic (As)—TWWmg/L0.1AG 12-2011 Art. 11 (p.10) Gt>0.11>0.1 and ≤0.11≤0.1
19TE34CCadmium (Cd)—TWWmg/L0.1AG 12-2011 Art. 11 (p.10) Gt>0.11>0.1 and ≤0.11≤0.1
20TE37CChromium (Cr)—TWWmg/L0.1AG 12-2011 Art. 11 (p.10) Gt>0.11>0.1 and ≤0.11≤0.1
21TE38CCopper (Cu)—TWWmg/L0.5AG 12-2011 Art. 11 (p.10) Gt>0.55>0.5 and ≤0.55≤0.5
22TE41CNickel (Ni)—TWWmg/L0.5AG 12-2011 Art. 11 (p.10) Gt>0.55>0.5 and ≤0.55≤0.5
23TE43CZinc (Zn)—TWWmg/L1AG 12-2011 Art. 11 (p.10) Gt>1.1>1 and ≤1.1≤1
24TE44CMercury (Hg)—TWWmg/L0.01AG 12-2011 Art. 11 (p.10) Gt>0.011>0.01 and ≤0.01≤0.01
25TE45CLead (Pb)—TWWmg/L0.1AG 12-2011 Art. 11 (p.10) Gt>0.11>0.1 and ≤0.11≤0.1
26TE49CGrease and oils—TWWmg/L15NOM-001-SEMARNAT-1996 (p.15) Mx>16.5>15 and ≤16.5≤15
27TE50CFloating matter—TWWPresent-AbsentPresent-AbsentAG 12-2011 Art. 11 (p.10) GtPresent-Absent
28TE51CColour—TWWPCU400AG 12-2011 Art. 11 (p.10) Gt>440>400 and ≤440≤400
29TE52CWater reuseYES-NOYES-NOST teamNO-YES
30TE55CArsenic (As)—Sludgemg/kg dry matter (104 °C)50AG 236-2006 para lodos—Application in soil Gt>55>50 and ≤55≤50
31TE56CCadmium (Cd)—Sludgemg/kg dry matter (104 °C)50AG 236-2006 para lodos Gt>55>50 and ≤55≤50
32TE58CChromium (Cr)—Sludgemg/kg dry matter (104 °C)1500AG 236-2006 para lodos Gt>1650>1500 and ≤1650≤1500
33TE59CCopper (Cu)—Sludgemg/kg (dry weight)1500NOM-004-SEMARNAT-2002 (p.6)—Excellent Biosolid Mx>1650>1500 and ≤1650≤1500
34TE62CNickel (Ni)—Sludgemg/kg (dry weight)420NOM-004-SEMARNAT-2002 (p.6)—Excellent Biosolid Mx>462>420 and ≤462≤420
35TE64CZinc (Zn)—Sludgemg/kg (dry weight)2800NOM-004-SEMARNAT-2002 (p.6)—Excellent Biosolid Mx>3080>2800 and ≤3080≤2800
36TE65CMercury (Hg)—Sludgemg/kg dry matter (104 °C)25AG 236-2006 para lodos—Application in soil Gt>27.5>25 and ≤27.5≤25
37TE66CLead (Pb)—Sludgemg/kg dry matter (104 °C)500AG 236-2006 para lodos—Application in soil Gt>550>500 and ≤550≤500
38TE71CHelminths—Sludgeegg/g (dry weight)10NOM-004-SEMARNAT-2002 (p.6) Mx>11>10 and ≤11≤10
39TE72CTotal coliforms—SludgeMPN/g (dry weight)1000NOM-004-SEMARNAT-2002 (p.6) Mx>1100>1000 and ≤1100≤1000
40TE74CSalmonella—Sludge-300NOM-004-SEMARNAT-2002 (p.6) Mx>330>300 and ≤330≤300
41TE76CScope of sludge management%100ST team<33.33≥33.33 and <66.67≥66.67 and ≤100
42TE78CIdentification of potential sludge consumers/usersYES-NOYES-NOST teamNO-YES
43TE80CQuantification of GHG emissionsYES-NOYES-NOST teamNO-YES
44TE83DOperation ManualYES-NOYES-NOST teamNO-YES
45TE84DRegular maintenanceYES-NOYES-NOST teamNO-YES
46TE85DCapacity sufficiencyYES-NOYES-NOST teamNO-YES
47TE86DAccessible Sampling and processing equipmentYES-NOYES-NOST teamNO-YES
48TE87DDischarge standards complianceYES-NOYES-NOST teamNO-YES
49TE88DAnalysis frequency compliance—watersamples/year2AG 236-2006 para lodos Gt<2-≥2
50TE89DAnalysis frequency compliance—sludgesamples/year2AG 236-2006 para lodos Gt<2-≥2
51TE90DCertificationYES-NOYES-NOST teamNO-YES
52TE91DHealth risk assessmentYES-NOYES-NOST teamNO-YES
53TE92ECurrent management of health risksYES-NOYES-NOST teamNO-YES
54TE93EHealth and safety equipmentYES-NOYES-NOST teamNO-YES
55TE94EPerformance of risk assessmentYES-NOYES-NOST teamNO-YES
56TE95ECurrent management of risksYES-NOYES-NOST teamNO-YES
57TE96EEnvironmental impact assessment (EIA)YES-NOYES-NOST teamNO-YES
58TE97EEfforts to reduce or manage environmental impactsYES-NOYES-NOST teamNO-YES
59TE98EPresence or risk of groundwater pollutionYES-NOYES-NOST teamYES-NO
60TE99EPresence or risk of surface water pollutionYES-NOYES-NOST teamYES-NO
61Ec2APer capita cost of WWTUSD/hab (inhabitants)/year4–8WHO>8.8>8 and ≤8.8≤8
62Ec7ABudget deficitYES-NOYES-NOST teamYES-NO
63Ec8AValorisation of by-productsYES-NOYES-NOST teamNO-YES
64S1BPersonal interest in wastewater management problemsscale 1–4between 1–4ST team≥1 and <2≥2 and <3≥3 and ≤4
65S2BPersonal awareness of wastewater management problemsscale 1–4between 1–4ST team≥1 and <2≥2 and <3≥3 and ≤4
66S3BWillingness to be informed about the wastewater management problemsscale 1–4between 1–4ST team≥1 and <2≥2 and <3≥3 and ≤4
67S4BAccessibility to informationscale 1–4between 1–4ST team≥1 and <2≥2 and <3≥3 and ≤4
68S5BPossibilities for providing a recommendationscale 1–4between 1–4ST team≥1 and <2≥2 and <3≥3 and ≤4
69S9BPersonal acceptance of the current wastewater managementscale 1–4between 1–4ST team≥1 and <2≥2 and <3≥3 and ≤4
70S10BPerception of social acceptance of the current wastewater managementscale 1–4between 1–4ST team≥1 and <2≥2 and <3≥3 and ≤4
Variables and Thresholds for SA in Tepeji
Gt: Guatemala Regulation   Mx: Mexican Regulation   ST-Team: SludgeTec Team   WHO (2006): Guidelines for SUWA—Vol 2
No.Code (ID)VariableUnitThreshold ValueSourceRedYellowGreen
1TE9BTemperature—WW°C40AG 236-2006 Art. 28 Gt>44>40 and ≤44≤40
2TE12BTotal Nitrogen—WWmg/L80AG 236-2006 Art. 28 Gt>88>80 and ≤88≤80
3TE13BTotal Phosphorus—WWmg/L20AG 236-2006 Art. 28 Gt>22>20 and ≤22≤20
4TE19BFaecal coliforms—WWMPN/100 mL10,000AG 236-2006 Art. 28 Gt>1100>1000 and ≤1100≤1000
5TE22BpH—WWpH unitbetween 6–9AG 236-2006 Art. 28 Gt<6 and >9-≥6 and ≤9
6TE23BArsenic (As)—WWmg/L0.5NOM-002-SEMARNAT-1996 (p.41) Mx>0.55>0.5 and ≤0.55≤0.5
7TE24BCadmium (Cd)—WWmg/L0.5NOM-002-SEMARNAT-1996 (p.41) Mx>0.55>0.5 and ≤0.55≤0.5
8TE25BChromium (Cr)—WWmg/L0.5NOM-002-SEMARNAT-1996 (p.41) Mx>0.55>0.5 and ≤0.55≤0.5
9TE26BCopper (Cu)—WWmg/L10NOM-002-SEMARNAT-1996 (p.41) Mx>11>10 and ≤11≤10
10TE29BNickel (Ni)—WWmg/L4NOM-002-SEMARNAT-1996 (p.41) Mx>4.4>4 and ≤4.4≤4
11TE31BZinc (Zn)—WWmg/L6NOM-002-SEMARNAT-1996 (p.41) Mx>6.6>6 and ≤6.6≤6
12TE32BMercury (Hg)—WWmg/L0.01NOM-002-SEMARNAT-1996 (p.41) Mx>0.011>0.01 and ≤0.01≤0.01
13TE33BLead (Pb)—WWmg/L1NOM-002-SEMARNAT-1996 (p.41) Mx>1.1>1 and ≤1.1≤1
14TE38BGrease and oils—WWmg/L50NOM-002-SEMARNAT-1996 (p.41) Mx>55>50 and ≤55≤50
15TE39BFloating matter—WWAbsent-PresentAbsentAG 236-2006 Art. 28 GtPresent-Absent
16TE40BColour—WWPCU500AG 236-2006 Art. 28 Gt>550>500 and ≤550≤500
17TE47CTotal Nitrogen—TWWmg/L30WHO>33>30 and ≤33≤30
18TE54CSodium (Na)—TWWmeq/l9WHO>9.9>9 and ≤9.9≤9
19TE55CElectric conductivity—TWWµS/cm30WHO>33>30 and ≤33≤30
20TE56CFaecal coliforms—TWWMPN/100 mL1000NOM-003-SEMARNAT-1997 Mx>1100>1000 and ≤1100≤1000
21TE58CHelminths—TWWegg/L5NOM-003-SEMARNAT-1997 Mx>5.5>5 and ≤5.5≤5
22TE60CTotal Suspended Solids (TSS)—TWWmg/L100WHO>110>100 and ≤110≤100
23TE61CpH—TWWpH unitsbetween 6.5–8WHO<6.5 and >8-≥6.5 and ≤8
24TE62CArsenic (As)—TWWmg/L0.1WHO>0.11>0.1 and ≤0.11≤0.1
25TE63CCadmium (Cd)—TWWmg/L0.01WHO>0.011>0.01 and ≤0.01≤0.01
26TE64CCyanide (CN)—TWWmg/L2NOM-001-SEMARNAT-1996 (p.14) Mx>2.2>2 and ≤2.2≤2
27TE65CChromium (Cr)—TWWmg/L0.1WHO>0.11>0.1 and ≤0.11≤0.1
28TE66CCupper (Cu)—TWWmg/L0.2WHO>0.11>0.1 and ≤0.11≤0.1
29TE67CNickel (Ni)—TWWmg/L0.2WHO>0.11>0.1 and ≤0.11≤0.1
30TE68CZinc (Zn)—TWWmg/L2WHO>2.2>2 and ≤2.2≤2
31TE69CMercury (Hg)—TWWmg/L0.005NOM-001-SEMARNAT-1996 (p.14) Mx>0.0055>0.01 and ≤0.01≤0.01
32TE70CLead (Pb)—TWWmg/L5WHO>5.5>5 and ≤5.5≤5
33TE71CGrease and oils—TWWmg/L15NOM-001-SEMARNAT-1996 (p.14) Mx>16.5>15 and ≤16.5≤15
34TE72CFloating matter—TWWAbsent-PresentAbsentNOM-001-SEMARNAT-1996 (p.14) MxPresent-Absent
35TE73CColour—TWWPCU400AG 12-2011 Art. 11 (p.10) Gt>440>400 and ≤440≤400
36TE74CWater reuse%between 0–100ST team<33.33≥33.33 and <66.67≥66.67 and ≤100
37TE88COdoursYES-NOYES-NOST teamYES-NO
38TE89CSolid waste management-YES-NOST teamNO-YES
39TE91COperation ManualYES-NOYES-NOST teamNO-YES
40TE92CRegular MaintenanceYES-NOYES-NOST teamNO-YES
41TE93CCapacity sufficiencyYES-NOYES-NOST teamNO-YES
42TE94CAccessible Sampling and processing equipmentYES-NOYES-NOST teamNO-YES
43TE95CDischarge standards complianceYES-NOYES-NOST teamNO-YES
44TE96CAnalysis frequency compliance—waterYES-NOYES-NOST teamNO-YES
45TE98CCertificationYES-NOYES-NOST teamNO-YES
46TE99CHealth risk assessmentYES-NOYES-NOST team0--
47TE100CCurrent management of health risksYES-NOYES-NOST teamNO-YES
48TE101CHealth and safety equipmentYES-NOYES-NOST teamNO-YES
49TE102CPerformance of risk assessmentYES-NOYES-NOST teamNO-YES
50TE103CCurrent management of risksYES-NOYES-NOST team0--
51TE104CEnvironmental impact assessment (EIA)YES-NOYES-NOST teamNO-YES
52TE105CEfforts to reduce or manage environmental impactsYES-NOYES-NOST team0--
53TE106CPresence or risk of groundwater pollutionYES-NOYES-NOST team0--
54TE107CPresence or risk of surface water pollutionYES-NOYES-NOST teamYES-NO
55Ec2APer capita cost of WWTUSD/hab/year1–1.5WHO>8.8>8 and ≤8.8≤1.5
56Ec6ABudget deficitYES-NOYES-NOST teamYES-NO
57Ec7AValorisation of by-productsYES-NOYES-NOST teamNO-YES
58S1BPersonal interest in wastewater management problemsscale 1–4between 1–4ST team≥1 and <2≥2 and <3≥3 and ≤4
59S2BPersonal awareness of wastewater management problemsscale 1–4between 1–4ST team≥1 and <2≥2 and <3≥3 and ≤4
60S3BWillingness to be informed about the wastewater management problemsscale 1–4between 1–4ST team≥1 and <2≥2 and <3≥3 and ≤4
61S4BAccessibility to informationscale 1–4between 1–4ST team≥1 and <2≥2 and <3≥3 and ≤4
62S5BPossibilities for providing a recommendationscale 1–4between 1–4ST team≥1 and <2≥2 and <3≥3 and ≤4
63S9BPersonal acceptance of the current wastewater managementscale 1–4between 1–4ST team≥1 and <2≥2 and <3≥3 and ≤4
64S10BPerception of social acceptance of the current wastewater managementscale 1–4between 1–4ST team≥1 and <2≥2 and <3≥3 and ≤4

Appendix F

Sustainability Assessment Results Per Variable (Panajachel, Guatemala)
R: Red   Y: Yellow   G: Green
No.Code (ID)VariableUnitMeasured/Gathered DataCategory
1TE7BTemperature—WW°C23.50G
2TE8BBiological Oxygen Demand (BOD)—WWmg/L1060.00R
3TE9BChemical Oxygen Demand (COD)—WWmg/L1150.00R
4TE10BTotal Nitrogen—WWmg/L33.05R
5TE11BTotal Phosphorus—WWmg/L26.65R
6TE12BFaecal coliforms—WWMPN/100 mL2.75 × 1015R
7TE14BTotal Suspended Solids (TSS)—WWmg/L610.00R
8TE15BpH—WWpH unit7.27G
9TE19CTemperature—TWW°C22.68G
10TE20CBiological Oxygen Demand (BOD)—TWWmg/L287.50R
11TE21CChemical Oxygen Demand (COD)—TWWmg/L224.00R
12TE22CTotal Nitrogen—TWWmg/L33.50R
13TE23CTotal Phosphorus—TWWmg/L16.19R
14TE24CFaecal coliforms—TWWMPN/100 mL1.32 × 1011R
15TE29CTotal Suspended Solids (TSS)—TWWmg/L565.00R
16TE31CpH—TWWpH units6.80G
17TE33CArsenic (As)—TWWmg/LNot detectableG
18TE34CCadmium (Cd)—TWWmg/LNot detectableG
19TE37CChromium (Cr)—TWWmg/L0.10G
20TE38CCopper (Cu)—TWWmg/L0.01G
21TE41CNickel (Ni)—TWWmg/LNot detectableG
22TE43CZinc (Zn)—TWWmg/L0.12G
23TE44CMercury (Hg)—TWWmg/LNot detectableG
24TE45CLead (Pb)—TWWmg/LNot detectableG
25TE49CGrease and oils—TWWmg/L367.50R
26TE50CFloating matter—TWWPresent-AbsentPresentR
27TE51CColour—TWWPCU648.00R
28TE52CWater reuseYES-NONOR
29TE55CArsenic (As)—Sludgemg/kg dry matter (104 °C)53.00Y
30TE56CCadmium (Cd)—Sludgemg/kg dry matter (104 °C)1.00G
31TE58CChromium (Cr)—Sludgemg/kg dry matter (104 °C)60.00G
32TE59CCopper (Cu)—Sludgemg/kg (dry weight)100.00G
33TE62CNickel (Ni)—Sludgemg/kg (dry weight)21.00G
34TE64CZinc (Zn)—Sludgemg/kg (dry weight)0.15G
35TE65CMercury (Hg)—Sludgemg/kg dry matter (104 °C)Not detectableG
36TE66CLead (Pb)—Sludgemg/kg dry matter (104 °C)61.00G
37TE71CHelminths—Sludgeegg/g (dry weight)9.00G
38TE72CTotal coliforms—SludgeMPN/g (dry weight)9 × 1013R
39TE76CScope of sludge management%NegligibleR
40TE78CIdentification of potential sludge consumers/usersYES-NO0.00G
41TE83DOperation ManualYES-NONOY
42TE84DRegular maintenanceYES-NONOR
43TE85DCapacity sufficiencyYES-NONOR
44TE86DAccessible Sampling and processing equipmentYES-NONOR
45TE88DAnalysis frequency compliance—watersamples/year2G
46TE89DAnalysis frequency compliance—sludgesamples/year2G
47TE90DCertificationYES-NONOR
48TE91DHealth risk assessmentYES-NONOR
49TE93EHealth and safety equipmentYES-NONOR
50TE94EPerformance of risk assessmentYES-NONOR
51TE96EEnvironmental impact assessment (EIA)YES-NONOR
52TE99EPresence or risk of surface water pollutionYES-NOYESR
53Ec2APer capita cost of WWTUSD/hab/year1.00R
54Ec7ABudget deficitYES-NOYesR
55Ec8AValorisation of by-productsYES-NONoR
56S1BPersonal interest in wastewater management problemsscale 1–44.00G
57S2BPersonal awareness of wastewater management problemsscale 1–44.00G
58S3BWillingness to be informed about the wastewater management problemsscale 1–43.60G
59S4BAccessibility to informationscale 1–42.40Y
60S5BPossibilities for providing a recommendationscale 1–43.40G
61S9BPersonal acceptance of the current wastewater managementscale 1–41.20R
62S10BPerception of social acceptance of the current wastewater managementscale 1–41.30R
Sustainability Assessment Results Per variable (Tepeji, Mexico)
R: Red   Y: Yellow   G: Green
No.Code (ID)VariableUnitMeasured/Gathered DataCategory
1TE9BTemperature—WW°C21.00G
2TE12BTotal Nitrogen—WWmg/L115.38R
3TE13BTotal Phosphorus—WWmg/L4.71G
4TE19BFaecal coliforms—WWMPN/100 mL2.40 × 103R
5TE22BpH —WWpH unit8.85G
6TE23BArsenic (As)—WWmg/L0.00G
7TE24BCadmium (Cd)—WWmg/L0.02G
8TE25BChromium (Cr)—WWmg/L0.05G
9TE26BCupper (Cu)—WWmg/L0.02G
10TE29BNickel (Ni)—WWmg/L0.05G
11TE31BZinc (Zn)—WWmg/L0.02G
12TE32BMercury (Hg)—WWmg/L0.00G
13TE33BLead (Pb)—WWmg/L0.00G
14TE38BGrease and oils—WWmg/L5.41G
15TE39BFloating matter—WWAbsent-PresentAbsentG
16TE40BColour—WWPCU100.00G
17TE47CTotal Nitrogen—TWWmg/L120.62R
18TE55CElectric conductivity—TWWµS/cm1.84G
19TE56CFaecal coliforms—TWWMPN/100 mL2400.00R
20TE60CTotal Suspended Solids (TSS)—TWWmg/L46.00G
21TE61CpH —TWWpH units8.32R
22TE62CArsenic (As)—TWWmg/L0.00G
23TE63CCadmium (Cd)—TWWmg/L0.02R
24TE64CCyanide (CN)—TWWmg/L0.64G
25TE65CChromium (Cr)—TWWmg/L0.05G
26TE66CCupper (Cu)—TWWmg/L0.02G
27TE67CNickel (Ni)—TWWmg/L0.05G
28TE68CZinc (Zn)—TWWmg/L0.02G
29TE69CMercury (Hg)—TWWmg/L0.00G
30TE70CLead (Pb)—TWWmg/L0.10G
31TE71CGrease and oils—TWWmg/L5.00G
32TE72CFloating matter—TWWAbsent-PresentAbsentG
33TE73CColour—TWWPCU100.00G
34TE74CWater reuse%100.00G
35TE88COdoursYES-NOYESR
36TE89CSolid waste management-NOR
37TE91COperation ManualYES-NONOR
38TE92CRegular MaintenanceYES-NODailyG
39TE93CCapacity sufficiencyYES-NONOR
40TE94CAccessible Sampling and processing equipmentYES-NONOR
41TE95CDischarge standards complianceYES-NONOR
42TE96CAnalysis frequency compliance—waterYES-NONOR
43TE98CCertificationYES-NOYESG
44TE100CCurrent management of health risksYES-NOYESG
45TE101CHealth and safety equipmentYES-NOYESG
46TE102CPerformance of risk assessmentYES-NONOR
47TE104CEnvironmental impact assessment (EIA)YES-NONOR
48TE107CPresence or risk of surface water pollutionYES-NONOG
49S1BPersonal interest in wastewater management problemsscale 1–43.71G
50S2BPersonal awareness of wastewater management problemsscale 1–43.57G
51S3BWillingness to be informed about the wastewater management problemsscale 1–43.29G
52S4BAccessibility to informationscale 1–41.86R
53S5BPossibilities for providing a recommendationscale 1–42.71Y
54S9BPersonal acceptance of the current wastewater managementscale 1–42.64Y
55S10BPerception of social acceptance of the current wastewater managementscale 1–41.64R

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Figure 1. The general method used in this research project. Highlighted blocks are the four building blocks in our method. This paper deals in detail with two blocks: Baseline Description and Sustainability Assessment.
Figure 1. The general method used in this research project. Highlighted blocks are the four building blocks in our method. This paper deals in detail with two blocks: Baseline Description and Sustainability Assessment.
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Figure 2. Location of the pilot sites.
Figure 2. Location of the pilot sites.
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Figure 3. (a) A first boundary explorations map for the Panajachel case, showing different scales of analysis that were initially found to be of interest: the plant scale (red dot), the subcatchment (light green), the municipality (yellow), the watershed (blue) and the province (orange). The large water body in the center is the Atitlan Lake. (b) The abstraction of “real-world” boundaries into boundaries for the modeling process. (c) The system model for the Panajachel case, showing the systems components in the scale they (mostly) operate in.
Figure 3. (a) A first boundary explorations map for the Panajachel case, showing different scales of analysis that were initially found to be of interest: the plant scale (red dot), the subcatchment (light green), the municipality (yellow), the watershed (blue) and the province (orange). The large water body in the center is the Atitlan Lake. (b) The abstraction of “real-world” boundaries into boundaries for the modeling process. (c) The system model for the Panajachel case, showing the systems components in the scale they (mostly) operate in.
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Figure 4. Data sources per type. (a) Panajachel pilot site; (b) Tepeji pilot site.
Figure 4. Data sources per type. (a) Panajachel pilot site; (b) Tepeji pilot site.
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Table 1. Steps followed in the construction of the extended dataset framework.
Table 1. Steps followed in the construction of the extended dataset framework.
Bottom UpTop Down
1System model analysisResearch literature review
2Stakeholder input (assessment workshop) on locally relevant data items and indicatorsPolicies and regulations review
3-Technical guidelines review
Table 2. Criteria used to prioritize the data items in the extended dataset framework and to create a site-specific dataset framework.
Table 2. Criteria used to prioritize the data items in the extended dataset framework and to create a site-specific dataset framework.
CriteriaPriority
1aStakeholders chose the item during Assessment WorkshopP1
PLUS (+)
1bLiterature on wastewater management mentions it
2Locally applicable regulation calls for the parameterP2
3Thresholds to compare current value against are availableP3
Table 3. Colour ranking in Sustainability Assessment.
Table 3. Colour ranking in Sustainability Assessment.
Data TypeCriteriaRanking
RedYellowGreen
Real number10% toleranceMV > TH × 1.1TH < MV ≤ TH × 1.1MV ≤ TH
PercentageRange divided into 3 equal parts (33% each)MV < 33% or
67% ≤ MV
33% ≤ MV < 67%67% ≤ MV or
MV < 33%
Absolute values (e.g., yes/no questions)No yellow range, unless mentioned otherwiseYES/NO
Present/Absent
Outside pH range
-YES/NO
Present/Absent
Within pH range
Social variables (dataset IIb)Scale 1 to 41 ≤ MV < 22 ≤ MV < 33 ≤ MV ≤ 4
MV: measured value; TH: threshold (numeric thresholds where normally defining a maximum, not a minimum).
Table 4. Extended dataset framework, overview of subsets and number of data items in each.
Table 4. Extended dataset framework, overview of subsets and number of data items in each.
SubsetDescription ScalesNumber of Data Items
Dataset 0
Context indicators
Understanding of context: geographical location and characteristics, poverty and employment indicators, etc.50 data items for 4 scales01WWTP7
02Municipal18
03Subcatchment13
04Watershed12
Dataset I
Technical-Environmental
Technical and environmental variables (e.g., population served, chemical parameters of water bodies and of effluents, WWTP management)380 data items across 4 scales01WWTP211
02Municipal31
03Subcatchment70
04Watershed68
Dataset II
Socio-Economical
Economic, financial, budget variables. Dataset IIb useful to understand the social acceptance of the systemIIa.
52 data items for 4 scales
01WWTP16
02Municipal17
03Subcatchment7
04Watershed12
IIb.
10 data items, across scales
Social space
(cross-scale)
10
Total data items492
Table 5. Site-specific dataset frameworks for both pilot sites, after prioritizing the EF.
Table 5. Site-specific dataset frameworks for both pilot sites, after prioritizing the EF.
Tepeji Dataset FrameworkPanajachel Dataset Framework
DatasetScaleNumber of ItemsDatasetScaleNumber of Items
Dataset 0
Context
013Dataset 0
Context
011
023020
034030
045040
Total15Total1
Dataset I
Technical Environmental
01107Dataset I
Technical Environmental
0198
02150215
03150355
04180418
Total155Total186
Dataset IIa
Social-Economic
017Dataset IIa
Social-Economic
018
025028
030030
043045
Total15Total20
Data IIb
Multi-scalar Social
Total10Data IIb
Multi-scalar Social
Total10
Total items in framework195Total items in framework218
Grey shaded areas indicate the data that used in sustainability assessment
Table 6. Data gathered for all scales, data gathered specifically for Scale 01 and data finally computed into the sustainability assessment: Panajachel pilot site.
Table 6. Data gathered for all scales, data gathered specifically for Scale 01 and data finally computed into the sustainability assessment: Panajachel pilot site.
All Scales: Data FoundScale 01: Data FoundScale 01: Data Found and Useful
Total ItemsItems Found% FoundTotal ItemsItems Found%Number of Items Found and Useful%
Dataset 011100.0011100- *- *
Dataset I1868847.31987374.495271.23
Dataset II312374.19181688.891062.50
Total21811251.381179076.926268.89
* NOTE: Dataset 0 contains context data and was not used directly in the sustainability assessment.
Table 7. Data gathered for all scales, data gathered specifically for Scale 01 and data finally computed into the sustainability assessment: Tepeji pilot site.
Table 7. Data gathered for all scales, data gathered specifically for Scale 01 and data finally computed into the sustainability assessment: Tepeji pilot site.
All Scales: Data FoundScale 01: Data FoundScale 01: Data Found and Useful
Total ItemsItems Found% FoundTotal ItemsItems Found%Number of Items Found and Useful%
Dataset 0151066.6733100.00- *- *
Dataset I1559360.001078175.704859.26
Dataset II251872.00171270.59758.33
Total19512162.051279675.595557.29
* NOTE: Dataset 0 contains context data and was not used directly in the sustainability assessment.
Table 8. Sustainability performance per dimension: Panajachel site.
Table 8. Sustainability performance per dimension: Panajachel site.
Variables Per Category% Variables Per CategoryDimension Average
DimensionR *YGTotalRYGTotalValueColour
Technical-Environmental (TE)272235252%4%44%100%−0.08Y
Economic (Ec)3003100%0%0%100%−1.00R
Social (S)214729%14%57%100%0.29Y
Total or Average323276260%6%34%100%−0.26Y
* R = Red. Y = Yellow. G = Green. ND = No Data.
Table 9. Sustainability performance per dimension: Tepeji site.
Table 9. Sustainability performance per dimension: Tepeji site.
Variables Per Category% Variables Per CategoryDimension Average
DimensionR *YGTotalRYGTotalValueColour
Technical-Environmental (TE)150334831%0%69%100%0.38G
Economic (Ec)0000NDNDND0%NDND
Social (S)223729%29%43%100%0.14Y
Total or Average1723655NDNDNDNDNDND
* R = Red. Y = Yellow. G = Green. ND = No Data.

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