Water Reuse: Contribution of a Decision Support Model

Abstract

Sustainability seeks to strike a balance between preserving the environment and meeting human needs without compromising future generations. In this context, and considering the effects of climate change on water availability, water reuse is emerging as an alternative to conventional water sources for various purposes, contributing to sustainability. Water reuse projects are, in general, not simple to implement due to different technical, environmental, social and economic aspects. In this paper, a support decision model for water reuse projects is presented, identifying relevant indicators and parameters. Based on a literature review, four indicators or dimensions (technical, social, environmental and economic) and twelve parameters (e.g., WWTP safety, transport complexity, existence of legislation, risk to health and environment, energy consumption, degree of acceptance and required investment and operation and maintenance costs) are proposed. The Fuzzy Analytic Hierarchy Process (FAHP) method is used as a component of the model to determine the weights of the indicators and parameters in order to allow the calculation of a reuse feasibility index (RFI). The developed model was applied to the city of Aquiraz, Ceará, Brazil, and the RFI found was 82%, which means that the water reuse project had very high viability. The results underwent a sensitivity analysis, which confirms the consistency of the conclusions.

1. Introduction

The issue of water scarcity has become a global concern due to increasing human demand for water, primarily for irrigated agriculture and urban areas. Irrigation accounts for the largest share of water usage worldwide, consuming 70% of global water, while industrial and municipal uses account for about 20% and 10%, respectively [1].

According to [2], two-thirds of the global population, approximately 4 billion people, experience severe water scarcity for at least one month each year. Additionally, half a billion people face severe water scarcity throughout the entire year.

In this context, the reuse of treated wastewater has gained prominence. Water reuse in large cities and metropolitan areas can reduce the need for freshwater abstraction from reservoirs, increase the availability of water for inland regions, support landscaping, and provide an alternative water source for industries and irrigation areas [3].

Water reuse allows conventional water sources to be saved for drinking water purposes and reduces the environmental impact of disposing wastewater into surface or groundwater resources. In other words, it contributes to water sustainability, increasing its availability to meet present and future needs for more noble uses, and by reducing treated wastewater discharged into receiving environments, it protects ecosystems from pollutants released into the environment [4,5,6].

Globally, according to [3], 11.6% of wastewater is reused for some purpose but with large differences between countries. Europe, for example, has a very high average rate of wastewater collection and treatment at around 93%, but the average water reuse rate is much lower at less than 3%. In Cyprus, it is around 92%, while in France, Spain and Portugal, the percentages rates are 10%, 16% and 1.5%, respectively. The implementation of wastewater reuse projects is, in general, not easy and/or simple due to multiple and complex aspects related, namely, to technical, environmental, social and economic dimensions [3,4,7,8,9] and the way to balance or weight these different aspects to support a decision. As such, scientific research that investigates the barriers and potential of treated wastewater reuse is crucial, especially in areas where water scarcity is already a reality. From the literature review [3,4,7,8,9,10,11], four indicators (technical, social, environmental and economic) and twelve parameters (e.g., WWTP safety, transport complexity, existence of legislation, risk to health and environment, energy consumption, degree of acceptance, and required investment and operation and maintenance costs) with direct influence on a decision for water reuse were identified.

Thus, this research aims to provide a practical solution for quick application, supporting a preliminary decision on whether or not to implement water reuse for suitable purposes.

A decision support model that incorporates indicators and parameters, along with their respective weights, provides a valuable framework for evaluating the technical, social, environmental and economic feasibility of water reuse in a city or region. By applying this model, it is possible to assess in the early stages of the study whether implementing a treated wastewater reuse program in a given region has the potential to be economically, technically, socially, and environmentally viable.

The general objective of this paper is to develop a methodology to support decision-making for water reuse.

The study carried out was analytical–descriptive and operational research to support decision-making. Traditional operational research, with its objectivity and economic rationality, ended up being restricted to problems that were not very relevant from a social point of view, although important from a technical point of view. Descriptive studies, however, have shown that, in practice, decision-makers often violate the rules of rationality. From there, Soft Operational Research emerged, which views decision-making as a social process in which decision-makers necessarily interpret and understand reality differently [12]. In social processes, such as decision-making, it is necessary to take into account the values, objectives, aspirations and interests of the individuals who have to make decisions [13], and to this end, there must be tools to help decision-makers define their problem. One of these tools is multicriteria decision support methods, which are based on a constructivist approach.

Multicriteria methods were basically developed under the precepts of two schools: the American School, which develops multicriteria decision-making methods called MCDM (multicriteria decision-making), characterized by adopting the scientific paradigm of rationalism, and the European School, which develops multicriteria decision support methods called MCDAs (multicriteria decision aids), which are characterized by adopting the scientific paradigm of constructivism [14].

According to [14], it is important to define the scientific paradigm that will be adopted in the development of a model so that, as criticisms of the established model arise, the precepts of the adopted paradigm and the objectives that are proposed are made explicit. For this author, the choice of a particular paradigm is due exclusively to the values of the researchers or consultants who adopt it, making it impossible to determine which one is the best.

In the constructivist paradigm, the existence of an external reality is emphasized, but, unlike the objectivist view in which reality is independent of the subject, in this view, the subject has an active role in the production of knowledge since this reality is perceived by him/her, and with reference to this reality, the subject is seen as being engaged in constant interaction and the need for adaptation [12,14]. In the constructivist paradigm, the interests, values and objectives of the various groups involved in a decision-making process must be taken into account [13].

This assumption that decision-making is a unique event since it is a social process causes the emphasis to shift toward models and solutions that meet the local context rather than seeking generalizable truths. The model is intended to provide decision support for those decision-makers whose model was constructed at a given moment in time and for that specific decision. Therefore, constructivist methods are a tool that aims not only to evaluate and, eventually, choose an alternative but mainly to create new (and better) alternatives; identify decision opportunities; guide strategic thinking; interconnect decisions; guide the process of obtaining information; facilitate the involvement of multiple influence groups; improve communication; and allow the decision-maker to reflect on their values and objectives [14,15].

Considering that in this work, one of the aspects influencing the decision to water reuse is the social aspect, the constructivist paradigm was adopted. Thus, an MCDA method was used with the objective of hierarchizing the indicators and parameters that influence the decision to water reuse.

An MCDA process begins with the determination of objectives, which should be as clear as possible to facilitate subsequent phases. The MCDA analysis carried out in this work began with the identification of the indicators and parameters to be used and continued with their respective estimation in order to allow the evaluation of alternatives, namely, reusing or not reusing treated wastewater from a Wastewater Treatment Plant (WWTP).

Once the relevant indicators and parameters for water reuse decisions have been identified, the relative importance of each of them must be quantified. This quantification was carried out using weightings, which are crucial for obtaining the decision support model, such as the AHP (Analytic Hierarchy Process).

The AHP method, introduced by [16], is one efficient method for decision support. The traditional AHP method, however, cannot reflect the uncertainty of human thinking. As a result, the Fuzzy AHP (FAHP) method was developed to solve hierarchical uncertainty problems. The FAHP method, used in this work, is a systematic approach to decision support problems that uses concepts from fuzzy set theory and hierarchical structure analysis [16,17].

The theory of fuzzy sets and fuzzy logic allows us to capture the natural phenomenon of imprecision and uncertainty in human thought, expanding traditional logic by including instances of partial truth, and can be used to deal with diffuse and uncertain problems in the real world [18].

Thus, the multicriteria Fuzzy Analytic Hierarchy Process (FAHP) method was used to determine the weights of indicators and parameters and subsequent construction of the decision support model based on the potential reuse feasibility index (RFI). The results were subjected to a sensitivity analysis that indicated consistency of the global performance results obtained by the Fuzzy AHP method.

2. Materials and Methods

Based on the indicators and parameters identified in the literature review, the weights were established using the Fuzzy Analytic Hierarchy Process (FAHP) method, as described by [17]. Eight steps were developed for the FAHP method, as follows:

2.1. STEP 1: Development of the Hierarchical Structure

In step 1, the hierarchical structure was developed based on 4 indicators (criteria) and 12 parameters (sub-criteria), as shown in Figure 1. The parameters were grouped under the indicators technical (C₁), social (C₂), environmental (C₃) and economic (C₄). Parameters are denoted as C_ij. For example, in Technical Indicator (C₁), three parameters include transport complexity (C₁₁), safety and resilience of the treatment solution (C₁₂) and the existence of appropriate legislation (C₁₃). After establishing this hierarchical structure with 4 indicators and 12 parameters, FAHP can be used to determine their relative importance to support water reuse.

2.2. STEP 2: Obtaining Judgments from Experts

Experts were asked to rate pairwise comparisons between indicators and between parameters within an indicator (e.g., C₁).

Table 1. Judgment of technical indicator parameters by expert 1.

	EMI+	MMI+	MI+	SMI+	EI	SLI−	LI−	MLI−	ELI−
C11				x						C12
C11		x								C13
C12				x						C13

illustrates the partial judgments of expert 1 for parameters in indicator C₁ with a “fuzzy” nine-point scale, according to [16,19]. The judgments for the other indicator and parameters by expert 1 and the other 4 experts were conducted in a similar manner. For simplicity, only C₁’s parameters are illustrated from this step onward.

As for the importance options (qualitative judgment), they are extremely more important (EMI+), much more important (MMI+), more important (MI+), slightly more important (SMI+), equally important (EI), slightly less important (SLI−), less important (LI−), much less important (MLI−) and extremely less important (ELI−).

Table 2. Conversion of qualitative judgments into numerical judgments for the technical indicator parameters.

Experts	Stage 2: Judgment of Experts
	C11 × C12	C11 × C13	C12 × C13
1	3	7	3
2	1/5	1/7	1
3	1/3	1/3	1
4	1/5	1/3	3
5	1/3	1	3

shows the conversion of qualitative judgment into numerical judgment using the nine-point “fuzzy” scale, according to [16,19], of the five experts. Expert 1 classified the complexity of transport (C₁₁) as slightly more important than the safety and resilience of the WWTP (C₁₂), receiving a score of 3. When comparing parameter C₁₁ with C₁₃ (the existence of legislation), expert 1 classified C₁₁ as much more important, receiving a score of 7. And finally, when comparing C₁₂ with C₁₃, he considered that the safety and resilience of the WWTP are slightly more important than the existence of the legislation, receiving a score of 3. When the comparison classified one subcriteria less important than the other, such as the judgment of expert 2 when comparing C₁₁ x C₁₂, the score received is 1/5, and this was used for the LMI− (1/3), MI− (1/5), MMI− (1/7) and EMI− (1/9) comparisons.

2.3. STEP 3: Converting Expert Judgments into Fuzzy Triangular Numbers

Step 3 involved converting the judgments into fuzzy triangular numbers acknowledging the subjective and uncertain nature of these assessments. The conversion was based on a nine-point fuzzy scale with a fuzzification factor (Δ) set to 1. The triangular numbers are represented as (l, m, u), where l is the lower limit, m is the modal value, and u is the upper limit, as follows:

(1, 1, 1) if the relative complexity was judged to be 1;

(x − Δ, x, x + Δ) if the relative complexity was judged with x (x = 2, 3, …8);

(8, 9, 9) if the relative complexity was judged to be 9;

(1/(x + Δ), x, 1/(x − Δ)) if the relative complexity was judged to be 1/x;

(1/9, 1/9, 1/8) if the relative complexity was judged to be 1/9.

Table 3. Fuzzy triangular numbers of the 5 experts’ judgments for the technical indicator parameters (C₁).

Experts	Stage 3: Conversion of Judgments into Fuzzy Triangular Numbers
	C11 × C12			C11 × C13			C12 × C13
	l	m	u	l	m	u	l	m	u
1	2	3	4	6	7	8	2	3	4
2	1/6	1/5	1/4	1/8	1/7	1/6	1	1	1
3	1/4	1/3	1/2	1/4	1/3	1/2	1	1	1
4	1/6	1/5	1/4	1/4	1/3	1/2	2	3	4
5	1/4	1/3	1/2	1	1	1	2	3	4
Min (l), Max (u) and Geometric Mean (m)	0.17	0.42	4	0.13	0.64	8	1	1.93	4

shows the judgment of the 5 experts and the fuzzy triangular numbers of the judgments for the parameters of the technical indicator (C₁). The last line of the table shows the minimum value of the value of (l), the geometric mean of the modal value (m), and the maximum value of the value of (u) of the 5 experts’ judgments, calculated according to Equations (1)–(3). These values from the last line were used in step 4.

2.4. STEP 4: Aggregation of Expert Judgments into a Fuzzy Judgment Matrix

In Step 4, the fuzzy judgment matrices were constructed for each comparison based on the fuzzy triangular numbers obtained from the experts. The aggregation of these judgments followed Equations (1)–(3), where (l_ijk, m_ijk, u_ijk) is the comparison, pair by pair, between the criterion (or subcriterion) i and j evaluated by the kth judge in step 3, and K is the number of judges:

l_ij = min (l_ijk),

(1)

$${\mathrm{m}}_{\mathrm{i}\mathrm{j}}=\sqrt[\mathrm{k}]{{\mathrm{\Pi }}_{\mathrm{k}=1}^{\mathrm{k}}{\mathrm{m}}_{\mathrm{i}\mathrm{j}\mathrm{k}}},$$

(2)

u_ij = max (u_ijk),

(3)

where l_i indicates the minimum rating value of all experts; m_i is the geometric mean of the weighting made by experts in relation to criterion i (modal value); and u_i indicates the maximum score given by the experts. In this paper, the evaluation was carried out by five experts. It is worth noting that (l_ji, m_ji, u_ji) = (1/u_ij, 1/m_ij, 1/l_ij).

With the values in the last line of Table 3, the fuzzy judgment matrix was obtained, as shown in

Table 4. Fuzzy judgment matrix for technical indicator parameters (C₁).

	C11			C12			C13
	l	m	u	l	m	u	l	m	u
C11	1	1	1	0.17	0.42	4	0.13	0.64	8
C12	0.25	2.37	5.88	1	1	1	1	1.93	4
C13	0.13	1.55	7.69	0.25	0.52	1	1	1	1

, which, as an example, presents the results of the parameters of the technical indicator (C₁).

2.5. STEP 5: Consistency Check

A consistency check is necessary to verify whether the judgments are random, a situation in which the matrix has a high degree of inconsistency, or based on the judge’s scientific knowledge [19].

The consistency ratio (CR) was calculated using the standard approach of the Analytic Hierarchy Process (AHP), where consistent judgments satisfy the condition CR < 0.1. The CR is given by Equation (4) [16]:

$$\mathrm{C}\mathrm{R}={\displaystyle \frac{\mathrm{C}\mathrm{I}}{\mathrm{R}\mathrm{I}}}={\displaystyle \frac{{\mathrm{\lambda }}_{\mathrm{m}\mathrm{a}\mathrm{x}}-\mathrm{n}}{\mathrm{R}\mathrm{I}(\mathrm{n}-1)}}$$

(4)

where CR is the consistency ratio, CI = the consistency index; n = number of criteria in the matrix and must be smaller than the eigenvalue; λmax is the main eigenvalue of the judgment matrix; and RI = the random index based on matrix size n, according to [16].

Briefly, according to [16], the consistency ratio (CR) can be evaluated by the modal value m. Thus, the matrix constructed to check consistency is formed with the modal values of the matrices from step 4.

As an example,

Table 5. Consistency check based on the fuzzy judgment matrix for the technical indicator parameters (C₁).

Matrix A				Sum	Eigenvector w	N	A × w	λ	λmax	IC	IR	RC
	C11	C12	C13
C11	1	0.42	0.64	2.066	0.1979	3	0.6016	3.03	3	0.003	0.52	0.006
C12	2.37	1	1.93	5.30	0.5081		1.5457	3.04
C13	1.55	0.52	1	3.07	0.2939		0.8639	2.93
	Total			10.43

presents the verification of the consistency of the judgments of the subcriteria of the technical criteria (C₁).

The sum of the values of each row of the matrix and the total sum were calculated. From there, the eigenvector (w) of each row of the matrix was calculated as the division between the sum of the corresponding row by the total sum. The value of n corresponds to the number of criteria in the matrix. In Table 5, the number of criteria is 3. Therefore, the value of n is 3.

Next, the vector product (Aw) was calculated from the multiplication of each row of the matrix by the eigenvector. The lambda λ was then calculated from the quotient between the vector product and eigenvector. From λ, λmax was obtained as the average of the values of λ.

The RI (random index) was taken from [16]. Considering that n is equal to 3, according to [19], the RI value is 0.52. The CI (consistency index) was calculated from Equation (4) as (λmax − n)/(n − 1).

As the CR was less than 0.1, matrix A in Table 5 appears to be consistent; that is, the judgment was not random.

This procedure was carried out for all the other subcriteria assumed in this paper and the criteria–pair judgment matrix.

2.6. STEP 6: Defuzzification of the Judgment Matrix

Step 6 involved converting the fuzzy numbers in the pairwise comparison matrix into crisp numbers through defuzzification. The degree of confidence (α-cut) and the risk aptitude (λ) of the decision-maker were used, with both ranging between 0 and 1. A large α-cut or a large λ shows more confidence or a more optimistic view of the situation, respectively. In the present paper, the defuzzification process was carried out based on Equations (5)–(7), using α-cut = 0.5 and λ = 0.5, as proposed by [17]:

Z^α_ijl = (m_ij − l_ij)α + l_ij,

(5)

Z^α_ijr = u_ij − (u_ij − m_ij)α,

(6)

Z^λ_ijα = λZ^α_ijr + (1 − λ)Z^α_ijl,

(7)

where m_ij, l_ij and u_ij are elements of the matrices constructed in step 4. Equations (5) and (6) generate 2 matrices, whose elements are calculated by the respective equations. The defuzzified matrix is then formed by the elements calculated with Equation (7), which takes into account the elements calculated by Equations (5) and (6).

Table 6. Defuzzified matrix for the parameters of the technical indicator (C₁) using Z_ijα^λ from Equation (7).

	Z11	Z12	Z13
Z11	1	1.253	2.354
Z12	2.718	1	2.216
Z13	2.730	0.571	1

demonstrates a case of defuzzification, with an α-cut of 0.5 (moderate confidence) and λ of 0.5 (moderate risk attitude) for C₁. For example:

Z^α=0.5_12l = (m₁₂ − l₁₂)α + l₁₂ = [(2.37 − 0.25) × 0.5] + 0.25 = 1.310

Z^α=0.5_12r = u₁₂ − (u₁₂ − m₁₂)α = 5.88 − [(5.88 − 2.37) × 0.5] = 4.126

Z^λ=0.5_12α=0.5 = λZ^α=0.5_12r + (1 − λ)Z^α=0.5_12l = (0.5 × 4.12) + [(1 − 0.5) × 1.31] = 2.718

2.7. STEP 7: Determination of Local Weights

Step 7 consisted of determining the local weights of the indicators and parameters (criteria and subcriteria). According to [17], several methods are used to obtain the local weights (c_i) of the criteria and subcriteria. A simple method is through Equation (8):

$$c_i={\displaystyle \frac{1}{n}}{\sum }_{j=1}^n{\displaystyle \frac{Z_{ij}}{\sum _{k=1}^n{z}_{kj}}},$$

(8)

where c_i is the local weight of the indicator (criteria) and parameter (subcriteria), Z_ij are elements of the defuzzified matrix row, and ΣZ_kj is the sum of the values of each column of the defuzzified matrix.

Table 7. Local and global weight of indicators and parameters.

Indicator	Local Weight	Parameters	Local Weight	Global Weight
	Ci		Cij	Wij
Technical	0.354	Transport complexity	0.340	0.12
		WWTP safety and resilience	0.391	0.14
		Existence of legislation	0.268	0.10
Social	0.242	Degree of acceptance	0.435	0.11
		Degree of need	0.332	0.08
		% of sewage collection and treatment	0.233	0.06
Environmental	0.132	Risk of public health	0.676	0.09
		Energy consumption by WWTP	0.130	0.02
		Risk to the environment	0.194	0.03
Economic	0.272	Necessary investment	0.373	0.10
		Operation and maintenance charges	0.133	0.04
		Economic benefits	0.493	0.13

demonstrates the use of Equation (8) by normalizing the column vectors and then averaging across the rows. For example:

$$c1={\displaystyle \frac{1}{3}}\left({\displaystyle \frac{1}{1+2.718+2.730}}+{\displaystyle \frac{1.253}{1.253+1+0.571}}+{\displaystyle \frac{2.354}{2.354+2.216+1}}\right)=0.340$$

2.8. STEP 8: Determination of Global Weight

Using Equation (9), the global weights (w_ij) of the parameters (subcriteria) were calculated:

W_ij = c_i × c_ij,

(9)

where c_i is the local weight of indicator I, and c_ij is the local weight of parameter j of indicator i. The results are presented in Table 7.

2.9. Decision Support Model

After finding the global weight of the parameters, which takes into account the local weight of the indicator and the local weight of the corresponding parameters, it is possible to calculate the potential feasibility index for water reuse based on Equation (10), whose classification is shown in

Table 8. Water reuse potential feasibility index (RFI).

Water Reuse Potential Feasibility Index (RFI)	RFI (%)	Classification
	81–100	Very High Viability
	61–80	High Viability
	41–60	Medium Viability
	21–40	Low Viability
	0–20	Very Low Viability

:

RFI = [∑ (W_ij × K_ij)] × 100,

(10)

where RFI is the water reuse potential feasibility index, W_ij is the global weight of the parameter, and K_ij is the constant of each parameter, according to Table 9, Table 10, Table 11 and Table 12, which are presented below.

The further away the WWTP is from the place where the water is used, and the more complex and difficult the transportation is, the worse it is for project implementation, as the higher the costs of transporting and storing treated wastewater will be. Therefore, the value of K is lower (Table 9).

The more reliable the WWTP, that is, the higher the percentage of compliance with the requirements or standards established in the legislation, the better it is for implementing a reuse project. Therefore, the value of K is higher (Table 9).

The percentage of attendance was calculated using the methodology described by [20,21,22].

If the region where a water reuse project is intended to be implemented does not have legislation including water reuse standards, the success of the project will be more difficult and complex, as confidence in terms of safety may be compromised. Therefore, in this situation, the value of K should be minimal. If, however, there is legislation, the more specific it is, that is, the more the local reality is reflected, the better it is, corresponding to a higher value of K (Table 9).

Table 9. Constant K of the “Technical Indicator Parameters”.

Transport Complexity C11	Distance (X) Between the WWTP and the Reuse Site (Km)	K11
	X ≤ 3	1
	3 < X ≤ 5	0.8
	5 < X ≤ 10	0.6
	10 < X ≤ 15	0.4
	X ≥ 15	0.2
Security and Resilience WWTP C12	% of attendance	K12
	81–100	1
	61–80	0.8
	41–60	0.6
	21–40	0.4
	0–20	0.2
Existence of Legislation C13	Scope	K13
	Municipal	1
	State	0.8
	National	0.6
	International	0.4
	Non-existent	0.2

Table 9. Constant K of the “Technical Indicator Parameters”.

Transport Complexity C11	Distance (X) Between the WWTP and the Reuse Site (Km)	K11
	X ≤ 3	1
	3 < X ≤ 5	0.8
	5 < X ≤ 10	0.6
	10 < X ≤ 15	0.4
	X ≥ 15	0.2
Security and Resilience WWTP C12	% of attendance	K12
	81–100	1
	61–80	0.8
	41–60	0.6
	21–40	0.4
	0–20	0.2
Existence of Legislation C13	Scope	K13
	Municipal	1
	State	0.8
	National	0.6
	International	0.4
	Non-existent	0.2

Implementing treated wastewater reuse programs is not without social challenges. In many communities, there are legitimate concerns about the safety and quality of treated water, as well as cultural and social issues related to the public acceptance of wastewater reuse. A lack of trust in authorities and government institutions can fuel resistance and create barriers to the adoption of these practices.

In the State of California, United States of America, for example, a reuse project was not successful in the 1990s mainly due to strong public opposition. This situation, according to [23], can be explained by the fact that public concerns were not adequately considered.

The public is a key stakeholder in any water management decision, and community members often play an important role in decision-making about water reuse projects. As in any water project, the success or failure of a proposed reuse project may reflect public perceptions of how the project relates to public health, safety, environmental protection and economic growth.

There is a natural aversion in society to water that is defined as contaminated, and sometimes, this feeling can translate into opposition to the use of treated wastewater, even when the water is demonstrably of high quality. In some cases, people may even prefer low-quality water from a “natural” source to high-quality water from an advanced wastewater treatment facility.

One of the reasons for the low percentage of water reuse in Portugal, according to [5], is, for example, the existence of fear associated with some lack of knowledge by the promoters of reuse projects and even the authorities involved in the approval and licensing of these projects regarding the health and environmental risks.

In developing countries, this becomes even more serious due to difficult access to adequate treatment technologies since treated effluent can transmit pathogenic organisms and potentially dangerous chemical substances. This difficulty can be justified by the costs of building, operating and maintaining a WWTP, which, when added to the costs of distributing and monitoring the reuse system, make this practice difficult to implement in some regions of the world [7,9].

The use of low-quality water without the need to meet the potability standard but suitable for the purposes for which it is intended and produced from the refinement of treated effluents will allow a considerable volume of water to be saved for public supply, which is socially, sanitarily and economically justified, especially in periods of prolonged drought.

Public education plays a key role in raising awareness of the benefits of reusing treated wastewater and building public trust. Educational programs and awareness campaigns should be developed in collaboration with local communities, taking into account their specific concerns and perspectives.

Thus, the participation of the agents involved is important in the decision-making process for water reuse, and the greater the acceptance of the population directly affected by the project, the better it is for its success. Therefore, the greater the value of K. The degree of acceptance can be measured through a social survey (Table 10).

The degree of need is related to the water crisis faced by the location where water reuse is intended to be implemented. The greater the crisis, the more justifiable, in principle, reuse will be. Therefore, the value of K is higher. In this paper, the degree of need was measured for the case study based on the Municipal Alert Index (MAI) established by [24], as shown in Table 10.

The higher the percentage of wastewater collection and treatment in the region, the better it is, as more water will be available for reuse. Therefore, the higher the value of K (Table 10).

Table 10. Constant K of the “Social Indicator Parameters”.

Degree of Acceptance C21	% of Acceptance	K21
	81–100	1
	61–80	0.8
	41–60	0.6
	21–40	0.4
	0–20	0.2
Degree of Need C22	Vulnerability Category	Class	K22
	High	1	1
	Medium-High	2	0.75
	Medium	3	0.50
	Low	4	0.25
Universalization of Sewage Collection and Treatment C23	% of Coverage	K23
	81–100	1
	61–80	0.8
	41–60	0.6
	21–40	0.4
	0–20	0.2

Table 10. Constant K of the “Social Indicator Parameters”.

Degree of Acceptance C21	% of Acceptance	K21
	81–100	1
	61–80	0.8
	41–60	0.6
	21–40	0.4
	0–20	0.2
Degree of Need C22	Vulnerability Category	Class	K22
	High	1	1
	Medium-High	2	0.75
	Medium	3	0.50
	Low	4	0.25
Universalization of Sewage Collection and Treatment C23	% of Coverage	K23
	81–100	1
	61–80	0.8
	41–60	0.6
	21–40	0.4
	0–20	0.2

Producing effluent with quality for reuse is not so simple. This is because traditional treatment, normally completed in the secondary stage, does not remove substances that must be absent for some reuse purposes, such as solids, pathogenic organisms, micropollutants and emerging contaminants. To remove them, tertiary treatment and disinfection are often necessary, which is normally accompanied by increased energy expenditure and increased use of chemical substances [8,25].

As for pathogens, their possible presence is often the main concern in reuse projects due to the risk of reused water becoming a vehicle for transmitting diseases and thus posing a public and/or animal health problem. Water, like any other substance, contains large quantities of microorganisms—bacteria, algae, protozoa, fungi, viruses and crustaceans—the vast majority of which are harmless to humans. However, some microorganisms are pathogenic, and their presence in water makes it a vehicle for transmitting numerous diseases, some of which are very dangerous and cause high mortality rates in developing countries, especially among children [5,10,11,25].

The form of exposure to pathogens varies according to the purpose and form of reuse, and maximum exposure may occur if there is ingestion of crops irrigated with reused water, direct contact of the body with surfaces wet with this water, or the inhalation of aerosols from this source. Even in these situations of maximum exposure, the intensity can be greatly reduced if the level of treatment of the wastewater reduces the presence of indicator microorganisms to very low levels [5,8,9,10,11].

In general, the concentration of hazardous chemical pollutants in treated wastewater is very low, often in the same order of magnitude as the concentration values found in groundwater, particularly with regard to heavy metals, pesticides and pharmaceutical products [5,10].

Specifically regarding compounds not being completely removed, according to [5,10], in most reuse applications, the health and environmental risks arising from the presence of these constituents are considered practically non-existent because they are adequately controlled. The presence of some constituents represents, in some cases, a benefit, such as in reuse in agriculture, through the fertilization provided by the nitrogen and phosphorus content of wastewater.

The technologies currently available for wastewater treatment allow for the virtually complete elimination of any type of chemical pollutant and pathogenic microorganisms present in wastewater, enabling the production of water that meets all the water quality criteria for human consumption from wastewater. In the vast majority of water reuse applications, the production of drinking water is not necessary, and the complementary treatment to enable the reuse of treated wastewater consists of its disinfection and preparatory treatment for disinfection (essentially, a reduction in turbidity) [5,10].

Thus, the risk to public health is related to the reliability of the WWTP performance; that is, the more reliable the WWTP’s performance, the lower the risk to public health (Table 11).

The impact of energy consumption on sustainability is a central issue in the debate on climate change and environmental preservation. In particular, WWTPs play a significant role, as they require energy to carry out fundamental sanitation processes. This consumption can have direct implications for greenhouse gas (GHG) emissions, depending on the energy source used.

Energy expenditure was identified as an environmental indicator parameter since energy consumption varies depending on the treatment technology, the distances between the production area and the area of use and the altitude.

The operation of WWTPs requires large volumes of energy, especially in processes such as aeration, pumping and the biological treatment of waste. In systems that use fossil energy sources, such as coal, oil or natural gas, there is an indirect contribution to GHG emissions, aggravating the greenhouse effect.

To mitigate the environmental impact of WWTPs, it is essential to adopt strategies that align energy consumption with sustainability. Some of these strategies include (1) energy efficiency through the modernization of equipment and the installation of intelligent monitoring and control systems, which help to optimize energy use, minimizing waste; (2) the integration of renewable sources, such as solar energy, wind energy, or biogas produced from the anaerobic digestion of sludge, making the plants more self-sufficient and reducing their dependence on fossil fuels; (3) the use of biogas; and (4) the reuse of waste generated in the process, such as fertilizers from treated sludge.

Energy consumption in WWTPs represents both a challenge and an opportunity in the context of sustainability. By investing in technological innovation, energy efficiency and renewable sources, it is possible to significantly reduce greenhouse gas emissions, contributing to a more sustainable and resilient future.

In this context, regarding the use of energy in wastewater treatment, especially electricity, this is the main environmental aspect considered in some studies, especially when this energy originates from fossil sources [8].

Thus, the greater the consumption of energy from non-renewable sources, the more GHG emissions and, therefore, the more harmful it will be to the environment (Table 11).

According to [8], access to clean water is becoming increasingly difficult and expensive due to environmental pollution, climate change and increasing demand. Thus, the use of water for non-potable purposes can be met by properly treated effluent.

The negative effects of wastewater reuse, particularly those resulting from agricultural use, such as groundwater contamination by nitrates, pathogens and soil salinization, can be overcome using predominantly domestic sewage that is efficiently treated and appropriate irrigation techniques.

Wastewater contains chemical and microbiological constituents that are not completely removed or inactivated in treatment plants. Residuals of some of these constituents in treated effluents may pose risks to public health and the environment. The control of these risks is based on knowledge of their origin and their impact on human health and the environment in general.

Having overcome the negative points, one of the advantages of reusing treated wastewater is that by reusing the treated effluent, it avoids being released into the soil or receiving water bodies, as normally occurs, and thus, possible pollution of the soil and surface and underground water resources is avoided.

In coastal areas, for example, which suffer from saline intrusion due to excessive use of underground water sources, water reuse can be beneficial both because it is another source of water and, therefore, reduces the pumping of groundwater and because of the possibility of recharging the aquifer with treated effluent, constituting a barrier to saline intrusion.

Thus, the risk to the environment is related to the possibility of the contamination of soil and groundwater and surface water due to water reuse. Therefore, the more reliable the wastewater treatment, the lower the environmental risk (Table 11).

Table 11. Constant K of the “Environmental Indicator Parameters”.

Risk to Public Health C31	Classification	K31
	Extremely High	0.2
	High	0.4
	Medium High	0.6
	Low Medium	0.8
	Low	1
Energy Consumption at WWTP C32	Classification	K32
	Extremely High	0.2
	High	0.4
	Medium High	0.6
	Low Medium	0.8
	Low	1
Risk to the Environment C33	Classification	K33
	Extremely High	0.2
	High	0.4
	Medium High	0.6
	Low Medium	0.8
	Low	1

Table 11. Constant K of the “Environmental Indicator Parameters”.

Risk to Public Health C31	Classification	K31
	Extremely High	0.2
	High	0.4
	Medium High	0.6
	Low Medium	0.8
	Low	1
Energy Consumption at WWTP C32	Classification	K32
	Extremely High	0.2
	High	0.4
	Medium High	0.6
	Low Medium	0.8
	Low	1
Risk to the Environment C33	Classification	K33
	Extremely High	0.2
	High	0.4
	Medium High	0.6
	Low Medium	0.8
	Low	1

If it is necessary to upgrade the treatment to produce water to comply with requirements, the water reuse costs will increase. Just as the further away the WWTP is from the place of use, the higher the costs will be. Therefore, in these situations, the worse it is for the viability of the project and, therefore, the lower the value of K (Table 12).

The more costly the maintenance and operation of the WWTP, the worse it is for implementing the reuse project (Table 12).

The greater the economic benefits arising from the implementation of the reuse project, such as increased agricultural productivity or reduced water consumption from conventional sources, for example, the better the viability of water reuse (Table 12).

Table 12. Constant K of the “Economic Indicator Parameters”.

Required Investment C41	Classification	K41
	Extremely High	0.2
	High	0.4
	Medium High	0.6
	Low Medium	0.8
	Low	1
Operation and Maintenance Charges C42	Classification	K42
	Extremely High	0.2
	High	0.4
	Medium High	0.6
	Low Medium	0.8
	Low	1
Economic Benefits C43	Classification	K43
	Extremely High	0.2
	High	0.4
	Medium High	0.6
	Low Medium	0.8
	Low	1

Table 12. Constant K of the “Economic Indicator Parameters”.

Required Investment C41	Classification	K41
	Extremely High	0.2
	High	0.4
	Medium High	0.6
	Low Medium	0.8
	Low	1
Operation and Maintenance Charges C42	Classification	K42
	Extremely High	0.2
	High	0.4
	Medium High	0.6
	Low Medium	0.8
	Low	1
Economic Benefits C43	Classification	K43
	Extremely High	0.2
	High	0.4
	Medium High	0.6
	Low Medium	0.8
	Low	1

Using the global weights obtained in step 8, a decision support model was developed to evaluate the feasibility of water reuse, based on Equation (10):

RFI = {[(0.12K₁₁) + (0.14K₁₂) + (0.10K₁₃)] + [(0.11K₂₁) + (0.08K₂₂) + (0.06K₂₃)] + [(0.09K₃₁) + (0.02K₃₂) + (0.03K₃₃)] + [(0.10K₄₁) + (0.04K₄₂) + (0.13K₄₃)]} × 100,

(11)

2.10. Sensitivity Analysis

Sensitivity analysis is used to obtain a measure of the robustness of the results. There are several sensitivity analysis methodologies described in the literature used to evaluate the stability of weights assigned by experts [15,16].

In this work, the approach described by [18] was used, by changing the local weights of the indicators by ±5%, ±10% and ±15% and reflecting the percentage of water reuse calculated by the model.

Thus, to evaluate sensitivity, the weight C_k can be subjected to a percentage varia-tion (vp) using Equation (12):

C_k = C_k0 ± C_k0 × vp,

(12)

where C_k0 is the local weight of the k-th indicator (criteria) with the greatest importance in the model (original weight), and vp is the percentage variation to which the weight was subjected.

The local weights of the other indicators (criteria) C_j are proportionally adjusted according to Equation (13):

C_j = (1 − C_k) × [C_j0/(1 − C_k0)],

(13)

where C_j is the new value of the weight assigned to the j-th indicator (criteria), and C_k is the weight of the k-th indicator (criteria) at a certain level of percentage variation, calculated by Equation (12). C_j0 and C_k0 are the values of the weights of indicators (criteria) j and k in the reference model (with the original weights).

The indicator with the highest local weight was the technical indicator (C₁), with a weight of 0.354. With an increase of 15%, the new weight values for the technical (C₁) and social (C₂) indicators were calculated using Equations (12) and (13) as follows:

C_k = 0.354 + (0.354 × 0.15) = 0.4072

C_j = (1 − 0.4072) × [0.2417/(1 − 0.354)] = 0.2218

Table 13. Sensitivity analysis results.

%	Technical	Social	Environmental	Economic
−15%	0.3009	0.2616	0.1433	0.2942
−10%	0.3187	0.2549	0.1397	0.2867
−5%	0.3364	0.2483	0.1360	0.2793
0%	0.3541	0.2417	0.1324	0.2718
5%	0.3718	0.2351	0.1288	0.2644
10%	0.3895	0.2285	0.1252	0.2569
15%	0.4072	0.2218	0.1215	0.2495

presents the changes made to the various indicators. The line referring to the 0% percentage in this work was called the base weight line, which is the reference for analyzing the changes applied.

The ranking of indicators remained unchanged across all scenarios from the sensitivity analysis (technical > economic > social > environmental), indicating the stability of the model outcomes despite the weight variations, which suggests that decisions based on the order of importance of these indicators will be rather consistent, not significantly affected by uncertainties in the attribution of the different weights.

Figure 2 shows the variation in the local weights of the indicators as a function of the percentage variations applied (±15%). A clear linear trend can be seen, which occurs because a percentage variation was applied uniformly. Furthermore, a quality of stability can be found in the order of importance of the indicators, as there are no lines crossing, which would indicate a change in their order of importance.

Figure 2 shows the stability and robustness of the indicator weights, as well as the significant difference between the weight of the technical indicator and the other indicators. This difference suggests that in future analyses or decisions, focusing on the indicators with the highest weight may be useful in decision-making.

3. Case Study

To illustrate the application of the proposed decision support model, we used the case study of the city of Aquiraz, located in the semi-arid region of Ceará, Brazil. Public data and expert consultations were used to determine the constants (K) for each parameter tailored to the local conditions.

At this stage, public bodies involved with the water sector and infrastructure in the area under study were contacted, such as the Ceará Water and Sewage Company (CAGECE), the Aquiraz City Hall, the Ceará State Water Resources Secretariat (SRH), the Water Resources Management Company (COGERH) and the Ceará State Research and Strategy Institute (IPECE).

For climatological and socioeconomic characterization, we used the information provided by [26].

To evaluate the safety and resilience of the sewage treatment plant, reliability coefficients were calculated, according to the methodology described by [20,21,22] for the parameters E. coli, Geohelminth Eggs, Electrical Conductivity, Sodium Adsorption Ratio (RAS), pH and Temperature and the parameters considered by [27] in the evaluation of treated wastewater reuse projects. The results of the treated wastewater quality monitoring at the Aquiraz WWTP were provided by CAGECE.

To understand the level of knowledge and acceptance of the population of Aquiraz regarding the reuse of treated wastewater for the purposes proposed by [27], a social survey was carried out to find out what level of knowledge the respondent had about the topic and for what purposes he agreed with the use of lower water quality.

Therefore, the following sections show the determination of the constant K for each parameter and the potential viability index for water reuse in the Aquiraz case study.

3.1. WWTP Safety and Resilience

According to the calculated reliability coefficients, the Aquiraz WWTP proved to be reliable for the purposes of reusing treated wastewater permitted by [27].

For most purposes, the reliability percentage was greater than 80%. Thus, according to Table 9, the value of K is 1.

3.2. Transport Complexity

In Aquiraz, there is still no infrastructure for storing and transporting treated wastewater. For illustrative purposes, a reuse project for irrigation was developed within the area next to the WWTP, with a distance between the production area and the area of use of less than 3 km. Thus, according to Table 9, the value of K is 1.

3.3. Existence of Legislation

Since the municipality of Aquiraz does not have legislation that deals with the reuse of treated wastewater, state legislation was considered, according to [27]. Therefore, according to Table 9, the value of K is 0.8.

3.4. Risk to Public Health

In accordance with [27] and the application of equivalent barriers to obtain a multi-barrier effect, according to the methodology described by [28,29], the Aquiraz WWTP produces adequate and safe water for the reuse purposes of the said resolution, with a low-risk classification. Thus, according to Table 11, the value of K is 1.

3.5. Energy Consumption by WWTP

Treatment at the Aquiraz WWTP is based on stabilization ponds, which consist of a natural biological treatment without the consumption of chemicals and energy. However, considering the increase in pumping for the reuse area, according to Table 11, energy consumption in the production area is considered Low Medium, with a K value of 0.8.

3.6. Risk to the Environment

Considering that with the practice of water reuse in Aquiraz, there will be a reduction in the discharge of treated effluent into the Pacoti River, and the low-risk classification in the risk assessment was made following the methodology described by [28,29], according to Table 11, the value of K is 1.

3.7. Degree of Acceptance

According to the social survey carried out, 52.5% agree with the use of treated wastewater for agricultural irrigation. Therefore, for this purpose, according to Table 10, the value of K is 0.6.

3.8. Degree of Need

According to [24], the city of Aquiraz was classified in 2023 in category 3, with medium vulnerability based on the Municipal Alert Index (MAI). Thus, from Table 10, the value of K is 0.50.

3.9. Percentage of Sewage Collection and Wastewater Treatment

According to [30], the sewage coverage rate in Aquiraz in 2022 was 44.3%. Therefore, according to Table 10, the value of K is 0.6.

3.10. Investment Cost

The investment required to implement water reuse in Aquiraz is related to the implementation of storage and transport infrastructures, which are still non-existent.

For illustrative purposes, the implementation of an agricultural reuse project in the area close to the WWTP was considered.

The project considered a 2-inch diameter pipeline to capture the treated wastewater in the last maturation pond, a sandbox, two 5 hp motor pump sets, a 3 m high elevated reservoir with a 20 m³ capacity and a distribution network for the areas to be irrigated.

Each area to be irrigated was 1 ha, and each hectare could be irrigated over 6 h by drip irrigation, with a flow rate of 8.3 m³/h. Thus, to irrigate the five areas delimited, a flow rate of 249 m³/day (5 × 6 × 8.3) was required, considering 6 h of irrigation per day.

As there is only 1 lagoon module in operation and the average flow rate is 68.62 L/s (approximately 5900 m³/day), the WWTP produces water at a flow rate sufficient for irrigation of the proposed areas.

Table 14. Materials and costs required to implement the proposed project.

Item	Amount	Value (USD)	Total Value (USD)
5 hp three-phase pump	2	674.83	1.349.66
2-inch tube	355.53 m	7.28/6 m	431.37
Drip irrigation set	5	1.804.08	9.020.40
Elevated reservoir (service and material)	1	30.585.07	30.585.07
Total	(USD) 41.386.50

shows the materials and costs required to implement the proposed project. The expected investment is USD 41,386. According to Table 12, this investment is classified as Low Medium. Therefore, K is equal to 0.8.

Water reuse is generally more expensive than collecting water from a natural freshwater source, but it is generally cheaper than desalinating seawater or building new dams. Since natural water sources are usually already being used, the cost of water reuse must be compared with the cost of any new water sources that are available. Water reuse costs vary greatly from place to place, depending on the physical characteristics of the site, water quality requirements, treatment methods, distribution systems, energy costs, interest rates, incentives and many other factors.

Regarding the price of treated wastewater to be used in irrigation, it is known that it must be competitive with the price of raw water charged by COGERH [10,23]. Currently, for irrigation with its own supply between 1440 and 18,999 m³/month, the price of water is BRL USD 0.51/1000 m³, a relatively low value but within the range recommended by [10] for the final price of water reused for non-potable purposes (0.25–1.60 USD/m³).

It is important to highlight, according to [7], that recovered wastewater is a source of water available throughout the year, guaranteeing the water supply for the proposed purpose and, therefore, the economic value of guaranteeing the water supply for a given need is considered, especially in conditions of scarcity.

Another factor that must be taken into account in the economic analysis is the population equivalent (PE), that is, the percentage of sewage collection and treatment coverage, because the higher this percentage, the lower, theoretically, the average cost per m³ of treated wastewater will be [7].

3.11. Operation and Maintenance Costs

As the WWTP is of the stabilization lagoon type, operation and maintenance are simple, as the treatment is of the natural biological type without the need for a specialized professional for operation, high energy costs and chemical products but with increased consumption of electrical energy with pumping for irrigation areas. Thus, according to Table 12, the value of K is 0.8, classified as Low Medium.

CAGECE was asked for data on the cost of operating and maintaining the Aquiraz WWTP, but the information was denied based on Article 24, item I, of State Decree No. 31,199, of 30 April 2013, which allows information not to be made available when it is classified as confidential. In this specific case, it was alleged that the information was strategic for the company’s business. It was also added that data collection is performed by the municipality and does not individualize the costs at the WWTP and that the cost data referred to water supply and sewage together, with no information only for sewage.

3.12. Economic Benefits

In terms of productivity, there is no real data because no experiments were carried out with the wastewater treated at the Aquiraz WWTP during the development of this paper. However, there are several studies that report an increase in agricultural productivity when using treated wastewater. According to [31], for example, the use of treated wastewater in eucalyptus irrigation increased productivity by 82.9% when compared to traditional irrigation.

Therefore, according to Table 12 in this study, the economic benefits are classified as High, with a K value equal to 0.8.

3.13. Potential Feasibility Index for Water Reuse in Aquiraz

Using Equation (11) and the obtained K values, the potential feasibility index for water reuse in Aquiraz is 82%, according to

Table 15. Potential feasibility index for water reuse in Aquiraz.

Indicator	Local Weight	Parameters	Local Weight	Global Weight	Kij	Wij × Kij
	Ci		Cij	Wij
Technical	0.354	Transport complexity	0.340	0.12	1	0.12
		WWTP safety and resilience	0.391	0.14	1	0.14
		Existence of legislation	0.268	0.10	0.8	0.08
Social	0.242	Degree of acceptance	0.435	0.11	0.6	0.06
		Degree of need	0.332	0.08	0.5	0.04
		% of ewage collection and treatment	0.233	0.06	0.6	0.03
Environmental	0.132	Risk to public health	0.676	0.09	1	0.09
		Energy consumption by WWTP	0.130	0.02	0.8	0.01
		Risk to the environment	0.194	0.03	1	0.03
Economic	0.272	Necessary investment	0.373	0.10	0.8	0.08
		Operation and maintenance charges	0.133	0.04	0.8	0.03
		Economic benefits	0.493	0.13	0.8	0.11
RFI (%)						81.85%

, and classified as Very High Viability, according to Table 8 (equal to or greater than 81%).

The sensitivity analysis was performed again, and the RFI for Aquiraz was calculated with the new weights of the indicators after the percentage variations in Table 13. The results are shown in

Table 16. Sensitivity analysis results for Aquiraz RFI.

%	Technical	Social	Environmental	Economic	RFI
−15%	0.3009	0.2616	0.1433	0.2942	80.80%
−10%	0.3187	0.2549	0.1397	0.2867	81.15%
−5%	0.3364	0.2483	0.1360	0.2793	81.50%
0%	0.3541	0.2417	0.1324	0.2718	81.85%
5%	0.3718	0.2351	0.1288	0.2644	82.21%
10%	0.3895	0.2285	0.1252	0.2569	82.56%
15%	0.4072	0.2218	0.1215	0.2495	82.90%

, where it is possible to observe that the value of the potential reuse feasibility index (RFI) in Aquiraz always remained in the “very high viability” category.

4. Conclusions

This work aimed to develop a simplified model to support the decision-making process for water reuse, integrating relevant indicators and parameters. Technical, social, environmental and economic indicators were proposed, each one associated with specific parameters that impact the feasibility of water reuse projects.

For the technical indicator, the identified parameters were (1) transport complexity (pipelines, reservoirs and the distribution of treated wastewater), (2) WWTP safety and resilience and (3) the existence of legislation defining the quality standards for treated wastewater reuse.

For the social indicator, the parameters include (1) the degree of public acceptance, (2) the degree of need associated with local water scarcity and (3) the coverage rate of wastewater collection and treatment in the region where water reuse is being evaluated.

For the environmental indicator, the risk to public health and the environment was identified, which is directly related to the safety and resilience of the WWTP. The practice of reuse allows the reservation of good water quality for more noble purposes, such as human supply, and this is also indirectly related to the environmental risk parameter. Energy expenditure was also identified as a parameter of the environmental indicator, depending on the treatment technology and the distance between the production site and the irrigated area, and its elevation. Depending on the energy source, greenhouse gas emissions may also occur, with impact on climate change.

For the economic indicator, investment costs were identified, as well as operation and maintenance costs throughout its useful life. Furthermore, for the economic indicator, the economic benefit due to the potential increase in agricultural productivity was considered.

This article aimed to fill some gaps, namely, regarding a tool to support the decision-making process regarding water reuse.

Previous studies on water reuse have mainly focused on public health risks, proposed specific requirements for water use and presented case studies. Compared to previous studies, the novelty of the presented model consists of developing a methodology to combine the FAHP method to allow the quantitative application of a decision support model for water reuse considering a limited number of variables.

The FAHP method is well known and has been intensively applied in various domains of knowledge, but as far as the authors know, it has not been applied to support models for water reuse.

By applying the model, it is possible to assess at the initial phase of a study whether the implementation of a program for reusing treated wastewater in a given city or region is viable.

The Fuzzy AHP methodology proved to be a viable and practical approach for determining the weights of the various indicators and parameters of the model.

According to the example and case study, sensitivity analysis shows the robustness of the results and conclusions. Small changes (positive or negative) in the weights do not affect the order of importance of the indicators, suggesting that decisions based on this model are consistent.

The city of Aquiraz was selected as a case study to demonstrate the applicability of the proposed model. Based on local data, the reuse feasibility index (RFI) was calculated to be 82%, falling within the “Very High Viability” category. This result suggests a strong potential for implementing water reuse initiatives in the region.

The list of indicators and parameters presented in the paper is not exhaustive and other relevant factors could be included depending on the local context and specific requirements of each project. The methodology used to determine the relative importance of the indicators and parameters of the model is flexible enough, regardless of the number or type of indicators considered.

As a suggestion for future work, the development of software to automate the Fuzzy AHP process would expedite the determination of the indicator and parameter weights. Such a tool would only require the input of expert linguistic judgments, handling all stages of the Fuzzy AHP method internally, to provide immediate results of the preliminary decision support for water reuse.