This study evaluates 10 case studies of decentralized wastewater treatment systems (DEWATS) and compares them based on their environmental and economic performance. The aim is to identify the best treatment system composed of specific treatment processes that can be used in the future for hospitals, universities/colleges, and small communities. The comparison was conducted using the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method of multiple criteria decision-making. According to the TOPSIS method, systems are ranked from 1 to 10 by normalizing and weighting the decision matrix criteria, measuring the alternative's distance from the ideal best to worst value, and then calculating the performance score of each system. The study found that the H4 system, which includes a septic tank, anaerobic baffled reactor, anaerobic filter, horizontal filter, and pond, achieved the highest performance score. Therefore, the processes of the H4 system are recommended as the best alternative for DEWATS. This study may therefore assist stakeholders on their decision to implement DEWATS in similar facilities.

  • The TOPSIS method is used to select appropriate DEWATS.

  • A comprehensive assessment using six criteria of two indicators: environmental performance and economic performance of DEWATS.

  • Determination of the performance score, separately for each indicator.

  • Ranking of systems based on the summarized performance scores for each system.

  • The evaluation of the best alternative for DEWATS, which comprises a set of DEWATS processes.

Wastewater treatment contributes directly and indirectly to all Sustainable Development Goals (SDGs). Regarding the 17 SDGs, SDG 6 is directly related to wastewater treatment, ‘ensuring availability and sustainable management of water and sanitation for all’ (Obaideen et al. 2022). Globally, an estimated 80% of industrial and municipal wastewater is discharged into the environment without any prior treatment, with adverse effects on human health and ecosystems (Lin et al. 2022). A sustainable solution for the treatment of wastewater is a decentralized wastewater treatment system (DEWATS), and over the last two decades, large numbers of decentralized wastewater treatment plants of different technologies have been installed all over the world (Singh et al. 2015). Massoud et al. (2009) stated that the most appropriate technology is the technology that is economically affordable, environmentally sustainable, and socially acceptable.

DEWATS are designed to operate on a small scale (EPA 2004; Massoud et al. 2009). They have a comprehensive approach to managing wastewater that involves collecting, treating, and disposing/reusing wastewater close to where it is generated. This approach can be applied to individual dwellings, clusters of homes, entire communities, institutional buildings, schools, or hospitals (Gutterer et al. 2009; Ali 2018). Capodaglio et al. (2017) highlighted that DEWATS not only reduces the negative effects of wastewater disposal on the environment and public health but may also increase the possibility of reusing wastewater. The extent of reuse depends on the community type, technical options, and local settings. According to Geetha Varma et al. (2022) and Sasse (1998), the following technical treatment technologies (processes) are typically seen in DEWATS. The primary treatment consists of sedimentation ponds, settlers, septic tanks, or biodigesters. The secondary treatment consists of anaerobic baffled reactors, anaerobic filters, or anaerobic and facultative pond systems. The secondary aerobic/facultative treatment consists of horizontal filters (HFs), and the post-treatment consists of aerobic polishing ponds. These processes efficiently treat wastewater from commercial and institutional facilities, including hotels, restaurants, office buildings, schools, hospitals, laboratories, and government and military institutions (EPA 2012; Gautam & Singh 2016; Ergas et al. 2021).

In this paper, DEWATS composed of different treatment technologies used in a variety of facilities is analyzed, including hospitals (ENPHO 2010a, 2010d; BORDA 2015; CDD 2019a), schools and universities (Gutterer et al. 2009; Riccardo Bresciani 2014; Bartaula 2016; CDD 2019b), and small communities (ENPHO 2010b, c). The main purpose of this study is to find the most appropriate system with suitable treatment processes within DEWATS for these types of facilities in the context of economic and environmental indicators.

Multiple criteria decision-making (MCDM) is one of the most accurate methods for decision-makers to choose acceptable alternatives concerning suitable criteria (Taherdoost & Madanchian 2023). MCDM includes different methods (Hajduk 2022). One of them is the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), which is the most popular and commonly used in MCDM across different fields (Çelikbilek & Tüysüz 2020). The TOPSIS method is a practical (Yahya et al. 2020) and useful technique for ranking and selecting alternatives (Roszkowska 2011). TOPSIS is applied for comparison in many areas: supply chain management and logistics, design, engineering and manufacturing systems, business and marketing management, health, safety and environment management, human resources management, energy management, chemical engineering, and water resources management (Behzadian et al. 2012; Yahya et al. 2020).

TOPSIS – comparative method for wastewater treatment systems

In this paper, TOPSIS is used as a comparative method in the selection of appropriate DEWATS treatment alternatives. Several works have highlighted the potential of using TOPSIS in the field of wastewater treatment (Table 1). The aim for implementing TOPSIS among other MCDM tools is outlined in the table, including the main application area in a given study. The set of papers does not have a particular order.

Table 1

TOPSIS in wastewater treatment

ReferencesAimMCDM used
Dang et al. (2023)  Select a wastewater treatment technology – for a factory TOPSIS, AHP 
Attri et al. (2022)  Multi-attribute sustainability assessment of wastewater treatment technologies – secondary treatment technologies Fuzzy SWARA, Fuzzy MOORA, Fuzzy TOPSIS 
Orhan et al. (2022)  Determining rehabilitation priority in wastewater systems ENTROPY, ELECTRE, TOPSIS 
Yahya et al. (2020)  Evaluation of wastewater treatment technologies – for municipal and industrial wastewater TOPSIS 
Yu et al. (2020)  Evaluation and selection of industrial sewage treatment projects – for industrial sewage treatment projects (technologies) Entropy, TOPSIS 
Zhou et al. (2018)  Selection of wastewater treatment plans – for the wastewater treatment plants selection problem IFS-TOPSIS 
Srdjevic et al. (2017)  Selection of best ordering of treatment technologies in wetland segmentation TOPSIS, AHP, ANP 
Mehtap (2015)  Integrated approach for the evaluation of wastewater treatment alternatives – for a case study conducted in Istanbul (four WWT technologies) DEMATEL, Fuzzy TOPSIS 
Jinxiang et al. (2013)  Finding out the best wastewater pollution control technology of high efficiency and low energy consumption – in the municipal wastewater treatment plants ENTROPY WEIGHT TOPSIS 
Mansouri et al. (2013)  Most suitable site for wastewater treatment plant TOPSIS, AHP 
Karimi et al. (2011)  Selection of anaerobic wastewater treatment processes – for an industry TOPSIS, FUZZY AHP 
Gómez-López et al. (2009)  Finding the best disinfection technique – for treated urban wastewater TOPSIS 
ReferencesAimMCDM used
Dang et al. (2023)  Select a wastewater treatment technology – for a factory TOPSIS, AHP 
Attri et al. (2022)  Multi-attribute sustainability assessment of wastewater treatment technologies – secondary treatment technologies Fuzzy SWARA, Fuzzy MOORA, Fuzzy TOPSIS 
Orhan et al. (2022)  Determining rehabilitation priority in wastewater systems ENTROPY, ELECTRE, TOPSIS 
Yahya et al. (2020)  Evaluation of wastewater treatment technologies – for municipal and industrial wastewater TOPSIS 
Yu et al. (2020)  Evaluation and selection of industrial sewage treatment projects – for industrial sewage treatment projects (technologies) Entropy, TOPSIS 
Zhou et al. (2018)  Selection of wastewater treatment plans – for the wastewater treatment plants selection problem IFS-TOPSIS 
Srdjevic et al. (2017)  Selection of best ordering of treatment technologies in wetland segmentation TOPSIS, AHP, ANP 
Mehtap (2015)  Integrated approach for the evaluation of wastewater treatment alternatives – for a case study conducted in Istanbul (four WWT technologies) DEMATEL, Fuzzy TOPSIS 
Jinxiang et al. (2013)  Finding out the best wastewater pollution control technology of high efficiency and low energy consumption – in the municipal wastewater treatment plants ENTROPY WEIGHT TOPSIS 
Mansouri et al. (2013)  Most suitable site for wastewater treatment plant TOPSIS, AHP 
Karimi et al. (2011)  Selection of anaerobic wastewater treatment processes – for an industry TOPSIS, FUZZY AHP 
Gómez-López et al. (2009)  Finding the best disinfection technique – for treated urban wastewater TOPSIS 

The information presented in Table 1 highlights the various practical uses where the TOPSIS was applied among other MCDM methods. These cases range from selecting a proper wastewater treatment technology for either domestic or industrial use, identifying the best site of the wastewater treatment plant and the best disinfection technique, determining rehabilitation priority, and selecting the best ordering of technologies in wetland segmentation (Table 1). There are cases related to the selection of treatment systems that vary according to the treatment technologies used in the particular system. In this paper, the focus is on selecting the best DEWATS served for an individual facility. The selection of an appropriate system through TOPSIS also reflects the environmental and economic performance of the DEWATS as the main indicators used in this paper.

Case studies

Four of the systems analyzed in this paper are for hospitals, four for universities/colleges, and two for small communities. These cases are chosen for comparison as they represent the most common facilities where DEWATS are applied. The systems are referred to as H1, H2, H3, and H4 for hospitals; U1, U2, U3, and U4 for universities/colleges; and C1 and C2 for small communities. Table 2 presents all case studies, and their abbreviated names as systems, that have to be evaluated for comparison analysis in this paper. The table also includes references for each case study in case there is detailed interest in the project for the given case study.

Table 2

Case studies used for system comparison with TOPSIS

Case studiesSystemsReferences
Hospitals H1 ENPHO (2010a)  
H2 ENPHO (2010d)  
H3 BORDA (2015)  
H4 CDD (2019a)  
Universities/colleges U1 Riccardo Bresciani (2014)  
U2 Gutterer et al. (2009)  
U3 Bartaula (2016)  
U4 CDD (2019b)  
Small communities C1 ENPHO (2010b)  
C2 ENPHO (2010c)  
Case studiesSystemsReferences
Hospitals H1 ENPHO (2010a)  
H2 ENPHO (2010d)  
H3 BORDA (2015)  
H4 CDD (2019a)  
Universities/colleges U1 Riccardo Bresciani (2014)  
U2 Gutterer et al. (2009)  
U3 Bartaula (2016)  
U4 CDD (2019b)  
Small communities C1 ENPHO (2010b)  
C2 ENPHO (2010c)  

The systems are compared based on two indicators: environmental performance and economic performance. The indicator of environmental performance refers to the treatment performance of any system. It includes three criteria: biological oxygen demand (BOD), chemical oxygen demand (COD), and total dissolved solids (TSS). The cost of construction (CC), the treated flow (Q), and the occupied space (OS) are criteria for economic performance as a second indicator (Figure 1).
Figure 1

Indicators/criteria of systems to be compared with TOPSIS.

Figure 1

Indicators/criteria of systems to be compared with TOPSIS.

Close modal

The processes that are used frequently in the systems of case studies are settling and septic tank (ST), anaerobic baffled reactor (ABR), biogas chamber (BCH) anaerobic filter (AF), horizontal filter (HF), vertical filter (VF), biofilter (BF), and aerobic polishing pond (APP). It is important to mention that the study is based on original data (Table 3) gathered from the literature (case studies).

Table 3

Data of indicators/criteria and treatment processes of the systems

 
 

Table 3 presents the data related to the environmental and economic performance criteria, used for comparing systems through the TOPSIS method. The table distinguishes each system based on specific treatment processes. The colored boxes in the table indicate similarities or differences between the systems in terms of the processes used.

TOPSIS method for case studies

The purpose of TOPSIS is to arrange the issues of comprehensive evaluation into a matrix, determine the ideal solution and negative ideal solution after normalization, and then calculate the distance between each evaluation alternative and the ideal solution or negative ideal solution (Tu et al. 2020). The paper by Khan & Sahabuddin (2022) outlines a five-step procedure for TOPSIS calculation. This paper divides the same five steps into seven steps to perform the TOPSIS calculation. The seven steps include: constructing the decision matrix, normalizing the decision matrix, weighting the normalized decision matrix, determining the ideal best (IB) and ideal worst (IW) solution, calculating the separation measure, determining the performance score (PS), and finally ranking the alternatives based on the PS.

Step 1: Construct decision matrix

In this step (Table 4), a decision matrix is formed using the following equation:
(1)
where Xij is the value of Si related to Cj, S is the system, C is the criteria, i is the number of systems, and j is the number of criteria.
Table 4

Decision matrix

SystemsCriteria
C1C2Cj
S1 X11 X12  X1j 
S2 X21 X22  X2j 
⋮ ⋮ ⋮ ⋰ ⋮ 
Si Xi1 Xi2  Xij 
SystemsCriteria
C1C2Cj
S1 X11 X12  X1j 
S2 X21 X22  X2j 
⋮ ⋮ ⋮ ⋰ ⋮ 
Si Xi1 Xi2  Xij 

Step 2: Normalized decision matrix

In this step, the normalized decision matrix is obtained using the following equation:
(2)

Step 3: Weighted normalized decision matrix

To find the weighted normalized decision matrix, the following equation is used.
(3)
where is the weight of the jth criterion.

Step 4: Determine IB and IW solution

In this step, the IB and the IW alternatives are identified:
(4)
(5)
(6)
(7)

Step 5: Separate each alternative from the IB solution and the IW solution

The separation measure of each alternative from the IB solution is as follows:
(8)
The separation measure of each alternative from the IW solution is as follows:
(9)

Step 6: Calculate performance score

PS is calculated as the relative closeness of the ith alternative Ai, concerning A+, and is defined as
(10)
where 0 ≤ PSi ≤ 1, i = 1, 2,…, m.

Step 7: Rank the alternatives

Alternatives are ranked according to PS.

The alternatives with the highest ∑PS are given first rank and successively the others.

The evaluation and the selection of wastewater treatment systems in general can be a complex procedure, as numerous evaluation indicators exist, differing by a lot of criteria. In this paper, the TOPSIS method is used to rank and select the most appropriate wastewater treatment system from 10 case studies of DEWATS in the context of 6 criteria. The evaluated criteria are defined under environmental performance and economic performance. Through TOPSIS, the weights and rating of each criterion are determined (Tables 5 and 6), after initial construction and normalization of the decision matrix (Tables 7 and 8). The main TOPSIS concept is to measure the distance of the alternatives from the positive ideal solution and the negative ideal solution (Table 9), to calculate the PS of each alternative. The most preferred alternative is the one that is closer to the positive ideal solution and also further from the negative ideal solution simultaneously in terms of distance measured. Considering all these steps, the ranking alternatives of DEWATS from 1 as the best to 10 as the worst is finally obtained and it is presented in Table 9 as well. Table 10 shows the same rank values considering the respective systems.

Table 5

Weighted normalized decision matrix

SystemsBOD (%)COD (%)TSS (%)Cost ($)Q (m3/d)Area (m2)
H1 0.1016 0.0569 0.0997 0.0526 0.0718 0.0797 
H2 0.0948 0.0959 0.1063 0.0187 0.0160 0.0199 
H3 0.1016 0.1149 0.1008 0.0567 0.0479 0.0356 
H4 0.1095 0.1161 0.1052 0.1459 0.2448 0.2678 
U1 0.0815 0.0865 0.0742 0.2122 0.1436 0.0676 
U2 0.1047 0.1054 0.0849 0.1538 0.1164 0.0774 
U3 0.1106 0.1105 0.1055 0.0305 0.0239 0.0625 
U4 0.0795 0.0779 0.1041 0.0279 0.0199 0.0135 
C1 0.1061 0.1102 0.1074 0.0374 0.0399 0.0383 
C2 0.0993 0.1066 0.1074 0.1082 0.0821 0.1235 
SystemsBOD (%)COD (%)TSS (%)Cost ($)Q (m3/d)Area (m2)
H1 0.1016 0.0569 0.0997 0.0526 0.0718 0.0797 
H2 0.0948 0.0959 0.1063 0.0187 0.0160 0.0199 
H3 0.1016 0.1149 0.1008 0.0567 0.0479 0.0356 
H4 0.1095 0.1161 0.1052 0.1459 0.2448 0.2678 
U1 0.0815 0.0865 0.0742 0.2122 0.1436 0.0676 
U2 0.1047 0.1054 0.0849 0.1538 0.1164 0.0774 
U3 0.1106 0.1105 0.1055 0.0305 0.0239 0.0625 
U4 0.0795 0.0779 0.1041 0.0279 0.0199 0.0135 
C1 0.1061 0.1102 0.1074 0.0374 0.0399 0.0383 
C2 0.0993 0.1066 0.1074 0.1082 0.0821 0.1235 
Table 6

Ideal best (IB) and ideal worst (IW) values

TypeBOD (%)COD (%)TSS (%)CC ($)Q (m3/d)OS (m2)
IB 0.1106 0.1161 0.1074 0.2122 0.2448 0.2678 
IW 0.0795 0.0569 0.0742 0.0187 0.0160 0.0135 
TypeBOD (%)COD (%)TSS (%)CC ($)Q (m3/d)OS (m2)
IB 0.1106 0.1161 0.1074 0.2122 0.2448 0.2678 
IW 0.0795 0.0569 0.0742 0.0187 0.0160 0.0135 
Table 7

Decision matrix – from collected data of case studies

SystemsBOD (%)COD (%)TSS (%)CC ($)Q (m3/d)OS (m2)
H1 90 48 91 39,683 90 800 
H2 84 81 97 14,103 20 200 
H3 90 97 92 42,713 60 357.24 
H4 97 98 96 110,000 307 2690 
U1 72.2 73 67.7 160,000 180 679 
U2 92.8 89 77.5 115,942 146 777.5 
U3 98 93.3 96.3 23,000 30 628 
U4 70.4 65.8 95 21,000 25 135.4 
C1 94 93 98 28,188 50 385 
C2 88 90 98 81,602 103 1240 
SystemsBOD (%)COD (%)TSS (%)CC ($)Q (m3/d)OS (m2)
H1 90 48 91 39,683 90 800 
H2 84 81 97 14,103 20 200 
H3 90 97 92 42,713 60 357.24 
H4 97 98 96 110,000 307 2690 
U1 72.2 73 67.7 160,000 180 679 
U2 92.8 89 77.5 115,942 146 777.5 
U3 98 93.3 96.3 23,000 30 628 
U4 70.4 65.8 95 21,000 25 135.4 
C1 94 93 98 28,188 50 385 
C2 88 90 98 81,602 103 1240 
Table 8

Normalized decision matrix

SystemsBOD (%)COD (%)TSS (%)CC ($)Q (m3/d)OS (m2)
H1 0.3047 0.1706 0.2992 0.1579 0.2153 0.2390 
H2 0.2844 0.2878 0.3189 0.0561 0.0479 0.0597 
H3 0.3047 0.3447 0.3025 0.1700 0.1436 0.1067 
H4 0.3284 0.3482 0.3157 0.4377 0.7345 0.8035 
U1 0.2445 0.2594 0.2226 0.6366 0.4307 0.2028 
U2 0.3142 0.3163 0.2548 0.4613 0.3493 0.2322 
U3 0.3318 0.3315 0.3166 0.0915 0.0718 0.1876 
U4 0.2384 0.2338 0.3124 0.0836 0.0598 0.0404 
C1 0.3183 0.3305 0.3222 0.1122 0.1196 0.1150 
C2 0.2979 0.3198 0.3222 0.3247 0.2464 0.3704 
SystemsBOD (%)COD (%)TSS (%)CC ($)Q (m3/d)OS (m2)
H1 0.3047 0.1706 0.2992 0.1579 0.2153 0.2390 
H2 0.2844 0.2878 0.3189 0.0561 0.0479 0.0597 
H3 0.3047 0.3447 0.3025 0.1700 0.1436 0.1067 
H4 0.3284 0.3482 0.3157 0.4377 0.7345 0.8035 
U1 0.2445 0.2594 0.2226 0.6366 0.4307 0.2028 
U2 0.3142 0.3163 0.2548 0.4613 0.3493 0.2322 
U3 0.3318 0.3315 0.3166 0.0915 0.0718 0.1876 
U4 0.2384 0.2338 0.3124 0.0836 0.0598 0.0404 
C1 0.3183 0.3305 0.3222 0.1122 0.1196 0.1150 
C2 0.2979 0.3198 0.3222 0.3247 0.2464 0.3704 
Table 9

Distance (d) from ideal best (IB) and ideal worst (IW), and performance score (PS)

SystemsdIBdIWPSdIBdIWPS∑PSRank
H1 0.0604 0.0338 0.3585 0.3013 0.0929 0.2357 0.5942 
H2 0.0256 0.0528 0.6733 0.3889 0.0064 0.0162 0.6895 
H3 0.0112 0.0675 0.8575 0.3419 0.0542 0.1369 0.9944 
H4 0.0025 0.0732 0.9676 0.0663 0.3650 0.8463 1.8138 
U1 0.0532 0.0296 0.3579 0.2243 0.2380 0.5148 0.8726 
U2 0.0255 0.0557 0.6857 0.2369 0.1800 0.4318 1.1175 
U3 0.0059 0.0695 0.9219 0.3520 0.0510 0.1266 1.0485 
U4 0.0494 0.0366 0.4256 0.3863 0.0100 0.0251 0.4508 10 
C1 0.0075 0.0682 0.9013 0.3538 0.0392 0.0997 1.0011 
C2 0.0147 0.0630 0.8103 0.2410 0.1565 0.3936 1.2039 
SystemsdIBdIWPSdIBdIWPS∑PSRank
H1 0.0604 0.0338 0.3585 0.3013 0.0929 0.2357 0.5942 
H2 0.0256 0.0528 0.6733 0.3889 0.0064 0.0162 0.6895 
H3 0.0112 0.0675 0.8575 0.3419 0.0542 0.1369 0.9944 
H4 0.0025 0.0732 0.9676 0.0663 0.3650 0.8463 1.8138 
U1 0.0532 0.0296 0.3579 0.2243 0.2380 0.5148 0.8726 
U2 0.0255 0.0557 0.6857 0.2369 0.1800 0.4318 1.1175 
U3 0.0059 0.0695 0.9219 0.3520 0.0510 0.1266 1.0485 
U4 0.0494 0.0366 0.4256 0.3863 0.0100 0.0251 0.4508 10 
C1 0.0075 0.0682 0.9013 0.3538 0.0392 0.0997 1.0011 
C2 0.0147 0.0630 0.8103 0.2410 0.1565 0.3936 1.2039 
Table 10

Rank of systems

 
 

Formation of decision matrix

A decision matrix is formed from the data collected for each compared system parameter.

Table 9 represents the measurement of the distances from the ideal values (IB and IW). PS is calculated separately for each indicator and it is presented in Figure 2 as well. The table summarizes the performance scores (∑PS) for each system and ranks them accordingly in the column named (labeled) rank.
Figure 2

Environmental and economic PS of systems.

Figure 2

Environmental and economic PS of systems.

Close modal

Table 10 shows the same ranking values as Table 9, but with the processes for each respective system included.

The TOPSIS analysis provided a structured and objective approach for evaluating wastewater treatment systems. The results show that each ranking represents a particular system with a set of DEWATS processes. The H4 system used for the treatment of wastewater discharged by a hospital is ranked as the best alternative. The H4 system has the highest performance score (∑PS) of 1.8138, and it is given the first rank. It is a more preferred alternative, being closer to the positive ideal solution (value) and also far from the negative ideal solution (value) simultaneously in terms of distance measured. The H4 system is composed of the ST+ABR, AF, HF, and APP. The system H4, incorporating the APP unit as the last polishing one, makes the system provide the best treatment performance, as well as economic parameters, if compared to the level of contaminant removal and water flow treated.

Compared with the H4 system as ranked number 1, the other systems that use more treatment processes are also ranked with the order values of 2, 3, and 4, and they are the H1, C2, and U2 systems. This indicates that incorporating multiple treatment processes typically leads to more effective outcomes. System U4 is evaluated as the worst alternative and ranked as the last since it is far from the IB value and closer to the IW value in comparison to other systems.

According to the ranking presented in Table 10, it is possible to determine the most appropriate processes for a specific DEWATS system. This can serve as an initial point to identify which processes are the most favorable and suitable for other DEWATS projects serving similar facilities as discussed in this paper. It should be emphasized that when analyzing alternatives using such methods, the evaluation is based on the model and weights assigned by the analyst. It is worth mentioning that even if two systems have similar processes, changes in the determination of criteria values or weights may result in changes in the final ranking.

Ranking of systems with similar processes, like H2 and U3 systems, can differ based on specific criteria. To comprehend the reasons for these differences, the discussion of the important criteria for both H2 and U3 systems with reference to the data presented in Table 7 is given below:

  • 1. BOD, COD, and TSS (%) – If the treatment performance of a system was better concerning the parameters specified in Table 7, which were set as criteria in the TOPSIS analysis, and then it could be a possible explanation for the changes in the ranking of the systems H2 and U3.

  • 2. CC ($) – If there are differences in capital costs between the two systems being considered, this could impact the final ranking of the systems. The CC is influenced by the amount of flow that needs to be treated and the occupied surface. Additionally, the type of substrate used for the treatment unit, such as for AF and HF, for both systems H2 and U3, and the dimensions of the treatment unit can also affect the CC. These criteria are interrelated and can influence each other, ultimately affecting the CC.

  • 3. Q (m3/d) – Systems that have higher treatment capacity can be considered more effective in some cases.

  • 4. OS (m2) – Depending on the treatment capacity, various systems can occupy different surfaces.

These systems even they use the same processes, but their weight is not the same. They may have differences in some criteria. Therefore, if a system is closer to the IB value for a particular criterion, that system will have a higher PS. This principle applies to other similar systems as well.

Effective wastewater treatment is crucial for achieving sustainable water management and sanitation goals. Selecting the appropriate wastewater treatment system can be a challenging and significant task for study. The TOPSIS method has been proven to be an effective decision-making tool for selecting the most preferred alternative among different wastewater treatment technologies (processes). The potential of using TOPSIS in the field of wastewater treatment has been highlighted several times, especially in selecting the best technology within wastewater treatment plants. This paper demonstrates the TOPSIS methodology for selecting appropriate systems within 10 case studies of DEWATS used in hospitals, universities/colleges, and small communities. The evaluation through TOPSIS is developed using six criteria of two indicators, namely environmental performance and economic performance. The criteria evaluated are BOD, COD, TSS, CC, Q, and the OS. The results show that the H4 system is top-ranked (first) and therefore the best system, which includes the APP as a follow-up process after HF, AF, and ST+ABR. The second most preferred system is the C2 system. These systems (H4 and C2) can be used for the treatment of wastewater discharged from facilities similar to those assessed in this paper. This paper shows that ranking systems through TOPSIS indicate that if there are systems consisting of the same processes, the rank value difference may be affected by other data, e.g. the amount of flow to be treated. Therefore, it should be noted that the results obtained from this paper might vary if more criteria are considered, which would provide a more sustainable assessment. The utilization of the TOPSIS method for the selection of appropriate DEWATS will be beneficial for decision-makers not only in the field of wastewater treatment but also in other fields of water management in general. By following this structured approach, stakeholders can ensure that their decisions are in line with sustainability goals, effectively balancing environmental and economic considerations.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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