Sustainability assessment of tertiary wastewater treatment technologies

Conference Proceedings

Sustainability assessment of tertiary wastewater treatment technologies: A multi-criteria analysis K.V. Plakas1, A.A. Georgiadis2, A.J. Karabelas1 1

Chemical Process and Energy Resources Institute, Centre for Research and Technology Hellas, Thermi, Thessaloniki, Greece 2

Environmental Economics and Natural Resources Group, University of Wageningen, Wageningen, The Netherlands

Abstract The Multi-Criteria Analysis gives the opportunity to the researchers, designers and decision makers to examine decision options in a multidimensional fashion. On this basis, four tertiary wastewater treatment (WWT) technologies were assessed regarding their sustainability performance considering a ‘triple bottom line’ (TBL) approach (i.e. economic, environmental, social). These are powdered activated carbon adsorption coupled with ultrafiltration membrane separation (PAC/UF), reverse osmosis (RO), ozone/ultraviolet oxidation (O3/UV) and the photocatalytic membrane reactor (PMR). In particular, the sustainability performance of the four selected tertiary WWT technologies was examined for a typical case of re-using the secondary municipal effluents. The participatory technique of Simple Multi-Attribute Rating Technique Exploiting Ranks (SMARTER) was utilized for assigning weights for each indicator. This sustainability assessment approach resulted in the development of a composite index as a final product for each WWT technology evaluated. The PAC-UF technology appears to be the most appropriate technology, attaining the highest composite value of the sustainability performance. A scenario analysis confirmed the results of the original scenario in five out of seven cases. In parallel, the PMR was highlighted as the technology with the least variability in its performance. Nevertheless, additional actions and approaches are proposed in order to strengthen the objectivity of the final results. Keywords: waste-water treatment; sustainability; composite index; SMARTER Introduction According to the World Health Organization (2014), the rapid increase of world population, in combination with the continuous urbanization and scarcity of good-quality water resources will lead to the acceleration of the upward trend in water reclamation and reuse. However, the essential element for successful implementation of a water reuse project is the capability of producing water of the desired quality, thus providing adequate public health protection while meeting the environmental and socio-economic goals than can be practically achieved at a given time. In this direction, an appropriate framework is required that considers institutional, financial and practical aspects to enable local stakeholders to take decisions and manage recycled water safely. In promoting and executing water reuse projects there is a need for the development of balanced decisions for the selection of the appropriate WWT technology on the basis of ‘triple bottom line’ (TBL) approaches to sustainability, i.e. by employing environmental, economic, and social criteria. On this basis, the multiple objectives and considerations involved in the decision-making process, for the selection of the appropriate wastewater technology, render it a complex problem to solve. The Multi-Criteria Analysis (MCA) is a tool that can rank the possible alternatives and distinguish the most preferable/acceptable option based on predefined objectives-criteria which are integrated in a single index for assisting the evaluation of each option (Dodgson et al., 2009). Moreover, the MCA can tackle the complex trade-offs between sociopolitical, environmental, ecological and economic factors by establishing a strategic and scientific theoretical framework as the 344

May 10-12 2015, Thessaloniki

benchmark for the analysis. As a result, MCA practices have been utilized in order to indicate the best technological solutions in several sectors such as energy, health and industry (Amer et al., 2011; Thokala et al., 2012). In the present study four tertiary WWT technologies are assessed, including powdered activated carbon adsorption coupled with ultrafiltration membrane separation (PAC/UF), reverse osmosis (RO), ozone/ultraviolet oxidation (O3/UV) and the heterogeneous photocatalysis coupled with low-pressure membrane separation (photocatalytic membrane reactor-PMR). The hybrid PMR technology has been successfully tested in laboratory pilots at authors laboratory (Patsios et al., 2013; Sarasidis et al., 2014; Karabelas, et al. 2014), and it is now scaled up for validation at appropriate end-users facilities, thus, paving the way toward its commercialization. The objective of this work was to explore the ranking of the aforementioned WWT technologies by investigating their sustainability performance in treating secondary municipal effluents for reuse based on a composite index from MCA. Methods Defining the sustainability indicators Table 1 summarizes the selected list of indicators for the scope of the integrated sustainability assessment. The selected indicators were used in several similar scientific studies that dealt with the treatment of municipal effluents as well (Kalbar, et al. 2012; Molinos-Senante, et al. 2014). Table 1 presents also the objectives and their direction (negative or positive depending on the desirable improvement), type and unit of each indicator (the latter if appropriate). The qualitative indicators should be quantified for the purpose of aggregating the selected indicators and of the final evaluation of each technological option. Toward these goals, the qualitative indicators were quantified using a nine-point scale in order to obtain numerical values (Table 2). Thus, the qualitative characterization of each indicator corresponds to a numerical value. Table 1. Selected battery of objectives and respective indicators to assess the sustainability of the advanced WWT technologies. Dimensions

Objectives

Economic

Minimization of costs Minimization of energy use Minimization of land use

Environmental

Minimization of xenobiotic accumulation Minimization of odour/noise/visual impacts

Social

Minimization of probability of mechanical failures Maximization of local development Maximization of public acceptance Minimization of the technological complexity

Indicators Investment costs Maintenance and Operation costs Energy consumption Land area requirements Xenobiotic efficiency removal Odour impact Noise impact Visual impact

Direction Negative

Type Quantitative

Units €/p.e.

Negative

Quantitative

€/m3

Negative

Quantitative

KWh/m3

Negative

Quantitative

m2/p.e.

Positive

Quantitative

%

Negative Negative Negative

Qualitative Qualitative Qualitative

− − −

Reliability

Positive

Qualitative

−

Employment

Positive

Quantitative

Employees/ p.e.

Public acceptance

Positive

Qualitative

−

Complexity

Negative

Qualitative

−

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Table 2. Nine-point scale for the quantification of qualitative indicators. Qualitative scale Numerical rate

Very low 1

Low 3

Moderate 5

High 7

Very high 9

The following step for the aggregation is the prioritization of the indicators in accordance with the relative importance of each indicator. Specifically, the selected weighting technique assists in the determination of the priorities of the indicators. Finally, the sustainability indicators are aggregated in a composite index for each WWT technology assessed. Weighting technique and the construction of the composite index In the examined case study, and under the time constraints, a linear multi-attribute model was developed utilizing the SMARTER (Simple Multi-Attribute Rating Technique Exploiting Ranks) weighting technique (Edwards and Barron, 1994). SMARTER introduces a relatively simple method with modest load-time, and has a well-established theoretical background. This technique involves respondents that are usually decision-makers competent to provide information about the prioritization of the criteria; subsequently, the priority weights are specified using the Rank Order Centroid (ROC) method. In this study, the relative importance of each indicator was estimated based on the opinion of decision makers, who can be represented by the current WWTPs operators throughout Greece. For this purpose, a brief survey questionnaire was delivered to thirty Greek WWTPs operators, with known activity and interest in water reuse. The sample was selected using the database of the Greek Ministry of Environment, Energy and Climate Change (2014), comprising only WWTPs that treat and re-use municipal effluents in other activities. Between October and the end of November 2014, a total of 13 questionnaires –out of the 30 distributed – were collected. After the analysis of the critical judgements that were provided by the sample, each of the twelve indicators was ranked (Table 1). Finally, the four advanced WWT technologies were ranked by employing the composite index given by equation (1), 4

C.I .i   w j ui , j

(1)

i 1

where CIi is the composite index of the ith advanced WWT technology, with 0  ui ,n  1 and i=1,2,3, & 4, n=1,2…12, and with

12

w n 1

n

 1 where 0  wn  1

Results and Discussion The respondents were asked to indicate the relative importance of each sustainability indicator for the selection of the most appropriate tertiary WWT among the four examined technologies (PAC/UF, RO, O3/UV, PMR). On the basis of the critical judgments for each sustainability indicator, an aggregation was performed in order to obtain the ranking of the weights (Table 3). It is, perhaps, not surprising that the respondents indicate the environmental aspects as the most important for the selection of the appropriate technology (55.5%). Indeed, it seems that there is an environmental trend from previous similar studies which have assigned great importance in the environmental sustainability as a key factor for selecting the appropriate WWT technology (Molinos-Senante et al., 2014). Once the numerical weights have been assigned to each sustainability indicator, a normalization of the performance matrix (Table 4) has to be executed in order to reformulate the entries for each indicator as single-dimensional units.

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May 10-12 2015, Thessaloniki Table 3. Weighting of the sustainability dimensions and indicators expressed in percentage. Dimensions

Indicators

Economic

Investments costs Maintenance and Operation costs (O&M costs) Energy consumption Land area required Xenobiotic efficiency removal Odour impact Noise impact Visual impact Reliability Employment Public acceptance Complexity

Environmental

Social

Weighting of each indicator (%) 13.4%

Weight of dimensions (%) 30.9%

17.5% 10.6% 3.0% 8.5% 5.4% 1.5% 0.7% 25.9% 3.2% 4.3% 6.8%

55.5%

14.3%

Table 4. Quantitative and qualitative data of the selected indicators for each WWT technology. Indicators

O3/UV

PMR

RO (with MF pretreatment)

Activated Carbon (PAC-UF) *

Investment costs (€/m3/d)

224.2

10

732.5

O&M costs (€/m3 ) Energy consumption (KhW/m3) Land area required (m2/m3/d) ** Xenobiotic efficiency removal

0.483

0.4

0.204

300 0.054 (PACsandfilter)

3.68

3.3

0.6***

0.2 (PAC-UF), 0.08 (PAC-sand filter)

0.0465 Diclofenac ~93% Atrazine ~40%

0.25 Diclofenac ~99,5%, Atrazine ~87,8%

0.25 Diclofenac>95% Atrazine >95%,

Odour impact

Very Low-Low

Very low

Moderate

Noise impact Visual impact Reliability Employment (Employees/m3/d) Public acceptance Complexity

Very Low Very low Medium

Very low Very low Medium

Moderate Moderate High

0.083333333 Diclofenac 79,5% Atrazine 74%, (Not in the case of PAC disposal - and without regeneration) Very Low Very Low High

0.0002083 Moderate Moderate

0.000458333 Moderate High

0.000458333 Low Moderate

0.000333333 Moderate High

Source: Data based on analytical calculations and results of experimental research performed by NRRE/CPERI/CERTH, as well as on technical and scientific literature review Comments: * UF considered in this study due to the beneficial effects of membranes on water quality (disinfection, total PAC and suspended solid retention), ** Estimations based on existed plants of varying capacity *** Post treatment excluded

Using the appropriate equations, the normalized performance matrix is derived. In case of the xenobiotic efficiency removal, two model compounds were considered as the most important for the examined case, i.e. diclofenac and atrazine as representative model compounds of pesticides and pharmaceutically active compounds, respectively. To facilitate the calculations, an average efficiency removal was taken considering the percentages of these two compounds. Moreover, the qualitative indicators were quantified, as was explained earlier in the methods section, using a nine-point scale (Table 2). Subsequently, the final outcome was determined using equation (1) in order to define the numerical value of the composite index for each WWT technology (Table 5). The sub-composite scores of the TBL approach are shown in Table 5 as well.

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Table 5. Dimensional scores and composite indices for each WWT technology. Dimensions

O3/UV

PMR

Economic Environmental Social Final Score (Composite index)

9.43 13.55 11.10 34.08

16.78 14.15 7.50 38.44

RO (with MF pretreatment) 11.38 43.78 10.00 65.16

PAC-UF 25.52 49.61 5.90 81.04

At first look, it seems that PAC-UF is the best technology among the four, with composite index 81.04, being the most sustainable option in the economic and the environmental dimension. The RO, PMR and O3/UV follow with composite indices 65.16, 38.44, 34.08, respectively. From the economic perspective, PAC-UF has moderate installation costs and usually limited operating costs, in contrast to the high capital cost of the RO and the significant investment and maintenance costs of ozonation/photolysis. Although the PMR has reduced capital requirements among all the examined technologies, the O&M costs are considered high because of the need for UV light lamps in the process. Thus, PMR stands in second place after PAC-UF. From the environmental perspective, PAC-UF is still considered as the most appropriate technology with minimum energy consumption, high reliability and high effectiveness to tackle odour/noise/visual impacts. However, in the social sphere, the O3/UV has the best performance with small differences from the PMR and the RO. Even if the PAC-UF technology emerges as the most appropriate WWT technology for the examined case study, it is necessary to take into account additional factors that could affect the final results. Particularly, considering that the relative importance of each indicator was based on the opinion of current WWTPs operators in Greece, there is still an inevitable level of subjectivity in the responses of the sample. Thus, to obtain a more holistic and thorough understanding of the differences between the TBL dimensions and integrate the importance of the weighting process in the selection of the technologies, a sensitivity analysis was performed. Scenario analysis In the foregoing presentation, a conceptual and methodological tool was described for the evaluation of four tertiary WWT technologies, where the required weights for a sustainability indicator (and generally for the TBL dimension) were assigned based on the preferences of current WWTPs operators throughout Greece. In order to make a ‘final check’ of the robustness of the findings, a scenario analysis is considered necessary. This scenario analysis would reveal whether changes in the weighting assignment would lead to changes in the final ranking, highlighting different alternatives as the best options. Following the approach of Molinos-Senante et al. (2014) and Karagiannidis & Perkoulidis (2009) in similar cases, a multiple scenario was adopted (Table 6) (Figure 1). In the original scenario (O Scenario) (based on the respondents preferences) the PAC-UF was the best technological option for the particular case study. When the weighting assignment was characterized by equal importance of its TBL dimension, the ranking was almost stable with a small difference between the RO and PMR (the latter reached the fourth position). In R1 and R2 scenarios, the ranking was still the same with PAC-UF as the dominant best option. However, in R3 scenario, when the social dimension had the relevant priority against the other two, the RO was the best choice with a slight difference for the PAC-UF, whereas, the PMR seemed to be the worst WWT technology of all. Remarkably, in the X1 scenario, when the economic dimension was considered more important, the PMR reached the second place among the rest. This happened because of the modest capital investment of the particular technology and the relatively low O&M costs. On the other hand, the RO and the O3/UV seemed to be the worst technologies. The X2 scenario maintained the same ranking with the original scenario with small variations in the numerical values. 348


However, in X3 scenario, where the social dimension was assigned the greater importance, the O3/UV reached the first place, whereas the PAC-UF was the worst technology. To sum up, in five out of the seven scenarios the PAC-UF seemed to be the best appropriate technology for the examined case. In parallel, the other two scenarios highlighted the RO and O 3/UV as the best options. Therefore, the results of the scenario analysis seem to provide support to the validity of the initial results (original scenario) that was derived from the multi-criteria analysis. Table 6. Weighting assignment of the scenario analysis. Scenario

Dimensional weights Environmental 55.5% 33.3% 25% 50% 25% 5% 90% 5%

Economic 30.9% 33.3% 50% 25% 25% 90% 5% 5%

O E R1 R2 R3 X1 X2 X3

100

O

X3

E 50

X2

Social 14.3% 33.3% 25% 25% 50% 5% 5% 90%

O3/UV R1

0

PMR RO PAC-UF

X1

R2 R3

Figure 1. Composite index scores for each WWT technology in eight scenarios.

Figure 2 shows the range of the composite values for each of the four WWT technologies based on the composite index scores of the aforementioned eight scenarios (including the original). It can be noticed that the variability of the sustainability composite index is larger in the PAC-UF and O3/UV. In particular, the PAC-UF has a maximum value 86.48 and minimum 45.47, whereas for the O3/UV the respective values are 75.52 and 27.29. The technology with the least variability is the PMR. The variability in the sustainability performance plays a crucial role in the selection of the final option; in fact, decision makers (or the involved stakeholders generally) occasionally exclude the most appropriate options (those derived from the analysis) even if they were the best available among alternatives, because of the potential risk associated with their uncertainty (Shehabuddeen et al., 2006). In the examined case, the PAC-UF and the O3/UV might be considered unfavorable options.

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100 90 80 70 60 50 40 30 20 10 0

MIN Original MAX

O3/UV

PMR

RO

PAC-UF

Figure 2. Variability of the composite index for each WWT technology.

However, even though the present results were based on the opinions and preferences of current WWTPs operators (considered to be experts), different individuals and groups, who have a vested interest, have to be involved in the decision-making process in order to reach the most acceptable and valid problem-solution. In this way, the legitimacy of the final selection could be enhanced and validated utilizing environmental governance decisions (at a local or international level). Therefore, the selection of the appropriate WWT technology should preferably be based on several groups of shareholders whose interests and opinions may be diverse. Such a process could enhance the economic efficiency, environmental effectiveness, equity, and political legitimacy of the environmentally sensitive decision regarding the particular issue. Hence, it would be advantageous, in future studies, to adopt a multi-stakeholder approach to investigate and simulate (at least partly) the concrete ranking of sustainable WWT based on priorities/preferences of those interest groups. Conclusions This research focuses on the evaluation of the performance of tertiary WWT technologies utilizing criteria-indicators of the three pillars of sustainability. The SMARTER weighting technique, used for ranking, facilitated the interpretation of the preferences of the considered stakeholders (WWTPs operators) and met the prerequisites for the composite evaluation. Among the four tertiary WWT technologies (O3/UV, PMR, RO and PAC-UF) evaluated, the PAC-UF treatment emerged as the most appropriate WWT technology for reuse of secondary effluents from WWTP of 150,000 p.e. because of the considered relatively low O&M costs, the low energy demands, the high reliable operation and the good effluent quality. The scenario analysis, including the composite indices of each technology for each scenario, reveals the superiority of the PAC-UF in five out of the seven cases. However, when the social dimension is assigned a greater importance, the RO and O3/UV attain the first place in the ranking. On the other hand, the PMR is shown to be the WWT technology with the least variability, which is a significant attribute. Consequently, this technology might be considered as the most rational option in order to ensure a minimum fluctuation in its sustainability performance under an uncertain, complex and evolving environment where the WWT technology would be operated. Although there are reservations on some aspects that led to the most appropriate and validated option, the MCA framework that was developed in the present study could facilitate the rather complicated decision-making process by providing a multidimensional overview of the alternatives. Therefore, the results of this study provide useful insights into a tool (to be further developed) helpful to decision-makers for selecting the most appropriate WWT technology.

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References Amer, M.; Daim, T.U. (2011) Selection of renewable energy technologies for a developing county: A case of Pakistan. Energy for Sustainable Development 15, 420-435. Dodgson, J.S.; Spackman, M.; Pearman, A.; Phillips. L.D. (2009) Multi-criteria analysis: a manual. Wetherby, West Yorkshire: Communities and Local Government Publications. Edwards, W.; Barron, H.F. (1994) SMARTS and SMARTER: Improved Simple Methods for Multiattribute Utility Measurement. Organizational behaviour and human decision processes 60, 306325. Kalbar, P.P.; Karmakar, S.; Asolekar, S.R. (2012) Technology assessment for wastewater treatment using multiple-attribute decision-making. Technology in Society 34, 295-302. Karabelas, A.J.; Plakas, K.V.; Sarasidis, V.C.; Patsios, S.I. (2014) The effect of humic acids on the removal of atrazine from water in a continuous photocatalytic membrane reactor. Global NEST Journal 16, 516-524. Karagiannidis, A.; Perkoulidis, G. (2009) A multi-criteria ranking of different technologies for the anaerobic digestion for energy recovery of the organic fraction of municipal solid wastes. Bioresource technology 100, 2355-2360. Ministry of Environment and Climate Change. Ειδική Γραμματεία Υδάτων. http://ypeka.plexscape.com/Default.aspx (last accessed November 2014). Molinos-Senante, M.; Gómez, T.; Garrido-Baserba, M.; Caballero, R.; Sala-Garrido; R. (2014) Assessing the sustainability of small wastewater treatment systems: a composite indicator approach. The Science of the Total Environment 497-498, 607-617. Patsios, S.I.; Sarasidis, V.C.; Karabelas. A.J. (2013) A hybrid photocatalysis–ultrafiltration continuous process for humic acids degradation. Separation and Purification Technology 103, 333-341. Sarasidis, V.C.; Plakas, K.V.; Patsios, S.I. (2014) Investigation of diclofenac degradation in a continuous photo-catalytic membrane reactor. Influence of operating parameters. Chemical Engineering Journal 239, 299-311. Shehabuddeen, N.; Probert, D.; Phaal, R. (2006) From theory to practice: challenges in operationalising a technology selection framework. Technovation 26, 324-335. Thokala, P.; Duenas, A. (2012) Multiple criteria decision analysis for health technology assessment. Value in Health 15, 1172-1181. WHO. World Health Organization. 2014. http://www.who.int/water_sanitation_health /wastewater/en/ (last accessed December 2, 2014).

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